What happens when the future of money stops being about speculation and starts being about people, ownership, and agency?

In this episode of Tech Talks Daily, I'm joined by Dr. Friederike Ernst, co-founder of Gnosis, to unpack a conversation that goes far beyond crypto price cycles or technical hype. This is a thoughtful discussion about where blockchain is heading and, just as importantly, where it could go wrong if we are not paying attention.

Friederike has spent more than a decade building foundational infrastructure for the Ethereum ecosystem, from smart wallets to decentralized exchanges and blockchain networks that quietly power large parts of Web3. But as she explains, the industry is now standing at a fork in the road.

One path leads to blockchain becoming a silent backend upgrade for banks and incumbents, improving efficiency while keeping power centralized. The other path is far more ambitious, using blockchain to return ownership, control, and financial agency to everyday people.

We talk about why financial infrastructure, despite working reasonably well for many of us in Europe, remains deeply inefficient, expensive, and exclusionary at a global level.

A major theme of this episode is usability. Friederike is clear that technology only matters if it improves real lives. She explains why early blockchain products asked too much of users and how that is now changing, with experiences that feel as simple as using a neobank or debit card while preserving true ownership under the hood.

The goal is not to make everyone a crypto expert, but to make financial tools that work seamlessly while remaining genuinely user-owned.

We also explore the darker possibilities. Like any powerful technology, blockchain can be used to empower or to control. Friederike does not shy away from the risks of surveillance, social scoring, and misuse, and she argues that the real battle ahead is cultural, not technical. Values like privacy, free expression, and personal agency need to be defended openly, or the technology will be shaped without public consent.

As we look toward 2026, this conversation offers a refreshing reminder that the future of money is still being written. The question is whether it will be owned by communities or quietly absorbed by the same institutions we already rely on.

After listening to this episode, where do you think that future should land, and what choices are you willing to make to influence it?

• Connect With Dr. Friederike Ernst
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In this episode of Tech Talks Daily, I sit down with Yuyu Zhang to unpack a shift that many developers can feel but struggle to articulate.

Yuyu's journey spans academic research at Georgia Tech, building recommendation systems that power TikTok and Douyin at global scale, and leading the Seed-Coder project at ByteDance, which reached state-of-the-art performance among open source code models earlier this year.

Today, he is part of Codeck, where the focus has moved beyond AI assistance toward autonomous coding agents that can plan, execute, and verify real engineering work.

Our conversation begins with a simple but revealing observation. Most AI coding tools still behave like smarter autocomplete. They help you type faster, but they do not own the work.

Yuyu explains why that distinction matters, especially for teams dealing with complex systems, tight deadlines, and constant interruptions. Autonomy, in his view, is not about replacing engineers. It is about giving them back their flow.

We explore Verdent, Codeck's autonomous coding agent, and Verdent Deck, the desktop environment designed to coordinate multiple agents in parallel. Instead of one AI reacting line by line inside an editor, these agents operate at the task level.

They plan work with the developer upfront, execute independently in safe environments, and validate their output before handing anything back. The result feels less like using a tool and more like managing a small engineering team.

Yuyu shares how parallel agents change both speed and predictability. One agent can implement a feature, another can write tests, and another can investigate logs, all without stepping on each other. Just as important, he walks through the safeguards that keep humans in control.

Explicit planning, permission boundaries, sandboxed execution, and clear, reviewable diffs are all designed to address the very real concerns engineering leaders have about letting autonomous systems near production code.

The discussion also turns personal. Having worked on some of the highest-scale systems in the world, Yuyu reflects on why developers lose momentum. It is rarely about raw ability. It is about constant context switching. His goal with Verdent is to preserve mental focus by offloading interruptions and letting engineers return to work with clarity rather than cognitive fatigue.

We close by looking ahead. The definition of a "good developer" is changing, just as it has many times before. AI is not ending programming. It is reshaping it, pushing human creativity, judgment, and design thinking to the foreground while machines handle the repetitive churn.

If autonomous coding agents are becoming colleagues rather than helpers, how comfortable are you with that future, and what would you want to stay firmly in human hands?

How do you capture every moment of a golf tournament spread across hundreds of acres, tens of thousands of shots, and dozens of players competing at the same time? That question sits at the heart of this conversation recorded at AWS re:Invent, where I sat down with Eric Hansen, VP of Product at the PGA Tour, and Elaine Chiasson, who leads the global golf team at AWS, to unpack how data and AI are reshaping the way fans experience the game.

