What does it mean when OpenAI sends a "distress signal" to the US government?

Last week, Sam Altman posted on X that OpenAI expects its annual revenue to reach $20 billion by the end of the year and "exceed hundreds of billions of dollars before 2030." Three days before that, its Chief Financial Officer hinted that the government should "guarantee" the company's infrastructure loans - although she later retracted her statement, saying she "misspoke."

In fact, a more important and market - overlooked basic fact occurred a week earlier - OpenAI's Head of Global Affairs wrote to the White House, requesting that the tax credit policy originally targeted at semiconductor factories be extended to areas such as AI data centers, server manufacturers, and electrical transformers.

The company claims that it plans to invest $1.4 trillion in capital expenditures over the next eight years. In comparison, this figure already exceeds Mexico's annual GDP and is equivalent to a startup spending about 5% of the US GDP just to build computing facilities for training larger language models.

As the current world's hottest AI superstar, why is OpenAI, unusually, sending a series of "SOS signals" to the US government?

There is nothing new under the sun. There are only two possibilities behind this: either OpenAI has truly found a return path that can justify large - scale public subsidies; or it has constructed a financial framework that cannot survive without permanent government intervention and will collapse otherwise.

On October 27, the letter from OpenAI to the White House revealed a structural dilemma: currently, the "Advanced Manufacturing Investment Credit" (AMIC) provides a 35% tax credit for semiconductor manufacturing investments. OpenAI hopes to extend it to AI data centers, AI server production, and key power grid components, including high - voltage transformers and the special electrical steel required for their production.

This request exposes a problem that OpenAI has not explicitly stated on other occasions: the bottleneck is no longer the chip supply but the power infrastructure - its construction cycle is measured in "years" rather than "quarters." The power consumption of a modern AI training cluster exceeds that of a small city, and the manufacturing cycle of the transformers that power it is as long as 18 to 24 months.

According to the letter from OpenAI, the US does not have a supply chain for the steel used in these transformers. This is no longer a problem that the software economy can solve. OpenAI is operating infrastructure comparable to that of a utility company but still trying to maintain the high valuation multiples of a technology company.

The request to extend AMIC essentially hopes that the government will re - classify AI data centers as strategic manufacturing, putting cabinets equipped with Nvidia GPUs in the same policy category as TSMC's Arizona factory.

This distinction is crucial: the products produced by semiconductor factories serve the entire economy, while the computing power produced by OpenAI's data centers mainly serves its own models. Expanding tax credits actually socializes the capital risk while keeping the profits private.

On November 6, the remarks of Sarah Friar, OpenAI's Chief Financial Officer, at a Wall Street Journal event triggered a public opinion crisis. She proposed that the government should "provide guarantees to facilitate financing." This carefully and vaguely worded statement could refer to direct loan guarantees or be understood as broader infrastructure support.

The outside world responded quickly and fiercely.

David Sacks, the White House AI director, directly posted a response: "There will be no federal bailout for AI." Republican governors and Democratic senators unanimously opposed it, believing that this was equivalent to shifting OpenAI's risks to the public. Friar later retracted her statement on LinkedIn, saying she "used the wrong words" and emphasizing that OpenAI was not seeking government guarantees.

Sam Altman immediately published a long article, emphasizing that the company has neither received nor hopes for government guarantees for its data centers, and limiting the government's role to supporting semiconductor manufacturing and the domestic chip supply chain, rather than endorsing OpenAI's infrastructure debt.

The problem is that OpenAI's letter on October 27 clearly discussed providing loan guarantees, grants, and cost - sharing agreements for AI supply chain manufacturers, advocating for direct federal funding to "reduce the delivery time of key power grid components from years to months," and even suggesting that the government establish strategic reserves of materials required for AI infrastructure, such as copper, aluminum, and rare earths.

