I love LLMs, I hate hype(geohot.github.io) |
I love LLMs, I hate hype(geohot.github.io) |
There are many things to be critical about but shoehorning an entire metro into the echo-chamber you're supposedly beyond yet can't help but orient your entire world view as the anti-SF-tech-bro all while running a startup and discussing AI on HN.
TLDR: SF is more than Paul Graham worship parties.
EDIT: Think I'm being misunderstood! author goes out of his way to blame shitty San Francisco.
> This is negative valence hype, not only is it not true, it’s mostly designed to make you feel bad about yourself and move to shitty San Francisco where everything really does suck like how these people claim.
The SF metro is possibly the worst in the entire world in terms of CoL vs QoL.
It has a higher proportion of unsheltered population living on the streets than almost any city outside of Africa except Manila and possibly Dhaka
false equating that author's AI hate is hating SF tech-bros? Oh I think I am being misunderstood, that makes me feel better about the insta-downvotes. Author states it plainly:
> This is negative valence hype, not only is it not true, it’s mostly designed to make you feel bad about yourself and move to shitty San Francisco where everything really does suck like how these people claim.
Whenever I visit SFO it's really funny seeing all the advertisements from startups above a population struggling to find housing.
Won't it be better to pay someone 100k in Reno than 180k in SF? Most collaboration happens online these days anyways.
Honestly 60k in Barcelona is like 200k in SF when you look at housing and public services.
We need to punish bad city governance for being bad.
Now we're at a point where that never happens, and where lipsync is almost a completely solved problem
If the issue here is simply that the quality is bad, one has to contend with the fact that it is undoubtedly exponentially improving and there's no reason we should expect that improvement to stop
I also don't have any interest in consuming AI generated art, but the same criticisms were levied at computer graphics and if we're comparing to CGI we'd be at the late 1970s in terms of nascency
That is a very astute and concise way to explain everything about how the frontier labs are behaving and how they're trying to push more people to pay token rates for the best models. At the current subscription prices ($100 or $200 a month for a generous, though bounded, amount of tokens), frontier models are a no-brainer, most folks and companies will use them. But, at token rates, 10x or 100x the cost of open models or what I was spending on the frontier models a month ago? That is a harder question to answer "yes" to. I certainly wouldn't spend $1000 a month for the best model, much less $10,000; my employer might pay $1000/month, but definitely not $10,000. The frontier labs need everyone to answer "yes" to spending 100x what they currently spend to justify the valuations, and it's just not going to happen as long as everyone knows how to make these models.
Both OpenAI and Anthropic are trying to figure that out now. Anthropic, in particular, has their finger on the trigger...they want to push people to usage-based billing for Fable. But, OpenAI released 5.6 Sol, competitive with Fable (or close enough), and it's available via subscription (even the $20 subscription!), and there's no moat keeping someone from switching. If Anthropic really does end Fable access on the subscription plans in a few days, I predict a large market move back toward OpenAI.
The market isn't going to bear the cost of making the frontiers investment make sense.
It would have been an interesting experiment to charge more for it right away and see what the market would bear, rather than tease it for long enough for it to be presumably superseded any time now by whatever is next.
I think an interesting question is going to be, if models are a commodity, who is going to want to foot the very expensive bill to train them? I'm sure training cost will drop.. eventually, but I doubt it will happen fast enough for any of these companies.
And we can't ignore the power of "good enough". GLM5.2 may not be as good as the SOTA models, but it can be good enough for most, of not all, of our needs.
It's running, privately, in my homelab.
I think we are entering what I call the "have it your way" era. If an open source project doesn't do exactly what you want it to do, fork it, or create a new version. It's too easy.
This makes me a bit concerned about the future of open source. Upstreaming used to be worth it, since maintaining a fork is effort too. But now the balance has shifted significantly. Especially with many projects becoming a lot stricter about contributing, and some becoming outright hostile to AI. I can't blame them. But I think the effect will be that improvements are less likely to make it back to the community as AI adoption increases.
You can use an LLM to create anything but you still need to know what it is that you're building, and you need to think through how everything should work or the LLM will just fill it with sausage. You can tell that the models are still quite jagged and limited by the mixed quality from a lot of the software that these presumed trillion dollar companies are putting out. The future is sausage.
