The next phase of the artificial-intelligence boom may be defined as much by capital markets as by technology. After several years of extraordinary private investment, some of the largest AI and technology companies ever built are moving toward — or have already entered — public markets. The critical question is no longer simply whether AI can generate economic value. It is who owns the financial risk when that value is priced into public securities.

That question matters because venture capital has accumulated a significant liquidity problem. PitchBook and the NVCA estimate that cash flows to venture limited partners have been negative by roughly $197 billion since 2022. Entering 2026, the U.S. venture market contained 859 unicorns worth approximately $4.34 trillion, while exit activity remained well below its 2021 peak. Funds can continue marking portfolio companies higher, but LPs ultimately require cash distributions rather than paper appreciation.

AI has intensified that imbalance. In Q1 2026 alone, approximately $242 billion — 80% of global venture funding — flowed into AI companies, with OpenAI, Anthropic, xAI and Waymo accounting for $188 billion. Capital formation has therefore become unusually concentrated in a handful of companies whose eventual exits could determine returns across large portions of the venture ecosystem.

The emerging IPO cycle sits at the center of that transition. A public listing can provide fresh capital for continued expansion, but it also creates something equally important for existing shareholders: a liquid market through which years of private-market appreciation can eventually be monetized.

The post-2021 technology market has not produced a uniform valuation cycle. Some prominent venture-backed companies remain materially below their previous private-market peaks. Others have recovered only gradually. At the same time, businesses associated with artificial intelligence, defense technology and strategic infrastructure have continued raising capital at sharply higher valuations.

The contrast is important. Klarna, Discord, Kraken and Plaid illustrate the valuation reset that affected much of the broader venture market. By comparison, companies such as Anduril, Shield AI and other frontier-technology names have continued to attract capital at substantially higher marks.

These figures should be understood as valuation snapshots, since they may represent different transaction dates, secondary-market marks or financing rounds rather than directly comparable real-time market values. The broader pattern, however, is clear: private-market performance has become increasingly bifurcated.

That creates very different starting points for public investors. A company reaching the IPO market after years of valuation compression is fundamentally different from one arriving after several rounds of substantial private appreciation.

In the second case, the earliest institutional investors may already have accumulated multiples of their original cost basis before public investors have access to the security at all.

This is where the conventional description of an IPO as simply a capital-raising event becomes incomplete.

A company typically begins with founders and seed investors, followed by venture-capital rounds and, in some cases, growth-equity, private-equity and strategic capital. If the company succeeds, each successive round generally occurs at a higher valuation, generating unrealized gains for the investors that entered earlier.

By the time the business reaches public markets, a substantial portion of the appreciation may therefore have already taken place.

The chart illustrates the process conceptually.

Public-market investors are not necessarily purchasing shares directly from venture or private-equity funds on IPO day. A primary offering raises capital for the company, and pre-IPO shareholders can remain subject to lockups and other selling restrictions.

But the IPO fundamentally changes the liquidity structure.

Before listing, an investor holding billions of dollars of private equity cannot easily sell that position into a deep public market. After listing, there is a transparent market price and a vastly larger pool of potential buyers. Once lockups expire and selling restrictions ease, earlier shareholders have a mechanism through which positions accumulated over many years can gradually be monetized.

The IPO can therefore represent an ownership handoff between capital pools.

That is not inherently problematic. Generating exits is one of the basic functions of venture capital and private equity. Investors provide risk capital early precisely because successful companies may eventually create liquidity events.

The important distinction is the entry point.

A VC investor may have financed the company when its valuation was measured in hundreds of millions or a few billion dollars. A public investor may first gain access when the same company is valued in the hundreds of billions. The two investors are participating in very different stages of the value-creation cycle.

For the coming AI IPO wave, understanding that distinction is essential.

The transfer becomes more consequential when the businesses approaching public markets have not yet demonstrated durable profitability.

Long-run IPO data underline the issue. Research from University of Florida professor Jay Ritter shows that only 24% of U.S. technology IPOs in 2025 were profitable on trailing earnings, compared with 44% in 2004 and 36% in 2005.

At the same time, the median technology IPO offer-price-to-sales multiple reached 11.8x in 2025, compared with 6.4x in 2004 and 4.5x in 2005.

Public investors have therefore been accepting higher revenue valuations at a time when profitability among technology issuers remains comparatively limited.

Frontier AI pushes that dynamic further.

OpenAI's audited 2025 accounts showed $13.07 billion of revenue against $34 billion of costs and expenses, producing a $20.92 billion operating loss. Its headline net loss was larger because of accounting charges, making the operating result more useful for assessing the economics of the underlying business.

At the same time, private-market valuation estimates for OpenAI have moved dramatically as new capital has entered the company. Rather than focusing on one specific headline valuation, the economically relevant point is that investors are assigning values in the hundreds of billions of dollars to a business that is still spending heavily to establish its long-term market position.

