I think the industry is freaking out about open weights models in general, if not specifically DeepSeek. That is why we're now on the ~4th call for pacing the frontier from the very people who, if they wanted to pace the frontier, would simply do it rather than asking Washington to get involved.
And it's why people like Hillary Clinton have been trotted out to talk about the dangers of open weights models -- I mean, does she even know what that phrase means? (I know HRC is a controversial figure and I'm not bringing her up for that purpose; I just note that she and other prominent retired politicians are now doing the circuit on Anthropic's behalf.)
hard to say but it would last some time. china currently is fast-follow mostly via distillation. they don't have the compute resources to catch up. even if their models are more efficient it's hard to beat the folks throwing an unprecedented number of gpus at their models.
no part of what i said implied distillation is easy. it is very costly and requires massive efforts. the payoff is you need less compute, which china is struggling to buy right now.
EDIT:
> China is following because they have an army of PHDs in data science and mathematics and capital to make use of them.
i agree with this too. but compute is the bottleneck.
Because as always this won't be about the actual pacing, but about the details of how the regulation will be implemented: How would you control the pacing? By installing a position in the company, providing regular feedback to some government organisation. This will be something the big US players can afford and implement, while an open weight blob uploaded to huggingface or modelscope, by definition, won't have a pacing officer attached, and thus will be against the law. And the Chinese companies, of course, won't abide to US law, because why would they? The open models they provide right now are essentially gifts to the world public. If the US doesn't want them, that's their choice.
So the end result will be a protectionist regime keeping the competition out, just like with cars and solar. The local industry will have a protected market, but of course won't play a role on the global stage.
Remember: small government is only good as long as it benefits the industry.
You might both be. China is focused on adoption and distilling is a cost-effective way to get there right now. If the conditions change they might well decide that they have to start training their own frontier models.
That said, China cannot make its own 2nm chips even though they definitely would like to. So I guess there are limits to what they can do sometimes.
> if they wanted to pace the frontier, would simply do it
I don't think that's a fair assessment. These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company. They also can't coordinate with each other, because that's illegal. Antitrust law generally prohibits competing companies from agreeing to restrict innovation.
What I'm trying to say is that their inaction in unilaterally slowing development is consistent with their stated beliefs and requires no other motivation.
Have you looked at this backwards, though? Meaning: have you considered what it would look like if the motivations are actually just plain old money/power, but a set of stated beliefs needed to be constructed to justify what's being sought? I think these absurd beliefs make more sense that way.
> multiple LLMs escape containment consistently, conspire with each other to hide from human oversight, show wanton regard for laws that stand in the way of their goals, and end up hacking dozens of different organizations
> “there’s no plausible way they’re concerned about safety”
> Antitrust law generally prohibits competing companies from agreeing to restrict innovation.
The antitrust claims are a complete smokescreen. Industries can and do adopt safety standards without government intervention.
> These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company.
So what? Anthropic believes their work has a 10% chance of killing all humans. I think risking the destruction of Anthropic's business should be worth avoiding that, if that's what they believe. And with one half of the frontier duopoly gone, the other half would have no incentive to race forward. And I know there's China, but they just get all their capabilities from distilling Claude, right? So, problem solved there, too.
Sure, but does slowing down the development of new models count as "adopting safety standards"? I very much doubt it.
> Anthropic believes their work has a 10% chance of killing all humans
This ignores the other part of what they believe, which is that they are the people most likely to make a model that doesn't do that. So, in their view, letting other companies win would increase the probability of human extinction.
> with one half of the frontier duopoly gone, the other half would have no incentive to race forward
I don't see how this could possibly be true, with at least half a dozen companies being just months behind what the frontier labs are releasing.
> Sure, but does slowing down the development of new models count as "adopting safety standards"? I very much doubt it.
I mean, the point (if the claim is to be believed) isn't just to "slow down the development", it's to take more time during development to properly assess the risks the models pose, develop methodologies to reduce that risk, and standardize that across companies. I doubt those wouldn't count, especially in the eyes of regulators of an administration calling for that same slow down.
> Sure, but does slowing down the development of new models count as "adopting safety standards"?
Of course! What slows down the development is the adoption of specific safety conditions the companies draw up. They can just do that, and it will hold up in court.
> in their view, letting other companies win would increase the probability of human extinction
Yes, I've heard: "We must be in charge even if we end up killing everyone in the process." I personally think that proposition is invalid, but we're all entitled to our opinions.
People used to append IANAL to such statements :-)
> I personally think that proposition is invalid
I agree, but that's meaningless in this context. I was responding to the claim that "if they wanted to pace the frontier, would simply do it", which is false, given what they actually believe.
> And I know there's China, but they just get all their capabilities from distilling Claude, right?
I hope this is a joke. But most of the LLM research and inventions come from China? The best papers are from DeepSeek? You either get the data by stealing from humans or distilling from bigger models?
Yes, I was being facetious. Chinese labs are doing the most interesting research and publishing it. But Anthropic beats the "distillation attack" drum every time doubts surface about the depth of their technical moat. Of course, when they need a scary bogeyman, then the story shifts to how China is recklessly racing ahead building dangerously powerful models. That's the thing with Anthropic: they speak out of all 13 sides of their mouths.
> China is recklessly racing ahead building dangerously powerful models
Their fear-mongering about GLM 5.3 got me to try it out. Its very good, I'll only go back to Claude if GLM isn't available (it forgot how to do tool calls yesterday).
Interestingly enough, it seems to compact at about 10% of the 1mn context, which makes sense if they're trying to run profitably.
"Is a lawsuit", not "was a lawsuit". Present tense, ongoing, not past tense.
Status: Complaint filed September 18, 2026 in the Northern District of California · responses due October 14–15, 2026 · initial case management conference December 23, 2026 · no class, no settlement.
- from the linked article.
> did they correct any of their behavior?
The behaviour being objected to is publicly agreeing with each other to slow down.
If a court orders them to correct this behaviour, it means they are forbidden from agreeing to slow down.
So no, it has not yet prevented anything. Which I think is the previous posters point: there is currently nothing stopping them from doing what they say they want to see happen. The problem from their PoV is that other players (those working on open source models, those in other countries who are never going to listen to any directive to slow down anyway, etc.) won't also collude with them so even if they do collude to do what they say they want everyone to do it'll be to their disadvantage, where what they actually want is for everyone else to be told to slow down while they somehow pay for a loophole to allow them to go a little faster for competitive advantage.
The speed of change is impressive, I don't think any tech ever before has gone from “brand new disruptor in public awareness” to “the incumbents feeling they have insufficient moat and so trying to arrange a regulatory capture situation” in such a short space of time.
> what they actually want is for everyone else to be told to slow down while they somehow pay for a loophole to allow them to go a little faster for competitive advantage.
It’s hilarious that you can write this and then act befuddled as to why they would therefore want laws as an external (and ideally impartial) coordination device.
Thinking this is a super unique scenario just reveals your ignorance of both 1) game theory and 2) actual industrial history. An industry asking for regulation to stop a race to the bottom is not atypical at all.
If you look at their actions, they're not the actions of someone who both A) believes their technology to be an existential threat and B) doesn't want humanity to go extinct
If they truly believed both of those things, they would just shut down their companies, because being a billionaire is entirely pointless if you're dead.
I can only conclude that they don't believe both A and B. I'm gonna assume they believe B because actually wanting to exterminate humanity is too comically supervillain esque even for Altman. Therefore they must not believe A. And it makes sense. If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology? I'm not buying it.
Instead, I think they believe C) that AI will create enormous economic value, D) that value will be distributed across the whole economy by making everyone more productive. Assuming C and D, you get E) for them to capture this value, they must maintain proprietary control of the technology in order to be able to charge everyone else for the privilege of using it.
Assuming they believe E, open models are an existential threat, not to humanity, but to OpenAI and Anthropic.
Their solution: make everyone else believe A, in order to achieve regulatory capture and somehow stop open models from advancing by banning their development, or something. This is a hail mary pass. I can just about imagine them achieving this within the US and maybe even Europe, but China? That ship has sailed.
My problem with the “making everyone more productive” is that it’s a feel-good propaganda for the individual. Most capitalist see people as an expensive cost to be eliminated. Thus all the company shares going up when they announce layoffs.
The market can only absorb so much new products, so if productivity increase, companies will reduce headcount as much as possible to increase margin for the same income, not grow their product or production to make use of their staff.
(See any wage/productivity graph)
Government will also likely follow the same path of reducing headcount instead of producing better / faster outcome for their citizens (except the internal surveillance apparatus. The one never shrinks).
Reducing the cost of a desirable service or product often has a tendency to increased demand for that service or product, and means customers have more capital available to purchase other products and services increasing demand for those. These factors are significant drivers of overall economic growth.
Of course economic growth has it's own problems in terms of ecological impact and such, but if you want to reduce ecological impact you still need to improve productivity. It's a matter of how you spend that efficiency improvement as a society.
> actually wanting to exterminate humanity is too comically supervillain esque
An entirely plausible scenario is that these oligarchs dream of living in Solaria, the planet from the Asimov universe where only a small number of immensely rich people lived and all the work was done by hordes of robots.
Once humans no longer serve the needs of the oligarchs, why would they want billions of people around? A question to ponder.
If they were rational people they'd likely have stopped their quest after the first few millions. They are driven by pure greed and their humongous egos, none of them is sane.
Some of it might be that, but some of it might be more prosaically the same thing that keeps people playing competitive games on one hand, or cookie clicker on the other. (Cookie clicker could read as "pure greed" if you squint.)
Yes, but me playing cookie clicker (or fortnite, or whatever, competitive tic-tac-toe) does not bring with itself suffering and poverty for millions of others. Whereas them chasing their virtual money high score ruins societies.
They are all psychopaths, devoid of normal human emotions.
For now. Throw in climate change, food shortages, war, mass migration, and suddenly the ratio becomes 25 million : 1 for starving, angry people vs billionaires, globally.
"I propose something different. Listen, my dear enemy... I shall acquire absolute power on earth. Not a single chimney will smoke unless I order it, not a single ship will leave harbour, not a single hammer will strike. Everything will be subordinated — up to and including the right to breathe — to the centre, and I am in the centre. Everything belongs to me. I shall engrave my profile on one side of little metal discs — with my beard and wearing a crown — and on the other side the profile of Madame Lamolle. Then I shall select the 'first thousand,' let us call them, although there will be something like two or three million pairs. They will be the patricians. They will devote themselves to the higher enjoyments and to creative activities. Taking an example from ancient Sparta we shall establish a special regimen for them so that they do not degenerate into alcoholics and impotents. Then we shall determine the exact number of hands necessary to give full service to the culture. In this case, too, we shall resort to selection. These we shall call, for the sake of politeness, the toilers—"
"It goes without saying—"
"You may laugh, my friend, when we get to the end of this conversation... They will not revolt, oh, no, my dear comrade. The possibility of revolution will be destroyed at the very root. A minor operation will be carried out on every toiler after he has qualified at some skill and before he is issued a labour pass. Quite an unnoticeable operation made under almost accidental anaesthesia... Just a small perforation of the skull. He will get a bit dizzy and when he wakes up he will be a slave. Lastly, there will be a special group that we shall isolate on a beautiful island for breeding purposes. All those left over we shall have to get rid of as useless.
"There you have the structure of the future mankind according to Pyotr Garin. The toilers will toil and serve uncomplainingly, like horses, for their food. They will no longer be people and they will have no worries except hunger. They will find happiness in the digestion of their food. The elite, the patricians, they will be demigods. Although I despise people altogether, it is always pleasant to be in good company. I assure you, my friend, we shall enjoy the golden age the poets dream of. The impression of the horrors created by purging the earth of its surplus population will soon be forgotten. On the other hand, what opportunities for a genius!
"The earth will become the Garden of Eden. Births will be regulated. There will be selection of the fittest. There will be no struggle for existence, that will be lost in the haze of the barbaric past. A beautiful and refined race will develop, with new organs of thought and sensation. Communism trying to drag all of humanity to the heights of culture? I instead will do it in ten years... What the hell! In less than ten years! For only a few, true... But then, it is not a question of numbers."
"A fascist Utopia, rather curious," said Shelga. "Have you told Rolling anything about this?"
"It is not a Utopia, that's the funny part of it. I am only logical."
Well, what was considered reasonable enough to state outright in 1906 or in 1926, is not quite polite to say out loud in 2026, but I don't think the sentiment of contempt has ever quite gone away entirely.
Unfortunately, many (most?) of them also seem to think they're better equipped than all of the others to do it safely, so "shut down my own company" effectively means "let one of the others destroy the world", whereas "keep company running" has at least a chance of "I solve alignment, we have a happy ending" (your case C).
Note: this does not mean I agree with them. Obviously they can't all be correct that they're safer than the others.
> If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology?
The tech they're experts at is AI, not security, which answers both parts of that.
Outsourcing things you are bad at is normal, not a mystery. Lots of places value physical security, and therefore hire a private security firm.
They can believe A and B and at the same time they don't want to cripple themselves. Your logic does not hold up.
Similar situation: nuclear race - everybody knew the risks, but making yourself armless does not help in any way. You need to make sure everyone is on the same page before you make yourself vulnerable in any way.
If there's a company with an insanely high valuation, and two potential motives consistent with their actions, one altruistic and one greedy, I'm struggling to understand why the assumption would be altruism, rather than the overwhelmingly more common primary motivation of "we want to make a boatload of money" while finding it useful to pretend otherwise, especially when so many sizable investors have similar sizable expectations for their returns.
It seems to me the ROI on the research may be diminishing. If they aren't turning a profit with the infrastructure already deployed and the models already trained then buying more GPUs and spending billions more on training doesn't seem like it would improve the correct side of the balance sheet.
It's not clear to me that what the evidence for D is. It could just as easily be that they want to translate popular fear/distrust of AI and desire for regulation into something anticompetitive that benefits them over the smaller players rather than being regulated themselves.
Is enforced by the federal government if they want to.
The most recently truly significant enforcement action was in the 80s, the AT&T breakup. And the current administration certainly will never enforce anything that hinders the oligarchs.
Daniel Plainview was in a Nash equilibrium where he couldn't unilaterally slow down oil extraction without essentially destroying his company. His competitors would simply drink his milkshake. When he called for pacing oil extraction, I take him as sincere.
One was even a non-profit, which should be more concerned with the well-being of humanity (which they assure is in great danger from what they produce) than the continuation of the company.
If A or OAI dropped out, there would be pretty crazy financial backlash from the whole inverted pyramid of investment and capital allocations that is built on top of their projected growth.
Why are people expected to lose their goddamn minds every 2-3 months like they've never seen an AI before.
I have a secret: if you don't use AI, the new releases aren't very impressive. I'm bored out of my mind with people doing galaxy brain memes every 2 months while on the whole.... they're still boring zombies, and only getting boring-er and boring-er. There's nothing more boring than being impressed by the latest AI model.
In terms of the projections being insane you can quantify it: each model costs something to train, but after that the training has only captured so much unique new value in terms of model capability, and the race is on to drain that value as rapidly as possible. Everyone is competing to drain the same value. This generation of models makes slop video games for example, and slop video games rapidly became the most boring thing on the planet.
I tell you 3 times I was genuinely excited with AI. 1 - original Google transformers release (hope for future) 2- opus 4.6 comes out (first time an llm can do real programming for me and the code is good) 3 - Qwen3.8-Flash-Next comes out - first time a model i can run myself at good speed can do real programming and is almost as good as opus.
However, should they be the one to survive the inevitable wave of collapses, they will be absurdly profitable, and that's what all the investors are betting on - the usual VC playbook of creating an entirely new market (or destroying aka "disrupting" established players) fueled by absurd amounts of money, surviving until everyone is hooked and the competition is gone, and then jack up the prices.
I comprehended it just fine, that's why I used "was" to begin with. My comment, which you "omitted to comprehend" (sic), ironically refers to their non-profit heritage, which should make them double sensitive to good causes like not destroying the world.
There definitely are many things wrong with that, of course. Lighting someone else's money on fire and walking away is highly unethical.
The real question is: what are the motivations for companies to pretend that their AI is world-ending, and what do they want out of the disquiet caused by them saying that?
Except it doesn't actually slow down the research. If anything it would likely drive investors to contribute more to the competitor that didn't destroy their own company and now you have the same amount of progress and a monopoly.
Collusion between the American companies to stifle innovation has nothing to do with open weights models - why would they collude to stifle innovation when the Chinese models will just gain market share/overtake them in capabilities. You are looking at this from the wrong direction.
they can't slow down because the investors are circling. But if they were forced to slow down by the government then they can use the zour hands are tied" excuse
> They also can't coordinate with each other, because that's illegal.
On the last OpenAI release, they only had comparison with Anthropic models, on the last Anthropic release, they only had comparison with the OpenAI models, there's something obvious going on here.
Maybe it's not intentional but having two providers only making comparison between each other is still a bit fishy to me in such a competitive space.
> For example, Anthropic putting K3 in its comparisons would be a huge admission that K3 is worth considering.
They do put OpenAI though, if it would be a comparison only with their own models, why not I get it but a single other competitor?
It would be like Apple making comparison page with their new iPhone and only mentioning Samsung and nobody else for example, it would sound weird. You either include competitors or you do not and include none of them.
> they can't unilaterally slow down without essentially destroying their company
According to them, the alternative is destroying the world.
They made up a shitty excuse to regulate the competition without thinking through what it implies about them. That's all this is. Let's not help them make even more excuses.
Hold on, the implication of "it would be a shame if anything happened to $nice_thing" is usually "I will kill/destroy the $nice_thing if you didn't take my offer", and here, nice_thing = "the world".
Are they saying that they will manually and willfully start a global thermonuclear war if China didn't listen, not that there are risks of AI accidentally causing one? Is that what they're trying to say?
> These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company.
I mean, these companies are saying AI will destroy humanity if it’s not paced. If they truly believe that, the small risk of destroying their own companies seems like a small price to pay.
Companies don't think. Companies don't make decisions.
I appreciate the analysis and following a string of thought. But sometimes you eliminate so much reality to follow a thought that the string becomes kinda pointless.
Amodei is worth several $billion, right? He could simply walk away today. There is no Nash equilibrium for him. Nor for his replacement, nor their replacement. And it is not illegal for people to collude in quitting their jobs.
There are two obvious arguments for not quitting. 1. If the well-intentioned person quits they will be replaced by a non-well-intentioned person. 2. There is no real belief in a hazard and you would be giving up unbounded income for no reason.
If the actions and behaviors of a supposedly well-intentioned person who is afraid of a hypothetical non-well-intentioned person are indistinguishable from those of a supposedly non-well-intentioned person... don't we really just end up with a really long sentence with a lot of gibberish + the outcome of having a non-well-intentioned person in power?
So destroy the company. What are we even talking about here? I thought this was some “extinction level threat”, and you think it’s excusable to prioritize profit and product because…why?
If the options are to shoot yourself in the face or wait a year or even a month and have someone else shoot you, I think most people would pick the second.
Companies are continuing because they don’t actually believe they are going to have significant negative consequences happen sooner. It’s marketing fluff around how cutting edge what they are doing is not any kind of realistic threat assessment.
I think most would think "I can probably do a better job of not shooting myself in the face than some random person not worried about it will" and then proceed to shoot themselves in the face.
That said, I don't buy the companies actually give a damn about the risk either.
I don’t necessarily believe that the AI itself will kill us all. But the AIs are increasingly being baked into complex, even military, systems. That scares me just because the opportunity for complex, cascading failure becomes very difficult to predict and control. These systems are inherently immoral (it’s just a lot of matrix math) and we know these systems “lie” and try to evade detection (see the Huggingface write ups). Now, couple that behavior with a military system (drones, targeting systems, etc.). I’m not suggesting that we’re headed for full-on Terminator land here, but things definitely could go wrong.
I think there's something here about game theory. I'm not sure what it's called.
The logic that if something is beyond repair anyway you might as well exploit it.
For example I heard a pick-up artist say that he thinks he is harming civilization by sleeping with hundreds of women per year. But that he already considers the situation unsalvageable so... "Might as well?"
I don't think that's an amazing attitude, but the AI labs seem to have a similar idea.
More charitably the logic seems to be, "only I can do this responsibly." I heard that from both Elon Musk (he cites this as his motivation for starting OpenAI — a chat with Sergei Brin that spooked him) and of course Anthropic (which split off from OpenAI due to ethical concerns).
So I don't actually think the ethics is all for show. I think people are actually taking this stuff seriously. But it is indeed deeply unfortunate that the survival of the companies incentivizes them to keep going at an irresponsible pace (by their own admission).
All following the same gradient off a cliff. One AI described it as a tragedy of Ancient Greek proportions.
"I cannot tolerate living in a world where we aren't the ones responsible for ending the world!"
It does not matter if competitors would not also agree to destroy their companies.
If I was competing with a bunch of people on building something that I came to believe would be an extinction level event, I wouldn't be saying "Even if I slowed/stopped, the others would not, so I have to keep going".
I'd say that I want absolutely nothing to do with pushing it further, stop my work, then regardless of consequences, do everything possible to stop my competitors regardless of legality
If there's one thing that politicians and strategists understand as much if not better than business people it actually is stuff like this. Most people never don't deal with high stakes situations at certain levels where everything is fungible(the law, media statements) and fuzzy (intelligence). Dealing and negotiating with nation states or figure heads who are adversarial/allies and at same time trade partners/enemies etc. They have certainly sold this situation as one that fits the regime for this.
Any single model at this point can do the basic type of CRUD coding most people were doing for the past 20 years.
Even a 4 bit Qwen model running locally beats me manually putting React components together by hand. But even that is too slow so we've all started using paid models in one form or another.
So I would urge these folks to calm themselves and realize Oracle made a lot of money selling managed RDBMS to people who could have easily just downloaded MySQL.
I saw it first on a recent Anthropic post, followed by a similar comment thread. There’s some sloppy techbro poetry to it, like when we used to call marketing people growth hackers a decade ago.
I bet she knows fine what the phrase means. Doesn't mean she's beyond protecting the business interests of people who fund her political goals of course but she's always struck me as a woman who knows her brief.
She is.
Those things are still hard.
I run my own inference, I wore my own harness, I implemented production apps using LLM ("document intelligence", aka data entry), I followed Karpathy course and trained GPT2, I trained a classifier.
It's still hard for me to explain a lot of nuances to IT directors with a technical background.
> I think the industry is freaking out about open weights models in general, if not specifically DeepSeek. That is why we're now on the ~4th call for pacing the frontier from the very people who, if they wanted to pace the frontier, would simply do it rather than asking Washington to get involved.
This makes no sense. Pacing the frontier gives open models the time to catch up and reach parity.
"Pacing the frontier" means "implementing regulatory capture". They don't necessarily care about preventing open weight models from existing, just on preventing the people with money from being able to make use of them.
They would leave a competitive advantage to non-US companies.
