Anyone Can Wrap an AI and Call It a Product with Sharon Gillenwater

Anyone Can Wrap an AI and Call It a Product with Sharon Gillenwater

What does it actually take to build something real in AI — not just wrap a tool and call it a product? Sharon Gillenwater has done it before. Fourteen years building a B2B SaaS company from scratch, bootstrapped, no venture capital, sold for $25 million. Then a private equity firm shut it down without warning. When she called to buy it back, they didn't know the company name, the revenue, or what the product did. So she rebuilt it. With AI. And a healthy amount of spite. Sharon is a four-time founder who has lived through dot-com, early SaaS, and now the AI wave. Her new company, ExecutiveIQ, helps enterprise go-to-market teams build the kind of relationships that actually close deals. Her view on the current moment is simple: anyone can wrap an LLM and call it a product. Without real domain expertise behind it, you have no moat. What You'll Learn Why most AI products have no moat: The companies that survive this wave will be the ones who actually know something — and can build on top of that knowledge in a way nobody else can replicate. Why human judgment still matters: The best products aren't fully automated. Sharon explains exactly where AI falls short and why the combination of domain expertise and human oversight is what actually wins. What it really costs to build with AI: Sharon burned through $1,000 of Anthropic credits in six hours while on holiday in Mexico. Everyone is figuring out the economics of this in real time. Sharon is one of the most grounded people I've had on this show. No performance, no hype — just someone who has built the hard way, lost something she cared about, and decided to go again. Connect with Sharon Website: executive-iq.com [http://executive-iq.com/] Newsletter: scalingwithsoul.substack.com [http://scalingwithsoul.substack.com/] LinkedIn: linkedin.com/in/sharonkgillenwater [http://linkedin.com/in/sharonkgillenwater] Uncomplicated: Humans vs. Machines is produced by Urban Podcasts [http://urbanpodcasts.co.uk].

You Can’t Fix What You Can’t See with Ghita El Haitmy of Techbible

What if the biggest challenge in tech isn’t finding the next AI tool, but knowing how to make sense of the ones you already have? Ghita El Haitmy came to that realization while building Techbible. What started as a platform to help teams discover new software quickly revealed a much deeper issue. Most companies don’t actually understand their own tech stack. They don’t know which tools overlap, which ones go unused, or how much they’re really spending. More importantly, they don’t have a clear way to decide what work should be automated, what should stay human, and how AI fits into real workflows. So Ghita pivoted. In a world where new tools launch weekly and AI models change overnight, the challenge isn’t discovery anymore. It’s visibility and judgment. It’s knowing what you already have, what you actually need, and how to use AI without creating more complexity. This conversation explores how teams can step back and build clearer systems around AI. It’s less about what to adopt next and more about how to make better decisions, avoid unnecessary complexity, and design workflows that actually support the way teams work. It’s a grounded, practical conversation about AI, tools, and the future of work, without the hype. What You’ll Learn Visibility Before Discovery: Why knowing what your team actually uses, and what it costs, matters more than chasing shiny new AI tools. Orchestration Is the New Skillset: Why understanding what each AI model is good at matters more than learning a new tool every week. How AI Spend Quietly Gets Out of Control: Why overlapping tools, agents, and subscriptions add up fast, and how to design workflows that reduce waste instead of creating more. What Should Stay Human: Why the future of work isn’t about automating everything, but about protecting the parts of work that require trust, context, and decision-making. Ghita’s perspective is a reminder that the teams who win won’t be the ones with the biggest tech stacks, but the ones who know how to think clearly about AI, design better systems, and use technology in service of the way they actually work. Learn More:  Website: techbible.ai [https://techbible.ai/] LinkedIn: Ghita El Haitmy [https://www.linkedin.com/in/ghita-el-haitmy-a9ab7767/]

