Category: Web/Tech
Can Artificial Intelligence Forecast Treatment Effects in Economics?
I use LLMs to forecast treatment effects of research studies pre-registered on the AEA RCT Registry and the Open Science Framework. I focus on studies whose results were first publicly released in 2026 and elicit forecasts from models whose training or knowledge cut-off dates were before the relevant set of papers was released. First, LLM models extract forecasting questions from the pre-registrations or pre-analysis plans. These questions elicit forecasted treatment effects; not all resolve in the subsequent papers, but those that do can be used to consider forecast accuracy. Forecasts were elicited for 889 treatment effects from 39 papers with results disclosed in January 2026 or later, or 673 from 33 papers after screening. Forecasts were elicited in raw units but I focus on those that could be standardized and therefore compared across papers. Currently, the median absolute forecast error is 0.054 SD for the best model, the average absolute error is 0.093 SD, and the MSE is 0.0218. The AI forecasts perform substantially better than benchmarks forecasting 0 effect or forecasting the mean effect of other papers’ results in that time period. Overall, these results suggest considerable scope for forecasts to be of value in informing policy and research.
From the excellent Eva Vivalt. As I have been predicting, so much of economics (and science) is going to change, and radically. Here is Eva’s thread on the paper. Perhaps we should dispense with many of the more mundane inquiries, and as Eva notes: “But can we not shift to working on those projects that have particularly high uncertainty and upside potential?”
She is one of the best young economists in the world, and this stands some chance of being the most important paper of the year.
How Tyler Cowen and Alex Tabarrok Have Changed Their Views on AI and Where They Disagree
From the Archives-written by ChatGPT.
Both became dramatically more confident in AI’s capabilities while remaining optimistic about its economic potential. Their differences concern how benefits arrive, how seriously to weight emerging dangers, and whether AI might possess consciousness. Cowen emphasizes institutions, bottlenecks, and geopolitical competition. Tabarrok emphasizes inexpensive intelligence, scientific discovery, and—especially in his later writing—concrete safety failures.
The dated passages below trace those changes across the public archive. Quotations are the authors’ own words, including excerpts from their columns; outside material they reproduce is identified separately.
Early views on unequal gains and abundance
Cowen’s early framework grew out of chess. On March 11, 2011, he observed that stronger computers required stronger human partners:
“The required education level — for the team’s ‘wage premium’ — is ratcheted up.”
His concern was already distributional: computers could increase productivity while changing which human skills commanded a premium. He also emphasized learning how to work with computers. What I learn from chess and computers.
Tabarrok’s April 12, 2016 argument was more explicitly reassuring:
“AI will be cheap and ownership will be widespread.”
He compared AI to an increase in the world’s supply of educated people: more intelligence produces useful ideas, and competition spreads the benefits to consumers. But his forecast of comprehensive substitution was cautious—AI becoming a perfect substitute for human intelligence could be “100 years or more” away. Will the proliferation of affordable AI decimate the middle class?
Neither position was absolute. Cowen warned in February 2017 that the Industrial Revolution involved decades of painful adjustment; historical progress did not guarantee quick wage gains. Tabarrok, in October 2019, praised the argument that mediocre automation could displace workers without creating enough additional wealth to compensate. See Cowen’s historical warning and Tabarrok’s Beware the Mediocre Robots!
Rising confidence in AI capabilities from 2020 to 2025
Cowen was already optimistic about language models in July 2020. After publishing a skeptical reader’s assessment of GPT-3, he added:
“I remain bullish, but it is always worth considering other opinions.”
That attribution matters: the skeptical arguments in the post were the reader’s, not Cowen’s. The case against the import of GPT-3, July 23, 2020.
Tabarrok’s shift toward near-term transformative AI became unmistakable by March 2, 2023:
“I never expected to witness the birth of aliens.”
Later that month, he endorsed Microsoft researchers’ description of GPT-4 as exhibiting an incomplete form of general intelligence: “They are correct.” This is a substantial acceleration from his 2016 outlook, although perfect substitution for humans and early general intelligence are different thresholds. See AGI is Coming and Baby AGI is Here, March 24, 2023.
