In this episode of Tech Talks Daily, I’m joined by Chase Garbarino, Co-Founder and CEO of HQO, a company reimagining the way commercial real estate connects with its occupants. From digital keycards to IoT-enabled spaces, HQO is building the operating system for real estate, already active across 250 million square feet in 24 countries.

We explore how commercial real estate, once known for its slow adoption of technology, is finally undergoing rapid transformation. Chase shares the story behind HQO’s creation, the drive to modernize the built environment, and why he believes cities are humanity’s greatest product. He outlines how hybrid work, flexible leases, and higher user expectations have pushed property owners to rethink what a workplace should offer.

But this isn’t just a tech story. It’s about connection. Chase reveals how HQO is helping create meaningful experiences through digital tools that serve every type of building user, not just those paying the rent. From commute integration to smart access, amenity bookings to workplace engagement, HQO’s platform is designed to be both invisible and impactful.

We also learn about the Quantum City Initiative, a new effort from HQO and MIT to help existing cities leap forward with shared learnings, digital infrastructure, and civic innovation. It is not about tearing cities down and starting from scratch. It is about helping them adapt, thrive, and compete in a connected world.

Chase closes by sharing one of his favorite stories: how a shoe company, a beer company, and a 3D printing firm all found community inside an HQO-powered building. These companies collaborated, socialized, and created something no one could have planned, all because the tech enabled real people to meet.

If you are curious about how smart buildings are becoming more human or how data and design can shape a more connected future, this conversation will spark new ideas.

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.

What happens when software development meets AI assistance, and anyone with an idea suddenly has the power to build it?

In this episode, I sat down with Ryan Frankel, President and CTO of Digital Brands and the mind behind HostingAdvice.com, to explore the rise of “vibeware” and how AI is shifting the development landscape.

Ryan has a fascinating journey that began with BASIC programming on an Apple IIc and led through military-grade signal processing to leading a portfolio of digital properties. At the center of our conversation is Vibeware.

AI-assisted development tools that are starting to lower the barriers for would-be creators. But unlike the hype you often hear, Ryan doesn’t sugarcoat the limitations. While AI can generate impressive snippets of production-ready code, we’re nowhere near a future where it can build and maintain scalable applications on its own. Context, debugging, infrastructure, and data architecture still require human oversight, and developers who understand these elements are more valuable than ever.

Ryan also explained how these tools are beginning to change how companies approach building versus buying software. AI-assisted development is giving teams more confidence to build custom internal solutions rather than defaulting to SaaS platforms. That trend could open the door for smaller businesses to create the kinds of tailored tools that were once only realistic for large enterprises.

Perhaps the most insightful part of our chat was Ryan’s analogy comparing AI-assisted coding to home cooking. Just as meal kits allow anyone to prepare a decent dinner, vibeware makes it easier for non-experts to build software. But when quality, scale, and performance matter, people still turn to professionals. Developers fluent in both fundamentals and AI tools will be the ones setting the bar.

We also touch on the future of engineering roles, the evolving skillsets needed, and how this new era mirrors the web development explosion of the early 2000s.

So where is the balance between automation and expertise? What role will junior developers play in a world where AI writes 30 to 90 percent of the code? And is the developer job market on the verge of a dip or about to expand in new directions?

On this episode of Tech Talks Daily, I sit down with Gene Hoffman, CEO of Chia Network, to explore a bold new vision for digital assets, investing, and regulatory-compliant blockchain innovation.

While much of the blockchain space has struggled with regulatory clarity, Chia has taken a different path, working closely with the SEC and other regulators to ensure long-term viability for the network and its applications. With a groundbreaking joint venture called Permuto, Chia is pushing that vision even further.

Permuto is set to tokenize equity investments directly on Chia’s independent layer-1 blockchain. The first application? Microsoft stock.

However, rather than simply offering a digital replica of a share, the platform will divide each into separate dividend and growth certificates. This new level of granularity can reshape the way institutions and individuals think about risk, returns, and liquidity. It’s particularly relevant for low-risk investors and retirees looking to isolate stable dividend income without exposure to market volatility tied to growth stocks.

