Mark Roberge writes, speaks, and invests in companies building and scaling go-to-market and sales teams, with a focus on how AI is changing that work. His posts discuss his experience building HubSpot’s CRM and his work with founders through Stage 2 Capital.
Mark Roberge is most useful for B2B SaaS go-to-market and sales team advice, especially how AI changes roles and retention.
Best for
A B2B SaaS company rethinking its go-to-market and sales organization for the AI era.
Not for
A consumer brand seeking sponsored posts, product demonstrations, discount codes, or reach and engagement targets.
Key takeaways
Retention is the best measure of product-market fit, so find an early leading indicator.
AI go-to-market prompts fail when the ICP ignores post-sale outcomes.
Sales roles are moving back to full-cycle sellers, with the future CRO closer to RevOps.
Check first: Confirm recent LinkedIn audience data and any documented survey results for the roles, industries, or seniority you need.
Main ideas
Retention is the best measure of product-market fit, so find an early leading indicator · AI go-to-market prompts fail when the ICP ignores post-sale outcomes · Sales roles are moving back to full-cycle sellers, with the future CRO closer to RevOps
Mark Roberge writes and speaks about building go-to-market teams, sales, and how AI is changing both.
Drawing on his work building HubSpot’s CRM and his conversations with founders and sales leaders, he turns broad trends into operating questions: which customers are a fit, how to tell whether they are getting lasting value, and what a sales team should do next.
His posts include practical lessons from The Science of Scaling and discussions of AI’s effects on sales roles and team design.
Roberge connects strategy to measures and decisions founders can use.
He argues that revenue and survey responses alone may obscure whether customers are finding lasting value, and illustrates a retention measure based on how many customers perform a valuable action at a regular interval.
He also shows how early customer interviews—including interviews with people who were not a fit—can inform sales discovery and qualification.
In his AI commentary, the same focus on outcomes appears in questions about customer retention, human accountability, and organizational adoption, not just product capability or faster selling.
Mark Roberge's ideas start from a sales and go-to-market operator's view: what actually predicts durable growth, and what changes when AI can do more of the work.
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Recurring priorities are retention as the real test of product-market fit, ICPs that include post-sale outcomes, sales roles rebuilt around full-cycle sellers, and AI adoption that keeps humans accountable. Roberge teaches through numbered mistakes, formulas, and named company examples, often from conversations with founders and operators.
Retention is the best measure of product-market fit, so find an early leading indicator
Roberge argues revenue can hide churn and surveys can produce false positives, so retention is the better test.
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Because renewal data takes a year, Roberge uses a leading indicator of retention: a behavior visible in a customer's first week or month that predicts whether they stay.
The formula Roberge gives is "P% of customers do E event every T time."
Roberge treats it as a "forever metric," not a one-time speed-to-value milestone, and says teams should know whether 30% or 90% of customers hit it before scaling.
Slack's LIR was 60% of customers sending 200 team messages a day. Roberge links an LIR builder powered by QuotaPath and recommends putting the metric in the board deck.
In one post Roberge calls the leading indicator a "forever metric," pushing back on using it only as speed to initial value.
AI go-to-market prompts fail when the ICP ignores post-sale outcomes
Roberge identifies three mistakes founders make when using AI to build GTM foundations: defining the ICP only by close rate, sales cycle, and ACV; treating the leading indicator of retention as a one-time speed-to-value number; and ignoring early customer interviews when writing the first sales playbook.
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Roberge argues the first 20 interviews, including with people who turned out not to be a fit, hold the richest inputs for qualification and discovery, connecting The Mom Test to Sales 101.
In a Harvard class with 50 founders from HubSpot's UNBOUND conference, Roberge taught The Science of Scaling and built Claude skills so founders could apply the principles.
Sales roles are moving back to full-cycle sellers, with the future CRO closer to RevOps
Roberge argues AI that can do 2-10x a human's work ends the SDR/AE/CSM specialization era and brings back the full-cycle athlete.
