The fact that AI models can be so easily distilled and replicated is such a stroke of luck.
10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly.
Rather, it seems that selling intelligence might end up as a race to the bottom.
Who woulda thought that just having access to enough textual inputs and outputs and a vaugely similar transformer architecture would be enough to copy-cat rather useful intelligence.
The internet created lots of monopolies with network effects and economies of scale.a low margin commoditized business that still attracted a trillion dollars of investment to get off the ground was not how I envisioned it happening either.
It reminds me of the seo antics out there. The search results page is the engine, much like how distilling is the "intelligence" for your chinese room machine
Even before LLMs, ML folks were already aware that you can use a model to teach another model. I doubt this is something AI companies put at the top of their investor materials, but it's been nice to see it play out.
That said, there are other moat factors like, a US company needing to use a US AI provider, sticky customers due to corporate onboarding friction, and others. Not nothing, but not as large a moat as some imagined.
Yes, but 10 or 15 years ago, I would have thought that there'd be more to it than just a slight modification on the ideas behind a CNN to get this level of AI.
There were somewhat good reasons to think it needed more than just this data-driven ML approach.
There's something startling about how (relatively) simple these networks are and yet how powerful they are. The main ingredient the AI darlings are using is vast amounts of compute and data. I don't want to take away anything from what the researchers came up with, but I suspect even they are surprised at how capable some of these models have become.
I think the only moat in the future will be the scale of hardware deployment. If one company is able to deploy an order of magnitude more silicon, they'll have a firm grip on a SOTA model and massive inference usage.
China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.
If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.
Yeah, very hard to predict the future at this point. But the Starship + Terrafab combo will be this type of order-of-magnitude-moat IF it works out. Big if.
If it doesn't work out, I think China's exponential terrestrial energy deployment will eventually give them the lead, IF they can get enough chips. Another big if.
well, a stroke of luck until the whole US stock market crashes & everyone's retirement funds get cut 40% I guess when people internalize this. it will have to happen sooner or later though I suppose
interestingly also, open weight models are also more effectively run in the cloud, so it creates a weird scenario where the frontier labs crash but the compute providers, not as much
I wouldn't be so sure about that. The popping of a bubble is usually just as irrational as its rise.
If investors start fleeing from senseless businesses in the AI sector, that does not mean that sensible businesses will be spared. These things follow herd mentality, and the primary drivers of the herd are greed and fear, not fundamentals or business logic.
The market (s&p500) crashing 40% puts us at levels we haven't seen since 2024, well into the creation of LLMs. Probably a worthwhile trade if it was either/or!
Yeah the last year has been astonishing, my portfolio is kicking ass. But I'm 10 years out from retirement and I am pretty confident a correction is coming; I hope the correction happens soon.
It’s not as bad as dot.com of course since all purely AI companies are private and the ones on the market have pretty decent cash flow outside of AI. But the stock market pattern is not that dissimilar, the largest increases are usually just before the crash.
Once they make a model better than Fable I’ll be switching to Codex. Their priorities in terms of consumers seem to be better. I do think Anthropic has some solid safety viewpoints, but I don’t necessarily think that either is entirely aligned yet with delivering exactly what humanity needs. Maybe the AI will help align the AI companies when it gets smart enough. That’s the real misalignment I’m concerned about.
It feels like 5.6-Sol is already fairly close to Fable, and in some ways exceeds it. Just the other day I had Fable draw up a solution for me, and then I fed it into 5.6-Sol and said how does this look ... it found an oversight and told me about it, and when I then fed that observation back into Claude it acknowledged the miss.
I've noticed also that 5.6-Sol is more concise with output than Fable (and let's not talk about Opus, which is even more wordy).
I don't think these companies have humanity's needs in mind when they're developing these models. Although the last part of your comment struck me as a bit comical, I genuinely believe that an AI can have way more empathy than a corporation. Afterall, a mimicry of empathy is probably better than no empathy.
It's a funny comparison. Comparing the empathy of some software to the empathy of a company. It's like saying my car was more empathetic than my school. How can those two objects even be compared is what i am wondering
We live in an odd time where 'software' (well neural networks) can be far more empathetic than summed product of a corporation.
