“Software 1.0 I’m writing code, software 2.0 I’m actually programming by creating data sets and training neural networks… so software 3.0 is kind of about… your programming now turns to prompting. And what’s in the context window is your lever over the interpreter, that is the LLM.”
You feed raw videos, like imagine a device, he takes raw videos or audio into basically what's a neural net and uses diffusion to render a UI that is kind of like, you know, unique for that Moment in a certain sense. And I kind of feel like in the early days of computing, actually, people were a little bit confused as to whether computers would look like calculators, or computers would look like Neural nets. And in the 50s and 60s, it was not really obvious which way it would go. And of course, we went down the calculator path and ended up building classical computing. And the neural nets are currently running virtualized on existing computers. But you could imagine, I think that a lot of this will flip, and that the neural net becomes kind of like the host process, and the CPUs become kind of like the co-processor.
The models now patch this, I think, but the new one is, I want to go to a car wash to wash my car, and it's 50 meters away, should I drive or should I walk? And state-of models today will tell you to walk because it's so close. How is it possible that state-of Opus 4.7 will simultaneously refactor 100,000 line code base or find zero-day vulnerabilities and yet tells me to walk to this car wash. This is insane. And to whatever extent these models remain jagged, it's an indication that number one, maybe something slightly off. Or number two, you need to actually be in the loop a little bit and you need to treat them as tools. And you do have to kind of stay in touch with what they're doing
I think it's some kind of a combination of verifiable plus labs care. Maybe one more anecdote that is instructive is from GPT 3.5 to GPT 4, people noticed that chess improved a lot. And I think a lot of people thought, oh, well, it's just a progression of the capabilities. But actually, it's more that, I think this is public information, I think I saw it on the internet. A huge amount of data of chess made it into the pre-chaining set. And just because it's in the data distribution, basically the model improved a lot more than it would just by default. So someone at OpenAI decided to add this data, and now you have a capability that just peaked a lot more. And so that's why I think I'm stressing this dimension of it, as we are slightly at the mercy of whatever the labs are doing
verifiability makes something tractable ... Because you can throw a huge amount of RL at it. ... if you are in a verifiable setting where you could create these RL environments or examples, then that actually sets you up to potentially do your own fine-tuning, and you might benefit From that ... is fundamentally technology that just works. You can pull a lever ... I do think there are some very valuable reinforcement learning environments that people could think of that I think are not part Of the... Yeah, I don't want to give away the answer, but there is one domain that I think is very... Oh, okay, sorry. I don't mean to vague post on the stage, but there are some examples of this.
a lot of people ... want to hire strong agentic engineers. ... most people are still not refactored their hiring process for agentic engineer capability, right? Like if you're giving out puzzles to solve, then this is still the old paradigm. I would say that hiring has to look like, give me a really big project and see someone implement that big project.
Like let's write, say, a Twitter clone for agents, and then make it really good, make it really secure, and then have some agents simulate some activity on this Twitter. And then I'm going to use codex 5.5 x-high to try to break your website that you deployed ... and they should not be able to break it. ... so maybe it looks like that, right?
because I'm still part of the system, ... Somehow, information still has to make it into my brain. And I feel like I'm becoming a bottleneck of just even knowing what are we trying to build, why is it worth doing, how do I direct my agents and so on. So I do still think that ultimately something has to direct the thinking and the processing and so on. And that's still kind of fundamentally constrained somehow by understanding. And this is one reason I also was very excited about all the LLM knowledge bases, because I feel like that's a way for me to process information. And anytime I see a different projection onto information, I always feel like I gain insight. So it's really just a lot of prompts for me to do synthetic data generation kind of over some fixed data.
the remarkable fact that Mount Everest, digital logic, the feminist movement, a ship’s anchor, the Renault Laguna, the Times New Roman font, The Hunt for Red October and the Japanese Art of Origami are all linked by a simple, if not humble, piece of mathematics accessible to primary school children.
