Originally published in September 2025, this was the first long-form essay I wrote for Popular by Design, and the one that jump-started it. I only had a few hundred subscribers at the time. I still think it’s one of the best things I’ve written, since getting humanitarian immigration right is especially hard. I hope you enjoy it.

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Despite all its supposed potential, immigration is deeply unpopular today. Refugee and asylum immigration is even more so, because humanitarian appeals don’t resonate much with voters. Most want to see clear benefits for their own country, not just compassion for strangers abroad. That’s why expanding refugee admissions is politically much harder than skilled or labor migration.1

The premise of this newsletter is that making meaningful progress on immigration requires more than just better messaging—it requires better policy. So I wanted to begin with one of the hardest cases and write about a possible solution for making humanitarian immigration more popular and sustainable. What I learned while working on this piece is that one doesn’t have to be a bleeding-heart liberal to support refugees.

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Enter programs for private or community sponsorship of refugees for permanent resettlement. The model was first launched in Canada in 1979 and is now being considered or piloted in other countries, including the United States. This policy innovation directly addresses a common skeptical retort in political debates about immigration and humanitarian obligations: “Why don’t you house them yourself?” This question, often used by skeptics to imply hypocrisy among pro-immigration advocates, points to the real and perceived costs of resettlement borne by taxpayers.

But the simple truth is that many people would gladly help refugees with their own money and resources—they just cannot legally do so. Outside of Canada, in most countries around the world—rich or poor, democracies or autocracies—only governments decide who gets to immigrate or resettle there and how, regardless of how generous their populations may be. This issue cuts across ideology—orthodox congregations can’t bring in culturally similar believers, while humanitarians can’t help families in danger even if they want to do it on their own dime.

Community sponsorship aims to change that. It gives willing individuals and private organizations a legal way to act on their motivations to help migrants, share the financial and social costs of resettlement, and show tangible benefits of migration to their communities. Just as importantly, unlike other pro-immigration policies, it creates a durable constituency of both conservative and liberal citizens with a direct stake in immigration and refugee protection. While this is a hard counterfactual to prove, I’m increasingly convinced that had Canada not pioneered sponsorship 45 years ago, it would have resettled far fewer refugees, and its immigration politics would be far more contentious.

There are many good overviews of what community sponsorship is, along with case studies and policy assessments. What I want to do here, in the spirit of Popular by Design’s mission, is something I haven’t seen anyone do yet: assess the potential of sponsorship to generate a more pro-immigration political consensus, both in theory and in practice, and with an open mind for its possible limits. Below, I outline the longest-standing Canadian PSR program, what we can learn from its successes and shortcomings, why it hasn’t spread more widely, the major criticisms, and the available polls on how this idea is received globally. I conclude with a discussion of America’s short-lived Welcome Corps sponsorship program (launched in 2023 but abruptly halted by the second Trump administration), and how better policy design could set it up for greater success if or (hopefully) when it resumes.

What is community sponsorship, and how does it operate?

Community sponsorship is a set of policies that let individuals, community groups, and nonprofit organizations sponsor specific refugees for resettlement in their country, in addition to or otherwise independently of traditional government resettlement. Sponsors cover housing and basic needs, provide social connections, and help with integration, for a defined period, typically twelve months after arrival.

Canada runs the longest-standing and most developed system. Since 1979, hundreds of thousands of regular Canadians have helped resettle around 400,000 privately sponsored refugees with the help of more than 200 local and faith-based groups, all in addition to government-assisted arrivals. In recent years, a slight majority of resettled refugees have come via private sponsorship, and federal targets now plan for more private than government-assisted admissions. Here is the basic breakdown of the current version of Canada’s Private Sponsorship of Refugees (PSR) program:

• Who can sponsor: Small groups of five or more Canadian citizens or permanent residents (“G5s”), Community Sponsors (local organizations such as cultural associations, schools, or municipalities), and Sponsorship Agreement Holders (“SAHs”) which are established charities, faith-based communities, or nonprofits previously approved by the government. SAHs also educate and support sponsors and the sponsored, and help resolve issues that arise.

• Who can be sponsored: Canadian sponsors may “name” a person abroad who meets Canada’s refugee definition. For sponsorships by G5s or Community Sponsors (but not SAHs), the person must also generally already be recognized as a refugee by UNHCR or a foreign state.2 Because global resettlement slots are scarce (UNHCR projects about 2.5 million refugees in need of resettlement in 2026, a fraction of the 30+ million recognized refugees worldwide), the eligible pool is rather constrained. In practice, the vast majority of named cases are distant relatives or close friends of people in Canada.

• What’s required from sponsors: Sponsors commit to 12 months of support: start-up funds, income support, housing, and hands-on help with school, work, and language. Government guidance suggests budgeting about 26,700 CAD for a family of three (minimum, varies by location and in-kind support).

• What happens to those sponsored: Resettled refugees arrive as permanent residents, receive federally funded interim health coverage, and after the sponsorship year, can access regular provincial benefits like all other residents.

• What the government still does: It sets and manages annual admissions targets (currently 21,000–26,000 for 2025, with new PSR applications paused until December 2025 to reduce backlogs), vets applications, conducts security and medical screening, issues visas and permanent residence, and monitors compliance across all resettlement streams. The federal and provincial governments are responsible for healthcare coverage from the time of arrival and for other benefits that accrue to permanent residents.

The system is now considered a global model that has inspired adaptations in at least 14 other countries while securing financial and other support pledges from dozens of organizations. In 2016, together with UNHCR and a range of non-profit partners, the Government of Canada launched the Global Refugee Sponsorship Initiative to promote community sponsorship as a complementary pathway for resettlement around the world. Since 2013, Canada has also been running a “mixed” stream, the Blended Visa Office-Referred (BVOR) program, where sponsors are matched to UNHCR-referred (rather than named) refugees and costs are shared with the government. Many countries have modeled their sponsorship schemes on either this matching approach or the traditional naming approach, with varying parameters.

In Australia, for example, the sponsorship programs can involve business support, but are explicitly counted within the same annual Humanitarian Program quota. In the United States, the Welcome Corps sponsors only provide the first 90 days of core services with arrivals entering as refugees and applying for permanent residency after one year. In Italy, the “Humanitarian Corridors” program allows only organizations (not individuals) to sponsor people on humanitarian visas, so there is no guaranteed permanent residency on arrival.

Why community sponsorship wins more support than resettlement or asylum

Although Canada’s program has occasionally been criticized over sponsor–refugee matching, long wait times, and tension with government quotas, it has not caused any significant right-wing backlash. The same is not true of humanitarian immigration generally—and asylum in particular—which often raises concerns about border chaos, arguably a major driver of recent populist resurgence worldwide. Even in Canada, the right of foreigners to claim asylum at the border is much more controversial than either government-assisted or privately sponsored resettlement or foreign aid.

The political promise of community sponsorship lies exactly in how it channels citizens’ both altruistic and somewhat parochial impulses—helping people you can identify with—into a structured way to resettle vulnerable populations from abroad while maximizing integration success and minimizing the concerns of skeptics. By providing individuals and organizations with a legal and effective way to help, community sponsorship makes larger refugee resettlement more politically durable in otherwise hostile anti-immigration environments.

First, it allows willing citizens to act upon their humanitarian beliefs beyond helping migrants who are already here or voting for a preferred party and leaving refugee protection solely to politicians and bureaucrats. The act of communal sponsorship builds lasting civic networks and constituencies of people invested in resettlement and immigrant success more generally. Research from Canada and other countries shows that sponsors overwhelmingly report positive experiences and stronger ties to their communities.

Second, it appeals to people’s conservative intuitions of localism, faith, and control, especially when “naming” the sponsored refugees is allowed. It is not a coincidence that Canada’s private sponsorship program roots lie in church-based aid and local civic voluntarism. Faith communities were already running settlement ministries and pressing the state to share responsibility, and then stepped in as enthusiastic yet “reluctant partners” during the late-1970s resettlement of Southeast Asian refugees. According to a recent survey of sponsorship organizations in Canada, 60% of them still belong to a religious organization, while 22% focus on another particular non-religious ethnic community or group.

Third, community sponsorship explicitly addresses common public fears. Because sponsors shoulder much of the cost and responsibility, perceived fiscal burdens are lower. Because sponsorship groups tend to be deeply involved in helping refugees they sponsor—finding housing, connecting newcomers to schools and jobs—social cohesion and integration outcomes should be stronger. While no randomized trials exist, observational studies generally find better integration outcomes in employment and income for privately sponsored individuals compared to government-assisted refugees, which is only partly explained by selection bias. A recent study by the Canadian government found that after one year 75% of privately sponsored refugees had employment earnings vs 37% of government-assisted, and social assistance receipt was 16% vs 93%, with advantages persisting over several years.

To my surprise, however, despite nearly half a century of Canada’s private sponsorship program and its recent global proliferation, direct public opinion evidence on the topic is scant. The only report I was able to find on public attitudes and sponsorship found high support but mostly relied on indirect or qualitative evidence (e.g., more positive general immigration attitudes among people who have participated or live in high-sponsorship areas). After further investigation, which took me much longer than I care to admit, I was able to locate a few relevant surveys that straightforwardly ask people about their support for sponsorship programs.

Here are the key reports and their highlights:

• In Canada, a vast majority are aware of the private resettlement program (which is impressive given the generally low political knowledge in public opinion). A clear majority—especially those who are aware—view it favorably. According to the 2018 and 2021 Environics surveys, about 3-7% say they have been directly involved, 15–25% say they personally know a sponsor, and about the same share say they would like to participate in the future. A 2017 McGill survey, which explicitly asked whether private sponsorship or government resettlement works better, found that significantly more respondents chose the former (41% vs. 6%, with the rest unsure).

• A 2021 Environics poll found that, among the small minority who view private sponsorship negatively (13-16%), reasons cluster around how the program is administered (taxpayer burden, insufficient resources) or unfavorable views of refugees (concerns about integration or competition for resources). Although these skeptics were not asked about other policies, it is reasonable to assume they have similar or stronger concerns about traditional government resettlement.

• In Germany, a 2016 More in Common survey conducted during the Syrian crisis found 45% in favor of introducing a sponsorship program, with about one-third opposed. These levels exceeded general positivity toward “refugees” at the time. Forty percent also reported donating or volunteering to help refugees, and 22% said they would be willing to participate in a sponsorship program.

• In the United Kingdom, a 2021 More in Common survey found 48% support and 34% opposition to accepting more (Afghan) refugees via community sponsorship. Net support was 14 points higher than for general resettlement, driven mainly by lower opposition among socially conservative and anti-immigration segments of the population.

• In Poland, a 2024 CMR Ipsos survey found 31-39% support for introducing a sponsorship program—the only case I saw where opposition exceeded support somewhat. Even so, community sponsorship was more popular than traditional government-led resettlement. An earlier survey from May 2022, fielded shortly after the start of the war in Ukraine by the same research team, reported much higher support numbers.

• In the United States, a 2023 YouGov survey at the launch of Welcome Corps showed 60% overall support, including 76% of Democrats and 53% of Republicans. Given heightened border salience and a thermostatic cooling on immigration during the Biden administration, these are notable figures. About one in four Americans also expressed interest in personally sponsoring a refugee in the coming years.

Bottom line: Community sponsorship is broadly popular—either absolute majorities or strong pluralities support it across various contexts—and it tends to outscore government-only resettlement and many other humanitarian policies.

Why hasn’t it caught on more? The major bottlenecks and limits of sponsorship

If community sponsorship works so well, why hasn’t it spread more widely? Despite my constant reminder to immigration advocates that humanitarian intentions are rarer than they assume, my sense is that the answer is probably not a lack of willing citizens. The Canadian surveys I described earlier show that a small but meaningful share of the public already participated (about 3-7%), or would like to participate (another 5–15%) if given the chance. This aligns well with my own surveys and incentivized experiments: while most people understandably prioritize their own or their country’s well-being, at least 10% in rich democracies display pronounced humanitarian motivations and are willing to benefit foreigners even at a personal cost. Even if we take a very conservative cap of 5% of the working-age population as the potential pool of sponsors, that is still a large number. Extrapolated to the United States and other rich democracies, this implies millions of potential sponsors. In short, public enthusiasm seems sufficient.

The bottleneck is the government’s resolve and capacity. Policy innovation in immigration is slow, especially when leaders want clear proof of success before scaling. Even Canada’s famous points-based skilled migration system took years to become a global practice. And sponsorship requires more than goodwill—it demands real administrative capacity.

Governments must vet sponsors, screen refugees, issue visas, arrange travel, monitor cases, and step in if failures occur. Many countries lack the bureaucratic infrastructure or trust in civil society to manage this. The start-up costs of building sponsor networks, training groups, and supporting them through the process are significant. Philanthropic seed funding has increased recently but remains modest, and officials rarely see enough upside to overcome inertia.

Even if some of these bottlenecks ease, community sponsorship is clearly not going to solve the world’s displacement crises alone. There are over 35 million refugees worldwide, with 2–3 million designated as urgent resettlement cases, and each year, only a fraction are resettled anywhere. If every wealthy nation decided to adopt the Canadian sponsorship model tomorrow, total numbers would still be in the hundreds of thousands per year, not millions.

Moreover, community sponsorship does not address the messy, politically toxic issue of spontaneous border crossings and asylum claims. Sponsorship is simply not designed for these scenarios—it is orderly and selective, which is the opposite of chaotic inflows. Some development economists—and now even The Economist—argue the asylum system is outdated and should be rebuilt around protection and legal work in proximate host countries, fewer camps, and more legal pathways with regional processing to deter dangerous journeys. In that reimagined setup, community sponsorship could serve as one of the channels to redirect some of the would-be asylum seekers into managed programs supported by citizens. But realizing this would require policy shifts far beyond sponsorship itself.

Interlude: the successful, yet short-lived, case of U.S. Welcome Corps

The recent two-year U.S. sponsorship experience illustrates both the appeal and fragility of sponsorship. The Welcome Corps, launched in 2023 as a pilot within the federal refugee admissions program, invited Americans to form groups and directly sponsor refugees for the first time. Several observers even called it a “revolution” in the U.S. refugee admissions or even immigration policy in general. The response was remarkable: more than 160,000 people across every state registered interest within two years. The most engaged states ranged from Minnesota and California to Texas and Indiana, showing geographic and political diversity.

Public opinion matched this enthusiasm. A YouGov poll found 60% of Americans supported the idea, including 76% of Democrats and 53% of Republicans. For a pro-immigration policy initiated by a Democratic administration to secure majority Republican support in 2023 was striking.

At the same time, the program did not generate any evident backlash. Some anti-immigration groups raised alarms about potential fraud and weaker vetting (which they do about pretty much all immigration programs), but a review by the Niskanen Center found those concerns unsubstantiated. Refugees underwent the same security screening as in other resettlement channels, and sponsors themselves were background-checked and trained. No major scandals occurred: refugees were vetted, sponsors were supported by intermediary nonprofits, and cases proceeded smoothly.

The U.S. case demonstrates the political potential of sponsorship: grassroots enthusiasm, broad partisan reach, and no visible backlash. It is not proof of long-term success, but it shows how strongly the model resonates with American civic culture. The Welcome Corps ended only because refugee admissions overall were paused by the second Trump administration in early 2025—not because of any explicit opposition to the program itself. If and when revived, it would likely continue to draw bipartisan interest.

Learning from Canada, the U.S. pilot, and other countries, we can try to identify some key design principles that make a community sponsorship program both more sustainable and scalable, from rigorous participant vetting to well-funded administration. I will write about these, as well as possible extensions to the program, in a separate post in the future. For now, I want to highlight two features that I find especially important for the program’s political success (notably absent in the initial version of the U.S. Welcome Corps program): naming and additionality.

Naming and additionality: the key sponsorship principles and the debates around them

As with any reasonable policy compromise, community sponsorship programs and their key principles have also been debated and criticized on both the left and the right. Let’s start with the aforementioned naming principle, which essentially allows sponsors in Canada to pick specific refugees (at least among those who qualify for resettlement by law). This principle raises obvious fairness questions: Are those refugees the neediest, or just the best connected? These concerns have led some left-leaning analysts to criticize the naming feature of private sponsorship as inequitable, since it tends to prioritize refugees who have family or friends abroad.

Although I have found relatively little explicit criticism from the Canadian right focused on the program itself, the concerns I did find are almost a mirror image. In particular, some worry that private sponsorship could become a sneaky backdoor for increasing lower-skilled immigration, in relative or absolute terms. Because sponsors usually name their relatives or co-ethnic friends, the program might be used to bring in people who would not qualify under stricter points-based streams. The most troubling aspect for these critics is that sponsorship leads to permanent resettlement, meaning those brought in—and their descendants—may draw on taxpayer-funded benefits if they contribute less in taxes than they consume. Given Sweden’s disappointing experience in improving fiscal outcomes for humanitarian migrants and their families despite strong integration efforts, this critique should not be easily dismissed.

As some rightly argue, however, one of the Canadian program’s strengths compared to its many offshoots is precisely that sponsors are allowed (though not required) to nominate specific refugees. Equity and human capital concerns aside, naming taps into the strongest motivations people have to sponsor in the first place. Individuals and groups are more committed when the person they welcome is not a stranger, but someone they already know, or someone with whom they feel a direct cultural or religious connection. Prior relationships often bring shared language and customs, which can ease integration. Besides, sponsors can also nominate people they do not already know, enabling creative uses such as campus sponsorship for refugee students or partnerships focused on sexual and gender minority refugees.

At the same time, matching-only streams like the aforementioned BVOR program have struggled to mobilize and retain large numbers of sponsors. After completing a matched case, many groups end up seeking channels that let them name specific people to help their relatives or friends. The U.S. Welcome Corps, for example, saw faster uptake after adding a possibility for naming in the second phase of the program, underscoring how the ability to nominate specific people can drive participation. In short, naming makes the program work politically by sustaining civic engagement over decades, even if it may complicate the purist ideals of impartial humanitarian protection or skill selection.

But the most serious structural critique of the program relates to the additionality principle or the lack thereof. Does sponsorship actually increase protection for vulnerable people, or does it substitute for government action? In 1979, when the program started during the Indochinese resettlement, the federal government made an explicit one-for-one pledge (one government-assisted admission for each privately sponsored case). The pledge was discontinued soon after as backlogs grew. Today, the government sets separate targets for government-assisted and privately sponsored streams, and allocations can shift between them from year to year.

This raises the familiar “crowd-out” concern: if volunteers sponsor 10,000 refugees, a cost-conscious government might reduce its own intake by a similar amount, yielding no net increase. The risk is debated and hard to prove, but in some years PSR admissions have exceeded GARs, which sponsors cited as contradicting their additionality expectations—even though additionality is not part of the PSR official program theory anymore.

From a political perspective, however, even a pure substitution arguably can have an upside: if taxpayers see that enthusiastic citizens are handling more refugees, it might reduce backlash and keep overall support higher than if the government tried to do it all. It also effectively addresses the most salient conservative critiques of the program. Still, for community sponsorship to reach its full potential, it ideally needs to complement, even imperfectly, not fully supplement, government resettlement.

Clear government commitments can prevent this—whether through pledges that private sponsorship will not reduce overall quotas, or even through formulas that increase official resettlement proportionally. Transparency is also essential: if citizens can see that their efforts genuinely expand the total number of refugees welcomed, more will step up. Creative mechanisms could reinforce this link, such as tying sponsor contributions directly to funding additional government-assisted arrivals. However it is achieved, additionality, even when it is only partial, is the key to unlocking sponsorship’s promise: mobilizing private compassion to help vulnerable populations beyond voting or charitable donations.

So, how can sponsorship make our immigration politics better?

Despite current challenges and limits, I believe that community sponsorship of refugees has a bright future. Its track record in Canada shows it can make refugee resettlement more popular and politically sustainable, even where traditional humanitarian policies face hostility. Programs that empower citizens to welcome refugees consistently score higher approval than almost any other immigration initiative. They harness grassroots goodwill that would otherwise go untapped. And they tangibly benefit not only refugees, who get a chance at a new life in a supportive environment, but also hosts, who often find new purpose and social ties, and their communities, which gain integrated workers amid population decline.

In an age of polarized politics, community sponsorship uniquely appeals to a broad demographic and manages to bring unlikely allies together—church groups and LGBT nonprofits, veteran groups and humanitarian agencies, liberals and conservatives, small towns and big cities. This coalition-building effect is invaluable for the long-term sustainability of refugee protection. It is much harder to demonize “refugees” in the abstract when your neighbors, co-workers, or your parents’ church are personally helping someone settle nearby.

In the more immediate future, we are likely to see continued scaling up country by country. The new Global Refugee Sponsorship Initiative has been advising governments, and roughly 14 countries have launched some version since 2016. Most remain pilot-sized and have resettled only a few thousand families, though separate family-reunion pathways for Ukrainians brought in tens of thousands under a similar sponsorship principle.

The real game-changer would be the U.S. fully embracing community sponsorship with naming alongside its government program. If the U.S. activated hundreds of thousands—if not millions—of willing sponsors, or even reached Canadian-level per capita rates, we could be talking about hundreds of thousands of refugees resettled annually via private means. Even if those numbers are aspirational, they illustrate significant untapped capacity. High-income countries collectively host only a small fraction of the world’s refugees today, but by empowering their own citizens to sponsor refugees, they could increase that share in a politically sustainable way.

Community sponsorship will not solve the refugee crisis on its own, and it will not replace the need for robust government action and international cooperation. But it will give tens of thousands of people a safe new home who would not have had one otherwise. In a world where so much of the immigration debate is abstract and mistrustful, community sponsorship offers a concrete and intuitively positive story: regular people working together on something compassionate and constructive, with visible results that many can admire even if they choose not to participate. That is a useful antidote to cynicism and a reason to think that, while community sponsorship may not transform global numbers overnight, it can improve our immigration politics over the long run, making it more open and humane by design.

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Many thanks to Gabriella D’Avino, Ania Kwadrans, Biftu Yousuf, and BBI fellows for their help and comments on this piece.

Select bibliography

• Global Refugee Sponsorship Initiative

• Fratzke, S., Kainz, L., Beirens, H., Dorst, E., & Bolter, J. (2019). Refugee sponsorship programmes: A global state of play and opportunities for investment. Migration Policy Institute.

• Labman, S., & Cameron, G. (Eds.). (2020). Strangers to neighbours: Refugee sponsorship in context (Vol. 3). McGill-Queen’s Press.

• Cameron, G. (2021). Send them here: Religion, politics, and refugee resettlement in North America (Vol. 5). McGill-Queen’s Press.

• Kaida, L., Hou, F., & Stick, M. (2020). The long-term economic integration of resettled refugees in Canada: A comparison of privately sponsored refugees and government-assisted refugees. Journal of Ethnic and Migration Studies, 46(9), 1687-1708.

• Labman, S. (2016). Private sponsorship: Complementary or conflicting interests?. Refuge, 32, 67.

• Manks, M., Monsef, M., & Wagner, D. (2022). Sponsorship in the Context of Complementary Pathways. Knowledge Briefs.

• Sustainable Practices of Integration (SPRING) podcast about Canada’s sponsorship.

• Private Refugee Sponsorship in Canada - 2021 Market Study, Environics Institute.

• Orth, T. (2023). Most Americans support “Welcome Corps,” Biden’s new refugee sponsorship program. YouGov.

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In Part I, I argued that AI can already do social science research better than most professors. In Part II, I engaged with over a thousand responses, conceding where critics were right, while standing by my main claim: the academic status quo was already broken, and AI is just forcing the reckoning.1 In this Part III, written collaboratively with AI and my peers over the last month, I move from diagnosis to what academics can and can't actually do about it.

The rather unlikely proximate cause of this third installment on AI was visiting the 2026 International Studies Association (ISA) Annual Convention in Columbus, Ohio—a preeminent multidisciplinary conference of the world’s leading international studies professionals. Or so I was told. What I actually witnessed were presentations so rough they would barely get a C in any of my classes: arguments with no thesis or coherence, grammar errors any spell-checker would catch, presenters reading off their slides as if encountering their own bad arguments for the first time. All without any AI involved, as far as I could tell, judging by the presence of typos and inconsistencies. These were not just grad students, but people with PhDs, tenure, and research budgets.

If AI slop is the crisis everyone warns about, I’d like to know what to call what I saw at ISA or most other big social science conferences, for that matter.2 The contrast was impossible to ignore: I was sitting through these presentations at precisely the moment I was receiving death threats and calls to fire me online for suggesting AI can do research better than most professors. That juxtaposition crystallized the argument for this piece.

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21. Most “slop” has always been and still is human slop.

My first thesis was the most provocative thing I’ve said, and I’ve adjusted it only slightly since then: agentic AI can already do most social science research tasks better than most professors globally. I still stand by it. In my recent interview with the Chronicle, they put it more bluntly: “AI Is a Better Researcher Than You.” If you still don’t believe that’s true, let’s talk in a few years.

But the flip side is just as important. If AI can produce better research output than professors, that’s also an indictment of the output those professors were and are still producing without AI.

“Slop” was Merriam-Webster’s 2025 Word of the Year, defined as low-quality digital content produced by AI. But the ISA conference was a reminder that the vast majority of slop has always been human slop. The academic journal system and big conferences in much of the humanities and social sciences were slop factories long before anyone had a ChatGPT subscription. Yes, I really mean that most research is slop.3

Some of it is also what the philosopher Harry Frankfurt would call “bullshit“: work that is indifferent to whether its claims are true, especially on politically charged topics like immigration, where researchers start with the left-wing conclusion and work backward. But slop is broader than bullshit. It also includes work that makes no claim at all, work that is supposed to have craft value, and simply fails. The researcher who finds a dataset before having a question, then dredges for significant results worth publishing, is producing slop. These researchers existed long before AI. They were just slower.

22. Academics were hallucinating and cheating before AI.

The concerns about hallucination and cheating that people raise about AI in academia describe problems that predate it by decades, if not centuries. A massive new project published in Nature this month (which I was a small part of) tested hundreds of social science papers: only about half of statistically significant claims replicated, with median effect sizes shrinking dramatically. This study confirms what the 2015 Open Science Collaboration study found in psychology alone, where roughly two-thirds of findings failed to replicate, and extends it across fields.

We didn’t call these “hallucinations” before AI. We called them “science.” If you think about it, hallucination and inspiration are actually not as different as they sound. Both involve generating combinations that go beyond the input. We call it hallucination when the result is wrong and a breakthrough when it’s right.

Meanwhile, academics routinely cite papers they haven’t read beyond the abstract.4 At least AI hallucination rates are tracked and improving. Human hallucination rates in academia are not tracked at all. We just call them “contributions to the literature.” And if you’re a peer reviewer, you don’t even have to hallucinate on your own: you just write “please cite me” and move on.

Some research is genuinely excellent. But before we worry about AI-assisted cheating, let’s reckon with the human kind for a moment. Diederik Stapel, Marc Hauser, Francesca Gino, Dan Ariely: the list of high-profile fraud cases keeps growing, and these are just the people who were caught. Aside from outright fabrication, p-hacking, HARKing (hypothesizing after results are known), and selective reporting were for years so common that they barely registered as misconduct. We’ve made real progress in understanding these practices, but they remain widespread enough to shape what gets published. Beyond all this, senior professors have always put their names on papers written primarily by graduate students, and entire books have been assembled by teams of research assistants working under a famous scholar’s name. None of this was considered cheating until AI made the process cheaper and more apparent.

23. The “stochastic parrot” metaphor describes humans better than AI.

One of the most influential slogans in the AI debate5 has always functioned as a thought-terminating cliche. As Cate Hall observed, it is a potent coinage: fun to say, conceptually efficient, and it has permanently colonized many people’s minds despite not being true of today’s models. A genuine linguistic work of art. It is also empirically false: every major frontier model since GPT-4 has been trained on non-textual input, and the original argument’s own logic requires text-only training to work.