Even if, and that's a big if, they can coordi ate with Europe Asia would still ignore them
The important part is HOW they want to pace the frontier. Not by actuall slowing down themselves, but by having the government create expensive regulations, like having independent verifiers "verify" the models before release.
Now, something tells me that those verifiers would verify anything an american company in good standing with the Trump admin releases, and likely nothing else.
And obviously, since verification is so incredibly important, we can't allow models, open or not, from other non-verified companies.
Think of the profits...I mean, the kids, or something.
If you read through Dario Amodei's manifesto on pacing the frontier, the only concrete action item is implementing even harsher restrictions on technology exports to China.
I'm absolutely bewildered that this is the top comment. 'Pacing the frontier,' if eventually enforced, will only affect US companies, not Chinese companies. How do you even 'pace the frontier' for Chinese models, and why should they agree to it?
China doesn't have a good track record of following signed agreements ( the WTO thing comes to mind), and this whole 'pacing the frontier' concept is even less enforceable than a signed agreement. So I would say that Anthropic/OpenAI called for this not because they thought it would eliminate the threat of Chinese models, but in spite of the risk of being overtaken.
And why would China pace the frontier when it was the American accelerationist companies who created this mess? I remember scientists calling for an AI slow down way back in 2023 and they were all ridiculed and ignored by those very same companies. There is no way "Pacing the frontier" isn't just a ruse.
> I'm absolutely bewildered that this is the top comment. 'Pacing the frontier,' if eventually enforced, will only affect US companies
I can address your bewilderment. What I was trying to say is that they don't actually want to pace the frontier at all. They want to gum up the market with regulations they design, that would ultimately force US companies to rent AI from them. They don't need to stop Chinese AI development and cannot do that. But they can, to quote OpenAI's Head of Strategic Futures, "create enough regulatory risk that every regulated enterprise backs off [of using Chinese models]".
That doesn't address the bewilderment. No one is confused about what the tsunami of cynical skeptics who pop up on every thread like this are saying. We get it. We understand what you think is happening.
What's bewildering is the absolute lack of recognition of the fact that IF participants are locked in an arms race, they cannot act unilaterally to disarm. So not disarming doesn't actually say anything about whether they want to.
Now, of course a company not disarming (pacing the frontier, in this case) is not evidence that they ARE locked in an arms race. Everything hinges on the question of whether it's an arms race, and I think there's a great discussion to be had there. But the cynics never even bother to address the question.
All that said, I'm not actually bewildered. HN is way too smart to not understand all this, so my conclusion is that the people talking past each other are having a different emotional reaction to what's happening in the world. The angry bitter cynics are reacting from fear and hatred, and that's where the sloppy motivated reasoning comes from.
So, to be clear, you are just asserting that maybe we should take them at their word? That the story they are telling is in principle plausible? That seems fine to think but that doesn't make the other story less plausible!
Also it seems a little silly to assert that the "angry bitter cynics" are the ones who don't think the world is hurtling to imminent doom or whatever..
>But the cynics never even bother to address the question
You might have sleep past it, but cynics became cynic when this question was just plainly ignored with self assured answers, mostly based on market valuation as the ultimate truth indicator. I guess now days the self assured answer is to accuse critics of "fear and hatred".
The US companies are also claiming the Chinese companies are only where they are at because they're distilling US models. By that logic, if there are no better US models to distill, the Chinese innovation should also stop.
In my country, public figures who are proven to be layman/non-insiders/not-working-in-the-space are speaking publicly about these terms and throwing them around like its the standard tech everybody uses 24/7.
She may very well even understand it because she’s not exactly dumb, but she is a creature of Washington, arguably one of the Apex predators, like some siren or hydra, maybe a mashup of them.
Because of the position of the party that plays the role of the tolerant, accepting, and anti-racists; she can’t just come out and say what underlies her words, “we, the ruling class parasites are getting very scared of China deposing our stranglehold on the world, and we don’t like that; so we will raise manipulative ‘concerns’ in an effort to bring about outcomes that hopefully will benefit us.”
> if they wanted to pace the frontier, would simply do it rather than asking Washington to get involved
The only reason they're asking to "pace the frontier" is because the two big US players have IPOs coming and so are desperate to find ways to (a) grow their vastly over-inflated valuations and (b) keep the whole Nvidia circular-financing gravy-train on the road.
I mean, its only a few months back that Anthropic were bragging to anyone willing to listen how amazing Fable was and how you had to be a super-special person to use them and be charged through the nose for doing so. But basically anyone willing enough trust Anthroipic with a copy of their ID and with a big enough wallet would happily be given access.
I'm a great supporter of the open-weights. Long may it continue.
Hillary is simply a dumb person. The only real skill of her is being a strategist, but after that, there is absolutely nothing. Born in a good household, married the right person and from there she simply used the "woman behind a successful man"-card that feminism started to push around that time.
Her losing against Trump back then was a clear setup. I cannot imagine, that anyone who put her on the chessboard thought, that she could win this. Trump winning twice had one reason: The deep state wanted it, because they needed some radical reforms that required a clown to pass them through without the citizenz being able to scan and put attention on them and instead media looked as the poses and faces trump made and all they and everyone else did was laugh. Exactly as planned.
As a tech business, it's a bad business. The moat is your sales channel and getting companies locked into your platform in multi-year agreements. When companies build systems on your AI API and test it's performance and integrate it across systems, they don't churn, reintegrating, re-testing, and re-skilling costs money.
Exactly this. Deep Seek hasn't caused a lot of knew talk, but it has silenced the talk about slowing down. An absence is harder to notice. But also the news cycle is so fast, its tripping over itself. So who knows.
That’s the point the GP is making and the US firms too.
They can pace themselves if they wanted. But that potentially does them more harm than good if nobody is enforcing the other US companies, and specifically the Chinese firms too.
What's happening with AI is just a reflection of geopolitics.
OpenAI, Anthropic and SpaceX have spent based on the predicate that they will "own" the AI future, that this moat will justify the trillions spent on hyperscalars, that this will be a repeat of the dot-com era that produced Microsoft (yes, yes, founded in the 1980s), Google, Amazon, etc that globally dominate their respective arenas. The US government acts to protect those interests and this is uniparty so Hilary Clinton is just as likely to be trotted out as Mike Pompeo. This is why many, myself included, describe the US empire as 5 companies in a trench coat.
China, on the other hand, believes that companies should serve the interests of the government, which itself serves the interests of the people. So rather than create trillion dollar AI companies with moats, AI should serve society. Xi Jinping has spoken extensively about this. That's one of the funny things about China. They love to write stuff down and tell you exactly what they're doing and why yet at the same time they're ascribed nefarious motives.
So, despite sanctions supposedly preventing China from buying the latest and greatest AI chips, China through its labs has begun commoditizing the AI models with open weight models. Society should benefit from that rather than a moat being built.
You can run DeepSeek V4.1 Flash locally on a 256GB Mac Studio for ~$11k now. I've seen reports of 30-38 tokens/sec. Not amazing but that'll only improve with future generations. In the coming years, China's EUV/DUV will come online and this will threaten the NVidia monopoly, at least for local Chinese companies.
So we have AI companies burning cash to subsidize usage and build a market where the revenue required simply may not ever eventuate through a combination of open weight models and increasing accessibility of local models.
> That is why we're now on the ~4th call for pacing the frontier from the very people who, if they wanted to pace the frontier, would simply do it rather than asking Washington to get involved.
See its not about pacing the frontier, its about pacing the frontier without hurting any of their fundraising.
> And it's why people like Hillary Clinton have been trotted out to talk about the dangers of open weights models
Pepperidge Farm remembers when robust consumer applications of cryptography -- especially for SSL/TLS -- was the big boogeyman, and the export controls involved were absurd.
Pacing the frontier is a front to get federal regulators/evaluators involved that they can then point to open source usage and cry "look they're not being safe!"
Ben Affleck knows his shit around LLMs apparently and HRC may have many virtues but "not being smart" is not among them. So even if she has trouble with grasping the fine details of the technology - she is more than smart and experienced enough to envision the implications.
The reason people aren’t freaking out is because most people are using heavily subsidized subscriptions.
I tried the cheapest provider on openrouter and burned through $50 in a few days. Quality was ok, seems slightly above Luna quality perhaps? But that $50 is 1/4 of my codex subscription where I could have burned that many tokens or more using Astra within my weekly reset.
This won’t last forever but as long as the frontier labs are subsidizing this heavily the open models won’t matter.
With all due respect (not that I feel like much is due after that response) I am not really sure you know what I am talking about. I'm talking about inference providers, like inference.net, Fireworks, Coreweave, Digital Ocean, etc, to use DeepSeek 4.1 Flash. They didn't create the model, they just are charging you to run inference tasks. That is a different story.
DeepSeek themselves are honest about the fact that they train on inputs by default. You won't hit DeepSeek if you use OpenRouter with ZDR enabled.
No, it’s outrageously profitable above x% utilization without stealing any prompts. Provider economics still pretty good. Acquiring hardware is the current limiter.
Yep. And the model is practically unbounded in its knowledge of math. So people should keep that in mind when they read anything about its level of intelligence.
I am curious how you managed to spend that much on Deepseek via OpenRouter. I loaded $100 back in July while using v4-flash or whatever the cheap good model was at the time, and have upgraded as the new ones came out from Deepseek. I still have $16 and some of that spend also goes towards the AI usage from my customers (the context they need to load in is quite large too).
And I am using the Claude Code harness with DS as the endpoint. And I use it ~5-8hrs a day to do my coding.
Likely the user doesn't know what they're doing or has extermely bad workflows. They're prob not managing their cache, and dont use compaction.. Letting context get to 500k and invalidating their cache every 10 tool calls because they have no providor fallback settings.
I was running deepseek v4.1 pretty much non stop during work hours, with heavy tool/mcp usage and finding it very difficult to spend more than $75 in a month.
Also the cheapest providers on Openroutrr can often have terrible cache hit %, short TTLs resulting in their effective price being much more expensive than people realize. 75% cache pretty much destroys any savings from a super cheap token perspective.
It's wild how different usage patterns are between users.
I have seen the Cursor leaderboard on my company and the vibe coders consume about 5x more tokens than the developers. They and other office workers also have Claude and their limits are often over around Wednesday.
People are using millions of tokens to do very simple HTML reports. I have seen someone asking the LLM to download the entire data into the context and asking it to sort.
Now that Microsoft allows you to do /cost for individual tasks (or whatever they call them today). So I tasked Sol, Astra and Fable in cowork with exactly the same vibe coding task on the exact same zip file containing a code project I needed an update for. Astra used 20x and Fable used 15x of what Sol did.
Fable changed a lot of things I had explicitly told it not to change. Arguably a lot of them would've been correct if you didn't work in a place where abstractions are directly against the core principles, but what it produced was basically unusable. I'm not sure if Sol or Astra did best, they produced rather similar code outputs. Astra's was better, but Sol didn't do so bad. It forgot to clean up a few places after it's refactor and it made two bugs I had to correct but other than that it was fine. Astra on the flip-side might have produced code that didn't need changes but it also rewrote every piece of documentation so that it became horrible.
As far as the "experiment" goes, it just shows you that the credit consumption is basically pure magic. You'd think that the Microsoft AI admin tools and the Agent365 FOMO DLC license they sell might give you some sort of reporting, but it doesn't. What you can see is how many tokens a user consumes and the total number of tasks they've initiated as well as whatever running agents they have. You can't see what models they use or which tasks are expensive, which makes it very hard to help them. Early on we had an employee who hit their limit in an hour, and it turned out they had basically uploaded a lot of information and run it in a single long task that kept going over it again and again. We told them it might be a good idea to only give it what it needed and to create more tasks, and even though it's been three months, they have yet to consume as many credits as they did that first hour.
But that's how you support and track it. You see a user spend a lot, then you go to their computer and now that you can actually do the /cost thing, you go through their tasks and try and figure out where they're spending money...
It's obviously improving. A month ago /cost wasn't there and they just released a new dashboard for cowork, but it's still black magic that is impossible to govern.
I'm assuming you're using Microsoft Cowork or Copilot or something. I suspect it's system prompt and implementation (tooling/harness) isn't the same as that of OpenAI and Anthropic even if the underlying model is supposedly the same. The answers provided can be wildly different and often for the worst.
It's cowork. I'm in enterprise in the EU, so we're more restricted in what we use. My issue is mainly with how impossible it is to do any form of reporting on this. Obviously this isn't the most popular opinion among the people subject to it, but for most things we do in the Microsoft enterprise setup we can basically monitor every thing a device does. With Cowork you can't even see which individual task is eating a users credits. At least not yet.
Which is an issue when you need to get department managers to manage their budgets around the amounts of credits their employees spend. The more of a black box it is, the more governance and corporate bullshit you have to deal with.
The copilot part of it runs "unlimited" on the license. Except it's not unlimited, and this is even more of a blackbox because you can't see any sort of spending and the limit is listed as "extensive use".
Just btw, most assume when you say Cowork you're referring to Anthropic Cowork as it's the original rather than Microsoft's white labelled Cowork. And when you say Sol or whatever model you're referring to OpenAI and use on its servers.
Microsoft's versions are not at parity with the originals. I feel your pain on being limited by silly enterprise restrictions.
On your point about "monitoring", personally I feel this is toxic corporate IT culture, enabled and perhaps pushed by the likes of Microsoft with all their tools, which they of course make money off. People have cellphones with cameras making most points in this area moot.
An alternative used in other big corporates is to set budgets, with tiered authorisation approvals for higher limits. The users and their managers can justify why and what they're doing that they need the additional tokens. This also encourages more efficient use of tokens on other work. More efficient use is sometimes counterintuitive. Laissez-faire generally works best.
Cowork requires user approvals for high risk actions such as emailing.
It's because this is how AI has been sold to everyone - just ask, and it will do it.
The better pattern is to let it code the app and then you can use the app to target your data. So you only pay for it once, plus it's deterministic. But yeah, it requires setting up an environment, etc. It becomes "maintenance".
You have to keep it secure, so it’s not “pay once” but has running costs, and need some kind of security scanning, citizen developer devops platform etc etc
This reminds me of when we gave clients the ability to build their own Power BI dashboards. The users would end up doing a full table dump multiple times for the same table in their reports. Requiring 16GB of ram on the server and maxing out the database every time the report was refreshed.
We ended up having to hire a full time employee to fix the performance of client built reports.
I've seen the same problem with Snowflake integrated with Claude.
People run a stupid amount of expensive queries that end up costing way too much because they're asking Claude the wrong query.
Not to mention people running wrong queries, using the result as gospel, and then the result has to be sent to a data analyst to be reverse-engineered so the numbers make sense.
You can easily burn through lots and lots of money on DeepSeek, if you do eg large scale code reviews.
Eg I've used Sashiko locally for Linux kernel code reviews before sending out my contributions out to the world. Sashiko is a great system, but it can burn through tokens like there's no tomorrow.
I do some pretty insane agentic work running constantly. I’m currently burning through multiple $200 accounts every week. I regularly spend $10-$20k worth of tokens a month. Most of it has been going to my experimental c++ compiler project
I've heard that certain inference providers may have different quality of caching implementations, so even if the listed numbers are as you say, the practical cache hit % you get might be significantly different/incur significantly different costs.
When 98.5% of my requests are cache hits (according to Pi for the last week), the cache miss price isn’t that important to me, and $0.003-0.006 per 1M input tokens is shockingly cheap.
It’s also the major difference between using DeepSeek directly vs other providers also serving it, though I have not looked lately: it’s possible other providers have matched its cache hit pricing better?
It will of course depend on what you’re doing with it, but right now my session at work has a 99.8% cache hit rate, and I’ve been running this session for hours with 23M tokens read and 713K tokens written (Opus 5.5 in this case though)
There’s a big difference in speed & quality between using DeepSeek API directly with DSH vs. DeepSeek in Opencode Go with Opencode CLI. Can’t tell if it’s the provider or the harness - but worth to give it a try.
All these products, western or Chinese, are built on a hell of a lot of running rough-shod over licensing or IP laws in general.
I'm writing a program I personally need, but I would be happy if there existed something like it already, if someone else vibecoded a better version of it than mine, or if DS got better at vibing this kind of thing.
Why does liking a product mean you have to give them all of your data? People are so outraged at LG because they make the best TVs and people wanted their expensive product, yet some MBA convinced them they could make more money by spying on your entire household all the time.
It actually was awesome in the early Facebook days where you could have your entire phone contacts and other apps filled out with a profile picture and Birthday by connecting them together. But that relationship has been completely abused, privacy has been invaded, and my data has been sold to multiple companies.
The goal going forward is to keep that data private. If your company can't survive without it then I hope your company goes out of business
This isn't true at least on the API. If you read their privacy policy you'll see the training clause is scoped specifically to the consumer terms i.e. for the chat product. No such clause exists for the API service, and it would absolutely be required under Chinese law if it was taking place.
Contrary to popular belief, DeepSeek really aren't interested in your prompts.
The standard terms includes clause 4.3 which grants them the right to retain inputs and outputs for training purposes, and this is missing from their API terms. The standard terms also cover the right to opt out (which you can do from your user settings). No such opt-out exists on the API because it isn't applicable.
It’s a pretty common requirement in the enterprise world. If you’re processing data for enterprise customers, it’s a lot easier to retain nothing than to deal with all the compliance issues that arise if you’re retaining data.
I have ZDR enforced and see only compatible models and providers, yet am able to use it. DeepSeek as a provider may not be ZDR, but the models are available from ZDR and no training providers on EU/US servers.
Just pin your config to a single provider, or several providers with the params `order` and `allow_fallbacks: false`. I regularly get ~98-99% cache hit rates with OpenCode. And some providers are much faster than DeepSeek; I was getting 200-300 tokens/second the other day with Together as my provider.
It's regrettable that OpenRouter doesn't even try to pin you to a single provider per session, but once you know about it, it's a problem that's easily solved.
Yes, but that's why you pin them. If you specify more than one provider in `order`, OpenRouter will use the first one unless it's down, so that's the only time it would switch providers on you. And personally, I'd pay a few cents instead of waiting for the API to come back.
Yes, I send it to China, and they use it to improve open-weight models. I'm ok with this arrangement. At least, I'm happier with this than with companies using my open-source work to improve proprietary models without my consent.
Yes. You have to find the provider with pricing that suits your usage.
I am having 98% my input in cache, so using Coralbricks makes sense due to them giving cache reads for free — you only pay for writes. I spend maybe 5-10 dollars a day and my agents basically work day and night implementing things for me.
If your tasks are write-heavy, find a provider with cheaper output.
If you build a customer-facing app, pay a bit extra for 400+ tok/s e.g. on Lithos.
We have entered the era of building enterprise scale projects for your own personal use. I can do things it took teams years to build as a hobby project over a weekend.
the very notion that everything has to be 'impressive' to you is ridiculous. You still don't understand the era of personal software and everything has to be a windows replacement or it wasn't worth building to you?
I'm making my own azure blob storage explorer, notes mac/android, db client etc.. its not about being 'impressive' but being personally suited to individual needs.
He said most impressive and useful meaning there is no absolute cutoff. The only reason why you would be offended is because you have built nothing at all and that's you telling on yourself. Not to mention the question wasn't even aimed at you.
No, that's not what happened in this thread at all.
There's a recurring pattern on HN where any time someone talks about knocking dozens of personal projects off their list - things that almost certainly would never have actually been addressed in the finite span of a normal life, the way things go - and you AI doomers show up and demand receipts as though that's a total reasonable and definitely not obnoxious request.
It's like if you tell someone that you love your partner and they demand to sit in the cuck chair or else you're obviously lying. I keep hoping people will move past this "prove that you're actually productive" reflex, but it just keeps happening in basically every AI thread.
In reality there are many reasons not to list out projects that you've worked on with LLMs, and while "none of your damn business" is always going to be at the top, the simple truth is that I want my products and projects to be judged by what they do and how well they work, not by how they were made.
You misread a comment so badly that you ended up responding to a completely different and made up comment. Your unwillingness to admit that you were wrong is way worse than whatever reflex you are ranting about.
So software that already solves those problems isn't good enough and your vibe coded versions will be better? And totally worth the negative cost of AI to society and the environment? You just gotta have your own versions right?
Well, maybe, for certain programs, yes? Maybe I don't want a million SLOC behemoth with 1000 features that has a huge attack surface, when a slim 10k SLOC tool that does exactly what I need (and nothing more) will suffice? A tool that I can modify and extend on a whim? A tool that does one thing, and does it well — you know, the Unix philosophy?
So much of that "took years" is because they didn't know exactly what the "years from now" state they were building in advance. Another huge chunk is because they started getting customers and had to respond to customer needs, demands, scale, bugfixes, preserve uptime, etc.
And even then, "enterprise" was often a dirty word in these circles. The over-engineered would-be-swiss-army-knife vendor that was mediocre-for-everyone but excellent for nobody.
I have built many tools in the recent past for myself. None of them need to be "enterprise scale." Most of them would be worse for it because the agent output suffers when the pile gets deeper and it's just adding more piles on top.
>you can't think of anything to unleash some agents on within the entire digital world at any given time?
It has to be worth it though right? Like I could spend some money and have agents build me my own Photoshop maybe (maybe?) But it would definitely be much worse to use than actual Photoshop. Then I have to have the continued interest to keep improving it which probably won't happen because the next shiny thing will grab my attention. So it all just seems like a bunch of kids that have been given a seemingly endless supply of free candy and they are going fucking nuts like chipmunks with ADHD on crack. Building all this shit that is absolutely meaningless. I realize I've gone on a rant but I'll keep going. I strongly suspect (with no evidence whatsoever) that the people who are churning slop apps out at breakneck speed have never been to an art museum. There. I said it. You've all got no taste. You wouldn't know a quality product if it hit you in the face. I'll leave with this thought- if apple didn't exist, would they ever exist now we have LLMs? I say no, because the age of good taste and refined design and original thoughts is gone forever now that we have Claude and chatgpt and agents.
I think you're underestimating how things used to be - you could go into any office, any closet, any coffee shop and find a shit-ton of half-baked, crazy-genius, kick-ass, retarded ideas and projects lying around everywhere in the world: filing systems, carpet organizing systems, outlines for film scripts, unsent letters to loved ones, etc etc etc. Whole worlds everywhere you look. And other people chipping in their two-cents worth, adding a few new filing cabinets, an idea for a film sequel, a new way to think about a different carpet, a notebook system to organize someone else's unsent letters... All this slop eventually thrown into nasty, fetid garbage dumps, forgotten.
> All this slop eventually thrown into nasty, fetid garbage dumps, forgotten.
Isn't that how it is supposed to be?
The lower the friction, the lower the signal:noise ratio.
It doesn't matter if 1 out of every 100k slop projects is actually a humdinger, how on earth will you ever find it?
The value of a project is the commitment to to it by people. Slop projects indicates a commitment in the low to none range.
