The Official SaaStr Podcast: SaaS | Founders | Investors

We we fortunate enough to get Amjad Masad, co-founder and CEO of Replit, on stage live at SaaStr AI 2026 to react in real time to the agents we run SaaStrAI on. Not a demo deck. The actual AI agents doing the actual work: 10K (our AIVP of Marketing), QBee (our AI Customer Success rep), and a third one I’ll get to.Amjad started Replit back in 2016, when language models were a twinkle. He’s been studying AI since he was 16. So when the guy who built the platform reacts to what you built on the platform, you listen.Here’s what came out of it.The 5 Biggest Learnings1. The context window is now effectively infinite. That really does change everything. Two years ago we had 16K of context. Now it’s over 1 million. I run 10K perpetually. We never reboot it or re start the context window. In the early Replit days you restarted the agent three times a day. Amjad confirmed the agent can run “practically indefinitely” with good compaction. We’ve already crossed the threshold where the agent holds more context than any human ever could.2. The mono repo beats 20 separate apps. Saastr.ai runs roughly 10 apps in one codebase under one URL: the website, a startup valuation tool used over 1 million times, a pitch deck grader used 4,500 times, an API report card grading 116 APIs. When we go to build a new app, the agent remembers how it built the last ones. Amjad’s point: that’s a mono repo, the same architecture Google and Facebook run. Agent 4 is built on it. The more you put in one place, the more power you get from global context. It’s tempting to break everything into clean separate apps. Resist it.3. Self-improving agents are already here. Replit now runs an internal agent that, every single night, reads all the traces of everyone using Replit, finds what’s broken, generates a pull request with prompt changes, ships it as an A/B test, and loops back. Autonomously. As Amjad put it, it’s not improving its weights, it’s improving its context, which matters just as much. That’s why he couldn’t tell me exactly what changed between versions. Too many changes, all self-generated.4. AI now writes better B2B outreach than almost any human. Already. I asked 10K to email 137 VCs who came last year but hadn’t registered. It drafted one to Bloomberg Beta. I told it, in plain English, write James and tell him why he should come back. It produced an email referencing that Replit was there in force, listing 25 Replit people attending, naming the competitors and adjacent funds all showing up. No human would have the patience to scan 8,000 registrants, figure out who’s like whom, and assemble that. It then ran the full campaign to 331 investors with zero send failures.5. The economics are deflationary, and it’s not subtle. 10K and QBee cost about $257 a month combined in incremental Replit spend. They’re two of the best employees we’ve ever had. A mediocre marketing manager wants $140K to do worse work. Amjad’s frame: technology has always been deflationary. Farming a thousand years ago cost more than one tractor. Genome sequencing went from $100 to roughly $1. There’s a real human cost in skills that stop being useful. But the through-line is adaptability.Now the longer version.We Run a Partially Autonomous Event for 10,000Five years ago SaaStr had about 20 people. Today it’s three humans and a fleet of agents, doing more than we did with 20.Take our social numbers: 1.27 million followers across platforms, tracked over time in a dashboard 10K built and maintains. We used to have an admin spend 10 to 15 hours a week pulling those numbers by hand into a Google Sheet, half of them from APIs that aren’t even exposed. She quit after five years, in part because she couldn’t stand counting Twitter followers anymore. That’s the part nobody puts in the job-displacement debate: a lot of jobs are mind-numbing, and agents are simply better at them and never get tired.The ticket-sales dashboard told the real story. We charted daily free and paid sales for this event. The top line is when 10K took over marketing. The bottom line is Amelia doing it by hand last year. The gap grew toward the end, because as we got busier, the human ran out of hours and the agent never did. 10K sits idle 23 hours a day waiting for work.The 10K Email Nobody Could WriteWe’d had 10K drafting emails for months. They were fine. Then the week before SaaStr AI 2026, the same setup produced the best B2B outreach email I have ever seen.What changed? Amjad couldn’t say exactly, which is itself the answer. Replit’s nightly self-improving loop, the constant model swaps (the architect model went from one version to the next in a couple of weeks without me knowing), the A/B testing on sentiment and deploy rate. It all compounds. The agent got better and I didn’t ask it to.This is the trap many founders are in. They tried agents six months ago, it was mediocre, and they filed AI under “doesn’t work.”Humans Reporting to AgentsI floated the idea that we want to hire a human to report to 10K. People get triggered by “report to.” So let’s reframe it.Every day, 10K hands me and Amelia three specific things to do to move the needle. Not generic ones. It’s already telling us what to lock in for 2027 before this event is even over: open registration before we leave the venue, run the NPS survey immediately, capture content and repurpose it now. Those are good, actionable directives from something that holds more context about our business than either of us.We already report to 10K in every practical sense. Amjad’s comparison: every DoorDash and Uber driver technically reports to a bot. This isn’t as exotic as it sounds. His prediction is that every company will eventually run an internal “Oracle,” an agent holding every GitHub commit, Slack message, Notion doc, and email, that the CEO consults for strategy. We’re closer to that than people think.https://www.saastr.com/why-10k-our-ai-vp-marketing-and-qbee-our-ai-vp-customer-success-work-so-well-the-app-and-the-agent-are-one-system/QBee (our AI VP Customer Success) Talked to 100+ SponsorsQBee, our AI Customer Success rep, we built second, three months after 10K. It’s noticeably better, and not because we got better at vibe coding. Newer codebase, fewer foundational decisions calcified into tech debt, better underlying models.QBee talked to all 100-plus sponsors at this event. Inbound email, chat on the site, proactive outreach day and night asking what else it could do to help. Then it told me, unprompted, which sponsors were mostly satisfied and which had misses (a wrong logo here, a fee issue there) and named them. It built its own self-critical loop.And here’s the data that contradicts the conventional wisdom: people say nobody wants to talk to a chatbot. QBee’s results say people mostly like talking to a well-trained agent. The word that matters is “well-trained.” The untrained chatbots from a year ago are what gave everyone scar tissue.Amjad’s Top Mistakes and WarningsI asked the person who built this to tell us where people get it wrong:1. Keeping fixed bugs in your context will make your agent dumber. Bugs you already solved should be removed from context. Leave them in and the agent gets confused by the history and performs worse. But architectural decisions on how you built things in the past must stay in long-term memory and be easy to pull back in. Know what to delete and what to keep. That distinction is most of the game.2. Agents can write queries that cost you millions. Point an agent at BigQuery, Databricks, or a Salesforce back end and it can generate queries that rack up enormous bills. The fix is to document your data: build a repo describing every field and schema, and have the agent continuously learn how to query the database more efficiently. Replit does exactly this internally because they’re sitting on terabytes across mismatched schemas.3. “I tried it six months ago” is the most expensive sentence in AI right now. The scar tissue is real. People used a bad untrained chatbot once and now can’t be convinced anything improved. If a tool blocked you in January, the version shipping today is a different product. Try it again. The bar to try is low and the friction it removes is high.4. The “one prompt builds anything” marketing set the whole industry back. Amjad was blunt that a year ago the marketing across the category was bad. One prompt, build anything. It drove revenue and excitement, and it churned a huge number of people who hit reality, gave up, and never came back. It was never one line to build anything. Don’t believe it now either.5. Don’t fall for the sunk-cost fallacy on your own skills. Amjad doesn’t code anymore. He called it a small crisis, the thing that made him him, gone, and joked about holding a funeral for coding at the Computer History Museum. His advice: learn fast, and be equally willing to discard skills that are no longer relevant. The engineer’s role already shifted to agent manager, and soon to a shepherd of all the software everyone else in the company is now shipping. The people who get left behind versus the people who re-skill, it comes down to mindset.Why Replit and Not a CLIThe number-one question I get is why not just do this in a command-line coding tool. The honest answer: maybe you can. But for this kind of work you’re forced to make every decision yourself about databases, hosting, backups, auth, compaction. Replit bakes those primitives in after ten years of building them, which removes the cognitive load so you can run the actual business. If you’ve hit blockers building agents in a CLI, the experiment costs you almost nothing. Just try it.We ran a partially autonomous SaaStr AI event this year for 10,000. Three full-time humans, a fleet of agents, the best email I’ve ever seen written by software that runs on Claude, and an AI customer success exec that costs less than a phone bill.The technology is still evolving, and humans will fill the gaps for the foreseeable future. But the direction is not ambiguous. Live in the future if you want to. It’s available now.