The giants knew this was coming, and soon 95% of AI tasks will be able to be done by open models (coding, research, cowork style work). So why pay a premium? Why use them at all? This leaves the labs with two options:
1) push the frontier in a way only massive scale can, and cash in on it (mythos level cyber security, recursive training, frontier science work). There’s big money for never before possible capabilities.
2) own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
Will it be enough to justify a Google level valuation? We’ll see how fast they can push it.
Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.
So long as the benefit:cost ratio is still sufficiently high, I don't think anyone gets fired for not scrimping. Better to encourage positive EV behaviour by your employees than to scare them away by firing them for not being perfectly optimal.
3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.
> own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
The problem with this is that there are incumbents in all those spaces doing their own AI agents / platforms, and they're the ones choosing the models they use internally and they sell to their own customers. The margins and the possibility to fine tunie using open weight models, as well as the guarantee they'll keep running at predictable costs (no US orders yanking access), make them a very appealing option.
And if you're a company that needs an AI powered legal software, would you buy it from OpenAI/Anthropic, or from someone who you've already bought legal software from before and has the domain knowledge?
The only reason people use google apps is because they are cheap and reliable. The user experience is awful. Have you ever tried to find a document you had open yesterday in drive?
Let's imagine that Anthropic/OpenAI fail to manufacture scarcity by villainizing Open Weight models (a sincere probability). What is left for these corporations to prop up their prices, or any margin at all? I expect scaffolding around tool use, supporting bespoke implementation and driving risk down for institutional adoption. (They might even build an insurance tool to protect accountants/lawyers from errors in compounded probabilism!)
A question for economists... It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
> It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
Isn't this the thing people have said about every new technology since the printing press? And it has been mostly true, but it has also been the case that the incumbents have fought hard to lock things back up again. Newspapers and radio stations buy each other up, the open web gets locked inside Facebook (which, 30 years ago, people were already worried about with AOL), people have computers in their pockets they can't run their own programs on anymore.
Interests are going to want to lock the new information thing behind a gate so they can charge a toll and censor what they don't like, same as it ever was. You don't win by default, you have to fight to stop them.
OpenAI, though they seem to backtrack it lately, have been slowly pushing forward of their launch of ads which would be a supplemental way to support cheaper use of their models. This is currently not as great a fit as the modern day banner ads, but it will be interesting to see where they go with that.
It would not be surprising if GPT and Claude get cheaper too as inference gets cheaper. Two years ago, o1 was the strongest model and cost much more than Fable, while being nowhere near as smart as a Qwen 3.6 35B that you can now run on a DGX Spark without much trouble.
True, outside of the dark tactics I imagined in the article, they will have to compete at lower costs. It's just that the current iteration does not feel cost competitive yet.
Probably they will, unless Claude and GPT become luxury brands like Gucci. Currently it makes no sense for them to invest into efficiency. They need to put everything into competing for the top spot as long as they still have a shot.
i agree with his statement that the big companies and the string pullers in government are inching toward banning open models.allowing the plebs unrestricted access to things seems against the wishes of the "you will own nothing and be happy" / "you will rent everything on the cloud and subscribe to your appliances" crowd such as blackrock and so on.
anyone who disagrees is not seeing the forest, only the trees.
With cache hit rates being effectively free, harnesses like Reasonix have let me do a month of work for less than 2 dollars. It's not even the subsidies making it cheap, American providers like Digital Ocean or Cloudflare host the same model with similar pricing.
Cloudflare's Deepseek V4 Pro prices are 4x more than Deepseek's for input and output tokens, and 100x more for cached input tokens, which is crucial for the tool uses of agents which cause multi-turn conversations.
Agent loops (particularly coding agents) have a huge amount of repetition, because the entire context is included in every model request. So long as it's at the start of the input and doesn't change, it will be able to hit the KV cache (assuming the model provider actually has the prefix in cache).
This only works because prompt caching is done by matching prefixes, not the entire input.
It probably depends on what you're doing, but imagine you're something in the shape of a search engine. How many user queries are unique vs. the same thing someone else searched for an hour ago?
I think this is very likely and something that everyone seems to be missing when valuing these AI firms. AI is not the new industrial revolution, it's the new cloud VM: a very useful commodity software offering.
