Venture capital runs on a simple lie we all tell ourselves: if revenue is going up and the product feels inevitable, the unit economics will sort themselves out later.
That lie worked in SaaS because “later” mostly meant: keep shipping, keep selling, and your marginal cost asymptotically goes to zero. Serving the 10,000th user was basically free. So you could fund growth first, then let operating leverage do its thing.
AI breaks that bargain.
Not because the tech isn’t real. Because the cost structure is. Every useful action can carry a variable compute bill. If you don’t model that bill at the level where value is actually delivered, you can build something that looks like a rocket ship and still never produce a venture outcome.
The trap is subtle: we’re pricing AI startups with SaaS heuristics.
“SaaS” implies 80–90% gross margins, predictable renewals, and the comforting idea that once you’ve built the product, delivering it is just bits on a wire. A lot of AI companies are selling outcomes powered by rented intelligence. That means inference is not a rounding error. It’s COGS. And COGS that scales with usage changes everything: pricing, fundraising, valuation, and the revenue you need in 8–10 years to generate real returns.
So what should you measure?
Not “blended gross margin.” That’s too easy to game, especially early when usage is volatile and you can hide subsidies inside a single line item. The metric that matters is:
Contribution margin per AI-driven action.
Pick the atomic unit your customer pays for: a document processed, a claim adjudicated, a sales email generated, a security alert resolved, a customer ticket deflected. Then do the unglamorous accounting: revenue for that action minus inference, retrieval, tooling, human-in-the-loop, and support. If that number is negative, you’re not scaling a product. You’re scaling a cost.
Negative contribution margin isn’t always fatal. But it demands a very specific story: a short, owned path to positive margins via model routing, caching, distillation, cheaper model mixes, better retrieval, product constraints, and—most importantly—pricing that captures value rather than compute consumption.
If your plan is “inference will get cheaper,” you’re speculating on an industry-wide cost curve you don’t control. Even if you’re right, your competitors get the same benefit. That’s not a moat. That’s weather.
This is where the “AI margin tax” shows up.
In the SaaS world, $100M ARR with high margins could translate into a clean unicorn-plus outcome. In AI, $100M ARR with materially lower margins often gets a materially lower multiple. Same revenue. Different business. Different valuation. This is why so many founders are confused right now: they hit impressive top-line numbers and still get treated like the exit is capped.
Investors need to internalize this because it changes portfolio construction. Venture is a power law; a small number of outcomes carry the fund. That means your “winners” must be huge, not just successful. And huge is a function of exit value, which is a function of revenue and margin profile.
If you’re investing at pre-seed and underwriting SaaS-style multiples on SaaS-style margins, but the company is actually a 25–60% gross margin business, you’re quietly chopping your upside in half. You can’t make that up with vibes.
Now the question founders always ask: what revenue do we need in 8–10 years for venture returns?
Annoying answer: it depends on the multiple you can justify at exit, and the multiple you can justify depends on whether you look like a durable software business or a variable-cost services machine.
A practical way to think about it:
If you want a venture outcome, you likely need to be on a path to $200M–$500M ARR (US and global from day one targets) within a decade and demonstrate a credible march toward 70%+ gross margins, strong retention, and defensibility. Yes, there are exceptions, category leaders can command premiums. But if your gross margin stalls below 50–60%, you’ll need far more revenue to hit the same exit value, and buyers may still cap the multiple because the business doesn’t scale cleanly.
So what changes in fundraising?
For founders: stop leading with architecture and benchmarks. Lead with business model physics. Show contribution margin per action today, the levers that improve it, and the milestones where cost curves bend. Your deck should make it obvious you’re building leverage, not just shipping intelligence.
For investors: stop asking “how fast can this grow?” as the first question. Ask “what happens to gross margin at 10x usage?” Ask “who owns the cost curve?” Ask “if your model provider changes pricing, what breaks?” Then price the round like you believe the answers.
And here’s the uncomfortable conclusion: many AI startups should not be venture-backed.
If your TAM is modest, switching costs are low, margins won’t clear 60%+, and revenue scales linearly with compute or headcount, you may still build a great company. It just might be a bootstrapped company, a profitable niche business, or a strategic acquisition—not a fund-returning outcome.
AI doesn’t kill venture. It kills lazy venture math.
The new rule is brutally simple: if contribution margin per AI-driven action is unclear, negative, or “we’ll fix it later,” you’re not building a venture-scale asset. You’re building an increasingly expensive demo.
And the market eventually always collects.