How Engineers Can Get Involved in Product Work | Teresa Torres (Continuous Discovery Habits author)

What happens to engineering when AI makes software easier to build? Marty Cagan, founder of Silicon Valley Product Group and coauthor of "EMPOWERED: Ordinary People, Extraordinary Products", argues that engineers' role in shaping the product becomes even more important. Their knowledge of technology helps them see new possibilities, challenge proposed solutions and decide what is worth building. We explore engineers' contribution to product innovation, what discovery looks like in an engineer's day, and why handing over an AI-generated prototype to be made production-ready can recreate the feature factory. Marty also distinguishes empowerment from autonomy, discusses how AI could help smaller teams own more of the product, and shares practical ways engineers can start changing how their teams work. In our conversation, we discuss: 1. Why engineers can be your strongest source of product innovation 2. Why using AI to speed up a feature factory can deliver failed products faster 3. How engineers can make time for discovery and help shape prototypes before a solution is decided 4. How empowerment differs from autonomy, and why smaller AI-assisted teams could reduce dependencies 5. How to start changing a feature factory from within through customer contact, usage data and small experiments Where to find Marty Cagan: • LinkedIn: https://www.linkedin.com/in/cagan/ • SVPG: https://www.svpg.com/team/marty-cagan/ Where to find Peppe: • LinkedIn: https://www.linkedin.com/in/peppesilletti/ • X: https://x.com/peppesilletti • Website: https://peppesilletti.io Where to find The Product Engineers Podcast: • Website: https://productengineers.com In this episode, we cover: (00:00) Introduction (00:56) Feature factories and empowered engineers (07:30) The product model and owning the problem (12:31) Four product risks and why engineers should push back (20:46) How AI changes product teams, discovery and delivery (26:25) The AI productivity paradox (31:53) Daily discovery and the prototype handoff trap (37:08) Developer tools and understanding your customers (42:58) Smaller teams, empowerment and autonomy (46:53) How engineers can start changing a feature factory Referenced: • EMPOWERED: Ordinary People, Extraordinary Products, by Marty Cagan and Chris Jones: https://www.svpg.com/books/empowered-ordinary-people-extraordinary-products/ • The AI Productivity Paradox, by Marty Cagan: https://www.svpg.com/the-ai-productivity-paradox/ • A Fresh Definition of the Product Role, by Marty Cagan: https://www.svpg.com/a-fresh-definition-of-the-product-role/ • Benedict Evans on Another Podcast: Most people and companies aren’t tool-builders: https://open.spotify.com/episode/4NkYXMBNyXqxihujGGSytc • Atlassian: The State of Teams 2026 (source of the 89%/6% figures): https://www.atlassian.com/blog/state-of-teams-2026 • Earlier episode with Meikel Ratz at PostHog: Why "Just Coding" Isn’t Enough Anymore: https://productengineers.com/episodes/season2/why-just-coding-isnt-enough-anymore/ • Product Therapy, hosted by Christian Idiodi (SVPG): https://podcasts.apple.com/us/podcast/product-therapy/id1738373011 • Claude Code: https://claude.com/product/claude-code • Cursor: https://cursor.com/ • OpenAI Codex: https://openai.com/codex/ • Lovable: https://lovable.dev/

What does business sense for engineers look like in practice? Arnon Cavaeiro joins me to talk about product engineering, product discovery and choosing which customer problems to solve. We get into revenue, costs and product strategy, and how understanding them helps you decide what to build next. The conversation moves from discovery to delivery, with practical questions engineers can bring to their next project. Why can a feature go unused even when a customer requested it? What is the smallest experiment that would test your riskiest assumption? Arnon shares how to use early signals before revenue catches up, make the business case for technical debt, and navigate the growing overlap between engineering and product management. In our conversation, we discuss: 1. Why understanding revenue and costs changes what engineers choose to build 2. How to judge customer problems by reach, pain and relative priority 3. Why customer requests need small experiments and clear product positioning 4. How leading metrics help you change direction before revenue catches up 5. How to connect technical debt and engineering decisions to business outcomes Where to find Arnon Cavaeiro: • LinkedIn: https://www.linkedin.com/in/acavaeiro/ Where to find Peppe: • LinkedIn: https://www.linkedin.com/in/peppesilletti/ • X: https://x.com/peppesilletti • Website: https://peppesilletti.io Where to find The Product Engineers Podcast: • Website: https://productengineers.com In this episode, we cover: (00:00) Introduction (03:23) Why engineers need to understand the business (07:15) Choosing problems worth solving (09:56) Small experiments and risky assumptions (13:16) Unused features, customer requests and positioning (16:57) Building as thinking, prototypes and taste (26:05) Outcomes, experiments and leading metrics (35:33) Making the business case for technical debt (41:40) Who owns product decisions? (47:08) Closing thoughts and where to find Arnon Referenced: • Taste Labs: https://tastelabs.com/ • Linear: https://linear.app/

