The Founder to Fortune Podcast

DevTool Founder-mode: Hiring for Grit, Reading Code, and Building TrustGuest: Ajay Tripathy, Former CTO of Stackwatch (exit: IBM)Episode Summary: Is the era of the "coder" coming to an end? Former Kubecost CTO Ajay Tripathy joins the show to discuss why the next generation of founders must pivot from writing code to owning business outcomes. We explore his "grit-first" hiring filter, how to engineer for business outcomes, ideal co-founder relationship and so much more.Timestamps:[01:01] – The Google Origins: Life inside the Borg project and the "Life is Short" catalyst for leaving.[06:14] – Vibe Coding & Early Days: Writing vanilla JavaScript in Nano and building the first prototype.[14:20] – The T-Shaped Partnership: How a technical founder and a product founder divide and conquer.[23:40] – Weaponizing the Roadmap: Why your first 10 customers should be your only product managers.[33:15] – Open Source Strategy: Using community adoption to de-risk experimental software.[43:30] – Hiring for Grit: Why Ajay hires Iron Man finishers and swimmers over "qualified" resumes.[53:00] – The 2030 Prediction: The shift from "writing" code to a 100% "reading and review" workflow.[01:05:00] – The IBM Model: Why the enterprise market cares about trust and outcomes over features.[01:21:00] – Moore's law for LLM: A technical look at maximizing hardware yield for AI workloads and what that could look like.About the Guest: Ajay Tripathy is a developer-tool founder and engineering leader. He was the co-founder and CTO of Stackwatch, where he led the creation of Kubecost. Following the company's acquisition by IBM, he now leads engineering initiatives focused on cloud optimization and AI-driven business outcomes. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Episode: Engineering Capital: Investing in Technical RiskGuest: Ashmeet Sidana (Engineering Capital)Host: Vidya Raman — Founder to FortuneEpisode overviewIn this episode, Ashmeet Sidana breaks down what it means to invest in technical risk—the “can this even be built?” kind—and why it creates leverage when founders get it right. We talk about what he looks for in first meetings, how to avoid PMF “progress theater,” why founders must learn sales, and what early-career investors can do to be genuinely valuable.Key takeawaysTechnical risk vs consumer risk (Google vs Facebook)Founding is not a job; the motivation bar is (intentionally) extremePMF: the only signal is paying customers; beware “playing house”Sales is a learnable skill — and non-optional for foundersEarly-career VC: do the work; on boards, talk lessLearning compounds; companies grow at the speed the CEO learnsChapters 00:00 — Opening + what to expect02:10 — Defining “technical risk”04:13 — What Ashmeet wants in a first meeting07:46 — The founder mistake that quietly kills outcomes17:54 — PMF: signals vs noise22:16 — Why founders must learn to sell24:06 — “Do the work” (for investors)27:34 — Boardroom calibration (talk ~1%)34:00 — Learning as the compounding advantageAbout the guestAshmeet Sidana runs Engineering Capital as a solo GP and is typically the first investor in companies taking technical risk. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Conflict between co-founders is inevitable.Letting it spiral out of control is optional.In this episode of Founder to Fortune, Vidya Raman sits down with Dr. Matt Jones — licensed psychologist, co-founder coach, and author of The Co-Founder Effect — to explore why the co-founder relationship is the single most under-managed risk in startups .Matt works exclusively with founding teams to improve communication, teamwork, and decision-making. In this conversation, he shares both deep psychological insight and highly tactical tools founders can implement immediately.Key Topics Covered • Why the co-founder relationship is the floor and ceiling of execution • The concept of emotional debt — and how it erodes trust • How to contain conflict so it doesn’t contaminate the business • Co-founder syncs vs. co-founder dates • Meta-communication: working on the relationship, not just in it • The dangers of rigid stories and confirmation bias • When you need co-founder coaching (and why waiting is risky) • Rethinking 50/50 equity splits • Recognition gaps between technical and business co-founders • The three relational languages: operational, psychological, archetypal • Power dynamics in complementary founding teams • The pursue/withdraw cycle • Why 3-founder teams add exponential relational complexityRapid-Fire Toolkit for Founders • Use breath to regulate before responding • Replace “you always…” with “I feel X when Y…” • Call for pauses in spiraling conversations • Repeat back what you heard (reflective dialogue) • After high-stakes meetings: debrief, regulate, then repairIf you are building a venture-scale company, this episode will change how you think about risk.Because most startups don’t fail from lack of intelligence.They fail from unmanaged relationships. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Tarang Vaish, founder and CEO/CTO of Granika with IIT and Stanford roots and experience across hardware, storage, SaaS, and AI. He argues people risk beats tech risk. Short, punchy takes on co-founder fit, solo founding difficulty, risk stacking, data compression for AI, and using AI like an intern. Practical stories from building hardware to petabyte-scale data platforms.