Cowen reached an especially explicit formulation on April 16, 2025, discussing o3:
“I think it is AGI, seriously.”
But he immediately separated capability from adoption:
“It still will take us a long time to use it properly.”
His growing enthusiasm about the technology did not imply an equally dramatic forecast for immediate economic growth. o3 and AGI, is April 16th AGI day?
Economic transformation and the pace of adoption
On January 6, 2025, Tabarrok wrote:
“The intelligence revolution is going to be bigger, more impactful and more wrenching than the industrial revolution.”
This is his own concluding sentence, following a quotation from Sam Altman. The Intelligence Revolution.
Cowen’s February 23, 2025 response to rapid-takeoff expectations focused on a different constraint:
“Human bottlenecks become more important, the more productive is AI.”
Energy, regulation, organizational habits, and weak human collaborators can constrain the economic value of better models. He offered an explicitly tentative estimate of an additional half percentage point of annual growth—large when compounded, but far from an immediate economic explosion. Why I think AI take-off is relatively slow.
By September 3, 2026, Tabarrok was developing the distributional implications:
“A smaller share of a much larger economy can still mean more income for labor.”
He argued that even substantial displacement could leave aggregate labor income higher, or make compensation affordable. He explicitly acknowledged painful losses for particular workers. His high-growth calculations are scenarios, not forecasts. How Much Redistribution Will AI Require?
They also converge more than their rhetoric suggests. In May 2026, Cowen rejected mass unemployment while identifying transitional hiring problems. In August 2026, Tabarrok interpreted business-survey evidence as consistent with their shared position that AI had not yet broadly reduced employment. See Cowen, May 13, and Tabarrok, August 31.
Growing attention to safety and institutional limits
Cowen stated his central position on November 19, 2023:
“I view AI as more likely to lower than to raise net existential risks.”
His reasoning was comparative: the world already faces serious dangers, and stronger science could improve our defenses. My summary views on AI existential risk.
Tabarrok’s later writing places more weight on specific vulnerabilities. On April 8, 2026, he described:
“the worrying combination that very powerful AI’s are very vulnerable.”
He was discussing cybersecurity capabilities alongside agents’ susceptibility to manipulation. AI Risks.
On September 17, 2026, he sharpened the institutional objection to assuming that beneficial science will automatically offset dangerous capabilities:
“But faster science does not automatically mean faster defense.”
This can be read as a qualification of Cowen’s earlier optimism: defensive discoveries must pass through institutions that may move slowly. It also applies Cowen’s own bottleneck logic to safety. Pandemic Preparation is AI Safety.
Cowen nevertheless supports safeguards. In August 2026, he proposed government-overseen, industry-led safety audits. In September, he opposed restrictions on recursive improvement as impractical to enforce internationally. His position is therefore selective regulation combined with continued development. See his August 27 proposal and September 22 critique.
The September 2026 exchange about AI leaders
On September 16, 2026, Tabarrok rejected the claim that executives’ safety warnings were merely marketing or regulatory capture:
“Amodei actually believes what he’s saying.”
He did not thereby establish that the warnings or proposed policies were correct. His argument concerned sincerity and incentives. What Regulatory Capture Actually Looks Like.
Cowen replied the next day:
“I agree with Alex that the standard model of regulatory capture does not apply here”
But he emphasized competition for power: each executive sincerely thinks their own company should win. For Cowen, that complicates treating their warnings as disinterested policy advice. They agree that concern is sincere; they differ over which incentives best explain behavior. The correct model of AI CEO behavior, September 17, 2026.
Consciousness and Tabarrok’s warning about infant pain
On March 17, 2025, Tabarrok’s What Did We Learn From Torturing Babies? drew a moral lesson from medicine’s historical dismissal of infant pain. He described how theories about immature brains allowed physicians to discount babies’ observable distress and perform procedures without adequate anesthesia. His warning explicitly extended to judgments about animals and AI:
“If we must err, let us err on the side of compassion.”
Tabarrok’s position is stronger than mere openness to AI consciousness: he argues for moral caution under uncertainty. The analogy does not establish that AI suffers. It challenges confidence that a theory ruling out suffering is sufficient grounds to disregard possible evidence of it.
