A former Google engineer argues the AI buildout has far outrun its revenue: roughly $725B in 2026 capex from five hyperscalers against about $25B in global AI services revenue last year. She stacks up the evidence of strain — collapsing free cash flow, power grid limits, failed enterprise pilots, rolled-back customer service agents, and legal liability from hallucinations (the Deloitte Australia report). Her conclusion is that the bubble is financial, not technological: AI survives like the internet did after dot-com, but the money will shift toward people who can verify and own AI output rather than just generate it.
- Microsoft, Amazon, Alphabet, Meta and Oracle are on track for about $725 billion of AI capital expenditure in 2026 — up 77% in a year from roughly $162 billion in 2022 — while global AI services revenue was around $25 billion last year, roughly $0.25 back for every $10 spent.
- The spending is no longer funded by spare cash: JP Morgan estimates AI capex went from about a third of hyperscaler operating cash flow in 2023 to roughly 93% in 2026, with combined free cash flow falling to about $7 billion in Q2 2026 from $60 billion at the end of 2025 and expected to go negative.
- DeepSeek's cheaply trained competitive model in early 2025 wiped roughly $600 billion off Nvidia's value in a single day — the largest one-day loss for any US stock — and forced investors to ask what the spending actually buys.
- Physical limits are binding: data center electricity demand rose 17% in 2025 (the AI slice closer to 50%), data centers now drive about half of new US electricity demand growth, and Gartner projects power shortages could constrain 40% of AI data centers by 2027.
- An MIT report found ~95% of enterprise generative AI pilots showed no measurable ROI, though the speaker notes the caveats: a six-month window, many marketing use cases, and internally built tools succeeding about a third of the time versus two-thirds for vendor tools.
- Amazon's internal 'Kiro Rank' leaderboard scoring engineers on AI usage led to 'token maxing' — agents pointed at made-up tasks to run up token counts — inflating Amazon's compute bill until the leaderboard was killed in May 2026.
- Liability is the real reason agents get switched off: a survey of 2,500+ enterprises found ~74% rolled back live AI customer service agents, and Deloitte had to refund part of a ~$440,000 Australian government report that cited nonexistent papers and a fabricated judicial quote.
- Hallucinations remain stubborn — a 2026 benchmark of 37 models found made-up answer rates of 15% to 52%, with some newer reasoning models worse than older ones, and Stanford put legal-question hallucination at 58–88%; insurers are now writing AI exclusions.
- Career advice: don't confuse the bubble with the technology, ask whether a tool would survive AI getting 10x more expensive, become the person who owns and verifies output rather than produces it, and move toward AI security, integration and evaluation work.
The $10-in, $0.25-out math
0:00–0:48
It states the core financial mismatch that the entire argument rests on, in a single comparison.
Big Tech is expected to spend around $725 billion on AI this year. Meanwhile, the entire global market for AI services generated only around $25 billion last year.
Token maxing kills Amazon's AI leaderboard
4:49–5:39
A concrete, specific example of AI adoption metrics backfiring inside one of the biggest tech companies.
Engineers started pointing AI agents at pointless made-up tasks purely to run up their token counts and climb the board, a move the industry has coined token maxing... a senior VP had to send around a note saying, 'Please don't use AI just for the sake of AI.'
For every $10 these companies are pouring into AI infrastructure, they're getting back only about $0.25 in revenue.
Capital moves at the speed of markets and power plants move at the speed of permits and construction.
Become the person who owns the output, not the person who produces it.
The dot-com crash didn't mean that the internet was fake, but it meant the money had run way ahead of the value.