Tech Talks Daily

David Hawig never set out to work in blockchain. He began his career in health tech, drawn to the potential of scaling impactful solutions. But it was the promise of a more transparent and user-controlled internet that ultimately led him to the Web3 Foundation, where he now serves as Director of Ecosystem Development and Investor Relations.

In our conversation on the Tech Talks Daily Podcast, David shares why Web3 is not just a trend or buzzword, but a complete rethink of how digital systems should function. He describes a world where users own their data, can verify transactions without relying on central authorities, and can move between services without friction. To David, the Web3 movement is not about hype or speculation. It's about enabling a future where the internet is built on truth, not trust.

At the heart of the Web3 Foundation's work is Polkadot, a protocol designed to solve the scalability and interoperability challenges that plague many early blockchain networks. David explains that Polkadot's sharded infrastructure allows workloads to be split across participants in a trustless way. This setup not only enables more transactions but positions the ecosystem to support millions of users as mainstream adoption accelerates.

That acceleration, he argues, is already happening. Thanks to new regulations like the Clarity Act and Genius Act in the US, enterprise adoption is becoming a reality. Where Web3 once attracted only startups and crypto-native communities, today major companies, including sports brands and entertainment groups, are actively building in the space. Unlike earlier efforts that felt more like PR stunts, these companies now see tangible benefits. Web3 can deliver faster, cheaper, and more flexible digital services, available 24/7, with no lock-in to single vendors.

David is especially passionate about removing the barriers that have made Web3 feel intimidating to the average user. While early blockchain projects often demanded technical knowledge and wallet key management, he sees a future where users interact with Web3 products as easily as they would a mobile app. Behind the scenes, cryptography and decentralization are doing the heavy lifting, but from the user's perspective, the experience is seamless.

One area David is particularly excited about is decentralized storage. As more businesses realize the risks of relying on centralized cloud giants, alternatives are emerging that offer both cost advantages and greater control. He sees this as a critical part of the broader shift toward self-sovereign infrastructure.

When asked if large corporations truly understand the scale of the disruption ahead, David is cautious but optimistic. Many established players, he says, still underestimate how quickly network effects can take hold. Once enough users and companies move into Web3 ecosystems, the old models will no longer be competitive. Whether it's financial services, social media, or identity management, the shift toward user-owned infrastructure will be difficult to reverse.

Looking ahead to 2026 and beyond, David points to the upcoming Jam upgrade as a major milestone. This next evolution of the Polkadot network is designed to dramatically improve scalability, supporting not just crypto-native transactions but also broader use cases in gaming, ticketing, payments, and more. The goal is clear: create a robust, low-cost, interoperable infrastructure capable of supporting millions of users across different networks and applications.

Before signing off, David leaves listeners with a recommendation. He suggests reading Man's Search for Meaning by Viktor Frankl, a book he returns to often when reflecting on motivation and purpose. It's a fitting choice for someone working at the edge of one of the most transformative shifts in modern tech.

The Web3 Foundation is not just funding protocols or building tools. It's laying the groundwork for a future where the internet belongs to everyone. And if David's predictions are right, that future may arrive faster than we think.

When most people think of Wikipedia, they picture an endless scroll of human-readable pages. But there's another side to this ecosystem, one designed not just for people but also for machines. It's called Wikidata, and if you haven't heard of it, that's exactly why this conversation matters.

In this episode of Tech Talks Daily, I'm joined by Lydia Pintscher, Wikidata Portfolio Manager at Wikimedia Deutschland, for a deep look into how structured, open data is quietly powering civic tech, cultural preservation, and knowledge equity across the globe.

Wikidata is the backbone that helps turn static knowledge into something living, adaptable, and scalable. With over 117 million items, 1.65 billion semantic statements, and more than 2.34 billion edits, it's become one of the largest collaborative datasets in the world. But it's not just the size that makes it impressive. It's what people are doing with it. Lydia shares how volunteers and developers are building tools for everything from investigative journalism to public libraries, all without needing deep pockets or proprietary infrastructure.

This isn't big tech. It's a global, grassroots movement making open data work for the public good. We explore how tools like Toolforge and the Wikidata Query Service lower the barrier to entry, allowing civil society groups to build sophisticated applications that would otherwise be out of reach. Whether it's helping connect citizens to government services or preserving disappearing languages, the use cases are multiplying fast.

Lydia also reflects on how Wikidata fosters a sense of purpose for contributors, offering a rare example of what many call the good internet, where collaboration outweighs competition and building something meaningful beats chasing virality.

If you're curious about where open knowledge is headed, how structured data can be a force for social impact, or why Wikidata might be the most important project you've never fully explored, this episode offers a window into a future where machines help humans build something better, together.

