A couple more thoughts here - the hard part is not just the data side of it, it's replaying/unplaying actions. Many actions are non-reversible. Code is clean in the same way that google docs is clean. But for many business processes, some actions just can't be unwound once started. If claude initiates a wire that it shouldn't, no amount of git technology will undo that wire.

Probably because telling someone not to do something works the 99% of the time they weren't going to do it anyways. But telling somebody "here's how to do something" and seeing them have the judgment not do it gives you information right away, as does them actually taking the honeypot. At the heart of it, delayed catastrophic implosions are much worse than fast, guarded, recoverable failures. At the end of the day, I suppose that's been supposed part of lean startup methodology forever -- just always easy in theory and tricky in practice I suppose.

I am about to go on a long rant, but there is so much money sloshing around the capital allocation machine going towards a vision of the AI managed and optimized future that the propaganda machine for these rose colored delusions must work in overtime. What disappoints me is the question of where the heck are the bears? Did they all go into hibernation 5 years ago when QE gave the retail kindergartener a handgun to pump low quality tickers to the moon? have we just societally accepted that everything should be a hyperreal version of sports gambling now and the world is and ought to be an efficient market of hyperstition?

I may be old and grumpy saying this, but this all sounds dumb and corny. I would like some of the very capable traders who make money repricing mispriced assets to find a way to make money deflating this bubble and bring this environment back to sanity. And I say this as someone who likes the capabilities of AI but continue to see it do little to none of the hard work solving incompressible problems that continue to create and retain enterprise value.

To get off my soapbox for a second and get back to your quoted passage -- what they're really saying is "We are working very hard to make this future coming, and we think so little of your intelligence that we believe you'll fall for the fear tactic of believing it's inevitable, ignoring the fact that it won't happen without someone's hands. And in this case, it is very much our hands, which are incentivized to not just do it but to do it so well that we ensure we do everything possible to make this happen. Part of which means persuading you that it is guaranteed to succeed. If we ever let the honest truth slip that what we're proposing is extremely hard to pull off with pure AI and we're just going to be a any other commercial real estate investor like anyone else, the jig is up."

That's what every single one of these kinds of hypocritical navel gazing faux-concern proclamations amount to for me. Astroturf.

The real moment of relief for me was the first time I used DeepSeek R1 to do a large task that I would've otherwise needed Claude/OpenAI for about 12 months ago and it just did it -- not just decently, but with less slop than Claude/OpenAI. Ever since that point, I've been continuing to eye local models and parallel testing them for workloads I'd otherwise use commercial frontier models for. It's never a perfect 1:1 replacement, but I've found that I've gotten close enough that I no longer feel that paranoia of my AI workloads not being something I can own and control. True, I do have to sacrifice some capability, but the tradeoff is I get something that lives on my metal, never leaks data or IP, doesn't change behavior or get worse under my feet, doesn't rate limit me, can be fine tuned and customized. It's all lead to a belief for me that the market competition is very much functioning and the cat is out of the bag, for the benefit of all of us as users.

I guess there is one thing that Gemini is objectively better at than either, which is long context, and it does seem to be by an order of magnitude. What boggles my mind is why Gemini is still not as good as the open weight frontier models yet. If they got just to parity with that along with their existing long context and strong token pricing, they'd be able to take over the coding market. Are they just biding their time to make their move? Hard to discern.

I'm not saying that they can actually do that per sé; switching costs are so low that if you are doing worse than an existing competitor, you'd lose that volume. Nor am I saying they are deliberately bilking folks -- I think it would be hard to do that without folks cottoning on.

But, I did see an interesting thread on Twitter that had me pondering [1]. Basically, Claude Code experimented with RAG approaches over the simple iterative grep that they now use. The RAG approach was brittle and hard to get right in their words, and just brute forcing it with grep was easier to use effectively. But Cursor took the other approach to make semantic searching work for them, which made me wonder about the intrinsic token economics for both firms. Cursor is incentivized to minimize token usage to increase spread from their fixed seat pricing. But for Claude, iterative grep bloating token usage doesn't harm them and in fact increases gross tokens purchased, so there is no incentive to find a better approach.

I am sure there are many instances of this out there, but it does make me inclined to wonder if it will be economic incentives rather than technical limitations that eventually put an upper limit on closed weight LLM vendors like OpenAI and Claude. Too early to tell for now, IMO.

[1] https://x.com/antoine_chaffin/status/2018069651532787936

My recommendation would be to use other tools built to support pluggable model backends better. If you're looking for a Claude Code alternative, I've been liking OpenCode so far lately, and if you're looking for a Cursor alternative, I've heard great things about Roo/Cline/KiloCode although I personally still just use Continue out of habit.

[1] https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct