This line: "this is my main argument against the valuation of frontier labs. It’s not that AI won’t create that much value, it’s that they won’t capture it."
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.
The funny thing about Fable, is we all but know it will be obsolete in a month or two. Between their embargo shenanigans (which IMO they could have avoided just by not pretending it was dangerous) and continuing to give access, whatever marginal advantage it had was essentially wasted.
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more for it right away and see what the market would bear, rather than tease it for long enough for it to be presumably superseded any time now by whatever is next.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 don't see how that matters? It's a treadmill. Sure Fable-v1 will be obsolete but the shiny new Fable-v2 won't. Don't view their shenanigans as regarding Fable but rather their current cutting edge model.
Yeah, I've started reaching for local models more. I'll use frontier models at the current cost for tasks that the local ones aren't great at, but when the rug pull inevitably comes, to me they're not worth 1000-2000 a month. And honestly, for my purposes I don't really need models to advance a lot. Like, I tried fable a couple of times and there just wasn't much there to justify its use to me. Opus did the same thing much cheaper.
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.
> But, at token rates, 10x or 100x the cost of open models or what I was spending on the frontier models a month ago
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.
In 5-10 years an Apple Watch will run a Fable level model locally. I don’t think we (hackers) should worry too much about token cost inflation. The current wave of providers, that’s another story.
And airline stocks are typically a bad investment and Delta has been called a bank that operates an airline due the the amount of their revenue that comes from credit card fees.
While I do agree there will be disruption we haven't seen yet, my company is already spending >$40k/day for a "frontier model", so who knows. Then again, they're not using that for coding
Who is going to end up capturing all this value being generated is going to be very interesting. Back in 1980, who’d have thought MS would capture the majority of the value from PCs over the next 3 decades, and not IBM?
So far, it seems to be the reverse of that disruption. Hardware companies, Nvidia, Apple, AMD, Intel, ARM, memory companies, are all having record-setting quarters, and it's actual profits, not subsidized by investors and circular investments (though the hardware companies are investing in the AI companies to keep the hype train rolling).
> where’s all this new magical software that the productivity improvements should imply?
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.
Remember: code is free as in "free puppy". FOSS communities were never valuable because of the code. It was the shared written and oral traditions that make the software useful, usable, and updated.
> that make the software useful, usable, and updated
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.
Developing scientific software is disproportionately hard though. Making it usable, useful and keeping it updated is even harder.
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.
You will likely end up in maintenance hell soon. This will likely not be much easier with AI because coding is not the hard/annoying part, it's the fact that you need to dust off every little project every time a tiny fix is needed, and that's a lot of toil in the long run.
Seems to me this would get easier or harder depending on how you write the code. Like if you write the code in something standard and unchanging like POSIX shell scripts or C99 or ES5 javascript, at least the ecosystem won't change out from under you. If you use rust or python or a bunch of node.js dependencies then you might have to edit the project just to keep up with ecosystem changes.
yeah I had this happen to me. Except when I go to maintain it, now cursor/claude are good enough to essentially handle it on their own, so it turns out to be very low effort to maintain.
Maybe? I ran across an old pre-LLM project of mine recently, and past me was an asshole and didn't leave a readme for future me. Meanwhile post-LLM projects at least have a readme that the LLM generated for me or my agent to read and pick up context on. Being able to ask an agent what is this repo, what's going on here? Hey just make it do this, instead of toilsomely digging in and doing it tmmyself, seems to say that might not come to pass.
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.
The fact that you're even saying this it is probably an admission that you do think it's making you dumber. Most people I know, who are honest with themselves, have admitted to me that they feel like it's making them dumber or "zombifying" them. This is also well studied already, https://arxiv.org/abs/2506.08872
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.
It's pretty easy to generalize this, but it doesn't match my perception. People who are using llms to do things they could have already done, but faster, probably have atrophying skill sets. People who are using these tools to accomplish significantly more difficult or complex work than they used to are absolutely finding new ways to push themselves. The problems are just much bigger.
