Category: Web/Tech

From the Master of Industrial Organization

Believing that AI will be incorrigible leads us to proposing the wrong solutions, including ones which are likely to increase the danger than decrease it. I think that an AI pause is using the wrong method to fix an ill-posed problem. The point of a pause would be to research AI until we have a deep enough theoretical understanding as to predict what an AI will do. I think this goal will always elude us. I cannot conceive of what this deep theoretical understanding would look like; I don’t think anyone actually does. It’s a call for a stop, not a pause.

The world would not stand still, though. The companies far behind the frontier now would doubtless learn more about what it is that the frontier AI companies are doing. If cooperation were to break down, we would have restarted the race with many more competitors. This is no improvement.

Nicholas Decker throughout.

Optimal liability for offensive and defensive AI

How much liability should AI providers bear when their services enable both attack and defence? Liability can improve welfare while increasing harm. Providers sell a common input to productive users, attackers and defenders. Within a defended contest, a higher common price reduces effort without changing attack success or attacker profits, saving resources and improving the target’s security payoff. Compensation weakens defence and raises attacker profits. Optimal liability balances these effects against productive exclusion. Greater competition can lower optimal liability; every such decline must end at an outcome retaining defence. With cybersecurity access fixed, monopoly can warrant partial liability but never full liability when provision is worthwhile. When guardrails preserving productive uses are available, strong competition favours universal guarding socially but encourages unilateral removal at insufficient liability. At a fixed provider count, sufficiently many productive users ensure a pure equilibrium with universal guarding under high liability. A universal-guarding requirement makes liability redundant. Under monopoly, adoption follows a unique liability threshold, while zero liability remains uniquely optimal for a range of parameters with sufficiently many productive users.

That is from a new paper by Joshua Gans.

Dartmouth Provost okie-dokie

On Aug. 26, a Semafor investigation using Pangram to determine whether recent guest columns in major newspapers were written using artificial intelligence identified a column by Schnell as “100% AI-written.” Schnell’s column in The Washington Post — titled “Universities are fighting AI cheating. But there’s a deeper problem.” — argued that “a degree should distinguish what students can do independently from what they can accomplish with AI.

”The Dartmouth subsequently reviewed publications written by Schnell before and after the November 2022 public release of ChatGPT. Pangram 4.0, the latest publicly available version of the AI detector, labeled all of Schnell’s written works prior to 2022 as “100% human-written,” while a test of nine of his works published this year returned a median “AI-written” percentage of 96%. Between 2019 and 2026, Schnell did not publish any academic works or op-eds for which he was the sole author credited, according to his online curriculum vitae.Pangram 4.0 assesses whether a work is AI-written by separating the submitted text into smaller segments, which are then identified as “human,” “AI-assisted” or “AI-generated,” according to a Pangram technical report.

…Schnell’s Sept. 18 email statement to The Dartmouth was itself “100% AI-written,” according to a Pangram test.

Here is the full story, you people crack me up…

Banning self-recursive improvement in AI models?

Some people are suggesting this, including Ezra Klein.

I do not understand how it is supposed to work.  Put aside the issue of foregone innovations, let us say I seek to access an AI from overseas.  Am I allowed to visit their website?  So many sites have AI behind them, including Chinese open source AI.  Do all of those sites get banned?  And how?  Firewall imposed on Americans?  And we cannot put the good open source models on our hard drives?  How enforced?  Does the government have to read the creation logs of the model (how can they?), and then decide whether I can use that model or not?

What if Anthropic licenses its IP to an independent subsidiary in the Cayman Islands and they keep on using code to improve the AI?  Or maybe only foreign nations are allowed to have the best models and the really fast rates of improvement?  How are multinationals supposed to operate across borders?  (Presumably you cannot just use the RSI of your foreign affiliate, but then the whole MNC is crippled and eventually rendered uncompetitive?)  How are we to keep “AI sovereignty” for Americans?

If the top U.S. labs have to give up on what is supposed to be really important, they will look very “catchable.”  The policy creates a big incentive for some other parts of the world to invest a lot more in compute.  How safe is that?  And in the short run, do we also have to stop American firms from selling their compute abroad?

Does this whole thing mean that regular companies can use AI to write code, but the main labs cannot?  Solve for the equilibrium there.  Or the whole code writing function goes away for everybody?

What is the political mechanism through which RSI progress gets turned back on again, and when?  Is it as rational as our current debate over data centers?  If the top companies are no longer building RSI models, how are they supposed to know how safe or unsafe they might be?