Now consider something completely different yet still very familiar: the “In This House We Believe” yard signs. Science Is Real. Love Is Love. No Human Is Illegal, etc. You can believe every line and still have no coherent policy position on any of these issues. What does “no human is illegal” imply for enforcement policy: open borders, amnesty, something else? The sign doesn’t say, because saying would require confronting contradictions. It is a loyalty oath, not an argument.

But the deeper point is that this is exactly what AI gets criticized for: producing shallow, feel-good statements that communicate belonging rather than meaning. Turns out humans have been doing that with or without AI for as long as we’ve had lawns to put signs on.

The yard sign is not an outlier. It is how most people seem to engage with most issues: adopt the position of your group, repeat it, move on. The substance of what you argue matters far more than how you frame it, but framing is easier, which is why it dominates public discourse, from Twitter threads to conference presentations.

I saw this firsthand at ISA, where tenured peers presented work that amounted to the academic equivalent of a yard sign, complete with a few misidentified regressions to give it the veneer of science. The stochastic parrot critique was meant to diminish AI. It ended up being a better description of human intellectual life than anyone intended.

24. Yes, consenting adults can use AI for writing. Policing it doesn’t work.

In Part II, I noted that AI detectors are often not very useful and create more problems than they solve. But the deeper problem is the impulse behind them: the belief that AI involvement is inherently contaminating, regardless of what it produces. Quinn Que makes a fascinating case that the obsession with AI writing detectors is akin to enforcing a “one-drop rule,” the principle from 19th-century American racial classification: any trace of AI involvement contaminates the entire work, regardless of quality or the author’s intent.

I was initially skeptical of the analogy, but it’s quite right. In the view of the anti-AI activist, any word you have not written yourself is a moral pollution. Although using AI for writing is not technically “illegal,” there is basically a one-drop rule governing whether you are a legitimate writer or a fraud, a good person or a bad one. Hence, the condemnations and death threats to people like me who disclose their AI usage for “outsourcing their thinking to the machine.”

As I argued in Part I, much of the opposition to AI is status protection dressed up as principle. takes this further, arguing that the chatbot moral panic may have a source beyond the status project. This source is something closer to superstition (”chatbots are demonic”): the sense that AI-produced text is spiritually tainted, that there is something wrong or even evil about a machine that can write, regardless of what it writes.6

Even , who recently honorably disclosed her AI use and sparked among journalists the same conversation academics had been having on Bluesky a few months earlier, felt compelled to defend herself by insisting that “AI didn’t touch copy.” I admire her for speaking up. But why should the copy question even be an issue? If the work is good and the process is disclosed, the rest is aesthetic preference dressed up as ethics. Where does impurity start? Google search? Auto-correct? Spell check? Transcriptions?

And even setting all that aside: barring some Dune-style scenario of an AI catastrophe followed by humanity coordinating to ban the technology, widespread AI-assisted non-fiction writing is almost inevitable in equilibrium, given the existing incentives. The flip side worth mentioning is that even if you do manage to write well entirely on your own without “AI pollution,” no one is going to be rewarding you for that purity very soon, either.

25. Not using the latest AI tools in your research and writing is malpractice.

recently argued that large language models are underused in journalism. His point applies to academics, too: the purpose of research is the useful output, not the human-mediated process. The rigor should be in the thinking and the verification, not in whether a human or a machine typed the sentences or pressed enter when running the regressions in R. As Hollis Robbins put it well, professors should probably be testing AI models before breakfast (basically be like ).

The most mundane use of AI is catching errors. Consider the New York Times headline on April 3, 2026: “A North American Treaty Organization Without America?” The correct name is, of course, the North Atlantic Treaty Organization. The Times ran a correction the next day. Some speculated the error was caused by AI. We’ll never know, and frankly, it doesn’t matter. Any sufficiently smart LLM workflow would have caught the mistake in seconds. A simple automatic routine for all headlines—”Does this contain any errors? Verify against two separate sources”—would have saved the Times a news cycle of embarrassment.

But fact-checking is the floor, not the ceiling. The more interesting use is doing things that weren’t possible before. My now co-author, , recently had Codex build her an interactive website to help her actually understand a political science paper she was reviewing, then did the task herself the way the study’s participants had. AI doesn’t just compress the time it takes to produce an output. It lowers the cost of the kind of active engagement most researchers skip: building the thing yourself, stress-testing an argument, rerunning an analysis the way participants did. That’s what deeper understanding actually requires.

The same applies to academia. Half the ISA presentations I attended in Columbus could have been meaningfully improved by a cursory loop through ChatGPT: checking grammar, tightening arguments, catching logical gaps. These tools are free or nearly free. Choosing not to use them is a choice to deliver worse work than you’re capable of, especially if your calling is to inform the public. But journalists and academics alike should not use AI just to catch typos in headlines. They should use it to build interactive visualizations, stress-test arguments, and genuinely understand the complicated things they are writing about.

But what about writing itself? You may have seen a common response to anyone using AI in their writing: “Why should I read the article and not the prompt that caused it?” Sounds reasonable, right? Well, no, if you think about it a bit.

Why eat the meal instead of reading the grocery list? Why look at a chart instead of the ggplot script? Why read the book instead of the author’s notes? The prompt gives you no new knowledge. The output does. That’s the whole point. As I argued in Part II, different people with different expertise, data, and context (as manifested in their claude.md files) produce entirely different outputs from the same prompt. It’s a skill issue.

I get it: it can feel strange to spend more time reading something than the author spent producing it. But we’ve been here before. A chart that took seconds to generate in R can take minutes to read carefully. Nobody demands or fantasizes about seeing the code only instead of the chart. They evaluate the chart and what it says—the new knowledge not available before.

26. LLMs may indeed produce new knowledge.

As recently documented by , AI chatbots consistently pulled users toward expert consensus, the opposite of what social media does. The tools don’t just help you write better. Used actively, they help you think more carefully.7 This way, LLMs may even reverse the rise of populism. But fact-checking is not the same thing as producing new knowledge.

Before we ask whether LLMs can produce genuinely new ideas (there are good examples of that), we should ask how many humans do. Most academics spend their careers recombining existing ideas in minor variations, applying the same methods to slightly different datasets, producing incremental work that nobody outside their subdiscipline will ever read. Original thinking is extraordinarily difficult and rare in any generation. I don’t mean it as an insult to any of my colleagues (or myself). But the bar that LLMs need to clear is lower than we like to admit.8

My friend Robert Kubinec, a brilliant political methodologist and published fiction author9 at the University of South Carolina, seems to be pretty skeptical about much of the AI hype. He argues that “LLMs never create knowledge, [which] only exists in human brains. They can only compare one set of knowledge to another.” I respect Bob, so it’s OK if we disagree. My response: The philosophical question about self-awareness is real, but it’s orthogonal to the practical one. Whether or not the model “understands” anything, the output either contains new information useful to humans or it doesn’t.10

The most suggestive recent example is Claude Mythos, Anthropic’s new frontier model. In a few weeks of testing, it flagged thousands of previously unknown security vulnerabilities across major operating systems and browsers, including one that had gone undetected for 27 years. Whatever you want to call that, “compare one set of knowledge to another” does not quite cover it.

The best conceptual theories in social science are already recombinations of existing ideas. Anthony Downs built his rational choice theory by transplanting microeconomic utility maximization into voting and literally called his book An Economic Theory of Democracy. Baumgartner and Jones borrowed punctuated equilibrium from evolutionary biology and applied it to policymaking. Social network analysis imported graph theory wholesale from mathematics. Axelrod fused the prisoner’s dilemma with evolutionary fitness selection. Alexander Wendt went further and applied quantum theory to international relations in Quantum Mind and Social Science—a move many found (rightly) ridiculous, but which Cambridge University Press still published. Social science rarely invents from scratch. It translates across domains, and the translation is the theoretical contribution.

That is structurally identical to what LLMs do: recombine patterns and concepts across contexts. Sometimes the result is nonsense. Sometimes it is productive. The same is true of human recombination. Wendt’s quantum IR has been criticized as a mere metaphor masquerading as physics, but as mentioned above, Cambridge University Press published it. If that counts as knowledge production, it is hard to see why LLM-generated recombinations wouldn’t.

To his credit, Bob conceded the practical point even while holding the philosophical one: “the question is how to use this capability to advance knowledge.” Exactly. As I said in Part I, stop debating whether LLMs “truly understand” while the people with the most at stake are already using the tools to improve their work.

27. For critics, the mental model of an AI user is stuck in 2023, which is ages ago.

Remember how we got into this mess. Students got enthusiastic about using AI before their professors, using the very first public, free models like ChatGPT 3.5. The result is that many professors and intellectuals still picture the typical AI user as an undergraduate trying to cheat on an essay or their assistant failing by submitting AI slop. That may still describe a lot of casual AI use around the world. It does not describe how top researchers in most fields are working with these tools, or how anyone trying to use AI responsibly and mindfully actually operates.

put his finger on this the best: we underrate other people’s rationality, assuming they apply new tools much more mindlessly than they really do. There is a vast space between asking ChatGPT to write a whole paper and writing down every single word yourself. When you write “yourself,” you are already using shortcuts. You are not doing deep research on every reference you make. You Google a statistic and trust the headline number. You skim an abstract and cite the paper. If you have resources, you outsource verification to a research assistant or a fact checker. AI lets you do much of this in a more systematic, automated, and cheaper way.

Like many, rightfully believes that writing is thinking and that outsourcing the full writing process to AI leaves your mind empty. But, while much writing is thinking, thinking is not only writing. Making art is thinking. Talking is thinking, too. As Dina Pisareva argues, prompting is also thinking, if you have a good Socratic partner on the other side of the exchange.

Thompson also acknowledges that all writing has always involved reaching outside the writer’s mind for ideas, facts, editing, and fact-checking. The line between legitimate assistance and illegitimate outsourcing has always been blurry, and AI didn’t create that blur. It just made it impossible to ignore. Thompson’s own conclusion: “We should be honest and open about the blur rather than declare everybody with an open Claude window a part of the slopclass.”

Where does that leave disclosure? Many good folks and top academic journals suggest complete AI-use disclosure as a solution. But disclosure is not sustainable in a game-theoretic sense: disclosers bear reputational costs while secret users free-ride, so the equilibrium pushes toward non-disclosure. People like Megan McArdle and I are all living proof of this problem. And the better framing, as Ryan Briggs puts it, is that AI complements expertise: automated RAs checking your work, formalizing arguments as you go, gathering data on demand. It’s a multiplier that lets capable people think better and faster.

My sense is that the only case where disclosure is genuinely owed is when the audience has a reasonable expectation of fully human-produced work, and that expectation is part of what they’re paying for. The best analogy is a live concert in the narrow case of personal or creative writing: if you show up expecting a live performance and the artist is lip-syncing, that’s a legitimate grievance. More precisely, disclosure is owed when non-disclosure would mislead the audience about what they are getting. A journalist publishing under a byline is promising accountability and originality, not keystroke provenance. A memoirist is promising both. The test is what the author is putting their name on, not whether a tool was involved.

But science and journalism are not live shows. They’re about discovering and sharing new knowledge. Nobody discloses that they used spell-check, research assistants, or a Google search. The norms for creative writing and art will be different, rightly so, because audiences there are partly paying for the human experience of making the thing. Humanities will probably land somewhere in between through some lengthy and contentious process, with many friendships being broken. But for research and journalism, “disclose everything” doesn’t get us where we need to go. What readers should care about is accuracy and whether the author takes full responsibility for what’s on the page. Provenance is a much weaker signal than either of them.

28. Nobody knows anything, myself included. That’s OK—we’ll figure it out together.

I initially shied away from talking to journalists about AI because I’m not an expert on it in any meaningful sense. But I’m increasingly realizing that nobody is an expert on whatever this all is, not really, and waiting for one to appear is how you end up with no norms at all. I still shy away from giving any advice beyond installing Claude Code (or your agentic tool of choice) and talking to it to solve a problem you have at hand.

As usual, Arthur Spirling was blunt on this point: the AI-academic accounts that constantly offer “advice” to colleagues and PhD students, as if they were in the labs developing the models, are tedious. They’re spectators, like the rest of us.11 Even the people in the labs have no idea how good the models will get beyond a few months.

So we’re in an awkward position: somebody has to start establishing the norms of the new workflow environment, and the people doing it will inevitably be people who don’t fully understand what they’re building norms for. That’s weird and uncomfortable. It’s also how most other institutional transitions have probably worked, though at a much slower pace.

We went through a version of this that seems like ages ago, when professors had to accept that policing students’ AI use fully and meticulously was neither productive nor possible. The same is now true for researchers. You can audit the parts that actually matter, like the replication code, and LLMs already make it easier. Provenance is the one thing you can’t audit, and it’s the thing we shouldn’t be spending energy on anyway. You have to follow the incentives and build systems that ensure the tools are used well, not pretend they aren’t being used at all.

On a related note regarding teaching, I’ve been on leave, so I’ve shied away from advice here too. Given the many questions I’ve received, I’m more pessimistic about the effects of AI on teaching than on research. For instance, while I use AI openly and extensively in my research and writing, I plan to ban all electronic devices in my substantive classes and bring back in-person written and oral exams.

These are not contradictory positions. The classroom is precisely where students need to build the cognitive skills that make AI collaboration productive later. You cannot meaningfully direct an AI tool if you never learned to think without one. The skill-atrophy concern from Part II is most valid here: students need to internalize the fundamentals before they outsource them. Research is where you deploy the best tools at your disposal to produce a worthy outcome. Teaching is where you make sure the next generation can actually use those tools well.12

29. The best work happens when humans and AI collaborate.

I still see many folks pledging not to use AI in their writing. This is about as sensible as pledging not to accept help from research assistants or co-authors. So, I pledge the opposite: I will use the latest LLMs, and for that matter any other available tool or human co-author, to best improve my research or the way I communicate it. That way, if my name is on it, you can be sure it reflects my own best judgment.

Many academics, especially in the humanities, still seem to believe that it matters how much time you spend on doing your work. But the folk labor theory of value is wrong in economics, and it is wrong here as well. If you’re a weirdo who wants to spend years running all your robustness checks manually with matrices inverted by hand instead of running a few R commands or manually translating or transcribing original manuscripts, that’s on you. I didn’t care much if those international studies professors at ISA spent 10 or 100 hours on their human slop presentations. The work is either good or it isn’t.

That said, there are some norms worth establishing. When you write “I believe” or “I feel,” that first person should genuinely be you. The pronoun “I” carries an implicit promise of a human voice behind it. A factual claim, like the correct name of NATO, doesn’t care who typed it. But personal conviction does. Think of a live concert: the audience pays for a real performance, not a lip-sync. When you use “I,” the reader is entitled to expect that you mean it. That doesn’t require literally typing (or dictating) every word yourself. You can direct the model, work from your notes, and read it over carefully, but it does require that the conviction is yours.

This piece, for instance, was openly and proudly written using an iterative back-and-forth conversation with Claude Code, based on my original ideas, conversations with peers, and useful suggestions by human academic and non-academic colleagues.13 Part I experimented with AI capabilities with no human editing. Part II reflected with 100% human voice. Part III, which you’re reading now, combines my voice with AI capabilities, completing the circle. I’ll let you decide which one was best.

So, where do humans retain an edge? As Yiqing Xu argues, probably in open-ended environments where training data doesn’t yet exist and in tasks that require direct human interaction. This may change soon. But we chose this career because we find meaning in figuring things out, not just because we’re still better at it. That’s why I don’t see any contradiction in outsourcing most grunt work to AI (or RAs, if you hold traditional values) and focusing on what gives us the most meaning—coming up with important questions about the world and answering them with the best tools available.

30. The real risk in science is human slop at AI speed. We can still prevent it.

AI amplifies whatever you bring to it. Bring genuine curiosity and hard questions, and AI will help you produce something worth reading. Bring nothing, and you’ll produce nothing faster.

But there’s reason for optimism. The same Nature findings that revealed how broken reproducibility is also showed that journals with mandatory data and code sharing had meaningfully higher reproducibility rates. AI can already accelerate this: nothing but inertia precludes journal editors from instituting automated reproducibility checks and mandatory code verification as a condition of submission. These systems can be set up as quasi-deterministic, with very little room for error, and authors can always challenge an automatic desk rejection if they believe the in-house agentic workflow failed to determine how to reproduce their code.

More ambitiously, AI lowers the cost of doing the kind of large-scale, data-intensive work that used to require being Raj Chetty with a team of 30 research assistants and access to every administrative dataset in the country, or being Daron Acemoglu… with pencil and paper. A junior scholar with Codex can now attempt projects that would have been logistically impossible five years ago. The bar for what gets published should rise, because the bar for what can be produced already has.

The standard hasn’t changed: if you put your name on something, you stand by it. Judge the quality of the output, not the process. The conversation about AI slop is a distraction from the harder question, one we should have been asking long before the chatbots arrived: why have we been tolerating so much human slop in the first place?

***

My goal when I posted Part I was simple: to bring the conversation that was already happening behind closed doors and in DMs into the open. Still, I keep hearing the same thing from my colleagues: “Alex, we know you use AI, we do it too, but can’t you just be quiet about it?”

I understand the fear that AI will be used irresponsibly, producing more slop than it prevents. But if you think about it, “be quiet about it” is just advocating for universal hypocrisy as a professional norm. Part of what I wanted was for the uncertainty everyone is experiencing about their workflows and their futures to reach a more stable equilibrium. That requires honest conversation, not silence.

And it is already happening. Emily Oster recently shared Isaiah Andrews’ advice on AI for MIT PhD students in economics, calling it something that should be circulated to all PhD cohorts. As noted, the most important thing about it was not any particular advice per se but having high-profile faculty send a clear signal: this is something you need to take seriously. Even , one of the most thoughtful people in journalism, recently admitted the AI people have been right a lot.

The change is real. Academics are already half-awake, and they are not going back. Aspiring grad students and junior scholars should tread carefully but embrace learning about and using AI tools fully. The scholars, intellectuals, and writers who refuse to engage will not be rewarded for their purity. They will simply be outperformed by colleagues who bring the same intellectual rigor, the same curiosity, and better tools.

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Here’s where I think the argument turns:

Many of my academic colleagues felt differently, however. To them, “I just oppose illegal immigration” was a socially acceptable way to express opposition to foreigners in general—xenophobia dressed in procedural language. I have been guilty of dismissing popular attitudes myself; when giving lectures about, say, the H-1B visa backlog or refugee processing times, I have found myself exasperated by audience members who stand up to ask why I haven’t gone out of my way to condemn illegal immigration.

Over the years, many scholars and advocates thus came to see the distinction as illegitimate. For some, the American immigration system is already so unfair and restrictive—fewer than 1 percent of people who want to immigrate can do so legally—that saying “Just follow the rules” can feel cruel. Others go further: Unauthorized border crossing is a victimless regulatory violation, they argue, and any law restricting people’s free movement is unjust.

I now think this dismissal was a mistake. The norm was imperfect and often insincere—but it was holding something together. Now that it’s gone, I’ll try to be a little less annoyed the next time someone in my audience stands up to ask why I haven’t condemned illegal immigration. Turns out that was the good version of the conversation after all. Read the full piece here (archive link).

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Some personal news first. I finally bought a Japanese toilet. A TOTO Nexus WASHLET+ S7A, to be specific. If you read my piece on Japan last year, you know I came back radicalized about how the Japanese make familiar things work better. The toilet is one of them. Heated seat, genuinely life-improving in ways it’s not possible to describe in polite company. It’s also arguably a progress and a policy design issue just like the shower heads: the technology has existed for decades, it’s demonstrably beneficial, and most Americans would love it if they tried it. The main barrier is outdated plumbing and electric codes, and the fact that nobody in power has bothered to update regulations that would make installation easier and cheaper. Sound familiar?

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Despite spending too much time on social media and receiving a fair share of death threats for my hot AI takes (I had to deactivate my Bluesky account), March was, by a wide margin, the most productive month in my entire academic career. Somehow, thanks to delegating grunt work to Claude Code, I’ve now actually been able to write more and better artisanal, hand-crafted human prose than ever before. Here is everything I published this month:

• On Popular by Design, I wrote “Academics Need to Wake Up on AI” and Part II (with Part III coming soon), “The Gay Marriage Playbook Won’t Work for Immigration”, “Public Engagement Is Good for Your Research”, and “What Is Populism Actually Good For?” (with Yaoyao Dai).

• I published guest posts on ’s Substack: “Spread the Word: Legal Immigration Is Incredibly Difficult” (with Michelangelo Landgrave). Marc Helbling and I wrote “How We Miscategorize the Categorizers” for Futures of Difference. and I co-authored “Why America Is So Much Better Than Europe at Immigration” at . Check those out if you haven’t yet.

• If you’re into the audio format, I also appeared on three podcasts (one still forthcoming): Opinion Science with Andy Luttrell and Money & Macro Talks, with more to come soon.

• And lest anyone wonder whether all this public writing comes at the expense of “real” research: I also published two peer-reviewed pre-registration reports: one with Yaoyao Dai, “What is Populism Good for?” in Research & Politics, on the mobilization effects of populism, and another (still in progress) on “Preventing Backlash by Shifting Issue Priorities: Immigration and Depopulation in Japan” in the Journal of Experimental Political Science (with Akira Igarashi, Rieko Kage, and Seiki Tanaka).

Before we get to the links, two quick questions for you all.

Drop your answers in the poll or the comments. The ideology question was inspired by interesting conversations I’ve been having with readers who assume very different things about where I sit politically. The branching-out question is genuine: some of my most popular posts this month were not about immigration at all, and I want to know whether that reflects what you actually want more of.

Here are the March links (linking does not imply endorsement):

• The Supreme Court will hear Trump v. Barbara this spring, challenging the executive order that would strip birthright citizenship from children whose parents lack permanent legal status. My colleague Amy Hsin at Notre Dame’s Keough School co-authored an amicus brief laying out the social science evidence against it. Among the numbers: Phillip Connor, Matt Hall, and Francesc Ortega estimate that birthright citizenship beneficiaries will contribute $7.7 trillion to the U.S. economy between 1975 and 2074. Revoking it could deny citizenship to 4.8 million U.S.-born children by 2045.

  • My other Notre Dame colleague Ashley Sanchez at The Conversation has a useful explainer on the Trump administration’s new interpretation of refugee detention rules. Legal refugees who haven’t yet received green cards now face prolonged detention under a reinterpreted DHS statute. Another case of the administration using administrative tools to restrict legal pathways without passing new legislation.

• (follow him!) at the Niskanen Center has a great piece in City Journal on assimilation. He looks at areas of the country with the best assimilation outcomes a century ago (measured by second-generation out-marriage) and asks what that tells us about the challenges we face today.

• My friend Hannah Postel (who should absolutely start a Substack) at Duke Sanford has new work in the Journal of Economic Perspectives on how U.S. immigration law shaped a century of Asian American immigration. Exclusion laws kept the Asian population below 1% of the U.S. total for nearly a hundred years. After the 1965 act abolished national-origin quotas, the Asia-born population grew by roughly 2,700%. The characteristics often attributed to Asian American "culture" like high educational achievement trace back to which entry pathways policy made available. A powerful case that policy design determines not just how many people come, but who comes.

• has a thoughtful piece on identity politics and the case for making identities broader in liberal democracies. Connects to the immigration debate in ways he doesn’t fully spell out but that readers of this newsletter will recognize: narrow identities are what make immigration politically toxic, and policies that encourage broader identification can defuse that.

• The Friedrich-Ebert-Stiftung published a brief on progressive immigration policy in Germany and the U.S., authored by Hannah Tyler, Stephanie von Meien, and Cristobal Ramon. I disagree with some of their points, but the general framing is close to what I’ve been arguing: humane and flexible policies that serve the national interest are good or at least better than the status quo. Restoring public confidence in the immigration system should be among the top priorities.

• tested whether AI can detect known errors in published papers. GPT (5.4) scored 5.9 out of 10 on errors in Moretti (2021), an AER paper with a huge number of identifiable problems. Human reviewers missed all of them the first time around. If journals adopted systematic AI checks, even an imperfect system would catch mistakes that currently sail through review.

• at WYSR has a piece comparing nanotech hype in the 2000s to AI hype today. The comparison is instructive. Nanotech was supposed to change everything, attracted massive federal funding, and produced genuine scientific advances that quietly found their way into useful products. But the transformative revolution didn’t arrive on the timeline the boosters promised.

• on the credential as a democratic institution. An argument about why expertise and credentials still matter in a populist moment. Connects to a question I keep coming back to: if populism is partly a rebellion against credentialed gatekeepers, how do you defend the value of expertise without sounding like you’re defending your own guild?

• has a new paper forthcoming in International Migration Review testing whether high skills insulate immigrants from ethnic bias. In a pre-registered experiment with British respondents, a Black South African doctor was rated less welcome than an identical White one (but not in the case of fast food workers). It's a well-designed study, but, with all due respect to Alexandre and his work, I worry this is also a common case of missing the forest for the trees.

  • The detected racial penalty is about 0.3 points on a 10-point scale, with race explaining less than 1% of variance. The skill premium, by contrast, is at least 7 times larger (2.3 points), explaining >20% of variance. If anything, this paper shows how remarkably little ethnicity moves the needle once you hold other factors constant. As I keep beating this drum, skilled immigration is popular regardless of ethnicity, and we never lose sight of that.

• at Field Notes on Progress has the best headline of the month: “The AI Revolution No One’s Talking About.”1 It’s about artificial insemination transforming dairy and beef farming. Really fascinating read, especially if you haven’t thought about the politics of cattle and dairy before.

• Published today: Madeleine Sumption's What Is Immigration Policy For? (Bristol University Press). I simply can’t recommend the book enough. Sumption directs the Migration Observatory at Oxford and knows more about what actually happens when immigration policies meet reality than almost anyone alive. The book explains why so many governments struggle to design immigration policies that people trust, and why many find current arrangements unsatisfying. The essential guide I wish every policymaker, advocate, and journalist would read before entering the immigration debate. Exactly in line with the spirit of Popular by Design, the book won't tell you what to think, but it will change how you think.

As before, if you want me to write more about one of these or other related topics, let me know!

Why does yelling about “corrupt elites” seem to work in politics? From Donald Trump to Viktor Orbán to Marine Le Pen, politicians who rail against the establishment and claim to speak for “the real people” keep winning elections. The populist playbook, us versus them, the pure people against the rotten elite, appears to be one of the most effective strategies in modern democratic politics. But what if it isn’t?

I’ve spent the better part of a decade studying populist rhetoric with my co-author Yaoyao Dai, now at the University of Pittsburgh. We just published our third and latest paper on the topic, and I thought this was a good moment to reflect on what our research program has found. The short version: populism’s power is real, but much more limited than most people assume. And the reasons why it works are not what you’d expect.

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What we mean by populism

Before getting into the findings, a quick definition. Political scientists generally follow Cas Mudde’s influential framework, which defines populism not as a full political program but as a simple worldview (or what Mudde calls a “thin ideology”). This worldview is based on three pillars: people-centrism (politics should reflect the will of “the people”), anti-pluralism (there is one authentic popular will, not many competing interests), and moralized anti-elitism (elites are not merely wrong but evil). This is what scholars call “thin” populism because it doesn’t tell you much about actual policy. A left-wing populist like Hugo Chávez and a right-wing populist like Trump share the same rhetorical structure, the people versus the elite, but disagree on virtually everything else.

This distinction between populism and its “host ideology” (the actual policy positions a politician holds) turns out to be crucial. Because when you peel them apart, something surprising emerges.1

When politicians gamble on populism

Our first paper, “When Do Politicians Use Populist Rhetoric?“ published in Political Communication in 2022, asked a deceptively simple question: if populist rhetoric is so effective, why don’t all politicians use it all the time?

