>I started using Spark 1.2 for development because if you're willing to let Meta train on your data it was dirt cheap
its free on opencode and i use it for personal projects. most of my personal projects are AI generated since its personal projects. nothing important are on them. it is hilarious if Meta is training their AI model with AI generated code.
The useful training data is when you clarify your intent, when you tell the model a different approach would be better, when you consistently refactor towards Y and away from X, and so on. The training data isn’t the code, it’s the session transcript. (Anthropic would call this a “distillation attack” against their model, but in this case the model is you!)
Thats not exactly 'validated'. Feels very noisy, it is not a good bar for either
- does this code do what the user actually asked
- is this code actually 'good'
There would be so many examples of coding projects that these models began or attempted to work in, that were abandoned because the models were floundering.
I would imagine the labs have some decent ways to produce novel requirements and then actually validate they are met, without the noisiness of implicit human feedback.
That said, the more I think about it, you are right, there's probably also very good ways to extract signal for all these sessions.
This is exactly what RLVR is, and the reason that models have improved so much at verifiable domains like coding and math while not so much on unverifiable ones like writing and UI design.
what's the moat for AI labs like OpenAI and Antrhopic as they seek trillion IPOs. a lot of people said Chinese models also distill US LLM models. if it is this easy to do. how do US AI labs justify asking for trillion?
I believe if this was true, we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
Even if they have Astra or whatever completely to themselves, how do they deal with the -product- side? Or sales, or account management?
They've been focused on juicing model's coding capabilities, but it's absolutely -not- "gen ai" enough to be doing the whole thing in agents, even 3 or 5 years from now, if only because there's so much context we _can't_ give to these models, without reverse centauring ourselves with cameras and mics and oodles of compute everywhere, and so far that hasn't exactly been playing out the way the frontier labs and the singularity folk hoped it would (meta glasses? humane? really?)
>we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
As a small business owner I see this. Random people contacting me to sell a software where I can instantly tell it is vibe coded. Subscription prices half or 1/4th of incumbents. Domain names registered in the last few months.
They don’t need to make you or me, people who actually make software, believe that every anthropic employee is actually and in fact, right now, a „10x software factory controlling thousands of agents“, they need to make investors and CEOs believe, and then hope that making that true is possible and that they can keep the fiction up long enough and get enough money and infra built out to make it actually true.
Like Uber replaced all drivers with self driving cars [1] in its 20 years of operation? Just because I want to paint the moon pink, doesn't mean it's feasible.
[1] If anything, Waymo has a higher chance and global Waymo adoption is probably 20 years into the future, at least.
> I believe if this was true, we'd already be seeing vibed stuff succeeding everywhere, and pricing out incumbents, but I guess I'm not seeing this? Like at all?
I'm substituting my own[0] vibe coding for buying[1] apps. Language mini-games to help with German? A few prompts. Fluid dynamics simulation for an airzooker? Vibed. A web app listening for a MIDI keyboard, upon which you can drop some .midi files, and get a rhythm action game to learn the piano? Vibed. Webcam for my Raspberry Pi? Vibed. Getting Marathon 2 (well, the open sourced and upgraded engine, Aleph One) working as a web app? Vibed. Isochrone maps? Vibed.
Half of this I can even get done with the free models.
> without reverse centauring ourselves with cameras and mics and oodles of compute everywhere, and so far that hasn't exactly been playing out the way the frontier labs and the singularity folk hoped it would (meta glasses? humane? really?)
Yeah, so fortunate that cameras are expensive and there aren't 6 on my table right now between laptops and phones. :P
Seriously though, what's saving humanity collectively from everything getting automated from the panopticon we'd already built before Transformer models got good enough for even the most basic of classification and translation tasks, let alone anything we now use them for, is that machine learning takes an obscene number of examples before getting competent. Any living creature that needed so many examples would starve to death before learning how to eat.
This difficulty is why, for all the billions of miles that Tesla cars have collectively driven, perhaps pushing trillions now, they're still not sold to the public without steering wheels. Tesla claim to make such vehicles now in the form of the Cybercab, but they're not for sale, and even then some of the pictures that get in the press still show steering wheels.
