Is the browser quietly becoming the most powerful and dangerous interface in modern work?

In this episode of Tech Talks Daily, I sat down with Karim Toubba, CEO of LastPass, to unpack a shift that many people feel every day but rarely stop to question.

The browser is no longer just a window to the internet. It has become the place where work happens, where SaaS lives, and increasingly, where humans and AI agents meet data, credentials, and decisions. From AI-native browsers to prompt-based navigation and headless agents acting on our behalf, the way we access information is changing fast, and so are the risks.

Karim shares why this moment feels different from earlier waves like SaaS adoption or remote work. Today, more than ever, productivity, identity, and security collide inside the browser.

Shadow AI is spreading faster than most organizations can track, personal accounts are being used to access powerful AI tools, and sensitive data is being uploaded with little visibility or control. At the same time, attackers have noticed that the browser has become the soft underbelly of the enterprise, with a growing share of malware and breaches originating there.

We also explore the rise of agentic AI and what happens when software, not people, starts logging into systems. When an agent books travel, pulls data, or completes workflows on a user's behalf, traditional authentication and access models start to break down.

Karim explains why identity, visibility, and control must evolve together, and why secure browser extensions are emerging as a practical foundation for this next phase of computing. The conversation goes deep into what users do not see when AI browsers ask for access to email, calendars, and internal apps, and why convenience often masks long-term exposure.

Throughout the discussion, Karim brings a grounded perspective shaped by decades in cybersecurity, from risk-based vulnerability management to enterprise threat intelligence. Rather than pushing fear, he focuses on realistic steps organizations and individuals can take, from understanding what data is being shared, to treating security teams as partners, to using tools that bring passwords, passkeys, and authentication into one trusted place as browsing evolves.

As AI reshapes how we search, work, and make decisions, the question is no longer whether the browser matters. It is whether we are ready for it to act as the front door to both our productivity and our risk, so are you securing your browser for the future you are already using today?

• Connect with Karim Toubba
• LastPass Threat Intelligence, Mitigation, and Escalation (TIME) team page
• Phish Bowl Podcast

What really happens when AI helps teams write code faster, but everything else in the delivery process starts to slow down? In this episode of Tech Talks Daily, I'm joined once again by returning guest and friend of the show, Martin Reynolds, Field CTO at Harness.

It has been two years since we last spoke, and a lot has changed since then. Martin has relocated from London to North Carolina, gaining back hours of his working week. Still, the bigger shift has been in how AI is reshaping software delivery inside modern enterprises.

Our conversation centers on what Martin calls the AI velocity paradox. Development teams are producing more code at speed, often thanks to AI coding agents, yet testing, security, governance, and release processes are struggling to keep up. The result is a growing gap between how fast software is written and how safely it can be delivered.

Martin shares research showing how this imbalance is already leading to production incidents, hidden vulnerabilities, and mounting technical debt.

We also dig into why this AI-driven transition feels different from previous waves, such as cloud, mobile, or DevOps. Many of the same concerns around security, trust, and control still exist, but this time, everything is happening far faster. Martin explains why AI works best as a human amplifier, strengthening good engineering practices while exposing weak ones sooner than ever before.

A significant theme in the episode is visibility. From shadow AI usage to expanding attack surfaces, Martin outlines why security teams are finding it harder to see where AI is being used and how data is flowing through systems. Rather than slowing teams down, he argues that the answer lies in embedding governance directly into delivery pipelines, making security automatic rather than an afterthought.

We also explore the rise of agentic AI in testing, quality assurance, and security, where specialized agents act like virtual teammates. When well-designed, these agents help developers stay focused while improving reliability and resilience throughout the lifecycle.

If you are responsible for engineering, platform, or security teams, this episode offers a grounded look at how to balance speed with responsibility in an AI-native world. As AI becomes part of every stage of software delivery, are your processes designed to safely absorb that change, or are they quietly becoming the bottleneck?

Useful Links

• Learn More About Harness
• The State of AI in Engineering
• The State of AI Application Security
• EngineeringX
• Follow Harness on LinkedIn
• Connect With Martin Reynolds

Thanks to our sponsors, Alcor, for supporting the show.

What really happens to a business when payments stop working, even for a few minutes?

I recorded this episode live at Dynatrace Perform in Las Vegas, inside the Venetian, surrounded by engineers, operators, and business leaders all wrestling with the same uncomfortable reality. Payment outages are no longer rare edge cases. They are becoming a routine operational risk, and the cost is far higher than many organizations realize.

To unpack that shift, I sat down with Victoria Ruffo, Software Engineering Team Lead at FreedomPay, for a grounded and practical conversation about resilience, observability, and what failure actually looks like in modern commerce.

