In this episode of The CTO Show with Mehmet, I’m joined by Ahmad Saleem, Founder and CEO of Podyssey.

Ahmad’s journey is anything but linear. From working as a geologist in mining and natural resources to earning a PhD in economics, moving into private equity, and eventually founding an AI startup, his path reflects deep curiosity, resilience, and systems thinking.

We dive into why podcast discovery is fundamentally broken, how AI and natural language processing can unlock the real value hidden inside long-form audio, and what it takes to build and scale a product in an uncertain, fast-moving market.

This conversation blends founder storytelling, product strategy, and honest reflections on failure, team building, and the future of content discovery.

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👤 About the Guest

Ahmad Saleem is the Founder and CEO of Podyssey, an AI-powered search engine designed to help people discover specific insights within podcasts rather than just episodes.

With a background spanning geology, economics, private equity, and natural language processing, Ahmad has been involved in nearly 20 ventures across his career. His work today focuses on applying AI to large-scale content discovery problems, particularly in long-form audio and multilingual environments.

https://www.linkedin.com/in/ahmad-saleem-ansari/

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🧠 Key Takeaways

• Why podcast discovery is harder than ever despite the explosion of content

• How AI and NLP enable searching inside conversations, not just titles

• The difference between finding podcasts and finding relevant moments

• Why categories and genres no longer work for modern podcast discovery

• Lessons learned from nearly 20 ventures and how failure reshapes founders

• How to build lean, distributed teams that move fast without sacrificing clarity

• Where AI agents fit and do not fit in long-form content consumption

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🎯 What You’ll Learn

• How Podyssey is rethinking podcast discovery at a global scale

• Why transcripts alone are not enough to understand context

• How founders should think about feature creep after product-market fit

• What changes when you build AI products in a remote, asynchronous world

• How experience with failure changes decision-making and leadership

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⏱ Episode Highlights & Timestamps

• 00:00 Introduction and Ahmad’s background

• 02:00 From geology and mining to economics and startups

• 05:00 Why podcast discovery is broken

• 09:00 AI, accents, transcription, and context challenges

• 12:00 From episodes to snippets: rethinking podcast consumption

• 15:00 Podyssey’s business model and monetization paths

• 17:00 Avoiding feature overload after product-market fit

• 20:00 Building lean, remote AI teams

• 25:00 AI agents and the future of long-form content

• 27:00 Failure, resilience, and restarting as a founder

• 33:00 The global future of podcasting and multilingual discovery

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🔗 Resources Mentioned

• Podyssey platform: https://www.podyssey.com/

Agentic AI is moving faster than enterprise readiness.

Boards are pushing adoption. Teams are deploying agents at speed. But security, control, and operational discipline are lagging behind.

In this episode, Mehmet sits down with Craig McLuckie, the co-creator of Kubernetes and founder of Stacklok, to unpack why most agentic AI initiatives break after the demo and what enterprises must do differently to make them durable, secure, and production-ready.

From MCP and context engineering to eval-driven development and why AI agents should never be treated like interns, this conversation goes deep into the realities CTOs, VPs of Engineering, and security leaders are facing right now.

This is not a hype conversation. It’s an operator’s reality check for 2026.

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👤 About the Guest

Craig McLuckie is a foundational figure in modern cloud infrastructure. He is the co-creator of Kubernetes, founder of the Cloud Native Computing Foundation, and former VMware executive behind the Tanzu portfolio.

Today, Craig is the founder and CEO of Stacklok, where he is focused on helping enterprises securely connect agentic AI systems to real-world infrastructure through open, controlled, and auditable platforms.

https://www.linkedin.com/in/craigmcluckie/

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🧠 Key Takeaways

• Why agentic AI represents a true epoch shift, not just another tooling cycle

• The real difference between demos, POCs, and production AI systems

• Why MCP is powerful but dangerous without proper control layers

• How context engineering is becoming more important than writing code

• Why eval-driven development replaces test-driven development in AI systems

• How enterprises should think about permissions, scope, and agent autonomy

• Why most AI failures are workflow problems, not model problems

• What 2026 realistically looks like for agentic AI adoption in the enterprise

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🎯 What You’ll Learn

• How to operationalize agentic AI without exposing your infrastructure

• Why treating AI agents like humans is a security mistake

• How to design guardrails without slowing teams down

• Where CTOs should focus investment to move from hype to ROI

• How leadership metrics and engineering evaluation must evolve in the AI era

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⏱ Episode Highlights & Timestamps