[0] if you can call anything vibe-coded "my own"
[1] or worse, given the popularity of subscription models in this era, leasing some SaaS
> I'm substituting my own[0] vibe coding for buying[1] apps. Language mini-games to help with German? A few prompts. Fluid dynamics simulation for an airzooker? Vibed. A web app listening for a MIDI keyboard, upon which you can drop some .midi files, and get a rhythm action game to learn the piano? Vibed. Webcam for my Raspberry Pi? Vibed. Getting Marathon 2 (well, the open sourced and upgraded engine, Aleph One) working as a web app? Vibed. Isochrone maps? Vibed.
When do you have time for all of this? I don’t mean the vibing part but the using the app part.
I agree that agents are super good for one shotting throwaway code for tasks that would have required manual human actions previously but I would never have bothered buying an app for that.
Not having to deal with IT support for relatives is a win though since now I can just throw it at an LLM!
> When do you have time for all of this? I don’t mean the vibing part but the using the app part.
Mix of this being spread over more than a year, that I'm not doomscrolling because HackerNews and Telegram are my main social media presences, and being unemployed/prematurely retired (which one depends on what one thinks of €1k/month passive income and no rent).
I am also weirdly unmotivated by opportunities to spend money, which is both how I got this passive income and lack of rent, and why it's borderline enough for me. FIRE is very easy when all your working life, you only spend rent+50%, the rest of your paycheque going to savings and investments; but most people can't do this.
Oracle makes billions selling SQL when you can just use a free version. This is no different. Enterprise has enterprise needs. It's really not that complicated or irrational.
Anthropic wants you to think that models that distill from them would be worthless otherwise. It’s not true, it’s just one part of the process. The whole narrative that Chinese and other models are only good because they distill is nonsense.
A lot of the freakout and "bye" posts on the OpenCodeCLI subreddit due to DeepSeek raising prices.
Stripe is a middleman. So is OpenRouter. So is OpenCode. Unless you own the data center and the hardware, how cheap can a middleman's tokens really be compared to the hyperscalers, without massive model compression?
Even DeepSeek itself is raising token prices. How much margin is there for a middleman like Stripe buying tokens in bulk from a data center and reselling them? Last i check Stripe do not run or own physical data center
That's old economy thinking. If you have shit tons of data about the behavior of large groups of people, someone will pay you for it. HFT firms paying for satellite pictures of shopping mall parking lots in order to get an advantage over their competitors is old news, but it gives you an idea of the game being played. Why do you think meta bought gif keyboard?
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
Whether AI works or is useful or not isn’t even the question anymore. It can fulfil every promise Sam Altman has been making and will still make no financial sense to justify these valuations.
I take it from [1] (transcript of recent DeepSeek CEO discussion with investors) that DeepSeek would disagree on the immediate catastrophic impact to the likes of OpenAI or Anthropic. The reason is even though technology parity mostly exists, only OpenAI, Anthropic et al have the inference capacity to gain market share and generate revenue. Chinese vendors don't have the chips needed to scale up inference and gain market share, and the DeepSeek CEO doesn't think this would happen in optimistic circumstances in the next 3 years, but thinks it might be possible in 5 years.
In summary, regardless of country of origin, availability of inference capacity is the moat protecting the likes of OpenAI and Anthropic, not technology superiority.
It only justifies their long term valuations if they can leverage that temporary monopoly for technological superiority (they can't) or lasting market share (they can't).
Chinese models prove there's no technical advantage, and the software side is heavily commoditized so there's not much advantages to market share either.
The question mark in my mind over the technological superiority is whether the additional volume of data they see due to capturing the top of the market allows them to do recursive self-improvement in a way nobody else can match, before any of the other labs can figure it out. That's the only runaway outcome I can see.
