Orbit - An Hg software leadership podcast

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.