Altman’s “Code Red”: A Symptom of Structural Strain in the AI Megacycle
Executive Summary
Sam Altman’s recent declaration of a “code red” at OpenAI, revealed through internal memos and subsequent reporting, marks a decisive shift in the narrative of the AI boom from exuberant expansion to a period of strategic anxiety and structural stress.
What began in 2022 as Google’s own “code red” panic at the disruptive rise of ChatGPT has now been inverted: the former insurgent is sounding an alarm about eroding advantage, rising costs, and intensifying competition from Google’s Gemini and Anthropic’s Claude.
This episode is not merely a tactical effort to refocus OpenAI’s teams on incremental product improvements. It crystallizes three converging crises in the current large‑scale AI paradigm.
Business‑model crisis
Financial disclosures and investor analyses indicate that OpenAI’s cost base for frontier models and associated compute infrastructure—tens of billions annually and cumulative commitments approaching or exceeding a trillion dollars over the coming decade—vastly outstrip present revenues and may leave a funding shortfall well above 200 billion dollars by 2030, even under bullish revenue scenarios.
Brian Merchant’s commentary, drawing on these trajectories, suggests that OpenAI may have effectively “overextended,” with cumulative capital raised and committed on the order of 100 billion dollars and a runway that looks perilously short if investor confidence wavers.
Competitive crisis
ChatGPT remains the dominant web‑based AI chatbot, but its market share has already fallen from roughly 87 percent of traffic to around 74 percent as Google’s Gemini, Perplexity, Claude and other systems accelerate their growth on top of vast incumbent platforms and cloud ecosystems.
In response to Gemini 3’s strong performance and rapid user gains, Altman’s “code red” memo explicitly orders a pause or delay of other initiatives, including advertising integration, AI agents for shopping and health, and a personal assistant called Pulse, in order to redeploy resources entirely toward shoring up ChatGPT’s core quality and reliability.
Political‑social crisis
Physical and environmental footprint of the AI boom is now colliding with public resistance and regulatory hardening.
A dedicated watchdog, Data Center Watch, estimates that about 64 billion dollars’ worth of US data center projects have been blocked or delayed in just two years by local opposition over water use, grid strain, noise, and land impacts, turning data centers into a new national flashpoint.
At the same time, leaders such as President Joe Biden, UN Secretary‑General António Guterres, and UK Prime Minister Rishi Sunak are increasingly framing AI as a strategic technology whose benefits must be balanced against existential, societal, and security risks.
Altman’s “code red,” then, is less a discrete panic than a highly visible manifestation of a deeper systemic contradiction.
The prevailing frontier‑model paradigm—ever larger, more general models trained on staggering compute using hyperscale data centers—relies on economics, infrastructure, and political tolerance that may not be indefinitely scalable.
The strategic question is no longer only which firm ships the next frontier model first, but whether this mode of AI development can be rendered economically sustainable and politically tolerable at all, and what adaptation is necessary from firms, regulators, and societies to avert a disorderly correction.
History: From Google’s “Code Red” to OpenAI’s
The phrase “code red” entered the AI lexicon in late 2022 when Google’s leadership reportedly invoked it internally after the sudden viral success of ChatGPT.
For a company whose research had underpinned many of the breakthroughs behind large language models, the spectacle of a smaller external lab seizing the public imagination—and potentially threatening the core search business—triggered a sense of existential urgency inside the incumbent.
That initial “code red” galvanized an accelerated product cycle that eventually produced the Gemini family of models and their deep integration into Search, Android, and Workspace.
OpenAI, by contrast, spent 2022–2023 as the avatar of the AI boom. It raised billions in equity and structured compute financing, cemented its strategic partnership with Microsoft, and achieved valuations in the tens and then hundreds of billions of dollars.
Its consumer product, ChatGPT, became the dominant gateway to generative AI, while its APIs provided a platform for a vast ecosystem of third‑party applications. Investor narratives coalesced around the idea that frontier models and artificial general intelligence (AGI) would unlock a new economic epoch.
