3244: From Mainframes to AI: Paula Paul on Building Software That Lasts

What happens when four decades of hands-on engineering experience meet today’s fast-moving AI and open-source ecosystem? In this episode of Tech Talks Daily, I sit down with Paula Paul, Founder and Distinguished Engineer at Greyshore, to explore that intersection. Paula began her tech career in the early 1980s writing code for IBM mainframes and has since become a trusted voice in enterprise modernization, cloud adoption, and open-source governance. Today, she advises organizations on how to build with confidence in an era defined by complex software supply chains, generative AI, and evolving SaaS models. We recorded this conversation during a time when many organizations are feeling the pressure to adopt AI and modernize legacy systems, but struggle with where to begin. Paula offers grounded insights on how to break down replatforming into value-driven streams and why a “big bang” transformation approach is often more risk than reward. She also talks about the critical role of open source in business today—not just from a tooling standpoint, but in terms of responsibility, transparency, and community. Paula’s perspective is refreshingly practical. She believes AI is a natural evolution of decades of computing and storage expansion but urges companies to start with targeted experiments and thoughtful team collaboration. She also shares her experiences as a woman in tech since the 1980s, reflecting on what’s changed and what hasn’t and how leadership dynamics continue to shape opportunities for women in the industry. Beyond the code, Paula draws a fascinating parallel between music and technology. As a board member of the Brookline Music School and an oboe and English horn student, she explores how musical thinking can enhance software development's rhythm, structure, and leadership. If you’re navigating modernization, curious about AI’s role in development, or wondering how to make open source work for your organization—technically and strategically—this episode has something for you. How are you balancing technical ambition with long-term value? Let’s continue the conversation.

What does it really mean to build software with joy? At the Team 25 event in Anaheim, I had the opportunity to sit down with Rajeev Rajan, CTO of Atlassian, to explore how one of the world’s most influential engineering organizations is redefining developer experience from the ground up. Rajeev shares how developer joy became a guiding principle for Atlassian, not as a feel-good initiative but as a core driver of performance and innovation. Rather than focusing solely on velocity or throughput, Atlassian measured how productive developers felt in their codebases. That metric has now improved from 49 percent to 75 percent, thanks to a deliberate strategy built on three pillars: better tools, stronger engineering culture, and empowered teams. We also dive into how this cultural shift helped accelerate the delivery of Rovo, Atlassian’s most ambitious AI product to date. Built-in record time, Rovo didn’t emerge from a top-down push but from an environment where engineers were supported to move fast without cutting corners. That includes training internal teams through AI School, rolling out dev agents, and prioritizing transparency over automation for automation’s sake. Rajeev also reflects on how India has become a global engine for AI and software development and why Atlassian invests in its global teams with the same level of trust and ownership as any other location. From ship-it hackathons to hands-on leadership and a grounded view of where AI helps and where it still falls short, this conversation highlights what it takes to build modern engineering teams that are both productive and fulfilled. So, if you’re building AI strategies, leading engineering teams, or rethinking how to scale innovation responsibly, what would change if developer happiness were your benchmark?

What if most product launches fail not because of bad ideas—but because of broken processes? In today’s episode of Tech Talks Daily, I’m joined by Stuart Jackson, Vice Chair of L.E.K. Consulting and co-author of the forthcoming book Predictable Winners (Stanford University Press, 2025). Stuart brings nearly four decades of experience in strategic growth and innovation, and his insights offer a much-needed reframe for businesses navigating the high-stakes world of new product and service development. We sat down to explore a topic many leaders shy away from: why 70–90% of product launches still miss the mark. Stuart breaks down the structural weaknesses that often derail innovation and explains why relying on gut instinct is no longer enough. Instead, we discuss how a rigorous, data-backed approach—augmented by AI—can dramatically reduce the risk profile of innovation while improving speed, accuracy, and return on investment. Stuart shares how leading organizations are integrating AI into every phase of their innovation pipeline—from idea screening and predictive analytics to launch planning and ongoing iteration. He outlines the rise of long-horizon thinking and how AI is enabling companies to identify emerging demand signals well before the competition. What I found particularly refreshing was Stuart’s take on what top innovators do differently. It’s not just about having better ideas—it’s about building the right systems. That means empowering cross-functional teams, systematically disaggregating risk, and knowing when to pivot versus when to persevere. This conversation isn’t about theory. It’s a pragmatic look at how to move from product guesswork to predictable success—whether you're building in-house or buying through M&A. For founders, product leaders, and anyone trying to modernize their innovation model, this episode offers grounded advice and a clear framework that you can start applying today. How are you thinking about AI as a tool for risk reduction in innovation? Are your product strategies built for speed or sustainability—or both? Let me know what you think.