Anthropic presents a similar tension. Recent reporting has associated the company with an exceptionally large prospective public valuation while its investment case remains heavily dependent on rapid future revenue growth. The company has also sought substantial external financing as compute requirements continue expanding.

Databricks offers an important counterexample. Unlike the frontier-model companies, it has developed a comparatively mature revenue base and has remained cash-flow positive. It has also continued accessing private capital rather than forcing an immediate IPO, illustrating that not every major technology business faces the same urgency to enter public markets.

The thesis is therefore not that all AI IPOs are weak businesses.

The more relevant question is whether some of the largest frontier-AI companies could reach public markets while their valuations still depend heavily on future growth, future margin expansion and continued access to large quantities of external capital.

What makes the current cycle unusual is the size of the companies involved.

The valuation estimates illustrate the magnitude of capital that public markets may eventually be asked to absorb. They should be treated as indicative snapshots rather than as perfectly synchronized market values, because several represent different dates and transaction types.

SpaceX is particularly important because it provides a live example of the transition from private to public ownership rather than merely a theoretical future case.

Following years of private-market appreciation, the company's 2026 listing opened its shareholder base to public-market capital. Earlier investors do not immediately liquidate their entire holdings at the IPO; lockups and other restrictions delay that process. But the creation of a liquid market fundamentally changes their ability to monetize those gains over time.

That is the mechanism that matters for the broader AI cohort.

The largest companies in the pipeline are no longer conventional venture-backed startups approaching modest public listings. Their private valuations are already comparable with some of the world's largest listed companies. Moving those businesses into public markets therefore requires a much larger pool of equity capital than previous generations of technology IPOs.

The IPO market is becoming not merely a venue for financing growth but a bridge between an enormous stock of private wealth and the much deeper liquidity available in public markets.

The valuation question becomes clearer when enterprise values are compared with the companies' current operating scale.

The dispersion is striking.

Several of the highest-valued technology companies sit far above what current revenue alone would imply. Their valuations instead embed assumptions about enormous future addressable markets, continued market-share gains, falling compute costs, improved monetization and eventual operating leverage.

That may prove entirely rational if AI becomes as economically important as investors expect.

But it also makes valuation unusually sensitive to long-term assumptions.

Consider a business valued largely on the expectation that revenue can compound rapidly for many years while margins expand substantially. Relatively small changes in expected growth, terminal margins or capital intensity can produce very large changes in the present value of that equity.

The issue becomes more significant when current earnings are negative. A mature profitable company has existing cash generation that can provide some valuation support. A loss-making company priced primarily on future revenue has considerably less protection if investor expectations change.

The public investor is therefore not simply purchasing today's operating business.

In many cases, the investor is purchasing a very large claim on a future business that has not yet fully emerged.

The same point becomes even clearer when the companies are considered through enterprise-value-to-revenue multiples.

The differences across the cohort are substantial.

These multiples should not be compared mechanically. SpaceX, OpenAI, Anthropic, CoreWeave, Cerebras, Stripe and Databricks operate different business models, carry different capital requirements and have different potential margin structures.

Nevertheless, the comparison reveals how aggressively the market is capitalizing future growth.

At the lower end, a company such as Databricks combines a significant revenue base with a valuation that is more closely connected to current operating scale. At the higher end, investors are effectively underwriting years of future expansion before current revenue approaches the level required to support today's enterprise value.

The distinction becomes especially important for businesses that remain structurally loss-making.

A profitable company can eventually validate a high multiple through earnings growth. A loss-making company valued primarily on revenue requires investors to believe that scale will eventually produce sufficiently high margins and cash flows.

If that transition occurs, today's valuations may ultimately look justified.

If it does not, the absence of established earnings leaves significantly less fundamental support beneath the equity.

CoreWeave demonstrates why the AI capital cycle cannot be analyzed purely through venture valuations.

The company has experienced exceptional demand growth. Q2 2026 revenue reached approximately $2.58 billion. Yet CoreWeave still generated a $626 million net loss, while quarterly net interest expense reached approximately $640 million.

At June 30, recourse and non-recourse debt totaled roughly $35 billion, while total liabilities had reached approximately $72 billion.

This reflects a fundamental characteristic of AI infrastructure: capacity must often be financed before the associated revenue arrives.

GPUs must be purchased. Data centers must be constructed. Power must be secured. Networking infrastructure must be installed. These requirements create enormous upfront capital needs.

As a result, the AI investment boom is increasingly connecting venture equity with debt markets, private credit and structured financing.

The Bank of England has highlighted the speed of that development. AI-related issuers accounted for a disproportionately large share of U.S. high-yield issuance for non-refinancing purposes during 2026, while private credit's role in financing AI investment has also expanded materially.