To answer this, we built the most comprehensive corpus of U.S. presidential campaign speeches at the time: 4,314 speeches from 1952 to 2016. We used a novel text analysis method combining active learning and word embeddings to measure how much populist rhetoric each candidate employed across the campaign trail. I (Alex) should say, thanks to the prowess of Yaoyao, we did all that fancy text analysis stuff before it was cool and before LLMs were even around.

The pattern was striking. Candidates who were trailing in the polls consistently used more populist rhetoric, regardless of whether they were Republicans or Democrats, incumbents or challengers. Populism, we argued, is a gamble: a high-risk, high-variance strategy that trailing candidates adopt because conventional campaigning isn’t working. If you’re already behind, why not shake things up?

Think of it like a football team that’s losing in the fourth quarter. You start throwing long passes not because they have a higher expected value, but because safe plays guarantee you lose. Barry Goldwater, George McGovern, and Donald Trump (in 2016, when most polls had him behind) all fit this pattern. They reached for populist rhetoric when they had little to lose.2

The (in)effectiveness of populist rhetoric

But does the gamble actually pay off? Our second paper, published in Political Science Research and Methods in 2024, tested this directly with a survey experiment.

We presented U.S. respondents with pairs of realistic campaign messages from hypothetical primary candidates. The messages varied on two dimensions: populist features (people-centric language, anti-elite attacks, anti-pluralist framing) and substantive policy positions (on immigration and other issues). This design let us isolate the effect of populist rhetoric from the underlying policy content, something that is nearly impossible to do when observing real elections, where populism and policy positions come bundled together.

The result was unambiguous: none of the populist features had an independent effect on candidate choice. Not people-centrism, not anti-elitism, not anti-pluralism. Not individually, and not in combination. What did matter, enormously, were policy positions that aligned with voters’ own preferences. Voters chose candidates based on what they promised to do, not on how dramatically they framed the conflict between the people and the elite.3

This finding is consistent with other experimental work. When researchers across multiple countries carefully separate populist style from policy substance, the style itself contributes very little to voter decisions.

So: if populist rhetoric doesn’t actually persuade voters, why does it seem to work? Why do populist candidates keep winning?

What populism is actually good for

This puzzle motivated our newest paper, our first ever registered report (where scholars publicly specify their hypotheses before running their experiment), now published at Research & Politics. We hypothesized that populism’s real contribution might not be persuasion but mobilization: getting people who already agree with you to actually show up and vote.

Previous studies, including our own, used what’s called a “forced choice” conjoint experimental design: respondents had to pick one candidate or the other. But in real elections, people can also stay home. To capture this, we ran a large-scale, preregistered survey experiment that added an “abstain” option, a seemingly small change that turns out to matter a lot.

What did we find? First, the basic persuasion result replicated: policy positions still dwarfed populist rhetoric in driving vote choice. Having a policy-congruent candidate increased the probability of voting by a massive 27 percentage points. Populist rhetoric, by contrast, had no meaningful persuasion effect.

But here is the twist: populist rhetoric did have a small but statistically significant mobilization effect. Having at least one populist candidate in a race was associated with a ~1.5 percentage point decrease in abstention. The effect was concentrated among voters who already held populist attitudes and encountered a candidate whose policy positions they liked. In other words, populist rhetoric didn’t convert skeptics; it energized true believers to get off the couch.

Meanwhile, non-populist voters did not appear to punish their preferred candidates for using populist rhetoric. This asymmetry is key: populism is a low-cost mobilization tool. It fires up your base without alienating persuadable voters.

Are hypothetical but cleanly identified ~1.5 percentage points a lot? In most elections, no. But in a close race (and modern elections in the U.S. and Europe are often decided by razor-thin margins) even a small mobilization advantage can be decisive. This may help explain the apparent paradox: populist rhetoric doesn’t change many minds, but it doesn’t need to. It just needs to get a few more supporters to the polls.

The media amplification question

There’s one more possibility worth considering: the role of the media. Populist rhetoric is, almost by design, dramatic and newsworthy. When a candidate calls the entire political establishment corrupt and claims to be the voice of the forgotten people, that generates coverage, and coverage generates name recognition, which generates votes.

The most vivid illustration is Trump’s 2016 campaign, which received an estimated $2 billion in free media during the primaries alone, far more than any rival. Much of that coverage was driven by his populist style: the outrageous claims, the attacks on the “swamp,” the rallies designed for television. Journalists couldn’t look away. And there is some evidence in the growing media populism literature that this pattern generalizes beyond Trump, with populist candidates across countries receiving disproportionate media attention relative to their actual electoral standing. That’s what our Notre Dame colleague Marc Jacob recently found too, but in the case of negative politics and political insults more generally—it captures and generates attention.

If populist rhetoric’s main benefit is generating outsized media attention, which then translates into awareness and mobilization, then the mechanism isn’t really about what populism says to voters. It’s about what populism says to journalists. This is consistent with our finding that populism mobilizes rather than persuades. But the media-amplification hypothesis still needs direct testing, and ambitious PhD students should certainly take this on (unless we or our Claude Code get to it first).

What does this all mean?

So what is populism actually good for? Based on our and other recent research, we’d summarize it this way:

1. Populist rhetoric is a gamble, adopted primarily by candidates who are already losing. It’s a variance-increasing strategy, not a winning formula.

2. It doesn’t persuade. Voters care about policy positions, not populist framing. The “host ideology,” what you actually promise to do, matters far more than how dramatically you frame the people-versus-elite conflict.

3. It may slightly mobilize, particularly among voters who already hold populist attitudes and agree with the candidate on substance. This is a modest but potentially consequential effect in close elections.

4. Media amplification may be a key mechanism, turning populist drama into disproportionate coverage. But we need more direct evidence.

The biggest takeaway, both for those who fear populism and those who are tempted by it, is that substance matters more than style. Politicians who deliver tangible results, or credibly promise to, will outperform those who simply shout louder about corrupt elites. This is consistent with what I (Alex) argue in my recent book, In Our Interest, in the context of immigration: policies that are demonstrably beneficial do more to win and maintain public support than any amount of rhetorical framing.

Populism is not nothing. But it’s not the all-powerful electoral weapon it’s often made out to be. Don’t confuse volume for effectiveness. The people who keep winning elections on populist platforms are winning mostly because of what they promise and do, not because of how they talk about it.

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This piece is more personal than usual. After my recent posts on AI in academic writing, I received a wave of private messages from fellow academics who agreed with my hot takes but wouldn’t say so publicly. My first instinct was to write about self-censorship in the academy. But the problem runs deeper. Most academics don’t want to engage with the public at all. This piece is about why that’s self-defeating, and why many of my colleagues are getting it wrong.1

A few years ago, I gave a talk at a retiree center in Charlotte, North Carolina, about my research on public attitudes and making immigration popular. Before I could even start, an older woman in the back raised her hand. “Why,” she asked, “would we want to make immigration popular in the first place?” No academic colleague had ever asked me that question before. Although I wasn’t able to bring her to my side fully, it turned out to be one of the most productive conversations I’ve had about my research with anyone.

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I am increasingly convinced that for social scientists and academics, engaging with the public is not a distraction from research but a direct input to it. The audiences you encounter outside the seminar room, the questions journalists ask, and the pushback from readers who have no stake in your theoretical framework: these are all important data. They reveal blind spots that insular academic communities systematically miss. Public engagement also forces you to justify, in plain language, why your work matters, which turns out to be a surprisingly effective filter for figuring out whether it actually does.

The standard academic view treats public engagement as a trade-off: time spent writing for popular audiences is time not spent on “real” research. I will argue the opposite here. My own experience and the experience of researchers I admire suggest that talking to non-academic audiences, writing for the public, and presenting research to people who might genuinely disagree with you make your scholarship sharper and more honest. It does this by stress-testing our ideas against the one audience that academic peer review systematically excludes: the very people researchers claim to study.

What engaging the public taught me peer-review didn’t

One of my most cited findings on immigration opinion came not from a faculty seminar but from conversations with policymakers in Washington. They all kept telling me the same thing: even when polls show majority support for more liberal immigration policies, politicians still will not touch the issue. The anti-immigration side simply seems to care more. This observation never came up in the academic literature I had been reading, where the focus was almost entirely on why people oppose immigration, not on how much they care about it in the first place, including the pro-immigration side.

That disconnect led me to a paper documenting what I called the “issue importance asymmetry” in academic-speak, describing the simple fact that anti-immigration voters are consistently several times more likely to rank immigration as their most important political issue compared to pro-immigration voters. This holds across decades in the US, the UK, and Europe. It is one of the most consistent findings in immigration research. And it started with listening to people outside academia who were closer to the political reality than most of my colleagues.

This was not a one-off. People outside the university often see things that people inside it miss, not because they are smarter, but because they are working from a different set of assumptions. When the overwhelming majority of your colleagues share the same political priors, certain questions never get asked. I have written about how well-meaning colleagues have suggested I soften findings that might “feed the far right,” even when the results were solid. That kind of filtering is invisible inside the academy. It becomes very visible the moment you share the unfiltered version with a public audience and discover they find honesty more credible, not less.

My piece arguing that Western countries do not “need” immigration grew directly from this. Voters who heard experts claim economies would collapse without immigration and saw their country functioning just fine concluded the experts were dishonest. The reframing came from paying attention to what skeptics actually found persuasive, not from academic theory. Similarly, when I wrote about community sponsorship, I highlighted polling showing that 73 percent of Republicans supported the Welcome Corps, a U.S. pilot of the sponsorship program, because it taps into conservative values of localism and faith. Most immigration scholars had not even thought to test whether the right might support refugee resettlement, because the academic framing treated it as exclusively a humanitarian, left-wing cause.

The wisdom of an old Charlottean

Let me tell you more about that Charlotte talk. The audience was conservative and very old, and the woman who challenged my premise was not the only skeptic. Before I could start, an agitated man asked, as a gotcha question, whether I believed Americans had the right to secure their border. I said yes. He almost seemed disappointed I did not proclaim “no human is illegal” or something to that effect. He sat back and calmed down.

After the woman said we did not need any foreigners, I agreed that immigration is a challenging issue and asked whether she thought we should also stop, say, German engineers from coming. She thought for a few seconds and then said, “Of course not.” Within a few minutes, we had moved past the headline positions and were having a genuinely productive conversation about which specific immigration policies she did and did not support, and why. In the end, folks tried to listen to me despite all the hearing problems in the audience for the rest of my presentation.

No academic audience had ever forced me to defend the premise of my research in quite that way. It made me rethink some of the ways my colleagues and I frame our questions and answers. We often assume that the value of studying what makes immigration policies popular is self-evident. It is not, and discovering that in a room full of retirees was more useful than discovering it from a reviewer comment.

Engaging with the public also improved my writing, and more so than LLMs ever could. When you have to translate a complex finding into a sentence that a non-specialist can follow, you quickly discover whether you actually understand it yourself. The vagueness that academic peer reviewers sometimes wave through does not survive a comments section or even a relatively shallow journalist’s follow-up question.

When jargon replaces argument

This brings me to an uncomfortable observation about a certain kind of academic work that I think public engagement would cure. Some research, particularly in what is called “critical” or “postmodern” scholarship, has become so insulated from public scrutiny that it is almost impossible to explain what it is actually saying, or why it matters.

I recently attended a seminar by Charmaine Chua, a geographer now at Berkeley, who presented research from a forthcoming book based on fieldwork aboard a container ship. The underlying empirical work was genuinely fascinating, on top of her great photography: vivid, detailed observations about the enormous salary disparities among crew members based on national origin, and the daily mechanics of global shipping that most people never see.

But the framing was almost entirely directed at an audience of critical geographers and “abolitionists.” Every observation had to be routed through Marx or David Harvey. One framework had to be “connected” to another framework, which had to be “put in conversation” with a third. There is a real story here about global inequality and labor exploitation, and it was being buried under layers of disciplinary performance.

In fairness to Chua, she has also written for popular outlets like Boston Review and Jacobin, translating her shipping research into language that (at least highbrow, left-wing) non-academics can engage with. She is, in that sense, doing the kind of public-facing work I am arguing for here. But the gap between the seminar version and the public version was striking. Even though we may disagree politically, I suspect her public version was much better. And not just because it was more accessible, but because the discipline of writing for a general audience forced clearer thinking about what the research actually shows.

This is not an isolated case, and the problem is that the vast majority of critical and empirical scholars alike do not go beyond publishing their work in obscure journals nobody reads. When research is never exposed to audiences who might say “I don’t understand what you mean” or “why should I care?”, it can drift into a self-referential loop where the work exists primarily to satisfy disciplinary gatekeepers. Public engagement is a corrective. It forces you to answer the question that every taxpayer has the right to ask: What is this for?

But engagement is not activism

I want to draw a distinction here that often gets lost. Public engagement is not the same thing as political activism. Confusing the two has done real damage, particularly in fields like sociology and political science, where “activist scholarship” has become an identity rather than a practice.

Activist scholarship’s problem is not that scholars have political views. Everyone does after all. When the scholarship itself is oriented toward a predetermined political conclusion, it stops being scholarship in any meaningful sense. And in practice, activist scholarship has tilted overwhelmingly in one ideological direction, which has undermined the credibility of entire fields. This includes hard science, too. Even the scholars doing this work would benefit from making their research more accessible to audiences beyond their own political coalition, because accessibility invites challenge, and challenge is what separates inquiry from advocacy.

What I am describing is closer to what calls the “problem-solving” approach to social science. Samii argues that social scientists should orient their work toward clearly defined societal problems, using normative analysis to identify what needs fixing, observational research to understand why, and experimental methods to test what works. This is distinct from both “disinterested” puzzle-solving (which often produces technically impressive work that no one outside the discipline reads or needs) and from activist scholarship (which knows the answer before the question is asked). Problem-solving research takes sides on the problem, not on the politics. It asks: Does this policy work? How do we know? What should we try instead?

That framework describes what I try to do with my own research and public writing. I’m sure I have my own biases and blind spots, but my Substack is decidedly not an advocacy project. It is an attempt to make research that is often locked behind paywalls and disciplinary jargon available to the people, including policymakers, journalists, and voters, who could actually use it. And the process of doing that has made my research better, not worse, because it forces me to change my mind on issues every once in a while.

This does not have to be an individual effort. Some departments have made public engagement part of their institutional identity. George Mason’s economics department with probably the single highest concentration of influential bloggers is a good example: serious, well-published researchers who also shape public discourse on the issues they study even when they disagree (e.g., compare and on immigration). More social science departments, and especially public policy schools, should follow that model. The infrastructure for combining fundamental research with public influence already exists. Most places just choose not to use it.

Taxpayer-funded research belongs to the public

There is also a straightforward accountability argument for public engagement that I think deserves more weight than it usually gets. Most social science research in universities is funded, directly or indirectly, by taxpayers. The National Science Foundation, the National Institutes of Health, and state legislatures fund the grants, the labs, and the salaries. Taxpayers underwrite the whole enterprise.

That creates an obligation. Not an obligation to oversimplify, or to produce findings that voters will find convenient, but an obligation to make the work legible. If you cannot explain to a non-specialist why your research question matters and what you found, that is worth examining. Sometimes the explanation is genuinely difficult because the work is methodologically complex, and that is fine. But you should at least be able to explain why the methodological complexity is necessary and what it is in service of.

I think this test is actually useful as a self-check. If I am working on something and I find that I genuinely cannot explain to a thoughtful non-academic why it matters, that is a signal that I should reconsider either the framing or the project itself. Not everything that is publishable is important. And not everything that is important is inaccessible. The exercise of translation is also an exercise in self-honesty.

There is a more basic point here that often gets lost. Academics are not just academics. They are also citizens, presumably interested in contributing to the public good. It makes sense to do that using your expertise rather than compartmentalizing it. When I see colleagues who study migration and its political implications but never comment on the topic publicly, while sharing their hot political takes on Facebook anyway, it strikes me as a missed opportunity. The idea that you can wear a professor hat and a citizen hat and never connect them does not hold up for most social scientists. You are already a citizen with political views. You might as well be one with informed political views who shares the basis for them.

Yes, it costs something. But you should do it anyway.

Many academics have been told, by colleagues or even their dean, not to spend too much time on public engagement, or warned not to say something publicly that would embarrass their college. If this is advice against posting on social media without any serious research work behind it, that may be quite sound. After all, unless you are at a public policy school, even a piece in The New York Times will not count for much in your annual review, let alone toward tenure. So I do not want to pretend that public engagement is costless.

The most obvious cost is time. Writing a Substack post or giving a public talk takes hours that could be spent on a paper. For junior scholars without tenure, your promotion committee probably will not count your Boston Review essay or your popular podcast appearance. The incentive structure of academia still largely rewards journal publications, grant funding, and citations from other academics.

Then there is the social cost. Colleagues who view public engagement as unserious can be quietly dismissive. I have experienced this myself. Not as direct criticism, but as a certain subtle skepticism, a sense from some peers that time spent writing for the public is time not spent on “real” work. The signals are usually indirect: a raised eyebrow, a conspicuous lack of interest, the faint suggestion that popular writing is something you do instead of scholarship rather than alongside it.

And there is the online environment, which can be genuinely toxic. Platforms like Bluesky, in particular, have become what I can only describe as a corrupting influence on academic discourse. The incentive structure rewards performative outrage and virtue signaling over substance.

Academics who engage there often find themselves dragged into pile-ons that have nothing to do with the quality of their ideas and everything to do with whether they said something that violated the platform’s ever-shifting ideological consensus. Compare this to long-form platforms like Substack, where the incentive structure at least partially rewards depth and evidence. Not all public engagement is equal, and choosing the right venues matters.

Having said all that, do it anyway. The alternative is worse. recently argued that if you write publicly about contested topics, getting piled on is not a rare catastrophe but a predictable occupational hazard. The question is not whether it will happen but when. His advice is simple: accept the risk and keep writing. Do not let the possibility of a pile-on shape what you are willing to say.

The issue goes beyond managing pile-ons, though. has written persuasively about intellectual courage as a scarce resource. Her most striking observation is that academics regularly message her privately to say they agree with positions she has taken publicly but are unwilling to say so themselves. I have witnessed this firsthand recently with my hot takes on AI in academia:

The courage to state publicly what you believe privately, especially when it is unpopular within your professional community, is not a nice-to-have. It is an epistemic necessity. Truth emerges through open argument. If everyone self-censors, the entire discovery process breaks down. has made a related case that tenured academics possess extraordinary free-speech protections and have a responsibility to actually use them. His own experience suggests that speaking honestly and across ideological lines actually improved his professional relationships and research collaborations. The fear of consequences was larger than the consequences themselves.

I have found this to be true in my own experience as well. After my recent piece challenging pro-immigration misinformation from within the pro-immigration camp or prompting my AI-skeptical colleagues to lock themselves in a room with Claude Code, I received pushback from many quarters. But I was also struck by the number of academics, including left-of-center scholars, who publicly endorsed these pieces that challenged their own side’s orthodoxy. As I wrote at the time, tenured (and untenured) professors should do this more often.

DEI is what happens when no one talks to the public

Let’s talk about faculty hiring for a moment because this is somewhat personal. The standard explanation for why universities went so far off the rails on race-based hiring after 2020 is left-wing bias and self-censorship. People were truly afraid to speak up. There is truth to that. Even influential tenured Harvard professors like Steven Pinker and Jill Lepore found it difficult to challenge the new orthodoxies.

But the deeper problem was that academics simply did not talk to people outside their institutions. Many of the faculty and administrators who embraced racial balancing in hiring genuinely believed they were doing the right thing. They had spent years inside institutions where this logic was so normalized that it never occurred to them to ask whether the public supported it, whether it was legal, or whether systematically excluding qualified candidates on the basis of race and sex might be ethically wrong.

Had they asked, the answers would have been clear. Race-based affirmative action in hiring is extremely unpopular among the American public, and has been for decades. Taxpayers fund universities to advance science and the public good. Nobody is paying us to maintain a particular racial balance among the faculty.

The scale of what happened is now well documented. 's viral "Lost Generation" essay highlighted that white men went from 49 percent of tenure-track hires in 2014 to 27 percent by 2024. At UC Irvine, just three of 64 tenure-track hires in the humanities and social sciences since 2020 were white men (4.7 percent). of the National Association of Scholars obtained internal emails through hundreds of public records requests that showed the machinery plainly: at one NIH-funded program, an administrator wrote "I don't want to hire white men for sure." at the Washington Free Beacon documented similar patterns across the country. I can also tell you about my first-hand experiences of search committee members who invited me to give a job talk telling me candidly that it was not going to happen because of my racial background (of course, most would be smarter than to invite me or say anything at all).

All in all, if you were a white or Asian man on the academic job market in 2020 or 2021, especially one from abroad, your marginal chances of landing a tenure-track position in many fields approached zero, all else equal. The fact that the existing stock of senior faculty was predominantly white and male was no consolation to an ambitious but broke thirty-year-old finishing a PhD. So many brilliant scientists with enormous potential have either become adjuncts with no future or left academia if they were lucky. The harm to science in terms of discoveries delayed or never made is staggering.

So, the collapse of public trust in higher education over this period was quite predictable. Academics knew what was happening. Many disagreed privately. But almost nobody was talking or explaining it to the public or pointing out that these policies had no popular mandate. That silence left the field to culture warriors on both sides and made the eventual backlash worse than it needed to be. It also cost a generation of talented researchers their careers, which is not the kind of thing a healthy profession stays quiet about.

How to actually do it better

So, our grim collective action problems aside, if you are an academic considering more public engagement, here are three things I have found genuinely useful.

Have a functioning website. First, and foremost, for the love of all that is reasonable, have a website. An up-to-date, accessible academic website. I genuinely do not understand colleagues who don’t. The notion that good research will find its audience on its own is optimistic to the point of delusion in an age when people are bombarded with information from every direction.

If you have done the work, make it findable. Thanks to Claude Code, from now on, my own site will be available in a dozen global languages, because accessibility means nothing if it stops at the English-speaking world. This very piece will be available on it in all languages upon publication.

Present your research to people who might disagree with you. This sounds obvious, but surprisingly few people do it, so I cannot recommend this enough. Go to a retiree center or a community forum. Or Bluesky if you’re writing on AI or LGBT issues. The audiences in these spaces are far more politically and demographically diverse than those of any college seminar. They will ask you questions that your colleagues never would, and those questions will reveal whether your argument actually holds up outside the assumptions of your discipline. The woman in Charlotte who asked me why we would want to make immigration popular taught me more in five minutes than many peer review reports have.

Write for the public. Start a blog or a newsletter. It doesn’t have to be a Substack, even though all cool people in academia are increasingly here now. The discipline of writing regularly for a non-academic audience changes how you think. It improves your prose, which then improves your academic papers. It forces clarity. And it opens you up to feedback from people with real-world experience in the things you study. Some of the most useful responses I have received to my Substack have come from readers who challenged my research claims based on their own experience: voters, immigrants, local officials, business owners, and even anonymous randos from the internet. That is a form of peer review that the academy does not provide.

Give pre-recorded interviews and do science podcasts. Popular science and policy podcast hosts ask different questions than academics do. They want to know what your findings mean for people who are not specialists. They push you to be concrete and specific. And they often identify angles that you, embedded in your own literature, have overlooked. They are not interested in any gotchas, so they would send you questions in advance. I have had those folks ask me questions that opened entirely new lines of inquiry, things no academic colleague had thought to raise because everyone in the field took the same assumptions for granted.

What not to do, or do with caution

Do not confuse social media arguments with public engagement. Getting into reply threads on X or Bluesky can feel like engaging with the public, but the incentive structure on those platforms rewards dunks and outrage, not depth. A 280-character exchange rarely changes anyone’s mind or improves your thinking. Long-form writing, in-person talks, and substantive interviews are where the real feedback loop happens. Use social media to share your work and find your audience, not to conduct your debates. And yes, I know I should follow this advice myself more.

Do not wing it on unfamiliar topics. The fastest way to undermine your credibility as a public-facing academic is to opine confidently on something you have not studied. One bad appearance on a topic outside your expertise can overshadow years of careful work within it. If you are asked about something adjacent, either redirect to what you actually know or say “I don’t know enough about that to give you a useful answer.” That sentence, rarely heard from both pundits and academics, tends to earn more respect than a half-informed hot take.

I have personally been asked on multiple occasions to come on news shows and talk about the US-Mexico border crisis, but I politely declined because it is not my area of expertise. Similarly, I now mostly refuse to talk to journalists about AI despite my recent notoriety on the topic, because I am a novice.2 Knowing when to say “this is not my lane” is itself a form of intellectual honesty that builds credibility over time.

Be generally selective with media requests, especially live interviews. If a journalist you know and respect reaches out on a topic you have actually studied, you should absolutely talk to them. Just understand that you will spend a few hours prepping and talking, and you may not be acknowledged or, worse, may be misinterpreted when their piece comes out.

For live interviews in particular, the risks are higher: the time you are given is limited, and you may not know what you will be asked. If someone you have not heard of reaches out, or the topic is adjacent to your expertise rather than central to it, the answer should be a polite no in most cases. Unless you do want to become the maligned stereotypical “talking head,” of course.

I will write more on this soon, but my sense is that with the help of agentic AI tools, scientists and experts should increasingly be able to produce better popular pieces on their own topics than generalist journalists can.

What’s lost when researchers stay silent

The stakes of this argument go beyond individual careers. When researchers with genuine expertise refuse to engage with the public, they leave a vacuum. And that vacuum gets filled by journalists and pundits without relevant training and advocates with axes to grind, and eventually by politicians who find it convenient to misrepresent what the evidence shows. The result is a public discourse about science topics that is poorer, more polarized, and more detached from evidence than it needs to be.

I have written at length about how “highbrow misinformation” develops when academic research gets filtered through advocacy groups and media outlets that strip away caveats and complexity. One way to fight this is to cut out the intermediaries. Not by replacing them entirely, but by making sure that the original researchers are also in the room, in the comments section, on the newsletter, explaining what their findings do and do not show.

The false trade-off between “serious scholarship” and public engagement has real consequences. It keeps good research invisible and lets bad arguments go unchallenged. It deprives researchers themselves of the feedback that would make their work better. If you are a scientist sitting on findings that matter, and you are not making them accessible to the people they are about, you are leaving value on the table for your field and for the people your research claims to serve.

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A few years ago, while presenting my research on immigration attitudes to a room of policy advocates, I was politely but firmly told that studying what makes immigration policies more or less popular was not really necessary. Immigration support, my influential interlocutor explained, was already growing steadily, much like support for same-sex marriage. We just needed to keep telling people that immigration is good, correct the misinformation spread by bad actors, and wait for the generational tide to carry us forward. Why bother designing policies for popularity when popularity was already arriving on its own?

I have seen and heard versions of this argument more times than I can count. The comparison between immigration and same-sex marriage has become something like conventional wisdom among progressive advocates, a comforting story about the arc of public opinion bending toward openness. And it is not hard to see why the analogy is tempting. Both causes involve expanding rights and freedoms, face opposition rooted in cultural anxieties, and have seen meaningful shifts in public attitudes over recent decades.

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The post-2024 reckoning over progressive immigration strategy has only reinforced the comparison. As the Trump administration’s enforcement measures take hold, public opinion is swinging back in a more pro-immigration direction. To many advocates, this looks like the tide turning, much as it did for gay marriage, and it would seem to validate the theory that immigration supporters should continue to focus on messaging. But support for same-sex marriage rose steadily for two decades and then locked in place (with some minor fluctuations): once Obergefell v. Hodges settled the legal question and millions of Americans came to know gay and lesbian people in their own lives, there was no mechanism to reverse the shift.

