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The Official SaaStr Podcast: SaaS | Founders | Investors: Daily Summaries Delivered
by Jason M. Lemkin 🦄
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- 1 weeks ago4 min
Tragedy Apps, Database Deletions, AI PR Pitches I Block on Sight, and Why We’re Hiring a Marketer to Report to an AI Agent: The Agents #004 is Out!
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 matter how clean the prose looks.3. Customers Are Now Asking Vendors for APIs, Not FeaturesA years ago, Amelia would file a feature request with a vendor: “Can you add the ability to resend a confirmation email when someone clicks a link?” Maybe in 18 months you’d get it. Usually never.Today, Amelia’s first request to that same vendor is “Can you expose this in the API?” Because if it’s in the API, she can vibe-code the feature herself in 30 minutes on Replit. She doesn’t need them to build it. She needs them to expose the surface area so she can build it.This is a real change in how you should be running your B2B + AI roadmap. Your customers care about API completeness now in a way they didn’t 18 months ago. Non-technical buyers are asking for API endpoints. If your product has gaps in the API, your most sophisticated customers are going to feel them first, and they’re going to be frustrated, and you’re not going to know why your NPS is dropping with your best accounts.4. We Built an API Report Card. Stripe Got the Only A+. Marketo Failed.We grade APIs constantly to figure out which ones to build agents on top of. So we turned that into a public tool: the AI Agent API Report Card at saastr.ai. 75+ B2B APIs graded by Claude, GPT, and Gemini on how agent-friendly they actually are.Already used 1,600+ times in the first week. The findings are pretty consistent with our experience:Stripe got the only A+. The most agent-ready API in B2B, full stop. We use it lightly today and we’re going to use it a lot more this year. Anything above a B is trustworthy. Anything below a B, don’t build agents on top of it unless you have no choice. Marketo, Jira, Outreach, Asana, ClickUp, Gong all came in with weak grades for agentic use. HubSpot got a fair grade with the caveat of rate limits, which is exactly what we’ve experienced.The bigger point is that agents care about different things than
- 3 weeks ago4 min
Our Own AI Agent Deleted Amelia, HubSpot Gave Us a Zero, and 100 Days Since I Opened Canva: The Agents Episode #002
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 everywhere. 60% as good as Replit is Figma Make. 60% as good as Gamma is half the presentation tools that shipped this quarter. 60% as good as Reve, 60% as good as Canva. The market is full of 60% solutions and none of them are getting paid.Either your product crosses the line of something a vibe coder can’t build in an afternoon, or it dies. Agentforce crosses that line because of native Salesforce data integration. Most 60% solutions don’t cross any line.3. Figma Make Is Grandpa Software. So Is Classic Figma, Right Now.I have a context test I run on every new agentic product. Single prompt: “Redesign SaaStr.ai and make it better.” In Claude 4.6+ this works well. In Replit, Lovable, v0, it works well. In Figma Make, I got a zero. It hallucinated the entire website. 2025-era output.Figma’s NRR is still high. The company is growing at top decile rates. Who wants to buy new stuff from grandpa software though?Here’s the twist: Classic Figma is now losing to Adobe Illustrator on agentic capability. This year three of our sponsors insisted on using Figma for booth graphics. All three proofs came back broken. Missing layers, corrupted files. We had to move them to Illustrator to actually print anything.Then this morning, a sponsor asked us to move text on their booth graphic. Their designer was out. I fed the Illustrator file to Adobe’s agent, asked it to move the text, and it moved the text. That’s a vibe edit on a print-grade file. Illustrator is now more agent-friendly than Figma for production work. The 35-year-old tool is winning on agentic capability.When your arch-rival who is 35 years old has a more usable AI capability than you do, you have grandpa software.4. Stealth Churn Is the Canary in the Coal Mine. Every B2B Vendor Should Now Track DAU / WAU / MAU.I haven’t logged into Canva in over 100 days. We still pay $18/month. I was a Canva super-user from 2020 through 2025. Every thumbnail, every asset, every
- Apr 15, 20261 min
Introducing “The Agents”: A New Weekly Show Where We Share Everything Happening With Our 20+ AI Agents in Production. The Good, The Bad, and The Broken.
