Jeanne DeWitt Grosser (Vercel, Stripe, Google)30 November 2025

What world-class GTM looks like in 2026

8Frameworks

15Insights

Episode overview

Jeanne DeWitt Grosser defines go-to-market broadly as every function that touches a customer or generates revenue, arguing that those functions should operate as an integrated lifecycle. She explains how Vercel uses go-to-market engineers and human-supervised agents to automate inbound qualification, analyze deals, and return customer signal to product teams. The conversation also covers segmentation, consultative selling, product-like customer journeys, PLG’s ceiling, pricing design, and building sales organizations that earn credibility with engineers.

Key ideas

Treat go-to-market as an integrated customer lifecycle spanning marketing, sales, technical sales, customer success, support, and partnerships.
Go-to-market engineers can encode legible, repeatable workflows into agents while retaining humans for review, feedback, and judgment.
Vercel reduced an inbound workflow from ten SDRs to one agent reviewer while holding lead-to-opportunity conversion flat and improving response speed.
Treat the buying journey like a product: map every touchpoint and make each interaction valuable, personalized, and experiential rather than transactional.
Effective segmentation combines buying complexity with product-specific revenue drivers such as growth potential, traffic, business model, and workload type.
Sales teams should have enough product depth to distinguish real market signal from one-off objections and act as an extension of product research.
PLG remains powerful for early growth but usually reaches a ceiling, so companies should build a repeatable sales motion before growth stalls.
Pricing should align customer value, supplier cost, packaging, and buying behavior, and should be iterated as deliberately as a product.

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

Frameworks in this episode

Sales4 steps

Conversation-Intelligence Deal Diagnosis (the Dealbot)

Run an AI agent over every call, email and Slack to find the real reason you win, lose, and stall

Read →

Sales3 steps

Discovery-Led Selling: Ask, Don't Pitch

Great salespeople talk under half the time and answer a question with a question about the question

Read →

Marketing3 steps

Go-to-Market as a Product

Design the buying journey as a sequence of experiences, and add value at every touch whether or not they buy

Read →

Strategy4 steps

Multi-Axis Revenue Segmentation

Plot customers on a graph of size plus the attributes that actually drive your revenue, not size alone

Read →

Finance3 steps

Price Like a Product (and Unbundle Deliberately)

Align price to where value and cost actually accrue, and kill defaulted freemium and mis-packed SKUs

Read →

Sales3 steps

Sell to Reduce Risk, Not Increase Upside

Four of five buyers buy to avoid pain or reduce risk — anchor the sale there, not on the art of the possible

Read →

Leadership3 steps

The 10-Minute PM Test for GTM-Product Partnership

Hire salespeople with enough product depth that engineers can't tell they aren't a PM

Read →

Sales5 steps

The GTM Engineer Agent-Deployment Loop

Turn a sales function into an AI agent by shadowing your best rep, then redeploy the humans up-market

Read →

People & resources mentioned

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

People · 8

Jeanne DeWitt GrosserMentionsGan was chief product officer at Stripe where she built their very early sales team from the ground up. She's currently COO at Versell.
“Today my guest is Gan Grosser.”
▶ Watch at ~1:30approx.
James DyettMentionsmentioned
“A huge thank you to Claire Hughes Johnson, Kate Jensen, and James Deiet for suggesting topics for this conversation”
▶ Watch at ~2:30approx.
Kelly SchaeferMentionsmentioned
“and Kelly Schaefer for the connection.”
▶ Watch at ~2:30approx.
Claire Hughes JohnsonMentionsClaire was most recently Chief Operating Officer at stripe for the past seven years
“A huge thank you to Claire Hughes Johnson, Kate Jensen, and James Deiet for suggesting topics for this conversation”
▶ Watch at ~2:30approx.
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
“Thanks for having me, Lenny.”
▶ Watch at ~5:30approx.
Jen AbelMentionsToday, my guest is Jen Ael, co-founder of Jellyfish, where she and her team help earlystage founders learn how to sell
“I had Jenna Ael on the podcast recently, and it was all about sales skills and how to sell.”
▶ Watch at ~57:30approx.
April DunfordMentionsApril Dunford was on this podcast and she has this book that she put out recently called the sales pitch
“the way April Dunford came on the podcast and talked about this”
▶ Watch at ~1:00:00approx.
Kate JensenMentionsyour former colleague Kate Jensen
“I'll read this quote from your former colleague Kate Jensen.”
▶ Watch at ~1:09:30approx.