The average Joe can easily vibe code apps that took a small startup just a few years ago. If developers are also using AI to build the same simple apps - then yeah. They're not pushing themselves hard enough, and probably not using their brains as much anymore.
alternatively, you might end up in 'good enough heaven' and not have to touch it for a decade because, you know, it does exactly as you need and you're not google, microsoft, openAI or antrhopic.
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.
Creating a fork of an active project only makes sense if you are its sole user (of the fork) and you really need exactly the modification you've been dreaming of.
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.
In the old world, this was because keeping in sync with upstream was hard. In the new world, it takes an hour. And because you're the only user, you can test in prod. Makes the whole thing faster. I have lots of forked and family-only software. Some are abandoned upstream etc.
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 law of conservation of energy also applies to software. If the price of software approaches zero, it is offset by the time and tokens required to modify and maintain it. Price, time, tokens are simply different expressions of energy.
I'm doing it right now to see what the cost is; I cloned the upstream and made a copy of the working directory and asked the Qwen3.6-35B-A3B model to merge my production files with the new upstream.
Since it's just a duplicate folder, I can always fall back if it fubars.
I understand the concern and it's fair but I am very curious about what happens when the two notions of "free" (free as in beer, and free as in freedom) start to diverge because the former gets easier to do.
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.
"This new tool allows for writing all this code ..... but every person and company, in unison, in a grand conspiracy, all decided to only write private software with it that they aren't releasing to the public in any way"
Doesn't have to be "every person and company, in unison, in a grand conspiracy" and other such strawmen.
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.
At least for me, the jump in productivity has resulted in building stripped down one-off software for my highly specific use-cases.
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.
This doesn't make sense, I enjoy making bread at home but it costs 10x and tastes like dog shit I dont want to spend my time perfecting the craft of making bread for my daily needs (maybe once in a while its a soothing activity), I want someone smarter than me to spend his entire life coming up and perfecting a solution and exerting more time and effort than I can afford and I am very happy to support him so I can stop worrying about it and focus on what I want to do
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.
>Makes perfect sense to anyone good at using these models.
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
I love LLMs too, but I am concerned about their cost. They are all still very subsidised. 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 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.
> 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.
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.
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.
> Using a full Claude Max 20x plan to 100% of weekly usage
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.
You can maybe run a local Sonnet-4.5-ish-level model (sort of) for less than the price of a new car, even at current massively inflated prices for fast RAM. This is probably not what you were looking for. But it's there. You could share one server between multiple developers. Maybe make a little AI co-op or something, with a pair of RTX Pro 6000 cards?
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.
I’ve got a pair of those cards and DS V4F is incredibly good. I’m happy I did what I did because I like this stuff but if you just want stuff then you are absolutely better off not spending $20k on two of these cards and using the API. This guy is absolutely correct.
Currently, because of the subsidies from the frontier models, demand is mostly for higher intelligence.
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.
GLM-5.2 is runnable and downloadable today on a MacBook studio that costs a stupid amount of money. No one can take that away from you except by force though, if you want to set it up today.
I get it, I want to agree, I really do like the “this is a new tool in the toolkit of the professional software craftsperson” argument…
…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.
> This is like shoving a sponge down your windpipe to remove mucus.
In my personal experience, not using a cotton-tipped swab for the task is like cleaning a plate loaded with gunk and burned-on patches with one's bare hands rather than choosing to use a sponge and/or brush. You can do it, [0] but it's much more work, much more time consuming, or you get an inferior result.
[0] In my case, I'd need to make one set of passes with paper wrapped around my smallest finger, and then another set with paper shaped into a tool that can lever the excess wax out from outer orifice of my ear canal.
> 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.
I think he now thinks agents can maybe program a little bit.
I felt the same way in 2024-2025. Then Sonnet 4 was released, and things started feeling different. Opus 4.5 was another step change for me. Everything feels like it's accelerating, and timelines are getting crunched. I guess in some ways I envy OP, who would "bet everything" against ASI - the truth is I don't know, and I don't think anyone knows, where this ends.
He didn't say he bets everything against ASI, he said he bets everything against ASI being a flash of light in the sky which destroys our chance of getting access to the wealth it creates.
That's a much less generous interpretation of his writing. "Yes we will birth superintelligence, but everything will just sort of work out for us humans". This seems like a silly take to me.
