“We’re Too Early for a Financial Model” and Other Lies Founders Tell Themselves

                                       

By: Wiss Team

Wiss Team

Emily Bennet, investing partner at a16z speedrun, recently published an essay that starts with a line I hear constantly: “We’re too early for a financial model.”

Why This Matters More Now Than Ever

Bennet’s core argument is solid: financial models at the seed stage aren’t about prediction accuracy. They’re about making assumptions explicit so you can test them against reality and adjust when they break.

But there’s a harder truth underneath. Look at what’s happening to established companies right now. Major retailers are discovering their business models don’t work when consumer behavior shifts. B2B software companies are watching AI compress their entire value proposition into features competitors offer for free. CPG brands are learning that decade-old distribution strategies suddenly don’t generate returns.

These aren’t small companies with bad ideas. They’re established businesses with sophisticated finance teams, and they’re still getting blindsided because their models didn’t flag problems early enough.

If companies with resources and experience are struggling to maintain model sustainability, what chance do early-stage founders have without any model at all?

What Bennet Gets Right: The Three Tiers

The a16z essay breaks financial visibility into three critical layers, and this framework is exactly right:

Tier 1: Financial Viability – Can you survive the next 12 months? Most founders know approximate burn rate. Far fewer can accurately answer “how much runway do we have?” within two weeks. That gap kills companies.

Tier 2: Structural Goal-Setting – What actually signals product-market fit? Not vanity metrics. Not activity. Actual indicators that users want what you’re building and will stick around.

Tier 3: Business Model Efficiency – Can the economics ever work? You don’t need perfect LTV calculations at the seed stage. You need a defensible hypothesis you can test: “We believe LTV will be $500, which means CAC must stay below $150.” Then you track whether reality converges toward that hypothesis or diverges from it.

Where the Essay Undersells the Problem

You don’t want to be surprised. The runway may disappear faster than expected. You don’t want to lack the metrics needed to raise your next round at reasonable valuations. Your business won’t be economically sustainable. The companies that fail aren’t the ones with bad ideas—they’re the ones that didn’t realize their unit economics were broken until they’d burned through it all! 

Disruption won’t wait. The article references how forecasting improves when assumptions are explicit and updated regularly. In 2026, that update cycle needs to be monthly, not quarterly. AI is changing competitive dynamics too fast for slower iteration. If your assumptions about CAC, conversion rates, or retention are three months out of date, you’re already behind.

Investors see through vagueness. Bennet mentions founders being “shocked when they lacked metrics needed to raise.” Let me translate: investors now assume you have a model. Not having one doesn’t signal you’re too early. It signals you’re not thinking clearly about your business. That’s disqualifying.

The Learning Speed Advantage

Bennet’s essay emphasizes that models accelerate learning in three ways:

1. They surface the real constraint (retention, pricing, sales efficiency, burn rate)
2. They create tight feedback loops (gaps become concrete questions)
3. They enable intentional pivots (you know which assumptions broke and why)

In practice, this is a competitive advantage early-stage companies have. You can’t outspend incumbents. You can’t out-resource them. You can out-learn them though!

Models make assumptions testable and failures legible. Without one, “$120 CAC instead of $40” feels like vague difficulty. With one, it’s a specific signal requiring a specific response.

That difference compounds. Companies that learn faster make better decisions faster, creating more learning opportunities and accelerating the cycle. Companies are learning slower burn cash proving things that could have been tested with a model.

Not Just Seed: Speed

Financial models at the seed stage aren’t about accuracy. They’re about learning speed. The companies that succeed aren’t the ones whose models were right—they’re the ones whose models were wrong in ways that taught them something useful, fast enough to adjust before running out of runway.

Emily Bennet is right that founders saying “we’re too early for a model” are really saying “we’re too early to think clearly about whether our business works.” That’s never true.

But in 2026, there’s an additional layer: you’re competing against founders who are thinking clearly, iterating faster, and using models to compress learning cycles. The gap between companies with financial visibility and companies navigating by gut feel is wider than ever and growing.

Build the model. Make assumptions explicit. Test them against reality. Adjust when they break.

Your Guide Through Early-Stage Financial Planning

Early-stage companies need financial visibility without the overhead of a finance team. Wiss’s Startup Advisory Services help founders build appropriate financial models, establish tracking systems, develop fundraising materials, and implement financial processes that scale from seed through Series A and beyond.

We’re not here to take over your finance function. We’re here to guide you in building the capabilities you need to make better decisions faster.

Schedule a conversation about financial modeling for your stage—no templates, just advisory.

This article references “The Number One Tool Early Stage Founders Overlook” by Emily Bennet, a16z speedrun investing partner, published January 29, 2026. It provides strategic perspective on financial modeling for early-stage companies and does not constitute specific financial or investment advice. Wiss & Company LLP provides accounting, tax, and advisory services to startups and venture-backed companies.

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