Resources · 41

StripeMentionscompany · Patrick Collison and John Collison
“Gan was chief product officer at Stripe where she built their very early sales team from the ground up.”
▶ Watch at ~1:30approx.
VercelMentionscompany · Guillermo Rauch
“She's currently COO at Versell where she oversees marketing, sales, customer success, revenue ops, and field engineering.”
▶ Watch at ~1:30approx.
YouTubeMentionswebsite · Google
“don't forget to subscribe and follow it in your favorite podcasting app or YouTube.”
▶ Watch at ~2:30approx.
DatadogMentionscompany · Datadog
“This episode is brought to you by Data Dog, now home to EPO, the leading experimentation and feature flagging platform.”
▶ Watch at ~3:00approx.
EppoMentionssoftware · Eppo
“now home to EPO, the leading experimentation and feature flagging platform.”
▶ Watch at ~3:00approx.
AirbnbUsescompany · Brian Chesky, Joe Gebbia, and Nathan Blecharczyk
“I saw this firsthand when I was at Airbnb, where our experimentation platform was critical for analyzing what worked”
▶ Watch at ~3:30approx.
LovableUsessoftware · Lovable
“I use it regularly and I could not recommend it more highly.”
▶ Watch at ~4:30approx.
LovableRecommendssoftware · Lovable
“Get started for free at lovable.dev.”
▶ Watch at ~5:00approx.
GoogleMentionscompany · Google
“You've done this for a long time at Google at Stripe.”
▶ Watch at ~8:30approx.
Project RosalindCoinedtool · Stripe
“we went and we started building project Rosland.”
▶ Watch at ~12:00approx.
Stripe ConnectMentionsproduct · Stripe
“you wanted to sell Stripe Connect versus Vanilla Payments.”
▶ Watch at ~12:30approx.
SalesloftUsessoftware
“someone in systems that was helping me configure Outreach or Salesoft”
▶ Watch at ~13:30approx.
OutreachUsessoftware
“someone in systems that was helping me configure Outreach or Salesoft”
▶ Watch at ~13:30approx.
ZoomInfoMentionscompany
“who went on to go to Zoom Info and then actually recently just founded a goto market startup”
▶ Watch at ~14:30approx.
AnthropicMentionscompany
“some of the folks doing that now work at OpenAI, they work at Anthropic”
▶ Watch at ~15:00approx.
OpenAIMentionscompany
“some of the folks doing that now work at OpenAI, they work at Anthropic”
▶ Watch at ~15:00approx.
ChatGPTUsessoftware · OpenAI
“they're doing chatbt on this”
▶ Watch at ~19:00approx.
LinkedInUseswebsite · LinkedIn
“they're looking up the you know person on LinkedIn”
▶ Watch at ~19:00approx.
Vercel AI CloudUsesproduct · Vercel
“everything that we're building with agents, we're building on Versell's AI cloud.”
▶ Watch at ~32:30approx.
VercelUsescompany · Guillermo Rauch
“everything that we're building with agents, we're building on Versell's AI cloud.”
▶ Watch at ~32:30approx.
FigmaMentionssoftware · Figma
“Enterprises like Atlassian, Figma, and Urban Outfitters use Stripe”
▶ Watch at ~33:30approx.
AtlassianMentionscompany
“Enterprises like Atlassian, Figma, and Urban Outfitters use Stripe”
▶ Watch at ~33:30approx.
StripeMentionscompany · Patrick Collison and John Collison
“Learn how Stripe can help your business grow at stripe.com.”
▶ Watch at ~34:00approx.
GongRecommendssoftware · Gong
“I think Gong has gotten just meaningfully more interesting in the last year.”
▶ Watch at ~34:30approx.
SlackUsessoftware · Slack Technologies
“Versel is incredibly heavy users of Slack.”
▶ Watch at ~37:00approx.
LostbotCoinedtool · Vercel
“that dealbot, the Lostbot version was like two days.”
▶ Watch at ~41:00approx.
Vercel SandboxUsessoftware · Vercel
“you have a sandbox if you're running untrusted code”
▶ Watch at ~45:00approx.
HotmailMentionssoftware · Microsoft
“It was a full year before Yahoo Mail caught up and even longer before Hotmail”
▶ Watch at ~47:00approx.
GmailMentionssoftware · Google
“my first job out of college was working on Gmail in 2004.”
▶ Watch at ~47:00approx.
Yahoo MailMentionssoftware · Yahoo
“It was a full year before Yahoo Mail caught up and even longer before Hotmail”
▶ Watch at ~47:00approx.
Apple PayMentionsproduct · Apple
“Apple Pay is super important.”
▶ Watch at ~1:02:30approx.
Stripe BillingMentionsproduct · Stripe
“you were going to want Stripe billing.”
▶ Watch at ~1:02:30approx.
Chrome UX ReportUseswebsite · Google
“Google publishes a crux score”
▶ Watch at ~1:03:30approx.
Google ChromeMentionssoftware · Google
“they have a bunch of data in Chrome”
▶ Watch at ~1:03:30approx.
Chrome UX ReportUseswebsite · Google
“crux rank is going to be super important.”
▶ Watch at ~1:06:30approx.
SierraMentionscompany · Bret Taylor and Clay Bavor
“So Sierra as an example, right? Like they are very clearly going after global 2000”
▶ Watch at ~1:14:30approx.
AtlassianMentionscompany
“I know Lastian was a long time PLG company”
▶ Watch at ~1:16:30approx.
v0Mentionssoftware · Vercel
“we were selling our front-end cloud and we were selling vzero”
▶ Watch at ~1:20:00approx.
Lenny's PodcastCoinedpodcast · Lenny Rachitsky
“You can find all past episodes or learn more about the show at lennispodcast.com.”
▶ Watch at ~1:25:30approx.
Apple PodcastsMentionssoftware · Apple Inc.
“you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.”
▶ Watch at ~1:25:30approx.
SpotifyMentionssoftware · Spotify
“you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.”
▶ Watch at ~1:25:30approx.