What does it take to make AI actually work at enterprise scale? In this episode of Tech Talks Daily, I'm joined by Ryan Steelberg, CEO of Veritone, to unpack the very real challenges and opportunities that come with bringing artificial intelligence into complex industries like media, law enforcement, and government.

Ryan has been building AI solutions long before it became headline news. He co-founded Veritone in 2014 with a mission to solve the unstructured data problem. Think audio, video, and other media that doesn't fit neatly into rows and columns. Now, Veritone isn't just talking about AI. It's powering more than 3,000 clients across sectors with real applications that drive measurable ROI.

We get into what it means to work with unstructured data at scale, and how Veritone processed over 58 million hours of media in 2024 alone. Ryan explains why traditional enterprises struggle to operationalize AI and how Veritone has evolved from a platform company into a creator of end-user applications designed for impact. This includes ad optimization for ESPN and video redaction for law enforcement.

What's especially compelling is Veritone's growing footprint in high-barrier sectors like federal defense and law enforcement. Ryan talks through what it took to become a prime contractor for the U.S. Air Force and Defense Logistics Agency, how Veritone adapted its stack for secure deployments, and why the key to adoption in these sectors isn't just tech. It's trust and proximity to mission-critical outcomes.

We also discuss the company's push into the $17 billion global training data market through the Veritone Data Repository. Ryan shares why VDR is gaining traction fast and how it positions the company as a key partner for the next generation of AI models.

This isn't a story of hype or futuristic promises. It's a grounded look at what it really takes to scale AI in some of the most demanding enterprise environments. Whether you're deep in the AI world or still figuring out where your business fits, Ryan's perspective is honest, strategic, and full of lessons you can apply right now.

Is the future of finance programmable? In this episode of Tech Talks Daily, I'm joined by Ryan Galvankar, founder of Plaza Finance, to explore how programmable derivatives and on-chain bonds are reshaping the way we think about risk, leverage, and asset management in crypto.

Plaza Finance isn't just another DeFi protocol. Since launching its core system on Base in April 2025, the team has introduced two standout tokens: bondETH and levETH. These programmable derivative tokens split Ethereum into customizable risk profiles, allowing both institutional and retail investors to choose their exposure with precision. Under Ryan's leadership, Plaza has already attracted over 600,000 testnet users, $2 million in early deposits, and $2.5 million in pre-seed funding.

Ryan explains why this matters. Today's DeFi tools tend to sit at either extreme—ultra-safe or wildly speculative. Plaza aims to bridge that gap with products that reflect the diversity of traditional finance, yet remain liquid and composable across Ethereum's ecosystem. He also shares why Ethereum staking plays a central role, and how integrations with lending platforms and cross-chain bridges will make these tokens even more powerful.

But Ryan doesn't stop at structured investing. He also launched OptFun, an ultra-short-term options trading platform that hit nearly $1 billion in trading volume within a month. It appeals to crypto natives seeking high-volatility speculation in a fair, on-chain environment.

Together, these two platforms reflect what Ryan sees as a split in the future of DeFi: one track of thoughtful, long-term financial products that institutions will trust, and another track of high-speed, high-risk experimentation for users who live for volatility. He also gives his take on the rise of tokenized real-world assets, AI-generated portfolios, and what it will take to truly decentralize the next generation of finance.

Whether you're deep into DeFi or still watching from the sidelines, this conversation sheds light on a pivotal moment where programmable finance is becoming real, and access to tailored financial tools is expanding faster than ever.

What if proving your right to work, rent, or even open a bank account didn't require digging out your passport or waiting weeks for manual verification? In this episode of Tech Talks Daily, I'm joined by Hamraj Gulamali, Head of Legal and Compliance at Zinc, to talk about how digital identities are changing the way we think about employment, data ownership, and trust.

Hamraj brings a unique perspective to the conversation. A barrister-turned-startup leader, he now sits at the centre of one of the UK's most forward-thinking background check platforms. Zinc's mission is simple but ambitious: to make employment identity verification faster, safer, and fairer. But as Hamraj points out, this shift isn't just about convenience. It's about control.

We discuss how digital IDs are already helping automate background checks, reduce onboarding delays, and improve accuracy in high-volume hiring environments. Hamraj explains why placing ownership of work identity back in the hands of individuals has the potential to unlock wider applications—from renting a home to streamlining banking processes.

But there's also friction. Some industries face structural and cultural resistance. Others fear the cost of digital transformation or worry about public trust. Hamraj unpacks why regulators have both helped and hindered progress. He shares his view on the UK's Trust Framework, the Online Safety Act, and why transparency, retention policies, and robust cybersecurity are essential for building public confidence.

