The AI Gold Rush: Seed Investing in a New Era

The venture capital landscape is undergoing a seismic shift, driven by the pervasive influence of AI. This conversation with Kevin Hartz and Bennett Siegel of A* Capital reveals that the traditional playbook for seed-stage investing is becoming obsolete. The core thesis is that while AI is creating unprecedented opportunities and accelerating innovation, it's also fundamentally altering the economics of startups and the expectations of founders and investors alike. The hidden consequence is a widening gap between firms that adapt to this new reality and those that cling to outdated models. Founders, investors, and anyone building in the technology sector should read this to understand how to navigate the intense competition and evolving dynamics of early-stage investment in the age of AI, gaining an edge by recognizing where true value creation lies beyond the hype.

The Unseen Currents Beneath the AI Wave

The narrative surrounding AI in venture capital often focuses on the dazzling speed of innovation and the potential for massive returns. However, Hartz and Siegel's conversation with Jack Altman unearths a more nuanced reality, one where the very definition of value and competitive advantage is being rewritten. The influx of large funds into seed rounds, the rise of younger, AI-native founders, and the changing economics of early-stage capital are not isolated trends but interconnected elements of a larger system. Understanding these dynamics requires looking beyond the immediate hype to the downstream effects and delayed payoffs that truly differentiate successful ventures.

The Giants at the Seed Table: Option Value vs. Partnership

One of the most striking observations is the growing presence of mega-funds in seed-stage investing. While these large firms can offer founders significant capital, their underlying incentive structure often differs from that of dedicated seed specialists. Hartz and Siegel suggest that these larger funds view seed investments primarily as "option value"--building a basket of companies to see what pops, with the ability to double down later. This contrasts sharply with the partnership model favored by A*, where the focus is on deep collaboration and shared risk from the outset.

"I think there's two facets. We used to joke at Coda, there were like two ways to make a billion dollars. The one way was like the benchmark way, like you find incredible Series A, you lead it, you partner, you have a generational public outcome, and you earn your percentage of the company. That's hard and takes a long time. Or the A*STAR way."

-- Bennett Siegel

This dynamic creates a subtle but critical tension. Founders may be drawn to the perceived validation and immediate capital from larger investors, but the long-term commitment and hands-on support offered by a firm like A* can be more crucial for navigating the inherent uncertainties of building a company. The "option value" approach, while potentially lucrative for the fund, can leave founders feeling like a line item in a portfolio rather than a true partner, especially during inevitable downturns or strategic pivots. The consequence is that companies backed by firms prioritizing partnership may develop more robust foundations and a stronger founder-investor alignment, leading to more durable success.

The Younger Founder: Navigating the New Frontier

The conversation highlights a significant trend: founders are getting younger. This isn't just a demographic shift; it's a reflection of how AI is democratizing creation and lowering the barrier to entry. Young founders, often digital natives who have grown up with AI tools, are uniquely positioned to leverage these technologies from day one. They are less encumbered by traditional software paradigms and more adept at building "systems of intelligence" and "systems of action" rather than just "systems of record."

"This is new for everyone. We're all rewriting the rules as we build these companies in real time, and young founders are situated very well because they're the first adopters of this technology. So it's why you're still seeing very large companies built by people under the age of 25 or 30 at an unprecedented rate."

-- Kevin Hartz

The implication is that traditional markers of founder experience, such as a long career in enterprise sales or SaaS development, may become less relevant. Instead, attributes like a deep understanding of AI, an ability to rapidly iterate, and a willingness to embrace new paradigms are becoming paramount. The advantage for investors who can identify and back these younger, AI-native founders early is immense. They gain access to companies that are intrinsically built for the current technological wave, potentially avoiding the costly process of retrofitting older business models with AI capabilities. The hidden cost for traditional investors is missing out on this wave entirely, backing companies that are already behind the curve.

The "Vibe Code" Problem: Durability in a World of Instant Creation

The ease with which AI can generate code and applications presents a fundamental challenge to traditional software businesses. If a functional app can be "vibe coded" in minutes, what is the enduring value of complex workflows or established software products? This question lies at the heart of the shift from traditional software to "systems of intelligence." Hartz and Siegel emphasize that the real value will come from companies building unique moats, not just replicating existing functionality with AI.