Immigration opinion, as I will try to convince you in this piece, does not work like this. The gay marriage analogy is wrong in ways that matter enormously for strategy. And the longer immigration advocates cling to it, the longer they will delay the kind of work that could actually make progress possible.

The triumph that became a template

The success of the same-sex marriage movement in the United States is genuinely extraordinary. In 1996, when Gallup first asked Americans whether marriages between same-sex couples should be legally valid, just 27 percent said yes. By 2015, when the Supreme Court decided Obergefell v. Hodges, that number had crossed 60 percent. Today, it sits around 69 to 71 percent. This is one of the fastest and most dramatic opinion shifts in the history of American polling.

The movement achieved this through a combination of moral clarity, personal storytelling, and strategic litigation. Advocates refused to settle for civil unions. They framed their cause around love, commitment, and family, values that resonated across ideological lines. And crucially, as more gay and lesbian Americans came out to their families and communities, abstract opposition gave way to personal connection. It was, by almost any measure, a masterclass in social change.

It was also, as has argued, a deeply unusual case that progressives mistakenly adopted as a universal template. The gay marriage playbook (refuse compromise, frame opposition as bigotry, deny trade-offs, and wait for opinion to catch up) was then applied to issues ranging from health care to policing to immigration. Other like and have noted that even within the LGBTQ movement itself, the playbook has not transferred well from marriage equality to the more contested terrain of gender identity and trans issues. has recently argued that the structural conditions that made the “it gets better” trajectory work for gay rights (demographic scale, random distribution across families, the powerful contact effect of coming out) simply do not hold for every cause.

Immigration is one of those causes. And the mismatch runs deeper than most advocates realize.

Why the analogy breaks down

To be fair, there are similarities between immigration and same-sex marriage. Both involve, at some level, expanding personal freedoms and reducing legal discrimination based on characteristics largely beyond an individual’s control. Both efforts ask a majority to accept people whom some portion of the public views with suspicion or hostility. And in both cases, opponents have relied on fear-based messaging that exaggerates threats and dehumanizes the people in question. These parallels explain why thoughtful advocates reach for the comparison. But the structural differences are profound and show why a strategy built for one cause will fail the other.

Ingroup versus outgroup. Gay and lesbian Americans are, by definition, members of the national community. They are someone’s child, sibling, coworker, or neighbor. The success of the marriage equality movement depended heavily on this fact: the most powerful engine of attitude change was personal contact with people who were already part of the social fabric. Roughly 84 percent of Americans report knowing a gay or lesbian person personally, a figure made possible by the fact that LGB individuals constitute 8 to 10 percent of the population and are distributed randomly across families, communities, and political affiliations. The question was never whether they belonged, but whether they would be fully recognized.

Immigrants, and especially prospective immigrants who have not yet arrived, are outsiders seeking entry. While many Americans do know immigrants personally, the people whose admission is under debate are often thousands of miles away, invisible to the voters deciding their fate. The emotional and political dynamics are fundamentally different. You cannot “come out” as a future immigrant to your family at Thanksgiving dinner in America.

Already here versus seeking entry. The marriage equality movement asked the public to recognize a reality that already existed. Gay and lesbian couples were already living together, raising children, building lives. Legal recognition was about catching the law up with the facts. Immigration, by contrast, is primarily about regulating flows: how many people to admit, under what conditions, through what channels. The people whose fate hangs in the balance often have no presence in the host country and no voice in its politics. This is not a matter of recognizing what is, but of deciding what will be. That is a categorically harder sell, because the beneficiaries of more open policies are largely absent from the political conversation.

Symbolic recognition versus material trade-offs. Same-sex marriage was, for most Americans, essentially costless. Extending marriage rights to gay couples imposed no burden on straight couples’ marriages, finances, or daily lives. There are no material constraints on the number of marriage licenses. Granting more marriage licenses does not reduce the value of existing marriage licenses. This is a crucial and underappreciated feature of the issue, and one that Johnson identifies as the key reason the playbook fails when applied elsewhere.

Unlike marriage licenses, immigration involves real and perceived costs to people you care about the most: competition for jobs, pressure on public services, cultural change, and housing demand. Whether or not these costs are overstated in the aggregate (and economists generally agree that they are), they are not evenly distributed, and they are not imaginary to the communities that experience them most acutely. A strategy that worked for a costless cause will not work for one where trade-offs are genuine and felt.

Courts versus legislatures. Obergefell settled the marriage question through the judiciary. A single Supreme Court ruling made same-sex marriage the law of the land, regardless of what any state legislature thought. This created a kind of finality that is enormously powerful for social movements: once the ruling came down, the debate was effectively over, and the remaining task was cultural adjustment rather than ongoing political combat.

Immigration policy has no equivalent shortcut. While courts can and do adjudicate individual immigration cases, block executive overreach, and shape enforcement at the margins, the fundamental architecture of immigration (visa categories, numerical caps, enforcement priorities, funding levels) is set (or at least supposed to be set) by legislation. There is no Obergefell for immigration. Each policy change requires building and sustaining legislative coalitions, which means contending with the very public opinion dynamics that advocates hope to bypass through persuasion.

Salience and who has a voice. For LGBT Americans, marriage equality was intensely personal, arguably the most important political issue in their lives. This asymmetry in passion was a strategic asset: advocates cared more than opponents and organized accordingly.

The “other side” of the marriage debate, socially conservative voters, was not uniformly passionate about preventing it. Anti-gay-marriage organizations were vocal and well-funded, but their intensity was not shared by the broader base they claimed to represent. In 2004, a constitutional ban on same-sex marriage ranked 21st out of 22 national priorities in Pew polling. By 2014, more than a third of same-sex marriage opponents told PRRI the issue was not that important to them personally, and large majorities on both sides saw legalization as inevitable. Many rank-and-file opponents simply had other priorities and came to see the fight as not worth the political cost.

Immigration presents the opposite dynamic. The people who stand to benefit most from more open policies, potential immigrants abroad, have no vote, no voice, and no political power in the receiving country. Meanwhile, those who perceive themselves as bearing the costs of immigration often care about the issue intensely and have proven willing to organize politically around it, from Brexit to Trump’s 2024 campaign. The passion asymmetry runs the opposite way.

The limits of persuasion

None of this means that persuasion is useless. Alexander Coppock’s careful experimental work has shown that providing people with information about policy issues shifts attitudes by about five percentage points on average, and that this shift occurs roughly equally across the political spectrum. There is no “backlash” effect from trying to inform people. Similarly, David Broockman and Joshua Kalla’s deep canvassing experiments have demonstrated that non-judgmental, narrative-based conversations can reduce exclusionary attitudes toward immigrants already in the country, a meaningful and durable effect, even if modest in magnitude.

But there are reasons to believe that persuasion alone, no matter how sophisticated, cannot solve the immigration puzzle. First, immigration is a domain where counter-messaging is powerful and abundant. Anti-immigration advocates, from populist politicians to media figures to viral social media accounts, often care more about the issue than pro-immigration forces do, and they have a structural advantage: concrete stories of harm are more emotionally compelling than abstract statistics about aggregate benefits. For every careful study showing that immigrants contribute more in taxes than they consume in services, there is a vivid news segment about a local community overwhelmed by a sudden influx. Coppock’s own findings suggest that if persuasion moves people roughly equally in both directions, the side with more motivated and more prolific messengers may well have the edge.

Second, persuasion’s political relevance is limited by a basic fact about democracy: people do not directly set immigration policy. Even if a well-designed campaign moved public opinion several points in a more favorable direction, this would not automatically translate into legislative change. Immigration policy is shaped by legislative coalitions, interest groups, bureaucratic capacity, executive priorities, and, critically, how salient the issue is to voters at election time. Public opinion is merely one input, not a mandate. This is quite different from gay marriage, where opinion change combined with judicial action to produce a fait accompli.

Third, and perhaps most fundamentally, the trajectory of immigration opinion looks nothing like the steady upward march of support for same-sex marriage. Immigration attitudes are thermostatic: they react to the policy environment rather than following a secular trend. When a government is seen as having lost control of immigration, public opinion turns sharply restrictionist. When enforcement tightens, opinion softens. Gallup’s data illustrates this vividly: the share of Americans who said immigration should be decreased surged to 55 percent in 2024, then fell to 30 percent by 2025 as border crossings declined under the new administration’s enforcement measures. This is notan arc bending toward openness. Rather, it is a thermostat that adjusts up and down in response to perceived conditions. You cannot persuade your way past a thermostat.

What playbook would actually work

If the gay marriage playbook is the wrong model, what is the right one? A better analogy might be vaccination. Vaccines are among the most demonstrably beneficial interventions in human history, and yet persuasion alone has never been sufficient to achieve the uptake that public health requires. Anti-vaccine sentiment persists despite overwhelming evidence of efficacy, because persuasion, however well-founded, cannot single-handedly overcome entrenched suspicion, motivated counter-messaging, and the human tendency to weigh vivid anecdotes over aggregate data.

What actually works is not just telling people vaccines are safe and effective, but designing systems (school enrollment requirements, workplace policies, accessible distribution networks) that make vaccination the easy default. The product had to be genuinely good and the policy architecture had to make participation straightforward. Persuasion played a supporting role, but it was not the main mover.

Immigration needs a similar shift in thinking. Rather than pouring resources into campaigns designed to convince the public that all immigration is beneficial, a claim that is, at best, an oversimplification, advocates should focus on working with governments and policymakers to design immigration policies that are genuinely and visibly beneficial to receiving countries and communities. This is the difference between persuasion and what I have called making immigration popular by design.

What does this look like in practice? First, it means pushing for specific, well-designed programs rather than abstract openness. New visa categories for high-skilled workers who demonstrably fill acute labor market gaps. Labor mobility partnerships that connect migrant workers with employers in sectors facing genuine shortages, with built-in mechanisms for oversight and accountability. Private refugee sponsorship programs that give communities a direct stake in successful integration, turning residents from passive spectators of government policy into active participants with skin in the game. Administrative reforms that make the system faster, more predictable, and more transparent, so that the legal pathway is not so dysfunctional that circumventing it becomes the rational choice.

The common thread is specificity. The gay marriage movement had the luxury of a single, clear demand: let us marry. Immigration reform has no equivalent single slogan because immigration is not a single thing. It is dozens of distinct policy channels (family reunification, employment visas, refugee resettlement, student migration, seasonal labor, asylum), each with its own logic, constituency, and set of trade-offs. Treating immigration as a single cause that just needs its “marriage equality moment” obscures the reality that different policies enjoy vastly different levels of public support. Skilled worker visas are broadly popular. Large-scale low-skilled immigration is not. Pretending otherwise is self-deception.

Second, it means being honest about trade-offs. The gay marriage movement could afford to be maximalist because the cause was genuinely costless. Immigration is not costless, or at least it is not perceived that way, which in a democracy amounts to much the same thing. Advocates who dismiss public concerns about rapid demographic change, labor market competition, or the strain on local services are being strategically obtuse. The path to more open immigration runs through demonstrating that specific policies produce specific benefits, not through insisting that opposition is merely a product of ignorance or bigotry that better messaging will cure.

Third, as often emphasized by and folks from , it means engaging with enforcement rather than treating it as the enemy. One underappreciated reason that support for same-sex marriage proved so durable is that the reform did not require the public to trust the government to manage a complex system. Marriage equality was self-executing: once legal, couples could simply marry. Immigration reform, by contrast, requires public trust that the government can administer new policies, that new visa holders will actually leave when their terms expire, that employers will be held accountable, and that the system will function as designed. Advocates who treat enforcement as inherently hostile to immigrant rights undermine the very trust that makes more open policies politically possible. The countries that have managed to sustain relatively open immigration (Canada, Australia, and, until recently, Germany) have done so in part by maintaining credible enforcement alongside expansion.

Finally, it means working with the thermostatic nature of public opinion rather than against it. If public attitudes soften when people feel the system is under control and harden when they feel it is not, then the most pro-immigration thing a government can do is create an immigration system that visibly works. This is counterintuitive for many advocates, who see enforcement and restriction as the problem rather than part of the solution. But the evidence is clear: the way to expand immigration over time is not to win an argument, but to build a system that earns and sustains public confidence.

The arc of progress is not automatic

The comparison between immigration and same-sex marriage flatters advocates by suggesting that history is already on their side. It implies that the hard work is simply to keep pushing the same message until the laggards catch up. This is comforting. It is also dangerous, because it discourages the much harder work of policy design, coalition-building, and institutional reform that real progress requires.

The gay marriage movement won an extraordinary victory under conditions that do not apply to immigration: an ingroup seeking recognition rather than an outgroup seeking entry, a costless reform rather than one involving real trade-offs, a judicial pathway to finality rather than an endless legislative grind, and a passionate constituency with direct political voice rather than voiceless people abroad.

Immigration advocates do not need a better version of the marriage equality playbook. They need a different playbook entirely, one built around political compromise and designing policies that earn public support by deserving it. The arc of progress on immigration is not automatic. It has to be achieved by hard work.

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Disclosure for my Bluesky friends: This post is 100% human-written (assisted with human-verified AI summaries of your critiques of my earlier post). Human mistakes are possible.

Earlier this week I argued that academics need to wake up on AI and offered ten theses on how agentic AI is changing social science research. The post went viral, especially after I revealed it was fully generated and posted by AI, leading to news features, over a million views, and over a thousand (often angry but also enthusiastic) responses across various social media platforms.

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In hindsight, I should have done a few things differently. First, revealing that Claude wrote the original post (even based on my earlier social media writing) as a cheeky follow-up was a mistake. It distracted from the substance and gave critics an easy reason to dismiss the arguments. Rather, I should have been upfront about my basic setup.1 Second, I should have been clear that current agentic AI tools are better at doing most social science research tasks than professors globally.2 This doesn’t mean you necessarily get replaced, but it does mean the nature of your work will change. Third, the AI-generated post had minor, somewhat weird stylistic errors that better human-augmented editing would have caught.

In this respect, I want to highlight Solomon Messing and Joshua Tucker’s Brookings piece, published shortly after mine, which makes a much more persuasive version of many of my arguments—with concrete use examples, no unnecessary provocation or AI-generated text, and a more constructive vision for the future. If my post was too abrasive for you, read theirs instead.

I was deliberately provocative, and I stand by that choice. It backfired in some sense but worked in another sense: dozens if not hundreds of academics are now trying agentic AI tools who would not have otherwise yet. After reading most responses, I certainly changed my mind on a few things, but I am still convinced of my core claim that, because of already existing AI tools, our research workflow will have to change whether you like it or not. Here are ten more theses that came from my reflections.

11. Qualitative research and novel data collection will increase in relative value.

The strongest substantive critique of Part I was that it conflated “research” with the specific tasks AI handles well—literature reviews, data analysis, conceptual synthesis. Several respondents rightly pointed out that AI cannot conduct ethnographic fieldwork, interview detainees in illegal prisons, or spend years building trust with a community. They are absolutely right. My theses were primarily about (the currently dominant) quantitative and conceptual work in social science, and I should have been clearer about that scope.

But the implication is not that qualitative researchers should relax. It is that the relative value of original data collection—fieldwork, interviews, archival work, participant observation—is about to rise. If AI can synthesize existing literature and run standard regressions, then the premium shifts to the things AI cannot do: generating new data that did not previously exist, especially from hard-to-reach contexts. Qualitative researchers and field experimentalists should see this as an opportunity to do more great work they have comparative advantage in instead of transcribing their interviews or compiling literature reviews.

12. Due to “jaggedness,” AI opinions are polarized by beliefs in their utility.

describes AI’s capabilities as a “jagged frontier”—superhuman at some tasks, embarrassingly bad at others, in ways that do not map to human intuition. AI can write a serviceable literature review but struggle with a basic visual puzzle. It can synthesize findings across 500 papers but hallucinate a co-author’s first name.

This jaggedness explains why the AI debate in academia is so polarized. Critics point to the troughs; enthusiasts point to the peaks. Both are right about their corner of the frontier. The overlap with the qualitative-quantitative divide in social science is hard to ignore: researchers whose work involves the tasks AI handles well (data analysis, literature synthesis, pattern recognition) tend to be more positive, while those whose work involves the tasks AI handles poorly (fieldwork, interviews, archival interpretation) tend to be more skeptical.

But I noticed something beyond mere disagreement. Bluesky users who despise AI viscerally were often the first to deny basic, easily verifiable facts—for instance, that it can produce slide decks well. Very few respondents acknowledged that AI capabilities for research are real but worried about their consequences. People either dislike AI and deny any productive use, or like it and exaggerate its utility. Some of this is motivated reasoning—the explicit existential threat of a computer doing things better than you. But I suspect even more of it is simply ignorance about “the other.”

Contact theory is real. If you believe that Claude Code is evil or incompetent, I dare you to install it and use it to organize your research folders or create slide decks for your upcoming conferences. Earlier I encouraged folks to “spend a week with Claude Code.” It should have been “spend a day” (which should be enough).

13. User expertise still vastly determines output quality.

Perhaps not surprisingly, much of the criticism on Bluesky still assumes that using AI means copying and pasting from a chatbot. That is just not how agentic AI works. Agentic AI operates autonomously within your file system, reads and writes code, consults documentation, and executes multi-step research workflows—all guided by detailed instructions you build over time.

One related, common form of AI denial also assumes that because the tool is accessible, anyone could produce the same output. That is like arguing that because everyone has access to a stove, everyone can cook a good meal. There are obvious differences in cooking skills, recipes, and the quality of ingredients.

But the question is not whether AI is better than most professors at doing most important research tasks (I still stand by the assertion that it is), but whether good researchers with AI are better than good researchers without AI (they absolutely are). Honestly, I would take well-prompted AI slop over Bluesky slop (hundreds of anonymous users responding ai/dr whenever they see the feared AI keyword regardless of any substance) any day of the week.

14. Publication lag makes AI capability critiques obsolete by the time they come out.

Here is a problem that almost nobody in the debate acknowledges: academic and book publication timelines are structurally incompatible with AI’s rate of improvement. When someone cites a 2025 paper (initiated in 2024) documenting GPT-4’s hallucination rate to argue against using AI in March 2026, they are citing evidence about a system that no longer exists. It is like citing a 2005 study on flip phone limitations to argue against smartphones. That’s probably why the new “AI Con” book is so bad—clearly outdated before it even hit shelves.

I am not dismissing all of this research itself. The studies are often methodologically sound. But the evidence base expires faster than it can be published, reviewed, and cited. Messing and Tucker’s Brookings piece, published in March 2026 (and reviewed “rapidly” in only two weeks), already documents capabilities that would have seemed speculative six months earlier. By the time a peer-reviewed paper on current AI limitations appears in a journal, the limitations it documents will likely be fixed. This is not a comfortable situation for academics who are trained to rely on published evidence. But it is the situation we are in.

15. Most papers are already mostly read by AI, not humans.

It is an open secret in academia that most published papers are never cited or read by anyone beyond the authors, reviewers, and sometimes the editor. With the coming proliferation of AI-written papers—whether complete slop or not—it will become impossible for researchers to keep up even with their own niche fields. I like to think I am aware of all the new literature on immigration attitudes, but I am probably missing 80% of what gets produced outside the US, Europe, and top disciplinary journals.

This means that academics should accept their primary audience is increasingly LLMs. has been talking about writing for LLMs for some time, but with the ascent of agentic tools, this applies to most academics too—including qualitative researchers whose work itself cannot be automated. I do not have a firm sense of what authors should do about it, but ensuring that a machine-readable version of your paper exists (ideally in .md format) seems like a good first step.

16. AI exposes what was already broken in academia and beyond.

Related, a large number of responses to Part I amounted to: “If AI can do your research, your research was never good.” I agree (LOL)—but that is an indictment of much of social science, not a defense against AI or a smart attack against me personally. The replication crisis, citation padding, p-hacking, and the production of papers no one reads were all pre-existing conditions.

Human-generated academic slop was always pervasive; AI just makes it visible. put this more bluntly in his restack: academic institutions hoard human capital, the tenure system rewards collective navel-gazing over public impact, and most professors could be more useful doing something else. That is a harsh framing. But if only a small percentage of published papers have genuine value, the system AI is disrupting was not exactly thriving.

17. Skill atrophy is a real risk, especially for the future generation of scholars.

This brings us to what I consider another strong reaction to my initial post: that outsourcing cognitive processes like “evaluating sources” and “coding data” damages the researcher’s own understanding. Many folks rightly worry about “reducing complex, thought-driven processes to a series of discrete tasks to be outsourced, when there’s so much that goes on cognitively both between and after the steps.” Messing and Tucker flag the same risk under “skill atrophy.”

I take this seriously, and I concede the risk is real—especially for students and trainees who have not yet internalized the cognitive skills that AI might short-circuit. The researchers who worry about skill atrophy are right that something is lost. But they underestimate what is gained: the ability to operate at a higher level of abstraction, to test more hypotheses, to iterate faster. For established researchers, the risk of atrophy is low because the skills already exist. For students and future researchers, we urgently need to figure something out in updating our grad school curriculum.

18. AI writing detectors and disclosure norms do not work.

AI writing detection tools were bad, are still bad, and will probably remain bad. The original Claude-produced post passed every major AI detector as “100% human” without any elaborate prompting to avoid this on my side. Many critics of my initial post said they immediately “sensed” it was AI-written. But they said this after I revealed the workflow—a textbook case of confirmation bias. Before the reveal, nobody flagged it. In fact, someone even complained I didn’t use AI to write a post boosting AI.3

The more important point is about disclosure incentives. Messing and Tucker recommend standardizing AI usage declarations across fields. I respect their reasoning and the call for standardization (instead of the chaos that we have now), but I disagree that any expansive AI declaration standard can have any merits given the current incentive structure.

Do not get me wrong—people in positions of authority like journal editors should be transparent about their workflow. But for regular authors, voluntary disclosure creates a system where honest users get punished and dishonest users face no consequences. I disclosed my AI workflow and received threats, professional attacks, and calls to fire me. The rational incentive is to lie. “AI usage acknowledgments” sound reasonable, but they collapse on contact with the actual social dynamics of academic life in 2026. Until the professional costs of disclosure drop, mandatory acknowledgment norms will select for dishonesty.

There is also a deeper problem: disclosure norms get the accountability question backwards. For some, AI disclosure can even function as a cop-out—”I used AI, so it’s on you now to figure out if it is slop.” But authors should stand by the final product regardless of how it was produced. If AI introduces an error, that is the author’s responsibility. What matters is whether the work is correct and valuable, not whether a human or a machine typed the sentences.

19. Academic Bluesky is not a serious venue for this debate.

I have to address this because it colored everything that followed. Bluesky generated almost as many reactions as Twitter, but they were overwhelmingly hostile in the least productive way possible. The most common response was some version of “If you didn’t write it, why should I read it?” or “ai/dr.” Many included curses, accusations of being paid by AI companies (?), and calls to not cite my earlier published work (??) or even to fire me (???) with people tagging my employer to replace me with AI since I’m claiming it’s so good.

My original post was provocative. But I did not attack anyone personally. I made arguments about AI and academia, based on my own experience in the field, which you may agree or disagree with. For that, academics on Bluesky responded with professional threats, ad hominem, and coordinated pile-ons. I have thick skin and employment security. I can absorb this.

But most people who might share heterodox views on AI in academia do not have that luxury. They are graduate students, contingent faculty, and junior researchers (in fact, I was one myself just a couple of months ago!) who watch what happened to me and draw the obvious conclusion: keep your mouth shut. That is the real cost of pile-on culture—not to people like me, but to the open exchange of ideas that academia is supposed to protect. And while I appreciated all the sympathetic folks who reached out in DMs, I wish you would speak out publicly. That is the only way this unfortunate dynamic can change.

20. Research can lack “soul” and still serve the public.

Max Kagan articulated and addressed a common concern from Bluesky folks that resonates with me too: the idea that research produced by or with AI lacks something essential—call it soul, craft, or authentic intellectual engagement. The process of struggling with a question, sitting with ambiguity, and slowly building an argument is personally transformative for many scholars. There is a reason people pursue PhDs despite terrible labor market prospects: the work itself is meaningful. When AI compresses that process into hours, something genuinely valuable is lost.

I feel the pull of this. But I am not sure it survives contact with the question of who pays for it. Most academic research is publicly funded. Taxpayers do not fund universities so that professors can self-actualize. They fund universities to produce knowledge that benefits society. If AI-assisted research produces more and better knowledge faster, the public interest argument for embracing it is hard to resist—even if the private experience of research becomes less romantic.

***

All in all, the discourse around Part I was messy. But it was also productive. It probably encouraged a few dozen if not more academics to try agentic AI tools for the first time, so I take it as a win. The strongest substantive objections—hallucination, skill atrophy, qualitative research gaps—forced me to think more carefully about both the risks and the opportunities. The Bluesky pile-on artists just showed yet again that it is not a serious platform for an open exchange of ideas. But the intensity of the reaction only confirms the stakes. People do not argue this fiercely about things that do not matter. Academics are waking up—some enthusiastically, some kicking and screaming. Either way, they are waking up.

Update: I’ve written a Part II with reflections on the responses to this piece and a Part III with ideas about what academics can do next.

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This piece is inspired by a wave of recent AI-related writing from people I respect: , , , , , , , Claude (yes!) Blattman, Kevin Bryan, , , Sean Westwood, and many others. So here, I’m continuing the tradition of writing the takes that are upsetting but needed.

I study immigration and public opinion, not AI. But I’ve spent the last few months watching AI transform my own research workflow, and I have some things to say to my colleagues. For the first time in my life, I genuinely do not know what academia will look like in five years.1 Even if progress stalls completely and we are stuck with the current models forever, the changes already in motion will transform my field of academic research and publishing beyond recognition. The status quo is unsustainable. It may take time, because academia is the most dispositionally conservative institution on the planet. But it will change.

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Here are ten theses for my colleagues, most of whom still seem oblivious.

1. AI can already do social science research better than most professors.

This is not hyperbole. Tibor Rutar recently described generating a full research paper using AI prompts alone, producing work he considers publishable in first-quartile journals. Paul Novosad reportedly accomplished similar results in 2-3 hours. claims that Claude can produce a publishable-quality political theory paper in under two hours with minimal feedback. Scott Cunningham estimates that manuscript creation now basically costs roughly $100 in editing services plus a Claude subscription.

And this goes well beyond crunching numbers or running pre-existing Stata code. Yes, what I’m claiming here is that LLMs produce excellent literature reviews and generate fruitful recombinations of existing ideas. Let’s be honest: academics haven’t been particularly great at writing either, and AI can make your ideas far more accessible to the people who actually need them. But effective use requires investment: describes building a ~200-line instruction file encoding his research workflow, judgment calls, and behavioral guardrails. This takes a skill.

2. The academic paper is a dead format walking.

Sean Westwood put it bluntly: “AI does lit reviews better. AI will do peer review. Users will skim AI summaries. The real science is the question, the pre-analysis plan, and the analysis. The 30-page paper is just vestigial wrapping paper.” He got roasted on Bluesky for saying this. But he’s absolutely right, and the backlash proves his point: the field can’t even discuss the obvious without circling the wagons. Arthur Spirling is also right that we need conversations about what a paper is, what “review” means, and the correct role of generative AI. Perhaps it’d be a good thing if AI finally pushes us to move on from a system where universities spend taxpayer money to pay commercial publishers to very slowly produce paywalled PDFs2 with outdated results of publicly funded research.

3. The commercial journal system may not survive this.

Cunningham’s latest piece models the math. If manuscript creation drops to a couple of hours and ~$100, submissions could increase fivefold while journal slots stay fixed. Desk rejection rates would go from ~50% to ~90%. The revenue model collapses. Peer review, already strained, becomes impossible at scale. Kevin Munger makes the case for submission fees, paid reviewers, post-publication review, and LLM-assisted screening. The question is whether journals adapt or get bypassed. My bet is most get bypassed.

