ElevenLabs’ Vision for Voice Interfaces | CEO Mati Staniszewski

Origin Story and Initial Vision

The inspiration for ElevenLabs came from a uniquely frustrating experience in Poland and other post-communist countries. When watching foreign movies dubbed into Polish, all characters - whether male or female in the original - are narrated by a single voice actor. This creates a terrible viewing experience that still persists today.

The Founding Moment:

• Cultural Problem: Poland and four other post-communist countries use single-voice narration for all foreign films
• Technical Insight: Understanding that transformer models and diffusion technology could create emotional, high-quality synthetic speech
• Vision Expansion: Realizing this wouldn't just change dubbing, but transform all human-technology interaction

Founding Partnership:

• 15-Year Friendship: Co-founders Mati Staniszewski and his partner have been best friends since high school
• Shared Background: Both from Poland, took same classes, love mathematics
• Deep Collaboration: Worked together, traveled together, lived together throughout the years
• Perfect Timing: Started in 2022 when crypto and metaverse were declining, giving them space to work before the GPT viral moment

Early Strategic Advantages:

1. Pre-Competition Window: Started before AI became mainstream, avoiding competition with major players
2. True Missionaries: Early hires were genuinely passionate about voice technology's potential
3. Global Talent Access: Hired the best researchers worldwide through remote-first approach

Timestamp: [0:00-7:58]

Strategic Hiring Framework

ElevenLabs built their foundation with a carefully structured team combining three distinct types of talent, optimizing for research excellence while maintaining product development capabilities.

Team Composition Categories:

1. Researchers - Create net new work, publish papers at top conferences, drive true innovation
2. Research Engineers - Take existing papers and improve them, optimize inference for better model serving
3. Full Stack Engineers - Build the product infrastructure and user-facing applications

First 10 Employees Breakdown:

• 4 People: Research and research engineering (including both co-founders)
• 2 People: Traditional engineering roles
• 3 People: Go-to-market and growth functions
• 1 Person: Additional research engineer handling data and model improvements

Remote-First Strategy:

• Global Talent Access: Hired researchers from Korea, Europe, and worldwide
• Best-in-World Focus: Prioritized finding top talent regardless of location
• Research Optimization: Early team heavily weighted toward research capabilities

Co-founder Expertise:

• Technical Leadership: Co-founder described as "the smartest person I know"
• Research Background: Previously worked at Google and university research
• Dual Role: Served as both researcher and technical leader

Timestamp: [1:33-3:45]

Mission and Product Offerings

ElevenLabs combines cutting-edge research with practical product deployment, positioning themselves as "the voice of technology" through two comprehensive platform offerings.

Core Mission:

Voice of Technology - Transforming how humans interact with AI through advanced voice capabilities

Two Primary Platforms:

1. Agents Platform:

• Conversational Agents: Build AI assistants for customer support
• Personal AI: Create personalized voice interactions
• Media Entertainment: Make content interactive and engaging
• Omni-Channel Experience: Seamless voice integration across multiple touchpoints

2. Creative Platform:

• Narration Services: Professional voice-over creation
• Dubbing Solutions: Multi-language content adaptation
• Music Integration: Add soundtracks and audio elements
• Holistic Suite: All-in-one creative audio production tools

Advanced Capabilities:

• Omni-Modality: Integration across different types of media and interaction
• Voice Empowerment: Expanding use cases where voice technology adds value
• End-to-End Solutions: Complete workflow from voice creation to deployment

Technology Focus:

• AI Voice Research: Continuous innovation in synthetic speech
• Product Deployment: Practical applications for real-world use cases
• Emotional Voice Creation: Voices that "actually represent emotions"

Timestamp: [3:51-4:33]

Strategic Timing and Market Positioning

Starting in 2022 gave ElevenLabs unique advantages that established their strong market position before AI became mainstream.

Perfect Market Timing:

• Pre-GPT Era: Started before ChatGPT's viral moment created massive competition
• Crypto/Metaverse Decline: 2022 was the final downhill moment for previous tech trends
• AI Neglect: Nobody was seriously working on AI, giving them clear runway

Two Key Strategic Advantages:

1. Development Head Start:

• Uninterrupted Focus: Time to build before the GPT moment triggered industry-wide AI rush
• Technical Foundation: Established core technology before competition intensified
• Market Understanding: Deep insights into voice AI potential before it became obvious

2. Missionary Talent Acquisition:

• True Believers: Early hires were genuinely passionate about voice technology's future
• Mission-Driven Team: People joined because they believed in the vision, not just career opportunity
• Quality Over Quantity: Attracted researchers who saw voice as the future interface

Competitive Landscape Evolution:

• Then: No major players focused on AI voice technology
• Now: Willing to compete with tech giants like Meta
• Advantage: Strong position for researchers to do their best work in audio and beyond

Vision Validation:

• Early Insight: Understood voice would become the future interface when others didn't care
• Normalized Now: Voice as future interface became widely accepted
• First-Mover Benefit: Established expertise and market position before mainstream adoption

Timestamp: [5:04-6:18]

Future of Film Creation

AI won't replace the entire movie-making process but will revolutionize specific components, creating a collaborative human-AI workflow that produces indistinguishable results.

