Strategy & market

Pavilion, formerly Revenue Collective, originally had strict membership requirements and an application process. After it opened sign-up to everyone, the company moved away from its focus on go-to-market executives and let in CEOs, and Sam says the impact of those decisions was still being worked through years later.

“we made it so that you could sign up online without talking to anybody and we let in so anybody can join. That's the single biggest mistake we've made.”

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Evan said early customers were largely startups and growing companies, where churn was higher and product-market fit was weaker than with mid-market and larger companies. He said the mid-market and enterprise segment shows much higher net retention. The company then honed its targeting toward that segment.

“obviously much higher net retention”

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Snehal said focused customer numbers could look small in the early days, but would compound, build the company's story and help build moats. He said that without a proper thesis, pursuing every customer could confuse the company. He added that focus would have saved Outdoo maybe the first six months of the year.

“Just going and going after everyone. without a proper thesis would essentially confuse you, could confuse you.”

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One host says tech founders often believe everyone will buy their product and that, while eventually everyone may, early on they have limited money. He recommends building an ICP and identifying which customers the product's current capabilities will deliver the most value to. He says founders who spread across customers from JPMorgan Chase down to small startups burn through money and then have a hard time getting another round of funding.

“build an ICP and try to figure out which customers at this point and the capabilities of your product are going to deliver the most amount of value to those customers”

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Shipley describes a case he did the week before of a CEO who joined a company that had been operating for 20 years and reached $8M in revenue. The company went from about 30 ICPs to four and moved faster, and reached $30M in three years. Shipley presents it as counter to the instinct that a larger total addressable market is always better.

“They went from like 30 to four and then moved faster.”

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McMahon describes building an ICP of customers that can buy, then cutting it by propensity to buy using factors specific to the product, then cutting by sales complexity, especially in startups. As an example, he says that for a small startup JP Morgan Chase and Morgan Stanley might top the first list and maybe two would survive the propensity cut. The complexity cut would remove such accounts because the startup lacks time for 120-day contract negotiations.

“I don't have enough time right now to spend 120 days negotiating a contract.”

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McMahon says technical founders often believe everyone will buy their product, so they pursue customers from JP Morgan Chase down to small startups. He says they burn the money and then have a hard time getting another round of funding. He advises deciding which customers the product's current capabilities will serve with the most value.

“They get burned, they burn the money, and then they have a really hard time getting another round of funding.”

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Dan separates product-market fit, where customers are happy and expand, from message-market fit, where the company wins new logos. He argues most teams define their ICP by where they win, when it should be defined by where happy customers will drive future growth, and then training sellers and improving marketing to raise win rates in that segment.

“I'd argue that really what we should be doing is defining our ICPs by where we have the happiest customers that will contribute to the future growth of our business.”

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G2 listing is free and 210,000 companies are listed across about 2,000 categories, with traffic unequal across categories. Eric said the team is not large enough to chase that many prospects, so the goal is to make the target smaller. He also said BDR coverage did not match the stated priority: a BDR leader who named enterprise as the top segment had only one BDR on enterprise, ten on SMB and fifteen elsewhere.

“we don't have a team large enough to chase 210,000 prospects”

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Mark said that the two common answers, where the most inbound demand is and where CAC is lowest, are both brutally wrong. He said ICP should be correlated with the highest LTV, meaning the customers who succeed, want to retain and buy more. He said the business should then work backward to acquire those customers profitably.

“It should be correlated to where you have the highest LTV.”

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Roberge says an ICP segment is one where customers see tremendous value, renew, expand and tell their friends. He notes this is sometimes not the highest inbound demand to the website and sometimes not the easiest or lowest-cost-of-acquisition segment to sell to.

“It really needs to be that the ICP is based on segments of the market where when people buy it, they see tremendous value, they renew, they expand, and they tell their friends.”

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He says founders put one rep in each major US city instead of focusing where the ICP is. If ICP analysis is done, he says it may show the company could put three or four reps right in the Bay Area, and if that is where the ICP is, that is where the company should stay.

“They have to put one in every major city in the United States.”

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Mark reacted to Andy's comment about not turning anyone away. He frames the ICP as having an engagement dimension: a green tier of A-plus fits you build email lists for and cold-outreach, and a yellow tier you aren't sure about but will sell to if they come inbound engaged. He says Andy's stance is probably a slight exaggeration and advises against accepting everyone. Some customer types will pull you down and add noise to your roadmap for a product you didn't build for them.

“This is my perfect green where I'm going to go and proactively sell to. This is my yellow where I'm not quite sure, but if they're engaged in inbound, I'll do it.”

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Dan said best-fit customers typically represent a small subset of the install base, and the ICP's purpose is to focus the organization on the accounts that will drive the business forward. He said that for demand gen teams with a $3 million budget to create great leads, the ICP typically fails them because it doesn't give them the information they need, so they end up leaning into quantity over quality.

“so your ICP is the bullseye of the dartboard.”

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Dan said that companies that haven't done this analysis will likely find fit in segments they never pursued. As an example, they may have organically acquired 35 to 40 customers in a segment they weren't proactively targeting that are growing at a healthy rate, being retained and giving positive NPS scores. He advised doubling down on these existing customer types rather than entering an adjacent market to find fit.

“you've organically acquired like 35, 40 customers in a segment that you haven't been proactively targeting that are growing at a very healthy rate”

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Dan said most founders who want a different ICP can't say what share of their market they have already penetrated. He suggested starting with the existing customer base and looking at customer lifetime value, retention and net revenue retention to find where fit is strongest before going to adjacent markets.

“Start by looking at your customer base and saying, you know, of our 500 customers, where do we have the strongest product market fit?”

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Mark Bersh argues Lauren Nemeth's ICP logic was about where people are seeing the most success and generating the biggest ROI, which he calls the value creation endgame. He says this points to product-market fit. He suggests plotting customer size against customer industry on lifetime value and customer value to decide where to scale.

“She said where people are seeing the most success.”

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Lauren Nemeth said Pinecone stopped trying to sell to everyone, since sales teams running in 50 directions is flawed. Based on evaluations of existing customers and conversations with partners such as Anthropic, she saw interest consolidate around ISVs and software companies. She said a vector database that prides itself on scale, high throughput and data freshness needed to focus on those major workloads.

“You'll see sort of a consolidation of interest really around ISVs or software companies today”

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Mark says a founder need not try to serve every region and industry at once, and that it is rare that boiling the ocean works. He suggests picking perhaps five cohorts, seeing which respond, then rerunning the test with only the strong cohorts. He says the early goal need not be a billion dollars in revenue, only getting to a meaningful number fast with high conviction that the segment can be expanded later.

“Maybe there's five different cohorts and let's see what happens.”

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Roberge respectfully adds to Bosworth's approach. He says teams often define ICP by close rates, CAC or inbound flow, which can produce a churny business. He says sometimes the highest-LTV accounts do not have the lowest CAC, and an ICP built on exceptional LTV segments lets you afford a higher CAC.

“sometimes the highest ltv accounts don't have the lowest cac and if we're building our icp based on where our inbound leads are coming from or where our close rates are higher you can end up with a very churny business”

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Mike said G2 had over 95,000 software companies to pick from, so each team would reach different assumptions about where to start. He said the better approach was to rank specific categories, for example category one through 2,100, to decide where to focus time and energy. He tied this to what G2 could sell and support at each package level.

“we have over 2100 categories we track. So like what's category one through category 2100”

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Asad's example is that a company could spend a million dollars of compute to distill a complete replica of a product like Granola. He concludes that much more product will be needed to win a category. The other hosts called the argument 'smart sounding' but wrong.

“a lot of things that are standalone companies will put on features and the amount of product you're gonna need to have. on a company to be able to win a category is going to just be that much more.”

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Keith said a good sequencer can now be built in a couple of weeks, and so can good forecasting tools built with frontier models on top of great data. He still can't imagine Lightfield doing everything in revenue, because he sees that as infinite and says some companies will endure.

“So I think that that layer, the sort of Clari Outreach, that layer I think will probably be sucked into the system of record.”

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Lightfield's ICP is high-growth tech companies, and Keith says they all want best-of-breed. He asks whether best-of-breed means every Outreach sequencer feature, or a system that understands the customer and sales process well enough to do the best agent work. His analogy is Microsoft Office: Teams and PowerPoint may not be the best individually, but shared data and directory access make the suite work.

“I think the bet that a lot of people are making now is actually if you have If you have the best modeling of your customers and your prospects, that makes those other features better than the points.”

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He points to restaurant software winners such as Toast and contractor software such as ServiceTitan, and notes that a restaurant runs more than one piece of software. He calls this a why-now argument for building new products into existing markets.

“you're better off going really deep and try to own the entire operating system, the tech stack of that business.”

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He says Clari was known for forecasting and Salesloft for engagement, and each has since expanded into the other's area. He argues that owning the workflow matters, but that a company must also be strong across all of it, not just one part.

“if you only solve one problem in the workflow you won't keep you in the room.”

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Cassie Young says she is sceptical of vibe-coded DIY CRMs, which she thinks will work in mid-market and enterprise until the first major security breach. She sees extreme risk for classic point solutions, and notes that most startups begin as a point solution or wedge, so they must accelerate the product roadmap to avoid being replaced.

