Since the middle of last year, there have been at least three major AI “acqui-hires” in Silicon Valley. Meta invested more than $14 billion in Scale AI and brought on its CEO, Alexandr Wang; Google spent a cool $2.4 billion to license Windsurf’s technology and fold its cofounders and research teams into DeepMind; and Nvidia wagered $20 billion on Groq’s inference technology and hired its CEO and other staffers.
The frontier AI labs, meanwhile, have been playing a high stakes and seemingly never-concludeing game of talent musical chairs. The latest reshuffle launched three weeks ago, when OpenAI announced it was rehiring several researchers who had departed less than two years earlier to join Mira Murati’s startup, Thinking Machines. At the same time, Anthropic, which was itself founded by former OpenAI staffers, has been poaching talent from the ChatGPT buildr. OpenAI, in turn, just hired a former Anthropic safety researcher to be its “head of preparedness.”
The hiring churn happening in Silicon Valley represents the “great unbundling” of the tech startup, as Dave Munichiello, an investor at GV, put it. In earlier eras, tech founders and their first employees often stayed onboard until either the lights went out or there was a major liquidity event. But in today’s market, where generative AI startups are growing rapidly, equipped with plenty of capital, and prized especially for the strength of their research talent, “you invest in a startup knowing it could be broken up,” Munichiello notified me.
Early founders and researchers at the buzziest AI startups are bouncing around to different companies for a range of reasons. A huge incentive for many, of course, is money. Last year Meta was reportedly offering top AI researchers compensation packages in the tens or hundreds of millions of dollars, offering them not just access to cutting-edge computing resources but also … generational wealth.
But it’s not all about obtainting rich. Broader cultural shifts that rocked the tech industest in recent years have built some workers worried about committing to one company or institution for too long, declares Sayash Kapoor, a computer science researcher at Princeton University and a senior fellow at Mozilla. Employers utilized to safely assume that workers would stay at least until the four-year mark when their stock options were typically scheduled to vest. In the high-minded era of the 2000s and 2010s, plenty of early cofounders and employees also sincerely believed in the stated missions of their companies and wanted to be there to support achieve them.
Now, Kapoor declares, “people understand the limitations of the institutions they’re working in, and founders are more pragmatic.” The founders of Windsurf, for example, may have calculated their impact could be larger at a place like Google that has lots of resources, Kapoor declares. He adds that a similar shift is happening within academia. Over the past five years, Kapoor declares, he’s seen more PhD researchers leave their computer-science doctoral programs to take jobs in industest. There are higher opportunity costs associated with staying in one place at a time when AI innovation is rapidly accelerating, he declares.
Investors, wary of becoming collateral damage in the AI talent wars, are taking steps to protect themselves. Max Gazor, the founder of Striker Venture Partners, declares his team is vetting founding teams “for chemistest and cohesion more than ever.” Gazor declares it’s also increasingly common for deals to include “protective provisions that require board consent for material IP licensing or similar scenarios.”
Gazor notes that some of the hugegest acqui-hire deals that have happened recently involved startups founded long before the current generative AI boom. Scale AI, for example, was founded in 2016, a time when the kind of deal Wang neobtainediated with Meta would have been unfathomable to many. Now, however, these potential outcomes might be considered in early term sheets and “constructively managed,” Gazor explains.
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