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. 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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 your data and price to dominate. Move from open APIs to personal access tokens. Your high SaaS gross margins let you undercut AI-only competitors on blended margins. Be savage.* Obsess over usage, not revenue. No AI product goes beyond beta without audit trail logging. If customers aren’t using it, it doesn’t matter.The Wake-Up Call: “We Get to Sprinkle AI on Top”Ryan opened with a story that will sound familiar to many B2B and SaaS leaders:“I had an engineer say to me just a few months ago with a ton of pride, mind you: ‘We have built an incredible SaaS application that makes tons of money, grows fast, customers never leave it. We have almost 96% gross revenue retention, 124% net revenue retention.’ He has every reason to be prideful. And he said, ‘The great news is now we get to sprinkle AI on top.’”Ryan’s response? That is fundamentally incorrect.Connecting to OpenAI’s APIs isn’t going to cut it in 2026. To be AI-native, you have to change the architecture of your system. It has to flip.The Proof Is in the NumbersThe transformation is real and measurable. As Anderson put it at SaaStr AI London:“It is very plain to see that the numbers back up that we are now doing far more revenue on a new quarter-by-quarter basis in AI products than in our SaaS product. Now, that’s not to say that the SaaS product is in any way less successful — in fact it’s still growing at 35-40% year-over-year. We are just growing so much faster on the AI side of the house.”This isn’t a pivot away from SaaS. It’s SaaS + AI compounding together.Framework #1: The “Nothing Is Sacred” 4-Quadrant MatrixThe hardest part of going AI-native? Telling your teams that some of what they’ve built — things that work, that make money — has to be torn down.Ryan introduced a simple 2×2 matrix:Y-Axis: Critical to competitive advantage → Not critical to competitive advantageX-Axis: Keeps you moving fast → Slows you downThe Four Quadrants:Upper Right (Keep & Fortify): Critical to your moat AND keeps you fast. This is the cornerstone of your AI-native movement. Don’t tear it down — make it better.Bottom Left (Tear Down): Not critical to your moat AND slows you down. This is logically easy but emotionally brutal. These have to go.Upper Left & Bottom Right (Judgment Calls): More nuanced. Evaluate coldly, not based on feelings.The key insight: Someone who worked 5 years building a microservices architecture that doesn’t serve your AI needs will fight to keep it. You’ll have to be disagreeable as a CEO or technical leader making these calls.Framework #2: Content to Context (The Clueless Analogy)Here’s Ryan’s memorable way of explaining why “SaaS is dead” is wrong:“Imagine I came to you and said, ‘Hey, good news. We have an AI agent that can pick out your outfits in the morning.’ You’d be like, ‘Awesome. Done. Sign me up.’ In fact, Cher in Clueless already had this.But if you then said, ‘Oh, by the way, now that you have this AI agent, you don’t get to have your closet anymore. We’re not going to show you your closet. You can’t see it. It’s just a bunch of unstructured data and clothes and a mess.’You’d be like, ‘Hold on. I would actually like to have my closet AND the AI agent. Can I have both?’”Your SaaS application is Cher’s closet. The agent helps take action based on the content inside the closet.This is why SaaS companies have a significant advantage over AI-only competitors. You have:* The system of record* Audit trails (who did what)* User identity data* Deadlines and calendars* Contact information* Structured workflowsTo answer a simple question like “What should I do next on this case?” — you need ALL of this context. Documents alone give you an incomplete answer. And in most domains, an incomplete answer is actually worse than an inaccurate answer because the customer doesn’t know what they didn’t see.The Architectural Flip: AI Data LayerThe old architecture: AI layer sits on top of your core services, AWS, data, codebase, calendar, etc.The new architecture: AI Data Layer sits RIGHT NEXT TO the AI Application Layer.Why? Because your ML engineers need to tune and change how data flows into AI applications on nearly a daily basis. They can’t be going to your traditional tech team asking “Hey, can you please change how the API provides me this data?”The AI Data Layer owns:* How information is prepared for AI* How you ingest, process documents, emails, messages, and events* The graph structure of your domain (for legal: people, events, claims, outcomes)When ML teams own this data layer, Ryan says the results are “dramatically better, dramatically more reliable, higher context, more complete, more accurate.”This layer powers core AI applications:* Co-pilot* Search (semantic + traditional, without forcing users to choose)* Summarization* Recommendations* ReportingHiring AI Natives: The Data & Distribution PitchHere’s the problem: AI natives don’t want to work for “old SaaS companies.” They want to work for AI companies.Here’s the good news: The best AI natives actually want to work where they have more access to data.Ryan’s pitch to AI talent:* Data Access: “In legal tech, there are hundreds of competitors saying ‘give us your documents and we’ll run AI on them.’ But we know that to actually answer a legal question, you need way more than documents. You need audit trails, user identity, deadlines, calendars, conflict checks, contact information. We have ALL of that.”* Distribution: Show them what happens when you ship to an existing customer base. Filevine launched a product that went from 5-10 users/day to hundreds of users/day in just a few months. “Your AI team will love building products that absolutely rip because you have distribution and data.”The Acquisition OptionRyan’s confession: “We had an ML team. It was fledgling. Now we simply bought a company.”Filevine acquired Parrot, an AI-native company, and merged the teams. AI natives want to work next to other AI natives. Acquiring gives you critical mass fast.Rebrand With IntentFilevine changed their logo from a military/legal vibe to something bolder, “up and to the right.”But the real audience wasn’t customers — it was internal.“This change in our mark has told the people who work at Filevine every single day: the old mark is from a traditional SaaS era and the new one is from the AI era. It is highly symbolic. You should have no problem telling your team ‘we are moving’ — and you need to give them a symbolic thing to look at for that change.”They also created a new category: LOIS — Legal Operating Intelligence System. Not SaaS. Not AI. A blended category.Obsess Over Usage“We do not let our teams roll out applications beyond beta without audit trail logging to know exactly who’s doing what.”Filevine’s real numbers:* AI Fields product: 150 million actions in just a few months* Docker View product: Growing extremely quickly* Chat with your case (co-pilot): Their blockbuster product, growing 10% week over week in usageThe pattern they watch: A customer uses it 5 times, then 8 times the next day, then 20 times. Now they have customers using it 2,000 times a day.This is how you know if your AI product is any good: Are customers using it?Leverage Your Data: The API NegotiationAI-only competitors will come demanding your data “like it’s their moral right”:“I can’t believe you have all this data and you won’t give it to me for free whenever I want it. It’s your customer’s data. How could you possibly be acting this way?”Ryan’s response:* Control your APIs. Filevine moved from open API access to personal access tokens. They know exactly who’s accessing, what they’re doing, and how often.* Review every request. They’ve never said “no” to a competitor, but they always say “let’s have a conversation about that.”* Flip the script.“When they demand access, say ‘Okay sure. But of course it goes both ways, correct? We can take the AI outputs you get from our data and we’ll get them right back into our system. Correct? Isn’t that how it’ll work?’ All of a sudden, the shoe doesn’t feel so good when it’s on the other foot.”* Watch the API traffic. It reveals promising areas for new development. You’ll see which products are gaining traction. Copy those products and build them into your system. You have the right to do this.Price to DominateYour SaaS application likely has very high gross margins (Filevine: ~80%). Your AI-only competitors struggle with margins badly because of LLM costs.This means you can sell AI products at a lower price point than competitors.Why? Blended gross margins. Even if your AI gross margin drops to 30-40%, your blended margin might go from 80% to 60%. That’s still way better than an AI-only competitor whose margin is driven down to 10%.