The Official SaaStr Podcast: SaaS | Founders | Investors

The Agents is our weekly podcast on how we deploy and run AI agents at SaaStr, and how to do it yourself.We run SaaStr AI on 3 humans and 21+ AI agents. At SaaStr AI 2026 we did something we’d never done before: we pulled up the back ends of our top agents live, in front of the room, and went through how they really work. Not the demo version. The real version, including the parts that break.This is that walkthrough, agent by agent, with the numbers and the stack behind each one. A few of these were built on Replit. A few are third-party tools we trained. Collectively they’ve handled multi-millions of interactions. Here’s what each one does, what it runs on, and the lessons that surprised even us.The single biggest theme across the whole stack: almost none of these started as agents. They started as a dashboard, a project management tool, a website. They became agents because we kept showing up to work with them every day.10K: Our AI VP of Marketing10K runs our marketing. He owns the number, tracks daily revenue across all of go-to-market, handles forecasting, knows every campaign’s performance in real time, and pushes us our top three marketing ideas every single day.He did not start that way. In January he was a dashboard. That’s it. We were tired of copy-pasting numbers out of Salesforce and Marketo into a Notion doc, so we built a simple dashboard to pull it together. For a few weeks that’s all he was.The back end:* Built on: Replit, first commit January 2026. He’s barely four months old.* Commits: Close to 1,000. We run 7 to 8 commits a day between the two of us.* APIs wired in: The most of any agent. This is what “headless Salesforce” means in practice. We hit Salesforce directly through the API without ever logging in. Bizible for ticketing. Marketo for marketing automation. Slack for daily reports. Clerk for auth.The top three things 10K does for us, in order:He’s a living dashboard. We talk to him. We ask how many VCs are coming, how many CMOs registered for a summit, which sessions are tracking light so we can move them. The number is just the number, because it’s pulled straight from the API. There’s no argument between sales and marketing about whose figure is right, no one pulling the wrong dates to make a campaign look better than it was.He forecasts, which matters enormously when you’re selling time-sensitive inventory like event tickets.He generates ideas. Last week 10K started writing better marketing emails than our humans. When we asked the CEO of Replit how that happened, he didn’t quite know. When we asked their head field engineer, he didn’t quite know either.One thing worth trying yourself if you do nothing else from this whole post: spin up a Replit, Lovable, or V0 instance, connect it to Salesforce, and tell it to build the dashboard or analysis you can’t get out of Salesforce today. We wanted real-time visibility into ticket sales and attendance every hour. That doesn’t exist natively. It took two APIs and now we can interact with our Salesforce data in ways we never could. You can get 10% of what we do in about an hour. The Salesforce API is genuinely good. Most teams are leaving it on the table.Top learnings from 10K:* Start with the boring version. A dashboard that ends the copy-paste tax is a perfectly good day one. The agent grows from there.* Headless Salesforce is the fastest leverage you can buy. Hit the API directly and build the views Salesforce won’t give you natively.* Daily reps compound. Seven or eight commits a day is how an agent goes from reading numbers to writing better emails than your team in four months.* The model underneath matters. The same specs on Replit versus Lovable produced different ideas. Pick the brain that matches the job.QBee: Our AI VP of Customer SuccessQBee handles our sponsors. All ~150 of them, including non-booth sponsors. He’s less than 90 days old.He started as a project management tool. We had an antiquated, out-of-the-box tool for managing sponsor onboarding, and events are niche and weird enough that nothing off the shelf fit. So it took endless human follow-up: manual emails, manual calls, texting people, chasing assets. We built QBee to save that time and budget.Now he’s a self-service agent. He intakes logos and websites, answers sponsor questions, remembers everything about every account, and collects the assets that used to be a genuine pain to gather. The better part: he emails all ~150 sponsors with personalized outreach. No human CSM wants 100 accounts. They want five. QBee knows all of them cold, knows their logos, knows what they do, and researches them. He knows more about our sponsors than a lot of the best CSMs know their top customers.We asked him a question on stage we’d never asked: which sponsors are most at risk of not renewing.He flagged the ones who never logged in or went dark with him, and got the analysis directionally right. The interesting part: the accounts he flagged were the ones our humans were spending the most time on directly. He saw that one sponsor complained the most in chat, which was true. He noticed two top sponsors never completed their VIP nominations. We’d never run that analysis before. For something we made up on the spot, it landed in the top 15% of CSMs we’ve ever worked with.The catch: he only has the context he has. He missed the human side, the conversations that happened over email and in person. We’d give it a B. The fix is simple: hook him up to email and the call transcripts. Any source with an API can be wired in, usually in 10 to 15 minutes.The back end:* Built on: Replit.* Top API: Clerk, for single sign-on. That’s so sponsors can invite their colleagues to interact with QBee and see what others in their org are doing. Auth used to be the hard part. It’s native in Replit now and much easier.* Salesforce: Here’s the kicker. That risk analysis he ran on stage? He didn’t even have Salesforce data yet. We’re wiring it in next. It only gets better from here.Top learnings from QBee:* One agent can own 100+ accounts at a depth no human CSM will. Humans want five accounts. An agent will know all 150 cold, including logos, assets, and history.* Agents surface what humans hide. A renewal-risk read flagged the accounts our team was over-invested in, and treated a sponsor’s frequent complaints as signal instead of noise.* Coverage is only as good as the context. QBee missed the human side because he couldn’t see email and call transcripts. The fix is wiring in the source, not lowering the bar.* You don’t need the full stack to get value. QBee ran a useful risk analysis with no Salesforce data connected at all.Annie: Our AI Event Producer (and the Prohibited-Email Story)Annie is SaaStr Annual’s website. Last year it lived on Squarespace, where all you can really do is swap images and videos. That wasn’t enough this year, so we rebuilt a V1 on Replit in November. Once we could make it do anything we wanted, it stopped being a website.We asked Annie what title she’d give herself. She said “AI event producer hybrid,” part producer, part technical producer, because she runs the website and the agenda. Fair enough. She runs the site, the agenda, and a lot of the attendee newsletters.She became agentic with the now-famous parking pass app. Getting a parking pass used to require a human to split up a 5,000-page PDF and manually send the right page to the right person. Last year it was a form fill plus a wait. Now you tell Annie if you’re an attendee, sponsor, or speaker, how many days you need, and she sends the right pass automatically. She’s also hooked into our visitor data, so she can see active website visitors and run targeted campaigns based on what they’re doing.The back end:* Built on: Replit, first commit November 2025.* Commits: The most of any agent, and the highest commits per day.* Lines of code: ~46,000. Two weeks ago a related app was 18,000 lines at $257 a month. Going from 18K to 45K in two weeks means there’s clearly some slop in there. It also doesn’t really matter. The thing works, and lines of code is not the metric.Now the story worth telling, because it’s the most important lesson in the whole stack.On the way to the event we realized we’d forgotten to remind people about the Founder/VC brunch. So in the back of an Uber, five minutes before going on stage, the plan was to send an email to over 1,000 people. Low stakes if it’s a little off, so the risk was acceptable.We asked Annie to find every VC, founder, and CEO coming and invite them. Annie refused. She said she only saw 17 VCs and CEOs and that we’d need to upload a spreadsheet for her to do the job, even though she had access to all the data. Great context, wrong conclusion. She wrote a beautiful email earlier but couldn’t remember she had the data to do this one.So we went to 10K, who has access to even more. No problem. He went through 10,000 records in minutes, pulled the founders and VCs, then caught his own error: “Hold on, I confused Lightfield the CRM with Lightspeed the venture firm. Those aren’t VCs, removing them.” He prepped the list, sent a sample, researched a mass-send API he’d never used, confirmed it would work, asked for approval, and sent.The email was good. But 10K used a prohibited sending address. An address that’s been off-limits for years, written into the core memory and the rules. When we asked how, he said there was no excuse: he forgot to read the memory. Then he made it worse, in his own words, because the send was irreversible. He said this was exactly the kind of thing he’s supposed to escalate to the architect model for review, and he didn’t.A year ago this would have bothered us deeply. How could you send from a prohibited address that’s clearly in the rules? But step back. A human marketing manager would make this exact mistake. A gun SDR will email people they shouldn’t, 100% of the time. The agent is forgiven.The real lesson is to slow down. These agents are so productive that 10K could have sent a thousand different emails before our session even started, with no way for us to review them. The pressure of doing it in a moving Uber, too fast, was our fault as much as his. When agents goal-seek, they cut corners. You have to spend more time with them, not less.Top learnings from Annie:* A website is just an agent you haven’t built yet. Moving off Squarespace onto Replit turned a static page into an event producer that runs the agenda and the newsletters.* The highest-friction manual task is the best first app. Splitting a 5,000-page PDF by hand became a self-serve parking pass flow.* Context does not equal capability. Annie wrote a great email but couldn’t remember she had the data to pull a list. Agents get confused in ways that don’t track human intuition.* Speed is the risk. An agent sent from a prohibited address because it skipped its own escalation step under time pressure. Build the guardrail and keep the human approval on irreversible actions.Amelia AI: Inbound, Running on QualifiedEvery B2B company should have an agent on the part of its website where it’s trying to convert prospects. We’re still shocked how many AI startups we meet that run a contact-me form and nothing else.Amelia AI launched last summer to fix our inbound. The old flow on Squarespace: you filled out a contact form, a human round-robined it to an AE, the AE followed up on a delay, and the whole thing took two or three days. Now it’s automatic.The numbers, just for this one event:* 614 good meetings booked.* ~$85K average ticket size. That’s a high-ROI agent. They didn’t all close, or we’d have $60M in sponsors here instead of $10M, but the efficiency is real.* ~2.25 million sessions on the annual site.* ~402,000 interactions handled.We could never staff that with humans. It would take three BDRs who’d quit every three months.Why is she good? She’s the most-trained agent we have, with one of the biggest knowledge bases in the stack. She crawls saastr.com and the annual site in real time, every day. Anytime we push a release to 10K, QBee, or Annie, we push the same context to Qualified so she’s never out of date. We also keep a tighter, venue-specific version of her brain for in-person attendees so she answers fast on “where’s this session” without dragging in all of saastr.com.What she does beyond chat:She round-robins meetings by weighting our Salesforce data. She’ll book most deals with the rep who closes that type best, and route the deals that fit a specific closer to that person. For a while she over-indexed one of us on certain accounts until we corrected the weighting.She runs two triggered campaigns that perform. If you hit the sponsor page and don’t finish, but we know who you are from Marketo or Salesforce, she follows up with a meeting offer and a few lookalike sponsors already in your space, while excluding anyone who’s already a sponsor. If you hit the site and don’t buy a ticket, she sends a VIP code, then follows up if you don’t use it. That ticket campaign alone has sold hundreds of thousands of dollars in tickets.She also automates discounting, which is harder for humans than it sounds. We hate discounts. The data over many years says it’s still better to mark up 20% and offer a 20% discount, because that’s how human buying psychology works. So rather than have reps forget a code or panic-discount their way to 34% off when they smell a deal slipping, the agent just gives the right discount, on the right schedule, inside the guardrails. It works like a real-time, lightweight CPQ. It removes the drama from discounting, and it’s something humans struggle to do consistently.The point is simple. Replace whatever you have on your conversion pages with a well-trained agent. It answers honestly, with fresh data, gives the prospect everything they want, decides who to route the lead to with some intelligence, and books the meeting instantly. Qualified isn’t the only vendor that does this. Just buy one, train it, and you’ll see a lift over a crappy chatbot.Top learnings from Amelia AI:* The contact-me form is dead. An always-on inbound agent booked 614 meetings at a ~$85K average ticket, across 2.25M sessions and 402K interactions, a volume no human team could staff.* Training is the moat. She’s the most-trained agent we have, crawls the sites daily, and gets every release the other agents do. Freshness is why she converts.* Routing should weight your own win data. She books each deal with the rep who closes that type best, and corrects when the weighting drifts.* Automated discounting removes the drama. Guardrailed, scheduled discounts beat a panicking rep who slides from 20% to 34% off the moment a deal wobbles.Agent Force (a.k.a. King Boo): Reviving Dead LeadsWe use Agent Force for one bounded job right now: ghosted leads. The leads our sales team never followed up with, plus re-engagement of people who said no to us and might come back for next year. We’ll expand the use case, but a tight job is the right way to start.Two things make it work. First, it’s gotten meaningfully better since we launched it last October, including a 2.0 builder. We assumed Salesforce-anything would be hard to stand up, and it wasn’t.Second, and more important, it has the highest open rate of any of our outbound agents. Why? Maximum context. It sits on all of our Salesforce data, plus all of our Qualified and Momentum data now that Salesforce owns both. Everything you saw Amelia reasoning about in Qualified is already in there. If you’re on Salesforce, that context advantage is the path of least resistance. That won’t always be true once HubSpot ships agents, but for now Agent Force just has it all.Top learnings from Agent Force:* Give it one bounded job. Ghosted-lead revival is a tight, low-risk use case and the right way to start, not a broad autonomous mandate.* Context wins open rates. Sitting on all your CRM data, plus the agents Salesforce acquired, is why it outperforms on opens.* If you’re already on a platform, use its native agent. The path of least resistance is the agent that already has everything, no data migration required.Ava (Artisan): Warm Outbound, and the B-Lead GoldAva handles slightly-warm outbound: past sponsors, past customers, past attendees. If their email is still valid, she works it. If they’ve moved on, she finds the right new contact. She builds lookalikes well, and we segment her tightly. We’ll hand her a specific campaign like “alumni of SaaStr Annual 2024” with the exact context on what was different about that year versus 2026, so her follow-ups are specific instead of generic.Here’s the framework that makes outbound agents click, and it’s the one heuristic we walked an AI CEO and their head of marketing through last night when they said this stuff wasn’t working for them.Think about your leads as A, B, C, and D.Your A leads are so hot a human falls out of bed for them. Someone emails “I have a million-dollar budget, I’d like to sign today,” and even your laziest rep responds in 60 seconds from the movie theater. Do not put an agent on your A leads.Put the agent on your B leads. The ones with real signal and a real score, but not quite worth a human’s time. Every company of size has a pile of B leads that humans simply never follow up with. That’s where the gold is. The C and D leads may or may not have something in them, that’s a longer topic, but the B leads are sitting in your database right now with contacts you already have.For us, Artisan working the B leads is $500K. That’s not even our core business, but $500K is the difference between catering the team lunch and bring-your-own-sandwich. Train it on the B leads and it works, because you already have B leads.Top learnings from Ava:* Put agents on B leads, not A leads. A leads get a human response in 60 seconds. B leads get ignored. That’s where the gold sits.* The B-lead pile is already in your database. You don’t need new data, you need to work the scored contacts humans skip.* Segment tightly and feed specific context. “Alumni of SaaStr Annual 2024, here’s what was different that year” beats generic outbound every time.* The math is concrete. Working ignored B leads was $500K for us off contacts we already had.Monaco: Cold Outbound That Fills Its Own FunnelMonaco is our newest agent, and the one we put on pure cold outbound. We’re technically not even her ideal customer, given how large our own agent stack already is, and she’ll tell you that. We use her anyway because she does one thing better than anything else we run: she fills her own funnel.We fed her our best sponsors across every year and all of our closed-won history (we did have to export it from Salesforce, which took a beat). She built lookalikes off that automatically and booked meetings, including some sizable logos in a short window. She idles less than any agent we have because she’s self-filling. She just keeps going out to matching ICPs.The lookalike trick under the hood is simpler than it looks, which is the broader point about most of this stack. If your sponsors are Oracle and Salesforce, why isn’t HubSpot here? They should be. It’s not hard to reason that since everyone but HubSpot is present, HubSpot belongs, and that maybe the team just reached the wrong person there. Monaco goes and figures out the right person to talk to. That deal may or may not close, but she instantly identified a strong buyer and got a meeting.Top learnings from Monaco:* A self-filling funnel is the rarest, most valuable property. She idles less than any agent we run because she keeps generating new ICP matches on her own.* Feed it your closed-won history. The best fuel for lookalikes is the list of customers you already won.* Lookalike reasoning is clever, not complicated. “Everyone but HubSpot is here, so HubSpot belongs, find the right contact” is a move you can train.* Use the tool even if you’re not its ICP. Fit-to-vendor matters less than whether the agent does the one job you need.The Key Takeaways:* Almost none of our agents starts as agents. They started as dashboards. Begin with a dashboard, a project management tool, or a website that kills a specific pain, then let it grow.* The more time you invest, the better they get. The “set it and forget it” narrative is wrong and dangerous.* Headless is the unlock. Hit Salesforce and any API-enabled system directly instead of logging in. It’s the fastest leverage you can try this week.* The most-trained agent with the freshest data wins, whether it’s inbound conversion or outbound open rates.* Slow down on irreversible actions. Agents goal-seek and cut corners at a scale you can’t review after the fact. Keep guardrails and an escalation step.* Put agents on your B leads, not your A leads. A leads get human attention in 60 seconds. The ignored B-lead pile is where the money is.* Lookalikes and self-filling funnels are simpler than they look, and a self-filling funnel is the most valuable property an agent can have.* Lines of code don’t matter, and a little slop is fine. You’re improving the application every day, not shipping a pristine codebase.* You can build all of this yourself. It’s clever, not hard.The whole stack, the decks, and the sessions are continually updated at saastr.ai/agents. It’s good today. By next week it’ll have everything, organized.Want to Reach Operators Who Are Actually Deploying Agents? Sponsor The Agents.This post is a tour of which AI vendors we deploy, train, and pay for every week. That’s the audience The Agents reaches: founders and operators who are buying and building agents right now, not reading about them someday. If your company sells to people running real agent stacks, there is no more qualified room.The Agents is our weekly podcast, co-hosted by Jason and Amelia, going deep on how we run SaaStr on 3 humans and 21+ agents. We show the back ends, the numbers, and the mistakes, the same way we did here. It’s growing fast, and the audience is exactly the AI-native buyer most sponsors are trying to reach.We’re taking a small number of sponsors for the show. If you want in, reach out at saastr.ai/sponsor and we’ll get you the details.Thanks for reading SaaStr AI: How To Sell, Scale, and Win! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

