The Official SaaStr Podcast: SaaS | Founders | Investors

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, or goes off-script.The agent remembers every instruction you give it. Every time. A human SDR forgets by Thursday.We see a lot of “AI SDR paralysis” from founders who test a tool, see the output, and say “it’s not that great.” Okay, but did you segment properly? Did you give it copy that already conver

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 person gets hit by a bus, the agents effectively stop functioning in any coordinated way.We actually asked our agents what they would do if our Chief AI Officer disappeared. The answers were… revealing.* The 10K version of Claude said it would need to hand over certain documents — documents that are stored locally on my laptop and probably nowhere else. It listed the upcoming campaigns for March and April. And then, interestingly, it flagged what it called “the vibe” for SaaStr Annual — it h

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.* Customer support is input-constrained. You have X customers asking Y questions per day. Make the team more efficient and you need fewer people. Legal is similar — you can’t create more legal problems just because your lawyers got faster.* Engineering is output-constrained. The roadmap is never finished. You can always create more. Make engineers more productive and you

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 AI magicWhat you cannot do is run 50% gross margins AND 40% S&M. That’s negative operating margin before you pay a single engineer. That’s burning cash with no path to profitability. That’s a company that dies.The trap I see founders falling into: they build an AI product with significant inference costs, then layer a traditional enterprise sales motion on top of it because “that’s how you sell to enterprises.” Now they’re paying for compute AND paying for a sales t