Shaun Clowes (CPO Confluent, ex-Salesforce, Atlassian)29 December 2024

Why great AI products are all about the data

10Frameworks

16Insights

Episode overview

Confluent CPO Shaun Clowes argues that product managers create leverage by focusing outside the building, seeking disconfirming evidence, and supporting market insights with rigorous data. He explains that AI product quality depends less on interchangeable models than on supplying timely, structured, relevant context and data. The conversation also examines why configurable business rules protect established B2B software, how product-led growth complements enterprise sales, and why varied career experiences can become professional superpowers.

Key ideas

Product managers should spend most of their thinking time on customers, markets, competitors, and real product usage rather than internal delivery mechanics.
Customer research must be appropriately sized, non-leading, structured, and analyzed for counterfactuals rather than used to confirm an existing strategy.
LLMs can synthesize interviews and competitor materials, but their usefulness is bounded by the quality, recency, structure, and breadth of the supplied data.
AI differentiation is primarily a data-management challenge: products need the right context and linked data at the right time, not merely a model and prompts.
Established B2B applications derive durable value from years of customer-specific business rules and workflows, making them much harder to replace than their interfaces suggest.
Data should function as a compass rather than a GPS; surprising results require checks of upstream causes, downstream outcomes, sample quality, and business impact.
Product-led growth and enterprise sales can reinforce one another, producing businesses with both many customers and substantial revenue.
Career choices can be treated as a bingo card of adjacent but different experiences that expand pattern recognition, versatility, and future leverage.

Transcript available · source text is retained privately and is not published

Frameworks in this episode

Innovation3 steps

Context Over Models: The Data-Management Bet

AI products win on getting good, timely, well-structured data to the model — not on the model itself

Read →

Productivity3 steps

Data Is a Compass, Not a GPS

Data disproves the ridiculous — it rarely hands you the answer, so validate findings before you trust them

Read →

Sales3 steps

Dual-Motion Growth: PLG and Sales That Feed Each Other

Win by having both many customers and lots of revenue — with PLG and sales feeding each other

Read →

Innovation3 steps

LLM as Disconfirmation Engine

Point the model at where your strategy does NOT fit — and reverse-engineer competitors from their public docs

Read →

Strategy3 steps

Meet the Bar and Be Different

Incumbents overshoot average utility — win by meeting the bar while being materially different

Read →

Productivity3 steps

Outside-the-Building Product Management

Spend 80% of your time thinking outside the building and argue every case from the market's point of view

Read →

Strategy3 steps

Seek the Counterfactual, Not Confirmation

The competitive edge lives in the data you're trying to prove yourself wrong with, not the data you hoped to see

Read →

Strategy3 steps

The Business-Rules Moat: Why 'Forms on Databases' Are Unclonable

B2B SaaS lock-in isn't the UI or data model — it's years of accreted, configured business rules

Read →

Self-Mastery3 steps

The Career Bingo Card

Deliberately take adjacent-but-different roles to fill in squares and become 'scribble-shaped'

Read →

Productivity3 steps

The Nielsen Number: Right-Size Your Research

Interview 7-14 people — fewer teaches too little, more teaches nothing new

Read →

People & resources mentioned

Attributed to the moment in the episode. Timestamps are approximate.

