Early Life & Foundational Influences

Geographic Progression & Expanding Worldview:

1. Suburban Warsaw beginnings - Started in a smaller, more limited environment
2. High school in Warsaw - Exposure to a much larger world that opened his eyes to possibilities
3. Mountain climbing metaphor - Understanding that climbing additional hills reveals increasingly more opportunities

Key Formative Experiences:

• Public school diversity - Attended school with any kids from the local area
• High school talent density - Met his co-founder in an environment with people who won competitions
• Selective admission process - Had to go through multiple steps to get into this higher-achieving environment

Core Philosophy Development:

• Scale of the unknown - Recognition that there's always more to discover and achieve
• Talent density motivation - Being surrounded by exceptional people became the most motivating factor
• Continuous exploration drive - The environment fostered a desire to explore more, learn more and do more

Family Influence & Hunger:

• Brother as trailblazer - Older brother went abroad to study, setting an example and motivation
• Community motivation cycle - High school friends motivated each other toward excellence
• Shared ambitious goals - Group commitment to studying at the best universities and excelling on exams

Timestamp: [1:22-4:09]

The Origin Story Behind Voice AI Innovation

The Catalyst Moment:

• Single voice narration problem - All Polish movies dubbed with one flat, emotionless voice for all characters
• Gender-blind dubbing - Same narrator voice used for both male and female characters
• Audiobook-style delivery - Movies narrated like audiobooks with no emotional variation
• "It's a terrible experience" - Recognition of a fundamental user experience problem

Prior Technical Foundation:

Hack Weekend Projects (Early 2021):

1. Recommendation system exploration - Testing new technology applications
2. Crypto risk analyzer - Built during crypto height, didn't work well
3. Speech analysis tool - Analyzed speaking patterns and provided improvement tips

The Technology-Problem Convergence:

• Audio technology insight - Previous hack weekend opened their eyes to possibilities in audio tech
• Market timing recognition - Understood this problem would need solving in the coming years
• Vision for the future - Anticipated all voices would eventually have original emotions and intonation

Evolution Beyond Dubbing:

Platform Expansion:

1. Research layer development - Had to fix fundamental technology components
2. Creative platform work - Expanded to broader creative applications
3. Agentic platform development - Voice as interface for technology interaction

Timestamp: [4:20-6:05]

Early Market Research & Product Discovery

Dual-Track Validation Approach:

Technical Track (Co-founder):

• Existing technology testing - Attempted to stitch together current tools for movie dubbing
• Quality assessment - Achieved good but not brilliant results
• Research pivot decision - Chose to step back and perfect individual components

Market Research Track (Mati):

• YouTuber outreach campaign - Scraped and personalized emails to content creators
• Value proposition testing - Pitched dubbing product for multi-language content availability
• Thousands of personalized messages - Systematic approach to market validation

Initial Market Response:

• 15% reply rate - From first batches of outreach emails
• Lukewarm interest - Not a burning problem for most creators
• Common objections:
• Skepticism about technical feasibility
• Requests for samples before commitment
• Operational concerns about implementation
• Platform limitations (YouTube doesn't support it)

The Pivot Discovery:

Real User Needs Emerged:

1. Post-production correction - Fix mistakes after recording
2. Script preview - Understand how content will sound before production
3. Voice replacement - Create voiceovers without speaking at all

Key Insight:

• Simpler problem identification - Users wanted basic voice editing, not complex language engineering
• Product-market fit signals - Real needs were much more straightforward than original vision
• Foundation for expansion - Simple use cases provided stepping stone to larger vision

Timestamp: [6:19-7:56]

Essential Insights:

1. Geographic progression shapes ambition - Moving from suburban Warsaw to the city expanded Mati's worldview and created hunger for greater possibilities
2. Talent density drives motivation - High-achieving environments with competitive peers became the most motivating factor for continuous growth
3. Bad user experiences reveal opportunities - Polish movie dubbing's terrible single-voice approach sparked the initial ElevenLabs vision

Actionable Insights:

• Validate with real users early - ElevenLabs discovered their actual market need through systematic YouTuber outreach, not assumptions
• Start simple, then expand - The pivot from complex dubbing to basic voice editing provided a clearer path to product-market fit
• Leverage community motivation - Surrounding yourself with ambitious, high-achieving people creates natural drive for excellence

Timestamp: [0:55-7:56]

People Mentioned:

• Piotr Dabkowski - ElevenLabs co-founder, met in high school, worked at Google before founding the company
• Luke - ElevenLabs team member who helped prep for the interview

Companies & Products:

• Google - Previous employer of co-founder Piotr
• Palantir - Mati's previous employer before founding ElevenLabs
• YouTube - Platform they researched for dubbing validation, noted limitations in supporting multi-language content

Technologies & Tools:

• Crypto risk analyzer - Early hack weekend project that didn't work well
• Speech analysis tool - Audio technology project that analyzed speaking patterns and provided improvement tips
• Recommendation system - One of their early hack weekend exploration projects

Concepts & Frameworks:

• Talent density - The concentration of high-achieving people as a motivating factor for personal and professional growth
• Hack weekend projects - Regular collaborative technology exploration sessions between the co-founders
• Dual-track validation - Simultaneous technical feasibility and market research approach

Timestamp: [0:55-7:56]

The Research Breakthrough That Changed Everything

The Original Problem Discovery:

1. Dubbing Challenge: Initially focused on solving dubbing problems for content creators
2. Research Pivot: Michael's research revealed they could build completely new text-to-speech models
3. Emotional Innovation: New models would be far more emotional and intuitive than existing solutions

Why Existing Models Weren't Enough:

• Uncanny Valley Effect: All existing models at the time had an immediately recognizable artificial quality
• Voice Replication Failure: Couldn't replicate voices with any meaningful accuracy
• Quality Gap: Clear difference between what existed and what creators actually needed

The Strategic Decision:

• Research-First Approach: Put dubbing aside to focus on the fundamental text-to-speech problem
• Custom Model Necessity: Only possible by creating their own models from scratch
• Foundation Building: Established the research layer before expanding to other applications

Timestamp: [8:02-8:43]

Investment Decision Framework for AI Model Strategy

The 2022 Context Factor:

• Pre-ChatGPT Era: This was during the "downfall of metaverse crypto days"
• Limited Public Awareness: Nobody was really thinking about AI yet
• ChatGPT Launch: Beginning of 2023 changed everything and spiked attention across the board

Key Evaluation Criteria:

1. Market Quality Assessment: As an investor, you could immediately tell existing solutions "just weren't very good"
2. Team Capability Question: The critical question becomes "will this team be able to solve and create something better?"
3. No Choice Scenario: In 2022, you didn't really have a choice - existing options were clearly inadequate

Modern Architecture Considerations:

• Single vs. Multi-Modal: Traditional approach was dedicated models for speech, image, video separately
• Combined Reasoning: New theme combines reasoning and speech together for better experiences
• ElevenLabs v3: Their most recent generation effectively uses this multimodal approach

Timestamp: [8:43-10:18]

The Nuanced Reality of AI Model Development

Use Case Dependent Progression:

• Narration Plateau: In narration specifically, new model generations won't make audio look drastically different
• Quality Ceiling: Similar quality levels being maintained rather than revolutionary improvements
• Research Commoditization: Eventually research advantages aren't enough - need to build product

The Scaling Laws Question:

1. Biased Perspective: Still feels like "just scratching the surface" of AI adoption
2. Adoption vs. Development: Clear difference between these two aspects of progression
3. Voice Space Advantage: Voice still hasn't hit the slower rate that LLMs might be experiencing

Future Trajectory Insights:

• Voice Curve Continues: Still seeing "pretty quick curve" in voice technology
• LLM Flattening: Acknowledges some flattening in the LLM space
• Combined Models: As they all combine, creates deeper understanding of everything
• ElevenLabs Strategy: Combines both research and product development together

Timestamp: [10:25-12:19]

The Competitive Moat Against Tech Giants

The Three-Pillar Defense Strategy:

1. Laser Focus Advantage:

• Early Days Clarity: When there were "so many different things you could do in AI space"
• Voice Specialization: Took the bet to "really own and win in the voice AI research and product space"
• Direct Alignment: All work is directly tied to voice applications

2. Exceptional Talent Concentration:

• Rare Expertise: Only 50-100 people worldwide working on voice at the top level
• Team Assembly: Co-founder P assembled "one of the best teams in the space"
• Top Talent Density: Have 5-10 people in the top 100 globally - "a mighty team"