Gene explains how this innovation could signal the next evolution of ETFs, offering flexible investment structures powered by blockchain. He also shares why Chia’s commitment to open-source technology and green consensus mechanisms has allowed it to attract serious institutional interest without compromising principles.

We also discuss Gene’s participation in the SEC’s upcoming Crypto Task Force Roundtable, where industry leaders will explore the convergence of traditional and decentralized finance. With deep ties to the crypto community and regulatory institutions, Gene offers a rare perspective on how to scale new financial tools in today’s regulatory environment responsibly.

Can blockchain finally deliver on its promise of transforming capital markets in a transparent, compliant, and useful way to everyday investors? Let’s find out.

When cybersecurity companies are racing to outpace evolving threats, innovation often starts in an unexpected place: revenue operations. In this episode of Tech Talks Daily, I sit down with John Queally, Senior Director of Revenue Operations at Clari, to explore why RevOps has become a vital engine behind the performance and resilience of cybersecurity leaders.

John brings a unique perspective from his journey through banking, analytics, and enterprise tech. What stands out is how rapidly the RevOps function has matured from a back-office support role to a central, strategic force. Especially in the cybersecurity space, where innovation requires ongoing investment and risk is measured in seconds, the pressure to run efficient, scalable revenue processes has never been greater.

We delve into why clean, trusted data is the backbone of any AI strategy and how 67% of revenue leaders still don’t trust the data they're using. It’s a staggering insight, and one that underscores the urgent need for cross-functional alignment. John explains how RevOps can serve as the connective tissue across sales, marketing, customer success, and finance, moving companies from a place where they're debating the accuracy of dashboards to making real decisions in real time.

He also shares a behind-the-scenes look at Clari’s work with cybersecurity firms like Okta, where implementing balanced pipeline strategies and streamlining task prioritization has unlocked measurable improvements. We discuss the rise of AI, but John doesn’t just repeat industry headlines. He calls out the "unsexy" truth that real AI advantage requires the hard work of data cleanup first and those who do it will pull ahead.

From operational transparency to building trust within revenue teams, this episode challenges assumptions about how data, AI, and RevOps intersect. And for anyone in cybersecurity or enterprise tech wondering how to scale effectively while preparing for what’s next, this conversation offers a grounded and insightful starting point.

Is your company still debating data? Or are you ready to turn trusted insights into action?

In today’s episode of Tech Talks Daily, I sat down with Bulent Cinarkaya, General Manager of Field Service Management at ServiceNow, to explore how AI is transforming the frontlines of field service. Often overlooked in the broader tech conversation, the technicians working outside the office are now seeing real, tangible improvements to their daily workflows thanks to advancements in intelligent automation.

Bulent brings a wealth of knowledge from working closely with global organizations that rely on ServiceNow to improve how they plan, dispatch, and support field teams. We talked about how agentic and generative AI are no longer theoretical tools; they are actively used to predict what technicians will need before arriving, automate access to resources, and reduce inefficiencies in task planning.

One of the most compelling parts of our conversation was how ServiceNow is using AI to improve productivity and enhance the human experience. From easing the onboarding of new technicians to capturing decades of experience from retiring experts, AI is helping teams bridge a generational gap in expertise. Technicians can now rely on intelligent systems to surface the correct information at the right moment, whether through summarizing technical documents or guiding them through complex tasks.

We also discussed the operational impact, with examples from customers like Bell Canada, Coursera, and British Telecom, who are seeing measurable improvements in scheduling accuracy and time to resolution. Bulent stressed the importance of unified data models, integrated platforms, and strong change management as organizations look to scale AI to ensure adoption and success.

This episode is a wake-up call for anyone still on the fence about AI in field service. AI is not only improving technician efficiency, but it’s also helping companies retain talent, meet rising customer expectations, and ultimately future-proof their operations.

So, how ready is your organization to move beyond proof of concept and turn AI into a field-ready advantage?