Project “Shallotpeat” and “Rough Times”: Sam Altman’s internal memo reveals OpenAI’s biggest crisis

And the $650 billion problem: Why OpenAI is doomed to succeed

In November 2025, the tectonic plates of the technology industry shifted fundamentally. For a long time, OpenAI was considered the untouchable monarch of the new AI age – a David showing the Goliaths of Silicon Valley how innovation works. But this aura of invincibility has begun to crack. With the release of Google Gemini 3 and the rapid rise of Anthropic's Claude models, the tide has turned. What began as a triumphant march toward artificial superintelligence has now transformed for OpenAI into an existential battle against technological stagnation and economic realities.

The situation is paradoxical: OpenAI has never been more valuable on the stock market, yet its technological leadership has never been more fragile. While Sam Altman's company, with a valuation of $500 billion, is venturing into territory usually reserved for established tech giants, a dangerous gap exists between its market value and its actual earning power. Annual revenue of $13 billion stands in stark contrast to massive losses and infrastructure commitments in the hundreds of billions. This aggressive growth model worked as long as OpenAI had the undeniably best product on the market. But that very premise has now crumbled.

With Gemini 3, Google has not only caught up technologically, but has overtaken OpenAI in crucial areas. Through the resurgence of pre-training and massive integration into its own ecosystem, the search engine giant demonstrates that deep pockets, proprietary hardware, and decades of experience in data processing ultimately outweigh the first-mover advantage of a startup. OpenAI's hasty strategic realignment—symbolized by the internal project "Shallotpeat"—is an admission that its previous bet on pure "reasoning models" has not paid off.

The following article analyzes the anatomy of this power shift. It illuminates how technical miscalculations, financial tightrope walks, and the resurgence of competition create a toxic mix that could redefine not only the future of OpenAI but also the structure of the entire AI industry.

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The former avant-garde of artificial intelligence is struggling for its future – while Google is shifting the balance of power with raw technological force.

The global race for dominance in artificial intelligence took a dramatic turn in November 2025. What had been considered OpenAI's secure leadership position for years became, within months, a precarious defensive posture. The release of Google's Gemini 3 not only marked a technological milestone but also challenged fundamental assumptions about the architecture of the AI ​​market. In an internal memo, OpenAI CEO Sam Altman warned his employees of rough times ahead and admitted that Google's recent advances could create temporary economic headwinds for the company. This unusually candid assessment reveals the fragility of a position that, until recently, seemed insurmountable.

The magnitude of this shift only becomes clear within the context of the sector's valuation logic. OpenAI currently has a valuation of approximately $500 billion, yet generates only $13 billion in annual revenue. This extreme divergence between market capitalization and actual revenue is based on the assumption of exponential growth and sustained technological superiority. Google's Gemini 3 undermines both of these assumptions simultaneously. The model outperforms OpenAI's GPT-5.1 in nearly all standardized benchmarks, demonstrating capabilities that OpenAI itself is still striving to develop.

The economic implications extend far beyond short-term shifts in market share. OpenAI burns through roughly eight billion dollars annually, posting a loss of five billion dollars last year. This deficit can only be sustained by continuous capital inflows, which in turn depend on investor confidence in its technological leadership. If that leadership erodes, the entire funding logic collapses. The situation is like a high-speed train running out of fuel while still traveling at maximum speed.

The primary source for Sam Altman's internal memo is The Information, a news publication specializing in the tech industry.

The memo was originally published by The Information on November 20, 2025. The original article is titled “Altman Memo Forecasts 'Rough Vibes' Due to Resurgent Google” or “OpenAI CEO Braces Possible Economic Headwinds Catching Resurgent Google”.

The Information's publication of the memo was subsequently picked up by numerous other media outlets, including:

• Times of India
• Economic Times
• Outlook Business
• Futurism

The memo itself was an internal communication from Sam Altman to OpenAI employees and was apparently leaked to The Information by a source within the company. In the memo, Altman warned of “temporary economic headwinds” from Google’s progress and stated that he expected “rough vibes.”

The anatomy of technological breakthrough

Google's success with Gemini 3 is based on a fundamental reassessment of a supposedly exhausted development methodology. Pre-training, the fundamental phase in which AI models learn from massive datasets, was considered by some in the research community to be largely exhausted. The scaling principles, which for years had promised predictable performance improvements through larger models and more data, seemed to be reaching their physical and economic limits. OpenAI responded by shifting its strategic focus to so-called reasoning models like o1, which improve their performance through longer thinking times during inference.

