The AI boom is becoming a balance sheet story, not just a technology story. Bridgewater Associates founder Ray Dalio has said before the artificial intelligence boom is in the early stage of a bubble, Reuters reported, but the sharper issue is not whether AI tools are useful. It is whether the financial structure around the boom can stay intact as spending rises, buybacks shrink and outside funding becomes more important.
Bridgewater analysis reported by Reuters estimated that Alphabet, Amazon, Meta Platforms, and Microsoft could invest about $650 billion in AI infrastructure in 2026, up from $410 billion in 2025. Bridgewater co-chief investment officer Greg Jensen said the cycle had entered a “more dangerous phase,” citing higher infrastructure demands and growing dependence on external capital.
Nvidia, the clearest financial beneficiary of the buildout, reported fiscal first-quarter revenue of $81.6 billion for the period ended April 26, 2026, up 85% from a year earlier. Its data center revenue reached $75.2 billion, up 92% year over year.
The capex strain
Meta’s own guidance shows how quickly AI investment is moving through corporate budgets. The company said Q1 capital expenditures were $19.84 billion. It also raised its 2026 capital expenditure outlook to $125 billion to $145 billion, from a prior range of $115 billion to $135 billion, citing higher infrastructure costs and data center capacity needs.
That level of spending changes the investor bargain. During the last decade, Big Tech’s premium valuations were supported by high margins, asset-light software economics, large cash balances and aggressive capital returns. The AI buildout pushes the sector closer to heavy infrastructure finance, where upfront spending can arrive years before the revenue stream is proven at scale.
Bridgewater’s analysis, as reported by Reuters, said the four major tech firms have cut share buybacks more aggressively to help fund AI capital expenditure.
Dalio’s broader argument is that bubbles become vulnerable when asset prices depend on continued inflows and holders eventually need liquidity. For AI, that risk does not require the technology to fail. It can emerge if the cost of funding rises, if equity markets weaken, if future revenue lags infrastructure spending, or if investors start demanding nearer-term proof of returns.
Real revenue, real risk
The current AI cycle is harder to dismiss than earlier speculative booms because the leaders are not pre-revenue startups.
But that strength also creates the central tension. The companies most able to finance AI are also the companies whose market values carry the most index weight. If investors decide that spending is outrunning visible returns, pressure on a small group of megacaps could move the broader market.
Reuters reported that Jensen warned a major stock market correction could restrict the ability to raise capital for future AI investment.
Dalio has also drawn a line between technological impact and investment outcome. His view leaves room for AI to transform business while still producing losses for some investors if expectations, valuations and financing assumptions move too far ahead of realized economics.
For now, the market is still rewarding the AI buildout, especially where revenue is already visible. The risk is that the next phase may be judged less by demos and model releases than by free cash flow, return on invested capital, margin pressure and the amount of outside money needed to keep the infrastructure race going.
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