Episode SummaryMost AI conversations start with parameter counts. This one doesn’t.In this episode, we go inside the origin story of smallest.ai, a company built on the contrarian belief that true intelligence can be achieved with compute-constrained, smaller models — especially when the goal is real-time speech intelligence that can run actual workflows in production. Sudarshan shares how his background in self-driving vehicles shaped his thinking on reliability, active learning loops, and why 90–95% of the work lives in data and labeling, not model training. We then zoom into real-world enterprise use cases like collections, outbound calls, and multilingual customer support, and talk through how CIOs can actually start with voice AI in a messy legacy stack. In the second half, we switch gears into his founder journey: using LinkedIn and Discord as core distribution and learning channels, building the largest voice AI community, and his unfiltered advice on cold outreach, selecting whose advice to listen to, and running asset-light experiments before raising large rounds. If you’re a founder building AI for the enterprise — or an executive trying to separate hype from deployable systems — this episode will give you a grounded way to think about small models, agents, and voice AI.Key Topics- Origin story of smallest.ai and the shift from self-driving to speech AI.- Why “small vs large models” is the wrong framing — and how to think in terms of specialized vs general-purpose agents instead- Building one of the world’s fastest text-to-speech and speech-to-speech systems- Emotional information in audio vs traditional speech-to-text → LLM → TTS pipelines- Handling multilingual, code-switching conversations (Hinglish and Spanish/English) in real-world deployments- The hidden 90–95%: data collection, labeling, and active learning loops inspired by Tesla’s approach- How CIOs and CTOs can actually start: quick-win use cases in collections and outbound calling with simple Excel-based feedback loops - Why legacy call center software is optimized for human agents, not infinite-capacity AI agents- Who ends up making the buying decision: CEOs, CIOs, heads of AI transformation, and VPs of collectionsBuilding a founder-led growth engine:- 30K+ LinkedIn connections- The largest voice AI Discord community- Leveraging community feedback to shape product and GTM- Founder advice: cold outreach, whose advice to ignore, asset-light validation, and benchmarking yourself against the bestNotable Quotes“We should stop talking about intelligence in terms of models. We should always talk about intelligence in terms of agents that do end-to-end tasks in the economy.” “Training is actually very quick. 90–95% of the work is the data — labeling it, fixing label errors, and feeding it back through active learning loops.” “For enterprises, start with quick wins. Collections is a great one — run outbound calls, compare the agent to your humans, and only then worry about integrating deeply into your systems.” “I wouldn’t take pitch deck advice from someone who’s never raised from a tier-one VC. Or engineering advice from someone who hasn’t written code in five years.” “Talking to a lot of high-agency people is a superpower — and social media is one of the fastest ways to make that happen as a founder.” About Sudarshan KamathSudarshan Kamath is the founder & CEO of smallest.ai, a company focused on building compute-efficient, real-time speech intelligence and specialized voice agents. Prior to smallest.ai, he worked on deploying deep learning systems for self-driving vehicles, building safety-critical systems that cannot fail. About Founder to FortuneFounder to Fortune is hosted by Vidya Raman, an investor and former operator who helps founders crack the enterprise market. Each episode dives deep into the realities of building, selling, and scaling products for enterprise customers — with operators, founders, and researchers who’ve actually done it.Subscribe on Spotify, Apple Podcasts, or YouTube, and leave a review if this episode helped you think differently about AI in the enterprise. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Pankaj Dugar, an accomplished leader in enterprise software who shaped the Databricks partner ecosystem, shares his insights on partnership strategies. He reveals why most partnerships fail right after the press release and the need for clear incentives to engage sellers. Pankaj emphasizes the importance of pre-building technical integrations to minimize friction and enhance customer value. He also discusses the necessity of founder-led sales before achieving product-market fit and the crucial focus on solving less glamorous yet impactful integration challenges.