One of the purposes of open weight models is to create a moat. If there were no open models available, I think we'd see much more and better models coming from Europe by now. Right now, any startup wanting to build and sell a model needs to be substantially better than the open models, which has become increasingly difficult and expensive.
This is what concerns me about how AI giants are planning to make money. Their product has already been commoditized at prices which for them are still subsidized to grab market share. Unless the giants invent a technological leap, their prices are going to be dragged down by open weight models and I don't see how they'll turn a profit.
If Anthropic announced AGI tomorrow, how much better would that model be than Fable 5? It's looking like the road to AGI is gradual and moat-less. Models seem capable of improving other models, and even without illegal distillations many are nipping at the heels of Anthropic.
Yeah, I think we're learning that we overestimated the relevance of recursive self-improvement in a singularity/intelligence takeoff scenario. We thought that once an AI could start improving itself, it would cause an exponential, self-reinforcing intelligence explosion.
Turns out that scaling up compute is much more important and also limits the upper end of intelligence.
The bigger mistake is assuming it would be better at everything all at once.
Suppose it can do 80% of what the 20th percentile human can do. That's a huge advance and very useful, but it means there are still things it's not very good at. If any of those things is (or becomes) a bottleneck, you're not getting the hockey stick graph.
I'm currently writing a blog post about data centres in orbit, and my current conclusion is that even though they can build one, they definitely can't put 1 million up there and would have better things to do if they could.
AGI? Too loosely defined. They lack a lot of competences which humans recognise when we see them but find it hard to put into words; on the other hand what they can do they already do faster than any human (and have greater breadth than any single human, but this usually doesn't matter because "coder" and "economist" and "translator" gets solved in human teams by hiring three people).
I do not think current ML has the tools to solve for quality. But we know it's possible for a really mediocre intelligence to make human level intelligence, because evolution made us, so for me the question of AGI is more a practical one: is it affordable?
(I also think not at the present time, but that's an "I think" not "I am analyzing it carefully").
Maybe you missed the part where starlink / orbiting datacenters don't really have to even make money as long as they partially fund rocket launch tests.
Or maybe you don't take Elon seriously when he talks about Mars.
> Maybe you missed the part where starlink / orbiting datacenters don't really have to even make money as long as they partially fund rocket launch tests.
I am only dismissing the orbital data centres, I do see a future for Starlink. One with competition, but a future nonetheless.
I'm old enough to remember the dot.com bubble and "we lose money on each unit and make up for it in scale":
If they don't make sense, they don't help. Putting a single one in space, or even a handful, is physically possible! But even optimistic Alphabet researchers (and Alphabet owns more of SpaceX than the entire IPO) say this only makes sense at $200/kg, while early Starship launch costs while they sort out reusability be at best $400/kg and the researchers don't expect $200/kg until the mid-2030s even with a high launch rate:
If the learning rate is sustained—which would require∼180 Starship launches/year—launch prices could fall to <$200/kg by∼2035
At $200/kg, and using the payload estimates elsewhere in the paper (the learning rate is based on mass rather than launch count), they'd need to launch 370,000 tons (4.4 ibid); even at the "good enough" cost, $200/kg, they'd need to spend $200/kg * 3.7e8 kg = $7.4e10. That's a hell of an R&D spend for the next 10 years of a company whose lifetime revenue (not profit) is reportedly $4.6e10.
My current draft has a few thousand words of additional problems, plus a bunch of things which I mention only to say why they are not, and some more where I say the research has yet to be done.
> Or maybe you don't take Elon seriously when he talks about Mars.
Used to, not any more. Has been too slow with Starship even before the fact that iteration with hardware is necessarily slowed down by a 2-year gap between launch windows.
There's not even been any news about demonstration models of either Mars-rated or Starship-rated Sabatier processors, which would be an easy win and also win points for both environmentalism and energy independence viz. Iran/Hormuz.
A new player beating Boeing to the ISS was once a pipe dream.
LEO constellations were once a pipe dream.
Launching thousands of satellites was once a pipe dream.
You should know that a) they are already running "AI" chips on their current sats. and b) they are already producing kW of power on orbit and have ~10k sats on orbit. You can watch Scott Manley's video on it, where he does some rough calculations and explains the overall architecture. There is nothing stopping them to do this, from an engineering perspective. If it makes commercial sense, that's another question, but 5-10-20 years in the future things might change there as well.