Not sure why you had to add the (attempted) qualifier. He started a company and is selling a box. That makes him a merchant. How successful that venture is, is a different question, but he absolutely is a merchant in this arena.
Yeah I don't think any of the labs have some secret sauce for intelligence either. It seems most of the advancements are still coming from hardware, making LLMs more efficient and throwing more compute and data at problems. And even those problems still require a lot of prompt engineering: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...
The secret sauce is training data. They’re not just taking advantage of more compute (which obviously is necessary but as mentions basically a commodity). They are paying billions to data labelers and making judgements about the nature of the training data they best need to make the product they want. This seems to get pushed aside as a minor point but it’s the primary differentiator of the big labs.
I'm pretty sure at this point that Anthropic is training mixture models (at least in the heavy pre-train) and deploying them dense with explicit loss on thinking trace coherence.
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.
While scaling laws hold (more weights = better), and time / financial costs are not trivial the incentives are in place to have MoE. MoE means you can have more weights without increasing the critical path of evaluating it.
I am curious what you believe the problems with it that would cause people to prefer using less weights. I'm not following what you mean by MoE can't have legible thinkings trace or tool use when existing models with MoE can.
even meta that sucks at doing anything is releasing frontier models. making an top ai is easier than making twitter clone( threads) if you have enough money.
>One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind. 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.
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.
Agreed. There's sort of this spiteful anti-hype here that I find very offputting, and ultimately I think it's because a lot of folks are going out and encountering opinions I never see. I hear wild conspiracy theories about data centers and the financials of involved companies that make their way to me from bluesky or instagram, often through here, but never the unstoppable tide of hype that people are allegedly[1] railing against. I do read Scott Alexander, but he's a lot more reserved than people make him out to be on this.
[1] Allegedly because I have no firsthand experience, not to imply doubt.
"Permanent underclass" is the notion that people who get involved at the ground floor will essentially get infinite wealth relative to the ones who don't. It's a little goofy, but more of the capitalism you'd expect from today's X than the communism you're imagining in yesterday's Twitter.
Agreed, but I do think this is a wholly different kind of hype. With crypto currencies it was the promise of modernizing value exchange, with some zealots promising the end of traditional currency.
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.
A lot of people died from Covid and if not for mRNA technology and extraordinary caregivers a lot more deaths would have occurred. That’s hype where it truly belongs. Don’t mix AI hype with Covid conspiracy theories.
> A certain cult likes to claim credit for things that are happening with or without them, and this is my main argument against the valuation of frontier labs. It’s not that AI won’t create that much value, it’s that they won’t capture it.
> 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?
As soon as we started unironically calling LLMs "AI" we went down the hype path. That has plenty of downsides, like stressing out the entire world and attracting cryptocurrency bros, but also the major upside massive of funding/acceleration.
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.
"It's powerful, and it's amazing, but it's not magic"
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.
Sure, nothing is magic. You can go look how a simple LLM works and build your understanding from there. But calling it "just software" is trivializing it in my opinion. I can write software, but I cannot write software that writes software.
> But calling it "just software" is trivializing it in my opinion
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.
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
> Calling it "AI" was possibly a net-negative but we don't know yet.
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.
>One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind. 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.
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?
May be true because humans, especially in the West, are big on performative humanitarianism but not actually considering the well-being of others (or changing their behavior solely for the benefit of others).
May not be true because it's a blind spot to assume that purely by being a player in the AI game (with no real attention paid to quality of result), you have increased odds of winning the game. That's true in the abstract, but practically, it requires a competent player to become true in reality.
> What I don’t like is two things. One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind.
> 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?
> 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.
Both can be true and I have both opinions also in me. Love the progress, worry about the consequences of not being careful with it.
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?).
I recently realized, that ever since I've had AI to "talk" to, I haven't had a stuck or "downtime" moment; there's always something to at least brainstorm on.
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?
Do you feel like the ideas you’re getting from brainstorming these days are the same level of quality as in the past? I’ve been doing some of the same, but I’ve also been feeling like the downtime where I’m genuinely stuck is where my most innovative solutions come to light. I’m not going as deep into problem spaces anymore.
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.