Makes perfect sense to anyone good at using these models. What doesn't make sense is that analogy. Typing prompts isn't even close to as difficult to baking bread.
LLMs can absolutely code and I can have a javascript app without ever seeing a line of javascipt code. What a blessing.
The perpetual underclass memes are just funny. How is it not chuckle worthy to see people mocking Anthropic's silly Fable marketing with relief that they have five more days to escape the perpetual underclass. Mom, how did we get so rich? Your dad took advantage of the Fable week.
And finally, lolbertarians had a couple of really hard years so it's great to see actual market competition where some of the largest companies in existence dance for my $20.
…but consider: the Q-tip. “Don’t use it to clean your ears”, but for most people that’s all they want to do with it, and empirical observation indicates that this dynamic results in either “using Q-tips irresponsibly” or “not using Q-tips”, with “uses Q-tips properly” being a small-to-vanishing proportion of the whole.
I wonder what he thinks was too harsh, still seems pretty bang on, I think it’s going to age well.
I think he now thinks agents can maybe program a little bit.
Having a thinking trace that is legible, coherent, and immediately implies the explicit turn output and/or tool use seems difficult if not impossible to reliably get from mixture models.
I predict MoE is a transitional technology, it's got too many problems and the benefits are...kinda grandfathered into the dogma at this point.
It's possible to use LLMs without logging onto twitter to be exposed to the people spouting off about a "perpetual underclass." I love the internet, but it really feels like (now more than ever) you have to be intentional about what sites you visit.
I’ve found them to be unavoidable to some degree.
[1] Allegedly because I have no firsthand experience, not to imply doubt.
(Genuinely curious, I hadn't ever seen that there though I don't go there much any more.)
This is what he wrote before.
> I’m calling it now, the adoption of AI agents into software development will be one of the most costly mistakes in the field’s history. Agents cannot program, and it’s taking longer and longer to realize that they can’t.
Now he's writng
> I love the progress. I’m so excited for the new LLMs, self driving cars, video generation models, and coding agents.
SMH now he writes about the hype. My brother in absolute Deity, *you* should have believed the hype.
He does say in this post:
> I’m getting better at using them and get some boost from the models. It is a new skill, and it’s not like I haven’t constantly been trying them. You have to be really careful, they can increase cognitive fatigue, and all the vibe coded stuff is still slop (where’s all this new magical software that the productivity improvements should imply?).
With this, I’m hearing (from supposedly reputable publications, in addition to random people) that this is going to end knowledge work in general and take out a large percentage of the world’s labor force. I’m being told to pick up a trade, and that the career I have and the knowledge I’ve gained is now worthless.
The worst part seems to be that it’s pretty much impossible to quantify any kind of impact these tools will have until after the impact is actually felt. We’ve been in limbo while the tech sector is just rotting.
So all people that don’t understand the thing being hyped.
> AI is something that’s happening mostly due to Moore’s law and general progress in computing, not something that they are doing.
But if these companies control the vast majority of compute power, which seems like the plan they are already executing, won't they capture most of the value from the progress of AI?
It's bullshit in the sense that they don't know for sure, but the author doesn't either. Why might or might not it be true?
> And two, this strawman jump from, oh hey, it’s a fancy autocomplete, smart compiler, better search engine, to it’s gonna like own the whole light cone bro like if you aren’t in SF and at the right parties there’s gonna be like a flash of light in the sky one day and you’re not even gonna know what happened but everything just Changed.
Haha, OP has a way with words.
In a way, both these emotional extremes (FOMO & the singularity) are just tools being used to continue driving the massive CapEx behind LLM improvement. Hate to love it? Love to hate it?
In the past when I couldn't figure out something, I'd take a break for a couple days, while going through Google → Stack Overflow → Reddit, and by the time you got to that point you rarely got useful answers, usually either trolls or silence.
Now I can just ask AI about fleeting ideas and always have a starting point for some area of some project to work on.
A lot/some of the concerns about the AI Age could be alleviated if people got UBI and a 4-day workweek.
like if AI's supposed to be so great why do we still have to work so much??
and if we don't have to work, how do we pay for food and bed?
I’ve also lost my ability to self-filter. In the past, I’d write down an idea and if I was stuck for too long, I’d discard it. Now I feel like I have an obligation to build everything.
