What about corporations or nation states during times of conflict - do you think it's accurate to model them as roughly as ruthless in pursuit of their own goals as future AI agents? 

They don't have the same psychological makeup as individual people, they have a strong tradition and culture of maximizing self-interest, and they face strong incentives and selection pressures to maximize fitness (i.e. for companies to profit, for nation states to ensure their own survival) lest they be outcompeted by more ruthless competitors. On average, while I'd expect that these entities tend to show some care for goals besides self-interest maximization, I think the most reliable predictor of their behavior is the maximization of their self-interest. 

If they're roughly as ruthless as future AI agents, and we've developed institutions that somewhat robustly align their ambitions with pro-social action, then we should have some optimism that we can find similarly productive systems for working with misaligned AIs. 

Human history provides many examples of agents with different values choosing to cooperate thanks to systems and institutions:

• After the European Wars of Religion saw people with fundamentally different values in violent conflict with each other, political liberalism / guarantees of religious liberty / the separation of church and state emerged as worthwhile compromises that allowed people with different values to live and work together cooperatively.
• Civil wars often start when one political faction loses power to another, but democracy reduces the incentive for war because it provides a peaceful and timely means for the disempowered faction to regain control of the government.
• When a state guarantees property rights, people have a strong incentive not to steal from one another, but instead to engage in free and mutual beneficial trade, even if those people have values that fundamentally conflict in many ways.
• Conversely, people whose property rights are not guaranteed by the state (e.g. cartels in possession of illegal drugs) may be more likely to resort to violence in protection of their property as they cannot rely on the state for that protection. This is perhaps analogous to the situation of a rogue AI agent which would be shut down if discovered. 

If two agents' utility functions are perfect inverses, then I agree that cooperation is impossible. But when agents share a preference for some outcomes over others, even if they disagree about the preference ordering of most outcomes, then cooperation is possible. In such general sum games, well-designed institutions can systematically promote cooperative behavior over conflict.

Nice! This is a different question, but I'd be curious if you have any thoughts on how to evaluate risks from BDTs. There's a new NIST RFI on bio/chem models asking about this, and while I've seen some answers to the question, most of them say they have a ton of uncertainty and no great solutions. Maybe reliable evaluations aren't possible today, but what would we need to build them? 

I think this kind of research will help inform people about the economic impacts of AI, but I don't think the primary benefits will be for forecasters per se. Instead, I'd expect policymakers, academics, journalists, investors, and other groups of people who value academic prestige and working within established disciplines to be the main groups that would learn from research like this. 

I don't think most expert AI forecasters would really value this paper. They're generally already highly informed about AI progress, and might have read relatively niche research on the topic, like Ajeya Cotra and Tom Davidson's work at OpenPhil. The methodology in this paper might seem obvious to them ("of course firms will automate when it's cost effective!"), and its conclusions wouldn't be strong or comprehensive enough to change their views.

It's more plausible that future work building on this paper would inform forecasters. As you mentioned above, this work is only about computer vision systems, so it would be useful to see the methodology applied to LLMs and other kinds of AI. This paper has a relatively limited dataset, so it'd be good to see this methodology applied to more empirical evidence. Right now, I think most AI forecasters rely on either macro-level models like Davidson or simple intuitions like "we'll get explosive growth when we have automated remote workers." This line of research could eventually lead to a much more detailed economic model of AI automation, which I could imagine becoming a key source of information for forecasters. 

But expert forecasters are only one group of people whose expectations about the future matter. I'd expect this research to be more valuable for other kinds of people whose opinions about AI development also matter, such as: 

• Economists (Korinek, Trammell, Brynjolfsson, Chad Jones, Daniel Rock)
• Policymakers (Researchers at policy think tanks and staffers in political institutions who spend a large share of their time thinking about AI)
• Other educated people who influence public debates, such as journalists or investors

Media coverage of this paper suggests it may be influential among those audiences. 

Mainly I think this paper will help inform people about the potential economic implications of AI development. These implications are important for people to understand because they contribute to AI x-risks. For example, explosive economic growth could lead to many new scientific innovations in a short period of time, with incredible upside but also serious risks, and perhaps warranting more centralized control over AI during that critical period. Another example would be automation: if most economic productivity comes from AI systems rather than human labor or other forms of capital, this will dramatically change the global balance of power and contribute to many existential risks. 

Interesting. That seems possible, and if so, then the companies did not violate that agreement. 

I've updated the first paragraph of the article to more clearly describe the evidence we have about these commitments. I'd love to see more information about exactly what happened here. 

I agree there's a surprising lack of published details about this, but it does seem very likely that labs made some kind of commitment to pre-deployment testing by governments. However, the details of this commitment were never published, and might never have been clear. 

Here's my understanding of the evidence: 

First, Rishi Sunak said in a speech at the UK AI Safety Summit: “Like-minded governments and AI companies have today reached a landmark agreement. We will work together on testing the safety of new AI models before they are released." An article about the speech said: "Sunak said the eight companies — Amazon Web Services, Anthropic, Google, Google DeepMind, Inflection AI, Meta, Microsoft, Mistral AI and Open AI — had agreed to “deepen” the access already given to his Frontier AI Taskforce, which is the forerunner to the new institute." I cannot find the full text of the speech, and these are the most specific details I've seen from the speech. 

Second, an official press release from the UK government said: 

In a statement on testing, governments and AI companies have recognised that both parties have a crucial role to play in testing the next generation of AI models, to ensure AI safety – both before and after models are deployed.

This includes collaborating on testing the next generation of AI models against a range of potentially harmful capabilities, including critical national security, safety and societal harms.

Based on the quotes from Sunak and the UK press release, it seems very unlikely that the named labs did not verbally agree to "work together on testing the safety of new AI models before they are released." But given that the text of an agreement was never released, it's also possible that the details were never hashed out, and the labs could argue that their actions did not violate any agreements that had been made. But if that were the case, then I would expect the labs to have said so. Instead, their quotes did not dispute the nature of the agreement. 

Overall, it seems likely that there was some kind of verbal or handshake agreement, and that the labs violated the spirit of that agreement. But it would be incorrect to say that they violated specific concrete commitments released in writing. 

Thanks, fixed!

Money can't continue scaling like this.

Or can it? https://www.wsj.com/tech/ai/sam-altman-seeks-trillions-of-dollars-to-reshape-business-of-chips-and-ai-89ab3db0