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Probably a first world problem, but with Opus 5.5's efficiency, the limits on the 5x plan are simply sufficient for my everyday work, even when running 2-3 sessions at a time. So I wonder when I would use Sonnet 5.5. More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity). So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day. So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point. Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
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I find that "vibe coders" (that is, people who do not know anything about programming, but nevertheless produce useful tools for themselves and others) are using a lot more tokens than we do as programmers. I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept. They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).
There's lots more you can do! Use the model to monitor your deployments after they get deployed. Have them fix and watch CI issues for you. Run adverserial review. Automatically watch metrics every day and highlight performance regressions. Start reviewing your previous sessions to find ways to statically reject different failure modes and have the agent have more success earlier on etc. Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?
It's the "semblance of understanding" you're holding on to that is keeping your demand limited. I'm holding onto it as well, but I think these companies are assuming that human understanding will no longer be relevant for most codebases going forward.
I created a team of agents using Opus 5.5 to review and address findings on a job system I have in a side project with medium reasoning, and I burned through the 20x plan weekly limit in 2.5 days. They were using GPT-6-Sol for reviews, and it also used 85% of my OpenAI x5 weekly limit. Three hundred something commits in total. OTOH, in the daily job, I have the team plan that's similar to 5x plan and I never had any limit problems, because I really need to understand be able to take responsibility for the code. Totally different uses.
> I want to retain some semblance of understanding How you do this (and how deeply) I think is really the limit. I am doing this by focusing heavily on the design phase with grilling and trying to continually improve process to need less effort in the review phase. Are your models doing automated reviewing and testing before pushing out the PR (themselves)? I think in the long run as models and the tools around them get better and cheaper, those that abdicate understanding will be able to achieve more. Although programmers think of that as irresponsible, ask yourself what does a tech lead do? And then what does a CTO do, etc?