Maybe it never mattered and the quantity of solutions is truly the most valuable thing.
You have to be careful and "remain yourself":
Like I've been trying to think of a generic save/load system for my game framework, but the ideas given by Codex so far don't suit my desired design/interface, BUT it makes me certain of how I DON'T want to do it heh
If I got lazy and just blindly took the AI's first suggestion, I'd end up in deeper tech dept.
You have to take advantage of and "exploit" the way LLMs work, which seems ideal for shaping vague ideas, by using the AI's fuzziness to help you decide what you do and don't want.
The blog has a tagline, "the singularity is nearer". I think belief in a "singularity" almost implies these things to some degree.
Wait, does this mean I'm better at something than geohot? All that time spent learning regexps wasn't a waste!
So far, all we have is more software running on computers. It's powerful, and it's amazing, but it's not magic.
Calling it "AI" was possibly a net-negative but we don't know yet.
But since its creators and as of my knowledge everyone else totally did not see it coming, that you can now give a vague prompt full of spelling errors - and get returned a working program - I would say it is pretty close to magic (as in we don't really understand why it works so good).
I also don't see how you cannot call it AI. Especially since simple chess engines and alike were called AI long ago. So it is not general strong AI and has no consciousness and no mind and is pretty dumb too often - but the general concept - getting from a some vague text to a working program has some connection to intelligence to me.
But it's not actually magic. Technical people understand that it's just software running on computers.
One of the lesser, but still underdiscussed ramifications is that I think it has limited the public's ability to comprehend the Yann LeCunn argument, that genuine AI is likely possible but that LLMs and transformers are a dead end and we need to explore different modalities
I’m not sure it’s net negative or not. I’ve found that it’s reductive though. We have this really broad field of artificial intelligence reduced down to at worst a “slop machine” and at best a single tool.
Imagine being a CS professor that studied AI in the 90s and how you have to over explain you don’t mean LLM chatbots to a layman.
It’s why con artists, scammers always flood every hype cycle. Greed ruins everything.
There is a lot of OSS software out there (e.g. in scientific communities) that I would say would barely qualify for each of those three attributes. The main reason it's valuable for the respective communities, is because it's the only thing that's available.
There is, of course, the question of if that's making me dumber. It might be, but there are other brain training things I'm doing outside of that to force my brain to do the thing.
I'd bet there's far more 'good enoughs' than anything else out there. One of the reasons microsoft office is constantly churning subscription, etc is because they solved good enough decades ago and need to justify valuations that just don't matter for most of their user's use cases.
Not everyone is a software developer having to churn out the 101th SaaS that's just because some MBA refuses to hire a dev.
I have seen so many unnecessary forks of popular projects that I think it's better to stick with the original, even if that means it won't be perfect.
As cost to software goes to zero, these things become easily possible. In the past, I'd only fork top-quality software (things like `xsv` etc. which is easy to edit. These days even complex PHP software I fork with little trouble.
With lots of software, the value is in the data model and algorithm choices. Sometimes I even just point Claude Code / Codex at an open-source thing I want to vendor some functionality into my personal setup with and it gives me what I want. The hard part for me is modeling the data well. That takes experience with encountering things and it's hard to replicate the edges. LLMs often don't get the rough bits right. But someone else's hard work usually has accounted for this.
The latter as always been more durable. Linux doesn't have the mindshare it does because it's "free" as in beer - it's because it's "free" as in freedom.
The price of freedom, of brewing your own beer, is sometimes higher than buying it from the store. But for many folks, the control over the supply chain is what makes it worth it. In LLM-land, it might take a little bit of time for folks to catch up -- or maybe a lot of that is already in motion as companies get paranoid (and rightfully so) to frontier labs getting a little grabby about data. If you need a ZDR environment, "free" as in freedom has a very high premium that you will pay and rightfully so.
Seems reasonable
We could try steelmaning this argument instead: it's enough that most big companies who would otherwise have incentives to contribute.
Before FOSS got in fashion, around the early 2000s, most commercial companies wouldn't touch it as contributors and were openly avert to it, and to open sourcing their stuff. This can be the case again.
I think the opposite: I think the frontier labs have good margins on their inference unit costs.
We can already see what it costs to run near frontier-size models. There are independent business pivoting to serving these models at reasonable prices and they're competing on OpenRouter for costs much lower than frontier labs.
> Is there any guarantee that I'll be able to run a Opus 4.8-level model on my personal computer before the big AI labs decide to hike up the prices?
I would bet good money on prices going down significantly, not up.
If we get to the point where you can run an Opus 4.8 model on your local computer, it's going to be even cheaper for a datacenter to serve it on their hardware. That means prices crash, not that they're going to rise.
1. Much of those profits have to be immediately reinvested into model training runs to avoid being lapped by competitions.
2. Unit costs are irrelevant when the labs don't price per unit, and instead charge, for instance, $200 / month for $10k worth of tokens.
This isn't a steady state. Whatever the current situation is, I doubt it's sustainable.
Using a full Claude Max 20x plan to 100% of weekly usage would easily cost you 2k through the API. While the Claude Max 20x plan is 200 a month.
Also, DeepSeek V4 Pro is cheap via any commodity API, and DeepSeek V4 Flash is essentially free at API prices like $0.09/M, $0.18/M out. This is generally not subsidized.
For a more practical local setup, Qwen3.6 27B on a used Nvidia 3090 (US$1300) or two is surprisingly nice. It needs clear instructions and you can't use it for hands-off vibecoding, but it's actually quite reasonable for hands-on programmers.
You're always guaranteed that you can stash away the open models!
If subsidies do end, demand for price efficiency per unit of intelligence will go way up.And because there's many players in the market, this demand should be met by at least some of them.
That's not sign of commodity actor, just the opposite.
It doesn't really, because whenever I ask them what did they actually create, its always a shitty dashboard or a finance tracker or something derivative and worse than what is out there
LLMs are poison for the brain, I'm almost certain of it, at least when used in the way most people are using them. If you drive everywhere because you don't want to walk (but you could), you're obviously going to be physically worse off than if you walked. This is the case with llms, if you have them do all the thinking, planning and action you're going to be cognitively worse off than if you didn't use them.
There's two reasons for that. The math is generally very unorthodox and alien for a seasoned developer, and software development practices are equally alien for the scientist who can understand and evolve the math behind it.
I have written a boundary element method evaluator for my Ph.D. not only math was alien, the required coding techniques for making it fast is very different for a standard developer. You have to have the perseverance and interest to do that. I chose that path intently and I do not regret a millisecond of it.
The problem is, if you don't have a dedicated team to continue that codebase (e.g.: like the Eigen team), your code is basically done and done. If somebody doesn't share the same passion, it's almost impossible for someone to take and carry it forward.
Oh, due to the math and optimizations, the code's structure need to be both documented and the next batch of developer(s) have to be tutored by the person who's giving the code to them.
Since it's just a duplicate folder, I can always fall back if it fubars.
Cost to generate all of the tokens divided by revenue generated by selling those tokens is what matters.
The subscription plans confuse a lot of people because that's what they see. They're not seeing the gigantic API bills from all of the tokens going into enterprise use cases.
The subscription plans are a small part of their income. Most users aren't maxing out 100% of their plan usage every week. I wouldn't be surprised if their average plan user was using less than 50% of their monthly quota each month.
Plans like that can produce a net increase in profit if they get consumers interested in the brand and pitching it at work. Giving them some extra token headroom on their $20/month or $100/month home plan is money well spent if it gets all of a company's developers advocating for enterprise plans with budgets exceeding $1000 per person.
I doubt many of their customers are on the 20X plan. Of those, I doubt many of them are using 100% of their weekly usage regularly.
Comparing the 100% maximum usage scenario of their most discounted plan against the API cost has been a trap in this conversation since it came out. I bet if we saw their financials it would be a tiny sliver in a pie chart somewhere.
The bigger mistake would be trivializing the rest of the technology involved just because LLMs are the newest piece. LLMs are only "magic" because they're built on a stack that was already "magic" without them.
LLMs are impossible without:
- operating systems
- programming languages
- compilers
- data centers / power grids / air conditioning
- servers / switches / routers
- CPUs / RAM / GPUs / SSDs
- fiber networks
- etc
Token prices are going down. Competition is global. A company could choose to keep their API prices high, but if another company comes in at 1/10th the price for 95% of the performance then they won't have many customers.