OpenAI's "Code Red": Is the Shallotpeat project now coming as an answer to Google's Gemini 3? Allegedly as early as next week…

OpenAI versus Google in the AI ​​race: An in-depth economic analysis of the market battle

The balance of power in Silicon Valley has shifted dramatically. Three years ago, it was search engine giant Google that was caught off guard by the sudden success of ChatGPT; now, the former pioneer OpenAI finds itself on the defensive. Internal reports paint a picture of a company under immense pressure: CEO Sam Altman has initiated a strategic realignment under the internal banner of “Code Red.” The trigger is the massive technological catch-up by competitor Alphabet, whose latest model, Gemini 3, is leading the benchmarks and whose new imaging model, Nano Banana Pro, is redefining standards.

To maintain its technological dominance, OpenAI is preparing for the accelerated market launch of a new reasoning model, rumored to be the mysterious Shallotpeat. This decisive move is even putting revenue-generating projects like shopping agents and advertising integrations on hold. But the race is no longer purely technological; it's a question of economic endurance. While Alphabet is posting record profits in the hundreds of billions thanks to its cloud infrastructure, OpenAI, despite its rapid growth, is struggling with exploding costs and deep losses. The following analysis sheds light on the financial abyss, the strategic sacrifices of "Code Red," and the question of whether OpenAI can withstand the pressure from the Google machine.

The official name of OpenAI's new reasoning model is not yet publicly confirmed. Internally, the model is being developed under the codename Shallotpeat, according to several reliable sources. This name appears in internal documents and memos from CEO Sam Altman, who describe the model as a targeted response to the performance advantages of Google's Gemini 3. The choice of the name Shallotpeat is programmatic and signals that OpenAI has identified the weaknesses of its existing models during pre-training and intends to address them specifically.

Besides the primary codename Shallotpeat, other names are circulating among developers and on benchmark platforms. For example, a model called Robin was spotted on LM Arena, which may represent a test version or variant. Furthermore, some technical analyses mention a related model called Garlic, described as an independent development of Shallotpeat, which is said to implement specific improvements in programming and logical reasoning.

Speculation about the final product name for next week's release ranges as far as "Red GPT," the latter a reference to the internal Code Red initiative. The official name is expected to be announced shortly before release, with OpenAI traditionally distinguishing between internal code names and public product names.

How a former monopolist became a challenger and is pushing the market leader onto the defensive.

OpenAI is preparing to launch a new reasoning model. Internal tests indicate it is outperforming Google's upcoming Gemini 3. The release is part of a strategic realignment that requires other projects to be sidelined. OpenAI plans to release the new reasoning model as early as next week, according to an internal memo from CEO Sam Altman, reported by The Information. Altman stated that the new model is currently performing better than Google's rival product, Gemini 3, in internal evaluations. It is unclear whether this is the Shallotpeat model. The accelerated launch is the spearhead of an initiative internally dubbed Code Red. OpenAI is responding to growing pressure from Google. The search engine giant increased the monthly active users of its Gemini chatbot from 450 million in July to 650 million in October, and Gemini 3 has outperformed the competition in numerous benchmarks. Altman warned internally of temporary economic headwinds due to the resurgence of the competitor.

To fully focus on improving ChatGPT and its new model, OpenAI is putting other commercial projects on hold, according to The Information. The introduction of advertising and the development of autonomous AI agents for shopping and healthcare tasks are being delayed. Further development of Pulse, a service for personalized briefings, is also no longer a priority. Instead, resources will be channeled into improving model behavior in ChatGPT, and especially image generation. OpenAI's own solution is under increasing pressure after Google released the new, extremely powerful Nano Banana Pro image model alongside Gemini 3. Three years ago, the situation was exactly the opposite: Google itself issued a Code Red alert to respond to the sudden threat to its own search engine posed by the launch of ChatGPT. At that time, the search engine giant restructured numerous teams to accelerate the development of its own AI models, which ultimately led to the release of the Gemini series.

