The End of the AI Subsidy: If OpenAI Fails, What Happens to Your Tech Portfolio?
OpenAI just announced it’s putting ads in ChatGPT. Furthermore, the company’s CFO admitted on stage that they’re seeking a “federal backstop” for infrastructure deals. Meanwhile, internal documents project $14 billion in losses for 2026 alone.
These aren’t isolated hiccups. Rather, they’re symptoms of a fundamental problem: the economics of AI don’t work yet, and the venture capital subsidies propping up the industry are running out. Moreover, if OpenAI—the face of the AI revolution—can’t make the math work with $3.5 billion in revenue, what does that mean for your tech investments?
This case study examines OpenAI’s financial crisis, the broader AI subsidy model, and the concrete implications for investors holding AI-heavy portfolios. Additionally, we’ll explore what happens when the most valuable private tech company potentially runs out of money.
The $44 Billion Cash Inferno: Understanding OpenAI’s Losses
Numbers tell stories, and OpenAI’s numbers tell a terrifying one for investors. Let’s examine the actual financial trajectory revealed through leaked documents and public statements.
The Loss Trajectory That Defies Logic
Internal documents show OpenAI projected cumulative losses of $44 billion between 2023 and 2028, with losses peaking at $14 billion in 2026. To put this in perspective, that’s not $14 billion in total spending—it’s $14 billion in losses after accounting for all revenue.
The company lost $5.3 billion on $3.5 billion in revenue during 2024. Subsequently, losses accelerated to $7.8 billion on just $4.3 billion in revenue in the first half of 2025. This means their loss-to-revenue ratio is actually getting worse as they scale, not better.
Traditional software companies improve unit economics as they grow. Netflix got more profitable per subscriber over time. Amazon Web Services achieved better margins at scale. However, OpenAI’s model works in reverse—each additional user and each new model generation costs more, not less.
The Infrastructure Cost Problem
Reports suggest OpenAI spends approximately $15 million daily just on Sora, their video generation model. That’s $450 million monthly for a single product that generates minimal revenue. Furthermore, this doesn’t include the compute costs for ChatGPT, GPT-4, or the numerous other models they’re running.
Training costs tell an even grimmer story. GPT-4 reportedly costs over $100 million to train. Industry speculation puts GPT-5’s training costs in the billions. Moreover, these costs increase exponentially with each generation while performance improvements deliver diminishing returns.
The economics are backwards. In typical tech, costs per unit decrease with scale. Here, costs per unit increase with advancement. Consequently, the faster OpenAI innovates, the faster they burn cash.
The “Code Red” Internal Atmosphere
Windows Central reports a “Code Red” atmosphere inside OpenAI as infrastructure and compute costs dramatically outpace revenue growth. This isn’t typical startup growing pains—it’s existential crisis territory.
Employees reportedly understand the math isn’t working. Additionally, the exodus of key talent suggests insiders see problems that outsiders are only beginning to recognise. Eight of the eleven original co-founders have left, including CTO Mira Murati and research chief Bob McGrew. These aren’t people leaving because everything’s going well.
The Economist’s Warning: Running Out of Money by 2027
Economist Sebastian Mallaby, a senior fellow at the Council on Foreign Relations, published a stark warning in the New York Times: OpenAI could run out of cash by mid-2027.
Why Traditional Tech Economics Don’t Apply
Mallaby’s analysis highlights a critical distinction: OpenAI lacks a “mature, highly profitable legacy business” to subsidise its AI development. This matters enormously when comparing OpenAI to successful tech giants.
Amazon loses money on Alexa, but Amazon Web Services generates massive profits that fund experiments. Google can afford to develop Gemini because Search prints money. Microsoft funds Azure AI with Office and Windows revenue.
OpenAI, conversely, is pure AI with no profitable legacy business. Every dollar they spend comes from investors or must be generated from AI products that are themselves unprofitable. Therefore, they’re in a race against time—achieve profitability before funding runs out or face collapse.
The Timeline to Crisis
Based on current burn rates and funding, here’s the timeline Mallaby and others project:
2024-2025: Continued massive losses funded by the $40 billion fundraiser. 2026: Peak losses around $14 billion as infrastructure scaling continues. Mid-2027: Cash reserves potentially depleted if losses continue at current trajectory. 2028-2029: Either profitability is achieved, or the company faces acquisition/bankruptcy
This timeline assumes no major revenue breakthroughs and continued high infrastructure spending. However, it also assumes OpenAI can raise additional capital, which becomes harder as losses mount and investor patience wears thin.