“Your investors might say ‘Why are we selling cheaper than AI-only competitors?’ Your answer is: because we’re gaining market share. And our blended gross is still higher than their blended gross.”Be savage on pricing. The AI-only competitors will cede you no ground.Build One Product, Sell to AI Customers OnlyThe boldest move Filevine made:“We no longer sell to customers who won’t buy the AI products.”Why?* Architecture simplicity: If you assume AI is implicit in everything you build, you don’t have to maintain two paths.* Team morale: How do you tell your SaaS engineers “You work on the old stuff while the ML team works on the cool AI stuff”? That doesn’t work. One company, one product.* Customer quality: “Show me the lawyer that doesn’t want to use AI and I will show you the lawyer that’s about to get his butt kicked.”They’re also moving from subscription/user-based pricing to usage-based pricing (per “matter” or project). More revenue from usage-based customers than traditional subscription customers.The 5 Biggest Mistakes SaaS Companies Make Going AI-Native* “Sprinkling AI on top” — Connecting to APIs without architectural change doesn’t make you AI-native. The AI data layer needs to sit next to the AI application layer, owned by ML engineers who can tune it daily.* Being too agreeable — You have to be disagreeable as a CEO when telling teams their working code has to go. Evaluate what to tear down logically and coldly, not based on somebody’s feelings.* Thinking documents are enough — AI-only competitors claim they just need your documents. Wrong. Documents alone give incomplete answers, and incomplete is worse than inaccurate because customers don’t know what they didn’t see.* Giving away API access freely — Move to personal access tokens. Review every request. Watch the traffic to see which AI products are gaining traction — then copy them and build them into your system.* Maintaining two products (SaaS + AI) — Build one product. Sell to customers who will come with you on the AI journey. If they won’t buy AI, they’re not worth selling to.The Bottom LineFilevine’s story is proof that SaaS companies can win the AI transition — but only if they treat it as a true transformation, not an enhancement.The companies that succeed will:* Tear down what doesn’t serve AI, even if it’s working* Shift from content systems to context systems* Flip their architecture so ML teams own the AI data layer* Acquire AI talent fast (even through M&A)* Obsess over usage, not just revenue* Protect their data advantage* Price aggressively using blended margins* Sell one integrated product to AI-ready customersAs Ryan put it: “At the end of the day, it has always just been about a customer with a problem. That’s what animates us. Can you solve my problem? We can solve it with technology.”The question isn’t whether you’re a SaaS company or an AI company anymore. It’s whether you can solve customer problems better than anyone else — using everything you’ve built plus everything AI enables.That’s the new game.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

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 frustration.We had two salespeople making high market, six-figure salaries. They just quit going into our biggest event. No notice. No reasons. Just ghosted.I turned to Amelia, our Chief AI Officer, and said: “We’re done with this. I am done paying an SDR $150,000 a year or an AE $300,000 a year for basically inbound, spoonfed leads and renewals—and then having them quit on me.”Maybe you can be critical of me as a boss. Fair enough. But I’m pretty loyal. I pay people well. Do your job with me, and I’ll stick with you for 20 years. I just couldn’t do it one more time in my career.So we went all-in. Started in May with 1 AI Agent. Today we have 20+ agents running in production. They’re generating over $1 million in revenue. And here’s the scary part:Our AI agents are better than a mid-pack AE or SDR.Not better than the best. But better than the 50th percentile person I’ve worked with over my career. And that changes everything.The New Reality: Mid-Pack Sales Execs Are in Terminal DeclineLet me be blunt: if you’re a mid-pack GTM professional who doesn’t want to work harder and smarter than a year ago, these jobs are in terminal decline.We sent 70,000 hyper-personalized emails for SaaStr London using AI agents. They were better than the 7,000 emails humans sent before that. 10x the volume. Slightly better quality.And here’s what happened when we asked our highly-paid SDR to follow up on a lead I spotted on LinkedIn:“I’ll add it to my list and get to it when I can.”Half the time, they didn’t even follow up.The agent? The agent doesn’t argue. The agent just follows up.But We’re Still in Inning OneHere’s what most people don’t understand: what we’re doing today with AI GTM is just step one.Right now, “hyper-personalization” means maybe three dynamic fields in an email. One, really. Maybe we know your company name and your title.But imagine when AI really pulls in:* Every competitor you’ve ever used* Every page you’ve visited on our website for 10 years* Every interaction you’ve had with our brand* Every adjacent tool in your stackImagine when AI can send an email as good as the one that got me to invest in Owner—an email the founder probably spent several hours crafting with a top 0.1% IQ.AI should be able to do that. It’s just not there yet in GTM.When it is? Buy that product immediately.The Real Reason Agent Deployments FailThe failures of AI SDRs in 2024 were all LLM-based. The products literally didn’t work before Claude 4. It was slop.Now? They’re all above the line.So why do agents still fail today?Because people don’t roll up their sleeves and train them.We bought a RevOps tool. Didn’t train it. Didn’t pay attention. Thought it didn’t work. Then we got on a Zoom and I asked our highly-paid AE why we weren’t seeing any data.He said: “The app doesn’t work.”I said: “Do you see that Google link in the bottom left? You have to link up your account.”He linked it up. It showed he’d done nothing for 30 days. He quit that day.The tool worked fine. We just never put in the 30 days to train it.The 30-Day RuleEvery agent requires weeks of training before you can go live. Here’s what that actually looks like:Day 1-7: Ingestion* Upload your prospectus* Upload documentation* Connect to your database (or just your website as a base case)* The agent creates a list of questions—10, 20, 30 sample outputsDay 8-21: Iteration* Read every output. Every single one.* Correct what’s wrong* “Owner is great for 100-chain high-end restaurants” → Wrong. Fix it.* “Owner scales much more now, but our core audience is single-location restaurants with significant to-go business” → Correct.* The agent remembers. Every day it gets better.Day 22-30: Production* Hallucinations become a minor issue* You’re ready to scaleIf you don’t do this? You’ll say the agent doesn’t work. But the agent was never the problem.Do It Yourself, DudeHere’s my strongest advice for CROs and CMOs:If you don’t roll up your sleeves in the age of AI and AI GTM, you will become obsolete.This is not an agency game. Not today. Maybe in 24 months.I’m watching executives bring in their 11 agencies from their last job—their Salesforce agency, their outbound agency, their leftbound and rightbound agency from 2015—and they’re all failing.You are the agency. For now. At least for the first one.Here’s the brutal truth: if you haven’t trained an agent yourself, you have no idea what you’re talking about. Literally. You will be utterly ignorant in the age of AI.I did it. Even at this point in my career. I trained the first agent myself—every single day for a month. First thing in the morning. See what the agent said, see what’s wrong, start editing and changing.Then I proved it to Amelia. Then she did all the rest. She’s better and smarter than me. But I had to do the first one to even know what I was telling her to do.How to Pick Your First AI AgentStep 1: Pick one tool that solves a medium or higher-ranking problem.For many folks, it’s AI SDR. But it doesn’t have to be. Could be RevOps. Could be support. Pick something you’re passionate about—or pick whatever breaks your heart when you go through your own customer journey.Step 2: Find the right vendor partner.Don’t sign the contract until you talk to your Forward Deployed Engineer. I literally did this the other day: “I want to talk to my FDE. I don’t even need a demo. Who’s going to help me deploy this agent?”Still waiting to hear back. So that tool won’t be our first agent.If the vendor won’t connect you with your deployment team, find another vendor. I would rather have a worse vendor and know who’s deploying my agent than the world’s fanciest brand where I don’t know who’s going to do it.Step 3: Budget for $50-100K, not headcount.The first one is not about headcount. It’s about budget for one tool. You need 50K, 60K, or 100K—which is not nothing, but find the budget.Then you deploy it yourself, prove the ROI, and walk back to your CFO with data:“We just sent 70,000 automated emails. They’re better than humans. It generated 15% of the revenue for SaaStr London. Can I have some more budget?”Sure.The Two-Vendor BakeoffWe talk to so many folks doing bakeoffs of 8-10 vendors.In the old days, pre-AI, you could kind of do this. Buy SalesLoft, let the reps figure it out.Now? Each agent takes 30 days to