The Founder to Fortune Podcast

DevTool Founder-mode: Hiring for Grit, Reading Code, and Building Trust

DevTool Founder-mode: Hiring for Grit, Reading Code, and Building Trust Guest: 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 [https://www.foundertofortune.org?utm_medium=podcast&utm_campaign=CTA_1]

Engineering Capital: Investing in Technical Risk

Episode: Engineering Capital: Investing in Technical Risk Guest: Ashmeet Sidana (Engineering Capital) Host: Vidya Raman — Founder to Fortune Episode overview In 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 takeaways Technical risk vs consumer risk (Google vs Facebook) Founding is not a job; the motivation bar is (intentionally) extreme PMF: the only signal is paying customers; beware “playing house” Sales is a learnable skill — and non-optional for founders Early-career VC: do the work; on boards, talk less Learning compounds; companies grow at the speed the CEO learns Chapters 00:00 — Opening + what to expect 02:10 — Defining “technical risk” 04:13 — What Ashmeet wants in a first meeting 07:46 — The founder mistake that quietly kills outcomes 17:54 — PMF: signals vs noise 22:16 — Why founders must learn to sell 24:06 — “Do the work” (for investors) 27:34 — Boardroom calibration (talk ~1%) 34:00 — Learning as the compounding advantage About the guest Ashmeet 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 [https://www.foundertofortune.org?utm_medium=podcast&utm_campaign=CTA_1]

Your Co-Founder Relationship Is Your Startup’s Biggest Risk

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 complexity Rapid-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 repair If 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 [https://www.foundertofortune.org?utm_medium=podcast&utm_campaign=CTA_1]

The #1 Risk First-Time Founders Always Underestimate (It's Not Technology)

Most first-time founders believe startups fail because of bad ideas, weak technology, or poor timing. In this episode, Tarang Vaish argues that the real failure mode is far less obvious—and far more dangerous: people risk. Drawing from his journey across hardware, data infrastructure, SaaS, and AI, Tarang shares hard-earned lessons on co-founder dynamics, solo founding, risk stacking, and founder mindset. He also offers a practical mental model for using AI effectively—by treating it like an intern, not magic. This conversation is for founders who want to think more clearly about risk, leadership, and what actually determines success in the early days. 🔑 Key Topics & Takeaways Why people risk is the most underestimated startup risk Why solo founding is exponentially harder—especially fundraising How first-time founders accidentally stack too many risks at once Why technical brilliance rarely saves a startup on its own The importance of co-founder complementarity, not similarity What “realistic optimism” really means for founders Why being transparent about founder ambitions can build trust Lessons from building across hardware, storage, SaaS, and AI Why data and security remain evergreen startup categories How to use AI effectively: treat it like an intern, with structure and feedback ⏱️ Episode Chapters 00:00 – Why most startups fail (and why it’s not technology) 01:01 – Tarang’s background: curiosity, IIT, Stanford, startups 03:23 – Early startup lessons from hardware and tight constraints 05:45 – Why ML and SaaS are harder to productize than they look 07:20 – The cloud tradeoff: easy to start, hard to scale 08:17 – Being upfront about wanting to become a founder 09:12 – How Granica emerged from real operational pain 12:21 – Signal vs noise: compression, AI, and learning efficiency 16:50 – Who actually buys AI and data infrastructure today 20:53 – Why everything eventually becomes a data lake 23:58 – Why data and security are evergreen founder bets 25:28 – 🔥 The #1 risk founders underestimate: people risk 26:10 – Solo founding, fundraising, and risk stacking 27:10 – Staying sharp as a founder 28:05 – Using AI like an intern (with a practical prompt tactic) 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 [https://www.foundertofortune.org?utm_medium=podcast&utm_campaign=CTA_1]

Small Models, Big Impact: Why the Future of AI Isn't Trillion-Parameter

Episode Summary Most AI conversations start with parameter counts. This one doesn’t. In this episode, we go inside the origin story of smallest.ai [http://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 [http://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 collections Building 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 best Notable 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 Kamath Sudarshan Kamath is the founder & CEO of smallest.ai [http://smallest.ai], a company focused on building compute-efficient, real-time speech intelligence and specialized voice agents. Prior to smallest.ai [http://smallest.ai], he worked on deploying deep learning systems for self-driving vehicles, building safety-critical systems that cannot fail.  About Founder to Fortune Founder 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 [https://www.foundertofortune.org?utm_medium=podcast&utm_campaign=CTA_1]