Tech Talks Daily

What if some of the most important applications of AI today have nothing to do with productivity, marketing, or enterprise automation, and everything to do with helping people survive crisis, displacement, and uncertainty?

In this episode, recorded at, I sit down with André Heller Pérache to explore how technology originally designed for customer service has evolved into humanitarian infrastructure supporting refugees and displaced communities around the world.

André shares the story behind Signpost, a global digital initiative from the International Rescue Committee that now operates across roughly 30 countries and 25 languages, helping register more than 20 million users while supporting over 500,000 digital social work consultations.

But this conversation goes much deeper than technology.

We discuss what happens when trusted information becomes as important as food, shelter, or medical support during times of crisis. André explains how Signpost was born from the realization that vulnerable communities were already living digitally through smartphones, WhatsApp, Facebook, and social platforms while much of the humanitarian sector still relied on traditional offline systems.

We also explore the responsible use of AI in high-stakes environments where mistakes can have real-world consequences for refugees, families, and vulnerable populations. André shares why the IRC sees AI as one of the humanitarian sector's biggest bets at a time when armed conflict, climate disasters, and shrinking budgets are putting enormous pressure on aid organizations globally.

From misinformation and trust to reducing cognitive burden and scaling empathy through technology, this episode offers a powerful reminder that behind every AI conversation are ultimately human beings searching for dignity, safety, clarity, and hope.

What if the future of AI is not one all-knowing assistant, but an entire workforce of specialized agents working together behind the scenes?

Recorded st Zendesk Relate, this episode features a fascinating conversation with Shashi Upadhyay about where enterprise AI is really heading, and why many businesses are still underestimating the scale of operational change required to make agentic AI work.

Shashi explains why Zendesk views AI agents as a new form of digital labor rather than simply another software feature. Instead of building one giant general-purpose assistant, Zendesk is developing coordinated networks of specialized agents designed for specific business functions such as billing, collections, refunds, returns, employee service, and industry-specific workflows across sectors like healthcare, banking, and e-commerce.

We also go behind the curtain inside Zendesk itself. Shashi shares how the company has transformed internally from a traditional seat-based SaaS business into an organization focused on measurable outcomes such as automation rates, customer satisfaction, and successful resolutions. He also discusses how AI is changing software development itself, enabling smaller engineering teams to move dramatically faster while reshaping how products are designed and built.

The conversation explores some of the biggest themes emerging across the AI industry right now, including outcome-based pricing, AI trust and guardrails, resolution learning loops, embedded AI, and the growing shift toward agent-to-agent interactions where personal AI assistants may eventually negotiate directly with enterprise AI systems on behalf of consumers.

We also discuss the fears many people have around jobs and automation. Rather than predicting catastrophic job loss, Shashi argues there is still enormous unmet demand for better service experiences, and that AI may ultimately allow businesses to finally deliver the level of customer experience people have wanted for years.

If you're trying to understand where enterprise AI moves next after copilots and chatbots, this conversation offers a clear and thought-provoking look at the systems, workflows, and cultural shifts already reshaping the future of work.

What if the cybersecurity industry has spent decades fighting the wrong battle?

In this episode of Tech Talks Daily, I sat down with Benny Czarny, founder and CEO of OPSWAT, to discuss why he believes the traditional "detect and respond" model is no longer enough in a world where AI is accelerating cyber threats faster than security teams can react.

Benny joined me to discuss his new book, Cybersecurity Upside Down, which combines personal stories from building OPSWAT with a bold argument for rethinking how organizations approach cyber defense altogether. His central belief is simple but provocative: detection-based security has trapped the industry in a losing cycle in which attackers need to succeed only once, while defenders are forced into a constant state of reaction.

During our conversation, Benny explained how his thinking evolved after realizing that even layering dozens of antivirus engines and sandboxing technologies still failed to stop malicious files reliably. That realization ultimately pushed him toward a prevention-first philosophy built around Deep Content Disarm and Reconstruction, or CDR. Rather than trying to determine whether a file is malicious, the approach assumes files may already be dangerous and regenerates clean, safe versions before they ever reach users or systems.

We also explored how generative AI is changing the cybersecurity landscape in ways many organizations still underestimate. Benny shared why AI is dramatically reducing the time required to create malware, weaponize exploits, and scale attacks, effectively giving even inexperienced attackers capabilities once reserved for nation states or advanced cybercriminal groups. He also raised concerns that AI data lakes could become contaminated with malicious content, creating entirely new risks for organizations rushing to deploy large language models without securing the data feeding them.

One of the most fascinating aspects of the discussion was the psychology and culture within cybersecurity teams. Benny argued that the industry often celebrates visible incident response activity while undervaluing quiet prevention. In a world dominated by alerts, dashboards, and SOC metrics, truly preventing attacks can almost appear invisible, despite potentially delivering far greater security outcomes.

We also talked about the sectors Benny believes are most exposed today, including energy, manufacturing, and critical infrastructure operators that still rely heavily on reactive security models while facing growing operational and regulatory complexity. He explained why some industries are advancing faster than others and why compliance mandates could become a major catalyst for broader prevention-first adoption.

Beyond cybersecurity itself, this episode also offered a fascinating look into Benny's entrepreneurial journey, what he learned building OPSWAT over two decades, how AI helped him research and structure his book, and why he is now even producing a cybersecurity-focused TV series called Into the Breach, designed to make complex security concepts easier for wider audiences to understand.

This conversation challenges many of the assumptions the cybersecurity industry has normalized for years. Whether you work in security, IT leadership, compliance, or want to understand how AI is reshaping digital risk, this episode offers a very different perspective on what modern cyber resilience could look like in practice.

What 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.

What 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?

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What 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

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What 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

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• Learn More about BQP

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What 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?

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What 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

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What 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?

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