11. AI and Investing I: Fundamentals for Picking AI-Related Stocks

Course Positioning

A finance literacy and industry analysis course for understanding the AI investment landscape through fundamentals, competitive advantage, market structure, and growth expectations.

Learning outcomes

Map the AI value chain across semiconductors, memory, networking, cloud, data centers, model labs, software, data, services, and vertical applications.
Analyze AI-related companies using revenue growth, margins, cash flow, capex, depreciation, balance sheet strength, valuation, and competitive positioning.
Distinguish real AI business value from AI washing, hype cycles, and unsustainable narratives.
Evaluate moats such as distribution, data, switching costs, ecosystem control, hardware constraints, developer adoption, and regulatory position.
Build an AI investment watchlist with thesis, catalysts, risks, valuation discipline, and position-sizing logic.
Understand major risks: cyclicality, customer concentration, export controls, commoditization, valuation compression, regulation, and fraud.

Course Design Snapshot

Positioning: A finance literacy and industry analysis course for understanding the AI investment landscape through fundamentals, competitive advantage, market structure, and growth expectations.
Audience: Retail investors, analysts, founders, business students, finance professionals, and professionals who want to understand AI market structure.
Duration: 8 weeks, 1-2 sessions per week.
Prerequisites: Basic financial statement literacy helpful. No coding required.
Format: Sector maps, financial statement analysis, unit economics, valuation exercises, risk analysis, and portfolio thesis writing.

Expanded Topic-by-Topic Coverage

Module 1. AI value chain

Module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy. Primary live activity or lab: Create an AI market map with public company examples.

Topics and coverage

chips

What it means: define chips clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

memory

What it means: define memory clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

networking

What it means: define networking clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

cloud

What it means: define cloud clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

data centers

What it means: connect data centers to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

model providers

What it means: place model providers inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

application companies

What it means: define application companies clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

services

What it means: define services clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

energy

What it means: define energy clearly and connect it to the module focus: AI value chain: chips, memory, networking, cloud, data centers, model providers, application companies, services, and energy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create an AI market map with public company examples.
Learners produce: Create an AI market map with public company examples.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Semiconductors and infrastructure

Module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power. Primary live activity or lab: Analyze one chip or infrastructure company using revenue drivers and supply-chain position.

Topics and coverage

GPUs

What it means: place GPUs inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

ASICs

What it means: define ASICs clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

memory

What it means: define memory clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

networking

What it means: define networking clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

foundries

What it means: define foundries clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

packaging

What it means: define packaging clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

cooling

What it means: define cooling clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

power

What it means: define power clearly and connect it to the module focus: Semiconductors and infrastructure: GPUs, ASICs, memory, networking, foundries, packaging, cooling, and power.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Analyze one chip or infrastructure company using revenue drivers and supply-chain position.
Learners produce: Analyze one chip or infrastructure company using revenue drivers and supply-chain position.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. Hyperscalers and cloud economics

Module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand. Primary live activity or lab: Build a simple capex-to-revenue thesis for one cloud provider.

Topics and coverage

capex

What it means: define capex clearly and connect it to the module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

depreciation

What it means: define depreciation clearly and connect it to the module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

utilization

What it means: define utilization clearly and connect it to the module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

cloud margins

What it means: define cloud margins clearly and connect it to the module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

model APIs

What it means: place model APIs inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

enterprise AI demand

What it means: define enterprise AI demand clearly and connect it to the module focus: Hyperscalers and cloud economics: capex, depreciation, utilization, cloud margins, model APIs, and enterprise AI demand.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Build a simple capex-to-revenue thesis for one cloud provider.
Learners produce: Build a simple capex-to-revenue thesis for one cloud provider.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Software and SaaS

Module focus: Software and SaaS: copilots, workflow AI, pricing models, margins, churn, seat expansion, and competitive pressure from platforms. Primary live activity or lab: Compare two software firms by AI monetization path.

Topics and coverage

copilots

What it means: explain how copilots changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

workflow AI

What it means: show where workflow AI appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

pricing models

What it means: place pricing models inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

margins

What it means: define margins clearly and connect it to the module focus: Software and SaaS: copilots, workflow AI, pricing models, margins, churn, seat expansion, and competitive pressure from platforms.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

churn

What it means: define churn clearly and connect it to the module focus: Software and SaaS: copilots, workflow AI, pricing models, margins, churn, seat expansion, and competitive pressure from platforms.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

seat expansion

What it means: define seat expansion clearly and connect it to the module focus: Software and SaaS: copilots, workflow AI, pricing models, margins, churn, seat expansion, and competitive pressure from platforms.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

competitive pressure from platforms

What it means: define competitive pressure from platforms clearly and connect it to the module focus: Software and SaaS: copilots, workflow AI, pricing models, margins, churn, seat expansion, and competitive pressure from platforms.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Compare two software firms by AI monetization path.
Learners produce: Compare two software firms by AI monetization path.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Data and vertical AI

Module focus: Data and vertical AI: proprietary data, regulated workflows, domain expertise, distribution, and customer lock-in. Primary live activity or lab: Evaluate whether a vertical AI company has a genuine moat.

