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Hosted by Kashif Manzoor

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Jun 2026

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"Open conversations. Real technology. AI for growth." Open Tech Talks is your weekly sandbox for technology: Artificial Intelligence, Generative AI, Machine Learning, Large Language Models (LLMs) insights, experimentation, and inspiration. Hosted by Kashif Manzoor, AI Evangelist, Cloud Expert, and Enterprise Architect, this Podcast combines technology products, artificial intelligence, machine learning overviews, how-tos, best practices, tips & tricks, and troubleshooting techniques. Whether you're a CIO, IT manager, developer, or just curious about AI, Open Tech Talks is for you, covering a wide range of topics, including Artificial Intelligence, Multi-Cloud, ERP, SaaS, and business challenges. Join Kashif each week as he explores the latest happenings in the tech world and shares his insights to help you stay ahead of the curve. Here's what you can expect from Open Tech Talks Conversations: • How organizations scale AI beyond pilots • Where AI implementations break down • Governance, risk, and maturity in GenAI systems • Career evolution in the age of AI The podcast is available on all major platforms, including Spotify, Apple, and Google. Each episode of the podcast is about 30 minutes long. "The views expressed on this Podcast and blog are my own and do not necessarily reflect those of my current or previous employers."

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June 14, 2026Episode 19030 min

AI Safety, AGI, and the Next Decade with Dr. Craig Kaplan

For most of my career, technology felt predictable. A new software platform arrived. A new programming language appeared. A new cloud service changed how we deploy applications. Every wave of technology helped people work faster. But AI feels different. Over the last two years, I have watched professionals across industries experience something I have never seen before. People are not simply using a new tool. They are having conversations with technology. A marketer can generate campaigns. A consultant can build frameworks. A developer can create applications in hours instead of weeks. And every week, the systems become smarter. Personally, I have experienced this while building AI frameworks, experimenting with coding agents, and working with organizations trying to adopt Generative AI. Many times I have found myself staring at a screen thinking: "How did it do that?" Not because the output was perfect. But because the pace of improvement was faster than expected. This raises an important question. If AI is becoming more capable every month, how do we ensure we build systems that remain useful, trustworthy, and safe? That is exactly what we explore in today's Open Tech Talks conversation with Dr. Craig Kaplan. Episode # 190 Today's Guest: Dr. Craig A. Kaplan, Inventor of the designs and Technologies that enable safe SuperIntelligence. He is a pioneer in artificial intelligence and the inventor behind technologies designed for safe Superintelligence. For more than four decades, he has worked at the intersection of intelligent systems, ethics, and innovation, developing architectures that help AI evolve safely and remain aligned with human values. Website: SuperIntelligence YouTube: iStudios What Listeners Will Learn: How AI evolved from symbolic systems to Generative AI The difference between AI, AGI, and Superintelligence Why are many AI researchers concerned about AI safety Enterprise AI risks leaders should understand today Why AI agents are becoming the next major AI wave The rise of multi-agent and collective intelligence systems How organizations can design safer AI solutions Why AI is shifting from a tool to a digital coworker The future impact of AI on jobs and knowledge work Practical guidance for responsible AI adoption Resources: SuperIntelligence

June 7, 2026Episode 18926 min

Everyone Wants AI But Few Know Why with Kevin Carlson

For many years, technology projects were relatively predictable. A new system was implemented, a process was automated, or an application was modernized. The challenges were technical, but the path was usually clear. Then Generative AI arrived. I still remember some of the early conversations with technology leaders. Almost every discussion had the same underlying question: "How quickly can we adopt AI?" Yet very few people were asking a more important question: "Why are we adopting AI?" Throughout my career in enterprise technology, ERP, cloud, and AI transformation, I've seen organizations succeed when they focus on solving real business problems. I've also seen companies chase trends because everyone else was doing it. Today's conversation reminded me that technology leadership is no longer about buying the latest tool. It's about balancing innovation, security, business value, and human judgment. As AI becomes part of every organization, the challenge is not whether to adopt it. The challenge is adopting it thoughtfully. Episode # 188 Today's Guest: Kevin Carlson, TechCXO Partner Kevin Carlson is a seasoned tech exec and a go-to expert on AI's real-world impact within businesses. He's been a CTO or CISO four times over, working across different industries in both North America and Europe, so he brings a genuinely practical viewpoint to how AI is changing business and the world. Website: TechCXO What Listeners Will Learn: Why do many AI initiatives fail despite large investments How technology leaders should balance innovation and business value The difference between AI hype and AI outcomes Practical approaches for introducing AI into organizations Why starting small often leads to bigger success Common mistakes enterprises make during AI adoption How security leaders should think about AI risks Data privacy considerations when using public AI models Why governance matters more than ever How AI is changing the role of developers Why communication and product thinking are becoming critical skills The rise of AI-assisted software development Resources: TechCXO

