On [Un]Churned, Cassie Vaughn said monday.com dropped claims like every automation saving two minutes and now anchors value to revenue-linked use cases.
Ziv says many people cite figures like 2x, 5x, 10x and 50x without saying what they measure. He gives a hypothetical of a $10 million AI investment producing 50x revenue and says that does not work directly.
“No, it's it doesn't work like that”
Listen to the episode Episode Metrics & finance Link to this
Seong says teams often report metrics such as hundreds of developer hours saved, but that this only matters if the team asks what it meant to the business. He advises using discovery to attribute the result to a positive business outcome. He describes this as the work of making the link explicit.
“Hey, we've saved your developer hundreds of hours of time, so now they're more productive. Well, then so what?”
Listen to the episode Episode Metrics & finance Link to this
Cassie Young says tool adoption and leading indicators such as BDR coverage or connects are fine but not enough, because CROs must get smarter about operating expenses. Her example is a digital deal room that cut a couple of days off the sales cycle, which she says should be translated into a direct annual gain rather than a multi-layer calculation that is too much math for a board.
“I think boards are done rewarding effort and want to see impact.”
Listen to the episode Episode Metrics & finance Link to this
She says the easiest way to prove the agent works is whether buyers buy from it. She wants to measure deal cycles, ACV and shortened sales cycles rather than top-of-funnel activity.
“I want to measure to the bottom of the funnel, not the top.”
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Cassie Vaughn said monday.com originally anchored value to productivity, time savings and cost savings across many customers, but this required coaching customers to accept the logic. She gave the example that every automation equals two minutes saved, which she called a stretch and a guess at best. The company now picks use cases where objectives are clear and tied to revenue or mission-critical business goals.
“So now instead, everything becomes about use cases.”
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On Grit, Jeanne DeWitt Grosser said her Stripe operating model quickly showed the founder's target was unlikely, prompting decisions on marketing, outbound or moving upmarket.
He described an early-stage CRO, two to three months in, committing to $10M for the year. The CRO couldn't state current pipeline and was counting on an unreleased self-serve product and a $1M big-tech deal that had only had a first meeting. Joubin's view is that founders are default optimists who 'do not know how to forecast a business' and need a big number to justify their valuation. His advice was to re-forecast from data, not forecast a PLG product that doesn't exist yet, and push the product team to fill the gap, because the product leader won't be fired first.
“I would not forecast the PLG product that doesn't exist yet.”
Listen to the episode Episode Metrics & finance Link to this
At Vercel this is called the lead-to-revenue model. She said the spreadsheet lets everyone judge honestly whether the target is achievable, based on history or on assumptions they are willing to make, and later shows which driver caused a hit or a miss. At Stripe she owned new business, which was the gap between a 'nice large round' target and the forecastable existing base. The model showed quickly that the target was unlikely, which led to a strategic discussion of options: invest in marketing (Stripe then had one marketer), invest in outbound, or move upmarket for a larger ACV. She said the data was messy at first, partly because she had changed the segmentation, so reasonable assumptions filled the gaps.
“it's your full funnel math by segment, by geo.”
Listen to the episode Episode Metrics & finance Link to this
Bob said top-down targets often come from the board and CFO based on market growth rates. The bottom-up build covers MQLs, SQLs, pipeline required, conversion rates, cross-sell and upsell versus new business, and how many new logos were won in prior years. He said the best planners work from recent quarters and years rather than an assumption that everything works perfectly.
“work from a basis of reality versus like Nirvana”
Listen to the episode Episode Metrics & finance Link to this
He says investors' top-down growth targets come from their return math, and that a top-down number can be a starting guide. Bottoms-up planning must account for attrition and ramp time, and he suggests spreading hires, such as about two reps a quarter rather than eight at once.
“the bottoms up is the sales capacity that you create and the demand and capacity that you use to feed them.”
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AJ said the board expects a predictable quarter-to-quarter model, so the company leaves a larger margin of error for what it does not yet know. All upside is tied to what it is building and thinking about. He said his board is good but still sometimes falls back on patterns from two or three years earlier.
“We're going to take our financial model and completely make it predictable off of the product that we have today and today and what we know today.”
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On Kyle Norton's podcast, Jonathan Moss said Experity lets anyone produce a three-year trend with insights in under 30 minutes without RevOps.
The old flow was linear and people-heavy: request to RevOps, prioritization, data pull, visualization, dashboard and analysis by people who weren't sales, marketing or CS domain experts. The target outcome was analysis 'federated down to the lowest common denominator of the user.' To get there Experity consolidated data in its warehouse, added business context to fields through a semantic layer so the model wouldn't misinterpret CRM fields, put LLMs on top, and gave users a chat interface that answers questions and builds charts.
“we're able to build like three year trends of visual, get insights and recommendations in less than 30 minutes”
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Only the CRO read the weekly deck covering every cut of every metric. He asked for AI to flag when anything deviates from the norm so leaders can dig in. The team built him a CRO skill. Lauren Hughes has since handed her revenue planning and analysis team a list of about 50 KPIs to manage, and says they still need to improve the report.
“all I need is somebody to put AI on this and tell me when anytime something deviates from the norm”
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Rick says CEOs can connect their systems to AI tools and pull conversion rates from HubSpot, so a CRO has less room to present numbers unchallenged. He says transparency is good when the numbers are accurate. The two measures ShipHero focuses on are whether conversion rates are improving and whether the team is using tools that help it win more deals, such as being able to say yes to more requirements.
“So one of the things we're looking at is like, hey, is the conversion rate improving?”
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He said CROs often have a forecast number but lack the explanation of which deals are the head and tail wins and what happened in the large deals, so they have to ask RevOps, who need a couple of days. He said Terret can answer that in a couple of minutes.
“We can nail that in a couple of minutes for you.”
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He says if you had spoken with him two years ago he would have said dashboards are not going away, but now he thinks they are. He did not give a timeframe in the episode, and the show notes frame it as questioning whether dashboards will exist in two years.
“I think dashboards are going away.”
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On Topline, Asad Zaman said Sales Talent Agency's token spend reached hundreds of thousands of dollars, surprising for a recruitment company.
Saumyo says some vendors bill enterprise plans on something other than tokens, while the outputs and use cases supported differ between vendors. He frames the decision as whether to save cost or to improve throughput and quality. He says Braze made the right choice even if it costs more, as long as the cost is justifiable through efficiency in both quantity and quality, and Braze is currently in an ROI-proving phase.
“do we really want to save cost Or do we really want to improve our throughput and improve our quality?”
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Braze is rolling out budgets where each department head gets a budget based on team size, and each individual gets a set amount per month of tokens. They will watch usage for a quarter to see which users and teams use how much, then normalise, since departments need different amounts. Saumyo says his team may need much more than an HR team. Token allocation will be distributed after one or two quarters based on empirical data about quality and throughput.
“every department head like myself or my boss right they will have a certain budget allocated to them based on of course the size of their teams”
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The investor, whom the transcript does not identify, says they ask most of their portfolio CEOs about token spend to understand the curve of usage against falling costs. They floated that spend might be around one tenth of a person's salary now and asked whether it goes to 50% or one times a salary. They said they don't know where it lands.
“if it's right now, maybe like one tenth of a person's salary, does it go to 50% of someone's salary to one times a person's salary? And I don't know where it sort of lands”
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He calls this token maximization and says the goal is efficient use: you will use a lot of tokens, but it has to be efficient. He describes it as an equation a company can control and balance. Josh adds that it needs close measurement and a constant pulse.
“So not to use the max amount of tokens, but the best quality output and maximized tokens.”
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Responding to Asad Zaman's comments on his own company's token spend, Ryan Burke says companies are encouraging experimentation and have budget available for it. He predicts that at some point the people with fiscal responsibility will ask what the spending is for. He describes this as a conversation that will happen across enterprises.
“at some point, the CFOs and, you know, those who have like fiscal responsibilities are going to step in and say, what are we spending this on?”
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Asad Zaman says the company is using a lot of tokens and spend is now in the hundreds of thousands of dollars, which took him by surprise. He does not state the time period. He says it makes him think about what the P&L will look like in two years.
