In the venture capital market, narratives can drive valuations to soar endlessly; however, in the public market, only cash flow can truly determine a company’s price.

This is an unwavering principle that has held steady in capital markets for centuries, yet is often overlooked amid the euphoria of technological revolutions.

Over the past two years, the rise of generative AI has fueled a wave of collective optimism among global investors. Capital has been willing to pay unprecedented premiums for the "future computing power civilization," treating losses as inconsequential and valuations as immune to gravity—so long as they are tied to the concept of AI.

However, as Wall Street begins to assess OpenAI’s IPO prospects, an uncomfortable truth is emerging: the brightest star in AI may become a stress test for the next bubble in the U.S. stock market.

As news spreads about OpenAI’s preparations for a public listing, investment banks are quietly reaching out to large institutional investors to gauge market reception for this unicorn. The response has been surprisingly lukewarm.

This is not merely a case of a single company’s IPO facing obstacles; it marks a pivotal turning point where the AI industry is shifting from being "narrative-driven" to "performance-driven."

When narratives clash with cash flow, OpenAI’s IPO journey could become the first test of the AI bubble’s resilience.

From Venture Capital Hype to Public Market Reality: OpenAI Faces a "Pricing Ceiling"

Over the past two years, artificial intelligence has nearly rewritten the storytelling logic of the tech capital market. The technological breakthroughs brought by generative AI have made capital willing to pay unprecedented premiums for the "future computing power civilization."

In this narrative, OpenAI is undoubtedly the central figure.

With the explosive growth of ChatGPT, this company, founded less than a decade ago, has rapidly become a symbol of the global AI industry. Its valuation has skyrocketed from a few billion dollars to approximately $850 billion in just a few years. This figure not only surpasses most traditional tech giants but even nears the GDP of some countries.

However, as the company prepares to enter the public market, the logic of capital begins to shift. The venture capital market focuses more on "potential," allowing companies to trade today’s losses for tomorrow’s monopoly; the public market, however, prioritizes "certainty," requiring companies to demonstrate the sustainability of their profit models.

For OpenAI, this shift has created a clear pricing conflict. According to leaked financing information, the company’s current valuation roughly equates to a price-to-sales (PS) ratio of 28 times its projected 2026 revenue.

This is an astonishing figure. For comparison, NVIDIA—the chip giant seen as the biggest beneficiary of the AI industry chain—still has a PS ratio of around 12 times.

This gap raises a sensitive question: If even the company "selling shovels" for AI computing power is only worth 12 times PS, why should the AI application layer be worth 28 times?

In business logic, infrastructure providers typically have higher certainty and more stable cash flows, while the application layer faces greater competitive uncertainty. OpenAI’s inverted valuation reflects the private market’s excessive optimism about AI application adoption.

When investment banks began to gauge institutional investors’ sentiment in the public market, the response was underwhelming. Essentially, this is not because investors are bearish on AI, but because they are beginning to question: Has OpenAI’s valuation already preemptively overdrawn a decade of future growth?

Public market investors are accustomed to using discounted cash flow (DCF) models to calculate value. When they factor in OpenAI’s projected years of losses, capital expenditures, and competitive discounts, they find the current valuation lacks a margin of safety.

This disconnect suggests that OpenAI is hitting an invisible "pricing ceiling." If it proceeds with an IPO, either the offering price will be significantly lowered, causing paper losses for primary market investors, or the stock will fall below its IPO price after listing, triggering secondary market turmoil.

Either scenario would signal the beginning of a reconstruction of the AI valuation system.

The Money-Burning Dilemma: Can AI Companies Actually Turn a Profit?

The capital market’s primary concern about OpenAI is not its technology but its business model. According to the company’s own projections, OpenAI will continue to incur losses until at least 2030.

This means that in the coming years, the company will still need to invest vast sums in model training, computing power procurement, and talent acquisition. This is a classic "money-gulping beast" model, burning through cash at a rate that leaves many traditional tech giants astounded.

The cost structure of generative AI dictates this reality. Training large models requires massive GPU clusters, and computing power costs have become the largest expense for AI companies. More critically, inference costs grow linearly with user volume.

