OpenAI is addressing a previously non-existent issue: whether AI’s future excess returns should be shared with the public. What truly affects valuation is not the “dividend” itself, but whether it will evolve from voluntary economic sharing to mandatory equity and governance transfer.

Over the past two years, the AI market has been focused on one question: Who can make the most money? NVIDIA orders, cloud provider capital expenditures, data center construction, model company valuations, and enterprise adoption speed have formed the backbone of this AI transaction cycle. Money is buying growth, betting on the profit pool, and discussing how much economic value AI can convert into company revenue.

But now, another question is starting to arise: If AI truly creates unprecedented wealth, should this money belong only to the company, employees, and shareholders? This is where the OpenAI Public Wealth Fund discussion is truly worth paying attention to. It is not a finalized regulatory policy, nor is the U.S. government immediately “seizing AI company equity.” More accurately, it is the first time the AI industry has put “how to allocate future excess returns” on the public policy table.

The counterintuitive aspect of this issue is that the market is not discussing distribution because it doubts AI’s money-making potential. On the contrary, precisely because more and more people believe AI will earn a large amount of excess profit, the political system is starting to inquire: Can these profits only be enjoyed by a few companies and investors?

According to a June 4th report by NOTUS, senior White House officials have had preliminary discussions with top AI companies regarding “voluntary equity transfer.” This direction is similar to the Alaska Permanent Fund, where the government or a public trust holds a portion of assets and shares some of the returns with residents. In a white paper released by OpenAI in April, the idea of establishing a Public Wealth Fund was also proposed. Large-scale model companies can contribute to this fund through funding, equity, or other means, allowing ordinary households without direct holdings in tech stocks, VC assets, or private equity to also partake in AI’s growth dividend.

Sanders’ version is more radical. He advocates for large AI companies to transfer a higher proportion of ownership to the public and allow the public to have a certain level of governance. The “50% stock tax” and board seats mentioned in the material represent the most radical political proposals in this round of discussions. However, the White House discussions are still preliminary probes as reported by the media, with no formal percentages, legal structure, or timetable. The OpenAI whitepaper represents corporate policy proposals, not government documents. While Sanders’ proposal is impactful, there is still a long way to go before it becomes actual policy.

Therefore, the most reasonable assessment at present is not “AI companies are to be nationalized,” but rather a new variable emerging in the AI valuation table: Would the most profitable AI companies of the future need to allocate a portion of their economic ownership to gain acceptance from society and regulatory bodies? This has limited short-term impact on the secondary market. Publicly traded AI assets such as NVDA, MSFT, AMZN, GOOGL, and META are still mainly driven by computational demand, cloud capital expenditures, order expectations, and profit realization. However, the impact is more direct for pre-IPO AI companies. If companies like OpenAI, Anthropic, or xAI were to go public in the future, investors would not only ask how much money they can make, but also inquire: how much of this money needs to be shared with a public fund, government, or other public mechanisms?

OpenAI has actively proposed a public wealth fund, essentially purchasing “social license” for its future expansions. The so-called social license is not an official license but rather the public, regulators, and political system’s tolerance for a company’s continued expansion. The more powerful the models become, the more discussions arise about replacing human labor. OpenAI is not facing the typical challenges of a tech company but rather a narrative pressure close to the level of the Industrial Revolution: If AI truly alters productivity, who will share in this portion of the gains?

Proactively designing a modest profit-sharing mechanism may shift the risk from “unknown political impact” to “long-term costs that can be estimated.” This is somewhat similar to a natural resources company entering a region and first designing a local employment, infrastructure, and revenue-sharing plan. What they need to address is not a one-time compensation but how future excess profits will be accepted by society.

The phrase “ceding ownership” can be intimidating, but the impact on valuation differs entirely based on the chosen path. The first is when a company voluntarily allocates a small percentage of non-voting economic interest to inject into a public wealth fund. The second approach involves governments acquiring economic interest through industrial policies, such as warrants. The third scenario is a Sanders-style mandatory high-percentage public ownership. A more realistic scenario is still the repeated discussion of a small-scale, voluntary, primarily economically focused proposal. It may not be implemented immediately, but it will become an unavoidable issue in AI company financing, listing, and policy communication.

For OpenAI, what is truly sensitive is not “whether to share,” but whether the sharing mechanism will affect the governance structure. Microsoft, venture capitalists, employee stock ownership plans, and strategic investors will all be concerned. Enterprise clients will also ask: If the government becomes an economic beneficiary in some sense, will procurement, data governance, and regulatory neutrality become more complex? Therefore, the market significance of this matter is not that AI company profits will be immediately cut off, but that the AI profit pool has been put into the public distribution framework for the first time.

The chain of evidence is already sufficient to show that the socialization of AI benefits is entering public policy experimentation; but it is not yet enough to demonstrate that the rules of the AI industry have changed. The next four key observation points are: First, see if companies outside OpenAI follow suit. Second, see if the White House and executive departments formalize. Third, look at financing documents and future prospectuses. Fourth, watch if the market price starts to reflect.

Therefore, there is no need to interpret this as a valuation collapse in the AI industry at present. The AI market previously only priced in growth, and now it is starting to price in distribution. If the final arrangement is just a small percentage, non-voting economic interest with clear disclosure, it resembles more of an insurance premium that AI companies pay for long-term expansion. However, if voluntary sharing is pushed into mandatory ownership due to political pressure, or even enters into board and governance arrangements, the valuation logic will significantly shift. At that point, what the market will discount is no longer a portion of profits but the control of the company and its long-term growth flexibility.

[BlockBeats]