According to SaaStr founder Jason Lemkin, the ultimate metric for judging whether an incumbent company has successfully integrated AI is not feature releases but growth re-acceleration. If revenue growth isn't picking up speed, the AI initiatives are merely performative. He points to Meta as a prime example of a company whose AI efforts are validated by this metric.
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A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.
The venture capital benchmark for elite growth has shifted for AI companies. The old "T2D3" (Triple, Triple, Double, Double, Double) heuristic for SaaS is no longer the gold standard. Investors now consider achieving $100M ARR in under three years as the strongest signal of exceptional product-market fit in AI.
Companies like Notion and Datadog are re-accelerating by targeting new, dedicated AI budgets. This is separate from shrinking 'efficiency tool' budgets. Growth comes from solving problems that unlock this specific new spending category, not just adding a minor AI feature.
For established software companies with sluggish growth, the path forward is clear: find a way to become relevant in the age of AI. While they may not become the next Harvey, attaching to AI spend can boost growth from 15% to 25%, the difference between a viable public company and a sale to a private equity firm.
While 2023 was a grace period for AI adoption, the tools matured significantly in 2024. Companies that failed to leverage agentic AI products to re-accelerate growth are considered to have fundamentally underperformed, as the opportunity was clear and present.
For established software companies, simply integrating AI is not enough. Investors are looking for a clear signal that AI is a true growth catalyst, not just a feature enhancement. The key question investors ask is whether AI will re-accelerate the company's growth. Without tangible proof in sales numbers, investor sentiment will remain neutral or bearish.
Seemingly small, quarterly AI improvements to Meta's ad platform (e.g., a 5% conversion bump) have a compounding effect. Performance marketers reinvest these gains back into the platform, creating a flywheel that reaccelerates revenue growth, explaining the stock's recent surge despite a mature business.
The traditional SaaS growth metric for top companies—reaching $1M, $3M, then $10M in annual recurring revenue—is outdated. For today's top-decile AI-native startups, the new expectation is an accelerated path of $1M, $10M, then $50M, reflecting the dramatically faster adoption cycles and larger market opportunities.
The power of Meta's AI-driven ad improvements lies in their compounding effect. Small quarterly boosts in ROAS (return on ad spend) are not one-off wins; performance marketers immediately reinvest these returns, creating an accelerating growth flywheel that fuels Meta's re-accelerated revenue growth.
Recent surveys suggest AI is underperforming, but the data reveals a stark divide. The 12% of companies that deeply embed AI into core processes are 3x more likely to see both cost reduction and revenue growth, creating a significant and compounding advantage over the majority who attempt superficial adoption.
