Riding Unicorns: Venture Capital | Entrepreneurship | Technology

What happens to software engineering when almost every line of code is written by AI?
In this episode of Riding Unicorns, James sits down with Mirko Novakovic, Founder & CEO of Dash0, the AI-native observability company helping engineering teams understand, monitor and fix problems in increasingly complex software systems.
Dash0 has grown at remarkable speed, 4x-ing ARR in six months and recently raising a $110m Series B. For Mirko, now on his third company, that growth hasn't come from a single breakthrough. His philosophy is much simpler: build the fundamentals brick by brick, continuously improve them, and let the effects compound. 
The conversation explores how AI is fundamentally changing software development. At Dash0, almost no code is now written manually, with engineers increasingly using natural language to create software and focusing their time on architecture, review and quality. Mirko believes the next step is automating the entire journey from prompt to production. 
We also discuss what it takes to scale a venture-backed company aggressively. Mirko explains why founders need to recognise when they have momentum and "step on the gas", why revenue growth is the metric that matters most in the early stages, and why sometimes you need to shorten your runway to capture the opportunity in front of you. 
Topics Covered
• Why AI-generated code creates a bigger need for observability
 • Growing Dash0 from launch to unicorn at exceptional speed
 • Why there is no "secret sauce" to building a great software company
 • How small improvements compound into significant growth
 • Knowing when to step on the gas and scale aggressively
 • Why revenue growth matters more than efficiency at the early stage
 • Saying no to $10m+ customer opportunities when you're not ready
 • Why the first engineers you hire must be exceptional
 • How AI is changing the role of software engineers
 • Why almost every line of code at Dash0 is now AI-generated
 • Building an AI-first organisation without restrictive procurement processes
 • Why Dash0 insists presentations are generated entirely by AI
 • Developers spending up to $20,000 per month on AI tools
 • Turning internal AI tools into new products
 • Why AI-native companies are documenting and systemising more of their knowledge
Mirko also shares his approach to hiring, why great people attract other great people, and how his experience building previous companies has shaped the speed and ambition behind Dash0.
This is a conversation about building at machine speed: using AI across engineering and operations, knowing when to take risk, and creating the organisational momentum required to build a category-defining software company.

How do you build a global AI company without burning hundreds of millions of dollars?
In this episode of Riding Unicorns, James sits down with Krish Ramineni, Co-Founder & CEO of Fireflies.ai, the AI meeting assistant used by millions of people around the world.
Fireflies started building with AI long before the current boom. After struggling to raise capital, Krish and his co-founder became obsessed with reaching "ramen profitability", keeping costs low and controlling their own destiny. They eventually raised venture funding, but haven't touched the capital from their Series A, reaching profitability in 2023 while continuing to grow rapidly.
The conversation explores how Fireflies created the AI notetaker category and engineered distribution directly into the product. Rather than relying on traditional enterprise sales, Fireflies focused on self-service adoption, word of mouth and a viral loop where every meeting introduced the product to new users. This helped Fireflies spread to more than 100 countries within its first year.
Krish also explains where Fireflies goes next: beyond meeting notes towards AI that understands conversations across meetings, email and Slack, identifies what needs to happen and eventually completes work while conversations are still taking place.
Topics Covered
• Building an AI company before the AI boom
 • Why Fireflies prioritised profitability from the beginning
 • Creating the AI notetaker category
 • Engineering virality and word of mouth into the product
 • Spending almost nothing on marketing until reaching tens of millions in revenue
 • Going horizontal when VCs advised Fireflies to focus on enterprise sales
 • Scaling a self-service product across 100+ countries
 • Building infrastructure for millions of users
 • Privacy, security and customer data in AI
 • Expanding from meeting notes into email, Slack and AI agents
 • Building a profitable company while maintaining venture-style growth
 • Krish's first-principles approach to leadership
 • Why iteration speed compounds into competitive advantage
 • The opportunity in open-source AI and inference infrastructure
Krish also shares why he runs Fireflies with an unusually flat organisation and more than 30 direct reports, and why he believes startups win by iterating faster than their competitors.
This is a conversation about building an AI company differently: staying lean, engineering distribution into the product, obsessing over economics and surviving long enough for the market to catch up with your vision.

