But if the job requires the best intelligence you can get with an LLM, then you use that.
Taking as an assumption that the quality of your product is a function of the quality of the inference you are using: if you use an inferior model because "what if it gets export controlled again" and your competitors don't, then your competitors are likely to win.
If you don't need frontier models for you job then this is all moot, but the thread started with
> You cannot build a business critical function on top of American SOTA frontier model
Which is silly. HN likes to roleplay bringing everythgin "business critical" in house because sometimes vendors mess up. Self host, don't use the cloud, run open models locally, built redundant supply chains in case of another covid, etc etc. Sometimes the risk is real, but most of the time the risk is rare and the cost of an interruption event is less than the cost of bringing everything in house or using lower quality vendors "just in case"
Beats which model in Claude? Whenever a "benchmark" doesn't put precise model numbers in their headlines I am immediately skeptical. Either they don't know the difference (bad) or they are benchmarking against weaker models (misleading, also bad).
It's like when studies say "AI is bad at X" and they used GPT-3.5 in current year.
The conspiracy version of this is each bad windows release is purposefully extra bad so the next "good" version is perceived as artifically well.
It's a shame too, I feel like the underlying OS has some really good engineering in it, but the layers of cruft and anti-features on top make for a poor overall product.
Right I get tha. The point I’m making is that from a users perspective it’s functionally very similar. A non deterministic llm or a non deterministic company full of designers and engineers.
The other theory is that helmet laws reduce casual cycling by adding an impediment to just hoping on and going. And that those short trips to the strore tend to be safer.
Personally my wife can't find a helmet that fits, Asians have rounder heads and north American helmet manufacturers are oblivious. As a result we never cycle.
Yes you use the right tool for the job.
But if the job requires the best intelligence you can get with an LLM, then you use that.
Taking as an assumption that the quality of your product is a function of the quality of the inference you are using: if you use an inferior model because "what if it gets export controlled again" and your competitors don't, then your competitors are likely to win.
If you don't need frontier models for you job then this is all moot, but the thread started with
> You cannot build a business critical function on top of American SOTA frontier model
Which is silly. HN likes to roleplay bringing everythgin "business critical" in house because sometimes vendors mess up. Self host, don't use the cloud, run open models locally, built redundant supply chains in case of another covid, etc etc. Sometimes the risk is real, but most of the time the risk is rare and the cost of an interruption event is less than the cost of bringing everything in house or using lower quality vendors "just in case"