Why do tech companies love AI so much?
Three reasons, in order of honesty: AI genuinely works at some tasks; it promises to convert labor costs into software costs, which is the highest-margin trade in business; and mentioning AI moves stock prices, so executives say it whether or not they've shipped anything. The third reason explains most of what you're noticing.
Why — the first-principles explanation
Start with the money mechanics, because they explain the intensity. Software's superpower has always been that it costs the same to serve one customer or ten million. That's why tech margins dwarf other industries. But huge parts of the economy resisted software — anything needing judgment still needed a person, and people don't scale. A support rep handles one call at a time, forever. AI is the first technology that credibly threatens to move judgment work across the line from the labor column to the software column. If that works even partially, it's the largest margin expansion available anywhere. That's not hype; that's why the money is real.
Then there's the fear, which is stronger than the greed. Executives lived through mobile, when companies that were fine one year were irrelevant three years later. The asymmetry drives everything: spend $10 billion on AI and be wrong, you lose $10 billion. Skip it and be wrong, you lose the company. Given that payoff structure, overspending is the rational choice even if you privately think it's a bubble. This is why capital expenditure keeps climbing past what current revenue justifies — single infrastructure programs now commit hundreds of billions of dollars.
And then the part you're actually sensing. Saying "AI" is nearly free and moves the stock. Building AI is expensive and slow. So there's an enormous gap between companies deploying it and companies announcing it, and both use identical language. When your toothbrush claims AI, that's not the technology reaching your toothbrush — that's a marketing department reaching the word. The word has become a financial instrument, semi-detached from the technology it names.
The honest synthesis: all three are true simultaneously. There's real capability, real economics, and a real bubble of language riding on top. The reason it's hard to tell them apart from the outside is that everyone involved is incentivized to keep them blurred.
An example that makes it click
In the 1990s, companies added ".com" to their names and their stock jumped — sometimes for firms that sold furniture and had no website. The internet was genuinely transformative and the name was a free money lever. Both things at once.
"AI" is that word now. Some companies rebuilt themselves around it. Some added it to a press release on a Tuesday. From the outside, on the day of the announcement, they look identical — and that's the whole point of saying it.
Key facts
- Software's marginal cost of serving an additional customer is near zero, which is the structural source of tech's high margins; AI extends that property to judgment-based work that previously required headcount.
- AI infrastructure commitments now run to hundreds of billions of dollars — the Stargate program alone announced $500 billion in intent, with over $400 billion committed across roughly 7 gigawatts as of September 2025.
- The Stargate flagship in Abilene, Texas involves over 25,000 onsite jobs, illustrating that AI capex is a physical construction and energy commitment, not just software spending.
- Announcing AI is nearly costless while building it requires multi-year, multi-billion-dollar capital programs, creating a persistent gap between AI marketing and AI deployment.
- Regulators treat AI claims as context-dependent and require evidence: NIST's voluntary AI RMF (released 2023-01-26) exists partly because AI assertions are hard for outsiders to verify.
▶ The 60-second explainer (script)
Why do tech companies love AI so much? Three reasons, and I'll give them to you in order of honesty. First: it actually works at some things. That part's real. Second, the money. Software's superpower has always been that it costs the same to serve one customer or ten million. That's where tech margins come from. But huge parts of the economy resisted software, because anything needing judgment needed a person — and people don't scale. A support rep handles one call at a time, forever. AI is the first technology that credibly moves judgment work from the labor column into the software column. If that works even partly, it's the biggest margin expansion available anywhere on earth. Third — and this is the one you're actually noticing — saying 'AI' is free and it moves the stock. Building AI costs billions and takes years. So there's a giant gap between companies deploying it and companies announcing it, and both use the exact same words. When your toothbrush claims AI, that's not the technology reaching your toothbrush. That's a marketing department reaching the word. There's also fear, which is stronger than greed. Executives watched mobile kill companies that were fine three years earlier. So the math is: spend ten billion and be wrong, you lose ten billion. Skip it and be wrong, you lose the company. Given that, overspending is rational even if you privately think it's a bubble. All three are true at once. That's why it's so hard to read from outside.
What authoritative sources say
People also ask
Is AI a bubble?
Parts of it, almost certainly — the language has outrun the deployment. But the infrastructure spending is real concrete and real power plants, and the capability at some tasks is real. Bubbles and real technologies routinely coexist; the internet was both.
Do tech companies actually use AI internally, or just sell it?
Both. Coding assistance and customer support deflection are the two areas with the most genuine internal adoption. Many other announced uses are pilots that never reached production.
Why does every product suddenly claim to have AI?
Because the word is free and it works on investors and buyers. There's no certification required to call something AI, so the label spread far ahead of the technology.
Will companies keep spending this much on AI?
Unknown. The spending is currently justified by expected future revenue, not current revenue. If that gap doesn't close, capex gets cut — but the fear of missing out keeps it going longer than pure math would.