At current margins, are humans even capable of writing the next steps of code that are required for further progress?  At whatever speed?

I do not find this to be a workable idea.  I think it is “people wanting to do something that sounds reasonable and less scary,” without thinking through the practical details.  I have made a related point before, but one does best with AI policy when you start with the stuff that otherwise you might put into your last two or three paragraphs of an essay (“It remains to be seen how…but we are all human beings and surely we should not give up hope…”).  Even if you believe in a deal with China, and I do not, we (and others) now know competitors can catch up more quickly than we had thought, at least if they think they have a chance of winning.  They might even buy some space compute from Elon.

C’mon people, I know for many of you this is a difficult take to swallow, because it feels like “we don’t care enough” if we do not take some dramatic steps.  Nonetheless the reality is that, a long time ago, we made a whole series of decisions that imply…we have no other choice than to just see this one through and get it more or less right.  The longer we deny that truth, the harder it will be for all of us.

Further results on AI and labor market reallocation

This paper documents how artificial intelligence has affected stocks and flows in the U.S. labor market. Combining CPS and JOLTS data with AI exposure and adoption measures from OpenAI and Lightcast, respectively, we construct labor force stocks, worker flows, and market tightness by AI exposure and adoption. Since the introduction of LLMs, workers with high AI exposure and adoption have experienced larger declines in job-finding and job-switching rates than other groups, while their within-job activity switching has increased noticeably relative to others. The positive effects of AI on nominal wage growth and hours worked attenuate markedly in the post-LLM period, suggesting that LLM-driven reallocation has operated through within-firm task reorganization and a reconfiguration of labor inputs, alongside weaker demand for workers most exposed to AI. To quantify LLM-driven reallocation pressure, we construct individual-level hirability and separability indices and showthat dispersion in job-finding prospects has risen markedly since 2023, largely driven by the AI factors. The natural rate of unemployment—recovered from the trend components of unemployment inflows and outflows by AI exposure and adoption, as well as from a theoretical model of structural unemployment—is estimated to have risen by about 0.1-0.2 percentage point since the introduction of LLMs, albeit with considerable uncertainty.

That is from a new NBER conference paper by Hie Joo Ahn and Nicholas A. Carollo.  Via Inclusive Productivity Network and Alex Imas.

Obama on agentic AI

Obama recently said:

“If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet.”

As Roon noted: “this sounds completely incoherent to me”

From Rob Saker:

An agent is a system that can plan, use tools, write code, query data, run experiments, check its own work, and keep going. That is how the work gets done. Treating “agentic” as optional decoration, as if the serious version of AI is a polite chatbot locked in a box while the unserious version “roams free on the internet”, is the kind of sentence you write when you’ve heard the buzzwords and never watched a lab actually use the technology. You do not cure cancer with a model that only answers questions. You cure cancer with systems that can read the literature, propose hypotheses, design assays, analyze results, rewrite the next experiment, and do that loop a thousand times faster than a human postdoc. That loop is agentic. Strip the agency out and you are left with a very expensive autocomplete.

I agree with those points, but my main concern is different.  How would we enforce such a prohibition on commercial agents?  Set up a Chinese-like firewall that bans Americans from accessing sites with foreign agents?  Monitor all those sites over time, so we know which suppliers to ban?  Ban VPN as well?  Give our government the power to inspect hard drives, in case agentic functions might be embedded there?  Set up FBI “phishes,” luring Americans in with the prospect of agent access from abroad, and then arresting them, as we do with child ****?

Something else?  How about insisting that all American (and foreign?) web sites set up tough captcha problems, so that agents may not be used (ha ha)?

You might also ask how an AI “agent” is to be defined, after all even O3 had some “agentic” abilities, such as opening up museum web sites to see which exhibits are on.  Is it typing things into boxes and filling out forms that is to be prohibited?  How much regulation of software would that require, how would that regulation actually occur, and what else would end up being restricted?

I do not think Obama intends to be supporting massive restrictions on freedom of speech and civil liberties.  Rather this is a good example of how some political factions will simply have “ideas about what might be good,” without having freedom concerns — or for that matter practicality and civil liberties concerns — center of mind in the first place.

And this would cause the immediate bankruptcy of both Anthropic and OpenAI, right?

I can readily imagine that, upon the advent of agentic AI, we need some significant changes in our cyber laws.  Now would be a good time to both commission and also cite some peer-reviewed academic literature on this matter!?  That is not an impossible thing to do, yet our public discourse seems oddly resistant to the notion.

Addendum: And do not forget the classic Gwern piece, remarkably early in its prescience.

