Loyalty Is Dead in Silicon Valley
Since the middle as 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 co-founders and research teams into DeepMind; and Nvidia wagered $20 billion on Groq's inference technology and hired its CEO and other employees.
The frontier AI labs, meanwhile, have been playing a high-stakes and seemingly never-ending game of talent musical chairs. The final reshuffle started three weeks agowhen OpenAI announced it was rehiring several researchers who had left 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 employees, has stumbled upon talent from the ChatGPT creator. OpenAI, in turn, just hired a former anthropic security researcher to be his “chief of preparedness.”
The hiring churn happening in Silicon Valley represents the “great dissolution” of the tech startup, as Dave Munichiello, an investor at GV, put it. In previous eras, tech founders and their first employees often stayed on board until the lights went out or there was a major liquidity event. But in today's market, where generative AI startups are growing fast, equipped with lots of capital, and valued above all for the strength of their research talent, “you invest in a startup knowing it can be broken up,” Munichiello told me.
Early founders and researchers at the buzziest AI startups bounce around to different companies for different reasons. A big incentive for many is of course money. Last year, Meta reportedly offered compensation packages to top AI researchers in the tens or hundreds of millions of dollarsand offer them not only access to modern computing resources, but also … generational wealth.
But it's not all about getting rich. Broader cultural shifts that have shaken the tech industry in recent years have made some workers worry about committing too long to one company or institution, says Sayash Kapoor, a computer science researcher at Princeton University and a senior fellow at Mozilla. Employers used 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 heady era of the 2000s and 2010s, many early co-founders and employees also genuinely believed in their companies' stated missions and wanted to be there to help achieve them.
Now, says Kapoor, “people understand the limitations of the institutions they work in, and founders are pragmatic.” The founders of Windsurf, for example, may have calculated that their influence could be greater at a place like Google that has a lot of resources, Kapoor says. He adds that a similar shift is happening within academia. In the past five years, Kapoor says, he has seen more PhD researchers leave their computer science doctoral programs to take jobs in industry. There are higher opportunity costs associated with staying in one place at a time when AI innovation is rapidly accelerating, he says.
Investors, wary of getting collateral damage in the AI talent wars, are taking steps to protect themselves. Max Gazor, the founder of Striker Venture Partners, says his team monitors founding teams “more than ever for chemistry and cohesion.” Gazor says it's also increasingly common for deals to include “protective provisions that require board approval for material IP licensing or similar scenarios.”
Gazor notes that some of the biggest acquisition 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 negotiated with Meta would have been unfathomable to many. Now, however, these potential outcomes can be considered in early term sheets and “constructively managed”, explains Gazor.