Victoria explains how FreedomPay supports merchants by orchestrating every part of the payment journey through a single platform, from terminal management to remote updates and even on-device advertising.

If you have checked into a hotel and noticed a payment terminal quietly branded "Secured By FreedomPay," there is a good chance you have already interacted with her team's work. That real-world exposure gives her a clear view of what happens when systems fail and why customers are far less patient than businesses often assume.

We talk about new research from FreedomPay, Dynatrace, and Retail Economics that puts a stark number on the issue. $44.4 billion in U.S. retail and hospitality revenue is at risk every year due to payment disruptions. But as Victoria points out, the most alarming insight is not the headline figure. It is the gap between how long customers are willing to wait and how long outages actually last.

Most consumers abandon a purchase after seven minutes, while many disruptions stretch on for hours. In those early minutes alone, the majority of revenue is already gone.

The conversation moves beyond statistics into lived experience. From lunch breaks cut short by declined payments to stadiums losing an entire event's worth of revenue in a single outage, Victoria shares why these failures are not abstract technical issues. They directly affect staff wages, customer loyalty, and long-term brand trust.

We also explore why cash-only backups and outdated terminals no longer reflect how people actually pay, and why uneven investment in resilience leaves many merchants dangerously exposed.

AI plays a central role in the discussion, but not in the way hype cycles often suggest. Victoria is clear that FreedomPay is not using AI to touch cardholder data or write payment code. Instead, tools like Dynatrace Intelligence help teams detect issues faster, identify patterns humans might miss, and move from reaction to anticipation. That shift, she argues, is where real value shows up, especially when seconds and minutes matter.

If you care about payments, customer experience, or the hidden connection between technical failure and business impact, this episode offers a timely reminder that outages do not have to be catastrophic if organizations plan for them properly.

As consumers grow less patient and systems grow more complex, are your payment platforms designed to absorb disruption, or are they quietly waiting to fail at the worst possible moment?

Useful Links

• Connect With Victoria Ruffo
• Learn More About Freedom Pay
• Whitepaper Payment Resilience in an Uncertain World
• Learn More About Dynatrace Perform

Thanks to our sponsors, Alcor, for supporting the show.

In this episode of Tech Talks Daily, I'm joined by Josh Haas, co-founder and co-CEO of Bubble, to unpack why the next phase of software creation is already taking shape. We talk about how the early excitement around AI-powered code generation delivered fast demos and instant gratification, but often fell apart when teams tried to turn those experiments into durable products that could grow with a business.

Josh takes us back to Bubble's origins in 2012, long before AI hype cycles and trend-driven development. At the time, the idea was simple but ambitious: give more people the ability to build genuine software without spending months learning traditional programming. That early focus on visual development now feels timely again, especially as builders wrestle with the limits of black-box AI tools that hide logic until something breaks.

We spend time on where vibe coding struggles in practice. Josh explains why speed alone is never enough once customers, payments, and sensitive data are involved. As he explains, most product requirements only surface after users arrive, and those edge cases are exactly where opaque AI-generated code can become risky. If you cannot see how your system works, you cannot truly own it, secure it, or fix it when something goes wrong.

The conversation also digs into Bubble's hybrid approach, blending AI agents with visual development. Rather than asking builders to trust an AI, Bubble's model unquestioningly emphasizes clarity, auditability, and shared responsibility between humans and machines. Josh explains how visual logic makes software behavior explicit, helping teams understand rules, permissions, and workflows before they cause real-world problems.

I learn how this mindset has helped Bubble-powered apps process over $1.1 billion in payments every year, a level of scale that leaves no room for guesswork.

We also explore Bubble AI Agent, where conversational AI meets visual editing, and why transparency and control matter more than flashy demos. From governance and rollback logs to builder accountability, this episode looks at what it actually takes to build software that survives beyond the first launch.

If you are building with AI or thinking about how software development is changing, this episode offers a grounded perspective on what comes after the hype fades. As AI tools become more powerful, the real question is whether they help you understand your product better over time, or slowly disconnect you from it.

Which path should builders choose right now?

Useful Links

• Connect with Josh Haas
• Learn More About Bubble

Thanks to our sponsors, Alcor, for supporting the show.

How do you turn a developer-first product into a growth engine without losing trust, clarity, or focus along the way?

In this episode of Tech Talks Daily, I'm joined by Sanjay Sarathy, VP of Developer Experience and Self Service at Cloudinary, for a grounded and thoughtful conversation about product-led growth when developers sit at the center of the story. Sanjay operates at a rare intersection. He leads Cloudinary's high-volume self-service motion while also caring for the developer community that fuels adoption, advocacy, and long-term loyalty. That dual perspective, part business, part builder, shapes everything we discuss.

