similar vibes as "640k ought to be enough for anybody"
I won't deny that the latest Claude models are fantastic at just one shotting loads of problems. But we have an internal proxy to a load of models running on Vertex AI and I accidentally started using Opus/Sonnet 4 instead of 4.6. I genuinely didn't know until I checked my configuration.
AI models will get to this point where for 99% of problems, something like Gemma is gonna work great for people. Pair it up with an agentic harness on the device that lets it open apps and click buttons and we're done.
I still can't fathom that we're in 2026 in the AI boom and I still can't ask Gemini to turn shuffle mode on in Spotify. I don't think model intelligence is as much of an issue as people think it is.
But think about the general user. They're using the free Gemini or ChatGPT. They're not using the latest and greatest. And they're happy using it.
And I am willing to bet that a lot of paying users would be served perfectly fine by the free models.
If a capable model is able to live on device and solve 99% of people's problems, then why would the average person ever need to pay for ChatGPT or Gemini?
Even Opus makes mistakes with dates or not understanding news and everything correctly in context with chronological orders etc, and it would be even worse with smaller and less performing models.
Scheduling, planning, researching products, shopping, trip plans, etc...
My experience is very different than yours. Codex and CC yield very differenty result both because of the harness differencess and the model differences, but niether is noticeably better than the other.
Personally, I like Codex better just because I don't have to mess with any sort of planning mode. If I imply that it shouldn't change code yet, it doesn't. CC is too impatient to get started.
Perhaps Opus is superior and I'm just jaded.
I come from Cursor before having adopted the TUI tools. Opus was nothing short of pathetic in their environment compared to the -codex models. I would only use it for investigations and planning because it was faster.
Like you've said, though, that could just be a harness issue.
It's the biggest thing that stuck out to me using local AI with open source projects vs Claude's client. The model itself is good enough I think - Gemma 4 would be fine if it could be used with something as capable as Claude.
And that's gonna stay locked down unfortunately especially on mobile and cars - it needs access to APIs to do that stuff - and not just regular APIs that were built for traditional invoking.
The same way that websites are getting llm.txts I think APIs will also evolve.
The world has moved on, that code-golf time is now spent on ad algorithms or whatever.
Escaping the constraint delivered a different future than anticipated.
it is economically not viable to try anymore.
"XYZ Corp" won't allow their developers to write their desktop app in Rust because they want to consume only 16MB RAM, then another implementation for mobile with Swift and/or Kotlin, when they can release good enough solution with React + Electron consuming 4GB RAM and reuse components with React Native.
Of course, it's never that simple in reality; you need developers who know each platform for that to work, because you must run the builds and tell the AI what it's doing wrong and iterate. Currently, you can probably get away with churning out Electron slop and waiting for users to complain about problems instead of QAing every platform. Sad!
But most likely, it's not. At a system level we don't want people to do that. It's a waste of resources. Making a virtue out of it is bad, unless you care more about bytes than humans.
In a 5-year lifecycle that's about 10,000 years of human labour wasted. Yes, I had to quadruple-check this myself.
Does it take 10,000 work-years of effort, per project, to train its developers to write reasonably performant code?
Of course not all of this would translate into actual productivity gains but it doesn't have to.
The ones that stick out are actively maintained, widely used, and well funded. It doesn't have to be a million active users, but they should be the first to get their act together.
Unfortunately the number of users and the collective value of their wasted time doesn’t make arguing for efficiency and performance any easier.
I once noticed my name in the Chromium OS credits due to a patch I had submitted to a library that's on every Chromebook. 1 million would be a small number for Chromebooks alone.
That said, I think it’s more of a collective action problem. The person who could pay for the refactor to operate in 640 K is not the same person who has to pay for the 16 GB. And yes, the 16 GB is cheap enough in comparison to other costs that the latter group doesn’t necessarily notice that they are subsidizing inefficient development.
Not that I agree of course :) I’m talking more of the net negative of everyone needing to buy 16gb sticks so developers can YOLO vibe-coded unoptimized garbage. But at least I think the former explanation is what stavros meant :)
The costs are borne by different people: development by the company, RAM sticks by the customer.
A company is potentially (silently?) adding to the cost of the product/service that the customer has to bear by needed to have more RAM (or have the same amount, but can't do as much with it).
I don't think it's even mildly controversial to say that there will be an inflection point where local models get Good Enough and this iteration of the pendulum shall swing to fat clients again.
The local models have their own advantages (privacy, no -as-a-service model) that, for many people and orgs, will offset a small performance advantage. And, of course, you can always fall back on the cloud models should you hit something particularly chewy.
(All IMO - we're all just guessing. For example, good marketing or an as-yet-undiscovered network effect of cloud LLMs might distort this landscape).
My thinkpad is nearly 10 years old, I upgraded it to 32GB of ram and have replaced the battery a couple of times, but it's absolutely fine apart from that.
If AI which was leading edge in 2023 can run on a 2026 laptop, then presumably AI which is leading edge in 2026 will run on a 2029 laptop. Given that 2023 was world changing then that capacity is now on today's laptop
Either AI grows exponentially in which case it doesn't matter as all work will be done by AI by 2035, or it plateaus in say 2032 in which case by 2035 those models will run on a typical laptop.