The Product Experience: a Mind the Product podcast

What changes when designers can test an idea in minutes rather than weeks? And which decisions should stay firmly in human hands?
Federico Casalegno, Executive Vice President at Samsung Electronics and Head of its Design Innovation Centers and Global Studies, joins Randy Silver to explain how Samsung combines human research, machine learning, digital twins and robotic testing to improve its products.
Using examples from earbuds, watches and rings, Federico walks through the process of collecting body-scanning data, defining measures for comfort and fit, and testing variations before putting physical products in people’s hands. He explains how some design tasks have moved from weeks to minutes, while real-world testing and expert judgement remain essential.
They also discuss building small, multidisciplinary teams, hiring for curiosity and collaboration, and keeping people’s needs at the centre of product development as the tools become more powerful.
Key takeaways
Define the problem before choosing the tools. Teams need a clear understanding of what they want to improve before building or applying AI systems.
Set meaningful measures. Comfort, fit and sensor performance give teams specific goals to optimise against.
Combine simulation with physical testing. Samsung iterates across digital twins, robots and real users.
Keep experts accountable for decisions. Data supports designers and engineers; they remain responsible for making the call.
Hire for curiosity and collaboration. New kinds of work require people who can apply their skills to unfamiliar problems and learn together.
Give faster production a clear purpose. The goal is to improve people’s lives through useful products and experiences.
Chapters
00:00 Introduction
01:01 Federico’s journey from social sciences and MIT to Samsung
04:42 Introducing computational design at Samsung
07:40 From body-scanning data to digital twins and robotic testing
12:23 Taking some design tasks from weeks to minutes
13:47 Giving designers better data for complex decisions
15:42 Hardware timelines and designing for a wider range of people
18:13 Generative data and the limits of synthetic research
21:20 Balancing automation, optimisation and creative judgement
24:11 Building tools around the problem
26:19 Prioritising requests and discovering new problems
28:18 Building multidisciplinary teams
30:57 Small teams and the value of creative friction
33:41 Why faster production still needs meaning
35:44 Keeping people at the centre as tools evolve
37:03 What leaders should ask before pushing teams to ship
38:43 What computational design could make possible next
40:56 Credits
We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 
Our Hosts
Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. 
Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

Rob Ferguson is VP of Technology and Strategy at Fireworks AI, the inference and model-customisation platform used by AI-native companies such as Cursor and Notion. 
He previously served as Global Head of AI at AWS and CTO of Microsoft for Startups, and has been a startup CTO through three exits. Rob joins Randy Silver to ask what a moat looks like when features can be cloned in days. They cover why competition is a good signal, why data and customer proximity now matter more than shipping speed, and why “done” is the most dangerous word in AI product development.
Key takeaways
Competition is evidence that a market is worth being in. Rob’s advice is to out-care and out-invest rivals rather than worry about copycats, as he saw at Automatic and Rdio.
Features alone no longer protect you. A launch that took two years to build was matched to 95 to 97 percent parity within days. What kept the original ahead was its cookbooks, community, clear point of view and understanding of its audience.
Rising above the noise is now the hard part. Rob’s example is a startup he advises whose cold email open rate has fallen from four percent to a fraction of one. You can no longer be just technically good. You need to be a tastemaker with a point of view.
The most capable AI systems use a mixture of models, not a single one. Teams that win start by measuring what matters, splitting work into small, verifiable parts and separating “hunch” tasks from deterministic ones.
Prototypes are now the start of the conversation, not the end of it. Rob’s best designers and PMs arrive with clickable prototypes, but the real work is the 50 or 60 iterations after that, and agreeing what “done” means before you show anything to a customer.
The second 80 percent of product development is still hard. Replacing a function with AI overlooks the many small jobs inside it. That is why forward-deployed teams, embedded with customers and focused on the work itself, are becoming standard.
Your data is worth more than the labs will tell you. Rob says Fireworks tried to calculate the discount it would need to offer customers to train on their data and couldn’t make the maths work, because the value was too high.
Product fundamentals haven’t changed: build, measure, learn. Good data, a good experience, knowing your customer and asking great questions still decide who wins.
Management teams should rebuild around customer proximity. Long-range plans matter less than teams that engage directly with customers, because a relationship is far harder to copy than a design.
Chapters
00:00 Introduction
 01:55 What a moat actually is
 03:33 Why customer relationships are the most defensible moat
 05:40 When features can be copied in days
 09:30 Distribution, drafting and staying power
 12:57 Where compute costs are heading
 17:56 Fireworks and the gold rush analogy
 20:44 Why the big labs don’t offer model choice
 26:00 Beyond the chat interface: mixtures of models
 29:48 Prototypes, vibe coding and the seductive word “done”
 33:17 The second 80 percent of product development
 37:09 Is AI producing revolutionary software?
 40:38 Curation as a core skill
 42:14 What product teams should do about moats
 47:48 What management teams should plan for
 50:11 Wrap-up
We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 
Our Hosts
Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. 
Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

