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

> But is the capability difference enough [..]

This is the (m/b)illion dollar question, isn't it? I think there's also a question of what do you think capability is exactly, and how the difference manifests itself.

On the one hand, when something becomes "good enough" that's a clear capability threshold. On the other hand, what's the limit of those capabilities, and equally as important, how does capability reflect on reliability?

We've seen "local models" lately improve on capabilities where they're "good enough" for some tasks. Reliability of solving those tasks is a bit harder to measure/benchmark/test. It'll get better as more people work with those models. But, something I've noticed in the past ~6months is that the frontier models are gaining a lot in both the breadth of capabilities, as well as the reliability of solving those tasks that they're capable of solving. I think this is where scaling (both compute and data) is showing, and where having more compute is simply better (more parallel exploration, more training data output, more broad data, etc).

There's also the problem of benchmarking true capabilities. The popular ones are getting old, and aren't as reliable as they used to be (not even touching on the subject of benchmaxxing, just thinking about their saturation, even with honest intentions).

So the question then becomes what will users prefer? Do you get the best of the best, or the one that's good enough? There might be a market for both, honestly. Not everyone does SotA stuff. And a lot of what people used to do in a company is probably mundane enough that a "good enough" model with "good enough" reliability can probably handle (w/ some supervision ofc).

What I'm more interested in is if things like Thaalas succeed and they get to provide local hardware that runs models "burned in silicon". That would be interesting, because speed and all the advantages of local models are a "quality" on their own. For example, right now I'd pay ~1k$ for an external hdd-sized block that can run a ~32B model that's popular right now, even knowing that it can only run that model. I have no idea if that's feasible or not, if it makes sense from a financial pov. But I'd buy one. And local inference on dedicated chips doesn't need to be "oss only". I'm sure oAI / etc would probably take the risk of licensing one of their -mini / -lite models provided that the risk of the weights leaking is small enough (and it probably is).

> This keeps a ceiling on how much or how fast the frontier labs can raise prices.

I generally agree, but from a different perspective. Up till now we've seen that the 3 labs influence each other's price points. When gpt5 came out at a radically smaller price, the others lowered them as well. Now with opus being SotA for coding, w/ 5.5 close behind, they've raised them back. Google seems to follow slowly. But there's hope that, being 3 top labs + 2 trailing (xAI & Meta), there'll be pressure once again. If any of those trailing labs manage to get to SotA again, the prices will drop once more. Some people say that open source also provides a pressure here, but I'm not yet convinced of this. There's still a question of who'll serve the models, at what scales, etc.

What about it? Energy production basically has to equal energy consumption in the medium term, so if the grandparent comment is correct, it is 2x per capita.

Dunno how trustworthy this source is, but it says ~35 MWh/person in China and 77 MWh/person in USA.

https://ourworldindata.org/grapher/per-capita-energy-use

"offshore Indian devs" are no slouches. They have access to the same GPT models and likely cost a tenth of the median US salary. Businesses are always looking to lower marginal cost. They will hire 1 software architect in US to write specs and 10 software developers in India to babysit 100 agents.

The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding _that_ new user costs ~$20k/year (assuming 100k server and 5 year depreciation).

This is true for traditional SaaS too, but the number of concurrent users that could be served by one machine and the cost of the hardware were both at least an order of magnitude better.

My friend is an exec at a US software company and they are preparing to lay off a few teams of programmers in their Eastern European locations and replacing them with a small number of US programmers + AI. He said they are much more productive and produce new features much faster.