“where I think there's extreme risk in that category are like the classical point solutions.”

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Cassie says full-platform plays are more compelling than partial-platform plays, which now feel more like features, because an end-to-end platform does the work that point tools did. Separately, she says buyers like having a throat to choke for security and governance, which makes her less worried about DIY replacing platforms. She says eight months earlier she would probably have backed a company solving one part of the problem, but now she expects buyers to adopt it for year one and then build it themselves.

“in these full platform plays versus things that even looked like partial platform plays now feel more like features”

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Cassie contrasts a classical wedge play, where a company takes one clear wedge with a broad platform vision, with a compound startup approach that spans the whole customer lifecycle. She says you have to take the compound approach to take share, and that a sales-coaching company building an invisible CRM is a harder path, which is her personal conviction.

“A native CRM like you kind of have to go at it from the compound startup approach if you're really going to take share”

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He described the products as sharing the same data and intelligence substrate, so the AI assistant and AI agent draw on the same workflows and knowledge base. When asked about timing, he placed it in the range of 10 to 30 million dollars of ARR.

“So there's just a lot of synergy and compounding value to build a multi -product platform little early.”

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He said there is too much competition for a point solution because every incumbent can build that feature, and that the sales cycle necessitates playing the platform game. He framed this as a meta observation about the post-AI market.

“you can't win as a point solution. There's too much competition. Every incumbent can spin up that feature.”

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She argues that point solutions focused on features are not true platforms of transformation, and that the embedded systems in organisations are innovating faster and releasing similar functionality. She says customers may then decide they already have that functionality. She says she is curious what will happen to these new products. Josh adds that some great companies will still emerge.

“they're point solutions, not platforms. The platforms are catching up.”

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He said these pillars are the North Star for everything, including the product roadmap, and that every AI initiative has to link to them. He said anything that cannot show a material impact on one of them does not get off the ground in the BRD process, a discipline he learned from a mentor at eBay who asked what the change would do for the customer and whether it was material.

“So in our particular case, it's quality, speed. cost and ease of translation.”

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Kropp described a phase where large companies ran about a thousand AI initiatives, mostly chatbots, which got employees hands-on but did not change the business. Companies are now moving to a few big rocks, meaning three to five focused areas with a senior sponsor and an objective tied to overall corporate goals.

“But it didn't create any value.”

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He said he had pulled these together in the week before the recording, currently in a PowerPoint, and said they need to live in a dashboard the team can share. He said he did not know what his extended team was doing with AI day to day. The aim is to spot overlap, such as people on different teams working on similar efforts, and pair them so they can combine work.

“I pulled together at least the you know the 65 to 100 initiatives that our three organizations are working on”

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Her one-on-ones with the leaders of support, customer success and professional services showed them describing the same AI builds. Directors and managers were building matching tools across the three organizations, because leaders had told everyone to learn AI and go faster. She said without someone to pull it together, the organizations would keep going down three siloed paths.

“there was no inventory of all the good AI goodness that was going on within our organization”

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Cassie Young says many organisations are trying dozens of AI initiatives without a clear owner or objectives. She compares this to the 2006 Yahoo peanut butter manifesto, where everyone spreads themselves thin across many things.

“one of the downsides of letting everybody experiment, do you know what I mean? Is you just end up without real focus.”

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Rob said his team is working on an AI roadmap that prioritises which tools to build or use. He asked that any agent built be tied to an OKR, since Ironclad uses an OKR model. He said it is easy to start building agents, but the business still has to run, so the team needs to focus on the right areas.

“If we're going to build an agent, let's tie it to an OKR So we know it's, it's moving a needle somewhere”

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Pablo said the MIT and McKinsey reports are both accurate, but that the technology works. He said the fundamental issue is that there has not been a strategy executed on AI, and that companies spread AI initiatives thinly across many departments and sub-departments.

“The tech works. The fundamental issue is one, there hasn't been a strategy execute on AI.”

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Ann says the hit to Crunchbase's revenue was anticipated, and the question for SaaS companies is how quickly they can define a go-forward strategy. She says those that think they are too important to lose users will face problems.

“Anyone in the SaaS software business that's not seeing a hit to their revenue from AI, I would be surprised.”

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Ann says Crunchbase took a hit to its own revenue from AI, which it had anticipated, and that she would be surprised if any SaaS business was not seeing one. She says the SaaS companies that are fine will be those attached to AI companies in some way.

“We've already got our MCP on the front end and that's the new SaaS.”

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He says companies that sit across an organization as the blood and guts, rather than as systems of record, will likely have a longer and harder road and will be disintermediated by new companies. He points to Sabre, which he says still exists as a shell of its former self, as an example of how long that decline can take.

“there's going to be this long tail of these other companies”

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Marc says he would have said he was still very bullish because he still uses both products, but after using the latest OpenAI image models for recent content, he would answer differently. He says that unless Figma and Canva develop proprietary technology, or become the publisher of the next great image model, they face trouble, even though the products are beautifully crafted and founder-led.

“Unless they can come up with some proprietary technology, unless they are the publishers of that next great image model and not OpenAI and not Gemini, then I think there's some trouble waters ahead here because they're beautiful products.”

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Sam says these companies traded multiple times on the way to around 200 to 300 million in revenue, and founders then left and brought in professional managers with no native sense for AI. He says this leaves technical and cultural debt, and that private equity ownership makes it worse, so product innovation and vision are unlikely. He contrasts companies where the founder remains, such as Salesforce with Benioff and ZoomInfo with Henry Schuck.

“And in so doing, the founders peaced out and they brought in professional managers. And the professional managers don't have any native or intuitive sense for what's going to happen with AI for the most part.”

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Kyle Lacy said Lessonly in the current market would get eaten alive, because its software was simple to use and did not have a moat. He said what still works from that era is the customer-first culture and the storytelling, which were built around the customer and part of the company's culture.

“Lesson Lee in this age would get eaten alive our software did not have emote it was really simple to use you could probably vibe code it down a weekend.”

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Queener says he had AI agents analyze whether he was right enough to deserve writing a new piece, and the first half of that piece reviews how the moats he expected have changed over the last 18 months. He frames the prediction as a bold call he made 18 months before the episode. The claim is his own assessment of his prediction.

“I thought I was prescient like I wrote 18 months ago, that 75% of all customer facing software companies would disappear and 50% of all categories would disappear at the same time.”

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Snehal said Outdoo received many leads that did not lead to attraction, and that buyers who were just using experimental AI budget did not commit later. He said the team needed customers who found the product essential, and that such customers may look like small numbers early on but compound over time.

“not just because they had some experimental AI budget and they wanted to try out few things and they just did not commit later on”

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Steve Cox says many AI-native companies come across his desk as acquisition targets. He describes them as having a similar profile: they went to market, grew to two, three or four million dollars of ARR quickly, and then struggled with retention after the first funding round.

“the amount of AI native companies that come across my desk now that you know are up for sale you know they've run out of funding they've done the first round and when you look at them they all have a similar profile which is you know they went to market you know they grew to two three four million of ARR pretty quickly and they've struggled with retention”

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Trae Stephens said the enterprises that spend meaningful dollars on SaaS will not multiply their spend because there are more vendors. He said they will tighten up and maintain discipline, and that he feels there is a market imbalance. He described competition as the killer of opportunity in Silicon Valley and said there is more of it now than ever.

“They're not going to spend exponentially more on SaaS because there's exponentially more enterprise SaaS companies”

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He said these platforms' retention data may look like tools people use once and never again because there is no clear use case. He said there will be a reckoning around the term ARR, and the vibe-coding platforms will be put through the wringer.

“one of them will go out of business and shut down.”

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He attributes the term to Cassie Young and Kyle Poyar, whom he spoke to. He says companies have been playing with many AI tools as a fun playground because they see productivity gains, and that this tinkering has led to bloat. He expects consolidation to be greater than before, driven by budgets and by the tinkering itself, not only cost cutting.

“there's like this apocalypse that's going to come”

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She says companies are consolidating for two reasons: to cut costs and streamline budgets, and to open budget for new software aligned with their AI initiatives. She says people are rethinking the technology they already have in front of them.

“I think you're going to see a lot of consolidation around tech stack”

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He notes that VCs love to ask about long-term moats. He now thinks most moats are ephemeral, especially while model capabilities are advancing quickly. In his view, advantage comes from moving quickly and adapting to an ever-changing 'chessboard'.

“I think what I've changed my mind on is most moats are illusion. Most moats don't exist. Most of them are ephemeral”

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Stebbings says he was mocked for investing in Lovable because it was 'a wrapper', and concedes it was one. He argues that wrapper companies win by building very valuable features very quickly and compounding that over time.

“it's about speed of decision -making, product execution, and building value over time very, very fast.”

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Josh describes a conversation with the founder and CEO of Zora, who is transforming his whole company but says engineering matters most because it keeps him ahead. Josh says this reminds him of what Ziv said about Gong: a company that stays far ahead and efficient in what it builds will keep a moat.

“if companies can stay so far ahead and be efficient with what they're building, that will continue to be a moat for them.”

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He says moats he once thought would let a company win a game have become the price of entry. He gives a basketball analogy: being fit gives you the right to play but does not win the game. He presents this as a shift he and others have experienced over the last 18 months.