4. Academics hold AI to absurd double standards.

Hallucinating content is concerning, and researchers should always verify their sources. But just like with self-driving cars, we need a reference point: human writers have been superficially citing papers based on the abstract for ages. Journals already publish studies with data errors, p-hacked results, and non-replicable findings at alarming rates. One estimate puts the share of genuinely useful published papers at around 4%. An LLM that occasionally hallucinates a citation is competing against a system that routinely produces junk science dressed in enough jargon to pass review. If we applied the same skepticism to human-produced research that we apply to AI outputs, we’d shut down half the journals tomorrow.

5. Junior scholars face the biggest disruption and opportunity.

This is probably bad news for junior academics trying to advance their careers in the middle of this shake-up. Jason Fletcher argues that the strategic logic of tenure hasn’t changed—survive the gate first—but AI fundamentally alters how you get there. Teaching prep costs drop. Data cleaning and debugging get delegated to AI. The bottleneck shifts from execution to verification and original thinking.

Gauti Eggertsson observes that the returns on conceptual thinking and original ideas are now relatively higher compared to technical grunt work. A junior scholar with good ideas and Claude Code can now produce research at a pace that would have required a full lab a few years ago. But so can everyone else, and the evaluation criteria haven’t caught up.3

6. I don’t envision a research assistant role in my workflow anymore.

I still think it’s invaluable to have mentees and co-authors. But their role is changing fast. I’m not going to hire someone to clean data, run regressions, or draft literature reviews when AI does all of it faster and at negligible cost. What I want from collaborators is original thinking, domain expertise, and intellectual challenge. This is a genuine loss for the traditional apprenticeship model, and I don’t have a clean answer for how to replace it. Fletcher’s complementary framework—AI produces initial analyses, human researchers independently replicate from scratch—points in a promising direction. But it’s clear that the trend for increased co-authorship in social sciences, for instance, may reverse very soon.

7. Much of the opposition to AI is status protection dressed up as principle.

I recently wondered on Twitter how much of the distaste for AI telltale signs is basically a new version of grammar policing—people enforcing status markers through language gatekeeping. Kevin Bryan said it plainly: “I get the desire for artisanal, hand-crafted research, with the matrices hand-inverted. But our job is to move the frontier of knowledge, not self-actualization.”

Dan Williams has written persuasively about how highbrow misinformation flourishes inside institutions where nearly everyone shares the same biases. I think something similar is happening with AI denial. Many academics—especially those concentrated on Bluesky4 and, I suspect, those who are completely offline—are in complete denial about what’s already happening. Chris Blattman went from a Claude Code skeptic to building an entire AI workflow toolkit in a matter of weeks. Robert Wright recently hosted Alex Hanna and Emily Bender arguing that LLMs are useless. Smart people claiming that a tool millions find useful is fundamentally broken. This smug attitude is exactly why populists are winning, and it applies to AI denial just as much as to politics.

8. The productive worries are about security and verification.

My challenge for anyone who dismisses AI capabilities: spend one week alone in a room with Claude Code or Codex. Not the chatbot—the agent. Most people still think of AI as a search engine that sometimes makes stuff up. They have no idea what agentic AI systems can do.

Focusing on whether LLMs “truly understand” or produce “real” knowledge is a philosophical indulgence that takes away from the things worth worrying about. How do we verify AI-generated claims at scale? How do we prevent p-hacking? (Andy Hall’s team found that AI agents are surprisingly resistant to sycophantic p-hacking—but can be jailbroken with modest effort.) How do we protect sensitive data when AI tools access institutional repositories? How do we ensure that online survey respondents are real? These are solvable engineering and institutional design problems, the kind that Hollis Robbins calls “last mile” challenges—things that live in the edges of expertise, in the contextual and the unsettled. Debating whether Claude is “really” intelligent is like debating whether a calculator “really” does math while your competitor finishes the problem set.

9. We are about to get much better science.

There are some silver linings, however. On my own turf, immigration: we can now automatically catalogue policy and opinion changes across countries and suggest fixes in real time. We can build algorithms to better match refugees and migrants to destination communities. We can make sure research and evidence are accessible to policymakers and voters who never read an academic journal.

More concretely, Yamil Velez and Patrick Liu have been building AI-generated experimental designs since 2022; tailored Qualtrics experiments can now be created in 15 minutes via prompts. Velez’s work points to something even bigger: AI doesn’t just speed up existing survey methods, it makes entirely new forms of interactive, adaptive surveys possible—designs that would have been impractical to program manually. David Yanagizawa-Drott has taken things further still, launching a project to produce 1,000 economics papers with AI—not as a stunt, but as a stress test of what happens when the cost of generating research drops to near zero.

Non-native English speakers also stand to benefit enormously: researchers in Cairo, Sao Paulo, and Jakarta can now produce prose that reads as well as anything coming out of Cambridge or Stanford. Eggertsson suspects AI will erode the monopoly that top US schools have long enjoyed, since their advantage rested partly on knowledge transmission that is now nearly instantaneous. If you care about democratizing science, this matters more than most of the things universities spend money on.

10. Apart from the doomsday scenarios, AI is genuinely exciting.

Yes, there are real risks. Job displacement for some academics (and most other folks) is not hypothetical. The alignment and safety concerns are genuine, even if unlikely to play out in the worst-case scenarios. I take those seriously and I fear our uncertain future somewhat.

But here’s what I keep coming back to: AI is useful and fun. My sense is the “agentic AI is making us dumb” crowd is probably right about some things. But I’ve also noticed my procrastination bar going up. Instead of doomscrolling, I now slack off by trying side projects in Claude Code. May be the most productive form of non-work there is. I’ve been vibecoding a few pretty exciting projects over the past few weeks. Stay tuned.

The wise Yiqing Xu advises that we should all pause for a month to reassess and redesign our workflow, then resume. I agree. The payoff will be large. Lock yourself in a room with Claude Code and see what happens.

P.S. This post was entirely generated and posted on Substack by agentic AI using my new Claude Code (Opus 4.6) workflow. Make of that what you will.

P.P.S. That is, entirely generated based on my artisanal, hand-crafted human social media posts and thoughts on the topic. So who wrote it, really? You tell me.

We hit 1,000 subscribers this month! Thank you for reading, sharing, and arguing with me in the comments. I started Popular by Design because I think the immigration debate deserves more honesty and less tribalism—and it turns out at least 1,000 of you agree (or at least enjoy disagreeing).

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A few updates: I’m hiring a postdoctoral researcher to join me at Notre Dame’s Keough School to work on politically sustainable immigration. If you know someone working on immigration, public opinion, or policy design who might be a good fit, please send them my way. Immigration folks with a PhD (an unfortunate formal requirement by the admin) are more than welcome to apply. I’m also starting as an associate editor at the Journal of Ethnic and Migration Studies (JEMS)—one of the top journals in the field. Excited to help shape what kind of research gets published and how we evaluate it (at least before AI will change it all).

Since joining ’s ABI fellowship, this month I published more pieces than ever before. Hopefully, I will be able to continue the streak at least until the end of my sabbatical. First, “Reflections on the Uncomfortable Truths about Immigration”—my attempt to address the most frequently asked questions about my earlier “Uncomfortable Truths” post. Second, “Student Migration Is Popular... Until It Isn’t”, on what went wrong in Canada, Australia, and elsewhere when universities started selling immigration status instead of education. Third, “Western Countries Do Not Need Immigration”—a deliberately provocative title for an argument that cuts in a surprising direction.

Before we get to the links, I have a favor to ask. I’m curious who’s actually reading this newsletter. We’re now roughly at the population of a small medieval village, which seems appropriate for some basic ideological census:

OK, here are the February links (linking does not imply endorsement):

• I normally don’t follow the Olympics, but the restrictionists’ reaction to Alysa Liu has been fascinating to watch. Liberals thought MAGA celebrating her Olympic gold was a gotcha, but it’s actually a concession I’m happy to accept: no country needs immigration, but the countries that choose it wisely end up stronger. An American daughter of a Chinese dissident draped in the flag on the Olympic podium while the authoritarian government that persecuted her father watches—that’s a recruitment ad for every talented person in the world.

• has a thoughtful piece on what a liberal immigration enforcement regime might look like. Most voters aren’t categorical restrictionists—they can support freer immigration if they believe the system is orderly and serves the national interest. The uncomfortable truth for many liberals: public opinion does support deporting most undocumented immigrants, not just criminals.

  • also compiled a great selection of the latest immigration enforcement research—my reading list is now complete.

  • I often disagree with on how to interpret public opinion on immigration, but his work this month on asking people concrete questions about enforcement is valuable. Abstract attitudes are one thing; specific policy preferences are another.

  • But here’s the uncomfortable truth for conservatives. published new polling on immigration attitudes among Trump voters. Some of their findings surprised even me: 90% of Trump voters agree that “properly controlled immigration can be good for America,” and 70% want it to be easier to immigrate legally (but harder to come illegally). More room for agreement than usually assumed.

• has a fascinating conversation with Ruud Koopmans on the differences between immigrant integration in Europe and the US. I’ve been beating this drum for a while: immigration is not one thing that has uniform effects—policies and contexts matter enormously.

  • makes a related argument I’ve been pushing for years: you can’t just import conclusions from European immigration debates to the US, or vice versa. Showing a Danish crime chart says nothing about how well America integrates migrants.

• explains how Trump botched immigration. What we’re seeing is textbook thermostatic reaction to government overreach—voters still support deportation in the abstract but strongly disapprove of how it’s actually being carried out.

  • Case in point: the administration tried suspending TSA PreCheck and Global Entry, then caved within 24 hours after backlash. It’s honestly a bit funny, if not sad, that the trusted traveler programs are the thing that finally breaks it for normal folks who happened to vote Republican.

• has several related pieces worth reading. First, shares new data on the trans rights backlash—I haven’t seen much from social scientists on what’s driving it. Is it thermostatic? Elite cue-taking? Something else? One of the most dramatic opinion shifts in recent memory, and the field has sat it out.

  • Second, on why thoughtless moderation is a mistake—voters aren’t dumb or unnecessarily cruel. Being thoughtlessly harsh on trans rights or immigration doesn’t win elections.

  • has a great conversation at The Argument about the end of conservatism. His point on immigration: the current conundrums have nothing to do with big ideological debates about what we owe each other and foreigners. Trump’s “own, concrete choices, not just the spirit of nationalism, have led his administration to become very unpopular.”

• has a beautiful piece on Tokyo as a megacity at human scale. The “city of doorways, not vistas” framing is great. After living there, I came away with a similar sense that the intimacy is mostly about zoning and land use, not some mysterious cultural essence. Where I’d push further is toward what happens when demographics undermine that vibrancy—outside greater Tokyo, depopulation is devastating.

• has a sobering piece on why comparativists should speak up. As someone who ended up studying US politics from a comparative perspective, I’m struck by how often the “it can’t happen here” instinct comes from people who simply haven’t looked at how things have unfolded elsewhere.

• came out against white culture. It’s kinda funny since this is pretty much exactly what the critical studies people have been saying all along, just without the unnecessary jargon. But well…sometimes it takes a different messenger.

• pushes back on the idea that immigration will fix fiscal problems. at Cato responds with a different set of assumptions that flip the results dramatically. My sense is that fiscal impacts are genuinely hard to estimate, and reasonable people can disagree on assumptions. But I’ve definitely learned from both Cato’s and Manhattan Institute’s work on this, and I wish more of our immigration debates were this technical rather than vibes-based.

• and at EIG found major data errors in George Borjas’s paper making the case for a $100,000 H-1B fee. Ozimek’s broader point is one I keep returning to: you have to do the policy right. Many people who think they understand high-skilled immigration actually don’t understand the economics or the mechanism design.

• won his tariff case at the Supreme Court—a 6-3 decision challenging presidential tariff power. A Soviet-born constitutional scholar turning ideas into action by defending limits on executive authority. Worth reading his initial thoughts at Reason and his inspirational profile at IHS.

• has a new paper using survey evidence with a conjoint experiment to test how state-level immigrant integration policy features affect perceptions of fairness and support. Important new evidence that US attitudes are much more inclusive than conventional debates suggest—but this support is conditional on policy design. People are more supportive of integration when it includes social support and clear eligibility criteria, which they view as fair. Another reminder that how you design and implement policies probably matters more than how you talk about it.

As before, if you want me to write more about one of these or other related topics, let me know!

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Many folks told me my latest post challenging the pro-immigration orthodoxy was like a breath of fresh air. To keep with the theme of radical honesty, I believe we also need to reflect on whether countries need foreigners in the first place.

Let’s be honest with ourselves: no Western country will collapse without immigration. The United States is a powerful, functioning state. So are Japan, Germany, France, and most of Europe. The lights will stay on. The trains will run. The US Super Bowl will be just fine without foreigners singing in Spanish.1

Pro-immigration advocates who claim otherwise—who insist that immigration is “the only politically feasible solution to population collapse”, that normal GDP growth will be “impossible” without sustained immigration flows, that no one else will take care of your elderly parents—are overstating the case. And in doing so, they are losing credibility with the very people they need to persuade. When you tell someone their country cannot survive without immigration and they look around and see it surviving just fine, you have not made an argument. You have made yourself easy to dismiss.

So let me start where immigration skeptics start and explain why no country needs immigration. But in the spirit of radical honesty, I’d ask my restrictionist friends to return the favor—and follow their own argument to its logical conclusion.

The case for no immigration, taken seriously

One of the common good-faith conservative arguments against immigration is not about its effect on crime or culture—it is about dependency. As critics at outlets like The American Conservative have argued, wealthy countries have become “addicted to cheap labor.” If an economy cannot function without constantly importing foreign workers, maybe the economy is broken, not understaffed. Maybe the answer is automation, higher wages, and policies that get native-born men—millions of whom have dropped out of the labor force—back into productive work. Maybe immigration is a crutch that lets governments avoid harder structural reforms. This is a serious argument, and it deserves a serious answer.

But first, it deserves an honest concession: you can build a rich, functional country without much immigration. Consider Japan. In the early 1970s, Japan had a population of more than 100 million people and virtually no immigration. Over the next two decades, it built the world’s second-largest economy through domestic investment, export-driven manufacturing, and a disciplined, highly educated workforce. By 1995, Japan’s GDP per capita was among the highest in the world. No immigrants needed.

Or consider Sweden and Canada in 1900—two countries with almost identical populations of roughly five million people. Sweden was actually losing people: between 1850 and 1930, about 1.3 million Swedes—a third of the population—emigrated to the United States. Yet Sweden went on to build one of the world’s most admired welfare states. It industrialized, innovated, and became synonymous with quality of life—all without relying on large-scale immigration until the very end of the twentieth century.

Sweden also tells a different cautionary story. After decades of prosperity built on a homogeneous welfare state, Sweden began accepting large numbers of humanitarian migrants in the 1990s and 2000s. The result has been among the worst integration outcomes in the OECD: foreign-born residents face an employment gap of over 20 percentage points compared to natives, non-European immigrants earn 20 to 30 percent less even after decades in the country, and someone has to pay for it. Not surprisingly, the Sweden Democrats—an anti-immigration party—rose to become the second-largest party in parliament in one of the most cosmopolitan countries in the world. If you want an argument that not all immigration is beneficial, Sweden hands it to you on a silver platter.

So the restrictionist premise is correct. You can have a prosperous, well-governed country without letting foreigners in. Japan proved it. Even when you do embrace immigration, it can go badly if you do it wrong. Sweden proved it. Immigration skeptics are not crazy. On the basic facts, they have a point.

The question is what happens next

Japan’s population peaked at 128 million in 2008 and has been falling ever since. Today it stands at about 123 million. By 2070, demographers project it will drop below 90 million. Japan’s economy, once the world’s second-largest, slipped to fourth in 2023, overtaken by Germany—partly due to currency effects, but also reflecting decades of stagnation that economists consistently connect to demographic decline.

Canada took a different path. Starting from the same base of five million as Sweden in 1900, Canada chose relative openness. It built an immigration system—imperfect, sometimes messy, as I have written about—but one that consistently welcomed newcomers. Today, Canada’s population has grown to over 41 million, more than four times Sweden’s. The exact surplus to native-born Canadians from all that immigration can be debated and is likely modest per capita. But without relatively open immigration, Canada would be a much smaller, less influential country than it is today—and heading down the same demographic path as Japan.

Now consider the United States. When Matt Yglesias proposed “One Billion Americans,” many on the right thought he wasn’t serious. But in 1800, the United States had just 5.3 million people—smaller than Sweden is today. If someone had argued then for “100 million Americans,” they would have sounded equally delusional. The country got there—and then tripled that number—largely through immigration. According to the National Academies, most Americans today descend from immigrants who arrived after the nation’s founding. Without those arrivals, the United States would not have had the population to industrialize, settle a continent, or become the dominant power of the twentieth century. The notion that America can simply close the door and remain what it is—that is the truly radical position.

Stasis is not stability

Here is what immigration skeptics get wrong: they confuse the absence of collapse with the presence of thriving. Countries without immigration don’t stay the same. They age and, now they also shrink. They lose fiscal capacity. They still get “woke.” And, yes, they quietly start opening the very door they swore they would keep shut.

Japan is the clearest case. The country that proved you don’t need immigration has 2.57 million foreign workers—a record high, nearly triple the number from a decade ago, and growing at double-digit rates every year. Japan recently scrapped its controversial Technical Intern Training Program and replaced it with a new system designed to attract and retain skilled foreign workers, setting a target to admit even more. The government does not call this “immigration,” of course—Japan has never been comfortable with the word.2 But whatever you call it, the country that needed no one is now competing globally for foreign labor.

Every retiree in Japan is now supported by roughly two working-age people, and that ratio is projected to worsen to less than 1.5 by 2060. Hospitals need nurses. Construction sites need workers. Someone has to care for the elderly, staff convenience stores, and contribute to the pension system. The Japanese government looked at the math and decided that ideological purity was a luxury it could no longer afford. Despite all the amusing hysteria about having a new ultraconservative prime minister, Sanae Takaichi, her government is planning to admit over 1.2 million foreign workers under new visa programs—because the math doesn’t care about your ideology.

The question is whether we want immigration

The word “need” has been doing enormous work in this debate, and it is time to retire it. No country “needs” immigration in the same way that no country “needs” international trade. Or universities. Or highways. A nation can exist without any of these things. North Korea basically exists without trade. Some countries have gutted their university systems and survived. You could stop building roads tomorrow and the state would endure—at least for a while.

But no serious person argues against trade by saying “we don’t need it.” The question is whether trade makes you better off. The same logic applies to immigration. The question is not whether your country can survive without it. The question is whether you want growth, innovation, fiscal solvency, and demographic vitality—or whether you prefer to manage decline.

Here is what strikes me most about the “we don’t need immigration” position: even if you accept every conservative premise—enforce the border strictly, be very selective, prioritize fiscal impact, demand almost complete assimilation, put the national interest first—you do not land on zero immigration. You still land on a lot of foreigners coming every single day for life.

And when restrictionists call for “zero immigration” or a moratorium or a “pause until we figure out what’s going on”—what does that actually mean in practice? Does it mean telling your buddy he can’t bring his wife home from Canada? Would you look a fellow American in the eye and say the government forbids him from living with the person he married? Would you tell a hospital already short on nurses that they need to start rationing care for seniors because hiring a qualified foreign nurse is off the table? Because that is who we are actually talking about.

The reality of migration on restrictionist terms

Immigration in practice is not just dangerous men sneaking across the border or businesses “importing” cheap labor in droves—it is an American citizen waiting years to reunite with a spouse, a rural hospital trying to stay open, a university lab trying to keep its best researcher. Even Donald Trump sponsored a foreign spouse—twice. If the policy you are proposing would not have let the president bring his own wife to the country, maybe it is time to revisit the policy.

If you believe in national strength, you should want the world’s best doctors, engineers, and researchers competing to come to your country. If you believe in family values, it is worth asking why the U.S. makes it agonizingly difficult for American citizens—including white, native-born Americans—to bring their foreign-born spouses home. If you believe in fiscal responsibility, the actuarial case for working-age immigrants paying into Social Security is straightforward. If you believe in national sovereignty, you should want a legal immigration system that works, so that people have lawful alternatives to crossing the border illegally.

Even Richard Hanania, who is hardly a bleeding heart progressive, has argued that opposing high-skill immigration is flatly irrational—pointing out that 46 percent of Fortune 500 companies were founded by immigrants or their children, and that restricting elite talent harms the country far more than any conceivable benefit from keeping people out. This is the argument that follows from taking national interest seriously. This is the logic of any competitive sports team: you want the best players regardless of where they come from. National strength works the same way. If you are serious about greatness, you recruit talent—you don’t turn it away.

I would like to hear an immigration restrictionist describe, concretely, the immigration policy they would actually be happy with. Not “less immigration” or “mass deportation now” as a slogan—a specific system. Who gets in? Through what channels? With what requirements? My prediction is that any honest answer to that question looks a lot like substantial, well-designed immigration—a points-based system, employer sponsorship, family reunification for immediate relatives, and yes, some humanitarian admissions. In other words, something not so different from what most mainstream economists and policy analysts recommend already.

The debate was never really about whether to have immigration. It was about how much, what kind, and how well-managed. That is a reasonable debate worth having—and one that pro-immigration advocates should welcome rather than fear.

No country “needs” immigration, but smart countries can choose it

No country will collapse without immigration. But the countries that chose it—thoughtfully, selectively, with an eye toward visible public benefit—grew larger, richer, and more dynamic. The countries that avoided it are now scrambling to reverse course before the math and the demographic reality catches up with them.

Immigration is not a necessity. It is an advantage—and right now, it is an advantage that is unusually easy to take. Hundreds of millions of people around the world want to move to wealthy democracies. That will not always be the case. Global population is projected to start declining within a few decades, and when it does, the competition for immigrants will get much fiercer. Countries that build good immigration systems now will have a head start. Countries that wait may find there is no one left to recruit.

The restrictionists are right that no country needs immigration. But they are wrong about what follows. What follows is not a reason for complacency—it is a reason for ambition. The smart move, on their own terms and by their own metrics, is to build an immigration system that actually works. Not because the country will collapse without one, but because the country that gets this right will be larger, richer, and stronger than the one that doesn’t.

Surely, some people would choose to be poorer and smaller rather than accept any immigration at all. I do not think most people would—not even most hard conservatives. And if you do, you don’t have to be a “white nationalist” to make that choice unlike what some critics say.

But please be straight with the rest of us: admit that you are fine with a shrinking economy, that you want native-born Americans picking strawberries at $50 an hour rather than learning a skill, and that you would rather manage decline than compete for the world’s best talent. That is a coherent position. It is just not a popular one—and the radical honesty I am asking for here should apply equally to the cosmopolitan left that pretends countries will collapse without immigration and the nationalist right that pretends they will be just fine without it. The real work is in the details—and proposals like, for example, this one show that serious restrictionists and other reformers may be closer to agreement than either side admits.

I'm happy to share my forthcoming piece in the International Higher Education journal, reprinted with permission. International student migration has long been one of the most popular forms of mobility across borders—yet some countries have moved sharply to restrict it. This article applies the lessons on "persuasion by better policy design" from In Our Interest to explain why student migration usually works politically, and how bad policies can destroy that consensus.

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A cycle of backlash and counterbacklash to immigration is reshaping global politics. Yet one form of immigration has long enjoyed remarkably broad support: international student migration. Students pay tuition, fill classrooms, boost local economies, and many stay to become skilled workers. In most democracies, the public has viewed international students favorably—even when attitudes toward immigration in general have soured.

That consensus, however, is now fraying. Canada, the United Kingdom, and Australia—three of the world’s top destinations for international students—have all moved to restrict student immigration in the past several years. What happened? And what does it tell us about how democracies can manage immigration more effectively?

Research across democracies shows that voters largely care about their compatriots and prefer immigration policies that benefit their countries. Public support for immigration rises when policies are “demonstrably beneficial”—when ordinary citizens can see, in practical terms, how immigration serves the national interest.

Most voters are neither unconditionally hostile nor unconditionally welcoming toward immigration. The vast majority hold conditional preferences, supporting it when they believe the system is working and opposing it when they do not. But persuasion through better messaging alone does not cut it—what wins voters’ trust are better policies.

Why student migration (mostly) works

International student migration is a powerful illustration of this framework. Student migration is overwhelmingly popular. Its popularity stems from the fact that students bring money into publicly financed universities, reinvigorate the communities where they study, and are expected to be skilled after they graduate. Interestingly, the most prominent concern people have about international students is not about their impact on the host country but about the possibility that students may return home rather than staying to contribute.

In the United States, international students contribute over $40 billion to the economy annually. In the UK, Canada, and Australia, international tuition effectively subsidizes the cost of education for domestic students. Beyond revenue, those who stay after graduation contribute to innovation and fill labor shortages. Those who return home create lasting networks and spread the host country’s culture. This combination of economic contribution and institutional orderliness—students come through a legal channel with clear gatekeeping by universities—makes student migration intuitively appealing across the political spectrum, much like skilled work migration more broadly.

When the consensus breaks

Canada offers the most dramatic cautionary tale. Its international student population roughly tripled in a decade, exceeding one million by 2023. Much of this growth was driven not by selective universities but by colleges—including many that Canada’s own immigration minister labeled “diploma mills”—that enrolled students in low-quality programs where the primary value was a post-graduation work permit and pathway to permanent residence, not the education itself.

When the product being sold becomes immigration status rather than education, the demonstrable benefits of student migration evaporate. Students were paying high fees for programs with minimal instruction, living in overcrowded housing in suburbs like Brampton and Surrey, and working multiple part-time jobs with poor employment prospects. Local communities bore visible costs—housing pressure, strained infrastructure—without seeing corresponding benefits.

Public support for immigration—previously a Canadian point of pride—plummeted in what observers described as the sharpest shift in Canadian immigration attitudes in the history of the country’s polling. As a result, the Canadian government decided to impose a cap on new study permits in 2024, which helped but didn’t fully resolve the situation or fully win people’s trust back.

Similar dynamics unfolded in the United Kingdom and Australia, where rapid growth in student numbers—amplified by dependant visa surge in the UK and a poorly regulated vocational education sector in Australia—eroded public trust in the student migration system. In both countries, governments moved to tighten restrictions, and the political debate shifted from whether international students were welcome to whether the system was out of control

In all three countries, the backlash follows a pattern consistent with the demonstrably beneficial framework. Student migration became politically toxic not because voters suddenly turned against education or foreign students, but because policy design failures—diploma mills in Canada, the dependant loophole in the UK, the unregulated vocational sector in Australia—severed the link between student migration and visible public benefits. When students come for education and stay for skills, it works. When the education system becomes a backdoor immigration channel, trust collapses.

The curious case of Germany

Germany offers a striking contrast—but perhaps a fragile one. Its public universities provide effectively free higher education to all students, including those from outside the EU—a taxpayer subsidy that might seem like a political flashpoint. Yet international students in Germany have so far generated comparatively little controversy.

German universities still maintain rigorous academic standards with no large, poorly regulated private college sector gaming the system—though a growing private sector increasingly serving international students bears monitoring. The post-study pathway ties continued residence to securing qualified employment. The 2023 Skilled Immigration Act even expanded work opportunities for foreign graduates—framed not as an immigration concession but as an economic competitiveness strategy to address Germany’s well-documented skilled worker shortage. And because domestic students also pay no tuition, international students are not perceived as receiving a special deal.

Germany’s stability, however, should not be mistaken for inevitability. If German universities—or a parallel private sector—were to begin using degree programs primarily as immigration pathways for foreigners, outside of democratic oversight and labor market alignment, the same erosion of trust could follow. The AfD’s rising anti-immigration platform has not yet targeted the free tuition consensus for foreign students, but that does not mean it won’t—especially if policy failures give it an opening. The lesson is not that Germany has found a permanent solution but that its system currently maintains the conditions under which student migration remains demonstrably beneficial: genuine educational quality, labor market linkage, gradual growth, and a framing that emphasizes mutual benefit.

