Is AGI Far? With Robin Hanson, Economist at George Mason University and Researcher at Future of Humanity Institute

**Erik Torenberg** (0:00)
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**Robin Hanson** (0:45)
We're on this upward growth trajectory. We have the potential to taking a big chunk of the universe and doing things with it, and I'm excited by that potential, so I want us to keep growing.
I see how much we've changed to get to where we are. My book, Age of Em, is about brain emulations. That's where you take a particular human brain and you scan it in fine spatial chemical detail where you fill in for each cell a computer model of that cell. If you've got good enough models for cells and a good map of the brain, then basically the IO of this model should be the same as the IO of the original brain. If we can get full human level AI in the next 16 to 90 years with the progress, then this population decline won't matter so much because we will basically have AIs take over most of the jobs and then that can allow the world economy to keep growing.

**Nathan Labenz** (1:32)
Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz joined by my co-host Erik Torenberg. Hello and welcome back to The Cognitive Revolution. My guest today is Robin Hanson, Professor of Economics at George Mason University and author of the blog Overcoming Bias, where Robin has published consistently on a wide range of topics since 2006, and where Eliezer Yudkowsky published early versions of what has become some of his most influential writing on AI.
Robin is an undeniable polymath whose approach to futurism is unusually non-romantic. Rather than trying to identify value buddies, Robin aims to apply first principles thinking to the future, and to describe what is likely to happen without claiming that you should feel any particular way about it. I set this conversation up late last year after my deep dive into the new Mamba state space model architecture, because Robin's 2016 book The Age of Em, which analyzes a scenario in which human emulations can be run on computers, suddenly seemed a lot more relevant. My plan originally was to consider how his analysis from The Age of Em would compare to similar analyses for a hypothetical age of LLMs, or perhaps even an age of SSMs. In practice, we ended up doing some of that, but for the most part took a different direction, as it became clear early on in the conversation that Robin was not buying some of my core premises. Taking the outside view, as he's famous for doing, and noting that AI experts have repeatedly thought that they were close to AGI in the past, Robin questions whether this time really is different, and doubts whether we are really close to transformative AI at all. This perspective naturally challenged my worldview, and I listened back to this conversation in full to make sure that I wasn't missing anything important before writing this introduction.
Ultimately, I do remain quite firmly convinced that today's AIs are powerful enough to drive economic transformation, and I would cite the release of Google's Gemini 1.5, which happened in just the few short weeks between recording and publishing this episode, as evidence that progress is not yet slowing down. Yet, at the same time, Robin did get me thinking more about the disconnect between feasibility and actual widespread implementation and automation. Beyond the question of what AI systems can do, there are also questions of legal regulation, of course, and perhaps even more importantly, just how eager people are to use AI tools in the first place. When Robin reported that his son's software firm had recently determined that LLMs were not useful for routine application development, I was honestly kind of shocked, because if nothing else, I'm extremely confident about the degree to which LLMs accelerate my own programming work. Since then, though, I have heard a couple of other stories, which, combined with Robin's, helped me develop, I think, a bit better theory of what's going on. First, an AI educator told me that failure to form new habits is the most common cause of failure with AI in general. In his courses, he emphasizes hands-on exercises, because he's learned that simple awareness of AI capabilities does not lead to human behavioral change. Second, a friend told me that his company hosted a Microsoft GitHub salesperson for a lunch hour demo, and it turned out that one of their own team members had far more knowledge about GitHub Copilot than the rep himself did. If Microsoft sales reps are struggling to keep up with Copilot's capabilities, we should perhaps adjust our expectations for the rest of the economy. And third, in my own experience helping people address process bottlenecks with AI, I've repeatedly seen how unnatural it can be for people to break their own work down into the sort of discrete tasks that LLMs can handle effectively today. Most people were never trained to think this way, and it's going to take time before it becomes common practice across the economy.