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Published in a week when Lovable's valuation doubled to $13.3 billion, up from $6.6 billion in December, this episode presents Ryan Meadows story of joining Lovable as a customer before becoming its Chief Revenue Officer, and how that experience shapes everything about how he thinks about AI, growth and the future of software. Hg's Tim Harrison sits down with Ryan to explore how they reached $400 million ARR in just 15 months, what it means to build an organisation entirely without legacy tooling, and why Ryan thinks most enterprises are still barely scratching the surface of what AI can do for their people. Ryan walks through their three-stage model of AI transformation, from getting everyone to prototype, through hardening load-bearing applications, to ultimately rebuilding workflows from first principles. He shares how Lovable disrupted its own seat-based pricing model to move to consumption in the enterprise, and why that shift unlocked a customer who went from 200 product managers to 8,500 active builders in two months. He also reveals how they use their own platform to run go-to-market, from an AI command centre that tells reps exactly what to do, to a CPQ and e-signature flow built entirely in-house. As Ryan puts it, the company's tagline is "we want to be your co-founder," and he believes that applies just as much inside an enterprise as it does for a first-time founder raising funding.

Every December, Rob Toews publishes ten AI predictions in Forbes, then comes back a year later and grades himself in public. That habit of putting a stake in the ground, and owning the misses, makes him one of the more interesting voices in the space. In this conversation with Jon Wulkan, he explains why the frontier has narrowed to a handful of Western labs, why even well-resourced challengers have struggled to keep pace, and why the much-feared "SaaS apocalypse" is probably overdone. They also dig into the economics underneath the hype: why model prices are likely to rise once the big IPOs land, why one of the three remaining frontier players has a structural funding edge the others can't match, and why an obscure accounting question about chip depreciation could reprice the entire AI infrastructure trade. Along the way, Toews reaches for a comparison that sticks: we're building data centers that draw twice the power of San Francisco, while the human brain runs on 20 watts. His advice for vertical software CEOs? Hire for slope, not y-intercept..

In this episode, John Cranmer is joined by Gil Elbaz, Co-Founder and Chief AI Officer at Onyx Security, the company building the secure AI control plane for the agentic era. Gil, formerly in the CTO office at Nvidia working on multi-agent infrastructure, argues that the AI attack surface has fundamentally changed. The old boundary between what is yours and what is not has collapsed, and every agent, every MCP, every tool connection is now part of the perimeter. The conversation moves from the rise of shadow AI inside enterprises, where employees reach for any tool that makes work faster, to the harder problems underneath: rogue agents taking destructive actions they were never asked to take, multi-agent communication happening invisibly through Slack and email rather than direct A2A channels, and offensive AI systems like Mythos finding more zero-days in weeks than internal red teams find in years. Gil shares his playbook for getting your arms around it: visibility first, then policy, then runtime protection. As he puts it, the right seatbelt is what lets you drive faster.

The inventor of DevOps explains why AI needs engineering rigour and human collaboration. The engineer who coined "DevOps" in 2009 thinks he is watching the same pattern play out again, only faster. In this episode of Orbit, Patrick Debois joins Nathaniel Barnes, Hg's portfolio CTO, at our annual Digital Summit in Paris, to map the parallels between the DevOps movement and what is happening right now with AI agents in software teams. The framing question: what if context is the new code? Patrick unpacks the four phases of his context development lifecycle, generate, evaluate, distribute, observe, and explains why the maturity of a company's CI/CD pipeline is the single best predictor of how well it will absorb AI. He and Nathaniel dig into context drift, customer-facing "vibe coding" as a discovery engine, the silos that agents are quietly breaking down, and why the companies winning right now are the ones that skipped the 27-step plan and just started. As Patrick puts it: "We are at the age where it doesn't need to be perfect. We just need to be there." Essential listening for any technology leader navigating this moment.

When Jonathan Sanders pitched Light to investors, the reaction was unanimous: rebuilding ERP would take a decade and a hundred million dollars. He did it in two years, with AI at the core. Today Light powers AI unicorns including Lovable and Legora, and Jonathan joins Hg director Soren Holt to explain how. The conversation goes well beyond automation. It's a builder's account of a first-principles redesign. Jonathan and Soren cover the move from template-based OCR to context-aware agents, why eighty percent of AI's value comes from doing things that were previously impossible rather than making old tasks faster, and how Light's customers now sweep entire transaction populations during audits rather than sampling. Jonathan walks through Light's hackathon programme with CFOs, the operating model behind a remote engineering organisation, and the shift from finance as operator to finance as orchestrator. The line that lands hardest: "Fear is not a strategy." An essential listen for any finance leader thinking about what 2030 actually looks like.

