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After funding 200+ SaaS startups, a clear trend has emerged at TinySeed. Vertical + Orthogonal SaaS is outperforming Horizontal SaaS. Lower churn. Better retention. Stronger exits. Rob Walling covers the State of TinySeed in Episode 812👇

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Startups For the Rest of Us

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Stuart Faught has been acquired 18 times on acquire.com. No plans to build a unicorn. Just trying to escape a corporate job that felt like Office Space. One tiny product at a time. Dentists. Orthodontists. HVAC. Med spas. Home care. Build vertical SaaS. Validate fast. Sell around 50k ARR when it stops being fun. Repeat. His rule: “If a buyer’s a jerk, I’m out. If they’re cool, I stay flexible.” Documentation. Niche clarity. Respect. That’s how you get 18 exits on Now he’s building with the same playbook. Full episode:

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Aaron Levie is one of the greats. He’s been the co-founder and CEO of Box for 20 years, of which public CEO for 10. And he shows no signs of slowing down, as one of the most active CEOs thinking about how AI agents will shape enterprise software. In our Greylock Partners Change Agents, Aaron and I discussed the question: happens when SaaS is no longer built for humans, but for a much larger scale of agents and humans orchestrating those agents? A few of my favorite topics from the episode: - Why 90%+ of enterprise data has gone unused to date and how agents finally make it accessible - Why SaaS categories won’t disappear, but will be rebuilt around agent orchestration layers - Why the system of record doesn't lose importance, but gains importance the enable agents - How abundance (not labor replacement) is the real shift agents bring to knowledge work - And the real moat in the agent era may be neither the model nor the interface but the workflow memory the agent accumulates over time Aaron has one of the clearest, most grounded takes on where agents for the enterprise can go and what happens to traditional enterprise SaaS. Full episode below:

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BREAKING: Manny Medina, Co-Founder & CEO of Paid, & former CEO of $4.4B, Outreach, joins me in London to discuss the $19.9T AI agent opportunity. Roughly $300 billion in SaaS revenue is “trapped” inside legacy pricing & delivery models After scaling Outreach from scratch to 6,000 customers, 220,000 active users, & $250M in ARR.. Manny (Manny Medina) saw firsthand how traditional SaaS monetization models break down when software shifts from user-driven actions to agentic systems performing complete end-to-end workflows. He founded Paid to close this gap. Early customers have already reported 20–40% revenue growth within six months of adopting the platform. With $33.3M in total funding, Paid is backed by Sequoia Capital, Lightspeed, EQT Ventures & Growth, gtmfund, & FUSE. (thank you to Max Altschuler at GTM Fund for the intro!)

Molly O’Shea

16,192 Aufrufe • vor 8 Monaten

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.
5:13

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 Aufrufe • vor 1 Jahr

.Intercom was on the brink of death—five straight quarters of $0 net new ARR, approaching negative growth. Then they went all‑in on AI. 6 weeks after GPT‑3.5 launched, they shipped a working prototype of Fin (Intercom’s AI agent). Fin grew from $1M → $12M ARR in 12 months. Now at mid–8 figures and >300% growth, it's on track to cross $100M ARR in less than 3 quarters, and will be growing faster than every public software company by year-end. In our real-talk conversation, Eoghan McCabe shares the brutal reality of transforming a late-stage, plateauing SaaS business into an AI-first company. What you'll learn: 🔸 Why Eoghan believes most late-stage companies won’t survive the AI transition 🔸 The “founder mode” transformation that required firing 40% of staff and resulted in 98% employee satisfaction 🔸 Why having “nothing to lose” is the ultimate advantage in AI transformation 🔸 How they transformed from a plateauing SaaS business to an AI-first company growing at 300%+ 🔸 How Intercom’s pricing evolved from “the most hated in SaaS” to a model for every AI company 🔸 How 12 years of therapy and a period of “ego death” shaped Eoghan’s leadership approach Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Great Question — Empower everyone to run great research: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 DX — The developer intelligence platform designed by leading researchers:
1:11

.Intercom was on the brink of death—five straight quarters of $0 net new ARR, approaching negative growth. Then they went all‑in on AI. 6 weeks after GPT‑3.5 launched, they shipped a working prototype of Fin (Intercom’s AI agent). Fin grew from $1M → $12M ARR in 12 months. Now at mid–8 figures and >300% growth, it's on track to cross $100M ARR in less than 3 quarters, and will be growing faster than every public software company by year-end. In our real-talk conversation, Eoghan McCabe shares the brutal reality of transforming a late-stage, plateauing SaaS business into an AI-first company. What you'll learn: 🔸 Why Eoghan believes most late-stage companies won’t survive the AI transition 🔸 The “founder mode” transformation that required firing 40% of staff and resulted in 98% employee satisfaction 🔸 Why having “nothing to lose” is the ultimate advantage in AI transformation 🔸 How they transformed from a plateauing SaaS business to an AI-first company growing at 300%+ 🔸 How Intercom’s pricing evolved from “the most hated in SaaS” to a model for every AI company 🔸 How 12 years of therapy and a period of “ego death” shaped Eoghan’s leadership approach Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Great Question — Empower everyone to run great research: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 DX — The developer intelligence platform designed by leading researchers:

Lenny Rachitsky

182,396 Aufrufe • vor 11 Monaten

What happens when you give a frontier agent access to a full company's history of docs, messages, tasks and more? As I'm onboarding to ClickUp, this feels like an absolute cheat code. "Cursor for your entire job." Full history of the company's thinking, decision making and operations are natively accessible w/ first-party tools and data access. No exaggeration this has reduced the amount of overhead to get ramped up (# of messages sent) by >60%. At Codegen we built an agent that straddles multiple applications - Slack, Linear, Notion, etc. - with many obvious limitations. Agents cannot search Slack historical conversations, for example, nor can you index Slack data. Any question concerning a historical decision is just out of reach. There are clear constraints of fragmentation preventing otherwise extremely capable models from performing productive tasks. Seems the biggest bottleneck to agent productivity in early 2026 is not capabilities or knowledge, it's the politics and incentives around SAAS fragmentation. Very bullish for all-in-one horizontal SAAS and what that unlocks
0:42

What happens when you give a frontier agent access to a full company's history of docs, messages, tasks and more? As I'm onboarding to ClickUp, this feels like an absolute cheat code. "Cursor for your entire job." Full history of the company's thinking, decision making and operations are natively accessible w/ first-party tools and data access. No exaggeration this has reduced the amount of overhead to get ramped up (# of messages sent) by >60%. At Codegen we built an agent that straddles multiple applications - Slack, Linear, Notion, etc. - with many obvious limitations. Agents cannot search Slack historical conversations, for example, nor can you index Slack data. Any question concerning a historical decision is just out of reach. There are clear constraints of fragmentation preventing otherwise extremely capable models from performing productive tasks. Seems the biggest bottleneck to agent productivity in early 2026 is not capabilities or knowledge, it's the politics and incentives around SAAS fragmentation. Very bullish for all-in-one horizontal SAAS and what that unlocks

Jay Hack

17,226 Aufrufe • vor 6 Monaten