Google pulls ahead with pre‑training; OpenAI's comeback plan named ‘Shallotpeat’
Google’s latest AI benchmarks have shifted the conversation from headline‑grabbing demos to the mechanics of model development. While OpenAI is rolling out a comeback plan codenamed “Shallotpeat,” the company’s own leadership is quietly acknowledging a gap. In a recent note, Sam Altman praised Google’s recent work, singling out the firm’s focus on pre‑training as a key differentiator.
That acknowledgment comes at a time when the industry is debating whether the massive data‑injection stage that fuels large language models still matters or has been eclipsed by fine‑tuning tricks. Here’s why that debate matters: pre‑training determines how broadly a model can understand language before any task‑specific tweaks. If a competitor can out‑learn the world’s text faster, it gains a head start that later refinements can only amplify.
Altman’s concession hints at a strategic recalibration for OpenAI. As he puts it, the next line explains why the foundational phase can’t be ignored.
Pre-training isn't dead, it's crucial What's interesting is the role that pre-training played in Google's success. In his note, Altman admitted that Google has "been doing excellent work recently," especially in pre-training. This fundamental phase, where an AI model learns from vast amounts of data, seemed to have hit its limits.
But Google's success shows that while massive performance leaps may not be on the immediate horizon, effective advantages can still be gained. This is a particularly sore spot for OpenAI, as the company has reportedly struggled to make progress in pre-training. This prompted OpenAI to focus more on "reasoning" models.
Google leads. OpenAI's internal memo paints a stark picture, warning that the company's recent advances in pre‑training could create temporary economic headwinds for its rival. Altman’s note acknowledges that Google’s work in the fundamental pre‑training phase—where models ingest massive data—has been excellent, and he stresses that pre‑training “isn’t dead, it’s crucial.” Yet the memo also hints at uncertainty: the vibes are “rough for a bit,” and the path forward is not fully mapped.
So, what will “Shallotpeat” deliver? The codename suggests a concerted effort to counter Google’s momentum, but details remain scarce. If OpenAI can match or surpass the scale of Google’s pre‑training, the competitive balance could shift; otherwise, the gap may widen.
Meanwhile, both firms appear locked in a race where foundational model training still matters more than ever. Whether OpenAI’s response will close the lead is unclear, and the broader impact on the market stays uncertain. Stakeholders are watching the internal dynamics, but no public roadmap has been released. The memo’s tone suggests caution rather than confidence, underscoring the competitive pressure.
Further Reading
- Google launches Gemini 3 with new coding app and record benchmark scores - TechCrunch
- The Scaling Wall Was A Mirage - Tomasz Tunguz
- AI Benchmarks 2025: Performance Metrics Show Record Gains - Sentisight
- A new era of intelligence with Gemini 3 - Google Blog
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Common Questions Answered
What is the name of OpenAI's comeback plan mentioned in the article?
OpenAI's comeback plan is codenamed “Shallotpeat.” The memo references this initiative as the company's strategy to regain competitive footing after acknowledging Google's recent advances.
Why did Sam Altman praise Google's recent work, according to the article?
Sam Altman highlighted Google's focus on pre‑training as a key differentiator, stating that the company has been doing excellent work in that fundamental phase. He emphasized that pre‑training isn’t dead and remains crucial for model performance.
How does the article describe the impact of Google's pre‑training success on OpenAI?
The article notes that Google's success in pre‑training could create temporary economic headwinds for OpenAI, potentially affecting its market position. OpenAI's internal memo warns that these advances may pose short‑term challenges for the rival.
What shift in industry conversation does the article attribute to Google's latest AI benchmarks?
Google’s latest benchmarks have moved the discussion from headline‑grabbing demos to the mechanics of model development, especially the pre‑training stage. This shift underscores the growing importance of data‑injection and foundational training phases.