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Roberge expects the future chief revenue officer to look more like today's revenue operations leader and questions functionally siloed tech stacks. The conversation also covers whether everyone still has a job in five years, building a virtual workforce, whether CRMs are enough, and the problem with AI SDRs.
Roberge reunites with Christopher O'Donnell, who co-built HubSpot's CRM with Roberge in 2013 and now founded Day AI, for a Science of Scaling episode on the future of sales.
This is the best time to be a fast follower, and vertical AI will sell the whole stack
From an NYSE interview, Roberge argues AI development tools have made copying products faster, while many first movers lack moats and cannot cut prices after raising at high valuations, opening the door for efficient fast followers.
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Roberge expects vertical AI software to sell an entire tech stack rather than a point solution, raising ACV and lowering cost. Roberge also argues change management beats product right now because user and organizational resistance is harder than proving the AI is best, and says MBAs are undervalued as deciding what to build matters more than building it.
Roberge spoke with the NYSE Wired team on a first trip back since HubSpot's IPO and laid out four numbered theses.
Humans stay in sales because they own outcomes, not only because of what they can do
Roberge summarizes a HubSpot Unbound conversation with Michael Hartman, a Stage 2 Capital LP and head of enterprise Americas at Anthropic.
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Hartman's argument is that humans remain in the loop because they must be accountable for decisions and outcomes, and that AI will eliminate human-only jobs rather than human jobs. Other points from Hartman include not waiting for an employer or school to teach AI, Anthropic's hybrid of traditional SaaS and AI-native tools, investing in counterbalancing operations to manage spend and spot redundancies, and weighting prior domain experience.
Hartman describes Anthropic's GTM team encouraging frontline employees to build their own agents and skills while operations teams manage spend and redundancies.
Stage 2's operator network is the support model: named LPs help with compensation, hiring, call reviews, and positioning
Roberge describes Stage 2 Capital's support model through Haize Labs' acquisition by Beacon Software.
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The LP base includes more than 1,000 go-to-market operators from companies such as Anthropic, OpenAI, Databricks, Cohere, and Salesforce. Roberge credits specific operators with specific work: Graham Collins of QuotaPath on the GTM system, compensation model, AE hiring scorecard, and candidate assessment; Matt Braley, former CRO of EliseAI, reviewing sales calls weekly to sharpen discovery and qualification; and Meini Heuberger, former head of sales at Cohere, on positioning, messaging, and buyer journey.
Haize built AI trust tools including judges, reward models, and automated red-teaming used alongside labs such as OpenAI and Anthropic.
Scaling lessons from a portfolio company: invert R&D, price for commitment, hire two reps
In a Science of Scaling conversation with Dan Lee, co-founder and CEO of Nooks, Roberge lists five practices behind Nooks's growth from Stanford to a nine-figure business.
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Nooks spends 70% of effort studying customer usage, prospect sentiment, and market signals to decide what to build and 30% building, inverting the traditional ratio.
Free is rarely right on day one, but profitability is premature, so Nooks priced for commitment.
Roberge compares copying early LLM and DevOps distribution to the 2012 PLG wave, while Nooks chose sales-led growth for its context.
Nooks's initial north star was delivered customer outcomes, not contracts or revenue, and it hired two salespeople rather than one so a struggling rep could be diagnosed as a bad hire or a systemic problem.
Nooks incubated out of Stanford and reached a nine-figure business.
Founder-led sales: the first playbook is already there, and admin work can be automated
In an SF Tech Week fireside chat with Andrew Smith, then VP of global sales at Cursor, Roberge covers defining an ICP for accelerated scale, finding first design partners in half the time, running executive first meetings that close at twice the rate, doubling selling time by automating pipeline management and forecasting, and the claim that founders have already built a first sales playbook without realizing it.
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Roberge also raises the unsolved question of what happens when the buyer is an agent.
The session was hosted with SpaceXAI, formerly Cursor, during SF Tech Week.
