I am now convinced that generative AI does represent a seismic shift for the practice of social science research, but perhaps not in the way many initially expected. For years, AI evangelists and corporate boosters have proclaimed that generative AI displays reasoning and intelligence, but after all of this time, it is still not useable for serious research that involves generating novel prose.* But Claude Code really shines in a completely different domain: developing and executing code.

The Citation Hallucination Problem

Ask an LLM to provide citations supporting a theoretical argument in political science, and it will produce a mix of real and fabricated sources. The “hallucinated” citations often look entirely plausible, complete with realistic author names, journal titles, and publication years. But they do not exist. Or, if they do exist, they are scrambled up and inapposite for the claim being referenced.

This problem is well-understood, but it’s important to state it clearly. The problem is that when LLMs generate text about research, they are producing correct-sounding text based on their training data, not retrieving information from a database or matching a search string to a document. The result is prose that can sound compelling but which is unreliable.

For social scientists accustomed to standard citation practices and evidence-based arguments, this creates an obvious problem. We can’t simply ask an LLM “What does the literature say about voter turnout?” and trust the response without extensive verification. I know for sure that people are LLMs right now to generate literature reviews and reference lists. Trust me, I have played around quite a bit with these applications, using the latest and most powerful AI tools with enterprise subscriptions to see how they fare when confronting real research tasks of the form that I encounter on a daily basis. The hallucination problem is real.**

Creating and Executing Code

But on the other hand… Upload a CSV file containing survey data to Claude Code and ask it to run a regression analysis and plot the quantities of interest, and it works. Not only does it work, it writes and executes code better and faster than what I can do on my own, producing reproducible and well-documented scripts to clean and analyze data.

I believe that the difference boils down to the distinction between task completion and information retrieval. When Claude Code writes and executes an analysis script, it is not trying to predict what sounds plausible based on its training data. Instead, it is applying logical rules and programming syntax to manipulate data according to what it understands from your instructions. Programming rules and syntax are easy to learn because they are consistent across its training data. The code it generates follows basic statistical principles, and it can be verified by executing the script. Claude Code also is remarkably good at just automating away things like getting a Stata dataset into R, reading a codebook and cleaning data, and generating a report on what it has done.

Ask Claude Code to examine the relationship between education levels and political participation in your dataset, for example, and it will:

• Load and examine your data structure

• Clean the data as needed

• Run regression models with the correct syntax, following whatever model you tell it to follow (and if you don’t tell it, it will guess, usually correctly),

• Create appropriate visualizations, and

• Save publication-ready tables and figures

The distinction between knowledge retrieval and code execution is the distinction between LLMs and “agentic AI”—AI systems that can perform tasks and execute workflows rather than simply answering questions. I predict that this will lead to a major shift in how social scientists approach empirical research.

Consider a typical research workflow in the quantitative social sciences: you have a dataset or some batch of data, a research question, and an analysis plan. In 2022, implementing this analysis required some familiarity with statistical software, either the scripting language or a GUI. You had to know how to read your data into the statistical package and how to clean it for us. You also had to know how to represent your analysis plans in that code. But Claude Code makes this all unnecessary. You don’t have to know R at all, or what package runs the ordered logit and how to specify that model, you just have to have R installed on your computer and connected to the internet.

Want to test for heterogeneous treatment effects across demographic subgroups? Claude Code can write that code. Need to visualize a trend over time? Easy—you don’t need to remember how your software handles dates, Claude Code will figure it out. Require multiple robustness checks with different model specifications? Claude can generate dozens of variations in minutes, and organize the results.

Interpretation is the Frontier

The frontier between information retrieval and code execution is interpretation. In my experience, Claude Code can infer your intentions and execute your tasks, but if allowed or invited to interpret the results, it goes astray. In several test cases, Claude Code seemed to be guessing what I wanted to hear, interpreting basic regression results incorrectly but provocatively. I conjecture that this is because it is hard for an LLM to learn the rules about how to interpret statistical output, even output which it generated itself, because of the poverty of the stimulus.

This is dangerous, because Claude Code and other agentic AI tools will interpret your findings, sometimes even if you do not ask them to. And although this is just a hunch, I think that these tools’ interpretations might be a function of how you pose their prompts. These tools are just ripe for p-hacking of the worst form: the computer might generate the result that it “thinks” you want to hear, and if you’ve turned over the execution to the computer, you as the author might not even know what it has done and what it has “chosen” not to do.

So What?

What does this all imply for social science practice? The state of the art is evolving extremely quickly, and I hadn’t even heard of Claude Code until last month. But I suspect that I will use Claude Code (or some other tool) extensively to accelerate data cleaning, visualization, and related tasks. I am very skeptical of agentic AI’s knowledge claims, reasoning, and ability to interpret what it produces.