AI Integration in Film Production:

Creative Development Process:

1. Idea Generation: Start with initial concept
2. Script Drafting: AI helps create first set of scripts
3. Iterative Refinement: Human-AI collaboration through multiple revisions
4. Final Result: Combination of human creativity and AI assistance

Voice and Audio Production:

• Voice Selection: Multiple iterations to find perfect character voices
• Line Re-recording: Easy ability to redo dialogue and delivery
• Sound Effects: AI-generated audio elements
• Custom Voice Creation: Develop unique voices for specific characters
• Production Integration: Seamless workflow from voice creation to final output

Platform Requirements:

• Solid Infrastructure: Robust platform needed to handle complex production workflows
• All-in-One Solution: Combined tools for complete audio production process
• Professional Grade: Enterprise-level capabilities for film industry standards

Human-AI Collaboration Model:

• Partial Replacement: AI handles specific tasks, not entire process
• Iterative Workflow: Multiple rounds of human guidance and AI execution
• Indistinguishable Results: Final output quality matches traditional production
• Enhanced Creativity: AI enables new possibilities while preserving human artistic vision

Timestamp: [0:18-0:47]

Essential Insights:

1. Cultural Problem as Innovation Catalyst - ElevenLabs was inspired by Poland's terrible single-voice movie dubbing system, demonstrating how local frustrations can spark global solutions
2. Strategic Timing Advantage - Starting in 2022 during crypto/metaverse decline gave them clear runway before AI became mainstream and competitive
3. Research-First Foundation - Built with 40% research talent in first 10 employees, prioritizing innovation over immediate product development

Actionable Insights:

• Remote-First Hiring: Access global talent by hiring best researchers worldwide regardless of location
• Missionary vs. Mercenary: Early team members who genuinely believe in the mission create stronger foundations than those chasing trends
• Pre-Competition Windows: Starting before mainstream adoption provides development time and talent acquisition advantages

Timestamp: [0:00-7:58]

The Genesis of Human-Like Voice Technology

The Pre-AI Foundation:

1. Partnership Origins - Mati and his co-founder met while working at different companies (Google and Palantir), collaborating on weekend hackathon projects across various domains
2. Technology Exploration - They focused on exploring new technologies and implementing ideas around them, less commercially focused initially
3. Audio Discovery - One of their experimental projects was in audio, which gave them insight into how they could work together effectively

The 2022 Launch Strategy:

• Foundational Approach: Their ideas were based on foundational technology stacks that would later prove relevant
• Parallel Innovation: When GPT-3 and ChatGPT emerged (December 2022/January 2023), it validated their direction rather than forcing them to scrap everything
• Technology Convergence: The GPT moment created amazing overlap possibilities - combining ChatGPT with voice technology made content creation much easier

Key Technical Breakthroughs:

1. Emotional Context Understanding - Making text-to-speech understand context and emotions in text (happy, dialogue, etc.)
2. Human-Like Expression - Bringing emotions across like a voice actor or human would when reading
3. Voice Recreation - Effectively encoding voice characteristics (style, accent, dialect, narrative vs conversational tone)

Timestamp: [8:05-11:28]

The Challenge of Human-AI Distinction

The Uncanny Valley Concept:

• Definition: The point where machines become indistinguishable from humans in communication
• Current Text Limitations: Most people can identify LLM-generated text within 10 seconds based on writing style patterns
• Audio Parallel: Similar challenges exist when voice AI is generated from text-based models

Detection Patterns in AI Content:

1. Obvious Tells - Specific formatting patterns (M-dashes, bolding) and unnatural writing flow
2. Lazy Implementation - Direct "write this for me" requests to LLMs are immediately identifiable
3. Successful Hybrid Approach - When humans write initial thoughts, use LLM for refinement, then remove AI-like elements

The Philosophical Question:

• Cultural Authenticity: Should AI voices maintain regional dialects and accents?
• Machine Identity: Perhaps machines should have their own distinct "dialect" rather than mimicking humans
• Use Case Dependency: Different applications may require different levels of human-likeness

Timestamp: [11:34-13:26]

Context-Dependent Performance Analysis

Customer Support Excellence:

• Baseline Comparison: AI voice systems compete against IVR flows, decision trees, or undertrained human agents
• Consistent Quality: LLMs provide reliable support without variation in training or mood
• Knowledge Access: AI can access comprehensive information instantly without human limitations

Implementation Success Stories:

1. Sierra Partnership - Using 11Labs voice technology with Sierra's customer service platform
2. United Airlines Integration - Dramatic improvement in customer experience compared to traditional support
3. Pre vs Post Comparison - Night and day difference in service quality and user satisfaction

Performance Advantages:

• 24/7 Availability: No human scheduling or fatigue limitations
• Consistent Training: Every interaction benefits from the same high-level knowledge base
• Scalability: Can handle multiple conversations simultaneously without quality degradation
• Cost Efficiency: Significantly more economical than maintaining large human support teams

Quality Factors:

• Pre-prompting: Proper setup makes AI responses less detectable and more natural
• Context Awareness: Better understanding of customer needs and appropriate responses
• Response Speed: Immediate access to relevant information and solutions

Timestamp: [13:32-15:57]

Essential Insights:

1. Foundational Innovation - ElevenLabs was built on foundational technology that proved prescient when the GPT revolution validated their approach
2. Emotional Intelligence Breakthrough - The key innovation was making AI understand emotional context in text and express it naturally through voice
3. Strategic Timing - Starting in 2022 before the ChatGPT boom positioned them perfectly for the convergence of text and voice AI

Actionable Insights:

• Partnership Development: Weekend hackathons and experimental projects can reveal strong co-founder compatibility and shared vision
• Technology Validation: Sometimes parallel innovation paths converge - staying focused on foundational problems pays off
• Use Case Selection: AI voice technology excels in customer support scenarios where it outperforms traditional alternatives

Timestamp: [8:05-15:57]

Current State of AI Voice Technology

Where AI Voice Excels Today:

1. Customer Support Applications - Many people cannot distinguish between AI and human agents in text-based support
2. Audiobook Narration - AI-generated audiobooks achieve quality where listeners cannot tell they're not human-narrated
3. Call Center Operations - AI has successfully passed the uncanny valley test in structured support scenarios

Current Limitations:

• Conversational Settings: AI hasn't passed the uncanny valley test for all use cases
• Immersive Media & Gaming: Still developing capabilities for complex interactive scenarios
• Personal AI Interactions: Real-time conversation with interruptions, quick pauses, and combined reasoning remains challenging

The Realistic Timeline:

Mati believes AI will eventually pass the uncanny valley test across all applications, though he acknowledges being biased as ElevenLabs' CEO. The technology continues advancing but hasn't achieved universal human-like interaction yet.

