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Build Your Own AI Company

You understand AI. Now build a business with it.

The landscape, the playbook, the mistakes everyone makes, and the ones who got it right.

For absolute beginners — no business experience required.

Chapter 1: The Landscape

Where does the money flow? Who's winning? And most importantly — where do YOU play?

The AI Industry in Concentric Rings

Foundation Layer
OpenAI, Anthropic, Google, Meta
They build the models
Infrastructure Layer
NVIDIA, AWS, Vector DBs, Frameworks
The plumbing
Application Layer
Vertical SaaS, Copilots, Agents
🎯 THIS IS WHERE YOU PLAY

The Wrapper Problem

Most "AI startups" are thin wrappers over GPT. They die when OpenAI adds their feature. Here's what happens:

📈 The Cycle of Death:
1. Build feature using GPT API
2. Get users, raise money
3. OpenAI ships your feature natively
4. Your customers leave for the "official" version
5. 💀 Game over

The antidote? Build something defensible. Own data, workflows, or distribution that OpenAI can't replicate.

Interactive: AI Company or Feature?

Look at these real AI products. Can you guess which survived platform updates and which got killed?

"The way to create something great is to create something people want" — solve real pain, not demo-day tricks.
— Paul Graham
"AI companies that win will look like regular companies that are great at AI, not AI companies that bolt on a business model."
— Sam Altman

📚 Go Deeper

Chapter 2: What's Working

Five business models that actually make money. Not theory — companies doing millions in ARR.

1

Vertical AI Agents

Own a niche deeply. Harvey (legal), Abridge (medical notes). Not "AI for everything" but "AI that replaces this specific $200/hr workflow."

Examples: Harvey, Abridge, Glean
2

AI-Native Workflows

Rebuild the tool around AI. Cursor, Replit. Not "add AI to existing tool" but "reimagine what's possible."

Examples: Cursor, Replit, Notion AI
3

Infrastructure Picks-and-Shovels

Sell to the gold rushers. LangChain, Pinecone, Weights & Biases. While others dig for gold, you sell the shovels.

Examples: LangChain, Pinecone, W&B
4

Data Moats

Your proprietary dataset IS your moat. Scale AI, Snorkel. Models commoditize, but unique data stays valuable.

Examples: Scale AI, Snorkel, Labelbox
5

Enterprise Deployment

Help big companies adopt AI safely. Consulting + tools. They want AI but don't know how. You show them.

Examples: Anthropic's Claude for Enterprise, OpenAI Enterprise

Interactive: Revenue Model Builder

Pick your target and see which business model fits best:

"AI is the new electricity" — but also: "Don't start with the technology. Start with the business problem."
— Andrew Ng
"Do things that don't scale" — applies to AI: hand-hold your first 10 customers, learn what they actually need.
— Paul Graham

📚 Go Deeper

Chapter 3: What's Failing

The graveyard of AI startups. Learn from their mistakes so you don't repeat them.

🌯

The Wrapper Trap

Built on GPT, no moat. OpenAI ships the feature natively.

🎪

Demo-Day Darling

Incredible demo, no real workflow. "Cool but when would I actually use this?"

💸

Cost Death Spiral

Giving away AI features, burning cash on inference. No path to margins.

⚖️

Hallucination Lawsuit

Shipped without guardrails. AI said something wrong, customer got burned.

🔨

Solution Looking for Problem

"We have this amazing model!" "What does it do?" "...anything?"

🔒

Data Privacy Fumble

Enterprise customers walked away. You trained on their data.

Interactive: Startup Autopsy

Six real AI startup stories. Can you identify which death pattern killed them?

Real Examples (Carefully Worded)

  • Jasper's decline after ChatGPT launch — from $40M ARR to layoffs in 6 months
  • Character.AI's pivot — amazing demos, but users didn't stick around
  • Copy.ai's commoditization — once unique, now one of dozens

The pattern? They solved a problem that the foundation models eventually solved better and cheaper.

"80% of AI startups will fail, same as every other category"
— Vinod Khosla
"The most common mistake startups make is to solve problems no one has"
— Paul Graham

📚 Go Deeper

Chapter 4: The Playbook

The dos and don'ts from people who've done it. Hard-won lessons from successful AI founders.

The DOs

  • Start with a workflow, not a technology ("What 4-hour task can I make take 10 minutes?")
  • Build evaluation before features (you can't improve what you can't measure)
  • Own your data pipeline (your data is your moat, not your model)
  • Price on value, not cost (save $500/hr of work? Charge $100, not $0.02 per token)
  • Ship ugly, learn fast (your first version should embarrass you)
  • Build for the future model (assume 10x improvement yearly)
  • Keep humans in the loop for high-stakes decisions

The DON'Ts

  • Don't fine-tune when RAG will do (cheaper, more flexible, easier to update)
🔗 Connection: If these terms are unfamiliar, take Build Your Own RAG (Course 2) and Build Your Own Fine-Tune (Course 3) first. The Playbook assumes you know the difference — those courses make it visceral.
  • Don't compete with foundation models (they have billion-dollar budgets)
  • Don't promise perfection ("AI that never makes mistakes" is a lie)
  • Don't ignore latency (correct answer in 30s loses to good answer in 2s)
  • Don't underestimate compliance (SOC2, HIPAA, GDPR add 6+ months)
  • Don't raise before customers (10 paying customers first, Paul Graham's advice)
  • Don't use AI where SQL works (simple problems need simple solutions)

Interactive: Founder Decision Simulator

Scenarios will pop up. Pick the best answer and see the reasoning:

"The gap between a great AI demo and a great AI product is larger than most people think"
— Andrew Ng
"The most successful AI companies will be the ones that figure out the UX"
— Sam Altman
"Make something people want" — still the best startup advice in the AI era
— Paul Graham

📚 Go Deeper

Chapter 5: Your Edge

What small teams can do that big companies can't. Why NOW is the best time to start an AI company.

Why Small Teams Win

🛒 The Model is a Commodity

You don't need to train GPT-5. You rent it for $0.002 per query. The playing field is level.

🚀 Distribution Beats Technology

The best model doesn't win. The best product does. And you can build better products faster.

🎯 Vertical Knowledge is the Moat

OpenAI doesn't know your customer's industry. You do. That's your unfair advantage.

⚡ Speed Beats Scale

A 2-person team can ship in a week what a big company committees about for a quarter.

Models

  • OpenAI (GPT-4, GPT-4o)
  • Anthropic (Claude)
  • Google (Gemini)
  • Meta (LLaMA)
  • Mistral (Mixtral)
  • Qwen (Alibaba)

Orchestration

  • LangChain
  • LlamaIndex
  • Haystack
  • Semantic Kernel

Vector DBs

  • Pinecone
  • Weaviate
  • Chroma
  • pgvector

Deployment

  • Modal
  • Replicate
  • Together AI
  • RunPod

Evaluation

  • Braintrust
  • Promptfoo
  • LMSYS
  • LangSmith

Monitoring

  • LangSmith
  • Helicone
  • Portkey
  • Weights & Biases

🚀 Build Your AI Startup Pitch

Fill this out and get a one-page pitch you can actually use:

"Live in the future, then build what's missing"
— Paul Graham
"In the AI era, the company that can best collect and use data will win"
— Andrew Ng
"If you're not embarrassed by the first version of your product, you've launched too late"
— Reid Hoffman

📚 Go Deeper

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