Loyalty Is Dead in Silicon Valley
From the middle Over the past year, there have been at least three major AI “acquisitions” in Silicon Valley. Meta invested more than $14 billion in Scale AI and brought in its CEO, Alexandr Wang; Google spent $2.4 billion to license Windsurf’s technology and integrate its co-founders and research teams into DeepMind; and Nvidia bet $20 billion on Groq’s inference technology and hired its CEO and other employees.
Frontier AI labs, meanwhile, are engaged in a high-stakes and seemingly endless game of musical chairs. The latest shakeup began three weeks ago, when OpenAI announced it was rehiring several researchers who left less than two years earlier to join Mira Murati’s startup Thinking Machines. At the same time, Anthropic, itself founded by former members of OpenAI, poached the talents of the creator of ChatGPT. OpenAI, in turn, just hired a former Anthropic security researcher to be its “readiness manager.”
The churn occurring in Silicon Valley represents the “great unbundling” of tech startups, as Dave Munichiello, an investor at GV, put it. In the past, tech founders and their early 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 quickly, have lots of capital and are particularly valued for the strength of their research talents, “you invest in a startup knowing that it could be broken up,” Munichiello told me.
Early founders and researchers of the hottest AI startups are moving to different companies for a variety of reasons. For many, money is of course an important motivator. Last year, Meta reportedly offered top AI researchers compensation in the tens or hundreds of millions of dollars, giving them not only access to cutting-edge computing resources, but also… generational wealth.
But it’s not just about getting rich. The broader cultural shifts that have rocked 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 researcher at Mozilla. Employers assumed that workers would stay on the job at least until four years, by which time their stock options typically had to vest. Back in the 2000s and 2010s, many co-founders and employees sincerely believed in their company’s stated missions and wanted to be there to help achieve them.
Today, Kapoor says, “people understand the limitations of the institutions they work in and founders are more pragmatic.” The founders of Windsurf, for example, may have calculated that their impact could be greater at a place like Google that has many resources, Kapoor says. He adds that a similar change is happening in academia. Over the past five years, Kapoor says, he has seen more and more doctoral students drop out of 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, fearful of becoming collateral damage in the war for AI talent, are taking steps to protect themselves. Max Gazor, the founder of Striker Venture Partners, says his team is selecting founding teams “for chemistry and cohesion” more than ever. Gazor says it’s also increasingly common for deals to include “safeguard provisions that require board consent for significant intellectual property licenses or similar scenarios.”
Gazor notes that some of the biggest acquisition deals recently involved startups founded well before the current generative AI boom. Scale AI, for example, was founded in 2016, at a time when the type of deal Wang negotiated with Meta would have been unimaginable to many. Now, however, these potential outcomes could be factored into early term sheets and “managed constructively,” says Gazor.
