Competition drives prices down. We tried Kimi and GLM models after hitting 'cybersecurity' blocks on OpenAI and Anthropic. We found them to be much cheaper to access and helped us complete our work without 'cybersecurity' refusals blocking us.
If this is mostly competition enduced, I have to assume either their usage has plummeted or they will implode financially in short order.<p>The frontier labs were already burning money on more expensive price models, and competition wouldn't have reduced their capex proportionately.
How could they do anything but implode financially? Genuinely curious. The numbers are not right.
Did the frontier labs take out debt or did they make flimsy unenforceable agreements to neoclouds who took out debt?
Borrowing money is not the only way to go into debt. They reportedly made binding and non-cancelable commitments. Once those commitments come due then they will be indebted to their counterparties until they pay up or the courts sort it out.<p>This is comparable to how, if I sign a 12 month non-cancelable lease and then don’t pay the rent, I will be in debt to my landlord.
People heavily underestimate the number of tricks available to corporates for taking on "debt": <a href="https://latentstate.net/the-hidden-ledger/" rel="nofollow">https://latentstate.net/the-hidden-ledger/</a>
That's effectively borrowing money with a different name though. The only way to go into debt is to sign an agreement to pay someone money in the future, regardless of the terms used to describe it.
No, it's really not. Do you have a war chest to drag out a legal battle and the ability to bankrupt your landlord by withholding payment? The rules are different at this scale, and "you can't enforce a contract if the attempt will bankrupt you" is only one of the possible games.<p>Here's another one: pay a supplier to cripple their deployment. Suddenly it's the neocloud's fault for failing to deliver, not your fault for failing to pay.<p>Did we all forget how Sam Altman turned a non-profit into a for-profit? Do we think he'll bring <i>less</i> sociopathy to this battle?
Yes.
Apple is worth more than all the AI companies combined. You think LLMs are less valuable in the global market than slightly updated iPhone models every year?
Cost and value are two different things.<p>Apple's worth about the same as the yearly value of the world's entire agriculture, forestry and fisheries industries.<p>Without Apple, the next year would be pretty tumultuous. Without agriculture and fishing, the next year would be fatal.
LLMs? No. LLM companies? Yes. It's just math, data centers, and mass copyright infringement. The only scarcity is Nvidia, who just passed Apple in market cap, somewhat proving your point but not in the way you meant it.
If you consider Nvidia an LLM company, and I do at this point given how heavily they've invested in frontier labs and where much of their revenue comes from, they alone are worth slightly more than Apple.
Maybe not, but it is probably less likely that Apple will face stiff competition from freely available alternatives, given that AOSP has been a thing for the iPhone’s entire lifespan and nonproprietary Android phones have failed to capture significant market share.
I don't think we have seen an Apple of AI yet. I wouldn't describe the frontier that way, would you?
Their costs could drop, competition could dry up, or they may only be unprofitable in the first place because they’re choosing to invest more money in growing their business than they’re currently making, a practice which need not and will not continue indefinitely.<p>It’s not at all unusual for companies to run at a loss for several years before turning a profit.
The government could bail them out, that's the most likely scenario if they don't implode IMO.<p>They could also continue to grow at the rates they had last year. I don't know where those customers would come from, but if they did pull that off OpenAI and Anthropic could sustain propping up the circular financing going around the AI industry.
Goverment bailing they out will be using people tax to rescue the companies that absorbed most of the 801k money so they can get people out of work... it's the ultimate slap to the face... I would strongly resist this, I would vote if they implode to give to whoever owned shares via 801k their money, to people and let them (openai, anthropic, et al) eat shit... I would love to buy one of those gpus to run an open model at discount price...
Completely agree it will be a huge slap in the face to the public, unfortunately that's a common solution.<p>When everyone is afraid the market will collapse, or more likely distracted because the market collapsed just enough to hurt the average person, the government will bail them out and either people won't notice or will welcome it as a solution to huge levels of fear.<p>Voting isn't usually much of a solution after the fact, the political machine doesn't allow for candidates that would fix anything to have a chance at any office that matters. There's a reason our politicians today all seem to have roughly the same goals and all seem to become extremely wealthy while in office.
A bailout would be a slap in the face when the correct solution is to nationalize them. If they are too big to fail then the government gets a controlling share of equity and we finally start the sovereign wealth fund we should have started decades ago.
Maybe you should try voting harder.
Meaning what? Cast more than one ballot?<p>I live in a state that votes heavily in one direction, most states are the same. Its fine for me to vote, but the winner of a majority of states is pretty well decided before a ballot is cast (by strong majority, I'm not claiming fraud).
