Tech Talks Daily

*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "27410bb7-d8c2-4aff-a02b-e0633691b6f9" data-testid= "conversation-turn-13" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:ab9c27ca-16aa-4b93-8b31-786a9be386c3-6" data-testid= "conversation-turn-14" data-scroll-anchor="true" data-turn= "assistant"> What if the next big shift in personal audio is not about blocking the world out, but staying connected to it?
In this episode of Tech Talks Daily, I sit down with Nicole from Shokz to talk about why open-ear headphones are suddenly everywhere, and why this category is moving from niche curiosity to everyday essential. For years, the audio market was obsessed with sealing users off from the outside world. Now the conversation is changing. More people want to hear their music, podcasts, and calls without losing awareness of traffic, fellow commuters, colleagues, or the world happening around them.
Nicole helps unpack what open-ear audio actually means in simple terms, and why it is resonating with runners, commuters, parents, office workers, and anyone trying to balance comfort, safety, and sound quality. We talk about the cultural shift behind this rise, from growing health and fitness habits to the way hybrid work and always-on lifestyles have changed how people use earbuds throughout the day.
We also get into why Shokz has become one of the defining brands in this space. Long before open-ear audio became a trend, Shokz was investing in bone conduction, open-ear design, and the kind of product research needed to make this category work in real life. Nicole shares how years of persistence, technical innovation, and consumer education helped the company move from specialist player to category leader.
During our conversation, we explore how real-world behavior shapes product design. That means thinking beyond audio specs and focusing on how headphones actually fit into daily life. Whether someone is running in the rain, commuting to work, wearing glasses, sitting in an office, or trying to stay aware while walking the dog, those everyday moments are shaping the next generation of audio devices.
Nicole also talks me through some of Shokz's latest product thinking, including the OpenDots One and the OpenFit Pro. From compact clip-on designs that feel almost like wearable accessories to new approaches around noise reduction in open-ear listening, this episode looks at how the category is becoming more sophisticated and more versatile without losing the awareness that made it appealing in the first place.
Looking ahead, we discuss whether open-ear audio will live alongside sealed earbuds as part of a two-device lifestyle, or whether it could eventually become the default choice for more people. We also touch on what comes next, from smarter audio experiences to the role AI and even connected glasses could play in the future of listening.
So if you have been seeing the phrase open-ear audio more often and wondering what all the fuss is about, this conversation will bring it to life. Are open-ear headphones simply having a moment, or are we watching a bigger shift in how people want to hear the world around them?

What happens when the real bottleneck in artificial intelligence is no longer training models, but actually running them at scale?
In this episode of Tech Talks Daily, I sit down with Satyam Srivastava from d-Matrix to explore a shift that is quietly reshaping the entire AI infrastructure landscape. While much of the early AI race focused on training ever larger models, the next phase of AI adoption is increasingly defined by inference. That is the moment when trained models are deployed and used to generate real-world results millions of times a day.
Satyam brings a unique perspective shaped by years of experience in signal processing, machine learning, and hardware architecture, including time spent at NVIDIA and Intel working on graphics, media technologies, and AI systems. Now at d-Matrix, he is helping design next-generation computing architectures focused on one of the biggest challenges facing the AI industry today: efficiently running large language models without overwhelming data centers with unsustainable power and infrastructure demands.
During our conversation, we explored why the industry underestimated the infrastructure implications of inference at scale. While training large models grabs headlines, the real operational pressure often comes later when those models must serve millions of queries in real time. That shift places enormous strain on memory bandwidth, energy consumption, and data movement inside modern data centers.
Satyam explains how d-Matrix identified this challenge years before generative AI exploded into the mainstream. Instead of focusing on training hardware like many AI startups at the time, the company concentrated on inference efficiency. That decision is becoming increasingly relevant as organizations begin to realize that simply adding more GPUs to data centers is not a sustainable long-term strategy.
We also discuss the growing power constraints surrounding AI infrastructure, and why efficiency-driven design may be the only realistic path forward. With electricity supply, cooling capacity, and semiconductor availability all becoming limiting factors, the industry is being forced to rethink how AI systems are architected. Custom silicon, purpose-built accelerators, and heterogeneous computing environments are now emerging as key pieces of the puzzle.
The conversation also touches on the geopolitical and economic importance of AI semiconductor leadership, and why the relationship between frontier AI labs, infrastructure providers, and chip designers is becoming increasingly strategic. As governments and companies compete to maintain technological leadership, the question of who controls the hardware powering AI may prove just as important as the models themselves.
Looking ahead, Satyam shares his perspective on how the role of engineers will evolve as AI infrastructure becomes more specialized and energy-aware. Foundational engineering skills remain essential, but the next generation of engineers will also need to think in terms of entire systems, combining software, hardware, and AI tools to build more efficient computing environments.
As AI continues to move from research labs into everyday products and services, are organizations prepared for the infrastructure shift that comes with an inference-driven future? And could efficiency, rather than raw computing power, become the defining metric of the next phase of the AI race?

How should businesses rethink infrastructure when applications, data, and users are increasingly spread across thousands of locations?

