Reports that OpenAI hasn't completed a new full-scale pre-training run since May 2024 suggest a strategic shift. The race for raw model scale may be less critical than enhancing existing models with better reasoning and product features that customers demand. The business goal is profit, not necessarily achieving the next level of model intelligence.

A strategic conflict is emerging at Meta: new AI leader Alexander Wang wants to build a frontier model to rival OpenAI, while longtime executives want his team to apply AI to immediately improve Facebook's core ad business. This creates a classic R&D vs. monetization dilemma at the highest levels.

Zuckerberg categorizes AI players by their AGI timeline predictions (optimist, moderate, pessimist), which dictates investment. He positions Meta's strong cash flow as a durable advantage to survive a potential bubble burst that would bankrupt unprofitable competitors like OpenAI.

To outcompete Apple's upcoming smart glasses, Meta might integrate superior third-party AI models like Google's Gemini. This pragmatic strategy prioritizes establishing its hardware as the dominant "operating system" for AI, even if it means sacrificing control over the underlying model.

A strategic rift has emerged at Meta. Long-time executives like Chris Cox want the new AI team to leverage Instagram and Facebook data to improve core ads and feeds. However, new AI leader Alexander Wang is pushing to prioritize building a frontier model to compete with OpenAI and Google first.

Meta benefits from a "do nothing, win" position in consumer-facing AI. The company can avoid costly R&D for new social features, knowing that any successful AI-driven application developed by a competitor can be quickly replicated and scaled across its massive user base, similar to how it handled Stories.

The new, siloed AI team at Meta is clashing with established leadership. The research team wants to pursue pure AGI, while existing business units want to apply AI to improve core products. This conflict between disruptive research and incremental improvement is a classic innovator's dilemma.

Initially, even OpenAI believed a single, ultimate 'model to rule them all' would emerge. This thinking has completely changed to favor a proliferation of specialized models, creating a healthier, less winner-take-all ecosystem where different models serve different needs.

With model improvements showing diminishing returns and competitors like Google achieving parity, OpenAI is shifting focus to enterprise applications. The strategic battleground is moving from foundational model superiority to practical, valuable productization for businesses.