Company empathy does exist, just look at how easy or hard it is to reach a company when you have a problem. How do they try to solve it for you? Is it a brick wall, for example Google when you have a problem. People quite often like dealing with small businesses because they can reach a singular human and have them as an interface to the problems they face now and in the future.
Agentic loops and the models underneath them can have a simulacra of empathy too. Not every model just blindly agrees with users, and some have a much better depth in picking up context clues that the user on the other end is having a hard time. Businesses just typically aren't running more expensive and fragile systems like that though.
You are basically saying you will switch from one evil to another because the other seems less evil for now.
It's funny how people make these alignment comments while ignoring how misaligned the leadership at these companies are right form the get go and they just play mental gymnastics to deflect those facts when confronted with them.
There are no open source models, at least not useful ones (yet) [0]. Open weight is not the same as open source. The current "open weight" models are just opaque binary blobs you can run on your own computer instead of through a web API.
Nemotron Super is sort of open source in the sense that Nvidia provides almost everything you need to replicate it from scratch. Of course it’s performance is not exactly stellar but it could be a good starting point for other research teams.
There is also https://apertus-ai.org/ but yeah, "not useful yet" if you were looking to replace your coding agent. Very useful if you are doing LLM research.
50% off at open router is also still applied so it comes out at $2 / $10 per 1M.
Feature request for Artificial Analysis, allow us to see these live prices on the pareto. It would amazing to also see what a 25,50,75,100 % utilised subscription costs compared to raw tokens.
Good timing. I'm not too happy having to pay MAX pricing to even access Fable, and I've had a couple situations where Fable missed things and GPT 5.6-Sol caught it. My needs are modest and I can get by on a $20 OpenAI subscription, so the odds are starting to look increasingly like I'm going to drop Anthropic altogether.
I think this is a move to get people off the subscription and move to API. The weekly usage is still awful altough it seems they're trying to fix it but I'm not hopeful.
The liquidity/fungibility of tokens/usage probably makes subscriptions really hard to offer in this space since there's a trivial market to sell your unused subscription usage, yet subscriptions are only good offerings when only a fraction of people can use the full allowance.
Seems like a really hard cat and mouse game to win for Anthropic/OpenAI.
if you're picking AI models for long-term sustainability you're doing it wrong. There's really no point in locking in model choice for anything more than a month or two these days.
What about companies purchasing enterprise contracts? Most contracts are minimum 12 months. At a minimum, to secure enteprise requirements like zero-data retention, you'll need to lock into a single provider.
These price reductions are mostly targeted towards self-serve customers on individual or small team plans, where individual choice matters and the friction of changing models/providers is low.
Exactly my and top commenter's point. "Temporary price reduction" and "production workloads" are two different worlds.
I'm against the idea that "there's really no point in locking in model choice for anything more than a month or two these days". At a minimum, enterprises are going to lock in a provider for a year due to enterprise contracts, which restricts their model choices. You sign for Anthropic, but now OpenAI models are "better". Or, you signed for AWS Bedrock: Oh no, you don't have access to deepseek-v4 because they're behind.
Using codex every day, in spite of which, I hope some day providers will just start naming their offerings small/medium/large, a bit like we eventually started doing in software testing. Trying to remember what Sol is or why it's better than the other thing is more cognitive effort than I can muster at this point. And that's a sure sign of commoditisation in itself
Tinfoil hat time: They saw everyone referring to Mythos, and later Fable, as the new “good” models when Anthropic released those, distinguishable from the “regular” Claude (or other companies’ models) for everyone, and didn’t have that distinction for the GPT model family. That’s why the planetary names were introduced.
I think model naming has been atrocious in general, in part because newer "lite" models surpass the capabilities of previous "pro" models (case-in-point: Gemini Flash which now surpasses the capabilities of the latest Gemini Pro, with a newer Flash Lite vying somewhat unsuccessfully for the old Flash price/positioning), but gpt 5.6's Sol/Terra/Luna split is really not bad at all - probably easier to understand than Starbucks' cup sizing!
The problem becomes when you add in the adjustable reasoning efforts and you end up with {model, reasoning_effort} combinations that end up completely obviating particular model classes altogether for at least some percentage of queries; e.g. with GPT 5.6 the price/performance Pareto frontier is dominated by permutations of either Luna and Sol, with Terra nowhere to be seen (but then if you need "large model smells" that aren't captured by your benchmark you can't even rely on this, as a model like Luna simply isn't capable of encoding sufficient world knowledge in its weights to perform certain tasks at any reasoning level but you might be able to get away with Terra on low reasoning, but no one seems to be covering this for some reason).