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Insights & moments

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

Myth Buster· 1

Myth Buster58:30

Most Customers Buy to Avoid Pain, Not to Chase Upside

Grosser cites a rule-of-thumb stat that 80% of customers buy to avoid pain or reduce risk, with only one in five buying to increase upside. Founders love selling the 'art of the possible,' but that vision-led pitch mainly resonates with other founders. For everyone else, especially enterprises, the real driver is derisking — not missing revenue targets, not being outdone, not causing brand damage.

~80% of customers buy to avoid pain or reduce risk; ~20% to increase upside.
The visionary 'art of the possible' pitch mostly resonates with other founders.
Enterprises are avoiding the risk of missing targets, losing to competitors, or brand damage.
Pivoting to a risk/pain message feels off-brand to founders but drives more buying behavior.
Echoes April Dunford: adopting a product is a career bet buyers don't want to get wrong.

“80% of customers buy to avoid pain or reduce risk as opposed to the other one out of five to increase upside”
Jeanne DeWitt Grosser · 58:30

#sales#positioning#psychology#enterprise

Hot Take· 3

Hot Take40:30

Build vs Buy: Agents Are Cheaper to Build Than You Think

Grosser's build-vs-buy calculus is shifting because these agents are neither hard nor expensive to build. The lostbot version took two days; the lead agent runs full-stack on Vercel for about $1,000 a year against an SDR function that cost well over a million in salary. She argues your own esoteric context and workflows are the real alpha, and that the CIO may shift from a procurer of software to a builder of it.