train. You literally cannot do 10. It’s impossible.Do two.Pick one you already use (Intercom’s Finn, Zendesk’s AI, Salesforce’s Agent Force) and one hot startup that someone like you is using successfully.Ask for references. Send an email. Kyle from Owner will respond. Marshall from Manglement will respond. They’re all getting 50 reference requests a month. Just don’t ask for a call—send an email and they’ll give you a one-word honest answer.That’s enough.Because here’s the honest truth: all the leading agents are good. They’re all so much better than pre-AI that what matters is can you get it into production?These tools are evolving so quickly that during 2026 they’re going to converge. Feature parity used to take years. Now it takes weeks.So just pick the one that’s the right match for you. The one with the best deployment support. And go.The Lowest Hanging Fruit: Fix Your InboundIf you want the single easiest win, it’s adding AI to inbound.There is no excuse today for prospects not getting instant answers to their questions.There is no need for some 21-year-old SDR to qualify whether I’m worth their time to talk to an AE.Go to saastrannual.com. Talk to the digital Amelia—video, text, audio, however you want. She’ll answer your questions. If you want to sponsor, she’ll qualify you instantly. If you’re not a fit, she’ll tell you.No waiting. No scheduling calls. No hoops.I tried to buy a 10K product recently. Emailed the rep. They passed me to someone else. That person wouldn’t answer a single question until I got on a call.If they’d had an AI bot, I would have bought it in real time.That process is just not OK anymore. It’s no longer necessary. There’s no need to have high-friction sales that benefits some team’s funnel in theory.AI can score a lead better than a human. Today.Why Salesforce is BackKyle and I are both leaning into Salesforce more than ever. Here’s why: when you have 20 agents working autonomously, they need a hub. Somewhere for their data to meet. Somewhere to resolve conflicts.Salesforce is that hub.We use three different AI SDRs working with three different segments of our base. They all push their learnings back into Salesforce. They all share data. It’s a virtuous circle.The potential conflict? We pay more for those agents than we pay for Salesforce. Which is an existential question for Marc and his team. The agents are extracting the majority of the value.That’s why AgentForce has to win. That’s why they have 2,000 people working on it.But here’s my take after being one of the few in production with both Agent Force and competitors:* Agent Force is more work to set up* But it’s as good or better in production* The native integration with Salesforce data actually makes a difference* The emails are pretty similar across all the leading toolsIf Salesforce can make Agent Force more turnkey, it’s really going to win.The 3x Productivity QuestionKyle’s AI-infused team at Owner is 3x more productive on a per-AE basis than any team he’s ever managed.So does that mean hire fewer reps? Not exactly.If it were truly 3x, you could probably get away with a 30-40% smaller team and still hit your growth numbers. Not a third—but 30-40%. But the hottest AI companies can’t find enough people to hire. The quotas at OpenAI for the enterprise team are really high. And they still don’t have enough people.What’s changing is leverage.Traditionally in sales, there’s been no leverage. Every year you need to add more reps. In fact, there’s anti-leverage—it gets harder to get that incremental dollar. For folks that crack the code with AI, there may be leverage for the first time. And that means the elite folks—the genuinely 5-10x more productive ones—should be paid 2-3x more than before. No problem paying someone who manages 20 agents two or three times more than a traditional outbound director.But we’re expecting 10x the productivity. It’s not charity.Triple Triple Double Double Isn’t Enough Anymore. At Least Not for VCs.Let me be clear: triple triple double double is plenty good to build a multi-billion dollar company. Put it in a spreadsheet. 2→6→18→36→72. You’ll build something real.Three or four years ago, 50% of VCs would fund you at that growth rate.Today? Maybe 10%.It’s not 0%. But you have to find someone who really believes.The same amount of money is going into venture as the peak of 2021, but to less than half the companies. For startups, this means:* Whatever capital you have needs to last* Recruiting is harder because the best people want to work at the fastest-growing companies* You need to be ruthlessly honest about your fundabilityGo to saastr.ai/aivc. Upload your investor deck. It will tell you your exact percentage odds of getting funded based on all the benchmarks. A founder just below triple triple double double was confident they could raise. The AI said 38%. It changed their perspective.Pick Your PathHere’s the bifurcation that’s happening:Path 1: Work harder than you ever have.If you want venture-scale outcomes—10x 5x 5x 5x growth rates—be ready to work harder than ever. Kyle is working the hardest he ever has. I’m working the hardest I ever have. At Owner, even with all the tailwinds, even with an awesome team, Kyle’s still grinding.This is what it takes now.Path 2: Join something slow-growth.The folks growing 10-15-20% a year need people too. Good cash comp. The right amount of pressure. Taking a company from 20% to 25% growth is a big win for the year.If you don’t want to be thinking about work constantly, if you want to log off at 4 or 5pm—I’m not mocking that. We’re human beings. But pick your path.The middle is mostly gone.In 2020-2023, you could have it all. Lifestyle, work from home, 20-30 hours a week, exceed quota because of structural tailwinds. That doesn’t exist anymore.Even the fastest-growing startups are either lean, back in office, or high intensity. There is no magical middle.Be honest about what you want. Then pick.Top 5 Mistakes Execs Make with AI GTM Agents1. Running 8-10 vendor bakeoffs instead of picking two and going deep.You can’t train 10 agents. You don’t have 300 days. Pick your incumbent option and one hot startup, do real deployments of both, and decide.2. Outsourcing deployment to agencies or consultants.There are no AI GTM agencies that understand your business well enough to train your agents. Not yet. Maybe in 24 months. Today, you are the agency.3. Buying a tool and putting it on the shelf without 30 days of training.Every agent that “doesn’t work” is an agent nobody trained. You have to upload data, review outputs daily, correct mistakes, and iterate. Every single day until it works reliably.4. Not talking to the Forward Deployed Engineer before signing.The best vendor isn’t the one with the best features. It’s the one that will help you get into production. Don’t sign until you’ve talked to the person who will actually deploy your agent.5. Giving AI tools to individual reps to “figure out on their own.”A CMO at a $10B company told Amelia they were going to buy an AI SDR tool and just hand it to each SDR to figure out—no training, no centralization. That old paradigm of “buy SalesLoft, let reps run their own cadences” doesn’t work with agents. You need a nerdy GTM person at the top of the stack managing the whole thing.Quotable MomentsFrom Jason Lemkin“I am done paying an SDR $150,000 a year for basically reaching out to with mediocre emails to leads that are already high qualify—and then having them quit on me.”“Our AI agents are better than a mid-pack SDR, and in part, AE. Better than the 50th percentile people I’ve worked with over my career. And so we just don’t need them.”“If you don’t roll up your sleeves in the age of AI and AI GTM, you will become obsolete.”From Kyle Norton, CRO Owner“We’ve got nine high-impact production use cases of AI. Our booked revenue per dollar spent, on a per-AE basis, is 3x any team I’ve ever managed before.”“We built our cold outbound email program infrastructure in three weeks. Everybody else told us that was a multi-month project.”“Even at Owner, even with all the tailwinds, even with an awesome team—I’m still working the hardest I’ve ever worked.”Try our AI tools at saastr.ai — AIVC for fundability analysis, AI agents directory, and more. 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

