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Top 10 Technology Leadership, Careers & Workforce Podcasts of 2026

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Engineering Leadership Podcast by ELC46 min

Implementing reusable and powerful agentic tools across your organization w/ Ozzie Osman #269

In this episode, Jerry discusses key insights on delegating to agentic tools while maintaining high levels of engineering ownership with Ozzie Osman, co-founder @ Monarch. Ozzie shares what it looks to pursue two paths when it comes to AI: agentic-forward and human-orchestrated pipelines. They also cover the specific AI tools that are used in Monarch, including Devin and Voltron; assigning tasks to be AI-first vs. human-led; determining which pieces of customer feedback lead to new features; and common challenges when it comes to AI-generated code and what the human review process for it looks like. ABOUT OZZIE OSMAN Osman (Ozzie) Osman is co-founder of Monarch Money. He is the lead author of the Holloway Guide to Technical Recruiting and Hiring. He has built products and engineering teams at companies including Quora and Google. Ozzie has also started two companies that have been acquired, and advised dozens of other startups. Sinch is the communications infrastructure the AI era runs on. There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke. Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries. Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message! Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction. Check it out here !

The Pragmatic Engineer Podcast1 hr 27 min

Design Engineering with Maggie Appleton

It’s fast, cheap, and extremely scalable. • O'Reilly Early Release: Scaling AI Adoption in Engineering – a free book on how to adopt and scale AI in a pragmatic way inside of engineering orgs. Complimentary, thanks to Antithesis. • Entire – every agent prompt, tool call, stored in your repo, and mirrored. — What can everyone else learn from designers and design engineers? As it turns out, there’s plenty, as I discovered when one of the best design engineers in the industry, Maggie Appleton , came onto the Pragmatic Engineer Podcast. She’s a staff research engineer at GitHub Next , where she builds prototypes to explore how software engineers might collaborate with AI in new ways. Maggie is at the intersection of design, anthropology, and web development, and was the first designer hired by AI startup Elicit, and Lead Design engineer at AI startup, Normally. Today’s episode is more visual than usual because Maggie brought her notebook along, so there are peeks inside its pages of prototypes and more: We got into designers’ work and how their design processes are adapting to and changing with AI. We explore why Maggie starts projects with pens and notebooks, what distinguishes design engineers from other designers, and why understanding engineering constraints leads to better collaboration with engineers. We also discuss how Maggie uses jigs to gain more control over AI agents, why human judgment and style still matter when models can generate designs, and how inconsistent AI capabilities can mislead us. Timestamps 00:00 Intro 03:24 From anthropology to tech 10:18 What does a designer do? 18:23 How Maggie works 24:55 The case for planning with physical tools 31:53 Why Maggie is learning woodworking 33:13 Design engineers and engineering constraints 38:49 How Maggie uses Figma 40:30 Design at GitHub Next 45:12 How has AI changed design 50:37 When models design and why humans are still needed 53:30 UX and UI 58:29 Capability gaslighting 1:00:33 One Developer, Two Dozen Agents, Zero Alignment 1:07:21 Craft and AI tells 1:14:17 Visual gardens, home-cooked software, and barefoot developers 1:21:02 Advice for engineers and lessons from anthropology 1:25:34 Book recommendation — The Pragmatic Engineer deepdives relevant for this episode: • What is “loop engineering?” • Design-first software engineering: Craft, with Balint Orosz • Are AI agents actually slowing us down? • Vibe Coding as a software engineer • How Codex is built • How Claude Code is built • From Chrome DevTools to AI Engineering, with Addy Osmani — Production and marketing by. For inquiries about sponsoring the podcast, email.

The CTO Lens47 min

Season 3: Episode 18 - Five Leadership Myths Put to the Test

Sophie and David are back for episode 18. This week, they've set themselves a challenge: run through the week's news without mentioning AI once (it does not entirely go to plan) and dispel five leadership myths. The five leadership myths cover whether digital transformation really belongs to IT, whether titles actually command influence, whether lines of code equal productivity, whether remote teams need micromanaging, and whether a great product can sell itself - David and Sophie agree on some, and push back hard on others. Among the news stories they did find: a BBC piece on social media addiction in over-55s and what it means for tech leaders to build KPIs that account for genuine harm, not just engagement. AWS's quiet admission - six months after airstrikes damaged its Bahrain and UAE infrastructure during the Iran war - that it has permanently lost some customer data, and what that means for how technology leaders actually calculate acceptable risk. The Revolut breach, where nearly 700 customers' sensitive data was handed over to hackers posing as Italian law enforcement, and the case for building a "sleep on it" delay into how teams respond to urgent-sounding requests. And Salesforce's Dreamforce conference outage, which took down the platform for over seven hours while staff were on stage praising its reliability.

