One of six curricula in the AI Career Curriculum
AI Startup
You build a company around the model: you decide what it's for and who pays for it.
8 curricula and 74 modules, free and MIT-licensed.
- AI WorkforceWork alongside the models
- AI StartupTurn it into a company
- AI GovernanceGovern, secure, and assure AI
- AI EngineeringBuild with the models
- ML EngineeringBuild and train the models
- AI InfrastructureRun the platforms models live on
Your relationship to the model
To you the model is a capability inside a business. You care whether it solves a problem customers will pay for, at a cost that leaves margin, with a story investors and regulators will accept. You make the calls about what to build, when, and with what money.
This org covers turning it into a company — the operating knowledge technical founders need: how startups work as a system, customer discovery and product-market fit, go-to-market, finance and fundraising, operations and governance, and the endgame. Each curriculum is one self-contained repository, and you learn by building real founder artifacts, not reading case studies.
What you own
- The problem, the customer, and the reason anyone pays
- Build-versus-buy and which bets to make with limited runway
- Pricing and margin, given what the model costs to run
- Hiring: which of the other four orgs' roles you need, and when
- Commitments to customers, investors, and regulators
What you don't own: The technical details of any single layer. You need enough fluency to ask the right questions of each and to know when an answer doesn't add up.
The tracks
Ordered by level. Each one opens with Module 00, which maps the role's place relative to the model before the technical work starts.
How startups work as a system, the dependency graph, stages, founder operating basics, reading the numbers
Customer discovery, business modeling, runway, pre-seed to seed fundraising, SAFEs & cap tables, founder-led sales
Architecture under uncertainty, build vs. buy, first engineering hires, tech debt as a business decision, scaling
Problem validation, PMF signals, positioning, ICP, founder-led sales, pricing & packaging, demand generation
Discovery to PMF, prioritization, outcome roadmaps, discovery/delivery cadence, experimentation & AI evals
Startup accounting, unit economics, three-statement forecasting, cap tables, convertibles, valuation, fundraising
Entity formation, founder legal architecture, employment law, HR ops, equity-comp policy, compensation
Endgame strategy, M&A structures & acquirer motivations, LOIs, diligence & data rooms, secondaries
Who you work with
No curriculum owns the model end to end. These are the handoffs with each of the others.
AI Workforce
Work alongside the models
- You hand them
- Decisions about which AI tools the company adopts and how roles change, plus the training and time to adapt
- They hand you
- Evidence of where AI changes the economics of a job, which is where the product and hiring bets are
AI Governance
Govern, secure, and assure AI
- You hand them
- The company's risk appetite, target markets, and customer commitments that set the compliance bar
- They hand you
- A defensible position for customers, regulators, and investors, and early warning on uses that would sink the company
AI Engineering
Build with the models
- You hand them
- Prioritized customer problems, what's been promised, and what 'good enough to ship' means for this market
- They hand you
- Working features, honest demos, and a clear picture of what the product can promise customers today
ML Engineering
Build and train the models
- You hand them
- The problems worth owning a model for, and the business case for the data and compute it takes
- They hand you
- An honest read on what's technically feasible, how long it takes, and what it costs to own a model versus renting one
AI Infrastructure
Run the platforms models live on
- You hand them
- Growth forecasts, cost targets, and reliability commitments that set the platform's budget
- They hand you
- Unit economics: what each request, user, or customer costs to serve, and how that changes with scale
What you'll work with
- Validation
- Customer-discovery interview plans, PMF measurement, ICP and positioning docs
- Finance
- Runway models, three-statement and driver-based forecasts, unit-economics and cohort models
- Equity & fundraising
- Cap tables, SAFE and convertible scenarios, investor funnels
- Operations & governance
- Entity and founder agreements, equity-comp policy, board packets
- Exit
- Deal-structure comparisons, LOI term reviews, diligence data rooms
How the tracks work
Learn
Each track is a sequence of modules: lecture chapters, hands-on exercises, quizzes, and projects. Start with Module 00 and work forward.
Check your work
Each curriculum includes worked exemplars: completed founder artifacts to compare your own against.
Built in the open
The content is written with AI assistance and under ongoing human review, so it can contain mistakes. Report anything wrong through the repo's Issues; fixes ship continuously.