To The Moon

Workflows workflows workflows…I’ve probably written about this in the past (hard to keep track of everything I’ve written at this point, so I’m sure there will be some repetitive content!) but I wanted to circle back to it. For SaaS companies, the conventional wisdom was that you developed a moat if you built a system of record - a platform that stored data. If you owned / controlled the data, you had a moat! Data has gravity. True, but that wasn’t the real moat. The real moat was the hundreds of workflows that grabbed data from that system of record and then got work done. Sometimes those workflows originated from the system of record platform itself. Sometimes the workflows originated elsewhere, and one stop of the workflow grabbed data from the system of record. The real moat is owning

Back in December I wrote about the fight to become the front door to the systems of record. In that post I wrote about distribution, who sits between the user and the data (and why sitting there is strategic). This post is an expansion of that post (and the 1 or 2 I wrote after about similar topics). What really should AI companies be racing towards? If systems of record won in the SaaS era (ie they had the durable moats), what’s the equivalent in the AI era? Of course the answer is still partially “the system of record”, where maybe you swap out “record” with something like “agents” or “work".” But let’s come up with something new :) Let’s start by looking at the SaaS era, and what qualities created durable successful companies. In SaaS, one main goal just about every company aspired towa

Q1 earnings season is just about done, and this Q has been great for software. Looking at the YoY growth in quarterly net new ARR added, this was the best quarter (by a long shot) in last ~5 years This chart uses a basket of ~50 public companies who report ARR or subscription rev. Not an exhaustive list, but a representative ones Another call out. while the aggregate net new ARR was high, 17% of the companies saw ARR shrink QoQ (so they added negative net new ARR). This was the second highest percent of companies who shrunk QoQ in last 5 years TLDR - aggregate was great, but very high dispersion! 😍 Been eyeing Tiger merch but short on Tiger Coins? Now's your chance. 🎁 We’ve selected 4 high-demand items across practial, lifestyle, and learning, now with a lower redemption threshold!

I’ve been investing in open source companies for nearly my entire venture career. I love open source businesses, think they’re generally great for the ecosystem, and can also create a lot of commercial value (but this can be tricky!). We’ve seen all kinds of open source businesses become successful. Databases ( $MongoDB Inc.(MDB)$ , Clickhouse, etc), Data Infrastructure (Databricks, $Confluent, Inc.(CFLT)$ , etc) Developer Tools ( $HashiCorp, Inc.(HCP)$ , $GitLab, Inc.(GTLB)$ , etc). And many other categories. The nuance lies in how you define “open source.” A lot of this comes down to what open source license the open sou

Last week I wrote a post on the opportunity for Neoclouds. At the end I teased out an idea that these businesses could really surprise people if chips retained value after a 4-5 year useful life, and I wanted to unpack that a bit this week. First - it’s important to go through some of the unit economics / business model of these Neoclouds to understand why the useful life of these chips matter. There’s largely three different types of “deals” different offtakers (ie labs, hyperscalers, AI natives, etc) make with these neoclouds. Bare metal, “managed kubernetes”, and “full cloud.” Bare metal is the most stripped-down offering. The neocloud delivers the physical GPUs, networking, and power, and the customer brings everything else (their own scheduler, orchestration, storage layer, software s

A Neocloud boom feels inevitable. Clicking out one layer, the data center infrastructure buildout feels like it could turn into one of the largest wealth creation moments ever in physical infrastructure. Now that I’ve spoken in absolutes like this, we can bookmark this post for later when we look back on “signs of the top” :) Let me caveat this post with the fact that I’m very AGI pilled. Just about any estimate for “tokens consumed by X date” or model progress or data centers built or total demand I’m taking the over. In all seriousness, the numbers are staggering. Rumors / reports peg Anthropic / OpenAI at ~3-3.5GW of capacity to end 2025. OpenAI has talked about getting to 30GW by 2030. Let’s assume Anthropic has similar plans. Just those two alone will bring on (or plan to bring on) ~5

Last October I wrote a piece saying OpenAI had their “App Store” moment after they released the Apps SDK. 7 months later that prediction doesn’t look great… I don’t think we’ve seen an explosion of custom ChatGPT apps. ChatGPT hasn’t turned into the “super app” yet. Hopefully one day they will! I think there may be a separate “app store” moment happening with Anthropic. BUT - more of a B2B app store moment than B2C apps. Over the last few months I’ve seen massive adoption of “skills” in Claude. A skill is essentially an "onboarding doc" for an AI agent - a folder of instructions (often just a markdown file) that Claude pulls in only when the task calls for it. Anyone in a company can write one in an afternoon, which is why I think the distribution dynamic looks less like a consumer app sto

Fun post this week. I want to write about my own (newly formed) definition of AGI. I think we'll hit AGI (or we can claim AGI) when as a society we decide the marginal unit of energy is better spent on a GPU (or whatever compute primitive exists at the time) than on a human. Said another way - when the energy consumed by compute becomes greater than the energy consumed by humans, we're making the implicit decision that we get higher utility out of sending energy to machines. All definitions of AGI are super wishy washy anyway, so why not through another into the mix! The reason I like this one is it's quite quantitative. I’ve run the math, and the answer is 2033 (as you’ll hear me describe later, it’s all a bit “funny math dragging assumptions to the right) but that’s what makes it fun! Fi

All three Hyperscalers ( $Amazon.com(AMZN)$ $Alphabet(GOOG)$ $Microsoft(MSFT)$) reported earnings this week. There was one quote from this earnings cycle that I think will get a lot less attention than it deserves. It came from Satya: “The basic transformation of any per-user business of ours - whether it is productivity, coding, or security - will become a per-user and usage business. That is the best way to think about it.” Big statement! The per-seat licensing model is the foundation that the entire modern SaaS industry was built on. It’s how so many IT budgets are structured. It’s how every renewal conversation goes. It’s how every comp plan is designed. Did

$NVIDIA(NVDA)$ I’m a perpetual optimist. It’s hard for me to see the world through any other lens! Sometimes I’m naive, but overall I think it’s a better way to live (and, generally, hard to bet against humanity’s resilience). One of the larger debates surrounding AI relates to the impact it will have on employment. One side (booo, the pessimists!) argues we’ll see a collapse in employment as AI takes everyone’s jobs. The other side argues some form of “Jevon’s paradox” - with massive positive economic benefits. Given my intro, I think it’s clear what side of that debate I fall on! There are many ways I’ve framed this in the past (I think I’ve even written about it in a prior week’s edition). However, I heard Jensen recently articulate it much mor