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.
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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.
Resist hiring quickly after finding traction. Instead, 'hire painfully slowly' and assemble an initial 'MVP Crew' — a small, self-sufficient team with all skills needed to build, market, and sell the product end-to-end. This establishes a core DNA of speed and execution before scaling.
A pre-product CRO conducts thousands of market conversations to validate demand and guide the product roadmap. This de-risks development by ensuring you build a product that customers will actually buy, a task more suited to a sales expert than a founder.
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.
Don't expect the parent company's sales force to sell your nascent product. Their focus on core business means they will ignore emerging tech. An internal incubator must have its own dedicated go-to-market team to find new personas and develop sales plays before a handoff.
To launch new products and compete with agile startups, embed a small "incubation seller" team directly within the technology organization. This model ensures tight alignment between product, engineering, and the first revenue-generating efforts, mirroring the cross-functional approach of an early-stage company.
In the AI era, marketing and growth roles are splitting into two distinct archetypes: the 'tastemaker' who has exceptional creative taste and intuition, and the 'engineer' who can technically analyze and orchestrate complex systems. Being average at both is no longer a viable path to success.
This emerging role applies engineering and AI to GTM functions, building agents to automate tasks like lead qualification and personalized outreach. This dramatically increases efficiency, allowing one person, with an AI agent, to do the work of ten.
Contrary to the belief that PMs are the earliest tech adopters, go-to-market functions (sales, marketing, support) are leading agent adoption. Their work involves frequently recurring, pattern-based tasks that are a perfect fit for automation, putting them ahead of the curve.