Eric explains why modern professional golf has more in common with Formula 1 than most people realize. Every ball struck, every position on the leaderboard, and every shift in momentum generates data that needs to be processed instantly. With more than thirty thousand shots across a single tournament and only a fraction of them shown on traditional broadcasts, the PGA Tour faces a constant challenge. How do you give fans context, insight, and a sense of presence when most of the action is never seen on screen?

Elaine shares how AWS has helped the Tour build the foundation to answer that question. From migrating decades of video and shot data into the cloud to applying generative AI for automated commentary, language translation, and real time insights, this partnership goes far beyond infrastructure. Together, they are experimenting with automated camera switching, AI driven production workflows, and personalized fan experiences that surface the right information at the right moment, whether you are following the leaderboard or a single favorite player.

The conversation also digs into trust and accuracy. Eric walks through how the PGA Tour validates AI generated commentary to ensure it stays aligned with the sport's standards, while Elaine highlights why operational discipline and governance matter just as much as innovation. They explore what hyper personalization looks like inside the PGA Tour app, how global broadcasts could evolve, and why the long term opportunity lies in making every shot matter for every fan.

As live sports move toward a future shaped by data, automation, and AI agents working behind the scenes, this episode offers a clear look at what that transformation really involves. So as golf continues to blend tradition with technology, what kind of fan experience do you want to see next, and how comfortable are you with AI calling the shots?

Useful Links

• Connect with Eric Hansen, VP of Product at the PGA Tour.
• Connect with Elaine Chiasson
• Learn more about AWS and PGA Tour

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How do you make sense of an industry that is changing at a pace few predicted, especially with SIGNAL London still fresh in our minds and Twilio unveiling the next stage of its vision for customer engagement? That question sits at the heart of today's conversation with Peter Bell, VP of Marketing for EMEA at Twilio, who joined me to unpack what the past year has taught both companies and consumers about AI's role in shaping modern experiences.

Peter begins by grounding everything in a single, striking shift. Only a year ago, AI-powered search barely registered in global traffic. Today it accounts for around a fifth of all searches. That leap signals a broader behavioral shift as consumers move instinctively toward conversational interfaces, which, in turn, leaves brands with a clear message. The clock has moved on. AI is no longer a nice-to-have. It is a direct response to how people now choose to discover, question, and buy.

Our conversation turns to the gap between customer expectations and the experiences they receive. Peter discusses why brands often struggle to integrate channels, data, and AI coherently. He explains how first party data has become the anchor for any serious AI strategy, why generic public models cannot solve brand-specific tasks, and why the most successful teams start with simple, tightly scoped problems. A password reset may not sound glamorous, yet it is the kind of focused use case that teaches teams how to govern data, automate safely, and build confidence in the process.

We also spend time on branded calling, RCS, and the evolution of voice. Peter breaks down what modern messaging now looks like and why trust sits at the center of every interaction. His explanation of Conversational Relay shows why natural voice exchanges finally feel within reach after years of frustration with rigid IVR systems. The thread running through all of this is clear. Consumers want speed and clarity, but they want reassurance too, and brands need to honor both sides of that equation.

Later in the conversation, Peter makes one of the episode's most compelling points. Brand visibility has become harder, not easier, because much of the early research now occurs within AI tools. Buyers form opinions long before they speak with a sales rep. That shift explains why so many B2B companies are returning to high-impact brand channels, whether that is F1 sponsorships or other standout moments that keep them in the initial consideration set.

We close with the topic that Peter believes will define the next stage of enterprise AI. Model Context Protocol. MCP has emerged as a quiet breakthrough, enabling LLMs to access data across CRM systems, files, and other software through a standard protocol. This removes one of the biggest blockers in AI projects: the practical challenge of connecting disparate data to a model built for a specific purpose. As Peter puts it, MCP gives companies a realistic way to make the special-purpose models that deliver reliable ROI.

It is a wide-ranging conversation shaped by SIGNAL London's announcements, the evolving customer journey, and a year in which AI moved from curiosity to expectation. I would love to know what part stood out most to you. Are you seeing the same shifts Peter describes in your own business, and how are you preparing for the year ahead?

Useful Links

• Interact with the Inside the Conversational AI Revolution report.
• Learn more about the Signal event
• Connect with Peter Bell, VP of Marketing for EMEA at Twilio.

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• Connect with Denny LeCompte, CEO of Portnox
• Learn more about Portnox

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