AI IPO Valuation Logic: From Cornerstone Investment to Public Pricing

Case Background: An AI Company’s Path to Public Markets

Recently, an AI company centered on large-model business updated its prospectus and plans to list on Nasdaq under the ticker “ANT.” According to disclosed information, the offering price range is set at $70 to $78 per share, with 825 million shares planned for issuance. At the top end of the range, the maximum fundraising scale could reach $64.3 billion. At the same time, some existing shareholders will additionally offer a certain number of Class A ordinary shares. Proceeds from this portion will go to the selling shareholders, and the company itself will not receive any funds. This is a typical secondary sale arrangement, through which existing shareholders can realize approximately $1.845 billion in cash.

This case is notable not only because of its large fundraising scale, but also because it reflects how the current capital market prices frontier AI companies: high expectations coexist with rational correction.

Cornerstone Investment: Locking In Certainty for a Public Listing

In this offering, the company signed agreements with several institutions in the semiconductor and technology sectors. These institutions agreed to subscribe to a total of $26 billion in ordinary shares through private placement, at the same price as the public offering. Such arrangements are commonly referred to in the capital market as “cornerstone investment.”

The core value of cornerstone investment lies in the following:

1. Locking in subscription funds in advance. When market sentiment is volatile, participation by cornerstone investors can signal confidence to the market and reduce the risk of a failed offering.
2. Supply-chain binding effect. Notably, the cornerstone investors include manufacturers across multiple upstream segments, including chip design, chip manufacturing, and memory. For AI companies that are highly dependent on computing power, establishing capital-level ties with the upstream supply chain may mean more stable cooperation in future compute procurement and capacity assurance.
3. Pricing anchor effect. The cornerstone investors’ subscription price equals the offering price, effectively backing the offering range with real capital and providing a pricing reference for other institutional investors.

Valuation Evolution: Convergence From Private Rounds to Public Offering

Understanding the company’s valuation trajectory helps clarify how the AI industry’s valuation system takes shape.

In the private-market stage, the company completed a $65 billion financing round in May 2026, reaching a post-money valuation of $965 billion. This valuation was driven by a small number of institutional investors in the primary market. With limited liquidity and few participants, such valuations often carry a strong “expectation premium.”

Closer to the listing, the company communicated with existing and potential investors during an “Investor Day” event. Market feedback indicated a valuation of approximately $1.5 trillion, below the previously rumored $2 trillion expectation. If employee stock options and other dilutive securities are further included, the final valuation on a fully diluted basis may fall in the $1.3 trillion to $1.45 trillion range.

This convergence process reveals several important patterns:

1. There is a systematic gap between primary-market valuations and secondary-market pricing. Private-round valuations are the result of negotiation, while IPO pricing must be tested by the public market. The scale of buyers is completely different, so pricing inevitably tends to be more conservative.
2. The fully diluted basis matters more. Ordinary readers may focus only on the word “valuation,” but professional analysis must consider potential dilution from option pools, convertible securities, and other instruments. Actual market capitalization should be calculated on a fully diluted share basis.
3. Cornerstone investment amount does not equal valuation. Although the $26 billion subscription amount is large, it represents a limited proportion relative to a valuation exceeding $1 trillion. It is more of a confidence signal than a pricing basis.

Deep Reasons for the Valuation Reset: The Capital Logic of AI

Why would a company with frontier large-model technology and a private valuation that has risen steadily proactively lower its valuation expectations before listing? From a technical and industrial perspective, three reasons can be summarized:

First, compute costs are high. Training and inference for large models require continuous investment in large volumes of GPUs and specialized accelerator chips. Capital expenditure is far higher than that of traditional software companies, directly weighing on profit margins and free cash flow, and further affecting the application of P/E-based valuation methods.

Second, the business model is still being validated. Although revenue forms such as subscription-based API usage and enterprise solutions are growing rapidly, customer retention and unit economics still require longer periods of public financial data for verification. Secondary-market investors naturally apply a discount to “unproven revenue.”

Third, the competitive landscape is uncertain. The rapid evolution of open-source models and price competition among leading vendors make future market share difficult to predict. Valuations must leave a margin of safety for these uncertainties.

Implications for Technical Practitioners

1. Reading a prospectus is one of the best forms of industry research. Data such as revenue structure, compute procurement contracts, and R&D spending ratios in a prospectus are first-hand materials for understanding the real cost structure of the AI industry chain.
2. Pay attention to “fully diluted valuation” and “net proceeds.” These two figures determine how much money the company actually receives and how much existing shareholders actually cash out. They are more informative than the “valuation” in media headlines.
3. Understand the trend of supply-chain capitalization. Chip vendors participating in the cornerstone investment of an AI company’s IPO shows that both sides of compute supply and demand are moving from a simple buyer-seller relationship toward deep capital-level binding. This will be an important feature of the future AI infrastructure market.

Conclusion

This IPO case demonstrates the full process of a frontier AI company moving from private financing and cornerstone subscription to public pricing. The valuation converging from a rumored $2 trillion to approximately $1.4 trillion is not negative news. Rather, it represents the capital market returning from irrational expectations to verifiable fundamentals. For developers, instead of focusing on the valuation number itself, it is more useful to study the compute economics, business models, and competitive landscape behind it. These are the real variables that will determine the long-term direction of the AI industry.