OpenAI IPO ambitions collide with financial reality as losses mount to billions

OpenAI Gets Set to Go Public: Can We Entrust the Financial Markets with ChatGPT and AI? | Newswise

Newswise -- OpenAI, the creator of ChatGPT, is gearing up to launch its Initial Public Offerings (IPO) this year. This financial manoeuvre would represent a pivotal shift for a project originally designed for the "common good" towards a market-driven logic. Established in 2015, OpenAI started out amidst growing anxiety regarding artificial intelligence (AI). Founded by Sam Altman and Elon Musk, the tech company adopted a non-profit structure and made no secret of its goal to develop AI that is "beneficial to humanity" and prevent it from remaining in the hands of a few dominant players. This ambition distinguished it from tech giants like Google, Microsoft, Meta, and Amazon, which were built on proprietary models and rent-seeking effects. In contrast, OpenAI intended to champion general public interest by emphasising open research and sharing knowledge. However, this orientation - symbolised by its name - quickly collided with a structural constraint: the astronomical cost of generative AI. Massive costs Unlike traditional software, where marginal costs tend towards zero (for example, the millionth copy of Windows costs Microsoft nothing), generative AI requires massive infrastructure. Every interaction mobilises computing resources, energy, and specialised equipment. A standard ChatGPT query, consisting of one question and one answer, costs between $0.01 and $0.10. Similarly, generating a high-definition image can cost between $0.10 and $0.20. While these amounts seem negligible in isolation, they become staggering when scaled to the billions of daily queries seen in 2026. This is explained by the underlying infrastructure, particularly the Graphics Processing Units (GPUs) supplied by players like Nvidia. These chips can cost tens of thousands of dollars to purchase and several dollars per hour via cloud access. OpenAI, like its competitors, depends on tens of thousands of these GPUs running continuously in massive data centers. According to some estimates, the necessary investments will reach hundreds of billions by the end of this decade. As early as the late 2010s, it became clear that a purely non-profit model could not meet such capital intensity. This is why OpenAI adopted a hybrid status in 2019, allowing it to raise funds while maintaining control through a foundation. It was a first foray into the market economy, albeit one tempered by the ambition to resist investor demands. Brutal acceleration with ChatGPT However, at the end of 2022, the chatbot ChatGPT radically changed the game, attracting 100 million users in just two months, before surpassing 900 million weekly users by early 2026. OpenAI's revenue surged from approximately $200 million (€173.15 million) in 2022 to over $10 billion (€8.65 billion) in 2025 - a sixty-fold increase in three years. This exponential growth was accompanied by the implementation of a business model with multiple revenue streams. For individuals, OpenAI offers paid subscriptions (ranging from $20 to $200 per month). However, the bulk of the revenue comes from enterprises, via subscriptions priced between $25 and $60 per user per month. A company with 10,000 employees thus represents several million dollars in annual revenue. Corporate money OpenAI additionally bills for the use of its models by companies that integrate them directly into their own solutions. Every use is metered, often on a massive scale. An application processing a million queries a day can generate tens of thousands of dollars in monthly billing. Finally, a growing portion of revenue comes from strategic agreements, notably with Microsoft, which integrates OpenAI technologies into its products under the Copilot brand. It is the sum of these flows - subscriptions, licences, third-party usage, and partnerships - that allowed OpenAI to reach approximately $1 billion in monthly revenue in 2025. Yet, this commercial rise masks an intrinsic economic fragility. A gigantic cash-burning machine Despite sharply rising revenues, OpenAI remains structurally loss-making. In the first half of 2025, the company reportedly generated approximately $4.3 billion in revenue while recording losses between $7 billion and $13 billion - more than $2 billion in losses every month. In total, cumulative losses could exceed $140 billion (€121.19 billion) between 2024 and 2029. This drift is explained by the very nature of OpenAI's business model, where every interaction incurs a cost alongside gargantuan necessary investments. Beyond infrastructure, Research and Development (R&D) is a major expense. To stay in the technological race against an increasingly competitive environment, OpenAI reportedly invested nearly $16 billion in R&D in 2025 alone. To this is added the cost of human resources, which is sometimes extraordinary. While base salaries for the most in-demand AI experts range from $250,000 - $700,000 per year, their total compensation - including stock and bonuses - frequently exceeds $1 million. In some cases, annual compensation even exceeds $10 million. Here again, bidding wars from competitors like Meta force OpenAI to match these offers for fear of seeing its key talent vanish. Nearing bankruptcy? In short, OpenAI's business is not enough to cover its costs, to the point that some analysts suggest that at this rate, it could be forced to file for bankruptcy as early as 2027. Recourse to external financing is