Timestamp: [16:03-17:45]

The Transparency vs. Deception Debate

Core Philosophy on AI Interaction:

1. Transparency Over Deception - Users should know when they're interacting with AI rather than humans
2. Results-Focused Approach - Speed of resolution matters more than human-like interaction
3. Context-Dependent Value - Human-like qualities serve different purposes across use cases

Practical Examples:

• Customer Support: Users prefer knowing they're speaking with AI if it means faster problem resolution
• Gaming Experiences: Emotional authenticity enhances immersion (like Darth Vader in Fortnite)
• Healthcare Support: Different demographics require different interaction styles

Key Insight:

"I don't actually care if it's a human or an LLM on the other side. In fact, I would rather know. And what I care about is how quickly can you get to resolution."

The focus should be on solving customer problems effectively rather than creating perfect human mimicry.

Timestamp: [17:50-18:44]

Gaming Applications and Emotional Voice Design

The Fortnite Darth Vader Project:

ElevenLabs collaborated with Epic Games to bring an authentic Darth Vader experience into Fortnite, focusing on creating maximum emotional impact and realism.

Voice Characteristics Achieved:

• Raspy Tone - Capturing Darth Vader's distinctive vocal texture
• Serious Emotional Range - Conveying the character's intimidating presence
• Immersive Quality - Making players feel they're interacting with the actual character

Context-Specific Design Philosophy:

1. Gaming Environments - Require emotional authenticity and character immersion
2. Customer Support - Needs efficiency and clarity over emotional connection
3. Adaptive Approach - Voice characteristics should match the use case requirements

This project demonstrates how AI voice technology can enhance entertainment experiences by delivering character-specific emotional authenticity rather than generic human-like interaction.

Timestamp: [19:14-19:40]

Demographic-Specific Voice Customization

The Japanese Healthcare Case Study:

ElevenLabs worked with a healthcare customer support company in Japan that discovered dramatically different communication needs between age groups.

Age-Based Customization Strategy:

Younger Users Preferred:

• Quick Responses - Fast, efficient information delivery
• Short Style - Concise, straight-to-the-point communication
• Direct Information - Minimal conversational padding

Older Users Required:

• Calmer Approach - More patient, gentle interaction style
• Longer Responses - Detailed explanations with context
• Slower Delivery - Paced communication allowing processing time
• Less Emotional Intensity - Softer, more reassuring tone

Implementation Results:

The demographic-specific approach led to much different results between groups, with each age segment achieving better resolution rates when the AI adapted its communication style to their preferences.

Core Principle:

Just as human customer service representatives adapt their approach based on who they're speaking with, AI agents should be versatile enough to help across all different use cases by matching their delivery style to user needs.

Timestamp: [19:53-21:21]

Questioning Silicon Valley's Certainties

The Current Industry Assumption:

Most people in Silicon Valley, including Mati himself, assume that AI will definitely pass the uncanny valley test across all formats:

• Text Generation - Indistinguishable from human writing
• Audio Format - Voices that perfectly mimic human speech
• Video Content - Visual content that cannot be detected as AI-generated

Current Reality Check:

Despite rapid improvements, distinguishing AI-generated content remains possible:

• YouTube Shorts - Audio quality still detectable as AI-generated
• Text Content - LLM-created content often identifiable
• Overall Detection - Most AI-generated content can still be recognized

The Uncomfortable Question:

Mati acknowledges feeling "almost dumb asking the question" because questioning scaling laws seems "sacrilegious" in Silicon Valley, but he wonders: Is there a possible world where that's not true?

Balanced Perspective:

While models are "getting way better," the assumption that scaling laws will continue indefinitely and solve all problems may need examination. The focus should remain on solving actual customer problems rather than achieving perfect human mimicry.

Timestamp: [21:38-22:54]

Critiquing the Industry's Misplaced Focus

The Problem with AI Buzzwords:

Mati observes that many Silicon Valley companies have become obsessed with AI and agent buzzwords that have proliferated the mission of companies, losing sight of fundamental business principles.

Misguided Priorities:

1. Solving Without Purpose - Companies focus on passing the uncanny valley test without understanding what problem they're actually solving
2. Technology-First Thinking - Prioritizing AI capabilities over customer needs
3. Buzzword-Driven Missions - Letting AI terminology define company direction rather than customer value

The Real Success Metric:

"At the end of the day, it doesn't really matter across all of that. It really matters about are you solving the problem for the customer in a quick and good way."

Core Philosophy:

• Customer-Centric Approach - Focus on delivering value rather than showcasing AI sophistication
• Problem-Solution Fit - Understand the specific problem before building AI solutions
• Practical Implementation - Choose the right level of human-like interaction based on use case needs

This critique reflects a mature perspective on AI development that prioritizes practical customer outcomes over technological showmanship.