Our conversation picks up on a theme I have been exploring across recent episodes. When technical work is explained clearly, whether that is security, performance, or reliability, it stops being background noise and starts supporting growth. Sanjay shares how Cloudinary approached this from day one, starting with founders who were developers themselves and carried a deep respect for developer trust into the company's DNA. Documentation that reflects reality, platforms that behave exactly as promised, and support that shows up early rather than as an afterthought all play a part.

What stood out to me was how early Cloudinary invested in technical support, even before many traditional growth motions were in place. That decision shaped a self-service experience that still feels human at scale. With thousands of developer sign-ups every day and millions of developers using the platform, Sanjay explains how trust compounds into referrals, word of mouth, and sustained adoption.

We also dig into developer advocacy and why community is rarely a single thing. Developers gather around frameworks, tools, workflows, and shared problems, and Cloudinary has learned to meet them where they already are rather than forcing them into a single branded space. From React and Next.js users to enterprise advisory boards, feedback loops become part of the product itself.

As AI reshapes how software is built and developer tools become more crowded, Sanjay offers a clear-eyed view on what separates companies that grow steadily from those that burn bright and stall. Profitability, experimentation with intent, and the discipline to double down on what works all feature heavily in his thinking. It is a conversation rooted in experience rather than theory.

If you care about product-led growth, developer trust, or building platforms that scale without losing their soul, this episode offers plenty to think about. As always, I would love to hear your perspective too. How do you see developer communities shaping the next phase of product growth, and where do you think companies still get it wrong?

Useful Links

• Connect with Sanjay Sarathy
• Learn more about Cloudinary

Thanks to our sponsors, Alcor, for supporting the show.

What happens when the rush toward AI collides with the messy reality of enterprise data that was never designed for it?

That is exactly where this episode with Kevin Dattolico from Syntax begins. Before we even hit record, we were swapping stories about music, travel, and a certain farewell concert that set the tone for a conversation that was both grounded and unexpectedly human. But once we got going, the discussion quickly shifted to one of the biggest blind spots I keep hearing about at tech conferences around the world. AI ambition is running far ahead of data readiness.

Kevin leads Syntax across the Americas, working with organizations that rely on SAP, Oracle, and complex cloud environments to run their businesses. In our conversation, he shares why many AI initiatives stall or quietly reset the moment they touch real production data. Proofs of concept can look impressive in isolation, but once AI starts interacting with live operational systems, the cracks appear. Inconsistent data, duplicated records, missing context, and governance gaps all surface at once. The result is confusion, unpredictable outputs, and a growing realization that the issue is rarely the model itself.

We dig into why ERP data has traditionally been trusted, while unstructured data across emails, documents, sensors, and logs often tells a very different story. Kevin explains where the real friction shows up when companies try to bring those worlds together, and why assumptions about data quality tend to break long before the technology does. It is a refreshingly honest look at what usually goes wrong first, and why leaders are often blindsided even after years of investment.

One of the strongest themes in this episode is the shift Kevin sees from AI-first thinking toward a data-first mindset. That does not mean abandoning AI spend. It means rebalancing priorities so those investments actually deliver outcomes the business can stand behind. We talk about what consolidation, cleansing, and transformation look like at enterprise scale, especially for organizations carrying decades of technical debt and fragmented systems.

The conversation also takes a thoughtful turn around governance, trust, and leadership. Kevin shares how the role of the chief data officer is changing from gatekeeper to enabler, and why modern governance has to support speed without sacrificing accountability. Along the way, he reflects on the risks of pushing ahead with weak data foundations, particularly in regulated industries where the cost of getting it wrong can be operational, reputational, or worse.

And then there is the moment that caught me completely off guard. When I asked Kevin to look back on his career and reflect on someone who made a difference, his answer led to one of the most moving stories I have heard in thousands of interviews. It is a reminder that behind every transformation story, there are people who quietly shape the path forward.

If you are wrestling with AI expectations, data reality, or simply wondering whether everyone else feels just as overwhelmed by this shift, this episode will resonate. The challenges Kevin describes are far more common than most leaders admit, and the opportunities for those who get the foundations right are real.

So as AI continues to dominate boardroom conversations, are you confident your data is ready to support the decisions you are asking it to make, or is it time to pause and rethink what sits underneath it all?

Useful Links

• Connect with Kevin Dattolico
• Learn more about Syntax

Thanks to our sponsors, Alcor, for supporting the show.

