Meta’s multi-hundred million dollar comp offers and Google’s multi-billion dollar Character AI and Windsurf deals signal that we are in a crazy AI talent bubble.
The talent mania could fizzle out as the winners and losers of the AI war emerge, but it represents a new normal for the foreseeable future. If the top 1% of companies drive the majority of VC returns, why shouldn’t the same apply to talent? Our natural egalitarian bias makes this unpalatable to accept, but the 10x engineer meme doesn’t go far enough – there are clearly people that are 1,000x the baseline impact.
This inequality certainly manifests at the founder level (Founders Fund exists for a reason), but applies to employees too. Key people have driven billions of dollars in value – look at Jony Ive’s contribution to the iPhone, or Jeff Dean’s implementation of distributed systems at Google, or Andy Jassy’s incubation of AWS.
The tech industry gradually scaled capital deployment, compounding for decades to reach trillions in market cap. The impact on the labor force has been inflationary, but predictable. But in the two and a half years post-ChatGPT, AI catch-up investment has gone parabolic, initially towards GPUs and mega training runs. As some labs learned that GPUs alone don't guarantee good models, the capital cannon is shifting towards talent.
Silicon Valley built up decades of trust – a combination of social contracts and faith in the mission. But the step-up in the capital deployment is what Deleuze would call a deterritorializing force, for both companies and talent pools. It breaks down the existing rules of engagement, from the social contract of company formation, to the loyalty of labor, to the duty to sustain an already-working product, to the conflict rules that investors used to follow.
Trust can no longer be assumed as an industry baseline. The social contracts between employees, startups, and investors must be rewritten. In the age-old tension between mission and money, missionary founders must prepare themselves for the step-function increase in mercenary firepower.
Hypercapitalist AI talent wars will rewrite employment contracts and investment norms, concentrate returns, and raise the bar for mission and capital required to create great new companies.
Talent
As a thought exercise, how much should Google have paid for Deepmind? In 2014, a $400M acquisition for a pre-revenue company seemed nonsensical. But with the leverage that comes with Google scale, the DCF value could be quite high – a few percentage points in net savings from their datacenter costs could make it a 100x+ return over a decade, and that’s in a pre-LLM world! In the context of Google paying $3B for Noam, they’ve probably already earned back that investment in his help getting Gemini training runs unstuck; the deal even looks modest with a year of hindsight.
From the Big Tech point of view, if AI is a $10T+ revenue opportunity, and your research team sized scales sublinear to revenue with a cap of a few hundred researchers, is the difference between spending $5M/year/researcher and $10M/year and $20M/year enough to stop you? $10B per year in researcher comp is less than a quarter of Meta’s annual capex. No matter the odds of ultimate product-market fit, the sunk cost is too large to turn back now.
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