Nathaniel Whittemore

But this isn't non-coders all of a sudden becoming product designers and engineers and trying to get in on the product building game.

This is people using code and websites to improve how they share things and collaborate with colleagues.

My argument would basically be that in the same way that building a slide deck or writing a document or interacting with a spreadsheet is a core knowledge work primitive, building websites and disposable web apps is also going to be a core knowledge work primitive going forward.

Codex Sites is a hyper simple version of that experience that's going to make that primitive much more accessible to a large number of people.

Like I said, I actually think that sites might be deserving of an entire operators episode to dig into different types of things that people might be able to do with it.

But if you had just one thing to play around with in the short term, that's where I'd be looking.

It's very clear that OpenAI and the Codex team see the Codex app as the new interface for knowledge work and are going to continue pushing to figure out all the implications of what you can do in this new type of environment.

But as I said at the beginning, the question of the next phase of enterprise AI is both one of interface, which we've been discussing with Codex, but it's also one of efficiency and cost management.

Uber, a company that has somehow found itself in the headlines as exhibit A in the changing tides of agentic AI, has now put a $1,500 monthly cap across token spending for all employees.

Now, I have a lot more to say about what I think does and doesn't work about that strategy, but we'll save that for another episode.

The point for us today is that cost management is that another vector of the next wave of enterprise AI is going to be cost management.

And interestingly, that seemed to be at the core of the announcements from Microsoft Build.

Nominally, the big announcement was seven new Microsoft AI models.

Image 2.5, Image 2.5 Flash, Transcribe 1.5, Thinking 1, Voice 2, Voice 2 Flash, and Code 1 Flash.

A family of models that were optimized around different sets of use cases.

And certainly just like any other time that we get model releases, there was a bunch of discussion of the benchmarks.

The headliner was MAI Thinking 1, a 1 trillion parameter model using a mixture of experts architecture for inference optimization that Microsoft tried to place as a model somewhere in the Sonnet 4.6 to Opus 4.6 type of range.

Now, to some, the discussion was just about Microsoft making progress in the model training game at all.

Wrote Sean Wang, you have to give Microsoft props for training all these in-house models from scratch and getting all of them to near state-of-the-art.

Mustafa Suleiman built a full-fledged NeoLab inside Microsoft in two years that Microsoft now fully controls from chip to model to harness.