If you have exponentially increasing use of your harness, then it's true that every day you capture exponentially more data, but it's also true that every day exponentially more data will slip through the cracks of your would-be monopoly and that data arrives at your competitors via various channels (competitor harnesses, subsidized reselling, etc)
The very exponential that you are relying on to give you runaway improvement is also giving exponentially increasing data to your competitors. All else being equal your competitors stay a step behind but you never develop a monopoly either. That's the best case for Anthropic/OpenAI. In reality, training data is just one variable, exponentials don't last forever, and your competitors will get better at capturing a bigger slice of training data.
If user data would become such a key ingredient (which it might, i actually remember noam shazeer talking about the importance of user data), i think chinese labs can still get it from china, as keep in mind it ahs a billion people behind the great firewall banned from using us llms. And btw broadly for any gap like this, you really gotta consider that if its becoming a bottleneck, chinese labs will find a way to buy it from one of the labs unless theres strict regulation at the government level
But is that data good? That's the question. As in, is my usage at work:
a) indicative of problems that aren't already out there in the wild? (no)
b) are the responses I'm getting so good and novel that the model can improve itself? (no)
It's the garbage in garbage out idea, just scaled up. If the model gave a bad answer, and I didn't catch it, and you now train on that I/O pair (my perhaps crappy prompt, the bad output), then you're not going to improve anything.
Yes, RSI seems to be the new AI industry McGuffin of 2026, just as agentic capability has become table stakes and scaremongering has become a punchline.
The Chinese models are adopting licensing quite rapidly and Xi will soon enough close them for security reasons. The most widely used model, integrated across Bytedance apps and operations, has never been open and is most closely associated with the state.
I would add that it is not just capacity, but also negotiation ability. With scale comes the ability to negotiate better prices than everyone else. Even if you can find capacity for your smallish user base, your inference cost can not match these companies unless you have a technical advantage for your inference cases. Squeezing the hardware requires request batching and caching which are far easier at scale and sustained user activity.
People can host these models is doing a lot of lifting here, these are models that depends on 5 digits on specialized installation to run on.
IMHO, this has the impact of softening the impact of data centers sitting unused in the long term if they can still serve open weight models, even if Anthropic or OAI have to scale down their expansion rate to pay the bills.
Regardless, reality has to give at some point; these valuations don't make any sense. We've been valuing GenAI as disruptive work, when in reality they're much closer to cloud providers with a beefy, one-pony-trick R&D department.
Is lack of inference chips due to the trading blocks by trump administration? What if Trump agrees to sell chips to china, would they collapse then? That's not a very strong position to be at
Most discussion in recent years about chip fabrication shortages, expansion, etc has focussed on leading nodes (<7nm) and AI/computer chips. But perhaps more quietly in the background, China has been rapidly building other semiconductor capacity such as power semiconductors used in electric vehicles, wind turbines, solar modules, train traction systems, etc. For example, Chinese-produced motor vehicles (37% of global motor vehicle production in 2025) in a year or two are targeted to use 100% domestically produced chips, and this production is decreasingly dependent on imports, even for factory tooling.
The report at [1] is a good summary of long term trends for China's rise in domestic self-sufficiency for semiconductor manufacturing. The report predicts "At current pace, China may achieve self-sufficiency in semiconductor manufacturing by 2027-2028, though trailing at leading-edge nodes". By contrast, before the first Trump presidency in 2017, a chart shows China importing 30% of all globally manufactured semiconductors (and increasing). Other reports on semiconductor fabrication equipment sales show the means, which is China having been and continuing to be in number (1) position for expenditure on semiconductor fabrication equipment.
The reports at [2] and [3] are also a good summary of long term trends for semiconductor foundry capacity predictions to 2031. A prediction is made that China's current 12% global semiconductor foundry supply capacity (across all semiconductor categories) in 2025 will expand to ~30% by 2031.
I have already begun winding down my spend on claude and OAI to make room for infra budget. Anecdotal, but I have no doubt a lot of others are doing the same, I very much agree the US players have major issues looming. What an exciting time to be alive!
No time like the present to pull out and reduce your exposure. I brought this up in my employer's forums 4 months ago and honestly it's been clear even before then. In particular, the upcoming IPOs of both oAI and Anthropic will likely be disastrous for the public - the floor is falling from under them and I don't know if they can be scrappy and work with fewer resources - their internal culture may not support this. We all knew in our hearts they're a commodity - just see how easily you can switch between the 2 of them - and now there are 10 more options costing a fraction.
