The AI founders Playbook
A Founder’s Guide to Building AI Startups: Lessons from Sequoia Capital, OpenAI, Nvidia, LangChain, and Ramp
Hey everybody welcome to Product Market Fit 🔥
I just spent the weekend deep in the weeds of what might be the most important AI strategy briefing of the year — Sequoia’s AI Ascent.
Based on the insights of the founders of OpenAI, Nvidia, Langchain, Anthropic and Ramp, if I were starting an AI company today, these are the 42 insights I’d keep taped to my wall 👇
SEQUOIA — THE HIGH-LEVEL STRATEGY
This is the biggest opportunity since the internet. Bigger than cloud. Faster than mobile.
AI isn’t one market — it’s eating all markets. Software, services, labor, infrastructure.
The value is in the application layer, not the models. Tools people actually use will win.
Foundation models are moving up the stack. Compete by going narrow and deep, not wide.
The best products start customer-back, not model-forward. Solve one job completely.
This is the Agent Era. AI will shift from chatbots to swarms of agents executing work.
Speed is everything. There’s a vacuum in every vertical. Whoever ships first, wins.
Distribution is built-in now. You’re launching into a 5.6B-person internet. If it works, it explodes.
The model doesn’t matter unless the product is great. Startups win on UX, not architecture.
Your moat is your workflow. Own the end-to-end journey. Don’t build tools, build outcomes.
HOW TO BUILD AN AI COMPANY (SEQUOIA’S PLAYBOOK)
Most users don’t know what AI can do. It’s your job to have the opinion.
Build an opinionated product. Guide the user. Don't ask them what they want.
The best AI products feel magical, not mechanical.
Go from tool → co-pilot → autopilot. That’s the value ladder.
95% of this is just company building. Team, execution, product. Don’t get distracted.
The AI-specific 5% matters most at scale. It’s your data flywheel, trust layer, and UX.
Build data flywheels. If your users’ behavior doesn’t make the model better, rethink your loop.
Moats = proprietary usage + performance lift. Otherwise, you’ll get commoditized fast.
Real revenue > vibe revenue. Adoption, retention, behavior change — not hype.
Don’t fear low gross margins early. Token costs are falling. Margin improves with scale.
THE AGENT ECONOMY IS COMING FAST
The next platform shift isn’t chat — it’s agents. Think agents that coordinate, reason, act.
Agents will collaborate like humans. You won’t prompt one — you’ll orchestrate dozens.
The three blockers left:
Memory (personal and long-term)
Protocols (how agents talk)
Security (trust, identity, auditability)
Whoever solves these problems first creates the “AI operating system.”
Trust is UX. If you can’t see or control what agents are doing, users will bounce.
RAMP — WHY MOST AI AGENTS FAIL
Most agents fail because they can’t complete workflows. They handle one step and get stuck.
Companies expose partial APIs. Agents don’t have full access to the app’s capabilities.
Ramp’s answer? Make agents use the UI. Literally. A headless browser “clicks” around the frontend.
Don’t rebuild your product for agents. Just let them operate the UI invisibly.
This gets you full feature coverage from day one. No need for custom tools or new infra.
LANGCHAIN — AMBIENT, EVENT-TRIGGERED AI
Ambient agents run in the background. They respond to signals, not prompts.
The Agent Inbox is a breakthrough. It’s the command center for human oversight.
Human-in-the-loop is not optional. Agents need approvals, interventions, and feedback.
Trust grows through control, logs, and reversibility. Let users audit and edit everything.
The real win? Agents that self-improve via your workflows.
OPENAI — STAYING ALIVE WHEN THE GIANT OWNS THE CORE
Don’t build the foundational layer. You will lose.
OpenAI wants to be the core API. Think HTTP for intelligence.
Compete on vertical depth. Build for one persona, one job, and go deeper than OpenAI ever will.
Small teams, big ownership. That’s how OpenAI ships fast even at scale.
The future = voice + code. Not just text. We’re heading toward agents that talk and build.
NVIDIA & ANTHROPIC — THE NEXT NEXT
Robotics is the next frontier. Physical AI is coming — trained 100% in simulation.
The best AI products of tomorrow won’t feel like software. They’ll feel like invisible teams, running in the background, making things happen.
