Podcast
Tech Talks Daily
Hosted by Neil C. Hughes · EN
If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change?
Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways.
Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses.
Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords.
We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make.
Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments.
Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas.
New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.
112episodes
Episodes
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Zendesk CEO Tom Eggemeier On Building The Autonomous Service Workforce
4d ago00:26:33Tap to summarizeWhat happens when customer service stops being a department and starts becoming an autonomous operational system? Recorded live at, this conversation with Tom Eggemeier goes far beyond chatbots, copilots, and AI hype cycles. Instead, we explore why Zendesk believes the future of enterprise service will be built around what it calls an "autonomous service workforce," where AI agents, human experts, workflows, analytics, governance, and orchestration layers all work together as one continuously learning system. Tom shares how Zendesk transformed its own internal operations using AI, achieving more than 60% autonomous resolution rates while simultaneously increasing customer satisfaction. We also discuss why the company is shifting away from measuring ticket deflection and toward measuring actual resolutions, what the Forethought acquisition means for Zendesk's long-term AI strategy, and why governance, permissions, and operational trust may become more important than the AI models themselves. But this episode is about much more than software. Tom explains why he believes the next phase of enterprise AI will fundamentally reshape workflows, organizational structures, and even the role humans play inside modern businesses. We unpack the rise of specialized AI agents, why AI-to-AI interactions could soon outnumber human interactions, and why many organizations are underestimating the operational redesign required to make agentic AI work at scale. We also discuss the hidden risks of fragmented AI systems, why disconnected tools continue to drain businesses, and how companies can balance autonomy with human oversight and empathy. If you've been wondering where enterprise AI is really heading beyond the headlines, this conversation offers a fascinating look at how one of the biggest players in customer experience is attempting to redefine service itself.
Transcribe →Atlassian's Sherif Mansour On Why Context Will Define The Future Of AI
5d ago00:35:00Tap to summarizeWhat happens when AI intelligence becomes commoditized? That is the question sitting at the heart of this episode recorded live at Team '26 in Anaheim, where I sat down with Sherif Mansour to unpack one of the biggest shifts happening in enterprise technology right now. For years, the AI conversation has focused on models, prompts, and raw capability. But according to Sherif, the real competitive advantage may no longer come from the intelligence itself. It comes from context. The workflows, relationships, decisions, knowledge, and operational history that exist inside an organization. In this conversation, Sherif takes me deep inside Atlassian's biggest AI announcements around Rovo, Teamwork Graph, AI-powered workflows, and the company's broader vision for what happens when AI moves beyond isolated copilots and starts operating across the flow of work itself. We explore why Atlassian believes organizational context is becoming the defining moat in enterprise AI, why the company is opening Teamwork Graph through MCP and external integrations, and how the industry is rapidly shifting from AI experimentation toward real operational execution. Sherif also myth busts some of the biggest misconceptions surrounding AI adoption today. We discuss the difference between automation and orchestration, why humans still remain central to decision-making, and how enterprises can avoid adding complexity while still moving quickly in the AI era. Along the way, we discuss real-world examples ranging from Formula One race strategy and procurement workflows through to AI-powered onboarding, engineering productivity, and the growing role of agentic systems inside large organizations. One of the most fascinating parts of the discussion centers around the evolution of enterprise software itself. Atlassian no longer sees AI as a standalone assistant sitting in a chat window. Instead, the vision is for AI to become deeply embedded into workflows, helping teams coordinate work, surface insights, and accelerate decision-making in real time. Sherif also shares why he believes the next major platform battle will not be over who owns the smartest AI model, but over who owns the operational context surrounding that intelligence. If you're trying to separate real enterprise AI progress from the hype cycle, this episode offers a thoughtful and refreshingly honest look at where things may actually be heading next. As always, I'd love to hear your thoughts. Is organizational context becoming the real competitive advantage in AI? And how prepared is your business for a future where humans and AI agents increasingly work side by side? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Why AI Is Still Blind to the Physical World and How Flexible Chips Could Change Everything
