# The mistake of conflating intelligence and power I had an interesting discussion recently. Someone asked me, what is intelligence? I said, the ability to achieve your goals across a wide range of domains. Okay, he says, then by that definition isn’t Donald Trump the intelligent person in the world, followed in quick succession by Xi Jinping and Vladimir Putin? To be clear, these people are obviously very competent and clever. But when you think of ASI, you don’t think of Trump, but more so. The person who kept pressing this question was correctly pointing out that I basically defined intelligence as power. And by this definition, Stalin was the most intelligent person who ever lived. Now, of course, you could change the definition of intelligence to something more like, manipulate abstract concepts and rotate shapes. But notice that the most powerful people in the world do not max out this quantity. The correlation between extreme power and this kind of intelligence might be even weaker than the correlation between extreme power and height. The physicists are not running the world. We tend to conflate power-seeking AI and superintelligent (in science and tech) AI. I’m not denying that AI can be power-seeking. Whatever skills and drives Donald Trump has could be embodied in a digital mind. I’m simply pointing out that the way AI systems are currently becoming smarter (by getting trained to be to be really good at specific economically valuable tasks like coding) is not that strongly correlated with power. We often talk about power in this way that misunderstands how it is actually derived in our world. Our intuitions are primed by games like Diplomacy or Go, which are designed to isolate and reward a g loaded kind of strategic reasoning. But in the real world, power is more the product of having the authority and trust to get lots of people to collaborate with you, rather than some galaxy brain scheming capability. Trump is not powerful because his brain, considered in isolation, is the most effective optimization engine on Earth. He is powerful because the government which hundreds of millions of people consider legitimate gives him a lot of authority. A group versus individual level analysis is useful here. As @GarettJones has written a lot about, individual IQ is only modestly correlated with individual income, but national IQ is strongly correlated with national outcomes. This is because intelligence has a lot of spillover effects - smarter societies cooperate more, save more, and can coordinate to build things like space shuttles and semiconductors. Richard Trevithick, who invented the high-pressure steam engine, died in poverty, buried in an unmarked pauper’s grave. But the fact that 18th and 19th century Britain had lots and lots of people like Trevithick contributed to Britain being able to set up a global empire and outcompete lots of backwards principalities around the world. It seems to me that the right mental model is that automated firms will outcompete everyone else in normal capitalist ways, rather than a single AI outthinking everyone else.

New blackboard lecture w @ericjang11 He walks through how to build AlphaGo from scratch, but with modern AI tools. Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn. Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second. Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside. Timestamps: 0:00:00 – Basics of Go 0:08:06 – Monte Carlo Tree Search 0:31:53 – What the neural network does 1:00:22 – Self-play 1:25:27 – Alternative RL approaches 1:45:36 – Why doesn’t MCTS work for LLMs 2:00:58 – Off-policy training 2:11:51 – RL is even more information inefficient than you thought 2:22:05 – Automated AI researchers

The Jensen Huang episode. 0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains? 0:16:25 – Will TPUs break Nvidia’s hold on AI compute? 0:41:06 – Why doesn’t Nvidia become a hyperscaler? 0:57:36 – Should we be selling AI chips to China? 1:35:06 – Why doesn’t Nvidia make multiple different chip architectures? Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!

The @ilyasut episode 0:00:00 – Explaining model jaggedness 0:09:39 - Emotions and value functions 0:18:49 – What are we scaling? 0:25:13 – Why humans generalize better than models 0:35:45 – Straight-shotting superintelligence 0:46:47 – SSI’s model will learn from deployment 0:55:07 – Alignment 1:18:13 – “We are squarely an age of research company” 1:29:23 – Self-play and multi-agent 1:32:42 – Research taste Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!