Angel investors & VCs
Venture · Investors
Full curriculum document
Angels and VCs evaluating and backing early AI ventures
Contents · 6 units
- 1Reading an AI product honestlyVEN-U1 · Module 1 · 40 GLH · Internal portfolio
- 2The AI market & where value accruesVEN-U2 · Module 2 · 40 GLH · Internal portfolio
- 3Technical due diligenceVEN-U3 · Module 3 · 50 GLH · Portfolio + exam
- 4Deal flow & portfolio constructionVEN-U4 · Module 4 · 40 GLH · Internal portfolio
- 5Valuation, terms & the dealVEN-U5 · Module 5 · 40 GLH · Portfolio + exam
- 6Backing builders & governanceVEN-U6 · Module 6 · 30 GLH · Internal portfolio
Overview & approach
An investment is a belief about the future priced under uncertainty. This track reduces that uncertainty from first principles: understand the technology well enough to judge the team, the moat and the risk before you commit.
The first-principles learning loop
- 1Problem
- 2Understanding
- 3Reasoning
- 4Design
- 5Implementation
- 6Testing
- 7Reflection
- 8Improvement
Every unit runs this loop, then goes round again — deeper each time.
Competencies & responsible practice
Core competencies
- Communication & collaboration
- Critical thinking & problem solving
- Creativity & imagination
- Citizenship
- Digital literacy
- Learning to learn
- Self-efficacy
Ethics & responsible practice
- Digital citizenship & online safety
- Data protection & privacy
- AI ethics, bias & fairness
- Environmental sustainability (compute cost)
- Financial literacy & entrepreneurship
- Inclusion & accessibility
- Mental well-being & healthy screen habits
- Career & life skills
The 6 units in full
VEN-U1 · Module 1 · 40 GLH · Internal portfolio
Reading an AI product honestly
Strand: Technical literacy for investors
Key inquiry: “How do you tell a real AI moat from a thin wrapper around someone else’s model?”
You ship: A technical-literacy brief scoring an AI product’s substance, defensibility and risk.
LO1 · What’s under the hood
- • Distinguish model, data, product and moat in an AI startup
- • Identify where the real defensibility (or lack of it) sits
LO2 · Risk lenses
- • Assess data, cost, safety and dependency risks
- • Ask the questions that separate signal from hype
Core competencies
- Critical thinking & problem solving
- Digital literacy
- Learning to learn
Ethics & responsible practice
- AI ethics, bias & fairness
- Financial literacy & entrepreneurship
Hands-on practice
- Tear down a real AI product and locate its moat.
- Draft the ten questions you’d ask its technical founder.
- Score substance vs hype on a shared rubric.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Assesses a product with support. |
|---|---|
| Proficient | Judges defensibility and risk independently. |
| Expert | Produces a sharp brief a partner could act on. |
VEN-U2 · Module 2 · 40 GLH · Internal portfolio
The AI market & where value accrues
Strand: AI market & value chains
Before this unit: A technical-literacy brief (Unit 1).
Key inquiry: “In the AI stack, who captures the value — models, infrastructure, data, or the application layer?”
You ship: A market map and thesis on where durable value and defensibility sit in AI.
LO1 · Value chain
- • Map the AI stack from silicon to application
- • Locate where margins and moats actually sit
LO2 · Thesis
- • Identify where an investor edge is real, not hype
- • Write a defensible market thesis
Core competencies
- Critical thinking & problem solving
- Learning to learn
- Communication & collaboration
Ethics & responsible practice
- Financial literacy & entrepreneurship
- AI ethics, bias & fairness
Hands-on practice
- Map the AI value chain and mark where value accrues.
- Compare two startups at different layers of the stack.
- Write a one-page market thesis and defend it.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Maps the market with support. |
|---|---|
| Proficient | Locates value and defensibility independently. |
| Expert | Produces a sharp thesis a partner could act on. |
VEN-U3 · Module 3 · 50 GLH · Portfolio + exam
Technical due diligence
Strand: Diligence & evaluation
Before this unit: A technical-literacy brief and market thesis (Units 1–2).
Key inquiry: “In five days, how do you verify what a founder claims their AI actually does?”
You ship: A completed technical due-diligence report on a real (or simulated) deal.
LO1 · Verify the claims
- • Design a diligence plan for a five-day window
- • Probe data rights, evaluation and reproducibility
LO2 · Team & scalability
- • Assess the team’s ability to execute and to scale the system
- • Surface red flags and unknowns explicitly
Core competencies
- Critical thinking & problem solving
- Communication & collaboration
- Self-efficacy
Ethics & responsible practice
- Data protection & privacy
- AI ethics, bias & fairness
Hands-on practice
- Run a mock diligence against a real data room.