The CTO Playbook54 min

111: Every technology decision you make now is a post-quantum decision

Quantum computing may not be breaking your security today, but the decisions you make now could determine how exposed your organization is when it does. Conor Deegan explains why CTOs need to start preparing before Q Day arrives. Coaching, podcast, and community to help you lead with clarity, confidence, and strategic impact. My guest this week is Conor Deegan, a security engineer, cryptographer, and co-founder of Project 11, focused on helping organizations prepare for the transition from classical cryptography to post-quantum cryptography. The quantum threat goes far beyond Bitcoin and digital assets. Conor explains how quantum computing could eventually affect the cryptography behind banking, communications, cloud infrastructure, authentication, health records, and everyday internet security. He breaks down what "Q Day" means, why organizations cannot simply wait until a powerful quantum computer exists, and how "harvest now, decrypt later" could put sensitive information collected today at risk in the future. The transition also comes with real engineering challenges. Post-quantum cryptography can require more computing resources, memory, storage, and bandwidth, while legacy systems and constrained devices such as IoT hardware can make migration even more difficult. Conor then shares a practical framework for CTOs: understand the threat, put someone in charge, identify critical systems and data, take the easy wins, put pressure on vendors, and stop creating migration debt. Conor welcomes direct contact on post-quantum migration questions.

Modern CTO18 min

Tech Titans: Why Plans Fail and Strategy Doesn't with Seth Godin, Author of This Is Strategy

Today, we're talking to Seth Godin, author of This Is Strategy . We discuss why strategy is a philosophy of becoming rather than a plan to execute, how status, affiliation, and freedom from fear quietly drive almost every decision people make, and why one of Seth's biggest professional failures came down to having a good idea and then walking away from it. All of this right here, right now, on the Modern CTO Podcast!

Soft Skills Engineering34 min

Episode 531: Shallow feedback and my coworker is a huge sloperator

In this episode, Dave and Jamison answer these questions: Listener Jame ain’t no dance asks,! Hey Space Therapists, I am a consultant that helps big companies to fix all kind of stuff. It’s ridiculous how many different hats I wear. One day I’m the interim PM, the other day I am telling SAP devs how to use a coding agent, next day I gather stakeholder requirements for a customer self service portal. I do all of these tasks on a fairly shallow level and all of that work is happening without much coordination from my company. I think I do a good, but it’s hard gathering feedback on my work, since no colleagues are involved and I can’t simply ask the customer without revealing insecurity. Send sophisticated tips and don’t make me quit. Cheers! Can I tell a coworker to stop using Claude? I work on a small team at a midsized company, and a coworker keeps dropping overly verbose pull requests that are full of AI jargon. These are increasingly hard to understand, and then when I ask for design intent clarification I often get sent a long document that is even harder to understand! And the code sucks! misleading docstrings, overly complicated functions, and weird places where the AI has convinced them that something needs to be backwards. And often times I end up having to fix failing tests (that my coworker probably didn’t run), or report back later that their changes have degraded something in prod or is causing a lot of logging noise. I worry that I’m doing the same thing and just not noticing. Code is starting to pile up in review and I don’t want to be rude and comment “ai slop” on 50% of the code! What can i do?

Refactoring Podcast1 hr 1 min

Building Tools for Defenders 🛡 — with Rahul Raina

Today's guest is Rahul Raina , co-founder of TRM Labs, which is a YC-backed company that works on solutions and products for security, fighting criminal networks, AI attackers, and more. With Rahul, we had a great chat that covered a lot of ground, starting with the tough reality of cyber defense today, and why attackers by default always have the upper hand when AI and technology improves. And then we moved to a deep dive into how the TRM team works and how they rebuild their whole product development workflow for AI, including reshuffling all the technical roles, compressing the developer process steps, and creating dedicated artifacts for human reviews. (00:00) Episode start (03:50) Introducing Rahul Raina (04:08) Why AI widens the gap between attackers and defenders (08:54) The asymmetry: attackers don't play by the rules (17:09) Inner loop vs outer loop: where the real bottleneck is (20:54) Redesigning the shipping framework around jobs, not roles (24:18) How to transition people: go slow before you go fast (34:24) What to hire for: problem solving, agency, humble craft (42:14) How builders review work: human-readable artifacts, not code (46:59) The harness interviews you: AI asks questions humans wouldn't (57:20) Build tools for understanding, not just for producing code - Today's episode is brought to you by Notion.

Engineering Enablement Podcast38 min

How Okta sets guardrails and context for AI agents

In this episode of Engineering Enablement, Robert Lucero, Chief Architect at Okta, joins host Brian Houck to discuss how agentic AI is changing identity and access management. They explore how organizations should define agent identities, why authentication and authorization remain foundational, and how sandboxing, fine-grained permissions, and just-in-time access can enable agents to operate autonomously without creating unacceptable risk. Robert shares what Okta has learned from driving AI adoption among security-minded engineers, what makes a repository ready for AI agents, and why strong testing, CI, and review processes matter even more as AI generates more code. They also discuss AI’s role in software validation, the need for human judgment, and whether AI will ultimately give the advantage to security teams or attackers.