I don't think people's argument is that it's impossible to put data centers into space. The argument is that the downsides (radiation, cooling, maintenance, power) are so severe that it is pointless to do it at scale.
Go back to the megathreads when this came up. Even here on HN. Plenty of people used the argument that it can't be done, for various reasons.
And my point was that at one point or the other there were many "downsides" for all the tech that SpaceX already has. Reusable boosters were seen as "uneconomical" and "pointless unless they can fly 10 times" by industry experts. They're now flying 30+times a booster.
LEO constellations were similarly "full of downsides" plus "all the companies that tried it went bankrupt in the 90s", so "it's pointless". And so on.
Pretty much everything about data centers in space is worse than having them on Earth. Apart from niche use cases, the only reason you'd talk about data centers in space is if you had a company with rocket ships and needed a story to tie your rocket ships to the current AI craze.
Microsoft tried to put datacenters into ocean [1] and then shelved the idea, because even that you have lower amount of failures, you still have failures and somebody has to go there and fix them. Which turns out to be problem.
And in ocean you don't have to solve for radiation nor cooling.
If just Elon was taking about data centers in space, you could take it with a grain of salt. But there are other serious players talking about it like Google and blue origin that it should be pretty clear it can't just be dismissed with "you didn't think about cooling!"
Yeah, and there's already been tech demonstrators for this. Starcloud-1 launched in '25 (on a F9) and demoed a CotS H100 in a ~60kg bus w/ 1kW of power. They ran inference on a "gemini" model (probably something small) and trained a GPT2 version LLM as a tech demonstrator.
Open weight and local hosting is far, far cheaper. In every respect. Even support is cheaper, over time.
However, it's difficult to sell this to businesses who want contracts and KPIs, not staff and commitments.
Regulated industries will favour the closed sources, either by choice or mandate. The interesting question is whether they will have better models, or worse models. History says they will receive a worse service, but continue anyway.
The token-economics for closed source models are different, they are optimizing for 200 USD tokens worth of software engineer monthly usage, they will increase per token price as models or harnesses are more optimized.
One thing it doesn't even mention is how good those models are. Evet since I moved to DeepSeek I had zero regrets. It performs exceptionally well. I honestly prefer it to ChatGPT (or Claude that I use at work).
I never used Fable, maybe it is that much better. DeepSeek has no problems with the workloads I give it though - if it only keeps marginally improving with each interaction I don't see myself needing to come back.
Aren't these open models so cheap because they're (partially) chinese gov. sponsored, and because they're stealing and redistributing the IP that comes in?
The giants knew this was coming, and soon 95% of AI tasks will be able to be done by open models (coding, research, cowork style work). So why pay a premium? Why use them at all? This leaves the labs with two options:
1) push the frontier in a way only massive scale can, and cash in on it (mythos level cyber security, recursive training, frontier science work). There’s big money for never before possible capabilities.
2) own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
Will it be enough to justify a Google level valuation? We’ll see how fast they can push it.
3) Buy all the RAM, increasing the barrier to entry to push back the tide a bit, in time for a juicy IPO.
4) Make it illegal to use anything but regulated models.
> Be apple where everyone else is Linux.
Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.
Won’t all they need to do is say “best in class, latest models, fastest” and wine and dine a few execs and those enterprise deals will be signed?
In this case the people tasked with using the product won’t actually mind.
Yes, exactly that. Be Azure and Office 365 and Sharepoint and AWS where everyone else is Debian Stable on a USB thumbdrive.
Office 365? Ew, Google docs, please.
No one is getting fired for using SotA.
If the price difference is 2x? Sure.
If the price difference is 50x? No way.
Tell that to Oracle
So long as the benefit:cost ratio is still sufficiently high, I don't think anyone gets fired for not scrimping. Better to encourage positive EV behaviour by your employees than to scare them away by firing them for not being perfectly optimal.
The CEO won't get in trouble, but the employee who can't justify a bad result/prompt?
Accenture says "yeah totally CEOs will pay a lot for literal nothing"
You forgot
3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.
Mythos was outperformed by small, specific local models in multiple oss project.
i'd love to hear about this! do you have examples?
https://aisle.com/blog/aisle-discovers-6-new-cves-in-curl-in...