3. Specialized Product Excellence:

• Research Superiority: Text-to-speech "blown everything out of the water"
• Benchmark Leadership: Speech-to-text beating OpenAI and Gemini on benchmarks
• Unique Capabilities: Music generation that "no big company has yet been able to crack"

The Product Layer Advantage:

• Creative Workflow Integration: Multiple additional steps for narration, voiceover, and dubbing perfection
• Voice Agent Infrastructure: Knowledge base integrations, functions, deployment, testing, evaluation, monitoring
• Platform Approach: All pieces coming together in a comprehensive platform
• OpenAI's Focus Gap: "Not investing as much time" in these specialized areas

Timestamp: [12:25-15:00]

Talent Retention Strategy Against Tech Giants

The Value Proposition Reality:

• Exceptional Impact: The impact that top research talent can create is "out of the scale across any of those companies"
• Early Stage Advantage: Especially valuable in the early days of company development
• Knowledge Premium: Companies like Meta pay for both the talent and the knowhow they bring

Strategic Talent Acquisition:

• Architecture Insights: Getting early people provides insight into models and architecture
• Acceleration Benefits: Can bring knowledge across and accelerate development
• Timing Matters: More valuable in early days than later stages

ElevenLabs' Competitive Edge:

• Continued Value Creation: The upside for ElevenLabs remains valuable even against big tech offers
• Research Impact: Top talent can create disproportionate impact in a focused environment
• Team Synergy: Having 5-10 people in the global top 100 creates multiplicative effects

Timestamp: [15:06-15:56]

Essential Insights:

1. Custom Model Necessity - ElevenLabs discovered through research that existing AI models had fundamental quality issues, leading them to build proprietary solutions
2. Investment Evaluation Framework - In 2022's pre-ChatGPT era, investors could easily identify inadequate existing solutions, making the key question about team capability rather than market need
3. Competitive Moat Strategy - Three-pillar defense against tech giants: laser focus on voice, exceptional talent concentration (5-10 people in global top 100), and specialized product excellence

Actionable Insights:

• Focus beats breadth in early-stage AI companies - ElevenLabs chose to "own and win" in voice rather than spread across multiple AI applications
• Talent density matters more than absolute size - having 5-10 world-class researchers creates multiplicative effects beyond what larger, less focused teams achieve
• Product layer differentiation is crucial even with superior research - platform integration, workflow optimization, and specialized tooling create sustainable advantages

Timestamp: [8:02-15:56]

People Mentioned:

• Michael - Researcher who discovered the breakthrough approach to building new text-to-speech models with emotional capability
• Andrew Reed - Mentioned as someone Harry spoke to before the show about competitive dynamics
• Kieran - Harry's partner who was involved in early investment discussions with ElevenLabs
• P (Co-founder) - ElevenLabs co-founder who assembled the exceptional research team

Companies & Products:

• OpenAI - Referenced as the major competitor that investors wondered would dominate the space
• ChatGPT - Mentioned as launching beginning of 2023 and changing AI awareness
• Gemini - Google's AI model that ElevenLabs beats on speech-to-text benchmarks
• Meta - Referenced as a company paying premium for AI talent and knowhow
• Anthropic - Mentioned alongside other companies competing for top AI talent

Technologies & Tools:

• ElevenLabs v3 - Their most recent generation using multimodal approach combining reasoning and speech
• Text-to-Speech Models - Core technology that ElevenLabs revolutionized with emotional and intuitive capabilities
• Speech-to-Text - Technology where ElevenLabs outperforms major competitors on benchmarks
• Voice Agents - Conversational AI systems requiring knowledge base integrations and deployment infrastructure

Concepts & Frameworks:

• Uncanny Valley - The phenomenon where existing AI models had immediately recognizable artificial quality
• Scaling Laws - The principle of AI model improvement through increased scale and data
• Single vs. Multi-Modal Models - Evolution from dedicated models per modality to combined reasoning approaches
• War for Talent - The competitive landscape for acquiring top AI researchers

Timestamp: [8:02-15:56]

ElevenLabs Funding and Product Market Fit Journey

Competitive Advantages Against Big Tech

ElevenLabs identified three key advantages that allow them to compete with major technology companies:

Speed of Innovation:

1. Research to Production Pipeline - New AI models get deployed to production almost immediately
2. Minimal Corporate Red Tape - Unlike bigger companies that face bureaucratic delays
3. Rapid Implementation - Critical product improvements happen without lengthy approval processes

Team Structure Benefits:

• Small and Mighty Team - Focused on quality over quantity in hiring
• Cross-Learning Environment - Team members can learn from each other effectively
• Quick Decision Making - No optimization for headcount but for the right people

Strategic Focus:

• Targeted Optimization - Focus on hiring the right people rather than many people
• Agile Development - Ability to pivot and adapt quickly to market needs
• Direct Impact - Every team member's contribution has immediate visibility

Timestamp: [16:01-16:47]

Pre-Seed Fundraising Difficulties

The pre-seed round in 2022 proved to be extremely challenging for the ElevenLabs founders:

Major Investor Concerns:

1. Research Capability Questions - How would they compete with established research teams?
2. Market Size Skepticism - Investors believed the AI voice market was too small
3. Defensibility Issues - Concerns about competing with incumbents from major tech companies

Fundraising Statistics:

• Rejection Rate: Spoke with 30-50 investors who said no
• Final Amount: Raised $2 million at $9 million post-money valuation
• Equity Sold: Just over 11% to the first investor, with others layering in

Additional Pressure Points:

• Accelerator Rejection: Turned down a US accelerator offer in early 2022
• Increased Expenses: Started spending more on GPUs and hiring first employees
• Personal Risk: Using savings from previous Google and Palantir positions
• Timeline Stress: Need to raise money became urgent as expenses grew

Timestamp: [17:01-18:48]

Strategic Investment in Core Infrastructure

After raising $2 million, ElevenLabs focused on two critical areas for acceleration:

Data Center Development:

1. Initial Setup - Built their first small data center in Poland
2. Geographic Expansion - Quickly moved to start purchasing infrastructure in the US
3. GPU Investment - Significant spending on graphics processing units for AI training

Team Building Strategy:

• Conservative Hiring - Added only two additional people initially
• Quality Focus - Each hire felt significant given the small team size
• Strategic Roles - Focused on positions that would directly accelerate product development

Infrastructure Philosophy:

• Self-Reliance - Building their own data center capabilities rather than relying solely on cloud providers
• Geographic Distribution - Early recognition of the need for US-based infrastructure
• Scalable Foundation - Investments designed to support rapid growth in AI model training and deployment

Timestamp: [19:49-20:22]

Strategic Timing of Funding Announcements

ElevenLabs developed a distinctive philosophy around when and how to announce funding rounds:

Announcement Strategy:

1. Product-Tied Releases - Every funding announcement coincides with a product launch
2. Customer Celebration - Use announcements to showcase arrival in specific sectors
3. Research Integration - Tie funding news to new AI model releases

Specific Timeline Example:

• Funding Closed: Q3 2022 (pre-seed round)
• Announcement Delay: Held announcement for several months
• Strategic Release: January 2023, aligned with beta product launch

Core Philosophy:

• Purpose-Driven Announcements - Never announce funding just for the sake of it
• Market Education - Use funding news to educate market about product capabilities
• User Acquisition - Leverage announcement momentum to bring product to users
• Sector Positioning - Demonstrate market arrival and credibility to specific customer segments

Timestamp: [20:41-21:21]

The Journey from Dubbing to Narration Success

ElevenLabs experienced a clear transition from struggling with product-market fit to finding strong market signals:

Initial Struggles with Dubbing:

• Poor Engagement: People were slow to reply to emails
• Weak Response: Sent samples weren't generating interest
• Clear Lack of Fit: No product-market fit throughout 2022

Breakthrough with Narration:

1. Market Shift: Pivoted from dubbing to narration and voiceovers
2. Strong Signals: Product-market fit signals started appearing immediately
3. User Engagement: Dramatic improvement in user response and interest

Three Key Validation Moments:

1. Viral Blog Post - "First AI that can laugh" got picked up by newsletters
2. Waiting List Growth - 1,000 people joined waiting list overnight
3. Power User Behavior - Audiobook author used platform extensively, copying and pasting entire 500-page book