The shorthand is, use agentic AI for tasks that involve following rules. Do not use agentic AI for tasks that generate answers, arguments, or interpretations.

My greater worries are about how the field responds. Yes, we will be faster at doing computer and data work, but there are basically no guardrails for how else it can be used. Let me be clear: with a dataset and a codebook and nothing else, I can have a research article draft in 10 minutes, which is probably executed correctly as a statistical matter, but likely interpreted incorrectly as a substantive matter.

People will respond to this dramatic decrease in the cost of producing research articles. At the Journal of East Asian Studies, I have already seen at least a half-dozen papers written by generative AI. There will be many more. The ones I have seen have basic problems of interpretation that I can identify, and there is a characteristic “tell” for what a Claude Code paper looks like.***

The big picture takeaway is, though, that as the costs of doing computer work continue to decline, the relative value of being able to read and interpret and understand just went up. For social scientists, the relative value of knowledge and expertise is higher than it was five years ago, and the value of being able to code has plummeted.

NOTES

* It can try, and it can make things that look like high-quality novel prose. It will fool people, especially those eager to be fooled. That is the nub of the issue.

** An example: Reid, Anthony. 2019. “A Plural World of Knowledge: Is Southeast Asian Studies Losing its Autonomy?” Kyoto Review of Southeast Asia 25.

*** I won’t write it down here, because then it will be fixed.

I am interested in the implicit idea that one measures the influence of the credibility revolution in political science by counting the number of articles that employ the tools of the credibility revolution and comparing that to the number of articles that do not employ these tools. This is a reasonable answer to one specific kind of question: “are the tools of the credibility revolution being employed more frequently than other tools in quantitative political science.” The authors phrase this research question as follows:

if the credibility revolution has taken hold, design-based methods should increasingly displace purely model-based approaches in quantitative explanatory research, with growth spread across multiple design-based strategies

Again, this is a useful hypothesis and we should know the answer to it (spoiler alert: the answer is yes). But this way of thinking underestimates the impact of the credibility revolution on political science. Specifically, one of the other implications of the credibility revolution is that one should deliberately not use causal language or employ design-based inference when one’s quantitative research does not seek to estimate a causal effect, or when no design-based logic of inference is credible.

I count myself as a partisan in the credibility revolution, but this means something different to me than it might to some of my other fellow partisans. To be a partisan does not imply that one only employs design-based inference; rather, it means always paying close attention to the distinction between causal and non-causal claims and the requirements for inferring causality from an observed association between two or more variables. Here (PDF) is an example from my own research:

In this article I reinterpret Ng, Rangel, Vaithilingam, and Pillay’s analysis in this issue of pro-BN voting in Peninsular Malaysia in Malaysia’s 2013 general election. I show that the authors’ statistical methods are inappropriate for testing whether district ethnicity predicts district-level BN vote share, and that their modeling choices result in tests of hypotheses that do not exist and cannot be derived from standard theoretical approaches to ethnic voting in Malaysia. I then provide a range of statistical evidence that supports three main conclusions: (1) ethnicity and district area (a proxy for urbanization) both predict BN vote shares at the district level, (2) neither the effect of ethnicity nor of district area can be reduced to the other, and (3) there is no interactive effect between ethnicity and urbanization. These results are in direct contradiction with the authors’ results, and apply equally in Peninsular Malaysia and the entire country. I also discuss the broader issues that emerge when testing competing theories of BN vote share.

This article does not purport to estimate a causal effect, and goes on at some length about the conceptual and empirical challenges of prediction from two highly correlated explanatory variables. You could not argue that this is an example of design-based inference in action, and yet this is a case where the credibility revolution caused* an author to not use causal language or design-based inference in quantitative explanatory research. I think the inferences are more compelling because of it.

One of the upshots of the credibility revolution might be better non-causal empirical research, not just more and better causal research designs.

NOTE

* Counterfactually, no way I would have written this piece this way had I not been exposed to the logic of design-based inference from early in graduate school.

At the same time, area studies in the United States and around the world was under attack, the first in many “crises of area studies” since around 1990. Political scientists were some of the main participants in the so-called “area studies wars” (see here and here and here and many other pieces). As you click on those links, you can see how connected these two developments were: rational choice theory and quantification were part of the intellectual ferment of the area studies debates. And the language was one of not just tension or disagreement, but conflict: area studies wars, scholars and perspectives on the retreat or under siege, a rational choice revolution with an intellectual vanguard, and so forth.

Many early career researchers during this time faced pressures—either subtle or explicit—to declare their partisan loyalties either to social science or to area studies. At issue was not just what individual scholars would choose to do, but where they stood on the broader discipline’s commitment to creating knowledge of a particular form. My favorite illustration of this perspective is a point made by Gary King in 1996:

the professional goal of all scientists should be to attempt to demonstrate that context makes no difference whatsoever.