In parallel, the physical infrastructure of the AI boom expanded at unprecedented speed. Hyperscalers and model labs committed to multi‑hundred‑billion‑dollar data center and compute programs, often financed through complex, circular deals in which chipmakers, cloud providers, and model developers effectively underwrote each other’s capital expenditure.
Analysts began to warn that AI might be entering a classic technology bubble: asset prices, capital commitments, and rhetoric about transformative productivity gains were surging far faster than demonstrated, broad‑based profit generation.
By 2024–2025, the landscape had shifted again. Gemini matured rapidly and was embedded into Google’s enormous distribution surface, from Search summaries to Android devices.
Perplexity, Claude and other challengers carved out growing niches in AI‑enhanced search and enterprise assistance.
At the same time, concerns about the sustainability of AI’s financial and infrastructural model became sharper as data center protests intensified and detailed forecasts exposed the sheer scale of the compute and capital required to sustain frontier scaling.
It is within this historical arc—an initial code red by an incumbent, followed by a euphoric boom led by OpenAI, and then a period of competitive catch‑up and mounting structural questions—that Altman’s own “code red” assumes its broader significance.
Introduction: “Code Red” as Metaphor and Political Act
Altman’s “code red” is formally an internal operational designation.
According to reporting in the Wall Street Journal, Reuters, Business Insider, and others, his memo to staff in early December 2025 declared a “code red” focused on improving ChatGPT, postponed multiple other projects, and followed on the heels of an earlier “code orange” warning in October.
He is reported to have urged employees to prepare for “rough vibes” in the coming months and to participate in daily “war‑room” calls aimed at accelerating improvements in speed, reliability, personalization, and breadth of responses.
Yet, as Brian Merchant emphasizes, “code red” in this context is not a technical severity classification so much as a metaphor for existential urgency and an instrument of narrative management.
Nearly everything at OpenAI leaks; Altman and his executives would have known that such a designation would quickly become public. The declaration therefore performs multiple functions simultaneously.
It rallies OpenAI employees around a singular mission at a moment of competitive pressure. It signals to investors and partners that leadership is responsive to the Gemini threat and is willing to sacrifice near‑term diversification in order to defend the core product. It also invites the broader ecosystem to interpret OpenAI’s situation through the language of crisis.
Merchant’s argument is that this language of crisis reveals more than it intends. A firm that has spent two years cultivating an aura of inevitability around its path to AGI and market dominance is now publicly acknowledging vulnerability, overextension, and dependence on investor patience.
What makes this moment analytically important is that it coincides with, and is amplified by, mounting questions about whether the underlying economic and infrastructural logic of frontier AI is itself reaching its limits.
Key Events: From Frontier Euphoria to “Rough Vibes”
The first key event in this sequence is the 2022 Google “code red” in response to ChatGPT’s launch. That internal alarm symbolized the realization within a dominant incumbent that its research lead had not translated into product dominance, and it catalyzed a rapid series of public releases and internal reorganizations that culminated in Gemini’s launch and integration.
The second key event is the explosive adoption of ChatGPT in 2023–2024. Usage rapidly reached hundreds of millions of monthly users, and ChatGPT captured close to 87 percent of web traffic to generative AI tools at its peak.
OpenAI secured billions in new funding, valuation leaps, and multi‑decade compute deals with partners such as Microsoft, Oracle, and others, sometimes involving cumulative compute and data center commitments on the order of hundreds of billions to over a trillion dollars.
The third key event is the emergence of a more plural, competitive market in 2024–2025. Similarweb and other trackers now estimate that ChatGPT’s share of generative AI web traffic has fallen to roughly 74 percent, even as its absolute user base continues to grow, while Gemini’s share has doubled or more, and Perplexity and Claude have achieved triple‑digit year‑over‑year growth in their respective segments.
This transition from near‑monopoly dominance to contested leadership constitutes the immediate competitive backdrop for Altman’s “code red.”