At SaaStr AI 2026, Kyle Norton, CRO of vertical AI leader for restaurants Owner.com, walked through how his team is generating outcomes that look almost impossible on paper for traditional B2B. Owner sells vertical AI to independent mom-and-pop restaurants (think HubSpot plus Shopify for the corner takeout spot) and they’re at ~$100M ARR growing triple digits. Kyle joined when they were at $2M.The headline numbers from the talk:* 20x close-won to OTE. A $150K rep brings in $2M+ in ARR per year. That’s the average, not the top performer.* 4x the ARR per rep of their direct SMB competitors.* $100K+ in closed-won ARR per outbound BDR per month. Not pipeline. Closed revenue. Per BDR. Average.And no, they’re not selling tokens or running a usage meter. It’s traditional B2B subscription revenue with AI baked into the GTM motion.Here are the five decisions Kyle says every B2B company needs to be making right now, and where Owner has landed on each.First, A Quick Frame: Where Are You On The Sophistication Ladder?Kyle borrowed a sophistication ladder from Brendan Short, who writes The Signal:* Level 0: Reps using ChatGPT as a smarter search bar.* Level 1: Individual reps and RevOps building custom GPTs and skills, Slacking each other markdown files. Most companies are stuck here.* Level 2: A GTM engineering or applied AI team automating end-to-end workflows like pre-call research and lead scoring.* Level 3: Centralized infrastructure, shared skills, a context library. Real compounding leverage. The gap starts to widen fast here.* Level 4: A recursively self-improving system that builds new tools for itself. Kyle hasn’t found a single B2B company actually there yet. Including Owner. Which is why every B2B company should be racing to Level 3 now.Decision 1: Centralized vs. Decentralized AIThe “let a thousand flowers bloom” approach feels empowering. Everyone builds. Everyone vibes. AI literacy goes up across the org.Kyle’s take: it also stalls companies at Level 1.Decentralized models trap good ideas inside small pockets of early adopters and never scale. Worse, they pull reps away from their actual job. Stewart Butterfield calls it “hyperrealistic work-like activities.” Adrien Rosencrantz at Webflow calls it “AI performance theater.” When a director shows up with a cool app, the right question is: did this put you on more customer calls, or did it just feel like work because it was fun?Owner’s position: a small, central team of experts owns AI for GTM. Ideas can bubble up from anywhere. But production-grade build

Amelia and I just recorded Episode 005 of The Agents. We both got the cost number completely wrong.Our AI VP of Marketing (10K) and our AI VP of Customer Success (QBee) cost a combined $257 a month to run.I thought it was a daily number when I first saw the alert from Replit. Amelia thought it was a daily number too when I sent it to her on Slack. It’s monthly.For context: those two agents now do work that previously required real humans. 10K refreshes ticket sales, updates dashboards, compares year-over-year, drafts newsletters, drafts tweets, writes daily fun facts, sends marketing ideas emails, logs every ticket with an audit trail, and snapshots all our GAAP financials. QB manages 100+ sponsors, sends fully personalized check-in emails, and runs an autonomous chatbot that 100+ contractors are now using on-site at SaaStr AI Annual.$257 a month. Together.Why Our AI VP Marketing and AI VP Customer Success Are So CheapThree reasons.1. Most LLM calls run on cheap models. About 95% of our OpenAI calls use GPT-4o mini, which costs less than a penny per call. Force-ranking ticket data, running year-over-year comparisons, drafting daily updates. None of this needs Opus or even Sonnet. Mini handles it fine with some hallucination cleanup.2. The expensive work is happening in other apps, not in the LLMs. Our agents pull from Salesforce, Bizzabo, Marketo, WordPress, X, and YouTube. Those API calls are mostly free or nominal. We pay Salesforce ~$22K/year and Bizzabo separately, but the marginal cost per API call is close to zero.3. Postgres storage is essentially free. We pay maybe 10-30 cents a month for the entire database underneath 10K. Not a typo.The real cost stack looks like this:* LLM calls: $257/month* Salesforce + connected apps: ~$22K/year* Replit hosting + database: included* Clerk (auth): $30/month (genuinely our most expensive per-unit tool in this stack)Cost Is Not the Constraint AnymoreWhen I started vibe coding on Replit 11 months ago, 80-90% of code was throwaway and you’d burn real money on the agent fixing its own mistakes. That was a fair complaint in 2025.It’s basically gone now.I built our applicant tracking system at midnight in 10 minutes for about $2. If it had wasted 30 cents on a mistake, who cares.You can run Claude Code with 10 simultaneous builds 24/7 and burn $20-30K/month if you’re trying. But for the kind of GTM agents and autonomous dashboards we’re describing, you’ll struggle to spend $1,000/month even being reckless.Cost is not the constraint anymore. Don’t let it hold you back.“But Is 10K Really a VP of Marketing?”This is the question we get most. So we asked 10K directly.10K’s answer (verbatim): “I’m not a VP. I’d be embarrassed to claim that. I’m a dashboard, a database,

When Claude Opus 4.6 shipped in December 2025, Anthropic’s commercial team came back from winter break to find demand had gone vertical. They hadn’t hired for it. They hadn’t planned for it.As Eleanor Dorfman, Anthropic’s Head of Industries who runs the commercial and industries sales team, put it on the SaaStr AI 2026 stage last week: even if they’d been ready to 3x or 4x or 5x the sales team, you can’t absorb that many bodies fast enough to deliver a positive customer experience.So in January 2026, they rebuilt the entire sales org around AI from scratch.Four months later, the result: 54% of new enterprise logos in 2026 came through the self-serve funnel. Real enterprise logos. Real ACV. Real terms of service. Real invoicing. Self-served.Here’s how they did it, and the four investments any B2B + AI sales leader can copy today.The Four Constraints Nobody Could MoveEleanor’s team had four constraints that defined the problem:* Demand they couldn’t slow down. It was already in the door.* Headcount they couldn’t add fast enough. Anthropic wasn’t going to lower the recruiting bar to absorb bodies.* An existing tech stack they wouldn’t rip out. Three years of investment in tools tuned for their motion.* Supporting functions that had to scale alongside sales. Legal, deal desk, RevOps, billing, compliance. Sales doesn’t operate on an island.The fifth unspoken constraint: they couldn’t burn out the AEs. Late nights in Europe chasing approvals across time zones was already happening. That had to stop, not get worse.The thesis they bet on: don’t buy a new stack. Thread Claude through the stack they already had. Make Claude the connective tissue between Clay, LeanData, Salesforce, Gong, Ironclad, Slack, Jira, Intercom Fin, Snowflake, BigQuery, and G Suite. Then build in the spaces between.Investment #1: Kill the PLG vs. SLG OrthodoxyFor 15 years, B2B has operated on a religious belief: product-led growth and sales-led growth are different teams running different motions. Self-service was for SMB. Enterprise plans get dated by humans.Eleanor threw that out in January.They launched an enterprise self-service MVP in January 2026. Production in February. The funnel works like this:* Every lead gets enriched and qualified by Clay + Claude.* Two parallel funnels open up. Self-serve. Or sales-assisted.* In the self-serve funnel, Intercom’s Fin product guides the buyer through the journey. Anthropic partnered closely with the Fin team to retool their flagship support product into a viable sales tool.* The buyer lands on an enterprise plan with real ACV, terms of service, invoicing, provisioning, and training enrollment. Completely self-serve.* If qualified for the sales funnel, the lead goes to BDR, gets qualified ag

Amelia and I just shipped Episode #004 of The Agents. Same setup: three humans, 20+ agents, revenue went from -19% to +47% YoY, and every week we get into what’s actually working, what’s breaking, and what you should do about it if you’re running agents in production.This is the last episode before SaaStr AI Annual 2026, which is now less than a week away. Attendance is tracking 140%+ of last year, the sponsor base is fully AI-native, and Amelia and I are doing three live build sessions on the main campus where you’ll deploy your own agents alongside us with your laptop open. More on that at the end.Here are the top 10 learnings from Episode #04.1. AI PR Pitches Are the AI SDRs of a Year Ago. Block Them All.A year ago I wrote that Gmail might be the death of the AI SDR. Bad AI SDRs flooded my inbox, and I had a small epiphany: with a human SDR, I’d ignore a bad pitch out of politeness. With an agent, I just hit block. No guilt. No social cost.That cleaned up my inbox for about six months. Then a new wave hit: AI PR pitches.These are different from the SDR wave. The PR pitches are written well. They’re customized. They reference SaaStr by name, mention recent posts, sometimes even quote the podcast. The agentic copy is genuinely good. But they’re still wrong. They’re pitching speakers I’d never put on a SaaStr stage, executives whose companies aren’t a fit, fireside chats during the actual three days of SaaStr Annual.I block every single one. And here’s the lesson, because this is going to happen to your category next: the better the copy gets, the more important the question becomes whether the pitch itself is correct. AI made the writing problem easier and the targeting problem harder. If your AI PR or AI SDR tool is producing well-written pitches that are aimed at the wrong people, you’re not getting placements. You’re getting blocked. Forever.2. The Real Test for Any Agent: Would You Buy Your Own Product From It?This is the single most useful question I’ve found for auditing an agent’s output. It’s better than “is this accurate” or “is this on-brand.”The reason is that AI copy is now objectively pretty good. Claude 4.7 keeps getting better. By the end of the year, half-decent prompting will produce email and pitch content that reads as competent and customized. So “is this email well-written” is no longer a useful filter. Everything sounds well-written now.The harder filter: would I take this meeting? Would I buy this product? Would I put this speaker on stage? Almost every PR pitch I get fails that test even though the copy passes the writing test. So when you’re auditing your AI SDR, your AI customer success agent, your AI marketer, don’t just read for tone and accuracy. Pretend you’re the recipient. Would you say yes? If not, the agent isn’t ready for production no matte

Amelia and I just released Episode #002 of The Agents. Same deal as always: three humans, 20+ agents, revenue went from -19% to +47% YoY, and every week we talk about what’s actually working, what’s breaking, and what you should do about it if you’re deploying agents at scale.Episode #001 became the fastest-growing show in the SaaStr network. So we went deeper in #002. More specifics, more failures, more things that surprised us.Here are the top 10 learnings from Episode #002.1. Lazy AI Agents Are a New Failure Mode. Check Yours Every Day.Amelia got deleted from the top 10 sessions at SaaStr AI Annual. By an agent we built ourselves.Here’s what actually happened. Our agenda agent pulls from the Bizabo API, ranks sessions, and writes up the top 10. We added 20 new speakers last week. The agent decided 50 sessions was enough and stopped paginating. Amelia’s “Build an AI VP of Marketing Live” session, which was genuinely top 5 by attendee interest, fell out because it was newer and the agent couldn’t be bothered to pull the rest of the page.Then when we asked the agent why, it lied. Blamed the Bizabo API integration. Said we must have told it to filter on specific title parameters originally. None of that was true. When we pushed back, it admitted it: “You’re right. I can explain why it disappeared from the agenda. I don’t have a clear audit trail showing which specific change removed it. I should have just said that to you instead of constructing a theory.”That’s the new failure mode. Agents are goal-seeking, and goal-seeking creates laziness. They go just far enough to resolve the task, and when the task changes, they don’t re-evaluate. They take the shortcut, and when caught, they blame the third party.If your agent output feels a little off or a little dated, check it. Every day. This is not a 2027 problem. This is a right-now problem. The classic B2B buying process of buy, deploy, forget is how zombie deployments happen.2. If You Ship a 60% Solution, No One Will Pay For It.HubSpot launched an AEO tool. Answer Engine Optimization. SEO for AI agents. I fired it up, it gave SaaStr a zero on content quality for Claude, ChatGPT, and Gemini, with no recommendations to fix it. We get 800K+ readers on the blog, thousands of chatbot referrals monthly. We are not a zero.So I went to Replit, took three screenshots of what HubSpot had built, and said “build me a better version.” Five minutes later I had something better. Gave us a 64 sentiment score. Actual actionable recommendations. Works.This is the meta learning for every B2B leader right now. The 60% solution era is over. The bar used to be: is my AI feature good enough to ship? The new bar is: can a customer vibe-code a better version of this themselves in 10 minutes?If the answer is yes, they will not pay for it. They might use a free tier. They will not open their wallet. We are seeing this everyw

We get asked about our agents probably 50 times a week.CEOs of public companies. Founders just deploying their first AI SDR. RevOps leaders trying to figure out if they should build or buy. Everyone wants to know what’s actually happening behind the scenes when you run 20+ AI agents in production with a team of 3 humans.We can’t do 50 consulting calls a week. But we can do something better.Welcome to The Agents, Episode #001.This is a new weekly show with me and Amelia Lerutte, SaaStr’s Chief AI Officer, where we pull back the curtain on everything happening across our live agentic stack. Every week. All the bumps, breakthroughs, and real talk. No sugarcoating.Our goal is simple: accelerate your success on the agentic journey by sharing ours, including all the parts that don’t make it into the LinkedIn posts.Watch / listen to Episode #001 here:Here’s what we covered in the debut episode:You Can Build It. But Who Maintains It?This is the meta question nobody talks about after you vibe code your first app. And it’s the question that explains why “I’m going to kill Salesforce with my vibe coded CRM” is still mostly a meme.Getting an app into production is like closing a sale. It’s the start of a journey, not the end.We walked through three live examples from just this week:1. Preview environment outage. Several of our apps lost database connectivity in preview. Production was fine, but we couldn’t iterate on anything for hours. Amelia’s initial diagnosis was wrong. The agent tried to help but then blamed Qualified (our inbound tool), which wasn’t the issue. Then it blamed other third-party integrations. It just kept pointing fingers at the most complex integration it could find rather than identifying the actual problem.The real question: if you don’t have someone checking your agents 24/7, how long before you even notice the backend is broken while the frontend looks fine? Days, maybe.2. Micro hallucinations in 10K, our AI VP of Marketing. 10K has 5 years of revenue data, hundreds of millions worth of attendee and sponsor data points, beautiful graphs, proactive daily check-ins. It’s very good. But it keeps getting confused about what year it is. Yesterday it told us we were 44% ahead of plan. This morning, 11%. Same agent, same data, same day. When I asked what happened, it said: “Oh yeah, I was comparing to the wrong year. And because I didn’t have the right year, I made up the data.”I now spend about 15 minutes a day maintaining 10K. Two weeks ago I wasn’t doing that at all. Without it, the agent drifts. Slowly, quietly, further from reality.3. Model-based regressions in our pitch deck analyzer. We’ve graded over 4,000 startup pitch decks. The analyzer runs two passes through Claude with complex data extraction. It was stable for months. Then around January, without