This is the clearest single answer to what Roberge thinks AI does to a sales organization.
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It is a full episode, and the post lists its topics so a newcomer can jump straight to the part they care about.
Roberge recalls being asked in 2013 to design, build, and scale HubSpot's CRM alongside Christopher O'Donnell, and credits the team rather than the two of them.
The conversation with O'Donnell, now founder of Day AI, covers why the split between SDR, AE, and CSM roles is ending and the full-cycle seller is returning, why the future chief revenue officer looks like today's revenue operations leader, and why functionally siloed tech stacks may disappear.
The post lists timestamps for each segment, from whether everyone still has a job in five years to the problem with AI SDRs.
For brands planning a campaign
A keynote or advisory engagement on AI-native sales design can be built around role redesign and stack consolidation rather than a product demo, and this episode gives the specific claims to anchor it.
This is his most concrete teaching piece: a short, numbered list of errors with a fix for each.
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Start here if you want to see how he turns ideas into something a founder can act on the same week.
Roberge describes hosting 50 founders from HubSpot's UNBOUND conference on the Harvard campus and teaching the core principles of The Science of Scaling, then building Claude skills with the group so founders could apply them.
The three mistakes he names are defining an ideal customer profile only from revenue-velocity metrics such as close rate, sales cycle, and contract value; treating the leading indicator of retention as a one-time speed-to-value measure instead of a metric that keeps checking whether the customer is getting value; and ignoring the first customer interviews, including interviews with people who turned out not to be a fit, when writing the first sales playbook.
He links the interview technique in The Mom Test to qualification and discovery sequences in sales training.
The post also states that The Science of Scaling is available on Amazon and that all proceeds go to mental health.
For brands planning a campaign
A workshop or hands-on session format is well supported by this post, and the specific three-mistake framing gives a speaker-ready outline.
Four numbered arguments, each stated plainly, that explain how he is advising and investing today.
Roberge writes that it was his first time back at the New York Stock Exchange since the HubSpot IPO. He argues that this is the best time in tech history to be a fast follower because AI development tools have made copying a product faster while many first movers lack a moat and cannot cut prices; that the next wave of vertical AI software will sell an entire tech stack rather than a point solution, which raises contract value and lowers cost, and makes niches that were too small for venture outcomes attractive to smaller funds; that change management beats product right now because the hard part is user and organizational resistance, not proving the AI is best; and that MBAs are an under-tapped talent pool now that deciding what to build matters more than building it.
For brands planning a campaign
Useful framing for advisory work aimed at vertical AI and legacy-industry software, and a reason to weight change management and implementation spending in any go-to-market recommendation.
This piece takes one idea and makes it usable, including a formula, a worked example, and a free tool.
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It is the best starting point for anyone who wants to understand the concept he returns to most often.
At an SF Tech Week event with SpaceXAI, formerly Cursor, Roberge asked the room to define product-market fit.
He walks through Sean Ellis's 40 percent test and calls surveys prone to false positives, then argues revenue has the opposite problem because it ignores churn, so retention is the better measure.
Because retention takes time to observe, he introduces the leading indicator of retention: something visible in a customer's first week or month that predicts whether they stay.
He states the shape of it as a percentage of customers doing an event every period, and gives Slack's as 60 percent of customers sending 200 team messages a day.
He says scaling when only 30 percent of customers hit the metric is a bigger risk than scaling at 90 percent, while acknowledging sometimes you have to.
The post links an LIR builder powered by QuotaPath and recommends putting the metric in the board deck.
For brands planning a campaign
A metrics or sales-software brand could co-market a tool with him, and the formula plus Slack example make a ready-made segment for a webinar or a partner landing page.
Shows how he extracts operating advice from a founder interview.
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Five numbered lessons, each tied to a specific decision, which is the pattern most of his episodes follow.
In a Science of Scaling conversation with Dan Lee, co-founder and CEO of Nooks, Roberge lists five practices behind the company's growth out of Stanford into a nine-figure business.
