Key Takeaways

  • The venture capital world is moving past the generic idea of a "great founder," instead demanding specific attributes tuned to a company’s position in the tech stack.
  • Earlybird's Andre outlines a three-tiered framework: Deep Tech (Hard Tech), Software Infrastructure and Frontier AI Models, and the Application Layer.
  • For Deep Tech ventures like Isar Aerospace or Marvel Fusion, founders need strong research foundations, IP, and a proven ability to translate R&D into production.
  • CEOs leading AI Frontier Model companies must have the credibility to attract a small, elite pool of research talent globally, differentiating from other tech startups.
  • This nuanced approach is core to Earlybird's Tech Stack Investment Framework, designed to generate alpha through precise founder evaluation.

The Earlybird's Tech Stack Investment Framework

Earlybird's investment professional, Andre, detailed the firm's framework for evaluating opportunities based on their place in the technology stack, recognizing that ideal founder traits shift dramatically across these categories:

Deep Tech (Hard Tech): really means hard tech. You can touch it. So, for example, we've invested in Isar Aerospace, which is a lower orbit rocket launcher, or Marvel Fusion, this is core fusion, or Greenlight, which is decarbonization, or Arago, which is a new chip infrastructure. So, this is really everything that you can touch. If we look in deep tech, deep tech, we want to see people who have a very strong research foundation. They have IP. They have published papers. They have the credibility and really know also what it means to bring research into production. If you want to produce something that's hard, it also means building production facilities, understanding process management, supply chains. So all of this stuff is very, very important.

Software Infrastructure & Frontier AI Models: everything that really enables them from the data collection, data infrastructure, and so on and so forth. If you look into the AI frontier models, we really want to also see the CEO specifically, someone have the credibility that you can attract the best research talent out there. So if you build for example a world model company or a voice infrastructure or an image or video model company, it's like whatever, say two dozens of exceptional people out there in the world and they want to work with the very best. And we want to see in the team, do you have the gravity to attract these best people?

Application Layer: it's really about taking existing components, assembling them, and really building a solution that's either for one specific department, so for sales, for marketing, for HR within an organization, or goes after a very specific sector, so construction tech, industrial tech, e-commerce, whatsoever. Whereas in the application layer, we look for people who understand the respective industry, the respective department, because you're selling into those and you need to understand the problem.

When This Works (and When It Doesn't)

This framework shines when deal professionals recognize that founder success is intensely domain-specific. As Andre explains, a founder who excels at building production facilities for rockets is unlikely to be the same person best suited to attracting world-class AI researchers. It provides a specific lens for diligence, forcing VCs to align team capabilities with the distinct challenges of a given tech layer.

However, this approach can fall short when a company blurs the lines between these categories, perhaps starting as an application layer and needing to develop proprietary infrastructure. It also demands that the investors themselves possess deep enough expertise across these diverse domains to accurately assess the credibility of a fusion scientist versus a sales-driven SaaS founder. Without that internal expertise, the framework risks becoming a theoretical exercise rather than a practical tool for allocation.

Why It Matters

This granular approach to founder evaluation signals a coming-of-age for venture capital, moving beyond simplistic narratives of entrepreneurial genius. For deal professionals, it means that a one-size-fits-all due diligence checklist for founding teams is now obsolete; assessing a deep tech founder's IP and production experience requires an entirely different lens than evaluating an AI application founder's market acumen.

For LPs, funds that articulate such specific investment frameworks, like Earlybird's, demonstrate a more sophisticated and disciplined strategy for managing risk and pursuing alpha across varied technological frontiers. This shift implies that capital will increasingly flow to funds with a clear thesis on who succeeds where, pushing out generalist funds that lack the specific expertise to evaluate talent across such divergent domains. It also suggests that operational assistance for portfolio companies will need to become equally specialized, mirroring the specific needs of founders in each tech stack layer.