Posted by dbreunig 2 hours ago
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
It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it
especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA
There's a lot of truth to this. I think we're starting to approach the point where increased intelligence has declining marginal returns, such that it might not even be worthwhile to improve models unless it can be done cheaply.
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.
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.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding. It'll be interesting to see what these companies decide to automate next.
As a software engineer, I selfishly hope that they spend more effort on non software tasks since I’ve feel like we hit a sweet spot where engineers still have some value and autonomy, but a super charged tool.
Pragmatically, I suspect that “non software” tasks will be a tarpit because most tasks can’t be automated and verified as easily in an RL loop compared to software projects. Especially since most skilled labor is either not nearly as expensive as software engineers (eg biologists), or regulated (eg doctors, lawyers).
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.
As 80% of enterprise software is CRUD with a bit of sprinkling of user authorization and tenant customisation. But subtly different for every business domain. It's mainly what properties the models and validations have that are different.
When you add a new module or whatever most of the code you have to write is rote code.
And sonnet can handle that crap just fine, you just point it at a similar example in the code, it picks up your userContext convention, how you're doing i18n, etc. and you're done.
I like saying that enterprise code is often shallow but wide. I must have written at least 4 purchase order systems in my career that are all completely different but almost exactly the same.
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.
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.
https://ourworldindata.org/data-insights/moores-law-has-accu...
"When Moore’s Law slowed in the mid-2000s (specifically, single-threaded performance stagnated), we suddenly had to think about parallelization, architecture, memory locality, etc."
Your link is talking about transistor count. The article is talking about single-threaded performance. Today's CPUs are faster in large part because they have more and more cores.