The team spends 70 percent of its effort studying customer usage, prospect sentiment, and market signals to decide what to build and 30 percent building, inverting the traditional ratio.
Free is rarely the right day-one price, but profitability is premature, so Nooks priced for commitment so customers would invest the effort to implement and use the product.
He notes founders copying the distribution strategies of early large language models and DevOps AI tools, and compares it to the 2012 product-led growth wave, while Nooks chose sales-led growth for its context.
Their initial north star was delivered customer outcomes, not contracts or revenue, and they hired two salespeople rather than one so a struggling rep could be diagnosed as a bad hire or a systemic problem.
For brands planning a campaign
Strong material for founder-facing advisory or content aimed at early-stage go-to-market teams, and the 70/30 research-to-build split is a quotable benchmark for a talk.
Gives a buyer-side view of AI adoption inside a major AI company, which is useful context before deciding whether his advice matches how large vendors really operate.
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Roberge summarizes a HubSpot Unbound conversation with Michael Hartman, a Stage 2 Capital limited partner and head of enterprise Americas at Anthropic.
The points he highlights: humans stay in the loop in sales because they must own decisions and outcomes, not only because of what they can do; AI will not eliminate human jobs so much as human-only jobs, with every role moving toward human and AI collaboration; people should not wait for an employer or school to teach them to work with AI; Anthropic's go-to-market team runs a hybrid of traditional SaaS tools and newer AI-native applications while encouraging frontline employees to build their own agents and skills, balanced against operations work that manages spend and spots redundancies; and hiring should weight prior domain experience, with customer success owning outcomes rather than just implementation.
He notes Hartman returns to the Stage 2 Summit in Redwood City on November 10.
For brands planning a campaign
A keynote can be paired with an enterprise AI practitioner for credibility, and the human-accountability framing is a safer message than job-replacement claims for cautious buyers.
Explains the investor side of his work with named, specific examples rather than general claims about supporting founders.
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Read it to understand what an advisory or portfolio engagement with him tends to include.
Roberge congratulates Leonard Tang and the Haize Labs team on their acquisition by Beacon Software, describing the original investment thesis as trust in AI, with Haize building judges, reward models, and automated red-teaming used alongside labs such as OpenAI and Anthropic.
He names the limited partners who helped: Graham Collins of QuotaPath on the go-to-market system, including the compensation model, account executive hiring scorecard, and candidate assessment; Matt Braley, former chief revenue officer of EliseAI, reviewing sales calls weekly to sharpen discovery and qualification; and Meini Heuberger, former head of sales at Cohere, on positioning, messaging, and the buyer journey, including helping buyers past uncertainty about what AI can do.
He adds that Stage 2's limited partner base includes more than 1,000 go-to-market operators from companies including Anthropic, OpenAI, Databricks, and Salesforce.
For brands planning a campaign
Concrete proof of the operating support behind an advisory offer, and the named examples of call review, hiring scorecards, and positioning work map directly to what a client could expect.
Roberge argues the SDR/AE/CSM specialization era is ending and the full-cycle seller is coming back, with the future CRO looking more like today's RevOps leader.
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He is skeptical of GTM AI that only makes yesterday's sales practices faster, and pushes instead for stripping go-to-market to first principles now that AI is foundational rather than bolted on. He also flags an open question for buyers: what happens when the buyer is an agent.
For brands planning a campaign
A good fit for a keynote or working session aimed at SaaS sales leadership that wants a structural argument, not an AI trends tour. Strongest when paired with his retention and ICP frameworks so the audience leaves with something to apply.
Roberge's recurring framework: an LIR is expressed as "P% of customers do E event every T time" — Slack's was 60% of customers sending 200 team messages a day.
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He argues retention is a better measure of product-market fit than revenue or survey answers, but waiting a year for renewal data is impractical, so the LIR is the early proxy. He treats it as a "forever metric" rather than a one-time speed-to-value number, and launched an LIR builder with QuotaPath to help teams draft a first version for their board deck.