> own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
The problem with this is that there are incumbents in all those spaces doing their own AI agents / platforms, and they're the ones choosing the models they use internally and they sell to their own customers. The margins and the possibility to fine tunie using open weight models, as well as the guarantee they'll keep running at predictable costs (no US orders yanking access), make them a very appealing option.
And if you're a company that needs an AI powered legal software, would you buy it from OpenAI/Anthropic, or from someone who you've already bought legal software from before and has the domain knowledge?
Google already owns the app layer, and hardware, and they are a frontier-level AI research firm.
I don't see how Anthropic or OpenAI survives being eaten by DeepSeek et al from the bottom of the stack and Google from the top.
The only reason people use google apps is because they are cheap and reliable. The user experience is awful. Have you ever tried to find a document you had open yesterday in drive?
I used their enterprise chat the other week coz one of the clients used it
It is truly amazing how bad it is. Made me miss using MS Teams. No software should make anyone miss using MS Teams
Uh? Recently and frequently opened documents always show up on the first screen as soon as I open the app or website.
Let's imagine that Anthropic/OpenAI fail to manufacture scarcity by villainizing Open Weight models (a sincere probability). What is left for these corporations to prop up their prices, or any margin at all? I expect scaffolding around tool use, supporting bespoke implementation and driving risk down for institutional adoption. (They might even build an insurance tool to protect accountants/lawyers from errors in compounded probabilism!)
A question for economists... It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
> It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
Isn't this the thing people have said about every new technology since the printing press? And it has been mostly true, but it has also been the case that the incumbents have fought hard to lock things back up again. Newspapers and radio stations buy each other up, the open web gets locked inside Facebook (which, 30 years ago, people were already worried about with AOL), people have computers in their pockets they can't run their own programs on anymore.
Interests are going to want to lock the new information thing behind a gate so they can charge a toll and censor what they don't like, same as it ever was. You don't win by default, you have to fight to stop them.
OpenAI, though they seem to backtrack it lately, have been slowly pushing forward of their launch of ads which would be a supplemental way to support cheaper use of their models. This is currently not as great a fit as the modern day banner ads, but it will be interesting to see where they go with that.
It would not be surprising if GPT and Claude get cheaper too as inference gets cheaper. Two years ago, o1 was the strongest model and cost much more than Fable, while being nowhere near as smart as a Qwen 3.6 35B that you can now run on a DGX Spark without much trouble.
True, outside of the dark tactics I imagined in the article, they will have to compete at lower costs. It's just that the current iteration does not feel cost competitive yet.
Probably they will, unless Claude and GPT become luxury brands like Gucci. Currently it makes no sense for them to invest into efficiency. They need to put everything into competing for the top spot as long as they still have a shot.
i agree with his statement that the big companies and the string pullers in government are inching toward banning open models.allowing the plebs unrestricted access to things seems against the wishes of the "you will own nothing and be happy" / "you will rent everything on the cloud and subscribe to your appliances" crowd such as blackrock and so on.
anyone who disagrees is not seeing the forest, only the trees.
With cache hit rates being effectively free, harnesses like Reasonix have let me do a month of work for less than 2 dollars. It's not even the subsidies making it cheap, American providers like Digital Ocean or Cloudflare host the same model with similar pricing.
Cloudflare's Deepseek V4 Pro prices are 4x more than Deepseek's for input and output tokens, and 100x more for cached input tokens, which is crucial for the tool uses of agents which cause multi-turn conversations.
How does caching help here? How much repetition is there in queries?
Agent loops (particularly coding agents) have a huge amount of repetition, because the entire context is included in every model request. So long as it's at the start of the input and doesn't change, it will be able to hit the KV cache (assuming the model provider actually has the prefix in cache).
This only works because prompt caching is done by matching prefixes, not the entire input.
It probably depends on what you're doing, but imagine you're something in the shape of a search engine. How many user queries are unique vs. the same thing someone else searched for an hour ago?
I think this is very likely and something that everyone seems to be missing when valuing these AI firms. AI is not the new industrial revolution, it's the new cloud VM: a very useful commodity software offering.
One of the purposes of open weight models is to create a moat. If there were no open models available, I think we'd see much more and better models coming from Europe by now. Right now, any startup wanting to build and sell a model needs to be substantially better than the open models, which has become increasingly difficult and expensive.
This is what concerns me about how AI giants are planning to make money. Their product has already been commoditized at prices which for them are still subsidized to grab market share. Unless the giants invent a technological leap, their prices are going to be dragged down by open weight models and I don't see how they'll turn a profit.
