"Frontier models" are caught in a financial dilemma of their own making --- they have spent such huge sums on development and as a result, they may have inadvertently priced themselves out of the market.

Energy costs are a huge factor for AI. He who has the lowest energy costs will likely be able to dictate market prices. And fossil fuels dependence doesn't look to be advantageous for AI.

1. I remain unconvinced LocalAI can work well for majority of businesses. It looks vaguely comparable on benchmarks, but it tends to be fragile and a lot of management overhead in reality.

2. Similarly, while Deepseek is comparable to Opus/Codex on benchmarks, for agentic work at scale I definitely notice the difference. That's not to say it's not economical, just that I definitely miss the big boys when I swap.

I kind of wish this was true, because the UK would be in a great place to compete with the US. But somehow people are happy to pay 3x the salary for an engineer in SF.

The contradiction here is that without frontier models, there'd be no foundation for models like DeepSeek to reference and catch up to. Is there an economic model that captures this kind of dynamic?

LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.

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.