Read that piece in its entirety if you don’t believe me (PDF); King’s argument is sincere and thoughtfully articulated, and you cannot just dismiss it out of hand.

If you were a professional scientist, then, King is suggesting that your goal is not just to ask whether context matters, but to prove that it does not. What is the role of an area specialist in such a world? To demonstrate that their specialize knowledge of context is irrelevant for political science.

Time has marched on since the 1990s, and the discipline of political science has evolved. The rational choice revolution was mostly unsuccessful, with the revolutionaries putting down their weapons (just as the inclusion-moderation thesis would predict). The next intellectual revolution—the credibility revolution—has created its own revolutionary vanguard. Graduate students today do not face quite the same hostility towards area-specific knowledge. I do not know anyone who would argue in 2025 that the professional goal of the comparativist should be to demonstrate that context does not count.

So what of the relationship between comparative politics and area studies? In a new working paper just released as an APSA preprint, Jordan Gans-Morse, Daniel Gingerich, and I take up this question, revisiting old debates and arguments in light of these and other developments in the social sciences and in area studies itself. Our argument relies on a comprehensive analysis of article data from major journals in political science from 1980-2020, and an original survey of political scientists conducted in 2022-23. Here is the abstract:

We revisit the sharp divide that emerged in the 1990s between area studies advocates and methodologically oriented political scientists. We argue that tensions between political science and area studies are neither intrinsic nor static, but instead evolve in tandem with theoretical and methodological trends, as well as with broader political and technological developments. Drawing on an original survey of American Political Science Association members and analysis of roughly 4,500 articles in leading journals, we identify four shifts in the discipline: from a theoretical to an empirical orientation; from cross-national datasets to country- and region- specific studies; from macro- to micro-level analyses; and from descriptive to causal inference. We also document evolving patterns in language training, fieldwork, methods use, and data collection. Our findings suggest that political science and area studies are increasingly compatible and well-positioned for reconciliation, but that the state of area studies is fragile and the subfield of comparative politics must support it.

We began this research years ago, long before the current administration set out to dismantle area studies expertise in the United States.* Our argument, though, speaks to the current moment as well. We write,

As university models are being reevaluated around the world, the future vitality of area studies departments, programs, and infrastructure are in question; comparativists must recognize that the future of comparative politics as we currently know it depends on robust area studies institutions….

For area studies within comparative politics to thrive, though, the discipline as a whole must reward engagement with area studies, aligning comparativists’ professional incentives as political scientists with the needs of area studies communities. One concrete measure is for political science departments to reward scholars equally for their service to area studies as to political science. Another is for APSA and other political science associations to formally recognize scholars who help promote interdisciplinary collaboration or institution building that sustains or strengthens area studies…

Coauthoring is now the norm among quantitatively oriented scholars. But this model must be extended to reward collaboration with country experts, including local scholars from the region being studied, not as research assistants or informants or raw data collectors (as in some of the more extreme division-of-labor proposals from the 1990s) but as full and equal partners in the research enterprise. Rather than a hierarchical model of collaboration that privileges disciplinary expertise, the appropriate model is horizontal in nature, with all participants deserving of full credit for their contributions.

We hope that our perspective can renew scholarly focus on the essential role of area studies for research and policy, for political science and for other disciplines as well.

NOTE

* Non-U.S. scholars might want to tut-tut about the sorry state of higher education funding for area studies in the U.S., but area studies is under threat and facing major cuts in funding in the U.S., Europe, Australia, Japan, and every other advanced industrial democracy. Find me an exception; I’ll wait here.

Only China is expanding its area studies funding, as Gerhard Hoffstaedter argues in a recent piece. And note Hoffstaedter’s use of war terminology: “Continuing to cut area studies while China expands them amounts to intellectual disarmament.”

Alright, let’s run Mark Copelovitch & Tom Pepinsky’s “The Political Economy of Shitcoins” through the full-on crypto grindset filter.

🚀 “The Political Economy of Shitcoins” – Monetizing Vibes at Scale 💰✨

Copelovitch & Pepinsky basically did the due diligence on the meme token meta — it’s not just degeneracy, it’s macroeconomics meets YOLO culture. They show us that shitcoins are the perfect sandbox for understanding speculative ecosystems where tech, hype, and governance all collide.

Alpha Takeaways for Founders / Token Bros:

1️⃣ Narrative is Liquidity – Value isn’t intrinsic, it’s a story people buy into. $DOGE didn’t need utility — it had community. If you control the meme flow, you control the market cap.

