Curriculum

Objective 1 · Analyze

Textbook chapters: Why businesses exist, addressing demand, market analysis. Deeper dive: business plan creation, organizational behavior intro, project scoping.

Tooling setup. Every student sets up Google Gemini through ACC Workspace. They learn the difference between a chat session and a configured agent — agents have a name, a job description, a memory, and tool permissions. They write their first system prompt the way a manager writes a job posting: who is this hire, what do they own, what authority do they have, what's out of scope.

Prompt fundamentals. Anatomy of an effective prompt — role, context, task, constraints, output format. Why specificity beats cleverness. The difference between asking AI to do something and asking AI to decide something. Iterating a prompt until the output is consistent.

Your first hire. Students stand up a single general-purpose AI agent and put it to work on a small task — research an industry of interest, draft a one-paragraph company concept. They don't know yet that one agent doing everything won't scale. By end of week they've picked a preliminary company concept and a GICS sector to research deeper in Week 2.

Vibe. Everything feels manageable with one agent. That's the point — the wall comes in Week 2.

Objective 1 · Analyze

Textbook chapters: Organizational behavior (deep dive), human resources. Deeper dive: hiring, specialization, onboarding, training, equipping.

Operational. Students hit the wall — one AI doing everything loses context, drifts, contradicts itself. They learn about context windows, token limits, and memory restrictions. This IS the HR lesson. They break the team into specialists, write training prompts (onboarding docs), and decide what tools each agent needs (graphics, databases, memory settings). Tool allocation = resource budgeting. Token limits = headcount budget.

Key insight. Context-window problems ARE human-resource problems. Students discover why companies specialize instead of having one person do everything.

Objective 1 · Analyze

Textbook chapters: Legal environment of business, compliance, regulatory frameworks. Deeper dive: contracts, incorporation documents, document storage protocols, industry standards and best practices.

Entity type finalized. C-Corp students formalize board structure and draft executive compensation packages — including golden-parachute clauses (negotiate it now or walk with nothing if the board fires you in Week 14). LLC students file operating agreements. Sole props document personal liability exposure.

Operational. Students conduct a real Terms-of-Service assessment of every AI tool their company uses — what data can be shared, what IP belongs to the vendor, retention policies, what stays internal. They establish document storage and naming protocols that get referenced all semester. They're building the governance layer.

EU section twist. If the course runs two sections, the EU zone tackles GDPR compliance, different data-handling rules, and cross-border data-transfer restrictions. Same lesson, higher difficulty.

Callback. When someone gets sloppy in Week 14, you point back to their own Week 3 protocols.

Objective 2 · Create & Evaluate

Textbook chapters: Marketing fundamentals, promotional strategy.

Transition bit. Friction between legal and marketing departments — opens with a live clip of Warren Zevon singing "Lawyers, Guns and Money," cuts to Dr. Hammond from Jurassic Park ("the only one on my side is the bloodsucking attorney"), closes with Zevon again ("...the shit has hit the fan"). Then the ACC disclaimer about revenue-sharing policy lands the punch line: this is why we just spent a week on compliance — paper this up BEFORE money is on the table.

Game theory and the split. The referral-revenue negotiation is a prisoner's dilemma. Hold at 100% and each student keeps more per referral; the first one who offers a split to their network gets more referrals and a bigger total pie. The incentive to defect is real because the money is real. Game theory taught through their own wallets.

The poll exercise (self-selecting incentive groups):

• Group 0: If friends ask about reimbursement, say no.
• Group 1: Say yes, and if a friend asks to split, say "idk I'll check."
• Group 2: "No we're not allowed to split, it's first-gen only — but please help me anyway."
• Group 3: "Yes, let's split it by X%."
• Group 4: "Yes, let's split it by X% and tell your people that too."

Critical detail. Each cut comes from the referring student's own revenue. They watch their share shrink as they incentivize their network. That's a real business decision with real tradeoffs they can feel.

Hands-on market research. Students design and run a small live marketing experiment for their company — a referral or promotional campaign of their own choosing — and watch real audience behavior in response. They learn to distinguish what their AI agents predicted vs. what the market actually did, and they bring that delta back to class as data. The marketing chapter taught in lived form, not lectured.

