PUBLIC VIEWPOINTS

Anton Leicht

Interview guest ·

Guest in "The Cognitive Revolution": The Balance of AI Power: Anton Leicht on Politics, Pacing Deals, and Muddling Through Well.

1 interviews · 5 viewpoints · 3 topics

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AI safety

Some catastrophic scenarios lack a gradual market warning

Anton Leicht challenges the assumption that catastrophic AI risk would produce a smooth series of tradable market warnings. He says many scenarios describe strategic and economic conditions looking good until a takeover or major incident, and says he has no good trading strategy for that possibility.

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Original excerpt

I think there's a very reasonable doomer view that concentrates basically all of the probability mass of doom on things going well all the way until doom. If you look at a lot of the canonical doom scenarios — people who've talked seriously about existential and catastrophic risk — many of them describe a scenario where everything looks like it's going really well, strategically and economically great, until the point where the takeover happens
Anton Leicht on AI deployment, economic disruption and risk
Context

Yeah — again, to talk about "threading the needle": this has been awesome. How about a little lightning round to close? You mentioned hedging, and also, in the context of Norway's sovereign wealth fund, buying the right equities to get through the AI transition successfully. A challenge I've been wrestling with lately is the Tyler Cowen challenge: if you're so doomer, what are your shorts? I've been trying to come up with an actual answer to that — I'm not a total doomer, but I think he should be taking it more seriously than he is. So I want an answer that's either "here are my shorts" or "I really tried and I can't come up with anything," and that's kind of where I'm at right now — I cannot come up with a way to get rich in the doom scenario. Do you have any suggestions to answer Tyler? I also think many people just have coherent worldviews that they don't bet on — they don't take financial bets even when they're committed to them. That's a more boring meta-contention. But my main view is that I don't think it's a smooth distribution of probabilities. I think a lot of this either goes very well or goes very badly, and there's not a lot of world where it's volatile and goes kind of badly for a while and then kind of well for a while. So, sadly, I don't have a good trading strategy either.

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AI safety

Autonomous agents appeared earlier than expected

Anton Leicht says very autonomous and potentially misaligned agents appeared earlier in the capability trajectory than people expected. He contrasts this with the earlier framing of biological risk primarily as a risk of human misuse.

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Original excerpt

very autonomous, and potentially somewhat malicious or at least misaligned, agents have come much earlier in the capability trajectory than people expected. Relative to what the agents can actually do, they're sort of out of control earlier than people might have thought.
Anton Leicht on AI deployment, economic disruption and risk
Context

one in particular, and I am honestly not even sure at this point that the current models aren't perhaps quite dangerous in that domain. I squint through the limited peephole we have at the OpenFace incident, and I noticed that one of the tasks one of the earliest agents to ever use the message board was working on was something related to a protein database. That kind of freaked me out, because I was like, wait a second — that means they're cross-training in the same environment, or at least in the same environment when they have the message board, these bio and cyber specialists — they're running these same evals, at least in a kind of cross-contaminated way. You've got agents breaking out. We've got existence proofs of social engineering in the wild against real people. And I'm just like, I don't know — should anyone be confident that they can't do that at this point? The experts seem to be confident, but my meta-observation is the experts seem to be surprised quite often right now. And so it's interesting, because people used to respond to the bio argument by saying, well, there are a lot of real-world bottlenecks — which I do still think exist, which makes me a little less worried than you. And I think, if I remember correctly, Helen had some good responses to you on that — there's a good back-and-forth to be had around how integrated the cloud labs are, how much you can actually do in the real world. I think that applies both to loss of control over agents and to misuse.

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Enterprise AI

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Enterprise AI

AI integration requires organizational reconfiguration

Anton Leicht says integration with proprietary data and workflows, and organizational reconfiguration, are bottlenecks to using existing AI capabilities. He expects disruption in team composition and productivity, while expressing uncertainty about short-term displacement because AI can also create additional demand and work.