Real-World Success Story:

• Platform Workaround: Author used small text box 500 times to process entire book
• Market Validation: Released audiobook passed as human content on platforms where AI was banned
• Organic Growth: Author brought other book author friends to the platform
• Positive Reviews: Content received great reviews from listeners

Timestamp: [21:28-22:50]

Evolving Definition of Product-Market Fit

ElevenLabs takes a nuanced approach to defining and measuring product-market fit:

Current Market Response:

• User Love: Clear evidence that users love the product
• Creator Adoption: Strong uptake from narrators and content creators
• Media Attention: Positive coverage driving additional user acquisition

Long-Term Perspective:

1. 5-10 Year Value: Focus on ensuring product provides value for extended periods
2. Self-Sustaining Growth: Looking for evidence of sustainable, long-term momentum
3. Future Value Creation: Continuous assessment of potential for additional value

Philosophical Approach:

• Conservative Assessment: Don't claim product-market fit prematurely
• Continuous Improvement: Always see opportunities to create more value
• Future-Focused: Definition tied to long-term sustainability rather than short-term metrics

Current Status:

• Getting Closer: Now closer to their definition of true product-market fit
• Still Growing: Recognize significant potential for additional value creation
• Sustainable Foundation: Building toward self-sustaining growth model

Timestamp: [23:08-23:46]

Essential Insights:

1. Competitive Strategy - ElevenLabs competes with tech giants through speed of innovation, minimal red tape, and a small but mighty team focused on quality over quantity
2. Fundraising Reality - Pre-seed was extremely challenging with 30-50 investor rejections, raising $2M at $9M post-money despite market skepticism about AI voice applications
3. Strategic Announcements - Company never announces funding rounds in isolation, always tying them to product launches, customer celebrations, or research breakthroughs

Actionable Insights:

• Build your own infrastructure early - ElevenLabs invested in data centers in Poland and the US immediately after funding
• Time announcements strategically - Hold funding news until you have meaningful product updates to share simultaneously
• Recognize product-market fit transitions - The shift from dubbing to narration showed clear user engagement differences
• Define long-term success metrics - Focus on 5-10 year value creation rather than short-term validation signals

Timestamp: [16:01-23:58]

People Mentioned:

• Peter Czaban - Co-founder of Polkadot cryptocurrency, early investor in ElevenLabs pre-seed round

Companies & Products:

• Google - Previous employer of ElevenLabs founders, source of savings for initial funding
• Palantir - Previous employer of ElevenLabs founders, provided savings for startup funding
• Polkadot - Cryptocurrency platform co-founded by early ElevenLabs investor Peter Czaban
• Credo Ventures - UK-based venture capital firm that participated in ElevenLabs pre-seed round

Technologies & Tools:

• GPUs - Graphics processing units that ElevenLabs invested heavily in for AI model training
• Data Centers - Infrastructure built in Poland and US for AI processing and model deployment

Concepts & Frameworks:

• Product-Market Fit - ElevenLabs' evolving definition focused on 5-10 year value creation rather than short-term metrics
• Research to Production Pipeline - ElevenLabs' competitive advantage of rapidly deploying new AI models to production
• Strategic Announcement Timing - Philosophy of tying funding announcements to product launches and customer milestones

Timestamp: [16:01-23:58]

Strategic Fundraising Timing & Focus

Key Fundraising Principles:

1. Tie announcements to product milestones - Don't celebrate numbers in isolation; connect them to meaningful product launches, user achievements, or hiring goals
2. Focus on user acquisition over media attention - Traditional press coverage often has minimal impact compared to grassroots community building
3. Avoid continuous fundraising mode - It's distracting and unproductive; instead, line up investors for future conversations

Most Effective User Acquisition Channels:

• AI-focused newsletters - Direct access to target audience interested in AI developments
• YouTube community partnerships - Leveraging creator networks and Discord communities for organic growth
• Reddit and Hacker News - These platforms picked up the product faster than traditional media
• Domain-specific forums - Engaging where actual users congregate rather than where they don't

Common Media Misconceptions:

The team experienced firsthand how traditional press coverage (major publications) had virtually no user impact, while grassroots channels like Discord communities and AI newsletters drove significant adoption. One major publication interview resulted in zero meaningful user growth despite extensive preparation.

Timestamp: [24:03-25:56]

Post-Launch Investor Engagement Strategy

The Distraction Problem:

When launches go well, founders face increased interest from:

• Investors seeking immediate meetings and pitches
• Event organizers requesting speaking engagements
• Media outlets wanting interviews and features

Recommended Approach:

1. Stay focused on product development - Resist the temptation to engage with every opportunity
2. Prioritize user feedback and enterprise interest - Early enterprise customers provide more value than media appearances
3. Line up investors for future rounds - Tell them you're not raising now but will reconsider in Q2/Q3

Strategic Investor Communication:

• Set clear timelines - "I'm not raising now, but will reconsider capital needs in [specific quarter]"
• Maintain relationships without commitment - Keep doors open for when you actually need funding
• Avoid continuous fundraising mode - It's unproductive and takes focus away from building

Testing Investor Value:

Use the interest period to evaluate potential investors by asking for specific help:

• Introductions to key contacts
• Assistance with hiring challenges
• Access to their network for business development

This approach serves as a litmus test to distinguish genuinely helpful investors from those offering empty platitudes.

Timestamp: [26:14-27:37]

Angel Investor Selection Framework

Three Core Categories for Angel Selection:

1. Domain Expertise Gap-Filling:

• Identify missing knowledge areas - Bring in angels who have expertise the founding team lacks
• Complement existing skills - Focus on areas where the team needs guidance and experience
• Technical or industry-specific knowledge - Especially valuable for specialized AI and voice technology challenges

2. Circle Validation & Access:

• Industry credibility - Angels who can validate the company within specific professional circles
• Event and community access - Participation in exclusive founder groups and industry gatherings
• AI founder network - For AI companies, having other successful AI founders as angels provides peer learning opportunities

3. Go-to-Market Expertise:

• Sales-led growth experience - ElevenLabs had strong self-serve and developer experience but needed traditional sales expertise
• Enterprise sales knowledge - Understanding how to scale from product-led growth to enterprise sales
• Market expansion strategies - Experience in scaling to new markets and customer segments

Strategic Approach:

Rather than focusing solely on brand names or check sizes, the framework prioritizes functional value - what specific expertise, access, or validation each angel brings to address the company's current growth challenges and knowledge gaps.

Timestamp: [29:18-30:13]

The NFDG Round Strategy & Execution

The Approach Timeline:

• March 2023: Investor interest began following strong product traction
• Strategic waiting period: Rather than jumping at first offers, the team optimized for ideal partners
• June 2023: Closed $19M round with Nat Friedman, Daniel Gross (NFDG), and Brian Kim from Andreessen Horowitz

Partner Selection Criteria:

1. Global credibility building - Ensuring Silicon Valley ecosystem recognizes ElevenLabs as a trusted, ambitious company
2. Admiration factor - Working with investors who had created something special themselves
3. Strategic value alignment - Partners who understood the vision and could provide meaningful guidance

The NFDG Connection:

• Nat Friedman reached out directly via email to the founding team
• In-person meeting in London - Friedman flew to London for face-to-face discussions
• Last-minute coordination - The meeting almost didn't happen due to scheduling uncertainty
• Hotel meeting setup - Conducted in a London hotel, demonstrating the informal yet serious nature of top-tier investor relationships

Strategic Outcome:

This round achieved the dual goals of securing capital while establishing credibility in the global AI ecosystem, particularly in Silicon Valley where many of the most ambitious AI companies are based.