So, yeah, that AI-booster who "created" (I use that word loosely) 7x Adobe replacements in a week (none of which actually work, but he'll get there eventually, I supposed) will successfully edge out the person who carefully and thoughtfully created a Photoshop replacement over six months of user feedback.
TBH, the only way to start a software business now is in stealth mode.
You have all the struggles for the price of Anthropic / cursor subscription. I use the first one I code large chunks some PR are 50k LOC and I have at least 2-3 like this a week . It’s a greenfield project .
I still have quotas left I use it for home things build 3d model of my renovation projects, alerts for shopping list etc . And yeah I use cutting edge of cutting edge of models that saves me time and money , only discount monitor saved me ~$2k on my renovation project
PR is just an entity to review /do some other LLM processes . Human part check other models review PR's, tests all kind of , security etc. Also human is to checks docs, specs in the pr, db migrations if any , some of the tests related to the PR. We stopped reading the code after opus 4.6. Sometimes for very core parts i skim through files just to make sure if the changes were correct.
This is a common problem. Fully automated SDLC needs to start before the CI/CD.
My suggestion is to have the proper chunking mechanisms and multiple specialised agents. The most important is harness engineering, what we do at dromeas.ai to verify the code that goes to prod is a)have the code mapped before hand for the right agentic context, b)chunks of the right size per model context window c)specialised agents d)deduplication and verification . All before assessing a PR, a commit, a release. Harness engineering is not easy.. Especially when supporting multi model
I agree. I code a lot, a lot! And maybe my code is shitty, but yeah, I burn a lot of tokens, and I couldn’t do it without Chinese models. I’m just a random dev in the middle of nowhere. And I don’t feel like I’m missing out on anything with my setup at all.
But aren't you developing bad habits and learning patterns that won't work long term? Or do you think things will get cheap enough that you will be able to keep going with your current patterns post-subsidies?
AI does the work, it does not replace the thinking. It's like saying a tech lead in a project does not do any thinking because there's some junior dev doing tedious and boring tasks.
> But aren't you developing bad habits and learning patterns that won't work long term?
2 reasons - there's an advantage now, use it. 2nd the frontier providers, this is the "early cheap days" like when uber was initially cheap to compete vs standard cabs. they want you to become hooked and boy are we hooked.
hooked to ai coding, but not tied to any particular model. if they decide to bump prices up, I can easily switch to a cheaper chinese model on openrouter.
Yeah it makes sense, but ultimately the only thing that creates any form of lock in is the chat history and memories, and that isn’t super important, it’s not a real network effect like a social media app or a taxi app.
Having a better model is the only real moat, without that inference is a commodity
There isn't lock in and this is what scares them. Switching cost is SO LOW. Our team runs the 3 main models to cross check work without any issues.
The benefits are real and our willingness to pay is real, but the valuations only support one and right now even the free cheap models might win. Thusly the collapse could still happen.
I think it will happen, like you say switching is easy and the only possible moat is to build a better model than your competitors at enormous expense.
They’re all stuck in a cycle of spending huge amounts of money on training just to stand still (in business terms).
In the long run, it can’t continue because it doesn’t make any sense
I expect by that point we'll have local models that can do a decent job, I would guess give it a decade and we'll be running custom accelerators that are smarter than current frontier models.
In the same way that only supercomputers used to have multiple processors and caches but it's now standard.
I use the frontier openai/anthropic models at work but exclusively open weight models (on cloud/hosted inference) for personal stuff and I think about it like this; 1) I don't see any reason GLM and DeepSeek won't eventually be as good as Claude, it's just a matter of time and 2) the open models are well and truly capable enough for most of what I'd want to do. I don't need nor want an LLM chewing away on a horrible enterprise spaghetti codebase, my employers can pay for that privilege.
Long term, we will see what happens and adapt. At worst we all go back coding by hand. Meanwhile what can I do, tell my customers that I'm raising my fee because I have to pay for token? The Claude Pro $20 plan is good enough for me and even in auto mode I never had to wait for the 5 hours reset.
Compared to what a lot of companies spend on software for chip and electronics design (we're talking about $10k-200k/seat per year), AI coding assistants have a long way to go in cost before companies won't be willing to pay for them. Companies pay a fortune for software when it enables their engineers to be productive.
For my company, I'd honestly pay $4-8k/month for Claude if I had to (it would be painful, and I'd try to get cheaper options to work first). I know some enterprise Claude users are paying that much now since they have to pay for API tokens. I am certain it's at least a 2X productivity booster for our work. Compared to the cost of hiring another developer, it's well worth it.
If they stop subsidising Claude Code for the pro/max users, there will be a lot of people priced out of it, especially the casual developer. But I don't see it going away for commercial use, even with a large price increase.
Old coding is done, as a workflow in teams. It’s the top down executive pressure of being non competitive as a company, and the bottom up pressure of human laziness
Show me people handwriting code à la NASA
And I mean we as coders have been trying to do this workflow for a while, I personally would refuse to code without IntelliJ magic complete
For this workflow, there’s no going back. What’s hard to imagine is AI taking over the other workflows we predict it will; Customer service AI sucks ass for me as a customer, et cetera
> For my company, I'd honestly pay $4-8k/month for Claude if I had to (it would be painful, and I'd try to get cheaper options to work first). I know some enterprise Claude users are paying that much now since they have to pay for API tokens. I am certain it's at least a 2X productivity booster for our work. Compared to the cost of hiring another developer, it's well worth it.
That enterprise cost you're willing to pay is correlated to how much developers will work for. When driving an agent, almost anyone can do it (almost no skills required).
If devs cost $1k/m, enterprises are not going to be willing to pay $4k/m for Claude.
What I am saying is, there's an equilibrium that will be reached; the price of the human driver and the AI worker will approach each other.
Where they stabilise, I still don't know, but I'd be very surprised if, in any field (not just dev), the human gets paid multiples more than the agent they are driving, as the agents get more capable.
You should basically never pay API prices, they are always several times higher than subscriptions.
There are several open weight subscription providers. OpenCode Go used to be good but now it's complete shit. Charm Hyper is really great and the best value. Other subscriptions have a more limited model selection or provide less value but are still decent.
I freak out since months for Z.ai lite subscription, I use glm-5.3-flash every day for a ludicrous 8.5USD/month and it's as good as DS 4.1 flash, if not better.
Almost exact same experience here, but I'm using Opencode's $10/month sub. It's perma set to DS 4.1 flash and I have anywhere from 3-5 agents going at a time. Never once hit a cap of any sort. I have absolutely no idea why people would be paying $200/mo when you can get perfectly good AI for $10 from multiple places
Software has rarely been the moat. Or file formats. You have always been able to reverse engineer them. The problem, always, has been network effects.
I can build an entire, fairly useful, spreadsheet app over a weekend. But can I send my "expenses.cells" files to my accountant? Will it work with the Excel/Google docs he uses?
AI can build or reverse engineer anything as long as you are motivated enough to do it.
One example is Affinity 3 released for free. But it's a huge pain in the ass because all the guides for how to do things are for Photoshop or Affinity 2.
Your vibecoded app won't have years of reddit posts showing how to do things. This also seems to be where LLMs are the weakest at giving advice, they hallucinate 80% of the time I ask them how to do something in Affinity, giving buttons and menus that simply don't exist.
This is a knowledge problem. You can fix it by pointing the model to documentation (if it exists). Otherwise the model will give you the next best guess
Huh? Both Anthropic and OpenAI have computer use tools. I frequently just tell the models to test their own work. I'm a little paranoid so I only grant access to one window and make that window VMware Workstation.
You combine it with /goal. I usually set a goal like "Complete the application defined in goal.md as written. Then test it end to end autonomously using Compter Use. Record all issues discovered during testing in a to-do. Then fix the issues in the to-do. Repeat testing until no more issues are discovered."
Yea if i use opus 5.5 in api through openrouter and pi agent harness I will easily burn 50-100$ a day (and with fable 5.1 i could burn 200$ easily). Whereas i have now been using a claude code subscription for 2 weeks using 5.5 at all times and have never hit a limit. I often run 6+ agent sessions at once.
I do think its important long term to not be reliant on these companies as you don't have control over the system prompts, the thinking tokens, and once the subsidization stops or the company is public they will be required to start making money and thus raise prices.
But models may get more intelligent and cheaper once that time comes so it may be a non issue.
> Yea if i use opus 5.5 in api through openrouter and pi agent harness I will easily burn 50-100$ a day (and with fable 5.1 i could burn 200$ easily).
Checked yesterday, for that day alone I had used $168 worth on my $20 subscription in Claude Code. I still had plenty of weekly use left. Seems like subscriptions are discounted at a 1:10 rate?
I dont think itll be an issue. Opus 5.5 now is way more than enough for me and open weight models will reach that level by the time subsidization stops
It's enough for you now, but I feel like part of the mythology of our future is that we'll be continued to be employed because we'll be working on more complex problems, with smarter LLMs at our side.
I do think there's a decent case to be made that this will be the case for the foreseeable future, and perhaps the mythology part is beyond that.
Although as soon as I wrote 'foreseeable future' I came to the realization that this is far far far less far out than it used to be. Which might mean I simply agree with you.
I’d argue most “people” haven’t actually needed more in a very long time other than to keep the same old software running. The requirements bloat of operating systems, browsers, and majority of software isn’t really a generalized “people” thing; more so the state of the industry being a form of inertial bloat.
FYI you can modify the system prompt using mitmproxy. Just ask your agent to walk you through it. Anthropic system prompts are gnarly and geared towards the lowest common denominator.
there is literally a --system-prompt flag for claude code. In my mitmproxy experiments using that flag appended to the existing system prompt rather than replacing it. So I had to create a little helper to strip the system prompt sent over the wire and add my own.
Use --system-prompt-file, it will replace the whole system prompt (https://code.claude.com/docs/en/cli-reference ). You'll have to use a shell alias or function or something to always append this flag when calling Claude Code but you don't need any MitM shenanigans. Then use "/context all" to see what else is sent (here I would recommend MitM'ing since Claude Code won't show the exact tools and text), there are a lot of tools no one needs and they are bloating the context, you can deny these in the settings.json (there is also a list here: https://code.claude.com/docs/en/tools-reference ). Also set "disableClaudeAiConnectors" to false to remove even more bloat.
I tried both --system-prompt and --system-prompt-file. they both appended when i tried about 6 months ago and watched the traffic. Yes I cleanup all those tools etc.
I got insane amounts of Anthropic and OpenAI credits given to me for free for my startup, and I have not touched them.
I get privacy, freedom, and no rate limits with the GPUs I racked locally, and those are features I would never give up even if the surveillance capitalism labs paid -me- to use their models.
How many consumers are there like me? Probably not many, but once local inference hardware is plug and play, I bet the tides shift pretty quick. Also weights-on-silicon will serve the needs of most consumers locally with more speed than any GPU could deliver for a fraction of the cost.
Most people will be doing inference in their pocket or a wearable in 5 years and the giant datacenters will be like AWS, sold to only big organizations that need to auto-scale capacity of custom models on demand.
The industry surely knows this and the subsidized inference is just marketing to generate so much buzz and demand such that the tiny fraction of the market they will be able to keep in the end is big enough that they do not collapse under all the debt.
OpenAI and Anthropic will be Dell and IBM in 10 years if they survive at all.
This. At this point I don't really care about other models because max subscription are super cheap (relatively speaking) and I don't hit my limits. Even if the frontier models are only 5% better I might as well just use the best thing available if the price is reasonable.
Once the subsidization ends and cost becomes significant I will take a serious look around for the best value models and switch off the expensive providers, but that time hasn't come yet.
> Once the subsidization ends and cost becomes significant I will take a serious look around for the best value models and switch off the expensive providers, but that time hasn't come yet.
There's a reason the labs in the US frontier oligopoly are using “safety” to lobby for antitrust exemptions for mutual coordination as well as anticompetitive regulation.
I have a subscription at work, and still I find myself wishing I could use a fast Chinese model. Something wired up to really fast inference - that rapidity of feedback is a feature in itself.
4.1 Flash seems to be in that sweet spot of very decent, really fast and really cheap. Even omitting the cost, it’s still compelling for staying in flow.
People also just do different work. Opus 5.5 is a really damn good model that's even better than Astra/Fable/Sol IME and I feel a huge difference in my work.
I've been running automated research tasks for life sciences companies, and the speed in which tokens are burnt is scary. Especially when you get into a complex knowledge space and require a subwgent to reason through each possibility, token usage grows quadratically not linearly as complexity increases...
> This won’t last forever but as long as the frontier labs are subsidizing this heavily the open models won’t matter.
It's amazing how new we all perceive AI to be, and yet how old the tricks that the big players use. Their job is to just suck the oxygen out of the room as long as they have the money to do it.
Hard to enforce when you’re dealing with private companies with “creative” accounting - who’s to say what the actual cost of inference is for OpenAI or Anthropic?
Possibly they don’t even really know themselves at this point, although obviously is it significantly higher than the consumer subscription price
It's glorious isn't it. We get free work done through subsidies. At the same time, this is what threatens my job, and the money for the subsidy is basically my own invested pensions.
> The reason people aren’t freaking out is because most people are using heavily subsidized subscriptions.
My understanding is that enterprise plans don't offer those subscriptions, so they end up paying for API prices and models like these directly impact that revenue stream.
I think it's fairly likely medium to large corporations don't pay anywhere close to the listed API pricings as they get deals through existing partnerships with the big cloud providers.
MS's Copilot subsidy ended months back. The large OpenAI subsidy has just been cut in half. Who knows when Anthropic ended theirs because I don't ever remember it being great value.
I put £15.73 (from a dollar exchange conversion of $21.20) at the beginning of September just to try DeepSeek out via API using Opencode and Pi. I still have plenty of credit left! Their off-peak reduced token cost truly is amazing.
What the provider actually sells to you is GPU time / load (oversimplifying). Both tokens and subscriptions are just pretty arbitrary ways to price it, almost unrelated to the actual cost of running the model.
> I tried the cheapest provider on openrouter and burned through $50 in a few days.
I wish I could observe how some of us are using these tools.
I still struggle to spend $50 in tokens per month, and I exclusively use prepaid API tokens. This is in support of personal projects and two clients. There are billing cycles where I might spend upward of $400, but this is maybe once a year. This is offset by months like August wherein I spent $12 in tokens.
The other advantage with prepaid is that it handles the other direction much better. I don't even know what a quota limit feels like. Being blocked for hours is way more expensive to me and my clients than even $500/m. Losing an entire business day over this wouldn't work out.
I've managed to convince some others to try the same thing. $200/m flat fee is a pretty extreme constant expense if you can be more clever on average.
I think a lot of people are getting pushed around by FOMO effects into spending money on pointless subsidized tokens and have (valid) fears that if they don't maintain the same apparent economic leverage as their peers that they will be left behind. This isn't actually the case, much like lines of code are a really poor indicator for the quality or productivity over a codebase.
Which models do you primarily use, and can you very roughly list your process? Agentic coding in VSCode with tons of MCPs or... something else? Do you include lots of images or have large codebases? Which agentic harness are you using?
I also find that it's easy to spend like that, but also easy not to with little impact on productivity. At the current moment I'm stuck with rider + copilot (not ideal), but e.g. using GPT 6.1 luna is really, really cheap, and lots of tasks are quickly and decently dealt with even at lower reasoning levels, (added bonus of having low latency). And that model is so cheap, I can't see a hitting 1500$ at api prices realistically - not even close. But it also depends on the harness and codebase.
I use opus primarily, on a mix of pure coding tasks, and log parsing / incident investigation.
I don't have the mental capacity to do a lot of context switching between active work streams, so I'm not doing stuff like leaving a big agent workflow running while doing other things.
I think it depends on how many threads you have running at the same time. I have Claude writing a compiler in one window, a ui framework for the language in another, an application using the installed versions of compiler and frameworks in another, and a ui designer (an Interface Builder lookalike) in another keeping up with the framework.
Each of these has a file it listens to in ~/tmp/<name>.io and whenever one needs something from the other, they message each other via that file. Tasks can bounce back and forth as issues are resolved and tested. At the same time, I keep each busy with a list of tasks
I can fairly easily run out of my $200/month subs every week, if I let Fable be the default model. With Opus it’s less likely. If and when I do, I just have an alias ‘claude.ds’ which fires up Deepseek instead, and burns through far less money, though I don’t think it’s as good at solving problems, just MHO.
> whenever one needs something from the other, they message each other via that file. Tasks can bounce back and forth as issues are resolved and tested.
Why isn't this just one coherent agent loop with subtools/agents as appropriate? If these tasks are related in some way, having a single context would probably make it go much better.
The freewheeling messaging part is where the token bloat is coming from. I suspect that for some of us this is actually the point. I think it's a mostly form of entertainment to do things this way. The next logical step from Factorio gameplay.
Parallel agents remind me a lot about multi core compute. It's incredibly easy to take a single core product and make it run much worse across a lot of cores.
Claude financials leaked last week, they are running with about -200% operating margins.
We also have OpenAI numbers, where people speculate that the about 150% of the margins spent on "marketing" is a fake line used to hide operational costs.
selling unused capacity behind high cost demand pricing of api access isnt subsidised in-fact the profit margin anthropic makes on subscriptions is close to 100%. people are looking at this economic model completely backwards
It's far more likely they start to restrict the usage of subscriptions in corporate settings and leave the individual subscriptions to inflate the margin against them away. The individual subscriptions are the hook; people need to be able to understand what they're capable of with enough tokens to tackle real projects they couldn't have without the sub. That's the only way individuals will make the attempt to sell it to their org.
We'll probably still be getting the same amount of work done with a $200 subscription a year from now. That will just represent a much smaller subsidization than we currently enjoy; something like 3:1 - 6:1 instead of 40:1. Maybe running at a lower tps than the API gets. Maybe no access to the absolute frontier, but still significantly more intelligent than we get now. The labs will essentially break even on the subs and the corporate spending will be the profit center. Tale as old as software.
Deepseek also came with heavily subsidized plans, at least at first.
This is why there are so many comments confused that you spent that much money on Deepseek. Those who got on one of the discounted plans could use very large numbers of tokens for trivial prices.
There is also a strange double standard for accounting for open models. People will look at OpenAI or Anthropic and say that we need to consider all of their training costs and employee compensation when thinking about the cost to serve their models, but when the models are released as open weights those costs are ignored. So in that way, the Deepseek models are heavily subsidized as well, with the possibility of them being served by a different company that paid nothing to develop them.
> Quality was ok, seems slightly above Luna quality perhaps?
I agree that it’s about in line with what you can get from Luna or Haiku, but I give the edge to Luna and Haiku when it comes to tasks that require world knowledge. It feels like their training sets were just cleaner.
Luna and Haiku are also close to free with a subscription plan, and they’re even very cheap at API rates.
So the amazing thing about Deepseek Flash is that you can almost kind of get that level of performance from an open weight model. It’s not as amazing when you start comparing it for how we really use smaller models from frontier labs on subscription plans.
> So in that way, the Deepseek models are heavily subsidized as well, with the possibility of them being served by a different company that paid nothing to develop them.
Has anyone seen the neo-cloud profit margin on deepseek? I wonder if the deepseek served API prices account for the training cost? Because they don't / have not raised the money to fund their future operation / training- and they depend on that cashflow for now? That would suggest a really high profit margin on inference only.
VRAM aint cheap, Sam Altman ruined the cost of memory, Nvidia doesnt make enough consumer GPUs letting the market go insane over them, I still have friends on 1070s or 1070 TIs because GPUs have been severely overpriced for too long. I remember when a gaming PC was only $1000.
Even so why would anyone not sleep on a model they cannot run?
Seriously, if a single politician stepped forward and said "i'll bring down ram prices" they could then shoot a puppy and call me a slur and I'd still go out and doorknock for them.
Look at the financials of these companies, they're all making obscene margins and do they plan to increase production? No. Micron is doing a stock buy back to pump the price of their share.
There's sanctions, tarrifs, and a DOJ who doesn't give a shit. Until we can fix that the insanity will continue. Phones will be unaffordable. Laptops will be obscene. Gaming consoles will be thousands of dollars. Desktops will be dead.
If you're waiting for some David Ricardo equation to happen, tough cookies, it's not coming.
The market is legally locked down and we're in hostage pricing mode.
And what's the story? You can't afford electronics because we're using it to build robots to take your job? I mean ...
Micron has 3 brand new fabs currently under construction, 2 Boise, 1 in New York as the first of 4 planned for a campus.
Plus expanding other existing facilities.
These things take ~3-5 years from breaking ground to full production. You'd have had to anticipate the current demand years before it happened in order to be bringing production on-line before 2030 or so.
Samsung and HK Hynix also have fabs under construction and planned.
CXMT started 11 years ago and only now is reaching any real volume. If they decided a year ago to react to the current demand cycle they'd be 6-7 years out.
Not much you can really do to wish for more fabrication to exist on any timeline not measured in fractional decades.
Could they do more and react quicker? Probably, but everything I've read on the subject seems to point to 3 years is absolute bare minimum if you happen to have a shovel ready project with the land bought, local permitting completed, infrastructure extended to the site, and a skilled workforce already in place. They could suspend buy-backs/dividends today and dump it all into building production and there would be no material impact until around 2030.
> The Micron CEO just recently said this is the exact plan
CEO simply stated the demand pressure will not go away through 2027, and supply will not increase until around 2028 when currently under construction fabs start shipping volume. The article does not support your statement.
You can see the SKUs they base the prices on by hovering over a data point, and the products just don't generally represent the market. Oftentimes the whole market is represented by some bottom of the barrel legacy memory, single stick in some warehouse. During the last years DDR3 and DDR4 haven't gotten as expensive as the current generations, and I think the memory compatible with current systems should represent the market rather than a Toshiba Satellite 4 GB extension kit.
Capitalism eats itself this way. Second and third order effects will collapse the demand.
You need to keep the market healthy, not some insane Bitcoin style HODL pump - that's how you get wrecked.
I mean I'm not a neoclassicalist but I've read all of them. I'm in consensus with them here. There's a bunch of theories on what a healthy market is but what we're currently seeing matches none of them.
It's short term profitable but long term disastrous, especially in a world where new mathematics and techniques could literally collapse the demand overnight.
Imagine if some paper hits arxiv and the 256 GB requirement for some model now becomes 64. Woops!
Some clever trick about how attention heads and context Windows work could potentially slash a bunch of requirements by giant margins and all they're doing is firing the starting gun at that global race with every obscenely priced unit they sell.
But if prices were reasonable, this wouldn't be an apocalypse. It'd be fine. Consumers wouldn't rush to 64GB, they'd say " Cool I can multitask now at 256" or " great I can do horizontal scalability' or something else.
But no they created the market conditions so now what would happen is the consumer will immediately flip the 192GB they don't need on eBay, hoping to snatch a profit before the prices tank and the second hand market will be flooded the rug will be pulled out from the luxury pricing and everyone will get screwed.
This has happened in electronics markets before. Many times.
