Analysis
BCA Research's former chief strategist Dhaval Joshi is pushing back on the single 'AI bubble' framing that's dominated markets coverage all year, arguing instead that investors are living through a 'rolling sequence of bubbles' -- capital sprinting into one AI-adjacent sector, inflating it fast, then rotating out just as fast into the next one, according to Fortune.
Joshi's test for what counts as a bubble is blunt: "If you can make a fortune in weeks or months, then lose it all just as quickly, that constitutes a bubble." By that measure, he points to a chain of 2026 examples that have little to do with AI models themselves and everything to do with AI-adjacent supply chains -- software-as-a-service stocks, silver and semiconductors have each seen the same sharp run-up-then-rotation pattern this year, with capital moving from one to the next rather than sitting still in any single trade.
## DDR3 memory and the $50,000 Corolla The starkest example is DDR3 -- an aging, largely obsolete RAM standard -- which surged roughly 600% in under a year as AI data-center buildouts strained memory supply chains and buyers scrambled for any available capacity, old or new. Paul Burchard, president of Artificial Genius, likened paying today's DDR3 prices to "paying $50,000 for a beaten-up 2007 Toyota Corolla" -- old technology repriced purely on scarcity, with no improvement in what it actually does. Silver has followed a parallel path, nearly tripling in price with no clear fundamental industrial-demand story behind the move, which Joshi treats as the same speculative pattern wearing a different commodity's clothes.
“## The earnings-quality question sitting underneath Joshi's sharper point is about earnings quality at the hyperscalers actually building AI infrastructure.”
## The earnings-quality question sitting underneath Joshi's sharper point is about earnings quality at the hyperscalers actually building AI infrastructure. Google went free-cash-flow negative for the first time in its history this year, and BCA Research strategist Peter Berezin's own modeling projects Microsoft, Alphabet, Amazon, Meta and Oracle will collectively see capital expenditure overtake free cash flow by 2027 -- meaning the companies funding the AI buildout are increasingly spending more than they generate, financing the gap through debt and equity rather than pure operating cash flow. Joshi frames the market's real underlying question as: "How is the E high?" -- shorthand for whether the reported earnings backing today's AI-infrastructure valuations are as durable as the price-to-earnings multiples assume.
That question lands in the same week Anthropic reported preliminary Q2 revenue above $11.5 billion, a genuine data point on the other side of the ledger: at least one AI-native company is showing hyperscaler-adjacent growth backed by actual usage revenue, not capex-driven narrative alone. Joshi's framework doesn't treat every AI-adjacent asset as equally fragile -- a rolling sequence of bubbles implies some trades are further along their inflate-and-pop cycle than others, and revenue-backed names look structurally different from commodity plays like DDR3 and silver that have no earnings story attached at all.
## Who else is watching the same signal Joshi isn't a lone voice. Arm co-founder Hermann Hauser made a similar case this week, telling CNBC that the AI revolution is real but so is the bubble risk sitting on top of it. JPMorgan CEO Jamie Dimon, Goldman Sachs CEO David Solomon and Amazon founder Jeff Bezos have all separately flagged some version of AI-valuation excess this year, even while their own institutions keep underwriting and financing the buildout -- a tension that's become its own recurring theme in 2026 markets commentary. OpenAI CEO Sam Altman has made similar comments about parts of the AI trade being overextended, an unusual admission from an executive whose own company's valuation depends on investors believing otherwise.
The practical takeaway for anyone with AI exposure: Joshi's 'rolling sequence' framing argues against treating an AI portfolio as one undifferentiated bet. A commodity-driven spike like DDR3's 600% run has no earnings underneath it and is the most exposed to a fast unwind; a revenue-growing frontier lab reporting real quarterly numbers is a structurally different risk, even if both get lumped into the same 'AI bubble' headline. Watch whether Microsoft, Alphabet, Amazon, Meta and Oracle's actual 2027 capex-versus-free-cash-flow numbers land where Berezin's model projects -- that's the concrete checkpoint that will tell you whether Joshi's earnings-quality warning was right, or whether the hyperscalers found a way to keep the E genuinely high.