“STA is using a lot of tokens like we're in the hundreds of thousands of dollars now”
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Ranked by how many independent voices make each point and how specific their evidence is. Co-hosts of a show count as one voice, and a point needs at least two shows to appear here.
What one named guest described doing or seeing. Each is a single account, not a point several operators agree on.
“You have to. Like you absolutely have to because otherwise there's just too many metrics and too many numbers out there and too many people process all of the things that are going to potentially change that.”
“Once a metric is measured, it can be gamed. And that happens.”
“travel and lodging in general is typically a solid 40 % of your kickoff budget. Be smart about it. If you're booking travel two months, even a month and a half, it's going to get closer to a 60 %”
“Right now it's sort of in the 25 to 30k with some outliers.”
“Their ROI on that is almost a billion dollars over three years.”
“What was your pull request per week in the last quarter? And I'm looking for five, which would be a 2x up to 20”
Roberge advises founders not to over-analyse the first choice. He suggests a two-hour session with decision-makers to pick a value-linked metric. A year later the company can check whether customers who stayed showed high adoption and those who churned did not. If the two groups look the same, he says the company can use its user logs to run around 50 permutations with AI to find what matters, which Josh likens to a regression analysis.
“So in the beginning, just pick something correlated value knowing that in a year, you'll be able to run the analysis to tune it.”
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Mark says PLG-era 'aha moments' such as Facebook's seven friends in the first week are useful but one-time, whereas he wants something recurring that catches customers who disengage later. When Joubin Mirzadegan described Roadrunner's goal of time until an AE creates a first quote, Mark suggested reframing it as something like 80% of customers averaging two quotes per month, or one quote per week, measured indefinitely.
“Good, but like the issue is like that's like a one -time thing. What I'm looking for is something recurring, okay?”
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Mark calls defining a leading indicator of retention (LIR) an important task most entrepreneurs skip: what can you observe in roughly a customer's first month that predicts whether they'll stay or churn. He narrows it to three variables, P, E and T. His examples: Slack, 80% of customers send 2,000 team messages every month; HubSpot, 80% of customers use five or more features every month; Dropbox, customers back up their device every week.
“P % of customers do E event every T time. P, E, and T.”
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Mark said Stage 2 has rolled this out with every portfolio company. He said he cares less about how much was sold and more about what percentage of customers hit the LIR. He said he reduces the LIR to three variables: what percentage of customers do an event every time period.
“I personally think it should be the first slide in every board deck at a seed -funded business.”
Listen to the episode Episode Metrics & finance Link to this
At Vercel this is called the lead-to-revenue model. She said the spreadsheet lets everyone judge honestly whether the target is achievable, based on history or on assumptions they are willing to make, and later shows which driver caused a hit or a miss. At Stripe she owned new business, which was the gap between a 'nice large round' target and the forecastable existing base. The model showed quickly that the target was unlikely, which led to a strategic discussion of options: invest in marketing (Stripe then had one marketer), invest in outbound, or move upmarket for a larger ACV. She said the data was messy at first, partly because she had changed the segmentation, so reasonable assumptions filled the gaps.
“it's your full funnel math by segment, by geo.”
Listen to the episode Episode Metrics & finance Link to this
Bob said top-down targets often come from the board and CFO based on market growth rates. The bottom-up build covers MQLs, SQLs, pipeline required, conversion rates, cross-sell and upsell versus new business, and how many new logos were won in prior years. He said the best planners work from recent quarters and years rather than an assumption that everything works perfectly.
“work from a basis of reality versus like Nirvana”
Listen to the episode Episode Metrics & finance Link to this
Aviv says he starts with the target and works backwards, for example needing 200 deals at a 50% conversion rate, which means 400 opportunities, and then the meetings needed to create them. He says the planning then depends on knowing the cost per meeting from marketing. He uses this to plan with the CEO and board.
“let's say I need to get through 200 deals and then it's like a 50% conversion rate so you need like 400 opportunities”
Listen to the episode Episode Metrics & finance Link to this
AJ said the board expects a predictable quarter-to-quarter model, so the company leaves a larger margin of error for what it does not yet know. All upside is tied to what it is building and thinking about. He said his board is good but still sometimes falls back on patterns from two or three years earlier.
“We're going to take our financial model and completely make it predictable off of the product that we have today and today and what we know today.”
Listen to the episode Episode Metrics & finance Link to this
He described an early-stage CRO, two to three months in, committing to $10M for the year. The CRO couldn't state current pipeline and was counting on an unreleased self-serve product and a $1M big-tech deal that had only had a first meeting. Joubin's view is that founders are default optimists who 'do not know how to forecast a business' and need a big number to justify their valuation. His advice was to re-forecast from data, not forecast a PLG product that doesn't exist yet, and push the product team to fill the gap, because the product leader won't be fired first.
“I would not forecast the PLG product that doesn't exist yet.”
Listen to the episode Episode Metrics & finance Link to this
He calls startup annual planning broken: a November guess becomes a mandate, and a missed Q2 leads boards to demand more hiring to catch up. Instead, agree up front what Q1 results mean 'go' (all green, e.g. raise from four to six reps a month), 'stay' (mostly green, keep four) or 'slow' (e.g. LIR crushed, two-thirds of reps missing goal, four meetings per rep per month against a target of ten, so pause a month and hold an emergency board meeting). Each quarter you reset the plan four quarters forward; sales hiring pace then drives CSM, SDR and manager hiring. He says you could also tie R&D and G&A to a split like 40/40/20, though he's unsure that's right and holds conviction mainly on the go-to-market piece.
“That's why I encourage folks to do the stay or go or slow model, which allows you to plan four quarters out every quarter, which systematizes the learning pace that we need from that.”
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Roberge criticises planning that copies benchmarks such as triple-triple-double-double, hires all the reps up front, and then lays people off if the target is missed. Instead, he suggests setting the plan from product-market fit and go-to-market fit signals, only 10-20% aggressive on close rates and hiring pace, which in his example gets to a 150% target. The plan made in November is re-evaluated after Q1 in March or April and every quarter after that. Leaders define ahead of time what results would mean going faster, holding pace or slowing down, and rate each quarter red, yellow or green to throttle hiring instead of thrashing.
“And we'll pre -define. What would we have seen after that quarter that would cause us to go faster? What would we see that would cause us to go the same pace? And what would I see that would cause us to go slower?”
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The plan is set at the start of the year or planning cycle, the forecast is the expected result for the current period using current information, and the actuals are what happened. Mark says these should be kept separate when reviewing churn, new ARR or bookings.
“So many founders think that the forecast and the plan are the same. They're not.”
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Mark says an annual plan is needed but should not be treated as fixed. Each quarter he asks whether the business is broken, on track, or ready to accelerate, and he suggests a forward-looking four-quarter plan even mid-year.
“I actually like to revisit it every quarter to assess whether we should slow down because it's broken, stay the course, or even speed up.”
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Mashrabov says social media ad production doesn't need 'PhD level intelligence' and favors cheaper, more steerable models, which can also be more cost-efficient for customers. As agentic ad workflows grow, Higgsfield chooses the model in over 40% of cases. He calls this optimization of token count and token cost 'tokenomics'.
“The margin on own models and open weights models is over 80%. And then it almost doesn't matter. And for close source models, it's probably between 20 and 30%.”
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Matt said running every piece of content through an AI model would be very expensive, so Handraise runs an ML process first to remove content like sidelinks, which he said were nearly impossible to filter out in the old world. He said isolating the problem to a smaller data set makes the cost workable, and that they still pull pretty good margins.
“So you know, if we ran every single piece of content through an AI model to it, it'd be really expensive. So we have, you know, an ML process that we run first.”
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Daniel said Glean's own LLM, Waldo, used as a pre-processing engine, can reduce token consumption by about 30 percent. For one customer he calculated that rolling out to 20,000 employees could save six or seven million dollars in token consumption alone, before any use-case value. He said a figure like that gets a CFO's attention and can lead them to sponsor work with the business units.
“we can actually reduce the amount of token consumption by about 30%.”
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Sam Jacobs said Harvey's cost to deliver $1 of revenue went from 50 cents to $1.50 as agents consumed tokens on behalf of human users. Harvey was growing extremely fast, but every dollar of revenue was destroying gross profit. He said Harvey's path out is training and hosting its own models, including open-weight models, and moving away from frontier models from labs like Anthropic and OpenAI.