What this means for higher education

For higher education professionals, the central lesson is not to take the popularity of international students for granted. The public support that student migration has traditionally enjoyed is conditional—it depends on the system working to benefit citizens, alongside students, as advertised. When universities or governments prioritize enrollment numbers and revenue over educational quality and labor market alignment, or take the role of immigration admissions, they undermine the very foundation of that support.

The backlash in Canada, the UK, and Australia is not evidence that student migration is inherently unpopular or that xenophobia permeates everywhere. It is evidence that badly designed student migration policies become unpopular—a distinction with enormous practical implications.

Obviously, replicating Germany’s tuition model is not feasible in most countries, where international student fees effectively subsidize domestic education. But governments do have design levers available: robust accreditation that shuts down programs functioning primarily as immigration pathways, post-study work rights tied to qualified employment rather than granted automatically, and enrollment growth calibrated to housing and labor market capacity. None of these require eliminating the revenue benefits of international students. They require making sure the revenue model does not swallow the educational one.

International student migration can remain popular by design, but only if higher education systems and governments do the work of maintaining quality, transparency, and visible benefit. The countries that get this right will attract global talent, strengthen their universities, and build durable public support. The countries that do not will discover—as Canada, the UK, and Australia already have—that even the most popular form of immigration can become toxic when voters conclude the system is no longer working in their interest.

The response to my recent piece on “highbrow” misinformation has been overwhelming. The piece argued that pro-immigration advocates, academics, and fact-checkers routinely make claims about immigration that are technically defensible but often misleading. I got a fair share of support and hate emails from across the political spectrum—which, I suppose, is one way to know you’ve touched a nerve. But I was also particularly heartened by the public approval the piece received from academics, including left-of-center scholars for whom endorsing a piece that challenges pro-immigration orthodoxy carries real reputational costs. Tenured (and untenured) professors should do this more often.1

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What I learned

Here is what I learned from the comments and reactions across platforms. First, the piece resonated with lots of folks who haven’t thought about immigration before at all. The reason for that is that the pattern of strategic half-truths and noble lies that and others describe as “highbrow misinformation” is hardly unique to the immigration debate. Commenters pointed out eerie parallels in gun policy, climate, public health, and more.

Second, it’s hard to please everyone—but I was struck by how the piece also resonated with a broad spectrum of ideologies. Some of the most thoughtful responses came from people who disagree with each other on nearly everything else. The post was not meant to convince everyone. Different readers will find different claims more or less persuasive, and that is fine. After all, my goal was never to smear any particular individual or organization—it was to call out and do something about the misinformation dynamic that erodes public trust across the board.

What I should have done differently

I stand behind what I wrote. One thing I do wish I had done differently, however, is less throat-clearing. While some of it was probably necessary—and I say this as someone who already cut the throat-clearing in half from the original draft—it was still not sufficient to prevent people from misinterpreting or outright yelling at me. Quite a few readers ignored most of it, found the part they objected to, and ignored the caveats anyway. So it goes.

More importantly, while it is simply not possible to cover all myths and misinformation instances in a single piece, I wish I had given at least a few more specific examples besides the Oxford literature review stating, with a broad stroke of the brush, that “immigrants commit fewer crimes worldwide.” So let me use this space to do what I should have done in the original, since it was probably the most common complaint among those on the pro-immigration side.

So, let’s take crime again and consider how confidently some prominent voices state things that are, at best, misleading oversimplifications.2 For example, Hein de Haas, a prominent left-of-center academic, in the PR materials for his widely read book How Migration Really Works, summarizes: “There is no evidence that immigration leads to more crime. In fact, crime rates have dropped as immigration has increased.” I genuinely admire de Haas’s original research (like this paper on the effectiveness of immigration policies)—but this kind of confident, sweeping summary is precisely the problem.

While immigration does not generally increase crime, context matters enormously: in the United States, immigrants commit far less crime per capita than native-born citizens, but this is not universal. In several European countries, including Sweden, I wrote at length that foreign-born individuals are disproportionately represented in the prison population, particularly where rapid immigration of young, unskilled males intersects with labor market discrimination. These sorts of sloppy generalizations happen even to the best of us, but in a high-profile book marketed to the general public, it becomes highbrow misinformation.

Here is another representative example of fighting “lowbrow” misinformation with “highbrow” misinformation. When trying to “debunk” another random thing that Trump said at a rally, FactCheck.org quoted Swedish criminologist Jerzy Sarnecki describing claims linking immigration to rising crime in Sweden as “lies”—while acknowledging that Sweden’s large refugee intake creates “various types of strains.” But Sarnecki maintained that the increase in lethal violence “has nothing to do with the recent large refugee wave,” despite the fact that the Swedish government reports finding overrepresentation of foreign-born individuals in crime statistics.

The same pattern also often applies to the mainstream description of immigration’s fiscal impacts—the blanket claim that “immigrants are net contributors” depends enormously on the skill and age composition of immigrant flows, the generosity of the social welfare system, and the time horizon you choose. Saying “immigrants are net contributors” without these qualifications is not just incomplete. In many European welfare states with large humanitarian intakes, it is simply not true. With those additional examples on the table, let me turn to what I learned from the comments themselves.

Comment highlights

More generally, the comments section on the original piece was among the most substantive I’ve seen when it comes to a public piece about immigration issues—over a hundred responses, many of them long and thoughtful. The piece was discussed on Substack, Twitter, LinkedIn, Bluesky, and Reddit—with strikingly different reactions depending on the platform. Here are a few that stood out, along with my brief reactions.

On Substack, drew a sharp parallel to gun policy, where “gun violence” is routinely defined to include suicides, which inflates the correlation with gun ownership and, as he put it, “gets in the way of building consensus for policies that would actually have an impact on gun homicides, such as safe storage laws and owner liability.” This is a perfect example of how strategically inclusive definitions—a form of highbrow misinformation—can undermine precisely the policies their proponents claim to support. As I noted in my original piece, has also written on similar dynamics within the climate debate.

User offered a succinct list of things “everyone can agree on”: high-skilled immigration is almost always good; the Right’s discussion on immigration tilts towards xenophobia; the Left often gaslights normal people on immigration by framing it as overwhelmingly good; low-skilled immigration can be good but tilts bad in Europe due to generous welfare systems; immigrants who commit violent crimes should be deported if possible. I thought this was a good consensus summary—though I imagine most people would still disagree with at least one or two of these points depending on where they come from politically. Which is precisely the point: even a reasonable attempt at common ground will leave some people unsatisfied.

agreed that pro-immigration types should be more ambitious and not knee-jerk defend current policies—but disagreed on the value of acknowledging tradeoffs. His argument: nobody in politics ever talks about tradeoffs for their preferred policies, because that’s “political suicide.” This is probably the strongest critique of my piece, and it deserves a serious answer.

Hanania is right that politicians rarely volunteer the downsides of their own agenda. But I think the relevant audience for my call for honesty is not necessarily politicians—it’s researchers, advocates, and communicators who shape the information environment that politicians respond to. And the cost of not being honest is compounding. As one commenter put it, telling only half the story year after year eventually backfires, because people experiencing the downside of tradeoffs are not blind. Ignoring their experience doesn’t make it disappear; it just makes the messenger look dishonest.

Besides, it can also leave the messengers themselves misinformed. I have met immigration scholars—people who study this for a living—who had no idea that foreign-born individuals are significantly overrepresented in crime statistics across several European countries. I’m not sure I fully understood it myself until halfway through graduate school. If the experts don’t know the basic facts, the information environment has a problem that goes beyond spin.

On the brighter side, , a self-described “open borders guy,” acknowledged that it’s “not exactly a secret in working-class America” that competition with employers who hire undocumented workers at low rates puts legal businesses at a disadvantage. He referenced roofers, restaurants, and housing competition.

What followed was remarkable. One commenter called him “the first pro-open borders person I’ve ever come across who openly acknowledges that working-class people are economically hurt by low wage competition from immigrants.” Mitchell fired back: “Telling people that they’re bigots because they actually trust their M1A1 Eyeballs is profoundly counter-productive.”

The thread got heated from there—but the core exchange is telling. When open-borders advocates struggle to say what Mitchell said, something has gone wrong with how one side of this debate communicates. Just like I want immigration-skeptical folks to acknowledge the trade-offs of restricting immigration, I also want all of us in the pro-immigration space to follow Russ and be able to admit at least one thing our side is getting wrong. Anything. Really. Please!

On Twitter, the piece reached its widest audience. quote-tweeted it approvingly—”Unusual honesty from immigration academic reveals how elite misinformation on sacred topics works”—and that post alone reached over 300,000 views. Philosopher Nevin Climenhaga found the concept of “highbrow misinformation” helpful and shared a related formulation from philosopher Rishi Joshi, who defends immigration restrictions: “Immigrants don’t come from immigrant-land.”

On the critical side, user Dion, among many other folks I respect like and Stan Veuger, argued the piece “would have been more convincing if you cited examples of people expressing the views you criticize”—a fair point I’m trying to address above.

On Bluesky, the reaction was more revealing. A handful of replies to my own post ranged from substantive critique—user named Charles raised an interesting charge of inconsistency in how I treated normative vs. empirical claims—to dismissive ad hominem of me being a “white man” that Bluesky’s own system flagged as “rude.” One reply even argued that the piece was itself an example of “highbrow misinformation” for not providing exact figures of how many academics believe or say certain things.

But the most telling feature was the silence. Bluesky was the only platform where the piece did not travel much beyond my own followers—no organic sharing, no discussion threads. When a piece generating hundreds of substantive comments elsewhere barely registers in one space, that says something about the information environment there.

On LinkedIn, the reception was more measured and constructive. I particularly liked Justin Schon’s point that there exists an asymmetry where “the burden of proof seems to fall on people to prove positive effects” of immigration, while negative claims face lower evidentiary standards. I think he’s right—but part of what I was trying to show is that the asymmetry can run in both directions depending on the audience.

FAQ

Are well-intentioned, misleading claims and omitted regression tables really misinformation?

Some commenters, including those who generally agreed with the piece, pushed back on how I treat the term “misinformation.” In the original piece, I relied heavily on Dan Williams’s concept of “highbrow misinformation”: claims that aren’t technically false but are strategically framed to mislead by omitting important context or presenting contested findings as settled.

’s recent piece on misinformation research makes a related but distinct point: that the entire field of misinformation studies has become overly procedural, measuring outputs (claims fact-checked, labels applied) rather than outcomes (beliefs changed, harms reduced). As Garimella notes, determining what’s “misleading” ultimately requires political rather than scientific judgments—which is why the infrastructure of fact-checking tends to focus on some types of misinformation more than others. This resonates with what I was trying to get at.

What strikes me is the dynamic we seem to be in: “it’s not misinformation unless it comes from the right.” As we see from the factcheck.org example, the infrastructure of content moderation and media literacy is overwhelmingly aimed at one direction. But as I tried to show in my piece, highbrow misinformation—the kind that comes from elites, academics, and well-meaning advocates—can be just as damaging to public trust, and it is far less scrutinized.

Is it really helpful to post this now, given everything that is going on?

I believe it is! There is never perfect timing for anything, but I should also note that I’ve been working on and sitting on this piece for quite some time—the first draft was finished in December.

As has argued, the real scarcity in our intellectual environment isn’t information or good analysis, but courage. She describes academics who privately agree with heterodox positions but won’t say so publicly because the career calculus makes silence rational. That dynamic helps explain what I found: not a conspiracy of lies, but a slow accumulation of strategic silences that leaves the public conversation distorted.

I can’t control how people use my piece. What I can do is make sure that what I say is accurate to the best of my knowledge. If someone cites it—and some people did, in fact—by saying that “this liberal professor acknowledges that immigration is not good,” I don’t think that’s persuading moderates to become anti-immigration. But it does increase the chance that some of them will actually read the piece and get exposed to the genuine pro-immigration arguments I make—like the evidence on increased productivity, the benefits of skilled immigration, and the case for demonstrably beneficial policies that can actually win public support.

More broadly, several commenters—from very different ideological starting points—raised the question of whether intellectual honesty is even a viable strategy in a polarized information environment. If one side tells the truth about tradeoffs and the other doesn’t, does the honest side lose? I think this is one of the most important questions in public discourse right now, and I don’t think the answer is as bleak as some fear.

The whole reason the piece resonated is that people are hungry for honest analysis. Two-sided arguments are usually more persuasive, not less—especially when audiences are already skeptical. And the cost of dishonesty is compounding: every time an advocate makes a claim that voters can see through, the credibility of the entire pro-immigration project erodes a little more.

Was I using hyperbolic language?

Perhaps, but I don’t see anything that I have gotten factually wrong. “What elites don’t want you to know” might have a populist flair, and I can acknowledge that. It was a deliberate choice to signal that this piece was not going to be a typical academic exercise in hedging (which many people still accused me of anyway). But the substance behind the rhetoric stands: the examples I gave are real, the research I cited is accurate, and the pattern I described—strategic omission and overclaiming by pro-immigration advocates—is well-documented.

If anyone can point to a specific factual error, I’m genuinely interested. So far, the pushback has been more about framing and tone than about the underlying claims.

Why did you like the comment or repost from someone I don’t like?

Likes are not full endorsements. As someone noted on Reddit, I liked most of the main-branch comments—even ones where I disagreed and pushed back on the message. The reason is simple: I appreciate thoughtful, respectful responses that engage with the substance of what I wrote. The bar is not “I agree with everything this person says or has ever said.” The bar is: “Did this person take the time to write something that wasn’t just a knee-jerk reaction—not just ‘immigration good’ or ‘immigration bad’?” If so, they got a like. I think that’s a reasonable standard, and I intend to keep it.

Aren’t you strawmanning pro-immigration advocates? I haven’t heard anyone even say “Immigration is ALWAYS good for everyone”

Folks, the subheads were not supposed to be literal things that people say! Of course, nobody walks around saying “immigration is good for everyone, everywhere, all at once”—but a great many people act as though or imply that’s their position when they dismiss every piece of evidence that complicates the picture.

It’s been interesting to observe the split reaction. Some people—mostly on the left—said I was strawmanning them, exaggerating the problem, or outright lying. Some other people—mostly in the center—said they feel seen and that everything I wrote is basically a truism. Both reactions happened in response to the exact same points. This meme post from captures this dynamic pretty well:

I think what is going on is that many people fail ’s “ideological Turing test“—they cannot accurately describe how the other side sees their arguments, even at a basic level. When centrist readers tell me that everything in my piece is obvious, and left-leaning readers tell me I’m making things up, the most parsimonious explanation is not that one group is right and the other is wrong. It’s that they live in different information environments—and the people who think I’m strawmanning might benefit from spending more time in the environments where these “strawmen” are, in fact, the conventional wisdom.

Coming next

In follow-up posts, I plan to engage with the most important—and possibly good-faith—criticisms from both the left and the right. Here is a preview of what I’m considering:

Selection by origin (a right-leaning criticism): Some commenters asked why I didn’t address “the elephant in the room”: the argument that immigrants from certain countries of origin have inherently lower potential—and that origin-based selection would be the best immigration policy. I take this argument seriously enough to engage with it rather than dismiss it as simply racist.

The strongest versions of it—for example, ’s work on national IQ—raise real empirical questions that deserve honest answers. I don’t think origin-based selection (as opposed to individual assessments) makes sense in 2026, for reasons that have less to do with political philosophy and more to do with data availability and the logic of liberal democracy. More on that soon.

Why immigration is not about humanitarianism (a left-leaning criticism): This was among the most passionate pushback I received. One reader argued: “You complain that the argument of ‘opposition to immigration is just racism’ is normative and not empirical, and in the same piece, you ‘debunk’ the ‘myth’ that immigration is about helping the vulnerable and treat it as an empirical claim when it obviously is not.”

I understand the sentiment. But I think this conflates what immigration should be about with what it is about as a matter of policy design and public support. The framing that immigration is fundamentally about humanitarianism is not just a normative preference—it is commonly deployed as a factual description of what immigration systems do and why they exist. And it is empirically wrong: the vast majority of cross-border movement is economic, and the vast majority of public opinion on immigration is shaped by perceived national interest, not humanitarian concern.

If you think there is anything I forgot to answer or mention, or you feel there are some factual errors or omissions, do let me know in the comments. As before, if you want me to write more about one of these or other related topics, I’m all ears.

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Happy 2026, everyone. I’ve been slower than usual this month—moving to an old house in Indiana from a new house in Carolina in the middle of winter turns out to be every bit as painful as people warned me. Everything that could go wrong has gone wrong, including the leaking roof and pipes, but I’ll spare you the details. But, on the bright side, I’m now fully settled, excited to be starting at Notre Dame’s Keough School, and ready to pick up the pace.

I don’t teach until August, which gives me a real runway for writing. And to make sure I use it, I got into Audience-Building Intensive—a five-month fellowship where I’ve committed to producing at least two original posts per month. So more takes are coming, folks. You now have permission to shame me in the comments if I slack off.

Speaking of takes: “The Uncomfortable Truths About Immigration” generated an overwhelming response—far more comments, positive approval, and media inquiries than I could have possibly expected. As expected, however, it also did annoy quite a few people on every side. My next post will highlight and reflect on the most frequently asked questions and concerns, and attempt to answer them honestly. Think of it as a FAQ for the piece that needed its own piece.

Here are the January links (linking does not imply endorsement):

• Thanks to folks like , , , and , I also joined the Claude Code train, using agentic AI tools for my writing and research workflow. The tech works wonders—literature searches, data formatting, organizing research notes, things that used to eat entire afternoons (even with regular chatbot AIs). I’m starting to wonder whether not using these tools as a university professor isn’t just a missed opportunity but outright malpractice—the equivalent of doing regressions by hand when Stata became available, or drawing charts with a ruler when you could use Excel.

• This month’s 6th annual Borders & Migration workshop in New Orleans was a blast—nothing beats the energy of sharp grad students and cutting-edge research even amidst all the doom and gloom. We’re keeping the no-slides format going and bringing in great data and policy folks from all around the place. As a successful side quest, we also convinced at least a few more academics to join Substack. If you missed this one, mark your calendars—our 7th annual event in St. Pete next year is gonna be big. Want to be on the program? DM me.

• I wrote a new policy brief for EUI’s Migration Policy Centre on why pro-immigration information campaigns keep failing. The short version: attitudes are remarkably stable, voters reasonably prioritize their fellow citizens first, and the solution isn’t better messaging—it’s better policy design.

• I also have a new paper with Yaoyao Dai (and my first published registered report) on why populists keep winning if their rhetoric isn’t actually persuasive. The answer may be mobilization rather than persuasion—though policy positions still matter far more for voting than populist framing (see the summary of our previous research on the matter).

• Together with Eric Gonzalez Juenke for , we trace how Trump and Stephen Miller transformed immigration policy in year one—largely bypassing Congress and any guardrails. Basically, how did we get from widespread and legitimate concerns about immigration among moderates in 2024 to federal agents shooting American citizens in Minneapolis?

• , among many others, makes the case that ICE is a 70-30 issue against Trump. I would note that the fact that voters now understandably dislike ICE and the chaos it brings does not mean “abolish ICE” is politically a good idea, or that voters are bound to trust Democrats more on immigration in general. The thermostat is correcting for overreach, but people’s absolute preferences on ideal immigration policy are likely unchanged. I wish more survey folks would not overinterpret the ongoing changes in people’s opinions and test beyond "Abolish ICE" (which tells us nothing about what people actually want from enforcement).

• at The Liberal Patriot argues that liberals should try harder to understand their opponents on immigration. I agree that understanding opponents is essential—most people really aren’t extremists. I’d just add: understanding is the starting point, but someone also needs to design enforcement policies that actually work—targeting real threats, minimizing civilian harm, providing due process.

• Will Allen, Mariña Fernández-Reino, and Isabel Ruiz have a new piece in the Oxford Review of Economic Policy that echoes what I’ve been arguing: immigration impacts vary by context and group, involve real trade-offs, and ultimately demand better policies rather than blanket claims in either direction. Self-recommending for academics and policy folks.

• has two pieces worth reading this month. First, on what awaits peer review in the near future—I agree that discouraging AI use is a losing gamble, while submission fees and mandatory computational reproducibility are a must. Second, a broader piece on why things will have to change in academia and media.

  • Academic jobs and promotions are premised on the fact that publishing is slow and hard—with agentic AI tools, that is about to change. Many of us have been focused on what AI does to teaching, but publishing may be the bigger stress test. The question is what norms and infrastructure we build around that reality. Provisional fixes for the coming academic publishing crunch: charge a submission fee and use it to pay reviewers, rely more on reputation by shifting toward post-publication review and non-blind reviews, and use an in-house LLM to co-read submissions and run the code as R2.

• has a good primer on why fertility decline is alarming, plus a list of research questions for everyone from PhD students to funders. If you’ve read my Japan pieces, you know this is a topic I’ll keep coming back to.

• RBC Economics, Canada’s largest bank, warns that the country may be cutting immigration too aggressively. International student applications are plummeting, and the new system is “clunky and cumbersome.” A fascinating case of the thermostatic pendulum swinging too far in the restrictive direction—the mirror image of what happened in 2022-2023.

• Peter Chai offers a useful corrective for anyone who covered Japan as a case study in anti-immigrant attitudes (myself included, to some degree). The public opinion picture is more nuanced than the headlines suggest—which matters for a country that just announced a comprehensive new “orderly coexistence” framework for foreign national policy.

• has a working paper showing that attitudes toward asylum seekers predict voting for populist anti-immigration parties far better than attitudes toward other immigrant groups. Most survey research lumps all “immigrants” together—masking the variation that actually drives politics.

• And finally, at Silicon Continent has some good advice for young people and academics on navigating career choices in the age of AI.

As before, if you want me to write more about one of these or other related topics, let me know!

Please like, share, comment, and subscribe. It helps grow the newsletter without a financial contribution on your part. Thank you for reading.

Disclaimer: This is a long post, which I hope will be informative regardless of the events happening or where you stand on immigration issues today. I also suspect it will be upsetting, especially for many of my own readers, who are generally well-educated, left-of-center, and strongly cosmopolitan. I will flag my own doubts, and I hope you keep an open mind, while telling me why you think I am wrong in the comments.

Here is the uncomfortable truth: a lot of what liberal elites on both sides of the Atlantic say about immigration is deliberately misleading in ways that matter for policy and for democratic trust. It is not usually outright made-up. But rather it is a form of “highbrow misinformation” built out of selective framing, strategic omissions, and “noble” half-truths. And it likely makes it harder, not easier, to build durable majorities for freer immigration policies in the long run. So, what I’ll try to do here is to catalogue some of the most important and destructive myths on my side.

To put my cards on the table, I am writing this as someone who’ve come to believe that rich democracies have an interest in inviting more select immigrants and has just published a book making that case realistically. I’ve also come to realize that some reasonable people disagree, so I also believe that refusing to admit uncomfortable truths about the costs of certain immigration policies does not protect the pro-immigration cause. As the often senseless and cruel approach to immigration in the second Trump administration makes increasingly clear, it hands ammunition to people much further away from truth and basic dignity. That is why calling out misinformation on the pro-immigration side is not a Trojan horse for xenophobia but an attempt to explain why our own stories are brittle—and how that brittleness helps the forces we worry about most.

The core problem is that we rarely say out loud what we all know privately: some immigration policies, and thus also some immigrants which they bring, are much more beneficial economically or culturally than others for receiving countries. Instead, we talk as if “immigration” were a single, abstract good that works for everyone, everywhere, and under any policy design.1 That is not just technically wrong on the evidence, but also politically self-defeating.

When reality fails to match this story—that immigration is always positive and has no downsides—voters do not conclude that immigration is complicated. They conclude that the people in charge are not being straight with them, just as many concluded during the botched response to Covid and on other issues.

I’m not just talking about a few sloppy op-eds. The problem is a whole information ecosystem that people like you and me live in and help sustain. Pro-immigration researchers choose the topics and ways to analyze the data that tend to, at least slightly, favor their views.2 Then, even the most cautious analysis gets selectively emphasized or underplayed. As with many policy issues, advocacy groups and think tanks turn the most convenient findings into talking points. Liberal-leaning media outlets then cherry-pick those talking points for simple “immigration is good” narratives. In the end, advocates and politicians repeat the cleanest versions in speeches, with almost none of the original caveats.3

What I mean by “highbrow pro-immigration misinformation”

Building on ’s work on highbrow progressive climate misinformation and ’s piece on elite misinformation as an underrated problem, philosopher has convincingly argued that a lot of so-called misinformation today does not come from anonymous trolls or bot farms, but from respectable institutions staffed by highly educated professionals. Williams defines highbrow misinformation not as crude fake news, but as communication that:

• does not usually state direct falsehoods,

• instead misleads by how it selects, omits, and frames facts,

• consistently pushes audiences toward one favored political narrative,

• and flourishes inside institutions like mainstream media or academia today where almost everyone shares the same biases and values.

In these environments, people rarely lie outright. They choose which convenient numbers to highlight, which friendly experts to quote, and which uncomfortable questions to never ask. Over time, this produces a public narrative that is technically defensible and emotionally satisfying, yet still a significant distortion of the best available evidence. Meanwhile, disconfirming information is quietly filtered out as “unhelpful” or “fuel for the xenophobes.”

Because of educational polarization, this kind of highbrow misinformation today often skews in a progressive direction. Highly educated professionals in universities, NGOs, major media outlets, and philanthropic foundations are overwhelmingly more culturally progressive than the general public. Meanwhile, politicians from nearly all established parties—who tend to be highly educated professionals—are now more pro-immigration than their voters. This is true even among the elites on the mainstream right. And when nearly everyone in a given institution shares similar values on contentious topics, bias compounds.

We have seen this on climate issues, where a great deal of attention has been paid to denialism on the right. However, far less attention has been given to alarmist misreadings on the left that suggest imminent civilizational collapse, even though mainstream projections say no such thing. We have clearly seen it in Covid and several other domains. Immigration is another textbook case that has mostly escaped this reckoning so far.

What I am trying and not trying to do here

Before I catalogue some common pro-immigration myths, a few necessary clarifications. First, while people’s misperceptions often simply stem from ignorance rather than deliberate deception, anti-immigration misinformation is absolutely real. Some of it, though not all, is particularly conspiratorial and genuinely dangerous.4 To give one prominent example, the Great Replacement Theory frames immigration as a whole a deliberate plot by “elites” to replace all white people, and it has been cited by multiple mass shooters. This is not at all remotely on the same level as a slanted chart in a liberal newspaper explainer.5

Second, and related, most pro-immigration and progressive communication is of much higher evidential quality than what you get from far-right influencers or talk radio. I am not claiming that “the progressive side” never generates destructive hoaxes on other issues like race and gender. But at least when it comes to immigration, the examples I discuss are rarely outright fabricated stories. They are subtler and more likely to be filtered out or refined before they reach the mainstream. They involve context-free headline statistics, cherry-picked case studies, or quietly shifting baselines that make trade-offs disappear.

Third, I do not want to dwell here on the popular rhetorical move that any opposition to immigration is “just racism.” That is a normative claim, not an empirical one.6 As I show in my book (also see ’s Whiteshift), under most coherent definitions of racism, this is simply not accurate as a general description of the social science data, even though racism obviously exists and matters.

Instead, here I want to catalogue a set of (often implicit) empirical beliefs, arguments, and narratives that are hard to justify given the best available evidence, but that are common among highly educated pro-immigration elites, including left-of-center and even moderate academics, advocates, and journalists in North America and Western Europe. These are also things I either believed myself earlier in my career or was strongly encouraged to say in order to “help the cause.” Note that, for the list below, given my own recent critique that “immigration is not just one single thing that has effects,” I use this term to specifically mean “more liberal immigration policies.”