At Team '25 in Anaheim, I had the unique opportunity to sit down with Stan Shepard, General Counsel at Atlassian, for a conversation that pulled back the curtain on how legal and technology are intersecting in the age of AI. Stan’s journey from journalism to law to shaping legal operations at one of the world’s most forward-thinking companies is as fascinating as it is relevant. What emerged from our discussion is a clear signal that legal teams are no longer trailing behind innovation—they’re often at the front of it. Stan shared how Atlassian’s legal function achieved 85 percent daily usage of AI tools, including the company’s in-house assistant, Rovo. This is remarkable when compared to the industry norm, where legal teams typically lag in AI adoption. Instead of resisting change, Stan’s team leaned into it, focusing on automation for repetitive tasks while reserving high-value thinking for their legal experts. We explore Atlassian’s responsible tech framework, their principles around transparency and accountability, and how these inform product development from day one. Stan also walked me through how Atlassian is navigating the emerging global regulatory landscape, from the EU AI Act to evolving compliance in the US. His insights on embedding legal counsel directly into product teams, rather than operating on the sidelines, reveal a model of collaboration that turns risk management into a growth enabler. For legal professionals, compliance leaders, and tech decision-makers wrestling with how to integrate AI responsibly, this episode offers a grounded, real-world blueprint. It’s not just about mitigating risk—it’s about building trust, preserving human judgment, and future-proofing your operations. If you’re wondering what responsible AI adoption looks like at scale, you’ll want to hear this one. So how are you preparing your legal and compliance strategy for the AI-powered workplace? Let’s keep the conversation going.

What if cloud storage could be simple, transparent, and genuinely affordable? In this episode of Tech Talks Daily, I sit down with Tommi Kannisto, founder of Storadera a company based in Estonia that's redefining the way businesses approach object storage. I first met Tommi during the IT Press Tour in London, where Storadera's fresh perspective on S3-compatible cloud storage stood out for all the right reasons. In our conversation, Tommi shares the journey behind building a hyper-converged cloud storage platform that challenges conventional thinking. From eliminating hidden fees to maximizing performance on traditional spinning disks, Storadera is built around an ethos of doing more with less. The platform’s architectural simplicity allows for efficient scaling, reliable performance with small files, and an operational model that avoids complexity by design. Tommi also walks us through how Storadera handles everything from data durability to sovereignty, with data centers already operating in Estonia and the Netherlands. With growing demand from regions focused on local control of data, we explore the opportunities emerging from geopolitical shifts and increasing global focus on sovereignty. Estonia’s digital-first culture and its thriving startup ecosystem form the backdrop to this innovation, and we discuss how a small country has produced more than its share of tech unicorns. As we look to the road ahead, Tommi explains how AI might soon play a role in optimizing read and delete operations to push storage efficiency even further. He also touches on the growing interest in Storadera from companies seeking alternatives to US-based hyperscalers and how demand from across Europe is shaping their expansion plans. If you’re exploring options beyond the cloud giants or curious about what’s brewing in Europe’s fast-growing tech scene, this episode offers a fresh, founder-led view of the storage market. Let me know what resonates with you. Are you considering new storage strategies in light of data locality or cost pressures? I’d love to hear your thoughts.