That introduces a second transmission mechanism.

If a highly valued software company experiences a valuation correction, equity investors bear the direct impact. If a heavily financed infrastructure provider experiences the same slowdown, stress can extend into lenders, creditors and financing vehicles.

Leverage can turn an equity repricing into a credit event.

The financial architecture becomes more complex because many of the largest participants in the AI ecosystem are also economically connected to one another.

Nvidia provides chips to AI companies while simultaneously investing across the ecosystem. Cloud providers fund, partner with or supply infrastructure to frontier-model developers. AI laboratories raise capital from technology companies and then spend substantial portions of that capital purchasing compute from the same broader network.

The resulting system contains multiple overlapping relationships between Nvidia, OpenAI, Microsoft, Oracle, AMD, CoreWeave and a growing number of private AI companies.

These transactions represent real economic activity.

GPUs are manufactured and delivered. Cloud services are consumed. Data centers are constructed. AI models are trained and deployed. Revenue is generated.

The concern is not whether the transactions are real. It is whether apparently independent revenue streams are increasingly exposed to the same underlying source of capital formation.

If Company A's revenue depends on Company B continuing to spend aggressively on AI infrastructure, while Company B's spending capacity depends on financing from Company C, and Company C benefits financially from Company A's growth, the ecosystem can become more correlated than conventional company-level analysis suggests.

That matters if the capital cycle turns.

A slowdown in frontier-model investment could reduce compute commitments. Lower demand could reduce expected data-center utilization. Leveraged infrastructure operators could experience weaker debt-service coverage. Chip orders could moderate. Valuations could decline. Financing conditions could tighten.

The same companies that require continued capital to sustain rapid growth could then encounter more expensive or less abundant financing precisely when they need it most.

Circularity does not necessarily make the AI investment cycle unstable.

It does make economic dependency and counterparty concentration critical diligence questions.

The final stage is the destination of the equity itself.

Private companies begin with relatively concentrated ownership. Founders, employees, venture funds, private-equity firms and strategic investors hold stakes that are difficult to trade and typically inaccessible to ordinary public-market investors.

The IPO changes that.

Over time, the securities can enter actively managed mutual funds, hedge funds, pension portfolios, ETFs and individual brokerage accounts. Major index inclusion may require additional trading history, liquidity, free float and profitability, so the transmission into retirement portfolios is not necessarily immediate.

But public ownership starts long before inclusion in a benchmark such as the S&P 500.

That is why the exit-liquidity mechanism matters.

A retail investor purchasing a newly public company is not automatically buying shares directly from a VC. Primary issuance may fund the company itself, and existing shareholders may initially remain locked up.

Yet the creation of a liquid public market gives those existing institutional holders something they previously lacked: a scalable route toward realization.

Private-market gains can gradually become public-market ownership.

For an individual investor, this changes the interpretation of what it means to be "early."

Buying a newly listed technology company may feel early because the security has only recently become publicly available. From the perspective of the company's capital history, however, the public investor may be arriving after seed investors, multiple VC rounds, strategic investors and growth-equity investors have already participated in years of appreciation.

The IPO may be the beginning of the public story.

It is rarely the beginning of the investment story.

None of this means that an AI crash is inevitable.

Artificial-intelligence demand is real. Revenue growth is real. Infrastructure is being built at extraordinary speed. Some companies already demonstrate sustainable economics, while others may ultimately grow into valuations that currently appear aggressive.

But the structure of the financing cycle deserves scrutiny.

Venture investors need liquidity. Several of the most valuable private AI companies still require enormous quantities of external capital. AI infrastructure is becoming increasingly leveraged. Corporate relationships across chips, cloud computing, model development and venture financing are increasingly interconnected.

At the same time, the valuations required to generate meaningful exits for today's private investors are reaching levels that can only be absorbed by the depth of the public capital markets.

That is what makes the coming IPO cycle different.

If AI growth remains extraordinary, public investors may finance the next stage of one of the largest technology expansions in modern history, while early private investors successfully realize the returns generated by assuming risk years earlier.

If growth or monetization disappoints, however, the same IPO cycle could become the point at which valuation risk that had previously remained concentrated inside private funds begins to spread across a much broader investor base.

The central question is therefore not whether venture funds should exit their investments. They should.

It is how much future growth has already been capitalized before public investors arrive, how dependent those valuations remain on continued financing, and at what price private-market gains are ultimately transferred into public-market portfolios.

That is the real handoff at the center of the AI exit machine.

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Reuters. (2026). Why SpaceX faces a longer wait to join S&P 500. https://www.reuters.com/legal/transactional/why-spacex-faces-longer-wait-join-sp-500-2026-06-05/ 

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