Who pays for the AI boom?

AI can change the world and still disappoint investors. A technology can deliver extraordinary value while the companies financing it struggle to earn a return. We have seen this before: the dot-com boom accelerated the commercial expansion of the internet, but many companies once worth billions disappeared. The technology fulfilled its promise; plenty of investments did not.

That distinction is becoming harder to ignore. Anthropic’s anticipated IPO, with reported valuation ambitions reaching $2 trillion, the semiconductor selloff in Asia and rising concern about neocloud debt are testing different assumptions behind the AI boom. Together, they raise a question that reaches well beyond public markets: how much of the ecosystem can sustain itself once customer spending has to carry more of the weight?

Sophie Shulman’s analysis in CTech connects these three pressures. For venture investors, the implications extend to where capital goes, how valuations are set, what counts as traction and how diversified our portfolios really are.

Extraordinary growth can still disappoint

Anthropic provides perhaps the clearest illustration. According to reporting by the Financial Times, the company told prospective investors that quarterly revenue reached $16.8 billion in Q3, up from $11.5 billion in Q2, alongside profitability on an adjusted basis. Or as Henry Blodget reported, while the growth of Anthropic has truly been unprecedented, it might be stalling. The company added more new revenue in Q2 2026 than Q3.

Q1: $4.8 billion
Q2: $11.6 billion (+$6.8B and +141%, sequentially)
Q3: $16.8 billion (+$5.2B and +45%, sequentially)

That is approximately 46% growth in three months, but media reports have mentioned that the expected price tag of roughly $2 trillion, is more likely to be closer to $1.5 trillion – still the biggest IPO of all time, but nevertheless a big haircut. These remain reported discussions, rather than a final IPO price. In the meanwhile, OpenAI is looking to raise $30 billion at $1.4 Trillion valuation ahead of said IPO, perhaps also with the goal of creating a clear pricing milestone at this point in time.

A business can grow exceptionally quickly and still fall short of the assumptions embedded in its valuation. That is why I find “is Anthropic too big to fail?” less useful than asking what has to happen for investors buying at these prices to earn an attractive return.

The accounting deserves scrutiny too. “Adjusted profitability” is not the same as free cash flow. Investors need to understand which costs are excluded, how revenue is recognised and what commitments sit behind it. Stock-based compensation, where excluded, still has a cost to shareholders through dilution.

At the same time, today’s economics are an imperfect guide to the eventual business. Cheaper inference could improve margins or be passed to customers through competition. Agents could generate much more usage while increasing the cost of serving each customer. Training requirements could change again.

Today’s numbers matter. They need to be examined alongside scenarios for how the business evolves, rather than extrapolated indefinitely.

The funding model is becoming part of the story

There is an important counterweight to the bubble debate: products such as coding agents give customers concrete reasons to use and pay for AI. Commercial adoption and financial fragility can develop simultaneously.

The question is whether the infrastructure supporting that adoption earns enough, quickly enough, to justify its cost.

Semiconductor investors are already reassessing expectations. CTech reported that the Philadelphia Semiconductor Index had fallen 23% from its peak, while Reuters described sharp selling in Asian chipmakers, including South Korean stocks and Japan’s Kioxia.

The pressure is particularly visible in neoclouds, the specialist providers financing and renting out GPU capacity. CTech reported roughly $50 billion of debt at CoreWeave, with yields on some bonds reaching around 15%. That market yield is not the interest rate on all existing borrowing, but it signals how investors are reassessing risk.

As expansion becomes more dependent on external capital, lenders and public-market investors gain greater influence over its pace. A project can have interested customers and still struggle to attract financing on acceptable terms.

I would be careful about claiming that every hyperscaler has exhausted its capacity to self-fund. Their balance sheets and cash generation differ. But the broader shift matters: financing conditions increasingly help determine how much infrastructure gets built, by whom and when.

When your investor is also your supplier

The circular financing debate sits at the centre of this shift. Nvidia is a major strategic investor in neoclouds, while also supplying the technology on which their businesses depend.

The documented relationships are substantial:

This graphic by Bloomberg, published in October 2025, illustrates how tangled the financial investments in AI are:

The mechanism is straightforward. A supplier invests in a customer. The customer raises more capital and buys the supplier’s technology. Those purchases support the supplier’s revenue and expand the market for its products.

There are legitimate reasons for these arrangements. Infrastructure requires upfront investment, engineering partnerships can improve performance, and suppliers may understand the opportunity better than generalist investors. Nvidia argues that financing enables infrastructure for real demand, with financing partners assessing utilisation, cash flow and residual value.

Supplier investment alone is not evidence of artificial revenue. But it changes the diligence question: how much activity is ultimately supported by customers earning or saving money outside that financing network?

Imagine a startup raises $100 million and spends $40 million on compute. Its provider records revenue and purchases equipment. These can all be legitimate transactions, but they are not independent proof of end-customer demand. Some of the activity is being financed by the same original pool of capital.

GPUs are valuable collateral, but their value can fall

The argument that GPU-backed lending is exceptionally safe rests partly on the usefulness and scarcity of the hardware. Yet collateral is only worth what another buyer will pay when a lender needs to sell it.

If compute rental prices fall, the income generated by a GPU can decline. Its resale value may fall too. The borrower’s cash flow and the lender’s collateral can therefore weaken together, precisely when protection is most needed.