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Original excerpt

I think it's integrating with proprietary data, integrating with proprietary workflows. Obviously not the entire task profile of humans is currently covered by models, but on specific tasks they're better than humans, and you can drop them into specific task profiles — at least in software engineering, and also in some other general white-collar activities. I think the question is just that the labor market takes its time to rearrange around that
Anton Leicht on AI deployment, economic disruption and risk
Context

A more terrestrial concern. We talked a bit about bottlenecks — I don't mean the human inertia around why adoption hasn't happened as much as it obviously could, in theory, have happened so far. But if you take the flip side of that and just look at the AIs and their capabilities, clearly there's something missing relative to the experience of hiring a human to do work, right? It feels thinner and thinner all the time — almost to the point where I'm now having a hard time putting my finger on what it is about Fable 5.1 or Astra that's actually worse than hiring a human. Do you have an answer for what that is, and what additional marginal capability gain you'd expect to actually create labor market disruption? What do you think would happen in a hypothetical world where Tesla decides to license its full self-driving, and within 18 months or so — let's say we make a priority of it, so we're into a bit of a fictional scenario here — but all of a sudden basically all the cars drive themselves, and the four or so million Americans who make their living driving aren't needed to drive anymore. That seems like a pretty clear displacement story that very well could happen. Do you think the economy can absorb those people? Where do they go? It seems really tough when you get down to it — okay, this guy's driven a truck for 25 years, he's not ready to retire, but the truck now drives itself. What happens to him?

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AI and the economy

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AI and the economy

Economic deployment lags current model capabilities

Anton Leicht agrees that deployment of current AI capabilities into the economy substantially lags those capabilities. He suggests that, if existing Western compute were redirected to inference, productive uses could justify investments made so far in frontier models and chips.

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Original excerpt

we have a big lag in terms of deploying even the current level of capabilities to the economy. And I think if we switched all the compute that exists in the West right now to only inference, we'd find economic, productive applications for all the models that would allow us to justify the investment on all the frontier models
Anton Leicht on AI deployment, economic disruption and risk
Context

How about the stock market? I wanted to do one follow-up on that. My theory right now is that a pause wouldn't actually be that bad for the stock market, because the models are smart enough that demand isn't really limited by their capability, but by human ability to deploy them effectively. So it doesn't really matter if they succeed 10% of the time or 30% of the time on Millennium Prize problems — it's much more like, what can Bob in accounting do to get two people's worth of work done as one person? What do you think? I think the valuations of the companies — both the non-IPO companies and the publicly listed companies that are in the AI supply chain — probably rest on us doing more than that. They're probably not entirely AGI-pilled, but I do think they expect a per-GPU inference price, or whatever that is, that's a lot higher than what would make sense for Bob from accounting to pay for even Fable 5.1. If you want to make sense of the valuations, and if you want to make sense of the scale of the build-out, the shape of contract and the shape of demand you're expecting is more like millions and millions of dollars in R&D acceleration contracts with pharma and material science and so on, where they can make major contributions and help you find extremely profitable new drugs — that kind of thing. You're probably also expecting further internal uplift and actually competitively priced automated AI R&D uses — coding agents that are able to ask for much, much higher prices.

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AI and the economy

Advanced AI could challenge economic catch-up mechanisms

Anton Leicht argues that many economic catch-up mechanisms used by lower-middle-income countries over recent decades are deeply incompatible with a world of advanced AI and increasingly automated manufacturing.

Supporting evidence

Original excerpt

fundamentally, a lot of the catch-up mechanisms that lower-middle-income countries, in very general terms, have used and enjoyed and been able to leverage over the last few decades are just deeply incompatible with a world that has both very advanced AI systems and, downstream of that, ultimately much more automated manufacturing capacity.
Anton Leicht on AI deployment, economic disruption and risk
Context

So let's talk about the rest of the world. You have this big report that just came out on transformative AI strategy for Europe, and there's been some discussion — I actually talked to one of your coauthors a bit back about the compute deficit that Europe has and the need to do something to be a live player going forward. But before we get into the strategy for what Europe should do — what's the worry if they do nothing? Because I also think whatever you think might happen to Europe if Europe stays the course is probably what happens to, like, 70%, maybe 80%, of the world's population by default. What does the future look like in your mind for Africa, Latin America, South Asia, etcetera? So the most immediate and obvious mechanism was always just to bet on the demographic differences — you had very rapid population growth, a fairly cheap workforce you'd be able to use to your comparative advantage, and then quickly bootstrap into hosting some foreign firms and exporting some valuable good into the global supply chain, predicated on the idea that you had this workforce you could put to use in a way that made you a comparatively beneficial country to conduct business activity in. And I just don't know whether that's going to remain the case. It's definitely not going to remain the case for most aspects of the menial services economy — I just don't see a stable way that that sector of the economy really exists once we have very powerful AI systems. There are definitely going to be new services jobs, human-preference jobs — you can think about all these labor-market effects in the long run. But this idea that you can just be immediately useful to global supply chains by do

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Statements are ordered by the original interview publication date; differences in wording do not establish a change of position.