To The Moon

The AI FactoryIf I zoom out for a moment and look at the current trajectory of AI infrastructure, it’s hard not to see an entirely new pattern forming. Inference keeps getting faster. Inference engines keep getting smarter. And the ecosystem around them keeps getting more modular and open. What once felt like specialized machinery locked inside a handful of labs is now drifting into the hands of every company with a GPU budget and a few strong engineers.Neoclouds like $CoreWeave, Inc.(CRWV)$ and $TOGETHER PHARMA LTD.(TGPHF)$ have rewritten the economics of GPU access. Inference clouds like Fireworks, Baseten and fal have done the same for reliable serving (and we’ve already separated into separate infere

If I had to simplify software market cycles, I’d say they come in two phases: the expansionary phase and the consolidation phase.In the expansionary phase, buyers scoop up software almost indiscriminately. There’s little concern for cost or efficiency, what matters is speed. It’s about accelerating product development, capturing market share, or outspending competitors to stay ahead, all under the assumption that growth will take care of everything else. During this phase, public markets shift their focus entirely to growth over profits. Take a look at the multiples chart I post later on breaking out multiples by high, medium, and low-growth companies. You can see the high-growth bucket has seen multiple expansion this year, while the mid-growth bucket has seen steady contraction.In the co

This week the 3 hyperscalers reported ( $Amazon.com(AMZN)$ AWS, $Microsoft(MSFT)$ Azure and $Alphabet(GOOG)$ $Alphabet(GOOGL)$ Google Cloud). What did we learn? Most importantly - they ALL called out still being meaningfully capacity constrained. CapEx guides are going up, data center builds are going up, power constraints are meaningful. This isn’t the telecom bust where the world laid fiber that was “dark” (ie unused). GPUs are being used the second the come online…Here are the numbers:AWS (Amazon): $132B run rate growing 20% YoY (last Q grew 17%)Azure (Microsoft): ~$93B run rate (estimate) growing 39% YoY (last Q gre

Cloud Giants Update:AWS ( $Amazon.com(AMZN)$ ): $132B run rate growing 20% YoY (last Q grew 17%)Azure ( $Microsoft(MSFT)$ ): ~$93B run rate (estimate) growing 39% YoY (last Q grew 39%)Google Cloud ( $Alphabet(GOOG)$ $Alphabet(GOOGL)$ includes GSuite): $61B run rate growing 34% YoY (last Q grew 32%, neither are cc)ImageFor SG users only, a tool to boost your purchasing power and trading ideas with a Cash Boost Account!Welcome to open a CBA today and enjoy access to a trading limit of up to SGD 20,000 with upcoming 0-commission, unlimited trading on SG, HK, and US stocks, as well as ETFs. Find out more here.Other helpful

1. $Microsoft(MSFT)$ Azure at a ~$93B run rate growing 39% constant currencyQuarterly YoY growth trends below Line chart titled Azure YoY Growth (cc) displays blue line plotting quarterly year-over-year growth percentages from about 27 percent in Q1 2021 rising to 39 percent in Q3 2025 across x-axis quarters labeled Q1 to Q3 from 2021 to 2025 and y-axis from 0 to 60 percent with data points at 27 percent, 28 percent, 31 percent, 34 percent, 39 percent Estimates of Net new quarterly ARR added:ImageYoY growth in quarterly net new ARR added:Image2. $Alphabet(GOOG)$ $Alphabet(GOOGL)$ Google Cloud at a ~$61B run rate growing 34% (not constant currency). Google cloud

For years, every model release followed the same pattern: a flurry of charts showing performance gains across MMLU, HumanEval, GSM8K, and whatever other benchmark happened to matter that quarter. It was a scoreboard for intelligence, a nd every model came with its proof point. A few points higher here, a few tenths lower there. But something changed. Those charts stopped being interesting (at least to me…). Every model sits within a rounding error of each other now, and people seem to have quietly stopped caring. Benchmarks have become saturated.It reminds me a bit of the “index wars” during the early days of search. Back then, Yahoo, AltaVista, and Lycos all bragged about the number of web pages they had indexed, and the bigger the number, the smarter the engine. Then Google came along, a

If we look back over the last few years there are pretty clear patterns of “hot topic debates” that seem to pop back up every so often around AI. One I want to discuss today is the broad topic of “will scaling laws hold.”It’s a nuanced question, because “scaling laws” really mean many things, all of which trace back to data and compute. The debate was broadly could you keep throwing more data and compute at the models to make them better, or would they start to plateau. Over time, nuance has emerged. It’s not just about watching performance scale with more data / compute, but watching performance scale based on where / when / what type of data / compute you throw at models to make them better. Regardless of the nuance, the debate seems to perpetually oscillate between “they wont hold!” to

OpenAI just had their app store moment, and we may look back on it as one of the most significant announcement of the company’s history. The company introduced “apps” inside ChatGPT, and a development platform called Apps SDK to help develops build custom native ChatGPT apps. You can now call an app by name, “ $Spotify Technology S.A.(SPOT)$ , make me a playlist,” “ $Expedia(EXPE)$ , find me a flight,” “ $DoorDash, Inc.(DASH)$ , order my usual,” and ChatGPT will handle the interaction in-line, blending natural language, APIs, and lightweight interfaces into a single conversational flow. It’s a simple but profound shift: ChatGPT is no longer just a reasoning engin

This week on Clouded Judgement: The New RL Training Grounds- Median software multiple: 5.1x- High Growth software median: 27.0x- Mid Growth software median: 7.8x- Low Growth software median: 3.9x- 10Y: 4.1%ImageMore on this from Clouded Judgement today:The earliest days of reinforcement learning were fun to watch. Algorithms trained on Atari games, Starcraft, Go. The appeal was obvious: constrained digital sandboxes with clear rules, infinite repeatability, and instant feedback. You could run agents a million times through Pong and see them get better.But those environments were toys. Beating Pong doesn’t teach you how to navigate a hospital system. A high score in Breakout doesn’t tell you much about running a logistics network. The truth is, if agents are ever going to matter in the ente

I’ve found myself having a similar conversation with a number of investors and founders recently, and wanted to flesh it out a bit into a post. It’s a similar topic to the ERR vs ARR debate. I’m calling this one the “Easy come, Easy go?” debate…Let’s first start out with an undeniable truth - the fastest growing AI companies are defying the laws of gravity when it comes to scaling. The growth some of these companies are seeing is eye watering. 0-$100m in ARR in less than 12 months! Sometimes faster! There are many reasons this type of growth is possible, but I think it boils down to the fact that many markets in AI are truly greenfield, can demonstrate ROI incredibly quickly, and these together lead to crazy adoption cycles and growth.Yet despite this, I’ve found myself less “sure” of the