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Financial powerhouses compared: Sales turbulence and profitability challenges

The financial performance of both competitors reveals fundamental differences in their economic starting positions. OpenAI recorded revenue of $4.3 billion in the first half of 2025, a 16 percent increase over the entire year of 2024. Its annualized revenue had already reached $10 billion by June 2025, while the company is targeting at least $12.7 billion for the full year 2025. Despite this impressive growth, OpenAI remains deeply in the red. Research and development costs amounted to $6.7 billion in the first half of 2025, while operating expenses for inference were estimated at $3.8 billion in 2024 and had already reached $8.65 billion by the first half of 2025. Losses in 2024 totaled approximately $5 billion, and the cash burn rate for 2025 is estimated at $8.5 billion. OpenAI spent approximately $3.8 billion on inference in 2024, while most of its $5 billion in research and development expenditures were used for experiments, test runs, and never-to-be-published models. R&D costs increased from $2.5 billion in 2024 to $6.7 billion in the first half of 2025, highlighting the enormous investments in model development and infrastructure scaling.

Alphabet, on the other hand, presents itself as a financially robust heavyweight. The third quarter of 2025 saw the company's first $100 billion quarter in revenue, reaching $102.3 billion, a 16 percent increase year over year. Net income rose 33 percent to $35 billion. Google Cloud saw revenue growth of 34 percent to $15.2 billion, while YouTube Advertising generated $10.3 billion. Capital expenditures on technical infrastructure totaled $24 billion in the quarter, underscoring the massive investment in AI infrastructure. This financial strength allows Google to make long-term investments in AI research and infrastructure without facing immediate profitability pressures. The cumulative cloud backlog reached $155 billion, indicating sustained growth and long-term customer contracts. The financial disparity between the two companies is striking: While Alphabet has already generated billions in profits and has a group-wide cash flow, OpenAI has to seek external financing and is struggling with massive operating losses.

Code Red as strategic fine-tuning: Economic implications of a focus

OpenAI's Code Red initiative is not merely a reaction to adversity, but rather a strategic fine-tuning of resource allocation under competitive pressure. Sam Altman launched this internal initiative to improve ChatGPT and delay other projects such as advertising, shopping agents, and healthcare AI agents. This shift in priorities reflects a classic economic adjustment to changing competitive realities. The implicit opportunity costs of the delayed projects must be weighed against the anticipated benefits of the accelerated model launch. OpenAI is focusing on improving user experience, personalization, speed, and reliability. The focus on image generation in response to Google's Nano Banana Pro demonstrates a defensive positioning in a strategically important market segment. The decision to pause advertising integrations, despite code findings in the Android app already hinting at such plans, suggests an internal prioritization that sacrifices short-term monetization for long-term market positioning. The Code Red strategy thus sends an economic signal to the market: OpenAI is accepting temporary economic headwinds in order to defend its technological leadership. The Pulse projects for personalized briefings and autonomous shopping agents are being postponed, even though they represent potential revenue streams. This resource allocation follows the economic principle of maximizing returns under uncertainty: the risk of losing ground in the core business is considered greater than the risk of missing out on side projects.

User metrics and market share dynamics: The quantitative competitive landscape

User metrics reveal a complex competitive dynamic between first-mover advantage and ecosystem integration. ChatGPT recorded between 700 and 800 million weekly active users in October 2025, while Gemini reached 650 million monthly active users during the same period. The growth rates are remarkable: Gemini increased from 450 million users in July to 650 million in October 2025, representing a growth of over 44 percent in just three months. ChatGPT's growth, on the other hand, slowed: From December 2024 to February 2025, the number of weekly users increased from 300 million to 400 million, a rise of 33 percent, but the growth rate decreased throughout 2025. Sources report that ChatGPT held a 60.4 percent share of the generative AI market in February 2025, while Gemini reached 13.5 percent. Microsoft Copilot uses OpenAI's models and achieved a 14.1 percent market share. The geographic distribution shows similarities: the US represents the largest user market for both platforms, with 15.1 percent for ChatGPT and 14.6 percent for Gemini. India is the second-largest market for both. The 25-34 age group is the most relevant for both platforms. However, usage intensity differs: ChatGPT users spend an average of 12 minutes and 9 seconds per visit, while Gemini users spent an average of 7 minutes and 8 seconds in October 2025. Pageviews per visit are 4.5 for ChatGPT and 4.52 for Gemini. The bounce rate for ChatGPT is 40.01 percent. The daily prompt count for ChatGPT reached 2.5 billion in July 2025. Monthly token processing at Google increased to 7 trillion tokens per minute. The number of enterprise customers for Gemini on Google Cloud grew 35-fold year over year. 92 percent of Fortune 500 companies use ChatGPT in some form. Google's search engine integration via AI Overviews reaches 2 billion monthly users. Gemini's Android integration reaches over 3 billion devices worldwide.