• 00:00 – Introduction and Craig’s journey from Google to Kubernetes

• 03:10 – Why agentic AI feels like a historic inflection point

• 06:05 – MCP explained and where enterprises get it wrong

• 10:45 – The security risks nobody is talking about

• 14:20 – Why AI agents should never be treated like interns

• 18:30 – The danger of permission sprawl and tool pollution

• 23:10 – Why most AI initiatives fail after the demo

• 28:40 – Eval-driven development vs traditional software thinking

• 34:15 – Context engineering as the new leverage point

• 38:50 – How engineering leadership and metrics must change

• 43:30 – What realistic agent adoption looks like in 2026

• 46:20 – Open source, ToolHive, and building durable AI platforms

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🔗 Resources Mentioned

• Stacklok: http://stacklok.com/

• ToolHive (Open Source MCP Platform): https://stacklok.com/toolhive/

In this episode, Mehmet sits down with Ghazenfer Mansoor, Founder and CEO of Technology Rivers, to unpack why so many software products fail quietly and what actually separates ideas that ship and scale from those that die early.

Drawing on two decades of experience and over 60 shipped applications, Ghazenfer shares hard-earned lessons on customer discovery, feature bloat, technical debt, AI with real ROI, and building system-powered businesses that scale sustainably, especially in regulated industries like healthcare.

This is a practical, no-fluff conversation for founders, CTOs, and operators building real products in a noisy AI-driven world.

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👤 About the Guest

Ghazenfer Mansoor is the Founder and CEO of Technology Rivers, a custom software development company with deep expertise in healthcare, HIPAA-compliant systems, and AI-driven operational automation.

He began his career as an early startup engineer, entered mobile development in its earliest days, and has since helped build and scale dozens of products. Ghazenfer is also the author of the upcoming book Beyond the Download, focused on building mobile apps people actually love and use.

https://www.linkedin.com/in/gmansoor/

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🧠 Key Takeaways

• Why most startups fail by building solutions before validating problems

• How feature bloat quietly destroys velocity, quality, and scalability

• The hidden cost of technical debt and why postponing it always backfires

• Why AI tools fail without clean data and mapped workflows

• How regulated industries can innovate without breaking compliance

• The shift from people-powered growth to system-powered growth

• Why founders should think like acquirers from day one

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🎯 What You’ll Learn

• How to identify the real problem worth solving before writing code

• How to prioritize features without killing your product roadmap

• Where AI delivers real ROI versus where it’s just pitch-deck noise

• How to design internal systems that create defensibility and valuation

• Why compliance and innovation are not opposites

• How to build products that users return to, not just download

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⏱️ Episode Highlights & Timestamps

• 00:02 Ghazenfer’s journey from early mobile engineering to healthcare software

• 05:10 Why most startup ideas fail before reaching scale

• 08:00 Feature race vs focus and why more features hurt products

• 10:15 Technical debt explained in simple, practical terms

• 14:00 AI in practice vs AI in pitch decks

• 17:30 Why workflows matter more than tools

• 19:45 Innovating in healthcare without breaking HIPAA

• 23:00 RAG, hallucinations, and building safe AI systems

• 26:45 Beyond the Download and building retention-first products

• 35:30 Moving from people power to system power growth

• 41:00 Thinking like an acquirer from day one

• 46:00 Final advice on AI, innovation, and staying relevant

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📚 Resources Mentioned

• Technology Rivers https://technologyrivers.com/

• Beyond the Download by Ghazenfer Mansoor: https://technologyrivers.com/l/beyond-the-download/

• HIPAA compliance principles

• Retrieval-Augmented Generation (RAG) architectures

• AI tools including Claude, ChatGPT, and Gemini