“They're just table stakes. So you don't have them, you can't play, but they're not a right to win”

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He argues that quickly changing product and code gives leverage to take advantage of the latest technology as it is built. He says this matters more than ever because the pace of change is so fast. He presents it as his view of what now matters most in building software.

“So the new currency now, and well, I guess it's always been, but it's like it's more important than ever before is how fast you can adapt, change, change your product, change your code.”

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Schuck said he believes most companies, perhaps nearly all, differentiate on how well they understand the customers they build for and how quickly they can ship software those customers value. He said a few companies get a moat, usually from a network or flywheel effect or a contributory data asset where every new customer strengthens the product. He said he would invest in building a moat once there is clear conviction about how it will be created.

“And I think like for most companies, maybe all, just about all companies, your competitive differentiation, I believe, is how well do you understand the customers you're building software for?”

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Stevie said every investor asks what protects a business, and she is a big believer that strong execution is the only real moat that can be built in many cases. Mark partly disagreed: he said you don't always need a moat and can outpace competitors without one, but a moat is a huge advantage if you have it, and pointed to Porter's barriers to entry.

“strong execution is the only real moat that you can build in many cases.”

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Brad says pulling the inner workings out of products that underpin Fortune 500 revenue is incredibly painful, so he expects replacement to take much longer than the disruption thesis suggests. A host added that companies typically bought at least five packages to attach to Salesforce and wrote their own code in each department, and Brad agreed that ripping it out would kill the patient.

“everyone can't vibe code their own salesforce and those systems of record are going to be pretty sticky.”

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He said upmarket he is not seeing large systems of record such as ERP or core CRM being replaced, but seeing them extended with better interfaces or dashboards built on Replit. Downmarket he sees customers building their own CRM on Replit instead of buying Salesforce. Internally, Replit wants reps not to touch Salesforce, with everything built on top automating it for them.

“the systems of record are staying there but what we are seeing is we're seeing the extensibility of the systems of record”

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He said he remains very bearish on companies switching away from systems like Salesforce or CRM, even as switching between AI models is easy. He said he does not believe customers will switch CRM systems, citing distribution, data and the trust companies have built with their customer base. He said he is not seeing switching happen in practice.

“I don't believe anyone's going to be switching CRM”

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Ghazi said that at a large enterprise, billing, finance, accounting, true-ups and delinquencies are tightly integrated with the front office, so a vibe-coded replacement would require rebuilding all of that plumbing. He said it could be done, but the question is whether to invest that time, and he sees it as a large uplift for organizations that have run for decades. Kyle added governance, security and stability as further heuristics for buying.

“if you're a big enterprise organization like that, you're not building your own CRM.”

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Ghazi said Replit moved from HubSpot to Salesforce and decided not to build its own CRM. On top of Salesforce, the revenue team uses internally built Replit apps such as a revenue co-pilot dashboard, customer health, CPQ and forecasting, and he said the extensibility is what matters.

“Salesforce is our system of record.”

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AJ Bruno says the delightful alternative Asad Zaman described and a system of record are very different use cases. He says a database is not set up to delight anyone, and that is acceptable because a system of record simply does what it needs to do. He says he does not want a Replit version of his CRM.

“And the delight, I'm not sure a database is ever set up to delight anyone. And that's, and that's okay. It's a system of record.”

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Liz says she still believes in the system of record, and acknowledges that people waffle back and forth on this debate. She says a company that is a system of record can be strengthened by AI and build a deeper moat.

“if you are a system of record you could be excuse me strengthened by AI and actually build like a deeper mode”

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He says large enterprises are too big and have too much invested to get rid of their ERP or Salesforce-type systems. He says he sees a lot of wrappers and additional capability built on platforms like Replit to make those systems more agile, with separate dashboards pulling data from Salesforce and other systems.

“You're not going to go get rid of their ERP system or their, you know, their sales force system or their CRM system.”

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He treats the ICP as parameters against which an account can be scored, informed by deals won and lost, and refined about monthly. Examples of disqualifiers include an account that has just bought a competing vendor, or a mid-tier bank just announced for acquisition. Backbase retuned its ICP after acquiring Casisto, an agentic conversational banking company.

“So an ICP to me is nothing more than a set of parameters against which you can score an account.”

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Alex says his team tracks the flow of capital and raises, pulls data from multiple sources, synthesizes it, and constantly updates top target lists. Scoring and market signals move accounts into tier one, which takes most seller time, and so change how sellers prioritize their pipeline cadence.

“We're tracking the flow of capital. We're tracking raises.”

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Adrian said the job combines Webflow's messaging and positioning doc, historical customer call transcripts and Slack activity, plus frameworks he borrowed from others. The output includes anti-ICP patterns, ICP patterns, customer quotes and jobs-to-be-done, and other agents use it. He can compare this week's file with last week's and look back a month.

“And so it's on a cron job. And so every week it just makes a new file and you can see the drift from the previous one.”

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Mark said the best organizations today may have had their ops team re-run ICP once a quarter using four or five segment attributes. He said AI could allow this to happen hourly across an effectively unlimited number of attributes. He said ICP can shrink because of new competition or economic conditions, or expand because of product releases.

“We can do it hourly on an infinite number of attributes.”

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John says Snowflake could initially only sell to tech and ad tech companies. As feedback shaped the product and new differentiators solved other pains and use cases, he says the ideal customer profile was continually updated.

“What we found initially is that we could only go to tech companies and add tech companies.”

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Asad Zaman attributes this to Eric from Benchmark and says it describes the problem SaaS companies face when hitting their numbers keeps them from changing. He says the bigger the company, the harder it is to burn down the existing model. The view is that most SaaS companies run AI work from five to eight after their core business from nine to five, when they should work the other way.

“every day that you are hitting your revenue targets as a SaaS company, you're destroying enterprise equity value.”

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Jaleh described trying several playbooks, including having sales sell only the new product and making the company more founder-led, and judging each by whether the company was moving as fast as possible. She said the team did not feel it could move that fast while splitting focus, so it spent about three quarters on trials before the November 2025 decision.

“so that took about three quarters. And then I say the last quarter was when I started to come to the decision and we rolled it out. So November of 2025 is when we made the decision to fully deprecate the existing product.”

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Mutiny initially planned for its existing SaaS business to fund the AI build. Jaleh said the zero-to-one phase is a benevolent dictatorship, while a scaled business depends on delegation, autonomy, goals and OKRs, and the two modes conflicted. She said the team was surprised when the CEO started making all the calls and sometimes pivoting on decisions already made, which is really bad to do in a scaling phase.

“The zero to one journey is a benevolent dictatorship. And running a scale business is all about delegating and autonomy and giving people goals and OKRs and planning and things like that.”

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Kellie said the decision was made before she joined about eight months ago, and the rebuild took two years of development. The new platform is fully agentic, so the AI does the work on a customer's contract assets rather than only managing files and workflows. She framed the choice as a bet that the company needed to rethink its solution space.

“they made the tough decision, right, which a lot of tech companies have a struggle with, or they wait. way too long right”

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Sam Jacobs links the Intercom pivot to the innovator's dilemma. He says a company has to be willing to go after a new direction even if reported numbers fall, and that the payoff may show up in a later year's story rather than the current one.

“even if my numbers go down in the short term”

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Sam Jacobs says Intercom gave up about $60 million of contracted seat-based ARR to back Fin. The company moved its marketing and energy to Fin and staffed the team building it, which he describes as pivoting the whole organisation rather than a single product.

“I think he said he gave up 60 million of contracted seat based ARR. They moved all of their marketing, every bit of energy, they staffed the team that was working on Fin.”

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He pointed to roughly a century of market history and to operators such as Berkshire Hathaway and Liberty Media. He said a business can grow only 5 to 10% a year organically and still generate a lot of cash flow, which funds further growth. He framed compounding as the source of most value creation.

“if you compound effectively, you will out return these super high growth stories, unless those super high growth stories eventually become compounders.”

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Karri Saarinen says Linear has valued staying alive as long as possible higher than increasing momentum or speed, and compares the approach to founders who play high-stakes games. He says the company reached default alive within two years of starting, never really spent the money it raised, and grew profit each year even while spending more. He says it was never a goal to maximise profit, and that the company can control its own destiny without worrying about the next fundraise.

“we kind of like valuing that in some ways higher than like just. increasing the momentum or the speed”

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Sam said the alternative to the fast growth model is a company intended to last 20, 30 or 40 years, with a machine designed to generate predictable cash while meeting a high standard of taste. He said he is building this kind of company himself.

“There's a different way to build a company that is a craft business.”

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Sam said SaaS companies in the middle, growing 30 to 50%, need a path out. That path is to become cash-flow positive and, whether or not they are valued at 10 to 15 times ARR, build a cash flow stream that could be paid back to shareholders, making the business more like a regular business.

“You're out of choices if you're not growing at AI rates.”

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Roberge says venture funding brings an expectation of a blitzscale plan. Two to four years in, he says, it works out about 10% of the time and fails about 90% of the time. The problem is that founders don't change the go-to-market system design even when it's clear they won't be a $10B company. He claims that switching to Rule of 40 to become default alive gives a better than 50% shot at returning hundreds of millions of dollars to founders, employees and investors, or at getting back on the unicorn track, and cites Henry at ZoomInfo as an example.