May 10, 2026Episode 18820 min

Beyond ChatGPT: The Future of Context-Aware AI with Martin Lucas

One thing I have realized after years of working in AI, enterprise systems, ERP, and now Generative AI, is that technology alone never changes industries. What changes industries is understanding people. The problem today is not a shortage of content. There is no shortage of tools. It is not even a shortage of AI models. The real problem is relevance. Why do people ignore most advertisements? Why do customers disconnect from brands? Why do organizations create more AI-generated content but still fail to create engagement? Because human decision-making is emotional, contextual, irrational, and deeply personal. And that is why today's conversation is important. For years, the world focused on machine learning models, automation, and now Generative AI. But very few people are asking a deeper question: Can AI actually understand human intent, context, and decision-making? Today's guest, Martin Lucas, has spent years exploring exactly that through deterministic AI and decision science. And personally, this topic resonates with me deeply. Because while building AI adoption frameworks and helping organizations modernize, I constantly see one challenge repeated everywhere: Companies are automating communication…but not improving understanding. They are generating more…but connecting less. This episode is not just about AI technology. It is about human behavior, trust, context, branding, creativity, and the future relationship between humans and intelligent systems. Let's dive in. Episode # 188 Today's Guest: Martin Lucas, Inventor of Deterministic AI He is the inventor of deterministic AI and decision science, proven across more than 100 global brands with results up to 76% above market performance. Website: Deterministic AI What Listeners Will Learn: What deterministic AI means in simple language Why traditional LLMs still struggle with consistency and context The difference between content generation and true understanding Why most ads and marketing messages fail today How human emotions influence decision-making Why AI-generated content often feels repetitive and disconnected How brands can create stronger emotional relevance with customers Why curiosity is essential for creativity and innovation The future relationship between AI, creativity, and human psychology How startups can build stronger brand positioning using behavioral understanding Resources: Deterministic AI

May 3, 2026Episode 18726 min

How GenAI Is Changing Surveys, Research, and Product Validation with Sharif Amlani

One of the biggest shifts I'm seeing right now is not only how AI is changing work, but how it is changing the way we test ideas. In the past, if a founder, researcher, product manager, or strategist wanted to validate an idea, the process was slow. Build a hypothesis. Run surveys. Wait for responses. Clean the data. Analyze it. Then maybe discover the question itself was not strong enough. Now, with GenAI, that whole cycle is being challenged. And this connects directly with my own work as well. When I work on AI strategy, GenAI maturity, or enterprise adoption roadmaps, the hardest part is often not the technology. The hardest part is asking the right question before building the solution. That is why today's conversation is important. Because we are moving from AI as a content generator to AI as a thinking partner. A system that can help researchers, founders, and teams test assumptions, explore user behavior, and sharpen decisions before spending time and money in the wrong direction. Today, I'm joined by Sharif Amlani, who brings together political science, research methods, data analysis, and generative AI to build tools for synthetic respondents and AI-powered research analysis. This is a conversation about research, validation, synthetic data, agents, and what happens when GenAI becomes part of the thinking process itself. Let's get into it. Episode # 187 Today's Guest: Sharif Amlani, Founder, HumanAI Sharif Amlani is the Founder and CEO of HumanAI, a UC Berkeley startup using generative AI to transform how we do research, analyze data, and expand what we know about the world around us. Website: HumanAI What Listeners Will Learn: How GenAI is changing research, surveys, and analysis What synthetic respondents are and where they can be useful Why AI-generated responses should support-not replace-real human validation How founders can test ideas earlier, before spending money on surveys Why talking to users remains the most important startup habit How AI agents can support analysis and reporting workflows Why consistency matters more than intensity when building a startup How market feedback can reveal a different customer than originally expected Resources: HumanAI