Timestamp: [23:21-23:59]

Essential Insights:

1. Uncanny Valley Progress - AI voice technology has passed the test in customer support and audiobook narration but still faces challenges in conversational and immersive settings
2. Transparency Over Deception - Users often prefer knowing they're interacting with AI, especially when it means faster problem resolution
3. Context-Driven Design - Voice characteristics should adapt to specific use cases rather than universally mimicking humans

Actionable Insights:

• Demographic Customization - Tailor AI voice interactions based on user age groups and preferences for optimal results
• Use Case Matching - Choose appropriate levels of human-like qualities based on whether the goal is efficiency (customer support) or immersion (gaming)
• Problem-First Approach - Focus on solving customer problems effectively rather than achieving perfect human mimicry

Timestamp: [16:03-23:59]

ElevenLabs CEO's Prediction on Voice AI Quality

Mati's Confident Assessment:

• 99.9% certainty that uncanny valley will be crossed in most cases
• The challenge isn't technical capability but societal preparation
• General population may be less attuned to AI voice detection than industry professionals

Critical Societal Implications:

1. Transparency Requirements - People need to know when they're interacting with AI agents
2. Content Labeling - Clear indication when content is AI-generated vs human-created
3. Default Assumption - Society may need to assume content is AI unless explicitly marked as human

The Preparation Challenge:

• Information Verification becomes crucial when voices are indistinguishable
• Whole world preparation needed for a reality where AI and human voices can't be differentiated
• Focus shifts from if it happens to how society adapts when it does

Timestamp: [24:05-24:48]

The Middle-to-Middle AI Integration Model

Mati's Vision for Film Industry:

• Not end-to-end replacement - AI won't create entire movies from scratch in the short term
• Middle-to-middle integration - AI assists specific parts of the production process
• Human-AI collaboration becomes the new standard workflow

The Iterative Production Process:

1. Script Development - AI helps draft initial scripts from human ideas
2. Voice Generation - AI vocalizes scripts and finds appropriate voices
3. Refinement Cycles - Human review and AI iteration create polished results
4. Hybrid Output - Final product combines human creativity with AI efficiency

Quality vs. Creativity Distinction:

• Technical Quality: AI can match human-level production values
• Creative Ingenuity: Original ideas and breakthrough creativity remain human domain
• Long-tail Content: AI enables more "okay" content that's still enjoyable
• Rare Creative Sparks: Completely new and different concepts still require human insight

The Attachment Challenge:

Even with perfect AI generation capability, human emotional connection to stories becomes more important:

• Real-life experiences and historical events
• Childhood memories and familiar universes
• Character relationships people know and love
• Shared cultural touchstones that create community

Timestamp: [25:24-29:11]

Approaching 50/50 B2C vs B2B Distribution

B2C Creative Applications:

• Content Creation: Narration and voiceovers for creators
• Music Production: Voice elements in musical compositions
• Creative Projects: Dubbing and audio content generation
• Individual Use Cases: Personal and artistic voice applications

B2B Enterprise Growth:

• Conversational AI Platform: Major driver of business revenue
• Voice Interactions: Primary focus with expanding capabilities
• Omni-channel Experiences: Voice and text integration for comprehensive customer engagement
• Enterprise Adoption: Rapid growth across business use cases

Market Balance Significance:

The approaching 50/50 split indicates:

• Successful diversification across market segments
• Strong product-market fit in both consumer and enterprise spaces
• Balanced growth strategy reducing dependency on single market
• Platform versatility serving different user needs effectively

Timestamp: [29:11-29:51]

From Cost Center to Customer Experience Enhancement

Traditional Support Evolution:

• Cost Center Optimization: LLMs excel at converting unstructured customer issues into structured, automatable formats
• Quick Adoption: Businesses rapidly implementing AI voice solutions for support workflows
• Efficiency Gains: Automated resolution of common customer problems

The Experience Shift:

From Reactive to Proactive Integration:

1. Traditional Model: Customers reach out via email only when problems occur
2. New Paradigm: AI agents integrated directly into website and product experiences
3. Continuous Engagement: Support becomes part of the entire user journey, not just problem resolution

Real-World Success Story - Italian Real Estate:

Challenge: Massive supply and demand mismatch

• Sellers unavailable to provide property information
• Buyers often unqualified or wrong fit for properties

AI Solution Implementation:

• AI Agent Integration: Buyers can call and get instant property information
• Information Aggregation: Data collected and provided to property sellers
• Qualified Lead Generation: Sellers can focus on genuinely interested, suitable buyers

Measurable Results:

• 20% to 60% improvement in qualification rates
• More people progressing to next steps in buying process
• Reduced friction in property matching

Industry-Wide Transformation:

AI voice agents are becoming integral parts of customer experience rather than just problem-solving tools, fundamentally changing how businesses interact with customers throughout their entire journey.

Timestamp: [29:51-31:54]

Essential Insights:

1. Uncanny Valley Certainty - ElevenLabs CEO predicts 99.9% likelihood that AI will cross the uncanny valley in voice technology, requiring societal preparation for indistinguishable AI voices
2. Movie Production Evolution - AI will transform filmmaking through middle-to-middle integration, assisting specific production processes while human creativity remains essential for original ideas
3. Business Model Balance - ElevenLabs approaches 50/50 revenue split between B2C creative applications and B2B conversational AI platforms

Actionable Insights:

• Transparency Implementation: Businesses should prepare systems to clearly identify AI-generated content and interactions
• Workflow Integration: Companies can leverage AI for specific production tasks while maintaining human oversight for creative direction
• Customer Experience Enhancement: AI voice agents can transform support from reactive problem-solving to proactive customer journey integration, with measurable improvements in qualification rates (20-60% in real estate example)

Timestamp: [24:05-31:54]

AI-Powered Customer Experience Transformation

ElevenLabs has revolutionized their customer support by implementing AI agents across their entire user journey:

Complete Agent Integration:

1. Website Experience - AI agents greet visitors the moment they open the ElevenLabs website
2. Product Navigation - Logged-in users get dedicated agents to help navigate features and functionality
3. Support System - Traditional support is now powered by AI agents for faster resolution
4. Sales Process - AI agents assist with sales inquiries and lead qualification