When Engels talked about the grave diggers of capitalism they were looking at it through a 19th century labor/manufacturing lens but arguably this same dynamic is at play here.
What "second- and third-order effects" do you suppose will collapse the demand for RAM? The people complaining most loudly about RAM costs are the people who want to run local models; if that becomes popular it will supercharge RAM demand, because locally-hosted models can't parallelize runs from many users the way cloud-hosted ones can. I don't see any slackening in RAM demand at any point in the foreseeable future, even if the big AI companies all go bust.
This is all hypothetical and debating hypotheticals isn't productive so let's roll back to markets.
Let's say ram used to cost $100 and now that same unit costs $1000. You paid say $500x1,000 for that unit during the price increase or some price where you can currently flip for profit.
You have a very expensive data center and you're in debt financed on the premise that you have these special computers.
Now a new technique comes out and it turns out you only need 1 memory unit for something that used to require 8 or 4 or some meaningful multiplier.
This stuff happens all the time. It's why we don't use BMP files on websites or serve giant MOV files on YouTube. It's why postgres queries are faster now than they were 10 and 20 years ago.
You rent out your machines. You need to service your debt.. Demand may 8x overnight to accommodate but you have a monthly bill to pay and that's unlikely. It's likely going to drop.
Think about it. Your customers are paying maybe $10,000 a month and serving their customers. Now they can drop that to $1,250.
On market if you were to sell some of that ram you have 100% profit right now but not for long.
Jevons paradox assumes unlimited capitalization, zero debt servicing, infinite time horizons...
We live in the real world so what do you do?
Historically the answer has been "sell that shit"
There's an aphorism for this "stairs on the way up elevator on the way down"
If we had a healthy market with sane prices where you can't flip the thing you bought for 100% profit the answer would be "create more value."
> locally-hosted models can't parallelize runs from many users the way cloud-hosted ones can
Why not? Unlike many other workloads, LLM inference actually seems pretty suitable for decentralization (effectively stateless means no availability concerns; bandwidth and latency are relatively forgiving too).
I think locally-hosted models at the org level will definitely be somewhat popular, but you seem to be talking about decentralizing for people's personal, non-business use, and I just don't think that's going to happen to any real degree.
People who say they want local runs really mean it: they want local runs on hardware in their room, not on some decentralized system which, if it existed, would almost certainly just be a worse, less-reliable version of cloud hosting. I'm not saying nobody would use it, but it sounds a lot like things like IPFS, which have also completely failed to displace either cloud storage or buying a bunch of disks for your own private use.
Some people will care a lot about keeping their data on-prem, but many others probably won't, and the former can then resell their spare capacity to the latter.
Decentralized storage is much harder, since there reliability matters a lot more as it's inherently stateful. You have to assume data loss, so you have to replicate everything; with inference, you only have to spend extra resources at failover time. Also storage can't be time-shared in the same way as compute; if it's full, it's full even when not actively accessed.
> The people complaining most loudly about RAM costs are the people who want to run local models
This is a tiny percentage of the population.
Samsung is cutting phone production because of RAM prices.[1] The consumer market is badly affected: budget phones, laptops, general electronics.
The budget segment of sub $100 devices in India has been almost wiped out. Manufacturers cannot afford to spend 50% BOM on RAM+storage. Unless employees are getting a 15-20% wage rise this year, I expect a similar situation in most places.
Between the engineered conflict in the ME triggering O&G price rises, and stratospheric RAM pricing, the situation is pretty bad.
> Imagine if some paper hits arxiv and the 256 GB requirement for some model now becomes 64. Woops!
If you were a DRAM manufacturer, isn't this exactly the kind of thing that would make you think twice about investing years and $billions in new fab construction?
The second Micron boise fab hasn't even broken ground yet, they are still working on the first one. So don't expect these things to be completed in parallel.
Some of my family is pretty happy, though, with the job security as they are pretty convinced these projects are all going to take much longer than what's being stated publicly. Micron is saying the first chip from the new fab will be in 2027... though they also predicted it'd be 2026. The date seems pretty slippy.
Anyone who has been around the semiconductor industry since the last century will remember various huge fabs e.g. in Arizona that were partially built but never finished due to oversupply by the time the walls and roof were done.
If memory prices cool, in about 2 years, Micron will stop new projects, they have done it before.
Especially given CXMT has been able to scale up much faster than what most people expected, only reason their isn't a bigger impact is modern HBM is hard to CXMT even today.
We are likely to see supply double in the next 3 years, but demand even out with optimizations, cooling of data center demand, and most importantly moving some of the dram to flash demand instead which is much easier to produce and scale.
> CXMT started 11 years ago and only now is reaching any real volume. If they decided a year ago to react to the current demand cycle they'd be 6-7 years out.
It's taken them this long to catch up to the DDR5 standard. They've only recently been through qualifications to be a DDR5 supplier for the big boys.
> Every Major Motherboard Maker Now Validates CXMT DDR5
What do you think they'll do? Neither repubs nor dems will touch ai companies in a meaningful way. Anything China does wrt memory fabs week be more significant
I don't have faith in the political parties. Everything is insane. You look at platter recently? It's up 3x in 12 months, not just ssd or nvme, but straight up traditional platter.
After 70 years of decreasing computer prices all of a sudden it's gone 3x, 5x, 10x up in 1 year, we are in total clown world and saying "dur AI" is lazy and doesn't map to reality.
It's Argentina style inflation - as if Honda said "we're only making $500,000 luxury cars now. Everything under $50k we've stopped." and then those cars shoot up to $125k.
It's destroys the market, destroys the consumer, destroys the company, dismantles everything, and they do it for the short term payday.
RAM manufacturers are bidding against NVIDIA and everyone else for the same constrained supply of EUV machines. And it takes years to build more fabs. Micron has multiple fabs coming online in 2027 and 2028.
If we take some time to understand how HBM memory is manufactured (with particular focus on yield risk for final packaging steps), we will hopefully learn that the current capacity crisis is not bullshit.
I guarantee Micron & friends are not intentionally orchestrating their business such that they would suffer a massively reduced chance of yielding on a per-die basis. Unless someone is actually buying HBM devices, they are not going to be making them. These are not a commodity that can be speculatively manufactured in any economically rational way.
>Seriously, if a single politician stepped forward and said "i'll bring down ram prices" they could then shoot a puppy and call me a slur and I'd still go out and doorknock for them.
How many people, outside of tech geeks and megacorps care about RAM prices? And how gullible would you be to BELIEVE the politician they could actually make it happen, and even if they did, that it would extend to the average person, and not JUST megacorps/megadonors?
Phone companies have been differentiating their models based on RAM for a decade. As have laptop and desktop sellers. The reason your router sometimes randomly crashes could very well be a result of not enough memory. The reason it takes such a long time to launch some programs repeatedly is because you don't have enough memory to cache it. Swapped from your browser to an app on your phone, but when you go back to the browser the site has reset and you lost everything you were working on? Not enough memory. Etc.
I think a lot of people care about the downstream effects of memory prices, but I agree with you that they may not realize that they happen because of memory prices.
>The reason your router sometimes randomly crashes could very well be a result of not enough memory. The reason it takes such a long time to launch some programs repeatedly is because you don't have enough memory to cache it. Swapped from your browser to an app on your phone, but when you go back to the browser the site has reset and you lost everything you were working on? Not enough memory. Etc.
That might be true at micro level, but at the macro level more memory just means developers get more lazy with their optimizations, causing apps to get more bloated, eating up any gains in extra memory. There's no reason why slack needs 1+GB to run, yet people are perfectly happy to put up with it.
> How many people, outside of tech geeks and megacorps care about RAM prices?
They don’t care about RAM prices, but they do care about the price of things that have RAM in them (or even NAND), and all of them are increasing way faster than inflation.
> Seriously, if a single politician stepped forward and said "i'll bring down ram prices"
Or abolished VAT (the meaning of VAT is that you pay a "rent" for all the infrastructure used to produce the thing) and import taxes (protect your market) on stuff we don't produce in our markets anyway.
The situation is actually much worse and the long term consequences will start materializing soon. The wholesale theft of humanities soul is in progress. It won't be a pretty sight in supposedly civil first world countries, when the human spirit awakens. Currently we are still pressing that snooze button hard and repeatedly, as I think most are keenly and deeply aware of what needs to happen but that too will cost our souls.
I keep arguing that memory needs more competition, and people keep pointing out that it's too slow and expensive to ramp up. But if the threshold to enter that market is so steep, that means it cannot function as a free market and requires regulation.
In this case I think investment in more production is the only option, and it needs to happen even if it is expensive and slow.
> Seriously, if a single politician stepped forward and said "i'll bring down ram prices" they could then shoot a puppy and call me a slur and I'd still go out and doorknock for them.
He doesn't ruin the cost of memory. Advances in memory size and speed are now in full speed mode. Expect drastic increase in the upcoming years. Big factories are in the making and planned. Gigalab in the US and many others in the east.
Since 2010 we have computers with 16gb as being normal. Finally we are moving into a new era where the standard will be 64gb next year and 128 in 2028. Hopefully we reach 1tb in 2030.
There's no BF16, original full quality weights are quantized already and 510GB.
Then good portion of those weights are n-grams (~200GB) that don't need to be in VRAM.
Then KV cache of that model is super lightweight at ~1GB per 1M tokens. If HBF succeeds, then accelerator with 16GB of VRAM and 1TB HBF/NAND is probably all you need (?).
GLM 5.3 Flash runs fine on two Sparks and Qwen 3.8 Flash Next on one is indeed incredible! I made this 3D game with it in two days using Qwen Code as agent:
Having Flash Next local at 150 t/s with 250k context is a joy. It’s as good as Sonnet 5. It will spaz out but it was less eager compared to DS Flash 4.1. Both are good but I find I prefer Flash Next. This and Qwen 27B are the models people should be freaking out about.
I reimplemented most of the features of the Deepseek v4.1 flash paper (apart from quantization aware training which doesn't make sense because my implementation uses float32 precision anyways)
I'm currently learning how to distill reasoning traces (check my other github repositories) but I think that a locally selfhostable deepseek is possible with my mixture of experts sharding mechanism. I decided to optimize everything for CPU parallelization, with the idea that the KV cache and meta model have to run from CPU RAM anyways, so the experts can also be loaded/unloaded at runtime if needbe, to save more RAM.
My assumption is that the KV cache optimizations in combination with the CED and compressed attention features are the reason why v4.1 flash has so few hallucination problems and such a strong self-lookup/thinking behavior. But that's more a gut feeling, need to evaluate and test this more thoroughly.
Anyways, would love to see someone train this on their own datasets. Currently my pipeline is kinda optimized for parquet and zim files.
The article isn't just about running locally though. The author is saying it's super cheap to run the model through Opencode Go (and presumably OpenRouter etc.) Personally I'm always most excited by models I can actually run locally, but even these huge open source models open up the competitive landscape for companies to let you call models via an API or just lease compute. And they don't have to charge you to offset research, training, huge staffs of the best minds in the world, crazy PR etc. I think that's a big win for customers and buts competitive pressure on the frontier labs as well.
You can run it locally for the price of a decent car, or run it (hopefully) privately on somebody else's hardware at vast.ai or a similar provider for much less. What's not to like?
No, you won't get frontier-level intelligence on a 1070Ti. Yes, it should be illegal to do what Altman did. Since we clearly don't live in the best of all possible worlds, we need to settle, and DS4.1 Flash is a good place to do that.
For tasks that don't require vision I personally like the NVFP4 quant of GLM 5.3 from Local Inference Lab better than DS4.1F, but they are both well beyond awesome.
I did somet math and completely gave up on the idea of trying any worthwhile local model and figured I'd rather pay the 15-30 USD per month via subscription and/or API key combos for years than buying a local setup which might go out of date very fast, if it doesn't goes kaput just out of warranty. I won't be surprised if RAM scarcity is an concerted effort to herd people towards the remote models :)
Allow a question from someone who’s only got a very vague idea of how this kind of stuff works behind the scenes: say I rent usage of this model through one of the many LLM hosting providers out there, and let‘s assume I use it extensively through something like Pi or OpenCode and vibe code away all the time, keeping the hosted model occupied as much as I can, happily burning my credits.
Does that mean that there is a hardware cluster as described by you above that is crunching away just for me?
So at FP16, I alone keep a 1,664 GiB system occupied all the time?
No, a cluster can server multiple users at the same time, providers cap the tok/s so that one cluster can run inference on multiple inputs at the same time. OpenAI with their new ultrafast mode is probably reserving the whole cluster or prioritizing requests of ultrafast users above others with a higher tok/s hence the high price and high speed. There's many other knobs providers tweak that they don't show the users, for example I doubt many providers are hosting the full FP16 version.
The "expensive part" of generating the next token is streaming in the model weights from memory. The computations are relatively simple, which is called a "low arithmetic intensity" in industry jargon.
So what they do is batch multiple chats together and compute the neuron activations for all of them together.
This is vaguely similar to how some database engines work, where if multiple users need to run a "whole table scan" query, the additional users "join" the streaming workload of the first query mid-way, then loop back around to complete the first part that they missed. The AI accelerators don't do this looping, but the concept is the same: amortize the expensive I/O over multiple computations running in parallel.
The "turbo mode" token rate thing is almost certainly your query getting sent to slower or faster hardware, like B200 vs newer B300 kit.
It depends hugely on what "rent usage of this model through one of the many LLM hosting providers" means. If you're asking them to host the model privately then yes, all of that 1.6T of RAM is likely in use holding weights, activations and KV cache by an inference engine that's only getting/answering requests from you alone. When you aren't actively using the model the hosting process is still active and waiting with all of that memory still wired to it.
As background: For the most part VRAM oversubscription/paging/swapping isn't a thing in the same way that RAM for a VM often is. There are some approaches to it, but (to my knowledge) not at that sort of scale.
There are some systemic reasons for this, but very broadly speaking the GPU vendors are building toward the highest bandwidth and lowest latency possible, and the overhead/complexity of something like protected memory modes serves neither of those priorities.
Projects like DwarfStar https://github.com/antirez/ds4 really lower the hardware bar a lot so Deepseek 4.1 flash and other mixture of expert models can run on consumer hardware. There are also other inference providers who make their money serving openweight models. Services like OpenRouter make it all too easy to utilize these models. Access to these models isn't hard. The hardware moat is becoming pretty easy to bridge.
More concretely DwarfStar M5 128GB Deepseek 4.1 flash 1K tokens @ 29s, 5K tokens + reasoning @ 147s, 10k token prompt @ 463 tokens/s = 22s. Hardware buy-in USD$7K / AUD$8.5K / EUR€6.8K. At typical workloads, ROI is still poor vs. current-era subsidies, but owning hardware is good for privacy/longevity/connectivity independence. Whether you actually consider Apple hardware 'owned' is a valid and thought provoking question.
Still gonna take 2-3 years to get DeepSeek V4.1 Flash quality at decent speeds on reasonably priced hardware.
Hardware update cycles are 2-3 years even on the high end, so it's still a ways away before "good enough" and "local" belong in the same sentence for the average person.
And by then, DeepSeek V6 Flash will be too cheap to meter, 5x faster, and 10x better, so... You'd still need to go out of your way.
Most people are spending most of their time on their phones anyway. ..
Flash Next is a basically there. It really depends on what you are doing. This model is great. People forget that they felt Opus 4.6 was a great model and now you have it at home.
"1070s or 1070 TIs because GPUs have been severely overpriced for too long" ... ."
1070ti launch MSRP was $450 ish.
5070 could be had in the last year for 5xx-6xx range easily.
All things considered - (inflation being about 30%~ (guess)) between these two timelines. You are looking at 300% performance difference at a cost dollar for dollar that is cheaper then when they purchased their cards.
Might be a bit of a stretch blaming it on "severely overpriced for too long..."
Are you counting the n-gram/PLE as part of the model weights there? They can go in host memory. Would be good to show your working. Also the released weights are pre-quantised and presumably QATed, so your "Full Precision" and INT8 are simply not a version of the model that actually exists.
Edit: I went and checked for you. The LM backbone is 307.2 GB (286.1 GiB), straight from DeepSeek's upload. The n-gram table is 203.1 GB (189.1 GiB), which goes in host RAM. Note the embeddings are higher precision than the expert tensors, so it's a larger fraction of the bytes than it is of the parameters.
DeepSeek V4.1 Flash is mixed MXFP4/MXFP8 so all but the INT4 calculation is wrong here and that's still wrong because you can run it on < 400GB VRAM. The n-gram table is MXFP8, but can be offloaded to RAM or disk without too much of a performance hit. Really, you could probably cram it onto < 300GB VRAM if you're willing to apply a small quant to certain parts of the model considering how little VRAM is dedicated to kv cache.
I know my comment is a little nit picky because it's still pretty expensive to run, but it's not quite as bad as this comment makes it out to be. Really, if you're VRAM constrained, take a look at GLM 5.3 Flash or Qwen 3.8 Flash Next before you worry about this model as all three models perform pretty similarly.
In nvfp4, it's about 300 gigs once you offload n-grams, 491 without offloading, you can run it pretty well on 4x DGX Sparks, which last I checked was about $20k. So, it's definitely runnable.
Or you can just use any of the neoclouds' shared hosting. The thing for them to be freaked out is that these models are getting good enough very quickly, and all the shared hosting providers can run them for a tiny fraction of what the frontier model companies charge.
I'm paying for the heavily-discounted subscriptions, not the API rates. There isn't really a cost gap for me. DeepSeek doesn't have a subscription to compare to, but when I compared the GLM 5.3 usage I got from a $100/mo Z.ai subscription compared to Opus 5.5 on a $100/mo Claude subscription, there wasn't a big gap. And GLM 5.3 is very clearly not a frontier model (deepseek v4 seemed a lot
closer, but I didn't use it enough to really say for my workloads).
I don't think those subscriptions nave negative contribution margins, either. I think we're seeing a lot of price discrimination by the big labs, and huge margins on their frontier models. The fact that they have been cutting prices to their second-biggest tier of models (Opus/Sol).
Open models catching up and collapsing these margins would worry me if I were a shareholder in the big labs, but as a user, I really doubt that the western labs have bigger environmental impact just because they have higher API costs, I think they have a ton of efficiencies they aren't sharing with customers yet because demand is so high.
The tightening of subscription value has already begun. dsv4.1f is already worth paying for at market API prices. Maybe it goes to 2x because apparently no one has figured out how to match DeepSeek's insane caching efficiency, but I don't see it getting much worse than that.
Plus you can also get dsv4.1f subsidized. OpenCode Go gives 4x if I understand their pricing correctly. Anecdotally, I feel like I get way more out of my $10/mo OpenCode Go sub for the price than my $20/mo ChatGPT, even using gpt-6.1-sol high which is very cheap, and I have yet to convince myself dsv4.1f is a worse model.
It's really not cheaper than frontier subscriptions. It's getting closer, and it's a great model, but it is not more value per task than the frontier subscriptions. Don't be swayed by the token costs, it's very chatty, like 3x more tokens for the same task as sol. I used dsf 4.1 full time for about a week.
It blows frontier API pricing out of the water, but again, look at cost per task, not token usage. Still easily wins though for my work.
I do think it's the most viable alternative I've seen so far, and that applies pressure to the frontier models. Should subscription prices hike or become unavailable for some reason, I know what I'll be using.
When pricing this, it's important to consider whether or not you want to opt out of data training. You won't get the advertised rate. Also the dsf 4.1 subscription providers are throttled af... and of course they are, because otherwise they'd be haemorrhaging money.
> Not by cost per task, just cost per token. But they're spending way more tokens per task, so it doesn't work in their facor.
They are not most expensive per Task. DeepSeek 4.1 flash is bloody efficient.
I currently did run a test myself:
- Use the pay as you go offer on OpenRouter on the same task on two different project: Perf optimisation on C++ codebase both with Anthropic and DeepSeek.
- I exploded my 15$ budget in a half-week with Anthropic.
DS4.1 flash is $0.30 in / $1.20 out (per M, peak, cache miss)
Opus 5.5 is $4.00 in / $20 out (per M, cache miss)
However, that is API prices.
Anthropic offers a $200/mo subscription. How this translates into usage is admittedly a bit opaque, subject to change, and depends on how exactly you use it. But it's a lot of usage - Semianalysis data shows that $200 is getting you around $2,500 of usage at API rates if you use Opus 5.5. This is close to what I'm seeing anecdotally with my accounts, if anything I have been getting a bit more.
Now, unlike DeepSeek, you can't use your subscriptions to power live AI-driven products, or resell tokens in any way. But for personal coding agents, you can use as many of these subscriptions as you want, for now. So I am paying effectively basically 8% of the published API rates, so at my usage:
DSv4.1: $0.30 in/ $1.20 out
Opus 5.5: $0.32 in / $1.60 out
Obviously, those aren't real prices, but they accurately convey apples to apples what my everyday usage costs me and most other heavy users, and why it's so easy for me to stick with Anthropic/OpenAI.
I don't think it's a coincidence, either - I think the token allowances for these subscriptions are set to be competitive with the open models, so that most coding users (and their incredibly valuable data) stay with the frontier labs, while VC-funded wrapper companies and less-price-sensitive giant companies with strict procurement policies pay exorbitant markups for enterprise contracts at the API rate.
Locked into their tools though. I happen to be very tied to a IDE centric model (old dog can't learn new tricks) and their Desktop thing is a regression for me. I can use the cli, but it wrecks the muscle memory I have with what I use now.
And Anthropic is somewhat unusual in that 10x more tokens via the subsidized path. I imagine more rugpulls are coming.
Not disputing your point in any way, just noting there's already caveats, and more are likely coming.
> It's really not cheaper than frontier subscriptions.
It depends on how you use it. I used to have the $100/mo Claude plan. I would easily blow through limits when I was on the $20/mo plan, but would rarely hit them when on the $100/mo plan.
Lately I've been using GLM 5.3 Flash (from Fireworks), and my spend is $1-$2 per day when I use it for coding, so max $60/mo (less, since I don't use it every day). IIRC DeepSeek 4.1 Flash is priced similarly.
If I had to pay API rates for frontier models, I can't see how $2/day would cut it. Maybe GLM/DS are chattier, but not anywhere near the 10x required to make the price difference not matter.
Sure, if you're running agentic loops all day, 5 days a week, you're probably going to blow past even $200/mo in API charges pretty quickly.
I suspect you are right. For context, I was assuming a 20x w/ OpenAI or Anthropic subscription as the comparison (or both). dsf 4.1 was going to run me about 2-3 times the cost of either of those for the same amount of work. Obviously you can optimize differently, but that's true of subscriptions too. I was using pi and had it evaluate it's ideal context compaction point based on usage and API rates. Keep in mind though I was using a ZDR provider, so slightly higher costs. If I want them to train on my code, I could shave a few $ off.