The fourth key event is the intensification of financial scrutiny. Leaked investor documents and media reports indicate that OpenAI expects to generate on the order of 13 billion dollars in revenue in 2025 but to lose around 9 billion dollars that year, spending roughly 1.7 dollars for every dollar of revenue.
Longer‑term projections shown to investors contemplate cumulative cash burn exceeding 100 billion dollars by 2029, with operating losses in some mid‑to‑late decade years reaching tens of billions annually.
HSBC and other analysts now project that OpenAI will remain free‑cash‑flow negative through 2030 and will require an additional 200‑plus billion dollars in financing to fund its compute and data center ambitions.
The fifth key event is the infrastructural and political backlash. The Data Center Watch report finding that 64 billion dollars in US data center projects have been blocked or delayed by local opposition since 2023 has been widely cited, including by Reuters and Fast Company.
Concerns about water consumption, noise, visual blight, grid strain, and property values have mobilized coalitions of farmers, environmentalists, and homeowners in states across the political spectrum, turning data centers into a new NIMBY flashpoint.
This grassroots resistance intersects with broader global regulatory developments, from export controls on AI chips and model weights to AI‑specific legislative initiatives in the EU and beyond.
The final key event is Altman’s own internal campaign. Reports indicate that in October 2025 OpenAI declared a “code orange” to focus on ChatGPT improvements, before escalating to “code red” on December 1 after Gemini 3’s strong reception. In the “code red” memo, Altman ordered a pause or delay of advertising products, AI agents for shopping and health, and the Pulse personal assistant, reassigning staff across teams to prioritize the core chatbot.
Merchant’s essay, “A ‘code red’ for AI,” then situated this internal drama within a broader landscape of financial overreach, infrastructural backlash, and political skepticism.
Facts and Key Concerns
The factual landscape underlying Altman’s “code red” falls along three primary axes: capital and cost structure; market dynamics; and political‑social constraints.
On capital and cost structure, internal financial projections and external analyses converge on a picture of extreme capital intensity. Reports based on leaked documents suggest that OpenAI expects about 13 billion dollars in revenue in 2025 accompanied by 22 billion dollars in total spending and a net loss around 9 billion dollars.
Cumulative spending on compute and data center capacity through the late 2020s is forecast in some analyses to approach or exceed 800 billion dollars, with total compute commitments running toward 1.4 trillion dollars by 2033.
HSBC estimates that even with aggressive growth—over 200 billion dollars in annual revenue by 2030 in some scenarios—OpenAI will still face a funding gap of roughly 207 billion dollars by that time.
Merchant, interpreting these trajectories, characterizes OpenAI as “massively overextended” and speculates that cumulative capital raised and committed may be on the order of 100 billion dollars, with only about a year of financial runway if investor support softens.
While that figure is an informed estimate rather than a disclosed statistic, it aligns with the scale of announced and rumored capital programs for compute and data centers.
In market dynamics, ChatGPT remains the single largest AI chatbot, but its dominance is no longer unchallenged.
Web traffic analyses indicate that ChatGPT’s share of generative AI chatbot traffic has fallen from roughly 87 percent to about 74 percent year‑over‑year, as Gemini’s share has climbed sharply and Perplexity and Claude have grown from small bases.
First Page Sage’s tracking of AI search and chatbot market share finds that ChatGPT still commands a majority of AI search‑related activity, but Gemini now holds over 13 percent, with Copilot and Perplexity also securing non‑trivial slices.
This diversification is occurring even as overall usage grows: ChatGPT’s weekly active users reportedly reached 800 million by October 2025, up from 700 million in August, but its relative share of the total market has slipped. The implication is that OpenAI’s revenue base is becoming more contested even while its cost base continues to climb.
Political‑social constraints introduce a third axis of concern. Data Center Watch’s detailed report documents that 18 billion dollars in US data center projects have been outright “blocked,” with another 46 billion dollars delayed, over the last two years amid local opposition and permitting struggles.
These projects span both Republican‑ and Democratic‑leaning states, and the political opposition is strikingly bipartisan, reflecting concerns over power prices, water usage, noise, environmental impacts, and historic preservation.