6d ago00:20:37Tap to summarizeWhat if the biggest limitation holding AI back isn't the model, the data center, or the algorithm, but the fact that most physical objects in the world still cannot communicate digitally? In this episode of Tech Talks Daily, I sat down with Richard Price, CTO and co-founder of Pragmatic Semiconductor, to explore why AI systems remain "half blind" to the physical world and what happens when everyday objects finally become intelligent, connected, and verifiable data sources. Richard shared how Pragmatic Semiconductor is taking a radically different approach to chip design by creating flexible, ultra-thin semiconductors built specifically for item-level intelligence. Rather than competing directly with traditional silicon, Pragmatic is designing lightweight, low-cost electronics that can integrate directly into packaging, labels, healthcare patches, wearable devices, and products that conventional chips cannot support economically or physically. During our conversation, we unpacked why the long-promised "Internet of Everything" has remained frustratingly out of reach for so many years. Richard explained that while silicon has powered decades of incredible innovation, scaling connectivity to billions or even trillions of everyday objects introduces major cost, energy, and sustainability challenges. Pragmatic's flexible semiconductor technology aims to solve that by reducing manufacturing complexity, lowering environmental impact, and enabling intelligence directly at the edge. We also discussed how embedding intelligence at the item level could reshape supply chains, sustainability initiatives, healthcare systems, and even consumer trust. From reducing food waste through smarter logistics to enabling wearable healthcare sensors with entirely new form factors, Richard painted a picture of a future where physical products can actively communicate their identity, condition, and history in real time. One of the most fascinating parts of the conversation centered on how businesses should prepare for this shift. As edge intelligence grows, organizations may need to rethink traditional cloud-heavy architectures and start designing systems in which decisions occur closer to the object itself. Richard explained how this could reduce latency, lower energy usage, and unlock entirely new categories of connected products. We also explored the sustainability side of semiconductor manufacturing at a time when AI infrastructure and hyperscale data centers are drawing increasing scrutiny for their energy and environmental impact. Richard shared how Pragmatic's thin-film manufacturing approach uses fewer chemicals, less water, and lower-temperature processes, while opening the door to more environmentally conscious digital infrastructure. Toward the end of the episode, Richard offered insight into some of the most exciting real-world applications already emerging, including healthcare patches, wearable sensing technologies, AR and VR devices, and electronics that could eventually conform to the human body itself. It is the kind of conversation that makes you rethink what a semiconductor can actually be. If you've ever wondered what comes after smartphones and smart devices, this episode offers a fascinating look at how flexible electronics could quietly become the foundation for the next generation of connected intelligence. Useful Links Connect with Richard Price Learn More About Pragmatic Semiconductor Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →How Quantum-Inspired Computing Is Solving Aerospace's Biggest Challenges
1w ago00:31:15Tap to summarizeWhat happens when an Air Force engineer with experience in intelligence, venture capital, and deep tech startups starts applying quantum-inspired computing to some of the hardest problems in aerospace and defense? In this episode of Tech Talks Daily, I sat down with Nathan Mason, VP of Strategic Growth at BQP, to unpack how quantum-inspired software is already helping organizations solve massive computational challenges without waiting years for fully mature quantum hardware. Nathan shared his fascinating career journey from military service after 9/11 through the intelligence community, business school, venture investing, and ultimately into the world of advanced simulation and optimization. He emphasized how data-driven thinking shaped his approach to high-stakes decision making and why gut instinct alone no longer suffices in an era driven by AI, complex systems, and operational risk. His insights provide valuable guidance for those interested in careers at the intersection of tech and aerospace. We also explored a question many business leaders are asking right now: what does "quantum in practice" actually look like today? Nathan explained how BQP is applying quantum-inspired approaches on existing CPUs and GPUs to improve simulation accuracy, accelerate modeling workloads, and help aerospace organizations make faster, smarter engineering decisions without simply throwing more hardware at the problem. This shows the tangible progress already happening, inspiring the audience with real-world impact. The discussion also tackled the commercial realities behind deep tech innovation. Nathan spoke candidly about the funding challenges facing startups working in quantum and defense technologies, emphasizing that moving beyond theory into operational deployment is difficult but achievable. This perspective encourages the audience to see obstacles as opportunities for innovation and persistence. Toward the end of the episode, Nathan shared thoughtful advice for students, engineers, and professionals looking to build careers in AI, aerospace, quantum, and defense. His message was simple but powerful: stay curious, keep learning, and never underestimate how a single conversation can completely change your career trajectory. If you've ever wondered how quantum computing moves from science fiction headlines into real-world business value, this episode offers a practical and honest perspective on how quantum-inspired software is already making a difference in aerospace and defense industries today. Useful Links Connect with Nathan Mason on LinkedIn Learn More about BQP Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Atlassian's Chief Design Officer on AI, Creativity, and the Future of Work