Lostbot took ~2 days (40 hours) to build; the lead agent 6 weeks.
The lead agent costs ~$1,000/year to run vs >$1M in SDR salaries — a 90%+ cost reduction.
Your own context, content and workflows are what unlock an agent's power.
She coins 'ERR' — experimental run rate revenue — for the current try-it-for-a-year AI spend.
The CIO may go from a procurer of software to a builder running thousands of internal agents.

“one of our learnings is that it's not that hard to build these agents and they aren't that expensive either.”
Jeanne DeWitt Grosser · 41:00

“that lead agent which runs full stack on Verscell will cost us about $1,000 to run for the entire year.”
Jeanne DeWitt Grosser · 41:30

#ai#build-vs-buy#agents#cost

Hot Take1:09:30

The 10-Minute Litmus Test: Salespeople Who Pass as PMs

Grosser's superpower is reputedly building sales orgs that engineers respect. Her litmus test: put an AE in front of 10 engineers and it should take them 10 minutes to realize the person isn't a product manager. The point is that reps need deep product depth — it earns credibility with engineering, and it lets a 20-person sales team act as an R&D extension by turning constant customer contact into product signal.

Litmus test: it should take 10 engineers 10 minutes to tell an AE isn't a PM.
Reps need incredible product depth for credibility with product and engineering.
The best GTM orgs are equal parts revenue-driving and R&D.
A 20-person sales team talks to more customers than a UXR/PM team — a signal source.
The skill is discerning signal from noise: real product gap vs objection to overcome.

“if you are an account executive in my org and I put you in front of 10 engineers at our company. It should take them…”
Jeanne DeWitt Grosser · 1:10:00

#sales#hiring#product#org-design

Hot Take1:19:30

Why Sales Comp Fights Against Flexibility

Grosser's hot take on sales compensation: while she believes in pay-for-performance, comp plans make an organization less flexible because you must decide what you value 12 months in advance. Her live example — Vercel wrote this year's sales plans before the AI cloud even existed, then had to incentivize selling it mid-year. She's still wrestling with how to keep the motivating upside of quota while retaining room to change your mind.

She's a fan of pay-for-performance but it locks in what you value for 12 months.
Vercel wrote this year's sales plans before the AI cloud existed, then introduced it mid-year.
Rigid comp plans make it hard to be innovative and pivot.
Many companies right now struggle to do annual planning at all.
The open problem: keep quota's motivating upside while staying flexible.

“when you know we wrote the sales plans for this year at Verscell the AI cloud did not exist.”
Jeanne DeWitt Grosser · 1:20:00

#sales#compensation#management

Explainer· 5

Explainer05:30

What 'Go To Market' Actually Encompasses

Grosser argues that go to market is far broader than just marketing and sales. She defines it as any function that touches a customer or makes a dollar, which at Vercel spans marketing, sales, technical sales, customer success, support and partnerships. She predicts the ~17 hyper-specialized GTM roles will start to collapse and be re-mapped around the jobs to be done across the full customer lifecycle.

Most people think GTM is just the 'tip of the spear': marketing and sales.
Her definition: any function that touches a customer or makes a dollar.
Includes sales engineers, post-sales platform architects, CS, support and partnerships.
GTM has gone through hyper-specialization (~17 roles) that she expects to collapse.
Better to map the whole lifecycle from awareness to a high-LTV five-year customer and orchestrate it like a product.