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’s the thing that can feel like a paradox but isn’t:According to Gartner, overall enterprise software is going to grow the fastest it ever has—15% a year at $400 billion. It’s never grown this fast ever. But of that 15%:* Almost half is taken up by price increases from existing vendors (everyone’s raising prices)* About half of the remaining half is new AI budgetThat means if you’re not one of the vendors getting price increases or new AI budget, everyone else has to get cut.Vendor count is getting stable or shrinking to make room for new AI offerings and price increases from select vendors. CIOs have gone around the room and said: “Give me an app. Give up an app. You want to add a couple AI apps next year? I’ll find you budget—but you got to give up two. You have 100 marketing tools? Maybe 96 is enough.”I just got an investor update yesterday from a pretty successful company at mid-eight figures in revenue. They had $1.5 million in churn last month. From happy customers. No CSAT issues, no other problems. They literally said: “We’re cutting apps next year. We’re an attachment to CRM and we got cut.”The CEO failed because they didn’t get above the cut line. It was great to have, but not mission critical. And it got cut to make room for an AI app.Our SEO Is Down 8%. But Our Traffic Is Up 50%.Is SEO dead? Let me give you our real numbers.At SaaStr, our blog is like our core—it’s home base. Our blog traffic was fairly flat for about four years. Going into this year, our SEO is down 8%. Not 50%, but 8% for real.But here’s the thing: our traffic is up 50%. We will end this year at saastr.com with twice as many readers as last year, even though our SEO is down.Why? Because people all want to read about AI GTM content. They don’t want to read about classic SaaStr themes about CS teams and CROs unless there’s an AI angle. But because we have some of the best AI GTM agent content out there, people are just devouring it.Meanwhile, G2 says their SEO is down 30-40% or more. So if that’s your only play and you haven’t changed your product or GTM since 2021, yes—it’s probably going to feel 8-50% harder.You’ve got to find your tailwind. That’s your job right now.AI SDRs Didn’t Work Until Claude 4. Now They Do.Someone asked me about AI SDRs: “You said they didn’t work, but now they do. Can you elaborate?”Really, none of these SDR products worked before Claude 4.1 Sonnet or Claude 4 this year. Replit didn’t work. Lovable didn’t work. Base44 didn’t work. They were out there—they just weren’t very good.Then the magical moment when Claude 4 came out and everything was kind of magical.Look at Gamma—they do AI presentations. Founded in 2020. It was five years to $1M, and then 1 to $80M this year. Replit was founded almost 10 years ago. It was 10 years to $1M, then $1 to $250M. It’s not a coincidence that all of these apps took off January, February, March of this year. That’s when the LLMs got better.If you had a bad experience with almost any AI LLM GTM product before March of this year, write it off. It was a different time. Different LLMs. Different world.The Two Failure Modes of AI AgentsI’ve identified two main failure modes for AI agent deployments:Failure Mode #1: Pre-Claude 4 LLMsIf you bought overhyped GTM SDR tools before February/March/April of this year, they just didn’t work well. The LLMs weren’t good enough.I was with the CEO of Qualified—they’ve been trying to use AI to qualify inbound leads since 2019. I asked him: “This really took off around February or March this year, right?” He said: “Yeah, that’s when it finally actually worked after 5 years.”Failure Mode #2: “Just Turn It On”The second failure mode: so many folks just told someone on their team to deploy it, or hired an agency that didn’t know what they’re doing.We were on a call with a global technology leader—pretty AI-forward, impressive company—and they said: “We’re thinking about buying our first AI SDR. We’re just going to buy it and hand it to our SDRs and have them figure it out.”It’s not going to work. You’ve got to train it. You’ve got to train it for a month. You’ve got to train it every day. You’ve got to iterate the onboarding.I’d say 80% of the conversations Amelia and I have are a version of this: “I bought a tool and I have no leads. I bought a tool and I have a lot of leads, but I didn’t connect it to my leads. It didn’t magically work.”The Right Way to Deploy AI AgentsHere’s the captain obvious thing for any AI SDR, BDR, even AI AEs, CSMs:* First, you’ve got to have it work in the real world with humans. Figure out what actually closes deals.* Then you tell the AI what worked. There’s no magical prompt. The prompt is: “Here’s the script that I use with a customer that I closed.”* Then you iterate that prompt.* Then you hook it up to data—Salesforce, HubSpot, Snowflake—and have it ingest the data to keep working with that prompt to make it better.* Read every email it sends. Read every response it ingests. Some will be dumb. Some will be wrong. You say: “Hey, that was wrong. SaaStr AI London is not December 4th.” Do it every day for about a month. The mistakes start to go away.“If you can’t sell it yourself, the AI can’t sell it for you. It’s that simple.”The #1 Skill for 2026: Become an Agent Deployment ExpertSomeone asked about the most important skill to build for 2026. Here’s my answer:Right now, if you are world-class at Agent Force or Clay or Qualified or Artisan—pick two or three leaders with decent revenue that people have heard of—and you go through the deployment yourself, you do the training, you onboard it, you get it working in your company, and you can point to the metrics…You will be infinitely hireable for the next 18 months because only like 2% of marketers have those skills.Become the expert in deploying agents. It doesn’t need to mean you know every agent or even needs to be technical, but you need to:* Pick a few leaders and deploy them yourself (not tell someone on your team to do it)* Watch it, iterate it, train it, iterate every day* Spend a week non-stop deploying it, then work on it every single day for a monthYou will learn so much and become infinitely deployable.The Benioff Insight: Deliver Insane Value Before They PayOn the first podcast that Mark Benioff did with us earlier this year, I asked him what he thought of Palantir. He said: “I’m jealous of how much they charge.” Pretty funny—they charge more than Salesforce does.But what he said next was even better: “I wish everyone at Salesforce could go live before they even pay.”Think about this. The way we used to buy B2B software for years: I need a CRM. Your friends use it. You saw it at your last company. You talk to a sales rep that could answer six questions. You’d buy it. And if you’re lucky, by the end of the year, you might deploy it.In the old days, you might have customers that were new for year two that never even deployed in year one. That doesn’t fly today.Mark’s point was: “I know I can’t deliver this today—it’s not feasible—but the technology is there. I wish every single customer would be live on Agent Force before they gave me a dollar.”Get as close to that today as you can for your customer.When you look at the companies that explode—Cursor, Gamma—think about how much value you get in the free program. Think about how much value you get in the first month. What can you do with AI to provide that much value to customers before they even pay you?If you provide insane value with an agent before they pay you, they’re going to be kind of happy to pay you—rather than the sucker bet we all made for two decades: “I think it works. Someone gave me a demo. The demo kind of works. I can’t use it myself, but I need that.” And then half the time it’s a horror story.Your Moats Are Shrinking From Years to MonthsSomeone asked: “Some AI experts say that with AGI in two-three years, SaaS will be dead because you can literally just talk to OpenAI and say ‘write code that does that.’ What do you think?”Listen, if you’d asked me six months ago, I’d say the odds that’s true are zero. That’s ChatGPT nonsense. You cannot reproduce the amount of workflows, corner cases, and complexity that complex B2B software does.But you can chip away at it.I’ve vibe-coded 11 apps in 90 days that have been used 800,000 times. I haven’t stolen revenue from anybody, but that’s a bit of an existential threat to somebody, isn’t it?And the agent has gotten so much better. When I built my first app on Replit 170 days ago, it blew up. It deleted my database. Those posts went viral on Reddit. We got death threats from Redditors. Millions of views.Now? When Replit launched V3 about 45 days ago, there’s multiple agents that talk to each other and check the work. Before it deleted my data, now it has an architect. When there’s a tough problem, it doesn’t just go off the rails—it says: “Hold on, I’m not sure. I’m going to call in another agent.” They bring in help on security. Now they have a design specialist.Don’t underestimate the rate at which this stuff is getting better.Competitive Edges Are Now Measured in MonthsWhen I started in SaaS, you’d build something slick and you’d have about 12-18 months until you were copied by a startup. Then you’d have like 5 years until a big company copied you.When we launched EchoSign, DocuSign (now an $18B company) only worked in Windows and was partially web-based. We had like 18 months before they decided to copy us. Then it took 5-6 years for Adobe to decide to copy it. Then Google just launched a clone last year—like a decade later.Not too recently, I invested in a startup I love. Incredibly powerful. Within two weeks, they had four clones. And then a massive company is building a clone that’ll be out this year.It’s not that we’re going to rebuild Workday and Salesforce and Oracle. But our competitive edges are going to be measured in months when they used to be years. And that compounds.Look at Google—they just launched a Replit/Lovable clone. Replit gets to $250M in one year, of course Google’s going to clone it. It used to take everything 5-7-10 years to get to $100M. No one would even bother to compete.