Companies told us AI would replace human workers and cut costs. Turns out the math doesn’t work. An Nvidia VP just confirmed that compute costs for his team exceed what they pay their employees. Uber burned through its entire 2026 AI budget in four months. And a startup CEO ran up a $113,000 monthly AI bill, with a four-person team.
This episode breaks down exactly why AI is costing companies more than the humans they laid off, what “tokenmaxxing” is and why engineers are doing it, and whether any of this spending is actually returning anything. Because at some point the ROI question stops being awkward and starts being a board-level emergency.
That’s the insidious part: they don’t get that solar panels get more efficient as they go, and usually less expensive, as they created that cost/improvement curve we all know is out there.
Ai may never hit that curve. The simplest LLM is so power-starved that we’re several dozen generations of tech below where we’ll even see that efficiency come up.
So does that mean we can make Google burn through money if we use Gemini daily for nonsense (or privacy-preserving/basic queries)?