> Do you feel like the ideas you’re getting from brainstorming these days are the same level of quality as in the past?
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.
I think big money/private equity/vulture capitalists tend to ruin everything. They set these unrealistic goals and force companies to do shady shit in order to meet these often unattainable goals or achieve unicorn status.
It’s why con artists, scammers always flood every hype cycle. Greed ruins everything.
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 vast majority of the target audience of this blog post would only consider moving to SF because of the tech scene. This isn't a mountain biking or asian food blog.
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.
How to you love this stuff so hard? I could newer love any ai generated music, book or artwork. Anything ai gemerated i have ever seem or heard was either disgustingly slop or indistinguishable from something else which was real. It‘s a like finding a cool track only to discover it‘s a lazy bootleg.
Yeah but it was only like 2 years ago that artists were arguing this on the basis that AI-gen images would consistently mangle hands
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
I'm sure most engineering is LLM-assisted already and nothing is wrong with it. It's just the one-shot vibe-coded low quality slop that spoils sentiment of this tools. Also many people are interested in what agents can build unsupervised as a test of "superintelligence".
This line: "this is my main argument against the valuation of frontier labs. It’s not that AI won’t create that much value, it’s that they won’t capture it."
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.
The funny thing about Fable, is we all but know it will be obsolete in a month or two. Between their embargo shenanigans (which IMO they could have avoided just by not pretending it was dangerous) and continuing to give access, whatever marginal advantage it had was essentially wasted.
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 don't see how that matters? It's a treadmill. Sure Fable-v1 will be obsolete but the shiny new Fable-v2 won't. Don't view their shenanigans as regarding Fable but rather their current cutting edge model.
They clearly didn't have the compute capacity ready to release Fable at any price that would have seemed "worth it".
Yeah, I've started reaching for local models more. I'll use frontier models at the current cost for tasks that the local ones aren't great at, but when the rug pull inevitably comes, to me they're not worth 1000-2000 a month. And honestly, for my purposes I don't really need models to advance a lot. Like, I tried fable a couple of times and there just wasn't much there to justify its use to me. Opus did the same thing much cheaper.
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.
> But, at token rates, 10x or 100x the cost of open models or what I was spending on the frontier models a month ago
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.
In 5-10 years an Apple Watch will run a Fable level model locally. I don’t think we (hackers) should worry too much about token cost inflation. The current wave of providers, that’s another story.
Just to clarify your implication: Fable subscription usage was just (re)extended to July 19
If they haven't relaxed the classifier this makes little to no difference as the model is essentially unusable no matter the token quotas.
And now their comment is good next week! The rug pull getting extended doesn't mean it won't come.
> It’s not that AI won’t create that much value, it’s that they won’t capture it.
Think airlines - both passenger and freight. They have never come close to capturing all the economic value they enable.
And airline stocks are typically a bad investment and Delta has been called a bank that operates an airline due the the amount of their revenue that comes from credit card fees.
Frontier labs will figure out all sorts of ways to wiggle into the value chain beyond being commodities.
When do you reckon they'll start doing that?
They are raking in 10's of billions, growing revenues astronomically as we speak.
That's not sign of commodity actor, just the opposite.
But, that's not what you said they'd do. I can switch to a different model with almost zero cost. That's the definition of a commodity.
They can do both things at once (and appear to be doing so).
While I do agree there will be disruption we haven't seen yet, my company is already spending >$40k/day for a "frontier model", so who knows. Then again, they're not using that for coding
What are they / you using it for in such quantities?
Who is going to end up capturing all this value being generated is going to be very interesting. Back in 1980, who’d have thought MS would capture the majority of the value from PCs over the next 3 decades, and not IBM?
So far, it seems to be the reverse of that disruption. Hardware companies, Nvidia, Apple, AMD, Intel, ARM, memory companies, are all having record-setting quarters, and it's actual profits, not subsidized by investors and circular investments (though the hardware companies are investing in the AI companies to keep the hype train rolling).
> where’s all this new magical software that the productivity improvements should imply?
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.
Remember: code is free as in "free puppy". FOSS communities were never valuable because of the code. It was the shared written and oral traditions that make the software useful, usable, and updated.
> that make the software useful, usable, and updated
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.
