Economists Got 5x Faster. But What About Quality?

Opening

Dear reader, something interesting is happening in economics right now. Dartmouth’s Professor Paul Novosad says AI has given him 5x more time to actually think about research questions. ETH Zurich’s Professor Elliott Ash says the productivity gains have him so energized that he wants to work even more.

Meanwhile, on economics social media, warnings are piling up about low-quality AI-generated content — the so-called “AI slop” — with people basically pleading, “please, just stop.” FT columnist Tim Harford tackled both sides of this head-on. I want to take his analysis a step further and argue that this isn’t just an economics problem — it’s a structural shift happening across knowledge production as a whole.

Three Things AI Is Changing: Productivity, Scope, Verification

Harford laid out three paths through which AI could reshape economics. Let’s walk through them one by one.

1. Productivity — “Economists Freed from Grunt Work”

Data cleaning, grant applications, formatting tables — these are the classic chores that eat away at an economist’s time. AI automates a substantial chunk of this. When Novosad says “5x,” he doesn’t mean the research itself is 5x faster — he means the time available for actual thinking has grown 5x.

Here’s the interesting part, though: this productivity boost hasn’t yet translated into visible results. According to Erzo Luttmer, editor of the American Economic Review, roughly 25% of submitted papers now disclose AI use — mostly for editing or coding assistance — but submission quality hasn’t noticeably changed.

Harford himself used an AI agent to analyze abstracts from NBER¹​ working papers. Since ChatGPT’s launch, average sentence length has dropped, but that’s just a continuation of a prior trend — meanwhile, word complexity actually increased. Submission volume at elite journals showed no clear break from existing trends either. Things got faster. They didn’t get better.

2. Scope — “Measuring the Previously Unmeasurable”

A more interesting shift than productivity is the expansion of research scope itself. Qualitative data used to be expensive to collect and hard to analyze systematically. Now economists are using AI to measure the effects of zoning regulations, analyze the impact of difficult job interviews, and mine massive volumes of corporate earnings calls to detect patterns in how firms respond to tariffs.

Progress in forecasting is also worth noting. This past March, the Bank for International Settlements (BIS) released BISTRO, a general-purpose foundation model for macroeconomic time-series forecasting. Built on a transformer architecture²​, this model — unlike traditional econometric models — doesn’t need to be custom-designed for a specific task. BIS reported that the model would have accurately predicted the persistence of 2021–2022 inflation — a period when most traditional models mechanically forecast “reversion to the mean” and missed badly.

3. Verification — “AI Catches Errors, Humans Make Them”

The third possibility is the subtlest, and the most important. Can AI help filter out errors in research?

There’s a tool called Refine.ink, co-founded by Northwestern’s Professor Ben Golub. It’s an AI-based paper review system that systematically detects mathematical errors, gaps in empirical strategy, and logical inconsistencies. According to Golub, it finds problems in at least a third of papers that had already passed peer review at top journals. University of Chicago’s Professor John Cochrane ran his own book on inflation through Refine and called it “the best comments I’ve received in 40 years as an academic.”

Several of economics’ top five journals are already experimenting with Refine. It seems to fit most naturally right before a conditional acceptance — as a final check that catches mistakes an author would otherwise be embarrassed by after publication. (There are plenty of similar services out there. Among the ones I’ve personally tried, I’d recommend https://jenni.ai/, which was built by a Korean founder.)

The Real Question: An Arms Race in Speed

So far, AI sounds like pretty good news for economics. But Harford poses a key question:

“Can AI find errors faster than humans can give up on finding them?”

This isn’t just a rhetorical flourish. An analysis of 70,000 reviews submitted to ICLR 2025 (a leading international machine learning conference) estimated that about 21% were written entirely by AI from start to finish. Not AI-assisted editing — one in five review reports was, in full, written by an LLM. Word is already circulating that “embarrassingly sloppy AI-generated reviews” have started showing up at economics journals too.

