Andrew Tulloch left Thinking Machines Lab to join Meta Platforms, a move framed as part of the growing AI talent war. Reports highlight a blockbuster compensation package and signal how big tech uses resources to recruit top AI researchers, with implications for startups, research diversity and hiring strategies.
Andrew Tulloch, a cofounder of Thinking Machines Lab, has left the startup to join Meta Platforms, multiple outlets reported on October 12, 2025. The departure, first noted by the Wall Street Journal and repeated by Reuters, TechCrunch and Yahoo Finance, is being presented as another high stakes win for big tech in the AI talent war. The hire raises questions about how compensation, compute and scale are reshaping who builds the next generation of AI.
Thinking Machines Lab, started by well known figures including Mira Murati, is one of several startups pushing frontier AI research outside of the largest companies. Startups often compete on agility and research focus, while large firms can offer massive computing resources and multimillion dollar compensation to attract top AI researchers. The current period has been described as an AI talent war, where established platforms and deep pocketed firms recruit researchers and engineering leaders to accelerate product development and model innovation.
For Meta, securing a recognized researcher from a high profile lab signals continued ambition in recruitment and product timelines. Adding experienced people can accelerate internal initiatives that depend on advanced model capabilities and lend credibility to public research efforts.
For startups and research labs, losing a founder or senior researcher is disruptive because these individuals often hold institutional knowledge, research direction and industry relationships. Startups must now weigh retention strategies that include meaningful equity, clear career paths and the promise of research independence if they cannot match big tech on pay.
For the broader AI ecosystem, repeated movement of senior researchers into large platforms can concentrate expertise and technological leverage. That concentration may speed advances in consumer and enterprise AI, but it also raises concerns about the diversity of approaches and independent research capacity. Policymakers and funders may increase focus on ways to preserve open and nonprofit research initiatives.
This hire aligns with broader AI workforce trends in 2025. Capital and compute remain necessary but not sufficient; the human layer of top researchers and engineers still determines who controls the fastest paths from research to product. As the AI talent war continues, companies, funders and policymakers will need to consider how to maintain a balance between concentrated product power and a diverse research ecosystem.
Andrew Tullochs move to Meta is both a single personnel story and a reflection of systemic dynamics. The key question going forward is whether the field can preserve multiple research approaches while enabling rapid product innovation at scale.