“by June, Harvey's gross margin due to rapid, agentic Adoption right agents going out and consuming tokens on behalf of their human users Harvey's gross margin had fallen to negative 50 %”
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He says tokens used to serve a specific customer count as COGS, while tokens used to build a broader platform count as R&D. He says early gross margin is messy and will be a bumpy journey, since initial AI product investment is expensive and token optimization improves it over time.
“allocating the token spend and the human labor costs between COGS and R&D is a delicate but really important distinction”
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The speaker says a company can show token usage or AI compute growth rather than blended margin. They argue a new revenue stream can be split out so that the revenue story shows up before margin pressure does, though the back-end billing for AI usage is not straightforward to attribute.
“the revenue should speak before the margin compresses.”
Listen to the episode Episode Metrics & finance Link to this
Cassie Young says tool adoption and leading indicators such as BDR coverage or connects are fine but not enough, because CROs must get smarter about operating expenses. Her example is a digital deal room that cut a couple of days off the sales cycle, which she says should be translated into a direct annual gain rather than a multi-layer calculation that is too much math for a board.
“I think boards are done rewarding effort and want to see impact.”
Listen to the episode Episode Metrics & finance Link to this
Cassie Young calls this her number one hack for CROs. She says many CROs do not understand the P&L or how to tell the story of what is happening, and that they need to understand how the CFO thinks about the function and where the board is pressure-testing the CFO.
“my number one hack for CROs is like you better be taking your CFO to to lunch.”
Listen to the episode Episode Metrics & finance Link to this
She says CROs understand the top-line mission but often not the full P&L, including gross margin, operating margin and cash. She advises strengthening the areas where the two already agree, then asking the CRO to put on an enterprise hat at the executive table and rethink resource allocation to hit the bottom line.
“find common ground on how they're building the bookings model”
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Cassie Young calls this the PRIME framework: Productivity (she uses revenue per employee), Retention (net or gross), Investment efficiency (net magic number and CAC payback), Momentum (top-line growth) and Expense reduction. She says not every tool needs all five, but she would not back a go-to-market solution unless it obviously hits at least one, and she offers it as a buyer framework for CROs.
“I won't touch it if it's not obvious to me that it doesn't hit one of those five things”
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He said leaders should not wait for someone to question the budget. He suggested asking what the impact would be if 10% of the budget were taken away, and said that if it is hard to state the negative consequence to the business, the function may be overspending.
“If you find it really hard to answer that question in terms of the negative consequence to your business that I think you you may be overspending in your function.”
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He gave the example of a CCO at a $500 million run rate business offered 1% more revenue to invest. He said the leader should accept only if they can commit to a specific return, such as freeing account management capacity for net new ARR or investing in professional services to unblock adoption, and otherwise say no.
“You would have to sign up for more return on that.”
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Mark Roberge described go-to-market leaders who report a missed quarter with a huge pipeline for the next one, repeatedly. He said he would ask for that exact pipeline slide in three months and ask what happened to those accounts. He would believe the optimism only if the team closed deals at a consistent rate, and called repetitive pipeline stories buying time for failure.
“I want you to show me that exact slide in three months when we come back here and tell me what happened with those accounts.”
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Carlos said some reps prefer not to put deals into the forecast, so their deals appear to close quickly when in reality they were kept in the back pocket. He said analysing conversion rates, meaning how long deals spend in each stage, helps leaders learn from reps with fast cycles and also spot this behaviour.
“if the conversion rates are unbelievably fast, then usually what that means is there are some sales reps who are a little bit reluctant to put things in the forecast”
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His example: an apparent conversion problem may come from inbound share falling from 70% to 60% as outbound pipeline grows. He credits a head of BizOps and data early in his time at Owner with controlling for confounding variables. He said dumping data into Claude invites confidently wrong answers.
“have you controlled for mix shift?”
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The old flow was linear and people-heavy: request to RevOps, prioritization, data pull, visualization, dashboard and analysis by people who weren't sales, marketing or CS domain experts. The target outcome was analysis 'federated down to the lowest common denominator of the user.' To get there Experity consolidated data in its warehouse, added business context to fields through a semantic layer so the model wouldn't misinterpret CRM fields, put LLMs on top, and gave users a chat interface that answers questions and builds charts.
“we're able to build like three year trends of visual, get insights and recommendations in less than 30 minutes”
Listen to the episode Episode Metrics & finance Link to this
With data now easy for FP&A, IT or HR to access, she worries that conclusions drawn without go-to-market context could be damaging. Her boss set the rule because they do not trust that the context exists in other departments. She would like tools that give everyone the right context.
“nobody can produce any sort of statement about go-to-market analytics without running it through me first if you're outside of my team”
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Cassie says by Series B-plus board meetings, companies often break out gross and net retention by current ICP customers versus legacy ICP customers. Early churn can be a normal and healthy part of finding the right customer, but companies should be honest about which lost customers were inside the ICP. She warns against losing customers who should be inside it.
“in series B plus board meetings you see the numbers for both GRR and NR broken out by current ICP customers versus legacy ICP customers”
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said Ziv Peled ([Un]Churned), Seong Park (Revenue Builders), Cassie Young (The Revenue Leadership Podcast), Amanda Kahlow (Topline), Cassie Vaughn ([Un]Churned)
Ziv says many people cite figures like 2x, 5x, 10x and 50x without saying what they measure. He gives a hypothetical of a $10 million AI investment producing 50x revenue and says that does not work directly.
“No, it's it doesn't work like that”
Listen to the episode Episode Metrics & finance Link to this
Seong says teams often report metrics such as hundreds of developer hours saved, but that this only matters if the team asks what it meant to the business. He advises using discovery to attribute the result to a positive business outcome. He describes this as the work of making the link explicit.
“Hey, we've saved your developer hundreds of hours of time, so now they're more productive. Well, then so what?”
Listen to the episode Episode Metrics & finance Link to this
Cassie Young says tool adoption and leading indicators such as BDR coverage or connects are fine but not enough, because CROs must get smarter about operating expenses. Her example is a digital deal room that cut a couple of days off the sales cycle, which she says should be translated into a direct annual gain rather than a multi-layer calculation that is too much math for a board.
“I think boards are done rewarding effort and want to see impact.”
Listen to the episode Episode Metrics & finance Link to this
She says the easiest way to prove the agent works is whether buyers buy from it. She wants to measure deal cycles, ACV and shortened sales cycles rather than top-of-funnel activity.
“I want to measure to the bottom of the funnel, not the top.”
Listen to the episode Episode Metrics & finance Link to this
Cassie Vaughn said monday.com originally anchored value to productivity, time savings and cost savings across many customers, but this required coaching customers to accept the logic. She gave the example that every automation equals two minutes saved, which she called a stretch and a guess at best. The company now picks use cases where objectives are clear and tied to revenue or mission-critical business goals.
“So now instead, everything becomes about use cases.”
Listen to the episode Episode Metrics & finance Link to this
said Joubin Mirzadegan (Grit), Jeanne DeWitt Grosser (Grit), Bob Ranaldi (Revenue Builders), Mark Roberge (Revenue Builders), AJ Bruno (Topline)
He described an early-stage CRO, two to three months in, committing to $10M for the year. The CRO couldn't state current pipeline and was counting on an unreleased self-serve product and a $1M big-tech deal that had only had a first meeting. Joubin's view is that founders are default optimists who 'do not know how to forecast a business' and need a big number to justify their valuation. His advice was to re-forecast from data, not forecast a PLG product that doesn't exist yet, and push the product team to fill the gap, because the product leader won't be fired first.
“I would not forecast the PLG product that doesn't exist yet.”
Listen to the episode Episode Metrics & finance Link to this
At Vercel this is called the lead-to-revenue model. She said the spreadsheet lets everyone judge honestly whether the target is achievable, based on history or on assumptions they are willing to make, and later shows which driver caused a hit or a miss. At Stripe she owned new business, which was the gap between a 'nice large round' target and the forecastable existing base. The model showed quickly that the target was unlikely, which led to a strategic discussion of options: invest in marketing (Stripe then had one marketer), invest in outbound, or move upmarket for a larger ACV. She said the data was messy at first, partly because she had changed the segmentation, so reasonable assumptions filled the gaps.