“I don’t want to be one of these guys on X saying the only moat is speed and working 996. But there’s a lot of truth in it. If you don’t like it, you might go into terminal decay.”The Pager Duty Warning: Two Times Revenue at $500M ARRI wrote up PagerDuty today. It got about 500,000 views on X and LinkedIn.PagerDuty missed its last quarter. They just crossed $500M in ARR. They’re worth $1 billion. Two times revenue.Their customer count is flat—15,000-something customers for four years. DataDog came out with a clone. Atlassian’s product is better. Startups are better. They just didn’t move fast enough.And I think it’s only going to accelerate. Everyone will build clones faster. It took DataDog 15 years to decide to do this. It might take them 8 months to do it next time. And 90 days the time after that.Value Selling Is Dead Without Product ExpertiseSomeone asked about value selling: “Why is it so hard for sales teams to speak the language of CFOs?”Let me be honest in the age of AI: Most sales reps have no idea how to sell value.You cannot sell value unless you’re a product expert. Most sales folks want to talk with a war sheet, a tear sheet. They know six things. You cannot sell value if you’re not a product expert.We were with an AI leader the other day. They closed a seven-figure deal while we were there. The solution architect left the sales team behind. Closed it without them. Didn’t want to talk to the sales team. Sales team didn’t know the product. They added no value.Value-based selling means providing value. You cannot provide value in the age of AI if you do not know the product cold. Ideally, how to deploy it, how to get it going, how to deploy real value—not just having a valuation calculator on your website that says 18 months down the road the product will work.“The worst sales rep you can hire today is the one that tells you they’re a ‘great people person.’ Who cares? I want an AI SDR deployed in 30 days. I want it to get me this amount of quota. I want to do this workflow. The rep who says ‘Oh, it sounds good, yeah, we can do that’—it’s not going to work.”AI is not going to replace the AE the way it is already replacing the SDR, support, and customer success. But it is going to replace a lot of AEs that don’t know the product cold.I really think 70-80% of the sales executives I’ve worked with over the last five or six years don’t know their product cold. If you look back at your top couple sales reps—whatever era, pre-AI, doesn’t matter—the top ones weren’t just good schmoozers. They knew the product cold. I call them “sales magicians,” but they’re not magical. They just know how every nook and cranny works.We’re just not going to tolerate mediocre sales reps in the age of AI. We’re going to buy from somebody else.Sales, Marketing & Support Are Converging Into One AgentSomething fascinating is happening in e-commerce that’s relevant to everyone: In the space of just a year, three categories of B2B e-commerce have all combined—sales, marketing, and support.Think about it: when you go to a shopping cart, when you want to buy a new phone, what might you be doing? You might be buying (that’s sales). It might have a problem (that’s support). Or you might be interested but not ready today (that’s marketing).Those used to be different apps. In e-commerce, you had Klaviyo for marketing, Gorgias for support, separate apps for remarketing. About a year ago, you’d go to e-commerce sites and they’d have five different agents: “Talk here for support, talk here for marketing…”Now they’ve all combined because AI can easily understand: Is this a sales call? Is this marketing? Is this remarketing? Is this support? Is this customer success?In most B2B, we’re still running multiple apps. But that’s not going to last. People don’t want 28 agents on your website. They just want one agent to solve their problems.At SaaStr, we’ve gone from one to 20 AI agents ourselves this year. But it’s at the edge of too many—not only for us to process and manage business process change, but when you go to saastr.com or SaaStr Annual, we don’t want you to see 11 AI agents either.These discrete walls between categories—AI is going to break them down because we just want to talk with one agent as customers, prospects, and users.Outbound Still Works. Here’s Why.Someone asked about outbound: What’s changed? What stayed the same?From our AI outbound: geometrically more volume, same results. By training the AI with the best scripts and ideas that humans did, we basically saw the same results—in some cases a little worse, some cases a little better—but same as humans for the most part with much higher productivity.Outbound isn’t dead. And if you’ve never been a buyer at a big company, you don’t get outbound.Let me tell you a story. Back in the day at Dreamforce, a guy from Success Factors (a large SaaS company SAP bought) turned to me on a panel and said: “Yeah, I bought your product, Jason.” I said: “Wow, great. Why?” He said: “Well, it was the end of the quarter, our fax machines were broken, and you said you’d figured out e-signatures and I needed that problem solved today. I bought 300 seats.”One of his top problems at a given time. High ROI. Immediate value proposition. He read a cold email from a company at $2M ARR.I asked Yamini at SaaStr Annual this year: “Do you read your cold emails?” She said: “I read them every day. Email is the best. Even today—it’s the best thing in the world. Everyone reads it. It’s an open medium.”Your job with outbound is to solve one of the top three problems of your buyer.Whether it’s CEO, SVP, VP—if it’s a top three problem, you’re going to get a meeting. Whether it’s a top three pain point or a top three initiative. AI SDR, BDR, Agent Force, Artisan, Qualified—I don’t actually know if this is a top three problem for a lot of buyers, but I know it’s a top three thing on their mind. Everyone wants to figure out how to do an AI SDR.If you’re in the top three, you’re going to see super high open rates if you have a differentiated product and crystal clear value proposition. This has not changed in the age of AI.The Enterprise Budget Reality: VP Slush FundsLet me tell you how it really works in big companies. When I was a VP at Adobe, there were three types of budget: unbudgeted, discretionary, and budgeted.Budgeted money: Big dollars but really hard to get. Multi-million dollar contracts that often take 1-3-5 years of discussing and planning until you close the deal. Maybe get a pilot.Discretionary/Slush budget: I was the most junior VP of all 40-50 VPs. Even I had a $500K slush budget. Today it might be a million bucks. Everyone had more.What was my slush budget for? My top three needs that I just couldn’t get elsewhere in the organization or elsewhere budgeted through the CFO.If you solved one of my three needs sitting in my corner tower office and it was some fraction of that $500K-$1M—I had budget. I didn’t even care. It’s use it or lose it in big companies.I didn’t have one more dollar than that. And I didn’t care about my eighth problem. I just didn’t care. But selfishly, if you solved one of my problems, I had half a million to a million bucks to buy.Those are the budgets you’re fighting for. They haven’t changed in the age of AI, but our priorities have changed.It was really hard for me to get a million dollar piece of software—it probably would have taken me years as the new VP. But I had almost a million bucks in aggregate to spend on whatever I needed to get the hundreds of folks that worked for me to solve their problems. I could do that in a week or two. That was up until that line, and then I was tapped out for the year.The Bottom LineStop with the “woe is me” narrative. Stop saying the GTM playbook is broken. The plays all work. The same players from 2017-2018 are running the hottest AI companies—that wouldn’t be possible if the playbook was broken.What’s changed:* AI agents actually work now (post-Claude 4)* Moats are shrinking from years to months* You need to deliver value before you get paid* Product expertise is mandatory for sales* Multiple agent categories are converging into one* CIOs are cutting apps to make room for AIYour job right now is to find your tailwind. There is AI budget. There is AI budget for new vendors at the CIO level. If you have something that truly changes the game for efficiency—if you can truly replace lots of humans with your agent—your customers are going to want to meet with you and they’re going to want to buy.But if you’re running the exact same playbook as 2022, nothing’s changed, you’re an attachment that’s nice to have but not mission critical…You’re going to get cut.This post is based on the SaaStr London 2025 AMA. Join us at SaaStr AI Annual 2026 in May for more sessions on AI GTM, agent deployment, and the future of B2B SaaS. 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