Formula: P% of customers do E event every T time. Slack example: 60% of customers sending 200 team messages a day.
Roberge allows that scaling while only a minority of customers hit the LIR is sometimes necessary — the point is to know where you stand, not to wait for ideal conditions.
For brands planning a campaign
A concrete, teachable framework that can anchor a sponsored workshop where founders or sales leaders draft their own LIR. More useful than a generic retention talk because it gives the audience a specific artifact to take away.
Roberge says founders building ICP prompts and skills with AI define the ICP using only revenue-velocity metrics — close rate, sales cycle, ACV — and miss post-sale outcomes such as customer value creation, lifetime value, and the leading indicators of long-term retention.
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He also argues the first 20 customer interviews, including interviews with customers who turned out not to be a fit, are the richest inputs for the qualification and discovery sequences in a first sales playbook, connecting The Mom Test to Sales 101.
For brands planning a campaign
Specific enough to run as a hands-on ICP exercise or a content piece with a clear before-and-after. Works for a founder audience that already has some pipeline data to work from.
In fireside chats with sales leaders, Roberge covers defining an ICP for accelerated scale, finding first design partners faster, running executive first meetings that close at twice the rate, doubling selling time by automating admin like pipeline management and forecasting, and the claim that founders have already built a first sales playbook without realizing it.
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For brands planning a campaign
Suits a founder-audience event or sponsored session with a live component, since the material is structured as steps rather than a single argument. The "you already have a playbook" angle gives a sponsor a non-obvious hook.
Vertical AI, full-stack products, and the fast-follower window
From an NYSE conversation: Roberge calls this the best time in tech history to be a fast follower, because AI development tools have accelerated product copying while many first movers lack strong moats and cannot cut prices after raising at historic valuations.
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He expects the next wave of vertical AI to sell an entire tech stack rather than a point solution, argues change management beats product given end-user discomfort with AI, and says MBAs are undervalued as the build-to-research ratio flips.
For brands planning a campaign
Fits a market- or investor-facing panel rather than a sales training session. The change-management-over-product point is a distinctive angle for enterprise adoption content, and the fast-follower thesis gives a sponsor something more opinionated than a category overview.
From a HubSpot Unbound conversation with Michael Hartman of Anthropic: humans stay in the loop in sales not only because of what they can do but because of what they must own, and accountability is why the human role endures.
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The argument is that AI eliminates human-only jobs rather than human jobs. Other points: don't wait for an employer or school to teach AI; Anthropic's GTM team blends traditional SaaS tools with AI-native applications while investing in counterbalancing operations to manage spend and spot redundancies; and prior domain experience should carry more weight.
For brands planning a campaign
Strong for an enterprise audience weighing an AI rollout, especially where buyers are wary of hype. The accountability framing gives a sponsor a serious, non-promotional position to attach to.
A Science of Scaling episode with Dan Lee of Nooks yields several transferable lessons: Nooks spends 70% of its effort studying customer usage, prospect sentiment, and market signals and 30% building, inverting the traditional R&D ratio; "free" is rarely right on day one, so price for commitment instead; PLG is context-dependent rather than a universal best practice; the initial North Star was delivered customer outcomes rather than contracts or revenue; and hiring two salespeople at once makes it possible to tell a bad hire from a systemic problem.
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Case-based teaching: take one company's specific operating choices — research-to-build ratio, pricing, North Star metric, first sales hires — and generalize the decision.
For brands planning a campaign
Good case material for a founder or sales-leadership session, and specific enough to be actionable rather than inspirational. A sponsor could build a session around one of these decisions rather than the whole list.
A post on Haize Labs' acquisition by Beacon Software shows how Roberge describes Stage 2's support model: an LP base of more than 1,000 go-to-market operators from companies including Anthropic, OpenAI, Databricks, Cohere, and Salesforce who contribute compensation models, AE hiring scorecards, candidate assessment, weekly sales-call reviews, and positioning and buyer-journey work.
