Recently $Anthropic(ANT)$ their initial S1 statement. A S-1 is a document companies file with the SEC in preparation for listing their shares on an exchange like the NYSE or NASDAQ. The document contains a plethora of information on the company including a general overview, up to date financials, risk factors to the business, cap table highlights and much more. The purpose of the detailed information is to help investors (both institutional and retail) make informed investment decisions. There’s a lot of info to digest, so in the sections below I’ll try and pull out the relevant financial information and benchmark it against current cloud businesses. As far as an expected timeline - typically companies launch their roadshow ~2-3 weeks after filing

Andrej Karpathy had a really interesting post on X over the weekend: Here’s the tweet in a visual funnel format I think this diffusion question is a really important one. In particular, how quickly does AI diffuse? What Karpathy’s X post shows (in his opinion) is that diffusion is happening slower. PNC research shows that only ~2% of US households are paying for some sort of AI service. What is the diffusion gap? It’s the delta between what you can accomplish with models (ie how powerful they are) and how the average person uses models. Here’s why I think it matters. A big question in the market today is “will the revenues match the promised CapEx commitments.” It’s a big reason why all the semi-cap stocks got clobbered this week, and questions like “Will

In the early days of the cloud era, when dissecting costs most people just looked at the basic price of compute and storage (for services like EC2 and S3). Overtime, the real focus shifted to egress. In today’s AI world, the initial focus is on the input/output pricing per 1M tokens, but the real focus is on the cache read rate. What is the cache read rate? The cache read rate is what a model provider charges to essentially re-reat input tokens it processed / stored from a prior call. If you think about an agent loop with the same system prompt, same tool definitions, etc, all of this history / context doesn’t need to be resent and reprocessed every time (and this turns out to be a majority of input tokens). Here’s some data on the headline prices everyone looks at (just a blended input/ou

Lots of drama this week on math proofs! I’m sure most have seen the back and forth between folks at OpenAI and Anthropic about a Navier Stokes proof. The point of this post is not to dig in on all the details (too much commentary on that online already, and too much speculation…) Instead - here’s a very high level of one part of the saga (and then something that I found interesting). Producing a proof of the Navier Stokes existence and smoothness problem is one of seven Millennium Prize Problems that each have a $1m prize attached to it (ie solve it, get a $1m prize). And OpenAI said they did it. 10k agents working in parallel for 3-4 days, >100 pages of manuscript and a formalized proof. The drama around this are allegations that OpenAI piggybacked off research two professors had done

This week we got a peak into OpenAIs new model Astra. I thought this was an important model because it’s rumored to use a new technique called recurrent depth. In this post I want to discuss what that means and what the implications are. But first - a quick timeline of some of the larger breakthroughs in model architecture: 2017: The Transformer era. The leap here was self-attention mechanism. Before 2017 recurrent neural networks (RNNs) processed text word-by-word. The now famous paper Attention is Al You Need introduced the Transformer architecture which made parallel training possible 2018-2020: Decoder-Only Architectures. The leap here was autoregressive scaling and in-context learning. Early on transformers used a encoder-decoder setup (like BERT). OpenAI flipped this by scaling decod