In this episode of Tech Talks Daily, I sit down with Mark Cree, President and Chief Operating Officer at Scale Computing, to talk about why the future of enterprise infrastructure is moving closer to where data is actually created.
This conversation was recorded following the 66th edition of The IT Press Tour, where some of the most interesting conversations in enterprise infrastructure centered on what happens when businesses move away from oversized, monolithic stacks and start focusing on practical, distributed solutions. From retail stores and airports to remote industrial sites, the edge is becoming a critical part of modern IT strategy.
Mark shares how Scale Computing has spent years building an edge-first platform designed to run critical workloads reliably across everything from a single location to tens of thousands of distributed sites.
Mark also reflects on his own journey through the technology industry, which includes founding companies acquired by Cisco and NetApp, working as a venture capitalist, and leading major storage initiatives at AWS. That experience gives him a unique perspective on how enterprise infrastructure has evolved, particularly as organizations reconsider the balance between centralized cloud environments and local processing closer to users and devices.
During our conversation, we explore why edge computing is becoming increasingly important for AI workloads, especially when large volumes of data are generated outside traditional data centers. Mark explains how processing information locally can reduce costs, improve performance, and enable entirely new use cases, from monitoring customer behavior in retail environments to running intelligent systems in remote locations.
We also talk about the ongoing reassessment happening across enterprise IT teams following major industry shifts, including changes in the virtualization market and growing concerns around vendor lock-in. Mark explains how Scale Computing is positioning itself as a flexible alternative by combining virtualization, containerization, networking, and security into a platform designed specifically for distributed environments.
Looking ahead, Mark shares his perspective on where enterprise infrastructure is heading over the next five years. As smaller AI models become more capable and organizations seek greater control over their data and systems, the role of edge platforms may become even more important.
 Instead of relying solely on massive centralized environments, companies may find new value in distributing intelligence closer to the places where real-world activity happens.
So as organizations rethink how they deploy applications, manage data, and control infrastructure, is the next big shift in enterprise IT happening right at the edge? And how prepared is your organization for that change?

*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "725fc4fc-24d2-4e38-b390-212d15f98453" data-testid= "conversation-turn-11" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:61d93b93-c3cb-4640-8f43-5f9021614702-5" data-testid= "conversation-turn-12" data-scroll-anchor="true" data-turn= "assistant"> How confident are you that your business could recover from a cyberattack, cloud outage, or infrastructure failure in minutes rather than hours or even days?
In this episode of Tech Talks Daily, I explore the changing nature of enterprise resilience with Joseph D'Angelo and Cassie Stanek from InfoScale, now part of Cloud Software Group.
Our conversation looks at why many organizations still rely on backup and replication strategies that were designed for a very different era of IT. In a world of hybrid infrastructure, multi-cloud deployments, and increasingly complex application stacks, those traditional tools often protect the data but often fail to restore the business services that depend on it.
My guests shares how InfoScale approaches resilience from the application layer outward. Instead of focusing on individual components such as storage or infrastructure, the platform looks at the relationships between applications, services, and data so entire systems can be orchestrated and recovered as a coordinated unit. That distinction becomes especially important during a ransomware attack or cloud outage, where restoring a single database rarely brings a digital business back online.
We also discuss how growing regulatory pressure is changing the conversation. Enterprises are no longer expected to simply claim they have disaster recovery processes in place. Increasingly they must demonstrate, test, and prove that recovery capabilities actually work. Cassie explains how controlled "fire drill" rehearsals allow organizations to validate recovery plans without disrupting production systems, creating defensible proof that systems can be restored when it matters most.
We also look ahead to the next phase of resilience, where environments will increasingly diagnose, adapt, and respond to disruptions in real time. Instead of reacting after an outage occurs, operational resilience will rely on predictive analytics, anomaly detection, and automated response capabilities that allow systems to self-correct before users ever notice a problem.
Throughout our discussion, one theme becomes clear. IT resilience is no longer just an infrastructure conversation. It has become a business continuity strategy that directly affects revenue, customer trust, and competitive advantage. As organizations depend more heavily on digital services, the ability to recover quickly from disruption is becoming one of the defining capabilities of modern enterprise technology.
So after listening, I'm curious about your perspective. Do you think most organizations are truly prepared for operational resilience in a multi-cloud world, or are many still relying on backup strategies that were built for a much simpler IT environment?

Have you ever bought a ticket to a show and wondered why the experience still feels strangely disconnected, with one app for ticketing, another for marketing, another for refunds, and a dozen spreadsheets held together by late nights and good intentions?
In this episode of Tech Talks Daily, I'm joined by Ritesh Patel, co-founder of Ticket Fairy, to talk about the technology behind live events and why it has lagged behind other industries in some surprisingly familiar ways. Ritesh makes the case that most organizers are operating more like creative founders than corporate operators, building "mini cities" for a weekend with tiny teams, tight budgets, and very little margin for error. That reality shapes every technology decision, and it explains why fragmented tools and siloed data can become a hidden tax on the business.
Ritesh walks me through Ticket Fairy's full stack approach, bringing ticketing, marketing, CRM, logistics, and payments into a single system, and why unifying data changes the economics of running an event. We dig into practical examples that go beyond vague AI talk, including how small workflow fixes can speed up entry, improve the on-site experience, and even translate into real revenue uplift once you multiply time savings across thousands of attendees.
We also get into where AI agents and large language models are already finding a foothold in events, particularly around unstructured documents like artist specs, supplier agreements, and operational paperwork that can swallow hundreds of hours. Ritesh shares why "AI-native" should mean more than a writing assistant in a text box, and what it looks like when AI becomes an extension of a lean events team, including a prototype voice agent designed to handle common ticket-holder questions without creating new support bottlenecks.
If you're interested in the real business mechanics of events, and how SaaS, payments, data, and AI can quietly shape everything from entry lines to repeat attendance, this conversation offers a fresh way to think about an industry that touches all of us, even when we don't think of it as a tech story.
And as a bonus, Ritesh leaves a music recommendation that sent me back to an album I had not played in years, Burial's Untrue, with "Archangel" as the track to start with. After listening, tell me this, where do you think unified data and practical AI will make the biggest difference in live experiences over the next couple of years, on the promoter side or the fan side, and why?