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

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

What a week for security breaches... Claude Code source code leaked via a misconfigured npm package, exposing 500,000 lines of code and an entire unreleased feature roadmap. Mercor got hit through a compromised LiteLLM dependency, with Lapsus$ claiming 4TB of stolen data including source code, databases, and contractor video interviews. And the axios npm package, one of the most widely used libraries in JavaScript with 100 million weekly downloads, was hijacked by state actors who injected a cross-platform remote access trojan. All within about 48 hours. The common thread? Trust in the software supply chain (and soon to be agent supply chain…) is incredibly fragile. A single misconfigured file, a single compromised maintainer account, a single poisoned open-source dependency...and the whol

One thing I’m starting to believe - the companies who figure out pricing and packaging the fastest will have a big edge in the early days of this AI phase shift. I think it’s one of the hardest problems right now for any AI company! What makes pricing so difficult in an entirely new (and expensive) line item has entered COGS - inference. Whether you’re paying OpenAI / Anthropic directly, or paying someone else to run open source models, inference costs are exploding (and we’re just getting started….). A big question becomes - how can you price your product such that you don’t torpedo your business into perpetual negative gross margin land (or said more positively, how can you price your product to more tightly align with value delivered). A couple weeks ago I wrote a post titled “Get in th

Every week I meet with founders building in the agent space. And lately, I keep hearing the same concept come up over and over - digital twins (or some version of this). When a concept starts showing up as frequently as this one, my ears generally perk up. Digital twins are the thing perking up my ears! And I think they’re about to become one of the most important concepts in AI. I think they could become a layer that helps scales AI to the masses (and consumption of AI). So what actually is a digital twin? The term originally comes from manufacturing. You’d build a digital replica of a physical asset (a jet engine, a factory floor) to simulate and monitor it. With AI it’s the same core concept, but with a totally new application. In the AI era, a digital twin is just representing knowledg

There seems to be endless debate around AI companies, and whether they have “upside down P&Ls” that will forever lose money, or if they will turn into cash cows in the future. Whether this sentiment is pointed at the large labs like OpenAI and Anthropic, or upstarts like Cursor, I hear it all the time! And I can’t tell if the bears just want to confirm their priors on AI negativity, if the bulls just have blind naive optimism, or if anyone really has a pov grounded in real analysis. As an early stage VC I certainly fall into the “perpetually optimistic” camp, so you can apply the appropriate filter to this post :) But for this post I wanted to focus on the profitability debate centered around the large labs, and why I think they’ll turn into wildly profitable business. There’s three ma

As always, these posts are more of a brain dump of “what I’m thinking” about…And lately I’ve been thinking about a pattern that keeps showing up when I study the biggest infrastructure winners of the cloud era, and what it means for AI companies today. Here’s the general idea: the biggest infrastructure winners of the cloud era monetized the core consumption primitive of the platform. In the cloud era, that primitive was compute, storage, and network I/O. In the AI era, it increasingly looks like tokens. Let’s unpack. When cloud computing first started taking off, the core primitive of the platform was very clear: compute. Everything that happened in the cloud ultimately boiled down to compute cycles running somewhere inside a data center. Storage, networking, and databases all mattered of

By now I’m sure everyone has seen Jack Dorsey’s tweet. For those who haven’t seen it (or don’t care to read it), he announced a ~40% headcount reduction at $Block, Inc.(XYZ)$ (formerly Square). This is a massive move… You rarely see headcount reductions this large. Throughout the post he used the word “intelligence” - which really can be replaced with “AI.” “we're already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that's accelerating rapidly” and later: “we're going to build this company with intelligence at the core of everything we do.” I’ve broadly seen two different reactions to thi