I strongly believe there will be 3, very large, markets that emerge in the “model / token” market: Frontier tokens (ie from the large labs like OpenAI and Anthropic) Vanilla open weight tokens (Deepseet, Moonshot, etc) Customized (RL / SFT) open weight tokens So much talk recently makes these seem zero sum… And of course, they do take share from each other. If open weight models never existed closed models would take it all! BUT - zero sum assumes the size of the pie is constant…which it isn’t. The market is growing much faster than any one group can steal market share from the other. So everyone grows! This won’t happen for ever. Eventually the market will mature and we’ll settle on a more “stable” market share. But for now (and for longer than people will expect), hypergrowth persists! I

The vast majority of earnings calls from public companies are pretty boring. But every now and then a CEO pulls back the curtain a bit and goes into a level of detail that I find fascinating. One of those calls was Jay Kreps from Confluent (on their Q1 ‘23 call) discussing the TCO advantages of using Confluent vs open source Kafka. On that call Jay went into a lot of detail about the differences between paying a cloud vendor vs hosting your own software. Jay’s one of the best infrastructure entrepreneur, so getting a peek into his brain like that was pretty cool. Yesterday was another moment where I really geeked out on an earnings call. This time it was Andy Jassy from $Amazon.com(AMZN)$ discussing the ROI on AI capex (which is clearly a very hot

I debated the title for this article a lot. The other option was “The World Isn’t Binary.” I’m still not exactly sure which title better describes this article, but I’ve found the principal of living with a positive sum mentality more impactful in my life so I went with that title. Anyway..on to the post! There are no shortage of big debates right now in AI. Open vs closed models. Nvidia vs custom silicon. Will chips have value after 5 years. Will AI kill software. God model vs fleet of specialized models. Are we in an AI capex bubble or not. Model layer vs application layer. Will models commoditize. The list goes on! These were just a few that came to mind… I won’t address all of these today, but the answer for most is “you’re not thinking big enough.” I’m clearly a hyper-optimistic perso

There’s a new open weights model out this week many are talking about - Kimi K3. It’s not technically open weights YET, but on their launch blog, they said" “The full model weights will be released by July 27, 2026.” At first pass, what stood out initially is the size of the model. K3 is a 2.8 trillion parameter MoE (16 of 896 experts active per token), which makes it the largest open model ever released (I think this is true, but haven’t fully fact checked everywhere). It’s ahead of DeepSeek V4-Pro at 1.6T (which I believe was the largest before K3). It has a 1M token context window, native multimodality, and seems to be built for agentic work. A lot of the launch materials / demos focused more on things like long-horizon coding, demos where it navigated massive repos, multi-hour autonomo

Alex Karp had a pretty spicy segment on CNBC last week talking about a lot of things (many of which took shots at the large AI labs). The part I want to focus on today was the concept he talked about of “owning your weights.” In summary: should enterprises “own” their models or rent them (ie buy from a large lab). Should you own your weights to give yourself more control, better cost management, maybe even better performance with with RL’s models, and ultimately remove a single point of failure (or reliance). This could come in the form of developing your own models, or RL’ing open weights models on your specific workflows / data. There’s a future version of this post that will focus more on “owning your harness” vs “renting a harness” from the large labs, but I’ll save that one for anothe

I think there are two big questions hitting AI markets right now: If $SpaceX(SPCX)$ (via xAI) and Meta are all of a sudden turning around and renting out their compute capacity, are we really in a compute crunch or is the system starting to fill up with excess? What does the rise of open source models and the end of tokenmaxing mean for Anthropic and OpenAI? On point 1 - SpaceX famously created ~$2.32B of monthly revenue by selling (ie renting out) about 450k of GPUs to Anthropic, Google and Reflection. Details below. SpaceX lands another computing deal, this time with Reflection, an open source model development company. $150m / month for GB300s. SpaceX the Neocloud! Deal 1 with Anthropic Colossus 1 and Colossus 2. Anthropic took all of Colossus

There’s a saying that will become more mainstream very soon. And that saying is “Time to Power.” Time to Power is starting to matter more in the infrastructure buildout. There are of course bottlenecks everywhere, but access to power is a big one. Why does it matter so much? Let’s look at a hypothetical data center build. These oftentimes will cost billions, or tens of billions, and the majority is financed with debt. As a debt provider the main question you’ll try and answer is “what is the likelihood the borrower will be able to pay back this loan, and under what timeline.” To answer that question, they’ll dig in on when the cash register will start ringing for the borrower (ie when the borrower will generate revenue). You could procure the chips, acquire the land, do everything necessar

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