This reminds me of Antirez's "Don't fall into the anti-AI hype" [0]

In a sentence: These foundation models are really good at optimizing these extremely high level, extremely well defined problem spaces (ie multiply matrices faster). In Antirez's case, it's "make Redis faster".

There have been two reactions: "Oh it would never work for me" and "I have seen months of my life accomplished in an hour", and I think they're both right. I think we should be excited for Antirez, (who has since been popping off [1]), and I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work.

[0] https://antirez.com/news/158 [1] https://antirez.com/news/164

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poisonfountain 4 hours ago | parent | next [-]

>I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work

I don't believe that anymore, to be honest. Models are starting to get good at ambiguity. Claude Code now asks me when something is ambiguous. Soon, all meetings will be recorded, transcribed and stored in a well-indexed place for the agents to search when faced with ambiguity (free startup idea here!). If they can ask you now, they'll be able to search for the answers themselves once that's possible. In fact, they already do it now if you have a well-documented Notion/Confluence, it's just that nobody has.

It's probably harder to RL for "identify ambiguity" than RL'ing for performance algorithms, sure, but it's not impossible and it's in the works. It's just a matter of time now.

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TranquilMarmot 2 hours ago | parent | next [-]

> Soon, all meetings will be recorded, transcribed and stored in a well-indexed place for the agents to search when faced with ambiguity (free startup idea here!)

We were doing that over at Vowel a few years back, unfortunately it didn't pan out because you're competing directly against Zoom, Google Meet, Microsoft Teams, etc. They are all (slowly) catching up to where we were as a scrappy startup 4 years ago.

It was truly game-changing to have all of your meetings in an easily searchable database. Even as a human.

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wuschel an hour ago | parent | prev | next [-]

Unfortunately you can't record meetings in many jurisdictions, including court sessions. Hence we have to rely - for worse, or perhaps even for better - on human driven note taking.

	▲	poisonfountain an hour ago \| parent [-]
		You're downplaying the AI lobby here. They're eating down copyright laws, something that seemed impossible just a couple of years ago. Screwing privacy laws is just the next step. Also, we are seeing a cultural shift around that as well. Now people bring "AI notetakers" to Zoom calls without even asking for your permission. People are already acting like privacy laws don't exist anymore, it's going to be even easier for the AI lobby to take it down now. Just like piracy normalized copyright infringement, opening the path to the current rulings around "fair training".

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Yokohiii 2 hours ago | parent | prev | next [-]

So self chosen total surveillance and transparency so your fav LLM can be better?

	▲	DennisP an hour ago \| parent [-]
		Could always use a local LLM for stuff like that. One of my relatives works for one of the big audit firms and that's what they do.

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2 hours ago | parent | prev | next [-]

[deleted]

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risyachka 2 hours ago | parent | prev [-]

In coding the ambiguity is very, very limited and constrained compared to any non dev job that involves any decision making

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wood_spirit an hour ago | parent | prev | next [-]

I have found Claude et al good at quickly implementing the algorithm I have in mind effectively, as long as I ask lots of control questions and check code. They aren’t good at inventing non-mainstream algorithms though and often slip staggeringly short term shortcuts in though. They are still a tool and not yet the craftsman who wields tools effectively. This will steadily change, and the corners where the obscure algorithm wins will erode further too.

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dinfinity 4 hours ago | parent | prev | next [-]

> I think the rest of us should rest easy knowing that LLM's can't [...]

What if (when?) (AI-assisted) research moves AI beyond LLMs? Do you think that can't happen?

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kubb 3 hours ago | parent [-]

Not in the next decade. Won't get funded.

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dinfinity 2 hours ago | parent | next [-]

Private investment in the US has grown from 100 billion in 2024 to almost 300 billion USD in 2025 [0]. Add public investments worldwide and private investments in at least China and Europe.

I'm pretty sure money is not going to be the blocker.

[0] https://hai.stanford.edu/ai-index/2026-ai-index-report

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2 hours ago | parent | next [-]

[deleted]

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kubb 2 hours ago | parent | prev [-]

The money will go to LLMs.

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dgellow 2 hours ago | parent [-]

Why not both? You don’t need 1trillion allocated before you have a proof of concept to demonstrate your non-LLM model, and once you have a PoC you will definitely have the larger investors interested

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kubb 2 hours ago | parent [-]

You will need 100s of billions to make a viable POC.