Topics and coverage

proprietary data

What it means: connect proprietary data to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

regulated workflows

What it means: show where regulated workflows appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

domain expertise

What it means: define domain expertise clearly and connect it to the module focus: Data and vertical AI: proprietary data, regulated workflows, domain expertise, distribution, and customer lock-in.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

distribution

What it means: define distribution clearly and connect it to the module focus: Data and vertical AI: proprietary data, regulated workflows, domain expertise, distribution, and customer lock-in.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

customer lock-in

What it means: show where customer lock-in appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

Practice and evidence of learning

Learners complete or discuss: Evaluate whether a vertical AI company has a genuine moat.
Learners produce: Evaluate whether a vertical AI company has a genuine moat.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. Financial statements

Module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs. Primary live activity or lab: Read a 10-K/annual report and extract AI-relevant signals.

Topics and coverage

revenue quality

What it means: define revenue quality clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

gross margin

What it means: define gross margin clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

operating leverage

What it means: explain how operating leverage changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

free cash flow

What it means: define free cash flow clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

dilution

What it means: define dilution clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

debt

What it means: define debt clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

reinvestment needs

What it means: define reinvestment needs clearly and connect it to the module focus: Financial statements: revenue quality, gross margin, operating leverage, free cash flow, dilution, debt, and reinvestment needs.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Read a 10-K/annual report and extract AI-relevant signals.
Learners produce: Read a 10-K/annual report and extract AI-relevant signals.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. Valuation and scenarios

Module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases. Primary live activity or lab: Create bull/base/bear scenarios for one AI-related stock.

Topics and coverage

multiples

What it means: define multiples clearly and connect it to the module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

discounted cash flow intuition

What it means: define discounted cash flow intuition clearly and connect it to the module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

growth duration

What it means: define growth duration clearly and connect it to the module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

margin expansion

What it means: define margin expansion clearly and connect it to the module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

downside cases

What it means: define downside cases clearly and connect it to the module focus: Valuation and scenarios: multiples, discounted cash flow intuition, growth duration, margin expansion, and downside cases.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create bull/base/bear scenarios for one AI-related stock.
Learners produce: Create bull/base/bear scenarios for one AI-related stock.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Portfolio construction and risk

Module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline. Primary live activity or lab: Write an investment memo with thesis, valuation, catalysts, and risk controls.

Topics and coverage

position sizing

What it means: define position sizing clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

diversification

What it means: define diversification clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

thesis drift

What it means: define thesis drift clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

rebalancing

What it means: define rebalancing clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

fraud alerts

What it means: define fraud alerts clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

behavioral discipline

What it means: define behavioral discipline clearly and connect it to the module focus: Portfolio construction and risk: position sizing, diversification, thesis drift, rebalancing, fraud alerts, and behavioral discipline.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Write an investment memo with thesis, valuation, catalysts, and risk controls.
Learners produce: Write an investment memo with thesis, valuation, catalysts, and risk controls.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Core labs and builds

AI value-chain map lab across public equities.
Annual report lab: extract business segments, AI claims, risks, capex, and customer concentration.
Moat lab: classify companies by data moat, hardware moat, distribution moat, cost moat, or no clear moat.
Valuation discipline lab: compare growth narrative with revenue, margin, and cash-flow evidence.

Capstone

Create a complete AI investment memo for one public company or a basket of companies. The memo includes business model, AI exposure, financials, competitive advantage, valuation scenarios, catalysts, key risks, red flags, and decision rules.

Assessment design

Market map accuracy and value-chain reasoning.
Financial statement analysis quality.
Valuation scenario realism.
Final investment memo with balanced risks and no hype.

Recommended tools and datasets

Company filings, annual reports, investor presentations, financial statements, spreadsheets, valuation templates, sector maps, earnings-call transcripts, SEC/FINRA investor protection resources.

Instructor notes

The course should explicitly train learners to avoid AI hype, fraud, and overconcentration. Strong investing education is mostly about process, risk, and valuation discipline.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.