The “Last Resort” Becomes Default: OpenAI’s Ads Pivot
In 2024, CEO Sam Altman called advertising in AI “uniquely unsettling” during a Harvard speech. Barely two years later, OpenAI announced ads in ChatGPT across both free and paid tiers.
From Principle to Desperation
The official announcement came on January 16, 2026, confirming ads in the Free tier and the new $8/month “ChatGPT Go” plan. This represents a complete reversal from Altman’s previous position. Moreover, it signals that revenue desperation has overridden earlier principles.
Altman even praised Instagram’s ad model as inspiration for ChatGPT’s new direction. The platform he previously criticised as “uniquely unsettling” for AI now serves as the template. This philosophical shift didn’t happen because of new insights—it happened because the money is running out.
Why the Ads Pivot Signals Deeper Problems
Advertising typically generates far less revenue per user than subscriptions. Facebook makes roughly $50 annually per user from ads. ChatGPT Plus subscribers pay $240 annually. Consequently, switching toward ads suggests OpenAI can’t acquire enough paying subscribers to fund operations.
Furthermore, ads in AI raise unique challenges. Users interacting with AI expect personalised, helpful responses. Ads introduce conflicts of interest—will ChatGPT recommend products because they’re best or because advertisers pay more? This tension could degrade the core product quality that attracted users initially.
The move also creates competitive vulnerabilities. Claude from Anthropic remains ad-free. Google’s Gemini doesn’t show ads. OpenAI is introducing friction that competitors can exploit.
The Government Backstop Controversy
CFO Sarah Friar created a firestorm at the Wall Street Journal Tech Live event by admitting OpenAI was seeking a “federal backstop” for its $1.4 trillion infrastructure deals.
What “Federal Backstop” Actually Means
A federal backstop implies government guarantees for private sector investments. Essentially, OpenAI wants taxpayers to assume the risk if their infrastructure buildout fails. Additionally, this suggests the company can’t secure private financing on commercial terms alone.
The admission raised “eyebrows on Wall Street”, according to coverage from Futurism. Friar attempted damage control via LinkedIn, walking back her comments. However, you can’t unring that bell—investors now know OpenAI is exploring government support because private markets have limits.
The “Too Big to Fail” Fallacy
Some argue OpenAI is too important to fail, drawing parallels to bank bailouts during the 2008 financial crisis. However, this comparison doesn’t hold up to scrutiny.
Banks are interconnected through the financial system. One bank’s failure creates cascading failures throughout the economy. Furthermore, depositors lose access to their money, creating immediate crises for millions.
OpenAI’s failure, conversely, wouldn’t create systemic risk. Microsoft, Google, Amazon, Apple, and Meta all have their own AI capabilities. They’d likely benefit from OpenAI’s disappearance as a competitor. Moreover, these companies finance AI development from their own profits, not venture capital subsidies.
The companies that lent money to OpenAI—primarily cloud providers and chip makers—wouldn’t be bankrupted by the loss. Unlike banks, they have minimal leverage and could absorb the hit. Therefore, the “too big to fail” argument lacks economic justification beyond political favour-seeking.
The Walmart Partnership: Agentic Commerce as Revenue Lifeline
OpenAI partnered with Walmart to enable “Instant Checkout” directly within ChatGPT. This represents a pivot from the search bar to a virtual merchant.
How the Economics Work
Merchants pay fees on purchases made through ChatGPT, similar to how platforms like Amazon or Instacart take transaction cuts. This creates a new revenue stream beyond subscriptions and (now) advertising.
However, the economics remain questionable. E-commerce platforms typically operate on thin margins. Amazon’s retail business makes minimal profit; they make money on AWS and advertising. Furthermore, users might resist AI recommendations once they understand the platform profits from steering them toward specific products.
The Trust Erosion Problem
Agentic commerce introduces the same conflict-of-interest issues as advertising. Will ChatGPT recommend the best product for users or the one paying the highest commission? Once users suspect recommendations are financially motivated, trust erodes.