Timestamp: [30:13-31:56]

Essential Insights:

1. Grassroots beats traditional media - AI newsletters, Discord communities, Reddit, and Hacker News drove more user growth than major publication coverage
2. Post-launch focus discipline - Successful launches create distractions; founders must prioritize product development over media opportunities
3. Strategic investor relationship building - Line up investors for future rounds while testing their helpfulness through specific asks

Actionable Insights:

• Tie funding announcements to meaningful product or user milestones, not just numbers
• Engage with investors between rounds by asking for concrete help with introductions or hiring challenges
• Select angels based on domain expertise gaps, circle validation needs, and go-to-market knowledge requirements
• Avoid continuous fundraising mode - it's distracting and unproductive for building the actual business

Timestamp: [24:03-31:56]

People Mentioned:

• Nat Friedman - Co-founder of NFDG who directly reached out to ElevenLabs and flew to London for meetings
• Daniel Gross - Co-founder of NFDG (Nat Friedman Daniel Gross fund) who participated in the Series A
• Bryan Kim - Partner at Andreessen Horowitz who co-led the $19M Series A round

Companies & Products:

• Andreessen Horowitz (a16z) - Venture capital firm that co-led ElevenLabs' Series A funding round
• NFDG - Investment fund founded by Nat Friedman and Daniel Gross that invested in ElevenLabs
• Tech Crunch - Technology publication mentioned as example of traditional media with limited user acquisition impact
• Hacker News - Technology forum that proved more valuable for user acquisition than traditional press
• Reddit - Social platform where AI communities picked up ElevenLabs faster than mainstream media

Technologies & Tools:

• Discord - Community platform that became one of ElevenLabs' most valuable user acquisition channels
• YouTube - Platform where creator community partnerships drove significant user growth

Concepts & Frameworks:

• Self-serve vs Sales-led Growth - Business model distinction between product-led growth and traditional enterprise sales approaches
• Angel Investor Selection Framework - Strategic approach focusing on domain expertise, circle validation, and go-to-market knowledge
• Grassroots Marketing - Community-driven user acquisition strategy that outperformed traditional media coverage

Timestamp: [24:03-31:56]

Unique Investor Approach

The API Testing Difference:

1. First to actually test the product - Only investor who tested ElevenLabs APIs before investing
2. Provided constructive feedback - Pointed out specific issues with voice quality and stability parameters
3. Saw value despite problems - Recognized potential even when the product wasn't perfect

Exceptional Pattern Recognition:

• Minimal information needed - Could understand the vision with very little explanation
• Context infilling ability - Drew from past experiences to provide relevant insights
• Immediate comprehension - Grasped exactly what ElevenLabs was building from first conversation
• Right questions from start - Challenged the team with appropriate strategic questions

Investment Execution:

• Moved quickly - Fast decision-making and execution
• Mutual enthusiasm - Both sides were keen to partner (though ElevenLabs tried not to show it too much)
• Strategic positioning - Helped position ElevenLabs as a top-tier company alongside Daniel Gross

Timestamp: [32:02-33:18]

Pre-Investment Partnership Strategy

Demonstrating Value Before Investment:

1. Celebrity introductions - Connected ElevenLabs with celebrities for voice licensing opportunities
2. Two weeks of active help - Provided support and resources before any commitment
3. Immediate network activation - Leveraged their connections to solve real business challenges

Executive Commitment:

• Brian flew to London - Partner traveled internationally for face-to-face meetings
• Real-time negotiation - Signed preliminary term sheet during the London visit
• Collaborative decision-making - Mati consulted with co-founder Piotr on speaker during negotiations

Strategic Positioning:

• Tier one fund recognition - Helped position ElevenLabs as a top-tier company
• Competitive advantage - Stood out among classic tier one funds through actions, not just reputation
• Partnership before investment - Showed genuine care and commitment before financial commitment

Timestamp: [33:29-34:30]

Speed as Competitive Advantage

Product and Investment Parallel:

• Execution speed essential - Critical for both product development and investment decisions
• Market reality - Speed is "the only thing you have" in current competitive landscape
• Founder preference - Mati thrives in fast-moving environments

The Road Show Problem:

1. Common founder approach - Many want 10-day investor meetings plus week to decide
2. Efficiency opportunity - Could save 17 days with immediate term sheet decisions
3. Increasing trend - More founders choosing lengthy processes over speed

Strategic Implications:

• Competitive differentiation - Fast investors can win deals others miss
• Resource optimization - Eliminates unnecessary time waste in fundraising
• Partnership focus - Allows more time for actual business building versus fundraising theater

Timestamp: [34:30-35:11]

Key Investment Decision Factors

Primary Optimization Categories:

1. Valuation considerations - Pure financial terms and company pricing
2. Dilution management - Ownership percentage retention
3. Brand name value - Investor reputation and market signaling
4. Network access - Connections and relationships the investor brings
5. Specific partner fit - Individual relationship and expertise match

Common Founder Mistakes:

• Unclear optimization goals - Many don't know what they're actually optimizing for
• Wrong focus areas - Optimizing for 20% valuation differences instead of partnership value
• Road show mentality - Treating fundraising like a comparison shopping exercise

Partnership-First Approach:

• Value beyond money - Investors can provide significantly more than just capital
• Long-term relationship - Focus on partnership quality over short-term financial gains
• Strategic support - Network, expertise, and ongoing guidance matter more than marginal valuation differences

Timestamp: [35:46-39:05]

Exploding Term Sheet Strategy

Personal Stance:

• Doesn't like them - But understands the investor reasoning
• Received multiple - ElevenLabs experienced several exploding offers
• Learning process - Initially didn't understand them, but gained clarity over time

Investor Perspective Understanding:

1. Stalking horse fear - Smaller funds worry about being used to bump other term sheets
2. Pricing out concern - Fear that valuations will rise beyond their investment capacity
3. No credit problem - First investors get trampled by later, higher offers

Founder Responsibility:

• Avoid manipulation - Don't use first term sheets just to inflate others
• Focus on partnership - Value investor contributions beyond just money
• Reasonable benchmarking - Order of magnitude differences matter, 20% differences don't
• Strategic optimization - Prioritize long-term partnership value over marginal financial gains

Timestamp: [37:42-39:05]

US vs European VC Differences

American VC Advantages:

1. Risk appetite - Much more willing to take bigger risks
2. Growth mindset - Conversations focus on "how do we bet bigger" rather than risk mitigation
3. Network effects - Incredible connections and partnership opportunities
4. Resource commitment - More willing to invest time and resources pre-investment

ElevenLabs US Investor Portfolio:

• Andreessen Horowitz - Celebrity introductions and network access
• Nat Friedman & Daniel Gross - Technical expertise and AI industry insights
• Sequoia with Andrew - "Another level" of partnership and support
• Iconic team - Providing "crazy help" and resources
• NEA - Additional US network and support

Fundamental Approach Difference:

• Optimization focus - Americans optimize for upside rather than downside protection
• Partnership intensity - Higher level of engagement and support
• Scale thinking - Focus on how to grow bigger rather than minimize risk
• All US-based - ElevenLabs' major investors are predominantly American firms

Timestamp: [39:12-39:57]

Essential Insights:

1. Unique investor diligence - Nat Friedman was the only investor who actually tested ElevenLabs APIs before investing, providing constructive feedback while recognizing the underlying value
2. Pre-investment partnership - Andreessen Horowitz demonstrated value by introducing celebrities and providing two weeks of active support before any financial commitment
3. Speed as differentiator - Fast decision-making in both product development and fundraising provides significant competitive advantages in today's market

Actionable Insights:

• Founders should optimize for partnership value over marginal valuation differences (20% variations aren't worth sacrificing long-term support)
• Test potential investors' commitment through their willingness to provide value before investment decisions
• American VCs tend to focus on growth optimization rather than risk mitigation, offering different strategic advantages than European counterparts

Timestamp: [32:02-39:57]

People Mentioned:

• Nat Friedman - Former GitHub CEO who uniquely tested ElevenLabs APIs before investing and provided technical feedback
• Daniel Gross - Former Y Combinator partner and AI expert, mentioned alongside Nat Friedman as angel investor
• Bryan Kim - Andreessen Horowitz partner who flew to London to meet with ElevenLabs team
• Piotr (P) - ElevenLabs co-founder who provided guidance during term sheet negotiations
• Andrew - Sequoia partner mentioned as providing exceptional support

Companies & Products:

• Andreessen Horowitz (A16Z) - Tier one VC firm that demonstrated pre-investment value through celebrity introductions and active support
• GitHub - Platform where Nat Friedman and Daniel Gross built their reputation, mentioned as source of admiration
• Sequoia Capital - Major VC firm providing "another level" of partnership support
• NEA - US-based venture capital firm mentioned as ElevenLabs investor

Technologies & Tools:

• ElevenLabs APIs - Voice AI technology that Nat Friedman uniquely tested before investing, identifying issues with voice quality and stability parameters

Concepts & Frameworks:

• Exploding term sheets - Time-limited investment offers used by VCs to prevent being used as stalking horses
• Road show fundraising - Extended process of meeting multiple investors before making decisions
• Stalking horse - First investor whose term sheet is used to generate competing offers

