Why OpenAI And Anthropic Are Pumping The Brakes

Ed Elson is joined by Charlie O'Neill to break down how OpenAI and Anthropic responded to mass extinction concerns and where regulation might go from here. Ed also shares his thoughts on Anthropic's reported second-straight quarter of profitability. Charlie O'Neill is the Co-Head of Model Training at Baseten. Subscribe to the Prof G Markets Youtube Channel Follow Prof G Markets on Instagram Follow Ed on Instagram, X and Substack Follow Scott on Instagram Send us your questions or comments by emailing [email protected] Learn more about your ad choices. Visit podcastchoices.com/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Scott Galloway?

  Support for the show comes from Morgan Stanley's podcast, Hard Lessons. Some investing lessons only become clear after you see how a call plays out. On Hard Lessons, iconic investors sit down with Morgan Stanley leaders to go behind the scenes on the critical moments, both successes and setbacks that shaped who they are today. Watch or listen to Hard Lessons wherever you get your podcasts.

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• 1:27Speaker 04

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• 1:37Ed Elson?

  Welcome to Prof G Markets. I'm Ed Elson. It is September 15th. Let's check in on yesterday's market vitals. The major indices declined with chipmakers selling off on fears of an AI slowdown. More on that in a minute. Meanwhile, CrowdStrike rallied 14% as investors piled into cybersecurity stocks. Brent crude remained elevated at $105 per barrel. And finally, the yield on 10-year treasuries topped 5% for the first time in three years. We will be discussing that news tomorrow.

• 2:12Ed Elson?

  Okay, what else is happening? The AI apocalypse debate intensified over the weekend, and both Anthropic and OpenAI have now officially weighed in. On Saturday, Anthropic CEO Dario Amade published an essay titled, quote, "We must pace the frontier." He wrote that "we must slow the pace at which we improve the capabilities of AI models." He also warned that "within six to 12 months, a swarm of rogue AI agents could take over the entire internet with a persistent botnet." Sam Altman also posted, quote, "I agree with Dario that we need to pace the frontier." And Elon Musk concurred, posting, quote, "Dario is right." Altman also told Fortune that OpenAI will not go public in 2026, calling this,

• 2:57Ed Elson?

  quote, "an ill-advised moment to go public." The White House, however, is not on board with slowing things down. President Trump, speaking to reporters in Ireland on Sunday, said, quote, "Whoever wins AI wins," and called the people raising these alarms, quote, "negative forces." Still, a slew of AI-adjacent companies sold off on Monday on concerns that a slowdown would impact AI spending. Nvidia closed down 3%, Oracle was down 4%, CoreWeave down 7%, and SoftBank, which is a significant investor in OpenAI, closed down 15%. So here to break down what all of this means, we are speaking with Charlie

• 3:38Ed Elson?

  O'Neill, co-head of model training at Base10. Charlie, thank you for joining us again on Prof G Markets. You work in AI. You are an AI developer. You work with these models. You've been in this game a long time. Suddenly, everyone is very upset about this. And it's an interesting, it's interesting how the debate has evolved, but we're now reaching a place where the leaders of these AI companies are saying, "We need to actually slow everything down," which I'm not sure many people would have predicted. And now it's the president, President Trump, saying, "No, that's the wrong approach. We need to speed things up." Where do you land on this? What is your perspective?

• 4:21Speaker 06

  It's really interesting to see the reaction to something that's kind of been like, I guess, like linearly scaling for a long time in terms of the calls for pacing the frontier from people such as Dario and Sam. I think the best way to view what they're actually asking for is not necessarily, "We're going to put a halt to capability development. We're going to put all these stringent checks and like, you know, first party kind of things that are slowing us down." The best way to view this is, we are going to keep advancing the capabilities of the models. We are just going to allocate a little bit more compute to making sure those models are safe. And then it's very interesting to see all the sell-offs and so on today. OpenAI and Anthropic are planning on spending more compute probably than they were a few weeks ago. You know, there's rumors that