“Even though it's very clear that we're not a blitz scale, that we're not a grow all cost, that we're not going to be a $10 billion company, we still apply the same go -to -market system design and approach. Wrong. We got to shift.”

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Schuck described a hypothetical 25 to 50 million dollar staffing firm with a 40 to 50 percent cash flow margin, which he said would produce about 12 million dollars a year in cash flow on a 25 million dollar business. He said he was 100 percent sure he could build one, and argued that this avoids the rat race of VC-backed competitors constantly trying to disrupt your business.

“then just build a business that has low equity value, but high cash flow value.”

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She said it is common for these functions to roll up to three separate leaders, which produces overlapping strategies rather than one integrated experience. She has repeatedly seen support and sales use different segmentations, which she considers unworkable, and has seen marketing spend misaligned with where sales puts people. Owning all of it makes aligning them her problem.

“one of the things companies don't nail is the human-based customer experience is a product just like the actual product.”

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Amanda Kahlow believes the go-to-market functions held in silos will collapse together within roughly 12 months. In her picture AI handles research, communication, onboarding and building for customers, while humans manage first touch through close, renewal and upsell and act as a checkpoint on outcomes. She presents this as her belief about where the roles are heading.

“in 12 months from now, I truly believe there will be a collapse of like the go -to -market functions and silos, like marketing, sales, customer success, they're all gonna collapse together.”

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He reasons that growth comes when a company delivers on value for its customers, so the whole customer lifecycle should be mapped across the organization and people aligned to it. At PartsSource he has taken on the growth side of the role over the last nine months, with the aim that everything done pre-sales aligns with onboarding and retention after the sale.

“growth really comes when you deliver on value for your customers.”

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Jo Massie said she owns everything customer, including marketing, customer success and about 90% of sales. Her reasoning was that digital and marketing overlap heavily, and that product usage drives customer acquisition, so the functions fit together. She said the combined digital team can now operate much faster than when its work was split across teams.

“At some point digital and marketing become very closely intertwined.”

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AJ Bruno says QuotaPath has no choice: without this move it becomes a forgotten SaaS tool, and SaaS tools are not even getting 1x multiples right now. He wants QuotaPath to become the premier AI-native services company, creating a category the way Gainsight once did. He sees Gainsight and QuotaPath as non-competitive allies in getting buyers to accept the new model, and says the old SaaS mentality must change for companies to survive.

“if we do not make this move, we will be a forgotten SaaS. Tool like everyone else and no one no one cares and you're not even getting 1x multiples on SaaS tools right now”

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He frames software as a means to an end, like the wheel, and says selling a tool makes less sense when AI does the work. He puts it as wanting to sell the fish rather than the fish pole.

“We're now in a world where AI can do much of the work itself. And so selling a tool doesn't make as much sense anymore.”

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Chuck Ganapathi says the role of agentic software should be to deliver the customer's outcome, not to hand the technology to customers to execute. He says Gainsight launched Atlas, an AI-native renewals service that it sells as a managed service, and that Gainsight is no longer just selling software.

“We believe that's going to be the future of software.”

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Chuck Ganapathi says that in SaaS the ROI from a project has to be owned and delivered by the customer, and the vendor only supplies the software. He says most software companies have always found it hard to prove their product, and that this is why Gainsight is moving into services that deliver outcomes.

“the ROI has to be owned and delivered by the customer themselves”

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Asad Zaman contrasts the move with trading one house for another, where the old house still exists. Here, he says, the better SaaS model will not remain an option and will depreciate over the next couple of decades, if not within the next decade. That makes the harder agentic model possibly the only game in town. Sam Jacobs agrees.

“it's going to be a depreciating asset over the next couple of decades, if not just within the next decade”

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Drawing on 12+ years selling MongoDB to developers, Dev Ittycheria said developers are quick to jump to the 'shiny new toy'. He used this to explain teams shifting meaningful usage from Anthropic to OpenAI's Codex. He framed the open question as what Anthropic ships next and what alternatives emerge.

“having sold to developers the last 12 plus years is that there's not a lot of loyalty. Developers are very quick to use to switch from one tool to another or frankly use multiple tools”

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Asked what he is long and short on, he said he is short on API lock-in and long on OpenAI's consumer ads opportunity, which he says has not been fully used yet. His reasoning is that labs need hundreds of billions in annualized revenue, and he only knows of ads and great hardware as businesses that reach that scale. He believes OpenAI's consumer business may keep it afloat whatever happens at the API layer.

“one thing I'm long is the consumer ads marketplace on OpenAI which has obviously not been fully utilized to this day, but I think remains one of the biggest opportunities in AI”

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Angelopoulos says Anthropic's API revenue has been booming but is 'easy come, easy go': anyone spending that much money that fast is able to switch vendors. He calls it 'totally feasible' that within the next couple of years an open-source model does 90% of the work at 10% of the cost, and that some businesses could have all their needs met by a 'mini lite' version of the next Kimi. He calls this a structural problem for the labs.

“People are assuming a degree of lock in the APIs that I don't think is necessarily going to exist. Anthropic revenue has been booming, booming, booming on the API. But to some extent it's also easy come, easy go.”

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Tunguz said that when a model company releases a state-of-the-art model, it has about 41 days to commercialize it before it is knocked off the top. He said individual users can be kept for a while, but they will move on to the next model. He framed this as the current state of the art rather than a fixed rule.

“And so that's kind of the state of the art today. You literally have 41 days.”

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Ann says a software company with valuable data can license that data to enterprises for their AI front ends, so revenue from data may rise even as seat-based software revenue falls. She says this is the strategy vendors should be working out now.

“You can have a way to license the data within that software application and have another revenue stream.”

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Ann says Crunchbase recognised before she joined that AI would likely take a large part of its business, so it prioritised datasets that competitors could not copy. She says the second part is supporting the new solutions being built on top of its data through a data distribution partnership strategy.

“the best way to do that is to develop proprietary data sets that they can't replicate.”

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She describes Red Canary as a data business, like ZoomInfo or DiscoverOrg, whose differentiator was how it ingested and normalised data to build predictive models before agents existed in the software. The deal was announced at the end of May and closed in August, and she says AI changed a great deal in the year after.

“more of the value the potential for AI agents based on this unique data asset that we had”

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Sam Jacobs describes a call with the president of this business. He says it is growing faster than last year and is on track to reach $300 million, which he attributes to AI analysis of datasets it previously could not analyse effectively.

“AI has given them the ability to analyze data sets that they previously had access to, but had no ability to effectively analyze.”

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Matt said the core problem is building a product well positioned for enterprise accounts, selling to them from the start, and building something valuable enough to command a $30K-plus check. He contrasted this with TrendKite, which sold many small deals and ended up with customers whose businesses and demands differed from enterprise companies. He described it as a deliberate choice to skip the SMB trap.

“how do we sell to those enterprise accounts day one and build something that's valuable enough where, you know, we can demand a, or command like a, you know, 30k plus check from those types of companies?”

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Bret Taylor said the company's thesis was that a lot of its impact on the economy and the world would come through the largest companies, so it set the Fortune 100 as its ideal customer profile. He said serving these companies requires people who understand legacy systems, mainframes and regulation, which is why parts of the business were staffed with experienced people. He called starting at the top of the market very intentional and said it is unusual for a company that has been in the market for two years.

“we started kind of at the top end of the market, which was very intentional.”

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Mark says founders traditionally jumped to the enterprise too soon. They assumed a big logo would bring others, underestimated how long a million-dollar deal with Goldman Sachs takes, and ran out of cash first. He pushed them toward smaller companies, partly because you can't walk into Goldman Sachs with a buggy first product. He says that is still somewhat true, but that 'maybe' going to the enterprise sooner is the answer in AI, a pattern he sees at Writer.

“But eventually you go to the enterprise, maybe you go there sooner, and that becomes the answer.”

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Manny sees agent companies solving very narrow problems in very narrow industries. He argues that owning the whole niche makes them very profitable even if they aren't huge. He cites Veeva, which does a few things only for pharmaceuticals and turned out to be a huge business.

“So they're solving very narrow problems in very narrow industries.”

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Responding to OpenAI's announced product for law, Stebbings calls it 'complete bullshit', citing the very deep functionality needed to serve the largest law firms. He also points to multi-year sales cycles with partnerships. Mashrabov replies that general tools for internal teams outside law firms are definitely happening, and both cite Solve Intelligence as a specialized winner.

“if you want to sell into these law firms, it's a multi -year sales cycle”

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Jean said the company serves not-for-profits, local government, professional services and higher education, and generally avoids large enterprise deals. He said Unit4 rarely bumps into SAP or Workday, except sometimes in North America. He said this focus makes customers stick because they can do interesting things on the platform.

“we focus on the mid -market. We don't really play in the enterprise space.”

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Aman said Toast's view is that the future is vertical, and that for each sub-vertical the company builds capabilities brick by brick. He said that however much innovation sits on top, operational complexity and workflows must be solved first. He used grocery stores, bottle shops and other retail categories as examples.

“we believe the future is like vertical. It's not horizontal.”