As we look ahead, Hamraj offers a clear vision for how verified digital IDs could simplify everyday tasks, slash manual admin, and reduce the compliance burden across multiple sectors. This episode cuts through the noise to offer a calm, informed, and practical take on one of the most overlooked shifts in how we live and work.

Whether you're in HR, tech, compliance, or just tired of carrying your passport across town on your first day of work, this conversation will get you thinking about what needs to happen next.

What does it take to build intelligent systems that are not only AI-powered but also secure, scalable, and grounded in real-world needs?

In this episode of Tech Talks Daily, I speak with Srinivas Chippagiri, a senior technology leader and author of Building Intelligent Systems with AI and Cloud Technologies. With over a decade of experience spanning Wipro, GE Healthcare, Siemens, and now Tableau at Salesforce, Srinivas offers a practical view into how AI and cloud infrastructure are evolving together.

We explore how AI is changing cloud-native development through predictive maintenance, automated DevOps pipelines, and developer co-pilots. But this is not just about technology. Srinivas highlights why responsible AI needs to be part of every system design, sharing examples from his own research into anomaly detection, fuzzy logic, and explainable models that support trust in regulated industries.

The conversation also covers the rise of hybrid and edge computing, the real challenges of data fragmentation and compute costs, and how teams are adapting with new skills like prompt engineering and model observability. Srinivas gives a thoughtful view on what ethical AI deployment looks like in practice, from bias audits to AI governance boards.

For those looking to break into this space, his advice is refreshingly clear. Start with small, end-to-end projects. Learn by doing. Contribute to open-source communities. And stay curious.

Whether you're scaling AI systems, building a career in cloud tech, or just trying to keep pace with fast-moving trends, this episode offers a grounded and insightful guide to where things are heading next. Srinivas's book is available on Amazon under Building Intelligent Systems with AI and Cloud Technologies, and you can connect with him on LinkedIn to continue the conversation.

AI is finding its way into almost every corner of customer service, but is it really what customers want? According to Kinsta's recent survey, the answer is overwhelmingly no. An incredible 93 percent of respondents said they would rather speak to a human than an AI chatbot when they need support. Nearly half even said they would cancel a service if it relied solely on AI-driven support.

In this episode, Roger Williams, Community Manager at Kinsta, breaks down the story behind those numbers and explains why his team continues to invest heavily in human-first support. Kinsta has built its reputation on 24/7 access to real engineers who understand the complexities of WordPress hosting, resulting in a 98 percent customer satisfaction score and consistently high ratings on platforms like G2 and Trustpilot.

Roger shares how Kinsta blends human expertise with AI tools in a way that enhances, rather than replaces, the customer experience. He talks about the role empathy plays in building trust, the importance of empowering support staff to own conversations, and why support should be viewed as an opportunity to strengthen relationships rather than just a cost to be reduced.

We also discuss the growing perception that AI in support is more about saving money than improving service, how business leaders can avoid falling into that trap, and what the future of open web hosting could look like as AI capabilities mature. Whether you are in tech leadership, run a customer-facing team, or simply value a good support experience, this conversation offers insights that go beyond the AI hype and focus on what truly matters to customers.

In a business climate shaped by rapid technological disruption, shifting geopolitical landscapes, and evolving customer expectations, strategy cannot remain static. JD Carter, Chief Strategy Officer at Vasion, believes the key to success lies in constantly aligning vision with execution while adapting to market realities in real time.

In this conversation, JD shares how his role involves continuously monitoring external signals such as technology shifts, regulatory changes, and economic pressures, then translating those insights into operational action. We explore his approach to looking beyond the company vision by breaking it down into achievable missions that link long-term goals with day-to-day work across cross-functional teams.

A major focus of the discussion is AI readiness and how organisations can move beyond hype to real impact. JD outlines a five-stage process for becoming AI-ready, starting with digitising and centralising documents and data, followed by cleaning and structuring information for use with large language models. He explains how automating repetitive workflows, modernising infrastructure, and ensuring interoperability across systems such as ERP and HR platforms create the foundation for orchestrated automation at scale. Governance and access controls complete the picture, ensuring that AI deployment meets both internal and regulatory standards.

We also look at how Vasion balances short-term market needs with a long-term platform vision. JD describes how leveraging the company's market-leading print infrastructure products supports current growth, while investing in R&D drives the development of a multi-product SaaS platform designed to integrate AI across the enterprise. A customer-first mindset shapes every decision, from pricing to product development, with continuous engagement through advisory boards, surveys, and direct conversations ensuring that partner strategies align with customer priorities.

To illustrate these principles in action, JD shares how Vasion responded to market demand for system-generated print job support by developing a SaaS-based output automation product. This pivot addressed a gap created by ERP and EMR vendors moving customers to the cloud and positioned Vasion at the start of its multi-product journey.

