Venture capital has turn out to be a mechanism for extracting executives from trillion-dollar corporations and paying them what it takes to build in an AI-driven world.
We now not finance corporations – we buy access to several hundred individuals who have built artificial intelligence systems in them Google, OpenAI AND Meta.
Secure superintelligence was founded by Ilya Suckew (were-OpenAI chief scientist), Daniel Gross (were-Apple AI Manager) i Daniel Levy (former researcher at OpenAI). The company employs roughly 20 employees. It has raised $3 billion so far at a $32 billion valuation, with no product or any revenue. They have three executives from trillion-dollar corporations who understand how you can build superintelligence. This alone requires $1 billion in capital per team member.
It became a textbook.
Microsoft agreed to pay AI overkill $650 million use his models and hire Deep Mind co-founder Mustafa Suleiman as CEO of Microsoft AI along with most of the 70-person Inflection team. Meta CEO Mark Zuckerberg apparently offered Andrzej Tulloch until $1.5 billion over six years. Google hit A $2.4 billion non-exclusive license agreement Windsurfing and hired a CEO, co-founder and chosen research and development staff. Meanwhile, CEO of OpenAI Sam Altman publicly stated that Meta offered top OpenAI talent 100 million dollars bonuses for signing a contract.
The traditional formula of the project is reversible. It was once easy: raise $20 million, spend 95% on growth and hiring, and allocate 5% to management. Currently, most of the capital is flowing towards recruiting a handful of managers who understand how you can operate in an AI-native environment.
But most founders cannot compete in $20 million bidding wars. And so it is an asymmetric game and it is 10x cheaper.
Why executive judgment is a recent, scarce resource
In the agentic era, AI systems write code, process data, provide customer support, and automate operations. The rarest resource has turn out to be the assessment of management staff who know how you can effectively coordinate these systems.
Consider what this implies in practice. Ten years ago, you would hire 200 engineers for $100 million. Today, that very same capital could fund five FAANG executives at $10 million each, with the remaining $50 million spent on computation, AI tools, and a skeleton crew of 20 to 30 people overseeing the autonomous agents.
Managers at pioneering AI corporations receive huge bonuses because they have knowledge that does not exist elsewhere. They know what is possible with current AI capabilities, understand the economics of model training and inference costs, and can anticipate regulatory frameworks before they are codified.
What does this mean for capital formation
This change creates a recent power dynamic. Founders who can attract brand executives unlock fundraising rounds that may be inconceivable based on traction or revenue alone. VCs are increasingly evaluating deals based on “who is building them,” moderately than traditional metrics corresponding to customer acquisition cost or gross margin.
The purest signal: AI Journeyemploying 19 employees, was taken over by MongoDB Down 220 million dollars — $11.6 million per worker. In the agent world, team size has turn out to be irrelevant.
How to compete with an asymmetric textbook
Most founders reading this text are not in a position to supply brand managers equity packages value $10 million to $20 million.
But a counterintuitive strategy emerges: Instead of directly competing for executives who have built AI systems in pioneering labs, focus on operators who have integrated them at scale in Fortune 500 corporations. A technology executive who has rolled out LLM programs to 50,000 employees at the company JPMorganChase Or Walmart understands enterprise AI adoption patterns that the majority OpenAI researchers don’t understand.
Here’s the asymmetric approach we see working:
1. Hire managers as “translators,” not “builders.” Former Vice President of Engineering z Data cubes who has integrated AI into enterprise workflows is more invaluable to a B2B AI startup than a DeepMind scientist. They are 10 times cheaper and often higher meet real market entry challenges.
2. Offer board seats, not just equity. The most compelling offer to executives earning $800,000 in FAANG corporations is not just equity – it is offering: (a) a board seat they’d never get at a large company; b) significant share in a rapidly growing company; and c) the ability to compress 10 years of skilled development into two to three years. The value proposition is not “getting rich” – it’s autonomy, influence, and an accelerated path to becoming a recognized AI operator.
3. Build technical credibility through advisory networks, not executive hiring. Instead of hiring one $5 million executive, allocate $500,000 to 10 advisors from Google, Meta and Microsoft who can provide technical validation during enterprise sales cycles.
4. Reach out to executives in “golden handcuff” situations. The best candidates aren’t those getting $100 million offers – they’re neglected vice presidents at trillion-dollar corporations who have built artificial intelligence systems but are stuck in organizational politics. They have the expertise, they’re willing to go away, and they’ll join for $2 or $3 million equity packages if you’ll be able to define a clear path to relevance.
Companies winning without huge war chests are not attempting to recruit Anthropic for research talent. They are aimed at enterprise operators who understand how AI systems are actually deployed at scale and build networks of credibility moderately than costly organizational charts.
An actual compromise
We are witnessing the emergence of a technical aristocracy. Several thousand people now have compensation packages previously reserved for successful founders as wealth transfers from high-profile tech jobs to an elite class of operators. Venture capital has essentially evolved from a growth capital fund to a talent acquisition fund.
The AI gold rush will eventually end, but the economic structure it creates will endure. In the age of agency, you do not raise capital to rent engineers – you raise it to rent executives who know how you can coordinate AI agents that do the real work.
The query is not whether the rules have modified. Have. The query is which version of the recent game are you playing: are you competing for $20 million in executives from pioneering AI labs, or are you building asymmetrically with $2 million of corporate operators and credibility networks. Both paths work. Only one is available to most founders.