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The post credits named operators with specific contributions during the company's pursuit of GTM fit.
For brands planning a campaign
Relevant to a brand that wants to reach founders through operator programs and portfolio support rather than through stage content. Shows a track record of convening practitioners, which matters if the campaign includes a community or advisory component.
Roberge says he worked on designing, building, and scaling HubSpot’s CRM in 2013.
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He also promotes The Science of Scaling content and events involving HubSpot, but these posts do not establish a current employment or sponsorship relationship.
Mark Roberge's posts suit a reader who follows B2B sales and go-to-market and wants a practitioner's view rather than a trends roundup.
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The through-line is measurement and first principles: retention as the real test of product-market fit, a leading indicator of retention that can be read early, and ICP definitions that include what happens after the sale.
Roberge teaches through numbered mistakes, named companies, and short frameworks, usually pulled from conversations on The Science of Scaling or from fireside chats at HubSpot's UNBOUND and SF Tech Week.
To explore, start with the retention and ICP material, then the founder and operator episodes; the NYSE and Anthropic conversations cover the market and enterprise-adoption side of the same argument.
Both work inside the HubSpot ecosystem and write about turning platform features into something a revenue team can actually use.
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LaRoche reflects on HubSpot's Unbound announcements and argues for process-driven AI over content-focused AI, the same practical-over-hype line Roberge takes when saying go-to-market AI has to be rebuilt on first principles.
LaRoche writes as a HubSpot implementation partner, so the focus is data integrity, client adoption, and delivering projects to customers. Roberge approaches the same ground as an investor and former executive, framing it around sales-org design, retention metrics, and founder go-to-market decisions.
Both treat retention as a growth lever and tie it to CRM adoption.
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Gamboa's NRR-versus-acquisition post and HubSpot Service Hub write-up make the case Roberge makes with the leading indicator of retention: existing-customer outcomes decide whether a good sales quarter holds.
Gamboa's material is consultancy case work — training a sales team, rebuilding CRM structure, and reporting adoption and sales lift. Roberge supplies a metric and a board-deck argument rather than an implementation story.
Both host interview podcasts for B2B SaaS founders and both spend episodes on the first sales hire, compensation and quota design, and product-market fit as the line between learning and scaling.
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The Full Stack CEO episodes cover the same hiring and go-to-market questions Roberge raises on The Science of Scaling.
Kpodar's show is built around guests walking through early-stage hiring and sales leadership decisions. Roberge tends to pair guest conversations with his own frameworks, such as the leading indicator of retention, and pushes further into AI-era org design.
Mark Roberge’s posts suggest a strong content fit for B2B SaaS companies reassessing go-to-market and sales in the AI era.
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He discusses AI-native sales design, customer retention, sales roles, and practical founder decisions.
That shows what his content addresses, not who follows or engages with it.
Before booking for a brief that depends on reaching specific job roles, industries, or seniority levels, request recent LinkedIn audience data or documented survey results that address those attributes.
Mark Roberge's partnership activity in the reviewed posts runs through investing work at Stage 2 Capital and media produced with HubSpot For Startups.
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Companies appear alongside Roberge as podcast guests, as co-hosts of on-stage conversations, as portfolio companies supported by go-to-market operators, and as tool partners. None of the posts publishes sponsorship inventory, pricing, editorial terms, or a booking route, so the formats below describe the shape of existing collaborations rather than a published offer; terms would have to be confirmed directly.
Speaking and teaching sessions
Buyer requirement
A defined room of founders or operators and a concrete go-to-market topic.
The reviewed posts show a class taught for 50 HubSpot UNBOUND founders on Harvard's campus and main-stage conference appearances.
No speaker fee, format guide, or booking route is published, so scope, audience size, and terms would need to be confirmed directly.
Podcast and fireside conversations
Buyer requirement
A named company executive or founder with a specific scaling story to tell.
