We Have Never Seen Anything Like This Before
In July 2026, the AI industry is simultaneously more powerful and more fragile than it has ever been. Chinese startup Moonshot released Kimi K3, the largest open-weight model ever built, matching Anthropic’s best models at a fraction of the cost. OpenAI’s leaked financials show $38.5 billion in losses in a single year. The US Treasury is quietly warning that the AI bubble is bigger than the dot-com era. And yet the hyperscalers are spending more than ever.
Is the AI crash about to happen, or is this just the growing pains of a genuinely transformative technology? The honest answer is that both sides have real evidence.
The Case That a Crash Is Coming
OpenAI Is Burning Money Faster Than Any Startup in History
The numbers are stark. OpenAI lost $5.09 billion in 2024. Losses increased nearly eightfold to $38.53 billion in 2025, according to audited financial documents obtained by Ed Zitron and verified by the Financial Times. The company projects $14 billion in losses for 2026 and cumulative losses of $115 billion through 2029 before reaching profitability sometime in the 2030s.
To bridge this gap, OpenAI is seeking $100 billion in new funding at a valuation of up to $830 billion. It has offered the US government a 5 per cent stake in the company. It offered private equity firms a guaranteed minimum return of 17.5 per cent. These are not the actions of a company with a clear path to profitability.
The Circular Financing Problem
Money is travelling in a loop. Nvidia has committed up to $100 billion to OpenAI. That money will go back to Nvidia to buy GPUs. Microsoft’s $625 billion cloud backlog is 45 per cent tied to OpenAI. Microsoft invested in OpenAI, OpenAI committed to buy Microsoft cloud services. The revenue looks real on paper but the cash is circulating among a small group of companies. Jim Cramer has compared it directly to the dot-com bubble.
Chinese Open-Source Models Are Undercutting US Pricing
This is perhaps the most consequential development of 2026. Kimi K3 from Moonshot AI performs close to Anthropic’s Fable 5 on independent benchmarks but costs $15 per million output tokens against Fable’s $50. DeepSeek-V4-Pro costs $0.87. Z.ai GLM-5.2 costs $4.40. Chinese models now account for more than 60 per cent of token usage on OpenRouter, the popular AI model marketplace. Six of the top 10 models on the platform are Chinese.
Coinbase cut its AI spending by 50 per cent by switching to Chinese models. Cursor built its frontier coding model on top of Kimi. Even Microsoft is testing DeepSeek as a cheaper option for Copilot. The pricing pressure is real, and it is not going away.
Enterprise AI Is Not Delivering
Only 25 per cent of enterprise AI initiatives have delivered the expected return on investment, according to an IBM survey of CEOs. Only 16 per cent have scaled enterprise-wide. The gains that do exist are concentrated in code generation, customer support, and drafting. Most other use cases show no measurable payback. Meanwhile, only around 5 per cent of ChatGPT users pay for a subscription.
Infrastructure Spending Is Hitting Real-World Limits
$64 billion worth of data centre projects have been blocked or delayed by local opposition. Oracle borrowed $43 billion to build data centres and is now planning job cuts while scrapping Stargate expansion plans with OpenAI. Meta and xAI are both leasing their data centre compute to competitors because they built too much capacity.
The Case That a Crash Is Not Happening
The Companies Spending the Money Are Already Profitable
This is the strongest argument against a bubble. In the dot-com era, the companies driving the excess were pre-revenue startups. In 2026, the companies spending $725 billion on AI infrastructure are Google, Meta, Microsoft, and Amazon. These are among the most profitable enterprises in human history. Google reported $91 billion in capex guidance from a company generating hundreds of billions in annual revenue. Meta updated capex guidance to $70 billion while reporting $51.2 billion in quarterly revenue.
The Infrastructure Build-Out Has Decades of Runway
Morgan Stanley estimates that nearly $3 trillion in global data centre capex will flow through the economy between 2025 and 2028, with more than 80 per cent still ahead. This is not speculative spending. It is an industrial build-out feeding directly into construction jobs, power investment, and services spend. Morgan Stanley expects AI infrastructure to contribute roughly 25 per cent of US GDP growth.
Short Asset Lives Reduce Long-Term Risk
Microsoft disclosed that $37.5 billion of its quarterly capex went to short-lived GPU assets with three-to-five-year useful lives. This is structurally different from building a highway amortised over 50 years. Faster obsolescence means faster cost recovery cycles and reduces the risk of stranded assets. If a data centre investment does not pay off, the hardware can be redeployed elsewhere.
The “No GDP Impact” Argument Is Historically Normal
Critics point to the lack of measurable US GDP impact from AI investment as evidence of a bubble. But transformative general-purpose technologies have always taken time to show up in productivity data. The internet showed no measurable productivity impact for 15 to 30 years after its commercialisation. Electricity took decades. If AI follows this pattern, the lack of 2025 GDP impact is early-stage behaviour, not failure.
Capex Is Still Funded from Earnings, Not Debt
Fidelity’s analysis notes that AI-related capital expenditure has been funded almost entirely from earnings rather than debt. The bond market is not denying capital. This is a sign of financial health, not speculative excess. Unlike the dot-com era where companies were borrowing to survive, today’s hyperscalers are writing cheques from their own cash flows.
The Honest Verdict
Both sides are arguing from real data. The truth is that some parts of the AI industry are in a bubble and some are not.
OpenAI’s financial position is genuinely concerning. A company burning $38 billion a year while facing pricing pressure from Chinese open-source models that cost 97 per cent less does not have an obvious path to profitability. The circular financing arrangement between Nvidia, Microsoft, and OpenAI creates an echo chamber of apparent growth that could unwind quickly if any one party loses confidence.
But the hyperscalers are not OpenAI. Google, Meta, Microsoft, and Amazon are spending from a position of strength. Their AI infrastructure build-out is a rational response to genuine demand, not a speculative gamble. The fact that they can fund it from earnings, not debt, matters enormously.
The most likely outcome is not a single dramatic crash but a slow unwinding of the weakest positions. OpenAI will either find a path to profitability, get acquired, or restructure. Chinese open-source models will continue to compress margins across the industry. Enterprise adoption will accelerate as the technology matures but will not produce the returns that current valuations assume.
The AI crash narrative is oversimplified. Some parts of this market will correct. Others will keep growing. The mistake is treating the whole industry as a monolith when the real story is the widening gap between the companies that can afford to build and the companies that cannot.