therefore indispensable to cover these losses. To sustain its growth, OpenAI has already raised approximately $58 billion since its inception, including more than $13 billion from Microsoft. In 2025, an exceptional funding round reportedly raised up to $40 billion more, pushing its valuation to several hundred billion dollars. At the end of March 2026, a new $122 billion funding round - notably involving Amazon ($50 billion), Nvidia, and SoftBank ($30 billion each) - brought the valuation to $852 billion (€737.6 billion). Yet, these amounts remain insufficient given the requirements. Industrial dependency Dependency on industrial partners appears particularly problematic. Microsoft provides OpenAI with its cloud infrastructure via Azure, while Nvidia plays a key role upstream by providing GPUs. Much like the Gold Rush era, when shovel sellers grew rich at the expense of prospectors, it is the infrastructure providers in the AI sector making a fortune, not the model designers. In practice, every AI query generates revenue for infrastructure providers, amounting to a form of "invisible tax" captured upstream. In 2025, Nvidia generated nearly $73 billion in net profit on approximately $130 billion in revenue, and its stock market valuation is 1.5 times higher than the entire Paris stock exchange! Governance missteps OpenAI's economic tensions have spilled over into its corporate governance. The hybridisation of a public interest mission with private financing mechanisms resulted in a complex structure. A non-profit foundation controls a for-profit "public benefit corporation", which is funded by investors and tasked with raising capital and developing activities - all while theoretically remaining subordinate to the foundation's public interest mission. This construction, designed to avoid purely financial logic, quickly fuelled tensions between different stakeholders. Elon Musk's departure in 2018 was the first signal of a strategic disagreement. In 2020, several researchers left OpenAI to found Anthropic, citing differences over safety and governance. However, it was primarily the crisis of November 2023 that fully revealed the system's fragilities, when the board of directors suddenly announced the firing of Sam Altman, citing a lack of transparency in his communications. Within hours, the situation spiralled into an open crisis. Nearly all employees threatened to leave the company if Altman was not reinstated. Microsoft, the main partner and investor, publicly supported Altman and even discussed the possibility of hiring him and his teams. Faced with this pressure, the board was forced to reverse its decision within days. Sam Altman was reinstated, and the board's composition was profoundly overhauled. This episode highlighted internal tensions, specifically the difficulty of making divergent logics coexist within the same company: ethical posturing, industrial imperatives, and investor demands. Intensifying competition In addition to these internal constraints, competitive intensity is particularly fierce. Google, the inventor of generative AI, is making rapid progress with Gemini. Anthropic, with Claude, has established itself in certain segments, particularly programming, while emphasising safety. China's DeepSeek has claimed to use less expensive processors. France's Mistral AI advocates for a frugal approach and European digital sovereignty. In a sign of this shifting landscape, Apple which initially partnered with OpenAI to include ChatGPT for certain Siri features - has chosen to replace it with Gemini. In this context of ecosystem reorganisation, OpenAI's position, while still central, is being challenged. Intensifying competition reinforces the need for ever-greater financial resources. The stock market: lifeline or mirage? OpenAI's Initial Public Offering (IPO) is presented as a response to these constraints: a way to fund massive investments and consolidate a weakened competitive position. An IPO could raise between $50 billion and $100 billion by selling 10% to 20% of the capital. Such an operation would constitute one of the largest in the history of financial markets. However, this transformation involves delicate trade-offs. A listed company is subject to profitability and transparency requirements that may clash with the experimental nature of artificial intelligence. Added to this is the persistent dependence on Microsoft and Nvidia, which limits the company's strategic autonomy. Most importantly, there is no indication that an IPO would suffice to resolve OpenAI's structural problems. At best, without a significant shift in the business model, it would only delay its bankruptcy by a few years. The economic model of generative AI remains fundamentally unstable today. A question beyond OpenAI Beyond the case of OpenAI, one can legitimately question the current functioning of an economy dominated by tech giants. Artificial intelligence is establishing itself as an essential infrastructure whose effects far exceed the economic sphere. For some analysts, control over AI now carries the same geostrategic importance as the possession of nuclear weapons. Consequently, a civilisational question arises: can we entrust the development and direction of such a technology solely to financial markets? Can we imagine Elon Musk or Mark Zuckerberg personally owning the equivalent of one or more atomic bombs? OpenAI's IPO will not provide the answer alone. However, it will constitute one of the first large-scale tests. This article is republished from The Conversation under a Creative Commons license. Read the original article. Frédéric Fréry is the Co-Director of the ESCP Tech Institute and a Professor in the Management Department at ESCP. This article was originally published on The Choice by ESCP: https://escp.eu/thechoice/tl-dr/openai-gets-set-to-go-public-can-we-entrust-the-financial-markets-with-chatgpt-and-ai/

