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.

Economist Bernd Hobart argues that large enterprises are too risk-averse for early AI adoption. The winning go-to-market strategy, similar to Stripe's, is for AI-native companies to sell to smaller, agile customers first. They can then grow with these customers, mature their product, and eventually sell the proven solution back to the legacy giants.

General Catalyst's CEO notes a change in enterprise AI GTM strategy. The old model was finding product-market fit, then repeating sales. The new model involves "forward deployed engineering" to build deep trust with an initial enterprise client, then focusing on expanding the services offered to that single client.

By starting before the ChatGPT boom, ElevenLabs secured two key advantages: less competition for top research talent, allowing them to hire "true missionaries," and a crucial head start to develop their technology before the market became saturated with competitors.

Instead of a single centralized growth team, ElevenLabs creates dedicated "sharded" growth teams for each product line (e.g., consumer app, creator tools). These pods act as mini-CMOs for their product, supported by a horizontal team of channel specialists like SEO and performance marketing leads.

ElevenLabs' defense against giants isn't just a better text-to-speech model. Their strategy focuses on building deep, workflow-specific platforms for agents and creatives. This includes features like CRM integrations and collaboration tools, creating a sticky application layer that a foundational model alone cannot replicate.

To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.

ElevenLabs' CEO sees their cutting-edge research as a temporary advantage—a 6-12 month head start. The real, long-term defensibility comes from using that time to build a superior product layer and a robust ecosystem of integrations, workflows, and brand. This strategy accepts model commoditization and focuses on building durable value on top of the technology.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.