Last week I wrote that we, a firm known mostly for its focus on B2B SaaS, have invested in startups working on autonomous navigation for spacecraft, personalized cancer vaccines, and micro-drones that hunt mosquitoes (stealth), among other companies that significantly expand the scope of what software investing meant traditionally.

Today, I’m excited to share that we participated in Anthropic’s Series G.

A VC that has “early stage” encoded in its freaking *name* and that prides itself on the craft of seed investing, participating in a financing at a $380 billion valuation?! You might wonder if we’ve lost our minds.

How it happened

The opportunity came about when our portfolio company Vercept was acquired by Anthropic in January/February, and we got the chance to not only roll over our position but double down. The timing made the decision easier than it might sound. Anthropic had just disclosed that its revenue run-rate had grown from $1B to $9B over the course of 2025, an unprecedented pace at this scale. We’d also observed that since the release of Opus 4.5 and 4.6, developers had been switching to Claude in droves, and it helped that we experienced the capability jumps firsthand. On top of that, there were rumors about a much more capable model in the pipeline (since announced as Mythos).

What does this mean for our strategy?

We’re not turning Point Nine into a late stage fund. And we’re not changing our name to two point zero. ;-) We continue to focus on early stage investing. This is an exception, because of a special opportunity to invest in a one-of-a-kind company.

In early April, Anthropic announced it had crossed $30 billion in annualized revenue. A few weeks later, it reportedly crossed $40B. As crazy as it sounds, Anthropic’s revenue would be even higher if the company had more compute, since the market for the highest-intelligence tokens is supply-constrained. To say that these numbers are “exceptional” would be an understatement. It’s unheard of in the history of capitalism.

What better company to make an exception for?

Why We Invested in Anthropic was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

Over the last few years, something happened though. A sizable portion of the companies we invest in these days deal not only with bits but with atoms too, so to speak (and sometimes photons, molecules, and proteins).

In the last two years, we’ve partnered with startups working on:

• 200-ton autonomous dump trucks for open-pit mines (Sensmore)
• Personalized cancer vaccines (Serova)
• Autonomous navigation for spacecraft (EraDrive)
• Micro-drones that hunt mosquitoes (stealth)
• A nitrogen-fixing, protein-rich crop that doesn’t exist in agriculture today (stealth)

Not your father’s SaaS portfolio. ;-)

We still invest in software, but in many cases it’s software and AI for the world outside of offices:

• Hula Earth’s on-device AI identifies nearly 10,000 animal species, giving landowners a real-time picture of the biodiversity on their land.
• Draxon uses VR to train airport ground handling crews.
• Sereact is teaching warehouse robots to pick, place, and sort objects they’ve never seen before.
• Rerun is building the data infrastructure for robotics and computer vision.
• Upciti uses sensors and cameras to provide cities with real-time data to optimize operations.
• (Stealth) is building the operating system for weather modification.

Where we still invest in pure software, it’s mostly foundation models or agentic systems that require extraordinarily deep domain knowledge to build, such as:

• Vercept (foundation model for computer use, recently acquired by Anthropic)
• Poolside (foundation model for software agents)
• Forithmus (foundation models for medical imaging)
• Findable (AI buiding intelligence for real estate)
• Zauber (AI agents for sea and air freight forwarders)
• (Stealth) (training methods for deeply superhuman coding LLMs)

A lot of these companies are working on things that could have an enormous impact on the world, using technologies that would have been impossible to build just a few years ago, which makes our jobs as early-stage investors more interesting than ever.

If we try to put them on a map (because that’s what VCs do :) ), we can see that the majority of our companies live at the intersection of “Engineered World and AI” or “Nature and AI”:

Of course there’s no perfect way to do this. You can argue some companies should be moved around a little or you can pick different dimensions … but we like this way of looking at it because it shows the three major themes we’ve been gravitating toward over the last few years.

Many of the above products are delivered as-a-service, so often it’s still a SaaS business model, albeit with different pricing, so not everything we’ve learned about SaaS has become irrelevant. But as you can see, what we’re looking for in a software company today vs. some years ago has (not surprisingly) changed quite dramatically.

2010-style SaaS investing is dead. Software investing has never been more exciting! (*)

(*) I realize this ending sounds like ChatGPT slop. I swear I wrote it myself!

What Investing in Software Looks Like in 2026 was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

One of the big challenges for many founders right now is to get their organization fully “AI-pilled”. In contrast to 1–2 years ago, it’s now very rare to have people in tech startups say that they “don’t believe in AI”, pointing to all the things AI can’t do yet. However, in many companies there’s still a massive discrepancy between the way founders (and some of the best team members) use AI compared to the rest of the org.

The strongest published benchmarks include Zapier’s hiring rubric, Fin’s 2x productivity program, Ramp’s Glass platform, and Jobber’s eight-rung engineering ladder, which was inspired by this blog post. They agree that AI fluency is observable, repeatable, and measured against output, not adoption alone.

This index condenses that consensus into 6 levels (L0–L5) based on 13 questions across 5 dimensions (Culture, Talent & Workflows, Throughput, Tooling & Context, Product, and Accountability & Governance). It shouldn’t take more than 10–15 minutes to complete and gives you an overall score as well as detailed report card with more granular scores and recommendations.

Head over to www.ai-pilled.com and let me know how you’ve scored! :)

How AI-pilled are you?

How AI-pilled Are You? was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

Okay, I didn’t build a real SaaS application. Sorry for the clickbait, but given what happened to my Knowledge Hub, I couldn’t resist.

A bunch of people have asked me about the status of my Knowledge Hub, which I wrote about a few weeks ago. How well does it work? Did I get PMF inside Point Nine? Things are changing so fast that I’ve been hesitant to write this post … by the time I hit the “publish” button, things might look different again. ;-) But I owe y’all an update, so here goes.

TL;DR

I’ve moved to a much simpler and probably better solution. Instead of worrying about a vector database, embeddings, RAG, hybrid search, a custom MCP server, and a process supervisor, I’ve just connected our data sources (Attio, Slack, Zendesk, etc.) directly with Claude, and it seems to work just fine.

A Quick Recap

About a month ago, I vibe-coded a Knowledge Hub: ~43,800 lines of Python, six data source connectors, a vector database, MCP servers, and more. I wrote about it here and also shared this (AI-generated) tech doc.

It was promising. It sort of worked. But it kept breaking. Connections got lost, MCP servers went down, sync jobs failed in non-obvious ways. Some sources kept failing silently. The app would happily report that it had synced, but nothing had actually made it into the database. Other times, documents were in the database but for some reason weren’t being retrieved. Plus all kinds of little things that didn’t work.

All of this is fixable, and better AI coding models and tooling come out almost on a daily basis, making it easier. But while working on it, I’ve tried a much simpler approach in parallel … and realized that the previous approach was heavily over-engineered (for our use case). It also helped that Anthropic released improvements at breakneck speed, including more plugins and better connections to GMail/G-Drive/G-Cal.

