LS

Lambda School Financial Model

• 10-month, online, full-time software engineering program.
• No upfront cost; Lambda bears training cost risk; student pays only on successful outcome.
• ISA terms: 17% of income for 2 years, triggered at $50,000+ salary, capped at $30,000 total.
• Data-led admissions: aptitude and potential battery; 5,000+ applications/month.
• Mastery-based progression; live classes from industry practitioners; 95 curriculum iterations logged.
• Full-time placement team; pursuing deep employer partnerships.
• Flywheel: better students → better employers → better alumni → better word-of-mouth → lower cost of capital.

• 22 million PSE (post-secondary education) students in the US.
• 840 million PSE students globally.
• 3.7 million unfilled tech jobs in the US.
• 261k new tech jobs created annually in the US.
• 71k unfilled technical Salesforce roles in the US.
• University tuition rose ~200% since 1987 (inflation-adjusted); early career salary flat/negative over same period.
• No explicit TAM dollar figure provided. Future expansion labeled: Placement, Upskilling, Financial Products, Healthcare, Engineering, Sales, Skilled Trades, Finance.

• Primary revenue: ISA repayments - 17% of placed graduate's annual salary × 2 years, capped at $30,000/student.
• Revenue is deferred: cash arrives 6–18 months post-enrollment depending on job-search duration.
• Revenue recognition follows employment event, not enrollment.
• Future revenue lines mentioned but not modeled in deck: employer partnerships/placement fees, upskilling, financial products.
• No pricing for any future product lines disclosed.

• 5,000+ applications per month.
• Students in all 50 US states.
• Lambda Today: ~3k enrolled/active students (pictogram, approximate read).
• Average pre-Lambda salary: $23,000.
• Average placed salary post-Lambda: $74,000.
• 95 curriculum iterations.
• No revenue figure, cohort size, placement rate, or time-to-placement disclosed.

• Implied LTV per placed student: up to $30,000 ISA cap (17% × $74k avg salary × 2 years ≈ $25,160 at average placed salary, below the $30k cap).

• Competitors referenced only implicitly (Dev Mountain 1k students, City College of San Francisco 20k, University of Phoenix 477k) as scale comparisons, not as named competitors.
• Moat framed as flywheel: best student selection data → best outcomes → best employer relationships → best alumni network → lowest cost of capital.
• Curriculum iteration velocity (95 iterations) cited as differentiation.
• No direct bootcamp competitor (General Assembly, Flatiron, Hack Reactor) named in deck.

Archetype + why: ISA portfolio / income-share lender model, layered on a cohort P&L. Lambda is economically a lender that funds human capital: each enrolled student is a loan-like asset with probabilistic repayment. The right model is a cohort-based ISA portfolio model - track each cohort's deployment cost vs. expected ISA cash inflow, with credit-loss adjustments for non-placement and default. Secondary layer: operating P&L (instruction + admissions + placement costs vs. ISA revenue recognized). This is not a standard SaaS or DTC model.

Forecast horizon & granularity:

• 5 years, monthly granularity for cohort cash flow (ISA receipts are lumpy and lagged).
• Quarterly roll-up for P&L and balance sheet.
• Cohort vintage tracking: each cohort modeled separately through enrollment → graduation → placement → repayment → cap-out.

Key drivers & assumptions:

Scenarios (Base / Bull / Bear - which variables flex):

• Base: 70% placement rate, $74k avg salary, 5% monthly enrollment growth, $10k cost/student.
• Bull: 80% placement rate, $80k avg salary (employer partnerships lift salaries), 10% enrollment growth, costs scale sub-linearly.
• Bear: 55% placement rate (tighter job market), $65k avg salary (more students near $50k floor), 0% enrollment growth (capacity constraint), cost/student rises to $14k.

Required sheets / outputs:

1. Cohort tracker - one row per monthly cohort: enrollment count, graduation date, placement date, ISA start, monthly ISA receipts by period, cumulative receipts vs. cap, write-off on non-placement.
2. ISA portfolio roll-forward - outstanding ISA receivables, new originations, collections, defaults, carrying value.
3. P&L - revenue (ISA receipts by period), COGS (instruction, section leads, content), gross profit, OpEx (admissions, placement, G&A, tech), EBITDA.
4. Cash flow - critical: Lambda funds training upfront and waits 13–24 months for repayment; model cash burn carefully.
5. Unit economics summary - LTV per cohort member, cost per placed grad, LTV/CAC, payback period.
6. Scenario toggle - placement rate, avg salary, enrollment growth rate, cost/student as flex inputs.
7. Sensitivity table - LTV and IRR sensitivity to placement rate (50–85%) × avg placed salary ($55k–$90k).