What happens when AI reshapes intellectual property, and decentralized storage rewrites data sovereignty? In this episode of Tech Talks Daily, we explore that intersection with a deep dive into the work of StorX Network, a platform rethinking cloud storage from the ground up.

Our guest joins from StorX, a decentralized cloud storage network designed with privacy, security, and user empowerment at its core. At a time when data privacy is eroding and centralized providers are struggling to keep pace with evolving threats, StorX offers a radically different approach. Their system encrypts data using the user’s private passphrase, fragments it into smaller pieces, then distributes multiple copies across a global network of autonomous nodes.

The result? A storage solution that is trustless, censorship resistant, and economically more efficient, often cutting costs by up to 90 percent compared to traditional providers.

We discuss how StorX is positioning itself in a world increasingly concerned about surveillance, ransomware, and digital control. Much like AI is forcing conversations around copyright and ownership, decentralized storage is surfacing urgent questions around who controls data and how it's accessed.

This episode is not just about technology, it’s about the philosophical shift in how we think about trust, control, and freedom in digital spaces. We unpack why decentralized architecture matters, how privacy-preserving systems can scale, and where innovation is heading next.

If you're building applications or storing sensitive data, this is a conversation worth tuning in for. Because as digital life becomes more complex, where and how we store our information will define what kind of internet we want to live in.

Want to hear more stories at the intersection of privacy, decentralization, and innovation? Subscribe and stay tuned.

What happens when the world's most powerful AI systems are measured by the same yardstick?

In this episode of Tech Talks Daily, I spoke with David Kanter, Founder and Executive Director of MLCommons, the organization behind MLPerf, the industry's most recognized benchmark for AI performance. As AI continues to outpace Moore’s Law, businesses and governments alike are asking the same question: how do we know what “good” AI performance really looks like? That’s exactly the challenge MLCommons set out to address.

David shares the story of how a simple suggestion at a Stanford meeting led him from analyst to the architect of a global benchmarking initiative. He explains how MLPerf benchmarks are helping enterprises and policymakers make informed decisions about AI systems, and why transparency, neutrality, and open collaboration are central to the mission.

We explore what’s really driving AI’s explosive growth. It’s not just about chips. Smarter software, algorithmic breakthroughs, and increasingly scalable system designs are all contributing to performance improvements far beyond what Moore’s Law predicted.

But AI’s rapid progress comes with a cost. Power consumption is quickly becoming one of the biggest challenges in the industry. David explains how MLCommons is helping address this with MLPerf Power and why infrastructure innovations like low-precision computation, advanced cooling, and even proximity to power generation are gaining traction.

We also talk about the decision by some major vendors not to participate in MLPerf. David offers perspective on what that means for buyers and why benchmark transparency should be part of any enterprise AI procurement conversation.

Beyond the data center, MLCommons is now benchmarking AI performance on consumer hardware through MLPerf Client and is working on domain-specific efforts such as MLPerf Automotive. As AI shows up in smartphones, vehicles, and smart devices, the need for clear, fair, and relevant performance measurement is only growing.

So how do we measure AI that is everywhere? What should buyers demand from vendors? And how can the industry ensure that AI systems are fast, efficient, and accountable? Let’s find out.

In this episode of Tech Talks Daily, Neil is joined by Jose Soto, VP of Engineering at Converge Marketing, to discuss how data democratization transforms performance marketing. Jose highlights a common bottleneck in marketing where engineering teams act as the data gatekeepers, often slowing down marketing efforts. He explains how empowering marketers with self-service access to data through intuitive platforms speeds up decision-making and drives measurable growth for brands.

Jose talks about how Converge has broken down data silos by creating clean data pipelines and user-friendly tools that allow non-technical users to interact with data confidently. Instead of relying on engineers, marketing teams now have the freedom to access data, build reports, and analyze trends in real-time. This shift has led to improved agility, better collaboration, and faster campaign optimizations.

The conversation also explores the impact of AI and machine learning on marketing. Jose discusses how these technologies are helping marketers make more precise, data-driven decisions by enabling predictive analytics, optimizing creative messaging, and even automating campaign management. As AI continues to evolve, marketing teams can make more informed decisions with greater accuracy.

For businesses aiming to stay ahead in the ever-changing marketing landscape, this episode offers valuable insights on empowering teams, streamlining operations, and leveraging data to foster growth.

Listen in to discover how Converge is breaking down data barriers and preparing for the future of AI-powered marketing.