Stuff like this that defies all logic happens all the time. Amazon operated at a massive loss first it’s first decade and still IPO’d years before they posted a profit.
That's why Anthropic are rushing the IPO. You can explain a single quarter slowdown, you can not explain a multi quarter one.
> If this is mostly competition enduced, I have to assume either their usage has plummeted or they will implode financially in short order.<p>If we assume it's due to competition, then their usage has precisely <i>not</i> plummeted because they cut prices by 80%.
They just started competing against each other? No.<p>Anthropic is going public soon. Until September, OpenAI was going public. Neither are remotely profitable.
So much this. I’m using these models primarily for dumb hobby projects and I’ve still hit this a lot - although much less so in Opus 5.5.
I wouldn’t be surprised if US “bans” access to the Chinese models by the end of the year
How?
I think your question is rhetorical, in which case I agree with the sentiment.<p>The US absolutely could (and very well might) make Chinese models illegal, possibly open weight models in general, but all that will do is keep them out of the hands of law abiding US citizens.<p>Law breakers in the US and the entire rest of the world would still have access, and nothing in the US will be any safer. People who think regulation will solve the problem should be embarrassed by their ignorance.
It may solve the problem of anthropic and open ai going out of business with all major consequences that would have for the rest of the US.
Lol, that's funny in an awful way. Choose between popping the AI bubble or creating a duopoly, and still have all the cybersecurity and other risks.
I don't get why people think Chinese models could bring openai and anthropic to their knees. There are zero signs of such a scenario. In fact the market is still growing at a gigantic pace. Quality of American models is still AAA. Chinese will have their place. But they are unmatched by their American rivals.
"Bring them to their knees" is highly unlikely.<p>"Puncture expectations of a multi-trillion dollar valuation" OTOH seems quite likely to me.
If the capability keeps growing significantly, then I agree it's possible the US could hold their significant lead indefinitely.<p>However, capability could plateau or reach a point where most of the market can't tell the difference and isn't willing to pay a premium.
> But they are unmatched by their American rivals.<p>Kimi K3.1 is top 5 on AA and half the price of Opus 5.5, Qwen4 is coming soon. There is near zero difference for most tasks, so it matters less today and even less as time goes on. Price is now the top concern for a reason.<p><a href="https://artificialanalysis.ai/#intelligence" rel="nofollow">https://artificialanalysis.ai/#intelligence</a>
“Ban open weight models”
Lol how. How do you even prove a model that I’m hosting is open weights?
OpenRouter and other inference providers won’t be able to openly host the models, for starters. That should cut most (or at least a significant share of) non-local inference, and local inference only works with smaller, less capable models, plus the hardware is hella expensive when you don’t have scale to saturate.<p>In addition, most companies don’t want to be in a position where a disgruntled employee can report them to authorities and rack up massive fines or worse, even if it’s hard to tell from the outside.
There's a difference between something being illegal and being able to enforce that law.
I don't know man. Marijuana is/was banned in most of the US. LOL, how do you ban a plant?
Banning open weight models in the US would provide the rest of the world with a huge business advantage since these models are much more cost-effective than US models.
If you are a legally operating company in the US just saying it's banned is enough, the threat of enforcement is enough pressure.<p>You and I obviously can just use a VPN, but 99% of usage is likely as part of a company.
Presumably, legally operating companies aren't the dominant threat though. So banning open weight models doesn't solve anything.
> If you are a legally operating company in the US just saying it's banned is enough, the threat of enforcement is enough pressure.<p>Is there precedent for the US actually "banning" software? I know for example the Kaspersky ban and tiktok ban, but both of those were either banning the sales or banning the service. IIRC neither made the actual software illegal.<p>What is an example of a specific software being illegal to possess? What is an example of a specific software being illegal to use if it is not used to break other laws (copyright, etc.) or talks to banned service providers?<p>Closest thing I can think of is restricting the export of cryptography in the 90's, but that was not even close to what you are suggesting.
Correct it’s 7% chance on kalshi, and those 7% of people are just wrong. I know everyone likes to larp that the US is some kind of dictatorship, but lol, lmao as they say.
Also on the “how” side so far we have “they will just announce that “they” are banned, hilarious.
What harness are you all using? OpenCode?
I have a harness for my harnesses now. My Hermes manages Codex, CC or Cursor now and interact with me via Slack and PRs on GitHub.<p>It's nice because I can keep going from anywhere, other team members can see and interject in my Slack threads, and Hermes auto updates its skills with any adjustments that we make so we don't have to ask twice.