“it's your full funnel math by segment, by geo.”
Listen to the episode Episode Metrics & finance Link to this
Bob said top-down targets often come from the board and CFO based on market growth rates. The bottom-up build covers MQLs, SQLs, pipeline required, conversion rates, cross-sell and upsell versus new business, and how many new logos were won in prior years. He said the best planners work from recent quarters and years rather than an assumption that everything works perfectly.
“work from a basis of reality versus like Nirvana”
Listen to the episode Episode Metrics & finance Link to this
He says investors' top-down growth targets come from their return math, and that a top-down number can be a starting guide. Bottoms-up planning must account for attrition and ramp time, and he suggests spreading hires, such as about two reps a quarter rather than eight at once.
“the bottoms up is the sales capacity that you create and the demand and capacity that you use to feed them.”
Listen to the episode Episode Metrics & finance Link to this
AJ said the board expects a predictable quarter-to-quarter model, so the company leaves a larger margin of error for what it does not yet know. All upside is tied to what it is building and thinking about. He said his board is good but still sometimes falls back on patterns from two or three years earlier.
“We're going to take our financial model and completely make it predictable off of the product that we have today and today and what we know today.”
Listen to the episode Episode Metrics & finance Link to this
said Jonathan Moss (The Revenue Leadership Podcast), Lauren Hughes (The Revenue Leadership Podcast), Rick Smolen (Topline), Justin Shriber (Topline), Ziv Peled ([Un]Churned)
The old flow was linear and people-heavy: request to RevOps, prioritization, data pull, visualization, dashboard and analysis by people who weren't sales, marketing or CS domain experts. The target outcome was analysis 'federated down to the lowest common denominator of the user.' To get there Experity consolidated data in its warehouse, added business context to fields through a semantic layer so the model wouldn't misinterpret CRM fields, put LLMs on top, and gave users a chat interface that answers questions and builds charts.
“we're able to build like three year trends of visual, get insights and recommendations in less than 30 minutes”
Listen to the episode Episode Metrics & finance Link to this
Only the CRO read the weekly deck covering every cut of every metric. He asked for AI to flag when anything deviates from the norm so leaders can dig in. The team built him a CRO skill. Lauren Hughes has since handed her revenue planning and analysis team a list of about 50 KPIs to manage, and says they still need to improve the report.
“all I need is somebody to put AI on this and tell me when anytime something deviates from the norm”
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Rick says CEOs can connect their systems to AI tools and pull conversion rates from HubSpot, so a CRO has less room to present numbers unchallenged. He says transparency is good when the numbers are accurate. The two measures ShipHero focuses on are whether conversion rates are improving and whether the team is using tools that help it win more deals, such as being able to say yes to more requirements.
“So one of the things we're looking at is like, hey, is the conversion rate improving?”
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He said CROs often have a forecast number but lack the explanation of which deals are the head and tail wins and what happened in the large deals, so they have to ask RevOps, who need a couple of days. He said Terret can answer that in a couple of minutes.
“We can nail that in a couple of minutes for you.”
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He says if you had spoken with him two years ago he would have said dashboards are not going away, but now he thinks they are. He did not give a timeframe in the episode, and the show notes frame it as questioning whether dashboards will exist in two years.
“I think dashboards are going away.”
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said Saumyo Mukherjee ([Un]Churned), Ziv Peled ([Un]Churned), Ryan Burke (Topline), Asad Zaman (Topline)
Saumyo says some vendors bill enterprise plans on something other than tokens, while the outputs and use cases supported differ between vendors. He frames the decision as whether to save cost or to improve throughput and quality. He says Braze made the right choice even if it costs more, as long as the cost is justifiable through efficiency in both quantity and quality, and Braze is currently in an ROI-proving phase.
“do we really want to save cost Or do we really want to improve our throughput and improve our quality?”
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Braze is rolling out budgets where each department head gets a budget based on team size, and each individual gets a set amount per month of tokens. They will watch usage for a quarter to see which users and teams use how much, then normalise, since departments need different amounts. Saumyo says his team may need much more than an HR team. Token allocation will be distributed after one or two quarters based on empirical data about quality and throughput.
“every department head like myself or my boss right they will have a certain budget allocated to them based on of course the size of their teams”
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The investor, whom the transcript does not identify, says they ask most of their portfolio CEOs about token spend to understand the curve of usage against falling costs. They floated that spend might be around one tenth of a person's salary now and asked whether it goes to 50% or one times a salary. They said they don't know where it lands.
“if it's right now, maybe like one tenth of a person's salary, does it go to 50% of someone's salary to one times a person's salary? And I don't know where it sort of lands”
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He calls this token maximization and says the goal is efficient use: you will use a lot of tokens, but it has to be efficient. He describes it as an equation a company can control and balance. Josh adds that it needs close measurement and a constant pulse.
“So not to use the max amount of tokens, but the best quality output and maximized tokens.”
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Responding to Asad Zaman's comments on his own company's token spend, Ryan Burke says companies are encouraging experimentation and have budget available for it. He predicts that at some point the people with fiscal responsibility will ask what the spending is for. He describes this as a conversation that will happen across enterprises.
“at some point, the CFOs and, you know, those who have like fiscal responsibilities are going to step in and say, what are we spending this on?”
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Asad Zaman says the company is using a lot of tokens and spend is now in the hundreds of thousands of dollars, which took him by surprise. He does not state the time period. He says it makes him think about what the P&L will look like in two years.
“STA is using a lot of tokens like we're in the hundreds of thousands of dollars now”
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said Mark Roberge ([Un]Churned, Topline), Aviv Canaani (The Revenue Leadership Podcast), Kyle Norton (The Revenue Leadership Podcast)
Roberge suggests extracting payback period back to its first-principles inputs: new opportunities per rep per month, close rate, sales cycle, cost to generate an opportunity, cost to close, and ACV. He says a board can review Q1 actual outputs alongside Q2 inputs to see where unit economics are likely to land. He describes this as managing the business one or two quarters ahead of peers.
“can we just algebraically extract payback period back to number of new opportunities per rep per month”
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In his hypothetical board pitch he would attack three of the four variables: active opportunities, close rate and sales cycle, leaving ACV alone. He would attack opportunities through selling time, then list the AI initiatives likely to move it and report the impact each quarter.
“I'd start off with like, what is the end goal?”
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He illustrated it with a rep working 20 opportunities at a 10% close rate on $100,000 ACV with a two-quarter sales cycle, giving $100,000 of productivity per quarter. He used the formula to break an output metric down into four variables that can each be targeted.
“It's the number of active opportunities that our rep is working, times the ACV, times the close rate divided by the sale cycle.”
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Aviv says a CEO might set a $10 million quarterly target, and the CRO should answer with the marketing budget required, the AEs or solution consultants needed, and the resulting waterfall model. He says DataRails has been able to do this over several quarters. He describes this as what the CEO and board want from a CRO at a startup.
“this is how much marketing budget I need. this is how many AEES or solution consultants and so forth and you can just build that waterfall model”
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Aviv says he starts with the target and works backwards, for example needing 200 deals at a 50% conversion rate, which means 400 opportunities, and then the meetings needed to create them. He says the planning then depends on knowing the cost per meeting from marketing. He uses this to plan with the CEO and board.
“let's say I need to get through 200 deals and then it's like a 50% conversion rate so you need like 400 opportunities”
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Kyle Norton says that revenue is driven by pipeline and conversion rates, not AEs multiplied by quota, and that the number of AEs affects conversion. He credits Jenny Dingis at Cleo with the same view. Aviv agrees that the number of meetings per AE per day should be calculated, since win rates fall once AEs are overloaded.
“What drives revenue is pipeline times conversion rates and conversion rates are impacted by having enough account executives to manage the the pipeline the funnel generated.”
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He said to return after a quarter with the miss framed as the plan versus actuals, such as planned reps added per month versus reps actually added, and planned inbound leads versus those achieved. He said this framing mitigates the risk of being penalised for the miss.