Behavioral Impact:

• Enhanced User Openness: Users become more expansive when sharing use cases because they feel unjudged
• Unlimited Time: Customers can take as long as needed to explain their requirements
• Better Experience Delivery: More detailed user information leads to improved personalized experiences

Sales Acceleration:

• AI BDR Integration: Users can accelerate their journey by providing detailed information to AI business development representatives
• Faster Routing: Enhanced information gathering allows quicker connection to the right human specialists
• High Adoption Rates: The AI-assisted sales process has seen significant user adoption

Timestamp: [32:00-33:47]

Global Team Structure and Growth

ElevenLabs has scaled to 330 employees worldwide with a strategic hybrid approach to operations:

Operational Model:

• Remote-First Culture: All employees can work remotely
• Strategic Hub Investment: Physical locations for in-person collaboration when desired

Major Global Hubs:

1. London - Primary European operations
2. New York - North American headquarters
3. San Francisco - West Coast tech hub
4. Warsaw - Eastern European base

Expanding Presence:

• Tokyo - New Asian Pacific operations
• Bengaluru - Indian market expansion
• São Paulo, Brazil - Latin American growth

This distributed yet connected approach allows ElevenLabs to tap into global talent while maintaining collaborative spaces for teams that prefer in-person work.

Timestamp: [33:47-34:20]

Strategic Long-Term Alignment Through Employee Liquidity

ElevenLabs raised $100 million in a secondary-only round with a clear strategic philosophy:

Core Reasoning:

• Long-Term Vision: Building something that will stand the test of time over the next 5-10 years
• Team Alignment: Ensuring employees are motivated for sustained long-term commitment
• Risk-Reward Balance: Allowing early liquidity while maintaining incentives for bigger outcomes

Employee Impact:

1. Early Liquidity: Team members can realize some value from their equity risk
2. Continued Motivation: Remaining equity stakes incentivize work toward even larger outcomes
3. Missionary Mindset: Passionate, mission-driven employees remain focused on long-term impact

Cultural Benefits:

• Enhanced Alignment: Employees become more focused on reaching ambitious goals like becoming a $121 billion company
• Sustained Passion: Capital changes don't affect the motivation of truly committed team members
• Performance Culture: Similar to high-performance athletes, top talent wants to be part of a winning team

Timestamp: [34:20-35:57]

Balancing Success and Sustained Performance

Managing team motivation post-liquidity requires careful cultural cultivation and realistic expectations:

Success Scenario Management:

• High-Performance Culture: Like elite athletes, top performers want to remain part of winning teams
• Continued Excellence: Money doesn't change the drive to perform at the highest level
• Cultural Foundation: Proper culture setting ensures motivation remains intact

Challenge Scenarios:

1. Short-Term Difficulties: Well-established culture can withstand temporary setbacks
2. Extended Struggles: Longer challenging periods become more difficult to navigate
3. Funding Reality: Poor performance typically means no up-rounds, maintaining natural incentives

Emotional Reality of Leadership:

• Persistent Anxiety: Even experienced leaders feel intense stress during difficult moments
• Decision Fatigue: Constant small decisions accumulate and drain mental energy reserves
• Caring Deeply: Emotional investment in outcomes remains high regardless of financial security

Transition Challenges:

• IC to Leadership: Individual contributors moving to management roles often struggle with the shift from meaningful work to constant decision-making
• Increased Demands: Higher positions bring exponentially more requests for decisions and input

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Essential Insights:

1. AI-First Customer Experience - ElevenLabs has transformed their entire customer journey with AI agents, from website visits to sales support, creating more open and detailed user interactions
2. Global Hybrid Growth - The company has scaled to 330 employees across major hubs in London, New York, San Francisco, Warsaw, and expanding to Tokyo, Bengaluru, and São Paulo
3. Strategic Secondary Funding - Their $100 million secondary-only round demonstrates commitment to long-term vision while providing employee liquidity and maintaining performance incentives

Actionable Insights:

• Implement comprehensive AI support across all customer touchpoints to improve user experience and gather better information
• Build remote-first culture with strategic hubs to access global talent while maintaining collaboration opportunities
• Use secondary funding strategically to align team motivation for long-term success while providing early liquidity rewards
• Prepare for decision fatigue in leadership roles and establish systems to manage the constant stream of choices required

Timestamp: [32:00-39:56]

Organizational Structure and Decision-Making Philosophy

ElevenLabs operates on a micro-teams model that emphasizes distributed decision-making and employee empowerment:

Team Structure:

• 330 total employees with product side organized into 20 teams of 5-10 people each
• Each team works on different segments of the product
• Teams operate with high autonomy and ownership

Decision-Making Framework:

1. Leadership Filtering System - Amazing leads handle micro-decisions and filter issues upward
2. 10% Rule - If decisions are within plus/minus 10% impact, individual teams should proceed without escalation
3. Ownership Culture - Teams are positioned to take full ownership and make decisions independently

Empowerment Philosophy:

• Trust-First Approach: Teams are fully trusted to make decisions
• No Punishment Policy: Employees won't be penalized for taking initiative and running ahead
• Quick Iteration: Teams encouraged to "run towards the fire" and fix problems without lengthy approval cycles
• Reduced Bureaucracy: Eliminates the need to constantly ask leads "is it right?" before proceeding

This structure only works when people take ownership and feel empowered to make decisions themselves, creating a culture where teams can flourish through rapid execution.