Yes but with gemini specifically they said that pro was still in training. And the comparison isn't really atrocious unless Gemini 3.5 Pro is worse than Gemini 3.5 flash
The "until at least Nov 21st" thing presumably mainly affects teams that pin to GPT-5.6 Sol (maybe after extensive testing) such that they won't be switching to GPT-5.7 or GPT-6 or whatever new model is released between now and November.
Does it mean that subscriptions get more tokens? I’m testing it now for coding instead of claude and it’s very important to understand if I get more due to the price reduction.
These price drops are absolutely bonkers. Gotta love competition! Glad we didn't end up with a duopoly of openai and anthropic, we got a glimpse of what nightmare that would've been and it wasn't pretty
Which is not that great for people using less than 50% every week, because the next reset date moves forward too. In essence, it is redistributing compute from people who haven't used their quota much to those who have.
Though I think they gave a banked reset this time.
The Chinese are coming after these greedy-ass frontier labs. Today Xiaomi unveiled it's own inference machine .... I bet it's gonna be cheaper than Nvidia DGX, shipped with open source models that anybody can have at home.
But can these really be trusted? There was just a HN post which proofed that you can train a model to behave completely different on a certain day. How do we now, that these models do not find a way to call home when they see interesting informations (probably irrelevant on a personal level, but corps, government and military might care).
ChatGPT already notifies the authorities if it thinks you’re doing something illegal. Fable downgrades itself if it thinks you’re doing something even vaguely suspicious.
I'm not sure I could characterize the frontier labs as greedy, given that they've been consistently losing gargantuan amounts of money.
The people who give them the money are greedy, and hopefully in for a rude awakening. Starting from Nvidia's vendor financing which has a very direct benefit to them, through to every company and oligarch investing into data centres in the hopes of being one of the ones left capitalizing on capturing the livelihoods of the majority of what remains of the "middle class".
It's either hopium or a truly horrific dystopia. Something's going to have to give.
The fact that AI models can be so easily distilled and replicated is such a stroke of luck.
10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly.
Rather, it seems that selling intelligence might end up as a race to the bottom.
Who woulda thought that just having access to enough textual inputs and outputs and a vaugely similar transformer architecture would be enough to copy-cat rather useful intelligence.
The internet created lots of monopolies with network effects and economies of scale.a low margin commoditized business that still attracted a trillion dollars of investment to get off the ground was not how I envisioned it happening either.
Where is the actual evidence of distillation? I keep seeing this repeated ad nauseam but I must have somehow missed the evidence.
Distillation a pretty well documented technique that actually pre-dates LLMs https://arxiv.org/pdf/1503.02531
Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit
Musk confirmed in federal court that xAI does it: https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.
Here's an example: https://github.com/microsoft/Build25-LAB329
there is no evidence. it shortcuts post training by a huge margin this is true. but that is all.
It reminds me of the seo antics out there. The search results page is the engine, much like how distilling is the "intelligence" for your chinese room machine
Even before LLMs, ML folks were already aware that you can use a model to teach another model. I doubt this is something AI companies put at the top of their investor materials, but it's been nice to see it play out.
That said, there are other moat factors like, a US company needing to use a US AI provider, sticky customers due to corporate onboarding friction, and others. Not nothing, but not as large a moat as some imagined.
Yes, but 10 or 15 years ago, I would have thought that there'd be more to it than just a slight modification on the ideas behind a CNN to get this level of AI.
There were somewhat good reasons to think it needed more than just this data-driven ML approach.
There's something startling about how (relatively) simple these networks are and yet how powerful they are. The main ingredient the AI darlings are using is vast amounts of compute and data. I don't want to take away anything from what the researchers came up with, but I suspect even they are surprised at how capable some of these models have become.
I think the only moat in the future will be the scale of hardware deployment. If one company is able to deploy an order of magnitude more silicon, they'll have a firm grip on a SOTA model and massive inference usage.
China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.
"Who knows" is the right answer, I think.
If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.
But as you said, who knows.