What happens when PMs, designers, and AI all start speaking the same language?In this episode, Vidya Raman sits down with Hailey Nevins (Director of UX Foundations at MongoDB) and Wenbo Wang (founding designer and former Databricks/Cloudera product designer) to explore how vibe coding is collapsing the old boundaries between design, product, and engineering.You’ll hear how GenAI is forcing UX to evolve—from pixel pushing to taste-driven orchestration—and why the best design teams now operate at startup speed without sacrificing rigor.We go deep into:* How “vibe coding” changes collaboration between PM, UX, and Engg* Why the new frontier isn’t just design systems—but design velocity* The rise of hybrid roles like “design engineer” and what they signal* How to build guardrails and evals for GenAI-powered products* When chat interfaces work—and when they absolutely don’tAnd just wait till you hear their hot takes on AI “killing” the wrong kind of design work, why hallucinations can actually make UX better, and what founders get wrong about hiring designers too late.If you care about product velocity, UX craft, or what “taste” means in an AI-first world—this conversation will challenge how you think about building.Relevant links:Hailey Nevins on LinkedInWenbo Wang on LinkedInOpen Lovable This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

In this engaging conversation, Kady Srinivasan, CMO of You.com shares her journey from software engineering to becoming a marketing leader across various industries. She discusses the importance of defining an Ideal Customer Profile (ICP) in B2B marketing, the impact of AI on marketing strategies, and the evolving role of marketers in a fast-paced environment. Kady emphasizes the need for discipline in narrowing down ICP, the significance of content creation, and the necessity of hiring the right marketing talent. She also highlights the importance of judgment in marketing and the need for continuous learning in the ever-changing landscape of marketing.Takeaways* Defining a clear Ideal Customer Profile (ICP) is crucial for B2B success.* Discipline is necessary for narrowing down ICP and avoiding distractions.* AI has drastically increased the speed at which marketers must operate.* Multi-threaded marketers can drive outcomes across various disciplines.* SEO is not dead but GEO and AEO are becoming vital.* Content creation is still table stakes for differentiation in the market.* Hiring the right marketing talent depends on the go-to-market strategy.* Judgment in marketing comes from experience and learning from failures.* Sales leaders should be prioritized in early-stage startups with outbound strategies.* Continuous learning and adaptation are vital in the marketing field.Relevant links:Kady Srinivasan on LinkedInYou.comFounders of You.com: Richard Socher and Bryan McCannSome links to the resources that Kady referred to:MavenGrowthXEvery.toWatch us on YouTube here. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Ely Kahn’s career spans government, startups, and now leading product at SentinelOne. From shaping U.S. cybersecurity strategy at the White House to building one of AWS’s early security products to launching SentinelOne’s fastest-growing AI product line, his journey offers a rare vantage point on how security and product innovation intersect.In this episode, we dig into:* How AI is transforming threat hunting and investigations—and what that really means for teams on the ground.* The surprising role of “digital twins” in scaling product management.* Why cybersecurity, despite the noise and consolidation pressures, might actually be one of the best spaces for founders.* And the future of AI pricing, product velocity, and reinventing core parts of the security stack.If you’re a founder, operator, or product leader navigating the next wave of AI and security, this conversation will leave you rethinking both the challenges—and the opportunities—ahead.Links:Ely Kahn on LinkedInListen on YouTube This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.foundertofortune.org

Join Jim Fisher, a veteran GTM leader who scaled Cloudera from $50M to $1B ARR, as he shares invaluable insights for founders. He discusses the three golden KPIs crucial for any go-to-market strategy and reveals why getting the Ideal Customer Profile right is critical. Discover the surprising truth that consistency, not just revenue, signals a successful GTM approach. Fisher also dives into the transformative role of AI in sales and provides practical advice on avoiding common pitfalls that can derail growth.