Timestamp: [40:02-41:39]

Fighting in the Eye of the Hurricane

ElevenLabs operates in a unique position where they compete directly with major AI companies while being in a market that matters deeply to both consumers and tech giants:

The Competitive Landscape:

• High-Stakes Market: Voice AI is a priority area for OpenAI, Anthropic, Google, and other major players
• Consumer-Driven Demand: The market matters significantly to end consumers, making it attractive to big tech
• Constant Pressure: Every major model release creates potential competitive threats

ElevenLabs' Dual Strategy:

1. Foundational Research Excellence

• Model Competition: Must create models better than OpenAI, Gemini, and other major players
• Proven Track Record: Successfully competed in text-to-speech, then speech-to-text, then music orchestration
• Continuous Innovation: Ongoing battle to maintain technological leadership

2. Application Platform Superiority

Beyond having the best voice models, ElevenLabs focuses on comprehensive platform functionality:

• Enterprise Integration: Seamless connection with CRM systems, Salesforce, ServiceNow, Google Drive
• Advanced Features: Function building, appointment scheduling, refund processing
• Production Tools: Deployment, testing, evaluation, version control capabilities
• Outcome Focus: Helping customers understand impact on CSAT scores and cost-benefit ratios

Competitive Advantage:

The combination of best-in-class foundational models plus superior application platform creates multiple defensive moats that pure research companies typically don't address.

Timestamp: [41:39-45:03]

End-to-End Creative Workflow Solution

ElevenLabs has built a sophisticated platform that handles the entire creative production process for voice content:

Creative Production Workflow:

1. Voice Selection Process - Multiple iterations to find the perfect voices
2. Line Recording and Editing - Redoing lines until they meet quality standards
3. Multi-Layer Integration - Combining voice, sound effects, and music in tracks
4. Asset Management - Accessing previously created content and custom voices
5. Collaborative Review - Team members can check and approve work
6. Production Integration - Bringing custom voices into the production flow

Platform Capabilities:

• Voice Creation Tools - Custom voice generation and management
• Audio Mixing - Professional-grade combination of multiple audio elements
• Asset Library - Storage and retrieval of past creative work
• Collaboration Features - Team-based review and approval processes
• Production Workflow - Streamlined process from concept to final output

Complexity Management:

The platform recognizes that creative production involves a complex flow requiring a solid platform to manage effectively. Rather than forcing creators to use multiple disconnected tools, ElevenLabs combines all functionality in one place.

This comprehensive approach differentiates them from competitors who may excel in individual components but lack the integrated workflow that creative professionals need.

Timestamp: [45:17-45:55]

Strategic Multi-Front Approach

ElevenLabs deliberately competes across multiple areas simultaneously, which creates both challenges and strategic advantages:

The Multi-Front Reality:

• Research Competition - Competing with major AI labs on foundational models
• Application Platform - Building comprehensive workflow solutions
• Creative Tools - Developing end-to-end production capabilities
• Enterprise Integration - Competing in B2B software markets

Strategic Benefits of Multiple Fronts:

Risk Distribution:

• Fewer Points of Failure - Success doesn't depend on winning just one battle
• Hedged Bets - If research isn't amazing, great product experience can compensate
• Flexible Positioning - Can leverage external research advances while maintaining product superiority

Competitive Moats:

• Integrated Advantage - Competitors typically don't address the full stack
• Compound Value - Success in multiple areas creates exponential rather than linear benefits

Generational Company Vision:

• Single Success = Amazing Company - Winning one front creates a strong business
• Multiple Wins = Generational Impact - Winning many fronts creates lasting significance
• Team Motivation - Teams understand that collective excellence creates something special

The Challenge Requirement:

"You cannot create something generational if you do one or two problem solves - you need to solve a double digit amount of different problems to really create something special."

This philosophy drives their willingness to take on the complexity and sleep-disrupting stress of fighting multiple competitive battles simultaneously.

Timestamp: [45:55-46:57]

Philosophy of Lasting Impact

Mati Staniszewski's motivation for building a generational company stems from a deep philosophy about creating lasting positive change:

Core Motivation - Extending Impact:

• Positive World Impact - The work ElevenLabs creates has genuine positive effects on society
• Maximum Reach - Desire to extend that positive impact as broadly as possible
• Legacy Thinking - Creations should outlive their creators and continue providing value

Generational as Shorthand:

"Generational [is] shorthand for that where all of us can create something that will go beyond what we can do ourselves in the future"

The Compound Effect Philosophy:

• Technological Advancement - Contributing to humanity's technological progress
• Universal Benefit - Creating value that extends to everyone, not just immediate users
• Beyond Individual Capability - Building something larger than what any individual could accomplish alone

Personal Fulfillment Through Impact:

• Exciting Proposition - The idea of leaving something meaningful for the world is inherently motivating
• Collective Achievement - Success comes from the entire team creating something special together
• Future-Oriented Thinking - Focus on what the creation will become rather than just current success

This perspective explains why ElevenLabs is willing to take on the complexity and challenges of competing on multiple fronts - it's not just about building a successful business, but about creating lasting technological advancement that benefits humanity broadly.

