The investment thesis for new AI research labs isn't solely about building a standalone business. It's a calculated bet that the elite talent will be acquired by a hyperscaler, who views a billion-dollar acquisition as leverage on their multi-billion-dollar compute spend.

Paying billions for talent via acquihires or massive compensation packages is a logical business decision in the AI era. When a company is spending tens of billions on CapEx, securing the handful of elite engineers who can maximize that investment's ROI is a justifiable and necessary expense.

Instead of simple cash transactions, major AI deals are structured circularly. A chipmaker sells to a lab and effectively finances the purchase with stock warrants, betting that the deal announcement itself will inflate their market cap enough to cover the cost, creating a self-fulfilling financial loop.

Demis Hassabis chose to sell DeepMind to Google for a reported $650M, despite investor pushback and the potential for a much higher future valuation. He prioritized immediate access to Google's vast computing resources to 'buy' five years of research time, valuing mission acceleration over personal wealth.

The most lucrative exit for a startup is often not an IPO, but an M&A deal within an oligopolistic industry. When 3-4 major players exist, they can be forced into an irrational bidding war driven by the fear of a competitor acquiring the asset, leading to outcomes that are even better than going public.

The AI fundraising environment is fueled by investors' personal use of the products. Unlike B2B SaaS where VCs rely on customer interviews, they directly experience the value of tools like Perplexity. This firsthand intuition creates strong conviction, contributing to a highly competitive investment landscape.

The massive partnership between Nvidia and OpenAI was negotiated directly between founders, bypassing investment bankers entirely. This highlights a trend where major strategic deals are executed outside of traditional financial institutions.

The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.

After reportedly turning down a $1.5B offer from Meta to stay at his startup Thinking Machines, Andrew Tulloch was allegedly lured back with a $3.5B package. This demonstrates the hyper-inflated and rapidly escalating cost of acquiring top-tier AI talent, where even principled "missionaries" have a mercenary price.