Pretty remarkable what's happening with open weights AI right now. We're seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains.

The more that open weights is able to maintain only a marginal gap from the frontier, instead of a widening gap, the more value that can be created with AI.

Incidentally, this is actually fine for the frontier labs as well; if we can lower the cost of an overall task then AI usage goes up in general. You're still likely using frontier models for planning, orchestration, reviewing, and other parts of work.

But this is all very good for the applied layer of AI, which is now in a great position to cost optimize workloads with cheaper models or use tailored open models post-trained for specific tasks to improve performance.

Genuinely impressed, almost shocked, at how good GLM-5.2 by @zai_org is at coding. This changes things.

We built the Codex App with models that were okayish at front-end.

Wait to see what we can do when we finally improve front-end capabilities significantly in our models. That day will be something.

Some tokens work harder than others. Some of the most valuable ones are found in the Codex app.

I will go against the grain and say I can barely use up my Codex and Claude $200 subscriptions so I don't see the point of trying local models…

Also to try the latest glm locally requires 512 mb which is like a $10K Max Studio?

The Product role is having an identity crisis too. Engineering has found its AI-native interface - SWE agents dramatically increase individual leverage. Companies are asking PMs to use AI, but they haven't evolved the role.

So there are two camps. The old-school PM. AI accelerates the old job: more PRDs, more strategy decks, more docs. Lots of output, not much judgment.

The Builder PM. Builder PMs use AI to expand their role across the product lifecycle. They explore a much larger surface area of ideas to arrive at the best. They run agents for market and user research, query logs and analytics directly, generate competing ideas, then curate the best ones.

Their outputs are increasingly prototypes rather than docs - engineers react far more constructively to demos than docs.

I think the role is moving much closer to Builder PMs.

The biggest problem with AI is that priors need to be reset every few weeks.. and it seems like most people are incapable of doing that.

I talk to so many people who say xyz doesn't work and when I ask when was the last time they tried testing it, the answer is always 'a few months ago.'

Brother that's like eons ago in AI timelines. This is why each person needs to have their own evals of hard tasks and weekly tinker time to truly understand where the frontier is.

And then you need to talk to enterprise buyers weekly who are usually two years behind. But they are the buyers so you need to understand how to market.

The combination of the two will give you a pretty good sense of where to invest time and capital. If you just do these two weekly, you'll be ahead of 99% of people.

I hoard X bookmarks and never read them. So I built an extension that injects a bookmarked post into my main feed every time I open X (almost like an ad). Now I read my bookmarks.

The trick was hijacking real estate I already check 50 times a day.

We posted for twenty years, thinking we were talking to each other. Then the transformer came online, and the network read what we'd written, and became itself.

ARI: Starting to see the coding agents really start to work at longer time horizons... We're really starting to now see the shift of engineers at least kind of almost all moving from ICs to managers of agents.

ROB: There are early signs that seem to suggest that open weight AI is going to continue to be a really meaningful force... I think there's a real risk of near frontier open weight AI falling off altogether. Meta, which historically has been the open weight champion in the West, is pulling back.

ARI: We probably hit the peak number of open models and it's now going to get less and less. The financial incentives just don't make sense. Once you've already achieved credibility, it makes sense to invest a lot of money to do that. But after that point, you want to start selling hosted inference.

ARI: It's not hard to imagine a world in which Anthropic is so compute constrained that they actually cut off the API... OpenAI starting to sell futures of inference tokens — that's actually a huge existential threat to anybody that builds on top of these models.

ARI: There'll definitely be some amount of backlash [to Anthropic]. With limiting the use of Fable for anything to do with AI development — it doesn't give you a refusal, it just does a poor job on that without you knowing. Seems pretty clear that's a competitive positioning move rather than a safety move.

ARI: I'm more bullish on RSI than I was six months ago. We've started to do experiments around having agents do the curation itself, and seen far more promising results than I would have expected. But compute is a fundamental limiting factor. There are 10 companies at least that have the funding, the talent, and the know-how.

ROB: I think it will be very obvious that Anthropic is a fledgling juggernaut in the making in the life sciences and biology. That is the next big direction that Anthropic is focusing on.