People · 10

Shaun ClowesMentionsthis was actually developed originally in the our retention program with Casey Winters and Sean Klaus
“today my guest is Shan klous sha is Chief product officer at confluent”
▶ Watch at ~1:00approx.
Lenny RachitskyMentionstoday we've got another very special compilation episode something I've been pulling on more and more with the podcast and the newsletter
“thank you Lenny it's really awesome to be here”
▶ Watch at ~5:00approx.
Steve BlankMentionsmentioned
“Steve blank and people used to talk about you should be spending 80% of your time thinking about things going on outside the building”
▶ Watch at ~9:30approx.
Sachin RekhiMentionshe was a product leader pretty well known in the growth community and he was a leader at LinkedIn for a long time
“Sachin Rey on on the show you may have uh he was a product leader pretty well known in the growth community”
▶ Watch at ~17:30approx.
Mike KriegerMentionsMike is chief product officer at Anthropic the company behind Claude. He's also the co-founder of Instagram.
“at the lenning friend Summit Mikey creger talked about how he had kind of the two types of PM groups within anthropic”
▶ Watch at ~23:30approx.
Charlie MungerMentionsHis words and documents were assembled into Poor Charlie's Almanack.
“Charlie Monger has this great quote I looked up just to make sure I get it right”
▶ Watch at ~55:30approx.
Elena VernaMentionsElena Vernon who's been on the podcast is just like a huge proponent of everyone will be product LED self-serve eventually
“Elena Verna had an awesome podcast episode talking about product let sales and kind of the combination of these two things”
▶ Watch at ~56:00approx.
Tom KennedyMentionshe was our general counsel so like Chief legal officer basically and a lifelong lawyer
“a guy called Tom Kennedy he was our general counsel so like Chief legal officer basically and a lifelong lawyer”
▶ Watch at ~1:01:00approx.
Marty CaganCoinedMarty has been helping product teams and product managers improve their craft processes and careers for over 20 years
“inspired how to how to build products that people love by Marty Kagan and the Silicon Valley product group”
▶ Watch at ~1:14:30approx.
Tim RobinsonMentionsmentioned
“that Main guy he's so funny I forget his name Tim Sweeny or something like that”
▶ Watch at ~1:15:00approx.

Resources · 34

ConfluentMentionscompany
“sha is Chief product officer at confluent”
▶ Watch at ~1:00approx.
MetromileMentionscompany
“Chief product officer of metromile a a public auto insurance technology company”
▶ Watch at ~1:00approx.
SalesforceMentionscompany · Salesforce
“previously he was Chief product officer at moft which is a billion dooll business within Salesforce”
▶ Watch at ~1:00approx.
ReforgeMentionscompany · Brian Balfour
“he also created two of the most popular reforge courses”
▶ Watch at ~1:30approx.
AtlassianMentionscompany
“prior to that he spent 6 years at atlassian where he ran the jir agile”
▶ Watch at ~1:30approx.
JiraMentionssoftware · Atlassian
“he spent 6 years at atlassian where he ran the jir agile”
▶ Watch at ~1:30approx.
InterpretMentionssoftware
“this episode is brought to you by interpret interpret unifies all your customer interactions”
▶ Watch at ~2:30approx.
BuildBetterMentionssoftware · BuildBetter
“this episode is brought to you by build better”
▶ Watch at ~3:30approx.
ChatGPTUsessoftware · OpenAI
“you can put a bunch of customer interviews into chat GPT and you can say hey chat GPT this is my strategy”
▶ Watch at ~15:00approx.
OpenAIMentionscompany
“in your experience you're just using Straight Up open AI J gbt Claud not like any specific tool”
▶ Watch at ~17:00approx.
ClaudeMentionssoftware · Anthropic
“in your experience you're just using Straight Up open AI J gbt Claud not like any specific tool”
