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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.
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.
There were somewhat good reasons to think it needed more than just this data-driven ML approach.
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
It's about evidence this is an active force in competition in LLMs.
It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.
Make sure to stay updated!
If I run out of tokens on ChatGPT of course I will try Claude. I never ran out of Google searches so no reason to try Bing
At some point Google gets suspicious of your persistent searches and makes you solve captchas and puts cooldowns on your searches.
More like the other way around - Claude burns tokens faster than any other LLM.
ChatGPT is AI for the average non-techie the world over, but the average non-techie isn't eager to pay for it. The more progress that's made, the less incentive to pay - most people are happy with the total garbage spewed by google AI overview. They'd be happy with google's 30b MoE gemma, whose performance will likely be squeezed down to something that can run on a phone in 2-3 years. Why would they pay $20 a month?
It's why OpenAI is pushing a variety of things such as ads and offer a more polished ui/ux than the competition, I think. The models are already good enough for people who just want to know how much sugar to add to their cake or when's the next basketball match their team plays - it's OpenAI's game to lose those people, by annoying UX and whatnot. If they can make a few bucks off of every one of their non-paying users it'll stretch their runway immensely. Those users will never go to Antrophic or some cheap Chinese model, but they might defect to Google because a popup on Android / in Chrome told them to.
I had a discovery call last week with someone who did not realize he could use ChatGPT for work. It was a revelation that he could drag a PDF into ChatGPT and it could summarize it for him.
FWIW, guy in his late-30s in a pretty senior sales role.
Wouldn't that just be price fixing? If they arrive at their prices independently and they all happen to be similar, fine. But if they're all "smart" and coordinate so none of them undercuts the other, that's probably illegal.
Personally, I came to this conclusion early this year. To acquire the data that AI Companies are using to train their models is low cost and once they have it, they can refine and store it. Creating the LLM takes a bit of money but it is not a serious blocker. Clearly, the Chinese companies can make AI so they will drive down costs. There is a need for good AI (Not just Great AI) and it is not cost prohibitive to make good AI (The same with specialized AI).
My prediction is that AI will spilt into two categories, Great AI (High Cost) and Good Enough AI (Low Cost). Which for the long run of AI and companies that use AI, this is good.
And with the sheer volume of data created from that, coupled with benign-seeming prompts like "plan out your reasoning in a document before implementing" that could never be patched without breaking existing customer workflows... there's more than enough for someone to distill on. Even if that only gets them to not-quite-frontier, if you're pushing the frontier every few months, they're only ever a few months behind you.
The primary resource you need to train LLMs is money and China has plenty of that.
Besides, identity verification that actually works at scale is a much harder problem than identity verification which is good enough to satisfy your compliance people and regulators. Especially if the fraudsters have a major world government standing behind them, and if their aim is to be identified as a real customer, not one customer in particular.
I live in Germany where people won't stop whining about electricity prices, and I pay 75€/mo.
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.
Referring to a baseless prediction by Sam Altman that AI will become like electricity without any push-back? Who really thinks Sam is working toward that future?
He already worked to undo every early promise made (non-profit, open source models, strong governing board, strong ethics/alignment/security focus). He's flip-flopped on other things like first characterising Trump "an unprecedented threat to America", then contributing 1M USD to Trump's inaugural fund far exceeding his earlier political contributions. Lately OpenAI, under his supervision, has also been working with Anthropic to lobby regulators in Washington for restrictions on open weights models - why so if not to undermine a free market in favour of an oligopoly?
Beyond that, you have the simple fact that most of his personal wealth and very probably the fate of OpenAI hinges on AI inference NOT becoming an interchangeable commodity.
I mean.. Honestly. The naivete is downright astounding.
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.
But as you said, who knows.
You can put AI chips in datacenters in the desert for far less than $100/kg. With lots of solar power available, the option to easily access your hardware and far less radiation issues.
The datacenter in space story really only exists to make it possible for Musk to sell X to SpaceX and make more money from the IPO. That's all. There is no engineering reason.
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.
The economy he and his ilk want to build is infinitely worse.
A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.
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/
While the training code and data are the true source. Since if you want to robustly modify the LLM that's actually what you need.
But since "compilation" (training) is extremely compute intensive this isn't something accessible to anyone without an entire datacenter.
Anyway semantics aside having the binary is still infinitely better than dealing with an api as far as privacy and control go.
you need to "literally" go read the definition of open source software or even ask an LLM to define it for you. Weights + inference code are not the source code they're more like the compiled binary. Making modifications to the behavior of a model with additional training is like writing a mod for minecraft. Sure, you can change things but it doesn't make it open source.