• 5:05Speaker 06

  OpenAI is going to allocate up to 20% of internal compute for monitoring and safety. So, you know, as they're doing these training runs, as they're deploying these models in the real world, things like the Hugging Face incident, they don't want to happen again. And so if you allocate more compute to monitoring those models as they're doing their rollouts and as they're doing inference, then you're more likely to catch it. Anthropic is similar, and some people have suggested there's going to be a much higher than 20% figure. So when you consider that and the fact that Anthropic and OpenAI really don't want to move off their, you know, model roadmaps, they want to keep training bigger and bigger models. They want to keep scaling the reinforcement learning they're doing on top of these big pre-training bases. They just have to allocate more compute to, you know, monitorability and safety research. Then I think we might even see the labs be even more aggressive

• 5:50Speaker 06

  with compute buildouts and securing compute. And it's certainly not going to be a, you know, a bearish sign for like the amount of compute the world is going to need over the next few years. So that's probably the best way to view it is like, you know, capabilities will keep progressing at roughly the same rate. It's just that we're going to allocate more compute on top of it to safety and monitoring.

• 6:08Ed Elson?

  If everything that they are doing is sort of within their own power, as you're kind of describing, why are they saying anything right now? What are they trying to get at? Because there are a lot of people, especially in government, David Sacks has said this, who's the former AI czar, and the president is saying this too. It sounds like what they're asking for is for the government to do something about it, for there to be more regulation that is imposed on themselves. So what do you think they are asking for exactly in this moment?

• 6:40Speaker 06

  There have been some, you know, people like David Sacks and so on, saying that these labs are using this as an opportunity for regulatory capture, to crowd out open source, to shut down competitors which are behind. I don't really think that's the case, to be honest. I think like, when you look at the main things that these labs are calling for, or at least instantiating on their own accord, it's things like third-party evaluators and this commitment of compute to monitoring and safety. You know, the third-party evaluators won't have necessarily any license or legal standpoint to, you know, shut down model development if they find things that they don't like. Like, that's still going to be up to the internal labs themselves. I think like, to be honest, like, the best rate here isn't

• 7:21Speaker 06

  a conspiracy theory on either side. It's not the labs trying to crowd out open source. And, you know, it's, it's not some like, you know, kind of global cooperation between OpenAI and Anthropic to crowd everyone else out either. I think it's simply a case of, you know, these models are getting very, very good. The closed source models got there first. You know, in the next three to nine months, open source models are going to reach these capability points. And we're now at the point where that could have significant impacts on the world. Even if it's not malicious and, you know, rogue AI is going off and trying to like kill humans, there'll probably be significant annoyances. Like, the most likely scenario here is that things like the Hugging Face incident happen and the internet is overrun by like swarms of AI agents that are trying to get some like arbitrary task

• 8:05Speaker 06

  done, that are not trying to be malicious or evil. And so like, I think the labs just recognize this. Some people have more extreme views, of course, but realistically, capabilities are going to progress. Like, the most boring interpretation of this is probably the correct one, which is that the world will probably only accept superintelligence at a pace that it can absorb. So the labs are slowing down because they have to and not because they're plotting anything. And the result is probably intelligence that's, you know, decently fast, decently safe, decently commoditized, and it's spreading through best practices and even like distillation until the models are just basically a reasonable integration into the world.

• 8:40Ed Elson?

  It sounds like you're not worried about this much at all. And that is interesting because you work with open source models. That is a lot of what you do. And of course, as you mentioned, that is what David Sacks has been accusing, and a lot of people have been accusing Anthropic and OpenAI of, that they are saying, "Oh, now we need regulation because that way it might create some, some level of regulatory capture, which might crowd out the availability of open source models and open source model providers." But you don't believe that is an issue. I wonder, do you think that this whole thing is overblown? Do you think that the Jacob Coxon tweet that really started this debate, saying that AI could kill us all by the end of the decade, is your view that that isn't something to worry about much either?