Doing this, I realized that by simply plugging everything into C̶l̶a̶u̶d̶e̶ Claude Code directly, I could probably get 90–95% of the value for about 10% of the effort. Especially 10% of the maintenance effort … and that matters a lot. A simpler AI Command Center system (Claude suggested this title when I discussed the specs with it) means I can iterate faster, because there’s so much less complexity to manage.

Side-by-Side: Knowledge Hub vs. AI Command Center

Here’s a quick comparison of the two systems (mostly written by Claude):

The main advantage of the Knowledge Hub is speed, because it provides the AI with a pre-indexed search. With the AI Command Center, question answering is pretty slow because everything happens live. But as mentioned before, the latency advantage of the Knowledge Hub comes with quite a lot of operational complexity. If it turns out that the AI Command Center isn’t good enough from a speed and answer quality perspective, I might go back to the Knowledge Hub — or take a closer look at systems like Glean or Dust.

New Features!

Because the system was suddenly so much lighter, I could experiment more freely. So I started adding things:

Task management. I made it my task manager to capture ideas and to-dos, replacing what I’d previously used ChatGPT for (which I wrote about before). The main advantage vs. the ChatGPT solution is that ideas and tasks are now saved in a Notion database, which is much more robust than relying on the context window of ChatGPT.

Smart screenshot workflows. When I paste a screenshot of a LinkedIn profile, it triggers research on that founder and their company. When I paste a screenshot of a WhatsApp conversation about scheduling, it triggers a Blockit scheduling workflow.

Morning digest. Every morning, it compiles a summary — emails, Slack messages, calendar updates — so I can start the day with a quick overview. I don’t know yet how useful it will actually be, but hey, why not? It took almost no effort to add.

But the most exciting feature I’ve added is a super simple but pretty interesting “scout”, which works surprisingly well so far, where Claude is instructed to find interesting founders/companies that I should take a look at. It looks for interesting themes in various places, does a first round of research, saves them as leads and presents them to me.

With this feature, I ran into a pretty serious issue: researching companies involves a large number of tool calls, and this kept clogging up the context window to the point where Claude would just stop working. I tried a bunch of approaches to slim it down. Nothing worked well enough … until I switched from Claude Chat to Claude Code for this workflow. The key difference isn’t that Claude Code has a larger context window (it doesn’t). The big advantage (no news to you if you’re an engineer) is that Claude Code can spawn sub-agents — farming out parts of a complex task to separate agents, each with their own fresh context window. This way, the parent agent’s context doesn’t balloon.

Since it works much better for the scouting/research use case and since I frequently run into context size issues with various types of questions, I’ve just migrated the entire thing to Claude Code (I told you there’s a chance that it might change before I’m done with this post).

And what about the Crustacean Revolution?!

The obvious question some of you might have: Why am I not using OpenClaw for this? Good question. For now, I’m moving pretty fast with what I have, but I’m experimenting with OpenClaw in parallel, and it’s well possible that I’ll soon switch to a more agentic, OpenClaw-powered setup … I’ll keep you posted!

AI Killed My SaaS was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

As you may have seen, about two weeks ago I finally decided to build a knowledge aggregation system. Having seen a steady stream of similar projects on my T̶w̶i̶t̶t̶e̶r̶ X timeline, I wanted to build something like a “second brain” that would sync all of our company’s data into a searchable, AI-powered knowledge base. Think Glean (which I’ve heard is great but unfortunately not available for smaller companies), but homemade.

13 days and ~43,800 lines of Python later, here’s an update.

What it does

Knowledge Hub (the not very creative working title) pulls data from the primary systems that we run on at Point Nine: Gmail, Google Drive, Slack, Zendesk, Attio, and Granola.

The system can also ingest data from ChatGPT. Given how many conversations we’ve all had with ChatGPT over the last few years, I thought this might make sense, but it’s still TBD how useful this will be in practice. So this is an experimental feature in an already experimental application. The Dropbox integration has been built but not yet enabled in production. Since we use Dropbox to store sensitive documents, I want to invest more time into security testing before enabling it.

All data gets processed into a vector database (Qdrant) with semantic search. It’s then connected to Claude via MCP servers (local for Claude Desktop, remote for Claude.ai in the browser). Using Claude, you can ask natural-language questions like:

• “How is Company X doing?”
• “Have we seen Company Y?”
• “What did we discuss with Company Z?”

…and get AI-powered answers with sources.

In addition, I’ve also created a Slack bot that allows you to get information from the vector database in a faster way. It uses regex-based question classification and only hits Claude for the final answer (with tighter constraints — shorter responses, no extended thinking). This makes it snappier for quick lookups (what you expect from a Slack bot) while Claude with MCP gives you Claude’s full reasoning power for a larger variety of questions.

Architecture

At a high level, the system is split into three main layers.

• At the top sits a simple web app that handles authentication, user management, and the dashboard. This is the part users interact with directly.
• Below that is a set of background services responsible for syncing data from external systems, running scheduled jobs, and exposing the knowledge base via MCP servers and a Slack bot.
• At the bottom are the data sources themselves and the vector database, where all content is ingested, embedded, and queried.

If you’re interested in more technical details, here is an (AI-generated) technical documentation.

Key Features

• Hybrid search — combines semantic (meaning-based) and keyword search
• Multi-user support — Google OAuth login; each user connects their own accounts
• Role-based access — admin vs. user permissions
• Claude integration — MCP servers let Claude Desktop and Claude.ai search the knowledge base directly
• Slack bot — team members can query the knowledge base from Slack
• Automated sync — scheduled background jobs keep data fresh

275 commits. 95 pull requests. Zero lines written by a human.

As you probably guessed, I didn’t write a single line of code myself. Every line was written by either Replit’s agents or Claude Code. I described what I wanted, tested the result, and told the AI what wasn’t working (many, many times).

Besides that, I occasionally asked the AI for a full code review and for suggestions for improvements, with a lot of rinse and repeat.

I did have to deal with pull requests, merges, version conflicts, etc., mostly because I messed up things when I tried to have several AIs working on it in parallel, and because it quickly went way out of my technical comfort zone. Because I don’t know how a system like this has to be built, the iteration went much slower than it would have if an experienced software developer had done it.

Things I’ve struggled with

• Process supervision. The MCP servers kept crashing. It took a lot of back and forth to implement health checks, auto-restart logic, and process monitoring. Neither I nor the AI anticipated this upfront.
• Parallelization. Initial sync was painfully slow. We were processing everything sequentially. (Am I starting to refer to the AI and me as “we,” a team?) Once we added parallel processing and batching, we saw a big speedup.
• Edge cases. All kinds of little things that created bugs that had to be fixed, in most cases without me even looking into what was going on.

I’m still battling with several issues: the MCP servers occasionally go down, the dashboard sometimes shows inconsistent numbers, sync jobs fail in non-obvious ways, and the Granola import is messy. I’m pretty confident that we (yes, we!) will be able to fix all of this though.