For now, the demand for compute is still outpacing the supply, causing GPU prices to go up. As reported by A16Z:

Rental rates (and thereby residual values for GPUs) are supposed to decrease over time, but that’s not really what’s been happening. As intelligence gets cheaper, demand for compute is only rising, causing GPU pricing to climb upwards for the latest chips (and stay pretty for the older ones). Even the A100 is pricing at-or-above what it was at the beginning of the year.

The chips do not need to become obsolete for this to hurt. They can remain productive while being worth considerably less than the financing originally assumed.

Long-term customer contracts help, but their protection depends on counterparty strength, cancellation rights, delivery conditions and margins. They also do not remove construction delays, power constraints or refinancing risk.

In Reuters’ October 19 reporting, S&P Global warned that interconnected financing structures could leave major technology companies with more AI-related liabilities than were currently visible. It did not quantify the additional exposure or see an immediate need to change ratings.

Parts of this market require infrastructure and credit underwriting alongside venture judgement. Funding an uncertain product and financing a fleet of depreciating machines demand different questions.

Could Wall Street prevent the glut it fears?

There is a potentially self-correcting mechanism here. If lenders fear excess capacity and stop financing new projects, fewer projects get built. That could reduce the very oversupply they worry about.

But timing matters. Capacity already under construction may still come online. Existing debt still needs servicing. Demand could weaken faster than supply adjusts, and a company can run out of cash before the market reaches a healthier balance.

Financing discipline could therefore improve the economics of future infrastructure while inflicting losses on investors who funded the current buildout. A sustainable long-term market does not guarantee that every participant survives the transition.

This also means a slowdown need not affect everyone equally. Operators with strong customers, manageable obligations and operational capacity could fare very differently from those still financing construction or relying on repeated fundraising.

What the boom is doing to venture capital

The effects reach far beyond infrastructure. According to the Q3 2026 PitchBook–NVCA Venture Monitor, AI accounted for 82.7% of US venture deal value through September. Funds of at least $500 million captured 78.1% of fundraising capital while representing just 6% of funds closed.

These are shares of dollars, not shares of startups. They help explain how record investment can coexist with difficult fundraising for smaller managers and companies outside the most sought-after deals.

For early-stage investors, one danger is importing the economics of enormous AI rounds into every seed investment. A frontier lab, a GPU cloud and an application startup may all carry the AI label, but their capital requirements, margins and routes to an exit differ fundamentally.

Strategic investors also have a different return equation. A chipmaker can benefit from equity appreciation and hardware sales. A cloud provider can benefit from equity appreciation and compute consumption. A venture fund primarily needs its ownership to turn into cash proceeds. The same investment price may make sense for one investor and much less sense for another.

Concentration can also hide behind apparent diversification. S&P Global Market Intelligence has highlighted overlapping exposure to AI companies across different investment managers.

I would take that concern further. An LP could own a model developer through venture, a neocloud through growth equity, data centres through infrastructure and related loans through private credit. Different fund labels may still conceal dependence on the same customers and spending assumptions.

Meanwhile, higher private valuations do not automatically produce liquidity. A funding round can improve reported returns without distributing cash to LPs. That distinction matters for the firms trying to raise their next fund and the founders depending on them.

The opportunity for early-stage founders

As an investor backing Israeli founders at pre-seed through Remagine Ventures, I remain excited about making these capabilities useful. I also believe a healthy ecosystem needs investors willing to back non-obvious companies before they become consensus bets.

For those companies, revenue quality deserves as much attention as revenue growth. Revenue from venture-backed customers is real, but customers dependent on frequent fundraising may cut spending together. A startup can appear diversified by customer count while remaining exposed to a single financing cycle.

Margins deserve the same scrutiny. Cloud credits and cheap pilots can obscure the cost of production workloads. Retries, human review, support and heavier usage all count. The useful measure is the cost of reliably delivering the result the customer wants.

For consumer AI, gaming and creative tools, that means understanding whether novelty turns into retention and willingness to pay. More content generated or more tokens consumed can signal engagement, but neither establishes a durable business on its own.

The questions I would keep returning to are:

• Does the customer renew because the product delivers a meaningful benefit?
• Do margins hold after credits expire and real operating costs are included?
• Does the business become stronger as models improve and inference gets cheaper?
• Is differentiation accumulating through distribution, workflow integration, proprietary data or trust?
• Can the next milestone be reached without assuming another unusually generous funding round?

The AI boom is creating extraordinary capabilities and a financing network in which suppliers, customers, investors and lenders increasingly overlap. Understanding both is becoming essential to understanding the opportunity.

I remain optimistic about what founders can build. The discipline is to keep asking where the cash ultimately comes from, who retains the margin and what must happen for shareholders to earn a return. The dot-com era showed how profoundly a technology can succeed while individual investments fail. We should be ambitious enough to back the opportunity and clear-eyed enough to remember the difference.

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Eze Vidra

Co Founder and Managing Partner at Remagine Ventures

Eze Vidra is the founder of VC Cafe and the co-founder and managing partner of Remagine Ventures, a pre-seed fund investing in ambitious founders at the intersection of AI, technology, entertainment, gaming, and commerce with a spotlight on Israel.

He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google's first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.

He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.

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Latest posts by Eze Vidra (see all)

• Venture Capital in the age of Consensus - October 21, 2026
• Who pays for the AI boom? - October 20, 2026
• The rise of the inference economy - October 19, 2026