Developing scientific software is disproportionately hard though. Making it usable, useful and keeping it updated is even harder.
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.
You will likely end up in maintenance hell soon. This will likely not be much easier with AI because coding is not the hard/annoying part, it's the fact that you need to dust off every little project every time a tiny fix is needed, and that's a lot of toil in the long run.
Seems to me this would get easier or harder depending on how you write the code. Like if you write the code in something standard and unchanging like POSIX shell scripts or C99 or ES5 javascript, at least the ecosystem won't change out from under you. If you use rust or python or a bunch of node.js dependencies then you might have to edit the project just to keep up with ecosystem changes.
yeah I had this happen to me. Except when I go to maintain it, now cursor/claude are good enough to essentially handle it on their own, so it turns out to be very low effort to maintain.
Maybe? I ran across an old pre-LLM project of mine recently, and past me was an asshole and didn't leave a readme for future me. Meanwhile post-LLM projects at least have a readme that the LLM generated for me or my agent to read and pick up context on. Being able to ask an agent what is this repo, what's going on here? Hey just make it do this, instead of toilsomely digging in and doing it tmmyself, seems to say that might not come to pass.
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.
The fact that you're even saying this it is probably an admission that you do think it's making you dumber. Most people I know, who are honest with themselves, have admitted to me that they feel like it's making them dumber or "zombifying" them. This is also well studied already, https://arxiv.org/abs/2506.08872
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.
It's pretty easy to generalize this, but it doesn't match my perception. People who are using llms to do things they could have already done, but faster, probably have atrophying skill sets. People who are using these tools to accomplish significantly more difficult or complex work than they used to are absolutely finding new ways to push themselves. The problems are just much bigger.
The average Joe can easily vibe code apps that took a small startup just a few years ago. If developers are also using AI to build the same simple apps - then yeah. They're not pushing themselves hard enough, and probably not using their brains as much anymore.
Socrates thought the same of reading and writing, that it would weaken the memory and isolate people from one another.
1966 saw the peak of calculator protests, where math teachers claimed similar things of calculators.
TBF I suspect both of those cases are true. Just that the benefits ultimately far outweighed the costs.
alternatively, you might end up in 'good enough heaven' and not have to touch it for a decade because, you know, it does exactly as you need and you're not google, microsoft, openAI or antrhopic.
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.
Creating a fork of an active project only makes sense if you are its sole user (of the fork) and you really need exactly the modification you've been dreaming of.
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.
In the old world, this was because keeping in sync with upstream was hard. In the new world, it takes an hour. And because you're the only user, you can test in prod. Makes the whole thing faster. I have lots of forked and family-only software. Some are abandoned upstream etc.
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 law of conservation of energy also applies to software. If the price of software approaches zero, it is offset by the time and tokens required to modify and maintain it. Price, time, tokens are simply different expressions of energy.
You still have to track upstream and merge conflicts. Or else you have to get LLMs to fix all the CVEs in your fork.
I'm guilty of creating a fork that just goes off the rails, but still needs to keep up with upstream. I do it via a skill and seems to work good enough for now: https://github.com/midasvo/findroid-ce/tree/main/.claude/ski...
I'm doing it right now to see what the cost is; I cloned the upstream and made a copy of the working directory and asked the Qwen3.6-35B-A3B model to merge my production files with the new upstream.
Since it's just a duplicate folder, I can always fall back if it fubars.
I understand the concern and it's fair but I am very curious about what happens when the two notions of "free" (free as in beer, and free as in freedom) start to diverge because the former gets easier to do.
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.
"This new tool allows for writing all this code ..... but every person and company, in unison, in a grand conspiracy, all decided to only write private software with it that they aren't releasing to the public in any way"
Seems reasonable
Doesn't have to be "every person and company, in unison, in a grand conspiracy" and other such strawmen.
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.
At least for me, the jump in productivity has resulted in building stripped down one-off software for my highly specific use-cases.
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.
This doesn't make sense, I enjoy making bread at home but it costs 10x and tastes like dog shit I dont want to spend my time perfecting the craft of making bread for my daily needs (maybe once in a while its a soothing activity), I want someone smarter than me to spend his entire life coming up and perfecting a solution and exerting more time and effort than I can afford and I am very happy to support him so I can stop worrying about it and focus on what I want to do
> This doesn't make sense
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.