“For me, I think of it as any function that is going to touch a customer or make a dollar.”
Jeanne DeWitt Grosser · 06:00

#go-to-market#sales#org-design

Explainer10:30

The Rise of the Go-To-Market Engineer

Grosser describes a new role that has emerged in the last 18-24 months: the go-to-market engineer. This person brings technical prowess and AI to bear on GTM, breaking down each function's workflows and turning the legible, deterministic ones into agents while keeping a human in the loop. Vercel started with inbound (the most 'legible' workflow) before moving to outbound and install-based sales.

GTM engineers bring technical skill and AI into every GTM function.
They break workflows down and turn the deterministic ones into agents.
Vercel started with inbound because that workflow is most 'legible' (writable, replicable).
The goal is lifting sellers from ~30-40% of time with customers toward ~70%.
Vercel's first three GTM engineers were technical sales engineers, all former front-end developers.

“you've seen the rise in probably the last like 18 to 24 months um of the go to market engineer”
Jeanne DeWitt Grosser · 10:30

#go-to-market#ai#gtm-engineer#hiring

Explainer23:30

SDR vs AE: Pipeline Generators vs Closers

For non-salespeople, Grosser draws the clean distinction between the two front-line sales roles. SDRs (inbound or outbound) generate pipeline — qualifying whether a prospect is worth a more expensive AE's time. Account executives are closers who take a prospect from interested to paying, and the sale grows more complex moving from SMB (single decision maker) up to enterprise (procurement, committees, migrations).

SDR = sales development rep, in charge of generating pipeline.
Two SDR types: inbound (handles 'contact sales' requests) and outbound (targets prospects).
AE = account executive, the closer who takes a prospect from interested to paying.
SMB sales tend to have a single decision maker and be transactional.
Enterprise adds economic buyers, technical buyers, procurement and committees.

“So SDR is typically in charge of generating pipeline.”
Jeanne DeWitt Grosser · 24:00

“So it's their job to take somebody from, okay, hey, I'm interested in learning about your solution.”
Jeanne DeWitt Grosser · 25:00

#sales#roles#enterprise

Explainer46:30

Treating Go-To-Market Like a Product

As technical differentiation commoditizes, Grosser's thesis is that the experience of being sold to increasingly differentiates a company and drives buying decisions. So she designs the buying journey as a set of deliberate experiences. Her signature Stripe example: replacing the boring discovery call with a whiteboarding session where the customer drew their own payments architecture and left with a valuable asset.

As software commoditizes, the selling experience itself becomes a differentiator.
Design the buying journey as unique experiences, not flat transactions.
Stripe replaced the standard discovery quiz with a collaborative whiteboarding session.
Customers drew their own architecture and left with an asset and a mental model.
A related principle: add value at every touchpoint even if the customer doesn't buy — they often return years later.

“actually the experience that you have of being sold to will increasingly actually uh differentiate a company and uh drive buying decisions if uh products…”
Jeanne DeWitt Grosser · 48:00

“what we started to do at Stripe was that first session was a whiteboarding session and we would actually get together and have you, you…”
Jeanne DeWitt Grosser · 49:00

#go-to-market#sales#customer-experience#stripe

Explainer1:00:30

A Primer on Segmentation Beyond Small/Medium/Large

Segmentation is how you carve up all the companies on the planet by how differently they buy. Small/medium/large is a rational start but insufficient; Grosser layers on axes that change how you sell. At Stripe that meant growth potential and business model; at Vercel it's the Chrome CrUX traffic score and workload type. A small company with huge traffic (e.g. OpenAI) gets promoted into the enterprise motion.

Segmentation = carving up the company universe by how differently they buy.
Small/medium/large captures buying complexity but isn't enough on its own.
Stripe added growth potential (consumption model) and business model (B2B/B2C/platform/marketplace).
Vercel layers in the Chrome CrUX traffic score and workload type (e.g. e-commerce vs crypto).
A small, high-traffic company like OpenAI gets promoted into the enterprise motion.
Rule of thumb for founders: pick ~three attributes that narrow your ICP — beyond three is too detailed.