What’s less clear is how good the system will ultimately be at answering non-trivial real-life questions. Based on my tests so far, it provides pretty impressive answers on tasks like “Write a memo about company X” or ”How is company Y doing?”. But once we (I mean my human colleagues and I) start using it seriously — asking more sophisticated questions across more diverse topics — it’s well possible that it won’t be good enough to be genuinely useful.

My expectation is that my AI and I will be able to implement a few more relatively easy wins, but that pushing quality meaningfully beyond that will be hard. So my guess is that we’ll eventually end up using a product built by someone who knows what they’re doing. :-)

Build vs. Buy

This is not an argument against vibe coding or against customization. Keep in mind that (i) I’m not an engineer and I did this part-time over two weeks, (ii) what I’ve tried to build here is a somewhat complex system, and iii) RAG across large, messy, heterogeneous datasets is not a solved problem. What I’ve built only scratches the surface of what’s needed to do this well. Finding needles in haystacks — reliably and verifiably — requires much more: better chunking strategies, reranking, evaluation frameworks, feedback loops, domain-specific tuning…

So in this particular case (small company, complex system, difficult problem), SaaS is still the better choice. In many other cases, the answer will be different, which leads us to the trillion-dollar question:

What kind of software will be replaced by custom-built solutions?

Given the speed at which AI coding models are improving, I’m careful with predictions, but my current thinking is:

1) For a small company like ours: If the solution exists, we should buy SaaS. It doesn’t make sense to reinvent the wheel (unless you do it for the fun and the learning, like me).

2) For a large enterprise: For a company paying millions of dollars for SaaS and with internal technical resources, the build-vs-buy calculus changes. I have strong conviction that if not now, then in the near future, a small team of engineers with AI assistance will in many cases be able to build and maintain a custom solution for a fraction of the cost of traditional SaaS. If I look at what I as an amateur have managed to build in less than two weeks, I can only imagine what a good developer, directing several AI coding agents in parallel, can get done in a few months.

I don’t think the replacement of SaaS by custom-built applications will happen fast. For most companies, reducing IT/software spend is not a top priority. If a piece of software works well, replacing it just to save money will usually not be a priority. It’s different when the existing software doesn’t do a great job and I think that’s where the “let’s just build it” conversations will start. But at some point, inevitably, the question will be asked more frequently: “Why are we spending $2 million on this and $500k on that?”

Now, software companies obviously have access to the same (or even better) tools as those enterprises. And arguably they should be able to leverage AI even more. What I mean by that is, if SaaS is under threat because software development becomes 10–100x cheaper, maybe the solution for SaaS companies is to produce 10–100x more software to keep the pendulum where it is.

Some more thoughts on vibe coding

When we talk about vibe coding, we’re actually talking about two developments:

1. Non-developers building simple software. People using Lovable, Replit Agent, or Bolt to build simple applications that they wouldn’t have been able to build before. Landing pages, internal tools, basic automations.
2. Engineers becoming hyper-productive. For complex systems, you’ll still need engineers. I built a 43,000+ LOC system without writing code, but I also spent 13 days debugging issues that an experienced engineer would have anticipated. I believe engineers paired with AI are becoming unbelievably productive. One person doing what used to take a team. Small teams doing what used to take hundreds of people.

If you’re a non-technical person thinking about building something with AI assistance and you’re overwhelmed or intimidated: just start. Start on Replit or Lovable. Just ask the AI what to build and iterate. If you get error messages, paste them into the AI chat. If you get stuck, ask Claude Opus 4.5 for help. If you don’t know how to give Claude access to the code, ask ChatGPT or Claude how to do it. You’ll get a good response, and in many cases, the AI will not only give you the answer but can do it for you straight away. You’ll be amazed by how far you can get. LFG!

Some Learnings from Vibe Coding a Knowledge Hub in 13 Days was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

A few weeks ago, I started using ChatGPT as a to-do list app. I just dump everything that’s on my mind into it, very “raw”, usually in voice mode. ChatGPT organizes these inputs into tasks, removes completed ones, and suggests next steps. When I want to get an overview, I simply ask, and ChatGPT returns an organized list of recently finished tasks and pending tasks grouped by priority.

Here’s an example for an input …

… and how ChatGPT organizes the list:

It’s not a sophisticated setup, not even a Custom GPT. It’s just a chat in a project that I’ve pinned to the top and called “Tasks”. I just ramble every open loop in my head, usually on my way to work, and let the model sort it out.

The only customizations are an Attio export to help with name recognition and spelling and some custom instructions, but to be honest, it worked surprisingly well even before I added those. Well, maybe not surprising in early 2025 — context windows have become so long, and LLMs have improved so much in the last years — but I think my 2022 self would be surprised that a general-purpose AI could outperform dedicated to-do list apps.

Why traditional to-do list apps never worked for me

This is a bit embarrassing for someone who’s a productivity geek otherwise, but I’ve never been a great user of traditional tasks managers. I’ve tried many — it’s a busy category with a lot of great products — but none of them stuck.

One problem is that it’s hard (at least for me) to truly make the to-do list app a comprehensive “system of record” style list that includes all of my tasks. Tasks come from everywhere — email, Slack, WhatsApp, meetings. If you’re not super disciplined in consolidating everything into one place, your to-do-list app doesn’t reflect the full picture, and you end up with multiple half-complete lists.

In theory, it should only take a few clicks to copy & paste a task over, but my guess is that except for a small-ish group of GTD heroes, most people aren’t disciplined enough to do this consistently. Another option is to set up automations (e.g. using Zapier) that automatically add tasks from everywhere to your to-do-list, or forward tasks via email to a special address that then adds the task to the list. Definitely possible, but again, not so practical for most people.

Then there’s the second problem: I kept forgetting to use them (don’t laugh, I know this makes me sound stupid). And if you can’t rely on yourself checking the to-do-list app, then for an important task you send yourself an email or put a post-it on your screen, which obviously defeats the purpose.

Why ChatGPT works better (for me, for now)

Using ChatGPT as my task manager works well because:

1. Capture friction drops to zero. Natural language is the best UI for this type of data entry, and voice recognition is now so good that it works perfectly in voice mode.
2. My tasks live where my attention already is. I use ChatGPT all day, so here’s no chance I’ll “forget” about it.
3. The model adds (some) structure/enrichment. This will get much better with more context and integrations, but even without that, it automatically groups subtasks, suggests next steps, etc.

So far, I haven’t had problems with context drifts or hallucinations. I think after a few weeks of heavy back-and-forth, the model might lose track of what’s done, so I might have to start a new chat and copy & paste the latest status over at some point.

It’s still a bit too early to tell if I will ultimately stick to ChatGPT as my to-do-list app (I’m still in the honeymoon phase, and I’ve honeymooned with other to-do-list apps before that didn’t become lasting happy marriages). But so far so good.