Timestamp: [32:02-39:57]

Investor Due Diligence and Partnership Quality

Key Evaluation Criteria:

1. Back-reference checks - How investors behave when companies are failing, not just succeeding
2. Support during difficulties - Whether they become more helpful during tough times rather than less
3. Track record verification - Getting specific examples of investor behavior in crisis situations

US vs European Investor Differences:

• US investors: Consistently positive back-references due to higher volume of company ups and downs
• European investors: Mixed results when conducting the same reference checks
• Experience factor: US investors have more practice managing company failures and recoveries

Standout Example:

Brian Kim's exceptional performance during a failed investment:

• Served as lead investor in a round that didn't work out
• Recovered money for all investors - described as "rare" in the industry
• Put in significant work to execute the recovery quickly and effectively
• Left strong positive impression despite the company failure

Timestamp: [40:03-41:09]

Small Team Philosophy and Organizational Structure

Core Principles:

1. More people doesn't fix problems - Additional headcount rarely solves fundamental issues
2. Higher ownership model - Small teams have greater responsibility and accountability
3. Faster iteration cycles - Teams can see results and adapt to reality much quicker

Current Organization Structure:

• Total company size: 250 people
• Actual structure: ~20 independent teams of 5-10 people each
• Team focus: Each team executes on specific projects with high autonomy

Team Organization by Product Areas:

Product Teams:

• Studio interface team - Responsible for core user experience when logging in
• Voice agent suite team - Handles entire voice agent product line
• Enterprise components team - Focuses on business-level features
• Self-serve elements team - Manages individual user tools

Support Functions:

• Talent team - Separate dedicated team for recruitment
• People team - HR and employee experience
• High independence model - Each team operates with significant execution autonomy

Benefits of Small Team Approach:

• Teams can move extremely quickly on their specific projects
• Direct connection between effort and results
• Beautiful execution model with faster feedback loops

Timestamp: [41:17-42:51]

The Dubbing Crisis: When a Partner Beat Them to Market

The Setup - Late 2023:

ElevenLabs had developed all the core components internally:

• Text-to-speech technology
• Voice cloning capabilities
• Speech-to-text functionality
• Complete dubbing solution ready for optimization

The Crisis:

1. Shared components with enterprise client for their use
2. Announced timeline - ElevenLabs planned to launch dubbing publicly in September
3. Partner launched first - Released dubbing solution two weeks before ElevenLabs
4. Massive media attention - Partner received all the credit and user excitement for the breakthrough

Impact on Company Morale:

• Research team frustration - How did the client build it faster with their components?
• Go-to-market team anger - Lost potential clients and competitive advantage
• Leadership disappointment - Their signature idea was executed by someone else first
• Company-wide demoralization - Two years of planning seemingly wasted

The Partner's Perspective:

• Weekend hack project - They treated it as a quick intern project
• Significant revenue impact - Generated tens of millions in revenue from the launch
• Timeline coincidence - Claims their launch timing wasn't influenced by ElevenLabs' announcement

Key Lessons on Crisis Management:

Stage 1 - Acknowledge the Reality:

• Don't pretend everything is fine - Be authentic about the situation
• Express genuine frustration - Let the team process what went wrong
• Analyze mistakes honestly - Go through what the company did wrong

Stage 2 - Move to Action:

• Transition quickly from anger to problem-solving
• Focus on execution - Relentless execution will prove itself over time
• Learn from mistakes - Ensure the same error doesn't happen again
• Implement consequences - If mistakes repeat, there must be repercussions

Timestamp: [43:03-47:38]

Speed vs Quality: The Dual Approach

The False Choice:

Many assume companies must choose between speed of execution OR quality of research/access to resources, but ElevenLabs believes in pursuing both simultaneously.

Balanced Strategy:

• Speed of execution remains critically important in competitive markets
• Quality of research and access to GPUs are equally essential
• Combined approach - The company focuses on excelling in both areas rather than choosing one

Timestamp: [47:44-47:55]

Essential Insights:

1. Investor evaluation strategy - ElevenLabs conducts back-reference checks specifically focused on how investors behave during company failures, not just successes
2. Small team philosophy - The company maintains ~20 independent teams of 5-10 people each, believing more people doesn't solve fundamental problems
3. Crisis management approach - When facing major setbacks, acknowledge the reality and frustration first, then quickly transition to action-oriented problem-solving

Actionable Insights:

• Due diligence on investors should include specific examples of their behavior during portfolio company failures
• Organizational structure can prioritize small, autonomous teams with high ownership over traditional large departments
• Cultural crisis response requires authentic acknowledgment of problems before moving to solutions
• Competitive differentiation should focus on both speed of execution AND quality of research simultaneously

Timestamp: [40:03-47:55]

People Mentioned:

• Bryan Kim - Lead investor who successfully recovered money for all investors during a failed investment, demonstrating exceptional crisis management

Companies & Products:

• ElevenLabs - AI voice platform with 250 employees organized into ~20 small teams
• Unnamed enterprise partner - Client who launched dubbing solution before ElevenLabs using their components

Technologies & Tools:

• Text-to-speech technology - Core component of ElevenLabs' voice platform
• Voice cloning capabilities - Technology for recreating voices
• Speech-to-text functionality - Component used in dubbing solutions
• Dubbing solution - AI-powered technology for translating speech while maintaining original voice characteristics

Concepts & Frameworks:

• Back-reference checking - Due diligence method focusing on investor behavior during company failures
• Small team organization - Management philosophy favoring 5-10 person autonomous teams over large departments
• Crisis management stages - Two-stage approach: acknowledge reality/frustration, then transition to action-oriented solutions

Timestamp: [40:03-47:55]

Research Strategy & Market Position

ElevenLabs has built a comprehensive competitive moat through three key pillars:

Core Competitive Elements:

1. Research Leadership - Continuous investment in voice technology research
2. Product Excellence - Phenomenal product experience built in parallel
3. Ecosystem Development - Strong distribution network and brand recognition

Market Advantage Timeline:

• Current Lead: 6-12 months ahead of competition depending on use case
• Strategic Value: Sufficient time to execute superior product development
• Research ROI: Head start advantage lasting 1-3 years maximum

Parallel Execution Strategy:

The company doesn't rely solely on research advantages but simultaneously builds exceptional product experiences from day one. This dual approach ensures that when competitors catch up technically, ElevenLabs maintains leadership through superior user experience and market position.

Timestamp: [48:02-48:46]

Infrastructure Investment Strategy

ElevenLabs made the strategic decision to build proprietary data centers rather than rely on third-party providers like CoreWeave or traditional cloud services.

Financial Justification:

1. Break-Even Timeline - Two-year horizon for ROI compared to renting
2. Continuous Training Needs - Ongoing model development requirements
3. Data Transfer Optimization - Reduced costs for large-scale data operations

Operational Benefits:

• Faster Experimentation - Immediate access to compute resources
• Greater Control - Direct management of infrastructure and performance
• Cost Efficiency - Long-term savings on compute expenses

Risk Assessment:

The decision assumes moderate improvements in GPU infrastructure. While future innovations could disrupt this equation, current analysis shows positive ROI and operational advantages that justify the investment.

Timestamp: [48:46-49:55]

Market Reality & Strategic Perspective

The CEO acknowledges that many AI application companies currently face challenging unit economics, but views this as a strategic phase rather than a fundamental flaw.

Current Market Assessment:

• Poor Unit Economics: Most AI application companies have difficult financial metrics
• Strategic Rationale: Companies prioritize market position over immediate profitability
• ElevenLabs Advantage: Healthier unit economics due to controlling research, product, and distribution

Long-Term Strategy Justification:

1. Model Cost Optimization - AI models will become more cost-effective over time
2. Brand Trust Building - Early market leaders establish customer loyalty
3. Signal Utilization - Customer data provides competitive advantages

Market Outcome Predictions:

• Winners & Losers: At least one company will succeed, but some will fail
• Market Size: Large enough for multiple successful companies
• Competitive Dynamics: Bold bets against tech giants like Google can succeed through superior branding and execution

Timestamp: [49:55-52:10]

Strategic Framework for Market Approach

The ElevenLabs CEO provides clear guidance on when to pursue broad horizontal strategies versus focused vertical approaches, based on their own successful horizontal launch.