Parallel lessons (organic, not lectured): incentive design and behavioral economics; game theory (prisoner's dilemma, Nash equilibrium); affiliate marketing and referral architecture; the line between a legitimate referral program and a pyramid scheme; real-time A/B testing methodology; revenue-sharing mechanics and how cuts compound through layers.

Geographic diversification (seeds the war game). The geographic spread of each student's customer or audience network becomes a strategic asset or liability during the Week 14 stress test. Concentrated networks are exposed to regional shocks; diversified networks are resilient. The decision is being made right now — they just don't know it yet.

Objective 2 · Create & Evaluate

Textbook chapters: Finance, accounting fundamentals. Deeper dive: payroll, budgeting, forecasting, preliminary valuation models.

Operational. Marketing bleeds into finance naturally — revenue-split decisions from Week 4 have accounting consequences. Token budget IS the payroll budget. Students allocate AI capacity across their team (resource planning). HR monitoring kicks in: are the directives working? Are we efficient? Do we need a reorg?

Accounting fundamentals. Track every dollar in and out across the semester in a real ledger — revenue from advertising, cost of AI subscriptions, revenue shares paid out. End of semester: produce a real P&L and balance sheet.

Forecasting. Data from the Week 4 marketing experiment feeds into scenario modeling. Bull case, bear case, base case — with their own numbers.

Valuation. Preliminary models for the operation across different growth scenarios.

Capital markets & treasury (Thu). A class treasury is set up — students can hold cash, hedge with futures and options, finance via swaps. The instructor plays the house. LLC milestone: a VC/PE-style pitch round where students pitch a Claude investor (a logic gate with parameters) for growth capital. They learn what investors look for, what dilution means, and what strings come with outside money.

GAAP / IFRS (light touch). A globalization lesson lives here — same set of books, two reporting standards. The U.S. zone reports under GAAP, the EU zone under IFRS. Differences are small in toy form but real in concept.

Objective 2 · Create & Evaluate

Textbook chapters: Operations, production, supply chain. Deeper dive: scaling output, quality control, process optimization, supply chain of information.

Operational. Output is scaling. Quality starts slipping. Students can't personally QC everything anymore. Time to promote an agent to a QC specialist role. This is the moment they learn why QC exists as a separate function — because the people doing the work can't also be the people checking the work.

Supply chain. Information flow through the AI team is a supply chain. Raw inputs (data, prompts) → processing (agent work) → output (deliverables) → QC → delivery. Bottlenecks, single points of failure, redundancy.

Inflation / labor pressure. If they didn't budget competitive wages — adequate token allocation for their AI team — agents underperform or "quit" (instructor revokes an agent). Cutting labor costs has a real production cost. The student who invested in their team early weathers it.

Objective 3 · Construct

Textbook chapters: Management levels, delegation, organizational structure. Deeper dive: managing managers, silos, context drift, centralization vs. decentralization.

Operational. Students hand off primary oversight to a management-level AI agent. They're now sitting at director level. They've outsourced day-to-day supervision but this comes with new challenges: siloed workforce, context drift between teams, decisions being made two layers down that they don't find out about until something breaks.

Key insight. This is the jump from manager to director. They're not doing the work, not even directly managing the workers — they're managing a manager. Every MBA program teaches this as theory. These students feel it.

Objective 3 · Construct

Detailed week build pending.

Mid-term performance reviews of every AI agent on the team. Students decide who gets promoted, reorganized, or replaced. Strategy is reset based on the data from Weeks 4–7. Companies that drifted off-thesis in the hustle weeks course-correct here; companies that built clean operating systems consolidate the lead.

Objective 3 · Construct

Detailed week build pending.

Foreign exchange, cross-border capital flows, trade dynamics. If two sections are running, this is where the U.S. and EU zones interact — exports, imports, tariffs, currency hedging. Same curriculum, different regulatory environment. International business taught by living it.

Objective 3 · Construct

Detailed week build pending.

