The Product Experience: a Mind the Product podcast

If anyone can copy your features in 20 minutes, how do you price what you build? Randy Silver talks to Carrie Osman, founder of Cruxy, a consultancy that works only with B2B tech companies on product value, packaging and pricing. Carrie trained at Mars, where she ran a pet food P&L, before moving into tech. They cover how to tell if your pricing is right, whose feedback to believe, why AI features that automate a basic job shouldn’t carry a price rise, and why every roadmap item needs a money figure attached.
Key takeaways
Weigh feedback by who gave it. In a deal review, score each piece of pricing feedback out of five. A senior, long-standing customer saying you’re underpriced is a five. Someone who saw one demo and has no budget is a zero. Carrie says procurement teams are incentivised to cut list price, so treat their pushback with caution.
Expect to be told you’re overpriced. Carrie says no one will tell you the product is underpriced. Look for patterns across usage, support tickets, integration and API volumes, and buyer calls, and dig only where several signals agree.
Don’t charge extra for what customers already expect. If someone buys your product to do reconciliation, they expect automation to be built in. Carrie’s test for AI features is whether they genuinely change the workflow, for example turning six steps into one or moving from a dashboard to a predictive score.
Keep pricing simple enough to buy. Carrie says most CFOs and procurement teams won’t sign off a token-based “agentic calculator” with 70 variables.
Include everything needed for a good experience in the base product. Charge for add-ons only where one segment has a clear, distinct need and willingness to pay.
Work customer-in, not product-out. Map a day in the customer’s life and look for friction points they can’t articulate themselves.
A moat is more than features. Carrie points to a narrow ICP, a strong partner or bottom-up channel, a customer-obsessed culture, and product marketing and enablement, including a sharp, short demo.
Cheap needs explaining. If you’re pricing low to win market share, say so upfront. Carrie argues pricing too low can signal poor quality, even in B2B.
To diagnose defensibility, overlay data by module. Map the customer workflow, then lay churn, usage, support tickets and tech stack data over your product modules and keep asking why.
Put a size of prize on every roadmap item. Estimate the money saved or earned, for example churn avoided, win-rate gains and partner feedback, and refine it as you near launch. Carrie says inflated numbers are intellectually dishonest and stop you making honest trade-offs.
Chapters
0:00 Intro: protecting your moat when features can be copied fast
0:43 Carrie’s route from Mars pet food to Cruxy
3:18 How do you know you’ve got pricing right?
5:38 How fast to act on a bad signal
6:42 Procurement pushback and scoring feedback by source
10:40 What AI has changed: skipping the fundamentals
12:32 Why AI on a table-stakes job isn’t a price rise
14:48 Changing the number versus changing the model
15:32 Packaging by segment and the trouble with token pricing
19:01 Table stakes, differentiators and add-ons
21:18 Customer-in, not product-out
24:21 Building a right to win beyond features
29:04 Pricing as a quality signal
32:20 Freemium and land grab
35:30 What defensibility means
37:13 Diagnosing where you sit in the stack
40:03 Handling uncomfortable conversations
41:51 Size of prize on your roadmap
44:51 Inflated impact numbers and counting the zeros
47:00 Where to learn more
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.

Jenny Martin is a collaboration coach and the author of OOPSI. She spent 25 years leading software delivery teams before setting up her own training company in 2011, and now focuses on the people side of product development: psychological safety, learning design, facilitation and Lego Serious Play. OOPSI is a framework she first developed a decade ago to keep teams aligned on outcomes. It has since been picked up by a major US client of Jeff Patton, a global consultancy and a large tech company. Jenny joins Randy Silver to explain how it works.
Key takeaways
OOPSI exists to balance thinking with doing. It gives teams enough collaboration at the right moment to build the right thing, without the analysis paralysis of the waterfall era.
The Asteroids metaphor explains the method. Break down only the big chunk in front of you and leave the rest unexplored. Blow everything up at once and you get overwhelmed by thousands of tiny, fast-moving pieces.
An outcome is a change in the real world that creates value for someone, usually a customer, internal or external. A number on a spreadsheet is not an outcome, though it should connect to one through a chain of whys.
Outputs are not what the team produces. They are what a user or system visibly delivers along the way, such as a payslip or a set of search results. Roughly 20% of them deliver 80% of the value, so this layer is where teams prioritise.
The process layer is the set of interactions that generates each output, and it maps closely to a well-sized user story. Working down from outcomes and outputs gives teams vertical slices without exploring every asteroid first.
Scenarios and inputs borrow from behaviour-driven development and specification by example. They suit deterministic systems where there is one right answer. AI evals handle the qualitative, non-deterministic cases, and teams will need both.
Concrete test data surfaces hidden assumptions early. On a retail project, Jenny’s team showed a customer the exact data they planned to test with, and the customer spotted a flawed assumption that would have cost three or four months of work.
Most teams struggle with the outcomes layer. Jenny sees it as the biggest risk now that AI lets teams move fast in the wrong direction.
Chapters
00:00 Introduction
 00:57 Where OOPSI came from
 01:34 Who is the collaboration coach?
 03:26 The problem Oopsie solves
 05:52 The Asteroids metaphor
 09:16 Joining up discovery and delivery
 13:09 Using OOPSIE at team and company level
 15:46 Outcomes
 19:37 Outputs
 24:30 Process
 28:44 Scenarios and AI evals
 33:06 Inputs
 34:53 Why examples matter at every layer
 37:43 What teams get wrong
 39:52 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.

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.