Choose Horizontal When:

• Novel Technology: Launching something very new and different
• Unknown Customer Subset: You know there's a bigger market but haven't identified specific segments
• Broad Applicability: Product has clear value across multiple use cases

Choose Vertical When:

• Domain Expertise: You have deep knowledge of a specific industry
• Defined Category: Clear understanding of the competitive landscape
• Established Market: Category already exists with known customer needs

ElevenLabs Example:

Despite advice from investors like Philippe to be more targeted in their ICP (Ideal Customer Profile), the company succeeded with a horizontal approach because voice AI was genuinely novel technology with broad applications across industries.

Timestamp: [52:16-52:54]

Organizational Structure Philosophy

ElevenLabs made the unconventional decision to remove all job titles, following examples like Stripe, to optimize for impact and flexibility.

Core Principles:

1. Impact Over Hierarchy - Individual contribution matters more than organizational level
2. Immediate Decision-Making - New team members can be impactful from day zero
3. Fluid Leadership - Anyone can transition to leading teams or functions quickly

Practical Benefits:

• Reduced Distractions - Eliminates politics around title assignments
• Enhanced Mobility - Lower tenure employees can manage higher tenure staff based on merit
• Faster Execution - Small teams operate without bureaucratic constraints

Internal Structure:

While titles are eliminated, clear decision-making structures exist within sub-teams. Each team has a designated lead who makes final decisions when consensus isn't reached, but this leadership role isn't permanent or title-based.

Flexibility Advantage:

Unlike traditional companies where titles typically remain forever, ElevenLabs' structure allows leadership roles to evolve based on performance and team needs.

Timestamp: [53:07-55:00]

Talent Development Strategy

ElevenLabs tackles the common European tech criticism about lacking experienced executives who've seen significant scale by focusing on internal talent development rather than external hiring.

Growth-First Approach:

• Internal Development: Prioritize growing existing talent over external hires
• Mentorship Integration: Match current team members with experienced US advisors
• Network Leverage: Utilize investor connections for guidance and expertise

Strategic Philosophy:

The company prefers taking calculated risks on people's growth potential rather than bringing in external executives. This approach builds loyalty while developing capabilities that match the company's specific culture and needs.

European Building Reality:

The CEO acknowledges that building in Europe is indeed "building on hard mode" compared to the US, but believes there are unique advantages that offset these challenges.

Timestamp: [55:06-55:55]

Essential Insights:

1. Competitive Moat Strategy - ElevenLabs maintains 6-12 months market lead through research investment while building superior product experience
2. Infrastructure Investment - Building proprietary data centers provides cost savings and operational control with two-year ROI timeline
3. Unit Economics Reality - AI companies face challenging margins short-term, but market size supports multiple winners with strong branding

Actionable Insights:

• Consider horizontal market approach when launching novel technology with broad applications
• Eliminate organizational titles to optimize for impact and leadership flexibility
• Develop internal talent with external mentorship rather than hiring experienced executives
• Build proprietary infrastructure when continuous model training and data transfer justify the investment

Timestamp: [48:02-55:55]

People Mentioned:

• Philippe (Investor/Advisor) - Advised ElevenLabs to be more targeted in ICP rather than horizontal approach
• Anton Osika (Lovable Founder) - Referenced as phenomenal marketing person and founder who said building in Europe is "hard mode"

Companies & Products:

• Stripe - Example company that also eliminated job titles in their organizational structure
• CoreWeave - Cloud infrastructure provider mentioned as alternative to building own data centers
• Replit - AI coding company mentioned as example of challenging unit economics
• Lovable - AI application company referenced for unit economics discussion
• Google Firebase - Google's product competing in similar space, noted for poor brand recognition

Technologies & Tools:

• GPU Infrastructure - Critical component for AI model training and inference operations
• Data Centers - Infrastructure investment for training and inference operations

Concepts & Frameworks:

• Unit Economics - Financial metrics measuring profitability per customer or transaction
• ICP (Ideal Customer Profile) - Strategic framework for defining target customer characteristics
• Horizontal vs Vertical Strategy - Market approach framework for startup positioning

Timestamp: [48:02-55:55]

European Talent Strategy

Key Talent Insights:

1. Exceptional European talent exists - The challenge isn't finding skilled people, but providing them with ambitious opportunities
2. Global ambition from European base - Building from Europe while competing globally, not just for European markets
3. Mission-driven workforce - Team members work weekends and feel genuine ownership in company success

Talent Acquisition Strategy:

• Direct outreach over inbound filtering - Early days focused on targeted recruitment rather than managing application volume
• Technical skill prioritization - Increasing emphasis on engineering and technical capabilities across all departments
• Global team distribution - Building local outposts in Brazil, Japan, India, and Mexico with go-to-market and engineering presence

Addressing Misconceptions:

• Work ethic comparison: European team members often outwork their US West Coast counterparts
• Central/Eastern European dedication: Team shows exceptional commitment with weekend work and genuine company investment
• Missionary mindset: Employees demonstrate true ownership beyond just working hard

Timestamp: [56:01-58:17]

Hiring Strategy & Mistakes

Major Hiring Insights:

1. Speed of separation decisions - When uncertain about a hire in early weeks/months, separate quickly rather than giving extended chances
2. Interview process evolution - Transition from outbound recruitment to managing high-volume inbound applications due to AI buzz and company scale
3. Founder involvement sustainability - Continue interviewing every hire even at 250+ employees to maintain culture and quality

Hiring Challenges:

• Hardest role to fill: Researchers (definitively the most difficult position)
• Managing inbound volume: Filtering through applications from people attracted by AI buzzword or company reputation
• Assessment timeline: Limited interview time requires quick but accurate candidate evaluation

Scaling Approach:

• Continued founder interviews: Plans to interview candidates up to 1,000 employees if possible
• Rapid growth trajectory: From 250 to 400 employees by end of year (60% increase)
• Global expansion hiring: 30-50 people already scheduled to join, additional 100+ planned

Timestamp: [58:26-1:01:32]

Revenue Growth Announcement

Major Revenue Achievement:

• $200 million ARR crossed - Just announced during this interview
• Exceptional growth trajectory: From $35M (end of 2023) to $200M in approximately 15 months
• Previous milestones: 20 months to reach $100M ARR, then 10+ months to $200M ARR

Business Model Insights:

1. Revenue per employee efficiency - Strong metrics with $200M across 250 employees
2. Enterprise focus - Large enterprises now represent majority of business
3. Sticky revenue model - Relatively stable customer retention, especially for enterprise clients

Customer Composition:

• Largest contract size: Approximately $2 million
• Primary use cases: Call centers, customer support, and personal assistance platforms
• Conversational agent platform: Core focus on building comprehensive voice AI solutions for enterprise customers

Strategic Approach:

• Efficiency vs. growth balance - Willing to temporarily reduce revenue per head to capture market share before competition
• Long-term retention focus - Prioritizing customer lifetime value and net revenue retention over short-term efficiency metrics

Timestamp: [1:01:32-1:03:55]

Essential Insights:

1. European talent excellence - Exceptional technical talent exists in Europe, particularly Central/Eastern Europe, with work ethic often exceeding US counterparts
2. Revenue milestone achievement - ElevenLabs just crossed $200M ARR, growing from $35M at end of 2023 in approximately 15 months
3. Global scaling strategy - Building from Europe with global ambition, expanding to Brazil, Japan, India, and Mexico while maintaining founder involvement in hiring

Actionable Insights:

• Make hiring/firing decisions quickly when uncertain about candidates in early weeks rather than giving extended chances
• Focus on technical skill sets across all company departments, not just engineering roles
• Maintain founder involvement in hiring process even at scale to preserve culture and quality standards
• Balance revenue per employee efficiency with strategic market capture before competition arrives

Timestamp: [56:01-1:03:55]

Companies & Products:

• Lovable - European company showing similar global ambition to ElevenLabs
• Aura - Swedish company mentioned as example of ambitious European startup
• Cintesia - Another European company demonstrating global competitive ambition

Geographic Markets:

• Brazil - Target market for ElevenLabs expansion with local engineering and go-to-market teams
• Japan - Strategic expansion location for building local presence
• India - Key market for establishing engineering and business development outposts
• Mexico - Additional expansion market for local team building

Business Concepts:

• Net Revenue Retention (NRR) - Key metric for measuring customer value growth over time
• Revenue per head - Efficiency metric for measuring company productivity and resource allocation
• Conversational agent platform - Core business model focusing on voice AI solutions for enterprise applications

Timestamp: [56:01-1:03:55]

Revenue Growth Trajectory

ElevenLabs has achieved remarkable growth milestones with an ambitious target ahead:

Current Growth Pattern:

1. 20 months to reach $100M ARR
2. 10 months to reach $200M ARR (just announced)
3. Target: 5 months to reach $300M ARR

Strategic Growth Drivers:

• Enterprise Partnerships: Major deployments with Cisco, Twilio, and Epic Games
• Self-Served Distribution: Continued strong growth from creators and developers
• Integrated Solutions: Speech-to-text, text-to-speech orchestration for enterprise clients

The company aims to break records with healthy revenue growth while maintaining quality work standards.

