Riding Unicorns: Venture Capital | Entrepreneurship | Technology

Shah Ramezani, founder and CEO of Noah, builds stablecoin-powered infrastructure for faster global payments. He shares Noah’s journey from blockchain experiments to serving markets worldwide, why exceptional hiring is his top responsibility, and how his “anti-sell” approach finds resilient talent. The conversation explores calculated risk, building ahead of market shifts, AI agents, proprietary data, and fintech’s next transformation.

Yaron Valler, engineer, entrepreneur, investor and founder of Target Global, has backed 15+ unicorns including Delivery Hero and DocPlanner. He discusses the people skills behind venture success, why outsiders and immigrants often build boldly across borders, and how Europe can improve integration. The conversation turns to AI, frontier technologies and his book, The New Europeans.

Anatolii Papulov, co-founder and CEO of UK SpaceTech company NewOrbit, is developing satellites for Very Low Earth Orbit. He explores the promise of operating just 200km above Earth, the brutal challenges of atmospheric drag, and why space hardware demands years of testing. The conversation also covers elite engineering talent, raising $18.5 million, launch-capacity bottlenecks, SpaceX, and the future of space-based infrastructure.

James Pringle and Hector Mason look back on six years of conversations, career growth, and network-building. They compare the AI boom with the 2021 venture bubble, explore the hurdles to real-world adoption, and debate opportunities in marketplaces, robotics, hardware, SpaceTech, and Europe. They also revisit standout investments, valuation lessons, future unicorn predictions, and their ultimate dinner-party lineups.

AI is creating extraordinary new demand for electricity. But what happens when the grid can't keep up?In this episode of Riding Unicorns, James and Hector sit down with Karl Bach, Co-Founder & CEO of Axle Energy, the energy technology company building a virtual power plant from hundreds of thousands of electric vehicles, batteries and heat pumps.Rather than relying solely on large power stations to balance electricity supply and demand, Axle connects distributed devices across homes and businesses and intelligently shifts when they consume energy. Today, the company manages more than two gigawatts of capacity, roughly equivalent to the capacity of a nuclear reactor.The timing is becoming increasingly important. Renewable energy creates more variability in electricity supply, while AI and the rapid expansion of data centres are creating new sources of demand. In the UK, Karl says a new data centre can face a grid connection wait of around 10 years. Axle's technology can help provide flexibility to the electricity system in weeks rather than waiting years for new physical infrastructure to be built.Karl also shares the realities of building in a heavily regulated industry. Axle spent years helping push through regulatory changes required for distributed devices to participate properly in energy markets, while gradually building from its first EV charger partnerships into a platform managing hundreds of thousands of devices.Topics Covered• How virtual power plants work• Why EVs, batteries and heat pumps can help balance the electricity grid• Building more than two gigawatts of capacity from distributed devices• Why renewable energy increases the need for grid flexibility• The impact of AI and data centres on electricity demand• Why connecting a new UK data centre to the grid can take around a decade• Building a startup inside a heavily regulated industry• How Axle won its first customers before it had a product• Creating recurring revenue for EV charger manufacturers• Karl's experience leading growth at Bulb• What Bulb's collapse taught him about building in the physical world• Balancing startup speed with the consequences of getting things wrong• Hiring exceptional people inside a highly technical industry• Why Karl doesn't believe energy will ever become free• Building a company around a problem you genuinely care aboutKarl also discusses his experience at Bulb, once one of Europe's fastest-growing companies, and how its eventual collapse following the energy crisis shaped his understanding of the risks that come with building businesses exposed to the physical world.This is a conversation about one of the fundamental constraints on the next wave of technology: energy. How we produce it, how we move it, and how software can make the infrastructure we already have significantly more efficient.

