alt Hacker NewsAnyone else find it weird how within 2 months of Fable releasing all the major labs suddenly had Fable-level models? Trying to think of explanations:
1) AI researchers talk and change companies often, so techniques circulate. This feels implausible because training and shipping a new model ought to take longer than 2 months?
2) Distillation - also implausible for the reason above.
3) Benchmark hacking. AI companies have ways they can dial up performance artificially, and will reach for that to maintain the appearance of parity.
Other reasons?
Edit: Most replies are ignoring timing. It's the near-concurrent release of the same jump in capability that I find suspicious; not the fact that labs can catch up eventually.
Replies
There is a widespread belief that the nature of intelligence is scalar, like how a person can have 100x more wealth than another person. If this were true, then we’d probably see breakaway RSI from a single lab.
But I think we’re discovering that intelligence is about universality, not magnitude. This is analogous to how building a universal Turing machine wasn’t merely a matter of building a calculator that could multiply higher numbers. The difference is that with calculators we consciously theorized about what universal computation would require, then we built one as a step change. Despite it having low memory and slow speeds, the first one built was as theoretically universal as any computer we have today, in terms of the surface of computations it can perform.
With intelligence, it’s turned out to be less discontinuous, which I believe has convinced people that intelligence is a never ending exponential rather than an S curve approaching a horizontal asymptote. I suspect the LLMs we have today are the same kind of thing we will have in 5-10 years, but in 5-10 years we’ll consider them to be fully universal. At that point we’ll still have improvements in tokens per second and volume of context window, but not in capability per token.
4) There's nothing terribly special about Anthropic. No moat.
It's because Fable is just synthetic RL tasks + scale. The secret has been out for awhile now.
Timing doesn't seem odd to me. It just seems like https://en.wikipedia.org/wiki/Multiple_discovery which I've noticed happen in many areas.
> 1) AI researchers talk and change companies often, so techniques circulate. This feels implausible because training and shipping a new model ought to take longer than 2 months?
The assumed timeline (2 months) is slightly wrong because Fable (Latin) is essentially the same as Mythos (Greek) albeit with protections against cyber and biological misuse.
Mythos (Preview) was publicly announced in April 2026 [1] which means other labs have had 4 months to catch up, not 2 months.
Assuming everyone had access to Mythos from the start, your expression, similar to other folks would have been "Mythos-level intelligence" and not "Fable-level intelligence".
Yeah, I’m not convinced that there are any models as smart as Fable. Opus 5 definitely isn’t for all it has great benchmark scores. Fable displays judgement in a way I haven’t seen from any other model.
No, it has happened to almost every other "sota" model before. There used to be a meme with a circular arrow going through Anthropic, OpenAI, Google as a hype circle. Now we can drop Google and add a couple of Chinese companies.
It's not an explanation of why it happens, I am just pointing Fable is not an exception, it has happened with almost every other model release by all these companies over the last 2-3 years.
Possibility: They're all hitting the same plateau of what LLMs can do with their current architectures.
I'm not stating this as a fact, but it's a hypothesis I'm keeping in my mix.
what we're going through is the same thing as smartphones, the limiter is compute.
it used to be snapdragon came out HTC rushed out a janky phone everyone went omg htc is goat, then in the next few weeks and months others would impliment better versions and people would not notice those as much, finally sony would release a polished phone right as the next snapdragon cycle came.
eventually compute gains leveled off and apple won on taste.
nvidia/tpu is the new snapdragon. Anthropic and google both peaked on the first training run on a new tpu cycle.
you should expect amazing things within a few months of each other from everyone with access to chips and willingness to use them on a training run.
We haven't seen willingness from google to do that. So its currently xai,oai,anthropic, and probably soon meta.
I'm pretty sure both Anthropic and OpenAI haven't necessarily been secretive that they have internal models that are much more capable than commercially available ones.
It's probably a mix of all of that plus simply always keeping one in the chamber to 1up everyone else when the time is right.
I think model level is more a function of the state of hardware. Once it exists and is available (and if a lab can afford it), then they can train their own 1T, 5T, coming up next 10T model.
I understand Mythos became internally available on the 24th of February.
Other labs catching up in half a year seems about right.
More compute is coming online at all times.
I'm solidly in the "they are benchmaxxing" camp. This became very apparent with GPT 5.6 Sol. It, too, was widely hailed to have near-Fable level intelligence. But I used it non-stop for a week and realized that they had mostly just dialed up the relentlessness meter to eleven, most likely via heavy RLHF.
Last week I gave it a small-sized auth ticket to work on, then stepped away. I came back later that afternoon and found that it had worked for 3+ hours and written 25,000+ lines of code. I skimmed over the code and it looked like a small fix followed by a massive number of additional checks around it, including static analysis tooling.
I gave it to another GPT 5.6 and said "check this code and see if it addresses the ticket". It looked at it and said that 98% of it was garbage and should be thrown away (its own words). I then gave it to Fable, which said it was massively over-engineered. Fable's theory was that the agent implemented the fix first, but then compacted and lost crucial context, forgot what the original task was about, and kept going. After many compaction cycles it was completely lost.
Some people complain that Opus 5 stops before finishing a task. But to me, that behavior is vastly preferable to what GPT 5.6 Sol does.
> It's the near-concurrent release of the same jump in capability that I find suspicious; not the fact that labs can catch up eventually.
What are suspicious of? If the timing is similar maybe just everyone already are of similar capabilities and got there at a similar time?
> Anyone else find it weird how within 2 months of Fable releasing all the major labs suddenly had Fable-level models?
It means Anthropic had no real moat and no real lead. Is that weird to you?
> other reasons
Maybe research is sufficiently public and simple to reproduce or the next steps of how to improve things are sufficiently obvious to the smart people working on frontier AI.
Its possible no AI lab has any unique edge, and success is a combination of (a) having access to GPUs (b) having access to large amounts of data (c) know about the handful of techniques to build an LLM, of which nearly all are likely open source and documented in papers. So the cycle of growth is (a) and (b), get more GPUs and get more data and you have a better model.