Menlo Ventures’ Matt Murphy explains what AI startups founders must do differently

Anthropic hit a $47 billion revenue run rate by May 2026, up from $9 billion in 2025. That's not just fast growth. It's a category of speed that Matt Murphy, who led Menlo Ventures' $500 million Series D in Anthropic, says he's never witnessed in 25 years backing startups, not during the internet boom, not mobile, not even the first cloud wave.

According to Murphy, this changes the entire founder playbook. AI startups can't follow the old SaaS script of slow, methodical go-to-market and multi-year sales cycles. The window to capture market share is radically compressed because the technology itself is moving so fast and adoption is happening at enterprise scale almost immediately.

The implication is that AI founders need to think like infrastructure builders from day one, not app developers. You're not just solving a workflow problem. You're potentially becoming a foundational layer that other companies depend on, which means your architecture, reliability, and partnership strategy matter as much as your model quality.

This also explains why we're seeing massive early-stage rounds and why valuations feel disconnected from traditional metrics. Investors are betting on velocity and market position, not just revenue multiples. If you can go from pre-revenue to tens of billions in run rate in under two years, the normal valuation frameworks break.

For founders, this means you need to be ready to scale infrastructure, hiring, and partnerships far faster than you would have in previous waves. It also means the pressure to pick the right early customers and use cases is intense, because those first deployments set your trajectory.

The Anthropic case shows that AI companies are competing on a different clock. Missing a six-month window could mean the difference between becoming a category leader and becoming irrelevant, because your competitors and the underlying models are all evolving in parallel.

What this means for you: if you're building an AI product or service, your go-to-market timeline needs to compress. Don't spend a year in stealth perfecting features. Get to market, get feedback, and iterate in public. Try this prompt with your AI assistant: "I'm launching an AI tool for [your target audience]. What are the three highest-impact early adopter segments I should focus on first, and what's a 30-day plan to reach them?" Use it to pressure-test your launch strategy and identify where you can accelerate without sacrificing quality.