What happens when companies deploy AI agents before fixing the data those agents rely on?In this episode of Riding Unicorns, James and Hector sit down with Toby Mather, Co-Founder of Rig, the AI-native data infrastructure company building the data layer that allows humans and AI agents to work from the same trusted context.Rig started as a "data brain" sitting on top of platforms like Snowflake and BigQuery, making sense of fragmented company data so teams could use tools like Claude and Cursor to build applications, automations and analytics. It has since expanded into an end-to-end data stack covering ingestion, warehousing and context.Toby explains why LLMs alone aren't the answer to enterprise data. While language models are excellent at understanding context, businesses still need deterministic systems like SQL and structured tables when they want accurate answers from their data. Rig's thesis is that AI should make sense of the complexity, but the underlying calculations still need to be grounded in reliable data.The conversation also explores the rush towards autonomous AI agents. Toby argues that many companies are trying to run before they can walk: connecting agents to dozens of fragmented systems without first creating the clean, consistent data infrastructure they need. Get the context right, and the journey from human-in-the-loop to autonomous agents becomes much easier.Topics Covered• Why AI agents need better data infrastructure• Building a "data brain" for humans and AI agents• Why LLMs aren't always good at data reasoning• Why 55-year-old SQL and tables still matter in the AI era• Moving from human-in-the-loop to autonomous agents• How Rig uses Rig to run its own business• Building agentic workflows across CRM and customer success• Toby's lessons from building and selling Lingumi• Why founders should choose enormous markets• Landing major enterprise customers as an early-stage startup• The PULL framework for B2B sales• How to sell enterprise software without becoming a services business• Competing with incumbents like Snowflake and Databricks• Balancing customer feedback with founder-led product intuition• Why agentic customer success could be a major opportunityToby also shares some of the more surprising ways customers are already using Rig, from identifying overpayments hidden across thousands of invoices to building AI-powered customer success systems that identify problems and proactively contact customers.This is a conversation about the infrastructure beneath the AI agent revolution: getting company data into the right shape, giving AI the context it needs and building the foundations required for truly autonomous work.

What happens when AI moves beyond the digital world and starts transforming the companies that manufacture the things around us?In this episode of Riding Unicorns, James and Hector sit down with Robin Van Aeken, Co-Founder & CEO of Magentic, the AI company building digital workers for some of the world's largest manufacturers.Magentic's AI agents, or "mages", work alongside procurement and supply chain teams at companies including Siemens and Coca-Cola Europacific Partners. They connect with existing systems, analyse huge volumes of procurement data and identify opportunities to make supply chains cheaper, faster and more resilient.The economics can be significant. A manufacturer spending $10 billion a year on procurement might save $50 million by making its team 50% more efficient. But improving procurement costs by just 1-2% could generate $100-200 million in savings. Robin explains why this makes procurement one of the most compelling applications for enterprise AI.The conversation also explores how Magentic deploys AI agents inside complex enterprises. Rather than expecting autonomy from day one, its digital workers effectively begin as "interns", learning the organisation, its systems and its data before gradually earning the trust to take actions and eventually run significant parts of procurement and supply chain workflows.Topics Covered• Why the next major AI opportunity could be in the physical economy• Building AI digital workers for procurement and supply chains• Why AI can create more value by reducing procurement spend than headcount• How human-in-the-loop feedback helps agents develop domain expertise• Using AI across complex ERP systems like SAP and Oracle• Building trust with some of the world's largest manufacturers• Data privacy, sovereignty and deploying AI inside sensitive organisations• How Magentic's "mages" progress from interns to autonomous digital workers• Why AI agents increasingly interact with software like humans• Robin's journey from McKinsey to founding Magentic• What his co-founder's experience at OpenAI taught them about the physical economy• Building an enterprise AI company backed by Sequoia Capital• How to construct the right investor syndicate beyond simply raising capital• Why faster, cheaper supply chains could ultimately reduce the cost of physical goodsRobin also explains why the explosion in AI infrastructure is creating a huge new challenge for the physical economy. Data centres, chips, power infrastructure, cooling and other components all depend on manufacturing and supply chains that weren't designed for today's speed of demand.This is a conversation about bringing AI into the physical economy: deploying agents inside complex enterprises, making global supply chains more efficient and using software to unlock potentially hundreds of millions of dollars in savings.

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 knowledgeMirko 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 infrastructureKrish 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 companyGyve 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.