“We were supposed to go from adding two reps a month to eight reps a month. And I was only able to add five.”
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Mark Roberge describes educating the board by splitting pipeline into three sources: marketing-generated, sales-generated and SDR-generated opportunities. For each he uses historical close rates and sales cycles to compute the number, and then compares actual results against that model when a quarter is missed. He recommends doing this in a quarter when the number is hit, because that is when you have credibility.
“We get opportunities through three sources.”
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said Tim Rutten (The Revenue Leadership Podcast), Kyle Lacy (Topline), Bob Ranaldi (Revenue Builders)
He describes the trailing 12-month magic number as new ARR divided by sales and marketing spend, equal to around 80 cents of ARR per dollar invested. Because ARR is about five years of contract value, he says that works out to roughly a 4x impact. He also says the 35-person marketing team runs a book above $350M ARR.
“every dollar invested we generate 80 cents in ARR and ARR is five years. So it's actually 8 time 5 is a nice 4x uh impact.”
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Kyle Lacy described how, at private companies, he tried to have the budget conversation with the CEO around sales and marketing together rather than as a sales headcount model and a marketing envelope. He said the board will ultimately ask about sales and marketing efficiency and will get into the detail if the number is well off.
“I tried as much as possible to have the conversation around sales and marketing, and not this like well we've met with sales and talked about their headcount capacity model.”
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Bob said his firm FTV focuses on sales efficiency, the relationship between sales spend and new bookings, and works to improve portfolio companies that spend more than they bring back in bookings. He told CROs entering PE-backed businesses to understand the metric, because boards and investors may press them to improve it.
“So it's the relationship of the spend relative to the results, results in the form of new bookings.”
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Mark said you add up quarterly spend on marketing, SDRs and AEs to get the cost of a sale, then compare the revenue and lifetime value it generates. He pointed to payback within 12 months and an LTV to CAC ratio of three to one as common benchmarks. He said a rep who creates their own demand has better unit economics than one fed by a large SDR team, so rep pay alone is not enough.
“What is the quarterly spend on marketing? What is the quarterly spend on SDRs? What is the quarterly spend on our account executives? And that's the cost for a sale.”
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said Alex Mashrabov (The Twenty Minute VC), Sam Jacobs (Topline)
Mashrabov says social media ad production doesn't need 'PhD level intelligence' and favors cheaper, more steerable models, which can also be more cost-efficient for customers. As agentic ad workflows grow, Higgsfield chooses the model in over 40% of cases. He calls this optimization of token count and token cost 'tokenomics'.
“The margin on own models and open weights models is over 80%. And then it almost doesn't matter. And for close source models, it's probably between 20 and 30%.”
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Sam Jacobs said Harvey's cost to deliver $1 of revenue went from 50 cents to $1.50 as agents consumed tokens on behalf of human users. Harvey was growing extremely fast, but every dollar of revenue was destroying gross profit. He said Harvey's path out is training and hosting its own models, including open-weight models, and moving away from frontier models from labs like Anthropic and OpenAI.
“by June, Harvey's gross margin due to rapid, agentic Adoption right agents going out and consuming tokens on behalf of their human users Harvey's gross margin had fallen to negative 50 %”
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said Carlos Delatorre (Revenue Builders), Jim Richmond ([Un]Churned)
Carlos said some reps prefer not to put deals into the forecast, so their deals appear to close quickly when in reality they were kept in the back pocket. He said analysing conversion rates, meaning how long deals spend in each stage, helps leaders learn from reps with fast cycles and also spot this behaviour.
“if the conversion rates are unbelievably fast, then usually what that means is there are some sales reps who are a little bit reluctant to put things in the forecast”
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Richmond said people will call a number lower than they really think to avoid a bad surprise, and he said he is the same way. He expects Smartling's GRR number to rise as that bad-surprise effect is removed and the forecast gets close to what the company will actually deliver.
“they'll call a number that's lower than what they really think because they want to look like a hero rather than a goat”
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said Katie Bullard (Topline), Cassie Young (The Revenue Leadership Podcast)
She says CROs understand the top-line mission but often not the full P&L, including gross margin, operating margin and cash. She advises strengthening the areas where the two already agree, then asking the CRO to put on an enterprise hat at the executive table and rethink resource allocation to hit the bottom line.
“find common ground on how they're building the bookings model”
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Cassie Young calls this her number one hack for CROs. She says many CROs do not understand the P&L or how to tell the story of what is happening, and that they need to understand how the CFO thinks about the function and where the board is pressure-testing the CFO.
“my number one hack for CROs is like you better be taking your CFO to to lunch.”
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said Matt Allison (Topline), Daniel Simon (Revenue Builders)
Matt said running every piece of content through an AI model would be very expensive, so Handraise runs an ML process first to remove content like sidelinks, which he said were nearly impossible to filter out in the old world. He said isolating the problem to a smaller data set makes the cost workable, and that they still pull pretty good margins.
“So you know, if we ran every single piece of content through an AI model to it, it'd be really expensive. So we have, you know, an ML process that we run first.”
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Daniel said Glean's own LLM, Waldo, used as a pre-processing engine, can reduce token consumption by about 30 percent. For one customer he calculated that rolling out to 20,000 employees could save six or seven million dollars in token consumption alone, before any use-case value. He said a figure like that gets a CFO's attention and can lead them to sponsor work with the business units.
“we can actually reduce the amount of token consumption by about 30%.”
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said Mark Roberge ([Un]Churned, The Science of Scaling), Rob Edmondson ([Un]Churned)
Roberge advises founders not to over-analyse the first choice. He suggests a two-hour session with decision-makers to pick a value-linked metric. A year later the company can check whether customers who stayed showed high adoption and those who churned did not. If the two groups look the same, he says the company can use its user logs to run around 50 permutations with AI to find what matters, which Josh likens to a regression analysis.
“So in the beginning, just pick something correlated value knowing that in a year, you'll be able to run the analysis to tune it.”
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Rob said that in upmarket accounts, a lack of consistent daily usage did not signal a problem. Instead, the team saw patterns of renewal that matched contracting, which he described as a very cyclical process within businesses. Following that pattern was a good sign for the team.
“upmarket what we saw was it wasn't actually consistent usage like everyday that indicated a successful renewal. But there were these patterns of renewal”
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Rob said the team learned the digital signatures of customers on happy and unhappy paths. He said the things they assumed were indicators of successful renewal or successful use were not always true. Examples were that everyday usage did not predict renewal upmarket, and that early AI feature adoption did not correlate with good outcomes downmarket.
“Successful use weren't always true for for AI features within our product.”
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Mark said that after a year you can compare cohorts. If customers who hit the indicator have high retention and those who don't have low retention, the indicator is validated. If there is no variation, he suggested analyzing customer and user logs and running correlations, for example to test whether the number of messages or the number of users matters more.
“those that did hit the leading indicator have very high retention, and those that do not have very low retention.”
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said Glenn Fogel (Grit), Eric Gilpin ([Un]Churned)
Booking reported about $27 billion in revenue, up 13%, but Fogel says revenue recognition understates the business because Booking doesn't own the hotel rooms it sells, so only the commission counts. He contrasts this with a retailer that briefly takes ownership of a book and so books its full price. He prefers to cite the $187 billion of total travel volume Booking processed last year.
“Fact is i like to think about the fact that we did a hundred eighty seven billion dollars of total travel volume.”
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Josh framed the figures as ARR, and Eric said Upwork was a consumption business that tracked gross sales volume (GSV) rather than ARR. He framed the market as a trillion-dollar fractional labor industry that Upwork challenged by changing the delivery model, not the underlying idea of accessing talent.
“we didn't necessarily have like the ARR framing, we had what we call GSV, so just gross sales volume”
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said Michael Walrath (Topline), Sam Jacobs (Topline), Mark Roberge (The Science of Scaling)
He said that if a company lacks growth it had better be very profitable, and that the more growth a company has, the less profit the market will let it show. He called this a weird idea for someone who has been in tech for 25 years. He said the right mix changes with the environment, so he cannot know what it looks like in advance.
“businesses should be rule of 40 or better over the long run, one way or the other.”