Reach AGI to leapfrog whoever is behind. Burn everything to get there faster.
If Anthropic announced AGI tomorrow, how much better would that model be than Fable 5? It's looking like the road to AGI is gradual and moat-less. Models seem capable of improving other models, and even without illegal distillations many are nipping at the heels of Anthropic.
Yeah, I think we're learning that we overestimated the relevance of recursive self-improvement in a singularity/intelligence takeoff scenario. We thought that once an AI could start improving itself, it would cause an exponential, self-reinforcing intelligence explosion.
Turns out that scaling up compute is much more important and also limits the upper end of intelligence.
The bigger mistake is assuming it would be better at everything all at once.
Suppose it can do 80% of what the 20th percentile human can do. That's a huge advance and very useful, but it means there are still things it's not very good at. If any of those things is (or becomes) a bottleneck, you're not getting the hockey stick graph.
What is an "illegal" distillation? Terms of service are not laws, and clearly copyright laws are no barriers to developing AI models.
If AGI = Data from Star Trek, it would be a huge leap. Frankly, anything less I wouldn’t consider as AGI.
Why would the creator of AGI sell it to anyone, when they could keep it to themselves and corner dozens of markets?
'Reach AGI', the same way SpaceX will put data centers in orbit. A pipe dream.
I'm currently writing a blog post about data centres in orbit, and my current conclusion is that even though they can build one, they definitely can't put 1 million up there and would have better things to do if they could.
AGI? Too loosely defined. They lack a lot of competences which humans recognise when we see them but find it hard to put into words; on the other hand what they can do they already do faster than any human (and have greater breadth than any single human, but this usually doesn't matter because "coder" and "economist" and "translator" gets solved in human teams by hiring three people).
I do not think current ML has the tools to solve for quality. But we know it's possible for a really mediocre intelligence to make human level intelligence, because evolution made us, so for me the question of AGI is more a practical one: is it affordable?
(I also think not at the present time, but that's an "I think" not "I am analyzing it carefully").
Maybe you missed the part where starlink / orbiting datacenters don't really have to even make money as long as they partially fund rocket launch tests.
Or maybe you don't take Elon seriously when he talks about Mars.
> Maybe you missed the part where starlink / orbiting datacenters don't really have to even make money as long as they partially fund rocket launch tests.
I am only dismissing the orbital data centres, I do see a future for Starlink. One with competition, but a future nonetheless.
I'm old enough to remember the dot.com bubble and "we lose money on each unit and make up for it in scale":
If they don't make sense, they don't help. Putting a single one in space, or even a handful, is physically possible! But even optimistic Alphabet researchers (and Alphabet owns more of SpaceX than the entire IPO) say this only makes sense at $200/kg, while early Starship launch costs while they sort out reusability be at best $400/kg and the researchers don't expect $200/kg until the mid-2030s even with a high launch rate:
- section 2.4, https://arxiv.org/abs/2511.19468At $200/kg, and using the payload estimates elsewhere in the paper (the learning rate is based on mass rather than launch count), they'd need to launch 370,000 tons (4.4 ibid); even at the "good enough" cost, $200/kg, they'd need to spend $200/kg * 3.7e8 kg = $7.4e10. That's a hell of an R&D spend for the next 10 years of a company whose lifetime revenue (not profit) is reportedly $4.6e10.
My current draft has a few thousand words of additional problems, plus a bunch of things which I mention only to say why they are not, and some more where I say the research has yet to be done.
> Or maybe you don't take Elon seriously when he talks about Mars.
Used to, not any more. Has been too slow with Starship even before the fact that iteration with hardware is necessarily slowed down by a 2-year gap between launch windows.
There's not even been any news about demonstration models of either Mars-rated or Starship-rated Sabatier processors, which would be an easy win and also win points for both environmentalism and energy independence viz. Iran/Hormuz.
> will put data centers in orbit. A pipe dream.
Cheap access to space was once a pipe dream.
Reusable boosters were once a pipe dream.
A new player beating Boeing to the ISS was once a pipe dream.
LEO constellations were once a pipe dream.
Launching thousands of satellites was once a pipe dream.