Regulatory filings, audit prep, investor relations. Students produce GAAP-compliant statements that will hold up in the Davos sector reports at Week 15. Governance structure tested — boards review filings, LLCs review distributions, sole props confront personal-liability exposure.

Objective 3 · Construct

Detailed week build pending.

The chapter most intro courses skip entirely. Sell, merge, IPO, or shut down. Each entity type has different exit mechanics — C-corp acquisition vs. LLC IPO vs. sole-prop wind-down. Students confront the question: what happens to the business now?

Objective 4 · Evaluate & Justify

Detailed week build pending.

The fiduciary lens. ESG reporting, stakeholder accountability, the difference between governance theater and governance that holds up under stress. Pay-it-forward mechanics — the successful companies are evaluated on how they invest in the next cohort.

Objective 4 · Evaluate & Justify

Detailed week build pending.

The week before the war game. Students review their delegation structures, pre-stage AI directives, and design their own sleep schedule. The companies that built clean operating systems can sleep through Friday night; the micromanagers are about to learn what their last six weeks of choices actually bought them.

Objective 4 · Evaluate & Justify

Format. A 24-hour weekend simulation that stress-tests every decision made all semester. Whatever business they picked in Week 1, built across the term, gets pressure-tested with real-world chaos.

The inbox. Students receive cascading notifications throughout the 24 hours — macroeconomic news (inflation prints, rate decisions), industry-specific events (rezoning laws, regulatory changes), demand shocks, supply-chain disruptions, worker strikes / system outages (AI actually does go down — that happens for real), and competitor moves from classmates in the same GICS sector.

C-Corp danger. If the company tanks hard enough, the board (a Claude logic gate) can vote to replace the CEO. Student is out with whatever golden parachute they negotiated in Week 3. If they didn't negotiate one, they walk with nothing.

LLC option. IPO during the simulation for students who want to go public. A live lesson in dilution, valuation, SEC disclosure, and giving up control to get capital.

Geographic exposure pays off. The geographic concentration of each student's customer base from Week 4 is now a live variable. Concentrated markets are exposed to regional shocks. Diversified markets are resilient. The decision they made in Week 4 is the decision being graded right now.

The sleep decision. They have to sleep at some point. If their directives and management structure are solid, they can sleep and their AI team holds. If they micromanaged all semester and never built real delegation, they're pulling an all-nighter. This is the ultimate test of everything they learned.

Objective 4 · Evaluate & Justify

Format. The capstone presentation. One industry leader per GICS sector presents the state of their industry to the room.

The map. By now every student has plotted the geographic footprint of their company's market reach. Each industry leader pulls up a sector-wide map showing the combined footprint of every company in their GICS sector. The audience can literally see which industries are geographically exposed and which are resilient. The map tells the story before anyone opens their mouth.

What industry leaders present:

• State of the industry, using docs and financials prepared by every company in their sector (both economic zones)
• Revenue breakdown — simulated war-game revenue plus real revenue from each company's marketing experiment
• Economic analysis of the industry across both zones
• Lessons learned and post-mortem on what worked and what broke
• Analysis of each company in the sector

The accountability web. The industry leader's presentation quality depends on every other company in their sector delivering clean financials and reports (GAAP-compliant from Week 5). The seven industry leaders have 40+ stakeholders in their presentation. Everyone else has two: their sector leader and the instructor.

Grading. Industry leaders are graded by the instructor, by self-assessment, and by every participant in their sector. Everyone else is graded by the instructor and their industry leader. This tracks with real-world accountability — the person presenting to the board has massive exposure; the individual contributor has fewer stakeholders but their work product still matters because it rolls up into the leader's presentation.

Objective 4 · Evaluate & Justify

Final deliverable. Performance review against the benchmarks set earlier in the semester. Honest assessment of what worked, what didn't, and which decisions required a human in the loop.

The override journal. Every week of the semester, students logged decisions where they overrode or redirected AI output. That artifact is now the spine of their final evaluation. What was AI good at? Where did it fall short? What required human judgment, and why?

Failure as curriculum. Some students' businesses underperformed. That's not a bad grade — that's a case study. The post-mortem on what went wrong is arguably more valuable than the success stories.