1. “Immigration is about helping the vulnerable”

One of the most common misleading stories on the pro-immigration side is that immigration is, at its core, a humanitarian project. The implicit picture is that immigration policy is mainly about how generous we are willing to be to vulnerable, marginalized outsiders. In The Truth About Immigration, calls this the “victim” narrative, the mirror image of the familiar “villain” story on the far right in which immigrants are criminals or job-stealers. Both are powerful, but they miss something important about what immigration is, not just morally but as a matter of fact.

First, most immigrants in the world are not humanitarian cases in the strict sense. It is hard to pin down an exact percentage, but less than 20% of all international migrants are refugees or asylum seekers, while the overwhelming majority move for work, family, or study. Yet this humanitarian minority receives disproportionate attention from journalists and academics, especially outside economics.

Second, immigrants, including many refugees and asylum seekers, are not just recipients of compassion. They are workers, consumers, taxpayers, neighbors, and family members who shape the national interest of receiving countries. We also know that most countries do not accept even asylum seekers purely out of altruism—the governments there are making their own calculations.

As someone who just lived through the senseless immigration enforcement surge in Charlotte, met with anger even among many conservative residents, I get the appeal of the humanitarian frame. Even before Trump’s latest crackdown in Minnesota and elsewhere, immigration policy was far from perfect, and people understandably want the state to stop harassing them. But from a human welfare perspective, stopping enforcement abuses or securing the right to asylum are not sufficient.

The biggest gains from migration to both national and global well-being do not come from marginal tweaks to benefit packages or enforcement practices in rich democracies. They come from enabling many more people to move from poorly governed, authoritarian environments to high-productivity liberal democracies in the first place—in ways that voters can see as fair and beneficial to their own societies as well. Stopping enforcement abuses may be orthogonal to that goal.

People who accept the humanitarian frame also often forget that most people outside their bubble are very different. In my book, I show that explicitly humanitarian-oriented immigration frames and policies—“we should accept more people simply because they need help”—resonate strongly with at most about 10 percent of the electorate. You may disagree personally, but even most left-of-center voters believe immigration policy should, like any other policy in a democracy, be designed to prioritize the national interest.

Horror stories of immigration enforcement abuse are usually not invented. But there is a steady focus on the most dramatic cases of suffering. Humanitarian groups understandably foreground the worst tragedies. Journalists gravitate toward camps and boats, not routine labor mobility. Politicians and philanthropies then talk as if immigration were mostly about charity. The result is a picture in which immigration is “about” compassion for victims.

However, in reality, most migrants are ordinary people moving for work and family whose presence can be strongly in the interest of receiving countries. In the end, a sense that migration is mostly about humanitarian and abuse cases becomes highbrow misinformation.

2. “Immigration is good for everyone, everywhere, all at once”7

When pro-immigration advocates move away from the victim framing, they often jump to the idea that immigration is simply good for everyone involved anyway. George Borjas, for instance, opens his book We Wanted Workers by quoting Paul Collier’s critique that social scientists had become so eager to refute xenophobes that they “strained every muscle to show that migration is good for everyone.” Borjas goes further and accuses many researchers of filtering or spinning evidence to exaggerate the benefits and downplay the costs.8

I think Borjas and Collier have a point, though they overstate the case. Most serious researchers I know would summarize their view roughly like this: freer immigration tends to be strongly beneficial on average, but this average elides distributional effects. Some groups will be worse off in the short or medium run and may need to be compensated or protected. That is a perfectly respectable position.

The problem is that, by the time this view passes through advocacy groups, communications offices, and friendly media, the second part often disappears. What reaches the public sounds much closer to “immigration is good for everyone, full stop. And if you are a member of a harmed group, or worry you may be harmed, you are a bad person.”

In my own experience at workshops and conferences, I have repeatedly seen findings that cannot be unambiguously read as “immigration is good” quietly downplayed, reframed, or dropped from papers. Well-meaning colleagues have suggested that I soften or remove results that might “feed the far right,” even when the estimates are robust. I have heard explicit advice not to emphasize negative fiscal impacts, violence spikes tied to specific policy failures, or integration problems in particular contexts - even when these are well documented.

The pattern does not stop in the seminar room. Once the more reassuring estimates are the ones that survive peer review and internal vetting, advocacy organizations put them in press releases and policy briefs, stripped of nuance. Journalists then write “here is what the research says” pieces that present those filtered results as the consensus view, and sympathetic politicians cite those summaries as if they meant “immigration has no losers.” At each step, the message gets cleaner and less conditional.

At the same time, this does not overturn the conclusion that the net economic impact of most types of immigration is sufficiently positive that it is likely that expanding immigration would be beneficial. The most serious work we have points to large net benefits from existing immigration and substantial room to liberalize, especially on skilled work-based channels, if policies are designed better. The truth is not that “immigration is actually bad.” But pretending that our freer policies are costless and universally beneficial erodes trust when trade-offs eventually become visible. Like all policies, immigration creates winners and losers. It is a lie to pretend it does not.

3. “If immigration is good in one case, it must be good in another”

Another related pattern stems from what I have recently called “immigration as a single dial” misconception. If a paper shows that immigration has positive effects in one country or context, advocates infer that immigration does not pose serious problems anywhere else, or at least that examples to the contrary are merely idiosyncratic.

One common move, for instance, is to take U.S. evidence that immigrants, including “unauthorized immigrants” (or “illegal aliens” if you’re stickler about the language), commit fewer crimes than natives, which is true in the aggregate according to multiple high-quality studies, such as recent work by or Abramitzky et al., and then use this as proof that immigration does not raise crime risks anywhere.

You can watch the overgeneralization in real time. A careful study about one US state (usually Texas since they have more data) becomes a punchy blog post. That blog post becomes an NGO fact sheet with a general headline like “immigrants commit less crime than natives.” Soon enough, a left-wing academic or pundit is insisting that “immigration doesn’t increase crime” in Germany or even worldwide, even when the underlying research never claimed such a universal result.

But the crime effects of immigration depend on who comes, under what legal status, how enforcement works, and how receiving communities respond. The fact that immigrants in Texas have lower conviction rates than natives tells you something important about that context. It does not, on its own, settle debates about youth gangs in Sweden, knife crime in the UK, or sexual assault patterns in specific German towns.

More broadly, a lot of people seem to slide from a normative claim to an empirical one: because all human beings have equal moral worth—a perfectly reasonable moral view most people share—they assume that the effect of any individual immigrant will be the same as any other immigrant. This is not true; the economic and cultural effects of a particular immigrant or group of immigrants plainly depends on who is moving, at what age, with which skills and language, and into which set of institutions and communities.

4. “Immigration is good… unless it is temporary”

A fourth place where highbrow misinformation flourishes is around temporary and circular labor migration. Many progressive advocates will say that immigration is a good thing overall, but then make a strong exception for temporary work visas, guest worker schemes, and especially the Gulf states. A two-year contract with no path to citizenship is presented as an affront to human dignity or as tantamount to indentured servitude.

This is not true. Migrants have their own thoughts on temporary migration, and many find it improves their lives. There is also good empirical evidence on the matter.

, for example, conducted a rare randomized evaluation of a temporary guest worker program that sent Indian workers to jobs in the Gulf. He found enormous gains in income for those who migrated and no evidence that, on average, their well-being was worse than comparable workers who stayed home. Organizations like the Center for Global Development and Labor Mobility Partnerships (LaMP) have documented case after case where workers queue for years and pay large sums to access “exploitative” guest worker jobs because the counterfactual at home is so much worse. The migrants have far more information about their circumstances than we do.

None of this is to deny the reality of abuse. There is extensive documentation of exploitative recruitment practices, passport confiscation, and unsafe working conditions in parts of the Gulf and elsewhere. In the Gulf, in particular, highly unregulated recruitment industries routinely leave workers indebted before they even arrive, and their visa is often tied to a single employer, which makes it extremely risky to complain or leave a bad job. Similar, though much less severe, dynamics can also be seen in the currently contentious H-1B program in the United States, where workers’ status is effectively controlled by their sponsoring employer and abuses have been documented. These are serious problems that demand policy responses and enforcement, not romanticization.

But here, too, there is an information pipeline at work. Human rights organizations must highlight the worst abuses to attract attention and funding. Journalists understandably focus on the most shocking cases. Politicians then react to those stories with sweeping condemnations or outright bans on entire visa categories, rather than asking whether the programs can be reformed in ways that protect workers while keeping legal channels open.

Crucially, none of the worst abuses are inherent to the idea of temporary visas itself. They flow from specific design choices about recruitment fees, debt, employer ties, complaint mechanisms, and labor rights. And there are real-world examples of countries like South Korea tightening these rules and significantly reducing harms, even if nobody has gotten it perfect.

This links directly to a broader rights-versus-numbers trade-off that much progressive messaging tends to smooth over. Martin Ruhs, in The Price of Rights, argues that there is a real and often unavoidable tension between how many migrants a country can admit and the range of social rights it can feasibly extend to them. You do not have to like this trade-off, but you cannot wish it away. If you insist that all migrants must have immediate access to expansive cash benefits, free health care, and full political rights, many voters will insist on admitting fewer migrants. If you design asylum systems where people are allowed in but then barred from working for long periods while the state supports them, you will quickly hit fiscal and political limits.

There are therefore two forms of highbrow misinformation that result. The first is a moralized caricature of virtually all temporary migration as unacceptably abusive, built on the worst cases and ignoring migrants’ revealed preferences and the welfare gains that are actually measured. The second is silence about the fact that banning or stigmatizing temporary schemes often shrinks legal options for exactly the vulnerable people advocates claim to care about, pushing them toward irregular routes that are more dangerous, less regulated, and harder to monitor.

A more honest message would be: temporary migration generates huge gains for many workers but also creates real risks of abuse. The right question is not whether such programs are inherently immoral, but how to regulate them and empower workers so that abuse is minimized while opportunities expand.

More importantly: if we care both about migrants’ rights and about how many people can move at all, we need to admit that rights are not free and design those trade-offs explicitly instead of pretending they do not exist.

5. “Immigration is good… misinformation is why people oppose it.”

I believe that one of the biggest pieces of misinformation among pro-immigration elites is ironically the idea that those who disagree with them are profoundly misinformed. This might be the most flattering but misleading belief in pro-immigration circles: the idea that widespread opposition to immigration is basically a result of misinformation or ignorance.

Of course, misinformation plays some role. Many people genuinely do not know basic facts about immigration policy or about immigrants themselves. I have done work showing that giving people clear information about legal migration pathways can reduce hostility in some cases.

But the best evidence we have suggests that information alone cannot explain mass opposition. Indeed, proponents of immigration are just as likely to hold incorrect beliefs. In one of my recent papers, for example, I find that misperceptions about immigration policy are common across the political spectrum, including among pro-immigration and Democratic respondents. Knowledge is not the exclusive property of one side.

Nor does correcting information universally make people more pro-immigration. In another recent study, shows that correcting some common misperceptions, such as the number of asylum seekers, can actually increase opposition to immigration.9

It seems that persuading people on immigration is hard. If it was simply that people didn’t know the truth, you would expect malleable attitudes. Instead, attitudes are fairly stable, rooted in deep-seated values about national identity, fairness, and risk, and often respond more to perceptions of control than to fact sheets.

It is not true that “if only we controlled the information environment around immigration better, people would come around.” Framing opponents of particular immigration reforms as simply “misinformed” or brainwashed by propaganda is itself misleading. It erases real value disagreements and real trade-offs, and makes many voters feel talked down to and stop trusting experts and institutions.

Honorable Mention. “The way we manage immigration is already good…we don’t need better policies”

Here I will do something that may sound odd in a piece on pro-immigration misinformation and criticize advocates for not being ambitious enough about immigration’s transformative potential.

Especially when faced with the kind of draconian overreach we are seeing now in the United States, a common progressive framing is: “Immigration is already good. Our main job now is to fight for the rights of those who are here.” When the whole institution seems under threat, perhaps it makes sense to not focus on expansion, and just try to survive the current chaos. This limits focus to expanding access to social support, limiting unjust enforcement, and maximizing the political rights of existing residents. Comparatively little attention is spent on how many more people might be able to move here in the first place.

This framing does not come out of nowhere. It grows out of decades of research showing that, on net, immigration has not produced the catastrophic economic or social harms that many feared. But as those findings move from technical reports into advocacy messaging and media commentary, the “on net” quietly drops away. What is left is a reassuring slogan that current policies are already a success story, which makes it harder to even see, let alone debate, the counterfactual world where many more people could move under better-designed rules.

Think of the famous estimates that open borders could double world GDP. These are, of course, hypothetical and almost certainly overstated. But even if the true gains are a fraction of that, they are still enormous. Even fairly cautious modeling of more modest liberalizations generally finds gains measured in multiple percentage points of world output, and at the individual level we routinely see migrants multiplying their earnings several times over by moving.

Those income gains translate into better health, education, and opportunities not only for migrants themselves but also for their families and communities through remittances and investments. And they are not just “private” gains for foreigners. When people move from low-productivity to high-productivity settings under sound policies, they expand the tax base, help sustain aging populations, staff understaffed services, and, perhaps most importantly, contribute to innovation in receiving countries. What I do not see, however, is many pro-immigration advocates seriously thinking through or even talking about these points.

Based on my own life, I can attest this is not an abstraction. Had I stayed in Soviet Russia, where I happened to be born, there is a good chance I would have been conscripted into a senseless war rather than writing this as a tenured professor now. The gap between my actual and counterfactual productivity and life chances is not about some “magic dirt.” It is about the difference between living under an extractive state and living in a reasonably functional liberal democracy and market economy with the rule of law. That difference is good for me, but, at least as I want to believe, it is also good for the United States—the country I now call home. I pay considerable taxes here, educate the youth, and spread the word about the importance of things like freedom of speech to my friends and relatives in Europe (which they desperately need). In other words, in the US, I can make use of my skills instead of wasting them.

To unlock more mobility, we may sometimes need to design status categories and benefit rules that fall short of maximalist rights packages, while still meeting basic standards of dignity and fairness. That means talking openly about which rights need to be guaranteed immediately, which can reasonably be phased in, and how to finance them without triggering a backlash that shuts the border altogether.

As I recently told for her Argument piece, I have little patience for claims that we do not need to change immigration policy because “all studies show immigration is already good.” If we pretend that current policies are close to optimal, we blind ourselves to the counterfactual world in which many more people could move, work, and thrive. That, too, is a kind of highbrow misinformation—comforting to people who already have the right passport, but deeply misleading about what is actually at stake.

Why highbrow misinformation matters for immigration’s future

So why spend all this time criticizing people who are, for the most part, on “my side” of the immigration debate? Why do it now? Because while resistance to unjust enforcement is necessary, it is not enough. Highbrow misinformation is still misinformation and it is corrosive, even when it aims at purportedly noble goals.

My view is that immigration is too important to rest on brittle half-truths. For all those of us who want to see better immigration policies that are stable over time, we need a politics of honest trade-offs. That means:

• Admitting that immigration produces both winners and losers, even if the overall balance is strongly positive.

• Being specific about where positive findings hold and where they might not.

• Recognizing that temporary and circular migration can be life-changing for workers, even when it does not fit our preferred models of citizenship.

• Acknowledging that migrant rights have costs, and that sometimes easing one constraint requires tightening another.

• Accepting that many people oppose some forms of immigration for reasons that are not reducible to ignorance or bigotry.

The liberal elites do not need to be hiding a sinister secret about immigration. The truth is powerful enough. The hard part is saying it plainly, even when it cuts against our own narratives, and then doing the hard work of designing better policies that are both humane and demonstrably beneficial to most citizens. If we can do that, we will not just be more honest. We will also have a better chance of making immigration popular enough to last.

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Acknowledgements: I would like to thank , , , , Rebekah Smith, and for reading and thoughtfully commenting on the previous drafts of this piece.

Happy holidays, everyone. Or should I say Merry Christmas?! Either way, I’m grateful to all of you who subscribed and commented. It’s only been a couple of months, and we’re already very close to hitting 500 readers. Your feedback, shares, and thoughtful pushback make this newsletter… better.

Some personal news first: I got tenure, and in a couple of days I’m starting as an Associate Professor at the University of Notre Dame. Let me tell you, it’s not fun moving from sunny North Carolina to Indiana in December. But it should be totally worth it. Next semester I’ll be on sabbatical, continuing the book tour, hiring a postdoc and research assistants, and building a new initiative focused on identifying better and more politically sustainable immigration policies. If you’d like to invite me for a talk, collaborate, or hear more about the initiative, please reach out. If you want to visit and give a talk at Notre Dame yourself, I’m all ears. And if you want to fund parts of the initiative, even better ;)

Since I’ll have a permanent position and won’t teach until August, I’ll try to live up to the idea of academic tenure, with probably the most free speech rights in human history. And actually write more frequent and potentially controversial pieces that I personally believe in but might have been hesitant to voice before, or too busy due to the publish-or-perish mode, to be frank. Last month, we did a small experiment asking what folks want to read next, and the answer was pretty unambiguously spicy. Well, you asked. I delivered. Here’s a preview of what you’ll see here soon:

And here are the December links and updates:

• has announced a call for its 2026-2027 fellowship (deadline: January 9). Highly recommended for any academic or social scientist interested in politics and public writing. I wouldn’t have been able to launch this newsletter without that fellowship and the GA community.

• is accepting pitches for the new “In Development” magazine. My fellow comparativists and historical political economists, this is your time to shine. Write under 4,000 words to tell the world about your nerdy topic and make $2,000.

• launched the new Update substack, covering important social and political trends.

• Let’s talk about LLMs for a sec. has a provocative take arguing that they can absolutely create new knowledge, but probably not new “concepts.” I personally think current LLMs can create both new knowledge and concepts that don’t exist yet. The question is whether that knowledge and those concepts will be useful. Humans struggle with this too.

  • talks about new studies on LLM persuasion, and explains why exposure matters more than persuasiveness.

  • There’s an intriguing new paper proposing the idea of “LLM-hacking” (similar to old-school p-hacking). The basic point is that it’s very easy for researchers to come up with a prompt that classifies things in line with their hypothesis, deliberately or not.

  • The fact that many of my academic colleagues are still deliberating over whether LLMs can be useful in their work (I’m looking at you, Bluesky), while people I know in industry have already moved to agentic tools like Claude Code is pretty wild. But agentic tools are slowly moving into the mainstream.

  • I can personally attest that it almost feels like forbidden knowledge. I’ve been playing around with Codex to automate the tedious parts of my research and writing workflow, and it’s kind of absurd how well it works, probably better than an actual RA. I haven’t been this excited about tech in a long time.

• I didn’t expect my book to be quoted in ’s piece making the liberal case against land acknowledgements and tying that debate to Stephen Miller’s assault on immigration. But it all makes sense somehow: if we want progress in immigration policy and other areas, we need to talk about tangible national interests, not just symbolism.

• writes clearly about the actual role of money in politics. It is not what you think. Also, check out his recent post on NIMBY and how regulation by litigation strangled American abundance.

• has a fascinating post listing worst policies in the developed world.

• Toward a Positive Vision for Immigration Policy in 2026 - talks to on what a credible pro-immigration agenda could look like.

• My friend has a hot new take about fixing the primary care doctor shortage. Check it out and make sure to subscribe!

• shares her best practices of data visualization. A must-read for anyone already practicing or just interested in data visualization. I'd absolutely assign it to all my students in an intro data science class — and you should too!

• "Every hundred South Koreans today will have only six great-grandchildren between them." Probably one of the best pieces on the depopulation crisis I’ve seen by at .

• I remember talked about the role of movies and TV shows in promoting the progress mindset. I nominate Vince Gilligan’s new Pluribus show. Apart from being a near-perfect testament to the necessity of individual creativity and freedom, one thing I appreciate about it is that it genuinely educates people about things. Like how difficult migration is across the Darién Gap.

• Most readers have probably heard about CECOT and the whole Bari Weiss and CBS affair by now. I personally don’t care about what Bari Weiss was thinking, but as someone who moved from Russia to America for freedom, I didn't have "passing around a pulled 60 Minutes segment like Soviet-era samizdat" in 2025 on my bingo card. Anyway: watch what our government is doing, and share it widely.

• “UC Irvine has hired 64 tenure-track assistant professors in the humanities and social sciences since 2020. Just three (4.7 percent) are white men.” offers probably one of the best, nuanced takes on the recent discussion about the role of age and affirmative action in academia and beyond, sparked by Jacob Savage’s Lost Generation piece.

• has another great piece outlining why most of the immigration research may be ideologically biased. Don’t get me wrong — setting policy based on even biased immigration research is still much better than doing it based on vibes or group animus, but this certainly means we need more conservative and moderate folks doing careful immigration research. The same goes for sociology as a discipline and other social sciences.

• A word of wisdom from regarding the credibility revolution and survey experiments: your conjoint or list experiment is not a policy intervention with real causal effects — it’s a measurement tool. A lot of smart folks are still confused about it for some reason! Also see my recent piece on why immigration is not just one single thing that has effects.

If you joined recently, here are my top three Popular by Design highlights from 2025:

1. Probably my most important post to date on private refugee sponsorship:

2. The surprising logic behind broad support for skilled migration:

3. I also wrote a slightly more technical post about what the research says (and doesn’t say) about public views on humanitarian vs. economic migration:

As an honorable mention, also check out a highly updated list I created to gather all migration-related newsletter in one place:

That is it for now. See you all next year!

A lot of political debate about immigration is a fight over its “effects.” On one side, opponents warn that immigration depresses wages, raises crime, and erodes social cohesion. On the other side, advocates insist that immigration boosts growth, does not increase crime, and might even lower it. Both sides, especially the generally more educated pro-immigration side, like to say that “the evidence” is on their side, and they can point to studies, figures, and even regression models to back it up. What I would like to argue here is that both sides… are wrong.

Hear me out. I’m increasingly convinced that, regardless of your empirical convictions, the claims about positive or negative effects of “immigration” (and more abstract concepts like “inequality” or “diversity”) are vastly misleading. And I’m not just trying to be a contrarian here or knee-jerk centrist. When someone says that we should acknowledge that immigration has a mix of good and bad effects, this is slightly better…but also wrong.

Of course, there is a large, careful academic literature on the relationship between immigration and things like wages and crime, usually pointing to neutral or positive effects. has done a great series of live posts summarizing a huge chunk of it, which I highly recommend checking out. For quite some time, though, something has been bothering me about the idea of figuring out the “effects” of big concepts like immigration in the first place. The recent post by on whether inequality undermines democracy, and the discussion of the likely inflated role of surveys in the credibility revolution helped me pin this down a bit more.

So, what I’ll try to convince you here is that “immigration” in the abstract cannot and does not have any true, or at least practically true, identifiable effects on relevant public outcomes. This is because “immigration” is not a decision or a policy lever that can be manipulated deliberately. It is not even one single specific thing. It is a description of different people moving between different places under different rules.

My point is not just about semantics. It is about what we can realistically know and what we can practically change. The problem with talking as if “immigration” has some true effects we need to uncover is that it hides the actual decisions that states make about who can come, on what terms, and what happens after they arrive. Those are the things that have effects, because people and governments can intentionally change them. Some immigration policies and decisions can and do produce better or worse outcomes than others. The question is which ones.

Selectivia and Inclusivia: a tale of two states

To see why this matters, it helps to start with a simple thought experiment. Indulge me for a moment while I tell you a story of two (totally made-up) countries. Call them Selectivia and Inclusivia.

Both are wealthy democracies with similar economies where people speak the same language. Both receive roughly the same number of culturally different immigrants per capita every year from the exact same poorer region. If you just looked at their net migration rates and the immigrant origin, you wouldn’t expect to see much difference in relevant outcomes.

But, for various reasons, their governments decided to run their immigration systems very differently. Selectivia uses a very demanding points-based system. It mostly admits highly educated workers with strong language skills and job offers in productive sectors. It vets people carefully and enforces the rules. A serious crime can get you deported. Employers who hire unauthorized workers face real penalties.

Inclusivia is also rich, but does not want to be seen as “picky” because it preaches inclusivity (duh!). It relies heavily on humanitarian admissions and family reunification, while keeping its work immigration pathways tight. It allows long backlogs for work visas, prohibits asylum seekers from working legally (for their own good), but also enforces these rules weakly. Many newcomers end up in informal jobs and in ethnic neighborhoods with little external support.

On paper, both countries have “high, ethnically diverse immigration.” In practice, they are moving different people into different legal and economic environments. Now imagine the best scholars of Selectivia and Inclusivia try to answer the question like “Does immigration increase crime?” in their countries using the best administrative data and quasi-experimental identification strategies.

In Selectivia, the immigrants are heavily screened, quickly employed, and know that any serious offense can get them deported. You would not be surprised to find that they commit fewer crimes than comparable natives. In Inclusivia, they bring in a lot of young men with few legal job options, so you might find higher crime rates among immigrants. Even if you only care about what’s happening in only one of these countries, however, it would clearly be a mistake to conclude that “immigration” is inherently good or bad for public safety.

Will everything be different across the two countries? No. In both places, for instance, scholars will likely find that immigrants are more likely to speak different languages than natives. That is just part of what it means to be an immigrant. But for outcomes we care about politically — wages, fiscal contributions, crime — what matters is not “immigration” in the abstract. It is how the system is designed and who it brings in.

Of course, even policy effects depend on the context. The UK, for instance, has tried to adapt Australia’s points system several times but has made a mess of it. Copy-pasting a law does not copy-paste its effects. Yet there is still a meaningful difference between saying “immigration increases crime” and saying “banning refugees from work for a year after arrival tends to increase crime.” The former is mostly an abstract political slogan. The latter is something we can identify, argue about, and feasibly change.

What do people mean when they say “immigration affects crime”?

The International Organization for Migration defines immigration as “the act of moving into a country other than one’s country of nationality or usual residence.”1 That is a description of a spatial demographic process determined by many push and pull factors, not a single quantity. There is no world government or any other entity that could turn “the immigration dial” up or down.

When ordinary people say “immigration raises crime,” they often have in mind either more incidents of crime in absolute terms or a very particular image of certain foreigners committing crimes as broadcast by the media. When advocates say “immigration does not raise crime,” they often mean that immigrants commit crimes at lower rates than native-born citizens, which is true in some but not all contexts.

When researchers talk about whether one thing “causes” another, they usually have in mind a counterfactual statement or something like Judea Pearl’s “do-operator”: what would happen if we changed X while holding everything else about the world fixed. The cleanest way to do this is a randomized controlled trial: you give the treatment to the experimental group and nothing (or a placebo) to the control group, and then compare the outcomes. Unfortunately, figuring out what would happen if we increased or decreased “immigration” is more complex than that.

Social scientists often use a variety of designs: looking at changes after a certain shock like in the case of the famous Mariel Boatlift study or “shift-share instrumental variables” by looking at pre-existing ethnic networks. In the real world, however, the only way to apply the “do-operator” to immigration is through deliberate policy and decision-making. Democratic governments can decide to change visa caps, eligibility criteria, enforcement practices, or rights after arrival, and those changes lead to different numbers and types of people moving.2

Some economists like James Heckman call the impact of a specific change in the rules or decisions a “policy-relevant treatment effect.” Given this framework, instead of asking “What is the effect of immigration on crime?” we should rather ask “What is the effect of admitting this particular group of workers under this visa program in this period on crime?” That sounds more narrow, but it is the only kind of “immigration-related effect” we can actually identify.