You should know that a) they are already running "AI" chips on their current sats. and b) they are already producing kW of power on orbit and have ~10k sats on orbit. You can watch Scott Manley's video on it, where he does some rough calculations and explains the overall architecture. There is nothing stopping them to do this, from an engineering perspective. If it makes commercial sense, that's another question, but 5-10-20 years in the future things might change there as well.
> You can watch Scott Manley's video on it, where he does some rough calculations and explains the overall architecture.
I'm currently writing a blog post, and there's one big thing everyone, including Scott Manley, missed.
Once I realised it, I wondered what took me so long to spot this issue.
I don't think people's argument is that it's impossible to put data centers into space. The argument is that the downsides (radiation, cooling, maintenance, power) are so severe that it is pointless to do it at scale.
Go back to the megathreads when this came up. Even here on HN. Plenty of people used the argument that it can't be done, for various reasons.
And my point was that at one point or the other there were many "downsides" for all the tech that SpaceX already has. Reusable boosters were seen as "uneconomical" and "pointless unless they can fly 10 times" by industry experts. They're now flying 30+times a booster.
LEO constellations were similarly "full of downsides" plus "all the companies that tried it went bankrupt in the 90s", so "it's pointless". And so on.
Reusable boosters have clear upsides, though.
Pretty much everything about data centers in space is worse than having them on Earth. Apart from niche use cases, the only reason you'd talk about data centers in space is if you had a company with rocket ships and needed a story to tie your rocket ships to the current AI craze.
And you had a lot of stock to sell to bagholders.
Yet spacex is losing money … only StarLink is profitable.
Microsoft tried to put datacenters into ocean [1] and then shelved the idea, because even that you have lower amount of failures, you still have failures and somebody has to go there and fix them. Which turns out to be problem.
And in ocean you don't have to solve for radiation nor cooling.
[1] https://www.tomshardware.com/desktops/servers/microsoft-shel...
If just Elon was taking about data centers in space, you could take it with a grain of salt. But there are other serious players talking about it like Google and blue origin that it should be pretty clear it can't just be dismissed with "you didn't think about cooling!"
Yeah, and there's already been tech demonstrators for this. Starcloud-1 launched in '25 (on a F9) and demoed a CotS H100 in a ~60kg bus w/ 1kW of power. They ran inference on a "gemini" model (probably something small) and trained a GPT2 version LLM as a tech demonstrator.
Google also wanted to deliver internet from balloons and put everyone's real name on their YouTube comments. Not all their ideas are winners.
I think it's such a vague term. If you showed someone in 2010 what we have now they would say it's science fiction.
Open weight and local hosting is far, far cheaper. In every respect. Even support is cheaper, over time.
However, it's difficult to sell this to businesses who want contracts and KPIs, not staff and commitments.
Regulated industries will favour the closed sources, either by choice or mandate. The interesting question is whether they will have better models, or worse models. History says they will receive a worse service, but continue anyway.
> Regulated industries will favour the closed sources, either by choice or mandate
Until your country will appear on naughty list of US administration because your local politician did something what mildly inconvenienced US oligarch
I wonder whether Oracle is going to go bankrupt because of this
Why Oracle?
They're extremely exposed to a market crash due to their huge debt-funded compute contracts.
Having said that, while one can always hope, I would assume that Oracle is one of these companies that will be bailed out or find a way to survive.
oracle is licking so much boot, you'd need to also have the republican fascist party completely faall apparent.
The token-economics for closed source models are different, they are optimizing for 200 USD tokens worth of software engineer monthly usage, they will increase per token price as models or harnesses are more optimized.
It's so refreshing to read a short to the point article, which is not extruded into 10 pages with LLMs.
One thing it doesn't even mention is how good those models are. Evet since I moved to DeepSeek I had zero regrets. It performs exceptionally well. I honestly prefer it to ChatGPT (or Claude that I use at work).
I never used Fable, maybe it is that much better. DeepSeek has no problems with the workloads I give it though - if it only keeps marginally improving with each interaction I don't see myself needing to come back.
Aren't these open models so cheap because they're (partially) chinese gov. sponsored, and because they're stealing and redistributing the IP that comes in?
And the American ones are stealing and redistributing the IP of every single person who authored anything on the internet at some point.
Well I can't speak to the chinese gov part, but ALL the models are IP laundering systems. I'd rather IP get laundered into open source.
Technically correct, the worst kind of correct :)