However, Google demonstrated that the supposedly doomed processor still holds considerable potential. Demis Hassabis, head of Google DeepMind, succinctly summarized this insight: While there are no longer exponential performance leaps from generation to generation, the returns on investments in pre-training remain exceptionally good. Gemini 3 Pro achieves 91.9 percent on the GPQA Diamond benchmark for PhD-level scientific reasoning, surpassing GPT-5.1 by almost four percentage points. Even more impressive is its performance in abstract visual reasoning: With 31.1 percent on the ARC-AGI-2 benchmark, Gemini 3 nearly doubles the performance of GPT-5.1 and outperforms its own predecessor by more than six times.

The economic significance of this technological superiority manifests itself in concrete application areas. In algorithmic problem-solving, Gemini 3 Pro achieves an Elo rating of 2439 on LiveCodeBench Pro, almost 200 points above GPT-5.1. This is not an academic metric, but a direct indicator of the productivity of developers using these models. In a market where OpenAI generates 70 percent of its revenue from API access and enterprise customers, technological inferiority translates into immediate revenue losses.

OpenAI's pre-training problems became apparent during the development of GPT-5, where established scaling optimizations no longer worked. The company realized that traditional methods for improving performance had lost their effectiveness. In response, OpenAI developed GPT-5 with a significantly smaller pre-training budget than GPT-4.5, but compensated for this with intensive post-training optimization using reinforcement learning. This strategy proved successful in the short term, but created a structural vulnerability: OpenAI had specialized in a methodology that, while generating innovative capabilities, neglected the fundamental model foundation.

The strategic repositioning and the Shallotpeat project

Altman's memo not only diagnoses the problem but also outlines OpenAI's counter-strategy. At its core is the development of a new model, codenamed Shallotpeat, specifically designed to address the identified pre-training deficiencies. The name itself is programmatic: shallots grow poorly in peat soil, the substrate is far from ideal. OpenAI thus signals its recognition that the foundation of its existing models has weaknesses that cannot be eliminated through surface optimization.

The development of Shallotpeat is part of a broader strategic realignment. In his memo, Altman emphasizes the need to focus on highly ambitious bets, even if this temporarily puts OpenAI at a disadvantage. One of these bets is the automation of AI research itself, a meta-approach aimed at dramatically shortening the development cycles of new models. This is not merely efficiency optimization, but an attempt to fundamentally change the playing field: if AI systems can accelerate their own evolution, it could diminish the structural advantages of established players with massive resources.

The urgency of this strategy is underscored by OpenAI's financial situation. The company must achieve profitability by 2029 to meet its infrastructure commitments to Microsoft and other partners. These commitments amount to approximately $60 billion annually, compared to current cloud infrastructure commitments exceeding $650 billion over the next few years. The discrepancy between these commitments and current revenues of $13 billion highlights the scale of the problem.

At the same time, OpenAI is pursuing a diversification strategy to reduce its dependence on Microsoft. The partnership adjustment announced in January 2025 allows OpenAI, for the first time, to also utilize compute resources from competitors such as Oracle. While Microsoft retains a right of first refusal for new capacity, the exclusivity has been broken. For OpenAI, this potentially means faster access to the massive GPU clusters required for training new models. The Stargate initiative, a collaboration between OpenAI, Oracle, SoftBank, and Microsoft, is set to invest $500 billion in data centers over four years. The first facility in Abilene, Texas, is already operational with Nvidia GB200 GPU clusters.

The economic fragility of the business model

The business models of leading AI companies are based on an implicit bet on network effects and technological lock-ins. OpenAI has pursued this strategy with considerable success: ChatGPT reached approximately 700 to 800 million weekly active users in November 2025, double the number from February. The platform processes 2.5 billion queries daily and ranks fifth among the most visited websites worldwide. This user base initially appears to be an impregnable moat, but the conversion rates reveal a fundamental weakness: only about four to ten percent of users pay for a subscription.

Economic viability thus depends on two critical assumptions: first, that the user base continues to grow exponentially, so that even small conversion rates enable absolute revenue increases; second, that technological superiority binds users to the platform and switching costs to competitors remain high. Google's Gemini 3 undermines both assumptions. Technical parity, or even inferiority, makes OpenAI an interchangeable provider in an increasingly commodified market.

