We estimate $414 billion in overall AI ARR by the end of this year (ex-China) 1. $187 billion in ARR from Anthropic and OpenAI (split between the labs and clouds who resell their models). 2. We estimate that ad-based ARRs for the two internet giants - Meta and Google - have been boosted by $95 billion over the past 2.5 years thanks to AI (based on the excess of their realized, high ad growth rates vs normalized growth rates). We estimate that this will increase to $108 billion by year end. 3. The various clouds - excluding frontier lab revenue - should be about $66 billion in AI ARR by the end of this year. 4. AI native apps and SaaS are generating $33 billion in AI revenue. 5. Smaller line items such as other frontier AI (Grok, Gemini subscriptions, etc.) and how AI is benefitting other ad networks (such as Amazon, Applovin, Unity, etc.) add another $20 billion in ARR. Taking reasonable assumptions, such as a 6 year life for the equipment, this should give an overall 12% IRR on the $931 billion in AI capex made over the past 3.5 years. Link with the details below

I really like this $ASML chart

China just put the light source of an EUV machine on a trade show floor. Langdao Silex (Shanghai, founded 2024, spun out of Tsinghua's plasma lab) showed a laser-produced plasma EUV light source. 90% local parts. Mass production? 3-5 years away. That's their own staff talking. Now the bar: ASML production sources: ~600W ASML demo in Feb: 1,000W, path to 1,500W Langdao: no power number disclosed A source that makes EUV light is not a source that runs 24/7 at fab uptime. And the source is one subsystem. The Zeiss optics are the harder moat.

On Anthropic's slowdown: 1. August is the month when most people take a holiday, you would expect token consumption to be lower. However, both OpenAI and Anthropic still produced nice MoM growth. 2. September is the month that OpenAI released Astra. You would expect dollars to shift to Astra to try it out. I did a large coding project with Astra last month. It's definitely a smart model but overall Opus 5.5 is the far more productive and better model to work with. And now Anthropic will release the new Fable. We're still very early when it comes to token demand. My bet is that Anthropic will retain a strong market share when it comes to the most advanced workloads, which is where most of the ARR in this industry will be made. Open-source will be dominant for more commoditized workloads, which is where most of token demand will go, but revenues and margins will be much lower here.

$ASML High NA just crossed from science project to production tool. Feb 2025: Intel had ~30k wafers through High NA. Oct 2025: 300k across the fleet. Today: Intel alone is past 1M, with results matching or beating low NA on the same layers. Fleet availability: 80% in April. Now close to 90%. The adoption calendar: Intel 14A: first node built around it Samsung: 2028, first High NA in DRAM HVM TSMC: 2030 The tech is ready, and the ramp will start in the coming years. ~$400M per tool.

$ASML CEO: "Our supply as currently planned for both 2027 and 2028 will not fully meet the demand of our customers. Given this, we are investigating ways to increase capacity for 2028 beyond the 30% increase we previously communicated."

Unitree's CEO just shrank his own timeline for robotics' "ChatGPT moment". August: 2-3 years best case, up to a decade. Now: 1-2 years. What changed? GPT-6 Astra. Independent test, YAM robot arms, zero robot-specific training: Block into a bowl: 19/20 Puzzle piece into a slot: 2/20 The "brain" for rough pick-and-place is basically here. Precision contact work is not. Wang's own bar is 80% of tasks in 80% of unfamiliar environments. ChatGPT Astra clears the easy half. Winners: actuators, reducers, dexterous hands (the exact bottleneck Astra exposed). Under pressure: robot-specific model startups. source: SCMP

Theoretically, $GOOG has all the assets to compete at the leading edge in AI - talent, data, strong operating CF to finance training runs and inference capacity. They're even uniquely strong in semis and hardware with the TPU stack. Additionally, they have the best cloud platform in the world with GCP which I use as the back-end for all my apps and tooling. However, the big problem is that they have become a big, bloated and bureaucratic organization that is unable to move fast. Similar to the companies they outcompeted 20-25 years ago like Microsoft. At Anthropic and OpenAI there are small teams of talent that can build and ship fast. In comparison, now four years into the AI race, the Gemini web console is extremely buggy, their AI models are mediocre, and they don't have good AI desktop tools or even extensions for top tools used by coders like VS Code.

"Micron to buy back 30-50% of the company in the next 2-3 years" - UBS "Given FCF of ~$155B/~$225B/~$205B for C2027/2028/2029E, respectively, this implies the repo here could be in a similar ballpark as NVDA despite having an 80% smaller market cap. Said differently, this now lays the path for MU to buy back ~30% of the company over the next two years and potentially about half through C2029. Ultimately, we think the market will reward this stock with a much higher P/E multiple as it sees a cycle play out in which MU's gross margins probably don't go below 65-70%, but in the meantime TBV/share should approach $360 by CYE28 - keeping a solid floor under the stock given much higher RoE across the cycle going forward as memory is critically important in an AI world."

The Silicon Photonics Foundry market (Mizuho estimates) $TSEM $STM $GFS

SpaceX is printing money currently with their AI capacity, and pricing keeps moving up. $SPCX

Key findings from Deutsche's copper conference:

More bad news for $ORCL "SQL migrations that once took years can now happen in days and weeks." - $MDB Will only speed up further with Opus 5.5

$NBIS and $CRWV are two good plays on rising GPU pricing - JPMorgan upgrade on CRWV: "We are upgrading CoreWeave to Overweight from Neutral, led by the favorable pricing backdrop for compute given the strong demand backdrop as well as the incremental willingness of the company to lean into short-term contracts at premium pricing, which drives our revenue and margin forecasts higher in contrast to a lackluster share price performance dictated by investor concerns around capital intensity. The stronger pricing backdrop on account of a robust demand environment, which has strengthened through this year, is evident in the following drivers: 1) July pricing changes of 25% across SKUs; 2) frequent price increases across SKUs from peers like Nebius (not covered); 3) pricing for short-term compute contracts from some peer companies tracking almost 3x the pricing range for CoreWeave on longer-term contracts; 4) CoreWeave’s leverage of the opportunity evident in its recent press release highlighting contracts signed in F3Q at ~$40M/ MW; and 5) CoreWeave management highlighting that the robust pricing is already supporting 5-10 ppts of incremental contribution margins on contracts, relative to prior contracts, dispelling any concerns that the higher price is purely a pass-through of higher costs."

Payback on GPUs is currently massive -> $75M / MW revenue for frontier labs vs $10M / MW annual depreciation. Right chart shows that NVDA GPUs keep humming for more than 6 years.

Investing in ASIC accelerator plays got you almost twice the return of $NVDA over the last 2.5 years (+290% vs +150%) basket: 40% AVGO, 40% MRVL, 20% ALAB

GPT Sol 6 is priced at a 50% discount to Claude Opus 5.5, basically the same as Sonnet. OpenAI is obviously trying to grab as much market share as possible. Of course it's working, I just already changed some of my API calls. Sol is a very good model.