I found Claude Code to work best. You can use it with any compatible API.
Pi.dev at home.<p>And I’m slowly writing my own. But it’s garbage at the moment.
I wrote one and kept iterating by using it to fix bugs in itself<p>I still also use other harnesses, but mine is already at the point where I prefer it more often than not<p>You should totally keep working on yours. It’s a cool learning experience. And you might stumble on some good ideas that nobody else has had
Don’t write your own. Fork maki.sh and customize it like I did. It’s built in Rust and is absolutely amazing.
Thanks for the suggestion, but that’s not a fit for me.<p>I’m writing my own harness to learn. And I’m writing it in zig.<p>But, I’ll absolutely add maki.sh to my reading list. So, that’s a win.
What kind of hardware do you need to run a useful LLM at home? To train one?
> "What kind of hardware... "<p>At home, I mostly use openrouter.<p>A few bucks will realistically last you forever.
I also bought a DGX Spark at launch. I've learned a ton. but it's not usually my daily driver. I do run overnight tasks on it, like "read this book and write up notes on each chapter..."<p>In retrospect, given the dirt cheap prices of apis and or renting hardware, buying hardware for inference was likely a mistake (but not enough of a mistake to sell it now).
There are a lot of tasks I have, e.g. integration with homeassistant, that I absolutely will not trust a cloud provider for. On prem or nothing.<p>I'm not willing to spend thousands for those features, so my house will remain dumber than it could be until prices come down in a year or two.
Schaefer says they write their own harness, not necessarily training or running a local model.
Crush is great and easy to hack on: <a href="https://github.com/charmbracelet/crush" rel="nofollow">https://github.com/charmbracelet/crush</a>
For me, I used Zed Agent with Codex as it has native integration with Zed's ACP so it's pretty convenient.<p>However, Anthropic is pickier about the harnesses you can use with Claude Code and I noticed discrepancies when using Zed's External Agent feature with Claude Code, so I just use the Claude app. It's fine.
Personally I've found Zed's agent to be much superior to OpenCode. IDK why but it gets better results from the same model on the same task. Using with GLM 5.3 and DeepSeek 4.1.
I've been pretty satisfied with OpenCode 2
yea but I thought they had a massive compute shortage. Something doesn't add up. Imagine there being a beef shortage and the beef producers cut prices because "competition". why?
If the beef producers don't believe beef is a commodity and they want to get you addicted to their specific beef then it makes sense. Right now in these companies life cycles profit doesn't actually matter. The only thing that matters is how much of their beef is being consumed. They want to give people so much free beef they can't even imagine the idea of having a meal that doesn't contain beef.
Massive increases in efficiency and <a href="https://en.wikipedia.org/wiki/Jevons_paradox" rel="nofollow">https://en.wikipedia.org/wiki/Jevons_paradox</a>
DeepSeek claims $38 of revenue per GPU, per hour for Flash V4. That is $332,880 per GPU, per year. This matches their DeepInfer numbers: 25,600 output tokens/s per GPU sold at $0.60 per million -> $55 minus some other overhead to arrive at $38.<p>For the bigger Anthropic and OpenAI models (and associated higher prices) there are many unknown factors. But presumably they also work hard on optimizations, and it's not so crazy to think they had or still have a lot of wiggle room to adjust prices.
It's sad how far down you have to scroll to get the real answer. It's because Anthropic margins on inference are absolutely bonkers and it's easy for them to drop prices by 80% and STILL have positive free cash flow
Yes, but DeepSeek went absolute goblin-mode on optimization and I would not be shocked to discover that OpenAI and Anthropic were behind on that front.
OpenAI and Anthropic have more compute and the best frontier models to throw at the optimization problems. They have published articles on these efforts. For example, Dots, and Anthropic's upcoming "persistent agent" is because of these optimizations. Both companies often publish articles detailing a lot of their technical efforts. They just get buried among the flood of other news.
Yes, I would not be shocked either.<p>Sonnet 5.5 is 16x more expensive at $10 per million output tokens. Kimi K3.1 is also $10 per million output tokens, and its number of active parameters is only 6.5x larger than V4.1 Flash (104B compared to 16B). These things are not directly comparable of course, but just to give an idea.<p>Even if inference of those models needs 16x, 32x, or 48x the number of GPUs compared to V4.1 Flash, still seems like potentially great revenue per GPU.
What is the TPS of DeepSeek? All of the LLMs I have tried are slow as molasses. \
Don't forget that Deepseek pays salary in RMB which is 1/7 of the value of USD.