Yeah, very hard to predict the future at this point. But the Starship + Terrafab combo will be this type of order-of-magnitude-moat IF it works out. Big if.
If it doesn't work out, I think China's exponential terrestrial energy deployment will eventually give them the lead, IF they can get enough chips. Another big if.
Altman specifically has said in an interview that I listened to once that he envisions AI being as cheap as electricity.
Altman of *Open* AI? No idea why I would trust him without very convincing proof.
I hope it's a good bit cheaper than that, I pay close to $400/mo for electricity and I'm in no way interested in paying anything like that for AI.
Lol, of course what he left out is this will happen by inflating the cost of electricity rather than driving down the cost of AI.
He also wanted to do a non-profit.
He even raised money on that premise.
He is a pathological liar, so is Dario. Don’t rely on the benevolence or truthfulness of these people.
They will say whatever is beneficial to say in the moment.
Yeah, he sure does lie about a variety of things! He doesn't have the name Scam Altman for nothing.
well, a stroke of luck until the whole US stock market crashes & everyone's retirement funds get cut 40% I guess when people internalize this. it will have to happen sooner or later though I suppose
I'd take a market crash over a monopoly in the hands of a ghoul like Altman.
The economy he and his ilk want to build is infinitely worse.
In truth it crashes either way.
interestingly also, open weight models are also more effectively run in the cloud, so it creates a weird scenario where the frontier labs crash but the compute providers, not as much
I wouldn't be so sure about that. The popping of a bubble is usually just as irrational as its rise.
If investors start fleeing from senseless businesses in the AI sector, that does not mean that sensible businesses will be spared. These things follow herd mentality, and the primary drivers of the herd are greed and fear, not fundamentals or business logic.
America is pretty close to rhyming with nazi germany circa 1929.
Ok, I'll bite. What's your rationale?
The market (s&p500) crashing 40% puts us at levels we haven't seen since 2024, well into the creation of LLMs. Probably a worthwhile trade if it was either/or!
This is funny because the stock Market has been ahistorically high. My portfolio went up over 20 percent in the last 12 months.
A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.
Yeah the last year has been astonishing, my portfolio is kicking ass. But I'm 10 years out from retirement and I am pretty confident a correction is coming; I hope the correction happens soon.
It’s not as bad as dot.com of course since all purely AI companies are private and the ones on the market have pretty decent cash flow outside of AI. But the stock market pattern is not that dissimilar, the largest increases are usually just before the crash.
It's a 20% discount on input and a 33% discount on output through at least November 21, 2026; the revised pricing schedule is now
So Sol is still 20x Luna, but much more appealing when compared to offerings from Anthropic and others.Once they make a model better than Fable I’ll be switching to Codex. Their priorities in terms of consumers seem to be better. I do think Anthropic has some solid safety viewpoints, but I don’t necessarily think that either is entirely aligned yet with delivering exactly what humanity needs. Maybe the AI will help align the AI companies when it gets smart enough. That’s the real misalignment I’m concerned about.
It feels like 5.6-Sol is already fairly close to Fable, and in some ways exceeds it. Just the other day I had Fable draw up a solution for me, and then I fed it into 5.6-Sol and said how does this look ... it found an oversight and told me about it, and when I then fed that observation back into Claude it acknowledged the miss.
I've noticed also that 5.6-Sol is more concise with output than Fable (and let's not talk about Opus, which is even more wordy).
I don't think these companies have humanity's needs in mind when they're developing these models. Although the last part of your comment struck me as a bit comical, I genuinely believe that an AI can have way more empathy than a corporation. Afterall, a mimicry of empathy is probably better than no empathy.
It's a funny comparison. Comparing the empathy of some software to the empathy of a company. It's like saying my car was more empathetic than my school. How can those two objects even be compared is what i am wondering
We live in an odd time where 'software' (well neural networks) can be far more empathetic than summed product of a corporation.
Company empathy does exist, just look at how easy or hard it is to reach a company when you have a problem. How do they try to solve it for you? Is it a brick wall, for example Google when you have a problem. People quite often like dealing with small businesses because they can reach a singular human and have them as an interface to the problems they face now and in the future.
Agentic loops and the models underneath them can have a simulacra of empathy too. Not every model just blindly agrees with users, and some have a much better depth in picking up context clues that the user on the other end is having a hard time. Businesses just typically aren't running more expensive and fragile systems like that though.