Reuters has underscored that this resistance could slow the Trump administration’s push for rapid AI data center expansion, and political strategists already see data centers as a potential campaign issue around affordability and land use.
Overlaying these local dynamics is a growing body of national and international regulation and rhetoric.
The Biden administration’s January 2025 executive order on AI infrastructure characterizes domestic AI data centers as a “national security imperative,” pledging to accelerate their construction on federal sites but also to impose safeguards on safety, labor standards, and energy use. Concurrently, the Commerce Department has introduced export controls on AI chips and certain model weights to constrain adversarial access to high‑end compute.
At the global level, António Guterres has repeatedly warned of “potentially catastrophic and existential risks” from runaway AI development and called for a global watchdog.
The Bletchley Declaration, launched at the UK’s AI Safety Summit under Rishi Sunak’s leadership, frames frontier AI as a dual‑use technology whose extraordinary capabilities demand internationally coordinated mitigation of misuse and loss‑of‑control risks.
From these facts arise several interlinked concerns. There is the fragility of the current AI financial model, in which the leading non‑incumbent lab is expected to rack up cumulative losses in the tens or hundreds of billions before turning profitable, and may require over 200 billion dollars of fresh financing even under favorable scenarios.
There is the risk that market structure will drift toward an oligopoly of integrated incumbents—Google, Microsoft, Meta, perhaps a small handful of others—whose advertising or cloud cash flows can indefinitely subsidize large‑scale model development and discounting, leaving independent labs highly exposed to capital market sentiment and regulatory shocks.
And there is the prospect of mounting political backlash as communities and policymakers recast AI not primarily as an engine of opportunity, but as a governance problem and a driver of local environmental burdens.
Actual Statements by Global Leadership
Global political leadership has increasingly begun to articulate these tensions, often in language that directly bears on the issues underscored by Altman’s “code red.”
President Joe Biden, in a statement accompanying his executive order on advancing domestic AI infrastructure, framed data centers explicitly as a strategic asset.
He argued that “building AI infrastructure in the United States is a national security imperative” and warned that as AI capabilities grow, “so do its implications for Americans’ safety and security.”
Domestic data centers, he said, are needed both to “facilitate AI’s safe and secure development” and to “prevent adversaries from accessing powerful systems to the detriment of our military and national security.”
US Commerce Secretary Gina Raimondo has likewise stressed the national‑security dimension of AI compute and chips.
In unveiling new export controls, she stated that “as AI gains more capability, the threats to our national security intensify,” and emphasized that the goal of restricting advanced chips and model exports is to ensure that the most powerful AI tools are developed domestically or with close allies, not diffused to adversarial regimes.
At the multilateral level, António Guterres has warned that the combination of “runaway climate chaos and the runaway development of AI without guardrails” constitutes a dual existential challenge.
In his Davos 2024 address, he cautioned that while generative AI has “enormous potential for good,” every new iteration also raises the risk of “serious unintended consequences,” including the exacerbation of inequality, and he criticized “powerful tech companies” for pursuing profits with “reckless disregard” for human rights and social impact. He has called for a global watchdog and for AI risks to be treated on par with pandemics and nuclear war.
UK Prime Minister Rishi Sunak, convening the 2023 AI Safety Summit at Bletchley Park, described frontier AI as presenting both extraordinary opportunities and “doomsday scenarios” serious enough to warrant unprecedented international coordination.
The resulting Bletchley Declaration, endorsed by a diverse set of countries and companies, explicitly recognizes that those developing “unusually powerful and potentially dangerous frontier AI capabilities have a particular responsibility for ensuring the safety of these systems.”
These statements do not directly reference Altman’s “code red,” but they define the larger political context in which such a move unfolds: a world in which AI infrastructure, compute, and model development are no longer seen as purely private commercial endeavors, but as matters of national strategy, global risk, and democratic legitimacy.
Cause and Effect: How the Three Crises Interlock
The three crises identified in the executive summary—business‑model, competitive, and political‑social—are mutually reinforcing rather than discrete.