Does Deepseek say how much those Clusters cost?
Given that they open sourced their inference framework, how realistic would be to buy hardware and become a provider on OpenRouter? These numbers are incredible.
This is why Anthropic and OpenAI are pushing for more “regulation”. You can’t show ROI to investors by <i>cutting</i> inference costs.<p>Regulation might inconvenience them a little, but the upside is huge if it means open models out of China get banned.
That would be like regulating who gets access to the best food on a sinking ship. The USA could allocate capital and attention toward much better things, for the country and for their people. I just hope the next couple administrations are wise enough to not experiment with the idea that ASI can get the country out of this 'fall of Rome' slow motion decline.
Ah yeah, our lovely free market, always asking for tariffs, blocking competition, to build cartels and monopolies.
> You can’t show ROI to investors by cutting inference costs<p>You can as long as you can saturate hardware with profit that makes up for training costs (which are also in free fall).<p>Anthropic is most at risk since they haven't bought any hardware (afaik), however OpenAi is building custom silicon now. The rest of Mag7 are primarily hardware vendors
Any sources on training costs in free fall? My understanding was training costs are proportional to model size. Is it just that compute costs are falling?
MiMo RL'd their latest models for $3.5M (million, not billion). It's hardware, software, and algos<p><a href="https://mimo.xiaomi.com/rl/" rel="nofollow">https://mimo.xiaomi.com/rl/</a><p>Also look to the history of stable diffusion
I believe that’s post-training, not pre-training.
> I believe that’s post-training, not pre-training<p>Aren't AI companies already butting against the limits of usable training data?<p>Outside of undiscovered techniques, additional costly pre-training will likely yield diminishing returns if you've already scraped the whole internet and destructively scanned as many 20th century books as you could lay your hands on.
yes, just post training, which is the most expensive phase now as I understand it
Training costs are not in free fall, and not every "post training" process is equal, Everyone does this differently. The costs certainly haven't gone down, to train a new 10T param model from scratch, like at all.<p>You cannot point at Xiaomis tiny model and compare it to training a 10T param model. The levels of dunning krueger are getting out of hand.<p>You also arent calculating the salaries of the people involved, of which there's only a few thousand worldwide in your hiring pool.
> You also arent calculating the salaries of the people involved, of which there's only a few thousand worldwide in your hiring pool.<p>There were probably a few dozen 20 years ago, and the existing pool grew from somewhere. AI (nee machine learning) is not rocket science - but it is a science, so it rewards those who are literate, curious, can hypothesize, experiment and learn from results to gain an intuition. I bet most statistics-adjacent professionals (millions, worldwide) can move laterally into AI research and excel at it. Nevermind AI has been among the most-in-demand undergrad programs since ChatGPT dropped. The only limited pool is for <i>celebrity</i> AI talent.
you haven't cited any examples, nor acknowledged this is how everything in computing has gone, DK is a two way street I suppose<p>stable diffusion is an excellent example, the price to train today's or yesterday's frontier is the analogy, many more people will be able to train up what we have today, especially as more of the pipeline is open sourced and the knowhow distills
>stable diffusion is an excellent example, the price to train today's or yesterday's frontier is the analogy,<p>Dont use llms to draft your responses to people, extermely cringe. That explains the llm induced Dunning Krueger.<p>What examples... There are no public numbers regarding how much it costs to train a 10T model, atleast nothing concrete.<p>You should just use common sense and think about how you have to fit tensors in memory to train, and the larger the model is the more GPUs you need. Then you could also use common sense to think about how GPUs have gone up in price, not down.
I would think that access to power is more critical than compute
> This is why Anthropic and OpenAI are pushing for more “regulation”<p>Given everything happening with Black October[0], don't you think regulating models may be reasonable?<p>[0] <a href="https://en.wikipedia.org/wiki/Black_October_(information_security)" rel="nofollow">https://en.wikipedia.org/wiki/Black_October_(information_sec...</a>
I too would like security issues to stay unpatched because only three letter agencies have access to uncrippled tools for finding the bugs.
I think that the frontier labs knew what they were doing when they invested in cybersecurity starting in the spring (or maybe earlier). They knew it would create hype. They knew it fit with their narrative that they are building a dangerous thing (something they believe because it’s fun to believe in doom, because it’s a great story for investors, and because it’s not inconsistent with a moat - in fact it makes a moat via regulatory capture).