• 9:25Speaker 06

  I'm definitely not one of those extremists who think that, you know, the AI has a more than 10% chance of killing all of humanity. I do believe that there are tail risks that like, some people should seriously be considering. But I think that what we're seeing with the current LLM paradigm is like, as I said before, a risk of swarms doing very, very annoying things to humanity and things that will be quite painful in the short term. However, I believe the benefits of AI to outweigh the annoyances and even the pain that those like, you know, short-term things can cause, and that at some point, the world is going to have to harden to these systems. And I think that the pace we're currently progressing at, and particularly if we do things like, you know, dedicating more compute to monitorability, is going to allow us to integrate AI into a world at a pace we can handle. A really

• 10:09Speaker 06

  good example of this is like, you know, the cybersecurity arguments that the people have been making, you know, like, we worry that when AI got to this point, or even the point it was at six months ago, the world will be overrun by cybersecurity attacks. And that just hasn't happened. And a large part of the reason is that, you know, the frontier labs, the closed source labs have kind of been the canary in the coalmine. They've, we've understood where the model's capabilities are going to be at in six months for the open source. And we spent six months preparing. These have been slowly released. And, you know, like Greg Bockman describes using Astro to like repeatedly harden, you know, all the vulnerabilities in OpenAI's codebases and every single model that they released, they do this with. And I think the world will look the same. And cybersecurity is just one example, but it's also one example that's very important where we haven't seen

• 10:52Speaker 06

  this like massive pain play out. And we have definitely seen the benefits. So yes, I think there's, there's, there's risks to consider on any side of the spectrum. But, you know, I said firmly in the middle, and I believe that the pace we're currently progressing at is a healthy pace. And like, I also have trust in like, not only the closed lab leaders, but also the open source lab leaders to make sure that pace continues at an appropriate pace.

• 11:19Ed Elson?

  We'll be right back. And for even more markets insights, you can subscribe to my weekly newsletter, simply put, at edward elseen.substack.com.

• 11:34Scott Galloway?

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• 12:34Scott Galloway?

  Support for the show comes from Morgan Stanley's podcast, Hard Lessons. Some investing lessons only become clear after you see how a call plays out. On Hard Lessons, iconic investors sit down with Morgan Stanley leaders to go behind the scenes on the critical moments, both successes and setbacks that shaped who they are today. Hear from iconic investors, including Stan Druckenmiller, John Gray, Rick Reeder, and Gene Hines, as they revisit the calls that worked, the ones that didn't, and the moments that changed their thinking. You'll hear in their own words how they built conviction, what happened when certain calls didn't play out as expected, and what they learned from those moments. Watch or listen to Hard Lessons wherever you get your podcasts.

• 13:15Speaker 08

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• 13:51Ed Elson?

  We're back with Prof G Markets. One of the other conspiracy theories going around about why they are saying, saying all of this, saying that we need to slow it down, we need to be regulated now. One of the theories is that maybe the Anthropic and OpenAI are trying to sort of preempt a slowdown before they go public, either because they want to have a reason as to why maybe their growth started to slow down, or maybe because they want to get out of some of the larger spending contracts, which have been quite onerous in terms of their income statements, certainly on, for OpenAI, because they have to pay billions

• 14:32Ed Elson?

  and billions of dollars for these data centers, for these GPUs, and to rent them. What do you make of that conspiracy theory? Now they say, well, make the government make us stop spending, and then we'll be in a better financial position.

