Gee, AI needs to become cost conscious. The whole computer biome is a default to maximum computation and obviously the AI will slavishly recalculate the same decision it made yesterday and the day before. engineers are simply too lazy to work like that.
and away goes your billing on an exponential curve.
Let us now see if they can fix this.
Sir Escanor (𝘏𝘰𝘱𝘪𝘶𝘮 𝘚𝘭𝘢𝘺𝘦𝘳)
@EscanorReloaded
CEOs are quietly realizing the AI replacement plan has a problem.
Two problems, actually.
One: the token costs for running AI agents are now exceeding what they were paying the employees they fired.
Two: when the tokens run out, the AI stops. Just stops. No continuity. No workaround. Just a spinning wheel where your workforce used to be.
You fired humans to save money and bought a subscription that bills you into a corner.
The employees you let go knew what to do when things broke.
The AI just invoices you for the outage.
And then there’s the permission problem nobody wants to talk about.
To do its job, the AI agent needs access. Full access. Your systems, your patents, your contracts, your future plans. Everything you spent years building, handed over to a process that has no loyalty, no discretion, and no skin in the game.
You didn’t hire a replacement.
You gave a stranger with no soul the keys to everything you own.
Enjoy.
The company that bet its future on AI just told 100,000 engineers to stop using its best tool because it was bleeding them dry
Something very interesting is beginning to emerge from inside the AI industry itself.
For the past two years we have been told that AI would replace human workers, dramatically reduce costs and create unprecedented efficiency across every sector of the economy.
Markets soared on that promise.
Companies fired staff, announced "AI integration" and watched their stock prices rise accordingly.
But now some of the first major cracks are beginning to appear in the narrative.
Microsoft has reportedly started canceling large numbers of internal Claude Code licenses after costs spiraled far beyond expectations as engineers increasingly relied on the system.
Uber executives admitted their AI budgets were effectively blown apart within months of deployment.
Even Nvidia's own VP of Applied Deep Learning openly stated that for his team, the cost of compute had become "far beyond the costs of the employees."
What is becoming apparent is that large scale AI deployment may not actually reduce costs in the way investors were led to believe.
Quite the opposite.
The more powerful these systems become, the more they are used. The more they are used, the more tokens, processing power, energy, infrastructure and compute they consume. And at enterprise scale those costs become enormous.
The assumption was that companies would replace expensive humans with cheap AI.
Instead, they may end up needing: expensive humans supervising extremely expensive AI systems running on staggeringly expensive infrastructure.
And that changes the economic equation entirely.