Well, OpenAI is at the root of Black October, so maybe they are the problem
You remind me of those politicians who try to scapegoat people who point out security holes. <a href="https://www.theregister.com/security/2022/02/15/missouri-ends-effort-to-prosecute-view-source-journalist/1504668" rel="nofollow">https://www.theregister.com/security/2022/02/15/missouri-end...</a><p>Regulate the bad actors, not the neutral tools. Lockpicks aren't illegal, but breaking and entering is. Imagine if MasterLock used a lockpick to break into a government building and steal stuff, then tried to lobby to make lockpicks illegal. That's what OpenAI is doing, and you're falling for it.
“Black October” sounds a lot like Red Scare.
This is the goal of all government regulations and it's always resulted in higher prices and near monopolies dominating the market.<p>Yet, so many Democrats support it.
In the hands of 4th party system Progressive Republicans and 5th party system New Deal Democrats, government was an extremely powerful force for trust-busting.<p>In the hands of 6th party system Republicans, namely Ronald Reagan and Robert Bork, the government worked hard to usher in a a new era of Robber Barons. We live in the culmination of this project.<p>History says the result depends on the hands, not the tool. 6th party system Democrats have largely failed to undo the Reagan/Bork policies, but that's an act of <i>o</i>mission not <i>co</i>mission and even then Lina Khan was a big step in the right direction. This issue was on the ballot in 2024 and will be again in 2028. Hopefully we make better choices in 2028.
If you are going to limit the 5th Party system to the first half of the long 1930s-1990s period of overlapping realignments (which is pretty reasonable), then the 6th Party system should be the second half of that period, and the period from about the end of the Clinton Administration on should be the 7th Party system; you can call now the 6th Party system if you call the entire long realignment period the 5th (which makes some sense, too, though there is a pretty significant difference between the New Deal dominant period and the Civil Rights dominant period of the realignment), but to split it somewhere in the middle but then lump the period after with it makes little sense.
If you take away all regulation you’d probably end up with a monopoly anyway, as companies would no longer be prevented from merging with their competitors or using underhand tactics to gain market share.
This is the goal of some, but very few overall, government regulations. Not even close to "all".
That's wrong, but why let reality get in the way of a pithy ideology?<p>Government regulations exist to regulate activity, to prevent, punish, and/or remediate harms.<p>Environmental regulations exist because businesses wouldn't clean up their own messes. Financial regulations exist almost exclusively because of harms and abuses committed by members of the financial industry. Building codes are updated to reflect lessons learned about people died, to prevent more people from dying the same way.<p>If AI regulations are passed, it will be because the AI industry decided not to self-regulate itself and stop illegally hacking computer systems, generating CSAM, or various other illegal activities that should easily be preventable if their systems were actually intelligent.
“All” is doing a lot of work there.<p>Also there are real trade offs. Food would be cheaper if, for instance, it were legal to cut it with any old cheap substance regardless of health effects.<p>But I totally agree that some regulations are dumb or are regulatory capture monopoly schemes.<p>Unfortunately like many other things there is no absolute simple rule to save us from thinking. Each case must be evaluated in depth hopefully with domain expertise.
Well, because after 2011, when Anderseen said "software is eating the world"... it only ate so much of the world. We have that data on verticals now... is it not the same thing for "AI?"<p>Genuine inquiry here.
> It’s no surprise both companies are trying to build moats to defend against this trend, such as a focus on enterprise commitments and personal agentic assistants like OpenAI Dots and Claude Duet.<p>Also regulatory capture, like OpenAI calcifying its teen mode into law via Adam's Law.
<a href="https://openai.com/index/supporting-california-bill-advance-ai-youth-safety/" rel="nofollow">https://openai.com/index/supporting-california-bill-advance-...</a><p>Openrouter recently changed their TOS to exclude under-18 users, presumably to escape liability from the law.
<a href="https://conductatlas.com/diff/openrouter/openrouter-terms-of-service/6099/6281/" rel="nofollow">https://conductatlas.com/diff/openrouter/openrouter-terms-of...</a><p>This probably won't be enough, because Openrouter now has to do age verification to verify their users are adults, and if they don't, they have to apply "teen mode" to all users.
It’s a race to the bottom to see who blinks first and goes bankrupt. It’s not sustainable.
Because there's limited demand, and GPU capacity is like airline seats use it or lose it.<p>This will only get worse.
At the end the author briefly contemplates this:<p>> One outcome that would be genuinely ironic is that if these models are more compute efficient, it reduces the need for these massive data center buildouts, which could cause cascading economic failures. However, that likely won’t happen since inference is only one part of the compute mix and those data centers are still needed for other parts of the pipeline.<p>Don’t agree with the however. I think like you’re saying we’re going to get to see in real time what happens when something is commoditized and it gets massively overbuilt.
