interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/

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The mood has been a bit dour lately, so here are some less-obvious things that I expect to take shape over the next decade that I am excited about: 1. Space! Lots and lots of stuff is going to be happening in space by the mid-2030s, and right now the U.S. looks like it will be the leader by default, modulo the overhyped “race” to land a human on the moon again. Robots on the moon building and digging and cutting and adhering are more exciting than a person standing there taking pictures. The humans can come once the machines have built us habitats. 2. Increasing substitutability of hydrocarbons. This will lead to fewer supply chain disruptions and more stable, lower prices. This is a story about both supply elasticity and demand elasticity, and the geopolitical implications are underrated. Amazing for North America. 3. Private market exposure increasingly being an option for public investors, combined with Silicon Valley figuring how to use debt and other financial instruments more creatively than in the past. This seems like something America will be uniquely good at and I expect it to boost our already enviable lead in emerging technology capital allocation. 4. State and local government adoption of AI. This will vary widely by jurisdiction, but in at least some places you will see government administrative processes and public service provision have huge increases in efficiency. State and local government is where most of the American public services that people rely on are actually administered, so this is high-impact for people’s lives. 5. Advances in mathematics rapidly diffused to other fields. Historically it takes on the order of decades or centuries for discoveries from the bleeding edge of mathematics to be translated into practical applications, but I think AI will accelerate this dramatically. Lots of this will have the flavor of “small steps toward a much better world,” but it will accumulate. 6. Speaking of mathematics, lean! Lean is a programming language used by mathematicians to formally guarantee the truth of a proof. AIs can write lean and eventually they will be superhuman at it. This will have applications far beyond math, including cybersecurity and contract writing/negotiation/execution. 7. Fixing American’s shipbuilding problem by building boats instead. America has great boat-building capacity, but is awful at building ships. I want us to get better at building ships, but I am confident we can scale boat production. The Navy will soon have massive fleets of autonomous boats everywhere in the world. 8. New kinds of agriculture will become possible because of autonomy. Row-crop agriculture is heavily mechanized already, and automating the tractor, by itself, does less than you think for the economics of a farm. But with time, more labor-intensive crops will become economical. And vastly more diverse and complex crop arrangements will become possible too, which will be both ecologically, economically, and culinarily beneficial. The economics of agriculture could start to be kinder to small farms again. 9. Because of the previous item and advancements in biotechnology, logistics, and robotics, I expect that people are underrating how much healthier, more diverse, and flat-out better the American diet will be in 2035. 10. The subterranean will become an increasingly important field of human activity. New imaging sensors, including ones enhanced by an abundance of space-based imaging platforms, will make the subterranean more immediately legible. Better drilling and digging will become cheap and widely diffused. Among many other things, the opening up of the subterranean as an industrial frontier will change what we think of as a usable natural resource. 11. Greater variety and ornamentation in the physical world. Standardization is partially a function of scarce labor. Robotics can make beauty accessible again, if we choose to let it. There are many others, but I am out of time and space.

Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models “I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that. “With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor. “You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something. “That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’ “I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”

At the moment, the exposures to those two are via total return swaps on the perpetual futures.

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