The Human Bet is a belief in application companies. If you believe this enduring value, investing there also means looking beyond the applications themselves. The next question is what every successful AI-native company will inevitably need — not who wins, but what all winners depend on.
The Capability Layer
Application companies won't emerge alone. Every major platform shift creates an entirely new generation of tools built to serve them. We've seen this before: as modern consumer brands emerged, companies like Stripe, Shopify, Faire, and Klaviyo were built alongside them. They weren't the story themselves. They existed because a new generation of businesses suddenly needed capabilities that hadn't existed before.
Intelligence is creating the same dynamic. AI-native companies make decisions, build products, and serve customers in fundamentally different ways, increasingly running with far smaller teams than the businesses they're usurping. That creates demand for an entirely new software stack designed around how these companies actually work. We think this will become one of the most important investment categories of the next decade.
The opportunity here isn't automation for its own sake, but giving smaller teams the capabilities of much larger organizations: better judgment, faster execution, richer context, entirely new workflows. The founders who win will have alpha by understanding the industries they're serving well enough to know where the real friction lives — and fuel that alpha by owning and manipulating that intelligence.
That's why this category feels compelling to us. For nearly two decades, we've invested in brands, retailers, healthcare companies, financial services businesses, marketplaces, and consumer platforms as their workflows evolved, watching their constraints accumulate. AI finally makes many of them solvable.
For example: • Koah is the monetization layer for AI apps, a contextual ad network designed natively for the conversational interface. • Blue Labs is building emotional intelligence infrastructure that gives AI systems the capacity to understand human context at scale. • Marklo is an agentic revenue OS for commerce brands, connecting Shopify, Klaviyo, and paid channels into a single planning intelligence layer. Depthfirst applies AI to cybersecurity, purpose-built for the AI development stack.
The Control Layer
If AI is going to act for us, what makes those actions trustworthy?
The internet was built around a simple assumption: people initiate actions. People log in, approve payments, sign contracts, and make decisions, and identity, security, and payments were all designed for a world where a human sat at the center of every transaction. That assumption is changing. Agents are beginning to research, negotiate, purchase, schedule, and coordinate work with increasing autonomy, and as that happens, the question stops being whether an AI can do something and starts being whether it should be allowed to. We see this as one of the defining infrastructure questions of the AI era.
A new control layer needs to emerge, one that gives people confidence their AI is acting within the authority they intended: identity systems that carry intent, permission frameworks that determine what an agent can and cannot do, audit trails that make autonomous actions transparent, and payment infrastructure built for agents transacting with agents.
Making AI more capable is not really the point here. Making autonomous systems trustworthy is. When much of today's infrastructure was adapted from a world where humans performed every important action themselves, the next generation gets designed for a world where software acts constantly while people remain in control. We see that need emerging across our own portfolio, where the hardest questions about increasingly capable agents are no longer about intelligence. They're about authority.
For example: • Natural is rebuilding the payment stack for the agent economy, infrastructure enabling agents to transact autonomously on behalf of businesses and users. • ZeroClick is building the OS for agent-to-agent commerce, turning services into agent-purchasable offerings with controls for pricing, identity, and analytics.
The Model Layer
The Human Bet argues that enduring value lives in the relationship an application builds with its users, not in the underlying model, and that remains true as general-purpose models continue to converge. One exception is worth considering: specialist models built for a single domain, paired with applications designed around that expertise.
Not all specialist models are defensible for the same reason. Some are built on proprietary data that a general-purpose model simply cannot access, where the advantage is exclusive data that is regulated, relationship-driven, or otherwise unavailable outside a particular ecosystem rather than better engineering or more compute. That creates a structural moat because the constraint is access, not effort.
Others gain their advantage through focus. By concentrating training, evaluation, and product development on a single domain, they can outperform more general systems that have not yet made that area a priority. That advantage is real but not necessarily permanent: if a horizontal model decides the category matters, its scale and resources may eventually close the gap. So the investment question isn't simply whether a company is building a specialist model. It's why that model is better. A moat built on proprietary data can endure because it can't easily be replicated. An advantage built on specialization alone becomes a race, and the real question is whether the company can compound its lead through product, customer relationships, and data before the generalists catch up. Both paths can produce exceptional companies. They just rely on different forms of durability.
For example: Speechify and Suno represent the focused-training case, purpose-built for speech and music respectively, each paired with a consumer application, and, in Speechify's case, an API offering that extends the model's intelligence beyond the consumer product itself.
The Supply Side of the Human Bet captures three distinct bets, each driven by a different source of advantage. What unites them is timing. None of these markets are fully formed yet. The companies, products, and infrastructure that define them are still being created, which is exactly why they're interesting. What has to get built is still being built.