The AI Growth Story Meets Its First Reality Check | Investing.com UK

The AI story has been one of relentless momentum. ChatGPT marked the first chapter, transforming OpenAI into the central force behind the industry's expansion. The company now faces the challenge of supporting the scale of investment behind it. OpenAI has long stood at the epicentre of the artificial intelligence revolution. ChatGPT propelled the company into global prominence, driving one of the fastest revenue ramps in tech history. In early 2026, the company secured a massive $122bn funding round at $852bn valuation, placing it among the most valuable private companies globally. Yet a Wall Street Journal report published on 28 April introduced a more cautious tone. OpenAI missed several key internal targets. ChatGPT did not reach 1 billion weekly active users by the end of 2025, landing closer to 900 million by February 2026. Annual revenue goals were missed, along with multiple monthly targets in early 2026. The board has also begun scrutinising Sam Altman's aggressive push for additional compute capacity. These misses come as OpenAI races toward a potential IPO later in 2026 or 2027, despite projecting heavy losses, potentially $14bn in 2026 alone, and cumulative cash burn that could reach tens or even hundreds of billions before any hoped-for profitability in 2029 or beyond. Is this a manageable competitive hiccup in a still-growing market, or the first credible crack in the AI growth narrative? OpenAI entered 2025 with extraordinary momentum. Revenue had climbed from roughly $2bn in 2023 to $6bn in 2024, while ChatGPT's weekly active users surged from about 100 million to 400 million by early 2025. Internally, the assumption was simple: this pace will continue. It didn't. By late 2025, ChatGPT reached around 900 million weekly active users on an enormous scale, but still short of the one-billion target. More telling was the slowdown beneath the surface. Monthly user growth dropped sharply from 42% in early 2025 to just 13% by September. Around the same period, OpenAI faced a wave of online backlash tied to military-related partnerships, triggering a boycott among parts of its user base. While difficult to quantify precisely, the timing aligns with weakening growth and rising churn, suggesting that reputational pressure may have amplified an already natural deceleration. From a monetisation standpoint, fragility is noticeable. Only about 5% of OpenAI's 800-900 million weekly users are paying subscribers, leaving the company with massive scale but relatively thin revenue per user. Even with a $20bn annualised revenue in 2025, performance fell short of internal expectations and by early 2026, OpenAI was missing multiple monthly revenue targets, a sign that demand was no longer keeping pace with projections. On the other hand, Anthropic pulled ahead on efficiency. By March 2026, it reached roughly $19bn in ARR, with Claude Code alone generating over $2.5bn after 5.5× growth following Claude 4. The monetisation gap is striking. Anthropic earns about $211 per monthly user, compared to OpenAI's roughly $25 per monthly user. Retention adds further pressure. AI apps retain just 21.1% of users annually, versus 30.7% for traditional software, making long-term revenue harder to sustain. Source: Visual Capitalist Financially, the model is under strain. Deutsche Bank projects $143bn in negative cumulative free cash flow from 2024 to 2029, while OpenAI could post a $74bn operating loss in 2028 alone. Still, OpenAI's IPO ambitions remain alive. Polymarket assigns a 51.5% probability to a 2026 listing. The reaction was immediate and telling. Within hours of the Wall Street Journal report raising concerns about OpenAI's future profitability and its ability to fund ever-expanding compute needs, the broader "OpenAI ecosystem" traded sharply lower. Oracle, deeply tied to OpenAI through the multi-year Stargate data-centre initiative, reportedly a roughly $300bn computing partnership, fell around 7%. CoreWeave, one of the most exposed pure-play AI infrastructure providers with contracts ranging from billions to tens of billions of dollars, dropped 7%. In Tokyo, SoftBank, one of OpenAI's most visible financial backers, lost close to 10%. The shockwaves extended across the semiconductor complex. Nvidia (NASDAQ:NVDA), Broadcom (NASDAQ:AVGO) and Advanced Micro Devices (NASDAQ:AMD) saw pressure despite their diversified revenue base. At first glance, the magnitude of the move appeared disproportionate. OpenAI remains a private company, and its direct revenues are still a fraction of the global tech ecosystem. Yet, the sell-off was about what OpenAI represents and the structure underpinning the entire AI investment thesis. Over the past two years, a powerful narrative has taken hold. AI demand is effectively limitless, and the only real constraint is compute supply. That narrative has underpinned hundreds of billions of dollars in capital allocation decisions across the technology stack. At the centre of this ecosystem sits OpenAI, functioning as both a symbol and a demand engine. The model is circular and this is precisely the issue. OpenAI raises capital at increasingly elevated valuations. That capital is