The cost structure exacerbates this problem. Training large language models and deploying them operationally requires massive computing resources. OpenAI projects compute budgets exceeding $450 billion from 2024 to 2030, with total commitments of approximately $650 billion, some of which extend beyond 2030. These investments must be justified by revenue, which in turn depends on market share. A vicious cycle ensues: If OpenAI loses market share, revenue decreases, limiting its ability to invest further and thus further eroding its technological competitiveness.

Comparative analyses illustrate the scale of the problem. Anthropic, a direct competitor using the Claude model, is currently valued at $170 billion with projected annual revenue of $4 billion. OpenAI and Anthropic together would need to achieve combined revenues of over $300 billion by 2030 to justify their current valuations—assuming a free cash flow margin of 27 percent, comparable to Alphabet or Microsoft. By comparison, Nvidia, the leading provider of AI chips, is projected to generate only $350 billion in revenue by 2030.

Google as a structural advantage holder

Google's position in the AI ​​race differs fundamentally from OpenAI's due to its integration into an established ecosystem with diversified revenue streams. The company generates over $300 billion in annual revenue primarily through advertising and cloud services, enabling AI development to be viewed as a strategic investment that doesn't need to be profitable in the short term. This financial robustness allows Google to experiment and invest in areas where pure AI players like OpenAI face immediate pressure to generate revenue.

The distribution advantages are equally significant. Google integrates Gemini into its search engine, which processes billions of queries daily, into Gmail with over 1.5 billion users, into Google Docs, Sheets, and the entire Workspace suite. This omnipresence creates passive exposure: users encounter Gemini in their everyday digital workflows without having to actively search for AI tools. Even if GPT-5.1 or Claude Sonnet 4.5 perform marginally better in specific benchmarks, Google places its model in front of billions of eyes.

Technological vertical integration amplifies these advantages. Google develops its own AI chips using TPUs (Tensor Processing Units), controls the entire cloud infrastructure, and possesses unique training resources gained through decades of data collection. This control over the entire value chain reduces costs and enables optimizations that are unavailable to third-party providers. As one Reddit commentator succinctly put it: Google controls the hardware, the data centers, the distribution channels, and the information itself.

Historical precedents caution against overestimating early market leadership. Internet Explorer dominated the browser market in the late 1990s with over 90 percent market share and was considered insurmountable, but was marginalized within a decade by technically superior alternatives. Yahoo and AOL, once synonymous with internet access, were displaced by Google and others. First-mover advantages in technology markets often prove temporary if structural disadvantages such as a lack of vertical integration or financial fragility cannot be overcome.

The investor perspective and valuation risks

OpenAI's valuation of $500 billion represents one of the most extreme discrepancies between current earnings and market capitalization in the history of the technology industry. This valuation implies a revenue multiple of approximately 38, while established tech giants trade at multiples between 5 and 15. The justification for this premium rests on the assumption that OpenAI will capture a disproportionate share of the emerging AI market.

This assumption is increasingly being challenged by empirical developments. The most recent funding round in March 2025, which valued OpenAI at $300 billion, was five times oversubscribed. The subsequent round in November, which raised the valuation to $500 billion, was primarily raised through secondary sales of existing shares, not through fresh capital injections. This signals a shift in sentiment: early investors are taking advantage of partial realization opportunities, while new investors are less willing to provide additional primary capital.

The comparison to the dot-com bubble is unavoidable. Sam Altman himself has publicly stated that he expects an AI bubble, comparing market conditions to those of the dot-com boom and warning against excessive investor euphoria. At the same time, he is projecting trillions of dollars in spending on data center expansion and responding to concerns from economists by urging everyone to simply let OpenAI do its thing. This rhetoric is reminiscent of the hubris of the late 1990s, when fundamental valuation questions were brushed aside with references to a new paradigm.

Analysts from Reuters and other institutions have calculated that OpenAI and Anthropic would need to achieve combined annual revenues exceeding $300 billion by 2030 to justify their combined valuations. This would mean that the two companies together would need to generate almost as much revenue as Nvidia, the undisputed market leader in AI chips. Given the intensified competition from Google, Microsoft, Meta, and numerous other players, this scenario appears increasingly unlikely.

The situation is exacerbated by developments in the broader AI market. An MIT study suggested that 95 percent of companies are not seeing measurable returns on their investments in generative AI. This finding triggered a significant tech sell-off in November, with Nvidia falling by 3.5 percent and Palantir by almost 10 percent. Markets are reacting with increasing nervousness to any indication that the promised returns from AI are not materializing.