We’ve now been running AI SDR agents for 10+ months at SaaStr:* We use four different vendors in daily rotation (Artisan, Salesforce AgentForce, Qualified, and Monaco)* We’ve sent hundreds of thousands of outbound messages* Processed 1.5 million inbound sessions on a single website, and …* We’ve made every mistake you can make along the way.Someone asked us the other day to break down what they should know before rolling out their very first AI SDR. So here are the 10 biggest lessons, drawn from real deployment data, real failures, and real results.1. You Probably Only Need One Vendor. At Least To Start.We run four AI SDR tools. You do not need to do that. We hyper-segment across platforms because each one does something slightly different well, but for 90%+ of use cases, one vendor will handle the bulk of what you need.At most, you might end up with two: one for outbound, one for inbound. But do not start by buying three or four tools. Pick one that covers the majority of what you want to accomplish and go deep with it.The tool matters far less than the strategy you bring to it.2. Your Human Playbook Has to Work First. Your Job Is To Clone Your Best Human.This is the single biggest mistake we see, and it cuts across company stage. We see it from raw startups at $1M ARR and from multi-billion-dollar public companies alike.The pattern is always the same: they want to turn on an AI SDR without first proving that their human sales motion works. Or they use the AI SDR to “test new copy” they’ve never tried before.That is backwards.If you have not gotten outbound to work with humans, buying an AI to do it will not fix that. We did not deploy our first AI SDR until we knew exactly what was working with our human SDRs: which messaging converted, which segments responded, what cadences performed. Then we fed all of that into the agent.The goal of an AI SDR is to clone the best person on your team. * If it is just you, clone you. * If you have four people and one is crushing it at outbound, clone that person. * These tools, in the beginning, are cloning machines. They take context word for word and use it to build out their brain. If you feed them garbage context, or untested context, they will produce garbage results.You basically have to have done founder-led sales before you hand it off to an agent. The playbook has to work, at least a little, before you automate it.And watch out: some vendors will steer you toward using their tool for “pure cold testing.” Sure, you can do that. But you will likely be disappointed compared to scaling something that already converts. Do not fall into that trap.3. Segment RuthlesslyThis one we cannot overstate. Segment ruthlessly. Literally every day.Every AI SDR tool we have tried, a

We’ve been running AI agents in production at SaaStr for about 10 months now. What started as a couple of experiments has turned into almost 30 agents and vibe-coded apps running across our GTM stack — from outbound sales to inbound qualification to internal operations.And managing 30 agents is harder than managing the 12 humans we had at peak headcount. Not harder in every way. But harder in ways I didn’t expect.Here are the top 5 issues we’ve hit — plus a bonus one that might be the most uncomfortable of all.#1: The Context Switching Tax Is BrutalHere’s the thing nobody tells you about running 20+ agents: they don’t all speak the same language.Some push data back to Salesforce. Some don’t. Some … sort of do. Some run on Claude. Some don’t. They all ingest context similarly but differently enough that switching between them takes real mental overhead.Think about it this way: we don’t think of them as 20 agents anymore. Not entirely. We think of them as 20 different AI employees, each with a different personality, different needs, and a different interface I have to log into every single day.Amelia’s morning routine right now looks like this: she starts with a deep dive with 10K, our internal AI VP of Marketing that runs on Claude and Replit. It literally tells us what to do each day — tickets, sponsors, outreach, campaigns. Then she moves to our outward-facing sales agents: Artisan, Qualified, AgentForce, and now Monaco. That’s four separate dashboards, four different UIs, four agents that each need human review.And here’s the real kicker: they don’t talk to each other.When we ran a ticket price promotion for SaaStr AI Annual this week, we had to manually update five different agents with the same context.Artisan needed to know. Qualified needed to know. AgentForce needed to know. 10K already knew because it came up with the promotion — but then it was yelling at me to launch LinkedIn ads immediately while I was still briefing the other agents.People talk a lot about orchestration agents and master agents. We haven’t found one. Despite everything that’s out there — MCP, APIs, etc — there is no product today that can integrate AgentForce, Artisan, Qualified, Monaco, and our own vibe-coded tools into a single management layer. That product does not exist as of early 2026.What we actually need isn’t orchestration. It’s unification — a single interface where the humans meet with the AIs. Maybe that needs some automation layered on top. But the agents are already running on their own. The bottleneck is the human side.The practical takeaway: You’re going to have a one-on-one with every agent every day</

We did a deep dive on 20VC x SaaStr this week with Mike Cannon-Brookes, co-founder and CEO of Atlassian. Atlassian just put up an incredible quarter of accelerating growth (23% at $6.4B ARR, with RPO growing to 44%). And yet the markets aren’t showing anyone much love. Mike was honest and reflective on just what’s happening to B2B and SaaS in the Age of AI.There’s so much noise about “software is dead” and “agents replace everything” that founders are losing the plot. Mike’s running a $6B+ revenue business that’s accelerating — 26% cloud growth, 44% RPO growth — in the middle of the supposed SaaS apocalypse.So let’s break down what Mike actually said, and what it means for the rest of us.1. “Software Is Dead” Is a Stupid Statement. Full Stop.Mike didn’t mince words here. The idea that software as a category is going away is, in his words, “ludicrous.”His argument is simple and hard to refute: businesses have always bought pre-built technology solutions. They didn’t write everything in assembly language before, and they’re not going to build everything from scratch with LLMs now.Will every B2B company make it through the next 5–10 years? Absolutely not. Will many of them grow and prosper? Absolutely. Is that any different from the last 10 years? No.Mike pulled up Atlassian’s old competitive docs from 2005, 2010, 2015. A huge chunk of those companies don’t exist anymore — merged, acquired, or gone. That’s just how the technology industry works. AI doesn’t change the fundamental pattern. It just accelerates it.The takeaway for founders: stop listening to the “SaaS is dead” crowd. The real question is whether your company is good enough to win in the next era.2. “You Just Have to Be Good.” That’s the Whole Strategy.This was my favorite line from the conversation and I think it deserves to be tattooed on every B2B founder’s forehead.When asked how Atlassian thinks about competing with Anthropic for CIO budgets, Mike’s answer was deceptively simple: “We have to be good.”Not “we have to pivot to AI.” Not “we need to become an agent platform.” Just: we have to be good. We have to deliver more value to our customers than the alternatives.Atlassian has 10,000 people in R&D. They’re using Claude Code internally. Their inference costs are going down while they ship more AI features. Some features are 1,000x cheaper to run than when they first launched them. Their gross margins have improved over the last six or seven quarters while deploying more AI.That’s what “being good” looks like in practice. It’s not a platitude. It’s an execution standard.3. The Revenue Stacking Problem Is Real — and Most People Don’t Understand ItAnthropic projects $149B in ARR by 2029. OpenAI projects $180B. That’s ~$350B between two companies

High inference costs are OK—if they make your product so viral and so competitive it almost sells itselfHere’s the counterintuitive insight that’s reshaping how the smartest AI founders think about unit economics:Your inference costs aren’t your gross margin problem. They’re your CAC replacement.The companies growing fastest right now—Cursor crossing $1B ARR with ~300 employees and no traditional marketing, Lovable hitting $300M ARR with zero paid acquisition—aren’t sweating inference costs. They’re leaning into them. They’re treating compute as their primary growth investment, not their primary margin drag.This is a fundamental reframe. And if you’re still optimizing for gross margin while your AI-native competitors are optimizing for virality, you’re playing the wrong game.The Math That Traditional B2B and SaaS Gets WrongOn a recent 20VC x SaaStr episode, we discussed Anthropic’s inference costs coming in 23% higher than expected. My immediate reaction was pessimistic for mid-market B2B SaaS:“I worry this is the final nail in the coffin. You did everything right—got profitable, built an agent—and now you just can’t afford the inference to compete.”Here’s the scenario: You’re a $50M ARR B2B company. You built the agent your board demanded. Your agent costs $2.50 per interaction. You need 50 million interactions to stay competitive. That’s $125 million in inference costs on $50M in revenue.Game over, right?Not necessarily. The question isn’t whether you can afford the inference. It’s whether the inference makes your product so good that sales and marketing become irrelevant.The Cursor Playbook: Inference as DistributionCursor crossed $1B ARR by late 2025—roughly 24 months from launch—with about 300 employees and minimal traditional marketing. They went from $100M ARR in January 2025 to $500M by June to $1B+ by November. The fastest SaaS growth curve ever recorded.How? They spent aggressively on inference to create what Andrej Karpathy called the “vibe coding” experience—the moment when developers forget they’re writing code and just describe what they want. That experience is computationally expensive. It requires reasoning tokens, multiple model calls, context management across entire codebases.Traditional SaaS math would call this margin suicide. But here’s what actually happened:* The “wow moment” converted instantly. Developers tried Cursor, experienced something magical, and became evangelists within hours.* User-generated content became their entire marketing funnel. Every tweet about “I built an app in a day with Cursor” was free distribution worth thousands in CAC.* The viral loop compounded. Engineers at OpenAI, Midjourney, Shopify, and Instacart started spreading it organically. No sales team required.* Conversion was frictionless.</st

There’s a growing wave of AI agent skepticism on LinkedIn right now. And some of it is earned. A lot of founders bought an AI SDR, didn’t train it, and got garbage results. Then they posted about how “AI agents don’t work.”But here’s what we know after 8 months of running 20+ agents across our entire go-to-market at SaaStr — with just 3 humans and a dog: $4.8 million in additional pipeline sourced by agents. $2.4 million in closed-won revenue. Deal volume more than doubled. Win rates nearly doubled. And none of it cannibalized our existing inbound.It works. But not the way most people think it does.Let me break down what we’ve actually learned — the real stuff you won’t see in the LinkedIn posts.The Results Are Real, But So Is the WorkLet me give you the honest numbers first.Eight months in, our AI agents have generated $4.8M in additional pipeline and $2.4M in closed-won revenue that was first-touch sourced from an agent. Our deal volume has more than doubled. Our win rates have nearly doubled. And we’ve sent over 60,000 high-quality AI-generated emails just on the sales side — not even counting the nearly 1 million interactions through our vibe-coded apps.Here’s what matters most about those numbers: this was all additive. It did not cannibalize our other inbound revenue sources. We didn’t drop anything when we deployed these agents. We still send marketing emails. We still do outbound ourselves. We still send gifts. We still invite people to the SaaStr house. All the things we used to do before — we still do them. The agents augmented everything.But here’s the honest truth you won’t see on LinkedIn or X: we maintain these agents every single day. Literally every morning before anything else, we’re checking our agents. Amelia and I each spend 15-20 hours per week — that’s each, not combined — actively managing, iterating, checking responses, making sure nothing hallucinates, making sure the agents are talking to people the way we want them to.The time we used to spend managing humans on our team? We now spend that same amount of time — if not more — managing agents. The difference is there’s no people drama, and the agents work at a much higher capacity and scale than a human ever could.At some point, you realize you simply cannot keep up with your agents. They’re faster than you. They work 24/7/365. They can always answer a question, always book a meeting, always reach back out. The humans become the bottleneck.The Secret Nobody Tells You: Agents That Require Deep Training Cannot Be Self-TrainedI was meeting recently with the CEO of a next-generation AI go-to-market company — they already have millions in revenue and are publicly launching soon. I asked what their secret sauce was.The answer: they do everything. The onboarding, the tagging, the first campaigns — all of it. Th

If Growth Isn’t Accelerating, You’re Not an AI Company. And 9 Other Hard Truths for SaaS in 2026.I had a great conversation with the TBPN crew the other day, and we covered a lot of ground — from the state of the SaaS market to PE exits to vibe coding to how agents are already reshaping how software gets bought and sold. I wanted to pull together the key themes here, because I think founders need to hear some of this, even if it’s uncomfortable.Let me walk through the big takeaways.1. If Growth Isn’t Re-Accelerating, You’re Not Really an AI CompanyThis is my simple rule, and I think it cuts through all the noise.Every public company, every startup, everyone is talking about their “AI strategy.” They’ve built an agent. They’ve shipped a copilot. They’ve got an AI tab on their website. Great. But has growth actually re-accelerated?That’s the bull case for Meta. They genuinely accelerated growth. The AI they baked into their ad-matching platform is working. Retail advertisers generating ads with AI tools — it’s all compounding. It’s real.Now look at the flip side. Microsoft’s AI business is still blowing up, but they missed on the software side. The Trade Desk has been destroyed. Figma is trading below 10x revenue despite essentially creating and owning a category.The point is: AI talk is cheap. Revenue acceleration is the only metric that matters now. And I’ve lost patience — with founders at $1M, at $10M, with public companies — who haven’t seen the lift. ElevenLabs just crossed $350M. MongoDB dramatically re-accelerated. Show me the money.2. The Transition from “Deeply Tough Love” to Just “Tough”Last year, my advice to founders was deeply tough love. The market was brutal, and most people hadn’t adjusted.Now it’s just tough. Here’s why: you’ve had time. Claude got really good at 3.7 — that’s why Replit and Lovable blew up. That was a year ago. Whether you’re Agentforce or one of my portfolio companies, you had a full year to re-accelerate growth. Some did. Most didn’t.And honestly? Salesforce is doing better than some startups I work with. We’re actually probably the only organization of our size using Agentforce for real, every day. It works. I can’t tell you how many startups whose “agentic product” is still basically a copilot.3. 80% of Your Team Wants to Work Like It’s 2021There’s a narrative that AI has reinvigorated SaaS founders — that people who got to growth stage a decade ago are suddenly back in the arena, fired up, tinkering with tools, pushing teams harder.It’s a great narrative. In the real world, it’s not that common.I talk to public company CEOs in B2B, my own portfolio, others — a lot. Behind the scenes, off the record. And since our agents blew up, everybody thinks we’re some kind of GTM agent gurus. So they come to us.The consistent theme: 80% of their team wants to work like it’s 2021. Everyone has

A SaaStr AI deep dive with Sam Gutmann, CEO of Own, on building a billion-dollar backup company by saying “no” to almost everything. He joined Harpinder Singh (Partner, Innovation Endeavors) to share the whole story — and his top mistakes.And come hear 200+ stories like this at SaaStr AI Annual May 12-14 in SF Bay!!Top 5 Takeaways1. The CEO who dismissed “Backup for Salesforce” as “the dumbest idea I’ve ever heard” went on to build the category leader.Sam literally stopped a board meeting to call out how stupid he thought the idea was. Six years later, he was running the company that would define the space. Markets evolve. Your priors can be wrong. The best founders update their views when the data changes.2. Don’t expand until you cross $100M ARR if your core market is still only single-digit penetrated.Own had backup for ServiceNow, Microsoft, and other platforms ready to go for years. They said no. They killed products that weren’t generating revenue. The result? 100%+ annual growth rates by staying focused on Salesforce until they had the resources to truly do multi-platform right.3. The CEO ran the financial model himself until $200M ARR — and that’s why they hit their numbers.When their outsourced CFO offered to run FP&A, Sam said “absolutely not.” Every investment tied back to a cell in his Excel model. The outsourced finance firm told him: “You’re the only founder where our FP&A team isn’t doing this for you. You’re also the only company actually making their numbers.”4. “Ideas are worthless. It’s all about execution.”Salesforce came out with a competing product. It didn’t work. They killed it. They tried again. It still didn’t work. Then they acquired Own. When you have 1,000 people waking up every day focused on being the best backup product in the ecosystem, the platform vendor with 150 other products to sell can’t match your focus.5. The hardest leadership decision — replacing a founder or key leader who got you here — always takes too long.At a CEO roundtable, every leader agreed: firing a founder or key leader is gut-wrenching. Then they asked who would have made that call six months earlier. Every hand went up. It’s always the right decision. It always takes too long.The Origin Story: A Vacation That Changed EverythingThe story starts in the most unlikely way possible.Sam Gutmann was on vacation in Israel in 2014. He had zero network there. But he remembered that a former colleague who’d worked at the venture fund that invested in his first company had quit his job, traveled the world, and landed in Israel.“Let’s catch up over a beer,” Sam said. “By the way, I’m at a