Where this is going

Right now, the system is still extremely dumb compared to how it will work in 1–2 years from now (trust me on that!). ChatGPT is not yet connected to my email, WhatsApp, calendar, meeting transcriptions, Slack, files, and other systems. Connecting ChatGPT with these tools will be the real game changer because then:

• It will not only track tasks, it will (at least partially) start doing them (e.g. drafting emails).
• It will know when a task has been completed so it will require even less input from me for the monitoring part.
• It will add/suggest tasks e.g. from meeting transcripts, Slack messages, and other sources.
• It will remind/nudge me, whether it’s with a built-in reminder feature (recently introduced by OpenAI) or by putting something into my calendar.
• With deeper contextual awareness, it will know which tasks are more urgent than others.

This is all very much possible with today’s AI, I just need ChatGPT to finally plug into my stack. ;-)

In the future, ChatGPT might also render a suitable graphical UI on the fly (e.g., displaying checkboxes that I can tick off by clicking). As much as natural language is excellent for task input, it’s not necessarily the best interface for every type of action.

What does this mean beyond task management?

Looking just a bit further ahead, with richer context from various data sources and greater intelligence, AI will handle more and more tasks autonomously. In many cases, users will only need to approve actions drafted or suggested by the AI.

If (and that is still an IF) AI kills traditional task managers, what does that mean for other software categories? Before you cry out “SaaS is dead” too loudly, keep in mind that task managers are very simple applications. Replacing CRMs or ERPs — with their intricate business logic, complex data structures, collaborative workflows, and permission models — is a different feat. I don’t think that AI won’t replace these within the next few years, but longer term, it’s a very real possibility (as Satya Nadella said a few months ago). And even if AI doesn’t completely replace the application, what if it replaces the UI layer? That creates big challenges, risks and questions for application-level companies, particularly those where the UI is key to their differentiation or stickiness.

As per our previous posts, vertical and highly specialized applications should have less to worry about in this scenario. Then again, there’s always the question of how fast the big players will move. Granola, Lovable, Cursor all emerged quickly in areas one would think should be dominated by large incumbents. Will the same happen in the personal productivity space and other horizontal software categories?

How ChatGPT Became My Task Manager (And Why It Might Become Yours, Too) was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

Last week, I published a brief LinkedIn post with some thoughts on AI code generation, in reaction to the staggering growth numbers reported about Cursor. Apparently, Cursor went from $100M to $300M ARR in four months, which is truly unprecedented growth at this scale. Since then, I’ve watched this excellent interview with Cursor’s CEO Michael Truell on Lenny’s Podcast, and wanted to follow up with a slightly extended and updated version.

1. Code Generation is THE B2B Killer App of Generative AI

It’s far from the only one, of course. Generative AI is impacting software across many verticals, and the ultimate gen AI killer app is ChatGPT. But in B2B, and for a relatively homogeneous use case (compared to ChatGPT, which, like Web search, is used for everything), code generation stands out.

Since writing that a week ago, I’ve come across some real data that prove it. ;-) If we take these (slightly outdated) UBS numbers and break it down into the major categories, we get this list:

As you can see, code generation represents more than 28% of what UBS includes under “AI Native Revenue”. If we combine image, video, and audio generation into media/creative, that category tops the list, accounting for more than 38% of the total. I’ve found it interesting to see that taken together, code generation and media/creative account for more than ⅔ of the total. As a caveat, this is based on data from only 21 companies, but I doubt that looking at the long-tail would radically change the picture.

2. It won’t take too long until most code will be written by AI

One weekend ago, I built a level editor in Unity for a game my son is working on. (He only lets me touch the plumbing, I’m not good enough for the really interesting parts 😄). Most of the C# code was AI-generated. I barely understood half of it, but it works.

That raises a big question that I’m sure many people have asked themselves lately: what happens if we increasingly rely on AI-generated code? Are we comfortable pushing code to production that no human being has reviewed or understood?

You could argue that this isn’t new. For C# to run on a computer, it must be translated into CPU instructions (machine code) via several intermediate steps. I remember back in the Commodore Amiga days, games (and what was called “demos”) were coded in pure assembler, which sounds (and I think was) crazy. We’ve since moved up the abstraction ladder, trading raw opcodes for C, Python or JavaScript. Maybe in a few years people will find it laughable to use a language like this because we’ll be talking to computers in plain English?

So abstraction isn’t new. Developers have always relied on compilers to generate lower-level code that no human ever reads. What’s different is that in the past, those compilers were deterministic and rule-based. What’s new is the probabilistic, opaque nature of LLMs.

For what it’s worth, I strongly believe that we’ll get there and that it won’t take decades. AI-generated code will earn our trust quickly — because AI will simply outperform human developers. Once AI codes better than almost any human, the dynamic will be similar to self-driving cars. Will autonomous vehicles make zero mistakes? No. But if they prevent 90% of accidents, we should get them ASAP.

3. Cursor, Midjourney, ElevenLabs: PLG on AI Steroids

Pre-PLG, a software company’s growth rate was usually constrained by the speed at which it managed to hire and train salespeople. Companies like Slack, Dropbox, and Zendesk showed that product-led growth can enable faster scaling. Now companies like Cursor, MidJourney, and ElevenLabs are taking this to the next level … what I like to call PLG on AI steroids.

None of these companies achieved their growth by building out a massive sales team. You can’t sell that fast. It has to be bought. And the speed of adoption is driven by the insane value that users get, enabled by AI. Traditional PLG usually came with a 30 day trial for setup, education, feature discovery, etc. Some of these new AI tools compress the entire onboarding and activation process into minutes.

In 2012, I wrote about how SaaS companies should make the learning curve as smooth as possible and give the user as much gratification along the way as possible. Just like game designers need to teach the game to new users in many small steps, meticulously making sure that it never gets too difficult nor boring, B2B software companies need to create a frictionless and rewarding onboarding experience.

With AI, it’s as if Mario has discovered steroids (or rocket fuel). The jumps are bigger. The payoffs come faster. And the wow moments hit instantly.

The question is: what other categories will see this dynamic emerge? Who else can turn foundational models into a product with such extreme pull?

4. Where Does the Value Accrue in Code Generation?

There’s been a lot of debate about where value lands in the AI stack. Will applications like Cursor win, or will the value flow down to the model providers (in addition to Nvidia)?

The bear case for Cursor, which started out as a rather thin UX layer, was that most of their revenue goes straight to Anthropic. That view seems outdated, given that Cursor is now training its own models, purpose-built for code. As Michael Truell said in the interview with Lenny: “We definitely didn’t expect to be doing any of our own model development. And at this point, every magic moment in Cursor involves a custom model in some way.”

Poolside, a P9 portfolio company, started there from day one — training an LLM that is entirely oriented towards software development and that improves by completing millions of tasks across tens of thousands of real world software projects (what Poolside calls Reinforcement Learning from Code Execution Feedback). Their foundational model, which can be fine-tuned on how customers write software, what libraries and APIs they use, etc. is designed to enable developers to produce the best code they never wrote.

