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By continuously testing competitors and local LLMs? The reason for rising prices is that they (Anthropic) probably realized that they have reached a ceiling of what LLMs are capable of, and while it's a lot, it is still not a big moat and it's definitely not intelligence.
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Anything but the simplest tooling is not transferable between model generations, let alone completely different families.
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> Anything but the simplest tooling is not transferable between model generations, let alone completely different families.

It is transferable-yes, you will get issues if you take prompts and workflows tuned for one model and send them to another unchanged. But, most of the time, fixing it is just tinkering with some prompt templates

People port solutions between models all the time. It takes some work, but the amount of work involved is tractable

Plus: this is absolutely the kind of task a coding agent can accelerate

The biggest risk is if your solution is at the frontier of capability, and a competing model (even another frontier model) just can’t do it. But a lot of use cases, that isn’t the case. And even if that is the case today, decent odds in a few more months it won’t be

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> The cost of AI isn't.

This is why there are a ton of corps running the open source models in house... Known costs, known performance, upgrade as you see fit. The consumer backlash against 4o was noted by a few orgs, and they saw the writing on the wall... they didnt want to develop against a platform built on quicksand (see openweb, apps on Facebook and a host of other examples).

There are people out there making smart AI business decisions, to have control over performance and costs.

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The same way companies already deal with any cost.
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