Contrary to the belief that deep-tech startups should be purely technical, ElevenLabs prioritized distribution early. Their first 10 hires included 3 people focused on go-to-market and growth, enabling both self-serve and sales-led motions from the start alongside foundational research.

The traditional VC advice of conquering one market before moving to the next is obsolete in the fast-paced AI era. To outrun competitors, startups must treat GTM like venture capital: test multiple markets and strategies in parallel to quickly identify the few bets that will drive exponential growth.

Many founders mistakenly believe achieving product-market fit is the final step to explosive growth. However, growth only ignites after also finding a repeatable go-to-market fit, which translates the founder's initial sales success into a scalable process that a sales team can execute consistently.

ElevenLabs' growth demonstrates a powerful compounding effect. It took them 20 months to reach their first $100M ARR, 10 months for the next $100M, and only 5 months for the third. This accelerating ramp highlights the explosive potential of product-market fit in the current AI landscape.

Modern growth is a high-volume game of testing unique marketing 'angles' to sell one product to many different customer segments. The fastest-growing brands aren't just spending more; they're systematically testing hundreds of angles monthly and scaling the few that resonate.

Frame your go-to-market strategy as an engineering problem. Create a dedicated 'GTM engineering team,' including actual engineers, to build a programmatic stack and apply a rigorous test-and-learn mindset to every GTM motion, from outbound campaigns to event strategy.

The new playbook combines deep growth marketing knowledge with AI-driven efficiency. This allows founders to bring a brand to market, test it intensely for six months to gauge product-market fit, and then decide whether to scale or kill it, minimizing wasted time and capital.

A high volume of go-to-market experiments is justified because you don't need a high success rate. A single successful experiment can unlock another $100M in ARR, which more than covers the cost of the 99 failures. This reframes failure as a necessary part of a massive growth strategy.

Contrary to typical advice, ElevenLabs targeted multiple customer segments simultaneously. This worked because they first built a best-in-class foundational AI model, attracting diverse users. They then hired founder-type leaders to own and grow each vertical-specific product, treating them as separate business units.