▶ Watch at ~17:00approx.
LinkedInMentionswebsite · LinkedIn
“he was a leader at LinkedIn for a long time”
▶ Watch at ~17:30approx.
WorkdayMentionssoftware · Workday, Inc.
“let's say I was working at workday and I was trying to bring an AI bot”
▶ Watch at ~22:00approx.
AnthropicMentionscompany
“Mikey creger talked about how he had kind of the two types of PM groups within anthropic”
▶ Watch at ~23:30approx.
Lenny's Friends SummitMentionsevent
“at the lenning friend Summit Mikey creger talked about how he had kind of the two types of PM groups within anthropic”
▶ Watch at ~23:30approx.
SalesforceMentionscompany · Salesforce
“I spent a long time at Salesforce so I spent a lot of time in the CRM ecosystem”
▶ Watch at ~25:00approx.
AshbyRecommendssoftware · Ashby
“yeah it's Ashby it's the one all the cool kids are talking about now”
▶ Watch at ~35:00approx.
Retention and EngagementCoinedcourse · Shaun Clowes
“you created the reforge course data for product managers and also retention engagement course on reforge”
▶ Watch at ~38:30approx.
Data for Product ManagersCoinedcourse · Shaun Clowes
“you created the reforge course data for product managers and also retention engagement course on reforge”
▶ Watch at ~38:30approx.
ShopifyMentionscompany · Shopify
“I had the head of growth from Shopify on the podcast”
▶ Watch at ~43:00approx.
Wix StudioMentionssoftware · Wix
“Wick Studio gives you everything you need to cater to any client at any scale scale all in one place”
▶ Watch at ~44:30approx.
The Lean StartupRecommendsbook · Eric Ries
“they all but goodies is probably going to be uh the Lan startup that I still find actually really good”
▶ Watch at ~1:14:00approx.
Silicon Valley Product GroupMentionscompany · Marty Cagan
“inspired how to how to build products that people love by Marty Kagan and the Silicon Valley product group”
▶ Watch at ~1:14:30approx.
Inspired: How to Create Tech Products Customers LoveRecommendsbook · Marty Cagan
“inspired how to how to build products that people love by Marty Kagan and the Silicon Valley product group”
▶ Watch at ~1:14:30approx.
NetflixUsescompany · Netflix
“there's a new program on Netflix I think it's called detroiters I've been watching that”
▶ Watch at ~1:15:00approx.
DetroitersRecommendstv show
“there's a new program on Netflix I think it's called detroiters I've been watching that yeah it's really funny I really like that”
▶ Watch at ~1:15:00approx.
GleanUsescompany · Glean
“it's a pretty well-known startup now they recently raised a ton of money we've been using um Gina confluent for a long time”
▶ Watch at ~1:15:30approx.
GleanRecommendscompany · Glean
“it's just amazing like it's just amazing okay I can't I can't describe how good it is”
▶ Watch at ~1:16:00approx.
ListenMentionsbook
“it also connects with this parenting book I'm reading called listen that a previous guest recommended”
▶ Watch at ~1:18:00approx.
Radical CandorMentionsbook · Kim Scott
“it connects with like radical cander is similar like in theory of just carrying”
▶ Watch at ~1:18:00approx.
SydneyRecommendsplace
“Sydney is a really beautiful city and like it's kind of famous for its beaches”
▶ Watch at ~1:18:30approx.
Blue MountainsRecommendsplace
“there's an area called the Blue Mountains which is like an hour and a half drive from Sydney”
▶ Watch at ~1:19:00approx.
Data for Product ManagersRecommendscourse · Shaun Clowes
“the retention engagement course and the um data for product managers course so you know love to see folks get some value”
▶ Watch at ~1:20:00approx.
Retention and EngagementRecommendscourse · Shaun Clowes
“the retention engagement course and the um data for product managers course so you know love to see folks get some value”
▶ Watch at ~1:20:00approx.