"If you can 'vibe code' an app in a matter of minutes, why do you need so many engineers? What's the value of the workflows that you've built? But we're seeing on the application layers, people are going after systems of intelligence, they're going after systems of action. It's new types of spend, and we're going to continue to back those."

-- Kevin Hartz

The consequence of this democratization of creation is that many companies built on older software models will struggle to differentiate. Their value proposition will be eroded by AI-powered tools that can achieve similar outcomes faster and cheaper. The advantage, therefore, lies with founders who can articulate a vision for building durable revenue streams and defensible moats that go beyond mere code generation. This might involve proprietary data, unique integration capabilities, or entirely new business models enabled by AI. Investors who recognize this distinction can identify companies with long-term potential, while those focused on the immediate hype risk backing businesses that will be commoditized. The delayed payoff for building true differentiation, rather than chasing immediate AI trends, becomes a significant competitive advantage.

The Roll-Up Conundrum: Where AI Meets Old Business

The discussion around AI roll-ups, particularly in services-oriented professions, reveals a complex interplay between technology and traditional business. While the allure of applying AI to automate labor spend is strong, Hartz and Siegel express caution, highlighting the immense difficulty of integrating and transforming existing businesses. Private equity has long grappled with roll-ups, and AI doesn't magically eliminate the cultural and operational challenges.

The core issue, as articulated by Siegel, is that the economics often favor the founder acquiring the business over the venture capitalist providing the capital. The founder can raise VC money, dilute less, and acquire assets, essentially being "bought into a business with embedded asset value." For VCs, achieving venture-like returns requires dramatic appreciation of these acquired assets, which is far from guaranteed.

"I think this is actually a great business for founders. It used to be that you'd have to raise capital, you'd have to buy an asset, and you'd have to improve the asset, and you would keep 20% of the profits. Now a founder can go and raise venture capital dollars, dilute 20%, so keep 80% of the profits, and go buy a business."

-- Bennett Siegel

This reveals a critical downstream effect: the VC's role shifts from enabling disruptive innovation to managing integration and operational efficiency, a task for which many are not inherently suited. The conventional wisdom that AI can easily unlock value in established businesses is challenged by the reality of execution risk and misaligned incentives. The advantage for founders is clear--they can leverage VC capital to acquire businesses with less dilution. For VCs, the challenge is to find structures and targets where true value creation, beyond mere financial engineering, can lead to outsized returns, a difficult proposition in a market where VCs are often less adept at managing the bottom line than the top line.

Key Action Items

• Prioritize Partnership Over Pure Capital: When seeking seed funding, actively look for firms that emphasize deep partnership and hands-on support, not just capital infusion. This often means looking beyond the largest funds. (Immediate Action)
• Embrace AI-Native Founder Attributes: As a founder, highlight your fluency with AI tools and your ability to build novel systems of intelligence or action. If you are an investor, actively seek out younger founders with this inherent understanding. (Ongoing Investment)
• Focus on Defensible Moats, Not Just AI Features: For founders, articulate a clear strategy for building durable competitive advantages that extend beyond simply integrating AI into existing workflows. Differentiate by building unique data assets, proprietary integrations, or novel business models. (Immediate Action)
• Understand the "Vibe Code" Risk: If evaluating traditional software businesses, critically assess their long-term defensibility against AI-driven commoditization. Investors should be wary of businesses that cannot clearly articulate how they will evolve beyond current AI capabilities. (Ongoing Investment)
• Be Skeptical of AI Roll-Up Narratives: For investors considering AI roll-ups, conduct rigorous due diligence on the execution capabilities of the management team to integrate and transform acquired businesses, not just the potential for AI-driven automation. Recognize the inherent founder advantage in these structures. (Immediate Action)
• Map Talent Beyond Traditional Metrics: Investors should broaden their talent mapping to include younger founders, researchers transitioning to entrepreneurship, and individuals from companies known for breeding strong entrepreneurial talent, even if their backgrounds are unconventional. (Ongoing Investment)
• Invest in Long-Term Value Creation: Recognize that true competitive advantage in the AI era will likely come from patient investment in companies building deep, defensible moats, not chasing short-term trends. This may require waiting longer for payoffs. (Long-Term Investment, 12-18+ months)