then committed to long-term compute contracts with hyperscalers and infrastructure specialists such as Oracle (NYSE:ORCL) and CoreWeave (NASDAQ:CRWV). These companies, in turn, invest heavily in data centres, GPUs, and networking equipment, often sourced from Nvidia and its ecosystem. Strong infrastructure demand translates into robust revenues and earnings growth for these suppliers, reinforcing high equity valuations and enabling further capital deployment into AI. The system works as long as end-demand keeps accelerating. If OpenAI's monetisation -- the most visible "end demand" signal -- slows or misses targets, investors suddenly question whether the entire wheel can keep spinning at the required speed. Chief Financial Officer Sarah Friar reportedly warned leadership that the company might struggle to honour future data-centre contracts if revenue growth does not accelerate. This is a classic case of narrative violation. OpenAI has been the poster child of the AI revolution; any sign of strain at that level forces investors to reassess the entire ecosystem. Competition is also becoming more relevant. Models from Google (Gemini) and Anthropic are increasingly credible, particularly in enterprise and coding use cases. Pricing pressure and higher churn become more plausible outcomes, complicating the path to profitability. The episode also amplified concerns that had already started to build. High-profile investors had begun trimming exposure to AI leaders. Figures such as Peter Thiel reduced Nvidia positions. SoftBank sold shares. Bearish voices, including Michael Burry, pointed to OpenAI as a potential "linchpin" vulnerability. The WSJ report crystallised these concerns, resulting in a rapid repricing of expectations. The question now centres on whether the economics of AI demand can sustain the scale of investment currently underway. The market reaction reflects a broader reassessment of the AI investment narrative. One interpretation points to a company-specific dynamic. This looks, first and foremost, like a competitive miss rather than a collapse in overall AI demand. Total usage of generative AI continues to expand at a rapid pace; the pie is simply being shared more evenly across players. Google's Gemini is gaining traction on the consumer side, and Anthropic continues to build strong positioning in enterprise and coding applications. The emergence of credible alternatives reflects a maturing market. OpenAI itself remains a dominant force. It still commands the largest consumer base, continues to grow revenue at a pace that would be exceptional by any historical standard, and recently raised $122bn at a valuation exceeding $850bn, placing it among the most valuable private companies globally. This view is reinforced at the infrastructure level. Nvidia's data-centre business remains broadly diversified, supported by demand from hyperscalers, sovereign buyers, and enterprise workloads. At the same time, Microsoft (NASDAQ:MSFT), Alphabet (NASDAQ:GOOGL), Amazon (NASDAQ:AMZN), and Meta (NASDAQ:META) are all guiding for substantial AI-related capital expenditures in 2026, with combined figures in the $600-700 billion range. These investments are driven by their own ecosystems (cloud, search, advertising, and internal productivity) not solely by OpenAI's trajectory. Private markets also remain receptive. The ability of AI companies to continue raising capital at scale suggests that investor appetite for the theme has not disappeared. Capital is still available, and the long-term narrative around AI-driven productivity gains remains intact. On the other hand, a different interpretation focuses on the structure of the system itself. The circular financing dynamic that has powered the AI boom is also its most visible vulnerability. Capital flows from investors to AI labs, from labs to infrastructure providers, and from there into GPUs and data centres, often with limited visibility on end-user returns at scale. In that context, the distinction between a virtuous investment cycle and a fragile feedback loop becomes less clear. Cash-burn levels remain substantial even under optimistic assumptions. If inference costs fail to decline quickly enough, or if monetisation through enterprise adoption, agents, or new product layers progresses more slowly than expected the gap between investment and return becomes harder to justify. The scale of committed spending leaves little room for execution missteps. Additionally, a significant share of major equity indices is concentrated in AI-linked names, including Nvidia, Microsoft, Alphabet, Amazon, and Meta. A repricing of growth expectations across this group would not remain contained, it would translate into broader index-level volatility. Sentiment had already started to shift before this episode. Questions around the sustainability of capex intensity, the role of debt in financing infrastructure expansion, and the need for measurable productivity gains had been building for months. This is not the moment to call the top on AI. It is, however, the moment to be more selective and more disciplined. Exposure now needs to be differentiated across the value chain. Diversified hyperscalers remain more insulated. By contrast, companies with concentrated exposure to OpenAI are more sensitive to any shift in its spending trajectory. OpenAI itself should be treated less as a sector proxy and more as a single-name risk. Revenues are scaling quickly, around a $20bn run rate, but the issue is whether that growth can support $1,400bn in long-term commitments.