Tech Talks Daily

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We also explore why cash-only backups and outdated terminals no longer reflect how people actually pay, and why uneven investment in resilience leaves many merchants dangerously exposed.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