To The Moon

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

I’ve been thinking a lot recently about comments $Microsoft(MSFT)$ Satya made a few years back. If we rewind the clock to mid / late 2022, the biggest thing on software companies & investors’ mind was “when will the optimizations end.” The ZIRP period of 2020-2021 created a buying frenzy - no one was thinking about costs (when it came to cloud / cloud software spend), they were only thinking about growth and capturing more market share (oversimplification, but you get the main point I’m making). At the end of the day, the market was providing cheap and abundant capital (for public and private companies), and investors (for both public and private companies) were rewarding growth (ie placing the most emphasis on growth when determining valuatio

In the early days of AI, we saw the rise of “GPT Wrappers.” Companies that created a product that resembled a thin layer on top of a model. People loved to mock these products, saying all the value was in the model with everything around it commoditized. “Why would I use your app when I can just use ChatGPT directly?” Years later, we have a new name for “wrapper” which is now “harness.” OK that’s a crude analogy and not exactly apples to apples... a harness is really the code that determines what information a model sees at each step, what to store, what to retrieve, and what context to present. It’s the scaffolding around the model. But the spirit of the comparison is directionally right: there’s an enormous amount of value in what sits around the model, not just the model itself. And we

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