So while Cursor and Poolside took very different paths — Cursor initially focusing on UX and generating momentum with PLG, Poolside focused on R&D and enterprise readiness — it seems that they are converging on the same view: if you want win in code generation, you have to deliver the best possible user experience and be able to make money even if margins decrease — and for that, you have to own a large part of the stack, including the model.

I’m generally not convinced by the “thin wrapper” narrative that dismisses apps as shallow UI layers on top of someone else’s model. Over the past few years, we’ve invested in multiple companies that solve real customer problems and build meaningful products without training their own models. But code generation may just be a special animal.

The Best Code You Never Wrote was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

As we’re about to enter 2025, there’s as much excitement — and uncertainty — in the AI world as ever. On one hand, there are big questions about whether the scaling “laws” that have driven so much progress will hold up. The key question for the entire AI ecosystem is whether larger models will continue to get meaningfully better with orders of magnitude more compute for training and inference. On the other hand, it feels like progress in AI has never been faster, with foundational model providers as well as startups launching a continuous stream of new capabilities and products that often feel almost magical.

With so much up in the air, I wanted to share a few thoughts as we get ready for another wild year in AI. Don’t expect lots of bold predictions (my crystal ball is as blurry as ever), but here’s where my head’s at as 2025 begins.

1) Pre-training might approach diminishing returns, but it’s too early to declare the “end of scaling”

There’s a growing sentiment in the industry that we’re approaching “the end of the scaling laws”. The view is driven by the fact that GPT-5 hasn’t been released yet and that most of the (extremely impressive) recent improvements to OpenAI’s products stem from other innovations. As you may have seen, Ilya Sutskever, co-founder of OpenAI and SSI, recently added fuel to the fire by declaring the end of the pre-training era.

“The end of pre-training”,“the end of the scaling laws” and “the end of scaling” can mean different things, so it’s worth clarifying what exactly we’re talking about. The scaling laws for LLMs, described in a landmark 2020 paper by Jared Kaplan and several OpenAI researchers, state that model performance improves with larger models, more training data, and more compute. The optimal balance between model size and dataset size for a given compute budget was further detailed in the famous Chinchilla paper in 2022. Both papers state that each incremental increase of any of the three variables yields a smaller improvement than the previous one. So if we observe diminishing returns, it doesn’t make sense to talk about the end of the scaling laws: quite the opposite, this is exactly what the scaling laws predict.

Maybe this is just semantics, and perhaps what people mean when they talk about the “end of the scaling laws” is that we’ve reached a point at which scaling models further doesn’t yield practically meaningful returns. Industry experts have different opinions on the topic, and I guess no one really knows, but here are a few things to keep in mind.

First, performance improvements have never been solely about scaling the pre-trained model. Adding more parameters, data, and compute has been a key driver of the huge improvements from GPT-2 to GPT-3 and from GPT-3 to GPT-4, but it wasn’t just brute force. Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) were critical in making the models useful and played a key role in making ChatGPT so good. (1) The same is true for the new o1 and o3 models, where the key innovation was to force the model to “think” before it answers, breaking down a bigger problem into smaller, more manageable steps. (2)

Second, while general-purpose foundational models have already been trained on most of the text on the Internet (a key argument of the people from the “end of pre-training” camp), specialized domains like biology or chemistry remain underexploited. So there’s still huge potential for progress by training on more domain-specific data. It’s an open question to what extent this will lead to performance improvements outside of the specific domain (but there’s evidence that it works for code, i.e. if you train an LLM on more computer code, it will get better at general reasoning). Similarly, there are high hopes that data in different modalities, especially video, and synthetic data will solve the data saturation issue, but experts disagree on the extent to which this is going to work. (Synthetic data definitely works for coding; in other domains, it’s less proven)

Finally, even if we’re approaching a point where scaling pre-training becomes prohibitively expensive, we’re only starting to find out how much better models can get with greater inference-time compute. O1 has shown that you get much better answers if you give the model more time to “work” on a problem. With more compute, models can think through more steps and increase the likelihood of reaching the right answer further. (3)

Net net — my best guess is that LLMs will continue to get better with more parameters/data/compute, but the improvement curve won’t be as steep as it was in the past, and more and more attention will go to everything that happens post pre-training.

2) There won’t be another AI winter …

When a new technology gets hyped up, inflated expectations are often followed by a deep trough of disillusionment, which is why many people worry that the current phase of excitement will lead to an(other) AI winter.

I don’t think so.

There will, of course, be lots of failures. Pilots that don’t convert. Startups that go bust (including many that have raised 10s of millions before strong PMF). Disillusionment in areas where products fail to meet expectations or perform as advertised. And probably some spectacular failures of companies that have spent hundreds of millions on training models and didn’t manage to turn the investment into differentiated products with a sustainable competitive advantage.

But there won’t be a big across-the-board AI winter where people question the value of the entire field. AI already delivers way too much value today, be it in coding, medical transcriptions, translations, customer support, or as a productivity booster for tens of millions of people. I also think that the amount of capital and talent flowing into AI in recent years will ensure continued progress at a high pace, even if pre-training isn’t the main driver anymore.

So if there is another AI winter, it will be a very mild, Californian winter, as Richard Socher recently said on a podcast. No Berlin winter.

3) … but some of the high-flyers won’t make it

In the last few years, many AI startups grew very quickly from 0 to a few million dollars in ARR (and some to much more), at a pace that was extremely rare in the past. Several factors contributed to this phenomenon:

• It has become much easier to build AI-powered products, with surprising, impressive new capabilities that wow users and buyers. In some domains, AI has surpassed a quality threshold, unlocking massive demand and allowing many players to gain momentum, even with similar products (e.g., writing assistants).”
• In the wake of the ChatGPT launch, AI has become a door opener. Every company wants to try AI tools and solutions. Getting companies to do a pilot has become much easier. An example is legal tech. It used to be a laggard industry in terms of tech adoption; there’s been talk about AI for many years, but not much has happened. ChatGPT has catapulted the topic to the top of the attention of every major law firm. According to Clio’s latest Legal Trends Report, AI adoption in law firms has skyrocketed from 19% to 79% in just one year.

In many cases, startups deliver tangible value to customers in ways previously unimaginable. However, I’m afraid that many fast-growing startups will plateau when churn kicks in and pilots don’t convert. This risk is especially pronounced for easily replaceable point solutions (simple to adopt but just as easy to switch away from), add-on tools (temporarily successful but unsustainable if incumbents integrate similar AI capabilities quickly), or human-in-the-loop products, where revenue traction may not reliably indicate PMF. (4)

The AI wave is lifting many boats, but not all of them will stay afloat. This is, of course, typical for big technology waves, so it’s not a new phenomenon.

4) Startups will solve the “last mile problem” of AI

When ChatGPT arrived, many people in the tech ecosystem (myself included) asked themselves: If AI keeps getting better at this pace, what’s left for startups to build? Won’t OpenAI, Anthropic, or Google’s latest LLMs eventually do everything? Do you still need specialized business applications if in a few years you have an extremely intelligent AI system that has access to all of a company’s data?

