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- Hacker News
- > those who need done a small set of narrowly defined tasks with existing clear guardrails: repetitive physical labor in a controlled environment, call center and customer service chat work, etc.
I have no idea how people can so confidently say that call center work is a “controlled environment” or “repetitive”. It’s almost by definition not repetitive or controlled. Customer support is what I go to when the controlled environment has failed
by yunwal - I think bearish on LLMs for automation, and bullish for LLM+human experts in specific fields, is about the right expectation for current architectures.
Apart from issues with task generalization, or perhaps related to it, is the fact that LLMs have real trouble with timekeeping, and cannot estimate the real world time it will take them to do things very well. This plus the memory issues make dreams of long horizon agents, that could plausibly handle changing specifications, quite implausible with current architectures.
In narrow domains with more deterministic outputs though, this is less of an issue, and we see multiple agents succeed much better.
The fusion of that capacity, with humans in the loop able to better direct such agents and act as their temporal tethers, is where I think the real action will be for a while at least.
- Navier-Stokes is a well defined problem, or "a hard technical problem". Most problems in the business world lack a good definition and tacit knowledge is required to solve them. As far as I've seen, AI lacks any kind of tacit knowledge, strategic thinking, etc what so ever.
Take a customer service person, that as soon as AI agents replaced was hacked. Lots of tacit knowledge, that wasn't measured, or even probably in the job description, until AI agents had none and the gap was taken advantage of. Gap is probably a poor word here as it implies not a chasm, which could very well be the case.
Your analysis was excellent but short on one front, AI has endurance on it's side. Looking at the N-S solution, OpenAI had 10,000+ instances that kept trying around the clock. Assembling a human team to do that would require a lot of effort. Though, that society collectively choose not to, perhaps tells how valuable it really is (i.e. it's now easy to launch a Manhattan Project level of effort). So maybe it can also be said that AI is also good at marshaling resources.
by lissom - "are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers, "
No, they're really not.
They're priced in a way that would imply AI will be universal form of compute, alongside traditional deterministic systems - which it will be.
And that they will capture most of that ... which they won't.
The Frontier Labs are a very bad buy at a high price, but that partly has to do with wacky pricing, but actually mostly has to do with their relatively weak place in the value chain.
The money is going to Nvidia, who have the most powerful position.
A bit like how a retailer can take all the margins of some innovative product, if they own the channel.
AI is over-hyped, the Frontier Labs are over priced - but AI is here to stay, and will grow. Not like Skynet, but like a new form of compute. And it will take it's time, and the profits will be reaped by those with the power.
by bluegatty - Great article, but the lack of sentence capitalization makes it unnecessarily difficult to read.
Apologies if this comment is off-topic, but it really is quite egregious, and since the article was submitted by the author I presume they are open to the feedback.
by yshklarov - The premise in the very first point seems off:
> the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers...
Even assuming this is how the AI companies are being valued (they're not), the numbers are off.
The "value" of most knowledge workers -- based on what enterprises currently pay for them -- is $50 - 70 trillion annually. It's reasonable to assume that if AI drop-in-replaced all those knowledge workers, AI companies could credibly charge somewhere in that order of magnitude, because that's what the market is already bearing.
So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that, the entire AI industry would be valued at double-digit trillions at the least.
Yet cumulatively the industry (the frontier labs + the SWAG estimate of the AI parts of all the other players) are valued at, say, ~6 - 7 trillion? Which seems like a fair approximation of how much knowledge work they can currently automate.
by keeda - This is the most grounded and coherent take I’ve seen on the actual realizable value of LLMs.. pretty much since they came out.
> the classes of firms that can accept the use of fully autonomous LLMs are few, by my count just three:
1. those who can accept failure cheaply: firms that would otherwise hire interns, firms involved in rapid prototyping work, etc.
2. those who need done a small set of narrowly defined tasks with existing clear guardrails: repetitive physical labor in a controlled environment, call center and customer service chat work, etc.
3. those that can accept or already do by nature the costs of rigorous specification and validation: chip design, drug discovery, and other domains where failure on deployment is an existential concern.
the first two classes are price sensitive and arguably don't need the jump in reasoning quality you see going from cheap to frontier models. most of these firms will be best served by open models running on cheap hardware, perhaps even locally at the site of use. for the first and third classes, the type of fuzzy combinatorial search that has produced headline results in mathematics and security research seems more sensitive to agentic swarm width than reasoning capacity
…
This is just so on point. And for the third class (which I would extend to things like materials research as well), specification and validation are already by FAR the larger costs, so automating search and simulation is really not a massive game changer for the broader business.
by plaidfuji - This April 2026 paper is a fun and related read.
https://arxiv.org/html/2509.24239v4
Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for illegal moves. With illegal asks discarded, none of the bots could beat a chess model calibrated to 1100 ELO.
The author of the originating post says that "current frontier models need laborious oversight and guardrails on even the simplest tasks", and he's absolutely correct.
by carodgers