Why do today's most powerful AI systems still struggle to explain their decisions, repeat the same mistakes, and undermine trust at the very moment we are asking them to take on more responsibility?

In this episode of Tech Talks Daily, I'm joined by Artur d'Avila Garcez, Professor of Computer Science at City St George's University of London, and one of the early pioneers of neurosymbolic AI.

Our conversation cuts through the noise around ever-larger language models and focuses on a deeper question many leaders are now grappling with. If scale alone cannot deliver reliability, accountability, or genuine reasoning, what is missing from today's AI systems?

Artur explains neurosymbolic AI in clear, practical terms as the integration of neural learning with symbolic reasoning. Deep learning excels at pattern recognition across language, images, and sensor data, but it struggles with planning, causality, and guarantees. Symbolic AI, by contrast, offers logic, rules, and explanations, yet falters when faced with messy, unstructured data. Neurosymbolic AI aims to bring these two worlds together, allowing systems to learn from data while reasoning with knowledge, producing AI that can justify decisions and avoid repeating known errors.

We explore why simply adding more parameters and data has failed to solve hallucinations, brittleness, and trust issues. Artur shares how neurosymbolic approaches introduce what he describes as software assurances, ways to reduce the chance of critical errors by design rather than trial and error. From self-driving cars to finance and healthcare, he explains why combining learned behavior with explicit rules mirrors how high-stakes systems already operate in the real world.

A major part of our discussion centers on explainability and accountability. Artur introduces the neurosymbolic cycle, sometimes called the NeSy cycle, which translates knowledge into neural networks and extracts knowledge back out again. This two-way process opens the door to inspection, validation, and responsibility, shifting AI away from opaque black boxes toward systems that can be questioned, audited, and trusted. We also discuss why scaling neurosymbolic AI looks very different from scaling deep learning, with an emphasis on knowledge reuse, efficiency, and model compression rather than ever-growing compute demands.

We also look ahead. From domain-specific deployments already happening today to longer-term questions around energy use, sustainability, and regulation, Artur offers a grounded view on where this field is heading and what signals leaders should watch for as neurosymbolic AI moves from research into real systems.

If you care about building AI that is reliable, explainable, and trustworthy, this conversation offers a refreshing and necessary perspective. As the race toward more capable AI continues, are we finally ready to admit that reasoning, not just scale, may decide what comes next, and what kind of AI do we actually want to live with?

Useful Links

• Neurosymbolic AI (NeSy) Association website
• Artur's personal webpage on the City St George's University of London page
• Co-authored book titled "Neural-Symbolic Cognitive Reasoning"The article about neurosymbolic AI and the road to AGI
• The Accountability in AI article
• Reasoning in Neurosymbolic AI
• Neurosymbolic Deep Learning Semantics

Why does healthcare keep investing in new technology while so many clinicians feel buried under paperwork and admin work that has nothing to do with patient care?

In this episode of Tech Talks Daily, I'm joined by Dr. Rihan Javid, psychiatrist, former attorney, and co-founder and president of Edge. Our conversation cuts straight into an issue that rarely gets the attention it deserves, the quiet toll that administrative overload takes on doctors, care teams, and ultimately patients. Nearly half of physicians now link burnout to paperwork rather than clinical work, and Rihan explains why this problem keeps slipping past leadership discussions, even as budgets for digital tools continue to rise.

Drawing on his experience inside hospitals and clinics, Rihan shares how operational design shapes outcomes in ways many healthcare leaders underestimate. We talk about why short-term staffing fixes often create new problems down the line, and how practices that invest in stable, well-trained remote administrative teams see real improvements. That includes faster billing cycles, fewer errors, and more time back for clinicians who want to focus on care rather than forms. What stood out for me was his framing of workforce infrastructure as a performance driver rather than a compliance box to tick.

We also dig into how hybrid operations are becoming the default model. Local clinicians working alongside remote admin teams, supported by AI-assisted workflows, are now common across healthcare. Rihan is clear that while automation and AI can remove friction and cost, human oversight still matters deeply in high-compliance environments. Trust, accuracy, and patient confidence depend on knowing where automation fits and where human judgment must stay firmly in place.

Another part of the discussion that stuck with me was Rihan's idea that stability is emerging as a better success signal than raw cost savings. High turnover may look efficient on paper, but it quietly limits a clinic's ability to grow, retain knowledge, and improve patient outcomes. We unpack why consistent administrative support can influence revenue cycles, satisfaction, and long-term resilience in ways traditional metrics often miss.

