We built @AikidoSecurity to secure devs. Today, we're launching Noon to secure everybody else.

Introducing SKY-001, the first Engineering World Model. On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested: -> 51-125x faster - > 150-210x cheaper -> Higher IOU than all frontier models SKY-001 lets you dream a physical object before you build it. 18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch). The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.

The electrons must flow. I have spent the past few months mapping the energy to compute space, following the whole trip an electron takes, from the moment it leaves a generator to the moment it becomes a token. It started as a side project and slowly became most of what I read. The more I understood it, the less it looked like a set of separate markets and the more it looked like a single flow. An electron leaves a turbine at about 18,000 volts and is pushed up to 345,000, because high voltage is how power crosses distance without bleeding away in the wire. It runs a hundred miles of line, drops through a substation, and passes through a transformer the size of a school bus into the building. Inside, it falls back down. To 800 volts, then to 50, then below one, until it reaches the single volt a chip runs on. Some of that stepping is unavoidable, but a lot of it is not. The grid runs on alternating current, built a century ago to push power to motors. The chip wants direct current. So the standard path flips one into the other and back more than once. Every flip runs through a switch that is never perfectly on or off, and every one loses a little energy as heat. A good chain from the fence to the chip still arrives below 90 percent. Some of that loss is physics. Some is just history, and history can be deleted. The electron then reaches the chip, and most of the power it draws goes to waiting, not computing. To write a single token the chip reads billions of numbers out of memory and does a little math on each. Reading those numbers takes far longer than doing the math on them. A top chip finishes the math for a token in about fifteen microseconds. Then it waits four and a half milliseconds for the next numbers to arrive, drawing nearly full power the whole time. No frontier model fits on a single chip either, so it is split across many, each one reading from its own memory and reaching across to the rest. The gap is not an accident. Over the last twenty years the math a chip can do grew about 60,000x, while the speed of feeding it data from memory grew only 100x, and the links between those chips only 30x. Doing the math got cheap years ago. Feeding the chip the data it needs became the hard part. Even on a carefully tuned run the best teams in the world use only 38 to 43 percent of the chip they are paying for. So after all that work of carrying a clean watt to the silicon, the silicon spends most of its life waiting. Whatever it does produce leaves the building as a token, as light down a fiber. Seen whole, the same two problems run its entire length. It’s a lossy inefficient flow that leaks energy and performance, and it waits in a queue at almost every stage. The median power plant that switched on last year waited 61 months just to connect to the grid. A substation transformer takes over two years to arrive. Gas turbine slots are booked out to 2031. Even the chip waits, idle about a fifth of the hours it is powered on. Once I saw it this way, the companies worth watching all did one of two things. They shorten the flow, or they own a piece of it. Remove a loss, and more of each watt reaches the chip instead of leaking away as heat, which means more tokens per joule. Remove a step, and you skip both its energy loss and the wait for the equipment it needed. Remove a wait, and you pull a project's whole revenue forward, which is worth the most right now. A single year of a gigawatt pulled forward is worth over two billion dollars. That is why the same megawatt-hour sells for $271 to a buyer who can wait and nearly $5,000 to one who cannot. Owning is different. You hold a point the flow cannot go around, built of physics or law, and you charge whatever the detour would cost. The best companies do both, in order. Shorten to enter, own to stay. That frame is what the whole map and the essay are built on. About 450 startups so far, mostly North American, sorted across the eleven stages of the flow. It is not the full list, and it keeps growing. If you are building anywhere on this flow, I would love to hear from you!! The full essay and the map are in the comments.

Compute Desk is partnering with @NodalExchange to launch compute futures settled against our compute indexes. With the compute world’s nexus to electricity and power, Nodal is an exceptional venue for AI infrastructure companies, hyperscalers and financial institutions looking to hedge or gain exposure to compute, power or the spread between them. Reach out to @ComputeDesk to trade or hedge compute. H/T @ForgashEmily Bloomberg: bloomberg.com/news/articles/…