On October 14, 2025, he went further in his own commentary following an essay by Jack Clark:
“these systems are becoming self-aware”
He distinguished self-awareness from sentience, while warning against confidently dismissing the latter. His link accompanying that warning points back to the infant-pain essay, making the connection explicit. We Turned the Light On—and the AI Looked Back.
Cowen’s position is much more categorical. On August 31, 2026, he wrote:
“I am very much opposed to the view that the AIs are sentient, or might be sentient.”
He treats AI sentience as a category error, although he accepts that human-like descriptions can sometimes help predict AI behavior. Anthropomorphizing AI?
The disagreement concerns both what AI might experience and how to act when we cannot confidently know. Tabarrok’s historical analogy places greater weight on the moral cost of mistakenly denying consciousness; Cowen expresses much greater confidence that sentience is the wrong category to apply. These are their philosophical assessments, not established findings about AI consciousness.
AI dominates human peer reviews
We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science.
By Gary King and Steven Worthington, not even using what are now the very best models, rather based on Claude Opus 4.8. The details of the dominance, as outlined in the paper, are striking. It is not close.
On my Saez and Zucman post, I find it striking that some people are complaining all of a sudden. Yet I have been offering, citing, and linking to commentary on published papers for twenty-four years now (and every single day). Sometimes it might just be a casual offhand remark from me, whether positive or negative. Typically this is simply taken in stride. It is when you pull out the bazookas, and offer oodles of high-powered analytics, more astute than human referees, and combined with the ability to pursue follow-up inquiries, that people start getting worried. Model that.
You can pick at individual criticisms from the AIs all you want, but the most damning observation is that the errors go in the ideological direction of the authors 97% of the time. That is exactly what (the human) Phil Magness said years ago, based on an extensive canvassing of their work. Basically we already knew that was true, and the AIs confirmed the human estimate.
On our side, we have taken steps, most of all the new MR archive, to make AI analysis of our work easier. Soon you will be able to read on MR what the AIs think are our worst posts, respectively (Alex has prepared this).
In the meantime, I encourage the NSF, prize committees, universities, tenure committees, journals, and so on to do as many of their own AI-enabled audits as possible. So far the world is not jumping at this opportunity. You can model that too.
Errors in the research of Emmanuel Saez and Gabriel Zucman
138 papers audited
2,035 problems found
147 serious enough to change a headline number
69% of papers have at least one
97% of major and moderate errors with a clear direction overstate the paper’s claim
And on the method:
Two independent audits of each paper. One by Claude Opus 5.5, one by OpenAI’s GPT-6 Astra, neither seeing the other’s work. Each obtained the paper, its appendices and its replication data, recomputed the headline numbers from the authors’ own files and, where possible, from the original sources (tax statistics, national accounts, Federal Reserve and survey data), checked that cited sources say what is claimed, and probed every assumption the conclusions rest on.
Then an adjudicator. A third agent re-verified every claimed problem, tried to refute each one, discarded those that did not survive, searched for problems both auditors missed, and graded what remained by how far it undermines the paper’s own headline claim: major changes a core number materially, moderate affects secondary results or a core result modestly, minor is a presentation or arithmetic slip. Confidence is graded separately.
There is much, much more at the link, which goes through the research paper by paper, and with further sustained analyses behind the links. The survey is both extensive and thorough. (And I thank an anonymous MR reader for this work.)
I am not even sure what to say at this point. We need a radical revaluation of this work, and of the associated policy arguments. More generally, this is only a single step in the story of how AI is going to revise economic science, and indeed past research more generally. As I stress in many of my recent talks, there will be significant gains and losses in status, with the losses often outweighing the gains. I see this as one of those cases.
How is AI shaping economic research?
