AI Costs More Than The People It Replaced. By Jemma Green in Forbes.
The present moment looks like this: companies cutting human labour to fund artificial intelligence that currently costs more than the labour it replaces, in pursuit of productivity gains that most studies cannot yet verify, at a pace that is exhausting annual budgets in weeks. …
The technology that was supposed to make human labour obsolete is, at this moment, more expensive than the humans it was meant to replace. Companies are laying off workers to fund the very AI tools that cost more than the workers they just let go. …
Uber’s CTO, recently disclosed that the company burned through its entire 2026 AI coding budget in four months. By March, 84 percent of Uber’s engineers had adopted Claude Code, and roughly 70 percent of committed code now originates with AI. The usage was enormous. The corresponding value was murkier. Uber’s COO and President, Andrew Macdonald, conceded publicly that token usage didn’t seem to correlate directly with useful features shipped to users. …
When a resource becomes cheap enough to waste, people waste it without a second thought. When it becomes expensive enough to matter, they develop a sudden, fervent interest in efficiency. Artificial intelligence appears to be hurtling toward that same reckoning — except the waste is measured in billions, and it arrives on a heavy monthly invoice. …
For a long time, the working assumption was that costs were falling. Per-token prices have indeed dropped, and Gartner forecasts that running the largest models could be nearly ninety per cent cheaper by 2030. The catch is that consumption has scaled faster than prices have fallen. A study by Faros AI found that “code churn,” lines of code deleted versus lines added, increased by more than 800% under high AI adoption. More tokens in, more work thrown away.
AI is still subsidized, not profitable:
The prices companies are paying for AI usage now are not real prices. OpenAI, Anthropic, Google and Meta are all pricing inference below the cost of serving it, burning venture capital to buy market share. OpenAI spends nearly two dollars for every dollar it earns on inference. Sam Altman admitted publicly that the company loses money on its $200 per month subscriptions. The subsidy model started unwinding this year. …
In April 2026, Anthropic moved enterprise customers from flat rate plans to usage based billing tied to actual compute. GitHub followed weeks later with the same shift for Copilot, after years of quietly absorbing up to eight times the subscription value for heavy users. Analysts project that when pricing normalises to reflect real infrastructure costs, enterprise AI bills rise another 30 to 50 percent above current levels. …
Like the Internet bubble in 2000:
The parallel to the late 1990s is instructive. The internet was real technology. And it still produced a [stock market] crash. … The internet was real, it still crashed, and what followed wasn’t less internet — it was internet that finally paid for itself. AI is heading for the same sorting, and the divide is already visible.
Interesting times ahead for investors. A good hard crash in the AI stocks will not only hurt AI investors, but will suck liquidity out of the entire market as investors with loans scramble to meet margin calls, thereby driving down the prices of every other asset as well –especially the ones that are easiest to liquidate and have seen recent profits, such as gold and bitcoin. Bad debts are going to create a huge monetary problem, which will see emergency rate cuts and inflation — which will increase demand for gold and bitcoin.