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Aman said the early customers included food trucks, cafes, quick-serve, full-service, fine dining, a bar and a nightclub, and Toast built for those edge cases on purpose. He described the company's branding as built around the thousand little things that make restaurant tech what it is. Aman presented this as a deliberate strategy, not a lack of focus.

“And I think that was a deliberate strategy.”

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Angelopoulos says much of the company's future is putting 'an arena' inside every business. Companies would use their own organic usage traces to learn which models are best for their users and employees, measuring whether people get jobs done and CSAT-type satisfaction. He says buyers don't know how to define performance, which is 'very mushy' and not like CPU benchmarking. The data would also power company-specific routers for vendor independence and AI sovereignty.

“People don't know how to make the trade offs in performance and costs. They don't even know how to define performance right.”

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Josh says Gainsight's strategy is to meet people where they are and build the best-in-class MCP, but not forget the value of having a similar feature set and rich functionality within its own platforms. Otherwise, he says, everyone is completely beholden to the group of AI players behind the LLMs.

“we got to kind of do both we gotta meet people where they are and build the best in class best in the world mcp but not forget that there is a value to having a similar feature set and rich functionality within our own platforms”

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He described the next couple of years as an infrastructure-build period in which a technology bet could become obsolete within a month or two, so he said flexibility matters most. He said companies with the internal skills to manage this can reasonably build, while others can buy and build their own agents on top of a vendor platform.

“Don't tether yourself to one LLM. Don't tether yourself to one particular data structure.”

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He cited Anthropic having to pull Fable, calling it Mythos, and the emotional maturity of leaders like Dario and Sam Altman as reasons for concern. He said doing everything with Claude makes him nervous, and that specialisation gives a comparative advantage, so he expects better applications from focused vendors.

“feels like a lot of key man risk for one specific vendor.”

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Dan Lee said that if he could go back to the start he would be paranoid about how much value Nooks delivers to customers, and rigorous about the mechanism for telling a nice-to-have from a need-to-have. When host Mark River noted the false-positive risk because people tell you what you want to hear, Dan recommended The Mom Test.

“I think be paranoid about how much value you're delivering to customers.”

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Avoiding a long idea maze was an explicit goal at founding. Zhang says most founders shy away from being aggressive because it feels too salesy, and default to soft questions like 'would you use this?' Instead, he and Ashwin asked how much the buyer would pay, where the budget would come from, how ROI is justified internally and who is involved. Zhang acknowledges luck played a part.

“they'll appreciate if you're asking more tough questions, like how much we pay for this, where's the budget coming from, like how do you justify ROI internally, like who are the people involved?”

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Snehal said Outdoo's best customers were those whose champions gave feedback on regular calls, about every two weeks. The team asked these customers how big a problem they were solving and whether they could do without the product. Those conversations revealed that finance and insurance use cases were where the product was most essential.

“is the a product really really essential can you not do without our product right”

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Joubin Mirzadegan asked whether value moves up the stack if open source cuts app companies' cost to serve by 80-90% and fixes their gross margin problem. Hamid said that has been the firm's fundamental belief. He pointed to demand at Harvey 'going through the roof' and argued that in every industry cycle, big companies buy from vendors that package a complete solution and show up to implement it and deliver value to end users.

“I know they were called wrappers for a very long period of time and I think that's, it's always the, you know, the very lazy way of, you know, identifying these companies”

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Sam said banks are not vibe coding their own customer support or operations software. He said the value sits in an application layer that understands the customer and sets the guardrails, so that the model is only used where reasoning is acceptable. He called this a template for vertical software in other industries.

“They're not building their own. They're not vibe coding their own internal customer support software.”

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He compared models to electricity that is priced by time and access, with orchestration in the application layer doing computation to reduce cost and deliver value. He said the exception is if one model pulls far ahead, which he expects to find out next year.

“So I do think value goes to the application layer significantly more than the model layer.”

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He notes that software's high gross margins and multiples have been tied to market, margins and recurrence, and that professional services in SaaS exist to deploy the software product. He says the core of the AINS thesis is that AI will enable services at high gross margin, which makes them venture-backable.

“the core underpinning of the AINS thesis is that AI will enable us to deliver services at a high gross margin for the first time.”

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He says the offering has two design partners and will not launch until the fall, and it is not part of the 2026 revenue plan. He says it must be profitable from day one, and the 70% figure is above the roughly 50% he heard from other services leaders as typical.

“I think that the number, the real number there is actually above 70 % margin.”

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Chuck Ganapathi says services have always been a low-margin business when all the work is done by humans, and that BPO companies have long done outsourced work. He says what is different now is that Gainsight's services combine humans and agents in value creation, which a traditional outsourcing company cannot match.

“what we're doing is not labor arbitrage”

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Dev Ittycheria said a clever product on someone else's platform is frankly a feature, and he expects many such AI app companies to sell to larger companies for their distribution. Durable companies create a loop: usage generates data no one else has, that data improves the product, and the product attracts more usage. Competitors can then copy features but not interactions.

“are you building a feature that's masking as a company or a franchise, right?”

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Ann says Crunchbase has more private-market data from inception through Series K than anyone, covering company profiles, competitors, investors and funding rounds. She adds that its 80 million-plus software users give the company behavioural signals that others cannot access, which lets it judge whether a company is gaining or losing momentum.

“having all this data and our 80 million plus users of the software and being able to watch their digital body language that nobody else has access to”

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He said that at some point there will be limited training data available and models will peak, so whoever owns the user can use their interactions to improve models. He described this as the near-term winner, with a more uncertain outcome if AGI arrives.

“whoever owns the user will be able to build feedback loops to train their models better.”

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Liz says proprietary data is definitely the first durable advantage. A company may already have that data, or it may be a system of action that captures data and accumulates it over time. She says she still thinks this is real.

“Proprietary data is definitely the first one.”

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He describes telling his teams that there are two options when a market or customer need falls outside the current offering: solve it and be stretched to think differently, or say no and lose the business along with the opportunity to learn. He says post-sales organizations sometimes get frustrated when new work is requested, and he has been open to it throughout his CCO tenure.

“there's two options. Either we have the problem or like, and I'm happy to have it to be able to solve it, to stretch us to think differently, or we could have said no”

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Shipley says JPMorgan asked staff to hand-curate a list of open source security vulnerabilities across 10,000 applications, a list that was growing daily. When he asked why they used the product, he learned they were not looking for license compliance but for security. He repositioned the company around open source security, which he described as a much bigger market with faster growth, although some staff did not want to change.

“So we completely repositioned the company around open source security, which is a much bigger market, faster growth”

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Ed said Seismic started as a document automation platform that went on to define the sales enablement category, a term the founders had not heard until Forrester asked to cover them. He advised founders to qualify in and out quickly and to talk to companies whose use cases do not fit the picture in their heads. When Forrester came, he worried the sales enablement TAM might not be big enough, because the product was much broader.

“I think you also should think through the use cases that you have in mind and go have some conversations that don't necessarily fit what you have in your head because that's what happened to us.”

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Jeff Perry said that about 10% of the business was coming from LLCs, and at first Carta was not sure it could help them with ownership tracking but decided not to turn them away. Over time the demand became Carta's third business unit, built entirely around the LLC signal, and he noted private equity had also become influential in that area.

“during that time period, about 10% of our business was coming from LLCs, not just venture-backed C-corp companies.”

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QuotaPath started as a PLG company expecting reps to adopt it as a personal 'shadow accounting' calculator, share it with their team, and build conviction up to managers and finance. AJ Bruno says those levels turned out to be disconnected: the CRO just wants reps paid correctly and sees it as a distraction, while RevOps handles the plumbing and thinks about cost. He describes a translation problem across all three, and says PLG also kept QuotaPath down market with smaller customers whose use cases it wasn't yet fully handling.

“Well, it turns out those two are actually pretty disconnected. You have the CRO who's like, I just want my team to get paid correctly. It's a distraction.”

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Dan Lee contrasted Cursor, where engineers have budget and access to connect their tools, with sales reps who lack permission to connect Salesforce and have no discretionary budget. He said sales managers may also object when reps use many different tools, so the product-led motion made less sense for Nooks.

“whereas sales reps don't have permission to connect Salesforce uh and they don't have discretionary budget”

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Kyle Norton argues that efficiency gains are not simply pocketed as cost savings, because competitive pressure changes the outcome. He says that if rivals become equally more effective they will use the gain to expand output, and his own team is growing faster because its CAC payback pencils out.

“They're going to ship more with more people.”

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Bret Taylor said the question is whether having more people enables a company to gain more market share than its competitors. He said efficiency gains are not simply recouped as cost savings or passed to shareholders, because rivals have the same technology. He used US mobile carriers as an example: if one lowers prices the others must follow, and if one finds a new acquisition channel the others will too.

“everyone's going to absorb the impacts of the technology and then compete.”

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Chuck's rule of thumb is that if only one other company does something as well, it is still relatively rare, but if more than one does, it is orthodox. Orthodox things should be done well but not better than others, and the team should put its money, time and attention into the true separators.

“my rule of thumb is if one other company is doing it just as good as me, still pretty rare, but if more than one is, then it's orthodox.”

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Chuck says a company that is below its competitors on basics like billing, invoicing and responsiveness will frustrate customers, and he uses restaurant and bank analogies to make the point. Only once those basics are at median do differentiators have an effect.