You are basically saying you will switch from one evil to another because the other seems less evil for now.
It's funny how people make these alignment comments while ignoring how misaligned the leadership at these companies are right form the get go and they just play mental gymnastics to deflect those facts when confronted with them.
I don’t think anyone said anything about either being less evil? Just having more consumer oriented products..
Absolutely loving this price war, long live open source models.
> long live open source models
There are no open source models, at least not useful ones (yet) [0]. Open weight is not the same as open source. The current "open weight" models are just opaque binary blobs you can run on your own computer instead of through a web API.
[0] https://allenai.org/
Nemotron Super is sort of open source in the sense that Nvidia provides almost everything you need to replicate it from scratch. Of course it’s performance is not exactly stellar but it could be a good starting point for other research teams.
Nemotron is okay. Better than Olmo.
There is also https://apertus-ai.org/ but yeah, "not useful yet" if you were looking to replace your coding agent. Very useful if you are doing LLM research.
50% off at open router is also still applied so it comes out at $2 / $10 per 1M.
Feature request for Artificial Analysis, allow us to see these live prices on the pareto. It would amazing to also see what a 25,50,75,100 % utilised subscription costs compared to raw tokens.
Good timing. I'm not too happy having to pay MAX pricing to even access Fable, and I've had a couple situations where Fable missed things and GPT 5.6-Sol caught it. My needs are modest and I can get by on a $20 OpenAI subscription, so the odds are starting to look increasingly like I'm going to drop Anthropic altogether.
This stacks with the 50% discount in OpenRouter, making it $2/$10. https://openrouter.ai/openai/gpt-5.6-sol
I think this is a move to get people off the subscription and move to API. The weekly usage is still awful altough it seems they're trying to fix it but I'm not hopeful.
You're probably right.
The liquidity/fungibility of tokens/usage probably makes subscriptions really hard to offer in this space since there's a trivial market to sell your unused subscription usage, yet subscriptions are only good offerings when only a fraction of people can use the full allowance.
Seems like a really hard cat and mouse game to win for Anthropic/OpenAI.
Very exciting - if only anthropic would do the same.
Through OpenRouter you can get Sol for $2 input / $10 output which makes it a really attractive choice amongst frontier models.
Wonder if that makes it cheaper than using on the sub (ignoring reset shenanigans)
What good does a temporary price reduction do for production workloads? I'm not even running evals on something that is not long-term sustainable.
if you're picking AI models for long-term sustainability you're doing it wrong. There's really no point in locking in model choice for anything more than a month or two these days.
What about companies purchasing enterprise contracts? Most contracts are minimum 12 months. At a minimum, to secure enteprise requirements like zero-data retention, you'll need to lock into a single provider.
These price reductions are mostly targeted towards self-serve customers on individual or small team plans, where individual choice matters and the friction of changing models/providers is low.
if you've got an enterprise contract, doesn't that include pricing? a temporary discount on the base API rate probably isn't super relevant to that.
Exactly my and top commenter's point. "Temporary price reduction" and "production workloads" are two different worlds.
I'm against the idea that "there's really no point in locking in model choice for anything more than a month or two these days". At a minimum, enterprises are going to lock in a provider for a year due to enterprise contracts, which restricts their model choices. You sign for Anthropic, but now OpenAI models are "better". Or, you signed for AWS Bedrock: Oh no, you don't have access to deepseek-v4 because they're behind.
enterprises usually a. just get chatgpt/claude enterprise, or b. just pay the aws bedrock or azure bill
neither of these entail model lock-in
Do you have guarantees that the price of the model you’re using in production today won’t increase in the future?
Using codex every day, in spite of which, I hope some day providers will just start naming their offerings small/medium/large, a bit like we eventually started doing in software testing. Trying to remember what Sol is or why it's better than the other thing is more cognitive effort than I can muster at this point. And that's a sure sign of commoditisation in itself
Sun, Earth, Moon — it’s basically L/M/S like you want but a little less boring.
Why is large better than medium to the average end user of ChatGPT though?
I don’t think there’s a way to name these things that will satisfy everyone.
The naming schema actually tripped me up for a week or so.