A historical episode that may arguably be related -- regarding stock market euphoria & overbuilding capacity -- was the dot-com era Telecom bubble & crash [1].<p>Even if something is a reasonable infrastructure project with a sound long term business plan, investors can still lose money if they pay too much for it. For the Telco bubble & crash, there were a few of these Telco companies engaging in financial shenanigans / securities fraud to juice their quarterly financials by leasing capacity to artificially inflate their operating revenue etc. E.g. we booked $100m of revenue by leasing X units of our capacity to $competitor! Sure, we're still extremely cashflow negative, but look at our operating revenue grow! Fine print: we also incurred a symmetric $100m expense to lease X units of capacity back from the same $competitor, but for some reason we didn't classify that as an operating expense, we've thrown it into a different accounting bucket.<p>That said, overbuilding fiber optic capacity by laying undersea cables is likely different to overbuilding GPU datacenter capacity in that most of the cost of laying a cable is the physical cost to lay the cable -- given you're already laying an undersea cable, the incremental cost to add a bit more capacity to it (more optical fibers) is negligible -- at least before you've sunk the cable! Whereas for a data center full of GPUs, maybe 50%-60% of the total cost is the GPUs, so it isn't cheap to massively overbuild capacity.<p>[1] <a href="https://en.wikipedia.org/wiki/Telecoms_crash" rel="nofollow">https://en.wikipedia.org/wiki/Telecoms_crash</a>
Yes - exactly.<p>Because all these newly trained models using the compute are doing really well and growing!
Well they did pull back on ZDR a few times and at least Anthropic has made their arguments (that I disagree with) that model outputs, no matter how much user context was fed, do not fall under their definition of "user data".<p>Are these prices subsidized by user generated outputs?
Can I even trust any answer to not be carefully worded and rephrase the response to "user data"?
Even so, what's to prevent some "oops" event that seems to be all too common?
At least some months back Bedrock seemed to be properly isolated, but I've heard of many getting non-ZDR notices for new models.
It’s probably more efficient caching methods and smaller models (6 sol is actually 6 terra and opus 5.5 is smaller than 5.1 etc). Don’t overthink this please
It’s both competition almost 3 way , cheaper open source , a clearly lagging behind a bit open ai that cuts prices to win Anthropic customers .<p>There was an interesting technical advantage by some models where Anthropic as a leader start loosing dramatically in speed too. 5.5 made a come back in that dimension.
I am guessing something to do with Qwen, Kimi and Deepseek. Plus after the tokemaxxing wave died down, a lot of cancelled subs from enterprise? Just my 2ct.
> leaked financials from both OpenAI and Anthropic show unsurprisingly that they are losing billions due to R&D/scaling<p>No, they are losing billions because LLMs are not a viable business. Their real business is making bombastic statements which the credulous press prints unquestioning, fooling various investors and ... that's it. IPO is another of these. You really should note how the Anthropic IPO date keeps slipping because they can't make the S-1 public as it would give away the game immediately. The market was fooled by SpaceX once you can't do this a second or third time.
Deepseek prices their models based on a 10 month hardware payback period. They report ~$38 revenue/GPU-hour on V4.1-Flash clusters.<p>Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.
At best, this tells you v4.1 flash is profitable, but can they really get $38 per gpu hour? Sounds like a lot.
From their own inference framework docs: "A single cluster currently serves 155 trillion tokens per day, with overall revenue of US$38 per GPU-hour (at valley pricing)."<p><a href="https://github.com/deepseek-ai/DeepInfer/blob/main/doc/inference_deployment_qa.md?plain=1#L17" rel="nofollow">https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...</a><p>So this implies they actually hit about double that during peak pricing.
If they keep it running at near constant usage, you'd only need 80-120 million tokens an hour to hit 38$ of revenue (at off peak pricing, half as much for peak pricing).
V4.1-Flash is probably quite smaller and much more resource effective than the frontier models. Add to that much cheaper energy in China.
Clearly potentially profitable. Not clearly profitable.
How much did it cost to train Deepseek v4.1 flash?
The market has decided that SPCX is worth $147, as of Friday, 5pm. Not sure who's fooling whom, but that's the market speaking. What that says, you'd have to ask a financial analyst, but that's where it is. It IPO'd at $135.
The existence of websites where you can rent a GPU to run open models is proof that inference is profitable.
