So, I'm using AI both at work and for a personal greenfield project, and I have 20 years of pre-AI software development. Which isn't to say I'm amazing, just putting some context here of what my experience level is and the contexts I've used this tech in. First off, I doubt the "10x" number in general (I'm not seeing it personally or from other people), but lets say I pretend it's true for a second. 10x better/faster at what exactly? Like, if you have an unreleased greenfield app then sure, you can bang together features a lot faster. But what if you have an established app with real users? It's not just the time it takes to make the feature, you also have to consider documenting the feature, making sure it works well with the other features, making sure it's something customers actually want, making sure it's part of a larger coherent design, training customers, marketing the feature, etc. Like this notion of "we'll just go 10x faster!" falls apart really quickly when you're talking about making something that people will actually depend on and use.

I keep thinking about that (so far anonymous) company that blew 500 million dollars worth of tokens in a month, and what I desperately want to know is WHAT DID THEY BUILD WITH THAT?! Like, for that sort of money they should have created an earth shaking new business or something instead of becoming a cautionary tale that's rightfully too embarrassed to publicly own it.

The other thing with regard to factories/agriculture/whatever is, in revolutions with those things nobody needed to be convinced. Sure, people were (rightly!) concerned about the societal impact, but the utility of a factory was fairly obvious. And yet last year OpenAI spent more on their marketing budget than Coca Cola. The way this stuff is hyped and pushed has an air of extreme desperation. If it was so good people wouldn't need so much convincing!

Maybe not 10x; but it's fantastically talented at intuiting intent, reconstructing contexts, and making aligned judgement calls. You could throw Fable utter garbage and get great results. Fable felt like it was modeling me whereas gpt-5.5 is still very much is riding your prompt, your inputs. I have bit of humility here as this is basically how I felt about 5.4->5.5; but 5.4 was very much a scalpel-very spiky weird intelligence. 5.5 sits somewhere in-between, but still spiky and verbose- code-maxxed; not a great orator, not a good proactive "here's the skip-connection you probably should be thinking about but don't seem to be weighing" in the way that Fable is. Fable is crisp.

Let's say we're prototyping an interactive tree (this is totally made up, but you get the idea):

Take this data input and convert it to a Sugiyama-style tree with hand-drawn feeling lines connecting the nodes. We need the ability to activate a random subset of nodes with a paint splash. The whole thing should feel organic, incorporating small motion effects. As the nodes are activated, the edges should look like a hand-drawn painting effect drawing toward the node, and then SPLASH onto the end node as it changes from black to deep red. The background should utilize a muted paper color, and we must adhere to this color palette for all elements (PALETTE).

...then back-and-forth 10 prompts or so to get the prototype I was looking for. Comparing these types of things between Fable and Opus, something like this would be quite a bit less glitchy, prettier, and closer to the quick prototyping I needed than what I got with Opus 4.8.

Now, when I went deep into a complex codebase to fix a small issue or optimization that spanned many files and was fairly unique from anything in the training data, I didn't really see any noticeable difference between Fable and Opus 4.8.