1w ago00:29:53Tap to summarizeWhat happens when AI stops being a feature and starts reshaping the very craft of design itself? Live from, I sat down with Charlie Sutton for a conversation that went far beyond product interfaces and pixels. As Atlassian unveiled its latest AI ambitions around agents, context, and the Teamwork Graph, Charlie offered a fascinating look at the human side of that transformation and why design may become even more important as AI becomes embedded into the way we work. Charlie shared how Atlassian approaches design at scale across products like Jira, Confluence, Loom, and Rovo, explaining why every interaction should feel intentional and cohesive, even when built by hundreds of people across dozens of teams. But this conversation quickly moved into much bigger territory. We explored how AI is changing the relationship between designers, developers, and business teams, and why the traditional barriers between idea and execution are rapidly disappearing. One of the most thought-provoking parts of the discussion centered around democratization. Charlie argued that while AI tools have dramatically lowered the floor for creativity, they have also raised the ceiling for what users now expect from software experiences. Anyone can prototype an app today, but expectations around quality, coherence, trust, and usability are climbing just as quickly. We also unpacked the growing shift from prompting AI to delegating work to AI agents. Charlie explained why assigning work to agents increasingly resembles managing human teammates, from defining goals and success criteria to understanding strengths, limitations, and context. That naturally led us into a deeper conversation about trust, transparency, and why users must always feel they can "pop the bonnet" and understand what AI systems are doing on their behalf. Another major theme throughout the episode was context. Charlie shared why Atlassian sees organizational context as one of the defining challenges of the AI era and how the Teamwork Graph is helping connect people, projects, conversations, and knowledge across the company. He compared this moment to the first time many of us used Google search and suddenly realized the scale of what was possible. We also discussed how AI adoption is unfolding differently from previous technology waves. Instead of adoption trickling down from hardcore technical users, Charlie is seeing rapid experimentation from marketing, HR, and design teams looking to reduce repetitive work and communicate ideas more effectively. Even his own mother, he joked, has become an AI power user before he has. From AltaVista nostalgia and Ask Jeeves memories to serious conversations about the future of human creativity, this episode captures a rare and honest perspective on where design, collaboration, and AI may be heading next. How will organizations balance personalization with shared experiences as AI becomes embedded into every workflow, and what role will human creativity play when everyone suddenly has access to the same powerful tools? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →AI, Engineering, And Formula One: The Tech Driving the Atlassian Williams F1 Team
1w ago00:28:31Tap to summarizeWhat happens when one of the most iconic teams in Formula One decides to rethink how work gets done behind the scenes completely? Last year, Atlassian Williams Racing made headlines when Atlassian entered Formula One as both title partner and technology partner. At the time, many people saw the partnership as another high-profile sponsorship deal. But over the last twelve months, something much bigger has been unfolding inside the Williams organization. At Team '26 in Anaheim, I sat down with Andrew Boyagi and Matt Harman to unpack how AI, data, workflows, and organizational transformation are reshaping life both at the factory and on the grid. This conversation goes far beyond racing. Matt explains how Williams is reducing the time between "idea to track," compressing development cycles so upgrades arrive at race weekends weeks earlier than before. One striking example involves reducing front wing lead times by a factor of three through parallel workflows and better collaboration, allowing performance gains to reach the circuit three race weekends sooner. Andrew shares how Atlassian's system-of-work philosophy is being applied in one of the most data-intensive environments on earth. We explore how tools like Jira, Confluence, Loom, Rovo, and Teamwork Graph are helping engineers, strategists, operations teams, and factory staff make faster decisions with less operational friction. We also discuss how AI is changing engineers' roles, why organizational context matters more than raw intelligence, and how Formula One teams balance human instinct with AI-driven precision in race strategy decisions. Matt offers fascinating insight into how AI helps teams process decades of historical race data in real time while still relying on human judgment in critical moments. Along the way, we explore the cultural transformation underway at Williams, including the shift away from endless meetings toward faster, outcome-focused collaboration. Matt explains how tools like Loom and Confluence are helping teams make decisions more efficiently while spreading knowledge more effectively across specialist departments. Andrew also reveals some eye-opening metrics from the partnership so far. Since rolling out Atlassian's Teamwork Collection, teams have reportedly increased throughput by 83%, while low-value meetings have been reduced by 863 hours in a single month across 200 people. Perhaps the biggest takeaway from this episode is that Formula One may actually be a perfect reflection of the challenges facing every modern business. As Andrew puts it during our conversation, Formula One is ultimately "an enterprise performance problem," just operating at 300 kilometers an hour with millions of people watching every weekend. If you've ever wondered what enterprise transformation looks like when milliseconds matter, this episode offers a fascinating look inside one of the most ambitious AI and workflow transformation journeys happening anywhere in business today Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Ohana's Human-First Approach To AI In Flexible Short-Term And Mid-Term Rentals