Muse

It is excellent.  It has one of the best user interfaces I have seen on any product.  The model is good and the speed and clarity exemplary.  It integrates with your calendar and email, if that is want you want, very easily and smoothly.  It all feels very…American.

Kudos to the team!

Internet diffusion and religious decline?

Using a panel of 81 countries across six World Values Survey waves (1990–2022), we examine whether growth in internet diffusion is associated with religious decline. We construct a six-item composite religiosity index, align annual macro series to wave periods, and estimate country fixed-effects models with one-wave lags and a broad set of modernization, demographic, welfare-state, and cultural controls. We also estimate models separately for individual dimensions of religiosity. Across specifications, lagged internet penetration is negatively associated with religiosity within countries over time. These results remain robust under period dummies and in models estimated without lags, although reverse-ordering tests indicate that prior religiosity can also predict later internet use. Associations are strongest for denominational belonging and self-identification as a religious person. Overall, internet diffusion emerges as a robust macro-level correlate of within-country religious decline.

Here is the full article by  and Via the excellent Kevin Lewis.

Reimagining research papers as interactive and reliable AI agents

That is a new Nature paper by Jiacheng Miao, Joe R. Davis, Yaohui Zhang, Jonathan K. Pritchard, and James Zou.  Here goes:

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

As I have been saying for a while now, there is much more of this coming down the pike.  Via Charles Klingman.

What Regulatory Capture Actually Looks Like

It’s amazing how a theory can take over a brain. Consider the idea that people believe what serves their interests. As heuristics go, it’s a good one. I use it all the time. Yet when Dario Amodei says AI is dangerous, perhaps even an extinction risk, some people conclude he must be running a marketing campaign. That is stupid. Which is more likely, that a useful heuristic sometimes misfires or that “our product might kill you” is a clever way to sell it? Death threats are a poor marketing strategy.

We also have plenty of evidence that the fears of AI experts are sincere. Amodei, Altman and Musk were all publicly warning about AI risk long before they had AI companies to promote. The worry runs well beyond the executive suite; rank and file researchers share it. And it extends outside the industry altogether, to computer scientists with no product to sell, among them Nobel laureate Geoffrey Hinton. Hinton left a high-paying job at Google precisely so he could speak out and Hinton is not in a Berkeley polycule with Eliezer Yudkowsky, at least as far as I know. Whatever else you may say about the belief that AI presents a serious risk, plenty of AI researchers believe it sincerely.

Similarly, when Amodei recently proposed to slow the pace and install independent safety teams at AI companies many people jumped to the conclusion that this was regulatory capture. Sorry, but no, that theory doesn’t make sense. To see why, we should review the theory of regulatory capture.

Regulatory capture came out of the political science literature especially Marver Bernstein’s 1955 classic, Regulating Business by Independent Commission. Bernstein argues for a regulatory life cycle: Gestation, Youth, Maturity, Old Age. A scandal brings a bureaucracy into existence—or gives an existing one new powers. The public’s attention, like Sauron’s eye, fixes on the issue of the day: something must be done. The thalidomide scandal, for example, helped establish the modern FDA.

Gestation gives way to a youthful burst of reform and the do-gooders come to Washington ready to battle the industry. Inevitably, however, the public’s eye looks elsewhere. But the industry never looks away. It lobbies Congress, hires former regulators, trains future ones, and supplies much of the information the agency needs. As the agency matures, accommodation replaces confrontation. By old age, the regulator has become the industry’s protector. The classic example is the ICC, created to regulate the railroads but it eventually came to shield them from competition from the trucking industry.

Notice that classic regulatory capture takes time, it’s a process of erosion rather than a battle, it happens in the shadows, in the backrooms, away from the public’s eye. As Culpepper argues in Quiet Politics and Business Power, business power goes down as political salience goes up. Regulatory capture and lobbying does a good job explaining why roasting coffee beans was defined as “domestic manufacturing”, thereby lowering Starbuck’s tax rate by 2%. It does less well at explaining big cross-industry issues the public cares about such as environmental regulation or race and gender discrimination regulation. Finally, don’t confuse capture with firms making the best of a bad situation. Philip Morris supported the 2009 Tobacco Control Act not because FDA regulation was Philip Morris’s unconstrained ideal but because it knew regulation was coming and it wanted a seat at the table to nudge the rules in its favor. That’s ordinary political bargaining—or rent-seeking—not evidence that the regulator has been captured.