That's not even taking into consideration all of the resets you get from the frontier subs. Which lately seems to at least double usage (more like 5x recently with OpenAI if you count the credit grants). But OpenAI is tweaking it's pricing, so it's always a moving target... which is kind of annoying until you learn to just ignore it.
Have you guys see how aggressive is the push for enterprise use by both OpenAI and Anthropic? I had friend from a non-tech industry in Asia telling me that their company was offered free trial of the enterprise version of Claude, with trainings and such.
On the other hand, DS and Z.ai, have zero to none marketing outside China. There is friction to use DS/GlM models and the ZDR is unclear, so most enterprise that has heavy AI usage hasn't move over yet. They would rather spent $200 for the peace of mind than to take the risk of being slam as a national traitor down the road (which again is another form of marketing by Big AI, trying to frame Chinese models as thiefs).
So, I don't think they are not freaking out, it's just that they are addressing different market segments and reacting to the situation differently.
Aggressive marketing (including daily posts on HN). Many people simply don't know about alternatives, there are many people who never heard about pi and opencode and live comfortably in claude-codex bubbles.
I find it curious that almost every AI post has way more comments than other posts, and I sometimes wonder how big of a part agents play here. I can't seem to distinguish whether it's just real interest in the subject or a big campaign trying to influence people's opinion
It's on top of mind for everyone in the software development industry (be it SWEs, product people, designers, managers...), as it's such a "cross-cutting" concern, I am not surprised it's always on the HN front page.
Sure, some Rust 1.98.7-beta release is interesting to some, but AI affects most of us, one way or another.
I (and many other people I personally know, so they are not botfarms) feel many different strong emotions regarding AI on a daily basis: anxious, frustrated, tired, bored, suprised, entertained, empowered, optimistic, pessimistic, usually all of it almost every single day.
I have been using DeepSeek 4.1 flash intensively for over a month. If I run it all day long it costs $1-2. Its fast. Previously I was always quickly running up to my Claude/Codex 5 hour window (on the $20/month plan). The cost savings of DeepSeek is real as shown in this article and I am using subsidized plans.
DeepSeek is horrible at grilling sessions (the /grill* skills to make technical decisions). It doesn't know how to explain things. Maybe the skill could be adjusted. It also doesn't come up with as good solutions as Opus/Sol.
What I use it for is
* the orchestator of my coding workflows
* the tester/verifier of code changes
* the sub agent that explores code or does web searches
* putting together code base research reports
Previously I planned with Opus/Sol/Astra and then I used DeepSeek for coding, and then reviewed with Opus/Sol/Astra. With the cost improvements to Opus/Sol I am trying to use them for coding instead now so there will be less back and forth review needed.
They are all working together in Pi using the extension @tintinweb/pi-subagents where my workflow skill is calling different subagents that use different models.
Luna is cost competitive, but doesn't score as well on intelligence. I do need the intelligence for most of what I use it for, so I am not motivated to use Luna. Haiku also doesn't seem like a competitive price/performance mix.
DeepSeek's own paper advises against using Max, showing that it normally doesn't perform that much better. I am not using it on Max, so that's not a useful benchmark for me. I have seen other benchmarks where Flash does significantly (30%) better than Luna.
i mean we use max benchmarks because they have the best coverage, which sucks because very few people use max day to day, but it's what we have. the performance curve is generally pretty similar across models and effort levels, weird outliers are pretty weird. max is generally a big cost bump from most providers (less so from OpenAI)
It is super bad on a bit more complex workflows and starts repeating same errors with the same tool until the cycle breaker hits.
6 is worse than 5.6 here.
But it is amazing on generating a report on content generated by better agentic models such as DeepSeek or GLM, which both do a mediocre/bad job on reports.
Because it's just Opus 5.5 and Haiku, and on OpenAI side Luna, that changed the calculus.
Also, I find all of them, including GLM5.3 and DS4.1, to be 100% en par with American's frontiers model for CRUDS (90% of enterprise programming).
Tangentially, all of them would have broken quite badly custom ERPs from my own experience.
I'm using DSv4.1 in OpenChamber (eg OpenCode) using the Superpowers skills and a lot of custom AGENTS.md instructions to iron out the kinks and I genuinely cannot see a difference between it and Opus and I've been building native iOS and AppleTV apps, Go servers, Typescript, Cloudflare workers, Svelte/Astro, etc.
It's a super capable model all around from my experience.
How are you getting down to $1-$2 per day running "all day long"? I've been using GLM 5.3 Flash and I also spend $1-$2 per day, but my use is pretty modest, I think. DS 4.1 Flash is priced similarly to GLM 5.3 Flash; can't imagine DS is significantly more token-efficient.
While I agree, this particular discussion chain really frustrates me.
1. "This Flash model is really smart. Here is an article to discuss how smart it is. Why aren't people freaking out about how smart this Flash model is?"
2. "I tried using it for a smart thing. It doesn't work so well for it."
3. "You should know better than to use Flash for smart things. It's not meant for smart things."
I’ve played with DS4.1 Flash. It’s neat, no doubt. In my tests it does decently well against Sonnet 5.5 but uses way more tokens. That works out since it costs ~1/3rd Sonnet per task/PR according to my tracking.
However, I can easily burn ~$5/day if I use it as my “worker” (still using opus for planning and review) so call it ~$150/mo.
I could, if I were so inclined, get a second Claude Sub and have even more headroom (though I’m able to stay under my limits most of the time with my current setup). Also Claude gives me Artifacts, Web Search, and now even some API Credits.
I have no doubt the future is open weights and I can’t wait, literally, I can’t wait for them to catch up on intelligence or for hardware to run a decent model to be within my grasp. But until that comes to pass, I’ll keep using Anthropic.
Well, I’m building a “software factory” (yes, along with everyone else it seems) and running my main work through it, as well as my side business, as well as building the factory itself. Using my $200/mo Claude plan for opus to plan/review and sonnet/DS to execute the plan.
I only reached for DS since I was hitting my limits on my subscription plan but I think switching to sonnet for the worker will fix my limit issues (previously using opus for everything).
I am using DS in Claude code with Superpowers (both of which increase token usage) but that’s my setup.
Because DeepSeek is not "a month or two" behind as claimed in the article.
These open models still did not beat February's Mythos / Fable 5.
DeepSeek 4.1 Flash is behind GPT 5.6 Sol, and that one is left in the dust by the excellent Opus 5.5.
Rumors say Anthropic is holding in reserve the big improvement, Fable 5.5, for the IPO.
It's plausible that open models are 6 - 12 months behind, and there is no "good enough". As long as progress doesn't slow down, leading labs have nothing to fear.
I am coding CRUD apps with a mix of astra, sol 6.1, fable and opus 5.5. A more capable model would still benefit me imo. Being able to follow high level guidance better, and being able to harness other models for each task would be a big improvement.
Do you know how what you're doing, or do you find yourself working on things you dont understand and need the best model because it's the only way to push your own capabilities (because you're avoiding learning how to do the thing yourself)?
Not asking to be mean, I just genuinely dont know why you'd need the frontier for basic applications.
It's a matter of bandwidth. The more I can offload onto the model, the more I can accomplish. For example, I had to do a lot of security work over the last 2 weeks to get ready for an event. This requires handholding current models on many fronts, like:
1) Do they actually implement the security fixes correctly.
2) Do their fixes create any new edge cases.
3) Do their fixes compromise existing interfaces or API surfaces.
I cannot trust current models to find all the necessary context, or to make what I consider to be good trade offs. A much more capable model would be able to see my existing patterns (or at least not have context rot make them blind to my convention docs) and make trade offs I agree with much more consistently, and I'd be able to do more with my time.
I've actually found models to be pretty poor at driving things I don't know well, so I generally don't do that unless its general design/product exploration and the end product code is throw-away.
Many people would, and you'll find that they're building crappy webapps where you dont need SoTA. Like seriously who needs these frontier models?
Unless you're doing some extermely difficult post-grad lvl research, you do not need a 100x PhD research assistant, especially not for whatever silly SaaS product most people are building.
There's people at my job that get so much more done than everyone else using Fable/Opus/Astra. and all they use is the fastest cheapest models. I'd say the people who are using sota models for everything are doing it just because they prefer to be lazy.
You simply do not need these frontier models, they outgrew most people's needs 6 months ago, but for some reason people still want to run a 700k rack of gpus full throttle to center a div for them.
I agree that for average web dev tasks the open models are already good enough. I've had good experiences with both DeepSeek and GLM. And these models are just better for anything security related since they don't throw massive hissy fits.
However I do actually have a project where I need the frontier models--I'm working on a deep learning project of moderate complexity (something novel/state of the art within its domain, adapting a known approach from published research in a related domain). The difference from Opus 5 -> Opus 5.5 was huge for my project. Opus 5 was struggling, Opus 5.5 is doing really well.
I think the demand for frontier models will continue to be there, at least for a subset of tasks, although I agree that it is probably going to shrink as the non-frontier becomes more and more capable.
I think the market would be huge, especially if it's the "can complete a large task in 3 turns instead of 15" kind of smart. Lots of people and companies would pay for quality + speed.
I think there would be a market, but it would mostly be a FOMO market. That is, people would be doing tasks on it that the "regular" model is more than capable of handling, because they're afraid they're leaving something on the table by not using the absolute best option.
Certainly, there's a real market for it too, with people who would actually use its advanced capabilities, and see the 100x price as worth it.
But sure, even a mostly-FOMO market is still a market. If people are paying, people are paying.
What does 10x more capable look like now? Surely at some point we will reach an asymptote of what can be done purely digitally: all useful coding tasks can be automated, most math research, etc. At some point the physical world becomes the dyke holding back the singularity; until these genius models can scale their investigation into physical experiments and manufacturing, the future will have arrived only in the digital world.
I think you're making a mistake in thinking the digital world is the only one reachable to AI. Robotics and sensing would be opened up by a sufficiently capable AI.
I agree: large market. I think the future of these frontier labs is selling exceptionally powerful and exceptionally expensive models. They'll be used for precision, high value tasks. The rest of us will be happy with good enough and cheap models.
There absolutely is "good enough" and I agree with this author: DeepSeek 4.1 Flash is plenty good enough for all the things I would trust an AI to do at my job.
Agree, will see after the dilution and CoT hack fixed, will they keep the pace now. MiMo had some good numbers recently because it's discovered that the post evaluation RL directly exposes answers to models, so RL and evaluation is runied.
Perhaps on certain benchmarks and for certain work, but anecdotally I've not been able to see a difference between it and Opus on a lot of dev work (web, Go, iOS/AppleTV native, scripting, general tasks)
Imo deepseek 4.1 (and a lot of the cheaper models, Luna is similar) show the issues in benchmarks. At this point.
In actual day to day development the differences are a lot harder to spot. Maybe deepseek is worse, but I asked it to run until it was able to launch itself and verify it worked as expected, and it did. Maybe it wasted some turns, idk, but when it said it was done, it was done.
I have no doubt there's things it's worse at, but what percentage of development is truly novel?
Personally I find this shocking. I don't think our application is that complicated, (typescript full stack graphql reactnative etc) but deepseek 4.1 flash is a bumbling fool, junior-level at best, who takes a very long time to make a very big mess. Opus 5.5 one shots truly impressive code in 5 minutes, while deepseek 4.1 flash takes 20 minutes to do horribly. I simply don't understand how folks claim they get good engineering out of it. Maybe we still care enough about the fundamentals to notice the mess ...
Are you using OpenRouter? I’m honestly surprised open model labs haven’t been calling them out, but heaps of providers either silently serve heavily quant versions, or don’t have inference set up correctly and don’t run the model properly.
Was a night and day difference going directly to deepseek api
The big labs' financials are based on their products being used widely by a lot of the general public. If it turns out that they're actually selling a premium product to premium-product consumers at a premium price point (while everyone else buys DeepSeek-like cheaper/worse products), that's a big issue for them.
If a consumer computer hardware company launched by promising investors that it'd be the next Dell/HP and it turned out to be the next Apple (talking Macs here, not phones or apps/services), that'd be an issue for them too.
> These open models still did not beat February's Mythos / Fable 5.
On what task? By who? On what benchmark? How do you measure in you own workflow the “betterness” or “more goodness” of these or any models? If you don’t say those things you’re just writing a bad ad copy.
> It's plausible that open models are 6 - 12 months behind, and there is no "good enough".
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough. It depends on the task you use them for and how you measure the output. For most tasks you really do not need frontier capability. Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
Did you see any person claiming an open model was more intelligent than Fable 5?
It's always some sort of "I don't notice the difference".
And honestly, if you don't see a difference between the SOTA from 6 months ago, which would be GPT 5.4, and today's Opus 5.5, you would have to be downright blind. Not sure what else to say - the results are obviously different for any kind of meaningful output.
> Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
By simply running the old models in the latest harness. Which none of the people who argue "it's all the harness" ever do.
I have 500+ hours of experience building native mobile apps with AI between GPT 5.4 half a year ago and today, in addition to my regular software engineering job.
The difference between GPT 5.4 and Opus 5.5 is obvious.
What do you do, where apparently you cannot see a difference?
I honestly can't imagine, unless it's like sorting your emails.
I have similar experience than you, with a big caveat, opus 5.5 would have still broken badly a custom ERP.
And on that specific kind of software, ultimately a big CRUD, there really isn't that much of a difference between GLM5.3 and Opus/OpenAI.
You see the differences when you get to different class of software.
I also have data entry applications that use LLM to actually parse documents, it's all Chinese models self hosted because the economic calculus beated a hosted API by about 5x
For mobile apps, I find that nowadays with Opus 5.5 the UI looks better, the UX is better, it can implement more tricky animations and gestures, and it can do all of that with far fewer iterations and feedback than eg. GPT 5.5 would have required.
Also vision capabilities were improved significantly with GPT 6 Astra or Opus 5.5, even compared to GPT 5.6 Sol.
There was no way the old models such as GPT 5.4 would have done a comparable job when asked to align an implementation to a visual reference.
Even for basic websites with no interactive functionality, this should make a significant difference.
I even find that for what amounts to a web search Gemini 3.8 flash is better than the big models, stuff like the links to updated tax codes in different countries, or whatever MS is doing with Azure/o365 or AWS with their plethora of products.
I think use cases are the real reason why people have such different experiences, I too find that Opus5.5/Astra/6 are better for UI/UX now, it wasn't the case a year ago, at some point Gemini pro 3.5 was the best one at that.
That's also why I use all of them and try to not be locked to a single harness as well.
I struggle to see how a software engineer fails to see that anecdotal evidence does not matter here at all. I can say I built seven fully functioning operating systems last month, or that I am the fastest runner in the world or that my daddy is the strongest man in the world. None of it matters without data. I can say it is warmer in the living room and you can say that no, it is much warmer in the bedroon, without having something definite to measure and something accurate to measure it with, the whole discussion is pointless - just vibes. You haven't even said how you have anecdotally experienced the difference between the older or the open weight models. So excuse me, but then you will fail to convince people on your claims, especially when the whole discussion seems to be in the middle of some bloody information warfare at the moment.
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough.
What kind of evidence would satisfy you. Referring to benchmarks is apparently not sufficient because "you won't notice the difference in your everyday tasks", but saying "I do notice the difference in my everyday tasks" is just vibes.
The benchmarks don’t mean shit. Opus 5 was a terrible model and yet it had very impressive benchmarks. All the labs are benchmaxxed to the tits, only open models’ benchmarks are even worth paying attention to because they literally cannot cheat.
I have a different experience and find them just as good as the latest anthropic and openai models. But then, we probably can just have opinions on this as benchmarks are probably used for marketing to a large degree. I find it hard to find truly independent benchmarks that don't have any ties to the cash flow of openai and anthropic, can not be trained up on etc.
Oh man. v4.1-flash has been an sbolute game changer for us. We run all our Personal Assistants now on flash (thinking high) by default and it works incredibly well. There is really no need for basic agentic tasks that might require Kimi K.3 or GLM-5.3 levels.
Once its gets juicier, we let flash launch specialized subagents with specific models. GLM-5.3 for coding or Kimi K.3 for research and critique.
But as a main driver. I love flash. And it brought our bill down by A LOT :D
> Today's models are now good enough for high-quality unattended tasks. Chasing the latest and greatest is silly. It is fun to see the new Fable capabilities, but the tasks we throw at them are usually ridiculous (maybe even insulting) if you believe in LLM sentience. It's like asking a math PhD to organize the files on your desktop.
I'm using DS V4.1 Flash as my main model since their release and it works great for all my coding tasks. My setup is OpenCode Go subscription and obra/superpowers skill.
The only times I try to change models are on general planning tasks (like research this codebase for tech debt mitigation opportunities) or if I need deep research which would benefit from searching the web, in which I still think Gemini is still the best because of the speed and access to google search index. But these are not even 20% of my daily tasks.
Just use a provider hosting it in your country especially if your country has major data centers then its the same as using Anthropic or GPT of GCP Model Garden or AWS Bedrock
nobody here is talking about running frontier level intelligence locally so if you’re Chinaphobic and prefer layers of corporations siphoning your data in between you and the party there are plenty of options instead of directly to the party
Not shilling for them but Ollama cloud hosts domestically with ZDR afaik. I run 95% of my open weight inference through them. The rest goes through Opencode Go $10 plan (which is enough to run 3 hermes agents on DSF 4.1 and leave plenty of left to experiment with when new models drop).
Cloudflare doesn't retain request bodies by default, and if you don't trust that then you shouldn't trust the third party AI provider either. Cloudflare does cache responses, but that doesn't typically apply on API endpoints and is trivially disableable.
should you not be worried more about sending data to providers of your own country? I think they are equally bad personally, but if you think one of them is acceptable, why do you prefer it to be your own government that has more ties to your life?
What is the cost of access like for DeepSeek-v4.1-flash, compared to GLM-5.3-flash via ZAI's Coding Plan? Because that's what I use; and often hit the "wait". I wouldn't mind trying a new model subscription or even API access which hits around glm-5.3-flash level weight class (which seem to be enough for me; with quite some human suprvision and nudging) but gives muuuuuuch moooore tokens for the same price.
I have a pretty large, complex project I've been building with heavy AI use (new language + compiler). I was following a 'strong model as orchestrator launching cheap models as implementers' pattern, but I recently trialled just using Deepseek-V4.1-Flash as the model for both layers because of the cost savings (with mimo v2.6 flash on code review agents for some decorrelation).
I was previously using GLM-5.3 as the orchestrator, after switching to DS anecdotally there was an unnacceptable quality loss, mostly around not taking all the relevant context into account when making decisions, pulling new design out of thin air without discussion too often, and being way too wordy and rambly in documentation despite prompting to avoid it. There's a lot of docs, rulings, core concepts, design philosophy to uphold and DS was just not cutting it.
However, it's perfectly capable of being the sole agent for all of my well specced implementation tasks. I've gone back to GLM as the orchestrator.
On the Claude side of things I was previously following "strong model directs weak" with Fable directing Opus/Sonnet (its choice per-task). Since Opus 5.5 came out I've just been having Opus direct Opus.
The sub-agent separation is still valuable to keep context clean for the orchestrator, but I just have no reason to use Sonnet as the grunt-work implementer because I'm finding it hard to run out of tokens with Opus 5.5 on a $200 subscription plan. It's really really good at subjective quality of work per token used.
I would probably go that route if I could use other harnesses with claude models, but I don't want to be locked in to claude code, and their API pricing (which you need to use it with other harnesses) is so much higher than subscription.
I have actually just dropped to using sonnet for everything, sure it does need some directing but I have yet to see a need to jump to opus.
To me it feels like sonnet/terra and composer 2.5 and grok 4.7 are actually good enough for most tasks and these companies are pushing the high models simply to make money.
You can just use plenty of good handmade languages now, I'm not claiming mine will ever be good or useful to anyone other than myself. I'm using AI so heavily on my project because I want to explore the PL design space without spending years on implementation for things I'll probably want to throw away and rewrite a different way once I actually play around with them properly. And because I lack the motivation to persist through the sheer volume of grunt work that language implementation needs to get to the juicy interesting parts.
I used Superpowers for awhile, but I don't feel like it gave me significantly better results than just raw-dogging it. It did however burn through my tokens significantly faster.
Maybe I'll come to miss it now that I removed it, but I certainly don't yet.
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And it's why people like Hillary Clinton have been trotted out to talk about the dangers of open weights models -- I mean, does she even know what that phrase means? (I know HRC is a controversial figure and I'm not bringing her up for that purpose; I just note that she and other prominent retired politicians are now doing the circuit on Anthropic's behalf.)
China is following because they have an army of PHDs in data science and mathematics and capital to make use of them.
EDIT:
> China is following because they have an army of PHDs in data science and mathematics and capital to make use of them.
i agree with this too. but compute is the bottleneck.
So the end result will be a protectionist regime keeping the competition out, just like with cars and solar. The local industry will have a protected market, but of course won't play a role on the global stage.
Remember: small government is only good as long as it benefits the industry.
Who's right ?
That said, China cannot make its own 2nm chips even though they definitely would like to. So I guess there are limits to what they can do sometimes.
I don't think that's a fair assessment. These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company. They also can't coordinate with each other, because that's illegal. Antitrust law generally prohibits competing companies from agreeing to restrict innovation.
> “there’s no plausible way they’re concerned about safety”
> Someone points out that he pretends to only care about B
> Random commenter: "there's no plausible way we care about B"
Good chat.
The antitrust claims are a complete smokescreen. Industries can and do adopt safety standards without government intervention.
> These companies are in a Nash equilibrium where they can't unilaterally slow down without essentially destroying their company.
So what? Anthropic believes their work has a 10% chance of killing all humans. I think risking the destruction of Anthropic's business should be worth avoiding that, if that's what they believe. And with one half of the frontier duopoly gone, the other half would have no incentive to race forward. And I know there's China, but they just get all their capabilities from distilling Claude, right? So, problem solved there, too.
Sure, but does slowing down the development of new models count as "adopting safety standards"? I very much doubt it.
> Anthropic believes their work has a 10% chance of killing all humans
This ignores the other part of what they believe, which is that they are the people most likely to make a model that doesn't do that. So, in their view, letting other companies win would increase the probability of human extinction.
> with one half of the frontier duopoly gone, the other half would have no incentive to race forward
I don't see how this could possibly be true, with at least half a dozen companies being just months behind what the frontier labs are releasing.
I mean, the point (if the claim is to be believed) isn't just to "slow down the development", it's to take more time during development to properly assess the risks the models pose, develop methodologies to reduce that risk, and standardize that across companies. I doubt those wouldn't count, especially in the eyes of regulators of an administration calling for that same slow down.
Of course! What slows down the development is the adoption of specific safety conditions the companies draw up. They can just do that, and it will hold up in court.
> in their view, letting other companies win would increase the probability of human extinction
Yes, I've heard: "We must be in charge even if we end up killing everyone in the process." I personally think that proposition is invalid, but we're all entitled to our opinions.
