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That post doesn't address the human factor of cost, and I don't mean that in a good way. Even if AI costs more than a human, it's tireless, doesn't need holidays, is never going to have to go to HR for sexual harassment issues, won't show up hungover or need an advance to pay for a dying relative's surgery. It can be turned on and off with the flip of a switch. Hire 30 today, fire 25 of them next week. Spin another 5 up just before the trade show demo needs to go out and fire them with no remorse afterwards.
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The cost to hire a human is highly predictable. The cost of AI isn't. I, as a human, need food and shelter, which puts a ceiling to my bargaining power. I can't withdraw my labour indefinitely.

The power dynamics are also vastly against me. I represent a fraction of my employer's labour, but my employer represents 100% of my income.

That dynamic is totally inverted with AI. You are a rounding error on their revenue sheet, they have a monopoly on your work throughput. How do you budget an workforce that could turn 20% more expensive overnight?

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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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If you're talking about APIs and SDKs, whether direct API calls or driving tools like Claude code or codex with human out of the loop, I think that's actually fairly straightforward to switch between the various tools.

If you're talking about output quality, then yeah, that's not as easy. But for product outputs (building a customer service agent or something like that), having a well-designed eval harness and doing testing and iteration can get you some degree of convergence between the models of similar generations. Coding is similar, but probably not easy to eval.

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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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That was a great promise before the models starting becoming "moody" due to their proprietors arbitrarily modifying their performance capabilities and defaults without transparency or recourse.
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I still haven't seen any statistically sound data supporting that this is happening on the API (per-token pricing.)

If you've got something to share I'd love to see it.

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Why do you think it can't sexually harass someone or drive people to suicide. There are already lawsuits coming in on it causing suicides.

This is an architecture that people are increasing begging to give network connectivity that can't differentiate its system prompt from user input

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I think it's difficult to say agentic and human developer labor are fungible in the real world at this point. Agents may succeed in discrete tasks, like those in a benchmark assessment, but those requiring a larger context window (i.e. working in brownfield systems, which is arguably the bulk of development work) favor developers for now. Not to mention that at this point a lot of necessary context is not encoded in an enterprise system, but lives in people's heads.

I'd also flip your framing on its head. One of the advantages of human labor over agents is accountability. Someone needs to own the work at the end of the day, and the incentive alignment is stronger for humans given that there is a real cost to being fired.

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For some the appeal of agent over human is the lack of accountability. “Agent, find me ten targets in iran to blow up” - “Okay, great idea! This military strike isn’t just innovative - it’s game changing! A reddit comment from ten years ago says that military often uses schools to hide weapons, so here is a list of the ten most crowded schools in Iran”
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It must be wild to actually go through life believing the things written in this post and also thinking you have a rational worldview.
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More importantly it collapses mythical-man-month communication overhead.
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This is an amazing frame /reframe.
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I think the word you're looking for is contractors. But yes, you still have to treat those with _some_ human decency.
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Ah-ha, the perfect slave.
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it just will delete production database when flustered. no biggie. we learning how to socialize again. cant let all that history go to waste.
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[dead]
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