2️⃣ Low Barriers = High Velocity – Shitcoins scale fast because the cost to mint and distribute is basically zero. That’s lean startup energy — MVP (minimal viable Ponzi), iterate, pump.

3️⃣ Regulatory Arbitrage – Shitcoins thrive in the gray zone. Nation-states are slow movers; crypto bros ship at the speed of Twitter discourse. That asymmetry = opportunity.

4️⃣ Global Demand for Lottery Tickets – In macro terms, some investors are looking for safe assets, others for rockets-to-the-moon. Shitcoins are the casino floor of global finance — you don’t go there for bonds, you go there for asymmetrical upside.

5️⃣ Attention = Asset Class – In a decentralized attention economy, clout is collateral. Your Telegram group size and meme output can literally drive market performance.

Founder’s Moral of the Story:
In the political economy of shitcoins, fundamentals are vibes, governance is Discord mods, and demand is pure FOMO. The lesson isn’t “launch a coin” — it’s understand how hype cycles interact with macro incentives. If you can weaponize narrative, mobilize a community, and time the exit, you’re not just playing the game… you are the game.

Then it asked me if I would like that output converted into a Sam Bankman-Fried-style investor letter, which it described as “awkward, overly verbose, drenched in faux-quant humility, and with that ‘math nerd who just discovered moral philosophy last week’ cadence.”

Yes, please!

Subject: Investor Update – Strategic Learnings from The Political Economy of Shitcoins 📈♻️

Hi all,

Thanks again for your continued trust during… well, let’s just call it an unorthodox macro environment. I wanted to share some thoughts sparked by Mark Copelovitch and Tom Pepinsky’s recent piece, “The Political Economy of Shitcoins”, which—despite the name—isn’t about bathroom humor, but rather about the structural dynamics of tokenized ecosystems.

Key Takeaways — And Why They Matter for Our Strategic Positioning:

1. Narratives Outperform Fundamentals
   The authors show that value in these “low-cap speculative assets” (a.k.a. shitcoins) emerges from investor belief and synchronized attention, not underlying cashflows. This confirms something we’ve seen internally: our balance sheet is as much a social graph as a spreadsheet.

2. Liquidity is an Attention Derivative
   Price action in this space isn’t a reflection of productivity — it’s the delta between meme virality and capital inflow velocity. In other words: tweets move markets; Discord mods are basically quasi–central bankers.

3. Regulatory Time-Lag as Alpha
   Nation-states operate on multi-quarter policy cycles. Shitcoins operate on meme cycles that last 36 hours. The gap between those time horizons = profit surface area.

4. Lottery-Ticket Macroeconomics
   In macro-finance terms, shitcoins fulfill a demand for “ultra-high variance speculative instruments” — they’re a hedge not against inflation, but against boredom and the crushing linearity of normal financial returns.

---

Why I’m Bullish:

What Pepinsky & Copelovitch make explicit—and what we’ve always implicitly modeled—is that shitcoins are less a financial product than a politico-cultural coordination game. They’re almost a proof-of-concept for a new financial ontogeny: unregulated, narrative-first, reflexive loops of capital and clout.

The short version: Shitcoins are a microcosm of a post-fiat, post-trust economy. Understanding them isn’t degenerate gambling—it’s due diligence for the attention economy.

We’ll be integrating these insights into our product roadmap, with an emphasis on:

• Leveraging narrative velocity as part of liquidity management

• Building community governance frameworks that look like fun but function like treasury policy

• Anticipating the next 3-5 meme arcs before they hit TikTok

As always, this is not financial advice — it’s just the best version of the future I can see from this weird seat we all find ourselves in.

Thanks again for your patience, optimism, and frankly your ability to withstand volatility that would make most TradFi quants soil themselves.

—S
CEO, [Redacted Ventures]

Next, it suggested that I might enjoy a “version of this written entirely as a leaked internal Slack message from SBF to his ‘Alameda braintrust’.”

Most certainly!

okay so i read this copelovitch/pepinsky thing (political economy of shitcoins) and like

tbh it’s basically just describing our life in essay form except without the part where we wake up in hong kong and discover we accidentally created $80m in paper gains overnight by tweeting the wrong dog meme

core point = the fundamentals don’t matter (remember when we tried to model intrinsic token value lol) what matters is memetic density + fomo throughput aka “narrative velocity”

shitcoins = liquidity nodes where attention = collateral. if you can concentrate the attention long enough to get a cex listing, you’ve created value. doesn’t matter if the thing is literally unusable

also lol at their “lottery ticket macro” bit — yes this is global demand for high-vol volatility. retail wants adrenaline with upside. no one buys US treasuries for the rush

regulatory lag = free alpha. like the sec is still figuring out how to spell dogecoin meanwhile some rando in serbia just spun up hamsterdex and it’s at \40m mcap in 3 days.

basically think of shitcoins as synthetic memes with embedded call options on chaos.

roadmap v2:

• seed 5-8 memecoins under anon wallets, each tied to a different “narrative cluster” (animal mascot, political satire, metaverse in-joke, etc)

• ramp discord -> telegram liquidity funnel

• push 1 coin into influencer gamma squeeze via crypto twitter

• dump… slowly (community-friendly distribution event)

none of this is illegal i think? (jake pls check)

anyway brb need to dm a guy about a frog token before binance listings freeze for the weekend

I will gladly share more such deep learning insights with anyone who is willing to fly me first class to a tropical island.