When Xi Jinping did the announcement of their open weights push, they might as well cancelled their IPOs....
Yes buuuut…. I do quite a bit of day trading (maybe closer to scalping) for the first few hours the market is open, everyday. Anecdotally: despite everyone knowing its valuation was ridiculous, I rode that SpaceX train pretty hard and made a pretty penny.
I close-out all my positions by end-of-trading everyday… so when the day came when there was a very clear and very scary indicator during early trading hours, quickly followed by SpaceX’s catastrophic fall right after opening bell, that was the end of my involvement….
And I fully expect oAI and anthro to be the same way. They’re being propped up with private loans, subsidies, and other tricky bookkeeping techniques. You would think their CEOs would pivot away from their current public personas. Ironically, they are like a poor man’s Elon Musk… and that doesn’t bode well for their companies
The frontier labs will do well if they pivot their offering towards more capable, larger-scale models that are inherently harder to both train and deploy for commodity suppliers. Their existing investments in gigawatt-scale datacenters are quite optimal for this. "Commodity" inference need not comprise the whole market.
I don’t think this works, for a few reasons. First, intelligence gains from scaling the models bigger is sublinear now. So they could eke out a little extra performance, but the increased cost will eventually eclipse the economic value gained from this.
Second, humongous models are impractical even for them to deploy widely. They’re best used as teachers for smaller, more efficient models that can crank out the volume they need to sell.
Finally, there is a data wall. Sure, they can keep scaling RL on math problems and code. But with everything else, where will the supervision come from when they need several orders of magnitude more?
I agree. And even if they were able to do it for one more round, it's not a sustainable strategy. What they (Anthropic and OpenAI) need to do is build platforms and integrate verticals.
assuming the technology of model architectures does not gain any further breakthroughs that returns us back to the gains previously seen. I'm of the opinion that we still have some discoveries on the mathematical side of the fence to go that will improve models further.
> I'm of the opinion that we still have some discoveries on the mathematical side of the fence to go that will improve models further.
That's assuming the infrastructure needed to develop models stays available financially and supply wise. A lot of the services used to train and develop models are supplied and funded by people who are looking for multiple returns of investment. If/when OpenAI and Anthropic valuations fall and they inevitably get acquired, will Meta/Alphabet/Microsoft still want to spend lots of money for unclear returns in the short-term? Nvidia and co are on a one way train service to hype town. I don't think they will be happy to get on a coach to hype town Temu version. The shareholders likely won't.
Also, the backlash against LLMs is growing rapidly. AI content, data centres, etc is quickly gaining negative connotations outside of visual and music artists circles. While existing models are going nowhere, developing more advanced models is very quickly getting unpopular. LLMs Data centres increasing people's bills, Anthropic destroying old books, chat bots giving unethical advice to vulnerable people, etc. It won't be long before LLM infrastructure becoming an electoral issue.
Will a small research oriented community be big enough justify maintaining the apparatus needed to produce infra tech at a profitable level post OpenAI?
Most of the money will come from companies/corporations who will be required to buy safe AI. The public will be just banned from buying which might make it hard (ie: site/payment blocked) but not impossible. It could be good enough for the big whales.
A large portion of the economy is currently tied up in the musical chairs shell game that is AI hype. When the music stops there are going to be CEOs looking for handouts and justifying it with spooky national security buzzwords. How we respond to that will depend on whether it happens in an admin that is famously captured by the industry or not.
Many believe, including myself, that the market is currently propped by a massive AI bubble. Nearly a US $1 trillion is being spent this year, and more is planned for next year. All of this is for a "build up". There is no pay out. The major AI companies are taking in massive losses in the hopes that they will eventually be able to cash out.
The math is not looking good to me. The effect will be like the dotcom bubble. But much much bigger. Because the numbers are so much bigger.