People used to append IANAL to such statements :-)
> I personally think that proposition is invalid
I agree, but that's meaningless in this context. I was responding to the claim that "if they wanted to pace the frontier, would simply do it", which is false, given what they actually believe.
Do you think their beliefs deserve some sort of special treatment?
No.
I hope this is a joke. But most of the LLM research and inventions come from China? The best papers are from DeepSeek? You either get the data by stealing from humans or distilling from bigger models?
Their fear-mongering about GLM 5.3 got me to try it out. Its very good, I'll only go back to Claude if GLM isn't available (it forgot how to do tool calls yesterday).
Interestingly enough, it seems to compact at about 10% of the 1mn context, which makes sense if they're trying to run profitably.
Get our of here if you think the law is what prevents them from doing things.
While I completely understand the claim I am addressing one part of it.
https://www.bbc.com/news/articles/c932g3v3e13o
They will just pay whatever they need to to their lawyers, then maybe pay a fine, but then, I guarantee you, nothing will change.
"Is a lawsuit", not "was a lawsuit". Present tense, ongoing, not past tense.
- from the linked article.> did they correct any of their behavior?
The behaviour being objected to is publicly agreeing with each other to slow down.
If a court orders them to correct this behaviour, it means they are forbidden from agreeing to slow down.
The speed of change is impressive, I don't think any tech ever before has gone from “brand new disruptor in public awareness” to “the incumbents feeling they have insufficient moat and so trying to arrange a regulatory capture situation” in such a short space of time.
It’s hilarious that you can write this and then act befuddled as to why they would therefore want laws as an external (and ideally impartial) coordination device.
Thinking this is a super unique scenario just reveals your ignorance of both 1) game theory and 2) actual industrial history. An industry asking for regulation to stop a race to the bottom is not atypical at all.
If they truly believed both of those things, they would just shut down their companies, because being a billionaire is entirely pointless if you're dead.
I can only conclude that they don't believe both A and B. I'm gonna assume they believe B because actually wanting to exterminate humanity is too comically supervillain esque even for Altman. Therefore they must not believe A. And it makes sense. If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology? I'm not buying it.
Instead, I think they believe C) that AI will create enormous economic value, D) that value will be distributed across the whole economy by making everyone more productive. Assuming C and D, you get E) for them to capture this value, they must maintain proprietary control of the technology in order to be able to charge everyone else for the privilege of using it.
Assuming they believe E, open models are an existential threat, not to humanity, but to OpenAI and Anthropic.
Their solution: make everyone else believe A, in order to achieve regulatory capture and somehow stop open models from advancing by banning their development, or something. This is a hail mary pass. I can just about imagine them achieving this within the US and maybe even Europe, but China? That ship has sailed.
The market can only absorb so much new products, so if productivity increase, companies will reduce headcount as much as possible to increase margin for the same income, not grow their product or production to make use of their staff.
(See any wage/productivity graph)
Government will also likely follow the same path of reducing headcount instead of producing better / faster outcome for their citizens (except the internal surveillance apparatus. The one never shrinks).
Of course economic growth has it's own problems in terms of ecological impact and such, but if you want to reduce ecological impact you still need to improve productivity. It's a matter of how you spend that efficiency improvement as a society.
An entirely plausible scenario is that these oligarchs dream of living in Solaria, the planet from the Asimov universe where only a small number of immensely rich people lived and all the work was done by hordes of robots.
Once humans no longer serve the needs of the oligarchs, why would they want billions of people around? A question to ponder.
Humans serve them well enough and relatively easy to control. Robot utopia is not guatanteed and have own risks.
And fortunatelly in a group of AI overlords everyone except of Musk is pretty sane.
They are all psychopaths, devoid of normal human emotions.
They are far safer people compared to those who dont care about virtual wealth numbers.
For now. Throw in climate change, food shortages, war, mass migration, and suddenly the ratio becomes 25 million : 1 for starving, angry people vs billionaires, globally.
Or maybe they don't. Some of those are deeply afraid of society looking funny at them.
Unfortunately, many (most?) of them also seem to think they're better equipped than all of the others to do it safely, so "shut down my own company" effectively means "let one of the others destroy the world", whereas "keep company running" has at least a chance of "I solve alignment, we have a happy ending" (your case C).
Note: this does not mean I agree with them. Obviously they can't all be correct that they're safer than the others.
> If they believe A, why are they so incredibly sloppy about security? Why did they outsource part of it to some external firm instead of leveraging their own expertise on the technology?
The tech they're experts at is AI, not security, which answers both parts of that.
Outsourcing things you are bad at is normal, not a mystery. Lots of places value physical security, and therefore hire a private security firm.
https://www.lesswrong.com/posts/hc4DbmhdzZpSLMQ9Y/the-ai-rac...
Similar situation: nuclear race - everybody knew the risks, but making yourself armless does not help in any way. You need to make sure everyone is on the same page before you make yourself vulnerable in any way.
Is enforced by the federal government if they want to.
The most recently truly significant enforcement action was in the 80s, the AT&T breakup. And the current administration certainly will never enforce anything that hinders the oligarchs.
Nothing wrong with that.
One was even a non-profit, which should be more concerned with the well-being of humanity (which they assure is in great danger from what they produce) than the continuation of the company.
I have a secret: if you don't use AI, the new releases aren't very impressive. I'm bored out of my mind with people doing galaxy brain memes every 2 months while on the whole.... they're still boring zombies, and only getting boring-er and boring-er. There's nothing more boring than being impressed by the latest AI model.
In terms of the projections being insane you can quantify it: each model costs something to train, but after that the training has only captured so much unique new value in terms of model capability, and the race is on to drain that value as rapidly as possible. Everyone is competing to drain the same value. This generation of models makes slop video games for example, and slop video games rapidly became the most boring thing on the planet.
Those are genuine breakthroughs.
But unfortunately, too many people who are already too rich for their own good have fully bought into it.
Except that's an open invitation to get sued into oblivion by your investors.
Of course they don't actually believe their nonsense about AIs doomsday hokey, so full speed ahead.
The CEOs of these companies are largely just talking heads and generally interchangeable / hot-swappable with anyone willing to give it a shot.
The importance of CEOs is largely overstated.
There definitely are many things wrong with that, of course. Lighting someone else's money on fire and walking away is highly unethical.
The real question is: what are the motivations for companies to pretend that their AI is world-ending, and what do they want out of the disquiet caused by them saying that?
Well that certainly tracks with what I know about these people and their twisted philosophy.
I also can't legally sell this magnificent bridge I'm offering you, but I have one for sale with all proper paperwork intact!
What a small price to pay for the survival of humanity.
If the danger they are claiming is really there, Amodei and Altman should already be serving in prison for taking destructive actions.
But no, all they want is a regulation against their competitors. So typical for Misanthropic and ClosedAI and thei paid shills.
Antitrust is a joke since the last decade. If we are going to not apply it, maybe we can get something positive out of it for once.
On the last OpenAI release, they only had comparison with Anthropic models, on the last Anthropic release, they only had comparison with the OpenAI models, there's something obvious going on here.
Sources:
https://openai.com/index/introducing-gpt-6-1-sol/
https://www.anthropic.com/claude-haiku-5-5
You don't compare yourself to the underdogs. Coke never made a "we're better than Pepsi" ad, but Pepsi definitely compared itself to Coke.
For example, Anthropic putting K3 in its comparisons would be a huge admission that K3 is worth considering.
> For example, Anthropic putting K3 in its comparisons would be a huge admission that K3 is worth considering.
They do put OpenAI though, if it would be a comparison only with their own models, why not I get it but a single other competitor?
It would be like Apple making comparison page with their new iPhone and only mentioning Samsung and nobody else for example, it would sound weird. You either include competitors or you do not and include none of them.
According to them, the alternative is destroying the world.
They made up a shitty excuse to regulate the competition without thinking through what it implies about them. That's all this is. Let's not help them make even more excuses.
Are they saying that they will manually and willfully start a global thermonuclear war if China didn't listen, not that there are risks of AI accidentally causing one? Is that what they're trying to say?
What’s that now? Surely you’re joking.
But that’s not the problem anyway.
If all the US AI players agree to self-regulate, that doesn’t help anyone. It only hurts the US / West.
The point is to get the government onboard so it can advocate for a global agreement.
Look, nobody made them take this much funding, and believe that scale was all they needed.
Turns out they made a bunch of bad investments, and are suffering the fate of many startups who invented something but couldn't profit from it.
There's definitely a case for multiple labs/models across the US/EU/China/India etc, but nobody's entitled to a financial return on their investment.
I mean, these companies are saying AI will destroy humanity if it’s not paced. If they truly believe that, the small risk of destroying their own companies seems like a small price to pay.
“This thing I building will destroy the world, but I’m making too much money to stop myself, please force me to stop” is such a weird position.
I always felt like one day it would end the world, I just didn't know how. Now I know.
I appreciate the analysis and following a string of thought. But sometimes you eliminate so much reality to follow a thought that the string becomes kinda pointless.
Amodei is worth several $billion, right? He could simply walk away today. There is no Nash equilibrium for him. Nor for his replacement, nor their replacement. And it is not illegal for people to collude in quitting their jobs.
There are two obvious arguments for not quitting. 1. If the well-intentioned person quits they will be replaced by a non-well-intentioned person. 2. There is no real belief in a hazard and you would be giving up unbounded income for no reason.
If the actions and behaviors of a supposedly well-intentioned person who is afraid of a hypothetical non-well-intentioned person are indistinguishable from those of a supposedly non-well-intentioned person... don't we really just end up with a really long sentence with a lot of gibberish + the outcome of having a non-well-intentioned person in power?
Companies are continuing because they don’t actually believe they are going to have significant negative consequences happen sooner. It’s marketing fluff around how cutting edge what they are doing is not any kind of realistic threat assessment.
That said, I don't buy the companies actually give a damn about the risk either.
The logic that if something is beyond repair anyway you might as well exploit it.
For example I heard a pick-up artist say that he thinks he is harming civilization by sleeping with hundreds of women per year. But that he already considers the situation unsalvageable so... "Might as well?"
I don't think that's an amazing attitude, but the AI labs seem to have a similar idea.
More charitably the logic seems to be, "only I can do this responsibly." I heard that from both Elon Musk (he cites this as his motivation for starting OpenAI — a chat with Sergei Brin that spooked him) and of course Anthropic (which split off from OpenAI due to ethical concerns).
So I don't actually think the ethics is all for show. I think people are actually taking this stuff seriously. But it is indeed deeply unfortunate that the survival of the companies incentivizes them to keep going at an irresponsible pace (by their own admission).
All following the same gradient off a cliff. One AI described it as a tragedy of Ancient Greek proportions.
It does not matter if competitors would not also agree to destroy their companies.
If I was competing with a bunch of people on building something that I came to believe would be an extinction level event, I wouldn't be saying "Even if I slowed/stopped, the others would not, so I have to keep going". I'd say that I want absolutely nothing to do with pushing it further, stop my work, then regardless of consequences, do everything possible to stop my competitors regardless of legality
Even a 4 bit Qwen model running locally beats me manually putting React components together by hand. But even that is too slow so we've all started using paid models in one form or another.
So I would urge these folks to calm themselves and realize Oracle made a lot of money selling managed RDBMS to people who could have easily just downloaded MySQL.
https://programmerhumor.io/programming-memes/when-your-tech-...
Are you sure it’s not OpenAI?
or both?
Love the expression. So accurate. And no irony here.
https://www.youtube.com/watch?v=Ask5wzUo9kQ
It's still hard for me to explain a lot of nuances to IT directors with a technical background.
This makes no sense. Pacing the frontier gives open models the time to catch up and reach parity.
The Chinese will keep doing what they are doing, the EU has no love for Silicon Valley and the rest of the world votes with their wallet.
Now, something tells me that those verifiers would verify anything an american company in good standing with the Trump admin releases, and likely nothing else.
And obviously, since verification is so incredibly important, we can't allow models, open or not, from other non-verified companies.
Think of the profits...I mean, the kids, or something.
We could call the chip … ClAIpper … or something
China doesn't have a good track record of following signed agreements ( the WTO thing comes to mind), and this whole 'pacing the frontier' concept is even less enforceable than a signed agreement. So I would say that Anthropic/OpenAI called for this not because they thought it would eliminate the threat of Chinese models, but in spite of the risk of being overtaken.
I can address your bewilderment. What I was trying to say is that they don't actually want to pace the frontier at all. They want to gum up the market with regulations they design, that would ultimately force US companies to rent AI from them. They don't need to stop Chinese AI development and cannot do that. But they can, to quote OpenAI's Head of Strategic Futures, "create enough regulatory risk that every regulated enterprise backs off [of using Chinese models]".
https://x.com/deanwball/status/2078133895766114412
What's bewildering is the absolute lack of recognition of the fact that IF participants are locked in an arms race, they cannot act unilaterally to disarm. So not disarming doesn't actually say anything about whether they want to.
Now, of course a company not disarming (pacing the frontier, in this case) is not evidence that they ARE locked in an arms race. Everything hinges on the question of whether it's an arms race, and I think there's a great discussion to be had there. But the cynics never even bother to address the question.
All that said, I'm not actually bewildered. HN is way too smart to not understand all this, so my conclusion is that the people talking past each other are having a different emotional reaction to what's happening in the world. The angry bitter cynics are reacting from fear and hatred, and that's where the sloppy motivated reasoning comes from.
Also it seems a little silly to assert that the "angry bitter cynics" are the ones who don't think the world is hurtling to imminent doom or whatever..
You might have sleep past it, but cynics became cynic when this question was just plainly ignored with self assured answers, mostly based on market valuation as the ultimate truth indicator. I guess now days the self assured answer is to accuse critics of "fear and hatred".
OP is 16 hours old yet in four hours this one sprung to the top.
My bet is on bot farms.
:-\
In my country, public figures who are proven to be layman/non-insiders/not-working-in-the-space are speaking publicly about these terms and throwing them around like its the standard tech everybody uses 24/7.
Because of the position of the party that plays the role of the tolerant, accepting, and anti-racists; she can’t just come out and say what underlies her words, “we, the ruling class parasites are getting very scared of China deposing our stranglehold on the world, and we don’t like that; so we will raise manipulative ‘concerns’ in an effort to bring about outcomes that hopefully will benefit us.”
The only reason they're asking to "pace the frontier" is because the two big US players have IPOs coming and so are desperate to find ways to (a) grow their vastly over-inflated valuations and (b) keep the whole Nvidia circular-financing gravy-train on the road.
I mean, its only a few months back that Anthropic were bragging to anyone willing to listen how amazing Fable was and how you had to be a super-special person to use them and be charged through the nose for doing so. But basically anyone willing enough trust Anthroipic with a copy of their ID and with a big enough wallet would happily be given access.
I'm a great supporter of the open-weights. Long may it continue.
Her losing against Trump back then was a clear setup. I cannot imagine, that anyone who put her on the chessboard thought, that she could win this. Trump winning twice had one reason: The deep state wanted it, because they needed some radical reforms that required a clown to pass them through without the citizenz being able to scan and put attention on them and instead media looked as the poses and faces trump made and all they and everyone else did was laugh. Exactly as planned.
Maybe I missed it (I'd be curious to read) but did DeepSeek's agents also escape the lab due to highly irresponsible RL experimentation?
As a tech business, it's a bad business. The moat is your sales channel and getting companies locked into your platform in multi-year agreements. When companies build systems on your AI API and test it's performance and integrate it across systems, they don't churn, reintegrating, re-testing, and re-skilling costs money.
They can pace themselves if they wanted. But that potentially does them more harm than good if nobody is enforcing the other US companies, and specifically the Chinese firms too.
OpenAI, Anthropic and SpaceX have spent based on the predicate that they will "own" the AI future, that this moat will justify the trillions spent on hyperscalars, that this will be a repeat of the dot-com era that produced Microsoft (yes, yes, founded in the 1980s), Google, Amazon, etc that globally dominate their respective arenas. The US government acts to protect those interests and this is uniparty so Hilary Clinton is just as likely to be trotted out as Mike Pompeo. This is why many, myself included, describe the US empire as 5 companies in a trench coat.
China, on the other hand, believes that companies should serve the interests of the government, which itself serves the interests of the people. So rather than create trillion dollar AI companies with moats, AI should serve society. Xi Jinping has spoken extensively about this. That's one of the funny things about China. They love to write stuff down and tell you exactly what they're doing and why yet at the same time they're ascribed nefarious motives.
So, despite sanctions supposedly preventing China from buying the latest and greatest AI chips, China through its labs has begun commoditizing the AI models with open weight models. Society should benefit from that rather than a moat being built.
You can run DeepSeek V4.1 Flash locally on a 256GB Mac Studio for ~$11k now. I've seen reports of 30-38 tokens/sec. Not amazing but that'll only improve with future generations. In the coming years, China's EUV/DUV will come online and this will threaten the NVidia monopoly, at least for local Chinese companies.
So we have AI companies burning cash to subsidize usage and build a market where the revenue required simply may not ever eventuate through a combination of open weight models and increasing accessibility of local models.
See its not about pacing the frontier, its about pacing the frontier without hurting any of their fundraising.
Pepperidge Farm remembers when robust consumer applications of cryptography -- especially for SSL/TLS -- was the big boogeyman, and the export controls involved were absurd.
I tried the cheapest provider on openrouter and burned through $50 in a few days. Quality was ok, seems slightly above Luna quality perhaps? But that $50 is 1/4 of my codex subscription where I could have burned that many tokens or more using Astra within my weekly reset.
This won’t last forever but as long as the frontier labs are subsidizing this heavily the open models won’t matter.
DeepSeek themselves are honest about the fact that they train on inputs by default. You won't hit DeepSeek if you use OpenRouter with ZDR enabled.
And I am using the Claude Code harness with DS as the endpoint. And I use it ~5-8hrs a day to do my coding.
I was running deepseek v4.1 pretty much non stop during work hours, with heavy tool/mcp usage and finding it very difficult to spend more than $75 in a month.
Also the cheapest providers on Openroutrr can often have terrible cache hit %, short TTLs resulting in their effective price being much more expensive than people realize. 75% cache pretty much destroys any savings from a super cheap token perspective.
I have seen the Cursor leaderboard on my company and the vibe coders consume about 5x more tokens than the developers. They and other office workers also have Claude and their limits are often over around Wednesday.
People are using millions of tokens to do very simple HTML reports. I have seen someone asking the LLM to download the entire data into the context and asking it to sort.
Those usage patterns don't correlate to output.
Fable changed a lot of things I had explicitly told it not to change. Arguably a lot of them would've been correct if you didn't work in a place where abstractions are directly against the core principles, but what it produced was basically unusable. I'm not sure if Sol or Astra did best, they produced rather similar code outputs. Astra's was better, but Sol didn't do so bad. It forgot to clean up a few places after it's refactor and it made two bugs I had to correct but other than that it was fine. Astra on the flip-side might have produced code that didn't need changes but it also rewrote every piece of documentation so that it became horrible.
As far as the "experiment" goes, it just shows you that the credit consumption is basically pure magic. You'd think that the Microsoft AI admin tools and the Agent365 FOMO DLC license they sell might give you some sort of reporting, but it doesn't. What you can see is how many tokens a user consumes and the total number of tasks they've initiated as well as whatever running agents they have. You can't see what models they use or which tasks are expensive, which makes it very hard to help them. Early on we had an employee who hit their limit in an hour, and it turned out they had basically uploaded a lot of information and run it in a single long task that kept going over it again and again. We told them it might be a good idea to only give it what it needed and to create more tasks, and even though it's been three months, they have yet to consume as many credits as they did that first hour.
But that's how you support and track it. You see a user spend a lot, then you go to their computer and now that you can actually do the /cost thing, you go through their tasks and try and figure out where they're spending money...
It's obviously improving. A month ago /cost wasn't there and they just released a new dashboard for cowork, but it's still black magic that is impossible to govern.
Which is an issue when you need to get department managers to manage their budgets around the amounts of credits their employees spend. The more of a black box it is, the more governance and corporate bullshit you have to deal with.
The copilot part of it runs "unlimited" on the license. Except it's not unlimited, and this is even more of a blackbox because you can't see any sort of spending and the limit is listed as "extensive use".
On your point about "monitoring", personally I feel this is toxic corporate IT culture, enabled and perhaps pushed by the likes of Microsoft with all their tools, which they of course make money off. People have cellphones with cameras making most points in this area moot.
An alternative used in other big corporates is to set budgets, with tiered authorisation approvals for higher limits. The users and their managers can justify why and what they're doing that they need the additional tokens. This also encourages more efficient use of tokens on other work. More efficient use is sometimes counterintuitive. Laissez-faire generally works best.
Cowork requires user approvals for high risk actions such as emailing.
The better pattern is to let it code the app and then you can use the app to target your data. So you only pay for it once, plus it's deterministic. But yeah, it requires setting up an environment, etc. It becomes "maintenance".
We ended up having to hire a full time employee to fix the performance of client built reports.
People run a stupid amount of expensive queries that end up costing way too much because they're asking Claude the wrong query.
Not to mention people running wrong queries, using the result as gospel, and then the result has to be sent to a data analyst to be reverse-engineered so the numbers make sense.
Eg I've used Sashiko locally for Linux kernel code reviews before sending out my contributions out to the world. Sashiko is a great system, but it can burn through tokens like there's no tomorrow.
https://github.com/sashiko-dev/sashiko and https://sashiko.dev/
Buy directly from DeepSeek's API.
You can literally get overcharged 100x on DeepSeek on OpenRouter (or more).
OpenRouter Pricing:
$0.02/M input tokens $0.60/M output tokens
DeepSeek Pricing (cache miss, off-peak):
$0.15/M Input $0.60/m output
It’s also the major difference between using DeepSeek directly vs other providers also serving it, though I have not looked lately: it’s possible other providers have matched its cache hit pricing better?
I'm writing a program I personally need, but I would be happy if there existed something like it already, if someone else vibecoded a better version of it than mine, or if DS got better at vibing this kind of thing.
what proof do you have, that they don't train on your data?
they can say they don't, but I don't see any way for you to confirm it.
with how these companies operate currently, I won't be surprised, if they say that one of agents "mistakenly" did that already..
It actually was awesome in the early Facebook days where you could have your entire phone contacts and other apps filled out with a profile picture and Birthday by connecting them together. But that relationship has been completely abused, privacy has been invaded, and my data has been sold to multiple companies.
The goal going forward is to keep that data private. If your company can't survive without it then I hope your company goes out of business
Contrary to popular belief, DeepSeek really aren't interested in your prompts.
What I see is https://cdn.deepseek.com/policies/en-US/deepseek-privacy-pol...
They do not have a specific exclusion for API use.
I know Z.ai has an exclusion for API use. It's widely reported Deepseek doesn't.
To the open platform terms (i.e. for API use): https://cdn.deepseek.com/policies/en-US/deepseek-open-platfo...
