The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...

I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis

Most of the things I work on are at least security adjacent. At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.

Maybe Fable can do the same things better than other models, but having to tiptoe around to avoid tripping safeguards makes GPT 5.6 so much easier to work with that I don’t even bother with Fable (or Opus 5) now.

Reading this as someone who switched over to ChatGPT after (and largely because of the changes made in) the Fable release, it reads a bit naive. Not only do I find Sol to be as good, if not better than, Fable it is also faster, better behaved and has a much more coherent writing style. You also don't randomly get the Opus downgrade. OpenAI seems to be pulling this off due to their partnership with Cerebras so I wouldn't make any comparisons to Moore's law just yet considering it seems like we're just getting started in that department. Anthropic could (and should) do the same thing. It certainly feels like model development is at a point where it would be worthwhile building special purpose silicon for the models we have now since they are capable enough that they would still be useful even when/if further advancements are made. If anything, I think Anthropic's problem has more to do with their micromanagement of what users can do with their models, they're creating an undue amount of overhead for themselves by over-policing usage and capabilities.

As models train up the intelligence ladder, many common tasks will hit fully diminished returns, and instead it'll just get progressively cheaper to do that task. But the tasks that AI is capable of doing are also expanding. I'm not sure 'Some tasks don't require the peak of the frontier' is worth worrying about, from an AI finance perspective.

I guess Moore's law analogy is weak. CPU speed has hit a limit in that case. What has hit a limit in AI case? Newer versions of the models are still flowing with more and more capability.

For the users, I feel it is more like "free lunch started", with all these awesome open-weight models being thrown around, breaking the monopoly of a few biggies.

This concept of a free lunch was never true. In a competitive dynamic, speed and performance were always worth optimizing, comparing, and improving.

One of the primary reasons for this is that computers operate in a vast range of orders of magnitude. There’s several orders of magnitude between cache local cpu operation and dram, then several to disk, then several to network, then several to globally durable guarantees. When your code has literally thirteen orders of magnitude to optimize under, there’s never a free lunch. You always need to understand your stuff.

What are all these rote coding tasks people do that they can farm it out to lesser models?

I've been offering Deepseek V4 Flash for free in www.freepi.ai and I've started using it as my main driver as well.

Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.

So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.

He started with thinking were to send what.

I throw everything at claude Opus.

While some people start thinking like OP, A LOT of people just start exploring ai.

And others which are already using it, only understand half of it and just use what they are allowed to use. Claude, GitHub Copilot, Curser, etc.

I just use Fable for reviews of specs and code then hand off to Opus to work on. Works well.

>> GLM 5.2 is worth focusing on. It came out the same week as Fable and is roughly 1/9th the cost (and ~1/5th the cost of Opus 5). Is GLM 1/9th the quality of Fable? Perhaps, for certain classes of tasks. But for most rote coding it’s more than sufficient. Especially when provided with great context. I frequently chat with Fable to interrogate and shape a design, before handing off a brief to GLM.

People say stuff like this a lot, but I have a different take.

The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.

Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.

So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.

over time greater intelligence will be expressed in smaller and cheaper models. we are still somewhat near the beginning of this bc we are finally starting to understand what makes a model truly intelligent/capable.

With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).

The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.

Are people still using deepseek-v4-flash everywhere? I found after the price increases, mimo-v2.5 seems far more attractive.

"When Moore’s Law slowed in the mid-2000s" it did not, in fact, slow down in the mid 2000s, or at all.

https://ourworldindata.org/data-insights/moores-law-has-accu...

Somewhat weird that the article was released today but did not mention GLM 5.3.

If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better? I get the "came out at the same time as fable" thing, but still.. no mention at all?

Yes, weights aren't out yet, but neither are the ones of Fable.

Doesn't feel well informed enough to give advice.