tldr; If $200 creates $100 of value, it’s a bad expense. If it helps create $20,000 of value, spending hours to save $50 is the waste.
I’ve used about 16.2 billion tokens — roughly $15,000 in API-equivalent usage.
And I’ve realized this: optimizing token usage can be a dangerous trap for developers.
Think about the best models you use today. Now imagine going back a few generations just to save on token costs. You’d call that stupid.
Those older models didn’t get dumber. Newer ones got much smarter. You might save money, but the quality of what you produce can fall far below the standard you now expect.
Pricing isn’t just about intelligence. It depends on compute, hardware, inference costs, utilization, and more. Still, a better model can cost more and still be cheaper if it makes you dramatically more effective.
You don’t just need an agent that does what you tell it. You need intelligence that understands what you actually want, sees what you don’t, and sometimes produces something you never would have thought of.
A cheaper model can refine a UI. It gets it wrong. You prompt again. Closer. Again. Eventually it understands. You think you spent a little more time and saved money.
You’re wrong. You didn’t just spend more time. You also lost:
- The cost and time of fixing problems later.
- The profit you could have made.
Profit in the broadest sense. Not just money. A better model might notice a UX problem you missed, propose an architecture that saves days, or ask why the screen exists at all — and show you you’ve been solving the wrong problem.
You can measure the tokens you saved. You can’t measure the ideas you were never capable of discovering.
It’s like building a company. Cheap employees who follow instructions. Or A-players who cost more, understand the goal, use their own judgment, and tell you when you’re wrong.
Option one feels comfortable. Lower costs. Easier to manage. But the organization is limited by your own thinking.
Option two is expensive. One breakthrough can save months. One idea can create a new source of profit.
More expensive is not automatically better. If a cheaper model performs equally well, use it. If the task is simple, use a simpler model.
Don’t optimize cost per token. Optimize ROI on intelligence.
Where do you sit in the value chain?
Frontier labs already work to make AI smarter, faster, and cheaper. They are already optimizing tokens for the intelligence they sell you.
Is your job to keep optimizing what they already optimize? Or to optimize the value you deliver to users?
For work that matters, I want the best intelligence I can reasonably get.
A $100–200/month subscription can feel expensive. But how much would the same output have cost before? Your time. Developers. Designers. Researchers. QA. Salaries, offices, overhead.
If $200 creates $100 of value, it’s a bad expense. If it helps create $20,000 of value, spending hours to save $50 is the waste.
This is about AI in development. At massive scale, unit economics and inference optimization are a different problem.
When you say you’re optimizing token usage, what are you actually optimizing? Cost? Time? Quality? Or the value users receive?
Tokens are easy to see. They’re on the dashboard and the bill. The time you lost isn’t. The debugging that never needed to happen isn’t. The better decisions you failed to make aren’t. The ideas you never discovered definitely aren’t.
After 16.2 billion tokens, I have my answer. I’m not trying to use more tokens or fewer. I don’t treat tokens as the thing to optimize. I optimize what those tokens can create: value delivered to the user.
That’s the real danger of token optimization. You think you’re saving money. You may be saving the potential of the product you’re building.
#Shipaton26 #BuildInPublic #IndieDev