Timestamp: [47:03-47:50]

Essential Insights:

1. Micro-Teams Structure - ElevenLabs operates 330 employees in 20 teams of 5-10 people, emphasizing distributed decision-making and employee empowerment with a "10% rule" for autonomous decisions
2. Dual Competitive Strategy - The company fights tech giants like OpenAI and Google on two fronts: foundational research excellence and comprehensive application platforms that integrate with enterprise systems
3. Generational Vision - CEO Mati Staniszewski pursues building something generational not for ego, but to create lasting positive impact that extends technological advancement for humanity beyond individual capabilities

Actionable Insights:

• Organizational Design: Implement trust-first management with clear decision-making boundaries to enable rapid execution without bureaucratic delays
• Competitive Positioning: Build multiple defensive moats by combining best-in-class technology with superior application experiences rather than competing on single dimensions
• Long-term Thinking: Frame company building around lasting impact and technological contribution rather than just financial success to motivate teams toward exceptional collective achievement

Timestamp: [40:02-47:56]

AI Research Talent Competition

The Current Landscape:

1. High-Stakes Competition - Top AI researchers are being valued like elite athletes, commanding premium compensation packages
2. Critical Timeline - The next few years are viewed as the most crucial period for AI research and innovation adoption
3. Talent Retention Strategy - Beyond compensation, companies must create environments where researchers can see their work reach users quickly

ElevenLabs' Approach:

• Rapid Iteration Cycle: Research work gets to users almost immediately, creating a short feedback loop between real-world application and creation
• High-Density Teams: Very small teams where everyone is exceptional, maximizing collaboration and impact
• Immediate Impact: Researchers can see their innovations being used by real people quickly

The Long-Term Question:

Will models eventually replace researchers? Many current researchers believe AGI will arrive within years, potentially changing the entire compensation landscape. This creates interesting dynamics where some candidates negotiate based on the assumption that traditional work may become obsolete.

Timestamp: [48:02-50:07]

AGI Timeline Perspective

Mati's Position:

• Longer-term spectrum: Doesn't believe AGI will happen in the next 2-3 years
• 5-10 year timeframe: Expects dramatic shifts to occur over this extended period
• Foundational vs. Real-world: Distinguishes between technical breakthroughs and practical adoption

The Research Community Divide:

1. Researcher Consensus - Most AI researchers believe AGI will happen within years
2. CEO Skepticism - Mati takes a more conservative timeline despite working with these experts daily
3. Knowledge Gap - Acknowledges researchers likely know more than he does about technical possibilities

Implementation Challenges:

• Business Process Integration: Organizations will need significant time to adapt workflows
• Human Adaptation: People require time to learn how to work effectively with AGI systems
• Real-world Deployment: Technical capabilities don't immediately translate to widespread practical use

Impact on Work Categories:

Middle-ground prediction: Super smart and creative people will use AI tools as superpowers rather than being replaced entirely. The key requirement will be wanting to do the enhanced work.

Timestamp: [50:07-52:54]

Extreme Recruiting Scenarios

The AGI Negotiation Strategy:

The Ultimate Deadline Pressure: ElevenLabs encountered a candidate who used AGI predictions as a negotiation tactic, essentially saying:

• 3-Year Window: "I want to maximize what I can get in the next three years"
• World-Ending Logic: "Because in three years, the world will change completely due to AGI"
• Self-Defeating Prophecy: The candidate viewed their own work (along with everyone else's) as contributing to making traditional employment obsolete

Why This Strategy Failed:

1. Company Impact: Impossible to explain how this mindset affects other employees
2. Bad Precedent: Sets unrealistic expectations across the organization
3. Conviction Problem: Very difficult to convince someone AGI won't happen when they spend their entire professional life working toward it

The Philosophical Dilemma:

The Expertise Paradox: How do you argue against AGI timelines with the smartest people who are most deeply embedded in the technology? These researchers are the most knowledgeable about AI capabilities, yet their predictions create challenging business dynamics.

Timestamp: [53:01-54:39]

The Credibility Gap

The Central Question:

If the smartest, most knowledgeable people in AI believe AGI is imminent, why don't their CEOs? This creates a fascinating disconnect between technical experts and business leaders.

Mati's Honest Assessment:

• Partial Belief: Acknowledges there's "some percentage" and "good degree of percentage points" that researchers might be completely right
• Qualification Gap: Admits he's less knowledgeable than the experts he works with daily
• Uncertainty: Recognizes this as a "big question" that more knowledgeable people should address

The Practical Reality Check:

Current AI Limitations: While researchers discuss post-AGI compensation models, practical challenges remain:

1. LLM Quality Issues: Still struggling to make AI text not sound obviously AI-generated
2. Limited Enterprise Success: Only a few areas like coding and customer support have worked really well
3. Gap Between Promise and Reality: Significant distance between current capabilities and AGI predictions

The Disconnect:

• Researchers: Focused on technical possibilities and breakthrough potential
• Business Leaders: Dealing with current limitations and implementation challenges
• Timeline Tension: Technical optimism vs. practical deployment realities

Timestamp: [54:39-55:54]

Essential Insights:

1. AI Talent War Reality - Top researchers are being valued like elite athletes, with companies competing intensely for talent through both compensation and work environment
2. AGI Timeline Divide - A significant gap exists between researchers (predicting AGI in 2-3 years) and business leaders (thinking 5-10 years) creating unique recruiting and business challenges
3. Practical vs. Technical Gap - While technical breakthroughs may happen quickly, real-world adoption and business process integration will take much longer

Actionable Insights:

• Retention Strategy: Beyond high compensation, create environments where researchers see immediate real-world impact from their work
• Timeline Management: Prepare for both scenarios - rapid AGI development and longer implementation timelines
• Recruiting Reality: Expect AGI predictions to become part of candidate negotiation strategies, requiring clear company positioning on these timelines

Timestamp: [48:02-55:54]

AI Adoption Reality Check

Silicon Valley vs. Reality Perspective:

1. Personal Experience - AI provides a "sounding board" and increases productivity, but hasn't fundamentally changed major life considerations like compensation, real estate value, or expectations about solving cancer
2. Behavioral Lag - Even living in Silicon Valley at Kleiner Perkins, the transformative changes predicted haven't materialized in daily life decisions
3. Longer Timeline Belief - Believes widespread adoption will take much longer, even with access to "the smartest human in your pocket"

Company Building Reality:

• Hiring Paradox: Despite AI capabilities, companies like OpenAI continue aggressive hiring (50%+ of their software written by AI, yet still hiring tons of engineers)
• ElevenLabs Growth: Scaled from under 100 to 300+ people in 12 months, planning to double to 500+ in next year
• Deployment Complexity: Go-to-market requires significant human resources for international expansion and enterprise partnerships

Key Insights:

• Reality hits you across the face - gap between AI promises and practical implementation
• Product Layer Critical: Best foundational models still need solid product layers to derive value
• Geographic Variation: AI conversations more frequent in SF due to higher density of founders and investors

Timestamp: [56:00-58:09]

Global Perspective on AI Discourse

Current Living Situation:

• Dual Base: Living between London and New York
• Travel Pattern: Quarterly travel approach - one continent per quarter to maximize efficiency
• Strategic Positioning: Balances international presence with deep work focus

Geographic AI Conversation Differences:

1. San Francisco: Much higher frequency of AI discussions due to density of founders, companies, and investors
2. London & New York: AI conversations exist but are less frequent
3. Ecosystem Indicator: Uses conversation frequency as a litmus test for growing AI ecosystems

Transportation Innovation Examples:

• Waymo Expansion: Coming to New York as a test of AI adoption
• Wave in London: Self-driving software for cars that "works really well"
• Quality Benchmark: Waymo represents "about as good as it gets" for autonomous vehicles

International Business Impact:

• Global Client Base: Clients across US, South America, Asia, and Europe
• Scaling Challenges: International operations require significant human resources for complex enterprise deployments
• Market Complexity: Different regions have varying levels of AI adoption and conversation

Timestamp: [58:53-59:43]

Strategic Headcount Growth Planning

Headcount Philosophy:

• Output-Driven Approach: Headcount viewed as corollary to desired output, not a goal in itself
• Project-Based Scaling: Increase headcount based on specific projects and scaling needs
• 12-Month Target: Planning to double headcount to 500+ people in next 12 months

Growth Drivers:

1. International Expansion: Significant scaling needed across multiple continents
2. Enterprise Complexity: Products becoming more complex in enterprise settings
3. Multi-Modal Research: Increasing focus on combining voice models with other modalities

Key Hiring Areas:

Product Development:

• Frontier Technology: Building on cutting edge of agents and creative work
• Enterprise Deployment: Bringing technology to Fortune 500 companies
• Full Stack Engineers: Great deployment engineers for enterprise solutions

Go-to-Market:

• Technical Sales: Highly analytical people excited about technological shift
• Zero-to-One Mindset: People comfortable with early-stage market development
• Customer Partnership: Working deeply with customers on deployment strategies

Organizational Context:

• Rapid Growth: Scaled from under 100 to 300+ people in just 12 months
• Global Presence: Clients across US, South America, Asia, and Europe requiring local support

Timestamp: [59:48-1:03:51]

Personal Well-being During Hypergrowth

Sleep and Health Management:

• Generally Good Sleep: Reports sleeping well despite intense work demands
• Travel Challenges: Frequent travel and time zone changes make consistent sleep difficult
• Quarterly Travel Strategy: Alternates between travel-heavy quarters and deep work quarters

Emotional Perspective:

1. Genuine Enjoyment: Finds the work genuinely enjoyable rather than overwhelming
2. Once-in-Generation Opportunity: Views AI transformation as rare chance to be "the voice of technology"
3. Team Motivation: Entire team feels fortunate to work on this generational shift

Travel Management:

• Continental Focus: Tries to stay on one continent per quarter
• Maximized Efficiency: Concentrates travel in specific quarters, then focuses on deeper work
• Time Zone Strategy: Attempts to measure and maintain proper sleep cadence despite frequent zone changes

Philosophical Outlook:

• Historical Significance: Recognizes AI is changing work processes and making people "smarter, quicker" across lifetimes
• Rare Opportunity: Emphasizes the unique chance to be the voice of technological change
• Team Alignment: Shared sense of purpose across the entire organization

Work Satisfaction Indicators:

• Not Overwhelming: Work feels engaging rather than burdensome
• Purpose-Driven: Clear sense of mission and impact
• Sustainable Approach: Balances intense work with strategic rest periods

Timestamp: [1:01:02-1:02:08]

Personal Definition of Perseverance

Core Definition:

Persistence Through Adversity: "When the hard times are there, are you still fighting and going after it and trying to do the best you can to go through this?"

Key Components:

1. Continued Fighting: Maintaining effort when circumstances become difficult
2. Best Effort Commitment: Giving maximum effort regardless of challenges
3. Perseverance Mindset: Focus on pushing through rather than giving up

Practical Application:

• Simple Philosophy: Direct, straightforward approach to defining resilience
• Action-Oriented: Emphasis on "fighting" and "going after it" rather than passive endurance
• Quality Focus: Commitment to doing "the best you can" even in tough situations

Timestamp: [1:03:58-1:04:15]

Essential Insights:

1. AI Adoption Reality - Despite Silicon Valley hype, real behavioral change takes much longer than predicted, even with transformative technology
2. Scaling Paradox - Companies like OpenAI and ElevenLabs continue aggressive hiring despite AI capabilities, highlighting the gap between automation promises and practical needs
3. Geographic AI Discourse - AI conversations vary significantly by location, with San Francisco leading due to higher density of tech ecosystem participants

Actionable Insights:

• Headcount Planning: View hiring as output-driven rather than goal-oriented, scaling based on specific projects and market needs
• International Strategy: Global expansion requires significant human resources for complex enterprise deployments across different markets
• Work-Life Balance: Manage hypergrowth demands through strategic travel planning and maintaining perspective on once-in-generation opportunities

Timestamp: [56:00-1:04:26]