The standard terms includes clause 4.3 which grants them the right to retain inputs and outputs for training purposes, and this is missing from their API terms. The standard terms also cover the right to opt out (which you can do from your user settings). No such opt-out exists on the API because it isn't applicable.
See here:
https://openrouter.ai/providers/
It's regrettable that OpenRouter doesn't even try to pin you to a single provider per session, but once you know about it, it's a problem that's easily solved.
If you don't want to mess about client side with pinning, set a guardrail on Openrouter that limits the available providers to only the official one.
I thought the advantage of DeepSeek is that you can host it on a server of your choosing.
I am having 98% my input in cache, so using Coralbricks makes sense due to them giving cache reads for free — you only pay for writes. I spend maybe 5-10 dollars a day and my agents basically work day and night implementing things for me.
If your tasks are write-heavy, find a provider with cheaper output.
If you build a customer-facing app, pay a bit extra for 400+ tok/s e.g. on Lithos.
Lots of guys buying articles on TechCrunch saying they’ll build this, he’s bootstrapped
It’s PERFECT!
you can't think of anything to unleash some agents on within the entire digital world at any given time?
you motivate your own personal work only via gauging its' usefulness to others and your own prospects?
sheesh.
built anything for the sake of building yet?
God I hope so.
There's a recurring pattern on HN where any time someone talks about knocking dozens of personal projects off their list - things that almost certainly would never have actually been addressed in the finite span of a normal life, the way things go - and you AI doomers show up and demand receipts as though that's a total reasonable and definitely not obnoxious request.
It's like if you tell someone that you love your partner and they demand to sit in the cuck chair or else you're obviously lying. I keep hoping people will move past this "prove that you're actually productive" reflex, but it just keeps happening in basically every AI thread.
In reality there are many reasons not to list out projects that you've worked on with LLMs, and while "none of your damn business" is always going to be at the top, the simple truth is that I want my products and projects to be judged by what they do and how well they work, not by how they were made.
That is a pretty strong statement. And without even anecdotal evidence, it becomes very weak.
Hence the question "what's the most impressive and useful thing you have made"
But no one really has any examples. Just half baked slop they never got over the finish line.
And even then, "enterprise" was often a dirty word in these circles. The over-engineered would-be-swiss-army-knife vendor that was mediocre-for-everyone but excellent for nobody.
I have built many tools in the recent past for myself. None of them need to be "enterprise scale." Most of them would be worse for it because the agent output suffers when the pile gets deeper and it's just adding more piles on top.
It has to be worth it though right? Like I could spend some money and have agents build me my own Photoshop maybe (maybe?) But it would definitely be much worse to use than actual Photoshop. Then I have to have the continued interest to keep improving it which probably won't happen because the next shiny thing will grab my attention. So it all just seems like a bunch of kids that have been given a seemingly endless supply of free candy and they are going fucking nuts like chipmunks with ADHD on crack. Building all this shit that is absolutely meaningless. I realize I've gone on a rant but I'll keep going. I strongly suspect (with no evidence whatsoever) that the people who are churning slop apps out at breakneck speed have never been to an art museum. There. I said it. You've all got no taste. You wouldn't know a quality product if it hit you in the face. I'll leave with this thought- if apple didn't exist, would they ever exist now we have LLMs? I say no, because the age of good taste and refined design and original thoughts is gone forever now that we have Claude and chatgpt and agents.
What's not to like ?
Isn't that how it is supposed to be?
The lower the friction, the lower the signal:noise ratio.
It doesn't matter if 1 out of every 100k slop projects is actually a humdinger, how on earth will you ever find it?
The value of a project is the commitment to to it by people. Slop projects indicates a commitment in the low to none range.
So, yeah, that AI-booster who "created" (I use that word loosely) 7x Adobe replacements in a week (none of which actually work, but he'll get there eventually, I supposed) will successfully edge out the person who carefully and thoughtfully created a Photoshop replacement over six months of user feedback.
TBH, the only way to start a software business now is in stealth mode.
I still have quotas left I use it for home things build 3d model of my renovation projects, alerts for shopping list etc . And yeah I use cutting edge of cutting edge of models that saves me time and money , only discount monitor saved me ~$2k on my renovation project
I mean, why even pretend you’re going to “review” something that large? Just build everything on main.
It will take shortcuts and now the entire premise is busted. You now need to build a code review process for large PRs.
My suggestion is to have the proper chunking mechanisms and multiple specialised agents. The most important is harness engineering, what we do at dromeas.ai to verify the code that goes to prod is a)have the code mapped before hand for the right agentic context, b)chunks of the right size per model context window c)specialised agents d)deduplication and verification . All before assessing a PR, a commit, a release. Harness engineering is not easy.. Especially when supporting multi model
Is Lithos actually fast for common usage?
2 reasons - there's an advantage now, use it. 2nd the frontier providers, this is the "early cheap days" like when uber was initially cheap to compete vs standard cabs. they want you to become hooked and boy are we hooked.
Having a better model is the only real moat, without that inference is a commodity
The benefits are real and our willingness to pay is real, but the valuations only support one and right now even the free cheap models might win. Thusly the collapse could still happen.
They’re all stuck in a cycle of spending huge amounts of money on training just to stand still (in business terms).
In the long run, it can’t continue because it doesn’t make any sense
In the same way that only supercomputers used to have multiple processors and caches but it's now standard.
For my company, I'd honestly pay $4-8k/month for Claude if I had to (it would be painful, and I'd try to get cheaper options to work first). I know some enterprise Claude users are paying that much now since they have to pay for API tokens. I am certain it's at least a 2X productivity booster for our work. Compared to the cost of hiring another developer, it's well worth it.
If they stop subsidising Claude Code for the pro/max users, there will be a lot of people priced out of it, especially the casual developer. But I don't see it going away for commercial use, even with a large price increase.
Old coding is done, as a workflow in teams. It’s the top down executive pressure of being non competitive as a company, and the bottom up pressure of human laziness
Show me people handwriting code à la NASA
And I mean we as coders have been trying to do this workflow for a while, I personally would refuse to code without IntelliJ magic complete
For this workflow, there’s no going back. What’s hard to imagine is AI taking over the other workflows we predict it will; Customer service AI sucks ass for me as a customer, et cetera
That enterprise cost you're willing to pay is correlated to how much developers will work for. When driving an agent, almost anyone can do it (almost no skills required).
If devs cost $1k/m, enterprises are not going to be willing to pay $4k/m for Claude.
What I am saying is, there's an equilibrium that will be reached; the price of the human driver and the AI worker will approach each other.
Where they stabilise, I still don't know, but I'd be very surprised if, in any field (not just dev), the human gets paid multiples more than the agent they are driving, as the agents get more capable.
There are several open weight subscription providers. OpenCode Go used to be good but now it's complete shit. Charm Hyper is really great and the best value. Other subscriptions have a more limited model selection or provide less value but are still decent.
https://cortecs.ai/detailedServerlessView/deepseek-v4.1-flas...
I can build an entire, fairly useful, spreadsheet app over a weekend. But can I send my "expenses.cells" files to my accountant? Will it work with the Excel/Google docs he uses?
AI can build or reverse engineer anything as long as you are motivated enough to do it.
Your vibecoded app won't have years of reddit posts showing how to do things. This also seems to be where LLMs are the weakest at giving advice, they hallucinate 80% of the time I ask them how to do something in Affinity, giving buttons and menus that simply don't exist.
You combine it with /goal. I usually set a goal like "Complete the application defined in goal.md as written. Then test it end to end autonomously using Compter Use. Record all issues discovered during testing in a to-do. Then fix the issues in the to-do. Repeat testing until no more issues are discovered."
I do think its important long term to not be reliant on these companies as you don't have control over the system prompts, the thinking tokens, and once the subsidization stops or the company is public they will be required to start making money and thus raise prices.
But models may get more intelligent and cheaper once that time comes so it may be a non issue.
I use a personal Claude account for personal projects and can let rabl run for an hour and barely make a dent into my usage
On the enterprise I have to be a lot more careful or I can burn through 2k in a week
The excuse they give is the guarantees you get with enterprise plans that they won’t look at your data
Checked yesterday, for that day alone I had used $168 worth on my $20 subscription in Claude Code. I still had plenty of weekly use left. Seems like subscriptions are discounted at a 1:10 rate?
EDIT: That was a delightfully thorough analysis. Nice to see Anthropic taking the value crown, only because Opus 5.5 is such a joy to use.
Although as soon as I wrote 'foreseeable future' I came to the realization that this is far far far less far out than it used to be. Which might mean I simply agree with you.
I'm assuming this is a sarcastic response to the person who said "640K [RAM] ought to be enough for anybody!"
Because, as somebody who was around when we had 640 K RAM, people certainly weren't happy with that amount.
...the absolute state of the token maximizers.
The system prompt is injected into your context on the server side.
here is a collection of public system prompts from claude code: https://github.com/Piebald-AI/claude-code-system-prompts
I get privacy, freedom, and no rate limits with the GPUs I racked locally, and those are features I would never give up even if the surveillance capitalism labs paid -me- to use their models.
How many consumers are there like me? Probably not many, but once local inference hardware is plug and play, I bet the tides shift pretty quick. Also weights-on-silicon will serve the needs of most consumers locally with more speed than any GPU could deliver for a fraction of the cost.
Most people will be doing inference in their pocket or a wearable in 5 years and the giant datacenters will be like AWS, sold to only big organizations that need to auto-scale capacity of custom models on demand.
The industry surely knows this and the subsidized inference is just marketing to generate so much buzz and demand such that the tiny fraction of the market they will be able to keep in the end is big enough that they do not collapse under all the debt.
OpenAI and Anthropic will be Dell and IBM in 10 years if they survive at all.
Once the subsidization ends and cost becomes significant I will take a serious look around for the best value models and switch off the expensive providers, but that time hasn't come yet.
There's a reason the labs in the US frontier oligopoly are using “safety” to lobby for antitrust exemptions for mutual coordination as well as anticompetitive regulation.
Something about renting that much compute doesn't sit right with me so I stick with the $20 subs.
4.1 Flash seems to be in that sweet spot of very decent, really fast and really cheap. Even omitting the cost, it’s still compelling for staying in flow.
I've been running automated research tasks for life sciences companies, and the speed in which tokens are burnt is scary. Especially when you get into a complex knowledge space and require a subwgent to reason through each possibility, token usage grows quadratically not linearly as complexity increases...
It's amazing how new we all perceive AI to be, and yet how old the tricks that the big players use. Their job is to just suck the oxygen out of the room as long as they have the money to do it.
And that this is even legal is a scandal all on its own.
Possibly they don’t even really know themselves at this point, although obviously is it significantly higher than the consumer subscription price
You can't jack up the prices on your product if your competitors can just clone it and resell its essence for pennies on the dollar.
My understanding is that enterprise plans don't offer those subscriptions, so they end up paying for API prices and models like these directly impact that revenue stream.
“With my OpenCode Go sub of $10/month, DeepSeek is basically unlimited.”
I wish I could observe how some of us are using these tools.
I still struggle to spend $50 in tokens per month, and I exclusively use prepaid API tokens. This is in support of personal projects and two clients. There are billing cycles where I might spend upward of $400, but this is maybe once a year. This is offset by months like August wherein I spent $12 in tokens.
The other advantage with prepaid is that it handles the other direction much better. I don't even know what a quota limit feels like. Being blocked for hours is way more expensive to me and my clients than even $500/m. Losing an entire business day over this wouldn't work out.
I've managed to convince some others to try the same thing. $200/m flat fee is a pretty extreme constant expense if you can be more clever on average.
I think a lot of people are getting pushed around by FOMO effects into spending money on pointless subsidized tokens and have (valid) fears that if they don't maintain the same apparent economic leverage as their peers that they will be left behind. This isn't actually the case, much like lines of code are a really poor indicator for the quality or productivity over a codebase.
I also find that it's easy to spend like that, but also easy not to with little impact on productivity. At the current moment I'm stuck with rider + copilot (not ideal), but e.g. using GPT 6.1 luna is really, really cheap, and lots of tasks are quickly and decently dealt with even at lower reasoning levels, (added bonus of having low latency). And that model is so cheap, I can't see a hitting 1500$ at api prices realistically - not even close. But it also depends on the harness and codebase.
I don't have the mental capacity to do a lot of context switching between active work streams, so I'm not doing stuff like leaving a big agent workflow running while doing other things.
All through claude code.
Each of these has a file it listens to in ~/tmp/<name>.io and whenever one needs something from the other, they message each other via that file. Tasks can bounce back and forth as issues are resolved and tested. At the same time, I keep each busy with a list of tasks
I can fairly easily run out of my $200/month subs every week, if I let Fable be the default model. With Opus it’s less likely. If and when I do, I just have an alias ‘claude.ds’ which fires up Deepseek instead, and burns through far less money, though I don’t think it’s as good at solving problems, just MHO.
Why isn't this just one coherent agent loop with subtools/agents as appropriate? If these tasks are related in some way, having a single context would probably make it go much better.
The freewheeling messaging part is where the token bloat is coming from. I suspect that for some of us this is actually the point. I think it's a mostly form of entertainment to do things this way. The next logical step from Factorio gameplay.
Parallel agents remind me a lot about multi core compute. It's incredibly easy to take a single core product and make it run much worse across a lot of cores.
Also isn't an open weight model also subsidised? Training isn't cheap and you are not paying for it.
We also have OpenAI numbers, where people speculate that the about 150% of the margins spent on "marketing" is a fake line used to hide operational costs.
It probably will. Moore's Law is still churning away in the background.
Frontier models might get more expensive, but that's a moving target. For any particular capability point, the models will only get cheaper.
We'll probably still be getting the same amount of work done with a $200 subscription a year from now. That will just represent a much smaller subsidization than we currently enjoy; something like 3:1 - 6:1 instead of 40:1. Maybe running at a lower tps than the API gets. Maybe no access to the absolute frontier, but still significantly more intelligent than we get now. The labs will essentially break even on the subs and the corporate spending will be the profit center. Tale as old as software.
This is why there are so many comments confused that you spent that much money on Deepseek. Those who got on one of the discounted plans could use very large numbers of tokens for trivial prices.
There is also a strange double standard for accounting for open models. People will look at OpenAI or Anthropic and say that we need to consider all of their training costs and employee compensation when thinking about the cost to serve their models, but when the models are released as open weights those costs are ignored. So in that way, the Deepseek models are heavily subsidized as well, with the possibility of them being served by a different company that paid nothing to develop them.
> Quality was ok, seems slightly above Luna quality perhaps?
I agree that it’s about in line with what you can get from Luna or Haiku, but I give the edge to Luna and Haiku when it comes to tasks that require world knowledge. It feels like their training sets were just cleaner.
Luna and Haiku are also close to free with a subscription plan, and they’re even very cheap at API rates.
So the amazing thing about Deepseek Flash is that you can almost kind of get that level of performance from an open weight model. It’s not as amazing when you start comparing it for how we really use smaller models from frontier labs on subscription plans.
Has anyone seen the neo-cloud profit margin on deepseek? I wonder if the deepseek served API prices account for the training cost? Because they don't / have not raised the money to fund their future operation / training- and they depend on that cashflow for now? That would suggest a really high profit margin on inference only.
VRAM & Memory Requirements by Precision
• FP16 (Full Precision): Requires ~1,664 GB of VRAM (e.g., an 8x B300 288GB cluster).
• INT8 Quantization: Requires ~832 GB of VRAM (e.g., 8x H200 141GB).
• INT4 Quantization: Requires ~416 GB of VRAM (e.g., 8x A100 80GB)
VRAM aint cheap, Sam Altman ruined the cost of memory, Nvidia doesnt make enough consumer GPUs letting the market go insane over them, I still have friends on 1070s or 1070 TIs because GPUs have been severely overpriced for too long. I remember when a gaming PC was only $1000.
Even so why would anyone not sleep on a model they cannot run?
Memory companies have price fixed multiple times. They've paid hundreds of millions in fines. wikipedia even has a page on it. https://en.wikipedia.org/wiki/DRAM_industry_price_fixing.
Look at the financials of these companies, they're all making obscene margins and do they plan to increase production? No. Micron is doing a stock buy back to pump the price of their share.
The Micron CEO just recently said this is the exact plan https://www.theregister.com/systems/2026/10/01/ram-supply-se...
There's sanctions, tarrifs, and a DOJ who doesn't give a shit. Until we can fix that the insanity will continue. Phones will be unaffordable. Laptops will be obscene. Gaming consoles will be thousands of dollars. Desktops will be dead.
If you're waiting for some David Ricardo equation to happen, tough cookies, it's not coming.
The market is legally locked down and we're in hostage pricing mode.
And what's the story? You can't afford electronics because we're using it to build robots to take your job? I mean ...
Nobody is coming to save us. That's our job.
wonder what voting would be like?
gamer vote ++
datacenter hater vote --
datacenter lobby ++
micron lobby --
Micron has 3 brand new fabs currently under construction, 2 Boise, 1 in New York as the first of 4 planned for a campus.
Plus expanding other existing facilities.
These things take ~3-5 years from breaking ground to full production. You'd have had to anticipate the current demand years before it happened in order to be bringing production on-line before 2030 or so.
Samsung and HK Hynix also have fabs under construction and planned.
CXMT started 11 years ago and only now is reaching any real volume. If they decided a year ago to react to the current demand cycle they'd be 6-7 years out.
Not much you can really do to wish for more fabrication to exist on any timeline not measured in fractional decades.
Could they do more and react quicker? Probably, but everything I've read on the subject seems to point to 3 years is absolute bare minimum if you happen to have a shovel ready project with the land bought, local permitting completed, infrastructure extended to the site, and a skilled workforce already in place. They could suspend buy-backs/dividends today and dump it all into building production and there would be no material impact until around 2030.
> The Micron CEO just recently said this is the exact plan
CEO simply stated the demand pressure will not go away through 2027, and supply will not increase until around 2028 when currently under construction fabs start shipping volume. The article does not support your statement.
Costs did go nuts, but there are signs of easing in the market of late. CXMT is starting to have an impact and priced will probably fall in 2027.
You need to keep the market healthy, not some insane Bitcoin style HODL pump - that's how you get wrecked.
I mean I'm not a neoclassicalist but I've read all of them. I'm in consensus with them here. There's a bunch of theories on what a healthy market is but what we're currently seeing matches none of them.
It's short term profitable but long term disastrous, especially in a world where new mathematics and techniques could literally collapse the demand overnight.
Imagine if some paper hits arxiv and the 256 GB requirement for some model now becomes 64. Woops!
Some clever trick about how attention heads and context Windows work could potentially slash a bunch of requirements by giant margins and all they're doing is firing the starting gun at that global race with every obscenely priced unit they sell.
But if prices were reasonable, this wouldn't be an apocalypse. It'd be fine. Consumers wouldn't rush to 64GB, they'd say " Cool I can multitask now at 256" or " great I can do horizontal scalability' or something else.
But no they created the market conditions so now what would happen is the consumer will immediately flip the 192GB they don't need on eBay, hoping to snatch a profit before the prices tank and the second hand market will be flooded the rug will be pulled out from the luxury pricing and everyone will get screwed.
This has happened in electronics markets before. Many times.
When Engels talked about the grave diggers of capitalism they were looking at it through a 19th century labor/manufacturing lens but arguably this same dynamic is at play here.
Let's say ram used to cost $100 and now that same unit costs $1000. You paid say $500x1,000 for that unit during the price increase or some price where you can currently flip for profit.
You have a very expensive data center and you're in debt financed on the premise that you have these special computers.
Now a new technique comes out and it turns out you only need 1 memory unit for something that used to require 8 or 4 or some meaningful multiplier.
This stuff happens all the time. It's why we don't use BMP files on websites or serve giant MOV files on YouTube. It's why postgres queries are faster now than they were 10 and 20 years ago.
You rent out your machines. You need to service your debt.. Demand may 8x overnight to accommodate but you have a monthly bill to pay and that's unlikely. It's likely going to drop.
Think about it. Your customers are paying maybe $10,000 a month and serving their customers. Now they can drop that to $1,250.
On market if you were to sell some of that ram you have 100% profit right now but not for long.
Jevons paradox assumes unlimited capitalization, zero debt servicing, infinite time horizons...
We live in the real world so what do you do?
Historically the answer has been "sell that shit"
There's an aphorism for this "stairs on the way up elevator on the way down"
If we had a healthy market with sane prices where you can't flip the thing you bought for 100% profit the answer would be "create more value."
Or they could stay at $10,000 per month since they are willing to pay that much already.m, so they just use AI more and in more places.
Why not? Unlike many other workloads, LLM inference actually seems pretty suitable for decentralization (effectively stateless means no availability concerns; bandwidth and latency are relatively forgiving too).
People who say they want local runs really mean it: they want local runs on hardware in their room, not on some decentralized system which, if it existed, would almost certainly just be a worse, less-reliable version of cloud hosting. I'm not saying nobody would use it, but it sounds a lot like things like IPFS, which have also completely failed to displace either cloud storage or buying a bunch of disks for your own private use.
Decentralized storage is much harder, since there reliability matters a lot more as it's inherently stateful. You have to assume data loss, so you have to replicate everything; with inference, you only have to spend extra resources at failover time. Also storage can't be time-shared in the same way as compute; if it's full, it's full even when not actively accessed.
This is a tiny percentage of the population.
Samsung is cutting phone production because of RAM prices.[1] The consumer market is badly affected: budget phones, laptops, general electronics.
The budget segment of sub $100 devices in India has been almost wiped out. Manufacturers cannot afford to spend 50% BOM on RAM+storage. Unless employees are getting a 15-20% wage rise this year, I expect a similar situation in most places.
Between the engineered conflict in the ME triggering O&G price rises, and stratospheric RAM pricing, the situation is pretty bad.
[1] "There is no profit even if we sell"…Samsung to cut smartphone production by 30% (https://www.mt.co.kr/en/tech/2026/10/08/2026100709554237233)
If you were a DRAM manufacturer, isn't this exactly the kind of thing that would make you think twice about investing years and $billions in new fab construction?
Some of my family is pretty happy, though, with the job security as they are pretty convinced these projects are all going to take much longer than what's being stated publicly. Micron is saying the first chip from the new fab will be in 2027... though they also predicted it'd be 2026. The date seems pretty slippy.
Especially given CXMT has been able to scale up much faster than what most people expected, only reason their isn't a bigger impact is modern HBM is hard to CXMT even today.
We are likely to see supply double in the next 3 years, but demand even out with optimizations, cooling of data center demand, and most importantly moving some of the dram to flash demand instead which is much easier to produce and scale.
It's taken them this long to catch up to the DDR5 standard. They've only recently been through qualifications to be a DDR5 supplier for the big boys.
> Every Major Motherboard Maker Now Validates CXMT DDR5
https://www.techtimes.com/articles/321572/20260725/every-maj...
After their recent IPO, they have more than enough cash to ramp up in a major way.
