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.

Franz Faerber spent 26 years at SAP building Hana before doing something unusual - walking away to build a competitor. Four years in stealth gave Everest Systems the rare luxury of rethinking ERP from first principles, and what emerged is a philosophy that challenges the entire business software model. Faerber's core thesis: application features are no longer defensible because AI can replicate them too easily. It is deep domain knowledge embedded in platforms that enable radical personalization. His "live sandboxing" technology allows users to experiment with production data in isolated environments - something he was never able to do in 26 years at SAP - and represents a fundamental shift from selling configured software to enabling customers to generate their own. The vision is provocative: 80% of software becomes commoditised infrastructure, while 20% becomes bespoke customization, and markets grow as companies build differentiated software instead of accepting generic solutions.

The conversation reveals hard-won wisdom about fighting bureaucracy, the viscosity of enterprise software markets, and why Germany remains his deliberate choice despite Silicon Valley's pull. His analysis of why technology companies struggle to become business software providers offers a refreshing counter to theories around OpenAI or Microsoft absorbing vertical markets. Most personally, his career reflections capture the tension experienced leaders feel between corporate safety and the life-enriching risk of building something new. At an age when contemporaries were settling into senior roles, Faerber chose to start over, arguing AI makes this the first time in ERP history when newcomers can genuinely out-innovate giants in years, not decades.

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.

In this candid anniversary conversation, Hg's leadership team of Matthew Brockman and Nic Humphries reflects on 25 years of building one of Europe's most focused software investors. Humphries shares the challenge of convincing colleagues to abandon multiple sectors for pure software focus, while Brockman opens up about his leap of faith in 2010, leaving Apax Partners for an uncertain bet on Hg's vision and the turbulence of 2012 that eventually led to success.

The conversation hones in on AI as the next major platform shift and Brockman's concept of the "last mile"—the deep understanding of customer workflows required to transform AI capability into practical business solutions. Their discussion reveals a firm that has spent 25 years accumulating the pattern recognition, operational capabilities, and entrepreneurial culture perfectly suited for an era where success depends less on investment judgement and more on building products that solve real workflow problems—making this milestone feel less like a celebration of the past and more like preparation for the defining challenge ahead.