How do you build a consumer product that people genuinely love, while still making the economics work?
In this episode of Riding Unicorns, James sits down with Gyve Safavi, Co-Founder & CEO of SURI, the award-winning electric toothbrush company combining design, performance and sustainability.
Before founding SURI, Gyve spent eight years at Procter & Gamble, ultimately leading brand management for the £500m Gillette Female Shave Care business across EMEA. He later worked at AKQA and WPP, including alongside Sir Martin Sorrell, before moving into startups.
The conversation explores how those experiences translated into building SURI and what it really takes to create a modern consumer hardware brand.
Gyve explains how SURI was built around three principles: exceptional design, high-performance cleaning and sustainability. He breaks down how the team used consumer research to determine pricing before launch, why gross margin needs to be engineered into the business early, and how relentless negotiation with suppliers has improved unit economics as the company has scaled.
We also explore customer acquisition, manufacturing, co-founder dynamics and how SURI is using AI internally to build a more efficient organisation.
Topics Covered
• Gyve’s journey from P&G, AKQA and WPP into entrepreneurship
• Building SURI around design, performance and sustainability
• How to price a consumer product before launch
• The four questions SURI used to identify its optimal price point
• Why gross margin can make or break a consumer startup
• Negotiating with manufacturers as volumes grow
• Reducing customer acquisition costs through better execution
• Why relationships can create an unfair advantage for startups
• Winning a month of free advertising at London’s Outernet simply by asking
• Co-founder dynamics and SURI’s early “bitching sessions”
• Why founders should listen to customers more than investors
• Learning to handle setbacks and celebrate wins
• How SURI is using AI to operate more efficiently
• Why AI could make this one of the best periods to build a consumer company
Gyve also shares his future unicorn predictions and explains why his ideal dinner would include Jony Ive, Jeff Bezos and Mick Jagger.
This is a practical conversation about building a modern consumer brand: creating a product people want, getting the economics right and building an organisation capable of scaling with it.

What happens when you hear your own hit song playing in public, but never receive the royalty?
In this episode of Riding Unicorns, James sits down with Ryan Edwards, Founder & CEO of Audoo, the music technology company rebuilding how public performance royalties are measured and distributed.
The idea for Audoo came from Ryan’s own experience as a musician. After hearing one of his songs playing in a London department store and discovering there was effectively no reliable way to track the royalty, he started investigating how the system actually worked.
What he found was an industry still relying heavily on proxy data, manual surveys and people physically visiting venues with clipboards.
Audoo’s answer is the Audio Meter, a small connected device that identifies music being played in public venues without recording or storing conversations. The resulting data allows collecting societies to understand what was actually played and distribute royalties more accurately to artists and rights holders.
Today, Audoo operates across 16 countries, with its technology deployed in environments ranging from cafés and retailers to gyms and leisure centres.
Ryan shares how he built the first prototype on a Raspberry Pi at his kitchen table, validated the idea with the former Chairman of PRS for Music, and launched the first real-world deployment on the other side of the world in Australia during COVID.
We also discuss the unusual group of investors Audoo has attracted along the way, including Björn Ulvaeus of ABBA, Paul McCartney, Elton John, Adam Clayton of U2 and Adele, and how those relationships have helped build credibility and open doors across the music industry.
Topics Covered
• Why the public performance royalty system needed rebuilding
• The personal experience that led Ryan to start Audoo
• Building the first Audio Meter prototype on a Raspberry Pi
• Why privacy had to be designed into the technology from day one
• The challenges of combining software, data and physical hardware
• Landing Audoo’s first major deployment in Australia during COVID
• Why venues are willing to install Audio Meters
• How better music recognition data changes royalty distributions
• Raising capital from some of the biggest names in music
• Expanding Audoo across 16 countries and into the Middle East
• What Ryan has learned from eight years as a founder
• Why founders need to celebrate wins and know when something can wait until Monday
This is a conversation about spotting an antiquated system, using technology to rebuild it from first principles, and creating the data infrastructure that could finally ensure artists are paid accurately when their music is played.

What does it take to build a frontier AI model lab in a world dominated by OpenAI, Anthropic and Google?
In this episode of Riding Unicorns, James and Hector sit down with Yang Li, Co-Founder & CEO of Cosine, one of the UK's leading frontier AI companies building sovereign AI models for enterprise and government.
Cosine began by fine-tuning foundation models for software engineering before evolving into a full frontier AI lab, training its own models from scratch. Today, the company works with organisations across defence, financial services and critical national infrastructure, deploying AI securely inside highly regulated environments.
The conversation explores why sovereign AI has become a strategic priority, how smaller model labs can compete with the biggest players, and what the future of enterprise AI will look like.
Yang also shares how AI is changing software engineering, why synthetic data has become a competitive advantage, and the lessons he's learned building one of Europe's most ambitious AI companies.
Topics Covered
• How Cosine evolved from AI coding tools into a frontier AI model lab
 • Why sovereign AI is becoming critical for governments and enterprises
 • Competing with OpenAI and Anthropic without billions in funding
 • Building AI models using synthetic data and post-training techniques
 • Why defence, banking and healthcare are driving enterprise AI adoption
 • The future of software engineering in the age of AI coding agents
 • Air-gapped AI, on-premise deployment and enterprise security
 • The geopolitical race for AI infrastructure and compute sovereignty
 • Hiring world-class AI talent and building high-performance teams
 • Founder lessons on conviction, resilience and staying alive long enough to win
This is a conversation about the future of frontier AI, the growing importance of sovereign technology, and what it takes to build an AI company that competes on the global stage.