>Makes perfect sense to anyone good at using these models.
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
I love LLMs too, but I am concerned about their cost. They are all still very subsidised. 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?
> They are all still very subsidised.
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.
They may have good margins, but a few things are still true:
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.
> 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.
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.
Interestingly enough, geohot also has an article covering this: https://geohot.github.io//blog/jekyll/update/2026/06/18/pric...
That's commentary on company valuations.
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.
You’re right, my bad, I read that too quickly
The subscription based plans are heavily subsidized, but the direct API inference pricing (which larger companies need to pay) is profitable.
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.
> Using a full Claude Max 20x plan to 100% of weekly usage
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.
I thought hardware prices would always just keep going down.
That is a great comparison. The problem is when costs become prohibitive for new entrants.
You can maybe run a local Sonnet-4.5-ish-level model (sort of) for less than the price of a new car, even at current massively inflated prices for fast RAM. This is probably not what you were looking for. But it's there. You could share one server between multiple developers. Maybe make a little AI co-op or something, with a pair of RTX Pro 6000 cards?
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.
I’ve got a pair of those cards and DS V4F is incredibly good. I’m happy I did what I did because I like this stuff but if you just want stuff then you are absolutely better off not spending $20k on two of these cards and using the API. This guy is absolutely correct.
Guarantee is too strong a thing to seek, but healthy competition makes it highly likely that the supply/demand curve will meet at a healthy place.
You're always guaranteed that you can stash away the open models!
Currently, because of the subsidies from the frontier models, demand is mostly for higher intelligence.
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.
GLM-5.2 is runnable and downloadable today on a MacBook studio that costs a stupid amount of money. No one can take that away from you except by force though, if you want to set it up today.
I get it, I want to agree, I really do like the “this is a new tool in the toolkit of the professional software craftsperson” argument…
…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.
Qtips are made for cleaning your ears. It says not to do that so they are NOT sued every time some idiot fucks up their ear with one.
But the part of the ear that needs cleaning can be reached without a cotton bud. This is like shoving a sponge down your windpipe to remove mucus.
> This is like shoving a sponge down your windpipe to remove mucus.
In my personal experience, not using a cotton-tipped swab for the task is like cleaning a plate loaded with gunk and burned-on patches with one's bare hands rather than choosing to use a sponge and/or brush. You can do it, [0] but it's much more work, much more time consuming, or you get an inferior result.
[0] In my case, I'd need to make one set of passes with paper wrapped around my smallest finger, and then another set with paper shaped into a tool that can lever the excess wax out from outer orifice of my ear canal.
He says he might have been too harsh in his “eternal sloptember” post from may: https://geohot.github.io/blog/jekyll/update/2026/05/24/the-e...
I wonder what he thinks was too harsh, still seems pretty bang on, I think it’s going to age well.
> 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.
I think he now thinks agents can maybe program a little bit.
I felt the same way in 2024-2025. Then Sonnet 4 was released, and things started feeling different. Opus 4.5 was another step change for me. Everything feels like it's accelerating, and timelines are getting crunched. I guess in some ways I envy OP, who would "bet everything" against ASI - the truth is I don't know, and I don't think anyone knows, where this ends.
He didn't say he bets everything against ASI, he said he bets everything against ASI being a flash of light in the sky which destroys our chance of getting access to the wealth it creates.
That's a much less generous interpretation of his writing. "Yes we will birth superintelligence, but everything will just sort of work out for us humans". This seems like a silly take to me.
There's good reason to hate the merchants and their marketing. But builders are not merchants. They build with whatever tool is available.
Geohot is one of the (attempted) merchants, but maybe that is not going so well and he is changing his tune.
Not sure why you had to add the (attempted) qualifier. He started a company and is selling a box. That makes him a merchant. How successful that venture is, is a different question, but he absolutely is a merchant in this arena.
Yeah I don't think any of the labs have some secret sauce for intelligence either. It seems most of the advancements are still coming from hardware, making LLMs more efficient and throwing more compute and data at problems. And even those problems still require a lot of prompt engineering: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...