	▲	dgellow an hour ago \| parent [-]
		For a PoC? That sounds very unlikely. I think you’re off by at least 2–3 orders of magnitude

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drysine 2 hours ago | parent | prev [-]

Advanced Machine Intelligence (AMI), a new Paris-based startup cofounded by Meta’s former chief AI scientist Yann LeCun, announced Monday it has raised more than $1 billion to develop AI world models.

LeCun argues that most human reasoning is grounded in the physical world, not language, and that AI world models are necessary to develop true human-level intelligence. “The idea that you’re going to extend the capabilities of LLMs [large language models] to the point that they’re going to have human-level intelligence is complete nonsense,” he said. [0]

[0] https://www.wired.com/story/yann-lecun-raises-dollar1-billio...

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kubb 2 hours ago | parent [-]

Now check how much OpenAI got in their last funding round, and you have your answer.

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DennisP an hour ago | parent | next [-]

I don't think it's valid to draw broad conclusions from the funding of a new company vs. an industry leader. If AMI builds something that looks impressive considering the funding they got, then they'll get plenty more in the next round.

	▲	dinfinity 12 minutes ago \| parent [-]
		He must be trolling. AI is hands down the most researched topic in CS departments. Of the 10 largest companies (by market cap), only 3 aren't balls-deep in AI R&D. The fastest growing (private or public) companies by revenue are also almost all companies focused primarily on AI (Anthropic, OpenAI, xAI, Scale AI, Nvidia). And the money isn't even the most important part. It's all about mindshare and collective research time. The architectural concepts can be researched and developed on top of open models, so even individual relatively poor researchers unaffiliated to anything can make breakthroughs. Even the computing required for the legendary "Attention is all you need" paper could probably be recreated on con-/prosumer hardware in a month's time.

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drysine an hour ago | parent | prev [-]

1B is what Microsoft invested in Open AI in 2019[0]. That was enough to get the ball rolling.

[0] https://en.wikipedia.org/wiki/OpenAI#Creation_of_for-profit_...

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cyanydeez 3 hours ago | parent | prev | next [-]

I'd say it's a malefactor of:

1. Amazing, you just tweaked 1% efficiency

2. You idiot, you just spent an hour trying to trouble shoot a hallucinated api.

On average, it's really hard to tell which ones going to win here.

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dakolli 3 hours ago | parent [-]

Its not hard to tell at all, just look at how much it costs to run a 10T param model (especially with parallelized agents). Those costs are not worth the occasional slot machine-eque jackpot you get. For an entity like Google it might be worth it, but that's it. They definitely aren't going to let us use these things for cost they are now for much longer.

Imagine going back to 2020 and tell people in 6 years going to be able to spend $200.00 a month and be able to spin up $2mm in GPUs at full throttle to respond to your emails. None of this makes sense.

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Leynos 3 hours ago | parent | next [-]

You don't pay for a £200 a month account to respond to your emails, and if you are, I would tell you that you're wasting your money.

	▲	throw310822 2 hours ago \| parent [-]
		I don't know, I guess it depends from a) how many hours per month you spend answering emails, and b) how much more revenue you could get in that same time. $200 should be reasonably 2/3 hours of work? So that's about the amount of saved time per month to break even on your subscription. It's a steal.

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cyanydeez an hour ago | parent | prev | next [-]

oh, sorry, I'm not running a 10T param. Just local models for me. kk thx.

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ogogmad 2 hours ago | parent | prev | next [-]

Whenever you solve any hard problem, you start off by finding a complicated solution, which you then scale down to a simpler solution.

LLMs are a "complicated solution" in the sense that they're expensive. Once you know what they're capable of, you can scale them down to something less expensive. There's usually a way.

Also, an important advantage of LLMs over other approaches is that it's easy to improve them by finding better ways of prompting them. Those prompting strategies can then get hard-coded into the models to make them more efficient. Rinse and repeat. Similarly, you can produce curated data to make them better in certain areas like programming or mathematics.

	▲	cyanydeez 36 minutes ago \| parent [-]
		they're not _compplicated_, their complex. And solution implies they're not hallucinating the goat and how to fix it.

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snapcaster 2 hours ago | parent | prev [-]

Do you realize you're fighting a strawman or do you actually think this is a compelling argument?

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vonneumannstan 3 hours ago | parent | prev | next [-]

A Statement all but guaranteed to look incredibly short sighted by 2030.

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3uba 2 hours ago | parent | prev [-]

[dead]