These are valid concerns, but based on what I’ve seen in the last two years, I think it has become increasingly likely that in spite of (or, maybe paradoxically, because of) the fast increasing capabilities of foundational models, there will be more, not less, opportunities for AI startups. The idea is that the more capable the models get and the more people try them, the more startups are needed to solve the “last mile” problems that models alone can’t address.

There are a few reasons why better models could expand the opportunity set for startups.

A) Rapidly increasing expectations

When models could barely generate coherent text, a good enough summary or response was impressive. Now that GPT-4, Gemini 2, and other models can write essays, debug code, and much more, our expectations have shifted. Businesses want AI solutions that are reliable (no hallucinations), accurate (fact-based and grounded in company data), and trustworthy (secure and explainable).

B) Integration is hard

Enterprises must integrate models into complex systems, ingesting data from a variety of sources and in different formats, integrated with custom workflows, and ensuring outputs meet domain-specific requirements. RAG (retrieval-augmented generation) sounds simple in theory, but in practice, you’ll have to overcome various challenges. How do you chunk, store, and rank enterprise documents effectively? How do you manage latency when retrieving and feeding data? How do you prevent irrelevant or misleading context?

C) Agentic systems further increase the surface area

There’s little doubt that the future belongs to AI tools that can autonomously complete multi-step tasks. But if you give so much power to an AI, making sure that the system runs safely and reliably becomes exponentially more difficult and important.

If foundational models expand the opportunity surface faster than they can provide the complete solution covering the last mile, we’re entering a golden age for AI startups that take raw capabilities and turn them into robust, enterprise-ready products, so let’s hope that the theory proves right. 🙂

5) “Virtual employees” might turn out to be a gimmick

12–18 months ago, a fascinating new type of AI startups emerged: companies that offer digital workers with human-like attributes (and sometimes faces and names) to automate end-to-end jobs, e.g. in sales and customer service. If you’ve been to SF recently, you’ve probably seen Artisan’s billboards all over the city.

It’s a super innovative and fresh idea. Since their digital workers usually use the same tools as existing, human employees, these startups piggyback on existing platforms and minimize integration effort. It’s also an opportunity to attack incumbents with differentiated packaging and a potentially disruptive pricing model. For customers, it’s a very compelling value proposition: Keep your existing software and workflows, just add some AI employees to take over some of the work at a lower price.

So there’s a lot to like. However, I wonder if having “AI employees” with human-like attributes really makes sense in the long run, or if this is a temporary hack that allows startups to quickly gain traction in the current phase of AI adoption. I’m leaning towards the latter. A lot of jobs might have to be reconfigured if AI turns out to be good at some parts and less at other parts of a human’s job. For example, if AI can handle 80% of an SDR’s tasks but only 25% of an AE’s, you can’t just replace all of your SDRs with AI SDRs. There’s still a lot to be figured out as we adapt to working with intelligent software and agents, but my hunch is that AI employees with faces and names won’t be part of the endgame.

6) With agentic AI, we‘ll all have to rethink human-computer interaction.

With agentic AI — models that can browse the web, execute code, use external tools, or handle transactions — we have to rethink human-computer interaction from the ground up. We’re not used to giving software so much power, and one of the key challenges will be defining the boundaries of what these systems can and cannot do independently.

Imagine having an AI agent for travel bookings. Even a seemingly simple task like booking a flight can’t be easily delegated to an AI agent, as it requires trade-off decisions, such as choosing between a faster connection or a lower price. Even if your AI agent knows your general preferences, there’s a high probability that it won’t get it right every single time in every specific situation. Now think about giving an AI agent the keys to autonomously deal with complex, multi-step workflows (and to interact with other AI agents!) in a business, where the stakes are much higher.

Lots of challenges must be addressed as companies allow agentic systems to handle more and more complex tasks with less and less human supervision. A useful analogy might be training and managing a coworker who gradually gains higher levels of permission as they demonstrate competence. However, as noted earlier, such analogies might be akin to the skeuomorphic UI of the early iPhone (temporarily helpful but quickly outgrown).

I’ve spent most of the past 25 years, first as a founder and then as an investor, focused on building and backing web applications and software designed to improve human-computer interaction. By tomorrow’s standards, much of that software was pretty dumb. (5) The emergence of intelligent agents requires entirely new UI paradigms and I’m super excited to see how the smartest founders will define the future of human-computer interaction!

(1) I was going to write “smart” but that would invoke the inevitable response that these models aren’t smart but just stochastic parrots that are excellent at appearing to be smart. Don’t feed the trolls. ;-)

(2) ICYMI, OpenAI has taught the O1 model to use “chain of thought” before answering, which used to be a highly effective prompting technique before O1 came out.

(3) So Nvidia shareholders have a good hedge. If companies use less compute to pre-train models going forward, there’s a good chance that they will use more at inference-time … which could be much more, because in this case the need for chips grows with the number of users.

(4) If you want to build, say, an AI accounting product and you start by selling an accounting service with some automation and a lot of humans in the loop, you’re not proving much because there’s a clear, existing market for accounting services. The real test is if you can over time remove the humans in the loop. Doesn’t mean that starting with humans in the loop can’t be a great strategy, it just means that the typical steps of how startups prove PMF are reversed.

(5) Fun fact: my first internet startup, a comparison shopping engine I founded in 1997, used agents to retrieve pricing and shipping cost information from online shops (but those agents weren’t intelligent).

The Agents Are Coming. Winter Is Not. was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the last few years, we’ve met a number of startups building carbon accounting, emissions management, and similar software products. Although all of these companies are working on an extremely important problem, I’ve been somewhat skeptical about many of the solutions I saw. There are two (connected) reasons for my skepticism.

1) Carbon accounting has a “Shit in, Shit out” problem

In most sectors, Scope 1 emissions (direct greenhouse gas emissions from sources owned by the company) and Scope 2 emissions (GHG emissions associated with the purchase of electricity, heat, or cooling) account for only about 25% of total emissions. About 75% of total emissions comes from Scope 3 emissions, i.e., the indirect emissions in a company’s value chain. In sectors like agricultural commodities, construction, and real estate, Scope 3 emissions represent around 90% of total emissions. And yet, according to a BCG estimate, only 10% of companies comprehensively measure their scope 1, 2, and 3 emissions. The rest rely on rough industry estimates for the bulk of their emissions. You can’t blame them — existing consulting services and software solutions just don’t enable companies to collect verified supplier-level data in a scalable, cost-effective manner.

So the vast majority of companies lack the data they need to properly measure and account for their GHG emissions. As the famous saying goes, “what gets measured gets managed”, and the inverse is true, too. If you can’t measure it you can’t manage it.