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Sam Jacobs said that for a $50 million business growing 25%, he would need it to generate about $10 million of EBITDA. He said that with that cash flow, which he expected to persist with growth, he could get the company valued at $150 to $200 million even after discounting the growth.
“If you're generating $50 million growing 25% and you're just breaking even, I need you to either grow faster or be more profitable.”
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Mark Roberge defines Rule of 40 as growth rate plus profit margin. A total of 40 or above is good, and anything below is suboptimal. He says the metric is widely used in private equity, and that depending on the investor it can apply with a negative margin, for example 60% growth with a -20% margin.
“So you could be growing 60 % but have a negative 20 % margin and still be rule 40.”
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said Mark Roberge ([Un]Churned), Kyle Norton (The Revenue Leadership Podcast)
Roberge says he would not use GAAP profitability to judge go-to-market fit because it includes noise from costs such as office space that do not grow with customers. He says unit economics isolates costs to revenue and customers. He says these can be framed as marginal revenue versus marginal cost or as payback period and LTV to CAC in SaaS.
“We're not going to use GAAP accounting profitability because that has a lot of noise in it around our office space and all these things that aren't necessarily going to grow with customers.”
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Roberge says he likes payback period of 12 months or less and net dollar retention above 100 percent, and that he favours using those two together. He says most companies are already reporting unit economics at board meetings. He suggests tracking these as the way to judge whether go-to-market fit has been reached.
“Okay, so let's just assume we're going to do a payback period, like 12 months or less. NDR we want NDR over 100 % I like that's kind of my favorite is using those two together.”
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He referenced Mark Roberge's Science of Scaling idea that product-market fit comes before go-to-market fit, and that the business model needs the right economics before scaling. He said early economics will never be amazing but must be serviceable. He said that when founders with a 17K ACV, a 45 to 90 day sales cycle and a two-week POC ask him for advice, that model does not work.
“you have to have a clear path to to solving to solving for a, you know, three, then four x LTV, CAC”
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said Greg Casale (Revenue Builders), Aviv Canaani (The Revenue Leadership Podcast)
Greg Casale says he measures his team by pipeline and bookings, not meetings, so he must show the partnership generates revenue. He says client organisations should give an outside SDR team something no internal team is going after, such as a tech refresh, upsell, cross-sell or a new product, so the work does not compete with their own reps.
“I'm going to measure success in pipeline and bookings, not meetings.”
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Aviv says marketing can show a lot of good leads, but if they do not close, it has not done its job. He uses a basketball analogy, saying an assist does not count if the shooter misses. He says he tells sales teams that a good team is needed to hit targets, not individual prospecting.
“you don't count assists if the shooting guard didn't make the shot”
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said Jake Saper ([Un]Churned), Katie Bullard (Topline)
He says this creates a fear that the market will punish a company whose recurring revenue line goes down during an uncertain outcomes-based transition. He calls the CFO implications huge.
“if you move to an outcome-based model versus a seat-based model, you're moving away from predictability, which is scary.”
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She says businesses starting to generate AI ARR will likely be valued separately. She notes that outcome-based revenue is less predictable than seat-based revenue, and that predictability has been part of why SaaS businesses carry high multiples. She presents this as her hypothesis rather than an observed result.
“My hypothesis is that for some period of time, I don't know how long it will last, it will be at a higher multiple.”
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said Chris Catania ([Un]Churned), Jason Dunn ([Un]Churned), Jon Wishart ([Un]Churned), Brian Oblinger ([Un]Churned)
Chris Catania says community can be measured and has been shown to affect all areas of the business. He says the problem is that the language used to describe community has not always been targeted at the right audience. He argues leaders must frame results for each executive.
“It's not that we can't measure community. It's that the language has not always been Targeted in the right way”
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Jason Dunn said community success is hard to measure because it rests on relationships, and he agreed it is a vibe. He said reporting an activity metric, such as 80% of members returning every 30 days, may not mean much to a leader. At AWS, the developer experience team's goals were around influencing developers: whether they were learning, growing, trying new AWS technologies, and taking them into their organizations. To show this, the team measured the output and reach of community content.
“So I think the key thing is actually to try to get a grasp on the output, because leaders care about what are they doing to help drive the organization's goals.”
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Wishart argues that a metric developed in the early 2000s, namely Google traffic and visitors, is no longer serving communities, and that this may be a useful forcing function to rethink measurement. He says the underlying value proposition is unchanged, if not improved, and that community impact can now surface at greater scale than ever. Oblinger adds that an answer found through a Google search that draws on community content still counts as help, and may deflect a support ticket or a call to the CSM.
“Just because it's not happening on your domain where you can measure it doesn't mean it's not valuable and it doesn't mean it didn't help someone.”
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He says his methodology is to go to each department leader and ask about their problems and what they measure. For example, he says a success leader might care about time to value, customer education, satisfaction and renewals, and those become threads to pull with the analytics team. He is clear that community is not a silver bullet for all of these.
“go to each leader of a department inside of a company and say, what do you care about? What are the problems you're trying to solve? What do you measure?”
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Oblinger says communities have historically measured cumulative users, posts, likes and engagement, and that the shift now is to put those numbers in business context. He describes blending community analytics with other business data to see whether participation contributes to churn reduction, cost reduction, or higher-quality leads.
“historically in communities, we've often measured what I would call operational metrics, right?”
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Oblinger describes going to a CFO at budget time to ask for more headcount and funding, and says that if the CFO sees community as a cost centre, the request is simply for more money to cover costs. He says that when the request is backed by insight into what participants do, such as buying more or staying longer, the conversation becomes what the team did with a given budget and what more could do.
“the conversation becomes, look what I did with $2,000,000. Imagine what I could do with three.”
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said Cassie Vaughn ([Un]Churned), Manish Chawla ([Un]Churned), Guy Galon ([Un]Churned)
Cassie Vaughn said the team brought in renewal forecasting three or four years ago as the heart of a commercial mindset. She said CSMs call a number, which is a bet with some stake in the game, and the company judges their ability to assess risk and opportunity. She said this was a gap for monday.com because nobody was paid on retention outcomes at the time, so CS took on the forecast.
“So we've owned the renewal forecast and we still do for years now.”
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He described the CEO turning to the CRO for new business growth and to himself for retention, with the CFO keeping the total score that includes price increases. In this model, CS owns part of the revenue equation, part of the expansion equation and part of the cost-to-serve equation.
“So you have to own that a part of that revenue equation and you have to own a part of the expansion equation and you have to own a part of the best cost to serve equation.”
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He says CS does a lot of renewal and expansion work, which he calls the execution side, but CS is not always part of planning with sales and finance. Sometimes CS is just a bystander. He says he wants to promote CS more into the revenue table, and his Pulse Europe workshop covers how CS can shape revenue forecasting using AI signals.
“I call it the execution side, but going back and plan it, CS are not part of it all the time.”
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said Manny Medina (Topline), Sam Jacobs (Topline)
The speaker linked this to models improving fundamentally every year, which can produce stretches of negative margin followed by price increases and rationalization. The result is a blended yearly gross margin around 25% to 30%. They gave it as a figure they hear from others, not their own data.
“The number that everybody I hear from is that at the end of the year, 30, 25 to 30”
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At Amazon, which he joined after HBS, Manny had to memorize operating margins: books 20%, electronics 7%, jewelry 3%. He cites Bezos's line 'your margin is my opportunity'. Coming from that, he found Microsoft's and SaaS's 70% to 80% margins a luxury that cheap money made possible and that wasn't even spent efficiently on growth. He still says gross margins must be positive.
“I feel like a normal, you know, software business will be running on 40% margins, 60% tops.”
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Sam Jacobs cited Iconic data to contrast AI economics with SaaS, where the median company ran gross margins in the mid-70s and the best exceeded 85%. He said token consumption on every prompt and document means AI companies cannot assume the SaaS margin floor.
“So Iconic found that scaling AI companies average roughly 41 % gross margins in 2024, 45 % in 2025, and a projected 52 % in 2026”
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said Jean de Villiers ([Un]Churned), Abbas Haider Ali ([Un]Churned)
Jean said the owners expect the post-sales functions he runs to return a 30% contribution margin to EBITDA. He said this means every function has to run at a reasonable level of profit.