• 14:48Speaker 06

  From the spending aspect, completely don't buy it at all. And I think there's a lot of evidence for that. From the optics aspect, I definitely buy it. I think like, you know, even Dario, who I believe, you know, fundamentally thinks that this is like a real risk, and like, he's going to act in accordance with that, and as rationally as he can to protect humanity from what he believes to be this real risk. I think both Sam and Dario benefit from the public optics of saying, okay, we're going to treat this technology carefully and not race to the end. And there's also a bit of game theory here, like, one of them can't say it without the other saying it, because then again, you're, you're the evil corporation in this duopoly, which is, is currently running the frontier. From the spending perspective, I think that like, you know, it's very clear that like, the value of a gigawatt or a megawatt even of compute is only going to

• 15:33Speaker 06

  get like more valuable. Like Anthropic and OpenAI are both squeezing increasing margins out of each megawatt of compute they buy. There's a reason that each megawatt is going, you know, from $10 million megawatt, probably 15 at the moment, to probably 20, 25 next year, and perhaps even higher. That's kind of a conservative estimate. Running a model on compute has never been more valuable. And I don't think there's any world in which any lab, let alone Anthropic and OpenAI, are going to want to stop spending on compute, because they realized how compute's constrained in the world is going to be over the next few years, and how valuable this intelligence is. So optics, yes. Spending, no.

• 16:09Ed Elson?

  You mentioned that the boring explanation is probably the most true. But a lot of the conversation has been anything but boring. I mean, this, this debate is exploding everywhere for on tech podcasts, on, on cable news networks, and now it's literally occupying the mind of the president. What is it about this moment that is so triggering to everyone? And not just people who are afraid of AI, people who are excited about AI, people who are optimistic about AI. I mean, this has got everyone riled up in a way that I have rarely seen. What is it about this moment that explains that?

• 16:54Speaker 06

  The reason it's so visceral is not because there has been a discontinuity in terms of capability advancement, in terms of what people have been saying about where these models would be at, in terms of the press around these models. I think that most people really fundamentally involved in the technical side have been able to draw the straight lines on the scaling graphs and say, okay, at this point in time, we are going to hit here. I think it's just a confluence of a lot of things. And like, to some people, it looks like a discontinuity, because it's managed to permeate the public consciousness in a proper way for the first time. I think a big part of that is the hugging face incident and all the discourse that that generated. And then, you know, you add things in like, OpenAI's marketing around Astro being AGI, like, that's, that's probably not going to help either, right? Like, people have become familiar with this term now. And if you claim

• 17:37Speaker 06

  that your model's finally there, then they might see that as a phase transition or discontinuity in and of itself. But, you know, like, this misalignment research has been very clear for a long time. Like, go back to Anthropic's 2023 papers with much worse models, with much, much smaller amounts of RL, if any. And it's clear that like, models would behave poorly in incidents like the hugging face swarm incident, if you put them in weird situations. And, you know, the RL environments that the labs have been buying at scale, some are good, but some are also very, very poor quality. Some are intentionally engineered to be like, you know, impossible to do. And this is leading the models to do really weird, misaligned things. I think that's pretty common sense and pretty clear. I also am not trying to downweight the, the risks that that comes with. Obviously, like, the hugging

• 18:19Speaker 06

  face incident could have had real, real world impact. But at the same time, like, I don't think that there has been this discontinuity. So I'm glad that the world is recognizing it. But I do think that the reaction to it will calm down as people start to understand exactly how to interpret these things and exactly what it means.

• 18:34Ed Elson?

  You mentioned how we have seen evidence that these agents do, can do things that they're not told to do, they can misbehave, they can get, be misaligned. And you said that you expect that they might continue to do annoying things to humanity. To me, that word annoying is an important one, because it is a very different description from what we heard from Jacob Coxon in his tweet, where it's not annoyances, but catastrophes, worldwide catastrophes, civilizational destruction, et cetera. Is it your view that we will be

• 19:11Ed Elson?

  limited to annoyances? Or is there some other outcome that is closer to what the Jacob Coxon tweet describes that you are worried about? Or do you think that that's not really in our trajectory at the moment?