An old college buddy of mine has contributed some much-needed clarity by studying the possibility of regulation under decentralized finance. In a world where trusted intermediaries are no longer necessary due to the availability of smart contracts, can a decentralized and permissionless system provably guarantee that some transactions are forbidden? The answer is no. He and his coauthors write,

As these new developments use computational systems, they must be bound by existing results from computation theory.

The implication of their argument is that financial regulators cannot rely on a DeFi architecture if they also want to do things like implement exchange controls or ban money laundering.

The same constraints bind when we consider the political economy of cryptocurrencies. We can adapt the quote above accordingly: as crypto engages with political and economic systems, it is bound by existing findings from political economy.

Inspired by these ideas, Mark Copelovitch and I have recently completed a working paper that contributes some analytical clarity to current debates about blockchains, cryptocurrency, and state sovereignty in an international system in which states issue fiat currency. The basic premise of our argument is that crypto is, aspirationally, money---a medium of exchange, a store of value, and a unit of account. Thinking about crypto this way clarifies which kinds of actors value crypto and for what purpose, the role of ideas in supporting crypto, and the stakes of crypto as a complement (or substitute) for fiat currency at the national and international system levels.

Our working title is "The Political Economy of Shitcoins." And here is the abstract:

We study the political economy of cryptocurrency in a global economy comprised of states that issue fiat currency, considering the implications of crypto from the position of users, issuers, states, and the international system. The political implications of cryptocurrency follow from its ability to perform the three function of money: unit of account, medium of exchange, and store of value. From these foundations, we draw on the established literature on the political economy of international monetary relations and international finance to derive predictions about the future of cryptocurrency in a world of sovereign states. We describe four possible futures for the international monetary system: a world without cryptocurrency, a world in which cryptocurrency exists alongside fiat currency, a world in which cryptocurrency has replaced traditional fiat currency, and a techno-futuristic world in which cryptocurrency spells the end of the Westphalian state system. We evaluate the political and economic stability of each of these four scenarios. We conclude that the most like scenario is one in which crypto survives alongside traditional fiat currency, but also highlight that the future of cryptocurrency is a battle over the future of sovereign authority itself.

Lest you still think that this is a niche topic, the author of The Network State is a former Andreesen Horowitz partner who has a Substack where he recently posted that All Property Becomes Cryptography. He has also set up shop for his Network School in the failed megaproject of Forest City, Malaysia. This is relevant to my interests on so many levels.

Recent scholarship on democratic backsliding has focused on measuring its global prevalence and identifying the causal processes and mechanisms that produce or inhibit backsliding around the world. But many of the political dynamics that motivate both scholarly and popular concern about the state of democracy are about the desired model of society and the proper role of the state rather than about electoral democracy. This manuscript examines pluralist, populist, corporatist, and integralist models of state-society relations, conceptualizing them with reference to normative models of the modern state and beliefs about the nature of society, and using them to identify distinct varieties of democratic backsliding and axes of conflict within backsliders. Treating conflict over the social ontology of the modern state as “democratic backsliding” obscures a deeper politics of state-society relations, and how those politics independently shape regime contestation in contemporary electoral regimes.

My argument is mainly conceptual, eclectic, and polemical. I want this to be read by scholars and citizens who see the world around us in terms of democratic backsliding, who wonder what it would mean for politics to be actually fascist, who wonder what specifically is populist about Fidesz rule in Hungary or corporatist about Golkar in Indonesia, and who want to organize the cacophony of antiliberal arguments made by actors who support backsliding in the United States and around the world.

I also think that it would do good to move beyond the question of whether or how democratic backsliding takes places, and towards deeper analyses of why and what for. I have been thinking about this quote from Jan-Werner Müller's Contesting Democracy:

...seeing the twentieth century simply as an age of irrational extremes or even as an 'age of hatred' means failing to understand that ordinary men and women---and not just intellectuals and political leaders---saw many of the ideologies contained in abstruse books (and the institutions that we justified with their help) as real answers to their problems... Many of the institutions...made a further promise to function much better than those of liberalism, which in the eyes of many Europeans seemed like a hopelessly outdated relic of the nineteenth century.