“So Google publishes a crux score which is basically they have a bunch of data in Chrome and so they know that Lenny's site gets…”
Jeanne DeWitt Grosser · 1:03:30

“they're a top 25 traffic site on the internet. So for us, that's going to push them in our enterprise”
Jeanne DeWitt Grosser · 1:04:00

#segmentation#go-to-market#icp#stripe

Story· 3

Story11:30

Project Rosland: Mapping the Company Universe at Stripe

At Stripe in 2017, running lean with only four SDRs instead of the usual thirty, Grosser's team built 'Project Rosland' — a giant database with every company on the planet as a row and every sellable attribute as a column. The aim was a Mad Libs-style email where 80% was filled in from attributes. It largely failed at the time due to false positives, but Vercel is now rebuilding it and it works because AI can finally do the job.

Stripe's operating principle 'efficiency is leverage' meant she got 4 SDRs, not 30.
Rosland was a company universe: rows = every company, columns = sellable attributes.
Goal was a Mad Libs email, 80% auto-filled from company attributes.
In 2017 the false-positive rate was too high and it never really worked.
Vercel is now rebuilding it and it works because AI can be brought to bear.
Some collaborators went on to OpenAI, Anthropic, and AI-native GTM startups.

“can we create a Mad Libs, you know, where I will come up with uh sort of a a predefined email template, but 80% of…”
Jeanne DeWitt Grosser · 12:30

“And we were trying to do this in 2017. And uh it was very hard and didn't actually totally work.”
Jeanne DeWitt Grosser · 13:00

#stripe#outbound#ai#data

Story19:30

How One Lead Agent Did the Work of Ten SDRs

Vercel had 10 SDRs on an inbound workflow; a single GTM engineer built a lead agent in six weeks (at ~25-30% of his time) and now one person QAs the agent while the other nine moved to outbound. Crucially, they tracked the same KPIs an SDR is held to and held lead-to-opportunity conversion flat, while cutting the number of touches to convert because the agent responds instantly instead of leads sitting in a queue.

10 inbound SDRs collapsed to 1 human QAing the agent; the other 9 moved to outbound.
Built by a single GTM engineer in 6 weeks at ~25-30% of his time.
They tracked normal SDR KPIs: lead-to-opportunity conversion, touches, time to convert.
Conversion rate held flat — agent as good as humans — with fewer touches.
Speed advantage: no leads sitting in the queue or coming in overnight unanswered.

“it was 6 weeks before we felt confident going from 10 to one.”
Jeanne DeWitt Grosser · 20:30

“so the agent is as good as our humans were, but it's actually condensed the number of touches it takes to convert because it's so…”
Jeanne DeWitt Grosser · 21:30

#ai#sales#sdr#automation

Story35:00

The Dealbot That Found the Real Reason Deals Were Lost

Vercel dumps its Gong transcripts into an agent ('dealbot') and first ran it as a lost-opportunity review over Q2's biggest losses. The AE said the biggest loss was on price, but the agent — reading every Slack, email and Gong call — concluded they actually lost because they never reached the economic buyer and couldn't demonstrate ROI. It's now run in real time, feeding coaching prompts into per-customer Slack channels.

Gong transcripts feed an agent called 'dealbot'.
First use: lost-opportunity review over the quarter's biggest losses.
AE blamed price; the agent found the real cause was never reaching the economic buyer.
The lesson: the true loss was an inability to demonstrate/quantify value.
Now runs in real time, pushing coaching into per-customer Slack channels.
They treat GTM gaps like engineering bugs and run weekly sprints to fix them.

“the biggest loss that quarter uh according to the account executive was lost on price. And when you ran the agent over every Slack interaction,…”
Jeanne DeWitt Grosser · 35:30

#ai#gong#sales#coaching

Takeaway· 3

Takeaway1:14:00

PLG Has a Ceiling — Add Sales Before You Hit the Wall

Grosser says product-led growth still makes sense for most companies at the outset (unless you're explicitly enterprise from day one), but it typically has a ceiling: people won't self-serve a million-dollar deal. The mistake companies make is waiting too long to add sales, because building a predictable outbound engine takes time. She can't name a $100B company that is PLG only.

