Realize it's going to be 10-100x more expensive once you have no way back?
If anything, I would bet that next year you could get today’s flagship performance for significantly cheaper via an open-weights model.
Open-source models have caught up tremendously recently. Those who can’t or don’t want to invest a lot of money can already develop with Kimi and GLM without any problems. We don’t have to wait another year for that.
From experience, the same level of usage would have left me stranded on my CC 5 hr limit within an hour.
There were some difficulties with tool calls, in particular with replacing tab-indented strings - but taking no steps to mitigate that (which meant the model had to figure it out every time I cleared context) only cost relatively few extra tokens -- and it still came in well under 4.6, nevermind 4.7. And of course, I can add instructions to prevent churning on those issues.
I have no reason to go back to anthropic models with these results.
"No moat" indeed.
(Timeframes are hyperbolical).
I expect tomorrow’s models will be so much more capable that we will happily pay more.
But if not, we will still likely get today’s capabilities or more for cheap.
I don’t see a realistic scenario in which the AI genie is going back into the bottle because of affordability.
It seems like wishful thinking by people who dislike the new paradigm in software engineering.
This is at multiple levels if you have a remote API call as a key part of your workflow/software system.
1. Price risk - might be affordable today - but what about tomorrow?
2. Geopolitical risk - your access might be a victim of geopolitics ( seems much more likely that it used to be ).
3. Model stability/change management - you've got something working at the API get's 'upgraded' and your thing no longer works.
If you are running on open weight models - you are potentially fully in control - ( even if you pay somebody to host - you'd expected there to be multiple hosting options - with the ultimate fallback of being able to host yourself ).
I'm not all gloom and doom but the treatment of junior engineers is something I think we will either regret or rejoice. Either will have a spur of creative people doing their own independent thing or we'll have lost a generation of great engineers.
We’ve been coasting along on a single generation who have ruled with iron fists.
Company brain drain, knowledge leaves with your seniors if you decide to get rid of them, or they just leave due to the conditions AI creates.
I don't know if the above comes to fruition, there's a lot of questions that only time will answer. But those are my first thoughts.
If you fire all your SWEs they won't sit around twiddling their thumbs waiting for an AI collapse, they'll career shift. Maybe to an unemployment line and/or homelessness, maybe to something else productive, but either way they'll lose SWE skills.
If you close down all the SWE junior positions you'll strongly discourage young people training in the field. They'll do something else.
Then if you want to go back, who will you hire for it?
They are large language models. Not automated development machines. They hallucinate.
The goal post has not shifted since 2023 or so. Make an LLM that doesn't blatantly disregard knowledge it has, instructions it has been giving, over and over, and you win. If trillions of USD of investment can't do it, I'd be curious to see what can.
If the AI is not good enough, then don't fire the devs. If/when the devs are no longer needed, I don't see why the need would return later, that was my point.
If that was the case companies could just have their project managers managing Claude Code instead of developers, and they would immediately realize that using Claude Code to develop software is just as complex and geeky as it ever was - nothing changed in that regard.
A harness and a bunch of skills is just the new "think step by step" prompting technique. Don't just let the LLM rip and write a bunch of code, but try to get it to think before coding, avoid things like churning the code base for no reason, and generally try to prompt it to behave more like a developer not an LLM. Except it still is an LLM.
A coding agent is really not much different to a chat "agent" in this regard. You've got the base LLM then a system prompt trying to steer it to behave in a certain way, always suggest "next step", keep to a consistent persona, etc. None of this actually makes the LLM any smarter or turns it into a brilliant conversationalist, anymore than the coding agent giving the LLM a system prompt magically turns it into a software developer.
If you don't appreciate the difference between what an LLM or a coding agent can do, vs what a human can do, then I can't help you.
The consumer space is about extracting every ounce of personal data possible.
The b2b space is about "maximizing customer value" - that is, not maximizing the value of your product to the customer, but maximizing the value of the customer to your business. Lock them in and lock them down, make your product "sticky" so they can't leave without immense cost.