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Hot Take· 4

Hot Take05:30

Why Product Management Is Still Such a Random Discipline

Shaun Clowes argues that 15-20 years into the profession, product management is still strangely undeveloped — outcomes, behaviors, and individual performance all look random. He sees making PM reproducibly great as part of his life's work.

Product management is 15-20 years old but outcomes remain random
Individual PM performance is wildly distributed, unlike more mature disciplines
We are not reliably producing '10x' product managers
Something about the current state isn't getting at the truly value-added work

“the outcomes are random the behaviors are random individual performance is random”
Shaun Clowes · 06:00

“we could all say that we're not reliably producing you know 10 times product managers every day every day of the week”
Shaun Clowes · 07:30

#product-management#careers#hot-take

Hot Take20:00

LLMs Are Very Smart and Also Insanely Dumb

Clowes' core AI thesis: models are brilliant but only know what they were trained on or what you feed them in the moment — then they forget it. Combined with the decay rate of information, this means the real AI challenge for PMs is data management, not the model.

Models only know their training data or what you hand them in the moment
Information has a decay rate — new data loses value very quickly
An LLM is a 'synthesis machine' that is useless without fresh data to work on
Therefore AI's biggest impact on PM is data management, not the models

“these models are very very very smart but they're also like insanely dumb”
Shaun Clowes · 20:00

“you've got this synthesis machine which is this llm thing that's going to help you help you do synthesis but if it hasn't got all…”
Shaun Clowes · 20:30

#ai#llms#data#hot-take

Hot Take28:30

Why AI Won't Clone Salesforce Out of Business

Even if AI makes it trivial to build a forms-on-databases app, Clowes argues that drives the value of each new clone toward zero and strengthens incumbents. Even a headless, agent-only future still needs the business rules — and those rules are exactly what the incumbents own.

Easy cloning drives incremental value of each new app toward zero
No one gets fired for buying Salesforce — buyers default to the premier vendor
Even agent-only workflows must operate against the system's business rules
Most likely outcome: currently dominant companies get even more dominant

“imagine Salesforce had no UI it would still have those business rules that I was talking about and those business rules are what Define what…”
Shaun Clowes · 29:00

“the most likely outcome is that the is that the currently dominant companies are going to get more dominant”
Shaun Clowes · 30:30

#ai#b2b-saas#disruption#hot-take

Hot Take37:00

Data Is a Compass, Not a GPS

Having run data teams across many companies, Clowes now believes being 'data-driven' is easy to overdo. Data rarely gives you the answer — it tells you whether what you just said is ridiculous. It's for disproving hypotheses, not generating direction.

Treating data as the answer-giver makes you 'wrong or slow, sometimes both'
Data mostly disproves what you already think, rather than deciding for you
Your intuition already encodes a ton of data you've absorbed
Right-size data; don't use it as a weapon to force people your way

“data is more like a compass than a GPS right like if you look at data as a way of like giving you the answer…”
Shaun Clowes · 37:00

#data#decision-making#product-management#hot-take

Explainer· 5

Explainer07:00

Why a 10x PM Actually Delivers 100x Return

Because product management is fundamentally about leverage — helping other people have dramatically more impact — a great PM compounds. A 10x PM 10x's the return on 10x resources, producing 100x or more.

PM is about leverage: organizing goals so others have more impact
A 10x PM multiplies the return on already-amplified resources
That compounding is why the outcomes and benefits are so wildly distributed

“a 10 times product manager has 100 times return or more because because they're 10 timesing the return on 10 times resources”
Shaun Clowes · 07:00

#product-management#leverage#careers

Explainer15:30

Use LLMs to Prove Your Strategy Wrong, Not Right

Clowes' favorite AI move for PMs: paste customer interviews into ChatGPT and ask where your strategy does NOT fit what customers said. You can also feed in competitors' public documents to reverse-engineer their likely strategy, which he finds 'at times creepy' good.

Ask the LLM where your strategy does not fit customer input, not where it does
People over-index on finding what they hope to see
Feed competitor public docs into the LLM to summarize their probable strategy
Only works if you push at the edges and try to prove yourself wrong

“hey chat GPT this is my strategy tell me where my strategy does not fit what these customers talked about”
Shaun Clowes · 15:30

“people spend far too much time looking for what they're hoping to see not for what they're not looking to see”
Shaun Clowes · 15:30

#ai#llms#strategy#customer-research

Explainer17:30

Great PMs Swim in a 'Feedback River'

Borrowing a concept from ex-LinkedIn leader Sachin Rey, Clowes describes the best PMs as constantly swimming in a feedback river — deliberately surrounding themselves with user interviews, direct feedback, NPS, and competitor data. LLMs make maintaining that river far easier.

Concept credited to Sachin Rey (ex-LinkedIn): the 'feedback river'
Great PMs deliberately wash themselves in continuous information
At Confluent, LLMs cluster thousands of inbound requests semantically
Semantic grouping reveals which ideas are rising or falling in popularity

“they really smart product managers are constantly swimming in a feedback River”
Shaun Clowes · 17:30

#ai#customer-feedback#product-management#llms

Explainer21:30

Great AI Products Win on Context, Not the Model

If you're building an AI feature, the differentiator won't be the model (mostly replaceable) or even prompts — it's the context: getting good, timely, well-structured data to the LLM. Using a hypothetical Workday HR bot, Clowes shows how much linked data real 'smarts' require.