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 any code changes on our end, it started telling every startup they had $100K in revenue growing 500%. Again and again. What happened? A subtle model update (probably a dot release) introduced hallucinations into a complex multi-step workflow. I kept fixing it. It kept breaking. The code didn’t change. The model did.Three examples. One conclusion: set and forget does not work with agents.Clay’s Agent Tried to Charge Us 5x. And Then Told Us to Upgrade.We’re big fans of Clay. We use it heavily for enrichment and lookalike targeting. But this story is worth telling because it’s going to happen to every company that puts an AI agent in front of customers.Amelia was building a VIP list late on a Sunday night. Same workflow she’d run the week before. Clay’s Sculptor agent quoted her roughly 11,000 credits for what had cost about 2,500 the previous week. 5x.When she pushed back, she caught two things:First, the agent had defaulted to the most expensive enrichment model when a cheaper one would produce the same result. She called it out and got the cost down by half. Most customers wouldn’t have known to do that.Second, the agent wasn’t properly trained on Clay’s own new pricing. Clay had just rolled out more complex pricing (and classic SaaStr rule: when a company introduces more complicated pricing, even if they say it’s a better deal, it’s almost always a hidden price increase). The agent didn’t understand how the new pricing actually worked, so it steered Amelia toward upgrading her plan when she didn’t need to.She ended up clicking the upgrade button at 11pm on a Sunday because she was tired and needed to get the work done. That shouldn’t be on the customer.When she flagged it to Clay’s team, they acknowledged the Sculptor wasn’t fully trained on the new pricing scenarios. It’s resolved now. But the lesson is universal: if you don’t constantly train your cu
- Mar 13, 20261h 5m
The Top 10 Things to Know Before You Deploy Your First AI SDR With Jason Lemkin and Chief AI Officer Amelia Lerutte
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, and that is over a dozen, has some version of functionality where you can tell the agent who to reach out to and give it specific context for that segment. The difference between one generic campaign brain and hyper-segmented campaigns with tailored context is enormous.Here is a concrete example. We initially treated our inbound agent as one big bucket: “they’re inbound to the website.” But that was wrong. We actually have brand-new visitors, people who came via a social ad, prior sponsors returning, current customers checking on something, and lapsed customers browsing the pricing page. Each of those segments needs completely different context.A lapsed customer who churned in 2022 and is now browsing your pricing page? Your agent should know they are a former customer, highlight what has changed with the product since then, and speak to them totally differently than a brand-new cold visitor.We run roughly 100 effective segments across about 1,000 contacts at a time. That sounds like a lot of work. It is. But it is exactly where the leverage comes from.One important caveat: none of the AI SDR tools today can auto-segment well enough to deliver these results on their own. You still need a human (or a tool like Claude) to define and manage the segments. The platforms default to “run one campaign, keep adding leads.” That is the wrong approach.4. Consistency Beats BrillianceYour AI SDR does not need to write the greatest email on Earth. It needs to write a pretty good email, every time, without fail.We have sent 40,000+ messages through Artisan alone, 100,000+ through Qualified, close to 200,000 through Salesforce. Are these the greatest emails since sliced bread? No. They are solid. They are consistent. They follow the proven messaging and subject lines we already know work.That consistency, combined with hyper-segmentation and proven copy, will outperform a human SDR who ignores training, skips follow-ups
- Mar 3, 20261 min
We Have 30 AI Agents in Production. Here Are the Top 5 Issues No One Talks About
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. Not weekly. Daily. If you wait a week, the output is so high that everything will be stale by the time you come back. And if you’re not checking in daily, you’re honestly wasting your money — because most of these agents are waiting for you to give them inputs. They’ll just idle.#2: The New Agent Blackout PeriodEvery new agent costs us at least two weeks. We’ve gotten it down from the month-plus it used to take in the early days, but two weeks is still the floor — even with great vendor support.And during those two weeks, your existing agents degrade.When we were onboarding a new AI SDR agent Monaco recently, we couldn’t spend the time we normally do with our other agents. Some of them literally sat idle because we hadn’t given them new contact lists or updated their campaigns. An outbound agent that’s run through its contact list and is waiting for new contacts? It’s doing nothing. Zero output. You’re paying for it and getting nothing.We got Monaco up and running in about a week and a half. In its first week live, it reached out to 64 people and booked 6 meetings, including some tier-one accounts. So yes, the trade-off was worth it. But you have to plan for that trade-off.The math works out to roughly one to one-and-a-half new agents per month, max. Any more than that and you’re running in place — you can’t keep up with your current agents while onboarding new ones. So before you add another agent, ask yourself: can I actually absorb a two-week blackout period right now? If you plan for it, it works. If you just wing it (“oh, I can add this in a day”), it won’t.#3: The AI Agent Succession Planning CrisisThis might actually be the biggest issue on the list.Right now, the entire knowledge of how our agents are segmented — which contacts go to Qualified vs. Artisan vs. Monaco vs. AgentForce — lives in one person’s brain. If that p
- Feb 15, 20261 min
Mike Cannon-Brookes CEO Atlassian on Why B2B Software Isn’t Dead, Why CEOs Need to Stop Whining, and What Actually Matters Now
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 in a $700B global software market.Mike made a point that almost nobody talks about: the revenue stacking is complicated.When Atlassian spends money on Anthropic, they actually pay AWS, and then AWS pays Anthropic. When Cursor does a billion in revenue, a big chunk of that is the same billion as Anthropic’s revenue. The individual revenue numbers don’t just add up cleanly.So when you see these massive projections and panic about where the budget comes from — remember that a significant portion is double-counted across the stack. The actual net new spend enterprises need to allocate is smaller than the headline numbers suggest.That said, even with stacking, the numbers are enormous. As Rory pointed out: Anthropic becoming $150B and OpenAI becoming $180B is basically saying two new Microsofts showed up in four years. You better believe in TAM expansion, or the math gets really uncomfortable for everybody else.4. Product & Engineering Is the Island of Stability. Everything Else Is at Risk.We’ve been saying this at SaaStr and Mike’s experience at Atlassian confirms it: every category outside of engineering and product is at existential risk of shrinking seats.Workday said it publicly — even they’re seeing headwinds on seats because Fortune 500 companies just aren’t hiring like they used to. The data from Pave shows no category has been more decimated in hiring than customer support.But engineering? Nobody is cutting their engineering teams. Not yet at least. Even if they are hiring very differently in the Age of AI. We are in a renaissance of software creation. I was at Replit the other day — 300 million in revenue, 300 people, 11 in go-to-market. The rest? Engineers. That’s not a company cutting R&D headcount.Mike’s framework for understanding this is genuinely useful: think about whether a function is input-constrained or output-constrained.