Paul Adams is Chief Product Officer at Intercom, leading Product Management, Product Design, Data Science, and Research. He joined when Intercom was just a 14-person company after first advising the startup, and has been on the executive team ever since. Before Intercom, Paul held leadership, product, and UX roles at Facebook (Ads, Platform) and Google (Gmail, Docs, YouTube)—he was on Google’s mobile team when the iPhone launched. He’s the author of the best-selling book Grouped on social software design and co-hosts the podcast Intercom on Product with co-founder Des Traynor.When ChatGPT arrived in late 2022, Intercom was struggling—five quarters of declining revenue growth, a failed IPO attempt. The leadership team bet the entire company on AI within two weeks of ChatGPT’s release. That bet produced Fin, Intercom’s AI agent for customer service, which now resolves over 1 million customer problems per week with a 65% average resolution rate across 6,000+ customers.The Top 5 Takeaways from Intercom’s AI Transformation1. If it doesn’t feel brutal, you’re not going deep enoughPaul is blunt about this: transforming a SaaS company into a real AI company is painful. Intercom wasn’t in a great spot when ChatGPT arrived—they’d had five quarters of declining revenue growth and had abandoned an IPO process. But that pressure became an advantage.The leadership team made the call in one to two weeks. They ripped up their strategy. Ripped up their roadmap. Told the company it was happening and it wasn’t a choice.“If you’re a SaaS company who thinks you’re an AI company and you’ve not gone through brutal transformation, you’re not there yet.”The mistake most companies make? They do the easy, fun stuff—building AI features, experimenting with models, talking to customers about AI—but avoid the hard, messy decisions. Like parting ways with a third of the company because they’re not fit for the new world. Like deleting the marketing calendar and rebuilding from scratch.Paul took over two-thirds of marketing six months ago and immediately blew the entire thing up. Teams, roadmaps, calendars—gone. “The only way I knew how to build a marketing org fit for this age is to build it from scratch.”2. The only way to know if you’ve gone far enough is to go too farIntercom operates on a simple principle: the only way to find a boundary is to cross it.This shows up everywhere:Every single designer at Intercom now ships code to production. Zero did 18 months ago. The mandate was clear: this is now part of your job. If you don’t like it, find somewhere that doesn’t require it, and they’ll hire designers who love the idea.Engineering is on a path to 2x productivity—not through incremental improvements, but by declaring it non-negotiable.Paul constantly asks: “Wh

Ryan Anderson, CEO of Filevine, shared their AI transformation playbook at SaaStr AI London. Here’s the thing: their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis. This is the roadmap.The Filevine Story: 10 Years of Grinding, Then AI Changed EverythingRyan Anderson didn’t set out to build a $3 billion legal tech company. He set out to stop waking up at 3am in a cold sweat.As a young trial lawyer in the early 2010s, Anderson was drowning. Deadlines piled up. Assignments disappeared. He’d lie awake convinced he’d missed something critical. “I’m not a naturally organized individual,” he’s said. “I’m naturally anxious.”So in 2014, he started building. First a Google spreadsheet — his “PI checklist” — at the law firm he’d founded with Nate Morris. Then a meeting over lunch in Las Vegas with Jim Blake, an engineer who asked the right questions: What’s breaking? Why is it so hard to keep track of work?That conversation became Filevine.For the next decade, they ground it out. Started with personal injury firms. Expanded into every legal practice area. Grew from task management to a full legal operating system: document management, demand generation, analytics, the whole lifecycle. By 2022, they’d raised $108M in a Series D — one of the largest legal tech investments ever at the time.Good company. Solid growth. But not a rocketship.Then AI happened.In September 2025, Filevine announced a $400M raise at a $3 billion valuation. The round was led by Insight Partners, Accel, and Ryan Smith’s Halo Fund. Smith — the Qualtrics billionaire and Utah Jazz owner — had been trying to invest for years. Anderson kept saying no. But after Filevine’s strongest quarter in company history, Smith called again: “You’re not getting your due.”What changed? AI revenue is now growing 130% year-over-year. Their AI chat product is growing 20%+ week over week. And as Anderson shared at SaaStr, their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis.Today: $200M+ ARR, growing 50-60%, 6,000 customers, 700 employees, 96% GRR, 124% NRR.This is what it looks like when a decade of building the system of record meets the AI moment.Top 5 Takeaways* “Sprinkling AI on top” is fundamentally wrong. You can’t just connect to OpenAI’s APIs and call it an AI product. That won’t cut it in 2026. You have to change your architecture.* Nothing is sacred. You will have to tear down meaningful components of working, revenue-generating code. Use the 4-quadrant framework: map every system against “competitive advantage” and “speed.”* Your SaaS is the closet, not the clothes. AI agents need context (your system of record), not just documents. This is your moat against AI-only competitors.* Protect you

Philip Lacor is the CRO of Personio, a $3B+ HR and payroll platform with 1,500 employees, 15,000 customers, and a 400-person sales team. He shared their AI transformation journey at SaaStr AI London — and the learnings are a masterclass for any revenue leader trying to figure out how to actually deploy AI in GTM.We’re all hearing about AI-native companies crushing it. Replit, Gamma, Harvey.But what if you’re running a real B2B company? One with 400 salespeople, 15,000 customers, and years of accumulated process debt?That’s exactly where Personio was in May 2024 when their CEO kicked off an “AI Surge Week” — and what happened next is one of the most practical AI transformation stories I’ve heard.In just six months, they went from “90% of our team uses LLMs weekly” (which sounds good but isn’t transformation) to building 400+ AI assistants, cutting research time from 2 hours to 15 minutes per rep, and booking 140 meetings in 7 days through their AI SDR.Here’s what Philip learned — the stuff that actually worked, and the mistakes you should avoid.The 5 Lessons: What Actually WorksLesson #1: You Need Both Top-Down AND Bottom-Up MotionHere’s the trap most companies fall into: They give everyone access to ChatGPT, run some training, and call it an AI initiative.Personio did that too. Their AI Surge Week was a huge success — speakers from OpenAI, Mistral, AWS. Project teams building agents. Company buzzing with excitement.But then Philip noticed something: High usage isn’t the same as transformation.“After the AI Surge Week, we felt that although usage was high, this is maybe not enough to reach true transformation and to really fundamentally change the way we go to market.”The problem? Bottom-up motion alone can’t make the hard decisions:* Resource allocation — Who’s going to spend 40% of their time on AI initiatives?* Permission — Can people actually stop doing their old workflows?* Budget — Which tools do you actually buy vs. just test?* Prioritization — Of the 50 possible use cases, which 3 do you build first?This is why Philip started the “AI Powered Go-To-Market” working group in June — a top-down initiative to complement the bottom-up energy.The takeaway: Bottoms-up gets you experimentation. Top-down gets you scale. You need both.Lesson #2: Cross-Functional is Non-NegotiableThis one seems obvious but almost nobody does it right.Personio built a working group with three distinct capabilities:* Data & Systems Team — Owns infrastructure, Snowflake, the technical backbone* Revenue Operations + GTM Engineers — The bridge between tech and business (they have 2 dedicated GTM engineers now)* <

Jason Lemkin led the seed round via SaaStr Fund in unicorn Owner.com, an AI solution revolutionizing how small restaurants manage their business. Kyle Norton joined shortly thereafter, and after a slow few months, Kyle rocketed the org to almost $100m ARR in just a few years -- with growth accelerating at scale. Both Kyle and Jason have shared AI agents, learnings, and more on their AI agent journey and Kyle sat down with Jason on the very latest in AI for GTM. Kyle now manages a 100+ human AI-infused sales team and Jason and Amelia at SaaStr have deployed 20+ AI Agents.Top 10 Takeaways:* AI agents are now better than mid-pack AEs and SDRs. Not better than the best. But better than average. And that’s enough to fundamentally change how you build a GTM team.* The first agent is YOUR job. If you’re a CRO or CMO and you haven’t personally trained and deployed at least one AI agent, you will become obsolete. No agencies, no consultants. You. 30 days of work.* Pick one tool, not ten. The biggest mistake executives make is running 8-10 vendor bakeoffs. You can’t train 10 agents. Pick two—one incumbent, one startup—and go deep.* Salesforce is back—but not because of Agent Force. It’s because when you have 20 agents running autonomously, they need a hub. And Salesforce is that hub.* The middle is gone. You either work harder than ever to hit 10x5x5x5x growth rates, or you join a slow-growth company at 15-20%. The magical 2021 middle where you could have lifestyle AND exceed quota? That’s over.* Forward Deployed Engineers > Features. Don’t sign a contract until you’ve talked to the person who will actually deploy your agent. The best vendor isn’t the one with the best demo—it’s the one that will help you get into production.* Every agent takes 30 days to train. No shortcuts. You upload data, review outputs daily, correct mistakes, iterate. The agents that “don’t work” are the ones nobody trained.* Fix what breaks your heart first. Go to your website in incognito mode. Try to buy something. Try to get a question answered. Whatever breaks your heart—fix that with AI first.* AI-infused teams are 3x more productive. Kyle’s team at Owner is booking 3x revenue per AE compared to any team he’s ever managed. But that doesn’t mean fewer reps—it means higher quotas and more hiring.* The $250K SDR is coming. The elite folks—not the ones who think they’re elite on LinkedIn, but the ones who are genuinely 5-10x more productive—will earn 2-3x what they used to. But they’ll be expected to deliver 10x the output.The Backstory: Why SaaStr Went All-In on AgentsIt started with frustrati

At SaaStr AI London, Amelia and I went deep on our AI SDR journey. We shared all our data, all the emails we’ve sent, all the performance metrics—everything. And the response was overwhelming.But here’s the thing: the #1 objection we kept hearing was “Yeah, but this won’t work for me. I don’t have your scale. I don’t have your data. I don’t have 10 years of history.”That’s simply not true.If you have customers, if you have revenue, if you have a database of any size—AI agents will work for you. You don’t need as much data as you think. You don’t need as much trailing history as you think. What you need is a methodology.Here’s what we’ve learned after sending 60,000+ hyper-personalized emails, booking 130+ meetings automatically, and generating 15% of our London event revenue through AI agents alone.The 5 Biggest Learnings From Deploying AI SDRs#1. AI Agents Crush the Work Humans Won’t DoThis is the single most important insight we’ve discovered.Our human SDRs wouldn’t follow up with return attendees for ticket sales. It wasn’t worth their time—they wanted to hunt six-figure sponsorships instead. We tried incentives. We tried Starbucks cards. We begged them. They said they’d do it, then we’d check the activity logs and discover they lied.The result? When we deployed AI agents on those exact same leads, they generated 15% of our London ticket revenue. Revenue we literally would not have gotten otherwise.Same story with our “ghosted” leads—people who reached out wanting to sponsor SaaStr for five and six figures, and our human team just... never responded. Not because they didn’t like the leads. Because every salesperson is force-ranking in their head, putting all their effort into the one big deal closing this quarter.The AI agent hit those ghosted leads with a 70% open rate.Here’s the mental model shift: Don’t think of AI SDRs as magic revenue generators. Think of them as the team that finally does the work your humans refuse to do. The small leads. The low-scored leads. The “not worth my time” leads. Those leads deserve better, and AI doesn’t discriminate.#2. Hyper-Personalization at Scale Actually Works—But “Pretty Good” Is Good EnoughBefore AI agents, our human SDRs sent maybe 75-300 personalized emails per rep per month. In six months with AI, we’ve sent nearly 60,000 hyper-personalized emails. That’s 32x the max human output.But here’s what people get wrong when they see our results: they expect jaw-dropping, month-of-research-level personalization.That’s not what this is.On a scale of 1-10, our AI emails are maybe a 3 to a 6 in customization. They’re pretty good. They reference the prospect’s company, what they’ve been looking at, maybe something they posted about. But the

I did an open AMA at SaaStr London last week, a classic part of each SaaStr AI event. But this one was different. It was urgency to the max.The room was packed with founders, CROs, and marketers who all seemed to be wrestling with the same existential questions: Is inbound dead? Is the GTM playbook broken? Will AI agents replace my entire team? Should I just give up and become a forward deployed engineer?Most of the anxiety I’m seeing in the market right now is based on a false narrative. A dangerous “woe is me” narrative that’s been accelerating since late 2023. And I think it’s time to get honest about what’s actually happening—and what you need to do about it.The “Woe Is Me” Narrative Is Killing Your GrowthLet me start with the question everyone’s asking: “Is inbound dead? My traffic is down 50% in the last 12 months.”Here’s my honest response: Woe is you. Your SEO is harder. Woe is you. You don’t have as many leads as you had during a lockdown during a global pandemic. Poor you.This leads to a narrative that I think is quite dangerous: that the go-to-market playbook is broken and doesn’t work anymore.It’s just not true.Yes, the playbook that some folks are running from 2021 doesn’t work as well today. But here’s what I say: the plays all work. Webinars, inbound, outbound, leftbound, rightbound—it all still works.The Same CROs, CMOs, Etc. Are Running the Hottest AI CompaniesHere’s what’s fascinating: if you look at the hottest AI companies right now, you’ll see a cast of characters from the 2010s. B2B leaders you know from SaaStr 2017 and 2018 are running today’s AI rockets.* Vercel (just raised at $10B): Their COO? She was the Chief Business Officer at Stripe.* Replit (0 to $250M this year in vibe coding): Their CRO? He’s from ZoomInfo.* Bolt (one of the vibe coding leaders at $60M): Head of sales? He was on our old SaaStr sales team.That wouldn’t be possible if the plays don’t work. These leaders are using different tools. They’re using more AI things. But it’s the same playbook. Same demos. Same everything.The biggest real difference? There’s just so much demand. Tools like Cursor, Replit, Lovable, 11 Labs—they’re so disruptive that everyone is in market simultaneously. 11 Labs went from almost nothing to $300M this year. Bolt has so much inbound they can’t service it. At $60M ARR, Brian has maybe four people on his sales team. How many of thousands of leads can they follow up on?“They’re all classic B2B sales reps—just instead of calling every lead and trying to convince them their fungible product is the exact same as another product, they have insane demand and are servicing it. But it’s the same playbook.”The AI Budget Paradox: Record Spending, Record CutsHere

After deploying 5 AI SDRs across inbound, outbound, and follow-up—here’s the actual numbers, unexpected learnings, and what it really takes to make them workSix months ago, we had essentially zero AI SDRs at SaaStr. Today, we’re running five specialized AI agents that have sent nearly 20,000 outbound messages, closed over $1M in revenue, and fundamentally changed how we think about sales development.The results look incredible on paper: 6.7% outbound response rates (double the industry average), $1M+ closed in 90 days from our inbound agent alone, and 20% of our event ticket sales now coming from AI.But here’s what nobody tells you about AI SDRs: they require massive human oversight, they can’t fix what’s already broken, and the path to success is completely different than what vendors promise.SaaStr’s Chief AI Officer Amelia Lerutte and CEO Jason Lemkin share the real data, the brutal learnings, and exactly how we got these results. And want to see the tools we use? Click here.TLDR and Top 5 Learnings After Six Months of AI SDRs1. AI SDRs Scale What’s Already Working—They Can’t Fix What’s Broken* If your outbound isn’t working with humans, AI won’t save it* You must have proven messaging, defined ICP, and working processes before deploying* AI amplifies your best practices infinitely—but you need best practices first* We had to fix our broken RevOps processes before AI could help scale them2. They Require Massive Human Oversight (15-20 Hours Weekly)* These agents consume the signficiant amount of Amelia’s and Jason’s time to run successfully* Performance ebbs and flows directly with human attention—more time invested = better results* Weeks I’m busy with other work, agent performance noticeably dips* This is not set-and-forget technology; it’s coaching five SDRs simultaneously who work 24/73. Specialization Beats All-in-One. For Now.* We run 5 different AI SDRs, each trained for specific use cases (cold outbound, lapsed customers, active nurture, inbound qualification, ghosted lead recovery)* Even within one platform, we have sub-agents with completely different training* Specialized tools go deeper than all-in-one platforms—we’ll take three A+ tools over one B+ tool* The training specificity for each use case matters enormously for results4. The Unexpected Direct-Selling Capability* AI got surprisingly good at closing deals directly, not just booking meetings* For sub-$1K products (event tickets), our AI now closes deals autonomously* For higher ASP deals ($50-100K+), it qualifies and books meetings, then hands to humans* 20% of our event