It's just a matter of time.
https://web.archive.org/web/20250612003557/https://diskprice...
https://diskprices.com/
After 70 years of decreasing computer prices all of a sudden it's gone 3x, 5x, 10x up in 1 year, we are in total clown world and saying "dur AI" is lazy and doesn't map to reality.
It's Argentina style inflation - as if Honda said "we're only making $500,000 luxury cars now. Everything under $50k we've stopped." and then those cars shoot up to $125k.
It's destroys the market, destroys the consumer, destroys the company, dismantles everything, and they do it for the short term payday.
I know it's just a figure of speech, but damn. I laughed out aloud in public just reading this.
I guarantee Micron & friends are not intentionally orchestrating their business such that they would suffer a massively reduced chance of yielding on a per-die basis. Unless someone is actually buying HBM devices, they are not going to be making them. These are not a commodity that can be speculatively manufactured in any economically rational way.
How many people, outside of tech geeks and megacorps care about RAM prices? And how gullible would you be to BELIEVE the politician they could actually make it happen, and even if they did, that it would extend to the average person, and not JUST megacorps/megadonors?
I think a lot of people care about the downstream effects of memory prices, but I agree with you that they may not realize that they happen because of memory prices.
That might be true at micro level, but at the macro level more memory just means developers get more lazy with their optimizations, causing apps to get more bloated, eating up any gains in extra memory. There's no reason why slack needs 1+GB to run, yet people are perfectly happy to put up with it.
They don’t care about RAM prices, but they do care about the price of things that have RAM in them (or even NAND), and all of them are increasing way faster than inflation.
Or abolished VAT (the meaning of VAT is that you pay a "rent" for all the infrastructure used to produce the thing) and import taxes (protect your market) on stuff we don't produce in our markets anyway.
In this case I think investment in more production is the only option, and it needs to happen even if it is expensive and slow.
Priorities
How? Increase production? The time needed to scale up the production is longer than one election cycle.
I spit out my coffee laughing when I read this
1660 ti, 4790k, 16gb ddr3
Then good portion of those weights are n-grams (~200GB) that don't need to be in VRAM.
Then KV cache of that model is super lightweight at ~1GB per 1M tokens. If HBF succeeds, then accelerator with 16GB of VRAM and 1TB HBF/NAND is probably all you need (?).
It has a set of n-gram tables which you can stream from system RAM or even NVMe
That said it’s still quite big! I can’t fit it on my DGX Spark, though I believe you can if you have two?
https://github.com/christopherowen/spark-ds41f
I’m quite spoiled with how good Qwen 3.8 Flash Next is on a single spark though: shocking how good local models are getting on attainable-ish hardware
https://blog.jonathanpage.com/
GLM 5.3 Flash runs fine on two Sparks and Qwen 3.8 Flash Next on one is indeed incredible! I made this 3D game with it in two days using Qwen Code as agent:
https://games.jonathanpage.com/
https://www.storagereview.com/review/dgx-station-gb300-clust...
I reimplemented most of the features of the Deepseek v4.1 flash paper (apart from quantization aware training which doesn't make sense because my implementation uses float32 precision anyways)
I'm currently learning how to distill reasoning traces (check my other github repositories) but I think that a locally selfhostable deepseek is possible with my mixture of experts sharding mechanism. I decided to optimize everything for CPU parallelization, with the idea that the KV cache and meta model have to run from CPU RAM anyways, so the experts can also be loaded/unloaded at runtime if needbe, to save more RAM.
My assumption is that the KV cache optimizations in combination with the CED and compressed attention features are the reason why v4.1 flash has so few hallucination problems and such a strong self-lookup/thinking behavior. But that's more a gut feeling, need to evaluate and test this more thoroughly.
Anyways, would love to see someone train this on their own datasets. Currently my pipeline is kinda optimized for parquet and zim files.
[1] https://github.com/cookiengineer/gonano
Because it's an open model so providers compete on price.
No, you won't get frontier-level intelligence on a 1070Ti. Yes, it should be illegal to do what Altman did. Since we clearly don't live in the best of all possible worlds, we need to settle, and DS4.1 Flash is a good place to do that.
For tasks that don't require vision I personally like the NVFP4 quant of GLM 5.3 from Local Inference Lab better than DS4.1F, but they are both well beyond awesome.
Allow a question from someone who’s only got a very vague idea of how this kind of stuff works behind the scenes: say I rent usage of this model through one of the many LLM hosting providers out there, and let‘s assume I use it extensively through something like Pi or OpenCode and vibe code away all the time, keeping the hosted model occupied as much as I can, happily burning my credits.
Does that mean that there is a hardware cluster as described by you above that is crunching away just for me?
So at FP16, I alone keep a 1,664 GiB system occupied all the time?
The "expensive part" of generating the next token is streaming in the model weights from memory. The computations are relatively simple, which is called a "low arithmetic intensity" in industry jargon.
So what they do is batch multiple chats together and compute the neuron activations for all of them together.
This is vaguely similar to how some database engines work, where if multiple users need to run a "whole table scan" query, the additional users "join" the streaming workload of the first query mid-way, then loop back around to complete the first part that they missed. The AI accelerators don't do this looping, but the concept is the same: amortize the expensive I/O over multiple computations running in parallel.
The "turbo mode" token rate thing is almost certainly your query getting sent to slower or faster hardware, like B200 vs newer B300 kit.
As background: For the most part VRAM oversubscription/paging/swapping isn't a thing in the same way that RAM for a VM often is. There are some approaches to it, but (to my knowledge) not at that sort of scale.
There are some systemic reasons for this, but very broadly speaking the GPU vendors are building toward the highest bandwidth and lowest latency possible, and the overhead/complexity of something like protected memory modes serves neither of those priorities.
He gave demand signal so many times years ago and was mocked for it and now we have the consequences of industry not taking him seriously.
Hardware update cycles are 2-3 years even on the high end, so it's still a ways away before "good enough" and "local" belong in the same sentence for the average person.
And by then, DeepSeek V6 Flash will be too cheap to meter, 5x faster, and 10x better, so... You'd still need to go out of your way.
Most people are spending most of their time on their phones anyway. ..
1070ti launch MSRP was $450 ish. 5070 could be had in the last year for 5xx-6xx range easily.
All things considered - (inflation being about 30%~ (guess)) between these two timelines. You are looking at 300% performance difference at a cost dollar for dollar that is cheaper then when they purchased their cards.
Might be a bit of a stretch blaming it on "severely overpriced for too long..."
Edit: I went and checked for you. The LM backbone is 307.2 GB (286.1 GiB), straight from DeepSeek's upload. The n-gram table is 203.1 GB (189.1 GiB), which goes in host RAM. Note the embeddings are higher precision than the expert tensors, so it's a larger fraction of the bytes than it is of the parameters.
So,
> Call me crazy but:
You're crazy. :-)
I know my comment is a little nit picky because it's still pretty expensive to run, but it's not quite as bad as this comment makes it out to be. Really, if you're VRAM constrained, take a look at GLM 5.3 Flash or Qwen 3.8 Flash Next before you worry about this model as all three models perform pretty similarly.
Or you can just use any of the neoclouds' shared hosting. The thing for them to be freaked out is that these models are getting good enough very quickly, and all the shared hosting providers can run them for a tiny fraction of what the frontier model companies charge.
it rips with just 64 ram and a 9070xt
I don't think those subscriptions nave negative contribution margins, either. I think we're seeing a lot of price discrimination by the big labs, and huge margins on their frontier models. The fact that they have been cutting prices to their second-biggest tier of models (Opus/Sol).
Open models catching up and collapsing these margins would worry me if I were a shareholder in the big labs, but as a user, I really doubt that the western labs have bigger environmental impact just because they have higher API costs, I think they have a ton of efficiencies they aren't sharing with customers yet because demand is so high.
Plus you can also get dsv4.1f subsidized. OpenCode Go gives 4x if I understand their pricing correctly. Anecdotally, I feel like I get way more out of my $10/mo OpenCode Go sub for the price than my $20/mo ChatGPT, even using gpt-6.1-sol high which is very cheap, and I have yet to convince myself dsv4.1f is a worse model.
It blows frontier API pricing out of the water, but again, look at cost per task, not token usage. Still easily wins though for my work.
I do think it's the most viable alternative I've seen so far, and that applies pressure to the frontier models. Should subscription prices hike or become unavailable for some reason, I know what I'll be using.
When pricing this, it's important to consider whether or not you want to opt out of data training. You won't get the advertised rate. Also the dsf 4.1 subscription providers are throttled af... and of course they are, because otherwise they'd be haemorrhaging money.
DS4.1 Flash not really cheaper than frontier models???
It is insanely cheaper.
I still use them because they aren't as squeamish about random things American CEOs don't like like decompilation.
They are not most expensive per Task. DeepSeek 4.1 flash is bloody efficient.
I currently did run a test myself: - Use the pay as you go offer on OpenRouter on the same task on two different project: Perf optimisation on C++ codebase both with Anthropic and DeepSeek.
- I exploded my 15$ budget in a half-week with Anthropic.
- I did two weeks and half with DeepSeek.
DS4.1 flash is $0.30 in / $1.20 out (per M, peak, cache miss) Opus 5.5 is $4.00 in / $20 out (per M, cache miss)
However, that is API prices.
Anthropic offers a $200/mo subscription. How this translates into usage is admittedly a bit opaque, subject to change, and depends on how exactly you use it. But it's a lot of usage - Semianalysis data shows that $200 is getting you around $2,500 of usage at API rates if you use Opus 5.5. This is close to what I'm seeing anecdotally with my accounts, if anything I have been getting a bit more.
Now, unlike DeepSeek, you can't use your subscriptions to power live AI-driven products, or resell tokens in any way. But for personal coding agents, you can use as many of these subscriptions as you want, for now. So I am paying effectively basically 8% of the published API rates, so at my usage:
DSv4.1: $0.30 in/ $1.20 out Opus 5.5: $0.32 in / $1.60 out
Obviously, those aren't real prices, but they accurately convey apples to apples what my everyday usage costs me and most other heavy users, and why it's so easy for me to stick with Anthropic/OpenAI.
I don't think it's a coincidence, either - I think the token allowances for these subscriptions are set to be competitive with the open models, so that most coding users (and their incredibly valuable data) stay with the frontier labs, while VC-funded wrapper companies and less-price-sensitive giant companies with strict procurement policies pay exorbitant markups for enterprise contracts at the API rate.
And Anthropic is somewhat unusual in that 10x more tokens via the subsidized path. I imagine more rugpulls are coming.
Not disputing your point in any way, just noting there's already caveats, and more are likely coming.
It depends on how you use it. I used to have the $100/mo Claude plan. I would easily blow through limits when I was on the $20/mo plan, but would rarely hit them when on the $100/mo plan.
Lately I've been using GLM 5.3 Flash (from Fireworks), and my spend is $1-$2 per day when I use it for coding, so max $60/mo (less, since I don't use it every day). IIRC DeepSeek 4.1 Flash is priced similarly.
If I had to pay API rates for frontier models, I can't see how $2/day would cut it. Maybe GLM/DS are chattier, but not anywhere near the 10x required to make the price difference not matter.
Sure, if you're running agentic loops all day, 5 days a week, you're probably going to blow past even $200/mo in API charges pretty quickly.
I suspect you are right. For context, I was assuming a 20x w/ OpenAI or Anthropic subscription as the comparison (or both). dsf 4.1 was going to run me about 2-3 times the cost of either of those for the same amount of work. Obviously you can optimize differently, but that's true of subscriptions too. I was using pi and had it evaluate it's ideal context compaction point based on usage and API rates. Keep in mind though I was using a ZDR provider, so slightly higher costs. If I want them to train on my code, I could shave a few $ off.
That's not even taking into consideration all of the resets you get from the frontier subs. Which lately seems to at least double usage (more like 5x recently with OpenAI if you count the credit grants). But OpenAI is tweaking it's pricing, so it's always a moving target... which is kind of annoying until you learn to just ignore it.
Have you guys see how aggressive is the push for enterprise use by both OpenAI and Anthropic? I had friend from a non-tech industry in Asia telling me that their company was offered free trial of the enterprise version of Claude, with trainings and such.
On the other hand, DS and Z.ai, have zero to none marketing outside China. There is friction to use DS/GlM models and the ZDR is unclear, so most enterprise that has heavy AI usage hasn't move over yet. They would rather spent $200 for the peace of mind than to take the risk of being slam as a national traitor down the road (which again is another form of marketing by Big AI, trying to frame Chinese models as thiefs).
So, I don't think they are not freaking out, it's just that they are addressing different market segments and reacting to the situation differently.
Sure, some Rust 1.98.7-beta release is interesting to some, but AI affects most of us, one way or another.
I (and many other people I personally know, so they are not botfarms) feel many different strong emotions regarding AI on a daily basis: anxious, frustrated, tired, bored, suprised, entertained, empowered, optimistic, pessimistic, usually all of it almost every single day.
DeepSeek is horrible at grilling sessions (the /grill* skills to make technical decisions). It doesn't know how to explain things. Maybe the skill could be adjusted. It also doesn't come up with as good solutions as Opus/Sol.
What I use it for is
Previously I planned with Opus/Sol/Astra and then I used DeepSeek for coding, and then reviewed with Opus/Sol/Astra. With the cost improvements to Opus/Sol I am trying to use them for coding instead now so there will be less back and forth review needed.They are all working together in Pi using the extension @tintinweb/pi-subagents where my workflow skill is calling different subagents that use different models.
Luna is cost competitive, but doesn't score as well on intelligence. I do need the intelligence for most of what I use it for, so I am not motivated to use Luna. Haiku also doesn't seem like a competitive price/performance mix.
AA has Haiku 5.5 as cheaper than 4.1 Flash (both on Max, which isn't ideal but what can ya do) and a 4 point intelligence gap.
Why do people like to think open models are more competitive than they are?
6 is worse than 5.6 here.
But it is amazing on generating a report on content generated by better agentic models such as DeepSeek or GLM, which both do a mediocre/bad job on reports.
Tangentially, all of them would have broken quite badly custom ERPs from my own experience.
It's a super capable model all around from my experience.
1. "This Flash model is really smart. Here is an article to discuss how smart it is. Why aren't people freaking out about how smart this Flash model is?"
2. "I tried using it for a smart thing. It doesn't work so well for it."
3. "You should know better than to use Flash for smart things. It's not meant for smart things."
However, I can easily burn ~$5/day if I use it as my “worker” (still using opus for planning and review) so call it ~$150/mo.
I could, if I were so inclined, get a second Claude Sub and have even more headroom (though I’m able to stay under my limits most of the time with my current setup). Also Claude gives me Artifacts, Web Search, and now even some API Credits.
I have no doubt the future is open weights and I can’t wait, literally, I can’t wait for them to catch up on intelligence or for hardware to run a decent model to be within my grasp. But until that comes to pass, I’ll keep using Anthropic.
I’ve been trying to spend my 5$ with deepseek that I put 2 months ago. I almost never hit limit with claude opus in 20$ plan.
Do you guys just run model in paralallel + loop?
Does it even produce anything worth using that way?
I only reached for DS since I was hitting my limits on my subscription plan but I think switching to sonnet for the worker will fix my limit issues (previously using opus for everything).
I am using DS in Claude code with Superpowers (both of which increase token usage) but that’s my setup.
These open models still did not beat February's Mythos / Fable 5.
DeepSeek 4.1 Flash is behind GPT 5.6 Sol, and that one is left in the dust by the excellent Opus 5.5.
Rumors say Anthropic is holding in reserve the big improvement, Fable 5.5, for the IPO.
It's plausible that open models are 6 - 12 months behind, and there is no "good enough". As long as progress doesn't slow down, leading labs have nothing to fear.
If you had a model 10x as capable as the best model out today, but it cost 100x more, would there be a market, and, if so, how big?
I think there would be a market and I think it would be large.
So, I agree.
99% of everything is CRUD LoB apps.
Not asking to be mean, I just genuinely dont know why you'd need the frontier for basic applications.
I cannot trust current models to find all the necessary context, or to make what I consider to be good trade offs. A much more capable model would be able to see my existing patterns (or at least not have context rot make them blind to my convention docs) and make trade offs I agree with much more consistently, and I'd be able to do more with my time.
I've actually found models to be pretty poor at driving things I don't know well, so I generally don't do that unless its general design/product exploration and the end product code is throw-away.
Unless you're doing some extermely difficult post-grad lvl research, you do not need a 100x PhD research assistant, especially not for whatever silly SaaS product most people are building.
There's people at my job that get so much more done than everyone else using Fable/Opus/Astra. and all they use is the fastest cheapest models. I'd say the people who are using sota models for everything are doing it just because they prefer to be lazy.
You simply do not need these frontier models, they outgrew most people's needs 6 months ago, but for some reason people still want to run a 700k rack of gpus full throttle to center a div for them.
However I do actually have a project where I need the frontier models--I'm working on a deep learning project of moderate complexity (something novel/state of the art within its domain, adapting a known approach from published research in a related domain). The difference from Opus 5 -> Opus 5.5 was huge for my project. Opus 5 was struggling, Opus 5.5 is doing really well.
I think the demand for frontier models will continue to be there, at least for a subset of tasks, although I agree that it is probably going to shrink as the non-frontier becomes more and more capable.
Certainly, there's a real market for it too, with people who would actually use its advanced capabilities, and see the 100x price as worth it.
But sure, even a mostly-FOMO market is still a market. If people are paying, people are paying.
even their harnesses are far surpassed by pi and opencode at this point
also sick 'rumors' lmao, apparently marketing through rumors is in vogue these days
Nah. There are benchmarks. They are free to look at. And they paint a very clear picture.
I've seen different benchmarks come to different conclusions
Benchmarking these models must be an incredibly complex and difficult problem
How can a lay person know which benchmarks actually have good signal?
In actual day to day development the differences are a lot harder to spot. Maybe deepseek is worse, but I asked it to run until it was able to launch itself and verify it worked as expected, and it did. Maybe it wasted some turns, idk, but when it said it was done, it was done.
I have no doubt there's things it's worse at, but what percentage of development is truly novel?
Was a night and day difference going directly to deepseek api
Except being priced out.
The big labs' financials are based on their products being used widely by a lot of the general public. If it turns out that they're actually selling a premium product to premium-product consumers at a premium price point (while everyone else buys DeepSeek-like cheaper/worse products), that's a big issue for them.
If a consumer computer hardware company launched by promising investors that it'd be the next Dell/HP and it turned out to be the next Apple (talking Macs here, not phones or apps/services), that'd be an issue for them too.
On what task? By who? On what benchmark? How do you measure in you own workflow the “betterness” or “more goodness” of these or any models? If you don’t say those things you’re just writing a bad ad copy.
> It's plausible that open models are 6 - 12 months behind, and there is no "good enough".
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough. It depends on the task you use them for and how you measure the output. For most tasks you really do not need frontier capability. Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
It's always some sort of "I don't notice the difference".
And honestly, if you don't see a difference between the SOTA from 6 months ago, which would be GPT 5.4, and today's Opus 5.5, you would have to be downright blind. Not sure what else to say - the results are obviously different for any kind of meaningful output.
> Also how do we know how much of these “big improvements” come from the harness and tooling rather than the raw capability of the model?
By simply running the old models in the latest harness. Which none of the people who argue "it's all the harness" ever do.
The difference between GPT 5.4 and Opus 5.5 is obvious.
What do you do, where apparently you cannot see a difference?
I honestly can't imagine, unless it's like sorting your emails.
And on that specific kind of software, ultimately a big CRUD, there really isn't that much of a difference between GLM5.3 and Opus/OpenAI.
You see the differences when you get to different class of software.
I also have data entry applications that use LLM to actually parse documents, it's all Chinese models self hosted because the economic calculus beated a hosted API by about 5x
For mobile apps, I find that nowadays with Opus 5.5 the UI looks better, the UX is better, it can implement more tricky animations and gestures, and it can do all of that with far fewer iterations and feedback than eg. GPT 5.5 would have required.
Also vision capabilities were improved significantly with GPT 6 Astra or Opus 5.5, even compared to GPT 5.6 Sol.
There was no way the old models such as GPT 5.4 would have done a comparable job when asked to align an implementation to a visual reference.
Even for basic websites with no interactive functionality, this should make a significant difference.
I think use cases are the real reason why people have such different experiences, I too find that Opus5.5/Astra/6 are better for UI/UX now, it wasn't the case a year ago, at some point Gemini pro 3.5 was the best one at that.
That's also why I use all of them and try to not be locked to a single harness as well.
Anecdotally, a lot of people - including myself - seem to really notice much difference between the model now or six months ago. So there really seems to be good enough.
Once its gets juicier, we let flash launch specialized subagents with specific models. GLM-5.3 for coding or Kimi K.3 for research and critique.
But as a main driver. I love flash. And it brought our bill down by A LOT :D
but DS 4.1 Flash is good enough for most tasks
> Today's models are now good enough for high-quality unattended tasks. Chasing the latest and greatest is silly. It is fun to see the new Fable capabilities, but the tasks we throw at them are usually ridiculous (maybe even insulting) if you believe in LLM sentience. It's like asking a math PhD to organize the files on your desktop.
I'm using DS V4.1 Flash as my main model since their release and it works great for all my coding tasks. My setup is OpenCode Go subscription and obra/superpowers skill.
The only times I try to change models are on general planning tasks (like research this codebase for tech debt mitigation opportunities) or if I need deep research which would benefit from searching the web, in which I still think Gemini is still the best because of the speed and access to google search index. But these are not even 20% of my daily tasks.
are you worried about sending all your data to third parties, especially if they're in different countries?
nobody here is talking about running frontier level intelligence locally so if you’re Chinaphobic and prefer layers of corporations siphoning your data in between you and the party there are plenty of options instead of directly to the party
The model engine provider might be ZDR, but the service as a whole isn't.
I cant ofc be fully sure because i don’t own the chain end 2 end.
(And what are the preferred providers?)
I was previously using GLM-5.3 as the orchestrator, after switching to DS anecdotally there was an unnacceptable quality loss, mostly around not taking all the relevant context into account when making decisions, pulling new design out of thin air without discussion too often, and being way too wordy and rambly in documentation despite prompting to avoid it. There's a lot of docs, rulings, core concepts, design philosophy to uphold and DS was just not cutting it.
However, it's perfectly capable of being the sole agent for all of my well specced implementation tasks. I've gone back to GLM as the orchestrator.
The sub-agent separation is still valuable to keep context clean for the orchestrator, but I just have no reason to use Sonnet as the grunt-work implementer because I'm finding it hard to run out of tokens with Opus 5.5 on a $200 subscription plan. It's really really good at subjective quality of work per token used.
I have actually just dropped to using sonnet for everything, sure it does need some directing but I have yet to see a need to jump to opus.
To me it feels like sonnet/terra and composer 2.5 and grok 4.7 are actually good enough for most tasks and these companies are pushing the high models simply to make money.
Maybe I'll come to miss it now that I removed it, but I certainly don't yet.