Skip to main content

Learning Roadmap

How to Become a AI Venture Scout

A step-by-step, phase-based learning path from beginner to job-ready AI Venture Scout. Estimated completion: 5 months across 5 phases.

5 Phases
20 Weeks Total
Medium Entry Barrier
Intermediate Difficulty
← AI Venture Scout Overview Interview Prep →
Your Progress 0 / 5 phases

Progress saved in your browser — no account needed.

  1. AI Technology Foundations & Venture Capital Fundamentals

    4 weeks
    Goals
    • Understand core ML/AI concepts including transformer architectures, fine-tuning, inference optimization, and data pipelines
    • Learn the mechanics of venture capital - fund structure, deal flow, term sheets, and portfolio construction
    • Familiarize yourself with the AI startup landscape and key players
    Resources
    • Fast.ai Practical Deep Learning course
    • a16z 'AI Canon' reading list
    • CB Insights State of AI Report
    • Y Combinator YouTube channel - AI startup pitches
    • Sequoia Capital blog on AI market maps
    Milestone

    You can articulate the difference between foundation model companies, application-layer startups, and infrastructure plays, and explain how VC economics work.

  2. Deal Sourcing, CRM Mastery & Network Building

    4 weeks
    Goals
    • Build a systematic sourcing workflow using GitHub, HuggingFace, Crunchbase, and academic publications
    • Set up and operate a personal CRM for tracking founders and startups
    • Begin building a network of AI practitioners and founders
    Resources
    • Affinity CRM or Airtable templates for deal tracking
    • Samir Kaji's Venture Unlocked podcast
    • AngelList Venture content
    • Notion templates for investment research
    • LinkedIn advanced search and Sales Navigator tutorials
    Milestone

    You have a functioning sourcing pipeline that surfaces 10+ promising AI startups per week and a CRM tracking 50+ contacts.

  3. Technical Due Diligence & Investment Memo Writing

    4 weeks
    Goals
    • Develop a structured framework for evaluating AI startup technical claims
    • Write polished investment memos with technical depth and business clarity
    • Learn to assess data moats, model defensibility, and go-to-market fit
    Resources
    • Sequoia Capital investment memo examples (publicly available)
    • a16z marketplace and B2B frameworks
    • Andrej Karpathy's talks on AI product strategy
    • Writing guides from Farnam Street and Stratechery
    • Papers With Code for evaluating model benchmarks
    Milestone

    You can produce a professional-grade investment memo that a VC partner would take seriously, covering technical differentiation, market sizing, team assessment, and risk factors.

  4. Specialization, Deal Experience & Portfolio Strategy

    4 weeks
    Goals
    • Develop a personal investment thesis in a specific AI vertical (e.g., AI agents, developer tools, healthcare AI)
    • Participate in at least 3-5 live deal evaluations end-to-end
    • Understand portfolio construction, follow-on strategy, and LP reporting
    Resources
    • Kauffman Fellows program resources
    • Join a scout program or angel investing syndicate (e.g., AngelList, Scout programs from Sequoia, a16z, Lightspeed)
    • Tomasz Tunguz blog on SaaS and AI metrics
    • First Round Review articles on AI startups
    • Attend local AI meetups or online communities (e.g., Latent Space, MLOps Community)
    Milestone

    You have sourced or co-evaluated real deals, have a clear investment thesis, and can confidently present opportunities to senior partners.

  5. Scaling Impact - Thought Leadership & Ecosystem Influence

    4 weeks
    Goals
    • Publish original AI market research or trend analysis that establishes credibility
    • Build a reputation as a go-to scout in your chosen vertical
    • Develop skills in portfolio company support and board observation
    Resources
    • Substack or blog platform for publishing analysis
    • Twitter/X for building public AI investment commentary
    • Industry podcasts and panel opportunities
    • Advanced Python for automated startup data analysis
    • Ben Thompson's Stratechery for business model frameworks
    Milestone

    You are recognized in the AI venture ecosystem, receive inbound deal flow from founders, and are considered for associate or principal roles at top-tier firms.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

AI Startup Sector Map & Landscape Report

Beginner

Create a comprehensive sector map of a chosen AI vertical (e.g., AI code assistants, AI for healthcare) by researching 30-50 companies, categorizing them by sub-sector, stage, funding, and technology approach. Deliver a visual landscape and a 3-page written analysis with key trends and investment implications.

~25h
Market mappingCompetitive intelligenceResearch synthesis

Automated AI Startup Discovery Pipeline

Intermediate

Build a Python-based pipeline that monitors GitHub trending repositories, HuggingFace model uploads, and ArXiv papers to surface AI projects with commercial potential. Include automated scoring, Slack notifications, and a simple dashboard for tracking discoveries.

~35h
API integrationData engineeringSignal detection

Technical Due Diligence Deep Dive - 3 AI Startups

Intermediate

Select three real AI startups at different stages and conduct full technical due diligence on each. Evaluate their model architecture, data strategy, competitive positioning, and team. Produce investment-grade memos for each with a clear recommendation and supporting evidence.

~40h
Technical evaluationInvestment memo writingDue diligence frameworks

LLM-Powered Deal Sourcing Assistant

Advanced

Build a LangChain-based research agent that takes a startup name or description as input and produces a structured brief covering founding team background, technology stack, funding history, competitive landscape, and market sizing. Integrate with Crunchbase API, LinkedIn, and web search.

~45h
LLM orchestrationAgent designAPI integration

AI Investment Thesis Presentation

Advanced

Develop and deliver a 15-minute investment thesis presentation on an emerging AI vertical. Include market sizing, competitive dynamics, historical analogies, key risk factors, and a target startup profile. Present to a panel of mock partners and defend your thesis under questioning.

~30h
Investment thesis developmentPublic speakingPersuasive communication

Portfolio Construction Simulation

Advanced

Given a hypothetical $10M fund focused on AI, design a portfolio of 10-15 investments across stages and sectors. Model expected returns under different scenarios, justify each selection, and present a portfolio strategy memo that addresses diversification, follow-on reserves, and concentration risk.

~30h
Portfolio strategyFinancial modelingRisk analysis

Ready to Start Your Journey?

Prep for interviews alongside your learning — it reinforces every concept.

Practice Interview Questions Explore More Careers