# The Rebel Alliance

> Our view on the emerging agentic stack

**Published by:** [Union Square Ventures](https://blog.usv.com/)
**Published on:** 2026-06-25
**Categories:** ai
**URL:** https://blog.usv.com/the-rebel-alliance-2

## Content

We believe that the AI opportunity is too big and too important to be owned by any one company. Instead, we see a massive ecosystem emerging around the agentic AI stack. We refer to it as the Rebel Alliance, and we believe it's not just competing, but is structurally positioned to lead. What began as a simple "chat app" experience has matured into the agentic era. More powerful models, combined with better tooling to orchestrate and connect them, have made it possible to build truly capable agents in many forms. Coding agents are just the tip of the iceberg. This shift favors a different kind of architecture. A chatbot can be vertically integrated: one model, one interface, one company. But agents are increasingly deployed as infrastructure: programmatically, via harnesses and CLIs, and composed across services and systems. They look less like apps and more like the multi-layered web infrastructure we've seen before. As of this writing, agentic traffic on the web has surpassed human traffic for the first time in history. Over the coming years, agents will be woven into all of our online experiences. This Rebel Alliance -- a framing we first applied to the memory layer last year -- has grown into a full stack: models, orchestration, memory, execution, identity, payments, and more; with hundreds of companies competing and composing with each other. And as we'll argue below, there are structural reasons this architecture out-scales any integrated approach. Michael Mignano @mignano For the first time in ~3 years, it feels like the AI table has been flipped over. Yes, the labs and hyperscalers will have the highest chance of resetting it before everyone else, given their vast capital, frontier model, and compute advantages. But there is now a window for a 941 2:57 PM • Jun 15, 2026 The other bet – Fat Models One way of looking at AI right now is that the largest companies in the space are each building the same thing: a vertically integrated AI platform. Their own models, their own APIs, their own orchestration tools, their own agents, their own consumer & enterprise products. The bet is that if you control the model, you can own every layer above it. It appears as though the market is pricing in the "Fat Models" thesis, valuing OpenAI and Anthropic at close to $1T each, and propelling Google 4x in the last 3 years to over $4T . There's good reason for this: we've invented a new form of magic here, and each of these companies is bringing it to market at scale in its own ways. But we've seen technology cycles like this before. At USV, we’ve long been students of the cycles of technology development and deployment, and the way in which value accrues at different layers at different points in the cycle. In the early 2000s, after the deployment of the physical internet, our belief was that value would accrue at the application layer. In the early 2010s, after the invention of bitcoin, our belief was that value would accrue in open source protocols below the application layer. Today, after the invention and deployment of AI foundation models, the question is: will all value accrue to the models, or to a broad ecosystem around them? Looking back: IBM built the mainframe as a fully integrated system: hardware, software, services – then the PC ecosystem unbundled it. Microsoft owned the desktop, then the web unbundled it. The telcos built vertically integrated networks, then the internet unbundled them. Apple built the most powerful and integrated computing platform ever, and yet Android, the more composable system, runs on over 70% of the world's smartphones. AWS built a $100B+ platform spanning compute, storage, and ML — and yet an entire ecosystem of multi-billion-dollar companies emerged by specializing at layers above it. We believe the same pattern will play out with the agentic AI stack – where the best products at each layer will be built by teams who are obsessed with that layer. Many important companies will be built here. The market map below is an illustration of what we believe is well positioned against the Fat Model thesis: Forces at play Beyond a historical perspective, we are also always looking to identify the forces & pressures that are transforming large markets. Looking at the agentic AI landscape today, we see several major forces & pressures that are creating tailwinds for the Rebel Alliance: Fierce competition among models: While frontier models get the lion's share of attention, in reality there are new models emerging constantly, each with different capabilities, latencies, and cost profiles. This includes open-weights models, edge models, and a wide variety of commercial models. Independent evals suggest that while the frontier models still sit at the very top, the advantage is now measured in single-digit points on the workloads that matter commercially. Even a few months ago, most teams defaulted to the best frontier model regardless of price. That's starting to change, and we’re seeing both developers and enterprises cost-optimize their model usage as well as compose multiple models to solve complex tasks in the best ways. Alex Atallah @alexatallah Watch token share slowly diversify over time: openrouter.ai/rankings#marke… 41 8:04 PM • May 16, 2026 Application scope and user experience: Sun Microsystems co-founder Bill Joy famously said: "No matter who you are, most of the smartest people work for someone else." That applies with particular force to AI. The range of what agents can do — and the variety of industries, workflows, and real-world contexts they touch — is so vast that no single company can build the best solution for all of them. We see a near-infinite opportunity to rewrite the world using agentic AI as foundational infrastructure, and it will require many companies with focused effort. AI moving from experimentation to production: The initial wave of AI adoption was driven by frontier model demos and consumer chat products, environments where a single provider can own the whole experience. But as agentic AI moves into production — into enterprise workflows, regulated industries, and mission-critical systems — a different set of requirements takes over: data residency, compliance, audit trails, cost controls, and the ability to swap providers without rearchitecting. Teams want to assemble the right combination of components, not accept a single provider's defaults. This favors a modular stack. Agents are inherently distributed: Previous platform battles could be won by owning the whole stack in one place, but agents don't work that way. They move across data sources, services, and compute environments — more like bees to flowers than programs running on a single platform. An agent that ingests patient data, checks drug interactions, and surfaces a treatment recommendation could touch half a dozen systems in a single task. This forces open interfaces between systems. Open interfaces invite composability, and composability invites specialization. The resulting ecosystem can compound faster than any single provider could move alone. Where we're looking We are deeply engaged in understanding the layers of this stack, what great looks like in each, and where value might accrue. Some themes we’re particularly focused on right now include: Orchestration: Tools for developers, business users, and eventually consumers to compose and manage increasing numbers of agents in increasingly sophisticated (and secure) ways. As more agents do more things in more places, orchestration becomes the control plane. Harnesses: If the LLM is the brain, the harness is the body — connecting it to the world. We expect an increasingly diverse range of harnesses linking agents to both digital and physical environments. Memory: In a multi-agent world, control of your digital context becomes central. It especially makes sense to us that memory may factor out into an independent layer when many agents need to share and act on the same context. Browser: What does a "browser" for agents look like — not just for agents to surf the web, but for people to interact with their agents? This could become a new form of messenger, and a major new interface category. Routing and model marketplaces: When every deployment is multi-model, something has to match queries to the right model at the right cost. Routing is essential infrastructure for making model competition real, and for composing multi-model interactions. Identity: As agentic traffic vastly outnumbers human traffic, new forms of digital identity — including cryptographic identity rooted in both software and hardware — become essential for transactional integrity. Payments: Agentic commerce is in the very early innings but will become enormous. Agents will embrace payment protocols for digital micro-payments and eventually become major actors in generalized e-commerce. Much of the payments stack will need to be redesigned agent-first. In conclusion It's a wildly exciting time. We believe the Rebel Alliance will produce many of the most important companies of this era — not despite the power of the large model providers, but because of the ecosystem dynamics playing out around them. We are actively investing across this stack and want to hear from founders who are building in it. If you're working on any of the layers above — or ones we haven't thought of yet — we'd love to talk. (Special thanks to Scott Belsky, Clem Delangue, Roham Gharegozlou, Colin Hanna, Andrew Lee, Dave Morin, Satya Patel, Avi Peltz, Naval Ravikant, Dan Shipper, Jesse Walden, Signüll, Spencer Yen, and Mario Zechner for giving feedback on drafts of this post)

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