2) Carbon offsets can be (mis)treated as an “easy way out”

Many companies buy carbon removal or offset credits to improve their carbon footprint. While this is a good approach in theory (put a price tag on CO2 and let the market figure out who can reduce emissions or remove CO2 from the atmosphere most efficiently) it comes with major issues and limitations:

• First, many projects come with a huge amount of uncertainty. If you buy an offset certificate for one ton of CO2, how certain can you be that one ton of CO2 will indeed not be released into the atmosphere as a result of your credit? And if you buy a removal certificate, how certain is it that a ton of CO2 is permanently removed ? If you buy credits from a reforestation project, for example, you must be confident that the trees remain alive and healthy for several decades. That requires the forest to be protected against wildfires, droughts, pests, and other risk factors, which you can’t take for granted if we’re talking about a period of 30 years or more. Another issue is that it’s not always straightforward to verify the additionality of these projects, i.e., whether the emissions reductions or removals achieved by a project would have occurred without the financial support from carbon credit sales. This uncertainty is reflected in the price of carbon credits, which spans across a huge range (around $30-50 per ton of CO2 for reforestation, $400–1000 for Direct Air Capture).
• Second, there aren’t enough high-quality offset or carbon removal credits available to counterbalance the current global emissions. The idea that emissions reductions achieved in one area of the economy can compensate for emissions produced elsewhere makes sense, but getting to net zero requires the vast majority of companies to cut their emissions as close to zero as possible.
• Finally, by going the offsetting route, companies miss the opportunity to reduce GHG emissions by making improvements to their value chain (some of which might be cheap or even free).

As a result, I’ve always felt that there was a lot of greenwashing: You use an estimation of your emissions (which might be significantly off) and offset them by buying carbon credits (which might remove only a fraction of the CO2 they claim to do).

The need for reliable Scope 3 emissions data, and the huge difficulty in obtaining it, led Louis Coppey to develop strong conviction on industry-specific (AKA vertical) climate platforms long before I met the Root team. The thesis is that how you get Scope 3 emissions data — and what companies can do to reduce them — varies drastically across industries, making it impossible to build a horizontal software product that can be sold to various sectors without extensive service or customization. So when Eric and Maurice, co-founders of Root, told us that they’re building a climate platform tailored to agricultural supply chains, starting with a product that helps food and beverage companies collect primary data, it immediately clicked.

Enter Root Global

As you probably know, the food and agricultural industry is a major contributor to global GHG emissions. Around 25% to 30% of global emissions come from our food systems, rising to around one-third when we include all agricultural products. What’s probably less obvious is that more than 80% of the industry’s emissions occur with farmers and processors. Therefore, farm-level data is absolutely crucial to unlock reductions.

Collecting primary supplier data is time-consuming and expensive. A large FMCG brand might source milk, eggs, grain and other agricultural products from hundreds of thousands of farmers across the globe. In addition, they might source processed products like cheese or milk powder from thousands of food processors, and each of these food processors in turn might work with hundreds of farmers. This is where Root comes in.

Root’s software platform makes it easy for sustainability and procurement teams at food companies to collect verifiable primary data at scale. Farmers have to answer only a few questions, as most of the data that is needed for GHG emissions calculations is retrieved from existing documents and other data sources. Using this primary data, Root models each product’s environmental footprint according to the most up-to-date calculation methodologies, providing companies with the data they need to identify and act upon individual emission hotspots across their supply chains. Over time, Root aims to give precise indications of the carbon impact, cost, and timeline of reduction levers tailored to the entire agricultural value chain.

Root’s ambition goes way beyond data collection (as valuable as that is). By building a platform for all players in the food and agriculture value chain — farmers, processors, FMCGs, retailers — they’re creating a climate platform that helps companies across the food system to get to Net Zero. It’s an ambitious mission, but we can’t think of a better team than Eric, Maurice, Rodrigo, and their team of 24 Rooties to achieve it.

Our investment in Root Global

As I’ve mentioned above, my sense is that in the past, carbon accounting was often about estimating emissions and buying offers for marketing and PR purposes. I think we can and must do much better — by calculating emissions using primary data, setting reductions targets, and working towards them. And with increasing regulatory requirements, investor expectations, consumer demands (and temperatures), the time is now.

We’re super excited to announce our investment in the company (which has previously raised a pre-seed round from many good friends like Project A, P9 alumnus Robin Dechant and Cargo.one CTO Mike Rötgers) and are thrilled to work alongside (and root for) the team as they tackle this critical challenge. Root is currently looking for 10+ people across different roles. If you’re interested in joining them as one of their first 50 employees, please reach out!

Why We Invested in Root Global was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.

A few days ago, Clio announced its US$ 900M Series F. The funding round was one of the largest capital raises for a vertical software company and the largest software financing round in Canada ever. Point Nine led Clio’s seed round in early 2009, so we’re fortunate to have been shareholders for most of the company’s history. When we invested, Clio had around 50 customers and a few thousand dollars in MRR. Today, Clio is used by more than 150,000 legal professionals, has crossed $200 million in ARR, is endorsed by more than 100 law societies and bar associations worldwide, and has more than 1,100 employees.

In this post I’d like to look back at Clio’s truly amazing 16-year journey and share a few learnings that might be relevant for founders or investors.

1) Some of the best companies start outside of the Bay area

It seems obvious now, but when Clio started out, the idea that world-class companies could emerge from outside the Bay Area wasn’t widely accepted. Back then, European and Canadian startups had a very hard time raising capital. A good example is Zendesk, where we didn’t manage to raise a Series A in Europe. Those days are, fortunately, long gone. The financing landscape has changed dramatically, making it much easier to raise capital from anywhere in the world.

Of course, there are still big differences between the SF Bay area and smaller tech ecosystems. The Bay area and other tech hotspots continue to produce amazing companies, and being in one of these regions still offers unique advantages (access to the largest talent pools, close proximity to early adopters, a rich ecosystem of experienced mentors,..). What makes many companies from other places so interesting is that because the founders are not part of the tech echochamber and because they are exposed to different types of industries and companies, they work on different problems.

Our portfolio company Laserhub, for example, has built the first fully integrated platform for the procurement of customized metal parts. Laserhub is based in Stuttgart, which is home to Daimler-Benz, Porsche, and thousands of small to mid-sized metal processing companies. It’s a multi-billion dollar industry, but if you haven’t grown up in an area like Stuttgart, it’s unlikely that you’ve had the type of industry expertise that led Adrian to start the company.

Similarly, no one in Silicon Valley was thinking about software for lawyers in 2008. The analogy with Laserhub isn’t perfect because there is no shortage of lawyers in California. But apparently, SaaS for lawyers wasn’t top of mind for Bay area founders at that time, maybe because vertical software wasn’t en vogue with Silicon Valley VCs in 2008.

2) Some of the best companies don’t follow the T2D3 path

For many years, investors were obsessed with the T2D3 growth path. When the 2022 downturn started, investors began to take a more balanced view, valuing efficiency over growth at all costs. However, hyper-growth is still the #1 thing that gets investors excited. Since returns are driven by power law, that’s understandable. Especially for larger funds, it’s hard to generate top-quartile returns without being in one of the few-in-a-generation companies like Wiz, which grew from 0 to $500M ARR in four years.