Timestamp: [1:04:32-1:04:54]

Series B Funding Details

The $3.3 billion valuation round provides insight into ElevenLabs' strategic positioning:

Funding Timeline:

• Round Completion: October 2024
• Revenue at Term Sheet: ~$80M ARR
• Revenue at Signing: $100-120M ARR
• Public Announcement: January 2025

Valuation Metrics:

• 30x current revenue at time of signing
• Projected 2025 end-of-year revenue: $250-300M ARR
• Effective multiple: 11-12x projected annual revenue

Strategic Use of Capital:

1. Model Investment: Expanded spending on multimodal capabilities
2. International Expansion: Growth into new geographic regions
3. Enterprise Platform: Enhanced reliability and integrations with Salesforce, ServiceNow, SIP trunking

Timestamp: [1:05:00-1:06:35]

Strategic Resource Allocation Framework

ElevenLabs approaches expansion decisions through a disciplined framework:

Core Decision Criteria:

• Parallel Development: Can new efforts run without distracting core work?
• Resource Isolation: Ability to bring people and investment to new areas independently
• Risk Assessment: Weighing upside potential against core business disruption

Investment Philosophy:

1. Geographic Expansion: Bringing existing products to new markets
2. Product Extensions: Creating new experiences without affecting core functionality
3. Enterprise Enhancement: Building functionality that serves existing user needs better

Key Question Framework:

• Can we create new value without affecting what's important to current users?
• Is the potential upside worth the distraction risk?
• Can we execute in parallel rather than sequentially?

Timestamp: [1:06:42-1:07:20]

Voice Agents as Multi-Billion Dollar Business

The company's most significant growth opportunity lies in voice agent technology:

Market Potential:

• Current Status: Already huge on relative basis but just scratching the surface
• Revenue Projection: Multi-billion dollar revenue generating business potential
• Primary Focus: Voice agents for customer support management

Expansion Strategy:

1. Voice Agents: Companies using AI for customer support automation
2. Conversational Agents: Expanding beyond voice to omnichannel solutions
3. Integration Platforms: Email, WhatsApp, and classic customer support channels

Competitive Positioning:

• Partnership Approach: Public partnership with Decagon
• Horizontal Strategy: Treating all partners as good partners currently
• Verticalization Potential: May overlap more as both companies evolve their focus

Timestamp: [1:07:26-1:08:43]

Human-Agent Transition Dynamics

ElevenLabs observes a nuanced shift in how employees adapt to AI integration:

Current Resistance Patterns:

• Initial Pushback: Some resistance from employees toward agent implementation
• Transition Model: Moving from "human plus agent" to eventual "agent-only" scenarios

Emerging Workforce Structure:

1. Specialized Humans: More focused on complex, domain-specific tasks
2. Automated Tasks: AI handling manual work nobody wanted to do
3. Value Redistribution: Higher value assigned to domain expertise roles

Task Distribution Examples:

• AI-Suitable Tasks: Appointment scheduling, refunds, routine processes
• Human-Required Tasks: Patient care navigation, medical analysis interpretation, high-stakes decisions
• Safety Requirements: Proper safeguards and authentication for AI-handled tasks

Long-term Trend:

• Increasing Automation: Higher percentages of tasks automated over time
• Enhanced Human Value: Domain experts become more valuable as routine work is automated
• Collaborative Model: AI assists with easier tasks while humans handle complex decision-making

Timestamp: [1:08:55-1:10:19]

Investor Brand Value and Market Perception

Premium venture capital backing creates measurable business advantages:

A16Z Impact (Series A):

• Immediate Credibility: People respected the company in ways they didn't before
• Market Perception: Clear signal of validation from top-tier investor

Sequoia Addition (Series B):

• Doubled Perception: A16Z + Sequoia combination is rare and powerful
• Client Respect: Many clients specifically respect this investor combination
• Iconic Status: Incredible mix of top-tier investors

Nat Friedman & Daniel Gross (NFDG2):

• Technical Credibility: Engineers and users admire and trust Nat Friedman
• Investor Recognition: Other investors recognize the strategic value
• User Validation: Technical community respects their involvement

Business Development Benefits:

• Client Acquisition: Easier conversations with enterprise clients
• Partnership Opportunities: Opens doors that might otherwise remain closed
• Talent Recruitment: Attracts high-quality engineers and executives

Timestamp: [1:10:25-1:11:19]

M&A Activity and Strategic Partnerships

As a valuable strategic asset, ElevenLabs has navigated acquisition interest:

Acquisition Approach:

• Multiple Offers: Company has received acquisition offers
• Due Diligence Process: Always conducts basic diligence on offers
• Investor Communication: Keeps investors informed of acquisition interest

Offer Characteristics:

• Largest Offer: Didn't include monetary terms initially
• Strategic Conversations: Often begin as strategic partnership discussions
• M&A Evolution: Partners frequently pivot conversations toward acquisition possibilities

Decision Framework:

• Strategic Evaluation: Assesses each offer's strategic merit
• Partnership First: Often explores strategic partnerships before considering acquisition
• Investor Involvement: Maintains transparency with investor base throughout process

Timestamp: [1:11:25-1:11:58]

Essential Insights:

1. Explosive Growth Trajectory - ElevenLabs achieved $200M ARR in 10 months after hitting $100M, now targeting $300M in just 5 months
2. Strategic Valuation - $3.3B valuation at 30x revenue reflects strong investor confidence and projected growth to $250-300M by end of 2025
3. Voice Agents Opportunity - The company's biggest future revenue driver is voice agents for customer support, projected as a multi-billion dollar business

Actionable Insights:

• Balanced Growth Strategy: Focus on parallel development that doesn't distract from core business while expanding geographically and enhancing enterprise features
• Human-AI Collaboration Model: AI handles routine tasks while humans focus on high-value domain expertise, creating more specialized and valuable roles
• Investor Brand Power: Top-tier VCs like A16Z, Sequoia, and NFDG2 provide measurable business development advantages through enhanced credibility and market perception

Timestamp: [1:04:00-1:11:58]

People Mentioned:

• Nat Friedman - Co-founder of NFDG2, admired by engineers and users for technical expertise
• Daniel Gross - Co-founder of NFDG2, strategic investor in ElevenLabs

Companies & Products:

• Cisco - Major enterprise deployment partner for ElevenLabs' voice solutions
• Twilio - Enterprise client using ElevenLabs' communication platform integrations
• Epic Games - One of ElevenLabs' biggest deployment clients
• Decagon - Public partnership for voice agent solutions
• Salesforce - Integration partner for enterprise functionality
• ServiceNow - Enterprise platform integration for ElevenLabs
• Intercom - Customer support platform mentioned in competitive context

Technologies & Tools:

• SIP Trunking - Telephony integration technology for enterprise voice solutions
• WhatsApp Integration - Omnichannel communication platform for conversational agents
• Email Integration - Part of comprehensive customer support solution offerings

Concepts & Frameworks:

• Multimodal AI - Expansion beyond voice to comprehensive AI communication solutions
• Omnichannel Solutions - Integrated customer support across multiple communication platforms
• Human-Agent Collaboration - Transition model from human-assisted to fully automated customer service

Timestamp: [1:04:00-1:11:58]

Strategic Decision Making and Employee Liquidity

Acquisition Approach:

• Early temptation: Slightly tempting during Series A when growth trajectory was uncertain
• Learning mindset: Used initial conversations to understand M&A processes rather than seriously considering sale
• Clear position: Once they understood acquirers wanted to absorb the company rather than maintain independence, became a "flat no"
• Current stance: Even more confident in rejecting acquisition approaches now

Secondary Sale Strategy:

1. Regular implementation: Conducts secondary sales in almost every funding round
2. Employee inclusion: Offers tender offers for all employees with vested stock
3. Liquidity provision: Ensures employees feel there's actual liquidity available to them
4. Risk management: Allows team to take bigger risks knowing basic financial security is covered

Philosophy on Financial Security:

• Basic needs coverage: Essential to cover childcare, housing, and good quality of life
• Foundation for ambition: Once basic layer is secured, team can aspire to much bigger outcomes
• European ecosystem impact: Questions how many great European companies might not have sold if secondary liquidity options were available historically

Timestamp: [1:12:07-1:14:43]

Contrarian Views on Global Scale and Voice Technology

European Company Building:

• Core belief: You can build a company from Europe at global scale
• Market perception: Most people still don't believe this is possible
• Ecosystem progress: Acknowledges that leaders like Harry Stebbings are helping change perceptions, but widespread belief hasn't shifted yet

Voice as Primary Interface:

• Technology prediction: Voice will become the primary interface for most technology around us
• Market skepticism: Most people would disagree with this prediction
• Long-term vision: Sees voice interaction as fundamental to future human-technology relationships

Timestamp: [1:14:50-1:15:22]

Investment Philosophy and AI Company Analysis

Investment Choice:

• Primary pick: Would buy OpenAI at $300B valuation
• Secondary preference: Loves Anthropic but sees different strategic focus
• Reasoning: Based on actual product usage and consumer investment

Usage Patterns and Strategic Focus:

• Daily usage: Uses ChatGPT very frequently for consumer applications
• Anthropic testing: Uses occasionally for competitive analysis and space understanding
• Strategic differentiation:
• Anthropic investing heavily in coding solutions
• OpenAI clearly investing significantly in ChatGPT consumer solution
• Investment philosophy: Would happily invest in products he actually uses regularly

Alternative preference:

• Google consideration: Would prefer to invest in Google if it were an option, but wasn't available in the hypothetical scenario

Timestamp: [1:15:28-1:18:20]

Evolution in Product Innovation Strategy

Previous Approach:

• Research-first requirement: Would not pursue any product innovation without internal research initiatives
• Internal dependency: Required building research capabilities internally before exploring products

Current Strategy:

• External research integration: Now willing to explore products using outside research
• Speed over control: Sometimes develops products even without internal research capabilities
• Pragmatic flexibility: Shifted from rigid internal-first approach to more opportunistic product development

Strategic Implications:

• Faster iteration: Enables quicker product exploration and market testing
• Resource optimization: Allows leveraging external research capabilities while building internal ones
• Competitive advantage: Reduces time-to-market for innovative features

Timestamp: [1:16:46-1:17:10]

Strategic Interest in Tech Giants

Primary Choice - Google:

• Model access: Would gain knowledge of incredible Gemini V3 models
• Innovation insights: Access to Google's cutting-edge AI innovations
• Scale understanding: Experience the massive scale of operations
• Strategic position: Still believes Google has a good future and is catching up in many areas

Alternative - OpenAI:

• Model knowledge: Would understand incredible model capabilities
• Industry leadership: Access to leading AI research and development

Google Optimism:

• Competitive position: Believes Google is "definitely in the race" for AI leadership
• Recent progress: Acknowledges they are catching up in many places
• Future outlook: Confident they have a good future despite challenges to ads model

Timestamp: [1:17:48-1:18:33]

Regulatory Strategy for European AI Ecosystem

Primary Policy Change:

• AI law alignment: Would proxy most AI law to US law
• Regulatory harmony: Follow exactly how the US approaches AI-related regulation
• Implementation strategy: Create the same regulatory framework or establish a special European state

Alternative Approach:

• Opt-in jurisdiction: Create a special state within the European Union where companies can opt into US-style AI regulation
• Gradual transition: Allows for testing without forcing all of Europe into immediate change
• Reduced complexity: Acknowledges the significant repercussions of wholesale regulatory change

Strategic Rationale:

• Competitive parity: Ensures European AI companies aren't disadvantaged by regulatory overhead
• Innovation enablement: Removes regulatory barriers that might slow AI development
• Global competitiveness: Aligns European ecosystem with the most advanced AI regulatory environment

Timestamp: [1:18:51-1:19:23]

Balancing Personal Brand with Team Recognition

Uncertainty and Concern:

• Honest assessment: Admits he doesn't know the true importance of founder brand
• Team-first philosophy: Believes the people who build the company are what matter most
• Worry about attribution: Concerned that too much founder brand takes away from team recognition

Company Success Factors:

1. Research excellence: Incredible research capabilities
2. Engineering execution: Engineers grinding to create the best product experience
3. Go-to-market innovation: Team inventing new ways to combine self-serve and sales
4. Operational scaling: Growing from under 100 to 250 employees in 7 months while maintaining culture

Evolving Perspective:

• Initial skepticism: Previously worried founder brand detracted from team contributions
• Changing view: Mind is shifting toward seeing potential benefits
• Complementary approach: Considering how founder brand might elevate rather than overshadow team achievements
• Time constraints: Hyperrowth leaves less time to appreciate individual contributions

Timestamp: [1:19:28-1:20:47]

Peter Thiel's Risk Philosophy and Current Strategic Decisions

Guiding Philosophy:

• Core principle: "The biggest risk is not taking the risk" - Peter Thiel
• Decision paralysis danger: Staying in indecision or avoiding choices is the greatest risk
• Action orientation: Tries to take quick action when identifying risks he should have taken

Current Major Risk Consideration:

• Acquisition opportunity: Considering acquiring another company worth hundreds of millions of dollars
• Scale of risk: Would be a huge risk for bringing them into ElevenLabs
• Internal confidence: Believes they can do better internally than the acquisition target
• Decision inclination: Wants to take this significant risk despite the magnitude

Risk Management Approach:

• Rapid response: When identifying missed risks, tries to act quickly to address them
• Strategic boldness: Willing to consider massive financial commitments for strategic advantage
• Internal capability confidence: Believes in building rather than buying when possible

Timestamp: [1:20:53-1:21:45]

Essential Insights:

1. Strategic independence - ElevenLabs firmly rejects acquisition offers, preferring to build for massive long-term outcomes while providing employee liquidity through regular secondary sales
2. Contrarian European vision - Believes you can build global-scale companies from Europe and that voice will become the primary technology interface, despite widespread skepticism
3. Pragmatic product evolution - Shifted from requiring internal research before product development to leveraging external research for faster innovation

Actionable Insights:

• Employee retention strategy: Implement regular secondary sales and tender offers to provide liquidity while maintaining long-term focus
• Risk philosophy application: Apply Peter Thiel's principle that "the biggest risk is not taking the risk" to major strategic decisions
• Regulatory strategy: European AI companies should consider how regulatory alignment with successful markets could improve competitiveness

Leadership Philosophy:

• Team-first approach: Success comes from research excellence, engineering execution, go-to-market innovation, and operational scaling rather than founder brand
• Strategic boldness: Willing to consider hundreds of millions in acquisitions while maintaining confidence in internal capabilities
• Global ambition: Committed to proving European companies can achieve global scale in AI and technology

Timestamp: [1:12:07-1:21:56]

People Mentioned:

• Peter Thiel - Referenced for his philosophy that "the biggest risk is not taking the risk"
• Anton Osika - Mentioned as answering he'd buy Grok and sell OpenAI in a similar investment question

Companies & Products:

• OpenAI - Discussed as investment choice at $300B valuation, praised for ChatGPT consumer focus
• Anthropic - Mentioned at $170B valuation, noted for coding focus rather than consumer applications
• Grok - Referenced at $120B valuation in investment comparison
• Google - Praised for Gemini V3 models and catching up in AI race
• Cursor - Most popular coding tool used by ElevenLabs employees
• Eight Sleep - Mentioned as favorite consumer brand
• Google Maps - Cited as beloved consumer application

Technologies & Tools:

• ChatGPT - Used very frequently for consumer applications
• Claude - Used occasionally for testing and competitive analysis
• Gemini V3 - Google's AI models praised for incredible innovations

Concepts & Frameworks:

• Secondary sales strategy - Regular liquidity provision for employees through tender offers
• Voice interface prediction - Belief that voice will become primary technology interface
• Risk philosophy - Peter Thiel's principle about the biggest risk being not taking risks

Timestamp: [1:12:07-1:21:56]