John Collison: belief

Transcript context

…It could be. I was in software when the software was created. I was curious about the tape actually means. It feels like… When I think about AI predictions, one thing I'm really struck by is how, still in 2026, every time you open a chat window, it's contextless. It's got no memory. Now, to be fair, it's like, guys, it's been four years. Not even four years. It's been three and a half years. Just calm down, we'll get there. But I also interpret a lot of the current enthusiasm for OpenClaw and all that stuff as this super hacky backdoor into state management where your little claw will write a markdown file of what it's doing and then look at that markdown file the next time and things like that. But it just feels like state management and memory is going to be a huge deal and that will really change the character of AI products. It's really interesting. Long context is one of the biggest bottlenecks on speed of the model. Every single token you generate, it reads through all of the previous tokens, or maybe it reads through a subset of them, but reads through a lot of the previous tokens you've written. Memory bandwidth for that is really constraining. You can think of model-level ways to solve that problem, which is to say maybe I can compress it into fewer bytes or something like that. But it's interesting that the most effective way to solve it has been—it's really a combination of everything—but the most effective way to solve it has been once you hit your 300,000 token limit, have the model go back through it and compact.…

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