Models are mostly replaceable; prompts help but aren't the edge
The real answer is context — all the data you feed the model
A smart HR bot needs employee, benefits, legal, and policy data all linked
Getting good, timely, well-structured data to the LLM is where 90% of the work goes

“it's obvious that the real answer is the context like all the context you're going to give it all the data you're going to copy…”
Shaun Clowes · 21:30

“that's where 90% of the calories go”
Shaun Clowes · 23:30

#ai#product-development#data#context

Explainer25:30

B2B SaaS Is 'Forms on Databases' — and Business Rules Are the Moat

Uncharitably, Jira, Workday, and Salesforce are all just forms on databases — which sounds easy to clone but is unbelievably hard. The real lock-in isn't the UI or data model; it's the years of accumulated business rules and configuration that make the software uniquely yours.

All vertical/business SaaS is ultimately 'forms on databases'
Lock-in isn't the UI or data model — it's the business rules
Configuration makes the app native to your company and a black box to everyone else
Salesforce couldn't describe its own sales process without reading its Salesforce config

“the jira is a form on a database you know workday is a form on a database so sales force they all forms on databases”
Shaun Clowes · 25:30

“the real thing just staring everybody in the face is it's all about the business rules like that is what drives the lock in”
Shaun Clowes · 26:30

#b2b-saas#product-strategy#moats

Story· 2

Story56:30

Treat Your Career Like a Bingo Card

Clowes describes his deliberately varied career (Atlassian, Metromile, Salesforce, Confluent) as filling in a bingo card — chasing adjacent-but-different roles to become more versatile. The best people, he says, aren't 'T-shaped' but 'scribble-shaped': deep in many things at once.

He chose roles to fill in boxes he hadn't yet filled, building versatility
Each new domain becomes a pattern-match superpower for future problems
Pick adjacencies that stretch you — don't jump out of a plane never having parachuted
The best people are 'scribble-shaped,' deep in more than one thing, not just T-shaped

“my career's been a little bit like a bingo card like I've always been looking for to fill in boxes I didn't have filled”
Shaun Clowes · 57:00

“it's more like people are scribbl shaped”
Shaun Clowes · 1:04:00

#careers#professional-growth#story

Story1:08:00

Failure Corner: Killing the Product the Day a Customer Wanted to Pay

Clowes shares a product that should have obviously failed — an environmental-impact tool his company had no right to win in. After two years, the forcing function to kill it was, ironically, a customer finally wanting to pay millions. The lesson: call a spade a spade far earlier.

Right idea, but wrong company, wrong players, wrong time, wrong distribution
The product limped along in market for two years as a 'zombie'
They killed it the moment a customer was ready to pay millions — signing would trap them
Regret: it was obviously dead at six months; he should have said so and forced a decision

“wrong company wrong players wrong time wrong distribution like we had literally no right to win no right to play”
Shaun Clowes · 1:09:30

“the final straw was actually when a when a customer finally wanted to pay for it”
Shaun Clowes · 1:10:00

#failure#product-management#lessons#story

Takeaway· 5

Takeaway08:00

A PM's Job Is to Say No to 90% of Things

Clowes explains why PMs are the easiest role to criticize: the job is to say no to 90% of requests so you can say yes to the right 10%. That puts you behind the eightball from the start, so you have to quickly prove you have the right insights and data.

Saying no to 90% makes the PM the 'bad person' by default
You say no to 90% so you can say yes to the vital 10%
You have to get 'runs on the board' fast or you don't get another swing
This is why PMs are the easiest team member to single out and criticize

“your job is to say no to 90% of things that that get brought your away”
Shaun Clowes · 08:00

“so you're saying no to 90% so you can say yes to 10%”
Shaun Clowes · 08:00

#product-management#prioritization#careers

Takeaway09:30

Spend 80% of Your Time Thinking Outside the Building

The single biggest lever for PMs, per Clowes, is orienting outward. Most PMs get dragged into internal politics, scrum, and delivery — and you can never earn an A that way because the job is finding reliable, differentiated value in the market, not execution.