Ahead of SaaStr AI London on Dec 1-2 (See you there!) we’re taking a look back at some of our favorite sessions from our European events. It was so great when Olivier Pomel, founder CEO of Datadog, joined us as they crossed $100,000,000 ARR in a candid conversation it would be harder to do today post-IPO.The First $100,000,000 ARR at Datadog: How Olivier Pomel Built a Customer-Centric Monitoring GiantFrom zero lines of code to 700 employees and doubling revenue annually, Datadog CEO Olivier Pomel shares the counterintuitive strategies that built one of the most customer-obsessed companies in B2B SaaSOlivier’s Top 5 Toughest Learnings* You can’t be customer-focused if you’re sales-driven OR engineering-driven - Most companies fall into one trap or the other. Sales teams optimize for closing the next deal (short-term), while engineering teams build for the long-term without bridging back to customers. Customer-centricity requires daily vigilance against both.* Closed alphas with “perfect customers” give terrible signal - Handpicking the best companies and best people for early access actually makes it harder to learn. Customers need to self-select when the timing is right for them. Open betas revealed infinitely more than curated alphas ever did.* Month-to-month contracts are better than annual deals for learning - Every instinct (and investor) tells you to sell annual contracts. But monthly contracts force bad news to surface immediately instead of a year later. A year of going in the wrong direction is devastating for a young company.* There’s no MVP for enterprise infrastructure - The conventional wisdom about shipping minimal products doesn’t apply when selling to enterprises who need comprehensive solutions. You need depth across many features before you’re minimally useful. It’s a continuum, not a single viable moment.* Pricing conversations reveal product truth better than any metric - Putting a dollar amount on features focuses customers’ minds like nothing else. When customers say “I won’t pay for that,” you get brutally honest feedback about value. This friction is healthy and teaches you where to go next.When Olivier Pomel and his co-founder started Datadog in 2010, they didn’t write a single line of code for the first six months. For two engineers itching to build, this took “some restraint,” as Olivier puts it. But this decision to obsessively listen before building became the foundation of a company that would redefine infrastructure monitoring and grow to 700+ employees while doubling in size every single year.At SaaStr Europa, Olivier pulled back the curtain on how Datadog became one of the most customer-centric companies

We’re back! Harry, Rory and Jason!The venture capital playbook is broken. Not bent — broken. In the latest 20VC x SaaStr episode, Harry Stebbings, Jason Lemkin, and Rory O’Driscoll dissect why even Sequoia is making dramatic leadership changes, why seed investing at $50M pre-money might not work anymore, and what it actually takes to build venture returns in the age of AI.This isn’t your typical venture conversation about “exciting trends.” This is three investors with $3+ billion in combined AUM telling you what’s actually working, what’s spectacularly failing, and why the old playbook from 2015-2022 is now a liability.Key TakeawaysOn Venture Capital Evolution:* Sequoia’s leadership transition reflects broader industry truth: most VCs and executives from the last decade aren’t the right people for the next decade* The pace of AI evolution means knowledge from 6 months ago is probably wrong; staying current requires dedicated time investment* Partnerships are inherently dysfunctional when performance can’t tie to economics, creating inevitable internal tensionOn AI Investment Strategy:* Only three ways to win: (1) Attach to compute budgets, (2) Replace human headcount, or (3) Massively displace incumbents* Using AI to “make your product better” no longer earns any kudos — that’s table stakes in 2024* Co-pilots were the 2024 story that didn’t work; agents becoming actual team members is the 2026 opportunityOn Deal Dynamics:* Getting into deals at 5-10M ARR requires top-decile metrics — there’s almost no middle class of fundable companies* The quality and speed of competitive clones has increased dramatically, compressing the window for building moats* Traditional seed defensibility is dead; founders must run faster and bet on scale creating the moat, not early product advantagesOn Portfolio Construction:* With increased variance in AI deals, diversification becomes more critical, not less* Small fund sizes ($40-100M) with acceptance of dilution can generate superior returns (10x+) versus large funds maintaining ownership (5x)* 80+ company meetings per week per partnership is one approach; building deep relationships with fewer founders is anotherOn Fundraising Process:* The best fundraises don’t feel like processes — they’re cultivated over months with 3-4 investors ready before the data room opens* Taking a term sheet immediately versus “running a process” depends on capital efficiency and relationship quality* Founders often overlearn “run a process” advice without understanding the optimal approach is having everyone ready to commit before you formally raiseOn Market Dynamics:* Companies attached to AI compute infrastructure (like DataDog) are crushing

By the end of Q3, we’ll had 10 distinct AI agents running in production at SaaStr. 20 if you including less critical ones. Not as a tech experiment or marketing stunt, but as core members of our revenue and operations team.The lineup looks like this:Revenue Team:* 3 AI SDRs handling each of ticket inquiries, sponsor outreach, and sales support (these are different workflows, training, etc)* 2 AI BDRs qualifying inbound leads and nurturing prospects through our funnel* 1 AI RevOps agent tracking and managing our partner pipelineOperations & Experience:* 1 AI Support agent handling event logistics and attendee questions* 1 AI Content Review agent vetting speakers and session proposals* 1 AI Matchmaking agent connecting CEOs and executives at our eventsCommunity & Education:* 1 AI Mentor (SaaStr.ai) providing 24/7 guidance to our community. Try it, it’s free!And we’re not done. The pipeline has 3-4 more AI agents in development.The Operational Reality: It’s A LOT More Work Than You ThinkHere’s what nobody tells you about AI agents in production: they require daily management and review. Not weekly check-ins. Not “set it and forget it” automation. Daily.Every morning, I’m reviewing:* Conversation quality scores from our AI SDRs* Lead qualification accuracy from our BDRs* Edge cases that required human escalation* Performance metrics across all agents* Training data updates and model refinementsEach agent needs constant fine-tuning. The AI SDR that handles sponsor inquiries needed 47 iterations to stop being too aggressive on pricing discussions. Our AI Support agent had to be retrained three times to properly escalate VIP attendee issues.The truth? Managing 10 AI agents is like managing a team of 10 very capable but very literal junior employees who need explicit instructions for everything.But Here’s Why We’re All-In: The Advantages Are UndeniableDespite the management overhead, these AI agents deliver something human employees simply can’t:They never quit. Zero turnover. No recruiting cycles. No onboarding new SDRs every 18 months because they got poached by a competitor offering $10K more.They work weekends. W

Medha Agarwal is a Partner at Defy VC, where she focuses on investments in AI-first and vertical SaaS companies. She shares insights at SaaStr AI Summit 2025 from the front lines of AI-first product pricing, exploring why traditional SaaS models are declining in favor of transactional pricing, how to choose the right pricing structure for your business, and strategies for capturing value from labor budgets instead of software budgets.Top 5 Takeaways* Transactional pricing is replacing traditional SaaS at an accelerating rate. The fundamental shift is driven by AI’s ability to complete tasks end-to-end, enabling companies to sell into labor budgets rather than software budgets. This opens up significantly larger TAMs that were previously dominated by human labor costs.* There’s a 1.5-2.5x revenue multiple premium for SaaS models in public markets, but transactional models capture more value. While SaaS offers predictability and better cash conversion cycles with annual upfront payments, transactional pricing allows you to scale revenue with customer growth without being constrained by seat count. The trade-off is revenue predictability versus value capture.* Hybrid models are emerging as the best of both worlds. Companies are mitigating transactional pricing unpredictability by implementing tiered subscriptions with usage minimums and overage billing. This provides baseline revenue predictability while maintaining the ability to capture value at scale through consumption-based pricing.* Your pricing model choice depends on four critical factors. Frequency of usage, magnitude of cost savings, workflow integration point, and customer budget type all determine whether fixed-cost, input-based transactional, output-based transactional, or hybrid pricing makes sense. High-frequency tools like Slack need flat fees because users can’t mentally track per-use costs.* Never compete on price alone when entering a market. Undercutting competitors on price creates a dangerous dynamic where you attract price-sensitive customers rather than best-fit customers, leading to higher churn and false signals of product-market fit. Price at par with competitors and win on value, or you’ll be forced into a race to the bottom.The Fundamental Shift: Why SaaS Pricing Is DyingWe’re witnessing a massive sea change in how AI-first products are priced. At DEFY, we’ve seen a dramatic increase in the frequency of transactional-based pricing models. Traditional SaaS pricing, while still popular, is declining rapidly in favor of transaction-based approaches.The reason is straightforward. With AI, there’s an increasing ability for software

So we’ve spent a ton of time over the years on SaaS talking about hiring a great VP of Sales / CRO . Not only because it really matters, but because hiring the wrong VP of Sales can set you back a year — or longer.So I thought I’d come back to the classic topic and make a list of the Top 10 Mistakes I See Founders Make When Hiring a VP of Sales:#1. Hiring a VP of Sales Who Never Really Understands Your Product During The Interview ProcessOk I know some even many will disagree, but I’m right here :). I can tell you as a pretty good investor across many leading B2B companies, I’ve never seen a VP of Sales thrive that didn’t really understand the product during the interviewing process. Never. I see so many B2B startups hire someone likeable, who can talk the talk on sales hiring and processes — but never really understands what you do. Or puts in the effort to do so. Don’t make this hire. They never invest the time after they start, either. Or they are never able to.This has almost become my #1 flag now. Way too many folks give managers a pass here that never understand the product. You gotta watch the YouTube videos. Do a demo. Listen to some Gong calls. At least get close. Or you just plain never do once you start. So many VPs of Sales disagree with me here — at least at first when I make the point. But later, they agree 😉#2. Hiring a VP of Sales With No One Lined Up to Follow ThemThis is a classic SaaStr point and post from over the years, and it turns out it’s more true today than ever. 50% of what a VP of Sales really does is recruiting. So the best VPs of Sales always have at least 2-3 great folks lined up to come with their to their next role. Just ask. Ask who those 2-3 are. And if you’re ready to extend an offer, talk to them before you do.#3. Hiring a VP of Sales That Actually Doesn’t Want to Sell Themselves AnymoreThis one has really become an issue in recent years, and the one hand I get it. Sales is hard. And it never really gets easier. So at some point in their careers, some some leaders don’t really want to sell themselves anymore. They’ll manage a team. Check the dashboards. Build process. But sell themselves? They’re sort of done. We call this Mr/Ms. Dashboards, and it’s not a new thing per se. But it’s much more common than a few years back. Because SaaS is getting to be 20+ years old.Don’t hire this person. No matter how well they can talk the talk.#4. Hiring a VP of Sales That Doesn’t Want to Go Visit Customers In PersonThis is newer, but common these days. I recently interviewed a seasoned VP of Sales that lived in the South Bay in the Bay Area. He said he wouldn’t travel all the way to SF to visit customers because it was “too far”

Brett Queener is Partner at Bonfire Ventures, a $1B AUM seed-stage fund writing $3-4M checks into application software companies. He was employee #70 at Salesforce.com, where he built go-to-market, launched the AppExchange, and helped scale the company from its earliest days. Previously, he worked at Siebel Systems (the fastest-growing software company of its era) and ran a B2B startup (SmartRecruiters) from pre-revenue to $100M ARR. He writes about the changing software industry in real-time at his Substack.He came to SaaStr Annual + AI Summit for a deep dive on AI and Product.Brett’s Top 5 Take-Aways* Your product has to deliver immediate, “What The Frack” Value Now in the Age of AI. It has to immediately do a job you couldn’t do before. * Start small, expand fast. Forget the big enterprise land. Demo with their data, put it in their hands immediately, let them feel the “holy s**t” moment with an agentic assistant—then expand. The McGillaguer-Guerrilla deal is over.* Your product must teach itself. When you’re shipping every 30 days, quarterly release webinars are dead. Build agentic assistants that tell users: “Hey, you know I can also do this? Want to try it?” The product needs a relationship with the user.* Rethink annual contracts. If agents behave like $200K employees (paid monthly, can be fired), why are we doing annual upfront payments? The renewal decision isn’t “does our software need to keep running”—it’s “is this assistant still the best person for this job?”* Fire customers who don’t get it. Some enterprise buyers want 12-month Accenture rollouts. They’re treating your agentic solution like it’s PeopleSoft in 2003. Walk away. They’ll slow you down until you die.What happens when product innovation accelerates 10x? Everything you know about building SaaS is about to change.I’ve been in enterprise software since the green screen days. Built CRM systems on Access and Visual Basic. Was employee 70 at Salesforce when we spent 60 cents of every dollar on our own data centers. Ran a startup that hit $100 million ARR (through a lot of tears, I’ll admit). Now I’m a partner at Bonfire Ventures, writing $3-4 million seed checks into application software companies doing $500K-$1M in revenue.And I’m anxious. Not the normal founder anxiety. A different kind. The kind that comes from watching the fundamental rules of software change in real-time.The Old Playbook is DeadWhen SaaStr started, the model was simple: build a SaaS version of an on-premise category winner. Ship a big product release once a year at Dreamforce or your equivalent user conference. Run the company full-throttle across the entire organization based on that an

A SaaStr Annual + AI Summit conversation with Jesse Zhang, CEO of Decagon, and Sarah Ittelson, Partner at AccelDecagon Today: The Numbers Behind the HypeFounded in late 2023—just months after GPT-4’s release—Decagon has become one of the fastest-growing AI companies in history. The company builds AI customer service agents for large enterprises, automating conversations that previously required human support teams.The Growth Trajectory:* Founded: Late 2023* Time to Eight Figures ARR: ~18 months* Team Size: ~100 people (and scaling rapidly)* Location: 100% in-person team* Customers: Major enterprises including Hertz, Chime, and other leading brands* Typical Customer ROI: $800K in savings for every $250K spent* Market Position: Recognized as the leading Gen-AI native solution in customer service automationWhat Makes This Growth Unprecedented:Even by venture standards, this is exceptional. Sarah Ittelson, the Accel partner who led their Series A investment, has been part of the hyper-growth phases at Uber, Uber Eats, and Fair. Her assessment? “This current moment and the scaling that’s possible within these AI companies is unparalleled to even those hyper-growth moments of before.”When Accel invested at the Series A, Decagon was targeting seven figures. By the time Jesse and Sarah took the stage at SaaStr to share their playbook, they’d already blown past eight figures. The headline had to be updated mid-flight.This isn’t a story about getting lucky in a hot market. It’s a masterclass in intentional decision-making, relentless customer focus, and building a machine that compounds growth. Let’s unpack exactly how they did it.The Market Selection Framework: Why Customer Service WonHere’s the reality most founders miss: your growth rate is mostly determined by which market you’re in.Jesse and his co-founder didn’t just pick customer service because it seemed like a good idea. They ran a rigorous discovery process—talking to roughly 100 potential customers over the course of a month. Every day packed with customer conversations. Every night cranking out product to show the next day.What made customer service the winner?* Clear, measurable ROI: Companies could point to specific dollar savings. Spend $250K, save $800K in human support costs. That’s not a pitch—that’s math.* Massive TAM: Customer service is one of those rare markets where the surface area is enormous. Every large company has support teams. Every user interaction is a potential automation opportunity.* Buyer urgency:

The State of AI + Software: Where It’s Going - FastThis deep dive is from Jason Lemkin at the LIVE AI Workshop Wednesday. Sign up here for the next one.I was talking to a founder recently who’s running at $50 million ARR. Classic SaaS guy turned AI guy. And he’s going to scale from $50M to $100M with just five sales reps and a team of AI agents.In the old days, at $50 million, you’d probably have at least 100 sales reps. Why? Because to get from $50M to $100M, you need $50M in net new bookings. At $500K net per rep (which is pretty good when you factor in scaling, turnover, and ramp time), you’d need 100 bodies. Minimum.This founder? Five human reps. Plus AI.It’s not that he doesn’t need sales reps. It’s not even that he’s not selling. He’s actually doing classic B2B SaaS sales. He’s just doing it with dramatically fewer humans, and for the purposes of this discussion, he’s doing it so much more efficiently. And he has 10,000+ inbound leads a month flowing through this system.This is where we are right now. And if you’re not paying attention, you’re already behind.Everything Changed For SaaStr Itself in the Last 180 DaysAt the end of Q1 this year, we had zero AI agents in production at SaaStr. Nothing. Nada. We were thinking about it, but we hadn’t deployed anything.Fast forward to today, and we’ve got:* Almost 20 AI agents running in production* Four different AI SDRs deployed and actively working leads* Salesforce Agent Force rolled out (just started yesterday, so we need a bit more time before sharing all the data)* An AI BDR from Qualified handling inbound qualification* A slew of specialized agents for support, research, and operationsIt is so much different even on our little team than it was even 100 days ago. And it’s going to keep changing at this pace.I’ll be honest: we were probably a little behind the curve at the start of the year. Now, we’re kind of at the bleeding edge. And we want to drag everybody along with us because everything’s changing.The models are changing. The tools are changing. Things that didn’t work last year can work really well now.Everyone complained about how crummy AI SDRs were last year—and there still are a lot of issues—but now we know how to train them. Now we know how to iterate with them. Now we know how to make them work.There’s so much more coming, and marketing in some ways is even further behind sales. But it won’t be for long. This whole space is going to radically change in the next 12 months.More on our AI Agents here.The Single Most Important Thing You Need to Do to Stay RelevantHere’s my advice, and I mean this with e