Around 15 years ago, Kaheer Suleman and I started Maluuba with the vision of building Universal Turing Machines. Back then, we were one of the pioneer research labs building foundational modules of deep learning and Reinforcement learning with Turing award winners @Yoshua_Bengio and @RichardSSutton. There was no AI hype then, as there is now. Those were the good old days of doing pure scientific research unlike the n+1 research that we see today. Maluuba was later acquired by Microsoft and became @MSFTResearch Canada. Grounded in that same mission, we're finally ready to introduce @skyfallai to the world after a lot of experimentation in stealth over the last year. We're a frontier neo lab building the first Autonomous Enterprise by moving beyond the current LLM paradigm. For the last 5 years, the foundational model market has relied on a single paradigm: scaling laws for LLMs - more data, more compute, bigger models. However, the real world is messy and much more complicated. In order to achieve our long term vision, the next generation of AI models requires a different approach. Skyfall is solving the hardest open problems in frontier AI: long-horizon planning, data inefficiency, and brittle performance in dynamic real-world environments. To achieve the team's vision of a completely autonomous enterprise, the team is developing a next-generation frontier model (Enterprise World Models) using Continual Learning and World Modeling. Enterprise World Models can simulate the multi-layered consequences of strategic business actions. Our approach unlocks a new category in the foundation model market. To prove it, we're introducing Morpheus, a Continual Reinforcement Learning platform for AI researchers. I'm building this company with the people I trust the most: my longtime friend Kaheer Suleman (prev. Co-Founder of Maluuba) and my brother @omgiamgod (prev. YC founder). Sumit and Kaheer are the first principles thinkers I can trust to go to the end of the world with to achieve the mission impossible together. Together with a stellar team of 25 researchers and engineers, we're pushing a new frontier in AI forward. We unpacked our long term vision in today’s Forbes feature 🔗- read it to see what we’re building toward. Thank you so much Victor Dey for the interview. To achieve our goal of enterprise world models, we are soliciting bids to acquire small SaaS startups (up to $1M) and fully automate them. If you’re interested, submit your business here: skyfall.ai/acquisition Finally, thank you to our investors and advisors for believing in our vision since day one: @Fidelity , @sk121 (@touringcapital), @karam_n and @chrisarsenault (@inovia), @morgan_blumberg (@M13Company ), @stephpalmeri (@NextViewVC ), and @michaellitt and @mmccauley (@GarageCapital ), @jennydhe, @fchollet @NaveenGRao and so many others for supporting us in this journey.

Today we present Morpheus, a persistent enterprise simulation platform designed to make Continual Learning a reality. Morpheus is the world’s first real world Reinforcement Learning environment. Every Reinforcement Learning environment operates in the game world. Benchmarks like Atari, OpenAI Gym, MuJoCo, and Procgen are all small, game-like worlds that reset every few minutes. But the real world never resets. A business keeps running and evolving everyday. We tested how frontier LLMs would perform in realistic and dynamic business environments 🧬on Morpheus. The main conclusion was that LLMs are not continual learners. 🧵Here’s how we did it and what we learned:

Today we're launching @SpellbookLegal's biggest thing yet: Autonomous Contract Management It’s the first end-to-end AI infrastructure for contracts The world is speeding up. We are in one of the biggest investment cycles in decades. Behind every rocket launch, FIFA game, and datacenter, lies a web of hundreds of agreements. Agreements are the invisible threads that allow us to work together. We have infrastructure for finance (Stripe, Ramp), eCommerce (Shopify) and many business functions. But agreement infrastructure is lacking. This creates a painful bottleneck on our ability to work together. Online purchases take milliseconds. But agreements still take weeks. CLMs were supposed to be the answer, but were designed in a pre-AI era. AI fundamentally changed how computers can accelerate agreements. Spellbook is the most used AI contract review tool in the world, with ~5,000 customers in 80 countries Now we are expanding to deliver the first end-to-end, AI native stack for contracts. From the moment a deal lands in your inbox, to the day it renews years later, Spellbook’s AI supports teams every step of the way, across all business teams. It runs 24/7. While you sleep, it's reading the deals that came in overnight, flagging the parts that actually need a lawyer, and clearing the busywork that used to kill mornings. Nothing gets handed off between systems or slips after signature, and the intelligence stays with you for the life of the contract. AI for lawyers is great. There are 20 million lawyers in the world, and many are our users. But there are billions who touch contracts. We're excited to help everyone move faster and do more of what they love, by building the best AI-powered contract infrastructure in the world. Get early access: spellbook.com/acm