Well, the dotcom bust wasn't all that bad for the wider economy. No financial crisis. A shallow recession (and even that could have been avoided.)
Btw, the dotcom bust was real, but there was no dotcom bubble. Skeptics back then said that the valuations only made sense if tech companies were to dominate the economy in the future. Well, that future arrived more than a decade ago.
(More formally, if you had invested in a broad index of tech companies throughout the dotcom boom years, and had held this, you would have done reasonably well over the next twenty years.)
Just keep in mind that it can take a whole for things to play out. I’m someone who believe the US AI industry is completely unsustainable and built on sand, and will crash even if the current AI itself turns out to be very successful. But that doesn’t mean everything will burn to the ground next week. In a history book things will look very sudden but at normal speed that can easily take months to years to fully play out.
Also, take in consideration that the AI trade infected a lot of other trade in the economy, if you decide at some point to move your money to a place that is safe in case of a downturn be sure to carefully evaluate that’s actually the case
These crises are manufactured by the central banks.
Compare and contrast how the dot-com bust did _not_ lead to global financial crises. Nor did Black Monday, nor the recent string of bank failures in the US.
('Manufactured' above means that central banks are responsible. I make no judgement on intent here. Around 2008 it was incompetence by the Fed and ECB as far as I can tell. The Fed started paying interest on excess reserves and the ECB even increased rates. Twice. Amongst quite a few other missteps.)
A lot of the performance of these open source models might come from distilling the closed frontier models. If those can't raise the funds anymore to train newer and better models then the whole improvement cycle might slow down.
Another interesting potential market here will be 'LLM in a box'. All the hardware and other tooling in a prebuilt, but modular, package ready to go. Pay one up-front cost, get a system running [whatever open LLM] with a token rate of [x], optionally configured to be immediately ready for distributed usage. Basically the opposite of cloud stuff: no rent, no dependency, 100% guaranteed uptime, guaranteed security/privacy (at least subject to your own actions), and so on.
Palantir already offers a "turnkey AI datacenter", i.e. a rack with "NVIDIA Blackwell Ultra systems with eight NVIDIA Blackwell Ultra GPUs and NVIDIA Spectrum-X™ Ethernet networking for AI training and inference".
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
It is just a dedicated computer system, which should be managed by its owner, like any other on-prem servers.
I doubt that it has a good price/performance ratio, but it is a solution for those who feel that they do not want to search, buy, assemble, install and configure every HW/SW component.
Fair but the idea of "running your LLM setup" at every "need" level and corresponding cost does make sense.
For a lot of people (and orgs I'd guess) who just go and buy ≈$20 per month plans (or more for teams), they might not even need a fraction of that cost or capability. A lot of them don't even need it for coding or graphics. Even the API access based pricing aren't great from these frontier US AI houses. The distribution of "LLM being" offered will also give rise to many open-router like offering but at the end point level - direct interfaces to the customers. Pick your vendor sort.
AI shouldn't become another "search means Google".
It comes in a full sized shipping container and costs around $10M but money has stopped being connected to reality now anyway with all the AI company valuations being floated around, so who cares about a few million here or there.
“100% guaranteed downtime when you least can afford it and the support tickets are your problem.”
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
Still has to break even on the balance sheet, especially at a bootstrapped startup. We actually made most of the financial windfall in translation API fees oddly enough.
For our own model training we needed to do some large scale translation tasks of a large dataset (1M or so documents, 10 or so target languages), running full-size NLLB on-prem saved us an absurd amount of money vs Google Translate API.
(For reference doing 1M target docs into a single language in Google Translate API is roughly $120k list price. You can run full size NLLB on an 48GB NVIDIA A600 and the major difference for us was speed, but for this task time to completion wasn’t an issue.)
Exactly! As I've argued here on HN before, such an "LLM in a box" might end up being serviced/upgraded once or twice a year by a company very similar to the one servicing the coffee machine at the office. In contrast to databases, storage, etc. it doesn't matter much if the box breaks at some point – they'll just come by and replace it with a new one – and there's barely any software on the box to speak of, at least none that requires continuous development and feature upgrades, beyond rolling out security patches. This makes the business case drastically different from cloud and SaaS offerings, where most of the moat is in the software and the state maintenance (and the vendor lock-in of course). The LLM in a box is destined to become a commodity.
