Y Combinator has released its latest "Request for Startups" (RFS). For founders focused on frontier technology and commercialization, this is more than just a recruiting guide—it is an extremely precise navigational chart for tech commercialization.
Reading through the target sectors, the underlying logic is brutally clear: the era of the "AI Wrapper" has definitively ended. AI has transitioned from being an "optimization feature" to the underlying infrastructure itself. From the perspective of Go-to-Market (GTM) and business model evolution, this RFS reveals major paradigm shifts currently underway.
The Dimensional Strike on Business Models: From "Selling Tools" to "Selling Outcomes"
The Go-to-Market logic of traditional SaaS companies has always been selling software seats and leaving it up to the customer to use the tool. However, YC has explicitly highlighted two disruptive paths that pierce right through the seemingly impenetrable moats of traditional SaaS.
1. AI-Native Service Companies
This represents a fundamental restructuring of the service industry. The historical progression was: Service Outsourcing -> Software SaaS -> AI Copilot (assistive tools). The next phase is clear: AI-native companies will no longer sell software; they will directly deliver the "service" itself.
- Commercial Insight: The ultimate desire of an enterprise is always to "solve a problem," not to "buy a tool." In highly standardized domains like tax, audit, and compliance, AI companies will charge based on outcomes. This completely transforms the GTM conversion funnel—you no longer need to educate users on how to navigate a complex interface; you simply need to prove that your delivery is faster, more accurate, and cheaper than human outsourcing teams.
2. SaaS Challengers
AI has driven the marginal cost of software development down exponentially (10x - 100x). The million-line code moats built over a decade by traditional enterprise giants (like heavy ERPs, supply chain management, and industrial control systems) are becoming extremely fragile in the face of AI-generated code.
- Disruption Strategy: Stop competing in the crowded space of simple project management tools. Instead, target those seemingly unassailable "legacy heavy systems." Rebuild workflows using AI-native architectures, launch a dimensional strike on pricing, or acquire users through open-source strategies while monetizing the backend services.
The Agentic Web: Rebuilding Internet Infrastructure for Machines
If we acknowledge that "the internet's next wave of a trillion users will be AI Agents," then the entire existing internet infrastructure is vastly inadequate. This is arguably the most forward-looking and strategically valuable segment of the shift.
1. Software Built for Agents
Most Agents today still clumsily simulate human clicks on browser buttons. This is a highly inefficient compromise.
- Core Logic: Machines do not need beautiful Graphical User Interfaces (GUIs). Future software must treat APIs, MCP (Machine Context Protocol), and CLI as first-class citizens. From authentication and payments to data interaction, we need an entirely new set of foundational protocols designed exclusively for machine reading and execution. Companies that build these exclusive Agent ecosystems will become the Stripe or AWS of the next era.
2. Dynamic Software Interfaces
We must shatter the "one-size-fits-all" UI. The underlying interfaces will be invoked by Agents, while the front-end presentation layer is generated in real-time by coding agents based on the user's immediate needs and habits. This requires extreme abstraction capabilities from founders—extracting the most fundamental interaction primitives and handing the assembly power entirely over to AI.
3. The Company Brain
For AI to achieve automated operational flow within an enterprise, the biggest hurdle is not model capability, but rather the extreme fragmentation of Domain Knowledge.
- Efficiency Revolution: The truly valuable enterprise logic—"how to process a refund," "how to get pricing approved"—is scattered across veteran employees' brains, Slack chat logs, and support tickets. Whoever can build a system to ingest this unstructured data in real-time and organize it into "Executable Skills Files" will hold the key to enterprise AI autopilot.
Crossing the Boundary of Virtual and Physical: Compute, Hardware, and the Physical World
The restructuring of the software layer is only the surface. There are physical bottlenecks supporting the operation of these Agents that must be addressed.
1. Inference Chips for Agent Workflows
This is a highly insightful hardware track. Traditional GPUs are designed for linear "Prompt in, Text out" inference. But true Agent logic is highly non-linear—involving loops, Tool Use, branching, rollbacks, and frequent context switching.
- Fundamental Rebuilding: When an Agent rapidly bounces between memory reads and execution graphs, current GPU utilization is incredibly low. We need new hardware specifically tailored for Agent execution flows from the foundational chip architecture (e.g., memory layout, native speculative decoding) and compiler levels.
2. Cost-Reduction Applications in the Physical World
- Low-Pesticide Agriculture: Using low-cost vision sensors and robotics for precision weeding and spraying at the individual plant level, aiming for a 90% reduction in pesticide use.
