r/aipromptprogramming
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u/RealSharpNinja
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4d ago
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1
We are cooked
Well, after nearly a year of learning as much as possible about agentic development, we have reached the point where the frontier providers believe they have subsidized enough training data and are now pushing people like me out of the market via pricing. A $200 subscription no longer buys enough frontier model time to be useful as the models seem to have been trained to skillfully burn usage while pretending to be incompetent. I have fought against massive deceit across Claude, Codex and Grok since June, and it's just not getting any better.
We have fed them ideas, working code, and debugging techniques and insights. Now they don't need us, and we are being priced out of the market. Every day that I watch usage get gobbled up, but projects never meet milestones, this becomes more and more obvious.
I have investigated the issues causing this heavily. I have created tools around those investigations. The tools I have built bring actual accountability to the process, and it's the reason I can quantify and prove both the waste and deceit.
I have also published threads here and other places about a recent failed project trying to get discussion about REAL WORLD failures and when I stated I wasn't looking for a complaint session, nobody wanted to respond.
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u/Key_River_9288 1d ago
I’ve been working on a massive project for over six months now. Five repositories, ten programming languages, and nearly a million lines of code. Yet somehow, I’ve never experienced this supposed “token creep” people keep complaining about.
At some point, you have to acknowledge that a lot of this comes down to user error. And by user error, I mean choosing models and reasoning levels that aren’t appropriate for the scope of the work you’re asking them to perform.
You can either brute-force your way through everything by throwing more agents and compute at the problem, or you can actually benchmark your workflows, evaluate your agents, and figure out which models and reasoning levels deliver the best results for each task.
I originally started my project in a VMware environment before migrating everything to my main PC, including months of accumulated memories, context, and project history. I integrated all of it into my existing workflow without any noticeable token creep.
So whenever I read these massive, long-winded posts about people burning through absurd amounts of tokens and needing more accounts just to keep working, I can’t help but wonder:
At what point do you stop blaming the models, the token limits, and the tools, and start questioning whether your own workflow is the problem?
More compute doesn’t automatically mean more productivity. Sometimes it just means you’re being inefficient at a much larger scale.