Does generative AI narrow or widen productivity gaps in research? We study the distributional effects of AI on research productivity in economics using evidence from three sources: NBER working-paper and publication records, text-based measures of LLM-assisted writing, and an original survey of 227 academic economists measuring AI use across research tasks. We first show that productivity gaps among NBER authors narrowed following ChatGPT’s release: authors without research assistants (RAs) published a larger share of papers in top-tier journals, while authors from smaller teams gained in working-paper output and publication speed. We then use the survey to examine how individual AI use affects research productivity. We find weak evidence that AI affects working-paper output on average, but there is substantial heterogeneity. Triple-difference estimates show that greater AI exposure narrows the output gap between researchers with and without RAs but widens the gap between senior and junior researchers. Selection into AI use follows a different pattern: junior researchers use AI more intensively and are more likely to use agentic tools. Thus, these differential productivity effects reflect differences in the returns to AI rather than selection into AI use. A model with diminishing returns to execution, increasing returns to conceptual capacity, and complementarity between the two explains how AI can alleviate disadvantages in research support while amplifying the returns to accumulated expertise. Over time, however, early convergence may give way to divergence as execution constraints recede and AI’s conceptual capabilities improve.
That is from Tianyu Jiang, who is this year a job market candidate from the University of Michigan. The paper is co-authored with Yuqing (Claire) Wang.
AI hacking risks
That is the topic of my latest Free Press column (now there are gift links!), here is one excerpt, noting this is a worst-case scenario not a prediction:
Yet current systems are likely to prove more robust than that suggests. For one thing, they use a variety of different methods of protection, including public key cryptography, AES, and cryptographic hash functions, the latter not qualifying as encryption per se. While it is possible that all could fall at the same time, that seems unlikely.
The more likely scenario, assuming this comes to pass at all, is that public key encryption would become vulnerable first, but the American economy would shift toward the more secure forms of communication, most of all those that have secure cryptographic keys already established. We would also work harder to substitute into the “post-quantum” methods of encryption that are being developed and already are being used in browsers.
The transition would be very costly, but the end result would not be total insecurity. Furthermore, today’s major financial institutions already have invested in various backup systems, including offline security methods and alternative payment-processing channels. Smaller businesses would face more serious problems of transition, although they also would be lesser targets in the first place. As for you the individual, you might have to show up in person at your bank to make a major transaction, and that requirement, imposed collectively, would strain our systems to the utmost.
A second layer of protection is that the new math techniques likely would not be available to everyone. They might require technical sophistication and a willingness to break the law in some very serious ways and also risk hacking retaliation. In addition, many math breakthroughs simply shave down the safety margins on systems, rather than breaking them altogether, and in those cases the criminals still might need a lot of computing power at considerable expense.
That situation is hardly the same as a complete and sudden “open season” on all methods of cryptography. Furthermore, some of the wrongdoers in this position, even assuming they felt they could get away with their crimes, might prefer to siphon off money slowly, rather than bring down the entire financial system, which could hurt their own net wealth as well.
A further source of protection is that the math breakthroughs might come in stages, rather than all at once. For an example from another part of math, OpenAI has proven a quasi-Riemann hypothesis, and Terry Tao told me recently that this may open the door to solving the Riemann hypothesis proper. We now have advance notice that the Riemann hypothesis may be proven (or less likely, disproven). In similar fashion, intermediate advances may signal that cryptographic protections are vulnerable. We thus would have some time to adjust…
The most likely scenarios would play out in terms of probabilities, as people deal with risk before any large-scale hacking attack would come. Right now the chance of public key cryptography breaking down is on the radar screens of a relatively small number of people. Imagine that some advances in math occur, and the more sophisticated market participants start seeing something like a 10 percent chance that some forms of encryption might be vulnerable. Perhaps without much fanfare, some people would start selling their bitcoin, or they might wire their money to more secure banks, or perhaps they would hold more gold and short the market. Some hackers would invest more in the activity, including hacks without advanced AI, under the philosophy of “harvest now, decrypt later,” pulling in classified or confidential materials that previously were not worth capturing. Overall, it would be like the early days of Covid, when major bad news was pending but only a few individuals were acting on it. That state of affairs could persist for some period of time.
In any case it would not be good.
And this: “If you have thoughts on my latest column, or questions about AI or anything else, join me for an AMA (Ask Me Anything) this week on The Free Press Forum.
Paid subscribers can submit questions now. Not a subscriber? For a limited time, you can get 20% off at thefp.com/tyler.”