The secret sauce is training data. They’re not just taking advantage of more compute (which obviously is necessary but as mentions basically a commodity). They are paying billions to data labelers and making judgements about the nature of the training data they best need to make the product they want. This seems to get pushed aside as a minor point but it’s the primary differentiator of the big labs.
As a I said, compute and data. But LLMs can be distilled, so even their data is not much of a secret sauce.
I'm pretty sure at this point that Anthropic is training mixture models (at least in the heavy pre-train) and deploying them dense with explicit loss on thinking trace coherence.
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.
>I predict MoE is a transitional technology
While scaling laws hold (more weights = better), and time / financial costs are not trivial the incentives are in place to have MoE. MoE means you can have more weights without increasing the critical path of evaluating it.
I am curious what you believe the problems with it that would cause people to prefer using less weights. I'm not following what you mean by MoE can't have legible thinkings trace or tool use when existing models with MoE can.
even meta that sucks at doing anything is releasing frontier models. making an top ai is easier than making twitter clone( threads) if you have enough money.
I mean the problem with Threads was lack of user engagement. The same could possibly still be said about their models.
yes ofcourse. But engagement needs strategy and execution.
>One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind. 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.
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.
Those people are not just on Twitter. They’re here on HN, they’re at work, they’re at your next social gathering.
I’ve found them to be unavoidable to some degree.
I see a lot of them on Nextdoor and at my city council meetings.
Talking points like: "Data centers are just surveillance centers that are going to use AI to put us into a digital prison!"
Whatever all that means. I assume some of it is about Flock cameras.
Agreed. There's sort of this spiteful anti-hype here that I find very offputting, and ultimately I think it's because a lot of folks are going out and encountering opinions I never see. I hear wild conspiracy theories about data centers and the financials of involved companies that make their way to me from bluesky or instagram, often through here, but never the unstoppable tide of hype that people are allegedly[1] railing against. I do read Scott Alexander, but he's a lot more reserved than people make him out to be on this.
[1] Allegedly because I have no firsthand experience, not to imply doubt.
Does Xitter still have people complaining about class divisions?
(Genuinely curious, I hadn't ever seen that there though I don't go there much any more.)
"Permanent underclass" is the notion that people who get involved at the ground floor will essentially get infinite wealth relative to the ones who don't. It's a little goofy, but more of the capitalism you'd expect from today's X than the communism you're imagining in yesterday's Twitter.
Thank you, I really needed to read a sane voice. The relentless hype-onslaught is not easy to cope with.
Honestly, who likes any hype in anything ever? Especially if you genuinely like and understand the thing being hyped.
Agreed, but I do think this is a wholly different kind of hype. With crypto currencies it was the promise of modernizing value exchange, with some zealots promising the end of traditional currency.
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.
C-suites. Marketers. People with stock portfolios. Banks. Politicians.
So all people that don’t understand the thing being hyped.
Basically all people with monetary investment in the thing being hyped
Stocks and politics I guess.
This was a refreshing perspective. Thanks for writing it.
misunderestimate? So overestimate, or estimate exactly?
A lot of people died from Covid and if not for mRNA technology and extraordinary caregivers a lot more deaths would have occurred. That’s hype where it truly belongs. Don’t mix AI hype with Covid conspiracy theories.
it's kinda like riding an e-bike, but in heavy and unpredictable pedestrian traffic.
> A certain cult likes to claim credit for things that are happening with or without them, and this is my main argument against the valuation of frontier labs. It’s not that AI won’t create that much value, it’s that they won’t capture it.
> 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?
As soon as we started unironically calling LLMs "AI" we went down the hype path. That has plenty of downsides, like stressing out the entire world and attracting cryptocurrency bros, but also the major upside massive of funding/acceleration.
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.
"It's powerful, and it's amazing, but it's not magic"
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.
Yes, LLM agents are "magic" in the sense that "any sufficiently advanced technology is indistinguishable from magic"[1]
But it's not actually magic. Technical people understand that it's just software running on computers.