What if managing databases on Kubernetes didn’t require a team of specialists or endless configuration? In this episode of Tech Talks Daily, I sit down with Tamal Saha, founder and CEO of AppsCode, to explore how his team is building a more intuitive, scalable, and enterprise-ready approach to Kubernetes-native data management. Recorded during the IT Press Tour in London, this conversation traces Tamal’s journey from Bangladesh to Google, and ultimately to launching AppsCode in 2016. He shares how early experiences with Google's internal systems helped shape his vision for a cloud-native data platform built for modern application environments. What began as an open-source passion project has evolved into a comprehensive enterprise suite that includes solutions like KubeDB, Stash, Voyager, and KubeVault. We discuss the operational realities of managing databases in Kubernetes—from simplifying provisioning and backups to solving problems around TLS management, multi-tenancy, and even secret rotation. Tamal outlines the benefits of a Kubernetes-native architecture for platform engineers, DevOps teams, and developers who want to deploy faster, automate more, and keep full control over their environments. With real-world insights from enterprise deployments, including large-scale telcos modernizing their infrastructure, Tamal explains how AppsCode is helping organizations move from ticket-based database provisioning to true self-service. He also looks ahead to what's coming next, including support for vector databases, AI-powered provisioning interfaces, and deeper integrations with open telemetry and observability tools. Whether you’re running stateful workloads in Kubernetes today or exploring how to modernize your data layer, this episode is packed with insight into building platforms that work with developers, not against them. So, is it time to rethink your approach to data in the cloud-native era? Let me know what you think.

What happens when the need for rapid AI innovation runs up against the growing pressure for trust, accountability, and compliance? In this episode of Tech Talks Daily, I sit down with Mrinal Manohar, CEO of Prove AI, to explore how risk management can accelerate rather than hinder AI deployment. Mrinal shares how Prove AI is helping organizations build trust into their AI systems from the start. At a time when businesses are moving AI models into production, yet often lack visibility or safeguards, Prove AI offers a solution grounded in transparency and automation. Their approach uses distributed ledger technology to create tamper-proof audit trails for AI models. This allows teams to focus on innovation while having the infrastructure in place to meet evolving standards and regulatory demands. We discuss why traditional monitoring techniques fall short in an AI context, especially as models become more complex and decisions happen in real time. Prove AI’s infrastructure is designed to support continuous risk mitigation. By recording every event and decision with cryptographic certainty, they make it possible to prove safety, compliance, and responsible use without relying on labor-intensive manual audits. Mrinal also explains how Prove AI’s upcoming GRC product aligns with ISO 42001 and helps companies stay ahead of regulatory expectations. Whether you're deploying AI in customer service, manufacturing, or high-risk environments, the platform ensures clear oversight without disrupting speed or agility. This conversation covers practical examples of AI risk in action, from automated railway inspections to drive-through ordering systems. We also explore how distributed ledger technology is helping redefine AI governance, offering companies a way to move fast with confidence. If you're scaling AI and wrestling with risk, compliance, or trust, this episode will give you a fresh perspective on how to build guardrails that support growth—not slow it down.