“But those table stake things all have to be relatively at median for us to then have cool things that will really separate us.”

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Chuck says half of strategy is matching competitors on the ordinary things customers expect, such as billing, invoicing and response speed. The other half is two or three true advantages that customers would choose you for. He says a company should be able to tell a customer face to face why they should buy from it.

“Half of strategy is not frustrating your customers, so half of strategy is going through the orthodox table stake, things that we do day in and day out, and ensuring that we're not frustrating our customers relative to what they could get with competitors.”

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She described a two-hour leadership debate about building for what customers want now, like email, versus what will move the needle. She said they would reach parity on basics and put around 80% of resources into the future, acknowledging that people may not buy it yet.

“let's put like 80 % of our resources to the future.”

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Marc describes prospects whose engineer CEO planned to build the whole marketing function as agents, and says he told them good luck. He says many of these companies later returned complaining about outbound results, content resonance and lead generation. He argues that the engineering worldview that AI can do everything is dominant on X and is fed by posts saying everyone is cooked.

“many of those companies have come back to us months later after complaining why their outbound isn't working, complaining why their content isn't resonating, complaining why they have no leads.”

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Kyle said these customers did not go to college and struggle to get on a Google Meet, so the BDR has to help them with that step. He said massive companies already run fully self-serve motions at the bottom of the market, and that AI makes that experience better and may push it further up market.

“So we're trying to to give an agentic experience for the bottom of the market with like uh mediocre success so far.”

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Keith said Lightfield buys a lot of software even as a high-growth company. The reason is that it wants a vendor to own supporting and advancing the product, so it doesn't fall apart six months later when there are 30 reps. He was dismissive of the trend of people on Twitter saying they'll vibe-code their own CRM.

“part of the reason we want to buy software instead of building it ourselves is because we want someone else to own... supporting it, advancing it, you know, making sure that it doesn't fall apart six months from now when I've got 30 reps.”

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He argues that even if building is easier, it still consumes engineers on something non-strategic like CPQ, and Valley CIOs and CTOs who tinker aren't representative of CTOs globally. Roadrunner itself has a buy-first policy: buy anything that moves the company forward, and only put engineers on internal projects if they run out of roadmap. He acknowledges the build-versus-buy evaluation is changing.

“I would say we in Silicon Valley probably overestimate people's willingness to want to build things.”

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Kyle said his team built waterfall enrichment in late 2022 before Clay was widely known. RevOps does not get good access and his BDR leader cannot make changes, so he said he would use Clay. He said he does not think access should go down to the rep level, and that his company takes a different approach there.

“because it's all like we in internally built it, only two people can really interact with these systems. And like RevOps doesn't really get good access.”

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A host described FDEs today as an inelegant inefficiency, since the work has to be redone every time the model changes, and asked whether improving AI would remove the need for them. Tunguz said no, calling FDEs a very long-term phenomenon and comparing them to management consultants. He argued that someone with domain expertise will still need to validate that the AI is doing something properly, comparing it to structural engineers who stamp diagrams.

“Like FTEs today are an inefficiency.”

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He describes licensed professionals who must assign tariffs to every imported item in a tariff regime that changes constantly. He says AI should be well suited to the work, but a licensed person is still required, which he sees as an enduring angle.

“you're required to have this licensed person kind of in the loop, and I think that is kind of an interesting, enduring angle.”

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He said customers need high volume, which automation handles, and very high quality for certain types of assets. He said the change will be less about moving everything to AI and more about doing more with the right type of translation.

“I think it's going to be a combination”

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Margo Martin said Deltek's annual plan from last year had goals like getting customers live faster and reducing support calls. Once Claude let them go faster, things they had expected to take five or six years to roll out began launching this year. She said the speed-up made parts of the original plan nearly obsolete.

“the things we thought were going to take five years, maybe six years to roll out, we were starting to roll them out this year”

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She said Deltek can't lay out a five-year AI roadmap because everything is changing so rapidly. Given that pace, she said a three-month roadmap is about as useful. She wants the person in the new role to check every morning whether the company is still headed in the right direction.

“you may as well lay out a three-month roadmap because everything is changing so rapidly”

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Wade said you often do not know what a new model can do on day one, but over weeks and months you learn what experiences it allows. He said he finds the accuracy of three-year visions and one-year roadmaps to be much lower now.

“I find the percent of correctness of those documents to be just a lot lower these days.”

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Lochmiller says electrical manufacturing is not an ultra-high-margin business, though margins have risen with shortages. The main benefits are on-time delivery, end-to-end visibility into true costs (he cites Elon Musk's 'idiot index', the end-product cost relative to raw materials), and freedom to design from first principles. The Abilene campus was designed as a one-gigawatt-scale computer running one cohesive workload on a single RDMA fabric.

“it's not the main reason we're doing it is like stacking margin. It's more availability and getting availability, being able to deliver on time.”

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For the first two buildings in Abilene, a little over 200 megawatts, Crusoe committed to a one-year build when the next-closest of 34 other developers bid two and a half years. Vendors quoted a 100-week lead time for medium-voltage power distribution centres. Crusoe's internal team sourced the components and made them in 28 weeks. Lochmiller describes the AI data centre supply chain as 'whack-a-mole', with the bottleneck shifting over time.

“When we went out and canvassed the market for vendors that were supplying these medium voltage power distribution centers, the lead time for this one component was a hundred weeks.”

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Asked why Cowboy can't just use existing launch providers, Bhatt said there isn't enough mass-to-orbit or rideshare capacity available. He noted that SpaceX has said it will use much of its own payload for its own projects. To control how it operates, its scale and its unit economics, Cowboy concluded it had to treat launch as part of a vertically integrated system.

“there is nowhere near enough launch capacity that's available for what everybody in the world wants to do.”

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He says Robinhood vertically integrated a lot over its history to control unit economics. Cowboy Space is building much of its stack, including rockets, though not all of it. Some component technology can be bought, and each case is decided on whether it keeps the output cost competitive with the terrestrial option.

“if you want to control the unit economics of your output service, it necessarily leads you to vertically integrating, right? To controlling the sort of components that drive it.”

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He says the enterprise motion needs its own version of each go-to-market element, and that thinking this way helps move toward the right design even though a company cannot fully separate them. He presents this as a way to test an org design against the go-to-market system from his show.

“think about this as a separate business, a separate team, a separate demand gen motion, a separate pricing model, a separate quota, a separate product”

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Alex said enterprise revenue is in the board plan but late. Mark calls that conservative because the test is not being scaled yet, and a miss by half would not blow the overall number.

“If we miss it by half, it doesn't blow the overall number.”

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Usha says HiveBright set up a team of three people outside sales, customer success and implementation to secure the first Orbit customer and work out how to take it to market. For the first six months, the SWAT team joined sales cycles where Orbit could be pitched, and the team picked up the first million dollars of revenue that way before organization-wide rollout.

“we set up a SWAT team of just three people outside of sales, outside of customer success, outside of implementation.”

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Mark says Formlabs had product-market fit wherever it already sold, maybe manufacturing, noting it did a lot with companies such as Ford and GE, but medical devices were a different market. He argues founders are overly optimistic about applying what they learned in one product-market combination to another, and that the rep should not have been on a performance plan; he said a few product managers and engineers should have been working out the message.

“you got to think like a seed funded business. You don't have product market fit yet.”

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Kevin Egan said early enterprise reps need a clear line of communication into product, company leadership and legal. He said their ROI may be questionable, but what matters is whether they are the best communication avenues and can capture voice-of-customer information that helps the company decide whether going upstream is worthwhile.

“when you decide to go upstream you are starting over you almost need like a little seed funded team”

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Paid owns the record of what the agent did, the credits charged and the auditability of both, which Manny calls a financial record. He acknowledges a higher bar, since billing errors and credit misassignments are a real risk when code is increasingly writing itself. He says workflow tools are like being a shark: always moving, with customers able to churn at any minute.

“I don't want to build workflows anymore because workflows is like being a shark.”

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Using Solve Intelligence's patent-workflow system of record as an example, he says creative assets today are scattered across Dropbox, Google Drive, Miro and Frame.io. Marketers want to search content and check it against brand guidelines through natural interfaces, which semantic understanding now makes possible. He says Adobe and Canva built for a 'pixel first era'. Higgsfield invests in a harness that learns a customer's visual style over time, which he says Claude and OpenAI cannot necessarily do.

“So whoever can create a AI native system of records is going to win.”

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Joubin Mirzadegan says that if Roadrunner goes down on the last day of a quarter and a customer such as HubSpot misses its number, Roadrunner gets sued; it's written into the contract, and the CIO and possibly CRO would be fired. He calls it an accountability moat. Mark Roberge agreed: a product that might look like a model wrapper isn't going to be production-ready if built in-house, no one wants to take that risk, and they want to put it on a vendor.

“It's almost like a CYA moat. It's like an accountability moat”

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Liz says there are meaningful reasons a market can have zero tolerance for error, or face real regulatory or compliance risk, that create a moat. She hedges that she has not done a ton of work in this area but sees it in some places.

“You've got, like meaningful reasons where there can be like zero tolerance for error or you have like real stringent regulation or compliance risk like that can be a real thing.”