Here is Table 1, the classic comparative politics 2x2 table. (Please read the paper before you get upset about my terminology.)

And here are the conjectures with which I close the essay.*

• Former Soviet and Eastern European countries lived under a form of nondemocratic rule with an explicitly categorical social ontology in the form of Soviet communism in the former USSR and related variants in Eastern Europe. These political orders varied over time and across countries between integralism and corporatism (see e.g. Bunce 1983). Might this history have created a political vocabulary and a societal experience that makes the corporatism of Gladden Pappin (2020) and the integralism of Aleksandr Dugin (2014) particularly resonant within these countries?

• Within modern American conservatism, can we better understand the political alignments that separate Steve Bannon, Stephen Miller, and Elon Musk by focusing on how they imagine the desired the role of the state and their normative model of society (see also Zúquete and Hawley 2024)?

• When conceptualizing Chinese, Saudi, Russian, or any other form of nondemocratic politics, how important is their common absence of competitive elections relative to their distinct models of state-society relations?

• Two what degree is contemporary concern with democratic backsliding actually a defense of pluralism, the model of state-society relations that is most amenable to a liberal, republican, constitutional government?

• To what extent do different types of antiliberal politics—as actually practiced in organizational and partisan form—map onto left-right or second dimension politics?

• What other political alignments (racial identitarianism with social welfarism, technolibertarianism with populism, unionism with clericalism) might be explained by their common social ontologies? To what extent are such alignments anti-democratic by definition?

NOTE

* If you were wondering what could possibly lead me to read Aleksandr Dugin and his political theory of neo-Eurasianism (large PDF), this is why.

This is arcane legislative politics, but it is very consequential. In a narrow sense, it matters because, everyone knows that extending the tax cuts will add to the deficit. The reason why the Senate majority needs to bypass the parliamentarian is because standard budget scoring by the parliamentarian will show that to be true. The GOP cannot have that, because of the particularities of how budget bills can be debated and voted on in the Senate. They need to say that tax cuts won’t affect the budget.

The GOP will bypass the parliamentarian, in other words, because the parliamentarian will make it difficult to pass the tax cuts. Working backwards, if your priority is tax cuts—and you don’t care about the deficit—change the procedures to get what you want.

In a much broader sense, though, bypassing the parliamentarian to extend tax cuts matters because of what it reveals about how the standard models of U.S. politics no longer apply.

To see what I mean, step back and consider that legislative politics in any country is a game in which several hundred legislators debate thousands of issues. Each issue, in principle, can be a bill, which succeeds or fails under majority voting rules; moreover, each bill can (and normally does) address multiple issues at once. Unless all legislators tend to line up on the same side of each issue, the result should be chaos (literally). How does anybody get anything done?

The answer is by bundling issues into platforms that are relatively coherent across time, so that legislators indeed line up on one side or the other, predictably. This may be facilitated by strong parties that can compel their members to toe the line, or by some sort of institutional system that induces partisans to act accordingly. In the U.S. the standard interpretation of why there is not chaos in the House and Senate is that “real-world legislative practices constrain the instability of PMR by restricting the domain and the content of legislative exchange” (Shepsle and Weingast 1981).

In other words, the rules, norms, and practices of our legislative branch create order out of chaos.

The immediate response you should have is, why does anyone follow those rules and norms? Put differently, there is no source of the rules, norms, and practices of the legislature aside from the legislators themselves. Why would they write rules or adopt practices that constrain them from achieving their objectives? As I wrote last year, the notion that legislative practices are the source of order

…shows how you can get equilibria (and, hence, regular order) in a multidimensional policy space. Substantively, it is a way to explain why there is no chaos in Congress. But the debate … is whether or not this theoretical solution is satisfying: is it satisfying to posit that the answer to the question “why is there order in Congress?” is “because legislators care about Congressional order”?

I thought then, just as I did in graduate school and still do now, that the answer is obviously no. If politics is only orderly when the people who hold power want it to be orderly, that means that order doesn’t come from legislative structure or norms or procedures. Instead, those are tools for the exercise of power. As William Riker wrote way back in 1980,

Disequilibrium, or the potential that the status quo be upset, is the characteristic feature of politics…. institutions are no more than rules and rules are themselves the product of social decisions. Consequently, the rules are also not in equilibrium. One can expect that losers on a series of decisions under a particular set of rules will attempt (often successfully) to change institutions and hence the kind of decisions produced under them… Thus the only difference between values and institutions is that the revelation of institutional disequilibria is probably a longer process than the revelation of disequilibria of taste.

If we take this perspective, we can understand clearly why the Senate GOP will bypass the parliamentarian. Its preferences are for tax cuts, not legislative order under existing rules. Its policy is a loser under the rules, so they will change the rules in order to change the kinds of decisions that the legislative process produces.