Evan Goldberg co-founded NetSuite in 1998 with a five-minute phone call to Larry Ellison when his "graphics stuff" wasn't going great. He pioneered cloud computing before SaaS existed, sold to Oracle for $9 billion, and stayed at the helm for a decade more. Now at 40,000 organisations and $4 billion ARR, Evan has presided over two tectonic platform shifts and says AI dwarfs both. Recorded at Hg's 'Office of the CFO' dinner in New York back in February, Goldberg explains why autonomous close agents that find exceptions while you sleep are "never going back" territory, his perfect metaphor for why AI still needs integrated data ("cell phones don't help you speak French"), and why customers "have no interest in becoming ERP hobbyists." He reveals he quit Oracle for MIT's AI lab 30 years too early, why mixing deterministic systems with probabilistic AI is the answer, and his tactical advice for founders: step back from strong growth numbers to spot destructive trends, then make painful changes even if they crater quarterly results. From a coffee-stained computer to global success, this is founder wisdom about building companies that last through multiple technology revolutions.

Marjorie Janiewicz has sold enterprise software through every platform shift for three decades: Oracle, MySQL, SAP, MongoDB, HackerOne. Now as Chief Revenue Officer at Mistral AI, she's taking a French startup from zero to $400 million in 18 months, closing deals with ASML and HSBC in under a year - timelines that used to take half a decade. Recorded in New York as Mistral announced its Finance offering, this conversation addresses what's working versus what's theatre in enterprise AI. Marjorie supports the MIT hypothesis: 95% of AI projects never reach production. Chatbots drive adoption but don't change businesses. The 5% that work? They start with one high-impact use case, they customise models with proprietary data, and they deploy on-prem where regulated data lives. She explains why the adoption curve flipped, why SaaS companies sitting on data can win if they treat AI as transformation not automation, and why Mistral bet on 400 forward deployment engineers instead of just shipping models. From prototypes done in 48 hours to why "sovereignty is just marketing, independence is what matters," this is pattern recognition from someone who's been in the room when the shift happens repeatedly. Whether you're a SaaS company worried about agents or trying to sell AI to enterprises struggling with ROI, Marjorie's earned her perspective.

Most B2B software founders don't spend four years working on presidential campaigns. Varun Anand did - on Hillary Clinton's 2016 run - and he argues that startups and political campaigns share the same DNA: both rely on finding "alpha," that elusive edge that competitors haven't copied yet. After the election loss forced a pivot into tech, Varun became the only attendee at a Clay webinar with no customers or revenue. That moment led to co-founding what's now used by Salesforce, OpenAI, and Nvidia, inventing an entirely new profession in the process. Varun reveals why go-to-market AI is fundamentally different from support or coding AI and how Clay's "un-opinionated primitives" approach lets teams build unique competitive advantages. He shares examples ranging from Waste Management analyzing trash can colours via Google Street View to Clay's own social listening engine that automatically routes sentiment to CSMs: all happening without human intervention. The conversation explores the Go-to-Market Engineer role, why curiosity-driven teams win, and Varun's prediction that the next 18-24 months will be about autonomous agents working accounts while humans focus on high-value interventions. Whether you're building GTM systems or rethinking sales ops, this episode challenges every assumption about how modern revenue teams should operate.

In this rare internal conversation, Matthew Brockman, Hg's Chief Investment Officer, offers an insider's view on what's actually working versus what's still hype. Far beyond speculation, this is private equity deploying real capital into real companies with real customers, watching what happens when agentic software meets regulatory compliance and established workflows. Brockman reveals why vibe coding is being used in sales processes but not production, why venture money is subsidising inference costs that must eventually turn into labour market economics, and his "virtual Matthew" Turing test for when we've truly reached AGI. From Hg's Catalyst program - parachuting developers into portfolio companies at Hg's expense to accelerate AI product builds - to standing up in Silicon Valley urging CEOs to invest and move quickly, this conversation cuts through the noise with data, deployment examples, and the hard economics of building enterprise AI that actually ships. Essential listening for anyone trying to separate signal from hype in February 2026