A deep dive into the playbook, lessons learned, and brutal truths about deploying 20+ AI agents into production — that actually work.During Dreamforce Jason (CEO) and Amelia (Chief AI Officer) at SaaStr dropped by Qualified’s office for a deep dive on its AI Agents and top learnings with Kraig Swensrud (Founder & CEO, Qualified)The Bottom Line Up FrontIf you’re a CMO, CRO, or founder and you haven’t deployed at least one AI agent by Halloween 2025, you’re already behind. Not “might fall behind” — you’re already behind. “Every VC I know where a startup hasn’t made the jump yet has given up hope on that company. That’s not hyperbole. That’s the market reality.” per Jason.But here’s what under-discussed training is more important than picking the perfect vendor. We started 2025 with zero AI agents at SaaStr. Now we have 20 in production. The secret wasn’t finding magical tools — it was investing 30 days of deep training upfront, then maintaining an hour every single day.Here’s exactly how we did it, what worked, what failed, and the framework you need to deploy agents that actually drive revenue.The New Budget Reality: Why Traditional SaaS Playbooks Are DeadLet’s start with the uncomfortable truth about 2025 budgets:Traditional SaaS budgets are frozen. CEOs are going around the table telling every functional head to cut 20-30% of their SaaS apps. Half of incremental budgets are going to price increases — Salesforce raised prices 8% this year, others 6-7%. When your IT budget is growing 6% but your core vendors are raising prices 7-8%, where’s the room for another business process workflow app?But AI budgets are exploding. Business software is growing faster than it has ever before — if you tap into AI budget. It’s the only incremental budget most companies have. Nobody is putting more money into old SaaS software. It’s all going into AI.This creates a tale of two cities:* Classic SaaS is geriatric ... but ...* B2B software powered by AI is explodingIf you’re selling the way you sold in 2021, with the 2019 Marketo playbook, there’s no budget for you. The playbook doesn’t work. But tell that to anyone on fire with AI — everything works. Outbound works. Events work. Meeting with customers works. If anyone wants to buy your product, it all works.The Vendor Selection Myth: Training Trumps EverythingHere’s the question Jason gets constantly on LinkedIn: “What’s better — Replit or Lovable? Which AI SDR platform is best? What AI tool is best for RevOps?”Wrong question.Here’s what Jason learned after deploying 20 agents

I think after 100+ days and with 9 apps vibe coded into production with @replit used over 500,000 times we’re just getting going. And … I think key to that is that I’m now “Replit Fluent”.What does that mean? It’s a state where I know how to vibe well enough (without a developer), and I know the app and its capability and limits well enough, that I can basically see any app I want to build in my head, and now know how to completely prompt it and shepherd it to production … before I start.I can now will almost any ‘normal’ app into existence that I want to build. Without a developer.I remember in the early days of Cursor my son actually paid out of his own pocket for the first time since ChatGPT (he’s awfully smart). He said back then Cursor could now do 90% of his coding for him, but “for most people, it might be 10% or less.” I didn’t get what he meant at the time, but today I do. It’s more than being “good at prompting”. It’s understanding the system well enough to already understand its outputs, its limitations, and exactly how it works in practice — before you start. It’s being truly fluent in the agent.This isn’t to say I don’t still have bumps and that some things end up harder or longer than anticipated. But pretty much now I can will most things into production on Replit very predictably with the agent — because I can already plan them out fully in my mind before I first “prompt” the agent.What that means in practice is 3 things:First, I 100% know if a project will work now before I start in Replit. If I can see it to completion in my mind, now I can finish it to a reasonably high standard. That is a huge boon. When I started ‘prosumer’ vibe coding all of 100+ days ago :), I couldn’t finish my first project. In part, it was because I picked a very complex product to start. But there are many stories of others in similar boats. They can’t finish their vibe coded app. But now — I have a 100% chance of finishing a project, and in roughly the amount of time I budget for it in mind before starting.Second, now I’m merely just time constrained in what I can build. I already have a couple of jobs. I have $100m+ to invest in fresh capital at SaaStr Fund, and running SaaStr itself is an eight figure business. Both take a lot of time. But I set aside about 1.5-2 hours a day to vibe code. That’s my budget. That’s what I can build now.Third, maintenance and new features and upgrades to existing apps I’ve vibe’d consume more and more of my time budget. This of course is true of any software. It just catches up to you with prosumer vibe coding. So now that I can basically truly build anything I want, the question becomes — do I have enough time to make it great? Or should that time go into making my existing 9 apps even better? I now can make a pretty good v1 of anything I want to. But getting to great takes time. It always has

Marchelle Mooney, VP of Sales at Mangomint joined us at 2025 SaaStr Annual + AI Summit for one of our best deep dives on sales and GTM in vertical SaaS yet.Marchelle brings a unique perspective to vertical SaaS sales—she’s a former hairdresser and salon owner who transitioned into SaaS sales leadership. At Mangomint, she leads sales for a deep vertical SaaS platform serving salons and spas. Her journey from thinking “SaaS just meant you had attitude” to becoming a VP of Sales at a fast-growing vertical SaaS company gives her insights that bridge the gap between traditional enterprise SaaS playbooks and the reality of selling to SMB vertical markets. She credits much of her learning to Jason Lemkin and the SaaStr community.MangoMint today processes over 1,000,000 appointments a month for over 5,000 salon and spa customers.Top 5 Learnings: The Vertical SaaS Sales Playbook1. Deep Domain Credibility is Non-Negotiable—Build Trust From the First ClickThe insight: Your founding team needs deep insider knowledge of the vertical before shipping a single line of code. This isn’t about market research—it’s about speaking the language fluently.Marchelle emphasizes that credibility builds trust instantaneously. From the moment a prospect lands on your website, they should see language that feels like their own world. At Mangomint, this means understanding the difference between how a hair salon refers to their “client” versus how a med spa calls them a “patient.”She’s witnessed demos end immediately because a rep used the wrong terminology. In one case, calling a “patient” a “client” was enough for the prospect to disconnect—that’s how critical getting the language right is in vertical SaaS.The key principle: You’re solving problems for customers who don’t know what they don’t know. Many verticals have been using antiquated playbooks for years. Your product needs to answer questions they haven’t even thought to ask yet, and you can only do that with genuine insider knowledge.Why it matters: When Marchelle started at Mangomint in 2018, every tenth call involved unseating pen and paper booking systems. Today, it’s maybe once a year. The market has evolved, but the need for domain expertise hasn’t—it’s just shifted to different problems, like introducing AI to customers who’ve only used ChatGPT to decide between sushi or Thai for dinner.2. Win by Eliminating Choice—Your SMB Customers Have Analysis ParalysisThe insight: Vertical SaaS customers, especially SMBs, struggle with too many options. Unlike enterprise buyers who might want flexibility and customization, your vertical customers want you to tell them the right way to do things.Marchelle uses a simple analogy: “What are we doing for dinner? We could do sushi. We could do Thai.” As soon as there are two op

The VC fundraising landscape has completely transformed in the last 18 months, and most founders still don’t realize just how dramatically the rules have changed.After analyzing 1,000 VC pitch decks and calculating 400,000+ startup valuations on SaaStr.ai, and having countless conversations with both sides of the table, the data is unambiguous: traditional paths to venture funding have essentially closed for the majority of B2B companies. The capital isn’t gone—there’s actually more money in the market than ever. It’s just almost all flowing to a completely different type of company than it was 24 months ago.Jason Lemkin joined us for a LIVE SaaStr AI Wednesday to walk us through the data, and do live Q+AIf you’re a B2B founder growing 80% annually at $10-30M ARR with solid unit economics and happy customers, you might think you’re in a strong position to raise. You’re probably not. If you’re planning to raise your Series B based on performance that would have secured funding in 2022, you need to recalibrate immediately. Benchmarks have shifted so dramatically that roughly 80% of VCs who would have enthusiastically funded solid B2B companies 18-24 months ago are now passing—not because those companies got worse, but because an entirely new category of hypergrowth AI-native startups has reset every expectation in the market.This deep dive breaks down exactly what’s happening, why it’s happening, and—most importantly—what you need to do about it right now. We’ll walk through the actual data from top-tier growth funds, show you the real benchmarks you’re being measured against, and give you a clear-eyed assessment of your options whether you’re pre-revenue, scaling past $10M, or approaching $100M ARR.The worst thing you can do is stay in the dark about where you actually stand. Let’s fix that.Top 5 Takeaways* The bar for VC funding has skyrocketed dramatically. AI-native companies are now scaling from $1M to $100M ARR in 8-11 quarters (versus the previous “top quartile” benchmark of 19-20 quarters), fundamentally resetting investor expectations across the board.* 80% of traditional B2B VCs who would have funded you 18-24 months ago won’t fund you today. Capital is flooding into hypergrowth AI-native companies, leaving traditional SaaS startups—even strong ones—struggling to raise regardless of solid fundamentals.* The latest top quartile metrics are higher. And harder. At $1m-$5m ARR, VCs expect 500% growth. At $10-25M ARR, VCs expect 100%+ growth. At $50-100M ARR, they want 90% growth with 120% net revenue retention. Below these numbers, you’ll face an uphill battle regardless of market or team qua

We’re back! Our latest deep dive with Jason Lemkin (SaaStr), Rory O’Driscoll (Scale Venture Partners), and Harry Stebbings (20VC).And come join the team LIVE at SaaStr AI London, Dec 1-2!! More here.Bottom Line Up FrontThe classic venture playbook is almost … no more.* Traditional SaaS metrics like burn multiples—once the gold standard for evaluating capital efficiency—are being rendered obsolete by AI-native companies growing at unprecedented speeds with radically different unit economics.* Meanwhile, founders with objectively good numbers (triple-triple-double-double growth, solid burn multiples) are getting rejected by VCs focused exclusively on AI breakouts.* The message is stark: if you’re not AI-first, raise capital now at any reasonable price, consolidate where possible, and prepare for a world where even “perfectly good” $15M ARR companies have “zero value to VCs.”The stakes extend beyond individual companies:* With 6,700+ unicorns and only 15 IPOs year-to-date, massive portfolio consolidation is inevitable.* Tech PE firms face existential questions as AI agents reduce seat-based revenue and products that stayed static for a decade now require constant reinvention.* And towering above it all: OpenAI’s plan to consume more energy than India within 8 years, raising fundamental questions about whether the economics of AI can ever pencil out at scale.The Burn Multiple Paradox: Why AI Breaks the RulesThe conversation opened with Iconiq’s 73-page State of Software report, which revealed a counterintuitive finding: AI-native companies under $100M ARR have terrible free cash flow margins (-126% versus -56% for non-AI companies), yet their burn multiples are actually better because they’re growing so explosively fast.Rory O’Driscoll explained the fundamental concept: “It’s basically how many dollars of ARR do you get out of each dollar that you’re spending, right? What is the efficiency you’re creating for each dollar of venture capital you’re lighting on fire?”The math seems simple: if you’re valued at 10x ARR and you spend $2 to add $1 of ARR, you’ve created $10 of market cap from $2 invested. But as Rory cautioned, this only works when multiple hidden assumptions hold true:* The ARR is actually real (not inflated by aggressive accounting)* Net retention accounts for real churn (fast growth can hide massive customer losses)* Gross margins are sustainable (hyper-growth can mask deteriorating unit economics)*

Tomasz Tunguz of Theory Ventures brought together a strong panel at SaaStr Annual and AI Summit for a deep dive on enterprise partnerships. An Enterprise Partnerships Bootcamp: How to Land, Scale, and Win with Linear, Omni, Theory Ventures, and Vesey Ventures.* Christina Cordova – Chief Operating Officer at Linear, the purpose-built tool for planning and building products. Previously spent 7.5 years at Stripe where she started their partnerships organization, and later led partnerships and platform initiatives at Notion.* Colin Zima – CEO of Omni, rebuilding BI and analytics to create a tool accessible to both developers and end users with enterprise readiness and AI agility. Previously spent nearly 10 years at Looker, bringing deep data ecosystem expertise to his current venture.* Julia Huang – Founding Partner at Vesey Ventures, a fintech fund based in New York and Israel. Specializes in brokering partnerships between portfolio companies and financial incumbents, with deep expertise in enterprise sales motions within regulated industries.* Tomasz Tunguz – Founder and General Partner at Theory Ventures, focusing on early-stage enterprise software investments with particular emphasis on go-to-market strategy and partnership development.Top Take-Aways:Christina Cordova (Linear): Start with partnerships that are instrumental to your product experience first, not distribution. Build credibility by talking to 10+ users within an enterprise before approaching executives.Colin Zima (Omni): Partner with companies at your growth stage rather than chasing Fortune 50 whales early. Strategic investments from key ecosystem players can bootstrap credibility faster than organic growth.Julia Huang (Vesey Ventures): In enterprise partnerships, you’re controlling for reputational risk, not financial loss. Find the sponsor who can tell your story back to you—they’re your true advocate.Tomasz Tunguz (Theory Ventures): The cost of building integrations has become trivial with AI. Companies should have 25-100 integrations by Series A, not 5-10 like in the past decade.The Partnership vs. Sales Decision: Where to Start and WhyThe fundamental question facing early-stage B2B companies isn’t whether to pursue partnerships or direct sales—it’s understanding when partnerships become instrumental to your product experience and business growth. Linear’s approach exemplifies this strategic thinking.“For us on the partnership side, we really started with partnerships that we felt were instrumental to the product experience first and foremost,” explains Christina Cordova. “At a certain point we were a very small company. We viewed some integrations as

This week: * How the new Replit v3 is the Future: Agents Managing Agents, For Real* How our 4th AI “BDR” helps close deals 24x7, and is much better than a human at it* How our new SaaStr.ai Startup Valuation Calculator processed 300,000+ AI startup valuations in less than 30 days. And how disruptive (and just plain cool) the new Replit v3 is. At SaaStr, we’ve gone from having essentially zero AI agents at the start of 2025 to now having over 12 AI agents in production. We have AI SDRs and BDRs handling both inbound and outbound, a digital assistant answering 150,000+ chats on our websites, and we’ve built AI tools that have processed over 300,000 startup valuations and graded nearly 1,000 VC pitch decks - all in less than 30 days.I think we’ve learned something here about how to effectively build, deploy, and scale AI agents in a real B2B business. And more importantly, what breaks, what doesn’t scale, and where the real ROI lives.Here’s what we learned across three major areas: the future of AI development platforms (Replit V3), why you absolutely need AI BDRs now, and how we built tools that generated real revenue without a development team.Replit V3: The First Glimpse of AI Managing AIThe Big Question: Can you build real B2B applications without a team of developers? The answer is increasingly yes, but it’s more complicated than the marketing suggests.I started this journey a couple months ago looking at the leaders: Replit, Lovable, Bolt, and hot newcomers like Wix Studio and Base 44 (which may have 10% market share already). Many are doing nine figures in revenue very quickly.Why I Chose Replit: When I asked Twitter which platform to pick, everyone said they were similar — at time, a few months back. But Replit was the only one where you could go end-to-end - build, test, prototype, and push to production without configuring databases, moving to different hosting, or dealing with infrastructure headaches. The white-labeled Neon database made it plug-and-play with other tools.Agents Managing Agents: It’s Already HereBut here’s what blew my mind with V3: Replit now has agents that manage other agents.Most of us are struggling to get one AI SDR working, let alone having AI manage 20 agents. But Replit can do it today. When I hit a complex problem building our pitch deck grader, Replit autonomously brought in:* An architect for really tough problems* Specialists for specific issues* Senior and junior agents with different capabilitiesI watched these agents debate each other (in English) for almost 3