What makes that kinda complicated is that multi-user throughput of LLMs scale well but single-user performance often stays constant at low ends. If you could saturate e.g. 16 concurrent session-month of demand, you can just go buy 16 of 32GB GPUs and start charging monthly for inference. That could work if you had e.g. over thousand total employees with hundreds of devs eager to trying it out, but only if the company is also interested in a private inference experiment.
You're talking about multi-session vs. single-session throughput. A single user can easily leverage multiple sessions via e.g. subagent swarms, especially on a lower-end setup where any single session is going to be quite slow. Saturating utilization during off-hours is harder but potentially quite feasible by assigning lower priority, unattended tasks/inference loops.
I think at this point the question is: will the US government be willing and capable to justify the trillion dollar valuation for _one_ of the companies via regulatory capture? The US has a workforce of 170m, so 1.7 trillion would come down to 10k per person, or a discounted cashflow at 3% of 25 USD per month - not including private use, students etc.
It’s a common denominator if you want to do napkin-math for a whole national economy. Regulatory capture is like a tax on those people not on the beneficiary side, so if the government were to nationalize both supply (no export license for SOTA models) and demand (no foreign or self-hosted LLMs allowed), they’d end up making everyone else pay for it in some way or the other. The governmental utility function will then include only those using the services for direct economic benefit.
It is impossible to justify the absurd private valuations they have given themselves in collusion with investors.
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
its interesting, as it's typically the banks and against the public at large because the goal is to jimmy up valuations to justify IPOs then sell on opening; just like spacex.
It's what enron was doing; it's what most of crypto's offshoots were doing.
Sure you can blame the marks of the grift and say "well the public should know they're faking all this cash flow expectation".
It seems like you're either driving the grift economy or part of the collusion.
It's similar to how a cult operates, so I'll be frank: your skepticism seems biased.
Which is not unreasonable. Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
> Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
It's been a few years. Has anyone done this successfully yet?
There are a over a dozen EU open-weight providers. I’m not sure if they are even charging that much of an extra. EU-based clients have little reason to use non-EU inference providers.
I don’t have user statistics but my mail/domain registrar Infomaniak advertises Qwen 3.5 and Apertus, “a Swiss open-source AI model, developed by EPFL, ETH Zurich and CSCS”
Most are not necessarily free to host and monetize. At least one of them has a license that says if you are re-hosting the model then you need a license with that company that made the model.
"I just don't see how you justify a trillion valuation for US AI"
- military applications
- financial applications
- medical
- applied science
In all those cases it is achievable for those who have needed training data, and Chinese are not going to get them easily. US AI Labs are showing: give us the data, we will do wonders, promising "singularity"-level future achievements.
I'm sure US billionaires will find a way to extract those trillions from the public. They're smart, they can handle it. After all, they can ask AI for advice on how to do it.
Could you just tell us why you think they were worth billions before ChatGPT, instead of suggesting that we think on it? You seem to know the answer already, so please share it with the class.
agentic coding is real. If AI labs can take over the coding tool market, that's a billion+ market. LLMs work in coding, and AI labs can slowly expand into other white collar work. Vaporware? Hardly.
The American attitude is generally to let private companies build up a new industry so it can create jobs and pay taxes. However, in the LLM race, the Chinese open weight playbook pretty much killed that. China has basically commoditized LLMs. Chinese models are good enough, so the race has come down to who can offer the cheapest tokens.
Chinese open weight models are great for this turn, but American private models generate orders of magnitude more cashflow. This cashflow = investment in training future models. It's unclear how Chinese open weight companies are going to compete in future rounds if they can't raise the same capital for training runs.
The American business model is exceedingly efficient at building large businesses from zero. I wouldn't dismiss it as just a jobs creation thing.
It’s unclear where American labs future capital will come from. They pretty much exhausted private options at that point and it’s not clear how successful an ipo would be at the current time
Obviously to me, I express things from my point of view.