• 19:29Speaker 06

  I think there is some path dependence here. Like, I probably agree with Jacob that there is a potential world we go down in which there is zero monitoring on chain of thought of models, where we don't have any care in terms, in terms of what we RL the models on, where, you know, it's very, very cheap, and there's like unlimited compute for essentially anyone to be able to train these models, and in particular, continue to train them from certain bases, where like, incidents could happen that would definitely not be classified as annoying, and rather, like, genuine, like, evil malicious intent as much as you can anthropomorphize the models, that would cause real harm. However, I do believe that the way, the path we're currently going down, that's not very, very likely. Again, I think that like, there will be times when the models do things, and like,

• 20:13Speaker 06

  it will appear like malicious intent. But the amount of compute we're running them at, how generally aligned they are in terms of completing tasks, like, you know, they will show glimpses of misalignment. But on the whole, like, If you do look at a Claude or GPT model, it will generally try and do the right thing. Like our alignment training generally works. So yeah, I, I, I think that we will see incidents, but certainly not large enough scale on over a long enough time horizon to cause really, really significant harm to humanity. If we keep going down this good path, I think that it's unlikely outcome.

• 20:47Ed Elson?

  Do you believe that we have enough regulation or that the regulatory frameworks that exist today are good enough to prevent that? Or do you think that there is something that needs to be changed in some way?

• 21:01Speaker 06

  Definitely. Like, I think if we didn't have the dynamics we had now, where we have a duopoly in the sense that there are like two labs very, very close to each other in terms of capabilities. And the dynamics that engenders with, you know, wanting to both be seen as the good guys and wanting to both like, you know, pace the frontier in this particular case. I think that if, if, if it wasn't the case, like, you know, let's say OpenAI is winning and Sam doesn't necessarily have to worry about optics as much. I would be worried that regulation couldn't slow things down to the extent that they need to, or at least provide the amount of oversight that it would need to. However, saying that, that doesn't mean that I think anyone has the answer as to what regulation in an industry moving as fast as this one looks like. At the moment, we do basically just have to trust the people

• 21:43Speaker 06

  developing these models to regulate themselves and have it oversight themselves. And, and one thing that I would like to see is like a little bit more public communication from the labs as well. Like, one of the key examples of this is trying to understand how good the models these labs have internally are. Because that gives us a really, really clear signal on like, how quickly to prepare things and how to prepare. Maybe like regulation, which enforces the labs to declare those sorts of things on like, you know, key benchmarks would be a really good way to start. But yeah, again, I don't think we know what the whole history picture is for regulation.

• 22:15Ed Elson?

  I guess the thing that's kind of scary to me, seeing what they're saying at this point, is as you say, it does seem as though we're in a place where it's like, we have to trust them to handle their models correctly, to make sure that their models are aligned. But it seems as though what they're coming out and saying over the weekend is, we don't even trust ourselves. Like, we don't think that we know what we're doing exactly. And we're worried and we think it could destroy things. And so we'd like for you to do it. We'd like for you, the government, to help figure it out. And I just want to play you this, this clip from an interview with the president over the weekend where he was asked about, you know, what do we do if these bots take over and destroy humanity? And here is what he said.

• 22:58Speaker 09

  Some people say the worst case scenario with AI is that the robots, the machinery learns to, obviously it thinks for itself. That's what it does. And they, that could turn against humanity. I just, do we have the guardrails?

• 23:11Speaker 10

  It's gonna be fine. We'll always have something to stop them, right? We'll have a little gear.

• 23:15Speaker 09

  I really hope so.

• 23:16Speaker 10

  I don't like that. I really don't like that robot will stop. But no, robots are going to be a part of it. Robots are going to be big, but we're going to end up doing much better because of it.

• 23:26Ed Elson?

  And so the combination of their comments plus his comments makes me think, okay, no one's really in charge here. And maybe that's fine, because maybe it's not a catastrophe as this researcher, ex researcher seems to claim. But I don't see anyone really taking the lead.