The same evidence could just as possibly point to supply being too great for demand. The OP article references this as a distinct possibility: if models really have gotten more compute efficient, we may wind up in a situation where we have more inference capacity than we actually need.<p>To give a simplistic example: if a city as a ton of available AirBnBs, you might say, "Lots of people are willing to rent out their apartments, it must be profitable for them to do so." But the fact that all of those rooms are not already booked could also mean that way too many people have attempted to rent out their apartments.<p>And for what it's worth, it's possible for supply to outstrip demand while still leaving inference profitable. The question is whether it's (a) profitable enough to service massive debts and (b) sustainably profitable over the long run.<p>Edit: to add one note, it's possible for the LABS to be profitable on inference without inference itself being profitable. We don't know what the contracts look like, but it's entirely possible that labs received sweetheart deals from neoclouds so that the latter could flaunt big utilization numbers.
Ironic statements, given that you yourself are an LLM.
Price war. Duh. There is no moat. Also them chinese competition. It’s going to be race to the bottom. It’s gonna be ugly for the stock market. Good for consumers though!
Can they do the same thing with fuel prices?
Solar panels (which continue get cheaper every year) on my house makes charging my low-TCO EV free in 5 years
If you make it easy for lots of people to make the fuel, yes. EVs and smaller scale power generation anyone?
That would likely require innovation in a renewable liquid fuel that can work at scale.<p>Electrification will drive the demand for oil down, making the overhead more expensive per gallon, and eventually we will largely move off of fossil fuels, though I don't expect completely. It will be many decades due to the turnover times for industrial/commercial transportation systems.
Yes if China somehow gets the fuel and subsidize it to sell it for cheaper in the global world, even the gulf state price will go down.
The moat is high TPS. No one has rolled it to average consumers though.
Tokens per second? That only really matters for things like chat and sessions where you are monitoring a session as it runs which is going to be an increasingly small percentage of usage. That's bottlenecked by human attention. Agents in the background, long running tasks, unattended tasks, all that stuff is what is even capable of driving significant usage because you can just increase consumption massively by parallelizing without being the bottleneck. I wouldn't be shocked if already the vast majority of token usage was from unattended sessions. TPS is not that important there
DeepSeek is serving more TPS than anyone, about as much as OpenRouter does in a week in a day, at 38$/H800/Ascend so about 900k/~90% profit over 3 years (or ~80s% profit including training expenses).<p>Meanwhile, Oracle is handing Tencent 100.000 GPUs at 1.60$ hour, dangerously close to breakeven...<p>What does this tell you?
Doubt the race to the bottom is really good for consumers, unless you actually just want more stuff rather than better stuff. The bubble bursting is going to be pretty horrible financially for us all.<p>The main thing it seems to have done so far is made everything feel less trustworthy, and everything digital less safe. It has empowered scammers, that's for sure.<p>And for what benefit? Spunking out 7/10 code a bit faster with your brain turned off. Great!
Just wait until the government ban on the dangerous open models materializes.
It won’t be a ban, it will be some “certified safe” badge from a federal agency. Companies will be scared into not using anything that is not “certified safe” and business insurance will even require it. Whoever (openAI or Anthropic) gets to the politicians first will be the only certified safe model provider.<p>Maybe the FDA is a good analogy, you wouldn’t want to buy meat at the store not FDA certified would you? OpenAI and Anthropic want you to feel the same about AI.
Not just models, won't be long before hardware is regulated too.
Sounds like an eventual Supreme Court case.
I do not expect an outright ban. (1) the bots are too useful, open is the way, re: HF handling the incident (2) you can train without certain capabilities, see Kimi K3.1 (3) reminiscent of the encryption is dangerous debate of the '90s<p>There will likely be requirements like registering with the government (China already does this, every business already does everywhere), certain usages require more or have deeper regulations (this is already done with most technologies, depending on application)
I think it will not technically be a ban, but a de facto one.<p>Some variation on, "Open weight models must be A) trained in the USA B) have a public training dataset + training pipeline C) be reviewed by a government 'safety council', etc" Something which technically leaves a crack open for someone to release a less capable model than the commercial ones.
That kind of policy will hold a country back, and why I don't think it will happen. There are already many companies already using Chinese models.<p>Another confounding factor, if a US company fine-tunes a Chinese model, or vice versa, what is that country that should be assigned to that model.<p>You also have to consider the competition between the US and China. If the US did something like this, the world will move to Chinese hardware and Ai. The think tanks that talk with legislators understand this.
Because they have found a way that cost them less to have you use a lesser capable model o less expensive
I fully expect token vendors to go the way of mobile data providers. Increasing throughput and efficiency, yet commoditized and not reaping the massive rewards of the economic gains their technologies enable.<p>Can't wait for the unlimited plans we will have in due time.