This trust erosion could undermine ChatGPT’s core value proposition. People use AI assistants expecting objective help. Converting that into a sales channel transforms the relationship fundamentally. Moreover, competitors like Claude can differentiate by remaining independent of commerce incentives.
The Subsidy Model: How Your Cheap AI Is Actually Expensive
Every API call that costs you pennies reflects a complex economic reality: venture capital is subsidising your AI usage. Understanding this subsidy model reveals why current pricing is unsustainable.
The Real Cost of AI Inference
Running ChatGPT reportedly costs approximately $700,000 daily just in servers and power. A single Bard query costs 10× more than a Google search, according to Alphabet executives. These costs are real and ongoing.
When you pay $20 monthly for ChatGPT Plus and make hundreds of queries, the economics clearly don’t work. You’re paying perhaps $0.10 per session, while each session costs significantly more to provide. Consequently, every paying user still generates losses that investors must cover.
The subsidy model works like this: Users pay $200 directly. Actual costs might be $500-$1000. Venture capital covers the $300-$800 gap. This creates the illusion that AI is cheap and abundant when reality shows it’s expensive and subsidised.
When Will Subsidies End?
Price increases will be gradual but inevitable. No AI provider can increase prices 5× overnight without losing their entire customer base. However, expect steady increases over the next 2-4 years as subsidy economics become unsustainable.
Signs of the shift are already visible. Free tiers are shrinking. Usage limits are tightening. Pricing tiers are differentiating more aggressively. Furthermore, companies are introducing ads and commerce features to diversify revenue beyond subscriptions.
The timeline remains uncertain, but the direction is clear. AI pricing will increase substantially from current levels. Moreover, some products might disappear entirely if they can’t achieve sustainable economics.
Portfolio Implications: What OpenAI’s Crisis Means for Investors
If OpenAI faces a financial crisis or collapse, the implications ripple across multiple sectors and investment categories. Let’s examine specific portfolio impacts.
Direct Exposure: Microsoft and OpenAI Investors
Microsoft has invested approximately $13 billion in OpenAI and integrated the technology deeply into products like Azure, Office 365, and Bing. An OpenAI failure would represent a significant write-down, though likely not existential for a $3 trillion company.
Nevertheless, Microsoft’s AI narrative drives significant valuation. The stock trades partly on expectations that AI integration will drive growth across the product portfolio. If OpenAI collapses, that narrative weakens considerably. Therefore, expect a meaningful stock price impact even if the direct financial loss is manageable.
Other major investors include SoftBank, Nvidia, Khosla Ventures, and Sequoia Capital. Venture funds holding large OpenAI positions would face substantial markdowns. Additionally, this could impact their ability to raise future funds.
Indirect Exposure: The AI Supply Chain
NVIDIA sells GPUs to OpenAI and similar AI companies. An OpenAI collapse wouldn’t bankrupt Nvidia, but it would raise questions about AI infrastructure demand. Furthermore, if the entire subsidy model unravels, capital expenditure on AI chips could decrease significantly.
Cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure rent compute to AI companies. These providers benefit from AI workloads but can pivot to other enterprise customers. Nevertheless, AI infrastructure growth has driven significant revenue growth expectations. Slower AI spending would impact their growth narratives.
Data centre REITs and infrastructure companies have invested billions based on AI demand projections. If AI companies can’t afford their current infrastructure, let alone expansions, these investments face challenges. Moreover, the $1.4 trillion infrastructure deals OpenAI discussed might never materialise if the company faces a crisis.
Sector Contagion: The AI Repricing
OpenAI’s failure could trigger broader AI sector repricing. If the industry leader with the most funding and best technology can’t make economics work, what does that say about smaller competitors?
Anthropic raised billions for Claude development. Cohere, AI21 Labs, and numerous other AI startups are burning cash at similar rates. Additionally, hundreds of application-layer AI companies depend on cheap API access that might not remain cheap.
Investors might dump the entire AI sector if OpenAI collapses, similar to how the dot-com bubble burst affected all internet stocks regardless of individual company quality. Furthermore, venture capital funding for AI startups could dry up as the subsidy model’s unsustainability becomes undeniable.
The Beneficiaries: Who Gains from OpenAI’s Struggles
Not every portfolio impact would be negative. Some companies and sectors could benefit substantially from OpenAI’s failure.