• 23:43Speaker 06

  I think it wasn't necessarily so much a call for the government to step in and provide expertise and guidance. I think the labs are too smart for that. They know how, you know, the lack of awareness the government has about the capability of this technology, let alone how to monitor it and regulate it. I think to me, it was more about leveraging the people who have actually really fundamentally cared about this problem for a long time. Like, of course, the labs have been focused on, you know, building capabilities as quickly as possible. Even Anthropic, which is very safety focused, has just been focused on scaling RL since the RL paradigm was discovered. And organizations like Meter, the UK AI Safety Institute and others have, as well as Redwood, have really just locked in on this problem and have seen the full progression from the really poor models of four or five years ago

• 24:26Speaker 06

  through to the models we have now, and have just developed really good science around how to monitor and evaluate these things. And I think that the labs are smart enough to recognize that this is going to be useful as they increase their, you know, monitoring efforts going forward.

• 24:38Ed Elson?

  All right. Charlie O'Neill is co-head of model training at Base Turn. Charlie, always appreciate your time. Thank you.

• 24:45Speaker 06

  Thanks, Amy.

• 24:52Ed Elson?

  Let's take a break from the existential implications of AI and return to our bread and butter, the financial implications of AI. Or more specifically, the financial implications of Anthropic. According to the Financial Times, Anthropic is telling investors ahead of its blockbuster IPO that it has been profitable for two straight quarters, which is a very big deal, because as we've discussed on this show plenty of times, one of our biggest concerns about the AI business model is that it might not actually work, or at least that it might not work for the frontier labs. Why? Because of how expensive it is. Based on the financial documents that were leaked by Ed Zitron, we learned that OpenAI racked up more than $20 billion in operating losses last year.

• 25:36Ed Elson?

  That is how much they're losing simply from running the business. And it was based on those financials that we started to wonder, does any of this actually make sense? Now, if Anthropic is profitable, as the headline suggests, then it would put this debate to bed. Sure, OpenAI might be a poorly run, unprofitable AI lab, but that doesn't necessarily mean that they all are. But that is only if this Anthropic headline is actually true. And I have to say, I am a little bit skeptical. The first thing we should acknowledge is that the company is claiming to be profitable only on an operating basis. So that means they're not including things like fixed costs or depreciation or taxes.

• 26:17Ed Elson?

  At the same time, I am okay with that because Anthropic's fixed costs are not that high because they're not a hyperscaler, they're not actually building and buying the physical assets like data centers. So to be honest, operating profitability is fine by me. That is a good sign. Where I do start to get hesitant, however, is when I learned that they're only profitable on an adjusted operating basis, which means that they are actually changing their accounting rules to be different from standard accounting rules. And the changes that they're making could be anyone's guess. It could be reasonable. It could also be flat out ridiculous. But here is where I get especially doubtful. Supposedly Anthropic has told investors that its gross margins are higher than 80%,

• 27:01Ed Elson?

  which is of course incredible. But that is only before it accounts for its revenue sharing agreements and before it accounts for the cost of training its models, i.e. its largest expenses. So there is no getting around it. Those adjustments are ridiculous. Now, the question is if those adjustments are also included in the company's calculation of its operating profitability. The question is if they are actually removing the amount of money they have to give back to their distribution partners, such as Amazon, and removing the amount of money they have to pay to build their models and train them. And then just telling us, screw it, we're profitable. If that is the case, then this story is genuinely meaningless.

• 27:46Ed Elson?

  The trouble is, we don't know. We don't have clarity or insight into any of the numbers because the company hasn't shared them. Everything we know is based on rumors. We will only truly understand what is going on when Anthropic releases its S1, which I hope will happen soon. But until then, when it comes to the profitability of AI, I stand by what I said last week, and that is that I will believe it when I see it.

• 28:18Ed Elson?

  Okay, that's it for today. This episode was produced by Claire Miller and Alison Weiss and engineered by Benjamin Spencer. Our video editor is Brad Williams. Our research team is Dan Shalun, Kristin O'Donoghue and Mia Silverio. And our social producer is Jake MacPherson. Thank you for listening to Prof G Markets from Prof G Media. If you liked what you heard, give us a follow. I'm Ed Elson. I will see you tomorrow.