My brain's initial conception of the concepts was earth-relative, so I mapped it as:
Sol = big, it's the sun Luna = medium, in-between sun and earth, space Terra = small, terrestrial
Pretty weird when the moon is as much between earth and sun as the earth is between the moon and the sun.
Tinfoil hat time: They saw everyone referring to Mythos, and later Fable, as the new “good” models when Anthropic released those, distinguishable from the “regular” Claude (or other companies’ models) for everyone, and didn’t have that distinction for the GPT model family. That’s why the planetary names were introduced.
I think model naming has been atrocious in general, in part because newer "lite" models surpass the capabilities of previous "pro" models (case-in-point: Gemini Flash which now surpasses the capabilities of the latest Gemini Pro, with a newer Flash Lite vying somewhat unsuccessfully for the old Flash price/positioning), but gpt 5.6's Sol/Terra/Luna split is really not bad at all - probably easier to understand than Starbucks' cup sizing!
The problem becomes when you add in the adjustable reasoning efforts and you end up with {model, reasoning_effort} combinations that end up completely obviating particular model classes altogether for at least some percentage of queries; e.g. with GPT 5.6 the price/performance Pareto frontier is dominated by permutations of either Luna and Sol, with Terra nowhere to be seen (but then if you need "large model smells" that aren't captured by your benchmark you can't even rely on this, as a model like Luna simply isn't capable of encoding sufficient world knowledge in its weights to perform certain tasks at any reasoning level but you might be able to get away with Terra on low reasoning, but no one seems to be covering this for some reason).
Yes but with gemini specifically they said that pro was still in training. And the comparison isn't really atrocious unless Gemini 3.5 Pro is worse than Gemini 3.5 flash
The "until at least Nov 21st" thing presumably mainly affects teams that pin to GPT-5.6 Sol (maybe after extensive testing) such that they won't be switching to GPT-5.7 or GPT-6 or whatever new model is released between now and November.
Bummer, this does not affect the weekly usage on Codex through Subscription.
Your move, Anthropic
Does it mean that subscriptions get more tokens? I’m testing it now for coding instead of claude and it’s very important to understand if I get more due to the price reduction.
These price drops are absolutely bonkers. Gotta love competition! Glad we didn't end up with a duopoly of openai and anthropic, we got a glimpse of what nightmare that would've been and it wasn't pretty
Thanks to both China & capitalism
[dupe] https://news.ycombinator.com/item?id=49396590
completely offtopic but how are you always there with a valid dupe link?
Not for subscribers though
Subscribers already get random rolling resets.
Which is not that great for people using less than 50% every week, because the next reset date moves forward too. In essence, it is redistributing compute from people who haven't used their quota much to those who have.
Though I think they gave a banked reset this time.
How do you know? I see a “weekly usage limit” bar in my ChatGPT settings, but I’m pretty fuzzy about what makes it go down.
If I stick with Luna, I can make it through the week.
ChatGPT Work and Codex use that. Normal chat has a different, unspecified limit.
then what happens?
they discovered a great way to destroy their own stickyness and make ppl build generic ai solutions.
The Chinese are coming after these greedy-ass frontier labs. Today Xiaomi unveiled it's own inference machine .... I bet it's gonna be cheaper than Nvidia DGX, shipped with open source models that anybody can have at home.
But can these really be trusted? There was just a HN post which proofed that you can train a model to behave completely different on a certain day. How do we now, that these models do not find a way to call home when they see interesting informations (probably irrelevant on a personal level, but corps, government and military might care).
Above average levels of paranoia here, but one way you can prevent that is by not connecting the machine in question to the internet.
ChatGPT already notifies the authorities if it thinks you’re doing something illegal. Fable downgrades itself if it thinks you’re doing something even vaguely suspicious.
I'm not sure I could characterize the frontier labs as greedy, given that they've been consistently losing gargantuan amounts of money.
The people who give them the money are greedy, and hopefully in for a rude awakening. Starting from Nvidia's vendor financing which has a very direct benefit to them, through to every company and oligarch investing into data centres in the hopes of being one of the ones left capitalizing on capturing the livelihoods of the majority of what remains of the "middle class".
It's either hopium or a truly horrific dystopia. Something's going to have to give.
The company may be losing money but the people are getting enormously rich and cashing out religiously