Raising too much from debt is a bit dangerous if you plan to go public relatively soon and don’t have a good story for it (I don’t believe they have one). You can continue raising from VCs, but at some point the valuation and dilution starts to become a real issue, and will make your ipo even more difficult. Their options are pretty much limited to raising money from hyperscalers (with required compute spending, so more circular funding), which is what they are doing, but you cannot do that infinitely without having a good story to tell Microsoft/Google/Amazon investors. The market is more skeptical than it was a few months ago, I’m not convinced you can do that for years to come
I think as a counter point we continue to see investment and buildout. What do you mean the market is more skeptical? Of course the market doesn’t really have an opinion per se and aren’t all of these companies growing in valuation, revenues, and profits? At least the public ones.
Why are Anthropic's and OpenAI's annualized revenue about $50B each?
LLMs need massive amounts of compute to compete, so I wouldn't claim that the great (and leading, and likely to continue to lead) LLMs are commodities end-to-end, even if the non-executing-at-scale LLMs files and IP are commoditized. The execute, the compute, that is what breathes life into the model, which is otherwise weak or dead.
US AI labs really rub me the wrong way, especially with the doom and scare tactics they use. Both Altman and Dario keep talking about how AI will replace workers and how we should regulate LLMs for national security, Dario’s main point.
LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?
LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.
> "Anthropic believes that they are the ones who should have final say over how Anthropic is used; given that they think only they should be developing leading edge AI, they by extension think that only they should have final say over AI generally. When you further combine this realization with the company’s pronouncements about AI’s ability to conduct all economic activity, you realize that Anthropic’s leadership effectively wants to have power over everything and everyone."
To be fair, we're simultaneously mocking anthropic for believing in safety so much and also for them thinking they're the only ones that care enough about it. It's true that no one else seems to care as much. Judging by reactions from everyone, all their safety talk is very bad PR.
The criticism isn't that they believe in safety too much. It's that they patently using safety as a red herring with the actual goal of regulating away competition that they know the cannot beat.
The people involved have been talking about safety long before they had any users or a company.
"Concrete Problems in AI Safety" by Dario and another founder was published 2016, anthropic was founded 2021. Among a bunch of other examples, including other founders.
If safety is seen as a joke now, it was really seen as a joke back then. The reason they keep shooting themselves in the foot with bad PR is because they truly believe in risks.
I’m sorry, but this do-gooder concept doesn’t jibe with their other actions. For example, swindling their business partners (such as Figma). Filing an unnecessary trademark lawsuit against a customer whose brand predates Anthropic.
And for safety? Oh yeah, they literally sell the removal of guardrails in exchange for minimum spend commitments.
For example, swindling their business partners (such as Figma). Filing an unnecessary trademark lawsuit against a customer whose brand predates Anthropic.
Completely unrelated to AI safety.
they literally sell the removal of guardrails in exchange for minimum spend commitments
It seems blindingly obvious to me that you'd want to let trusted organizations use the models defensively, which means without guardrails, and that you'd probably gate that behind some kind of enterprise sales process, for multiple reasons.
Honestly, it just seems like you have an axe to grind and aren't really particularly knowledgeable (or curious) about AI safety.
1. This is not “unrelated to AI safety”. The things they seek in the name of AI safety always seem to be aligned with the path that will eliminate competition and give them maximum economic power. Thus, to disentangle what is sincere and what is ulterior, we have to look at the leadership’s character holistically. A ruthlessly unethical and cutthroat corporation that is also seeking unprecedented power doesn’t deserve the benefit of the doubt when it claims to be doing this for altruistic reasons.
2. You misstated what I wrote. My gripe is not that they have an enterprise sales process. It’s that in negotiations, they say “we’ll drop the safeguards if you agree to spend more”. I haven’t heard of any groundbreaking AI safety research concluding that spending more with Anthropic makes the models safer, but maybe Dario has a new blog post coming.
When someone says they are doing something to help you, but it really helps them, you have to choose if you trust what they say. One way to know if you can trust them is to watch how they behave.