- Interview a technical founder and pressure-test claims.
- Write the report a partnership meeting would rely on.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Completes diligence with support. |
|---|---|
| Proficient | Runs a rigorous diligence independently. |
| Expert | Delivers a decision-grade report with clear risk calls. |
VEN-U4 · Module 4 · 40 GLH · Internal portfolio
Deal flow & portfolio construction
Strand: Investment strategy
Before this unit: Diligence experience (Unit 3).
Key inquiry: “How do you build a portfolio that survives being wrong most of the time?”
You ship: A thesis, sourcing plan and portfolio-construction model with stage allocation.
LO1 · Thesis & sourcing
- • Articulate an investment thesis and a sourcing strategy
- • Use curated, vetted deal flow effectively
LO2 · Construction
- • Model stage allocation, ownership and reserves
- • Reason about power-law returns and portfolio math
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Communication & collaboration
Ethics & responsible practice
- Financial literacy & entrepreneurship
- Career & life skills
Hands-on practice
- Write a one-page thesis and test it against real deals.
- Build a simple portfolio-construction model.
- Review Demo-Day companies against your thesis.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Drafts a thesis with support. |
|---|---|
| Proficient | Builds a coherent sourcing and construction plan independently. |
| Expert | Produces a defensible strategy a fund could adopt. |
VEN-U5 · Module 5 · 40 GLH · Portfolio + exam
Valuation, terms & the deal
Strand: Deal structuring & valuation
Before this unit: A thesis and diligence practice (Units 2–4).
Key inquiry: “How do we price uncertainty and structure a deal that is fair and survives follow-on rounds?”
You ship: A valuation and term-sheet analysis for a real or simulated deal.
LO1 · Valuation
- • Value an early venture under uncertainty
- • Model dilution and follow-on across rounds
LO2 · Terms
- • Read a term sheet’s key economic and control terms
- • Negotiate fair, founder-aligned terms
Core competencies
- Critical thinking & problem solving
- Self-efficacy
- Communication & collaboration
Ethics & responsible practice
- Financial literacy & entrepreneurship
- Career & life skills
Hands-on practice
- Value a startup two ways and compare the results.
- Mark up a term sheet and flag the traps.
- Model how ownership changes over two rounds.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Reads terms with support. |
|---|---|
| Proficient | Values and structures a deal independently. |
| Expert | Delivers a decision-grade valuation and term analysis. |
VEN-U6 · Module 6 · 30 GLH · Internal portfolio
Backing builders & governance
Strand: Post-investment value
Before this unit: A thesis, diligence and deal analysis (Units 2–5).
Key inquiry: “After the cheque, how does an investor actually help a company win — responsibly?”
You ship: A value-add and governance plan for a portfolio company, tied to milestones.
LO1 · Value-add
- • Map concrete ways to help by stage (talent, customers, follow-on)
- • Set milestones and support a founder against them
LO2 · Responsible governance
- • Apply ethical, responsible-AI expectations to portfolio companies
- • Balance support with sound governance
Core competencies
- Communication & collaboration
- Citizenship
- Self-efficacy
Ethics & responsible practice
- AI ethics, bias & fairness
- Financial literacy & entrepreneurship
Hands-on practice
- Draft a 90-day value-add plan for a portfolio company.
- Define milestones and a support cadence with a founder.
- Set responsible-AI expectations for the company.
Proficiency (Emerging · Proficient · Expert)
| Emerging | Plans support with guidance. |
|---|---|
| Proficient | Builds a milestone-linked value-add plan independently. |
| Expert | Delivers a governance-and-support plan founders value. |
What you walk away with
A portfolio of 6 real, shipped artefacts — one per unit, assessed to standard.
- 1 A technical-literacy brief scoring an AI product’s substance, defensibility and risk.
- 2 A market map and thesis on where durable value and defensibility sit in AI.
- 3 A completed technical due-diligence report on a real (or simulated) deal.
- 4 A thesis, sourcing plan and portfolio-construction model with stage allocation.
- 5 A valuation and term-sheet analysis for a real or simulated deal.
- 6 A value-add and governance plan for a portfolio company, tied to milestones.
Proficiency rubric (Emerging → Expert)
| Proficiency level | What the evidence shows |
|---|---|
| Emerging | Completes the work with guidance; the artefact functions for the core case and the practitioner can explain what it does and why. |
| Proficient | Works independently, handles real-world edge cases, evaluates the result, and justifies design decisions from first principles. |
| Expert | Delivers work adopted by real users or partners, reasons explicitly about trade-offs — cost, risk, safety — and extends beyond the brief with original contribution. |