The reality is that very, very few companies can sustain this type of explosive growth over an extended period. Attempting to force it often leads to failure. As I wrote some years ago, the good news is that growing a little slower is not the end of the world. If you have a great product with high NPS, low churn, and an excellent position in your market segment, you have a decent chance of getting to $100M in ARR even if your growth rate starts dropping significantly below 100% year-over-year at around $10M in ARR. It just takes a few more years.

Except for the first few years, Clio never grew at 100% year-over-year. However, Clio has unusually high growth persistence, i.e. its growth rate didn’t go down much with increasing scale. In fact, the company even accelerated its growth in the last few years, disproving the Mendoza line idea, or at least being an exception to the rule.

While many hyper-growth companies have experienced massive slowdowns in recent years, Clio has not. One of the reasons is that hyper-growth was often fuelled by spending huge amounts on sales and marketing. So when these expenditures are cut back in a downturn, growth plummets. Clio never relied on excessive sales and marketing spend in the first place, so they didn’t have to cut back.

Another excellent example is Procore, the leading software platform for the construction industry. It took Procore 13 years to reach $10 million in revenue, so it was anything but a rocket ship. But then, in the following eight years, they grew from $10M to almost $900M.

3) The best companies all require huge persistence

I’m sure you’ve heard this many times before, but it’s worth repeating: the best companies all require huge persistence. Many founders would have given up before Procore reached $10 million in revenue, and surely most VCs would have abandoned a company with Procore’s early growth trajectory.

Not every startup idea is worth pursuing indefinitely. I’m not arguing for founders to keep going forever if things don’t work out. If an idea doesn’t work, you’ve pivoted once already, there’s no clear opportunity or market pull, it’s okay to give up. Life is too short. And don’t worry about losing money for your VCs; that’s baked into the model.

What I mean is that every company I know that made it big and looks like a huge success from the outside faced existential crises in the early days. And it doesn’t necessarily get better when you’re bigger. You just have different (and maybe bigger) problems.

A journalist recently asked me, “What made Clio win?”. My answer was that it began with Jack and Rian having the right insight at the right time. They knew what to build, for whom, and had the capability to build it. But ultimately, the #1 factor behind Clio’s success was their persistence. They faced numerous challenges and crises but never gave up.

4) Vertical software companies can become much larger than most people thought

For many years, most VCs didn’t want to touch vertical SaaS companies with a yardstick due to TAM concerns. What they’ve missed:

a) Higher market share

Vertical software companies usually face less competition, enabling them to capture a higher market share.

b) Ever-increasing ARPAs

Vertical SaaS companies can continually increase their average revenue per account through what’s become known as the layer cake strategy. What this means is that you continuously add not only new features, but new products, services and revenue streams. Louis goes into more detail here.

Clio began as practice management software for solo lawyers, offering a simple solution for time tracking, billing, and document management. Sixteen years in, it has become a true industry operating system, powering not every aspect of the legal process including client intake, client communication, court filings, accounting, and much more.

What’s more, Clio’s roadmap is as long as it’s ever been and there are no signs of deceleration at $200 million in ARR. Clio will keep adding layers to the cake, while also doubling down on its rapid market expansion upmarket and internationally.

Similarly, Procore, after more than 20 years, has captured less than 12% of its TAM in the US and less than 2% worldwide. The numbers are similar for other vertical SaaS winners like Toast, ServiceTitan, and Shopmonkey. These companies illustrate that the potential for vertical SaaS is much, much larger than most people thought 10–15 years ago.

5) The best companies write their own playbooks

Today there’s a well-understood playbook for vertical SaaS. You can find it on the Internet. ;-)

When Clio started, there was no playbook. Clio co-authored that playbook alongside a few other pioneering vertical SaaS companies of its generation. Similarly, when Zendesk began, there was no playbook for consumerized software and PLG. Zendesk, along with a few other SaaS companies that emerged between 2006–2009, helped create this playbook.

What does this mean for companies starting today? Founders should understand and leverage the playbooks that guided the success of the previous generation. Much of that knowledge remains highly relevant. But the best founders will go beyond these established frameworks and write the playbook for the next generation.

Don’t take this as advice to reinvent everything. There are many things I think founders shouldn’t try to reinvent. Don’t try to innovate when it comes to structuring an ESOP or sales compensation. Focus on innovation in product and GTM.

6) The best companies and investments are non-consensus and (eventually) right

You’ve probably heard it before, but I’ll mention it briefly because it’s so important. The idea goes back to Andy Rachleff, co-founder of Benchmark, who said that “in order to create something legendary, you have to have an insight that is non-consensus and right”. Imagine a 2x2 matrix. On one dimension, you can be right or wrong. On the other dimension, you can be consensus or non-consensus.

If you are wrong, you will fail, no matter what. But it turns out that just being right is not enough. There is one square you want to be in. And that is the square that is non-consensus and right.

Most people don’t realize that if you are in the right and consensus square, you will usually not achieve greatness. Your startup might have a good idea, but if it’s too obvious, multiple me-too competitors will get funded by me-too VCs. As competition floods the market, prices erode, and sales cycles lengthen. And the exit options become less attractive.

The path to greatness is to be non-consensus and right. Being non-consensus and right affords the startup the time to survive, adapt, and succeed after trial and error without fatal consequences. No one preys on them because no one believes their idea is important.

This gives the startup time to master differentiable and specific skills and build strengths for inevitable competitive battles that will come in the future. When you’re starting out, it’s way better if your potential competitors don’t care about what you’re doing.

Mike Maples, Jr. based on an interview with Andy Rachleff.

When we invested in Clio in early 2009, nobody wanted to invest. And vertical software was so unsexy that it took years before the first serious competitors emerged. Like Andy Rachleff said, this gave Clio the time to survive, adapt, succeed after trial and error, and eventually dominate the market.

7) The best tech companies are founder-led by a highly technical founder

Jack co-founded Clio with his co-founder Rian in 2008. Sixteen years later, Jack is still leading the company as CEO and he’s poised to continue for many more years, which is one of the reasons why we’re so excited about the future of the company.

There are exceptions, but based on our experience with companies like Clio, Zendesk, Jobber, Docplanner, and Brainly, we aim to invest in companies where (a) we think the founders can lead for many years or decades and (b) they are excellent technologists. One reason is that the best startups are so ahead of their time that even after ten or more years, they’ve only realized a fraction of their original product vision. To keep advancing, the founder’s vision is crucial. Another reason is that if there are sudden market or technology shifts (like with LLMs), you need leaders who deeply understand the technology and its implications and can adapt quickly. It’s hard for a hired CEO to do what Des Traynor did at Intercom when ChatGPT came out — refocus a large part of the company on AI, almost overnight.

8) Bonus learning: Always keep an eye on your spam folder. ;-)

Clio’s Journey to $200M ARR was originally published in Point Nine Land on Medium, where people are continuing the conversation by highlighting and responding to this story.