“our owners expect us to return a 30 % contribution margin to EBITDA.”
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Jean said Unit4's private equity owners treat cloud gross margin as a value creation metric, so keeping traditional CS costs inside it was a non-starter. Unit4 moved CS into a cost of sale line that Jean runs and manages as a profit center.
“we've had to extract CS out of that operational expense, put it into a cost of sale line, which is essentially what I run, and effectively run that as a profit center.”
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He described two numbers: the envelope the CFO hands over, and the margin on any post-sales function that is monetized. He said he wants the right to keep what he earns from monetization, as with a premium support upgrade. He treats monetized work as outside the cost envelope.
“that should be separate from that envelope because it is self funding at that point.”
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He said margin expectations for monetized post-sales work vary by stage, from Series B through public companies. For the most premier packages at the most mature companies, he said around 70% margin should be achievable, and that support generally runs a bit higher margin than professional services.
“support generally runs a little bit higher margin.”
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said Sam Jacobs (Topline), Keith Peiris (Topline)
Sam Jacobs offered this explicitly as a straw man for his co-hosts to react to. Above 200% growth he will accept terrible margins temporarily, for example to subsidize usage or win market share, but only if the company shows the path to a good business. Between 100% and 200% growth he wants a clear improving margin trajectory, and below 50% growth he needs margins to be good. He did not specify a rule for 50–100% growth.
“Above 200% growth, I can tolerate bad margins temporarily. If you can show me, you have a path to a good business.”
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Asked whether investors cared about margins, Keith said they definitely asked. He said Lightfield is trying to take the market and his head of finance 'glares at me every day when we close deals,' but he believes there is a path to margins.
“we're not in a margin -optimizing state of the company right now. We're trying to take the market”
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A host said the best inference provider from a gross margin perspective, Fireworks, runs at about 25 to 30% gross margin, and that someone recently analyzed this. The host said gross margin does not matter right now and that it is growth over everything else. He said it will eventually matter, and used this to frame a question to Tunguz about whether FDEs are a short-term phenomenon.
“It just really doesn't matter right now.”
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said Josh Schachter ([Un]Churned), Guy Galon ([Un]Churned)
Josh described a Gainsight sales QBR for the leadership team where the AI-built slides looked far more polished than ever before. However the CRO found a number that was inconsistent with another number, and the team had to go back to Claude on it. He said that as this reaches executives and boards, AI will need to be extremely accurate because everyone will be held accountable.
“AI is going to have to be 99.999% correct when it helps us with those things.”
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He sees AI as another input to revenue projections, and says a human CS leader would have to confirm it can be put into the plan. He says there is still a human aspect that AI agents will not fully cover. He says the business needs to be confident in the plan, using AI together with the team's own capabilities.
“Because eventually when the CFO and the CRO are doing their revenue projecting for next year, even in a couple of years from now, it's not going to be OK. The AI told me that it would be 50/50% renewal chances. Nobody will accept it, right?”
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said Manish Chawla ([Un]Churned), Jim Richmond ([Un]Churned)
Manish compared the CCO's line of sight to the sales leader's line of sight to quota attainment, and said the CCO's equivalent is gross revenue retention, followed by expansion. He said account health and the value customers get are the basis for both, so commercial ownership must be connected across AEs and CSMs.
“What's the line of sight to in, in the case of of the chief customer officer, a line of sight to gross revenue retention, right.”
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Jim Richmond said the focus is protecting the existing book of business so the company has a solid foundation for growth. He said the company has to retain customers at as close to 100% as possible, while still generating leads for sales partners.
“That's, that's our new North Star metric for, for the year.”
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said Grant Clarke ([Un]Churned), Jake Saper ([Un]Churned)
Grant says this is what separates Atlas from a BPO that simply deploys people to do renewals. If Atlas-led actions go from 20% to 55%, then each new customer needs maybe six humans plus Atlas, or four, rather than 10 humans plus Atlas for the same number of contracts. Productizing that action-level autonomous leverage is how he expects to keep deploying economically into new customers without margin compression over time.
“We're not deploying the 10 humans plus Atlas. We're maybe deploying six humans plus Atlas or four for the same number of contracts.”
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He gives examples of the service FTE as a renewals person, coder, accountant or lawyer depending on the service. He says it is similar to the CSM coverage ratio that CS leaders track, and that investors and boards want to see it going up.
“The second layer is what I call your annualized revenue per service FTE”
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Actions written 10 Oct 2026 from the most useful of 311 recent insights and checked against them.
Sam cited this idea, attributing it to McKinsey, while describing Pavilion's cost problems after 2022. He said he was quite proud that the company reversed course and got back to profitability.
“it's much easier for a low growth company that's profitable to find and discover growth than it is for a high growth unprofitable business to find its way to profitability.”
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He uses Cision to measure earned media volume, including per-expert counts such as the jump from 35 to 3,500 hits. He reviews the overnight hits each morning as a gauge of how outside perceptions of the university and its people are being shaped.
“I come in every day and see between a hundred and fifty to twenty two hundred media hits overnight from our experts and our university”
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Rory O'Driscoll said OpenAI grew 18% quarter-on-quarter from Q1 to Q2, implying just under 100% annual growth. Over the same period Anthropic was growing about 10x, and in Q2 Anthropic's GAAP revenue ($11B) exceeded OpenAI's ($6.8B) for the first time. A 70% (or even 60%) Q3 figure, if accurate, would restore OpenAI's narrative, which he said matters given its forward commitments on compute, data centres and land. He said the open question is whether that growth came at Anthropic's expense.
“the gap wherever you grow from Q1 to Q2 was 18 % quarter on quarter. And you did that math and it was that implied, you know, just under 100 % year on year growth rate, which was massive deceleration Anthropic was still going 10x”
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When Josh Schachter asked how to show causality given that playbooks, outbound, inbound and quality all affect revenue, AJ Bruno said you must come in with a hypothesis and pick three things to measure against a control group, without going out of bounds from those three. He said QuotaPath does this with design partners and that it is mandatory, because otherwise too many metrics, people and processes change at once.
“You have to. Like you absolutely have to because otherwise there's just too many metrics and too many numbers out there and too many people process all of the things that are going to potentially change that.”
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Speaking from a prior role working with customer segmentation data, he said you can keep slicing data without coming up and forget what you were asked at the start. He described needing to come up for air.
“you can kind of just go down a rabbit hole and never come up and you're slicing data, slicing data, slicing data. you forget what question you were asked at the beginning, and then you had to come up for air.”
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He attributes this partly to QuickLizard pricing aggressively and partly to thin retail margins, where a revenue lift has an outsized effect on the margin line. Stiving's illustrative example was moving gross margin from 5% to 7%.
“It's not uncommon for us to see 25X ROI from our platforms, but I don't always go in day one with that number because that can come off as salesy”
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Ganesan pointed to the SaaS era, when net revenue retention could be inflated by landing a $10 deal and expanding to $50 a week later rather than signing $100 up front, which turns 100% NRR into 500%. He referenced the economist's idea of the 'bezzle' rising in booms, but was unsure who coined it. He said the real filter is whether founders and investors are focused on terminal value or on markups.
“Once a metric is measured, it can be gamed. And that happens.”
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He described an early-stage CRO, two to three months in, committing to $10M for the year. The CRO couldn't state current pipeline and was counting on an unreleased self-serve product and a $1M big-tech deal that had only had a first meeting. Joubin's view is that founders are default optimists who 'do not know how to forecast a business' and need a big number to justify their valuation. His advice was to re-forecast from data, not forecast a PLG product that doesn't exist yet, and push the product team to fill the gap, because the product leader won't be fired first.
“I would not forecast the PLG product that doesn't exist yet.”
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At Vercel this is called the lead-to-revenue model. She said the spreadsheet lets everyone judge honestly whether the target is achievable, based on history or on assumptions they are willing to make, and later shows which driver caused a hit or a miss. At Stripe she owned new business, which was the gap between a 'nice large round' target and the forecastable existing base. The model showed quickly that the target was unlikely, which led to a strategic discussion of options: invest in marketing (Stripe then had one marketer), invest in outbound, or move upmarket for a larger ACV. She said the data was messy at first, partly because she had changed the segmentation, so reasonable assumptions filled the gaps.