(By the way, I have been very transparent on HN that I have an axe to grind, literally referring to it as “an axe to grind”. My axe is that I think the frontier labs are a symptom of a cancer on our society, and I personally wish to see them and their tactics fail.)
Yes, that’s obvious, and it’s leading you to some really specious logic.
Your simpler version still sidesteps the point that a trademark dispute has nothing to do with AI safety. Other than that you disagree with it, therefore they’re dishonest scammers, therefore AI safety is all bullshit?
A trademark dispute in isolation isn’t related. But as part of a broader course of conduct in which the pursuit of commercial advantage consistently trumps other values, it is. We can disagree on this.
What they believe now or care about is fundamentally not very relevant: allowing them a monopoly would be a big mistake. Google was supposed to do no evil and it probably started that way but nothing guarantees the future other than having alternatives.
If there are genuine society risks in a tech I don't want to discourage CEOs from talking about them. I feel like we've spent decades talking about how evil chemical companies (etc.) were about covering up issues in the 20th century. But yes, that's different to being a reason to ban external models.
I agree re: Dario quietly pushing for government control. He also said LLMs would replace a lot of entry-level information jobs, doubling the unemployment rate from 4-5% to 10%.
but this crap may take forever to play out even if the outcome is well-known. Self-driving is "here", it's obvious that once it's cheap enough having a human behind a car wheel or a freight truck wheel is an absurd waste of human life (kinda like digging canals with bare hands instead of an excavator), yet truckers and uber drivers are still employed. But everyone knows the writing is on the wall for them.
tintinweb/pi-subagents. Then I use superpowers and it spins up subagents automatically. Works really well and you can customise the models and efforts you want to use with local AGENTS.md.
I’m primarily using gpt-5.6 and then opus for reviews.
Their harness is indeed nice, but we're using it against our own AI Gateway at work. Right now we only expose GPT models on it, but I imagine it's possible to add our own models eventually
Open code is cool once I added the ctrl-o function to it (show thinking and command outputs at will instead of on by default), but sadly I don't think it got merged by the team.
...but we're talking about compaction, and opencode's compaction is (or was) terrible. I've seen so many horrible problems that I keep it disabled (with an envvar flag, because even the config flag to turn it off was broken).
I have so much gratitude to the frontier companies who did all the extremely complicated research and development, model by model. It already feels difficult to remember how much capital it really took. Thank you for getting us to this point.
>Anthropic was asked to pay book authors because it trained on pirated downloaded books.
No, it was asked to pay book authors because it pirated copies of books and stored them on their hard drives. The ruling had nothing to do with training.
Aaron Schwartz was re-publishing the journals, which is the core concern or copyright and indeed where the word "copyright" comes from. Anthropic could get in a lot more trouble if their models are caught reproducing copyrighted work from their training set wholesale.
I think the Aaron Schwartz case is incredibly vexing because he was obviously acting out of a sense of altruism without personal self-interest. I don't think he deserved the book getting thrown at him like that. But the whole copyright system, which people seem to think is simultaneously good and bad, kinda rests on not allowing those kinds of violations
I've linked this several times, but LLMs are capable of reproducing entire books. Researchers were able to extract books nearly verbatim: https://arxiv.org/abs/2601.02671
The judge explicitly ruled that training on the books was fine.
They just should have bought them, rather than pirating them.
Also LLM output is not IP (in itself) in the first place, nor would Anthropic want to claim it is and that they have rights to it - that would drive paying customers away.
> The court also held that the third factor favored fair use as to the purchased library copies converted from print to digital because the purpose of the copying was to keep the books in its library but with more favorable storage and searchability properties. This purpose required copying, there was no surplus copying and the source copy was destroyed. With respect to the pirated copies, however, the court held that because “Anthropic lacked any entitlement to hold those copies” and retained them “even after deciding it would not make further copies from them for training,” this third factor weighed against Anthropic for that particular use.
its free on opencode and i use it for personal projects. most of my personal projects are AI generated since its personal projects. nothing important are on them. it is hilarious if Meta is training their AI model with AI generated code.
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