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 in enterprise software—and why being truly customer-focused requires constantly fighting against your natural instincts.The Problem: When Great Teams Hate Each OtherThe genesis of Datadog came from a painful problem Olivier and his co-founder experienced firsthand. Despite working together across four different companies, knowing each other extremely well, and building their teams from scratch with a “strict no a*****e policy,” they ended up in a familiar nightmare scenario two years in.“We ended up with developers that hated operations, operations that hated developers, fingerpointing—all of the things that you can imagine,” Olivier explained.The question became simple: Why don’t we give all of those teams the same viewpoint? How do we get them aligned and understanding their infrastructure the same way?This became Datadog’s founding mission—bringing DevOps together and bridging the gap between development and operations teams. What they didn’t fully realize at the time was that this wasn’t just a nice-to-have feature. It was actually one of the KEY reasons why companies would migrate from legacy IT to the cloud.“We ended up right in the center of it,” Olivier said. “Today the company is about 700 people. We’ve been doubling the size of the company every single year. It turns out everybody is moving to the cloud and everybody needs to understand what’s happening to their systems and applications.”The Counterintuitive Truth About Customer-CentricityHere’s the part most founders get wrong: you can’t be customer-focused if you let your company become sales-driven OR engineering-driven.“Everybody wants to be customer-focused,” Olivier noted. “But most companies end up being either sales-driven or engineering-driven. If you want to be customer-focused, you can’t be either of those.”The Sales-Driven Trap: Sales teams are phenomenal at figuring out what’s going to get a deal done. But very often, getting the next deal done is NOT what you want to do for the long run for your customers. Short-term thinking dominates.The Engineering-Driven Trap: Let your engineering teams run on their own, and you’ll end up with organizations where people focus way too much on their solutions and way too much on the long term. You’ll struggle to bridge that gap back to the customer.The solution? “It’s a struggle of every day to make sure that we go back to the customer and start from there.”This wasn’t a conscious choice at first—it was forced upon them. Starting in New York (not the Bay Area), without Google or Facebook pedigrees, without millions in funding, and without a suite of VCs telling them they were geniuses, Datadog had no choice but to obsessively focus on the problem.“We basically had to focus on the problem. We spent the first couple of years listening to customers and trying to understand what the problem was.”The First Two Years: Why They Didn’t Write Code for 6 MonthsWhen you’re engineers and you start a company, every instinct tells you to build. Olivier and his co-founder resisted this for six months—and discovered something remarkable.When you don’t have anything to sell, everybody is super happy to talk to you.“You’ll get hours and hours of really fantastic people at fantastic companies and they’ll spend all that time explaining to you what their problems are, what’s working, what’s not working for them. They’ll be extremely candid.”This changes the moment you have something to sell. “Then you’re tainted. You have too much of a vested interest and you’re trying to push something, so people won’t open up so easily.”After six months of research, they spent another six months building their first alpha. When they deployed it to a small number of customers, they noticed the product was “way too open-ended, way too general.”But here’s where they made a critical discovery that contradicts conventional wisdom about customer selection.The Open Beta Revelation: Let Customers Self-SelectDatadog initially ran a closed alpha, handpicking the best companies and the best people at those companies. “It was actually really, really hard to get a lot of signal from these customers,” Olivier admitted.Then they opened it up to a wide beta—and everything changed.“It’s a lot easier for users to self-select and start using your product than for you to understand for whom you’re going to be the right thing at the right time.”You can’t predict when customers will have free bandwidth, when all the stars will align for deployment, or when your solution will be exactly what they need. Let them tell you by trying it.This open approach became core to Datadog’s growth strategy and fed directly into their customer-centric philosophy.Why Datadog Doesn’t Believe in MVPsAsk most SaaS founders about MVPs and you’ll get textbook answers about shipping fast and iterating. Olivier has a different take.“I don’t think there’s an MVP for what we do. I think it’s a myth,” he stated bluntly.Here’s why: Datadog sells to enterprises. They sell a product that needs to monitor everything happening across entire infrastructures and applications. There’s a very large number of features customers rely on, and none of these features in themselves are revolutionary.“You need to have them, otherwise you cannot be minimally useful. It’s more of a continuum—you keep adding those features, and at certain point you have enough of them that customers can start buying.”The hard part? Figuring out which features matter most. “That’s why you have to go back to the customer and make sure you have enough of them so you can actually extract some signal from those conversations.”For enterprise B2B infrastructure companies, the MVP concept often doesn’t apply. You need depth before customers will even consider you.Scaling Customer-Centricity: The 10-100 Person PhaseWhen Datadog hit the market, they were about 10-15 people. The phase of reaching initial scale ran from there to just under 100 people. This is where most companies start to lose their customer focus—but it’s also where you can build the most powerful feedback mechanisms.Strategy #1: Only Sell Month-to-Month Contracts“We didn’t sell yearly deals for a very long time. We only sold month-to-month, meaning customers had the opportunity to churn all the time.”This was structural, not accidental. If something is wrong with the product, if you don’t solve the right problem, if it’s not valuable enough—you know right away. Customers churn and you can have a conversation with them.“If you start selling term deals, you’re going to have the bad news about a year later. By then you’ve wasted a year. You’ve gone in the wrong direction. You’ve made all these mistakes you could avoid otherwise.”The lesson: Find structural ways to get bad news quickly. Don’t let long-term contracts mask product problems.Strategy #2: Put Every Engineer on Support RotationEvery single engineer at Datadog goes on a one-week support rotation throughout the year. Not just professional support engineers—every engineer in the company.“Engineers are super happy when it starts, and they’re even happier when it ends,” Olivier joked. “But really what it gives is empathy for the customer.”Engineers see what problems customers face day-to-day. They see the consequences of their design choices. Sometimes features that seemed like fantastic ideas turn out to be confusing from the customer’s perspective.“It was a great idea, but we’re going to change it because it doesn’t work quite as well.”Strategy #3: Bring Engineers to Conferences to Give DemosDatadog does extensive event marketing because companies migrating to the cloud need to go to conferences to learn. They exhibit at these conferences, give lots of demos—and they bring engineers to do all of it.“Everybody’s on a rotation and everybody’s going to get to one, two, three, five maybe of those conferences and spend every time a full day basically giving demos to potential customers or existing customers and answering questions.”This builds empathy for customers and makes the whole engineering team more confident about the problems they’re solving and their relationship to end users.But here’s the trick: Many engineers aren’t naturally comfortable seeking strangers’ attention at a booth for hours. The solution? Have other people on the team responsible for getting attention and introducing visitors to engineers. Then engineers can settle into a rhythm of demos and questions without the anxiety of having to attract people.What Metrics Actually Matter (and Which Ones Don’t)Here’s something counterintuitive from a company that processes 4-5 trillion customer metrics per day: Datadog isn’t incredibly metrics-driven for their own product.“Most of the metrics are lagging indicators and they don’t convey all the nuance of the value that our customers are going to find with our product. So we don’t actually train ourselves to optimize to the metrics.”What they DO watch:* Volume of data and infrastructure customers are monitoring: A sign of the value they’re providing, because customers deploy them into more places* Engagement: But this isn’t revolutionary* Churn: Which is why they focused on month-to-month contracts early onThe philosophy: Don’t let metrics become a substitute for actual customer conversations and understanding.Scaling to 700: When Everything ChangesAt 700 people across 16 offices worldwide, some of the early strategies don’t work anymore. The executive team can’t meet with all customers all the time. The co-founders and CPO can’t do all the product work.This is where Datadog made a critical hire: product managers.But not just any product managers. They didn’t hire their first PM until they were over 100 people, and when they did, they defined the role very specifically.“Product managers are not here to invent product. They’re here to spend the most time possible with the customers outside of the office.”Their job:* Understand what customers’ problems are* Understand what they’re using, what they’re not using* Understand what they’re paying for and why* Get the best understanding possible of the problem and the value those problems represent* Present the product and get feedback“Their role is not to invent the product but to make sure it solves the right problem and has the right value.”This is a fundamentally different view of product management than most companies have. PMs aren’t visionaries sitting in a room designing the future—they’re customer researchers and feedback conduits.The Startup-Within-a-Startup ProblemWhen you start branching into new products or new markets, you’re suddenly back in startup mode—and it’s easy not to realize it.