That’s the Playbook.
You don’t have to be early to AI anymore. But you can still be right.
Build vertical. Go fast. Design for trust. And never forget — you’re not managing code anymore. You’re managing systems that think.
— Guillermo
PS: You can check the full breakdown here of the AI Ascent here:
1. Where the Partners at Sequoia see the biggest opportunity in AI is rigth now
2. The State of AI according by AI worlds top minds.
🔍 FAQs – Building an AI Company in 2025 (Sequoia’s Strategy + Founder Tactics)
What is the best way to build a successful AI startup in 2025?
The most successful AI startups in 2025 are built around vertical applications, not general-purpose tools. Sequoia Capital and top founders recommend going deep on specific workflows, building trust through human-in-the-loop design, and generating proprietary data flywheels to create defensibility.
What does Sequoia Capital look for when investing in AI startups?
Sequoia looks for real traction, not hype. They evaluate startups based on:
Actual user behavior and retention (not vanity metrics)
Gross margin trajectory as token costs decline
Data flywheels that improve core business metrics
Depth of solution in a specific workflow or industry
Where will most of the value in AI startups be captured?
According to Sequoia, the majority of value will accrue at the application layer—startups that build full-stack solutions for specific problems, rather than in foundational models or infrastructure.
Why do most AI agents and copilots fail?
Most agents fail due to feature incompleteness and weak integration. They may complete a first task but fail on follow-ups due to limited API access. Companies like Ramp solve this by giving agents full UI access, enabling them to operate the product like a human.
What’s the “agent economy” and why does it matter for AI startups?
The agent economy is the emerging system where autonomous AI agents don’t just respond to prompts—they proactively collaborate, transact, and complete tasks across software. This shift allows small teams to operate like large ones, with agents acting like invisible employees.
How are startups using agents differently than chatbots?
Unlike chatbots, modern agents are ambient (running in the background), event-triggered (not prompt-based), and capable of long, complex workflows. Founders are building agent interfaces like LangChain’s “Agent Inbox” to maintain oversight and user trust.
How can an early-stage founder compete with OpenAI or Anthropic?
Avoid competing with foundation models. Instead, build vertical products on top of them. OpenAI encourages startups to use their APIs to solve high-friction, niche problems that require workflow ownership, domain knowledge, and tight UX control.
What are data flywheels in AI startups and how do they work?
A data flywheel is when product usage generates data that improves the AI’s performance, which makes the product better, which attracts more users. Sequoia emphasizes this as a core moat—but only if it drives real business outcomes like better retention or lower churn.
What’s the biggest mistake founders make when building AI applications?
Founders often treat AI as an add-on, bolting it onto traditional UIs. Instead, you need to rethink the product architecture entirely—designing the interface, logic, and experience around the model from the start. Claude’s team at Anthropic notes that AI should be treated as the core user, not a sidebar tool.
How do I make my AI product go viral or scale fast?
The internet is ready for distribution—if your product is good. Unlike the early cloud days, today’s users are connected and primed for discovery through TikTok, Reddit, X, and more. Build for virality with shareable outputs, free usage tiers, and lightweight onboarding.
What kind of team structure helps AI startups move faster?
OpenAI and Ramp both advocate for small, high-ownership teams with fast iteration cycles. Founders should prioritize velocity over hierarchy, and focus on launching simple prototypes over attending strategy meetings.
What’s next after chat-based AI tools?
Chat was just the beginning. The next phase is ambient agents (running silently in the background), voice interfaces, and autonomous coding assistants. Startups should be building for this future now.
How do I build trust with users when deploying AI agents?
Trust comes from visibility, control, and accountability. Features like an agent inbox, action logs, approval workflows, and rollback tools give users the confidence to rely on your AI without fear of errors.
How should AI founders think about gross margins and infrastructure costs?
Don’t let current infra costs scare you. Token and compute prices are dropping rapidly. Focus on building a product people love—strong margins will follow with scale and optimization.
What are the technical gaps still holding back agent-based AI?
Three major gaps:
Persistent memory for context and personalization
Common protocols for agent communication
New trust and security models for autonomous actions
Startups solving these will define the next 10 years.