Robert C. Martin, also known as Uncle Bob, joins The Product Engineers Podcast to discuss why technical expertise still matters when AI writes the code. The author of Clean Code and Clean Architecture explains how those principles help engineers steer AI coding agents towards better software. We get into Robert's use of static analysis, complexity and coverage metrics, dependency checks and mutation testing. He shares how he stays technically involved while reading less code, and why understanding customers is essential to choosing the right technical constraints. In our conversation, we discuss: 1. Why clean code and clean architecture still matter when AI writes the code 2. How static analysis and dependency checks help steer agents towards better software 3. How Robert combines acceptance tests, QA tests and mutation testing to check AI-written code 4. How engineers can stay technically in control without reading every line of code 5. Why understanding customers leads to better technical decisions Episode transcript: https://productengineers.com/episodes/season2/why-hardcore-technical-focus-still-matters-big-time Where to find Robert C. Martin: • X: https://x.com/unclebobmartin • Website: https://cleancoder.com/ • Videos: https://cleancoders.com/ Where to find Peppe: • LinkedIn: https://www.linkedin.com/in/peppesilletti/ • X: https://x.com/peppesilletti • Website: https://peppesilletti.io Where to find The Product Engineers Podcast: • Website: https://productengineers.com In this episode, we cover: (00:00) Why technical expertise still matters with AI (05:27) Agile, customer contact and organisational change (08:39) Learning from customers without losing technical depth (13:19) Connecting technical requirements to customer needs (18:00) Clean code, static analysis and quality metrics for agents (23:48) Enforcing architecture and checking behaviour with tests (28:26) TDD, reading less code and staying technically in control (34:27) Where deep specialists matter and who can build with AI (40:30) Where to find Robert Referenced: • Clean Code: https://www.informit.com/store/clean-code-a-handbook-of-agile-software-craftsmanship-9780135398579 • Clean Architecture: https://www.informit.com/store/clean-architecture-a-craftsmans-guide-to-software-structure-9780134494166 • Agile Manifesto: https://agilemanifesto.org/ • CRAP4J: the complexity-and-coverage metric discussed in the episode • Gherkin: the acceptance-test language used in Robert's workflow

Everyone tells engineers to talk to users. But what should you ask, and how do you turn the answers into better product decisions? Sara Iacozza is a Senior UX Researcher and Product Strategist with 7+ years of experience helping teams understand user behaviour and decide what to build. In our conversation, we discuss: 1. How understanding users helps engineers make trade-offs and find simpler solutions 2. How to practise user interviews with colleagues, an AI coach, and a researcher’s feedback 3. How to avoid leading questions, use plain language, and ask about real experiences 4. How to turn interview notes into shared insights and check AI analysis against the evidence 5. How to get closer to users by shadowing interviews, watching recordings, and talking to colleagues Episode transcript: Where to find Sara Iacozza: • LinkedIn: https://it.linkedin.com/in/sara-iacozza/ Where to find Peppe: • LinkedIn: https://www.linkedin.com/in/peppesilletti/ • X: https://x.com/peppesilletti • Website: https://peppesilletti.io Where to find The Product Engineers Podcast: • Website: https://productengineers.com In this episode, we cover: (00:00) Introduction and what UX research involves (06:35) Why user research matters for engineers (12:33) Learning to interview users, with practice and AI (24:19) Avoiding leading questions and polite answers (28:39) Using language your users understand (33:08) Turning conversations into insights and checking AI analysis (42:00) Making research part of the team’s work (45:20) Getting closer to users when access is limited (51:35) Where to find Sara Referenced: • Sara’s User Interview Coach (announcement and access link): https://www.linkedin.com/posts/sara-iacozza_chatgpt-user-interview-coach-activity-7296141061372993536-pWZQ • Continuous Discovery Habits by Teresa Torres: https://www.producttalk.org/continuous-discovery-habits/

Jon Yablonski is a senior product designer at Mixpanel and the author of Laws of UX. In this episode, we explore how software engineers can get better at UX design through observation, practice, and collaboration, starting with the products they already use and love. In our conversation, we discuss: ✦ Why engineers may already have more design skills than they realise ✦ How to train your design eye by studying products you use and love ✦ How engineering decisions shift complexity between your code and your users ✦ When familiar interface patterns help, and where novelty makes sense ✦ How Jon uses AI for prototypes while working through design ideas with pen and paper Episode transcript: [ADD EPISODE URL] Where to find Jon Yablonski: • Website: https://jonyablonski.com/ • LinkedIn: https://www.linkedin.com/in/jon-yablonski-b4269115/ Where to find Peppe: • LinkedIn: https://www.linkedin.com/in/peppesilletti/ • X: https://x.com/peppesilletti • Website: https://peppesilletti.io Where to find The Product Engineers Podcast: • Website: https://productengineers.com In this episode, we cover: (00:00) Introduction: what UX design really means (07:01) Why engineers aren’t “bad at design” (10:22) Bridging design and engineering (17:45) How to train your design eye (20:16) Laws of UX: complexity and trade-offs (27:29) Familiar patterns and when to break them (31:57) Designing better AI experiences (37:37) Using AI without outsourcing your thinking (41:49) Figma, AI prototypes, and design systems (47:31) Design is a skill you can practice