Episodes pair Roberge with guests such as Day AI's Christopher O'Donnell, Nooks' Dan Lee, and Anthropic's Michael Hartman, produced with Stage 2 Capital and HubSpot For Startups.
The posts describe no sponsorship slots, rates, or editorial terms for the show.
Go-to-market operator support
Buyer requirement
Access in the reviewed material runs through Stage 2 Capital as an investor or limited partner, not a purchasable service.
Stage 2's limited-partner base of more than 1,000 go-to-market operators is credited with hands-on work on compensation models, hiring scorecards, weekly sales-call review, and positioning for portfolio companies such as Haize Labs.
A Stage 2 Summit session is described as open to the firm's limited partners and founders.
Tool and product collaborations
Buyer requirement
A co-built tool tied to a specific metric or workflow, as with the leading-indicator-of-retention builder launched with QuotaPath. The posts give no terms covering branding, exclusivity, or revenue share, so commercial arrangements are undocumented.
A B2B SaaS company rethinking its go-to-market and sales organization for the AI era is the closest match in the reviewed material.
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Roberge's recurring argument — that the SDR, AE, and CSM split is ending and the full-cycle seller is returning, with the future chief revenue officer resembling today's revenue operations leader — gives a keynote or advisory engagement a structural thesis rather than an AI trends tour.
The supporting material is specific enough to build a session around: the three mistakes founders make when prompting AI to build go-to-market foundations, including defining an ICP only from revenue-velocity metrics and treating the leading indicator of retention as a one-time measure; the LIR formula, "P% of customers do E event every T time," with Slack's 60% of customers sending 200 team messages a day as the worked example; and the claim that founders have already written a first sales playbook through their earliest customer interviews, including interviews with people who turned out not to be a fit.
For an enterprise audience weighing adoption, the accountability framing from the Anthropic conversation is a useful non-promotional position: humans stay in the loop in sales because they must own decisions and outcomes, and AI eliminates human-only jobs rather than human jobs.
The NYSE interview adds market-level material — the fast-follower window, vertical AI selling a full stack rather than a point solution, and change management beating product — that suits a market- or investor-facing panel as much as a sales training session.
Commercial shape matters here.
The reviewed posts show teaching sessions, conference stages, a podcast produced with Stage 2 Capital and HubSpot For Startups, and operator support delivered through Stage 2's limited-partner network.
None of that publishes a rate card, deliverable list, or booking route, so scope, audience size, and terms have to be confirmed directly.
A campaign should also expect the advisory and speaking role rather than a reach-based sponsorship: no audience composition data was supplied, so a brief that depends on reaching specific job roles, industries, or seniority levels needs that data before booking.
When to choose a specialist
A consumer brand — a direct-to-consumer wellness or apparel company, for example — wanting a run of sponsored posts with product demonstrations, discount codes, and reach or engagement targets is a poor match.
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Every reviewed post sits inside B2B SaaS go-to-market: sales-role redesign, retention metrics, founder-led sales, and portfolio-company support.
There is no consumer product content, no lifestyle or personal-interest material, and no short-form video or creator-style product placement to point to.
The reviewed material also shows no sponsorship inventory, rate card, or booking route, and no measured audience composition, so a buyer cannot verify that a sponsored post would reach the consumer segment they are paying for.
A brief like this should look for creators whose own material shows the product category and whose audience data supports the reach target; Roberge's evidence supports advisory, speaking, and content work for B2B software companies, not consumer product promotion.
A narrower mismatch is worth naming too.
A company wanting hands-on technical content — implementation walkthroughs, code, or developer tutorials — would find the reviewed posts at the level of go-to-market strategy and operating advice rather than engineering detail.
The same lack of published terms and audience data applies.
A worked campaign example
A worked angle: a sponsored "AI-native sales design" session for a sales-engagement or revenue-operations software company selling to SaaS sales leadership.
