Comment by ieie3366
4 hours ago
Fable is not a tool for the average user. It’s a professional tool for highly complex work.
I would compare it to a extremely high end $15k PC, or an expensive pro-grade video camera, or a freight train, or a …
I would say at least 95% of the global population will not encounter a situation once in their life where it would be actually useful/warranted.
I wish Fable were as good as you make it sound. A plan created by Fable is good, but in my case, it always contain issues caught only when it's reviewed again (whether by itself, Opus, Sol etc.). That's (almost) not different from plans created by Sol, GLM 5.3 etc. The one thing where it's genuinely better is the front-end, but then again it's far from perfect, it just needs less iterations.
Fable does far better at considering the whole picture, weighing options, and making suggestions during architecture or refactoring discussions.
It’s more reliable and makes less dumb errors than Opus.
It still messes up, of course. But for my working style, I definitely prefer it.
>> but in my case, it always contain issues caught only when it's reviewed again
Yes but those issues will be much less severe with Fable-written plans than those written by lesser models. I know this because my workflows at both my regular job and my startup involve multi-step agent reviews via codified adversarial review skills. Fable as a reviewer will frequently find blocker-level issues with plans written by GPT 5.6 Sol, and sometimes with Opus 5. The opposite almost never happens. In fact I cannot remember the last time it happened.
I think highly complex work and “professional” work are basically completely orthogonal. You can have highly complex work you do as an amateur, where AI can be very useful. For example working through a difficult mathematical problem, building or contributing to an operating systems, or researching a highly technical topic for a hobby project such as microscopy, chip design or lithography.
And you can have extremely simple work that you nevertheless have to do as a professional. For example, drafting a routine customer email, summarizing a meeting, formatting a report, filling in standard documentation, or making a trivial code change.
So I don't think the $15k workstation / professional camera analogy really holds. Those are specialized tools whose capabilities are mostly useful within a fairly narrow domain. A general-purpose AI can be useful across thousands of completely unrelated tasks, including one-off problems encountered by ordinary people.
Yeah this is a fair point. I only go to it when I have some big architectural problem I want its help in working out. Or a super nasty bug.
It works much better on regular software development e.g. for complex refactoring where cheaper models would produce a lot of garbage results.
You have a $3 trillion bubble riding on this not being true.
Right? If we’ve already reached ‘good enough’ then there’s rough waters ahead.
I have a sneaking suspicion that someone at Google may be making the same bet, looking at the faster and faster Flash models which provide acceptable results to a lot of people (outside of coding).
For Anthropic. But not for the AI industry at large.
Cheaper, more powerful AI will continue to expand the bubble. Projects will get more ambitious. Everyone will build out their own custom little software. Code diversity expands and requires even more AI.
The bubble is _not_ on models becoming more intelligent and solving arc-agi-999.
They are already good enough at what they mechanically are.
You have to use the right harness, right verifiers (automatic where possible, human where not), etc much much more specific than a generic one like claude code or codex, and it will also be able to work within constraints and be the "proposer" of an imaginary optimisation problem and an excellent one at that. But you have to frame the task at hand in that manner or maybe even reorganise the task you do itself so it is more amenable to being framed that way. If you use it this way, it is _already_ massively economically useful. But it will take many years for it to actually be usable in that way, since you need DC capacity to come up first which is few years away and also well, massive organisations that have to integrate these will usually take many years to do so.
It is also useful albeit less so in cases like general SWE, where you still need a human in a loop for non-verifiable requirements, and also in other general usecases where information retrieval is too intractable and you need to carefully use LLMs as a component of the overall system.
I am not saying Fable or whatever the biggest models are are useless - they will certainly be useful for tasks at the frontier of the day - which is today complex exploits and open math problems, and well, tomorrow it could be something in biotech. But this is not what the entire bet is on at all - just automating day to day drudge at the tens of thousands of massive companies and governments we all know and love is more than enough. With the right training data (which _also_ is a bottleneck and takes time) you could even automate certain processes entirely. Sure, if we get a crazy medical innovation and end up saving trillions in healthcare great, but that's just a bonus.
None of this is to say that I think there is zero sketchy financial engineering going on
The issue is that, if there is a “good enough” point approximately here, it is only a matter of time before models become small and efficient enough not to need all those data centers. It should be good for companies that sell computers (like Apple) rather than putting a toll booth in front of a pile of numbers.
The bubble is based on the promise that these LLMs will cure cancer and find the solution to global warming. The pragmatic users of these tools (like you seem to be) are enjoying the subsidized use of the tools right now, but it's not a sustainable business model
I hate to be brusque but this is cope. GPT 5.6 Sol is just as good and cheaper