The Hard Part Isn’t AI. It’s Trust with Olivier Babin & Naunidh Bhalla of Tetrix

Private-market investing is still running on PDFs, scattered spreadsheets, and manual models, while public markets operate with real-time analytics and platforms like Bloomberg. The gap keeps widening, and AI seems like the obvious fix. But as Olivier and Naunidh explain, most of the real problems in private markets aren’t technical. They’re about trust, workflow, and the quality of the underlying data. Tetrix was built to solve those problems. In just a year, the team has grown 10x, signed dozens of institutional clients, and developed a platform that automates the most painful parts of data collection and analysis, while keeping people in the loop where judgment matters most. Instead of chasing hype, they spent months speaking with more than 400 investors, from VCs and LPs to asset managers, to understand where decisions actually break down. The answer wasn’t “more AI.” It was cleaner data, clearer context, and tools investors could rely on. What You’ll Learn - Where AI really helps: How Tetrix automates the slow, manual parts of private-market workflows and where human insight is still essential. - Why private-market data is so messy: And how better structure and context unlock stronger, faster analysis. - How to build AI people trust: Why transparency, accuracy, and deep customer understanding matter more than any single model. - How to scale in a conservative industry: Lessons from 400+ conversations, fast iteration, and staying close to real investor workflows. Olivier and Naunidh’s story is a reminder that the future of private markets won’t be fully automated. It will be augmented, pairing AI with the human judgment that drives the best decisions. Learn more Website: tetrix.co [https://www.tetrix.co/] LinkedIn: Olivier Babin [https://www.linkedin.com/in/olivierbabin15/] | Naunidh Bhalla [https://www.linkedin.com/in/naunidhbhalla/]

Why Most AI Fails and the Founders Fixing It with João Carvalho & Wael Abdelmalek of Uthereal

Most companies sit on years of hard-earned knowledge — but have no way to turn it into AI that’s private, reliable, and actually useful. In this episode, I’m joined by João Carvalho, a machine learning PhD who spent years at ETH Zurich studying why AI fails, and Wael Abdelmalek, former AWS architect who helped global enterprises turn their expertise into production-ready AI systems. They met at a hackathon in Zurich and quickly realized they were solving the same problem from opposite sides. That moment became Uthereal — one of Switzerland’s top startups and a platform that lets companies build secure, business-grade AI systems trained entirely on their own data, with their own guardrails, and without exposing anything to Big Tech. We talk about what it really takes to build trustworthy AI, why so many GenAI pilots never make it to production, and how companies can protect their data while still moving fast. What You’ll Learn - Why AI Fails: A clear look at hallucinations, reliability gaps, and why “80% accuracy” isn’tenough for expert domains. - Building Private AI: How Uthereal creates business-grade “ChatGPTs” that stay inside your cloud — fully private, fully controlled, and built on your own knowledge base. - From Pilot to Production: Why 95% of GenAI projects stall after the demo, and what companies need to do differently to make AI work in the real world. - Security & Trust: What most teams don’t realize about data leakage, model backdoors, and the risks of sending sensitive information to public LLMs. The Future of Work: Why AI is an empowerment tool, not a replacement — and where human judgment still matters. Uthereal’s story is a reminder that real AI value isn’t about chasing hype — it’s about turning what your business already knows into something scalable, secure, and built to last. Learn More Website: uthereal.ai/ [https://www.uthereal.ai/] LinkedIn: João [https://www.linkedin.com/in/utherealjc/]  LinkedIn: Wael [https://www.linkedin.com/in/wwael/] Uncomplicated: Humans vs. Machines is produced by Urban Podcasts [http://urbanpodcasts.co.uk].

Why Belief Is a Founder’s First Investment with Nomiki Petrolla

What if the most important thing you invest in as a founder isn’t money, code, or connections, but belief? When Nomiki Petrolla’s six-week-old daughter was on a ventilator fighting for her life, she walked into her house from the hospital, handed her baby to her husband, and quit her job as head of product at an AI company. That moment changed everything. After 15 years building products for other people, that crisis pushed her to bet on herself, and on the women who kept coming to her with ideas but no access. She launched a consultancy and helped 43 women build their first tech products in a single year, proving the problem was real: women with strong ideas had no clear path to build, test, or launch without technical skills, capital, or permission. That momentum became Theanna, a platform now helping hundreds of women go from idea to revenue with AI-powered roadmaps, real community, and a repeatable path to traction. This episode is about the moments that force reinvention, and why belief is the foundation of every founder’s journey. What You’ll Learn - AI as an Accelerator: Where AI truly speeds up execution, where it still falls short, and how Theanna uses Anthropic and GPT-based workflows to guide founders from idea to traction. - The Real Startup Timeline: Why “move fast” doesn’t mean skipping the hard parts, and how patience, resilience, and community compound over time. - Women, Access & the Gap: What happens when women receive less than 2% of venture capital, and how platforms like Theanna help close the gap by making early traction accessible. - Belief as a Founder’s First Investment: Why starting before you feel ready is often the only way to break through, and how to know when to pivot, persevere, or push harder. Nomiki’s story is a reminder that in the AI era, tools and community can democratize entrepreneurship — but it all starts with believing you can build something that didn’t exist before. Learn More Website [https://theanna.io] | LinkedIn [https://www.linkedin.com/in/nomikipetrolla/] Uncomplicated: Humans vs. Machines is produced by Urban Podcasts [http://urbanpodcasts.co.uk].