PLG suits most companies early — except explicit enterprise plays like Sierra.
PLG has a ceiling: people won't hand over a million dollars via self-serve.
Companies hit walls by waiting too long to add the sales motion.
Turning outbound into a predictable engine takes real time to build.
Effectively every company ends up building a sales org eventually.

“it does typically have a ceiling. So people are generally not going to, you know, go give a give you a million dollars via self-s…”
Jeanne DeWitt Grosser · 1:15:00

#plg#sales#growth#go-to-market

Takeaway1:21:00

Hire a Diversified Sales Portfolio

On who to hire, Grosser values a diversified portfolio. She wants people with real sales experience because sales is a skill, but pairs them with non-traditional backgrounds — especially consulting and banking — who are strong at the quantitative, consultative side. Mixing the two creates a richer learning environment: consultants realize sales is a skill and learn it, while AEs learn to talk P&L, CFOs and TCO.

Sales is a skill, so you need people with genuine sales experience.
Pair them with non-traditional backgrounds, especially consulting and banking.
Consultant/banker profiles are strong on quantitative, analytical, consultative selling.
The mix creates a richer learning environment where people teach each other.
AEs learn P&L, CFO conversations and TCO analysis from the analytical hires.

“I strongly believe that sales is a skill and so you want sales people with actual sales experience in your organization, but I think there's…”
Jeanne DeWitt Grosser · 1:21:00

#sales#hiring#team-building

Takeaway1:25:00

In Sales, Maybes Will Kill You

Closing on lessons from her past as a competitive diver, Grosser ties precision and getting back on the board after a bad dive to the replicability sales demands. She shares a line a sales guru once gave her: yeses are great, nos are great, maybes will kill you. The point is to get comfortable that a no is a great outcome — it hands you data you can act on.

Diving taught replicability and getting back up after a bad landing — both transfer to sales.
A sales guru's line: yeses are great, nos are great, maybes will kill you.
A no is a good thing — it gives you data you can act on.
Sales is about driving predictable, replicable outcomes and forecasting well.

“yeses are great, nos are great, may will kill you.”
Jeanne DeWitt Grosser · 1:25:00

#sales#mindset#resilience

Key ideas

Frameworks in this episode

Conversation-Intelligence Deal Diagnosis (the Dealbot)

Discovery-Led Selling: Ask, Don't Pitch

Go-to-Market as a Product

Multi-Axis Revenue Segmentation

Price Like a Product (and Unbundle Deliberately)

Sell to Reduce Risk, Not Increase Upside

The 10-Minute PM Test for GTM-Product Partnership

The GTM Engineer Agent-Deployment Loop

People & resources mentioned

People · 8

Resources · 41

Insights & moments

⚡Myth Buster· 1

Most Customers Buy to Avoid Pain, Not to Chase Upside

◆Hot Take· 3

Build vs Buy: Agents Are Cheaper to Build Than You Think

The 10-Minute Litmus Test: Salespeople Who Pass as PMs

Why Sales Comp Fights Against Flexibility

✶Explainer· 5

What 'Go To Market' Actually Encompasses

The Rise of the Go-To-Market Engineer

SDR vs AE: Pipeline Generators vs Closers

Treating Go-To-Market Like a Product

A Primer on Segmentation Beyond Small/Medium/Large

❝Story· 3

Project Rosland: Mapping the Company Universe at Stripe

How One Lead Agent Did the Work of Ten SDRs

The Dealbot That Found the Real Reason Deals Were Lost

▲Takeaway· 3

PLG Has a Ceiling — Add Sales Before You Hit the Wall

Hire a Diversified Sales Portfolio

In Sales, Maybes Will Kill You

Myth Buster· 1

Hot Take· 3

Explainer· 5

Story· 3

Takeaway· 3