A season finale should feel like a recap, but ours turns into a snapshot of how fast tech is reshaping real work and daily life. Hugh’s back on the ground in Los Angeles in a new Paramount role, taking robotaxi rides like it’s normal, while Hannah steps into a Product Director job at Ocado tackling last mile logistics and the delivery experience. We talk honestly about what we’ve learned after 18 months of making Tech Overflow and why the “curious minds” approach works best when we keep it practical.

Waymo becomes our unexpected lens on product design, autonomy, and human behaviour. What happens when there’s no driver and no social friction? Why do other drivers treat a self-driving car differently? And what does “polite” software feel like as a passenger when the rest of the city learns it can always cut in?

Then we go deep on AI agents, vibe coding, and the gap between “LLMs make building easy” and actually shipping something useful. Hannah tries to build an agent to book Pilates classes and discovers that the hard part is not motivation, it’s foundations: terminals, tooling, and knowing how to break the problem into steps. From there we unpack AI at work, including token usage as a blunt adoption metric, the meaning of tokens, and why most organisations are still learning how to use AI as a co-pilot rather than an autopilot. Listener Q&A covers LLM tiers like Claude Haiku, Sonnet and Opus, local models you can run on your own machine, plus data privacy, enterprise terms, and API retention. We also answer a classic question clearly: how contactless payments and Apple Pay work, end to end.

Subscribe for season three, share this with a friend who’s trying to “use AI properly”, and leave a review if Tech Overflow helped you make sense of modern technology. What should we build or explain next?

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Entrepreneurs get the glory, investors get the spreadsheets, and John Lilly says that’s exactly how it should be. John (former VC, now angel and board member, and newly involved with Gigascale Capital) joins us to demystify venture capital for curious builders: how funds are raised from limited partners, why returns follow a power law, and what investors actually do between writing a cheque and a company becoming real. Along the way, we unpack his simple working framework: see, win, decide, then help build.

The best moments are the stories. John explains how a small real-world signal helped him chase Instagram, how “winning the right to invest” can mean recruiting a key hire, and why a fast acquisition can still feel like a mixed outcome when you believe the company could be worth far more. He also shares the long arc of Figma, including an early “no”, a year of breakfasts, and the traits he looks for in founders: grit, follow-through, and the ability to learn without defensiveness.

Then we widen the lens to the present shockwave: AI and large language models. John talks about prototyping at speed, what it means for startup formation, and why the next constraints may be compute, chips, cooling, and power rather than ideas. Finally, we touch climate tech and hard tech investing, where energy, materials, supply chains, and data centre demand collide.

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This week Hannah is joined by guest host Derrick Connell to discuss how Microsoft's Tay went wrong, how Satya Nadella reacted in the moment, and what Derrick learned about innovation. Derrick also shares stories from shifts in technology and discusses his new book Twenty One Summers.

Derrick shipped a chatbot that survived for 18 hours as it went horribly wrong. It sounds like a punchline until you realise it helped rewrite how the industry thinks about AI safety. Hannah sits down with Derrick, a former Corporate Vice President who spent nearly three decades at Microsoft and led teams across Search and AI, to unpack what innovation looks like when you are shipping into the real world and the real world fights back.

We talk through the massive platform shifts Derrick lived through, from Windows and Office shipped on discs to cloud services that ship daily, plus what it took to build Bing while Google held the vast majority of the search market. Along the way we get practical about product development methods, why agile experimentation changed the pace of software, and how “scrappy” teams innovate when they are not expected to win.

Then we go deep on conversational AI. Derrick explains why China’s WeChat environment made early chatbots thrive, how training data and user behaviour shaped outcomes, and why the US launch of Tay on Twitter was vulnerable to bot attacks and manipulation. We also get into the leadership playbook after a public failure, the importance of asking “What did we learn?”, and how that moment pushed Microsoft to publish early AI ethics guidelines that influenced responsible AI practices across the industry.

If you care about AI product management, innovation leadership, chatbot design, LLM guardrails, and what it takes to build safer AI systems, this conversation will give you both a story and a framework. Subscribe, share with a friend who builds AI, and leave us a review so more curious minds can find Tech Overflow.

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