The business‑model crisis arises from the basic arithmetic of frontier AI. Training and deploying the largest models require enormous clusters of GPUs or custom accelerators, vast data center capacity, and specialized talent, all of which must be paid for up front. Revenues, by contrast, arrive more gradually as subscriptions, API usage, and enterprise contracts ramp over time.
For a lab like OpenAI that does not own an advertising platform or a global cloud infrastructure business, this mismatch is particularly acute: the firm is locked into multi‑year, multi‑billion‑dollar compute obligations just as competitive pressure erodes its ability to charge premium prices.
Altman’s “code red,” in which resources are pulled away from future revenue‑generating products to defend the existing flagship, underscores this tension: defending the present revenue base may come at the cost of delaying diversification needed for long‑term resilience.
The competitive crisis amplifies this vulnerability. Once multiple firms can access comparable hardware and talent, the marginal advantage from “scaling harder” diminishes, and product differentiation, distribution channels, and cost efficiency become decisive.
Google can embed Gemini into search pages seen by billions of users per day and into Android devices worldwide, cross‑subsidizing AI features from its advertising cash flows.
Microsoft can bake OpenAI‑based models into Office, Windows, and Azure, distributing costs and revenues across a vast software and cloud portfolio.
In such a setting, an independent or semi‑independent lab faces a compressed strategic space: it must simultaneously race to keep its models competitive, invest in productization, and manage extreme capital intensity—tasks that are easier for integrated incumbents with diversified balance sheets.
The political‑social crisis then feeds back into both cost and competition. Local resistance and regulatory friction make it harder, slower, and more expensive to build the hyperscale data centers needed to sustain frontier scaling.
Export controls, safety obligations, and potential licensing regimes for frontier models may raise compliance costs and delay deployments. Environmental and labor requirements attached to public‑sector siting of data centers, as in the Biden executive order, further condition the economics of infrastructure build‑out.
In extreme scenarios, public opposition could translate into moratoria, stricter zoning, or limitations on water and grid allocations for AI‑intensive facilities, tightening the physical bottlenecks that already constrain model scaling.
In Merchant’s framing, Altman’s “code red” is therefore not simply a response to Gemini’s latest benchmark wins or marketing blitz.
It is a symptom of the deeper reality that OpenAI’s strategy has been predicated on a race‑to‑scale paradigm whose underlying assumptions—about cheap capital, pliant infrastructure, and unbounded political tolerance—are becoming questionable.
If Altman is now effectively ordering his teams to drop everything but defending ChatGPT’s quality, it is partly because the firm cannot afford to concede even incremental ground while carrying such a heavy fixed cost base.
Steps Ahead: Strategic Adaptation for Firms, States, and Societies
For OpenAI and similar labs, the immediate strategic imperative is to reconcile frontier ambition with financial and infrastructural realism.
This likely requires a rebalancing away from exclusive focus on ever‑larger general models toward a portfolio that includes smaller, specialized systems tuned for high‑value enterprise and vertical applications, where willingness to pay and margins may be greater and compute demands more manageable.
It also suggests an intensified focus on efficiency—algorithmic, systems‑level, and hardware‑aware—so that useful capability gains can be extracted at lower incremental compute costs.
Business‑model diversification, through durable enterprise contracts, tools deeply embedded in organizational workflows, and possibly hardware or on‑device offerings, becomes critical to reducing dependence on a single flagship chatbot whose market share can be eroded by platform incumbents.
At the level of incumbents such as Google, Microsoft, and Meta, the challenge is to wield their structural advantages without provoking a backlash that leads to aggressive antitrust or AI‑specific structural remedies.
Given their ability to subsidize AI with large existing revenue streams, these firms will be under increasing scrutiny to demonstrate that they are not using loss‑leading strategies to crush independent competitors and entrench enduring dominance.
Proactive transparency around model capabilities and limitations, participation in open evaluation regimes, support for interoperable standards, and selective openness in research can mitigate some political risk, although it cannot fully offset the perception that a tiny number of platforms are coming to control the core infrastructure of cognition in the digital age.