1w ago00:38:05Tap to summarizeWhat happens when the biggest innovation in housing isn't a luxury tower or another short-term rental app, but a platform built specifically for everyone caught in between? In this episode of Tech Talks Daily, I sat down with Ezra Gershanok, co-founder of Ohana, to unpack how his team is quietly reshaping the overlooked middle-term housing market. For years, people relocating for internships, new jobs, temporary projects, or extended travel have faced two bad choices. Either pay eye-watering hotel and Airbnb rates for months at a time or lock themselves into inflexible long-term leases they never really wanted. Ezra experienced this firsthand while relocating during his time at McKinsey, while his co-founder faced similar frustrations at Apple. Instead of accepting the problem as unavoidable, they built a marketplace around trust, flexibility, and human connection. What struck me throughout our conversation was how Ohana sits at the crossroads of technology, real-world problem solving, and changing work culture. The company has already processed more than $37 million in payments over the past year, with average booking values around $8,000 and average stays approaching 80 nights. Those numbers completely change the economics and psychology of online marketplaces. These are no longer casual weekend bookings. These are high-trust decisions involving real money, real relocation stress, and real human relationships. We explored how Ohana uses AI behind the scenes while deliberately keeping the customer experience deeply human. Hosts and guests are introduced on live match calls. Security deposits are held in escrow. Support teams actively facilitate trust between both sides. Ezra shared how the company uses AI to scale communication and operational workflows without replacing human interaction, something that feels increasingly rare in today's race toward automation. The conversation also touched on how employer partnerships with companies like OpenAI, Palantir Technologies, and Oracle are creating predictable housing demand for interns and new hires moving into expensive cities like New York City and London. Ezra explained why the platform initially gained traction among Chinese international students and how those same network effects are now accelerating growth in London. We also discussed the practical side of building a startup with no-code tools like Bubble, scaling globally with a tiny core team, balancing community standards with rapid growth, and why execution still matters more than ideas. Ezra offered refreshingly honest insights about persistence, operational discipline, and why solving an underserved problem often matters far more than building flashy technology. This episode is a fascinating look at how AI can actually support more meaningful human experiences instead of replacing them. It is also a conversation about trust, housing, modern mobility, and the growing realization that the way we live and work no longer fits neatly into old systems. So how will platforms like Ohana shape the future of temporary living as work becomes increasingly global, flexible, and distributed? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Google Cloud Next 2026: Why AI Orchestration Changes Everything
2w ago00:23:54Tap to summarizeAt Google Cloud Next in Las Vegas, I sat down with Granville Valentine to talk about one of the biggest shifts happening in business technology right now, the move from isolated AI experiments to orchestrated, production-scale agentic systems. Granville leads Google Cloud's AI Go-to-Market organization across North America, working directly with major enterprises on adopting Gemini, customer experience AI, and multi-agent workflows. That puts him right at the center of how businesses are actually deploying AI in the real world, and where many are still getting stuck. In this conversation, we explore why so many companies discovered in 2025 that standalone chatbots were failing to deliver measurable ROI, and how orchestration-based AI systems are changing that. Granville explains why the future belongs to multi-agent workflows built around business outcomes rather than technology demos, with different agents collaborating around customer experience, commerce, upselling, support, and personalization. We also discuss the rise of proactive "digital concierges" that unify search, commerce, maps, personalization, and customer service into a single intelligent journey rather than the fragmented app experiences consumers are used to today. Granville shares practical examples from companies like The Home Depot and explains how businesses are using Gemini Enterprise for Customer Experience to create more natural and effective customer interactions. Another major theme in this episode is data. We explore how cross-cloud connectivity and universal context engines are helping organizations query data across multiple cloud environments without moving everything into a single platform first, dramatically reducing friction for companies trying to build agentic workforces. The conversation also touches on generative media, from video and image creation to interactive shopping experiences, and how businesses are using these tools to drive real engagement, customer retention, and revenue growth rather than simply producing flashy content. Most importantly, this episode cuts through the hype and focuses on execution. Granville explains why businesses need to stop thinking about AI as a standalone feature and start thinking about it as an operating model built around outcomes, experimentation, and continuous learning. Are businesses finally ready to move from AI experimentation to the agentic enterprise? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Global Electronics Association CTO on AI Infrastructure and Supply Chain Resilience