This is not a matter of semantics: OpenAI either wants direct federal funding for its construction or wants to reduce construction costs by 35% through tax incentives. Both involve government funds. Although loan guarantees and tax credits may differ in accounting treatment, in the public's perception, when a startup requests subsidies exceeding the annual budgets of many countries, this difference becomes insignificant.

Bloomberg once pointed out that OpenAI's spending plan "is under scrutiny because this unprofitable startup is seeking creative financing arrangements - including structures criticized as 'circular transactions'."

This point is worth elaborating on.

OpenAI has announced partnerships with Oracle, SoftBank, and Microsoft to build data centers and purchase GPUs. The structure of many of these transactions is as follows: OpenAI promises to purchase computing power from the infrastructure it participates in financing, and the revenue generated from these purchases depends on the products running on the same infrastructure. Microsoft invests in OpenAI, provides Azure computing power for training, and shares in the revenue from ChatGPT and API sales.

This model may work when growth is smooth, but what if the customer acquisition cost grows faster than the revenue? What if GPT - 5 is trained for eight months but only shows a slight improvement over GPT - 4? By then, circular transactions will no longer seem like complex financial engineering but more like a house of cards - each participant is both a creditor and a debtor.

At that time, federal support will no longer be a policy option but a bailout. In fact, OpenAI is sending a message to Washington: the infrastructure spending it has promised has exceeded the capacity of private financing, but for the national interest, this construction must proceed.

There are few precedents for a startup to promise to invest $1.4 trillion in infrastructure. This scale can only be compared with sovereign projects, not with venture - capital - backed enterprises. And the latter are usually explicit government projects that do not need to prove market viability first.

OpenAI's attempt is similar to SpaceX's Starlink, but the capital requirement is an order of magnitude higher, and the target customer group is more ambiguous. Starlink was established because the demand for rural broadband is clear and the willingness to pay can be measured.

OpenAI's bet depends on three assumptions: continuous exponential improvement in model capabilities, long - term corporate demand for AI services, and profit margins sufficient to cover infrastructure costs, including regional power grid upgrades. If any link goes wrong, the whole logic will no longer hold. The urgency of the AMIC lobbying indicates that OpenAI's management has calculated the numbers and seen the funding gap.

In an optimistic scenario, OpenAI achieves its revenue forecast: the company reaches an annual revenue of $20 billion in December and maintains growth, thus justifying the infrastructure investment in hindsight. The approval of the AMIC extension reduces the cost of AI hardware. OpenAI successfully transforms from a startup to an industrial - scale operator. Five years later, government tax credits are regarded as a wise policy to help the US lock in its leadership position in AI.

The middle scenario is more complicated: the revenue grows but fails to meet expectations. At that time, the collapse scenario is self - evident: model progress stalls, the scope of corporate AI applications falls short of expectations, and OpenAI cannot fulfill its infrastructure commitments. The circular financing chain breaks, revealing that the company's health depends on an illusion maintained by everyone.

Microsoft can absorb OpenAI through a merger, but other investors will lose all their money. Or Washington can directly intervene, nationalize the technology but retain the operation; or the company goes bankrupt, and its core researchers move to Anthropic, Google, or emerging startups that have learned from OpenAI's lessons.

The contrast between the lobbying strategy and the public information is worth pondering.

Companies with sustainable models usually do not need to seek interventions like the Chip Act before proving the existence of the market. However, while Sam Altman shows confidence and a growth curve externally, his policy team is requesting industrial support for manufacturers that have not yet crossed the "valley of death."

OpenAI has built a precision machine with extremely low fault tolerance: software companies can afford to make mistakes, but there is no turning back for infrastructure businesses. The $1.4 trillion commitment is either a negotiation strategy to seek government support or an indication that OpenAI has realized that the private market cannot meet its financing needs on acceptable terms.

If so, as part of the market, we must constantly remind ourselves: the risk of collapse is no longer a hypothesis; the only question is when the music will stop.

This article is from the WeChat official account "Silicon - Based Stardust", author: Shu Le. It is published by 36Kr with permission.