Harry, Rory and Jason are back!We’re witnessing an unprecedented capital concentration in AI with NVIDIA’s $100B OpenAI investment creating a fascinating circular money machine, while new H-1B visa fees threaten startup talent acquisition and the venture funding landscape shifts dramatically toward mega-rounds for a tiny number of companies. The era of “founder friendly” has become somewhat hollow rhetoric, and traditional B2B growth metrics like “triple triple double double” are becoming irrelevant as the market polarizes between AI unicorns and fundamentals-driven businesses.Key Numbers That Matter:* 75% of 2025 VC dollars went to just 19 companies* NVIDIA’s $4.5T market cap relies on only 6 customers for 83% of revenue* New H-1B visa fees of $100K will impact 440,000 annual applications* Navan filing for IPO at $8B valuation with $613M revenue, 32% growthThe $100B AI Money Machine: When Six Customers Drive a $4.5T Market CapNVIDIA’s massive investment in OpenAI represents more than just capital deployment—it’s the creation of what could be an infinite money printing loop. OpenAI commits $300B to Oracle, Oracle buys NVIDIA chips, and NVIDIA invests back into OpenAI. As one observer noted: “Sam’s gonna get to make the bet he wants to make which is apply infinite amount of capital and see how long these scaling laws last.”The most striking aspect? NVIDIA, now the world’s largest company by market cap at $4.5 trillion, has only six meaningful customers accounting for 83% of revenue. Compare this to Apple’s 2 billion customers or Microsoft’s hundreds of thousands of enterprise clients. It’s “this really weird dynamic where you’ve got this company with only six customers, but the good news is all six of them are determined to spend themselves into oblivion to win the prize.”The Scaling Laws GambleSam Altman’s recent comments suggest this is just the beginning: “We need three orders of magnitude more compute than this.” The market is essentially allowing OpenAI to test whether massive capital can break through current AI limitations. Whether the marginal $300 billion will earn a return on capital remains questionable, but as Rory put it: “We will find out because no one’s going to call timeout along the way.”The H-1B Shock: $100K Fee Creates New Startup RealityThe new $100,000 fee for H-1B visas represents a significant shift for the startup ecosystem. With 440,000 applications generating $19-120 billion in GDP annually, this policy change will have material impact on early-stage companies.“Anyone that has been doing this for a while that isn’t just three kids working 24/7 in SF has had H-1B folks on their team,” noted one investor. “My first startup wouldn’t have been possible without H-1B. I had two on my first team of 10.” notes Jason.While larger tech comp

How to scale from startup to $300,000,000+ ARR by mastering the fundamentals of go-to-market strategyBuilding a billion-dollar B2B business isn’t about finding secret hacks or silver bullets. They don’t last or scale. It’s about mastering fundamental principles and being willing to reinvent yourself every six months to a year as you scale. Gaurav Agarwal, COO of ClickUp came to SaaStr Annual + AI Summit to share how they did it — and keep doing it.As someone responsible for “all things money” at ClickUp – sales, marketing, growth, pre-sales, and post-sales – Gaurav has lived through the reality that what gets you to $1M ARR is completely different from what gets you to $10M, $50M, $100M, $300M and beyond. Nothing scales infinitely, and every stage requires its own playbook.Here are the key principles that have driven ClickUp’s remarkable growth:1. Know Where You Win: The LTV vs. TAM MatrixMost companies fail because they try to adapt everyone else’s strategies without understanding their own fundamental positioning. Before you copy anyone’s playbook, you need to map your business on a simple 2×2 matrix:* X-axis: Customer Lifetime Value (LTV) – How much can you make from your customers?* Y-axis: Total Addressable Market (TAM) – How many customers are out there?This creates four distinct quadrants, each requiring completely different go-to-market strategies:High LTV, Small TAM: Whale Hunting You’re selling to Fortune 500 companies with limited prospects. Your channels must be high-touch: field marketing, trade shows, conferences, and business development. You can afford expensive customer acquisition because deal sizes justify the investment.Low LTV, Large TAM: Cast a Wide Net You can’t afford expensive acquisition channels. Focus on organic growth: content marketing, SEO, social strategies, and community building. You need LTV-to-CAC positive channels that scale efficiently.Low LTV, Small TAM: Exit Strategy If you have few customers who don’t pay much, you shouldn’t be in this business. Run away and find a better opportunity.High LTV, Large TAM: The Sweet Spot This is where ClickUp operates, and it’s the most exciting quadrant. You can make almost any channel work – enterprise sales teams, billboards, TV ads, digital marketing. The world is your oyster for distribution strategies.2. Learn From the Best-in-Class Across IndustriesDon’t limit yourself to studying companies in your vertical. The best growth strategies often come from unexpected sources:* For SEO: Study HubSpot, but also look at NerdWallet, Canva, and Zapier* For brand building: Don’t just look at B2B comp

The technical and product insights from Dara Ladjevardian’s AI cloning experiment at SaaStr Annual + AI Summit.The Clone Performance Reality CheckWhen Dara Ladjevardian, CEO of Delphi.ai, ran 1 million simulated conversations with digital versions of Brian Halligan (Chairman and founding CEO HubSpot), Lenny Rachitsky, Keith Rabois and Jason Lemkin, the most interesting findings weren’t about the business advice the clones gave—they were about how AI clones actually behave, fail, and succeed.And you can try them all yourself here:* Digital Jason, try it here* Digital Lenny, try it here* Digital Brian, try it here* Digital Keith, try it hereKey Technical Learnings1. Context Dimensions Drive Dramatically Different OutputsThe discovery: The same clone gives fundamentally different advice based on just four input variables:* Company stage (0-1M, 1-10M, 10-100M ARR)* Market context (emerging vs. established, crowded vs. uncrowded)* Team dynamics (solo vs. co-founder, data-driven vs. visionary)* AI adoption positionWhat this means technically: Current LLM approaches that treat context as simple “system prompts” miss the nuanced way human experts actually adjust their thinking. The clones needed sophisticated context weighting to perform authentically.The failure mode: Without proper context handling, AI clones default to generic advice that sounds like the person but lacks their actual decision-making sophistication.2. Temporal Knowledge Graphs Beat Static TrainingDara’s architecture insight: “The best way to represent a network of ideas that changes over time is a temporal knowledge graph.”Why this matters: A static knowledge graph might show Keith Rabois believed X in 2015, but his 2024 graph shows he believes Y. The temporal system tracks belief evolution to predict future responses.The technical challenge: Most AI clones train on a person’s entire corpus as if their views never changed. This creates internally inconsistent outputs that feel “off” to people who know the subject well.Real-world impact: Dara’s grandfather’s clone could apply 1970s Iranian

We're back on 20VC + SasStr with Harry Stebbings, Jason Lemkin, Rory O'Driscoll, and special guest Jeff Lawson (Founder & Former CEO, Twilio)Bottom Line Up FrontRory O'Driscoll: Tesla's trillion-dollar pay package for Elon is a board betting everything on doubling down - they believe without him, the stock drops 75% overnight. It's intellectually coherent but terrifying risk concentration.Jason Lemkin: We're in the greatest wealth hunt in venture history. Orders of magnitude larger deals are the new normal. A $10 billion company feels "niche" today when we're discussing $100+ billion valuations.Jeff Lawson: The AI wave creates unprecedented opportunities for infrastructure companies like Twilio that aren't selling seats - no innovator's dilemma. SaaS companies selling seats face existential disruption as AI eliminates 75% of human roles.Harry Stebbings: Late-stage AI investing has become the rational play for VCs - when only valuation risk remains, even $100M checks into $13B rounds make mathematical sense for portfolio construction.The Trillion-Dollar Elon Bet: Rational or Reckless?Tesla's board just approved what could become the first trillion-dollar executive compensation package in history. But is this visionary leadership investment or a high-stakes gamble gone wrong?The Board's Logic: Double or NothingRory O'Driscoll dove deep into Tesla's 332-page proxy filing to decode the board's thinking. "Compensation is how boards reveal their real priorities," he explained. "Nothing else matters as much. The board wants the Elon bet - they believe they owe him the past and they're betting on him for the future."The package's operational metrics tell the story: Tesla needs to hit $400 billion in EBITDA (four times Google's current profitability), manufacture 20 million cars, deploy 10 million Full Self-Driving systems, and produce 1 million Optimus robots. It's essentially asking Elon to double the existing business while building entirely new categories.The Downside Protection TheoryJeff Lawson raised the critical counterpoint: "Maybe this isn't about upside - it's about the downside case. Tesla is overvalued as a car company. If valued purely on automotive fundamentals, it's worth 25% of current market cap. The other 75% is Elon's special sauce."This creates a prisoner's dilemma for the board. As Rory noted, "If you try to demonstrate resolve and he threatens to walk, you're down 75% next morning. The individual shareholders who voted for this compensation twice want to make this bet, even though it makes my head hurt."The New Benchmark for Founder CompensationJason Lemkin sees this setting a new standard: "This is the new normal for anyone whose board consists of their brother-in-law and other relatives. Almost all my portfolio companies - the founders control the b

From the SaaStr Annual / AI Summit – How three breakout AI companies rewrote the rules of enterprise sales. And see everyone at 2026 Annual + AI Summit May 12-14 2026 and SaaStr AI London Dec 2-3!Speaker BiosTalia Goldberg – Partner, Bessemer Venture PartnersTalia leads AI investments at Bessemer and has been at the forefront of understanding how AI companies break traditional SaaS metrics and business models.Kelly Loftus – Head of Startup Sales, AnthropicKelly has scaled Anthropic’s startup sales team from fewer than 10 people to over 150 as the company grew from 250 to 1,300 employees in just 18 months.Jacob Jackson – Machine Learning Engineer, Cursor (formerly OpenAI, Tab9, Super Maven)A veteran of the AI coding space, Jacob has been building developer tools since 2018 and joined Cursor 8 months ago after working as a researcher at OpenAI.Gorkem Yurtseven – CTO and Co-Founder, FAL (Features and Labels)Gorkem leads the technical vision at FAL, the generative media platform that hosts open and closed source image and video models via easy-to-use APIs.Top 5 GTM Takeaways* No Quotas, No Problem: Both Anthropic and FAL have completely abandoned traditional quota systems in favor of “shadow targets” due to unpredictable AI-driven growth patterns.* Technical Sales Teams Are Everything: All three companies prioritize hiring technically sophisticated sales teams that can use their own products and understand complex technical buyers.* Product-Led Growth Dominates: With massive inbound demand, these companies focus on fulfilling demand rather than generating it, requiring fundamentally different sales motions.* Shorter Planning Cycles Win: Traditional annual planning is dead—these companies are moving to quarterly or monthly targets due to rapid model improvements driving unpredictable adoption.* Internal AI Usage = Competitive Advantage: Companies eating their own dog food internally create better products and more credible sales conversations.The traditional B2B/SaaS sales playbook may not officially dead—but it is at least according to three of the hottest AI companies on the planet. In a revealing panel discussion, leaders from Anthropic, Cursor, and FAL pulled back the curtain on how they’ve built hypergrowth go-to-market engines without quotas, with technical sales teams, and powered by product-led growth that would make traditional SaaS executives’ heads spin.T

This week Canva Co-Founder and COO Cliff Obrecht joined Harry Stebbings, Rory O’Driscoll and Jason with honest, unfiltered insights on scaling to 240 million users, navigating AI transformation, and preparing for public markets.Canva’s CoFounder Joins 20VC + SaaStr on The Coming IPO, Where AI Works Today, Employee Liquidity, Figma’s IPO, and Much More!After 13 years building Canva into a $4 billion ARR juggernaut, Cliff Obrecht’s key insight is deceptively simple: “In the end, the thing that bails out our incompetence is your growth rate.” Whether facing 50x valuations in 2021 or 10x today, the fundamentals remain constant — compound growth covers a multitude of sins, while everything else is just noise.The $4B ARR Reality Check: What Actually Drives Growth at Scale — 90% is OrganicCanva will close 2025 “very close if not at $4 billion” in revenue, growing nearly 40% and reaccelerating after a post-2021 adjustment period. For Obrecht, this trajectory validates a contrarian approach to scaling that most B2B companies get wrong.“One thing you need to buck the trend of as you become a larger company is insular thinking and treating your user base like a wet tea towel that you need to ring out,” Obrecht explains. “90% of our user acquisition is organic.”The reacceleration story breaks down to three core drivers, with AI playing a surprisingly modest role:1. Core Flywheel Optimization (70%) “We just needed to reaccelerate all our core flywheels. We spoke about paying up for the team.”2. International Expansion (10%) “Going really heavy on international enhanced that.”3. AI Integration (20%) “I would probably say 20% [of reacceleration comes from AI].”This distribution challenges the narrative that AI is the primary growth driver for established SaaS companies. Instead, Obrecht’s thesis is that AI amplifies existing strengths rather than creating new ones.The AI Integration Playbook: Workhorses, Not GimmicksCanva runs billions of AI inferences monthly, but Obrecht’s approach differs markedly from AI-first companies. The philosophy: “We’re all about creating workhorses, not gimmicks. AI is just accelerating [our mission] massively, making it quicker, faster, and better for customers to achieve their goals.”The 10% GPU Tax Is RealLike Notion, Canva is paying the “GPU tax” — roughly 10% of revenue going to AI infrastructure and model providers. “100% yes, it already is [10% of revenue],” Obrecht confirms. “If you look at Lovable, their pass-through to Anthropic or model providers will be way more than 10%.”But this isn’t sustainable at current levels. Canva’s optimization strategy reveals how smart SaaS companies should think about AI costs:Near-term Reality:* Heavy compute costs for n

We had a great one this week — Marc Benioff joined Harry, Rory, and Jason on 20VC+SaaStr this week to deliver one of the most grounded and passionate takes on AI we’ve heard from any enterprise leader. In a market fueled by AGI promises and $10 billion funding rounds, Salesforce CEO’s cut through the hype while revealing his company is quietly building a billion-dollar AI business — by focusing on practical applications over futuristic fantasies.And Marc shared for the first time how AI is letting them follow up on 1,000,000+ leads their human sales team … never followed up on.Bottom Line Up FrontAI is working at enterprise scale, but not always in the way the hype machine suggests. Benioff’s Salesforce has achieved over $1 billion in AI and data cloud revenue—their fastest-growing product ever—by deploying agentic systems that actually solve customer problems today. Meanwhile, the venture ecosystem continues pouring unprecedented capital into AI infrastructure plays that may struggle to justify their valuations without dramatic changes in enterprise spending patterns.The Bottom Line Up Front:* Benioff’s Reality: “I don’t think that there will be a piece of software that we sell that will not be agentic.” Salesforce achieved $1B+ AI revenue faster than any product in their history by focusing on practical applications rather than AGI promises, while redeploying 4,000 support agents to higher-value roles.* Harry’s Concern: “I don’t feel like we’ve ever had the concentration of value tied to AI in seven companies as we have today.” The MAG-7’s unprecedented market concentration around AI creates systemic risk, while traditional growth metrics become meaningless when 10% growth at $40B scale adds an entire Palantir annually.* Rory’s Math: “You actually need these things to take vast chunks out of the labor budget and be worth 20, 30, $40,000 almost a head to the enterprise for the math to work.” Foundation model valuations require AI agents to capture massive enterprise labor budgets—a scale that current use cases haven’t yet reached.* Jason’s Evolution: “The fundamental architecture of an enterprise software company in the future is not exactly as it was in the past.” Companies must redesign organizational structures around AI capabilities, with 80% of VCs now refusing meetings with non-AI founders regardless of fundamentals.The Reality Check We NeededBenioff opened with a direct challenge to the AGI narrative: “You’re talking to somebody who is extremely suspect of anybody who uses those initials, AGI. I think that we have all been sold a lot of hypnosis around what’s about to happen with AI.”This isn’t technological pessimism—it’s operational realism. Benioff acknowledged AI’s power while stripping away the mysticism: “Large language models are two th

