Is AI Stalling Out? Cutting Through Capabilities Confusion, w/ Erik Torenberg, from the a16z Podcast

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today, I'm excited to share a recent conversation I had with Erik Torenberg, which originally aired on the a16z Podcast, about whether recent developments suggest that AI progress is slowing down or even stalling out. We begin with a discussion of recent arguments from Cal Newport, from The New Yorker, where he argued that progress on large language models has stalled, and from a recent episode of the podcast Lost Debates, where he highlighted, among other things, the negative impact that AI can have on students' learning. Now, while I absolutely share Cal's concerns about AI-enabled bad habits, like cognitive offloading, and more broadly question whether AI will ultimately prove to be good or bad for humanity overall, I think it's important to separate the question of impact from the analysis of capabilities' advances. And on the capabilities point specifically, while OpenAI's naming decisions have caused a lot of confusion, I point to the 100x expansion of context windows, the introduction of real-time interactive voice modes, the improvement in reasoning capabilities and the resulting IMO gold medals and other accomplishments, the dramatic improvements in vision and tool use, including general computer use, and the fact that today's frontier models are beginning to contribute to the hard sciences to argue that we have in fact seen qualitative advances. And in fact, while the capabilities frontier does remain jagged, and embarrassing failures are still fairly common, every aggregate measure, from the volume of tokens processed, to the size of the tasks that AI can handle, to the revenue growth we are seeing across the industry, suggest that overall progress remains pretty much right on trend. From there, we go on to discuss my expectations for AI's impact on the labor market, and why I think that some verticals, like accounting, where people really only want to buy what they absolutely have to have, will be most disrupted, while areas like software engineering might, for a while at least, maintain employment by dramatically expanding output. How advances in multimodality as they move beyond text and image and begin to deeply integrate reasoning models with specialist models in domains like drug development, material science, and robotics suggest a sort of base case for superintelligence. The possibility of AI protectionism, such as the proposed ban on self-driving cars that Senator Josh Hawley recently floated, and the possibility of a broader AI culture war. My concerns about recursive self-improvement and companies tipping into that regime without adequate controls. How much it matters that China now produces the world's best open source models, and why, though they probably will have a real impact on China's AI sector, I remain skeptical that chip export controls will make the world a better place. And finally, considering that the scarcest resource is a positive vision for the future, why I encourage everyone, regardless of their technical ability or cognitive profile, to get involved in shaping the future of AI development. The bottom line for me is that AI capabilities' advances have not stopped, and I don't expect them to stop for the foreseeable future. Frontier developers report a clear line of sight to at least two more years of similar progress, and their optimism is well supported by the last five years of history, which shows that over and over again, AI weaknesses that were expected to be hard to overcome have in practice been solved through continued scaling plus relatively minor tweaks to the core paradigm. By 2027 or 2028, I think labor market impacts will be undeniable, and it will be clear for all to see that AI models are making important contributions to science. And while there are indeed some investment bubble dynamics, and some new model releases between now and then will surely disappoint, the most dangerous thing we could do is convince ourselves that we don't have anything major to worry about. With that, I hope you enjoyed this discussion about recent AI capabilities advances and what's coming next with Erik Torenberg from the a16z Podcast.

**Erik Torenberg** (3:49)
Nathan, I'm stoked to have you on the a16z Podcast for the first time. Obviously, I've been podcast partners for a long time with you leading Cognitive Revolution. Welcome.

**Nathan Labenz** (3:57)
It's great to be here. Thank you.

**Erik Torenberg** (3:59)
So we were talking about Cal Newport's podcast appearance on Lost Debates. And we thought it was a good opportunity to just have this broad conversation and really entertain this question of, is AI slowing down?
So why don't you steel man some of the arguments that you've heard on that side, either from him or more broadly, and then we could have this broader conversation.

**Nathan Labenz** (4:19)
Yeah. I mean, I think for one thing, it's really important to separate a couple of different questions, I think, with respect to AI. One would be, is it good for us right now even? And is it going to be good for us in the big picture? And then I think that is a very distinct question from, are the capabilities that we're seeing continuing to advance and at a pretty healthy clip. So I actually found a lot of agreement with the Cal Newport Podcast that you shared with me when it comes to some of the worries about the impact that AI might be having even already on people. He goes over, looks over students' shoulders and watches how they're working, and finds that basically he thinks that they are using AI to be lazy, which is no big revelation. I think a lot of teachers would tell you that.