Tech Lead Journal59 min

Don't Outsource Your Thinking: How to Lead in the AI-Native Era - Emilie Schario

What’s the one rule that matters most when AI writes most of your code? Emilie says it comes down to a single line: don’t outsource your thinking. In this episode, Emilie Schario, co-founder of Kilo Code, unpacks what it actually means to lead in a world where AI can write most of your code. She explains why Kilo went all-in on being model agnostic, supporting over 500 models instead of betting on a single lab. Emilie shares her core rule for using AI responsibly: never outsource your thinking, especially when it comes to auth, billing, or security. She introduces a metric more companies should be tracking, spend per merged pull request, as a better signal of value than raw AI cost. Emilie also talks about the “killing problem” that comes with software becoming nearly free to ship, and why teams now discover product-market fit after shipping rather than before. She closes with how engineers are shifting from producers to reviewers, and what that means for anyone now managing a team of AI agents instead of just their own code. Key topics discussed: Why Kilo Code refuses to bet on a single AI model The one rule for not losing control to AI agents A better way to measure AI spend: cost per merged PR Why shipping software is nearly free, and why that’s risky How AI-native teams find product-market fit after shipping Why senior engineers adapt to AI agents the fastest Building a council of reviewers for high-risk code changes What the Anaconda acquisition means for Kilo Code Timestamps: (00:00) Trailer & Intro (02:27) What Is Kilo Code All About? (03:31) How Do You Avoid Getting Overwhelmed by 500 Plus AI Models? (04:34) How Does a Multi-Platform Approach Benefit Developers? (07:19) How Does Kilo’s Agent Harness Differ From Other Tools? (09:51) Why Will Model Choice Matter Less Over Time? (11:20) What Tools Help You Pick the Right Model for Each Task? (14:16) What Does the Anaconda Acquisition Mean for Kilo Code? (15:43) Why Should You Measure AI Spend Per Merged Pull Request? (18:54) How Do You Balance Speed and Control in AI Development? (20:28) Why Should Companies Treat AI as Operational Infrastructure? (23:31) How Do AI-Native Organizations Run Their Day-to-Day Workflows? (27:58) How Will High-Performing AI Organizations Differentiate Themselves in the Future? (30:11) What Is the “Killing Problem” Now That Shipping Software Is Free? (32:40) How Do You Shift to a Metrics-First Mindset? (34:24) How Is the Product Manager’s Role Changing in AI-Native Teams? (37:06) How Do You Balance Rapid Feature Delivery With Product Quality? (41:48) How Is the Software Engineer’s Role Evolving in the Age of AI? (43:57) What Is the Best Advice for Junior Engineers Entering an AI-Native World? (45:51) How Can Developers Handle the Cognitive Load of Code Reviews? (49:11) How Should Docs and Code Attribution Evolve With AI Agents? (53:06) How Can AI Improve Your Life Outside of Work? (55:04) 3 Tech Lead Wisdom _____ Emilie Schario’s Bio Emilie is VP, Engineering at Anaconda and the former cofounder of Kilo (acquired by Anaconda), where she focuses on turning AI investment into real engineering output. She operates at the intersection of AI tooling, engineering org design, and the day-to-day realities of running high-performing teams, helping organizations translate innovation into measurable impact. Show notes & transcript:. Buy me a coffee or become a patron .

The Tech Leaders Podcast48 min

#134: Daniel Hulme, Global Chief AI Officer at WPP, on Machine Consciousness and the Future of AI Agents

Seven years after becoming the first-ever guest on The Tech Leaders Podcast , Dr Daniel Hulme returns to share his thinking on how AI has evolved, where it’s heading, and what organisations should be focusing on now. As Global Chief AI Officer at WPP and founder of Satalia, Daniel has spent nearly three decades building AI systems and exploring some of the biggest questions around intelligence, consciousness and safe machine behaviour. In this episode, we unpack why many businesses are approaching AI in the wrong way, why chasing quick wins can be a trap, and where AI could have the biggest impact, from supply chains and marketing optimisation to governance and agent testing. Daniel also shares how WPP is using AI as a creative superpower, including a model trained on its own creative expertise to generate award-worthy ideas and help teams think bigger. Timestamps: Satalia Take over by WPP (11:45) Will AI kill agencies or make the best ones stronger? (16:28) Inside WPP: Creative, Production, Media & Enterprise (20:30) What advice would you give to entrepreneurs to put foundations in place to grow their brand using AI technology? (21: 45) AI Controls and Governance (24:10) Multi Agent Reasoning (29:35) What will the biggest societal impact of AI be within the next 5 years? (32:47) Is AI conscious? (38:56)