Every user query represents real costs in electricity and chip wear and tear. Given the current industry structure, the biggest winners in the computing power supply chain are actually hardware vendors, such as GPU manufacturers and data center infrastructure companies.

They collect "tolls"—regardless of whether AI companies are profitable, hardware vendors are guaranteed returns.

This explains why, in the AI wave, the biggest capital market winners are not the model companies but the upstream players in the industry chain. For example, server, power, and computing power chip companies have seen their stock prices continue to rise, while many AI application companies are still burning through cash.

Against this backdrop, OpenAI faces a structural dilemma: Technological leadership does not guarantee commercial success. If it cannot translate its technological advantages into sufficient pricing power, high costs will forever devour profits.

At the same time, the competitive landscape is evolving rapidly. The Claude model developed by Anthropic is catching up quickly, even beginning to compete head-on with OpenAI in the enterprise market.

As more tech giants and startups enter the large model race, AI capabilities are rapidly commoditizing.

When model capabilities converge, price wars become inevitable. This means OpenAI’s "moat" in technological leadership may be far shallower than the market imagines.

Once caught in a price war, its already thin gross margin space will be further compressed, and the timeline for profitability will be pushed back even further.

For public market investors, a company that is chronically unprofitable and faces fierce competition can hardly justify an $850 billion valuation.

The Potential Black Swan of the AI Bubble: The Market Impact of OpenAI’s IPO

In the history of the U.S. stock market, many tech bubbles have not burst at their peaks but rather showed cracks when star companies went public.

Before the dot-com bubble burst in 2000, many companies received extremely high valuations during their IPOs, but once they entered the public market, investors quickly realized that their business models could not support their valuations, ultimately leading to a reconstruction of the entire industry’s valuation system.

At the time, Cisco, as an infrastructure provider, held its value, while countless dot-com companies collapsed. OpenAI may be at a similar critical juncture.

If the company goes public at a valuation close to its current level, investors will, for the first time, re-examine the profitability of generative AI companies using public market standards.

Once the market disagrees on pricing, the IPO performance is likely to become a bellwether for AI industry valuations. If OpenAI’s stock performs poorly after listing, it will have a ripple effect, affecting all unlisted AI unicorns, making it difficult for them to raise funds in the primary market and even triggering a downward spiral.

More critically, OpenAI’s shareholder roster includes almost all of the world’s top venture capital institutions. From Silicon Valley funds to large tech companies, massive capital has entered at extremely high valuations in the private stage. These institutions face tremendous exit pressure.

If the public market cannot sustain this valuation system, the risks these investment institutions face are not just declining returns but potentially significant paper losses. For the entire AI investment ecosystem, this will be an important stress test.

Liquidity is the lifeblood of venture capital. If the IPO exit is blocked, the capital cycle in the entire primary market will stall.

In other words, OpenAI’s IPO is not just a company listing event; it could become a litmus test for industry valuations. It tests the public market’s tolerance threshold for the AI narrative.

After two years of generative AI frenzy, the capital market may soon face a critical question: Is AI a technological revolution or a capital bubble inflated by narratives?

OpenAI’s prospectus will be the first formal answer to this question.

Conclusion: Valuation Regression—The Second Half of AI Investment Is a Survival Game

OpenAI’s IPO dilemma is essentially the growing pains of the AI industry as it transitions from "infancy" to "maturity."

In infancy, growth is the only goal, and losses can be tolerated; but in maturity, profitability and cash flow are the cornerstones of survival.

For investors, this means the second half of AI investment will be a survival game. Companies that cannot prove their business model’s viability, overly reliant on financing infusions, will be eliminated in the process of valuation regression.

The real opportunities will belong to those companies that can control costs, possess unique data barriers, and quickly achieve positive cash flow.

OpenAI’s path to listing may be tortuous, but this is not necessarily a bad thing. It will squeeze out the excess in valuations and bring the market back to rationality.

When the halo of narratives fades, only true value creators will stand tall in the capital winter.

The AI revolution has not ended; it is simply evolving in a harsher, more authentic way.