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The Renaissance of the Pre-Training Era and Algorithmic Breakthroughs

Google's success with Gemini 3 marks a rehabilitation of pre-training as a primary source of performance gains. This development contradicts narratives that had proclaimed the end of scaling. The reality is more nuanced: While pre-training no longer delivers exponential leaps, systematic, substantial improvements remain achievable when the right methods are used.

Gemini 3's architecture integrates several algorithmic innovations. The model utilizes a mixture-of-experts structure developed by Jeff Dean, Chief Scientist at Google DeepMind. This architecture activates only a fraction of the parameters for each query, enabling efficiency while maintaining high capacity. Gemini 3 also demonstrates capabilities in multimodal integration that extend beyond simple text-to-image translation and include complex visual reasoning tasks.

Gemini 3's Deep Think mode represents Google's response to OpenAI's reasoning models. Instead of treating pre-training and reasoning as competing paradigms, Google integrates both. Deep Think achieves 41 percent on Humanity's Last Exam benchmark without aids and 45.1 percent on ARC-AGI-2 with code execution. These results demonstrate that the dichotomy between pre-training and test-time compute is a false dichotomy: optimal systems combine both approaches.

The significance of this finding for competitive dynamics cannot be overstated. OpenAI had specialized in test-time compute because pre-training scaling was no longer working. Google is now demonstrating that pre-training still has potential if approached correctly. This means that OpenAI has not only fallen behind technologically, but also strategically relied on a methodology that is proving to be incomplete.

Demis Hassabis articulated this integrated vision in several interviews. He emphasizes that the path to Artificial General Intelligence requires multiple innovations, not just scaling. These innovations include agent systems capable of tracking complex tasks over extended periods, world models that develop internal representations of physical reality, and meta-learning capabilities that allow systems to generalize from a limited number of examples. Google is systematically investing in all of these areas, while OpenAI has primarily focused on reasoning.

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The role of reasoning models and their limitations

OpenAI's o1 model and its successors represent a fundamental paradigm shift in AI development. Instead of primarily scaling through larger models and more training data, these systems invest computing time during inference to develop longer chains of reasoning. This approach has achieved impressive success in specific domains, particularly mathematics, coding, and formal logic, where verifiable results serve as feedback.

However, the limitations of this approach are becoming increasingly apparent. A study by Apple researchers demonstrated that reasoning models perform dramatically worse when problems are even slightly modified. Changing numbers or names in mathematical problems alone leads to noticeable performance losses. Even more serious: Adding logically irrelevant but superficially plausible information caused performance drops of 17.5 percent for o1-preview, 29.1 percent for o1-mini, and up to 65.7 percent for lower-performing models.

These findings suggest that reasoning models do not actually develop general problem-solving strategies, but primarily replicate learned patterns. They behave like students who have memorized specific types of problems but fail when faced with slightly varied formulations. This is not merely an academic critique, but has immediate practical implications: In real-world applications involving complex, multifaceted problems without standardized formulations, these systems remain unreliable.

The cost structure of reasoning models exacerbates their limitations. Unlike traditional models, where pre-training is the most compute-intensive phase, this relationship is reversed for reasoning models. Post-training and inference become the dominant cost factor, making scaling economically challenging. OpenAI has to expend significantly more compute for each o1 query than for comparable GPT-4 queries, without users being willing to pay proportionally more.

Google's integration of reasoning capabilities into pre-training-optimized models could prove to be a superior approach. Gemini 3 with Deep Think achieves comparable or better reasoning performance than o1, but is built on a stronger foundation. This suggests that the optimal architecture doesn't use reasoning as a replacement for pre-training, but rather as a complement to a robust base model.

Competitive dynamics and Anthropic's catch-up

Anthropic's Claude family, particularly Sonnet 4.5, is establishing itself as a serious third force in the AI ​​competition. Claude Sonnet 4.5 achieved 77.2 percent on the SWE-bench Verified Benchmark for real-world software engineering problems, making it the leading model in this critical application area. With parallel test-time compute, this performance increases to 82 percent, a level that neither GPT-5.1 nor Gemini 3 can match.

Anthropic's strategic focus on security and alignment creates a niche with a specific willingness to pay. Companies in highly regulated sectors such as finance, healthcare, and cybersecurity are increasingly prioritizing models that demonstrably integrate robust security mechanisms. Claude Sonnet 4.5 achieves 98.7 percent on security benchmarks and demonstrates reduced tendencies toward sycophancy, deception, power-seeking, and delusional reasoning. These characteristics are not mere marketing features but address real concerns of enterprise customers.