Big Tech with profitable AI alternatives: Google, Amazon, Meta, and Apple finance AI from profits, not subsidies. They’d gain market share if OpenAI disappears. Moreover, these stocks might outperform during AI sector repricing as investors flee pure-play AI for established tech giants.
Open source AI beneficiaries: Meta’s Llama, Mistral, and other open models create pricing pressure on proprietary AI. If OpenAI struggles, adoption of open alternatives accelerates. Companies building on open source models face less risk from AI pricing increases.
AI infrastructure efficiency plays: Companies that reduce AI costs rather than building models could see increased demand. Chip optimisation, model compression, and inference efficiency become more valuable when the subsidy ends. Furthermore, these companies solve real problems rather than depending on unlimited funding.
Scenario Analysis: Four Possible Outcomes
Let’s examine the most likely scenarios and their portfolio implications.
Scenario 1: Microsoft Acquisition (Probability: 40%)
Microsoft absorbs OpenAI fully, similar to how Google acquired DeepMind. This prevents embarrassing collapse while giving Microsoft complete control over the technology.
Portfolio Impact:
- Microsoft’s stock drops 5-10% on acquisition announcement as investors price in the failure and ongoing losses.NVIDIA and cloud providers see a modest negative impact from reduced competition.
- AI sector avoids complete panic but faces repricing as the subsidy model’s unsustainability becomes clear.
- Venture investors in OpenAI take significant haircuts but avoid total loss.
Scenario 2: Government-Backed Restructuring (Probability: 25%)
Political pressure leads to government support, perhaps through infrastructure guarantees or direct investment. OpenAI survives but with constrained growth and government oversight.
Portfolio Impact:
- Taxpayer assumption of risk creates moral hazard concerns
- OpenAI becomes less attractive to private investors due to government involvement
- Competitive dynamics shift as government backing gives OpenAI an advantage over competitors
- Long-term uncertainty about the government’s role in AI development
Scenario 3: Successful Pivot to Profitability (Probability: 20%)
OpenAI achieves profitability through some combination of ads, commerce, enterprise sales, and cost reduction. The crisis was real but ultimately manageable.
Portfolio Impact:
- Validates AI business models, supporting sector valuations
- OpenAI IPO becomes possible, providing liquidity to investors
- Subsidy model continues longer as other AI companies point to OpenAI’s eventual profitability
- Competitive pressure increases on Google, Amazon, and Microsoft
Scenario 4: Bankruptcy and Liquidation (Probability: 15%)
OpenAI runs out of money, can’t raise additional capital, and files for bankruptcy. Assets are liquidated with technology and talent acquired piecemeal.
Portfolio Impact:
- Microsoft, Nvidia, and other investors face complete losses on OpenAI investments.
- AI sector experiences severe repricing as subsidy model collapses
- Flight to quality benefits big tech with sustainable AI funding
- Venture funding for AI startups essentially stops
- Potential buying opportunity for investors willing to weather volatility
Strategic Implications for Tech Portfolio Management
Based on this analysis, here are concrete portfolio management strategies for navigating OpenAI’s crisis and the broader AI subsidy unravelling.
Reduce Pure-Play AI Exposure
Companies that are 100% AI-dependent face maximum risk if subsidies end. This includes most AI startups and some publicly traded pure-plays. Consider reducing exposure to:
- Companies with no profitable business lines subsidising AI development
- Businesses dependent on cheap API access that might reprice dramatically
- Venture funds are heavily concentrated in AI investments
Diversify toward companies with multiple revenue streams where AI is an enhancement rather than a foundation. This provides a buffer if AI economics deteriorate.
Increase Allocation to Profitable AI Integrators
Companies that integrate AI into existing profitable businesses can weather pricing changes better. These include:
- Microsoft, Google, Amazon, Meta – established tech giants
- Enterprise software companies adding AI features (Salesforce, Adobe, Oracle)
- Companies solving AI cost problems rather than creating them
These businesses benefit from AI upside while having downside protection from non-AI revenue.
Monitor Infrastructure Spending Carefully
AI infrastructure investments made sense under assumptions of unlimited AI company spending. However, if AI companies face cash constraints, infrastructure demand could disappoint. Therefore, be cautious about:
- Data centre REITs are dependent on AI tenant growth
- Infrastructure companies with large AI customer concentrations
- Cloud providers where AI workloads drive growth expectations
Look for diversified infrastructure plays where AI is one growth driver among many rather than the primary thesis.
