Replacing LLM yes/no calls with Jev to cut costs on thousands of decisions
The author estimates 10,000 simple decisions cost about $0.42 with Jev and classifying 1,018 AI studies about $0.08, arguing LLMs should only handle exploration and writing.
That $1,000 monthly AI bill might not be racking up because of complex tasks. It could be due to thousands of simple "yes/no" decisions that an expensive LLM is handling for you. Picture this: 10,000 simple decisions. Instead of running every single choice through an expensive generative model, "Jev" - the example cited in the article - costs around $0.42 for 10,000 decisions. And classifying 1,018 AI studies in a real-world case cost about $0.08. This is where true AI agent optimization begins. The LLM should handle exploration, planning, and writing. The decision layer should determine where to route a task, whether to proceed, what to approve, and what to send for review. The code should handle execution. And if your agent does this thousands of times a day, the difference between "generating everything" and "generating only what’s necessary" can quickly translate into significant cost savings. I see the same thing in creative AI workflows. When you have 10–20 services open for a single project, you’re paying for more than just subscriptions; you’re paying with your own time as you constantly switch between them. That’s why @Picsart makes sense here: 188 AI models plus a workflow engine, all in one workspace. You don’t have to keep jumping between tools, copying results from one service to another, and manually piecing the process together. Fewer unnecessary steps -> less wasted time -> more content from the same resources. AI is becoming cheaper. But if you simply ramp up usage, your bill might grow right along with your productivity. The next level isn't just using *more* AI. And learning not to pay AI for things it doesn't need to generate at all.
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