Claude Sonnet 4.5's ability to sustain complex, multi-stage reasoning and code execution tasks for more than 30 hours positions it as an ideal model for autonomous agents. This is a rapidly growing market where AI systems independently manage extended workflows. OpenAI and Google both compete in this segment, but Anthropic has gained an edge through early specialization.

Claude's pricing reflects this positioning. At three dollars per million input tokens and 15 dollars per million output tokens, Claude sits in the mid-price segment, cheaper than GPT-5.1 for many use cases, but more expensive than some open-source alternatives. This pricing structure suggests Anthropic's strategy: not mass market through low prices, but premium segment through superior quality and security.

Anthropic's valuation of $170 billion, with projected annual revenue of $4 billion, seems less extreme than OpenAI's multiple valuation, but remains ambitious. The investor logic differs: Anthropic positions itself as a takeover target or long-term player in an oligopoly market, not as a market dominator. This more modest ambition could paradoxically prove more sustainable than OpenAI's all-or-nothing strategy.

Data scarcity and synthetic solutions

A fundamental challenge for all AI developers is the increasing scarcity of high-quality training data. Epoch AI estimates that models are currently trained with 4.6 to 17.2 trillion tokens. The majority of freely available internet text has already been consumed. Future performance improvements can no longer be achieved primarily by simply increasing the size of training datasets, but require higher-quality or more diverse data.

Synthetic data, meaning training content generated by AI systems, is being discussed as a potential solution. The approach is inherently paradoxical: models are to be trained on data generated by previous models. This carries the risk of model collapse, where errors and biases are amplified over generations. However, carefully curated synthetic datasets with diversity and quality controls can generate rare edge cases that do not occur in natural data.

Google possesses structural advantages in data acquisition through its search engine, Gmail, YouTube, Google Maps, and numerous other services that continuously produce fresh, diverse, human-generated data. These data flows are not only voluminous but also longitudinally structured, making it possible to identify temporal patterns and developments. OpenAI lacks comparable data sources, which increasingly relies on partnerships with publishers, licensing agreements with media companies, and synthetic data generation.

The legal situation exacerbates this asymmetry. Several lawsuits from publishers and authors against OpenAI for copyright infringement could restrict access to historical data and make future scraping activities legally risky. Google can argue that crawling websites for search indexing is an established, legally sound practice that benefits AI development. This legal uncertainty places additional risks on OpenAI that established tech giants do not bear to the same extent.

Superintelligence as a long-term bet

Altman's memo repeatedly emphasizes the need to maintain focus on achieving superintelligence, despite short-term competitive pressures. This rhetoric is strategic: it justifies current investments and losses by pointing to transformative gains in the future. Superintelligence refers to hypothetical AI systems that surpass human intelligence in all relevant areas and are potentially capable of accelerating their own development.

Expert estimates for the timing of this development vary considerably. Analyses of over 8,500 predictions suggest a median between 2040 and 2045 for the achievement of Artificial General Intelligence, the precursor to superintelligence. Some prominent voices, such as Dario Amodei of Anthropic and Elon Musk, project significantly earlier dates, in some cases as early as 2026 to 2029. Sam Altman himself has named 2029 as a target date.

The economic relevance of this debate lies in the valuation logic: If superintelligence is achievable within five years and OpenAI remains a leader in its development, this justifies almost any current valuation. However, if superintelligence is 20 years away or OpenAI does not remain a leader, the basis for valuation collapses. Investors are thus betting not only on technology, but also on specific timelines and market positions in hypothetical future scenarios.

The automation of AI research, which Altman identifies as a key focus, could shorten these timelines. Systems that independently generate hypotheses, design experiments, train models, and interpret results would dramatically increase the speed of development. Google DeepMind is working on similar approaches, particularly by integrating AlphaGo-like planning algorithms into language models. The question is not whether such meta-AI systems will be developed, but who will implement them first.

Market structure and oligopoly formation

The AI ​​market is rapidly evolving into an oligopoly with three to five dominant players. OpenAI, Google, Anthropic, Microsoft, and Meta possess the financial resources, technical talent, and infrastructure to remain at the forefront of the competition. The barriers to entry are now prohibitive: Training a state-of-the-art model costs several hundred million dollars, requires access to thousands of cutting-edge GPUs, and demands teams of top researchers.