Here’s the structure of the problem:

• More than 5 million academic papers are published worldwide every year
• The pool of qualified reviewers can’t keep pace with that volume
• Peer review is poorly rewarded and weakly incentivized to begin with
• When AI steps in and says “I’ll handle it for you,” already under-motivated human reviewers have even less reason to try hard

This is a textbook case of what economists call moral hazard³​ — the phenomenon where safety nets make people behave more recklessly, the same way drivers with airbags tend to speed more.

Oz’s Lens

Productivity tools translating directly into better output quality is rarer than we assume. When Excel arrived, we expected better analysis to follow — what we actually got was “more spreadsheets.” It’s hard to argue that PowerPoint improved the quality of presentations, either.

I think AI is following the same pattern. Productivity tools make people who were already good at something even better — but they also let people who were already cutting corners cut even more of them. Professor Golub himself admits this happened in his own work — he’s described the experience of “an AI-written section sneaking in and passing for real work” even in his own paper.

What I’m watching for is whether tools like Refine can go beyond being simple “error detectors” and instead establish a new baseline for academic quality. If a world emerges where every paper goes through an AI review before submission, human reviewers could focus on what AI can’t catch — originality, theoretical contribution, contextual judgment. That wouldn’t be a threat. It could be a redesign of the division of labor.

The problem is that we have to survive the “flood of slop” during that transition. And right now, we’re right in the middle of it. Surviving the literal deluge of AI-generated content — that’s just… the task at hand. Whether it’s short-form video, blog posts, or academic papers.

Closing

AI is cutting down on economists’ grunt work, expanding the scope of research, and helping catch errors. But the evidence that “more research” is turning into “better research” is still thin. And there’s an irony here: the AI that catches errors and the AI that mass-produces them faster are the same technology.

This isn’t unique to economics. Code review, legal document review, AI-assisted medical diagnosis — the same question will repeat itself in every field where AI takes on a verification role: once a safety net exists, do people become more careful, or less?

If you want to dig deeper into this topic, I’d recommend looking directly at the BIS’s BISTRO paper and trying out a service like Refine.ink. Refine in particular has a free trial, so just running your own writing or reports through it is enough to get a feel for what AI verification can actually do.

References & Further Reading

• Tim Harford, “Economists have caught the AI bug”, Financial Times, 2026. : The column that sparked today’s issue. A balanced look at three ways AI is reshaping economics.
• Koyuncu, B. et al., “Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series”, BIS Working Papers, 2026. : A paper on the architecture and performance of a transformer-based macroeconomic forecasting model. The inflation-forecasting case study is striking.
• Pataranutaporn, P. et al., “Can AI Solve the Peer Review Crisis?”, arXiv, 2025. : A large-scale experiment where four LLMs evaluated 1,220 economics papers, revealing both AI’s discriminative power and its biases.
• Korinek, A., “AI agents for economic research”, NBER Working Papers No. 34202, 2025. : A working paper that concretely explores which stages of economic research AI can be applied to.

The author, Kwangseob Ahn, is a professor of business administration at Sejong University and lead consultant at OBF (Oswarld Boutique Consulting Firm). He teaches statistics and data analysis — business data management and business analytics — while leading GTM and AI strategy consulting in the field, designing the seam between technology and business. He has published academic research on a memory architecture for AI dialogue systems (HEMA) and runs Daily Arxiv, a daily curation of global AI papers. He holds a master’s from Korea University’s Graduate School of Technology Management and a KMBA. He is the author of Homo Brainless: The People Who Outsource Their Thinking.

Footnotes

1. NBER (National Bureau of Economic Research): a leading American economic research institution. The “working papers” it publishes are pre-publication drafts, offering the fastest window into the latest trends in economics research. ↩

2. Transformer Architecture: the neural network structure underlying large language models like ChatGPT. It works by “predicting the next word,” and BIS applied this same approach to macroeconomic time-series data. ↩

3. Moral Hazard: the economic term for the phenomenon where having insurance makes people behave more recklessly. The same structure applies when AI catching errors makes human reviewers less thorough. ↩