Soufi Esmaeilzadeh is product director for AI experiences in Google Search, where she leads AI Overviews and AI Mode. In 15 years at Google, she has watched search move from ten blue links to multi-turn conversation. 
She joins Randy Silver to explain how her team prototypes and ships at Google’s scale, why she runs lean “SWAT team” pods, and how she balances speed against the quality bar users expect from search.
Key takeaways
Users changed their behaviour first. Once natural language models let people drop keywords, queries grew longer and more specific, and follow-up questions became an expectation rather than a feature request.
Soufi frames AI in search as an evolution, not a disruption. Positioning it as a better way to serve the user’s existing need, rather than a threat to the core product, made it easier to win internal support.
Her team goes from hypothesis to scrappy prototype to real-data demo to a small beta as fast as possible. Debating in decks is the thing she most wants to avoid, because these are not one-way doors.
Fast prototyping makes the hard question “is it worth it?” Ideas are cheap, so product managers must weigh people, attention and tokens against other opportunities, and build well, not just quickly.
Quality bars can make or break a launch. Gen UI simulations and audio-over-image explanations landed because the team defined “good” early. A subpar first version would have fallen flat.
Soufi runs lean pods: one senior strategic lead and a small bench of ICs, with flat lines of communication. Extra layers strip out context, and without context it is hard to move fast and ship well.
Product basics matter more at speed. Clear hypotheses, shared goals, agreed metrics, and a joint definition of what good and not-good look like still come first.
A leader’s job in a fast-moving team is to clear the noise. Urgency energises people. Chaos, spinning and cross-team debate drain them.
For hiring, Soufi looks for three things: consumer product instinct, close attention to where the underlying technology is heading, and a zero-to-one builder mentality.
AI tools accelerate the work, but the team keeps challenging and cross-checking their output. Sharing where each person has hit the limits is a standing conversation, because those limits keep shifting.
Org design needs stable problem areas even when the technology shifts. Accountability over an area for two to three years lets people build deep opinions about what success looks like, while their methods change every few months.
Chapters
00:00 Introduction
 01:28 Soufi’s role and background
 03:09 How AI has changed search
 05:30 How AI experiences in search evolved
 07:08 Delivering AI answers at Google’s speed and scale
 09:04 Evolution, not disruption
 12:16 What AI means for publishers
 14:25 Overpersonalisation and quality feedback
 16:40 Testing and betas at Google’s scale
 20:40 Gen UI, simulations and visualisations
 23:39 Balancing cost and ambition
 25:30 Structuring AI product teams
 28:21 Keeping quality high as pace increases
 31:07 Changing processes and measuring speed
 33:47 Avoiding burnout and context switching
 36:10 Hiring and promoting
 37:53 Working with AI tools and where they fall short
 40:27 Designing teams for the long term
 42:52 Wrap-up
We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 
Our Hosts
Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. 
Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

