5 ms·
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 l
by momojo 5mo ago
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 https://antirez.com/news/158
[1] https://antirez.com/news/164 https://antirez.com/news/164
- dinfinity 5mo ago> 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?
- kubb 5mo agoNot in the next decade. Won't get funded.
- dinfinity 5mo agoPrivate 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 https://hai.stanford.edu/ai-index/2026-ai-index-report
- kubb 5mo agoThe money will go to LLMs.
- dgellow 5mo agoWhy 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
- kubb 5mo agoYou will need 100s of billions to make a viable POC.
- dgellow 5mo agoFor a PoC? That sounds very unlikely. I think you’re off by at least 2–3 orders of magnitude
- AlexCoventry 5mo agoYou only need to train a range of small models in order to establish a plausible scaling law, IMO.
- deleted 5mo ago[deleted]
- drysine 5mo agoAdvanced 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-billion-to-build-ai-that-understands-the-physical-world/ https://www.wired.com/story/yann-lecun-raises-dollar1-billio...
- kubb 5mo agoNow check how much OpenAI got in their last funding round, and you have your answer.
- DennisP 5mo agoI 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 5mo agoHe 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.
- 5mo ago
- ActorNightly 5mo agoI mean, Google already has Mu Zero, which Im willing to bet has evolved quite a bit in private because if anything is going to get us closer to actual AI its that. Realistically, one can build a AI capable of reasoning (i.e recurrent loops with branches) using very basic models that fit on a 3090, with multi agent configuration along the lines https://github.com/gastownhall/gastown https://github.com/gastownhall/gastown. Nobody has done it yet because we don't know what the number of agents is required and what the prompts for those look like. The fundamental philosophical problem is if that configuration is possible to arrive at using training, or do ai agents have to go through equivalent "evolution epocs" to be able to do all that in a simulated environment. Because in the case of those prompts and models, they have to be information agnostic.
- poisonfountain 5mo ago>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.
- deleted 5mo ago[deleted]
- risyachka 5mo agoIn coding the ambiguity is very, very limited and constrained compared to any non dev job that involves any decision making
- exfalso 5mo agoThat's.. not even close to being the case. It's literally a series of ambiguous questions and strategic decisions. Non-ambiguous is like a first semester algorithms class in university.
- antonvs 5mo agoThere seems to be a category of "coder" which fits the other commenter's description, where someone else makes all the significant decisions and they just write the code. Not coincidentally, that category seems most at risk from AI, because they're basically like a human version of a coding agent.
- TranquilMarmot 5mo ago
- cyanydeez 5mo agoI'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.
- dakolli 5mo agoIts 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.
- Leynos 5mo agoYou 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 5mo agoI 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.
- snapcaster 5mo agoDo you realize you're fighting a strawman or do you actually think this is a compelling argument?
- ogogmad 5mo agoWhenever 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.
- vonneumannstan 5mo ago>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. A Statement all but guaranteed to look incredibly short sighted by 2030.
- 01100011 5mo agoThe past few years has seen a great rise in casuals reminding us of AIs limitations only to be proven wrong in 6 months. I don't think we're close to AGI, but in 2 years I've gone from AI doubter to AI convert. It's not perfect, but I don't need it to be. The real question to me is if the system can pay for itself. Economics are racing against efficiency gains and it's anyone's guess which wins.
- byzantinegene 5mo agowhat are those limitations we're talking about? seems most of those the original limitations that people complained about were resolved through workarounds like tools and skills which are more software-engineering than llm advancement.
- vonneumannstan 5mo agoLong-term planning, context poisoning, task length reliability, etc. Improving all the time but certainly still constraints.
- nerdsniper 5mo agoThe biggest limitation I see right now is weak “theory of mind”. It’s why even though AI can generate very decent exposition, it sucks at generating narrative. This also reflects in weak performance at humor, art, and even shows up in exposition (resulting in reactions akin to “cool story bro, but why should I care?”) It’s why people can identify AI writing even if it doesn’t contain any LLMisms. AI’s generate text that almost looks like a human wrote it, but that no human would ever actually write - when we try to imagine what kind of person would have wrote this, we draw a blank - no one we’ve ever met would have written it like that - not even any archetype we’ve ever built an internal model for.
- 3uba 5mo ago[dead]
- wood_spirit 5mo agoI 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.
- DoctorOetker 5mo ago"... tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work." sounds like jobs involving legalese, politics, corruption and more generally involving pretending you don't understand something for which your income depends on not openly understanding something...