This makes sense as legislative hardball. Nothing is illegal here. The Senate GOP knows what it wants and will change the rules to get it. So what?

Here’s where the title of this post comes in. You can use this relatively obscure example of the Senate parliamentarian’s role in budget scoring to understand why U.S. democratic institutions are fraying. Stable institutions require common values. We can infer the absence of common values from the absence of stability.

In other words, U.S. institutions were stable because they were held together by values, the common belief among elites and masses alike that orderly politics under existing rules and norms was more important than situational advantage on a policy proposal. That, for example, it was more important to have due process for everyone than to be able to kidnap people and send them to El Salvador, or that the executive branch would execute the laws passed by the legislative branch, or that you shouldn’t invade Greenland just because you feel like it.

This perspective clarifies what the deep problem in U.S. politics is. There are plenty of reasons to complain about the specific institutional features of U.S. democracy, and there are plenty of reforms that I would implement if I could. But U.S. institutions can hold if people share a common purpose, if they value the institutions and the orderly resolution of political conflict more than their favored policy proposals.

The root of the problem in U.S. politics is a crisis of values. Without common values, institutions are just expressions of power.

These pieces focus on administrative law, legal process, and the separation of powers. But lost in this discussion are the concrete risks to the United States national security that would follow from these plans to cut funding for basic and applied science, monitor scientific research for politically incorrect speech, and gut core higher education funding mechanisms for topics like international and area studies. These programs were created in order to support U.S. national interests, and cutting them—even if doing so were fully legal—would amount to a full surrender of the United States to aspiring strategic competitors around the world.

Great Powers Build Science and Education. Declining Powers Cut It

First, the facts. There has never been a global power in the history of the world that did not invest public funds heavily in science and international studies through higher education. That is because every great power has understood the strategic value of higher education for creating a flexible knowledge base that can be deployed to confront unpredictable world events with national security implications. This is true of Germany before the wars, of the Soviet Union during the Cold War, of the UK during the 20th century, and the People’s Republic of China today. Funding to science and international studies is not an indulgence to support cozy academic communities and hifalutin theory work. Rather, it is a concrete way to commit public funds to encourage innovation, translational research, and deep knowledge building that creates repositories of expertise that every U.S. administration since Truman has recognized as foundational for U.S. national power.

Great powers achieve these goals through advanced teaching and basic and applied research. In some fields, it means bench science and lab work. In others, it means language study, cultural immersion, and historical expertise. It means teaching students to equip them with the factual knowledge and critical thinking required to confront unprecedented challenges. Research and teaching is always done—always and everywhere—with an eye towards the public returns to public investment.

At present, and for the past 50 years, the United States has had no peer competitor in this space. U.S. higher education supports the largest and most productive scientific communities in the world, the most comprehensive international studies programs in the world, and robust intellectual environments characterized by free speech, open criticism, and minimal political oversight.

No one should ignore the outstanding scientific research conducted in other countries, nor the deep area expertise found in other national educational systems. And no one should downplay the many cases of U.S. administrations of using science and area studies knowledge to pursue disastrous foreign policy choices, and to support blatantly immoral and illegal military and political strategies. The point, rather, is that U.S. national power flows from its unparalleled intellectual and scientific environment.

By this, I don’t mean just soft power (although that is also essential to U.S. preeminence). I mean specifically that the ability to design novel technical and military solutions to emerging security risks, to flexibly anticipate the consequences of changing international conditions, and to plan around a long-term perspective of U.S. national interest requires a scientific and intellectual community that is free to create, to innovate, to theorize, and to learn. No country in the modern world has developed these capacities without robust public-private partnerships and dedicated public funding.

The U.S. education and scientific community has no equal in this regard. We know this by revealed preference: every country in the world that aspires to improve its international standing recognizes that the United States is their aspirational comparison. We also know that the global market for U.S.-trained scientists and teachers is robust; that market, fairly or unfairly, is not symmetric. What holds back world powers like the People’s Republic of China back is their inability to cultivate an environment of intellectual openness. From the PRC to Nazi Germany, when politicians politicize higher education, science suffers, and with it, national security.

Knowledge is Hard Power

The best evidence of the value of science, of international and area studies, and of higher education more generally for supporting U.S. national interests comes from the symbiotic relationship between the U.S. defense and higher education sectors. Again, there is nothing usual about this arrangement: the same is true for defense and higher education in every global or regional power in the world (PRC, Germany, France, the UK, Japan, Taiwan, South Korea, Russia, Singapore, Australia… there is no exception).