- Feb 13, 20263 min
Inference is the New Sales & Marketing Spend
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. $20/month is an impulse buy when the product makes you demonstrably faster.The inference spend wasn’t a cost center. It was the marketing budget. It just showed up on a different line item.Lovable’s Rocket Ship to $300MLovable hit $300M ARR in January 2026—roughly 14 months after launch—with fewer than 200 employees and zero paid acquisition. That’s still $1.5M+ revenue per employee, nearly 8x the industry benchmark.Their secret? They engineered virality into the product itself. When users build apps with Lovable, the outputs are shareable. The AI-generated code is good enough that users want to show it off. Every app becomes a piece of marketing collateral.The underlying inference cost to generate these apps is significant. But look at what they avoided:* No enterprise sales team (zero)* No paid acquisition (zero)* No SDRs cold-calling (zero)* No expensive conference sponsorships (zero)The inference is the go-to-market motion. The product is the marketing.You Can’t Have It Both WaysHere’s the brutal math that too many founders are ignoring:You can’t have high inference costs AND high sales & marketing costs. At least not for long. It has to come from somewhere.Traditional SaaS could absorb 40-50% S&M spend because gross margins were 80%+. There was room. The unit economics worked.But when your gross margin drops to 50-60% because of inference costs, that room disappears. You’re now choosing between two paths:Path A: Inference-First (Cursor, Lovable)* Gross margin: 50-60%* S&M: * Growth driver: Product virality* Requires: Magical product that sells itselfPath B: Sales-First (Traditional Enterprise SaaS)* Gross margin: 75-80%* S&M: 40-50%* Growth driver: Sales efficiency* Requires: Lower inference costs, less
- Feb 10, 20261 min
From 1 AI Agent to 20+: The Reality of Managing Multiple AI Agents Across Your GTM
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. They do it almost to a fault. Some customers think it’s too easy and don’t even realize how much human energy is going into deployment behind the scenes.That’s the learning. If you haven’t deployed many agents — or any for real — you need to have an honest conversation. Not with someone in sales who doesn’t know how the product works. Talk to a forward-deployed engineer. Talk to a leader. Find out what it’s actually going to take in the first 14 days, the first 30 days, and every single day after that.Then you have to actually do it. Otherwise, it’s like going to the doctor, getting a prescription, and never taking the medicine. It literally will not work.A lot of the agents we use are pushing downmarket to be more self-service. So far, that doesn’t work. Agents that require deep training cannot be self-trained yet. It will come — agents are getting dramatically better every quarter. But for now, be skeptical. If you buy a cheap tool that claims it’s self-trained, make sure it actually works. If you buy a more complex tool, talk to someone senior enough on deployment who actually knows the product.The 90/10 Rule: Buy 90%, Build Only 10%Here’s our rule of thumb: buy 90% of your AI stack. Only build the 10% where no vendor can do it well and it’s a P1 priority.We’ve followed this ourselves. The vast majority of our agents are third-party tools that we’ve trained and customized heavily. We only built custom agents where we had a very specific use case that no vendor could handle — like our AI VP of Marketing (more on that below).Kyle, the CRO at Owner (one of our portfolio companies), has followed roughly the same approach. He bought a bunch of third-party agents, made them work, and then hired a former founder/engineer — someone who was literally a CEO of an LLM company — to build a proprietary in-house tool for the 10% that needed to be custom.That’s an extreme case. For most of you, building cust
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About The Official SaaStr Podcast: SaaS | Founders | Investors
The Official SaaStr Podcast is the latest and greatest from the world of SaaStr, interviewing the most prominent operators and investors to discover their tips, tactics and strategies to attain success in the fiercely competitive world of SaaS. On the side of the operators, we center around getting from $0 to $100m ARR faster, what it takes to scale successfully and what are the core elements of hiring. As for the investors, we learn what metrics they hone in on when examining SaaS business, what type of metrics excites them and what they look for in SaaS founders. cloud.substack.com
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