Format: a 45-minute keynote built on the role-redesign argument, followed by a 90-minute working session where attendees apply the three prompting mistakes to their own ICP definition and draft a first leading indicator of retention using the "P% of customers do E event every T time" formula.
The sponsor supplies the worksheet and hosts the follow-up.
Roberge's existing QuotaPath collaboration on an LIR builder shows a precedent for a co-built tool tied to the metric, though branding, exclusivity, and revenue-share terms are undocumented and would need to be set.
Why it works for this audience: each attendee leaves with a specific artifact — a revised ICP prompt and a draft LIR — rather than a general talk about AI, which matches how the reviewed sessions are structured, such as the class taught to 50 HubSpot UNBOUND founders on the Harvard campus and the SF Tech Week fireside chat on founder-led sales.
Session content can pull the three-mistake list, the LIR formula with the Slack example, and the change-management-over-product argument.
A named practitioner from the sponsor or a customer can co-present, which is the pattern the reviewed conversations follow.
The next step for attendees is the worksheet plus a short list of metrics for their next board deck; the sponsor gets the follow-up conversation.
What to confirm before booking: no speaker fee, format guide, or booking route appears in the reviewed material, and the audience composition for a session like this is not documented.
A buyer should ask for recent audience data for the channel being used and agree scope, attendee count, and terms directly.
The reviewed material also does not establish how much editorial control Roberge holds over any podcast or stage conversation.
Choose Kpodar if the brief is specifically about making an early sales hire—not redesigning the whole sales organization around AI.
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In a post about a Full Stack CEO episode, Kpodar lays out a useful decision: hire to discover the sales motion, repeat a proven one, or build a sales team.
The examples distinguish a self-sufficient seller who can replicate a proven playbook from a more senior operator who can own a territory like a “mini CEO.”
That gives a hiring-focused session a direct decision framework.
The reviewed posts focus on early sales hiring, interviews, compensation, and founder-led sales. They do not show the AI-era sales-role redesign or AI go-to-market analysis present in Roberge’s material, so this is a narrower fit for the stated brief.
Choose LaRoche if the company’s main obstacle is getting an existing HubSpot setup adopted and used well.
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LaRoche describes two projects where over-engineered CRM instances left teams struggling, and argues that implementation should fit the client’s capacity and build new habits at a suitable pace. A session or advisory engagement built around simplifying CRM configuration and improving adoption would be more directly grounded in this material than Roberge’s strategy-led AI-era org redesign.
The examples are about HubSpot implementation and partner guidance, not a broad redesign of sales roles or an AI-native go-to-market model. The reviewed posts do not establish LaRoche’s suitability for a keynote or advisory brief beyond those implementation concerns.
Choose Gamboa if the brief is centered on customer retention, expansion, or improving adoption of HubSpot Service Hub.
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One post contrasts acquisition spending with net revenue retention and describes health scoring, automated check-ins, and expansion triggers; another presents a client case involving CRM restructuring and training, with reported adoption and sales results. That makes Gamboa a more specific option when the desired campaign outcome is practical retention or CRM adoption work.
The examples are product- and implementation-oriented, rather than a demonstrated framework for redesigning the sales organization for the AI era. The posts also promote HubSpot-related services, so a buyer seeking an independent, cross-platform AI strategy should confirm the intended scope and perspective.
This profile draws on eight LinkedIn posts from Mark Roberge's account, covering The Science of Scaling episodes, a Harvard class for founders at HubSpot's UNBOUND conference, an SF Tech Week fireside chat, an NYSE Wired interview, and a Stage 2 Capital portfolio acquisition announcement.
The posts carry no publication dates in the reviewed material and were observed in October 2026.
Podcast and event content is described from Roberge's own written summaries, not from episode audio, video, or transcripts.
No rate card, booking route, or audience composition data appeared in the reviewed material; those remain open gaps for any engagement decision.
Three similar creators were compared using six of their posts.
Recent engagement
Across their last 8 LinkedIn posts, the median post got 190 reactions and 32 comments. Checked 2026-11-02.