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Joubin Mirzadegan frames the moat question as whether an incumbent like Salesforce will cannibalize its business and re-architect before a startup gets to distribution, or whether an enterprise will simply build the tool with Claude Code. Mark Roberge said competing with incumbents is not new; the new threat is the foundation model coming from below.

“the new one now is the foundational model from below.”

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He said many buyers are afraid of being the one who buys a point solution only to have a foundation model company build the same capability pretty quickly. He noted that point solutions are great in some instances, but in many cases a good-enough do-it-yourself build with some prompts is possible, and he does not want to buy technology that is instantly obsolete.

“I think a lot of buyers are afraid that they're going to be the dope who buys the point solution tool only to have one of the foundation LLM companies just be able to do that capability.”

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Mark says that besides the usual risk of legacy incumbents building the product into their platforms, native AI companies face a new risk from foundation models expanding up the stack. He describes a growing graveyard of AI startups that raised over $100 million and are now at zero. He gives 'probably more than half' as his estimate of how many failed for this reason. He says investors and job seekers must analyze the long-term moat against both pressures.

“there's already a growing graveyard of native AI startups that raised over $100 million and they're now at zero. And for probably more than half, it was because the foundational models integrated up.”

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He presents this as part of the journey that everyone has been feeling, describing founders chasing a thesis that keeps shifting. He gives the three-to-six-month cadence as what some of his founders face now. The figure is his description of his portfolio.

“some of my founders have to find and refine product market fit every three to six months. That wasn't the case five years ago.”

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Mark said product-market fit can erode because of technology, macro conditions, competitive landscapes or customer desires. He said he thinks LinkedIn Recruiter stayed in use for eight years because the other tools lost product-market fit. He argued that a business should measure product-market fit about three quarters before its peers do.

“I've never seen a business scale where product market fit does not become at risk because of movements in technology, macro conditions, competitive landscapes, customer desires.”

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Amanda Kahlow says most go-to-market teams are using AI for better forecasting, faster research and dashboards that can be queried, which makes existing people marginally more productive. Her thesis is that buying and selling happens in the conversation, so tools built around the conversation are yesterday's workflow. She describes the realistic end point as AI at the centre of outcome-driven buyer conversations aimed at shorter sales cycles and higher ACV, with real revenue impact.

“But I don't think that's gonna move the needle. That's going to create minor efficiency gains to make the people that you have more productive.”

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He said pilots will narrow to use cases with concrete value, such as automation and AI that helps humans in sales and support. Asked whether the music will stop, he said Cresta cares less about that and focuses on what drives long-term, undeniable value.

“I think as time goes by, and people start to converge on use cases that will drive very concrete, undeniable ROI, coding is probably one of them. And then Contact Center, CX Transformations, definitely the other one.”

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Lauren Nemeth said rag is probably the biggest workload coming into Pinecone and is the talk of the town. However, many rag use cases were simple chat over text, and those did not create ROI for ISV customers. She said the clearer ROI comes when AI takes over human tasks through agents, gen AI applications or lower hallucination rates.

“those weren't actually creating ROI for those ISVs.”

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For Higgsfield, the outcome is helping businesses sell more through AI ads. On network effects, he says AI does not replace them and he doubts agent swarms will in the next five years. Higgsfield encourages its community to open-source projects others can fork, as on GitHub; he says these grew from about 10 seeded projects eight weeks ago to over 10,000, and he believes this can become a moat over time.

“We do believe that there are only two ways of modern value creation or moats today. First is when you deliver the outcome.”

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Liz says that winning follow-the-leader accounts and early customers can create real network effects, as opposed to virality. She says the logo momentum from those accounts can be used to build such effects.

“I do think network effects are real, particularly in vertical software.”

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Azam said a PLG company whose board pushes it upmarket to drive higher ASPs faces a retooling of its product and the whole company in a lot of ways. A host added that a cultural shift is needed and often gets lost. Azam was describing patterns he has seen rather than a prescription for any one company.

“And that's a retooling of the whole product and the whole company in a lot of ways.”

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A host described companies that start with PLG only and build a financial model and company around it with no emphasis on enterprise. When the board then asks for bigger deals and to go upmarket, the company and its financial model are not built to support that. The host added that a cultural shift is needed and that this gets lost, especially with technical founders.

“They start in only PLG.”

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Carina Brockl says you have to dive into the product: can it meet enterprise security and workflow requirements, and does it still work with 500 people on the platform? In solar, enterprise meant a cluster of large nationwide installers. She says the product rarely starts out ready, so sales must collect requirements from customers and work with the product org to close the gaps.

“And it doesn't have to be perfect. I want to say that. But the company needs to be ready and to focus on the enterprise because the product will need work in there typically.”

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In a prior role she assumed that moving up-market, going sales-led or going international was a go-to-market effort, and only later brought in engineering. At Lattice the CTO asked where the company should invest because bandwidth was limited, and this forced the choice to move up-market rather than keep both motions.

“I've known that like that's a company wide effort and you need, you need everyone focused on it for you to be successful in that approach.”

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Kevin Egan said that building out Salesforce's enterprise motion started with hiring senior sellers who know how to get into large complex accounts. Around 2007 and 2008, landing a large bank and a large insurance company brought requirements the company had not seen before, so Salesforce built its own data center implementations for them. He said trust, security and people, process and technology all had to be in place to take on a bank like that.

“I think we actually built out our own data center implementations for those companies.”

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He said the go-to-market innovation of the previous era was significant, citing Dropbox as a PLG company and Zoom reinventing go-to-market around video conferencing. He said open source selling directly into the enterprise at Confluent was also a go-to-market innovation. He said investors now look for a founder who can execute a go-to-market move that produces a lot of leverage for the company.

“the go to market innovation is now significantly more important than it was. And if you can find a founder who can execute a beautiful go to market judo move and produce a lot of leverage for the company, then it's incredible”

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Brian said the technology in this space is easily swappable, while solving business problems and building champions and trusted relationships is not. He said sellers who anticipate needs and build for the incomplete software development lifecycle will win the accounts.

“technology right now in this space is easily swappable. What's not is solving business problems.”

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Benchmark (his partners Peter and Ev led the deal) invested in Instinct's $1B Series C at $10B despite competition from Meta's Muse. He compared the setup to Cursor and Cognition competing with the labs in coding. He said that when a category is that important, 'a lot of things can win', and that the labs will also have offerings.

“when something is that important, a lot of things can win. And so my view is basically something like there can be an amazing independent player like instinct.”

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He said Salesforce was dominant but the CRM market was big enough for multiple players, and he thinks the same will hold here. He framed this as his personal belief and said he has no crystal ball. He sees Replit focused on the knowledge worker rather than the developer and engineer.

“this is my personal belief is the market is so big that there's going to be room for different players in the market to go capture it”

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Manny says he used to argue around San Francisco that SaaS was dead and would take the shift lying down, but has changed his mind. SaaS companies already have knowledge and workflows embedded in their software, which makes them best placed to build agents on top of their stack. Their challenge is figuring out how to capture value, and if they don't, someone else will.

“So they are the primary beneficiaries of building agents on top of their stack. And they just need to figure out how to capture value. And if they don't, somebody else will.”

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He points to SaaS companies that recovered in the stock market after the SaaS selloff, with growth, retention and earnings numbers that he says remain strong. He says his concern is not the AI-disruption narrative itself.

“I think this will naturally play its course.”

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He called this the inconvenient truth of the SaaS apocalypse: there is very little evidence the software pie is being eaten in any meaningful way by AI-native companies. A host agreed, pointing to Yext's own churn data, where accounts spending over $50k a year (about 90% of revenue) show almost no churn, and said AI-era growers are generating other revenue rather than taking that share.

“actually there's very little evidence right now that that the software pie is being in any meaningful way by the AI native companies.”

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AJ Bruno says the market has over-rotated against companies like ServiceNow and that its distribution, at a scale comparable to Salesforce, is the key factor. He links this to the view that public incumbents such as Atlassian may work out how to adapt.

“ServiceNow is on the scale of Salesforce and their distribution and power distribution.”

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Kyle Norton says Salesforce's acquisitions, including Bluebirds, Momentum and Informatica, show it is all in on AI. He says the ecosystem gives Salesforce an advantage as the pivot point where integrations, partners, roles and permissions sit. Another participant adds that growing a $40 billion business may be hard to show in public markets, but that this doesn't stop Salesforce from delivering value to CROs and sales teams.

“I went from being a bear on Salesforce 24 months ago to being like very bullish on Salesforce now”

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AJ Bruno argued that the common view that AI will replace SaaS misses the point. He said AI is compressing the time value of money, so public markets are repricing software companies more quickly than before.

“What AI is doing is it's compressing the time value of money.”

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Grant credits Jake Saper's AINS playbook at Emergence Capital. In that playbook, early interest from many customers is a 'Mirage PMF', and taking them all on risks diluting the autonomous leverage you are building. The approach is to choose customers who participate in the build, accept a flatter early revenue curve, and focus on learning loops and proof points that the trained model succeeds more and fails less. Once the knowledge base, guardrails and evals are productized, you can take on 10 more customers at once without proportional cost. Josh Schachter calls it 'go fast to go slow to then go fast again,' and both add that it takes discipline.

“And you may have a little bit of a flattening of the curve early on in revenue. but you're doing that because you're working on the signals.”