If you're a healthcare leader, operator, or technologist trying to understand how AI, remote teams, and smarter operations can work together without losing trust or care quality, this conversation offers plenty to reflect on. As healthcare systems rethink how work gets done behind the scenes, what would it look like if stability and clinician well-being were treated as core performance measures rather than afterthoughts, and how might that change the future of care?

Useful Links

• Connect with Dr. Rihan Javid
• Edge Health
• Rinova AI

Thanks to our sponsors, Alcor, for supporting the show.

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Why do small business leaders keep buying more software yet still feel like they are drowning in logins, dashboards, and unfinished work?

In this episode of Tech Talks Daily, I sit down with Jesse Lipson, founder and CEO of Levitate, to unpack a frustration I hear from business owners almost daily. After years of being pitched yet another tool, many leaders now spend hours each week troubleshooting software instead of serving customers. Jesse brings a grounded perspective shaped by decades of building SaaS companies, including bootstrapping ShareFile before its acquisition by Citrix, and what stood out to me immediately was how clearly he articulates where the current software model has broken down for small businesses.

We talk about why adding more apps has not translated into better outcomes, especially for teams without dedicated specialists in marketing, finance, or sales. Jesse explains how traditional software often solves only part of the problem, leaving owners to become accidental experts in accounting, marketing strategy, or customer communications just to make the tools usable. From there, our conversation shifts toward what he believes will actually matter as AI adoption matures. Rather than chasing full automation or shiny new dashboards, Jesse argues that the real opportunity lies in blending intelligence with human guidance, allowing AI to work quietly behind the scenes while people remain the face of authentic relationships.

A big part of our discussion centers on trust and connection in an AI-saturated world. Jesse shares why customers have become incredibly good at spotting automated communication and why relationship-based businesses cannot afford to lose the human element. We explore how AI can act as a second brain, helping business owners remember details, follow up at the right moments, and show up more thoughtfully, without crossing the line into impersonal automation that turns customers away. His examples, from marketing emails to customer support, make it clear that technology should support better relationships rather than replace them.

We also look ahead to what small businesses should realistically focus on as AI evolves. Jesse offers practical guidance on getting started, from everyday use of conversational AI, to building internal documentation that allows systems to work more effectively, and eventually moving toward agent-based workflows that can take on real operational tasks. Throughout the conversation, he keeps returning to the same idea, that AI works best when it helps people become the kind of business leaders they already want to be, more present, more consistent, and more human.

If you are a founder, operator, or small business leader feeling overwhelmed by tools that promise productivity but deliver friction, this episode offers a refreshing reset. As AI becomes more capable and more embedded in daily work, the real question is not how many systems you deploy, but whether they help you build stronger, more genuine relationships, so how are you choosing to use AI to support the human side of your business rather than bury it?

Useful Links

• Connect with Jesse Lipson
• Connect with Jesse on X
• Learn more about Levitate

What happens when power, rather than compute, becomes the limiting factor for AI, robotics, and industrial automation?

In this episode of Tech Talks Daily, I'm joined by Ramesh Narasimhan from Nyobolt to unpack a challenge that is quietly reshaping modern infrastructure. As AI training and inference workloads grow more dynamic, power demand is no longer predictable or steady. It can spike and drop in milliseconds, creating stress on systems that were never designed for this level of volatility. We talk about why data center operators, automation leaders, and industrial firms are being forced to rethink how energy is delivered, managed, and scaled.

Our conversation moves beyond AI headlines and into the less visible constraints holding progress back. Ramesh explains how automation growth, particularly in robotics and autonomous mobile robot fleets, has exposed hidden inefficiencies. Charging downtime, thermal limits, and oversized systems are eroding productivity in warehouses and factories that aim to run around the clock. Instead of expanding physical footprints or adding redundant capacity, many operators are questioning whether the energy layer itself has become outdated.

One of the themes that stood out for me is how energy has shifted from a background utility to a board-level concern. Power density, resilience, and cycle life are now discussed with the same urgency as compute performance or sensor accuracy. Ramesh shares why executives across logistics, automotive, advanced manufacturing, and AI infrastructure are starting to see energy strategy as a direct driver of uptime, cost control, and competitive advantage.

We also explore the industry-wide push toward high-power, high-uptime operations. As businesses demand systems that can stay online continuously, the pressure is on energy technologies to respond faster, charge quicker, and occupy less space. This raises difficult questions about oversizing infrastructure for rare peak loads versus designing smarter systems that can flex in real time without waste.

If you are building or operating AI clusters, robotics platforms, or industrial automation at scale, this episode offers a clear-eyed look at why energy systems may be the next major bottleneck and opportunity. As power becomes inseparable from performance, how ready is your organization to treat energy as a strategic asset rather than an afterthought?