Francois Lopitaux is SVP of Product at ThoughtSpot, the analytics company built on the premise that you should be able to search your data like you search Google. He began his product career 18 years ago, when Salesforce acquired his Paris company, and went on to lead customer service, analytics and AI products there before joining two startups and then ThoughtSpot. He joins Randy Silver to explain how building agentic products differs from shipping deterministic software, from designing without control of the user path to keeping an LLM trustworthy when the answer has to be the same every time.
Key takeaways
AI has cut the time to prototype and validate, and ThoughtSpot estimates its development is two to three times faster. Productionisation still needs sound architecture, guardrails and experienced engineers.
When everyone can vibe code, discipline matters more. Lepiteau compares fast AI-assisted development to driving a Formula One car on a road built for a mini Austin: the speed is only useful if you can control it.
Agentic features are probabilistic, so product teams are now building guardrails around an LLM rather than a fixed step-by-step flow. That means more hypotheses and more testing.
Keep the end goal fixed and stay flexible on the technology. ThoughtSpot is now revisiting existing features to see where newly released models can replace what it built with LLMs.
The eval system is becoming the new PRD. It defines what good looks like and what must be avoided, in a product where the UI is a prompt and the path can't be predicted.
For analytics, where "what is my revenue this year?" must always return the same answer, trust comes from using an LLM only where it is needed and supplying a semantic layer. That layer defines how your business works, so the model behaves less like a new intern guessing at your terminology.
Adoption and repeat usage are the metric that matters most, backed by anonymised sampling of answer quality and follow-up questions. Enterprise customer interviews supply the qualitative temperature check.
Embedded and white-label products raise the bar on trust, because customers ship the product to their own customers and carry the accountability. Features must be rebrandable and opt-in, with flags so each customer can roll out at its own pace.
An AI feature is never finished. Evals are only a guess at how people will use the product, so teams must keep observing real usage after general availability. ThoughtSpot organises teams by product track, which makes that follow-up a natural continuation of the work rather than extra load.
Pruning matters more now that code is cheap to generate. Lepiteau's own team has built three versions of its conversational agent, and without cutting back old versions he warns of a monster where every addition breaks something else.
Lepiteau still hires technical, curious product managers. For a recent associate PM role, he asked candidates for a Git repo and a video explaining why what they built solves a problem.
Execution is speeding up, so he expects smaller teams and a falling PM-to-developer ratio. Deciding what is meaningful to customers is the remaining bottleneck.
2025 was about proving AI works. Lepiteau expects 2026 and 2027 to be about proving ROI, so teams should be able to switch models easily and pick the cheapest one that does the job.
Chapters
00:00 Introduction
 00:48 Meet François and ThoughtSpot
 02:55 How AI is changing the product development lifecycle
 05:18 Why discipline matters more in the AI era
 08:00 Building probabilistic features
 09:43 Why evals are the new PRD
 13:57 Building an eval system for agentic analytics
 17:15 Creating trust with a semantic layer
 19:30 Measuring the success of AI products
 22:12 Trust in embedded analytics
 24:19 Go-to-market for white-label and OEM products
 26:54 Rolling out at your customers' pace
 28:37 Why AI features are never finished
 31:20 Organising teams by product track
 33:42 Pruning and sunsetting features
 35:38 What to look for when hiring product managers
 37:56 The future of team size
 40:41 Managing AI costs and model flexibility
 42:49 Wrap-up
We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 
Our Hosts
Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. 
Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

When you use an AI tool, how much time do you spend correcting it? And would your customers be willing to do the same?
Kendra Vant, Chief Product Officer at Tapi, joins Randy Silver to explore the “reliability layer”: the people, checks and engineering that make AI products dependable. She explains why enthusiastic AI users often provide that layer themselves, and why product teams can’t assume their customers will.
Kendra shares four questions to ask before building AI into your product, from who catches mistakes to what reliability costs at scale. They also discuss designing for failure, the technical literacy she expects from product managers, and why faster prototyping makes experienced judgement so valuable.
Chapters
00:00 Introducing Kendra Vant
02:51 The gap between an AI demo and a reliable product
06:12 Accuracy, consistency and the user as the reliability layer
13:31 Four questions to ask before building AI into your product
18:27 Sponsor: Jira Product Discovery
19:01 Planning for failure and protecting customer trust
24:06 Customisation, pricing and the cost of scaling
26:37 Do product managers need to learn to code?
31:40 How Tapi uses AI to experiment and build
33:25 Why small teams have an advantage
35:05 Product judgement and the experience gap
38:08 Using AI to maintain legacy code
39:41 Ask better questions, write down answers and test your thinking
43:11 Where to follow Kendra
Key takeaways
Find the work your users are doing for the AI. Correcting answers, refining prompts and spotting mistakes can make a tool feel more reliable than it is. Consider whether your customers have the time, motivation and expertise to do that work.
Make someone responsible for reliability. Establish who specifies, builds and operates the checks around your AI. An impressive demo can conceal the fact that nobody owns this work yet.
Price the whole product. Human review, additional models, guardrails and ongoing maintenance all affect the cost of delivery. Check whether your reliability layer remains commercially viable as usage grows.
Design for the moments when it fails. Saying a model is wrong 15% of the time prompts a different conversation from saying it is 85% accurate. Identify the consequences for customers and plan how the product will recover.
Build your technical literacy. Kendra expects product managers to be comfortable interacting with Git and learning from the codebase. Understanding how software works helps you ask better questions and collaborate with engineers.
Recognise the value of experience. Faster tools increase what teams can build, but recognising flawed suggestions still requires judgement. Kendra raises an open question: how will newer practitioners develop that judgement as the work changes?
Use writing to sharpen your thinking. Break difficult problems into smaller questions, write down your answers and explain them to a colleague. The gaps often become clearer when you have to articulate your reasoning.
We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 
Our Hosts
Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. 
Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.