I've never done it because it takes too much effort, but today I had my AI bot start emailing @CityofJoburgZA to report broken traffic lights on my way to work. I've scheduled a follow-up email every day until it's fixed. Next, automated messages and phone calls.

It’s laughable that the “killer AI app” that the market was looking for was a secure VM and a few plugins.

Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products!

Some thoughts on $AMZN/AWS – Enjoyed @borrowed_ideas recent AWS piece, and a lot of respect for publishing the model. I think he's a good analyst, which is why it makes my disagreement with him on AWS even more fun. His base case is that AWS is worth ~$1.3T and AWS's AI capex earns roughly its cost of capital. I shake out pretty differently... a few thoughts: First, let’s get Amazon Retail out of the way. 2027 consensus retail EBIT (North America + International) is ~$56B. Walmart trades at ~25x CY27 EV/EBIT. Put Walmart’s multiple on Amazon’s retail earnings and retail is worth somewhere around ~$1.4T. I’d personally take the over on that. Retail is materially under-earning... burdened by Alexa, supply chain services, the grocery push, Kuiper/Leo (LEO satellites), and an international business that’s still early on its margin trajectory. But for the sake of argument, call it fair. Back that out of Amazon’s ~$2.9T EV and... ~$1.5T is left for two things: AWS and the frontier lab stakes. Mark the stakes below current headlines (so let's say Anthropic at $1.5T, OpenAI at $1.0T) and Amazon's stakes are worth ~$350B. That leaves ~$1.15T for AWS, or ~12.5x 2027E consensus AWS EBIT. That’s below his $1.3T base case... which already assumes AI CapEx is value-neutral. Put simply, the market is already assuming AWS's AI CapEx creates virtually no value. MBI's model splits AWS into three demand buckets: (1) the frontier labs, (2) enterprise / “other AI”, and (3) the core non-AI cloud – and I address each below. (1) Frontier labs. His model has Anthropic and OpenAI paying ~$8B per GW-year for Trainium and ~$11B for NVIDIA, with lab capacity flat after 2029. At those prices the bucket earns exactly its ~9.6% cost of capital. Trainium sold to labs actually earns a bit less. I’ll concede that renting GPUs to frontier labs is the lowest-return business of the bunch, with two customers each with enough bargaining power to negotiate hard on price / threaten to insource evenually. However, the hyperscalers are, long term, likely to be the low on the cost curve, for three reasons... (i) They’re the largest NVIDIA buyers in the world, so they get a discount on the most expensive part of the stack (ii) They have the cheapest power: long-running, pre-existing PPAs, and utilities are inclined to give them the best pricing (iii) They have a far lower cost of capital than anyone else renting out compute Some neoclouds have done a better job architecting efficient token-serving systems... But I haven’t heard a compelling argument for why that’s a durable advantage. Also... power is a binding constraint, and the hyperscalers are locking up the land, power and shell (look at the backlogs & build plans as % of total incremental capacit...). If we stay supply constrained, they’re incredibly well positioned. And there are plenty of worlds where regulatory pushback limits how much supply comes online, which rewards whoever already owns the supply. Shorter term... At ~$5.00/hr for B200s and ~$2.50/hr for H200s, my math has GPU rentals earning well north of 50% EBIT margins and ~50% cash-on-cash returns. For now, at least, the most commoditized version of this business isn’t earning anywhere near commodity returns. And AWS doesn’t just rent compute to the labs. Bedrock (like Foundry at Azure and Google’s "enterprise agent platform") takes a cut of the lab tokens it distributes. It's a capital-light royalty. These are holistic relationships. The hyperscalers lend the labs their balance sheets because they know they can distribute those tokens through the greatest enterprise distribution platform of all time. Enterprise / “other AI”. MBI prices it off what customers can theoretically afford to pay (less than the frontier labs): "~$20B per GW-year today, falling to ~$14B by 2030 (arguing Azure, GCP, OCI and the neoclouds compete on price). Tis bucket ends up earning ~2pts over its cost of capital. That framing treats enterprise AI as simply renting raw compute capacity. Some enterprises rent GPUs directly, but most enterprise demand will be inference, and inference is far more likely to be bought as a managed service vs. GPU hours. There are numerous reasons that these business should earn excess returns: (i) Security, identity and governance are already in place. Who can see what, where, when, and what did they see? That’s the bedrock (no pun intended) of identity and access management. Most orgs already run it in AWS or Azure... and it’s quite painful to rebuild. (ii) The model weights are hosted inside the hyperscaler, so your data isn’t sent to OpenAI or Anthropic. Plenty of companies still aren’t comfortable with that. (iii) You burn down existing spend commits instead of onboarding a new vendor, which is a big lift in a large org (people like one throat to choke). (iv) It fits natively with the rest of your IT environment. (v) Model-neutral orchestration: routing the right job to the right model at the right time, mix open-weight and frontier models, and avoid going all in on one lab that might fall behind or adopt policies you don’t agree with (vi) The largest SIs and their quasi-FDEs are building on these platforms. And the data we have suggests Bedrock, Foundry and Vertex are outgrowing direct lab spend. Starting from first principles (switching costs, barriers to entry, real managed-service benefits), I'd have a hard time arguing this "segment" barely out-earns its cost of capital over time. Core non-AI cloud. MBI's model takes it from ~$120B of revenue in 2025 to ~$360B in 2035, a ~11-12% CAGR from 2025 (~10.5% from 2026), with growth fading to 7% by the end. This is where almost all of his value sits, because it's the only segment where ROIC materially exceeds WACC. For starters, I’m not sure you can cleanly separate core cloud from AI anymore. An agent is simply a model working in a computer/terminal-like environment... therefore it needs a CPU, databases, file systems and storage (read: agent memory), and access to other apps for tool calling. Those are all traditional cloud workloads. Agentic AI pulls a tremendous amount of traditional infrastructure along with it. It’s why we’re seeing CPU shortages and an acceleration in "non-AI" compute/storage/networking spend. I’ll cede the point that you can separate the two. But even if you can, that ~10% CAGR number looks quite low. Last, the frontier lab stakes. The bear world where compute is a commodity and the labs scale so fast they buy their own compute is also a world where the labs are enormous businesses. In that world, the ~$350B worth of stakes should probably double or triple. Add it all up: lab dedicated compute + Bedrock that earn more than Amazon's cost of capital, enterprise AI that earns a managed-service/PaaS return, a core cloud that grows faster than 10%, and if not... lab stakes worth multiples of today... and I have a hard time seeing $1.3T being a justifiable valuation. I appreciate the work by MBI. And it’s great because it makes the assumptions explicit. I just happen to disagree with several of them. Thanks for posting @borrowed_ideas.

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