2w ago00:26:36Tap to summarizeWhat happens when AI growth collides with the physical limits of power, materials, and global supply chains? In this episode of Tech Talks Daily, I speak with Matt Kelly, CTO and Vice President of Technology and Standards at the Global Electronics Association, about the growing pressure on AI infrastructure and the supply chains that support it. Drawing on insights from thousands of member organizations across manufacturing, automotive, and electronics, Matt offers a practical look at what business and technology leaders should really be preparing for in 2026 and beyond. Our conversation begins with the shift from cost optimization to resilience and system-level performance. Matt explains why the old procurement mindset of chasing the lowest-cost supplier is rapidly being replaced by what he calls confidence-based sourcing. In a world shaped by geopolitical disruption, pandemic aftershocks, and surging demand for AI, organizations are discovering that cheap sourcing means little if critical components fail to arrive on time. We also discuss why dual sourcing has evolved from a procurement strategy into a business continuity requirement. Matt shares real-world examples of how something as small as a missing capacitor can prevent the delivery of million-dollar AI infrastructure systems. That single point of failure has pushed resilience metrics such as recovery time, geographic diversity, and validated backup suppliers into boardroom discussions. Another major focus of the episode centers on AI infrastructure itself. While many conversations around AI focus on software models and automation, Matt argues that the true bottleneck may soon become power availability. From server cooling and energy consumption to sustainable hardware design and material shortages, the industry now faces challenges that stretch far beyond compute performance alone. Matt also explains why fully localized supply chains remain unrealistic for the electronics industry. Instead, he advocates for a balanced model that combines trusted global partnerships with strategic regional sourcing for critical components and security-sensitive technologies. One of the strongest takeaways from this conversation is that AI infrastructure must now be approached as a system problem. Silicon design, packaging, thermal management, power delivery, sustainability, and supply chain strategy cannot be treated as separate conversations. As organizations race to scale AI capabilities over the next few years, are business leaders truly prepared for the infrastructure realities sitting behind the AI boom, or are we about to discover that resilience and energy matter just as much as innovation itself? Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com
Transcribe →Cognichip CEO Explains the New AI Race Happening Inside Semiconductor Design
2w ago00:29:30Tap to summarizeWhat happens when the pace of AI innovation collides with the realities of semiconductor development? In this episode of Tech Talks Daily, I speak with Faraj Aalaei, CEO of Cognichip and a semiconductor industry veteran with more than 25 years of experience spanning engineering, venture capital, and two successful IPOs. Faraj joins me to discuss why the future of artificial intelligence may depend on radically rethinking how chips are designed, manufactured, and scaled. Cognichip is developing the world's first Artificial Chip Intelligence (ACI®) to reimagine chip design. Founded by experts from Amazon, Google, Apple, Aquantia, Synopsys, and KLA, the company tackles high cost and inaccessibility in chip development, enabling hardware to evolve as quickly as software innovation. Backed by $93 million in total funding from Seligman Ventures, SBI Investment, Mayfield, Lux Capital, and Candou Ventures, Cognichip's ACI® reduces design cycles by 50%, cuts development costs by 75%, and optimizes power, performance, and efficiency. ACI® accelerates innovation and broadens access to semiconductor technology by making it easier, more affordable, and accessible to a broader range of innovators Faraj explains how the semiconductor industry now faces a growing bottleneck. While AI software can evolve at remarkable speed, chip development often still takes between three and five years and costs more than $100 million. That mismatch is becoming increasingly difficult to sustain as demand grows for specialized AI hardware, edge computing systems, and next-generation infrastructure. Our conversation also explores the geopolitical and economic shifts reshaping the semiconductor industry. Faraj shares his perspective on the emerging concept of "Pax Silica," the growing effort by governments to restructure global chip supply chains and reduce reliance on China. While many policymakers see this as a matter of national security and resilience, Faraj warns there may also be unintended consequences, including rising AI infrastructure costs, engineering shortages, and slower innovation cycles. One of the most interesting parts of our discussion centers on the idea that AI itself may become the missing scaling factor for semiconductor development. Instead of relying solely on larger engineering teams and longer development cycles, Cognichip believes AI-designed chips could dramatically accelerate innovation and make advanced hardware development accessible to far more companies and researchers. Faraj also reflects on his career journey from entrepreneur to investor and back again, sharing lessons from decades spent helping build the modern semiconductor ecosystem. From supply chain realities to the growing pressure on engineering talent, this episode offers a rare insider perspective on the technologies quietly powering the entire AI economy. As AI systems continue to demand faster, more specialized hardware, are we reaching the limits of traditional chip development, and could AI itself become the tool that reshapes the future of semiconductors?
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