Seems like the two hottest trends these days are personal AIs (Instinct, $Meta Platforms, Inc.(META)$ Muse, $SpaceX(SPCX)$ Grok Bot, Dots from OpenAI, etc) and Decision Models like Jev. I wanted to write a bit today about decision models! Jev (from the company TypeSafe) has taken the world by storm (it launched mid September, so very recently). Within 24 hours of launching on Vercel’s AI Gateway, Jev was used by ~13% of paid teams. This made it the fastest adopted model in the gateway history! The Information reported they’re talking to investors about raising a round at a $10b valuation. Then this week OpenAI announced their Decisions API, Databricks announced their ai_decide function (powered by a decis

Last year I wrote an article about how the front door to systems of record were evolving. I think a better title would have been something about how the “interaction layer” is evolving. The general idea of the post is that when we saw the internet arise it ended up “layering” platforms that used to own the customer interaction layer (and turned “front door” applications into middleware). The other point of the post is that whoever owns the interaction layer (ie the place where the end user takes actions) ends up capturing most of the profits. The example in that post was OTAs became the interaction layer for travel (in the age of the internet) and pushed down the GDS systems to middleware. This last week we started to really see a tangible example of how this may play out with AI, and iron

Dario dropped another blog post last weekend that went viral - “We Must Pace the Frontier.” As you could expect, it created an entire spectrum of reactions! In the blog post he talked repeatedly about “embedded evaluators” - and I kept thinking about the parallels to the IAEA (International Atomic Energy Agency). Side note - I hate drawing a parallel to nuclear…… I don’t like the doomer narratives. BUT I though the principle of the IAEA is similar to what Dario was proposing with ebmedded evaluators. So what is the comparison? In 1953 Eisenhower gave his “Atoms for Peace” speech at the UN. A few years later the IAEA was formed. In super simple terms, the IAEA’s job was to make sure the world could use nuclear technology, while verifying that whoever uranium was being enriched the levels we

FT recently dropped an article showing the spend split between different Anthropic models (indexed back to June). What was interesting about this chart (below) was that the recent growth across July wasn’t driven by Fable, but by Opus 5. Many people asked questions about this. Why isn’t Fable 5 driving more adoption? The overwhelming response to the question of “why has Fable 5 seen more lackluster adoption” was zero data retention (or ZDR). This topic has been front and center this summer. OpenAI wrote a blog post about it a few weeks ago. In my opinion, it’s becoming increasingly clear that users have a preference when it comes to data retention policies, and if you believe the majority of responses to Martin’s tweet, the labs have some decisions to make! So what is Zero Data Retention?

Whenever I’m thinking about making a private investment there’s (broadly) a couple main questions I ask myself: What could go right If things go right how big can it get What could go wrong The reality is the first two are kind of the same question, and the third question is completely irrelevant (in my opinion). I bring this up because so often the conversation around venture stage company investing is about the risks. Execution risk. Market risk. Competitive risk. Hiring risk. etc. What if the market isn’t big enough? What if they can’t execute on this vision? The deck is massively stacked against startups. The reality is only a miniscule fraction ever actually turn into big companies! And all the reasons to be hesitant are right! In a vacuum, the reasons to not invest in a startup almos

A few weeks ago I wrote an article “The Second Life of a GPU.” In that post I discussed a (hypothetical) scenario where GPUs retained value past their (presumed) 4-5 year useful life. What impacts would this have on clouds / neoclouds or the broader compute complex? Could compute contracts be “re-contracted” after their useful life? All some version of the same question - is the useful life of these chips actually much longer than 4-5 years. Well, this week on Coreweave’s earnins call we got a great nugget related to this. “What we are seeing today is that the upside of recontracting is real as we remain largely sold out of prior generations of NVIDIA GPUs in addition to the current SKUs. So as our earlier generation fleets roll off their original contract, they offer the potential to deli

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