April 19, 2026Episode 18628 min

The Hidden Challenges of AI Adoption in Enterprises

Over the past year, something has become very clear. AI is not just a technology shift. It is a leadership test. Across enterprises, startups, and even governments, the same pattern keeps repeating: Leaders are being pushed to act fast Teams are overwhelmed with change And yet, clarity is missing From the outside, it looks like a technology race. But from inside organizations, it feels very different. It feels like: uncertainty pressure and a constant question - "Are we doing enough?" In conversations with CIOs, architects, and business leaders, one thing stands out: The real challenge is not adopting AI. The real challenge is leading through it. That's why this episode matters. Chapter List: 00:00 Introduction to Silicon Valley Executive Academy 01:37 Understanding the Silicon Valley Playbook 03:20 The Impact of AI on Leadership 05:25 Leading Through AI Transformation 09:45 Managing Pressure as a Leader 11:21 Driving Growth with a Healthy Culture 13:39 Common Challenges for Executives 16:00 The Role of Emotional Intelligence in Leadership 17:20 Micro Joy Method for Leaders 18:58 Building Trust as a Leader 19:54 Identifying Red Flags in Leadership 21:20 Evolving Leadership Models 23:53 Advice for Emerging Leaders Episode # 186 Today's Guest: Victoria Mensch, CEO & Founder, Silicon Valley Executive Academy An executive leadership coach and strategist with over 25 years of experience in Silicon Valley's high-tech sector. With a PhD in Psychology and an MBA from UC Berkeley. Website: Executive Silicon Valley What Listeners Will Learn: Why AI adoption is fundamentally a leadership challenge How pressure and hype impact executive decision-making The difference between transformation and patching processes with AI Why culture and team alignment matter more than tools How leaders can manage uncertainty without burning out teams What early-career professionals should focus on in an AI-driven world Why trust, courage, and clarity are becoming core leadership traits

April 5, 2026Episode 18518 min

What I've Learned Helping Enterprises Adopt GenAI

80% of enterprise AI projects never reach production. After two decades helping enterprises adopt new technology, Kashif Manzoor breaks down the five failure modes killing enterprise AI initiatives, introduces the GenAI Maturity Framework, and shares three questions every CTO should ask before approving their next AI project. Episode #: 185 In this episode, you'll learn: The 5 failure modes killing enterprise AI initiatives The GenAI Maturity Framework (6 dimensions, 6 levels) 3 questions every CTO should ask before their next AI initiative Why the gap between perceived and actual AI maturity is where POCs go to die Practical actions you can take this week TIMESTAMPS: 0:00 - The POC graveyard (a real conversation) 1:30 - Welcome + Why this episode exists 3:30 - My journey: Oracle → Cloud → GenAI 7:00 - The 80% problem: Why enterprise AI fails 10:00 - Failure Mode 1: The Strategy Gap 12:30 - Failure Mode 2: The Architecture Gap 15:00 - Failure Mode 3: The Governance Gap 17:00 - Failure Mode 4: The Talent Gap 19:00 - Failure Mode 5: The Measurement Gap 21:00 - The GenAI Maturity Framework (6 levels explained) 24:00 - 3 Questions Every CTO Should Ask 26:30 - What's coming next 28:00 - Subscribe + Connect

March 15, 2026Episode 18426 min

Could Living Neurons Power the Future of AI with Ewelina Kurtys

Over the last couple of years, most of my conversations around AI have been about capability. How fast models are improving. How agents are becoming more autonomous. How enterprises can adopt GenAI safely. How teams can redesign workflows around intelligence. But this week, I found myself thinking about something deeper. Not what AI can do. But what does AI cost? And I don't just mean money. I mean energy. I mean infrastructure. I mean the hidden assumptions underneath the current AI boom. Because when we talk about the future of AI, most people immediately jump to models, chips, data centers, agents, and software stacks. But as someone who works closely with organizations trying to operationalize AI in the real world, I keep coming back to a harder question: What happens when the current compute model itself becomes the bottleneck? This is not a question most teams are asking yet. But it is a question serious builders should start paying attention to. This week, while reviewing different enterprise AI patterns and thinking through long-term architecture choices, I realized that much of the current AI conversation still happens within the assumptions of silicon, scale, and software abstraction. But what if the next major shift is not a better model? What if it is a different computing substrate altogether? That's exactly why today's conversation is important. Because this episode is not about another AI app. It is not about another wrapper. It is not about another productivity layer. It is about something much more fundamental: What might come after silicon, and how should we think about it today? Chapters: 00:00 Introduction to Ewelina Kurtis and Final Spark 00:52 Understanding Living Neurons and Their Potential 02:44 The Vision Behind Final Spark 05:34 Current Progress and Future Goals 08:27 Collaborations and Research Opportunities 11:17 Programming Living Neurons 14:02 Ethical Considerations in Biocomputing 16:59 Benefits of Biocomputing for Society 19:39 Advice for Aspiring Bioengineers 22:30 Commercial Aspects of Final Spark 24:24 Investor Insights and Future Directions Episode # 184 Today's Guest: Dr. Ewelina Kurtys, Scientist from FinalSpark Website: FinalSpark What Listeners Will Learn: Why the future of AI may require rethinking computation itself, not just models How energy efficiency is becoming a core strategic issue in AI What biocomputing means in simple terms How living-neuron-based computing differs from traditional silicon-based systems Why future AI progress may depend on alternative hardware paradigms How emerging scientific computing trends should matter to enterprise AI leaders today Why staying ahead in AI means looking beyond current tools and architectures Resources: FinalSpark