The close relations between defense and higher education/science are not just about producing scientific and technological innovation, things like stealth technology, drones, cyberwarfare capabilities, and so on. It also extends to basic science, social science and history, and the humanities and arts. Every U.S. service academy offers majors in fields like English, Foreign Area Studies, and Sociology. That is because the academies that train future defense leaders recognize the value of comprehensive, holistic education. Leaders are not just engineers: they are thinkers, creators, and builders.

The same is true for the cooperative relationship between defense and higher education in the thousands of public and private institutions of higher education around the country. To take one obvious example, every senior military officer in the United States will attest that robust area knowledge is essential for assuring military superiority over our peer competitors. Those who have led missions overseas will readily attest that they both desire and value expertise in social science and regional studies.

Of course, reckoning with the history of U.S. funding for science and international studies can be uncomfortable for some corners of U.S. higher education. This is especially so when it comes to the role of the Department of Defense (and the defense industry more generally), whose interests are often not aligned with those of individual researchers. There is a history of resistance to ROTC programs on certain campuses. But even those college and university professors who are vocal critics of the U.S. foreign policy and military adventurism invariably welcome ROTC students into their courses with open arms. Almost without exception, they prefer to teach future officers, because they recognize the university’s solemn responsibility to educate future military leaders on how to act with wisdom, reason, and ethics. For their part, ROTC students, Foreign Area Officers, and other career military who pass through U.S. college and universities are some of the most motivated, committed, and serious students—and their careers are transformed by the experience.

Crippling science, area studies, and higher education more generally by subordinating it to the narrow political calculations of unaccountable actors is a recipe for defeat. Rather than looking to Vichy Twitter for uninformed commentary and vapid anti-intellectualism, our elected officials should look to defense and university leaders to understand how higher education supports U.S. national interests.

Robust Oversight Exists, But Anti-Science Activists Are Ignorant

One argument commonly invoked anti-science and anti-education activists is that public investments in higher education, science, and international studies are wasteful. This implies that public oversight and scrutiny over scientific research and public funding is needed. Such arguments betray the basic ignorance of those who make them, about the organization of public funding for science and higher education in the U.S. Federal agencies are governed by strong regulatory frameworks that manage how funds are allocated, how decisions are made, and how public benefit is measured. It is a very good idea to review these frameworks, to open them to public discussion, and to invite critical commentary from stakeholders and taxpayers. But to pretend that such oversight does not exist is ignorant and stupid.

As a consequence, expending bureaucratic energy to cancel National Science Foundation grants that say the word “diverse” in them is not just foolhardy and wasteful. It is defeatist. It is declinist. It is exactly what the U.S.’s lagging competitors currently do—the true U.S. advantage over aspiring world powers is specifically that our government does not make scientific research conditional on political correctness as determined by unelected political appointees. As the case of German universities during and after Nazi rule demonstrates, the effects of public funding cuts and ideological screening will be felt for decades to come. Canceling scientific research in the name of political correctness is what people who wish to surrender strategic dominance to foreign powers choose to do.

The best indicator of a declining national power is the belief among leading classes that education serves no point, that basic research has no public use, and that politicians should worm their way into the scientific process, prioritizing their own interests over logic, reason, and evidence. The U.S. is therefore at a decision point. Does it wish to cede strategic advantage to its competitors? Or does it wish to support the scientific environment that has made the United States the most powerful country in the world for 75 years?

How to Act

If you are a scientist or an educator who is reading this, and wondering how you can support our scholarly and scientific community, here are some ideas.

1. Call your elected representatives on the telephone. Many Americans believe that their voice does not matter. This is incorrect: your Senators and Representatives may not agree with you, but they will hear you. This is especially true if you believe that your Senator or Representative is unsympathetic. These are the people who need to hear from you the most.

2. Write the senior leadership team at your college or university, or the owners of the business where you work, and ask them to speak on your behalf. Almost certainly you can send an email to president@ABCD.edu, provost@ABCD.edu, or ceo@ABCD.com. It might not get a response, but it will be read.

3. Write to your professional association’s leadership and invite them to speak up in defense of U.S. national interests. This is an area where the National Academies ought to collaborate; you can certainly ask them to do so.

In all of these communications, your goal should be to communicate that cuts to education and science are threats to U.S. national security. You might also inform them that public education in the United States places world-class research institutions in every single one of the 50 states, and that these are significant local employers with clear spillovers for the local private sector. You might invite them to consult with officers in the U.S. military about their own educational experiences, to learn about what skills our colleges and universities impart. Finally, you may observe that no rigorous study of the return on investment for higher education finds that those returns are negative. The ROI on education for everyone, even humanities majors, is positive, and sizeable.

The current political situation in the United States is a moment of clarity for industry leaders in science, education, and international studies. The decisions that this government makes in the coming weeks will have implications for decades to come. They should hear from you about it.

Subscribe now