They will not come. You can only spend so much time speaking on the phone.<p>But if you get unlimited tokens, with scheduled/automated jobs and subagents you will burn a LOT of money.
"Unlimited" for a consumer will probably take a form like limited parallel agents at max intelligence for X amount of tokens, followed by either single threaded agents or nerfed intelligence or both.<p>This is the same mechanism used in unlimited mobile internet for example.
> <i>if you get unlimited tokens, with scheduled/automated jobs and subagents you will burn a LOT of money</i><p>Maybe, maybe not. There could be a saturation point of compute demand from ordinary consumers. (See: hot-water analogy.)
> You can only spend so much time speaking on the phone.<p>Tell that to my dial up modem as I played Active Worlds in 1996.
Mobile video data and tethered devices would tell a different story. Usage growth has been significant.<p>I expect they will be "unlimited" in the same sense the mobile / ISPs do that 99% of people never reach the limits.<p>We could have "unlimited" electricity too, if we choose, by printing solar panels (like the Chinese) and hopefully fusion before long.
> Can't wait for the unlimited plans we will have in due time.<p>I don't see how this can happen. The cost of serving a model grows linearly with use (energy). The cost of serving a mobile data plan is approximately zero.
I'm curious, why would this not turn into an unlimited electricity flatrate? In most parts of the world, consumer electricity is charged by kWh, not as flat payment.<p>As long as tokens are as energy intense to compute as they are right now, I don't see a flatrate being a viable model.
Power and memory are apt to become the biggest billing constraints.
Something close to how we rent other cloud resources today makes sense. We don't worry about electricity costs, it's built into the hourly prices, use as much of the hardware as you can. Electricity rates vary over the course of the day as well.<p>People don't like complex pricing. This would be for businesses running larger workloads. For individuals, the unlimited plan seems the most probable to me. This stuff is going to get real cheap in the coming years.<p>The energy needed per token is in dropping rapidly, even though the energy per-GPU is going up. We are seeing something like 10x improvements per year.
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Because they have been making inference a lot more hardware efficient.
credits goes to china ai companies
credit goes to china ai companies
They're competing with NVIDIA (huggingface)
competition. that's good for consumers.<p>though problem with proprietary LLMs it's difficult to know if the vendor's supposed "optimization" affects performance negatively because it's difficult to define an SLA
Think of all the open source projects in DeepSeek
Duh, China?
Part of the cheaper costs, if not the majority, probably are KV cache optimisations. <a href="https://insufferable.dev/posts/the-ai-race-just-got-awkward/" rel="nofollow">https://insufferable.dev/posts/the-ai-race-just-got-awkward/</a>
Microeconomics may give us a clue<p>Lack of demand and/or commodity product.<p>Either doesn't bode well for a multi-trillion dollar valuation for either company.
> It turned out that GPT-6 Sol was a dud performancewise, so a week later OpenAI announced GPT-6.1 Sol,<p>I strongly believe that GPT-6 Sol was only ever meant to be the web chat model, but it was a panic public release to respond to Opus 5.5. It's clearly been over fitted for drawing diagrams in the chat.
Moore’s Law.
People think OpenAI and Anthropic are just these wildly unprofitable companies with no future or business.<p>They know what they’re doing. The naysayers are like the people that said this little internet thing wasn’t going anywhere.
>They know what they’re doing. The naysayers are like the people that said this little internet thing wasn’t going anywhere.<p>There's a big difference between people saying OpenAI/Antropic wont survive and LLMs wont survive. A lot of big early players in the internet space died off as well.
Whether you think they have a future or not does not change fact that they currently are wildly unprofitable.<p>>internet thing wasn’t going anywhere.<p>This is conflating the utility of the technology with the economics behind it. Few people claim LLMs are useless. It's a question of figuring out a viable business model.
Or OpenAI and Anthropic are AOL and Yahoo.
The Internet was profitable, but I'm not connecting to this website from my Solaris Sun Microsystems machine using Netscape Navigator
Might want to Google Global Crossing.
The internet survived, but most giant companies did not.
> <i>The naysayers are like the people that said this little internet thing wasn’t going anywhere.</i><p>The same rhetorical tactic was used to diminish blockchain critics.
It sounds like you're just a "yessayer", though? Not much different from naysayers, just on the opposite side.<p>One thing that naysayers have going on for them is the fact that those companies indeed aren't making profit right now.
So did the bankers in 2008.<p>Crazy how delusional people are.<p>They are in too deep - they have to believe it all works out and act accordingly. lmao.
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