Echoing Steve Blank: spend 80% of your time thinking outside the building
Most PMs get pulled into internal politics, scrum, and delivery
You can't win on execution — the job is finding unique market value
Always argue from the customer, market, and competitor perspective, then back it with data

“you should spend 80% of your time thinking outside the building”
Shaun Clowes · 09:30

#product-management#customer-research#strategy

Takeaway32:00

In the AI Era, Distribution Gets Harder, Not Easier

Distribution has always been the hardest problem, and AI is making it worse — cold email and LinkedIn outreach are drowning in LLM-generated spam that desensitizes everyone. Being a cheaper clone won't cut through; you have to be materially different, e.g. by bringing data into the workflow.

Distribution channels are getting more crowded and expensive
Half of LinkedIn outreach now reads as LLM-generated spam, worsening signal-to-noise
'Cheaper Salesforce' isn't a strategy — you must be materially better on some angle
Next-gen apps win by bringing data in as a first-class citizen (e.g. Ashby's ATS)

“in the world of AI it seems like distribution is more likely to get hard than easy”
Shaun Clowes · 32:00

“meet the bar and be different meet the bar and be different is is the way to cut through”
Shaun Clowes · 34:00

#distribution#go-to-market#ai#differentiation

Takeaway39:30

When Data Defies Intuition, Trust Your Intuition First

If a result contradicts your intuition strongly, the most likely explanation (Occam's razor) is that the analysis is wrong. Believe your intuition and go prove yourself right — and rigorously check whether the data is representative before presenting it, or you lose credibility.

A wildly counterintuitive result is usually just a broken analysis
Believe your intuition first, then try to prove yourself right
Occasionally the anomaly is real gold — but only diligence reveals which
Presenting a hole-filled analysis with authority costs you 'brownie points'

“first believe your intuition and go and prove yourself right”
Shaun Clowes · 39:30

“it would be better not to show up with an analysis that isn't clear then it would be to show up with an analysis that's…”
Shaun Clowes · 40:30

#data#analysis#decision-making#product-management

Takeaway1:17:00

People Don't Care What You Know Until They Know You Care

Clowes' guiding motto, learned after starting out as an 'engineer's engineer' obsessed with technical correctness: influence isn't about being right. It's built on trust, relationships, and caring about others' outcomes and incentives — everything good sits on that foundation.

He began his career prizing technical correctness and 'the one right answer'
Real influence isn't about being right — it's about trust and relationships
Care about others' outcomes and incentives first
Strong partnerships and progress are built on that foundation

“people don't care what you know until they know that you care”
Shaun Clowes · 1:17:30

#leadership#influence#careers#takeaway

Key ideas

Frameworks in this episode

Context Over Models: The Data-Management Bet

Data Is a Compass, Not a GPS

Dual-Motion Growth: PLG and Sales That Feed Each Other

LLM as Disconfirmation Engine

Meet the Bar and Be Different

Outside-the-Building Product Management

Seek the Counterfactual, Not Confirmation

The Business-Rules Moat: Why 'Forms on Databases' Are Unclonable

The Career Bingo Card

The Nielsen Number: Right-Size Your Research

People & resources mentioned

People · 10

Resources · 34

Insights & moments

◆Hot Take· 4

Why Product Management Is Still Such a Random Discipline

LLMs Are Very Smart and Also Insanely Dumb

Why AI Won't Clone Salesforce Out of Business

Data Is a Compass, Not a GPS

✶Explainer· 5

Why a 10x PM Actually Delivers 100x Return

Use LLMs to Prove Your Strategy Wrong, Not Right

Great PMs Swim in a 'Feedback River'

Great AI Products Win on Context, Not the Model

B2B SaaS Is 'Forms on Databases' — and Business Rules Are the Moat

❝Story· 2

Treat Your Career Like a Bingo Card

Failure Corner: Killing the Product the Day a Customer Wanted to Pay

▲Takeaway· 5

A PM's Job Is to Say No to 90% of Things

Spend 80% of Your Time Thinking Outside the Building

In the AI Era, Distribution Gets Harder, Not Easier

When Data Defies Intuition, Trust Your Intuition First

People Don't Care What You Know Until They Know You Care

Hot Take· 4

Explainer· 5

Story· 2

Takeaway· 5