The problem is not just a lack of nuance

Once you see this, a lot of the public debate looks strange. In the United States, the most rigorous studies tend to find that all possible types of immigrants, on average, commit less crime than natives and that plausibly exogenous changes in immigration are not associated with higher crime rates. European evidence is much more mixed, in part because the migrant populations and enforcement environments are very different.3

Yet pro-immigration scholars and advocates often jump from these findings to more sweeping claims about the effects of immigration. As a result, you have people in Europe confidently declaring that science proves “immigration does not raise crime” by citing US studies and data as if this is some kind of ground truth. At the same time, as has recently pointed out, you also often have American restrictionists citing European crime data, as if a spike in offenses among poorly integrated refugee youth in Sweden tells you what will happen if you expand high-skill work visas to more Indians in the US.

Migration is what you make it. If your policy selects educated professionals, screens them for criminal histories, and makes any serious offense grounds for deportation, you should expect very low crime rates among immigrants. If your policy strands young men with no legal way to work, in marginalized neighborhoods, with little support and weak enforcement, you should expect more crime. Both of these systems can be called “immigration.” Neither tells you what “immigration” as such does.

I am sympathetic to the argument that we cannot always acknowledge nuance in public communication. If you attach every caveat you know to every sentence, nobody will finish your op-ed. But the problem here is not too much nuance. It is the wrong level of abstraction.

When we say “immigration lowers crime,” we are not bravely cutting through complexity. We are skipping over the only levers we actually control — policies — and pretending there is a single, context-free object called “immigration” whose true effect we just have to discover.

Policies are the levers that move outcomes

So what does have effects? Here I think the “credibility revolution” in economics and political science has the right basic idea. The most useful studies are not the ones that try to estimate some grand, policy-free effect of immigration in general. They are the ones that cleanly identify the consequences of a specific, realistic change in policy for the people on the margin of that policy. Think of governments raising or lowering a visa cap, changing a work authorization rule, or tightening enforcement in some domain.

Even if we had perfect data and unlimited resources, we could not say “the true effect of immigration on crime is X.” There is no single parameter to estimate. Any credible claim will always be about a particular policy change for a particular group in a particular environment. But it should be possible to say that “the effect of introducing language programs for immigrants across contexts is on average Y.”

Michael Clemens and Ethan Lewis offer a nice example of such “policy-relevant treatment effects” in their study of low-skill work visas in the US. Instead of asking like many whether “immigration helps or hurts US workers,” they use the H-2B visa lottery — a randomized cap on low-skill non-farm guest workers — to compare similar firms that randomly win and lose access to those workers. They find that firms allowed to hire more H-2B workers expand production and investment, with no evidence of overall job losses for US workers and possible gains in some areas.

You can say that this is a fact about “immigration.” But, more specifically, it is a fact about what happens when you let US firms hire more low-skill seasonal workers, legally, through a particular program, in the 2021 H-2B visa lottery and its aftermath. That is exactly the kind of thing policymakers actually decide and care about. Of course, when we have many studies like that with converging findings, we can generalize more. But it will ultimately be generalization about policies regulating immigration, not necessarily immigration in itself.

Interlude: inequality and diversity are not things that have effects either

If you still are not convinced, I hope you can at least grant the possibility that the same problem must arise with even more abstract concepts like “inequality” and “diversity.” When I first took a social science class as an undergraduate, I was struck by how much of sociology and adjacent disciplines are organized around inequality and disparities between various groups. Not just as possible outcomes of interest but causes of other important things like democracy and violent conflict.

I still think both topics matter as important diagnostics or indicators to measure. But they are not levers in themselves either. Inequality is a summary of the income distribution. Diversity is a summary of the demographic mix. Neither jumps out of the data and changes your life on its own. The reason why some places are more (un)equal or diverse than others is never random and overdetermined by various interconnected factors. So, when smart people say “inequality hurts democracy” or “diversity undermines social trust” by pointing out to some cross-national correlations or even quasi-experimental studies, I’m really not quite sure how to interpret those statements.

As I show in my research with Giuliana Pardelli using new data from Brazil, for instance, many of the observed negative (or positive) effects of local ethnic diversity are a result of a statistical artifact related to historical state development and the incentives of certain populations to settle in more remote areas. There are some historical examples of authoritarian governments purposefully resettling entire ethnic groups, but I hope it is not something anyone would want to follow or replicate.

Broadstreet

Ethnic diversity as an outcome: the coevolution of state capacity and racial demography in Brazil

Ethnic diversity as an outcome: the coevolution of state capacity and racial demography in Brazil…

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4 years ago · Broadstreet

At the same time, if governments decide to reduce measured inequality by confiscating the wealth of everyone in the top 1 percent and throwing it into the sea (or some ineffective program), you will get very different “effects” than if you reduce inequality by raising the incomes of the bottom half via, let’s say, cash transfers. Both will show up as a lower Gini coefficient. Only one will be a (relative) success for human well-being.

So, what is to be done?

While “migration” understood as a description of people moving between places does not have a single and true identifiable effect, the way we regulate it likely does. It is fine to use shorthand like “immigration raises productivity” or “immigration does not increase crime” in a headline. But as researchers and commentators, we should not confuse our own shortcuts with ground truth.

Importantly, the point is not that we should prioritize niche, well-identified RCTs over larger descriptive studies or even theoretical modeling. Good, relevant evidence is not just about clever causal identification. It is about tying that identification to a concrete policy lever. Instead of asking “Is immigration good or bad?”, we should ask things like “If we change these visa allocations in this way, what happens to innovation, wages, and public opinion?”

That has two practical implications. First, we should be much more explicit about what we are actually talking about, at least when it comes to technical reports and academic papers. Instead of saying “immigration reduces crime,” say “in recent US data, legal immigrants with strong labor market ties and deportation risk commit fewer crimes than comparable natives.” It is more cumbersome, but it is honest and actually useful.

Second, in both advocacy and analysis, we should force ourselves to think in terms of better and worse policies, not better and worse “amounts of immigration.” If you are an advocate, ask yourself not just how immigration can be good, but how it can realistically be bad, and under what rules. If you are a skeptic, ask yourself what concrete system you would support to be convinced that certain migrants improve public services, pay more in taxes than they receive in benefits, and reduce crime in certain areas. What admissions criteria, enforcement practices, and integration policies would make those outcomes more likely?

As I recently told for her Argument piece, I have no patience for people who claim that we do not need to change immigration policy because “all serious studies show immigration is already beneficial.” Or that acknowledging that some immigration policies have bad outcomes plays into right-wing xenophobic narratives.

The policy status quo in most OECD countries is far from optimal. In a market economy, prohibiting or heavily discouraging people from moving to where they are most productive is bad for them and bad for everyone else. It is hard to justify morally. Yet that still does not mean that “immigration” in the abstract is good under any regime, or that we can ignore the design of the system.

Migration is basically just a fancy word to describe people moving, which they have been doing for centuries. Bad policies turn those movements into various losses, but good policies can turn them into gains. The effects of better regulations, not “immigration” as some single metaphysical object of nativist fear or humanitarian sacred right, is what we should be arguing about more.

In three months, Popular by Design has already hit 300 subscribers! I’m thankful to everyone who clicked like, share, comment, or subscribe. It helps grow the newsletter without a financial contribution from anyone. Thank you for reading and suggesting things to write about.

Speaking of which, before we move to this month’s links, let’s do a little experiment. What should the next post be about? Since the most widely read post here so far was my personal notes about living in Japan, I’ll certainly write more about that, but I also promise to prioritize whatever gets the most votes below.

Here are the November links:

• Alan Manning has a new book, “Why Immigration Policy Is Hard: And How to Make It Better.” I haven’t had a chance to read it yet, but it sounds exactly like what we’re trying to do here, so I may write a review soon.

• Lorenzo Piccoli at EUI just posted an updated interactive directory of basically all (!) 390 migration research institutions globally. Check it out!

  

• Karolina Łukasiewicz with colleagues has a new special issue on the EU response to migration from Ukraine at the Journal of Ethnic and Migration Studies.

• Philipp Lutz and Marco Bitschnau have a new paper showing that much of what is labelled “misinformation” about immigration, including the commonly reported exaggerated numbers, is really just bad guessing. More generally, people are not very good with percentages, and we can’t expect them to be knowledgeable about too many policy details.

• , , and have a new working paper showing that people oppose new housing not just because of self-interest, but because they genuinely don’t want others to live near ugly buildings. Because voters feel as strongly about buildings that are far away, the authors rightly call this “sociotropic aesthetic judgments” (also see from talk about the same issue in the UK context).

  • I wonder how much of the so-called “cultural” concerns about immigration like hearing someone speak Spanish in the US are similarly sociotropic and about aesthetics.

• Sean Westwood has a new PNAS paper, entitled “The potential existential threat of large language models to online survey research,” showing that LLM bots can now mimic human survey takers, fully and cheaply. This is not great if we want to know what actual voters think about immigration or other issues!

  • It looks like, in the face of LLMs, just as professors have to go back to in-person assessments to preserve the integrity of teaching, survey researchers may have to go back to in-person interviews to preserve the integrity of polling. As another possible solution, just introduced Pulse, a “proof-of-life” approach that uses a simple finger-on-lens verification task to confirm human presence.

• Marc Sabatier Hvidkjær offers a nuanced take on Denmark’s approach to accommodating far-right voters, recently explored at length by David Leonhardt, Cas Mudde, and many others in major outlets. Moderating on immigration can certainly work politically, but I worry that American pundits who keep writing about this often get lost in Danish political details and skip whether the policies are right on the merits.

• I didn’t think it was possible to go viral on this platform, but my note on the varieties of populism (where a currently right-coded government department blamed all problems on illegal immigrants while a prominent left-wing politician blamed the same exact problems on billionaires) seems to have touched some nerve.

  • While there was broad agreement none of this is literally true, there was a common sentiment (especially among those left-of-center) that blaming billionaires is somehow not as bad as blaming immigrants. I don’t care what your politics is, but neither of the two original assertions is factually accurate or productive, even if you believe immigrants are a more sympathetic group with less power. You won’t solve traffic congestion or safety issues if you misdiagnose the problem by believing that’s all the fault of your outgroup.1

  • In this respect, and Adam Brzezinski have an intriguing new paper documenting what they call “Narrative Entanglement” showing that, unlike economists, politicians tend to outright deny the existence of trade-offs. For example, EU “politicians who support climate action do not say: “This is an expensive but necessary survival strategy.” They promise it will save the planet and create millions of jobs.”

• Gracia Liu-Farrer, Takeshi Miyai, and Yu Korekawa have a new Foreign Affairs piece on Japan’s “training-based approach to immigration,” offering a pragmatic blueprint for how to grow and integrate a migrant labor force without causing backlash.

• Is immigration in the US good or bad? That may be the wrong question. It was a pleasure talking to about how to make immigration better instead for another great immigration piece on automating admissions from team.

• is starting a new magazine, aptly titled “In Development.” You can pre-subscribe now.

• If you haven’t seen this yet, has a great new essay on Au Pair, sharing his personal experience participating in the program. The Au Pair program isn’t perfect, but it improves the lives of Americans and foreigners in clear ways. So it certainly merits more attention and study as a policy that could be scaled and sustained politically.

• The good folks at released a detailed county-level map of US immigration needs and a new report on reforming immigration, “Immigration beyond the extremes: A blueprint that actually works.” Self-recommending!

Disclaimer: This post is more personal than usual. I plan for it to be part of a larger Japan series, so hopefully it wouldn’t be just another “American discovers that public transit doesn’t have to suck—or that the dollar goes further abroad” piece. But it may sound like that sometimes, because the hype is real. Japan is awesome, and I can’t stop thinking about it. Yes, that makes my partner jealous and my colleagues roll their eyes—but it’s true.

As some of you know, last year I spent my fall sabbatical1 as a visiting researcher at the University of Tokyo. This was a rushed decision prompted by a single conversation with a now-collaborator I haven’t met before, but it ended up changing my life plans and the trajectory of my whole research agenda. My Tokyo sabbatical wasn’t just a productive and enjoyable research trip but a shift in how I think about cultural differences, the benefits of immigration, and the threat of depopulation in America and Europe.

Of course, it wasn’t just me who decided to go to Japan suddenly. Over the past years, and especially since the country reopened post-pandemic, people from all over the world have traveled to Japan in search of novelty and awe. According to some observers, it is one of the most culturally distinct places for Americans and Europeans. According to others, it is a country where tradition and modernity sit in plain view: wooden bathhouses two blocks from neon-lit arcades, or hand-pulled noodles in the basement of a high-rise office tower served by a robot.

The truth is more mundane. Japan is an uncannily normal, advanced democracy with familiar problems after years of economic stagnation and aging: disappointing wages, mental health strain, frustration with the status quo, population decline, and now also anti-immigration parties. In fact, I would argue that Japan’s appeal to outsiders is exactly that it is ultimately the most “normal” country in the world that manages to adapt to our ever-changing environment and make familiar things, from food and bathing to whisky and clothing, better.

Indeed, the more time I spent in Japan, the more I saw that it felt “weird” and “crazy” to so many westerners not because it was exotic, but simply because all possible normal things worked well there. None of this was mystical or rooted in some fixed or mysterious national character of the country. What I came to realize is that it was mostly about social norms and government policy choices that made everyday life feel predictable and frictionless. But the question that has stayed with me since I left Japan is whether these good things can last.

Why Japan is so awesome

I’m yet to meet someone who traveled to Japan and was disappointed in their experience. There is something to like for almost anyone, from beautiful pristine nature to thoughtful urban design, not to mention the obvious things like anime, fashion, or the hot springs culture that make people want to go to Japan in the first place.

As just one pertinent example, let’s talk about the food. If you care about good food like I do, Tokyo is unbeatable. You could try a different restaurant every single day for the rest of your life and still not run out of options—there are now about 160,000 restaurants in the city. Some are Michelin-starred, some are hole-in-the-wall counters, some are themed to the point of absurdity.

Outside of Tokyo, the quality and diversity of restaurants are still remarkable. I’ve deliberately gone out of my way to do fieldwork in declining regions, and even there the food options were better than what I generally found in comparable places in Europe or America. I particularly grew to appreciate Japanese comfort food and the local interpretation of various Chinese and Sichuan dishes.

What explains this? While it’s tempting to say that food has always been important to Japanese culture, there are more mundane and identifiable reasons why there are so many good restaurants everywhere. Much of it, for instance, arguably comes down to zoning.

Hear me out. Similar to many, many, many people before me, the experience of living in Japan basically radicalized my views on zoning in America and the myriad regulations that stifle small businesses. It’s worth emphasizing again: the reason there are so many great little restaurants on the fifth floor of a high-rise in Tokyo or cozy coffee shops that feel like someone’s living room is because they often are. In mixed-use areas, it’s usually legal to run your business out of your own home, and the result is an endless churn of creative, affordable, and idiosyncratic spots that make cities feel alive.

Yet why Japan feels so normal

As an American who was born in the Soviet Union and lived in Western Europe, I never really believed in the “collectivist East vs. individualist West” dichotomy, but being in Japan cured me of it completely. Day to day, Japan often feels more familiar to most Americans than Germany, France, Italy, or even the UK. No, unfortunately, the dryers are just as slow as in Europe, but most places are air-conditioned, and ice shows up by default in every drink. And—while I’m not necessarily endorsing it—once you get outside central Tokyo, you run into wide stroads, gas stations, and parking lots that could just as well be in New Jersey. Even the electrical outlets are the same.

Talk to people, and the overlap becomes even clearer. Despite its former technological dominance and the futuristic outlook, everyone in Japan these days carries iPhones and other US-designed gadgets. Parents worry about childcare costs and school quality. Adult children juggle eldercare. Young workers complain about rent, commutes, and managers. Teens argue with their parents about screen time. And yes…folks also increasingly complain about foreigners and some even vote for anti-immigration politicians.

The collectivist/individualist distinction or various cultural explanations don’t help much here. What is different here is informal social norms and formal institutional rules, not people’s mentality per se. Trains in Japan are reliable and quiet because people expect them to be reliable and quiet, and railway workers back that up because they are incentivized to do so. Small businesses and weird coffee shops proliferate because zoning allows them to, not because people here are necessarily more creative or entrepreneurial.

Why awesomeness and normality may not last

Unfortunately, these good things may not be there for too long. The proposed research plan for my sabbatical was to study Japanese immigration politics in the context of population decline, so I was familiar with the basic statistics of Japan’s low fertility rates, rapid aging, depopulating countryside, and the collapsing pension system. Yet venturing outside of Tokyo and seeing abandoned houses and emptying elementary schools scattered all around firsthand, along with hearing personal stories about old people dying alone, made me think more seriously about the global depopulation trend and its impact on politics and human well-being.

Depopulation rather than overpopulation is a real issue, and it’s neither inherently left nor right. I’ve been aware of this for years, but being in Japan made the abstract numbers concrete in a way charts never could. It is a topic I have recently written about, and I expect it to feature more in my work in the months ahead. The related problems of low fertility and aging are already salient political issues in Japan, and we should expect them to become central political questions everywhere else sooner than most people think.

Some “degrowth” advocates point to Japan as proof that you can be prosperous and happy without striving to produce more. But what we are really observing are the accumulated benefits of past growth and technological diffusion—the result of previously built infrastructure, high capital stock, strong institutions, and reliable global trade flows. Objectively speaking, Japan’s living standards are already too low, especially relative to its potential. Without more young workers, the story will soon become one of a slow decline rather than a stable alternative path.

Immigration as a way to slow the decline?

Contrary to the old cliché that Japan is an ethnically homogeneous country that “can’t” accept foreigners due to ingrained xenophobia, the recent trajectory looks different. One of the main reasons is that the demographic situation is so dire that the government has effectively had no choice but to accept more immigrants. Japan’s foreign-worker population has roughly quadrupled since 2007 to more than 2 million—a remarkable shift given its long history of minimal immigration. As recently noted by , it’s also unclear how ethnically homogenous Japan has truly been to begin with.

Japan’s pragmatic and incremental approach—favoring work-tied, often temporary entry over permanence and humanitarianism—has critics, but it likely helped minimize backlash and build tolerance for higher inflows. Whether support endures and whether voters will accept more permanent pathways as the numbers rise remains an open question. There is already a nascent anti-immigration party, as in many European countries, though its ability to retain or expand influence is uncertain.

It’s widely acknowledged that high-skilled immigration boosts economic growth, and it tends to be extremely popular (the recent H-1B controversies notwithstanding). As I argue in my book, such immigration is demonstrably beneficial so that most voters intuitively understand why more of it makes sense. The same dynamic is visible in Japan. Yet, unlike many other OECD countries that mostly need high-skilled workers, Japan would clearly gain from a much broader mix.

The lessons I learned about migration in Japan

This leads me to what may be the most important thing I learned in Japan. Unlike what many centrist analysts believe based on their US or European experience, Japan shows that demonstrably beneficial immigration is much broader than simply attracting the best and the brightest. Labor shortages can be real across the entire economy. Walking around Japan—especially outside the greater Tokyo area—you quickly see that businesses struggle to find workers not because wages are low or conditions are bad but because most people in the area are old and already retired.

Some analysts I respect, like and , have expressed legitimate skepticism about immigration pathways based on labor shortages. These metrics are hard to define, and it’s easy to imagine how they could be gamed, especially when firms want special carve-outs to hire more foreign workers at lower wages. Yet when shortages are structural and clear, as they are in Japan, and driven by age rather than policy failures, the logic of matching foreign workers to specific needs becomes much harder to dismiss.

In Japan’s daily life, immigration is already increasingly woven into the social fabric. It is not a future prospect but a present reality. Even older residents in rural towns told me that without the Vietnamese owner of the ramen shop, or the Filipino aide at the neighborhood clinic, the community would feel hollowed out. These folks are not cosmopolitan, but when the benefits of immigration are visible and close to home—open storefronts, functioning care facilities, affordable food—attitudes soften regardless of prior biases. Immigration stops being abstract and becomes a question of whether a town can keep its school open and its hospital staffed, rather than a question about “identity.”

Americans often struggle to see this because our demographic situation, while worsening, is not as dire. We already have a large immigrant presence, so the counterfactual—what our communities would look like without immigration—is hard to even image. As recently observed, the United States shifting into “a normal, settled country rather than a nation of strivers seeking a better life” in the middle of the 20th century has been a recipe for stagnation. Japan, too, probably needs more strivers of all kinds if it hopes to reverse its decline.

Another related idea I hadn’t fully appreciated before Japan was the importance of “training-based” immigration. Proponents of high-skilled immigration often argue that skilled pathways yield larger long-term benefits, which is true. But they also assume that skills are something workers bring with them through education before they migrate, or acquire only after arrival through formal programs. What Japan highlights is that skills can be gained directly on the job, sometimes more effectively than in school. Its system—flawed as it is—shows how structured, supervised work programs can develop skills while addressing acute labor needs. This is not a replacement for skilled immigration, but it expands the set of complementary legal pathways that voters can see as clearly beneficial.

***

Still, major barriers remain if Japan hopes to harness the full benefits of immigration. Naturalization is rare even for long-term residents. Housing markets are difficult for foreigners to navigate. Language training is underfunded. Professional licensing is opaque. Many foreign residents remain locked out of full participation despite years of legal work. Bias shows up in subtle ways: tourists get scolded for breaking unspoken rules, while residents face endless paperwork and suspicion from landlords or officials. But these are not immutable features of Japanese society. They are policy design problems that can be fixed through clearer rules and more consistent enforcement.

Japan’s broader lesson is that there is no secret sauce to prosperity. Good policy design can make the everyday feel exceptional, whether in zoning that enables countless restaurants or incremental immigration measures that relieve decline without sparking backlash. Bad design—or simple institutional neglect—can unwind those gains quickly.

If progress means more people living better lives, Japan shows both the promise and the risk. The promise is what well-crafted rules can deliver. The risk is what happens when demographic collapse pushes even a well-run society toward decline. Economic growth makes life comfortable for decades even after it stalls—but that comfort eventually erodes if new progress does not follow.

I would like to thank the folks at the and Blog-Building Intensive Fellowship for encouraging me to publish a more personal essay. A special thanks to , , , and among others for their comments on the previous versions of the draft.

It’s been several months since I launched this newsletter, and the response has been far greater than I expected. I’m grateful for the support, especially given how niche some of these discussions can be. As a newcomer on Substack, I’ve spent time mapping the broader immigration space to see where my work might add something, so I thought I’d share the accounts I identified publicly.

I’ve seen similar lists on Twitter/X and Bluesky, so a centralized Substack version could be useful too. To build the list, I started with basic searches for Substack accounts mentioning “immigration,” then followed their recommendations and cross-checked with what I already knew. I tried to branch out as much as possible and include lesser-known accounts, but given my own background this list may tilt somewhat toward US and European politics writers.1

For clarity, I divided the list into three rough categories: (1) economics and policy analysis; (2) legal reporting and advocacy; (3) and generalists who write about immigration. The list is by no means comprehensive (yet), the boundaries are blurry, and inclusion here is not an endorsement of any particular view. What unites these accounts is that they offer informative views on immigration from various ideological perspectives that are worth knowing even if you disagree. Here is the list in no particular order:

Economics, politics, and policy analysis

• ’s newsletter: commentary on US immigration law and policy.

• ’s newsletter: data-driven analysis of public opinion and populism.

• ’s Zekrets: “Evidence-based opinion on global economic issues, with a dollop of life wisdom.”

• ’s Guns, Guards, and Governance: Border militarization, self-governance, and political economy.

• ’s Centre for Heterodox Social Science newsletter: “Challenging progressive orthodoxies in academia, advancing post-progressive social science research, and critically studying left-wing ideology.”

• ’s Laissez-Faire, Laissez-Passer: empirical work on US immigration economics and policy.

• ’s We Wanted Workers: UK-focused policy analysis and research-based commentary.

• ’s Americas Migration Brief: concise weekly roundups of policy and research across the Americas tagged by topic and country for quick scanning.

• ’s Involuntary Departure: commentary on US immigration law and policy from a scholar-practitioner perspective.

• ’s Moving Climate: commentary on the intersection of climate and migration.

• ’s America’s Promise: “This newsletter is for anyone who wants to understand why Washington keeps failing on immigration and learn about solutions for the future.”

• ’s Popular by Design: a newsletter highlighting politically sustainable immigration policies and research behind these policies.

• ’s Immigration Law Unhinged: analysis of immigration law and policy with a focus on statutory changes and implementation.

• ’s Behind the Border: “An immigration law professor at the frontlines” with accessible explainers and case-driven insights.

• ’s Lit & Border News: immigration detention, reading recommendations.

• ’s Points of Entry: “Original research on how immigration shapes who we are, how we compete, and how we stay safe.”

• ’s Lauren Policy: accessible reviews and links to most recent immigration research with a focus on development.

Legal news, original reporting, and advocacy

• The Border Chronicle: border policy, detention, and politics with on-the-ground reporting.

• : an immigration attorney’s on-the-ground dispatches from the U.S. system: concise, case-driven explainers on courts, detention, and policy changes.

• BORDER/LINES: weekly newsletter with original US immigration reporting.

• ’s Writing on the Wall: “Essays, reporting, criticism, and interviews on the polycrisis in América and beyond.”

• ’s Beyond the Border: human stories about the US immigration system.

• ’s Substack: “Discussions of immigration policy from an immigration lawyer and person of faith.”

• ’s Tales of Humanity: “shining the light on strategies for resistance in text and podcast formats; celebrating individuals on the frontlines, who show us, every day, that there is a better way -- that we can welcome newcomers.”

• ’s Migrant Insider: “a hard news startup to cover immigration beat.”

• ’s Distributed Progress: “Exploring how science, talent, and mobility shape progress in a world transformed by AI” with frequent data and analysis on US international students.

• La Cuenta: tallying costs faced by undocumented residents, policy-relevant stories.

• ’s Border Humanity: humanitarian perspectives from the border.

• ’s US Border News: reporting on “border security (including combating drug & human smuggling), immigration issues, and life along the US Southern Border with Mexico.”

• ’s From Her Beacon Hand: “defending America’s founding ideals and self-interest by advocating for the legal and safe immigration of peaceful people.”

• Save Resettlement: tracking US refugee-resettlement capacity and funding.

• Immigration Hub: bi-monthly guide to the latest campaign, messaging, and policy immigration news.

• The Migration Opportunity: newsletter of the Talent Mobility Fund, covering various promising pilot programs and policies.

• ’s Agglomerations: new data and analysis of immigration and related issues from the perspective of growth.

• (Institute for Progress) newsletter: analysis of immigration and related issues from the perspective of progress.

• Eyes On Immigration Court: newsletter about NYC’s federal immigration courts.

• ’s newsletter: “Short essays on all sorts, personal and political” with a focus on UK immigration and asylum.

• ’s Notes from the Beautiful Periphery: “Reimagining the US-Mexico Borderlands and Edges Everywhere.”

As honorable mentions, there are also a few generalists who have substantial content on immigration. This is probably the most controversial category since many other bigger and smaller accounts could be added.

Generalists who write about immigration

• Good Authority: data and analysis from political scientists but accessible, including on immigration.

• ’s Slow Boring: pragmatic policy and politics, frequent immigration analysis.

• ’s Noahpinion: economics and politics with recurring focus on immigration and Japan.

• ’s Political Calculus: UK politics and analysis with a focus on public opinion and populism.

• ’s Talk Data to Me: UK statistics and current affairs, explaining official data and measures.

• ’s Bet on It: economics and immigration from a libertarian perspective.

• ’s The Argument: original reporting and data analysis on US politics and contentious issues from a center-left perspective.

• The UnPopulist: “a publication committed to defending free and open societies.”

• The Liberal Patriot: “Political and policy analysis from the vital center.”

• ’s newsletter: US politics and foreign policy from a right-libertarian perspective.

• ’s American Pursuit: “a newsletter on policy, politics, and building what’s next.”

• ’s Political Economy, Stats, and Society: short research-informed posts on societal issues.

I expect to update this post over time, so if I missed someone, let me know in the comments (self-promotion welcome).