Let’s dispense with the fantasy right away. Your $20-a-month ChatGPT subscription isn’t paying for anything remotely resembling the actual cost of running modern AI. It doesn’t cover the data centers, the power bills, the GPUs stacked like gold bars in climate-controlled bunkers. Not even close.
Right now, no major AI company is profitable. None of them is even flirting with sustainability. They’re lighting venture capital on fire and calling it innovation, and the burn rate keeps accelerating.
So what’s the business plan?
It’s not complicated.
At some point, prices will rise. Not by a polite ten percent, either. When the switch flips, the increase will be brutal. But the companies can’t do that yet. First, AI has to become unavoidable.
The real product isn’t chatbots. It’s a dependency.
Businesses are being nudged, gently at first, to replace chunks of their workforce with machine labor. Once that happens, reversing course becomes prohibitively expensive. Institutional knowledge evaporates. Workflows warp around proprietary systems. Human staff disappear, and with them the ability to function without the platform.
After that, the trap is set.
Prices go up slowly enough to prevent mass defections but fast enough to extract monopoly rents. Switching back to humans would cost more than staying put. Switching to a competitor is impossible because there won’t be many competitors left.
This is the same playbook Big Tech always uses. Subsidize adoption. Starve alternatives. Centralize infrastructure. Then turn the screws.
Most businesses understand exactly where this leads, which explains the sluggish pace of adoption. Nobody wants to volunteer for a hostage situation. But resistance has a half-life. Quarterly pressures accumulate. Executives cave. And eventually the mousetrap snaps shut.
Until then, the AI giants are racing to vacuum up everything that matters: compute, energy contracts, chip supply, training data, talent. Hundreds of billions will be burned to ensure that no smaller rival can ever scale high enough to threaten them.
And if some genuinely different approach to artificial intelligence eventually emerges, something that doesn’t rely on gargantuan language models or planetary-scale computation, it may not matter.
By then, all the resources required to deploy it will already be locked into a few corporations whose primary innovation is finding new ways to charge for the future.
References:
- Gross, Grant. “CIOs Will Underestimate AI Infrastructure Costs by 30%.” CIO, 19 Dec. 2025
- Gross, Grant. “Vendor Pricing Experiments Leave CIOs’ AI Costs in Flux.” CIO, 1 Sept. 2025
- “AWS Hikes GPU EC2 Prices 15% for AI Workloads Amid Shortages.” WebProNews, 3 Jan. 2026
- Bradley, Tony. “The Real Friction Slowing Enterprise AI Adoption.” Forbes, 20 Nov. 2025
- Ohiri, Emmanuel. “Why AI Teams Need Cloud Infrastructure Without Vendor Lock-Ins.” CudoCompute Blog, 14 Jan. 2026
- “Why Cloud Spending Keeps Rising as AI Moves into Daily Operations.” CloudComputing-News.net, Jan. 2026
- “Amazon’s $38B OpenAI Deal That Sent Its Stock Soaring, Powering the Next Wave of AI Growth.” CarbonCredits.com, 6 Nov. 2025
- Khalili, Joel. “A Yann LeCun–Linked Startup Charts a New Path to AGI.” WIRED, 29 Jan. 2026
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