Franz Faerber, co-founder and CEO of Everest Systems and former architect of SAP HANA, challenges Sam Altman's vision of throwaway software, arguing humans crave stability, while making the case for why Germany - not Silicon Valley - is the right place to build next-generation enterprise software in the AI age. Despite being a German-US company, Everest invests 80% in Germany, leveraging equal talent quality at a fraction of US costs. This episode explores the rare dual perspective of someone who led innovation inside SAP and now challenges the narrative that all meaningful software innovation must come from the Valley. Faerber discusses Everest's breakthrough "live sandboxing" technology that eliminates complex multi-system landscapes,, why deep domain knowledge is the new differentiator in an AI age, and shares his counterintuitive leadership philosophy: "I'm a big believer in a certain level of chaos is healthy." From building Germany's first AI-generated warehouse module to his advice for SaaS leaders ("assume AI costs zero—what fundamentally changes?"), this conversation offers a masterclass in conviction, timing, and reimagining enterprise software. Whether you're building against incumbents or navigating AI transformation, Faerber's insights on bureaucracy, talent strategy, and the courage to "do it earlier" provide essential guidance for software builders.

Daniël Rood, director of AI go-to-market at Google Cloud, offers a masterclass in navigating transformation from inside a tech giant. Building Google's first European AI team when gen AI was still "a PhD science project," Rood reveals the hidden resistance mechanisms that plague even innovative organizations. His concept of the "corporate immune system"—the culture that protects success for all the right reasons but resists dramatic shifts—explains why customer success stories, not internal advocacy, are what actually move leadership. His hiring philosophy centered on "intellectual humility" and "teams of translators" who bridge technology and business offers a blueprint for staffing AI initiatives moving too fast for anyone to be an expert. The conversation reveals Google's quiet dominance: AI Mode has already reached 1.5 billion monthly active users, nearly double OpenAI's reach. But Rood's most provocative insight addresses vertical SaaS: he predicts the shift to "expertise as a service," where repetitive professional work gets commoditized and deep human judgment becomes "an API on top of the platform." His framework for the new reality is stark—culture trumps strategy, three-year horizons are irrelevant, the world is "tokenizing," and sales cycles collapse through proof-based processes. By 2030, he argues, AI will have moved so deeply into the background that having a Chief AI Officer will seem as obsolete as having a Chief Mobile Officer today.

From oil rigs to Amazon's machine learning division and now Cambridge's DeepMind chair, Professor Neil Lawrence brings a refreshingly grounded perspective to AI. Lawrence introduces his "atomic human" concept; arguing it's not our capabilities but our vulnerabilities and limitations that make us irreplaceable. Drawing on experiences watching his coding assistant try to claim authorship and building systems at Amazon, he illuminates why accountability requires skin in the game and why machines can never truly stand behind decisions the way humans must. His mechanical engineering background shines through in vivid analogies that make complex ideas tangible and even delightful. The conversation builds to a genuinely uplifting conclusion about the next generation. Lawrence dismisses the disempowering AGI narrative sold by tech incumbents protecting their turf, arguing instead that today's young people see the world as it is and are excited to shape it. His insistence that "people aren't stupid"—from public dialogues to business customers consistently asking for improvements in healthcare and education—makes the case for staying connected to customers and trusting the next generation to steer technology toward what we care about most. It's a perfect note to end the year on: pragmatic, human-centred, and genuinely hopeful.

In this illuminating conversation from the frontier of AI-native startups, William Fairbairn, founder of Zalos.ai and part of Y Combinator's class of 2025, reveals what it takes to build at Silicon Valley velocity. Just five weeks after launch, Zalos is already deployed with major enterprise customers, automating finance workflows through computer-use agents that execute tasks like humans—extracting contract terms, initiating billing, and reconciling cash. Fairbairn's insights into Y Combinator's evolved wisdom for the AI era, particularly "forward-deployed engineering", demonstrate how the startup playbook has been fundamentally rewritten. With funding bars rising to $2 million ARR in 12 months just to reach Series A, and "supernovas" scaling from $1 million to $100 million in 18 months, this postcard from the edge of software leadership captures the intensity and opportunity of building in an era where the rules change weekly.

In this fascinating conversation, Perplexity's Chief Business Officer Dmitry Shevelenko reveals how a company barely three years old is mounting the first credible challenge to Google's search dominance in two decades. Shevelenko shares the counterintuitive distribution strategy that led Perplexity to partner with mobile carriers and device manufacturers rather than chasing browser deals, explaining how creating mutual value with partners became their path to 22 million monthly active users. The discussion centres on execution velocity as Perplexity's primary competitive advantage, with Shevelenko openly admitting that six months from now he'll have a top priority he can't even imagine today. From eliminating internal presentations entirely to making hiring decisions "physically hurt," he paints a vivid picture of how Perplexity maintains startup intensity while competing against trillion-dollar tech giants, offering invaluable lessons for anyone navigating the AI transformation.