The debate over whether AI is in a bubble has become one of the most active discussions online. A short clip of Ben Horowitz commenting on the topic received more than half a million views in just a couple of days. A post suggesting America may need to spend $3.5 trillion a year on AI by 2032 has been seen more than 250,000 times. The conversation is loud on both sides, and people keep returning to it month after month.
One side argues that demand is simply too high for this to fail. The other side says the financing does not add up and the required revenue numbers are unrealistic. A third factor has entered the picture: free and open-weight models that change the math for everyone involved.
The Bull Case: Sold-Out Compute and Unmatched Demand
Ben Horowitz has shared that Amazon CEO Andy Jassy told him Amazon’s AI compute is sold out beyond 2028. The shortage is severe enough that Jassy indicated they do not expect supply to return even into the early 2030s. Horowitz contrasted this with the dot-com era, when more bandwidth was built than consumers could absorb. This time, he said, there is more demand than supply.
Amazon has reinforced the point in public statements. AWS AI demand is outpacing capacity through 2027, and the demand already booked for 2028 is described as striking. Multiple reports point to the same conclusion: compute is constrained.
Horowitz has also argued that OpenAI and Anthropic threaten nearly every stock people own. He noted that the world has never seen companies grow this large this fast, raising the question of whether there is enough capital to finance them. At the same time, he expressed confidence in the demand for their potential IPOs. Another investor added that people need to own these companies precisely because they sell intelligence broadly, and nearly every business is built on intelligence.
Model performance rankings continue to shift quickly. In one recent stretch, OpenAI’s GPT-6 and Astra brought OpenAI level with Anthropic roughly three weeks after launch. Then Claude Opus 5.5 pushed Anthropic back into the lead within three weeks. Trackers such as Vercel, Ramp AI, and OpenRouter show the two companies staying neck and neck, with the lead changing every few weeks. Anthropic has told investors that quarterly revenue is up nearly 50 percent, and Opus 5.5 is widely regarded as a strong release. Yet people show little loyalty to any single model. Users move rapidly to whatever is currently best.
The Bear Case: Revenue Requirements and Financing Risks
The other side focuses on the scale of the required returns. Gartner has estimated that more than $3.5 trillion in AI infrastructure spending could be exposed to potential impairment by 2030 if current investment trends continue. To recover the investments, cover financing costs, and earn a 12 percent return on invested capital, data-center owners would need roughly $8.5 trillion in cumulative revenue through 2030. That would require growing nearly nine times as fast as the industry grew over the past decade just to break even.
A Columbia professor has put the total cost of the AI build-out at about $10 trillion from 2025 to 2035. Relative to the size of the economy, that is larger than the railroad, highway, or internet build-outs—and three times larger than the internet boom. Other figures put the needed annual U.S. spending on AI services at about $3.5 trillion by 2030, or 8.8 percent of GDP. No single product category has ever reached that share of GDP. The build-out itself is estimated at $10 trillion from 2025 to 2032, again larger relative to the economy than previous major infrastructure booms.
Financing has shifted toward joint ventures, private credit, and special-purpose vehicles. That structure makes risks harder to see. While it may be premature to label the system a clear risk, better measurement and disclosure are needed while capital structures are still forming. Roughly 80 percent of NeoCloud providers are expected to cease independent operations. AI providers are projected to spend nearly $1 trillion on infrastructure, with an additional $6 trillion forecast between 2026 and 2030. Current combined run rates for OpenAI and Anthropic sit around $125 billion. Some projections call for roughly 5x growth over the next 14 months, but recent monthly figures show revenue growth tapering rather than accelerating. Anthropic’s reported quarterly numbers have moved from about $4 billion in one quarter to $11 billion in the next and still remain well short of $100 billion annualized.
Corporations are beginning to pull back on AI spending rather than doubling down. Revenue appears to be trading back and forth between the leading companies rather than expanding in a straight line.
Open Weight Models Change the Equation
A third category is now affecting both the bull and bear arguments: open-weight and freely available models. Meta released a model capable of running on two Mac Studios (or equivalent high-end hardware in the 40,000–50,000 range). While that hardware cost is not trivial, it shows that capable models can already run outside the large closed providers. Google offers substantial free services and can fund much of its own infrastructure. The debt-to-income ratios for these companies differ sharply from those of pure-play labs that have raised large amounts of capital.
Frontier closed models still lead open-weight models by roughly 10 percent on many benchmarks, yet the gap is narrowing. Multiple new open-weight models have appeared in a single week. Businesses are learning that these models can deliver strong results on hardware they control. For the trillion-dollar revenue targets to be met, spending must reach deeper into corporate budgets. If companies instead adopt open-weight options and slow their paid usage, the path to those revenue numbers becomes steeper.
What History Shows About Technology Bubbles
Major technology bubbles have never eliminated the underlying industry. They have revalued market capitalizations. The video-game crash of 1983–1985 saw revenue fall more than 97 percent and nearly wiped out the new entertainment category. Nintendo later revived it. The same pattern appeared in the dot-com, fiber-optic, and clean-tech cycles. Even if AI valuations drop 20–30 percent around an IPO, the technology itself is expected to continue advancing. The industry is more consequential than video games, and AI revenue is not projected to fall 97 percent.
A Middle Path: Technology Stays, Financing Faces a Wall
All three perspectives contain elements that are currently true. Demand exceeds available supply today. That demand has been supported by venture capital that cannot fund the build-out indefinitely, so costs are likely to rise and some loosening will occur. Open-weight models are being released by organizations with different motives—ranging from large U.S. technology companies to others outside the U.S.—and those models are already usable.
The technology itself is not going to disappear. People predicting total collapse are overlooking the persistent demand and the rapid improvement in open-weight systems. At the same time, the circular financing that has propped up spending cannot continue unchanged. A funding wall is likely. Prices should keep rising. OpenAI and Anthropic are expected to reach an IPO eventually. When they do, the offering is likely to be difficult; valuations could drop roughly 50 percent once the economics are fully visible. Companies currently spending two to three dollars to earn one dollar face a model that has never worked indefinitely. Revenue, not hype, will determine the outcome.
The practical winners are likely to be smaller businesses that adopt open-weight models running on hardware they control. Capable systems are already producing results that would have seemed futuristic only a few years ago, including on hardware that is six to eight years old. The advantage sits with organizations that treat AI as infrastructure they own, integrate into their own software, and run in their own environments rather than relying solely on external APIs and pricing schedules.
The debate will continue because the numbers are large and the stakes are high. Demand is real and currently constrained by supply. The revenue required to justify the capital deployed is also real and extremely high. Open-weight models are already shifting where that demand lands. The most durable path is building systems that remain useful even after financing conditions change.
Conclusion
The technology is not going to disappear. Demand remains higher than supply today, yet the financing that has supported the build-out cannot continue unchanged, and a funding wall is likely. OpenAI and Anthropic are expected to reach an IPO eventually, and that offering is likely to be difficult once the economics—spending two to three dollars to earn one—are fully visible.
The practical advantage sits with businesses that treat AI as infrastructure they own. Open-weight models already deliver strong results on modest hardware. Organizations that learn to run and integrate these models into their own systems, rather than relying only on external services, are best positioned as valuations adjust and costs rise.
