Zeta: The New Magic Number - Why SaaS metrics break on AI companies, and what to measure instead
In this solo episode of Very True, Alex makes the case that the biggest mistake an AI-powered business can make is treating itself like a SaaS business. It isn't one. This episode is the math behind why.
Every metric we learned to trust to tell us a company is healthy, net dollar retention, the magic number, LTV to CAC, was built on two assumptions: that revenue accumulates and stays, and that gross margins are high. SaaS had both, and they were load bearing. AI has neither.
Switching costs collapse to near zero when a customer can move every workload in an afternoon, and gross margin, the thing that was always solved in SaaS, is suddenly variable, not high, and moving underneath you every time a new frontier model ships. Take those two assumptions away and the inherited metrics aren't leading indicators. They're a house of cards.
Switching costs collapse to near zero when a customer can move every workload in an afternoon, and gross margin, the thing that was always solved in SaaS, is suddenly variable, not high, and moving underneath you every time a new frontier model ships. Take those two assumptions away and the inherited metrics aren't leading indicators. They're a house of cards.
So Alex went looking for what to measure instead, and found it somewhere he did not expect: a mechanical engineering class about what happens when a car hits a speed bump. A company absorbing a new model release is the same physics. Zeta, the damping ratio, is the number that says whether you glide over it or get thrown through the roof. The best part is you already have everything you need to calculate it. Two streams, revenue and cost, plotted against every model release. And unlike the SaaS benchmarks, bigger isn't better here. It's about finding consistency in a dynamic system, which comes down to one question every founder should be asking: Am I critically damped, or am I getting shaken apart?
Episode Highlights:
- SaaS was a business model, not a technology. Why talking about "SaaS vs AI" is a category error, and what actually made the SaaS gold mine work across all four DCF variables.
- The calculus under SaaS. The 2014 realization that ARR is the derivative of recognized revenue, why revenue is an accumulation function, and why that accumulation quietly cured almost every problem a SaaS company had.
- The two assumptions holding up every metric you trust. Accumulating contractual revenue and high gross margins. Net dollar retention, magic number, and LTV to CAC all sit on top of them, and none of them survive without both.
- What AI takes away. How switching costs collapse when everything is a prompt, why "they're losing money because people love it" breaks every law of finance, and why value is accruing to the harness, not the model.
- The speed bump. A sprung, damped mass hitting a speed bump, mapped onto a company absorbing a new model release. Overdamped companies barely react and get eaten on competition. Underdamped companies push everything to the newest model and watch gross margin crater. Critically damped is the whole game.
- Zeta as the new magic number. Why the SaaS magic number worked, why it isn't portable to AI, and why zeta is, now that Stripe's move to acquire OpenRouter and tools like Ramp mean you can read gross margin by product with a single prompt.
- Growth reveals problems instead of curing them. In a high-margin accumulation function, growth covers everything. Multiply an up-and-to-the-right chart by a negative gross margin, and the faster it grows, the faster it sinks.
Links & Resources:
Frameworks Substack: https://alexoppenheimer.substack.com/
Verissimo Ventures Substack: https://verissimo.substack.com/
Verissimo Ventures: https://verissimo.vc/
Follow Alex on LinkedIn: https://www.linkedin.com/in/alex-oppenheimer/
About Very True:
Hosted by Alex, Very True by Verissimo Ventures explores the honest, unvarnished stories of founders and the real problems they are solving. We look past the hype to find the truth in technology and entrepreneurship.
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