February 22, 2026Episode 18325 min

How Attackers Use AI And Why Your Defenses Might Still Fail with Adriel Desautels

Episode # 183 Today's Guest: Adriel Desautels, Founder & CEO, Netragard Adriel is a leader in cybersecurity with over 20 years of experience. Adriel founded Secure Network Operations and the SNOsoft Research Team, whose vulnerability research helped shape modern responsible disclosure practices. He later launched Netragard, pioneering Realistic Threat Penetration Testing, which he now call Red Teaming, and expanding into a broad range of security services. Website: Netregard X/Twitter: Netregard What Listeners Will Learn: Why "AI penetration testing" is often closer to automated scanning than real offensive testing How AI changes security risk mainly through volume and speed, not necessarily sophistication Where organizations get misled into a false sense of security Why "preventing breach" is unrealistic and why limiting damage paths matters more What cybersecurity professionals should focus on to stay relevant in the LLM era How AI may influence vulnerability research, but still struggles with novel exploitation thinking Resources: Netregard

February 7, 2026Episode 18229 min

Why 95% of AI Pilots Fail and How to Be in the 5% with Mindaugas Maciulis

Welcome to Open Tech Talks. Quick note before we start, thank you. The messages, the feedback, the "keep this practical" reminders… they've been incredibly helpful. Open Tech Talks has always been a weekly sandbox for technology insights, experimentation, and inspiration—with one objective: learn, test, and share what's real. Now, a personal moment from this week. A few days ago, I sat with a business owner who said something that stuck with me: "AI is everywhere… but I don't know where to start without breaking my business." And that's the truth for most companies, especially small businesses. Because "start with AI" sounds simple… until it touches real operations: leads that go cold, follow-ups that don't happen, teams that feel overwhelmed, tools that multiply, processes that nobody can explain clearly. Most AI projects don't fail because the model is weak. They fail because the process is unclear, the team is overloaded, and the strategy is missing. Let's begin. Episode # 182 Today's Guest: Mindaugas (Min) Maciulis, Founder & CEO of Strategic AI Advisors He works with CEOs, COOs, and operating partners in the $20M–$250M range who are ready to go beyond pilots and turn AI into real EBITDA growth. His proven 90-day sprint framework, AImpact OS, delivers measurable lifts across productivity, customer service, and sales. Website: Strategic Advisors What Listeners Will Learn: Identify the best "starting point" for AI using business pain, not hype Understand why AI pilots fail mostly due to adoption (not technology) Learn a practical approach to simplify workflows before adding automation See how SMBs can move faster than enterprises in the AI era Understand the difference between augmentation and transformation with AI Learn how to avoid tool overload and focus on measurable outcomes Resources: Strategic Advisors

January 30, 2026Episode 18132 min

AI Is Creating Technical Debt Faster Than You Think with Maxim Silaev

This week, I've been thinking about something slightly uncomfortable. Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable. And then I asked myself: If I were to rebuild this today in the era of generative AI… Would I build it the same way? The honest answer? No. Not because it was wrong. But because our assumptions have changed. Two years ago, AI was a feature. Today, AI is shaping architecture decisions. We're not just designing systems anymore. We're designing systems that design, generate, predict, and automate. And here's the tension I keep seeing in enterprise conversations: Everyone wants AI. But very few are asking: "What technical debt are we creating while chasing it?" That's why today's conversation matters. Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation. And this episode is not about hype. It's about responsibility. Because AI doesn't remove architectural complexity. In many cases, it amplifies it. Let's get into it. Chapters 00:00 Introduction to Technical Debt and Architecture 01:34 The Impact of AI on Technical Debt 04:12 Generative AI and Architectural Challenges 08:40 Adopting AI in Organizations 12:26 Building AI Strategies and Governance 17:33 Data Quality and AI Integration 22:43 Guardrails for AI Adoption Episode # 181 Today's Guest: Maxim Silaev, Technology Advisor and Enterprise Architect He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms. Website: Arch-Experts What Listeners Will Learn: What technical debt really means in the AI era How generative AI can unintentionally increase hidden system risk Why architecture remains critical despite AI coding tools The importance of governance and verification layers in AI systems How large enterprises are cautiously integrating AI Why strategy must precede AI deployment The evolving role of enterprise architects in AI-native environments Resources: Arch-Experts