Episode Summary
Executive Summary: Matty, co-founder of 11Labs, explains how the company grew from an audio research experiment into a leading AI voice platform, driven by a small elite team, fast product iteration, and heavy investment in proprietary research, GPUs, and data centers. He discusses early product-market fit, fundraising strategy, talent retention, European company-building, and why voice agents and conversational AI could become a major business line.
Main Topics: Origins of 11Labs and the Poland mindset (Priority: 5/5): Matty credits growing up in Poland and being surrounded by high-density talent in school with shaping his ambition, hunger, and belief that Europe can produce global companies. Idea genesis: dubbing, voice, and research (Priority: 5/5): 11Labs began with a plan to improve movie dubbing and creator voice tools, but quickly shifted toward text-to-speech and voiceover after early user feedback revealed a clearer pain point. Product-market fit and launch strategy (Priority: 5/5): The company found traction after pivoting from dubbing to narration/voiceover, with viral moments, creator adoption, and a disciplined approach to announcing rounds only alongside meaningful product milestones. Fundraising, investor selection, and signaling (Priority: 4/5): Matty describes difficult pre-seed fundraising, the importance of speed and help from investors, and why A16Z, Sequoia, and NFDG mattered beyond capital because they validated the company and opened doors. Small teams, culture, and organizational design (Priority: 5/5): 11Labs operates with small, highly autonomous product teams, no formal titles, and a bias toward hiring/retaining people who can move quickly and own outcomes. Research moats, data centers, and unit economics (Priority: 4/5): Matty argues that research creates only a time-limited advantage, so product and distribution are equally important; the company built its own data centers to improve economics and accelerate experimentation. Future of voice agents and global expansion (Priority: 5/5): He sees voice/conversational agents as a multi-billion-dollar opportunity, with 11Labs expanding internationally and into enterprise workflows like customer support, scheduling, and integrations.
Key Arguments: Voice is becoming the primary interface for technology, especially as AI agents mature. A proprietary model is necessary when existing tools are too poor to deliver the desired user experience; 11Labs initially had to build its own because off-the-shelf speech models were not good enough. Research alone is not a durable moat; product quality, distribution, and speed of execution are what sustain advantage over time. Small, autonomous teams outperform larger hiring sprees because they increase ownership and iteration speed. Investor value should be judged by domain expertise, strategic help, and behavior in bad times—not just valuation. Launching and fundraising should be tied to real product news and user momentum, not vanity announcements. Europe can produce globally ambitious companies, but founders need to think globally rather than building only for European markets. Owning compute infrastructure can make sense if long-term training and inference volumes justify the capital expense and improve experimentation speed. Enterprise voice agents have strong potential because many workflows can be automated while preserving human expertise for high-stakes cases. Secondary liquidity and tender offers reduce pressure to sell early and help founders and employees take bigger long-term bets.
Data Points: Time to $100M ARR: 20 months - 11Labs reached $100M in ARR in 20 months from launch. Time from $100M to $200M ARR: Around 10 months - Matty said the company doubled from $100M to $200M ARR in roughly 10 months. ARR at end of 2023: $35M - He said the company was around $35M in revenue at the end of 2023. Current ARR: $100M+ - Matty confirmed the company had crossed $100M in ARR during the interview. Largest contract: Around $2M - He said the biggest customer contract is about $2M, mainly in enterprise voice/call-center use cases. Pre-seed raise: $2M - The initial pre-seed round raised $2M. Pre-seed valuation: $9M post-money - He recalled the pre-seed price as $9M post-money. Early investor conversations: 30 to 50 investors - He said the team spoke with roughly 30–50 investors before closing the pre-seed. Last round valuation: $3.3B - Harry introduced 11Labs as having raised at a $3.3B valuation in the latest round. Total capital raised: Over $350M - Harry noted the company has raised more than $350M total. Company headcount: 250 people - Matty said 11Labs currently has about 250 employees. Expected year-end headcount: 400 people - He projected the company would reach 400 employees by year-end. Teams: About 20 teams - He described the organization as roughly 20 small teams of 5–10 people each. Research talent pool: 50 to 100 top people - Matty estimated the global elite voice-research talent pool at only 50–100 people. Top researchers at 11Labs: 5 to 10 people - He said 11Labs has 5–10 people among the top voice researchers. Research lead: 6 to 12 months - He estimated 11Labs’ research advantage versus competitors at 6–12 months depending on the use case. Early reply rate: ~15% - When emailing YouTubers about dubbing, the initial personalized outreach got about a 15% reply rate. Waiting list surge: 1,000 people - A viral blog post about the first AI that can laugh added about 1,000 people to the waiting list overnight. Secondary liquidity: Used in almost every round - Matty said 11Labs often includes secondary and tender offers for employees and founders.
Pivotal Quotes: "Voice will be the interface to the technology around us. It will be the primary interface for a lot of technology around us." — Matty: On why voice is strategically important and where 11Labs is headed. "The round should have another purpose, which is bring the product out and help you get the product into the users." — Matty: On why fundraising announcements should be tied to real product milestones. "The biggest risk is not taking the risk and not taking a decision or staying in the middle." — Peter Thiel (as referenced by Matty): Matty cited this as advice that shaped how he thinks about bold decisions and acquisitions.
Implications: 11Labs’ story suggests AI winners will combine proprietary research, fast shipping, and disciplined go-to-market, not just model quality. For founders, the lesson is to optimize for users, investor quality, and talent density. For the industry, voice and agents look likely to become a major enterprise and consumer interface.