Open-source models like Meta's Llama, Mistral, or Allen AI's Olmo offer alternatives for specific use cases, but lag behind proprietary frontier models in absolute performance. Their significance lies primarily in democratizing AI capabilities for developers without massive budgets and in creating competitive pressure that moderates API access prices.

China is simultaneously developing its own independent AI ecosystem with companies like Alibaba Qwen, Baidu Ernie, ByteDance, and other players. These models are increasingly reaching parity with Western systems, but are partially separated from the global market by differing regulatory frameworks, limited access to cutting-edge chips due to export controls, and language barriers. The geopolitical dimension of AI development could lead to parallel, regionally dominant ecosystems, similar to the fragmented internet.

For OpenAI, this oligopoly means that marginal positions are not stable. Either the company establishes itself sustainably as one of the few leading systems, or it is relegated to a second tier from which promotion is virtually impossible due to the capital intensity. Investors understand this dynamic, which explains the extreme valuation volatility: With binary outcomes, probabilities are continuously re-evaluated, and small changes in probability assessment lead to large valuation shifts.

Vertical integration as a strategic imperative

Microsoft's licensing of OpenAI's chip and system design IP in November 2025 signals a strategic realignment. The agreement grants Microsoft comprehensive access to OpenAI's proprietary chip design portfolio and could substantially shorten Microsoft's development cycles for next-generation AI processors. This is part of a broader trend toward vertical integration, where leading cloud providers seek to gain greater control over their hardware foundations.

Google has been developing TPUs for years, thereby controlling the entire stack from silicon to software. Amazon is developing its own Trainium and Inferentia chips. Microsoft is investing heavily in its own AI accelerators. This move toward custom silicon reflects the realization that general-purpose GPUs are suboptimal for specific AI workloads. Specialized chips can achieve orders of magnitude better efficiency for specific operations, reducing costs and increasing performance.

OpenAI lacks this vertical integration. The company relies on external chip suppliers, primarily Nvidia, and uses cloud infrastructure from Microsoft, Oracle, and others. These dependencies create cost disadvantages and strategic vulnerabilities. The partnership with Microsoft for IP licensing could be a first step toward closing this gap, but developing its own hardware takes years and requires expertise that OpenAI still needs to build.

The economic implications are substantial. Model operators with their own hardware control can reduce their costs by several orders of magnitude, enabling more aggressive pricing strategies or, alternatively, securing higher margins. Google can potentially offer Gemini at prices where OpenAI incurs losses because Google can dramatically reduce its costs through TPU usage. This is not a theoretical possibility, but a practical reality that is already influencing market dynamics.

From Netscape and Yahoo to OpenAI: Is history repeating itself?

The developments of 2025 mark the end of an era of undisputed leadership by individual pioneers in the AI ​​sector. OpenAI's position as a defining player in the generative AI revolution is fundamentally challenged by technological parity, the structural disadvantages of established tech giants, and financial fragility. The company faces the challenge of managing simultaneous crises: catching up technologically with Google, ensuring financial sustainability despite massive losses, strategically repositioning in a consolidating market, and coping with the operational complexity of rapid growth.

Google's success with Gemini 3 demonstrates that in technology-intensive markets, resource depth, vertical integration, and patient capital often offer structural advantages over agile innovation. The ability to absorb losses for years while products mature and economies of scale are realized is an invaluable advantage. OpenAI and similar pure-play AI companies must achieve profitability within timeframes dictated by investor expectations, whereas Google can experiment until solutions are truly market-ready.

The future of the AI ​​market will likely be characterized by an oligopoly of three to five dominant providers, each occupying different strategic niches. Google as a vertically integrated generalist with superior distribution, Microsoft as an enterprise-focused integrator, Anthropic as a security and alignment specialist, and Meta as an open-source champion for developer ecosystems. OpenAI's future position in this constellation remains uncertain and critically depends on whether the Shallotpeat project addresses the identified pre-training deficiencies and whether the company can establish a sustainable competitive advantage beyond its historical brand leadership.

For investors, corporate clients, and technologists, this realignment means a reassessment of risks and opportunities. The assumption that early market leaders will defend their positions is proving increasingly questionable. The speed of technological change, the capital intensity of cutting-edge research, and the power of established distribution channels are creating a dynamic in which structural advantages are often more important than historical innovation leadership. The coming years will show whether agile pioneers possess the resources and strategic vision to withstand the overwhelming power of the tech giants, or whether the story of Netscape, Yahoo, and other early internet pioneers will repeat itself in the AI ​​era.

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