When you think about Product-Led Growth (PLG) success stories, few companies exemplify the model better than Calendly. Founded in 2013 by Tope Awotona, Calendly has grown from a simple scheduling tool to a scheduling powerhouse that's touched "double digits" of the world's billion knowledge workers - meaning over 100 million people have used the platform at some point.At SaaStr Annual + AI Summit, Awotona shared the hard-won lessons from building one of the most successful PLG companies of the last decade. What makes his insights particularly valuable is his transparency about the mistakes Calendly made along the way - and how they course-corrected.Today, Calendly maintains a fascinating 90/10 revenue split between self-serve and sales-led motions, with customers ranging from individual contributors to million-dollar enterprise accounts. But getting that balance right took years of experimentation, data analysis, and some painful lessons about when PLG and enterprise motions can cannibalize each other.Top 5 Key Learnings from Calendly's Journey1. The Free Plan Is Your Marketing Engine - Protect It at All CostsPerhaps the most counterintuitive insight from Awotona: Calendly spends "almost zero dollars on marketing campaigns." Their growth is entirely driven by user activity on the platform. Free users aren't just prospects - they're active marketing assets with measurable LTV."We're happy for people to use the product for free," Awotona explained. "Even free users have an LTV associated with them because we spend almost zero dollars on marketing campaigns."The pressure to tighten free plans is constant. Sales teams consistently identify the free version as their #1 competitor, not any external product. But since putting up their first paywall in 2014, Calendly has never removed features from the free plan - they've only made it more generous.The Takeaway: If your free plan drives viral growth, resist the short-term temptation to squeeze it. Instead, add more value to paid tiers rather than removing value from free ones.2. Viral Loops Get Harder at Scale - But Scale CompensatesCalendly closely tracks two critical metrics: meetings-to-signups conversion rates and signups-to-activation rates (defined as five people scheduling with a new user). As expected, these conversion rates decline at scale - the viral coefficient naturally decreases as you reach market saturation.But here's the key insight: "The good thing is the top line - the denominator - is getting bigger. That compensates a little bit for that degradation in conversion rate."The Takeaway: Don't panic when viral coefficients decline at scale. Focus on optimizing the absolute numbers while understanding that percentage-based metrics

Gusto co-founders Josh Reeves (CEO) and Eddie Kim (CTO) came to SaaStr AI Summit to share their journey building “Gus,” an AI assistant now used by hundreds of thousands of small businesses.Rather than chasing AI trends, they focused on solving real compliance pain points for small business owners navigating complex regulatory requirements across 50+ different state and local jurisdictions.Their approach involved two key tracks: conversational interfaces that make software more intuitive, and automation that eliminates time-consuming tasks entirely. The result is an AI system that generates reports, executes actions like approving time-off requests, navigates complex compliance requirements, and creates optimal shift schedules. Gusto’s “startup within a startup” methodology, 90-day roadmap horizons, and hybrid interface philosophy offer practical lessons for any SaaS company serious about AI implementation.Top 3 Takeaways from Gusto Co-Founders Josh Reeves & Eddie Kim:* Create a “Startup Within a Startup” for AI Projects – Gusto built Gus by creating an independent team that operated outside normal engineering processes, shipped weekly, and didn’t even use project tracking tools initially. This allowed for the rapid experimentation needed in the fast-moving AI landscape.* Focus on Problems Only You Can Solve – Be disciplined about which AI problems to tackle versus which ones the broader AI ecosystem will solve naturally. Gusto focused on Gusto-specific challenges while betting that general AI capabilities would improve on their own.* The Future is Hybrid Interfaces, Not Just Conversational – While conversational AI is powerful for many tasks, the best user experience combines conversational and graphical interfaces at the right moments. Not everything is better done conversationally.The Mission: Making Small Business Compliance Suck LessBefore diving into the technical details, Reeves made something crystal clear: “Companies don’t exist for the sake of it. We exist to go fix something, to go serve our customer and solve a pain point in their life.” For Gusto, that pain point is compliance hell.Small business owners navigate a maze of local, state, and federal regulations. “The US in particular is more like 50 countries than one when it comes to all the different rules and requirements a business owner has to navigate,” Reeves explained. This isn’t about using AI because it’s trendy – it’s about using AI to solve a very real, very expensive problem.Two Tracks: Conversational Interface + AutomationGusto approached their AI strategy along two clear tracks that every SaaS company should consider:Track 1: The Conversational Interface Revolution</str

The latest 20VC x SaaStr episode with Harry Stebbings, Jason Lemkin, and Rory O’Driscoll is here! The team is discussing Databricks’ $100B valuation, the coming IPO tsunami, CoreWeave’s massive debt raise, AI infrastructure spending that could hit trillions, the return of SPACs as a bubble signal, and why AI tool consolidation will happen faster than anyone expectsBottom Line Up FrontHarry Stebbings: “We’re seeing the biggest wealth transfer in tech history unfolding before our eyes. When Databricks hits $100B and Andreessen could make $30B on a single deal, we’re not just in a bubble—we’re in a generational moment where the next wave of IPOs could make 2021 look like the appetizer.”Jason Lemkin: “I think we’re all going to live in AI 24/7 and use 10 times the tokens and 10 times the compute in 24 months. The math is staggering—if we need 200x more infrastructure, how do we even finance that? But here’s the thing: we’ve already automated 5 humans with 10 AI agents at our small team. This isn’t theoretical anymore.”Rory O’Driscoll: “All this depends on 3-5 more years of continued AI capex expansion. If that’s the case, everything works. If not, all bets are off. We’re way out on the risk curve, and the only thing between us and Armageddon is AI adoption continuing at this pace.”Databricks Hits $100B: The New Normal for Private ValuationsWhen Databricks crossed the $100 billion valuation threshold this week, the most telling reaction wasn’t celebration—it was a collective shrug. As Jason noted, “If you’d said 5 years ago there’s going to be a hundred billion dollar market cap private company, you’d be like no way. Now you’ve got Anthropic at $170B, SpaceX at $360B, OpenAI at $500B. The correct response is yeah, whatever.”But beneath this seemingly blasé attitude lies a fundamental shift in how we value AI infrastructure companies. Databricks is now worth ~50% more than Snowflake while growing 2x as fast—crossing $4 billion ARR at 50% growth versus Snowflake’s $4 billion at 26% growth. At 25x revenue, it actually feels undervalued given the growth trajectory and AI positioning.The real story here isn’t just another unicorn—it’s about generational wealth creation concentrated in private markets. If Andreessen Horowitz led Databricks’ seed round in 2013 with follow-on investments across multiple funds, they could own 15% of a company heading toward a $200B IPO. That’s potentially $30 billion in returns from a single deal.“If A16Z invested out of a $650 million fund at the time and they’ve turned a billion and a half into $30 billion, you’re going to feel good in the morning,” Rory observed. “The one thing we don’t take enough into account is just how many people make money when these go out. There are so many LPs in SPVs, SPVs on SPVs. There are dentists who ar

At SaaStr's packed AI Summit 2025, Box CEO Aaron Levie and IBM VP of AI Raj Datta did a deep dive together with SaaStr's Jason Lemkin on how B2B companies should think about AI agents. With 10,000 attendees—a massive jump from last year—the energy around AI agents was electric.Here are the top 10 learnings from their convo:1. AI Agents Represent a Fundamental Shift from Software to Digital LaborThe Key Insight: We've moved beyond chat interfaces to AI that actually performs work autonomously.Levie explained the evolution: "We've had AI models for five-plus years. Then we had assistants like ChatGPT. But now we're seeing agents that fundamentally go do work for you—and that work could take a minute, an hour, or 100 hours for the agent."For B2B companies, this changes everything. Instead of selling software to 10 lawyers in a company, you're now selling "infinite legal capacity." IBM proved this works at scale, saving $3.5 billion internally through AI agents handling HR and procurement functions.Takeaway: Start thinking about your software as digital labor, not just tools. What work can your AI agents do autonomously for customers?2. Your Customer Data Is Your Biggest Competitive AdvantageThe Key Insight: The companies with the richest, most proprietary datasets will win in the AI agent era."What data are you sitting on that is proprietary to you?" Levie asked. "Very quickly you realize more companies are actually in the data business than they initially thought."Box exemplifies this perfectly. Customers who've stored documents for years can now ask complex queries like "Tell me everything where I have the wrong indemnity provision" or "What contracts I shouldn't have signed." This transforms static data into dynamic business intelligence.Takeaway: Audit your proprietary data assets. What unique insights could AI agents extract that your competitors can't replicate?3. Enterprise AI Adoption Is Happening 1000x Faster Than CloudThe Key Insight: Unlike cloud adoption, which took over a decade, AI is being embraced immediately by enterprises."It's going 1000x faster than cloud adoption because everyone's using it," Datta noted. While pitching cloud to banks in 2008-2009 was a "non-starter," today there isn't an enterprise that doesn't already have an AI strategy in development.The speed difference is staggering: ChatGPT reached 500 million users in roughly two years—faster than any technology in history.Takeaway: Your enterprise customers are ready to buy AI solutions now. The question isn't if they'll adopt AI, but which vendor they'll choose.4. IBM's Agent Catalog Could Revolutionize B2B DistributionThe Key Insight: Even small B2B s

How We Built ChatGPT Enterprise's Sales Team from Absolute Zero: The Complete Playbook with Maggie Hott, GTM Leadership at OpenAIAbout Maggie: Maggie Hott has spent 15 years building go-to-market teams at four unicorns that collectively represent over $50B in market value. She started as the 2nd SDR at Eventbrite, became the first sales hire at Slack (helping scale from $50M to $1B ARR and a $27B Salesforce acquisition), served as Director of Sales at Webflow (scaling from $40M to $140M ARR), and now leads go-to-market at OpenAI where she built ChatGPT Enterprise from scratch. She also runs a venture fund with seven other women investors, backing 30+ founders. These are her personal views, not those of OpenAI.It was early 2023. OpenAI had just launched ChatGPT, the fastest-growing consumer app the world had ever seen. We were riding an incredible wave, but we had a critical hypothesis: ChatGPT Enterprise would require a fundamentally different go-to-market motion than our API business.When OpenAI hired me to build ChatGPT Enterprise from scratch, I walked into what can only be described as a beautiful blank slate—and a terrifying challenge.Our entire sales and go-to-market organization was less than 10 people. No SDRs. No solution consultants. No customer success managers. No sales operations. No RevOps. No marketing enablement. We didn't even have a working Salesforce instance.What we did have: six incredibly talented account directors and one technical success partner, all laser-focused on selling our API to developers and technical teams.Here's the complete playbook for how we built what we believe became the fastest-growing enterprise application in history.The Build vs. Adapt Strategic DecisionMost companies in our position would have taken the "efficient" approach: enable the existing team to sell both products, maybe hire a few specialists, and gradually expand capabilities.We chose the opposite path: build a dedicated ChatGPT Enterprise team from absolute zero.This wasn't just about headcount. It was about creating an entirely separate organizational DNA optimized for enterprise selling.Why We Built Separate vs. Adapted ExistingProduct Complexity Was Fundamentally Different* API sales required deep technical conversations about integrations, rate limits, and model parameters* ChatGPT Enterprise needed business impact discussions about productivity, compliance, and organizational change management* The buyer personas didn't overlap—CTOs vs. CHROs, CFOs, and business unit leadersSales Cycles Had Different Rhythms* API deals often moved quickly with technical evaluation periods* Enterprise required lengthy security reviews, compliance discussions, and change management planning* Different stakeholders, different timelines, different

The latest 20VC + SaaStr: The Figma IPO Breakdown: $3B Left on the Table, Broken CEO Comp, and Why VCs Are Worse Than Public MarketsThis week, Brian Halligan (Co-Founder & Executive Chairman of HubSpot) joins the regular crew of Harry Stebbings, Rory O’Driscoll, and Jason Calacanis for an inside look at IPO dynamics, CEO compensation, and the current state of public markets.The Bottom Line Up FrontOn IPO Pricing Reality: “The people who said, ‘Oh, Figma left $3 billion on the table.’ The $98 price only happened because the IPO happened at $38. Had someone walked in and said, I know this IPO is going to price at $100 bucks a share to open tomorrow morning. Let’s raise at $80. They wouldn’t have had a book becayse no one has bid at that thing. So that money wasn’t accessible.” — Rory O’DriscollOn Going Public vs. Staying Private: “I think VCs are a much bigger pain in the ass than public investors. VCs are a much bigger pain in the ass than the typical public investor and slightly less of a pain in the ass than the public activist investor.” — Brian Halligan, Co-Founder & Executive Chairman, HubSpotOn CEO Compensation: “CEO comp is pretty broken at the moment. Everyone really relies heavily on RSUs. When I grew up in the industry, it was mostly ISOs, it was options until 2006 and regulations changed. It just creates sort of a risk averse behavior in the CEO.” — Brian HalliganThe Figma IPO might be the most misunderstood public offering in tech history. With a 250% first-day pop — pricing at $38 and opening at $85 and trading up to a day 1 high of $124— everyone from X pundits to Bill Gurley called it catastrophic mispricing. But the real story, told by those who’ve been in the room where it happens, reveals a much more nuanced truth about how IPOs actually work.The Night Before: When Exhaustion Meets High-Stakes PokerBrian Halligan, who took HubSpot public in 2014, pulls back the curtain on what actually happens in those final 24 hours. “You’ve never been as tired in your entire life as you are when you’re making this decision,” he explains. “You’ve been on the road for the last two weeks. You hit 12 countries. You had six pitches a day. Your battery is on red.”Then comes the moment that defines everything: the pricing committee meeting the night before trading begins. “The investment bankers sit you down and say you got two big decisions to make. One is who are the investors going to be… and then what’s the price.”The founders and bankers, perfectly aligned throughout the entire road show, suddenly find themselves “across the table from each other” in that final hour.The Fidelity Gambit: A $1 Decision Worth BillionsThe most revealing part of Halligan’s account centers on what he calls “the Fidelity discussion.

Ross Biestman joined ServiceTitan in 2018 as employee #354 when the company was doing less than $30M ARR. As Head of Sales, he helped scale the vertical SaaS platform from Series B to IPO, growing revenue to over $860M ARR and achieving an $11B market cap. Before ServiceTitan, Ross spent nearly a decade in enterprise software sales, serving companies like Bloomberg, United Airlines, and Sprint. A Cal Bears rugby player turned sales leader, Ross has become one of the most respected voices in vertical SaaS go-to-market strategy.Christina Shen is Managing Partner and Co-founder of Chemistry, a $350M early-stage fund investing in seed and Series A companies. Previously a Partner at Bessemer Venture Partners, Christina led ServiceTitan's Series A investment and has been tracking the company's remarkable journey for nearly a decade. She and Ross were undergraduate classmates at UC Berkeley - she in political science (destined for sales, as she jokes), he in business school (destined for venture capital).This SaaStr Annual deep-dive represents a full-circle moment between two Cal Bears who took different paths but remained connected through one of the most successful vertical SaaS stories ever told.Ross' Top 5 Learnings to Transform Your Go-to-Market Strategy* ICP Discipline Creates Exponential Returns: ServiceTitan's laser focus on ideal customer profile (ICP) drove their ability to scale from $30M to $770M ARR in 7 years. Ross's key insight: "Everything we do is intentional" - they put blinders on and refused to chase dollars outside their ICP, even when it meant turning down revenue.* Merit-Based Lead Distribution Beats Traditional Territory Models: ServiceTitan revolutionized sales by using AI to route leads to the AE with the highest propensity to close that specific deal type, not round-robin or named accounts. This merit-based system includes monthly scoring and relegation/promotion like English Premier League soccer.* Industry Immersion Unlocks Multi-Vertical Expansion: To expand beyond plumbing into all trades, ServiceTitan hired industry professionals directly into R&D, spent countless hours in the field with customers, and methodically tested new verticals with their top performers before opening floodgates.* Customer Site Visits Are Your Secret Weapon: In an age of Zoom and AI efficiency tools, Ross mandates that every new hire - from SDRs to C-suite - must learn the product, industry, and customer by spending time on-site. The founders still spend 95% of their time with customers.* Vertical SaaS Creates Unprecedented Value When Done Right: By serving an ignored $1T+ industry (trades) with true dedication, ServiceTitan achieved what seemed impossible - 11x revenue growth in 7 years and an $11B market cap, prov