Inside OpenAI’s Code Red: The Secret Plan to Outrun Google’s Gemini 3

OpenAI's "Code Red": The Battle for AI Supremacy Against Google's Gemini 3

The landscape of artificial intelligence is experiencing a seismic shift, with Google recently planting its flag firmly on the AI hill. Following the impressive capabilities demonstrated by Gemini 3 and its proprietary TPU architecture, the perception of Google's lead in AI has grown significantly. This surge has prompted a decisive response from OpenAI, with CEO Sam Altman declaring a "Code Red" and confirming a new secret model, "Garlic," is in development to directly challenge Google's recent gains.

Google's Formidable AI Position

Google's strengths in the AI race are comprehensive. The company possesses a frontier model, robust AI infrastructure, custom silicon in the form of TPUs, substantial revenue, a wealth of talented researchers, and an immense amount of data. This combination positions Google as a formidable competitor, especially after Gemini 3 showcased remarkable scaling capabilities, seemingly surpassing what many in the industry believed was a "wall" in pre-training.

The success of Gemini 3 is largely attributed to Google's TPUs, their custom silicon designed specifically for running artificial intelligence workloads. These TPUs have been in development for years, and their benefits are now evidently coming to fruition, enabling Google to deploy a massive model that performs incredibly well. This achievement led many to count OpenAI out, but the competition is far from over.

The Scaling Wall Debate: A Crossroads for AI Research

Before Google's Gemini 3 demonstration, a consensus among leading AI researchers suggested that the "age of scaling" might be over. According to semi-analysis, OpenAI's researchers had not completed a successful full-scale pre-training run for a new frontier model that was broadly deployed since GPT-4o in May 2024, highlighting significant technical hurdles.

Ilya Sutskever, OpenAI co-founder and current founder of Safe Superintelligence, articulated this sentiment on the Dwarkesh Patel podcast. He stated that the industry is transitioning back to the "age of research," implying a need for new ideas and algorithms rather than merely scaling up existing methods. Sutskever noted that from 2012 to 2020 was the age of research, followed by the age of scaling from 2020 to 2025. However, he questioned whether a 100x increase in scale would still yield significantly different results, suggesting it might not.

Andre Karpathy, another prominent AI researcher, echoed similar views on the same podcast, while Yann LeCun has consistently maintained for years that large language models in their current form are reaching their limits and require new fundamental ideas. These perspectives underscored a belief that LLMs were at a dead end and that simply scaling them further was no longer effective.

However, Gemini 3 challenged this notion, demonstrating that scaling is "far from dead." This has led to speculation that Google's unique architecture or methods, particularly their TPUs, allowed them to overcome the perceived pre-training scaling wall.

OpenAI's "Code Red" and Strategic Reorientation

In response to Google's advancements and the industry's shift, Sam Altman declared a "Code Red" within OpenAI. This directive signifies an intense focus on improving the quality of ChatGPT and competing fiercely in the grand model race, even at the sacrifice of progressing other products. Internal memos, such as the Kobayashi letter, indicate that OpenAI is pausing projects like ads and shopping to concentrate on enhancing ChatGPT's performance.

Altman explicitly stated that OpenAI has "more work to do on the day-to-day experience of its chatbot." Key areas of improvement include enhancing personalization features for users, increasing speed and reliability, and enabling the chatbot to answer a wider range of questions. This strategic shift emphasizes that for the vast majority of users and use cases, the core intelligence of current models is already sufficient. What truly matters is the "day-to-day experience" and the surrounding "scaffolding" that makes the AI accessible and user-friendly.

For 99% of users, the models are good enough to serve their needs. ChatGPT has maintained its position as "the verb" for AI interaction, similar to how Google became synonymous with search. The ChatGPT experience is still considered "killer," and despite some frontier users switching to whichever model is momentarily superior, the majority of the population is unlikely to switch unless major issues arise with ChatGPT.

Introducing "Garlic": OpenAI's New Frontier Model

As part of its "Code Red" initiative, OpenAI is actively developing a new model code-named "Garlic." This model is specifically designed to counter Google's recent gains, particularly on the pre-training front. OpenAI's Chief Research Officer, Mark Chen, recently confirmed a supercharging of pre-training efforts over the last half-year, with a dedicated "superstar team" focusing on building this crucial muscle at OpenAI.

Chen explicitly stated that OpenAI believes there is "a lot of room left in pre-training" and that scaling is "not dead at all." He expressed confidence that these renewed efforts are leading to the training of "much stronger models," allowing OpenAI to go "head-to-head with Gemini 3 easily on pre-training."

"Garlic" has reportedly been performing well on OpenAI's internal evaluations when compared to Gemini 3 and Anthropic's Opus 4.5. It's suggested that "Garlic" incorporates bug fixes developed during the pre-training process of another rumored model, "Shallot Pete." Furthermore, Altman hinted at a potential "December surprise," which could mean the release of GPT 5.2 or even 5.5 by the end of the year, although "Garlic" is distinct from "Shallot Pete."

The Competitive AI Landscape

The AI market is intensifying. Google has seen significant user growth, expanding from 450 million active users to 650 million in just a few months. Google is also incredibly well-positioned financially, with diverse revenue streams and substantial cash reserves, allowing them to fund and potentially absorb losses in their AI division while competing with OpenAI and Anthropic.

OpenAI, while nearing a billion active users, has reportedly seen its growth slow down. Despite this, the company remains a formidable force. The intense competition drives innovation, pushing both giants to refine their strategies and technologies.

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Conclusion

The AI race between Google and OpenAI has entered a new, highly competitive phase. Google's Gemini 3 and TPU architecture have challenged previous assumptions about the limits of AI scaling, prompting OpenAI's swift "Code Red" response. With Sam Altman's focus on enhancing ChatGPT's user experience and the active development of powerful new models like "Garlic," OpenAI is signaling its intent to reclaim its lead. The debate over scaling versus research continues, but one thing is clear: the pursuit of advanced artificial intelligence is pushing the boundaries of innovation, promising exciting developments for users and the industry alike.