For policymakers and regulators, Altman’s “code red” is a prompt to move from reactive crisis management to deliberate architectural design of the AI ecosystem.
This involves at least three dimensions.
The first is competition policy
ensuring that access to compute, data, and distribution is not so concentrated that only a handful of firms can viably develop state‑of‑the‑art models.
Tools here range from traditional antitrust enforcement and merger scrutiny to more novel interventions around access to cloud infrastructure, chip supply, and data resources.
The second is infrastructural governance
designing permitting, zoning, and environmental review frameworks for data centers that are predictable and rigorous but not sclerotic, and that fairly internalize environmental and community costs while allowing strategic national infrastructure to be built.
The third is safety and rights governance
regimes for testing, auditing, and constraining frontier models that enable beneficial innovation while reducing the risk of catastrophic misuse, systemic bias, or democratic destabilization.
A further step is public investment in open, public‑interest AI infrastructure. Rather than leaving the entire frontier in the hands of a few corporate giants, governments and consortia could build shared compute facilities and support open‑source model development oriented toward scientific research, education, and critical public services.
Such initiatives could both diffuse concentration of power and provide a fallback if private capital retreats from over‑extended AI bets.
For civil society and affected communities, the expansion of data centers and AI deployments presents both a challenge and an opportunity. Grassroots resistance has already demonstrated its power to reshape or stall projects worth tens of billions of dollars.
The strategic question is whether this energy will be channelled into blanket obstruction or into negotiated compacts that secure tangible local benefits, robust environmental safeguards, and meaningful labor protections in exchange for hosting critical infrastructure.
Civil society organizations can also play a critical role in monitoring AI systems’ impacts on workers, minorities, and democratic processes, and in pressing for enforcement of the safeguards that governments and firms now increasingly pledge in principle.
Conclusion
Sam Altman’s “code red” should be read not only as an internal emergency code at a single firm, but as a revealing moment in the maturation of the AI megacycle.
It exposes the extent to which the dominant paradigm of frontier model development has become entangled in a triad of systemic risks: a financial structure that demands immense, long‑duration capital commitments on the promise of still‑uncertain monetization; a competitive landscape increasingly tilted toward platform incumbents with the capacity to treat
AI as a cross‑subsidized feature rather than a standalone business; and a political‑social environment in which the physical footprint and societal externalities of AI infrastructure are generating organized resistance and calls for tight governance.
The inversion of the “code red” narrative—from Google’s alarm at ChatGPT in 2022 to OpenAI’s alarm at Gemini and others in 2025—underscores how volatile and path‑dependent this space remains.
Today’s disruptor can become tomorrow’s overextended incumbent, particularly when strategies hinge on staying just ahead in a race whose barriers to entry are falling for well‑resourced rivals and whose resources—capital, energy, land, and public trust—are finite.
Whether this moment marks the beginning of a managed recalibration or the prelude to a more disorderly correction will depend on how effectively firms, governments, and societies can adjust course.
For firms like OpenAI, that means pivoting from a posture of permanent emergency and maximal scale toward one of disciplined prioritization, efficiency, and diversified value creation.
For incumbents, it means recognizing that structural advantage carries responsibility and that overreach could invite regulatory redesign of the market.
For policymakers, it demands a shift from reactive, model‑by‑model interventions to coherent strategies for competition, infrastructure, safety, and public‑interest capacity.
And for communities and civil society, it requires sustained engagement to ensure that AI’s gains do not come at the expense of environmental integrity, local autonomy, and democratic accountability.
If these adaptations succeed, the present “code red” may, in retrospect, appear as the moment when the AI sector began to transition from speculative arms race to more grounded, governed, and pluralistic development.
If they fail, the risks are not limited to one firm’s fortunes: they include an AI bubble whose bursting could reverberate through financial markets and infrastructure plans, and a deeper crisis of legitimacy that could trigger far more radical constraints on the technology’s trajectory than any single competitive setback has yet implied