1. https://en.wikipedia.org/wiki/Clarke%27s_three_laws
Sure, nothing is magic. You can go look how a simple LLM works and build your understanding from there. But calling it "just software" is trivializing it in my opinion. I can write software, but I cannot write software that writes software.
> But calling it "just software" is trivializing it in my opinion
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
I think calling it AI has been very negative.
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
> Calling it "AI" was possibly a net-negative but we don't know yet.
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.
>One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind. 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.
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?
May be true because humans, especially in the West, are big on performative humanitarianism but not actually considering the well-being of others (or changing their behavior solely for the benefit of others).
May not be true because it's a blind spot to assume that purely by being a player in the AI game (with no real attention paid to quality of result), you have increased odds of winning the game. That's true in the abstract, but practically, it requires a competent player to become true in reality.
> What I don’t like is two things. One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind.
> 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?
This guy is sooooo annoying with his stale takes.
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.
Both can be true and I have both opinions also in me. Love the progress, worry about the consequences of not being careful with it.
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?).
You act as a hype peddler in practically every LLM-adjacent thread. No wonder you’re taking this article so personally. Touch grass.
I recently realized, that ever since I've had AI to "talk" to, I haven't had a stuck or "downtime" moment; there's always something to at least brainstorm on.
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?
Do you feel like the ideas you’re getting from brainstorming these days are the same level of quality as in the past? I’ve been doing some of the same, but I’ve also been feeling like the downtime where I’m genuinely stuck is where my most innovative solutions come to light. I’m not going as deep into problem spaces anymore.
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.
> Do you feel like the ideas you’re getting from brainstorming these days are the same level of quality as in the past?
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.
Thanks, that makes sense! I’m realizing that’s how I’ve been learning to approach it too and seeing the best results.
I am calling your bullshit out and asking to provide even a singular example where you got 'trolled' seeking software development help.
^ Here's one
but seriously? You can have a look now yourself.
I haven't used Reddit for anything serious for years, but the times I or other people actually got useful answers or ideas is few compared to:
- A handful of mods deciding for thousands of readers that your question doesn't fit the "subreddit" (this happened a lot on /r/askscience)
- Low effort answers by karma farmers, basically copy-pasting docs etc
- "Why do you want to do this?" and other derailments completely failing to answer the question
- Actually literal trolling: "Your first problem was using xxx"
> What I don’t like is two things. One, this constant bullshit about some window closing, or the perpetual underclass, or falling hopelessly behind.
The blog has a tagline, "the singularity is nearer". I think belief in a "singularity" almost implies these things to some degree.
the author does not believe in the technological singularity.
That's what I gathered from the blog post - which made the title of the blog seem odd.
> But models are useful just like... all the regexes I never learned how to write and now never will!
Wait, does this mean I'm better at something than geohot? All that time spent learning regexps wasn't a waste!
FUCK U 2
I think big money/private equity/vulture capitalists tend to ruin everything. They set these unrealistic goals and force companies to do shady shit in order to meet these often unattainable goals or achieve unicorn status.
It’s why con artists, scammers always flood every hype cycle. Greed ruins everything.
I hate LLM. I hate people who push any digital artifact with LLM. Fuck you all.
You eat poop for fun
Fuck you
Your SF hate isn't a good look.
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 vast majority of the target audience of this blog post would only consider moving to SF because of the tech scene. This isn't a mountain biking or asian food blog.
> shoehorning an entire metro into the echo-chamber you're supposedly beyond
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 equivalency
ooh I like your site: https://webb.page
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.
damn, you all hate SF that much?
I don't hate SF it's just overpriced.
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.
How to you love this stuff so hard? I could newer love any ai generated music, book or artwork. Anything ai gemerated i have ever seem or heard was either disgustingly slop or indistinguishable from something else which was real. It‘s a like finding a cool track only to discover it‘s a lazy bootleg.
Yeah but it was only like 2 years ago that artists were arguing this on the basis that AI-gen images would consistently mangle hands
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
I've made ai generated art using family photos as the starting point, and it was wonderful. :)
I'm sure most engineering is LLM-assisted already and nothing is wrong with it. It's just the one-shot vibe-coded low quality slop that spoils sentiment of this tools. Also many people are interested in what agents can build unsupervised as a test of "superintelligence".