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Research and development investments as a competitive factor: The scaling economics of AI training

Research and development costs reveal fundamental differences in the scaling economies of the two companies. OpenAI invested $2.5 billion in R&D in 2024, with the majority spent on experiments and unpublished models. The first six months of 2025 saw R&D spending of $6.7 billion, suggesting an annual rate exceeding $13 billion. Inference costs skyrocketed from $3.8 billion in 2024 to $8.65 billion in the first half of 2025. This cost explosion reflects the economic reality of AI model training: the more powerful the models, the higher the computational costs. Most of the $5 billion in 2024 R&D spending went toward research runs, experimental training, or de-risking, while only $480 million was used to train actually released models such as GPT-4.5, GPT-4o, and o3. The implicit marginal costs of model improvement increase exponentially. OpenAI spent approximately $1.8 billion on inference in 2024, implying a margin of around 50 percent with a return on sales of approximately $3.7 billion. Total compute costs in 2024 were estimated at $6 billion. R&D spending for 2024 was $5 billion, with amortization occurring over several years. Sales and marketing expenses reached $2 billion in the first half of 2025. Employee salaries are also in the billions. Total capacity planning calls for the construction of 250 gigawatts of computing capacity by 2033, which would cost $10 trillion. One-gigawatt data centers cost between $32.5 billion and $60 billion and require two and a half years to build. Total funding needs for the next twelve months are estimated at $400 billion.

Google has a company-wide R&D budget of over $40 billion annually, with a significant portion allocated to AI research. Capital expenditures on technical infrastructure alone reached $24 billion in the third quarter of 2025. Total investments in AI infrastructure for 2025 are estimated at $85 billion. Google processes 980 trillion tokens monthly, nearly double the 480 trillion tokens processed in May 2025. Energy efficiency improved 33-fold, and the carbon footprint per prompt was reduced 44-fold. Google can amortize the costs of model training and inference through its existing cloud business and leverage synergies from its semiconductor development. In-house chip development using TPUs reduces reliance on NVIDIA and lowers the marginal cost of inference. Google's scalability advantages are substantial: The integration of Gemini into the search engine, Android, and Workspace enables immediate distribution to billions of devices without additional customer acquisition costs. Infrastructure costs can be amortized across the entire Alphabet portfolio, whereas OpenAI must bear all costs internally.

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Monetization strategies and long-term profitability: Paths to economic sustainability

The monetization approaches of the two companies differ fundamentally and have profound implications for their long-term profitability. OpenAI generates revenue primarily through ChatGPT subscriptions, API usage, and enterprise sales. The number of paying subscribers has reached ten million across Plus, Team, and Pro, plus one million commercial users. Annual subscription revenue is $2.7 billion and is projected to reach $4 billion by the end of 2025. API usage and enterprise sales further contribute to revenue diversification. OpenAI offers productivity features such as spreadsheet and presentation editing to integrate more deeply into enterprise workflows. Pricing follows a classic freemium model, with $20 per month for premium features. The return on sales from inference is approximately 50 percent, which should lead to improved scaling economics as user numbers increase. However, costs are growing faster than revenues: R&D spending increased from $2.5 billion in 2024 to $6.7 billion in the first half of 2025. Inference costs rose from $3.8 billion in 2024 to $8.65 billion in the first half of 2025. This cost structure necessitates continuous capital raising. OpenAI announced a $40 billion funding round in March 2025, led by SoftBank, at a valuation of $300 billion. Funding needs for the next twelve months are estimated at $400 billion. Long-term profitability depends on the ability to reduce inference costs and develop new monetization channels. While planned advertising integrations have been paused, they remain a potential revenue stream. The development of autonomous shopping agents and healthcare AI has also been delayed, even though these markets offer significant revenue potential. The intensity of competition forces OpenAI to sacrifice short-term monetization in favor of market positioning.