We discuss the signals leaders should watch to keep strategy relevant, including shifts in customer behaviour, the pace of technology adoption, and internal friction that may indicate misalignment. JD likens strategy to a GPS system, with vision as the destination and constant recalibration required to navigate roadblocks and changing conditions.

The conversation closes with a focus on embedding strategic agility into company culture. JD explains Vasion's Missions of Aspirational Performance system, which connects corporate values directly to execution by breaking down strategic goals into work that individuals can see contributing to the bigger picture. He also shares his personal three-pronged approach to continuous learning, combining formal education, informal learning, and mentor-driven guidance.

This episode offers practical insight for leaders navigating uncertainty, balancing present-day demands with future opportunity, and embedding adaptability into the DNA of their organisations.

MariaDB is a name with deep roots in the open-source database world, but in 2025 it is showing the energy and ambition of a company on the rise. Taken private in 2022 and backed by K1 Investment Management, MariaDB is doubling down on innovation while positioning itself as a strong alternative to MySQL and Oracle. At a time when many organisations are frustrated with Oracle's pricing and MySQL's cloud-first pivot, MariaDB is finding new opportunities by combining open-source freedom with enterprise-grade reliability.

In this conversation, I sit down with Vikas Mathur, Chief Product Officer at MariaDB, to explore how the company is capitalising on these market shifts. Vikas shares the thinking behind MariaDB's renewed focus, explains how the platform delivers similar features to Oracle at up to 80 percent lower total cost of ownership, and details how recent innovations are opening the door to new workloads and use cases.

One of the most significant developments is the launch of Vector Search in January 2023. This feature is built directly into InnoDB, eliminating the need for separate vector databases and delivering two to three times the performance of PG Vector. With hardware acceleration on both x86 and IBM Power architectures, and native connectors for leading AI frameworks such as LlamaIndex, LangChain and Spring AI, MariaDB is making it easier for developers to integrate AI capabilities without complex custom work.

Vikas explains how MariaDB's pluggable storage engine architecture allows users to match the right engine to the right workload. InnoDB handles balanced transactional workloads, MyRocks is optimised for heavy writes, ColumnStore supports analytical queries, and Moroonga enables text search. With native JSON support and more than forty functions for manipulating semi-structured data, MariaDB can also remove the need for separate document databases. This flexibility underpins the company's vision of one database for infinite possibilities.

The discussion also examines how MariaDB manages the balance between its open-source community and enterprise customers. Community adoption provides early feedback on new features and helps drive rapid improvement, while enterprise customers benefit from production support, advanced security, high availability and disaster recovery capabilities such as Galera-based synchronous replication and the MacScale proxy.

We look ahead to how MariaDB plans to expand its managed cloud services, including DBaaS and serverless options, and how the company is working on a "RAG in a box" approach to simplify retrieval-augmented generation for DBAs. Vikas also shares his perspective on market trends, from the shift away from embedded AI and traditional machine learning features toward LLM-powered applications, to the growing number of companies moving from NoSQL back to SQL for scalability and long-term maintainability.

This is a deep dive into the strategy, technology and market forces shaping MariaDB's next chapter. It will be of interest to database architects, AI engineers, and technology leaders looking for insight into how an open-source veteran is reinventing itself for the AI era while challenging the biggest names in the industry.

In this episode, I am joined by Charles Southwood, Regional Vice President, Denodo Technologies, a company recognised globally for its leadership in data management. With revenues of $288M and a customer base that includes Hitachi, Informa, Engie, and Walmart, Denodo sits at the heart of how enterprises access, trust, and act on their data. Charles brings over 35 years of experience in the tech industry, offering both a long-term view of how the data landscape has evolved and sharp insights into the challenges businesses face today.

Our conversation begins with a pressing issue for any organisation exploring generative AI: data reliability. With many AI models trained on vast amounts of internet content, there is a real risk of false information creeping into business outputs. Charles explains why mitigating hallucinations and inaccuracies is essential not just for technical quality, but for protecting brand reputation and avoiding costly missteps. We explore alternative approaches that allow enterprises to benefit from AI innovation while maintaining data integrity and control.

We also examine the broader enterprise pressures AI has created. The promise of reduced IT costs and improved agility is enticing, but how much of this is achievable today and how much is inflated by hype? Charles shares why he believes 2025 is a tipping point for achieving true business agility through data virtualisation, and what a virtualised data layer can deliver for teams across IT, marketing, and beyond.

Along the way, Charles reflects on the industry shifts that caught him most by surprise, the decisions he would make differently with the benefit of hindsight, and the one golden rule he would share with younger tech professionals starting their careers now.

This is a conversation for anyone who wants to understand how to harness AI and advanced data integration without falling into the traps of poor data quality, overblown expectations, or short-term thinking.