Calling these models "open source" is an old trap that software companies use to use. Free to download but then, once you're fully comitted, the trap snaps shut and you must pay up to continue.
Training data and code.
Is it just the supplementary data/code for how they were trained, not just the final product?
I don't mind open-weight models, but they are not open source. It's like bringing home a dog from the rescue and just hoping that it doesn't have a history of biting kids in the face. You just can't know, because you don't know the full history. You can try to add new training (fine tune) to tell it not to bite kids, but that's it.
This should not be surprising at all. Every new students spends tiny fractions of time learning knowledge that took many lifetimes to discover. This fundamental to the progress of intelligence and understanding.
It should not be surprising that AI can be distilled. It's the logical method of training; I would hope that each frontier model is in fact not trained 'from scratch' each time.
We should expect future frontier models are simply distilled versions trained by specialist models, the same way humans learn from a series of professors, papers and canonical books on each different subject material. Models like this can be trained incrementally, or a so called Mixture of Experts (MoE).
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).
FWIW, we use ChatGPT for our primary model and use Claude to do the reviews. This works better than ChatGPT doing it's own review even with a clean session/context.
I've done this tens of times between these two models and it works great in my experience. Sol initial back and forth with me. Commit. Let Grok review. Sol fix. Only then do I start reading the code.
Like, the quality of the anthropic models is fine, but they’re so incredibly slow. Claude reads files one at a time while codes dispatches tool calls three or four a time.
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.
For example, health insurance providers are renowned for not being empathetic. Charities are the opposite. Sometimes companies even build it into their identity, e.g. Cards Against Humanity.
As for AI, I haven't seen a strong difference in empathy but it's definitely true that the big AI companies at least try to make their models moral and empathetic. Even if it mostly ends up just being annoying.
I think that AI has an insane level of upside, it’s just that the greedy dumbfuck billionaires are getting their greedy little grubby paws involved. If left to researchers I think the sky is the limit, but unfortunately thy need assets, so there isn’t a clean solution to that.
Ideally, we could somehow separate AI from funding from malicious entities like billionaires, but right now that doesn’t seem possible. Hopefully in the future researchers with genuinely good intentions can have far more direct control than dumbass greedy old fucks, but we’ll just have to see.
I think the future can be bright in theory but we’ll have to see, making insanely powerful open-source models is the direct way to get around the billionaires so I think that’s our only option. Make open-source ASI you can run on a consumer computer.
I believe the reason we have not seen a Fable-level model from OpenAI yet is because doing so would box them in on costs just as harshly as it has boxed in Anthropic. They are letting Anthropic make this mistake.
If you can train a larger model then you can distill smaller models from it. You don't need to necessarily serve the larger model publicly. Distillation is much more effective when you have unrestricted access to the original model.
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.
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.
Making 2/10 permanent would be a killer move and make a strong argument against open-weight. For the sake of the open weight ecosystem I hope they do not.
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.
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
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).
It really reminds me of pay-to-win games at this point: Two currencies (credits, tokens), both with a floating, intransparent exchange rate between each other and real money, random airdrops...
[1] https://help.openai.com/en/articles/12642688-using-credits-f...
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.
neither of these entail model lock-in
enterprise SaaS LLM calls do lock in rates but don't lock in models for similar reasons to the above.
I tried it again today because of the discount, it told me it couldn't run acceptance tests because a .env file did not exist, and when I showed it the damn file it went "ah, it's there now". I think it was the first time I've ever had an agent try to gaslight me.
But I continued to work with it and found that it was mostly my own style of interacting that needed to change. In a way it is similar to a new co-worker, they have their own personality and ways of working. Once I figured that out I have been able to get very good work out of Sol.
Sol seems to work better when you are clear, precise, direct and unambiguous. The model seems annoyed if things aren't spelled out. Not micro-managing, it seems to have a high bar for specific intent.
When I get Fable to write out specs for Sol, I tell Fable that Sol is a nit-picking literalist that is exceptional at instruction following. So far this description has lead Fable to generate specs that Sol implements at a high quality.
Though I think they gave a banked reset this time.
If I stick with Luna, I can make it through the week.
The part I don't understand is how usage is "charged" against this limit.
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.
aside from the obvious IP theft problem, it's probably most dangerous for Chinese users outside China to use the Chinese models.
they discovered a great way to destroy their own stickyness and make ppl build generic ai solutions.