Building an economic model for the first time can feel intimidating. The good news: AI is genuinely useful here, not because it knows your numbers, but because it can help you think through the structure of your model, identify what you're missing, and pressure-test your assumptions before you discover the hard way that something doesn't add up.
The key reminder from the Week 5 reading applies doubly here: you still need to build it yourself. Use AI to think alongside you, not to hand you a spreadsheet you don't understand. An investor will ask you about every line — you need to own it.
Structuring your model
Before you open a spreadsheet, it helps to talk through the structure of your model. Different business models have very different economic shapes, and AI can help you figure out which tabs and categories you actually need for your particular venture.
Try this prompt
"My startup is [describe it — what you sell, to whom, and how]. Help me think through the structure of an economic model for this type of business. What are the key revenue drivers, the main cost categories, and the most important assumptions I'll need to make? What makes the economics of this business model different from a typical product or services company?"
Identifying your key levers
Every business has two or three numbers that really drive everything else. Finding yours early saves enormous time. AI can help you figure out which assumptions will have the biggest impact on your model's outcome — so you know where to focus your energy on validation.
Try this prompt
"Here are the main assumptions in my economic model: [list them — price, volume, CAC, churn, margins, etc.]. Which of these is likely to have the biggest impact on my path to profitability? Where should I be most focused on testing and refining my estimates with real data?"
Sanity-checking your revenue assumptions
Revenue assumptions are where most first-time founders are most wildly optimistic. AI can help you pressure-test whether your numbers are in a reasonable range — and identify what you'd have to believe for them to be true.
Try this prompt
"My startup projects [X revenue] in year one, growing to [Y revenue] in year two. Here are my assumptions: [describe them]. Does this growth rate seem realistic for a company at this stage in this market? What would I need to believe about customer acquisition, conversion rates, and retention for these numbers to hold?"
Working through CAC and LTV
Customer Acquisition Cost and Lifetime Value are the two numbers that matter most. If you don't yet have real data, AI can help you build a first-principles estimate and identify comparable businesses whose CAC:LTV ratios you might use as benchmarks.
Try this prompt
"Help me think through CAC and LTV for my startup. We sell [product/service] at [price] to [customer type], and we plan to acquire customers through [channels]. Walk me through how to estimate both CAC and LTV for this model, and tell me what ratio I should be aiming for to have a healthy unit economics story for investors."
Estimating costs you haven't thought of
First-time founders reliably underestimate costs. AI can play the role of an experienced CFO asking uncomfortable questions — surfacing the expense categories you forgot to include.
Try this prompt
"Here are the cost categories I've included in my economic model: [list them]. What am I likely missing? What are the most commonly overlooked costs for a [type of startup] at the early stage? Please be specific — I'd rather find these now than in front of an investor."
Figuring out your capital needs
Once you have a model, you can use it to figure out how much money you need and when. AI can help you think through the implications of your cash flow timeline and what that means for fundraising strategy.
Try this prompt
"Based on my model, my startup will reach cash flow breakeven around [month/year], and I'll need approximately [amount] to get there. Help me think through what this means for my fundraising strategy. How should I think about how much to raise, when to raise it, and what type of capital makes sense at each stage?"
Explaining your model to investors
Guy Kawasaki's point is that financial projections tell a story. AI can help you translate your spreadsheet into a narrative that makes the economics intuitive — and help you anticipate the questions an investor will ask.
Try this prompt
"Here is a summary of my startup's economic model: [describe the key numbers — revenue trajectory, main costs, path to profitability, capital needs]. Help me tell this as a coherent story for investors. What is the narrative arc of these numbers, and what questions should I be prepared to answer when I present them?"