Outsourcing plus local AI will soon become more economical vs. frontier labs

Agreed, but same data is listed right below the table.

How many of those wtf moments are simply from not “being in the room when it happened?” Most enterprise software is riddled with wtf moments demanded as one compromise or another.

Absolutely. We are in a phase of "free money" for AI. Just as with the dotcom bubble that leads to 1) lots of experimentation, and 2) lots of infrastructure buildout (which includes AI model training). Once the money dries up, some infrastructure (including models) will turn out to be profitable, most won't. And some experiments will turn out to be successful, most won't. Lots of useful things will come out of that, both the failed and the successful attempts. Just as the dotcom boom payed real dividends 5-10 years later and laid the groundwork for the world we have today

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I've been pretty happy sticking with codex 5.4 medium. I don't see a good case for switching to 5.5 at the cost of going through my token budget quicker.

There are misaligned incentives here between users just trying to get stuff done and AI companies competing on having the "smartest" model that passes benchmarks and continuously does some nobel peace price winning stuff. It's mostly overkill for the more mundane stuff normal people actually do with them. It's nice to have the option when you need that. But defaulting to that is not economical and a bit unnecessary.

There's also a difference between smart models and bigger context windows. Most of the progress in the last year was simply the context windows getting big enough to fit all/most of the stuff needed to solve issues. Before then, you had to carefully manage the context to not run out of space and they wouldn't fit much more than small hobby projects.

With sub agents, the parent agent doesn't need to be a frontier model. It can delegate to smarter agents. And most stuff it delegates shouldn't need a frontier model. Wouldn't it be nice if it could decide on a case by case basis.

The walled gardens offered by OpenAI, Antrhopic, and others currently default to one size fits all "frontier" models. This is not sustainable. They should evolve to using smaller and effective models most of the time with complexity based escalation as needed based on either estimated complexity or when the small models fail. I'm guessing some open source based alternatives to these walled gardens are probably already heading that direction.

The irony here is that with a walled garden, these companies are selling a premium experience. But in the current market that boils down to burning billions of investor cash to keep the GPUs going without much hope on profitability. Eventually surviving companies are going to have to compete on quality, cost and margins. The smart approach would be to dynamically adapt token and context window sizes instead of blindly defaulting everything to the best possible. Don't boil the oceans for a simple email summary or a simple web UI. That stuff already worked well enough with models even a few years ago.