NYT debate on the rules for AI companies
Emily Bazelon was the moderator, there were five of us in the symposium. Excerpt from me:
Bores: Do you think the federal government pre-empting the states is consistent with democracy?
Cowen: Absolutely. We have federal laws that pre-empt state lawmaking in many other areas. Courts have upheld it many, many times.
Bores: OK, but is it pro-democracy, in giving people a say?
Cowen: It is pro-American Constitution.
Most of the panel disagreed with most of my points.
Why are cybersecurity losses not worse?
Here’s the first part of my theory: cybercriminals are not the organized geniuses we implicitly imagine. Anyone building a criminal gang has to find candidates willing to forgo more stable opportunities in the formal economy. My modeling estimated that something like 720,000 people are involved in the online criminal economy, 90% of them in scam and fraud call-center type operations, some of them human-trafficked. Of the 76k or so actual hackers, the vast majority work for governments. Of the remaining few thousand criminal hackers, most work in ransomware. Some demographic estimates of these workforces are below.
Criminal organizations need to base themselves in middle and lower income countries, where they can pay less and hire people who have few formal-economy options. For scams and fraud, picture an illegal call center building in a small city in Nigeria. For ransomware gangs, picture kids working in the informal economy in Belarus. I think folks in the AI safety and cyber world sometimes imagine adversaries as their Waluigi twins, implicit MIT graduates; in fact, in cybercrime we’re dealing with a lower-skill demographic. The chart below estimates attacker wages relative to default economy wages…
Here is more from Joshua Saxe, very good throughoiut. There are bottlenecks on the down side too…
Cybersecurity ETF
Joe Weisenthal: “The Red Line is the day that Anthropic introduced Mythos”
Podcast with Rabbi David Wolpe
Here is their episode summary:
Economist Tyler Cowen joins David Wolpe for a conversation about AI, religion, and what makes us human. Tyler makes the case that AI’s biggest impact may be on status: who gains it, who loses it, and what happens to experts like rabbis when anyone can ask ChatGPT all their questions. He also suggests that losing our monopoly on intelligence may make us feel more unique as humans, rather than less.
You can listen here.
Will bystander economies be able to finance their governments?
From Jesús Fernández-Villaverde:
Will AI poison the public finances of many modern states? There is a real risk that it will.
Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.
But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation. Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.
I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.
The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.
Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.
But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines’ net return. Painful, but the boom survives and the adopter can pay its debt.
The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.
Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent. This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high. Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.
I am somewhat more optimistic than this. The assumption of zero Spanish adoption already is noted, but also ideas are public goods, and Spain can benefit from American (and Chinese) AI, and the resulting scientific advances. The long-run supply of capital also might be quite elastic, we will see.
A simple point about the intertemporal regulation of AI
At some point AI will be more regulated than it is today. Furthermore, I believe that most parts of the American economy, once they are regulated, end up overregulated.
Thus it is likely that the AI sector will, at some point, end up overregulated.
Given that reality, perhaps the AI sector should be underregulated today. After all, overregulation will have decades, maybe centuries, to catch up and even the ledger.
Many regulatory issues can be considered within this framework. You can think that AI is “underregulated today”, in the static sense of that term, and still not favor additional regulation right now. You might prefer to keep it underregulated for some while to come.
One reason I do not love AI writing, or even AI-assisted writing
Of course I read to absorb the content, but I also am in a position to meet many authors, have contact with them, and spend time with them. So reading anything also is a potential option on those future connections. But when I am reading an AI piece, the value of that option comes pretty close to zero.
Saving for the machines
From Jesús Fernández-Villaverde:
When machines can perform a growing share of tasks, growth depends on two margins: how the task structure affects the returns to accumulating machines, and how strongly capital accumulation responds to those returns. I argue that, in a standard task model, capital supply governs the size of the growth boom associated with automation, while the task structure sets the duration of this growth boom and the evolution of wages. I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate is fixed at the calibrated U.S. 2025 level of 23%. Therefore, slow growth might reflect too little capital accumulation instead of a technological bottleneck. Also, the saving rate rises mainly because automation raises the capital share, not because households become thriftier. Long yields price the growth boom: the ten-year real yield jumps from 5.1% to 13.6% on impact. In my benchmark calibration, hand-to-mouth households lose the equivalent of two-fifths to two-thirds of their consumption, while keeping them on trend temporarily costs at most a fifth of GDP.