Google pursues a different monetization strategy. Revenue from Gemini is not reported separately but is integrated into the overall revenue of Google Search, Google Cloud, and Workspace. The AI ​​Overviews in the search engine reach two billion monthly users and indirectly contribute to advertising revenue. Google Cloud revenue grew 34 percent to $15.2 billion, with Gemini playing a significant role for enterprise customers. Gemini's 85,000 enterprise customers on Google Cloud saw a 35-fold year-over-year increase in usage. Gemini's pricing is similar to OpenAI's, with $20 per month for premium features, but monetization is handled through Google's existing billing and distribution system. Alphabet's advertising revenue reached $74.18 billion in the third quarter of 2025, a 12.7 percent increase year over year. YouTube advertising reached $10.26 billion. Subscription revenue grew by 21 percent to $12.9 billion. Google can recoup the costs of its AI infrastructure through multiple revenue streams and leverage synergies from its existing advertising platform. Gemini's long-term profitability is therefore less dependent on direct subscriptions than on strengthening the overall Alphabet ecosystem. The $155 billion cloud backlog provides visibility into future revenue. Alphabet's operating margins are 30.5 percent, while OpenAI continues to post substantial losses.

Economic future scenarios and competitive forecasts: Paths to market maturity

The long-term economic prospects of both companies show different paths to market maturity and profitability. OpenAI needs to achieve massive economies of scale to reduce inference costs and recoup its R&D expenditures. With 800 million weekly active users and 10 million paying subscribers, OpenAI has a solid foundation for growth. Its planned expansion to one billion users by the end of 2025 would significantly improve its economies of scale. However, costs are growing faster than revenues. R&D expenditures increased from $2.5 billion in 2024 to $6.7 billion in the first half of 2025. Inference costs increased from $3.8 billion in 2024 to $8.65 billion in the first half of 2025. To achieve profitability, OpenAI needs to reduce inference costs per user and develop new monetization channels. The planned advertising integrations could generate substantial revenue, but user adoption is uncertain. Developing specialized enterprise solutions for healthcare, finance, and e-commerce could generate higher margins than standard subscriptions. The API platform for developers offers an ecosystem approach, but competition from open-source models like Meta's Llama and Mistral is intensifying. Altman's long-term vision calls for 250 gigawatts of computing capacity by 2033, which would cost ten trillion dollars. This ambition requires sustained funding of over 400 billion dollars in the next twelve months. OpenAI's valuation could reach 300 billion dollars, but profitability remains elusive. Competitive dynamics force OpenAI to balance short-term monetization with long-term market positioning. The Code Red strategy signals a commitment to competitive pressure and a willingness to sacrifice short-term gains. The question is whether the capital markets will fund this growth model in the long run.

Google is pursuing a different path to market maturity. Integrating Gemini into its search engine, Android, and Workspace enables slow but steady monetization without direct billing. AI Overviews reaches two billion users and improves search quality, indirectly boosting advertising revenue. Enterprise adoption is growing, with 85,000 customers and a 35-fold increase in usage. The $155 billion cloud backlog provides visibility into future revenue. The $85 billion capital expenditure on AI infrastructure in 2025 will be recouped across the entire Alphabet portfolio. In-house chip development using TPUs reduces reliance on NVIDIA and lowers inference costs. Energy efficiency has improved 33-fold, and the carbon footprint has been reduced 44-fold. Google can spread the fixed infrastructure costs across multiple revenue streams, while OpenAI bears all costs internally. Google's long-term strategy appears to be aimed at establishing AI as a commodity and monetizing it through its ecosystem. Cloud margins improve with increasing automation and scalability. Google's search engine market power remains intact with a 90 percent market share. Regulatory risks from antitrust proceedings, particularly the EU's $3.45 billion fine, could impact business models. In the long term, Google could offer AI services as part of its cloud and advertising platform, while OpenAI must focus on pure AI models. The question is whether the pure AI business model can be economically sustainable or whether it will ultimately be absorbed by the major cloud providers. Competitive dynamics show that Google can defend its market position through ecosystem integration and financial strength, while OpenAI relies on technological innovation and rapid growth. The economic sustainability of both approaches will be determined in the next three to five years.

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