By Pukar C. Hamal
Venture capital has become a mechanism for extracting executives from trillion-dollar companies and paying them whatever it takes to build in an AI-native world.
We’re not funding companies anymore — we’re buying access to the few hundred people who’ve built AI systems inside Google, OpenAI and Meta.
Safe Superintelligence was founded by Ilya Sutskever (ex-OpenAI chief scientist), Daniel Gross (ex-Apple AI lead), and Daniel Levy (former researcher at OpenAI). The company operates with roughly 20 employees. So far, it’s raised $3 billion at a $32 billion valuation, without a product or any revenue. What they do have is three executives from trillion-dollar companies who understand how to build superintelligence. That alone commands $1 billion in capital per team member.
This has become the playbook.
Microsoft agreed to pay Inflection AI $650 million to use its models and hire DeepMind co-founder Mustafa Suleyman as CEO of Microsoft AI, along with most of Inflection’s 70-person team. Meta CEO Mark Zuckerberg reportedly offered Andrew Tulloch up to $1.5 billion over six years. Google struck a $2.4 billion nonexclusive licensing deal with Windsurf and hired its CEO, co-founder and select R&D staff. Meanwhile, OpenAI CEO Sam Altman has publicly stated that Meta offered top OpenAI talent $100 million signing bonuses.
The traditional venture formula is inverting. It used to be simple: raise $20 million, spend 95% on growth and headcount, allocate 5% to executive comp. Now the majority of capital flows toward recruiting a handful of executives who understand how to operate in an AI-native environment.
But most founders can’t compete in $20 million bidding wars. And so there’s an asymmetric play, and it’s 10x cheaper.
Why executive judgment is the new scarce resource
In an agentic era, AI systems write code, process data, handle customer service and automate operations. The scarcest resource has become the judgment of executives who know how to orchestrate these systems effectively.
Think about what this means in practice. A decade ago, $100 million might have hired 200 engineers. Today, that same capital might fund five FAANG executives at $10 million each, with the remaining $50 million allocated to compute, AI tooling and a skeleton crew of 20 to 30 people overseeing autonomous agents.
Executives from frontier AI companies command massive premiums because they possess knowledge that doesn’t exist elsewhere. They navigate what’s possible with current AI capabilities, understand the economics of model training and inference costs, and can anticipate regulatory frameworks before they’re codified.
What this means for capital formation
This shift creates a new power dynamic. Founders who can attract marquee executives unlock fundraising rounds that would be impossible based on traction or revenue alone. VCs evaluate deals increasingly on “who’s building this” rather than traditional metrics like customer acquisition cost or gross margins.
The clearest signal: Voyage AI, with 19 employees, was acquired by MongoDB for $220 million — $11.6 million per employee. In an agentic world, team size has become irrelevant.
How to compete with the asymmetric playbook
Most founders reading this can’t offer $10 million to $20 million equity packages to marquee executives.
But there’s a counterintuitive strategy emerging: Instead of competing directly for executives who’ve built AI systems at frontier labs, target the operators who’ve integrated them at scale inside Fortune 500s. A chief technology officer who deployed LLMs across 50,000 employees at JPMorganChase or Walmart understands enterprise AI adoption patterns that most OpenAI researchers don’t.
Here’s the asymmetric approach we’re seeing work:
1. Hire the “translator” executives, not the “builder” executives. A former VP of engineering from Databricks who integrated AI into enterprise workflows is more valuable for a B2B AI startup than a research scientist from DeepMind. They’re 10x cheaper and often more relevant to your actual go-to-market challenges.
2. Offer board seats, not just equity. The most compelling pitch to executives earning $800,000 at FAANG companies isn’t just equity — it’s offering: (a) a board seat they’d never get at a big company; (b) meaningful ownership in a high-growth company; and (c) the chance to compress 10 years of career advancement into two to three years. The value proposition isn’t “get rich” — it’s autonomy, impact and an accelerated path to becoming a recognized operator in AI.
3. Build technical credibility through advisory networks, not executive hires. Instead of hiring one $5 million executive, allocate $500,000 across 10 advisers from Google, Meta and Microsoft who can provide technical validation during enterprise sales cycles.
4. Target executives in “golden handcuff” situations. The best candidates aren’t those getting $100 million offers — they’re the overlooked VPs at trillion-dollar companies who’ve built AI systems but are stuck behind org politics. They have the expertise, they’re ready to leave, and they’ll join for $2 million or $3 million equity packages if you can articulate a clear path to relevance.
The companies winning without massive war chests aren’t trying to out-recruit Anthropic for research talent. They’re targeting enterprise operators who understand how AI systems actually get deployed at scale — and building credibility networks instead of expensive org charts.
The real tradeoff
We’re witnessing the formation of a technical aristocracy. A few thousand individuals now command compensation packages previously reserved for successful founders, as wealth transfers from broad-based tech employment to an elite operator class. Venture capital has fundamentally transformed from a growth capital fund into a talent acquisition fund.
The AI gold rush will eventually end, but the economic structure it’s creating is permanent. In the agentic era, you don’t raise capital to hire engineers — you raise it to hire executives who know how to orchestrate AI agents that do the actual work.
The question isn’t whether the rules have changed. They have. The question is which version of the new game you’re playing: competing for $20 million executives from frontier AI labs, or building asymmetrically with $2 million enterprise operators and credibility networks. Both paths work. Only one is accessible to most founders.
Pukar C. Hamal is founder and CEO of SecurityPal, which eliminates the security review bottleneck that stalls enterprise deals for companies such as OpenAI, Figma and MongoDB. Born in rural Nepal, he built a profitable company with a 24/7 security operations command center in Kathmandu, proving that world-class execution doesn’t require Silicon Valley overhead. He writes about capital formation and the economics of AI-era operations.
Illustration: Dom Guzman
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