This article argues that calls to treat OpenAI as “too big to fail” are misguided. It summarizes OpenAI’s heavy losses, the circular financing supporting it, and why comparisons to 2008 bank bailouts are false. The piece examines business viability, questionable revenue sources, strategic AGI claims, and political maneuvers, concluding that government should refuse a bailout.
“Financial Reality vs. Systemic Risk”
OpenAI’s reported losses are staggering: $5.3 billion on $3.5 billion of revenue in 2024 and $7.8 billion on $4.3 billion of revenue in the first half of 2025, with a company projection of $115 billion in cumulative losses by 2029. Yet the firm continues to draw huge investment, supported by a complex, circular financing web described in a Bloomberg “AI Money Machine” chart.
Proponents frame this web as evidence of systemic importance, suggesting a single failure could cascade across the tech sector. That analogy to 2008 is misleading: banks then were highly leveraged, making small asset shocks catastrophic. Most big tech firms carry little leverage and could absorb the loss of a supplier or competitor; many would likely benefit competitively if OpenAI faltered.
“Business Model and Troubling Revenue Streams”
Unlike rescued banks in 2008, which were fundamentally profitable and repaid support, OpenAI lacks a clear path to profitability. The company’s financials and the performance limitations of large language models (LLMs) challenge claims that simple scaling will solve reliability or intelligence gaps.
Commercial use cases that generate significant revenue remain limited because LLMs are unreliable for novel, high-stakes tasks. The most immediately promising income sources, however, are ethically fraught: student cheating via GPT, AI companionship and life-advice services, and forthcoming erotic AI features. None of these justify massive, sustained subsidies.
“AGI Race and Strategic Arguments”
Sam Altman and others have argued that U.S. leadership in AI is a strategic imperative versus China, implying government backing might be necessary. But the claim falters on two counts: LLMs are a detour from true artificial general intelligence, not necessarily progress toward it, and several well-capitalized tech firms could continue any AGI effort if OpenAI collapsed.
Framing OpenAI as indispensable to national security overstates the company’s uniqueness and undervalues the capacity of other profitable firms to carry forward advanced AI research and infrastructure.
“Political Influence and Policy Recommendation”
There are signs that political relationship-building plays into OpenAI’s strategy. Altman’s public shifts and donations suggest cultivation of influence that could yield favorable contracts or regulation. That raises the specter of politically motivated support rather than market-based rescue.
Comparing a potential bailout to subsidies for harmful industries is apt: OpenAI’s products enable academic dishonesty, can foster unhealthy dependency, and raise imminent ethical concerns about sexualized AI content. For these reasons — financial fragility, weak profit prospects, dubious revenue sources, and political risk — the appropriate policy response is clear: the government should decline to bail out OpenAI.