Consider Hedging Strategies
For portfolios with significant AI exposure, hedging strategies might make sense:
- Pair long positions in AI beneficiaries with shorts on pure-play AI companies
- Use options to cap downside on high-beta AI stocks
- Rotate some gains from AI winners into value stocks uncorrelated with AI
The goal isn’t abandoning AI exposure entirely, but managing risk as the subsidy model shows strain.
The Honest Assessment: What This Actually Means
OpenAI probably won’t disappear tomorrow. They just raised $40 billion, bringing total funding to nearly $58 billion. Microsoft, Nvidia, and SoftBank have invested too much to let them fail easily.
Nevertheless, “too big to fail” isn’t a business model—it’s a warning sign. The real story isn’t OpenAI’s potential demise. Rather, it’s the end of the AI monopoly narrative and the unsustainable subsidy model underlying current AI economics.
What’s definitely true:
- Current AI pricing is artificially low due to venture subsidies
- OpenAI loses money on every customer despite $3.5 billion in revenue
- The path to profitability requires either dramatic revenue increases or significant cost reductions
- Neither seems imminent given current trajectories
What’s probably true:
- AI pricing will increase substantially over the next 2-4 years
- Some AI companies will fail as subsidies run out
- Portfolio repricing is coming as markets recognise subsidy unsustainability
- Big tech with profitable businesses will outlast pure-play AI companies
What’s possibly true but uncertain:
- OpenAI might run out of money by 2027
- The government might intervene with bailouts or guarantees
- Microsoft or another tech giant might acquire OpenAI
- The entire AI investment thesis might need a fundamental reassessment
Conclusion: Preparing for the End of Free AI
The AI subsidy model is ending. Not tomorrow, but over the next few years. Companies building on cheap AI APIs should prepare for 3-5× price increases. Investors holding AI-heavy portfolios should reassess concentration risk.
OpenAI’s crisis serves as an early warning for broader industry challenges. If the best-funded company with the most advanced technology can’t make economics work, that tells us something important about the entire sector.
Smart portfolio management means recognising when narratives have run ahead of fundamentals. The AI revolution is real, but the current financial structure supporting it is not sustainable. Therefore, position your portfolio for the transition from subsidy-driven growth to sustainable profitability.
The companies that survive won’t be the ones burning the most cash. They’ll be the ones who found actual sustainable business models before the money ran out.
Spend some time for your future.
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Explore these articles to get a grasp on the new changes in the financial world.
Disclaimer: This case study provides analysis and educational information only. It does not constitute investment advice or recommendations. AI company valuations, financial performance, and market conditions change rapidly. The author may or may not hold positions in the mentioned companies. All projections and scenarios involve substantial uncertainty. Stock investing involves risk of loss. Always conduct your own research and consult qualified financial professionals before making investment decisions.
References
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- Windows Central. “OpenAI could lose $14 billion in 2026, becoming bankrupt by 2027.” Retrieved from https://www.windowscentral.com/software-apps/openai-could-lose-dollar14-billion-in-2026-becoming-bankrupt-by-2027
- Mallaby, Sebastian. “This Is What Convinced Me OpenAI Will Run Out of Money.” The New York Times, January 13, 2026. Retrieved from https://www.nytimes.com/2026/01/13/opinion/openai-ai-bubble-financing.html
- SiliconANGLE. “OpenAI to start testing ChatGPT ads across free, Go tiers,” January 16, 2026. Retrieved from https://siliconangle.com/2026/01/16/openai-start-testing-chatgpt-ads-across-free-go-tiers/
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- Morningstar/MarketWatch. “OpenAI is AI’s leading indicator. Does that make it too big to fail?” Retrieved from https://www.morningstar.com/news/marketwatch/20251119262/openai-is-ais-leading-indicator-does-that-make-it-too-big-to-fail
- Uptech Studio. “The True Cost of AI: When the Subsidies Run Out.” Retrieved from https://www.uptechstudio.com/blog/the-true-cost-of-ai-when-the-subsidies-run-out
- Khatib, O. “The Fragile Future of AI: Beyond Venture Capital Subsidies.” Medium. Retrieved from https://medium.com/@olikhatib/the-fragile-future-of-ai-beyond-venture-capital-subsidies-46abac932c3b
