Why the Old Signals Don’t Work

The explosion of GenAI and the rise of agentic products have upended the venture playbook shaped during the 2010s SaaS boom.

With early revenue more reflective of hype rather than meaningful and durable user value, old SaaS-era metrics have lost their predictive power. In the world of AI, behavioral signals – frequency of use, depth of execution, and integration into user routines – are the true leading indicators of scalable potential.

Back then, annual recurring revenue (ARR) was the go-to signal for early traction. It captured what early-stage investors needed to see: real demand, real usage, and a scalable way to sell. If a company hit $1M ARR, it meant that customers were paying, sticking around, and likely to expand over time. ARR was a simple, shared answer to a complex question: is this working, and can it scale?

Since the launch of ChatGPT in late 2022, that signal has started to break down. Dozens of AI-native startups have crossed $1M ARR in 6 months (ex. Bolt.new hit $1M ARR in ~1 week, Lovable in ~2 months, Bebop in ~45 days), a milestone that once took 12-24 months. Enterprises are green lighting team-level pilots, and consumers are rapidly adopting lightweight tools to explore new workflows. Both are driven by urgency and FOMO, which has led to a wave of short-term, curiosity-driven trials with shallow commitment.

ARR now reflects momentum, not durability. Rapid adoption doesn’t guarantee users will stay — and momentum, unlike retention, doesn’t scale. For early-stage investors, that distinction matters: only durable behavior compounds. Overreliance on revenue leads to mispriced rounds, inflated expectations, and short-lived growth. Spotting the real winners requires a shift in perspective, one that starts with behavior, not revenue dollars.

Together, these behaviors reveal if a product is useful, trusted, and habit-forming — early signals that are stronger predictors of scale than ARR ever was.

The Core Shift: A New Order of Operations

In the SaaS era, scale followed a familiar pattern: Monetization -> Usage -> Habit. Customers, usually enterprise buyers, paid upfront., often through annual contracts, before they fully adopted the product. Revenue came first; behavior followed.

In GenAI and agentic AI, that sequence has flipped: Execution -> Habit -> Monetization. Products must first deliver utility, earn repeated use, and only then earn the right to charge. Whether it’s a solo consumer or team, users commit based on proof, not promise.

Why Traditional Metrics Fall Short

ARR isn’t the only metric that’s lost predictive power. CAC is noisy, inflated by AI hype and experimentation. MAUs are bloated by one-off interactions. Seat expansion doesn’t guarantee usage depth.

These tools move fast, but value shows up differently. The SaaS scorecard doesn’t explain who’s building habit, trust, or long-term defensibility. In GenAI, scale must be inferred from usage behavior, not just revenue curves.

Reframing the Scorecard

The shift from SaaS to AI-native products doesn’t just change how users behave, it changes how investors evaluate traction. Here’s how the old signals compare to what matters now:

Implications for Investors

In this new wave, early behavioral depth is a stronger signal of future scale than early revenue. It’s better to back a product with a small base of power users and a clear usage archetype than one with broad but shallow adoption. The strongest early-stage opportunities will earn trust through execution and habit, then monetize once users are hooked.

But depth alone isn’t enough. Investors should also evaluate whether behavior can translate into durable monetization. That means understanding not just what users do, but why they’ll pay.

Closing Framework: What to Look for, What to Believe

In the AI-native era, the most investable companies don’t win by monetizing first, they win by becoming indispensable. For early-stage investors, that means shifting from watching top-line numbers to watching behavior.

Here’s a simple framework to guide that shift:

• Look for Behavior: Repeated use, real delegation, and workflow fit are the new foundation of traction.
• Confirm Repeatability: Usage should be consistent, unprompted, and pattern-forming. Does it deepen or expand?
• Map to Pricing Power: What moment makes the product hard to live without? That’s the inflection point where habit earns the right to charge, and where usage starts compounding into real, defensible value.

In the native-AI world, the best companies won’t be the first to monetize, they’ll be the first to be missed while they are gone.

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