What if the way we work could finally match the way we want to work? At Atlassian’s Team '25, that vision is no longer a distant ideal.  In this special episode recorded live from the event, I sit down with Anu Bharadwaj, President of Atlassian, for an in-depth conversation about the future of collaboration and the company’s newly formalized System of Work. Anu offers a behind-the-scenes look at how Atlassian is rethinking productivity through a new lens. We discuss how the System of Work unifies teams, tools, and data to drive meaningful outcomes instead of isolated wins. Anu explains how this framework was designed to move beyond conventional work management tools, helping teams focus not just on doing more, but on achieving more together. One of the standout innovations is the Teamwork Graph. Built to provide context across tools and departments, it connects strategy to execution with clarity. We explore how this visibility allows leaders to uncover bottlenecks, align resources, and connect the dots between business goals and day-to-day work. It’s about making the invisible work visible and measurable. We also dive into Rovo, Atlassian’s new AI teammate designed to elevate rather than replace human contribution. Anu shares how AI tools are being developed to reduce cognitive load, eliminate friction, and give people more time to focus on what truly matters.  By embedding intelligence directly into the workflow, Atlassian is helping organizations reduce the time lost searching for answers and navigating silos. For leaders looking to modernize their operations, Anu outlines where to start and what to watch out for. Whether you are overseeing enterprise transformation or leading a fast-moving team, this conversation provides a practical view into how the Atlassian platform is helping organizations unlock better outcomes. What resonated most for me was the shift from chasing productivity to enabling performance. Atlassian is not just enhancing collaboration—it’s helping reshape how teams think, operate, and scale. If you're curious about the System of Work, the Teamwork Collection, or what was unveiled at Team '25, this episode is for you. It’s a glimpse into the future of work that prioritizes people, aligns efforts, and enables continuous learning. Let me know what you think. How are you planning to turn insights into action within your team?

In this episode of Tech Talks Daily, I sit down with James Smith, who leads ThoughtSpot’s business across EMEA. With over 15 years in the analytics space, James shares how AI is shifting the way organizations use data, and why that change is not just about technology, but also about mindset and culture. James explains how ThoughtSpot is helping businesses move toward a more autonomous model, where AI-powered tools handle repetitive queries and free up analysts for more strategic work. He shares how ThoughtSpot’s “Spotter” tool enables business users to ask and answer their own data questions, helping to reduce the bottlenecks that many central data teams face. But this isn’t about removing people from the process. It’s about enabling better collaboration between AI and human decision-makers, where transparency and context guide smarter actions. We also talk about ThoughtSpot’s internal motto, “2% done,” and how that mentality drives continuous innovation. In a world that’s changing rapidly, it’s a reminder that staying curious, challenging assumptions, and building from first principles can unlock new levels of performance. As demand for AI-powered insights increases, James highlights the growing importance of strong data foundations. It’s not enough to just add AI on top. Organizations need to invest in data quality, governance, and flexible platforms that support users at every level of maturity. From enabling better business decisions to giving non-technical users easier access to insights, James offers a grounded view of what AI can really deliver today. If you’re working to build a data-driven culture, or looking to put more power in the hands of your teams, this conversation offers practical ideas to guide that transformation. How are you preparing your business for this shift in analytics? Let me know.

In this episode of Tech Talks Daily, I sit down with Marc Lueck from Zscaler to explore a security mindset shift that more businesses are beginning to embrace. The conversation revolves around the "Resilient by Design" principle, which encourages organizations to stop relying on reactive threat detection and start building systems that anticipate risk and limit impact from the outset. Marc explains how today’s cyber landscape demands more than traditional firewalls and legacy systems. He breaks down why Zero Trust should no longer be seen only as a remote access solution, but as a consistent, context-driven access model for all environments. From on-premises communication to lateral traffic between applications, Marc outlines how this model helps reduce vulnerabilities and prevent attacker movement inside the network. We also dive into the challenges posed by AI-driven attacks, where phishing, malware, and reconnaissance are increasingly automated and customized to each target. With these tactics becoming more advanced, Marc stresses the importance of shifting away from pattern-based detection toward a strategy based on real-time context and intent. He shares insights into Continuous Threat and Exposure Management (CTEM), a new class of tools that help security teams understand where problems are likely to occur before they happen. This proactive focus is key to designing systems that are resilient under pressure, rather than relying on alerts after the fact. From his analogies of planning for hurricanes rather than rainstorms, to his thoughts on legacy systems and the future role of automation in threat response, Marc offers a practical look at what resilient infrastructure truly involves. So the question is, are your systems designed to recover and continue when something breaks, or are they waiting for the next emergency to arrive? Let’s find out.