Now let’s evaluate Amodei’s call for regulation in light of regulatory capture theory. AI regulation is in gestation. Public attention is fixed on the industry, and much of that attention is hostile. The big profits in AI lie in automating work, and job loss is a much more salient fear than extinction. AI politics is now loud–precisely the environment in which Culpepper predicts business power will be weakest. A mature industry can bend regulation to its purposes through revolving doors, longstanding relationships and obscure rulemaking. An industry under Sauron’s eye has much less power and faces much greater risk that politics will bend regulation to its purposes. Political actors are eager for an excuse to redistribute AI rents away from capitalists and toward favored groups (ala Peltzman).

Regulation will reduce AI profits. That doesn’t prove that every rule Amodei favors is innocent of self-interest, an absurd proposition. I suspect that Amodei’s ideal may be something like a single regulated AI monopoly—safe and reasonable profitable, like the old AT&T. But that’s not the profit maximizing outcome. If transformative AI can capture even a fraction of the enormous labor market–the world’s biggest market–laissez-faire would mean vastly greater profits. Amodei may simply prefer a smaller fortune and a safer world. That is perfectly consistent with self-interest playing a role; it is not consistent with the bastardized theory that profit maximization is the only thing that matters or that “capture” is universal.

Go ahead: argue that Amodei and other AI experts are wrong about AI risk. Ask whether his proposals favor Anthropic. But calling “our product might kill you” a clever marketing and regulatory-capture strategy isn’t sophisticated analysis. The facts don’t fit regulatory capture theory and trying to make them fit requires epistemically painful Ptolemaic epicycles. Even a dull Ockham’s razor cuts through that story to the obvious alternative: Amodei actually believes what he’s saying.

When discussing AI policy, start with China

Whatever your ideas for regulating AI, I say start with China.  Do not put China as an afterthought at the end of your proposal, mentioned in a vague wish that something good ought to happen and that maybe the future of humanity can be secured.

During the 2023 U.S.-China nuclear talks, America proposed missile-launch notifications, a nuclear crisis hotline, and processes to limit the use of outer space for military conflict.  China declined all of those.  Blame the U.S. if you wish (do we always keep our word?), but that is what happened.

China also broke off meaningful arms control talks.  You may think it is their right to play catch-up, and to be cynical of our motives, but that is what happened.

How well did China exchange timely information about Covid, and cooperate with stopping its initial spread?

Get the picture?

If you start with China in your discussion, you will end up with sensible proposals before getting too caught up in your moods of the day.  If you are reading proposals or for that matter tweets for AI regulation, and the writer does not deal with these China issues in a forthright and very specific manner, you should be very suspicious indeed.

Here is Ezra and Matt Sheehan, discussing related issues (NYT).

Callum Williams on cybersecurity prices

Share prices of cyber firms have jumped around a lot in recent weeks, leading one side or the other to claim victory. But the crucial point is that, relative to the historical norm, the market is not really pricing ANYTHING big to change. There was a much bigger move in cyber stocks in both 2020-22 (up) and 2022-23 (down) but no one read “AI x-risk” into this.

Here is the full post with graph.

Image

Hardly the final word, and I am myself more pessimistic than those numbers indicate.  But at least with this we are getting somewhere concrete and scientific rather than just scare stories.  As for meta-commentary on the discourse itself, you really should be asking who are the people insisting on data here, and who are the people trying to talk you away from focusing on the data so much.

Is it the screens? Or education systems?

The Dark Ages implies television and phones are the main cause of cognitive decline. This fails to explain the patterns in PISA scores. Why did England and Scotland fall so precipitously from 2000 to 2005 whilst America improved? Why did England and Estonia hold steady after 2015 whilst most other OECD countries declined? How have Singapore, Taiwan, Japan avoided decline altogether?

A better explanation is that a country’s education system is more important than its television diffusion.1 East Asian PISA and IQ scores have probably remained constant, or even risen, because of their rigorous education systems and intensive tutoring cultures. The two European countries which avoid PISA-malaise – Estonia and England – have more rigorous education systems than their neighbours. They (more or less) use the knowledge-rich curricula, direct instruction, and systematic phonics – techniques which their more progressive neighbours abandoned between 1975-1990.

Here is much more from Alexander Thompson, recommended.

Does AI assistance enhance or erode expertise?

From a new NBER working paper:

Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.

That is by David Autor, et.al.  Do note that over time the allocation of humans to tasks will evolve so that more of the humans become more productive, not less.  RCTs somehow have the odd disadvantage of requiring too many things to be held constant, and so they can miss the benefits of longer-term adjustments.