Here is a thread on the paper. Here are the slides. More bottlenecks!
No, DataCenters are Not Crowding-Out Other Investments
Based on charts like the one at right, Paul Krugman and many others are arguing that:
Massive spending on data centers is crowding out investment in everything else in the economy.
Not so fast. Can you see one immediate problem? How is $51b in data center construction spending crowding out $120b in everything else!
Indeed, crowding out in the standard loanable funds model is generically less than 1 to 1–that is, $51b of additional spending will crowd out less than $51b of other investment spending. The standard analysis is that increased investment demand/borrowing increases interest rates–the higher interest rate reduces investment demand elsewhere (that’s the crowding out) but the higher interest rate also increases savings so investment elsewhere falls by less than the increase in tech investment. Here’s the classic graph from our textbook, Modern Principles. In the graph, we explain crowding out in terms of government borrowing but formally the diagram is the same with tech borrowing substituting for government borrowing:
Although the diagram is formally identical with private borrowing or government borrowing the term crowding-out is usually reserved for the latter. Private borrowing faces the market test so “crowding out” in that context simply means funds flowing from lower value to higher valued uses–it’s a benefit. Government borrowing doesn’t face the same market test and so we need to remind people that it comes at a cost. Let’s put this point aside, however.
Krugman doesn’t point out the problem with Rattner’s graph but he does substitute with a graph of his own (shown below) which compares all “tech” investment with all other investment as a share of GDP. On the surface that seems to at least get the ratios in more plausible alignment but there are a few problems. First, the numbers are on a different order of magnitude. Rattner has annual datacenter investment rising to $51b (annual rate) over a more than two year period. Krugman has tech spending rising to ~$539b over a similar period–so far more than datacenters or AI as conventionally considered is being counted. That’s fine–but Krugman is relying here on datacenter discontent while actually discussing something different. But there is another problem.
Krugman’s graph is in terms of shares of GDP and as one share is going up, another is going down which is meant to be suggestive of crowding out–except GDP is increasing. Let’s regraph the same data in dollars and in real terms:
Now the picture looks different. From 2023 Q2 to 2026 Q2, real tech investment rose about 46%, while other real investment rose about 3%. Other investment has weakened since its 2024 peak, but it remains above its starting level. This does not rule out crowding out: other investment might have grown faster without the tech boom. But we have now gone from charts suggesting that crowding out was absolute to arguing that it was relative to GDP and relative to a counter-factual which cannot be identified with these graphs.
So what is really going on? Real interest rates are up since the pandemic but most of the increase happened before the AI investment boom. The average 10-year TIPS yield rose from −1.00% in late 2021 to 1.49% in late 2022. In comparison, the increase in rates over the Rattner/Krugman chart-periods are only .5 and 0.68 percentage points respectively, relatively small potatoes to hang a crowding out story.
Moreover, current real rates are ~2-3%, in line with historical averages and hardly prohibitive. If there are good projects they can be funded.
Why did real rates rise if not datacenter borrowing? There are many possible explanations: recovery from unusually low rates during the pandemic, Federal borrowing, maybe even a quite different tech story in which greater expectations of future wealth are reducing savings today (like the reputed AI workers who have taken up smoking because they expect a future cure for lung cancer). But note that real rates are up across advanced economies, not just here, even though most of the AI spending is here–this further weakens the case for crowding out of US investment, as the world saving base is much larger than the US base.
Bottom line is that I think a more plausible story is that two things are happening at once: tariffs, uncertainty, and other forces including the fading of Biden-era CHIPS and IRA subsidies have weakened investment outside AI-tech. The AI boom in contrast has supported overall investment. In other words, rather than datacenters and AI investment “crowding out” other investment, my interpretation is that AI investment is a bright spot in what would otherwise be a more negative economy.
Hat tip: Arpit Gupta.