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He says that is just a services business and a low-margin one that should not take venture capital. He says a 30% margin AI-based accounting business could be a great bootstrapped company, or it needs a credible path to higher margins.

“And that's just a service. That's not an AI native service. And that's a low margin business that should not take venture capital.”

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Brian said Cursor's co-founders argued that anything easily acquired is easily lost, and that building an enduring company requires marrying PLG with sticky enterprise sales. He said they researched companies that grew in PLG but never made the enterprise conversion and most of them failed.

“Companies that grew up in PLG but never made the conversion into enterprise, most of them, most of them failed.”

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At MongoDB, some developers and startups would never be reached by outbound sellers and needed a frictionless self-service path, while regulated large enterprises would never buy on a credit card and needed sophisticated sellers to break in. Azam said he worked backwards from getting the product into as many customers and segments as possible to an architecture combining PLG, high-velocity sales, customer success and strategic enterprise selling, with smooth handoffs between channels. He called this a constant work in progress.

“I never looked at it as an either or.”

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Mark Roberge says many companies run a bottom-up motion to their first $10 million, $50 million or $100 million and then find it fails when they go top-down. He attributes a lot of that failure to not understanding the decision-making unit in larger deals. He says bottom-up was rarely an option before the internet, freemium and product-led growth.

“But to become a billion dollar business, you actually have to exploit both.”

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Mark says he cannot think of a public PLG company without a vibrant large enterprise sales team. He notes Shopify reached $100 million in revenue without one, and argues that layering in a sales organization is necessary to unlock the multibillion-dollar opportunity.

“I can't think of one that doesn't have a vibrant large enterprise sales team.”

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At Shopify Plus, merchants that had outgrown the standard plans would not invest about $25,000 a year without a relationship with someone at the company, even when they agreed with the product checklist. Loren describes this as an emotional break in the buyer at a certain MRR or ARR, which is why Shopify Plus added a sales team.

“I'm not going to invest $25,000 a year, which for a startup is a decent amount of money, without having some kind of relationship with the company.”

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Dino cited Atlassian, Datadog, GitHub and GitLab as proof that a lot of selling can be done with the product itself. He said, however, that a PLG company can only take the motion so far from a scale standpoint, and can only unlock so much of its total addressable market without a more formal sales and marketing strategy. He said he is very pro PLG based on his experience at Snyk.

“you can only take it so far from a scale standpoint”

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Dan says an ICP segment needs three things in common: high lifetime value with happy customers who drive inbound, relative ease of winning compared with other segments, and a sizable segment that is healthy. He says the third test is the one go-to-market leaders most often miss.

“It needs to be high-LTV, happy customers that drive inbound.”

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Roberge says founders often try to prove every region and segment in year one, when the more useful step is to pick the market that is easiest to win repeatably. He tells founders that they think their ICP is what is on their website and in their pitch deck, while the real ICP is who their salespeople go after and close.

“Your ICP is who your salespeople go after and close.”

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Bosworth says her first ICP analysis looked at where Checkr landed deals, and landed them fastest, and at where it had reference customers, since selling without names to drop is hard. She says Checkr had most of the gig economy as customers, which gave it logos to reference. It began with three or four ICPs and now has five or six, and she calls the process ever-evolving.

“we owned the gig economy 90 of 95 of the gig economy works with checker”

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Asad Zaman says he has a feeling that customers will have less stickiness with platforms as AI makes it easier to move quickly. He contrasts the old software that never delighted him with a recent Replit experience he found delightful. He says he expects a significant shift in how these companies make customers feel.

“I have a feeling that where we're going, customers have less stickiness with platforms”

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Asad Zaman says some software companies had signs of strong customer relationships, such as high NRR, but their customers felt locked in with no good alternatives. He says the switching alternative was more painful than the status quo. He presents this as a reason to reassess the moats of those companies.

“there was a generation of software companies that had signs that they had really good customer relationships. You know, NRR is an example of that. But their customers felt more like prisoners.”

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She says it is becoming easier to keep data outside a software system, to access it in real time, and to move it with AI or integrations, which reduces the pain of switching that software once had. A host then asked her what version of stickiness she is still willing to underwrite.

“the switching costs have gone down and I think will continue to go down as it's easier to have your data outside of the system”

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The speaker says the cost of generating software has fallen so low that there will be 10 times more competitors to Salesforce, even though he does not think people will spin up their own CRM to replace it. He says this will dramatically reduce the pricing power and leverage Salesforce has with customers. Another host said he was not sure that was true.

“But there are going to be 10 times more competitors to Salesforce. And so the pricing power and leverage that Salesforce has with their customers is going to dramatically fall.”

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Sam Jacobs says the argument that certain legacy companies have lost leverage and need to rethink their strategy sounds valid to him, but the argument that the technology represents a fundamental existential risk to white-collar work has no precedent. Asad Zaman agrees, saying he does not believe in that risk yet and does not want to react to any one quick thing.

“My point is the argument that certain legacy companies have lost leverage and need to rethink their strategy in the wake of this new technology, that sounds valid to me.”

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Sam Jacobs said database and system-of-record vendors like Workday face pressure because accessing the data will no longer be the critical thing, so they will have less leverage. He said the core premise was that customers rarely switch, and that this is now less certain, while software as a category will keep growing around the agentic layer.

“therefore they will have less pricing power and less leverage”

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Asad Zaman said an acquisition can give a buyer a jumping-off point even when tools like Codex and Claude Code make building product easier. Companies that raise rounds are also hunting for small startups to acqui-hire. He cited Google's deal for the Windsurf founders to lead a division, which he said left the rest of the team out in the cold, as the kind of deal employees should worry about.

“They're buying capabilities, even though building product has become easier because of things like Codex and Claude Code.”

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Peter says that although there is a lot of M&A, the majority is bigger startups acquiring AI-focused teams to change their culture and product. He contrasts this with older companies being bought by PE funds to be milked for their terminal value. He says nobody can know what a company will be worth in ten years, which makes terminal value hard to rely on.

“the majority of that M&A is bigger startups buying AI made up teams to like invigorate their culture and change the way their product works.”

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Kaplan described a sequence: first paint a picture of what great could look like, then make clear the strategy to get there, then state the execution expectation attached to it, and said many people get it wrong. McMahon replied that there is often a big disconnect between the vision and what the rep will do tomorrow.

“First you gotta paint a picture of what great could look like.”

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Chuck says leadership believes investments and employee activities will move KPIs but rarely defines those activities or turns KPIs into activity metrics. He gives the example of a fry clerk at McDonald's who cannot see how the job ties to strategy, and says this conversion is hard work that many leaders skip or cannot do.

“one of the big disconnects in business is that everything that leadership does is hypothesis.”

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Chuck says a key test is the runway, meaning how long a competitor would need to neutralise the capability, and he distinguishes between something merely differentiated and a true advantage. He also says the substitutes for each advantage should be considered carefully.

“You come up with something really cool that a competitor can take away from you in a week. It's not competitive advantage.”

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After the three questions, McMahon asks what the company's real secret sauce is and how it would defend it. He says that if the answer is that it is not defensible, the warning sign is that somebody could do the same thing tomorrow.

“if they say it's not defensible, somebody could do the same thing tomorrow.”

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Mark Roberge proposed this litmus test: imagine a group of engineers from a large AI company leave and rebuild your product, and a top venture firm funds them with 20 million dollars to sell it for half the price. He said that if you cannot answer how you still win, you do not have a real moat. He said that many features that look like moats, such as a feature a competitor could build in two months, are not moats.

“how do you still win when these ankle biters are out there selling your exact product for half the price?”

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Mark said the test is to imagine five strong engineers leaving a large tech company with a $10 million term sheet, copying the product and selling it for half the price, then asking why you still win. He said a single feature is not a sustainable moat, because if a rival could build it in about three months the advantage is weak.

“What if five amazing engineers from Google quit got a $10 million term sheet from Sequoia and copied your product and sold it for half the price? Why do you still win? That's the moat test.”

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Her AI-native clients, which she names as Wispr Flow, Exa, Perplexity and Fireworks, have hiring plans so large that Go Nimbly cannot build process fast enough. A handful of SDRs make it work because inbound is so heavy, so in her view the go-to-market job is to maximise success that is already assured. She compares it to Zendesk in 2016, whose head of IT told her they had grown in spite of their operations.

“They are growing in spite of their operations because there's just no way for us to keep up with that speed.”

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He named AI leaders as examples of companies that may get revenue predictably because of market conditions for a time. He said individuals doing things differently does not mean an organisation lacks a system. On the example of a large asset manager, he said he did not want to touch it but would attribute it to brand.

“There are products and services and brands that can predictably get revenue because of the market. So they have a tailwind.”

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Chris Catania says he presents community as a service to the other parts of the business and learns each function's language to show where it adds to their work. He gives sales, customer experience and product as examples of teams it can serve. He says leaders should not silo community into one area.

“I see community as a service to the other parts of the business”

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Oblinger says many organizations start with one use case, such as how to deliver value or an experience through community, and then try to collect more over time. He lists support, success, product, marketing and developer relations as areas to integrate, and says the best communities have covered their bases across them.

“over time, what you want to do is try to get all of them. You want to collect them, all right?”

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