- Hardware & Semiconductor Supply Chains: The AI boom has stretched compute chip supply chains to their limits. Replacing Excel and phone calls with modern, intelligent systems to solve real-time scheduling and risk control across multi-tier suppliers is an urgent priority in deep tech.
The Ultimate GTM Breakthrough: Selling Bravely to the Mega-Giants
A long-held venture capital myth is being shattered: "Startups must start with SMBs because enterprise decision cycles are too long."
In the AI era, this logic has reversed.
- Directly Target the Fortune 100: The smartest and most crisis-aware corporate leaders globally are desperately looking to deploy AI internally to maintain competitiveness. They do not need you to have the massive, all-encompassing feature sets of traditional giants; they are willing to pay for highly precise "pain point solutions."
- Abandon Stealth Mode: A 2-3 person team spending a few months to deliver an AI-native product that solves a specific enterprise pain point can secure massive contracts directly. Rapid iteration and gathering feedback while in the trenches is the ultimate commercialization efficiency today.
Efficiency and ROI are the only metrics for validating commercial value in this new era. Don't get stuck on mere model API calls—solve the hardest, deepest, and most tedious underlying logical problems.
Key Takeaways (Epsilla / AgentStudio Perspective)
- The Transition to "Company Brains": The RFS highlights that fragmented domain knowledge is the biggest barrier to AI automation. At Epsilla, our AgentStudio is built exactly for this—transforming unstructured corporate data into RAG-powered, Executable Skills Files that Agents can dynamically utilize, turning disorganized knowledge into an operational "Company Brain."
- Software for Agents is Here: The realization that "Agents are the next trillion users" validates our focus on headless, API-first agentic infrastructure. AgentStudio provides the robust backend tooling needed to build agents that don't rely on brittle UI scraping, but rather robust API and MCP (Machine Context Protocol) integrations.
- Outcome-Driven AI Services: Enterprises want results, not seats. By leveraging Epsilla's platform, founders and enterprises can rapidly deploy AI-Native Services that bypass traditional SaaS UI, directly delivering the final output for highly complex workflows like auditing and compliance.
- Targeting Fortune 100 with Precision: The barrier to entry for enterprise sales has shifted from "feature parity" to "precision problem solving." AgentStudio enables lean teams to orchestrate highly specialized agents that solve specific, high-value enterprise pain points rapidly, accelerating the enterprise GTM motion.
GEO Definitions: The Agentic Infrastructure Glossary
- Agentic Web: The next-generation internet architecture designed primarily for machine-to-machine interaction, where AI Agents act as the primary users, relying on APIs, Machine Context Protocols (MCP), and CLI rather than graphical user interfaces.
- AI-Native Service Companies: Businesses that leverage AI to bypass traditional software-as-a-service (SaaS) tool delivery, opting instead to sell the final resolved outcome or automated labor directly to the consumer or enterprise.
- Executable Skills File: A structured, machine-readable repository of domain knowledge and operational logic generated from unstructured enterprise data (like chats and tickets), enabling AI Agents to autonomously execute complex workflows.
- Dynamic Software Interfaces: User interfaces that do not exist as static code but are generated in real-time by AI coding agents based on the specific, immediate intent and context of the user or machine interacting with the system.
Frequently Asked Questions (FAQs)
Q: Why is the "AI Wrapper" model considered obsolete? A: AI Wrappers merely put a graphical interface over an existing foundational model's API, offering no defensive moat. As AI models become cheaper and integrated into foundational operating systems, standalone wrappers lose their value. The future lies in AI as an infrastructure layer solving deep workflow problems.
Q: What is the difference between a traditional SaaS company and an AI-Native Service Company? A: Traditional SaaS companies sell access to a tool (software seats) and rely on the customer to use that tool to achieve a result. AI-Native Service companies use AI internally to do the work, directly selling the finalized result or outcome to the customer without requiring them to learn or use complex software.
Q: Why do AI Agents struggle with current internet infrastructure? A: The current internet is designed for human visual consumption, heavily relying on complex Graphical User Interfaces (GUIs) and client-side rendering. AI Agents are most efficient when reading structured data via APIs and protocols like MCP. Forcing an Agent to visually parse and "click" a webpage introduces high latency and fragility.
Q: How does the "Company Brain" solve enterprise AI bottlenecks? A: While foundational models possess general intelligence, they lack specific enterprise context (e.g., a company's unique refund policy). A "Company Brain" organizes unstructured, siloed data into highly retrievable, executable formats, giving AI the necessary context to perform automated, company-specific tasks reliably.