The Evidence You Can't Build in Eight Weeks

Global venture funding hit a record $300 billion in Q1 2026. It doesn't feel that way if you're not building AI infrastructure.

Seed deal count fell 30% year-over-year while total seed dollars rose. Capital is concentrating into fewer, larger rounds for a specific type of company. For everyone else, the institutional path has gotten materially harder. Only 20% of companies that raised seed in 2022 have gone on to raise a Series A, against a historical conversion rate of 50-60%.

That collapse has a specific cause. It's not macro. It's bar.

What investors need to see now

Current Series A requirements look like a 2019 Series B: $2-3M ARR minimum, burn multiple below 1.5x, LTV:CAC above 3:1, CAC payback under 12 months, gross margins above 70%. These aren't aspirational. They're table stakes for getting a first meeting.

Most of these metrics are straightforward to calculate if your data infrastructure is clean. None of them are straightforward if you've never built it.

Gross margin by product line requires revenue and COGS tracked at the right granularity, consistently, for long enough to be meaningful. CAC payback by channel requires marketing spend categorized by channel, tied to customer acquisition, connected to the revenue recognition timeline for that cohort. Cohort data requires that you've been tracking customers as cohorts from first purchase, not as aggregate monthly revenue.

This isn't data you assemble. It's data you generate, month by month, from a system designed to produce it.

Why eight weeks isn't enough

Most founders start thinking about institutional fundraising eight to twelve weeks before they want to begin outreach. They know they need clean financials. They don't realize they need 12-18 months of clean cohort history.

You can't reconstruct cohorts retroactively from a Stripe export and a spreadsheet. You can approximate, but the gaps show. Investors who have reviewed hundreds of Series A data rooms know immediately whether cohort data was built systematically or assembled. The difference isn't the numbers themselves. It's consistency, granularity, and whether edge cases were handled the same way across all periods. Assembled data has gaps in all three.

Raising institutional capital is an evidence problem. Evidence takes longer to produce than a raise takes to run.

A company planning to raise in Q1 2027 needs the underlying data infrastructure producing clean cohort and channel data today. Not in November.

What this means if you're not raising

CAC payback by channel tells you which marketing spend is working before you've wasted two quarters on it. Gross margin by product line tells you where to push revenue. Customer cohorts show whether retention is improving or deteriorating months before it appears in aggregate churn.

Companies that build finance infrastructure to support a raise often find sharper operations as the side effect. Companies that wait until they're raising usually don't raise, and also can't see why.

For most $2M-$20M companies right now, the fundraising environment isn't a question of timing. It's a design constraint. Either you're in the AI premium cohort with institutional tailwinds, or you're building a business that generates its own momentum. Either way, your evidence base needs to be 18 months old before you need it.