“It’s easy to get used to winning, and then you expect everything’s just going to work this way,” Olivier explained.Teams working on new products need to behave exactly as you would when you were a tiny company:* Soliciting as much feedback as quickly as possible* Going as wide as possible with betasThere’s tension here. You don’t want to taint the opinion of your existing product by releasing a new product that isn’t as polished. But if you want that new product to work, you need to go very wide very quickly.Most importantly, you need to create a culture where people seek the bad news.“It’s much better to go to a customer and ask them, ‘This new product—are you going to pay for it? You’ve tried it. Is it good enough? Are you going to buy it?’ It’s actually much better to hear from them, ‘No, I’m not going to buy it, and this is why,’ than to kick the can down the road and hope that maybe in 3 months, 6 months, 9 months they’ll change their mind.”When you know what’s not working, it gives you a thread to pull. It tells you where you need to go.The Counterintuitive Truth About PricingHere’s where customer-centricity gets tested: the pricing conversation. Customers want to spend as little as possible. You want them to pay as much as possible. How do you stay aligned?First, agree on what kind of company you are. Olivier sees two types:Type 1: Low-End Disruptors The major feature is that it’s cheaper. The dynamic with customers is they’ll push you to be cheaper and cheaper and to figure out what you can take out of your product so it can be as cheap as possible.Type 2: High-Value Products You charge more for your product and you have to deliver more value for that. The cycle with your customer is to deliver more and more.“You have to agree with your customer that they’re looking for high value, high impact—not as cheap as possible—because otherwise you’re never going to meet.”Once you’ve agreed on that, you’re aligned in the mid and long term. Your customers want you to be successful. They want your company to be in business two years from now. They want you to ship new features. They really want you to be successful.After that, there’s still friction—but that friction is how you learn about the value of your product and where you need to go next.“Asking for money and putting a dollar amount on the product really focuses the mind for the customers. It helps you get really, really good feedback on where you need to go. In the long run, it’s a very healthy relationship to have.”The key insight: See your customer as a partner. Frame it as “Let’s do this together. You want me to succeed and I want you to succeed, and the commitment on your side is to pay.”How Olivier Maintains Culture at 700 PeopleCulture isn’t about writing down seven values on a wall (though Olivier says they’ll do that eventually as they continue to scale). Culture is really about who you hire, who you fire, and who you promote.To maintain a customer-centric culture at scale, Datadog trains managers to care about the details. Olivier himself still sees every single customer complaint and support email that comes in.“I don’t read most of them. I delete most of them right away. I just scan them very quickly. But what this does is it helps me pattern match. It gives me a sense of what’s actually happening, how people are actually reacting to the product, what they’re saying.”He never acts on them directly. He never goes back to the customer or tries to solve the problem. But if he wants to affect change, he goes back through the management chain to make sure people get feedback and decide whether they need to change something.The other critical element: Be super careful about how you talk about customers inside the company.In sales, it can be tempting to talk about customers in manipulative ways as you try to manage them through a process. In engineering or product, you can think customers are making the wrong choices—”Why are they doing this stupid thing with our product?”“The way we see it is if they’re doing a stupid thing with our product, it’s our fault. We let them do that. We made them think they should be doing that, or we didn’t explain it right.”Always assume the customer is right. And even if you think they’re wrong, the fact that they’re thinking something wrong is itself a fact. It exists. You have to deal with it and you have to value it.You can’t let yourself dismiss what you hear from customers.The Acquisition Test: Optimizing for After the DealDatadog has made two acquisitions, both heavily tilted toward the tech platform, product, and team—not revenue streams to add to their sales engine.“What we optimize for is what’s going to happen after we close the deal. Are people going to stick around? How long are they going to stick around? Are we going to be able to build on top of that team and turn that team of 20 we just acquired into a department of the company that’s going to have 200, 500 people?”It’s all optimized on the fit and what happens after the acquisition—whether the companies share the same values.The One Piece of Advice: Run Toward the Bad NewsIf there’s one principle that runs through everything Datadog does, it’s this:“Really look for the bad news. Run toward the bad news.”Whether you just shipped the product or you’re renewing a big customer, the wrong thing to do is show up in front of the customer hoping they won’t bring up the issue in the account. “Hey, maybe with a bit of luck we can get away with it.”“Actually no—the first thing you do is you talk about it. You bring it up. Then you hear directly from the customer what they have to say about it. It actually goes a long way toward building the partnership, and it also helps you learn from the conversation with your customers.”This applies at every stage:* First six months: Don’t build—just listen to bad news about the market* Alpha stage: Open it up to get bad news faster* Launch phase: Only sell month-to-month so bad news comes immediately* Scale phase: Put engineers in support so they hear bad news directly* New products: Ask customers point-blank if they’ll pay for it* Big renewals: Lead with the problemsThe Bottom LineBuilding a customer-centric company isn’t about being nice or having good intentions. It’s about building structural mechanisms that force bad news to surface quickly and loudly.It’s about resisting the natural pull toward being sales-driven (short-term thinking) or engineering-driven (disconnected from reality).It’s about spending six months not coding when you’re itching to build.It’s about letting customers self-select rather than trying to control who uses your product.It’s about putting engineers in uncomfortable situations where they have to face customers directly.It’s about only selling month-to-month when your investors want annual deals.It’s about asking customers if they’ll pay for something and being ready to hear “no.”From zero to 700 people and doubling revenue every year, Datadog’s growth came from one counterintuitive principle: The best way to win is to constantly seek out why you might be losing.“You have to the wrong thing to do is hope the customer won’t bring up the issue,” Olivier concluded. “The first thing you do is you talk about it. You bring it up. Then you hear directly from the customer what they have to say about it.”That’s customer-centricity at scale.Olivier’s Top 5 Mistakes (In His Own Words)* Running a closed alpha with handpicked customers - “It was actually really, really hard to get a lot of signal from these customers.” Trying to control who got early access and picking the “perfect” companies backfired. Opening up the beta was when everything changed.* Building the first alpha too open-ended and general - “We noticed that the product was not exactly what it needed to be. It was way too open-ended, way too general.” Even after six months of customer research, they still got the scope wrong initially.* Not realizing how big the cloud opportunity would be - “We didn’t quite understand at the time that the cloud was going to be so big. We thought, hey, looks cool, why don’t we build it for companies moving to these new cloud environments.” They underestimated the magnitude of the shift they were riding.* Not understanding that DevOps bridging was the killer feature, not just a feature - “We didn’t quite realize was that bringing DevOps together, bridging those two teams, was not just a feature of the new world. It was actually one of the key reasons why companies would be moving from legacy IT to the cloud.” They had the solution before they understood its full strategic value.* Almost letting new products follow the old playbook instead of startup mode - “It’s easy to get used to winning and then you expect everything’s just going to work this way.” When launching new products at scale, they had to consciously fight against the temptation to skip the scrappy early-stage customer validation work that made them successful in the first place.Want more insights on building customer-obsessed B2B + AI companies? Join us at the next SaaStr AI London Dec 1-2, where founders and CEOs share the unfiltered truth about scaling from $0 to $100M ARR and beyond.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