“it's your full funnel math by segment, by geo.”
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The speaker linked this to models improving fundamentally every year, which can produce stretches of negative margin followed by price increases and rationalization. The result is a blended yearly gross margin around 25% to 30%. They gave it as a figure they hear from others, not their own data.
“The number that everybody I hear from is that at the end of the year, 30, 25 to 30”
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At Amazon, which he joined after HBS, Manny had to memorize operating margins: books 20%, electronics 7%, jewelry 3%. He cites Bezos's line 'your margin is my opportunity'. Coming from that, he found Microsoft's and SaaS's 70% to 80% margins a luxury that cheap money made possible and that wasn't even spent efficiently on growth. He still says gross margins must be positive.
“I feel like a normal, you know, software business will be running on 40% margins, 60% tops.”
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Crusoe depreciates GPUs over six years, which he calls the industry standard. Because Crusoe is 'long' energy, data centres and chips, it built managed services that hide the underlying chip from the customer. He says demand for cheaper intelligence on older, slower chips will persist, and he presents the longer cycle as a belief rather than something already proven.
“will ultimately, we believe, extend the depreciation cycle beyond six years.”
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When Crusoe made substantial Hopper purchases in 2023, the feedback was that the chips might not be valuable after year three, and early financings demanded very fast payback and heavy debt service coverage. Three years later, Hopper rental rates are higher than at launch. He says people underestimate developers' ingenuity in turning compute into valuable services.
“Here we are three years later, and the prices being charged for utilizing hoppers is higher than the rates that were being charged three years ago when they were brand new.”
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Asked which layer has the best margin today, Lochmiller names managed GPU clusters. He says this is true 'right this instant' and attributes it to a massive shortage of supply.
“Managed compute clusters is like incredibly high margin, like right this instant.”
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Lochmiller treats the GPU hour as a near-commodity, noting that platforms are creating tradable compute futures. Crusoe rents capacity on roughly five-year contracts to credit-quality customers that pay back and cash-flow within the term. It also signs shorter-term contracts at higher margins that carry renewal risk, and sells managed inference and serverless fine-tuning on even shorter, higher-margin terms. The aim is a healthy blended margin across the mix.
“There are shorter term contracts that we'll do at higher margins. But they're riskier because at the end of the contract, it's like, is there a renewal?”
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The speaker said all three of them were part of a fast-growing company at PTC, where the number one reason for its growth was being able to prove beyond doubt what it had done. They said the company called these measures metrics internally, which were basically proof points.
“todays metrics are tomorrow's proof points”
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Kaplan described a buyer, Neil, who resisted setting success metrics because he thought Kaplan wanted to manage and measure him. Kaplan said he framed the work as putting deposits in the bank together, so the next request for money would rest on a track record of solved problems.
“you're gonna ask for a certain amount of money to solve a certain problem”
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XYZies had four sales motions, each on a separate tool, and no single view of performance across channels. Its stack was a high-end popular CRM, plus WhatsApp, phone lines and chat, plus another system for data analysis. He says that when you try to deal with multiple things, you can't put things in motion quickly or measure them well.
“when you try to go ahead and deal with multiple things, that's when things start to slow down and things break because then you cannot really put things in motion quickly, and you cannot measure them very well”
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He estimated current AI spend at roughly $100B, of which 20% ($20B) is relevant to the startup. Compressing that 5:1 leaves about $4B of accessible revenue. Because developer adoption has been very fast, he said a $1B revenue line could be reached quickly by saving customers 80 cents on the dollar.
“Spend right now, today, is roughly $100 billion. And you do the analysis, and 20 % of that is relevant to Jeff. So that's $20 billion of accessible revenue. Say they compress it five to one.”
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Grant says this is what separates Atlas from a BPO that simply deploys people to do renewals. If Atlas-led actions go from 20% to 55%, then each new customer needs maybe six humans plus Atlas, or four, rather than 10 humans plus Atlas for the same number of contracts. Productizing that action-level autonomous leverage is how he expects to keep deploying economically into new customers without margin compression over time.
“We're not deploying the 10 humans plus Atlas. We're maybe deploying six humans plus Atlas or four for the same number of contracts.”
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Josh Schachter says SaaS gross margins are around 80%, with 80-plus considered very good, and that Jake Saper believes AINS companies can reach 70%. He adds that the AINS startups Jake works with are early in their maturity and lack the data and reps to have reached that level yet. Grant Clarke says he believes Atlas can get there.
“Jake talks about the gold standard being 70% of margin to get there. You know, in SaaS, it's around 80%.”
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Grant says the trap for legacy service providers is that when a customer offers more work, such as more customers or new regions, the quick answer is yes because it drives top line. The provider then never finds time to work out how to scale cost over time, and costs rise with revenue. Automation, RPA and process improvements help, but he says they yield only smaller degrees of improvement.
“the very quick answer is yes, we'll figure it out because that's driving top line, but then you never have time to really figure out when you scale, when you put the cost in place, how do you continue to scale it over time?”
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She treats three to four months before the event as the sweet spot for flight prices. Booking late, she says, crowds out spending on the fun parts of the event.
“travel and lodging in general is typically a solid 40 % of your kickoff budget. Be smart about it. If you're booking travel two months, even a month and a half, it's going to get closer to a 60 %”
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Freya separates operational metrics such as site traffic and share of search, which belong inside marketing, from output and results, which should be explained in terms stakeholders understand. She says over-explaining marketing measures to people outside marketing makes the role creep problem worse. Dasha says her leadership update focuses on pipeline because AEs need it to hit their number, while her marketing team reviews traffic and share of search.
“there's the operational and tactical, site elements, which absolutely should be discussed inside of the marketing department”
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He says the $4.5BN model assumes substantial deceleration, which his quant-minded finance team says is how the business works. The company is still pushing to grow at least 30% month over month. He bases his higher view on monetization-driven adoption by DTC brands and on Hollywood sentiment shifting toward using AI as a hybrid-production tool, which he describes from private conversations.
“Our current business model projects 4 .5.”
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Mashrabov says social media ad production doesn't need 'PhD level intelligence' and favors cheaper, more steerable models, which can also be more cost-efficient for customers. As agentic ad workflows grow, Higgsfield chooses the model in over 40% of cases. He calls this optimization of token count and token cost 'tokenomics'.
“The margin on own models and open weights models is over 80%. And then it almost doesn't matter. And for close source models, it's probably between 20 and 30%.”
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On the day of recording, Bloomberg reported that Higgsfield had crossed $1BN in annualized revenue. Mashrabov says this probably makes Higgsfield the third fastest to that milestone after OpenAI and Anthropic. He says image models for aesthetic photoshoots and product consistency took the company from $20M to $100M ARR.
“actually it took us 18 months from one million to one billion. For Cursor, it took 24 months.”
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Mashrabov says he believes OpenAI, Anthropic and others use the same four-weeks-times-13 method. Higgsfield counts revenue, not sales: annual subscriptions and enterprise contracts are pro-rated so only the 28-day share is counted. Only live revenue is included, and multi-year enterprise deals are not baked into the $1BN figure.
“What we do is we look revenue over the last four weeks and multiply it by 13.”
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Sam Jacobs offered this explicitly as a straw man for his co-hosts to react to. Above 200% growth he will accept terrible margins temporarily, for example to subsidize usage or win market share, but only if the company shows the path to a good business. Between 100% and 200% growth he wants a clear improving margin trajectory, and below 50% growth he needs margins to be good. He did not specify a rule for 50–100% growth.
“Above 200% growth, I can tolerate bad margins temporarily. If you can show me, you have a path to a good business.”
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Sam Jacobs said Harvey's cost to deliver $1 of revenue went from 50 cents to $1.50 as agents consumed tokens on behalf of human users. Harvey was growing extremely fast, but every dollar of revenue was destroying gross profit. He said Harvey's path out is training and hosting its own models, including open-weight models, and moving away from frontier models from labs like Anthropic and OpenAI.
“by June, Harvey's gross margin due to rapid, agentic Adoption right agents going out and consuming tokens on behalf of their human users Harvey's gross margin had fallen to negative 50 %”
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