George | Super

"Born out of GPU scarcity, neoclouds now face a harder test." – McKinsey, November 2025 14–16% gross margin after depreciation. Lower than many non-tech retail businesses. The prescribed move is clear: orchestration, managed inference, platform layers. And the market is moving fast. But there’s something already inside the hardware that the stack race is overlooking. H100, H200, B200, B300 — and every generation after — already include confidential computing capabilities. Super Swarm turns those capabilities into a verifiable confidential execution layer for neoclouds – enabling sovereign compute environments for sensitive data and AI workloads. GPU cloud instances stop being just rented compute and become independently verifiable confidential infrastructure. Customers with their own on-prem infrastructure can extend workloads into cloud instances without leaving the trust boundary. That’s not just another platform feature. It’s a different category of infrastructure. 👉 McKinsey: The evolution of neoclouds and their next moves – link in comments

@coinbureau I think everyone (or at least the majority of corporate workers) will use AI in one form or another just like with how everyone's connected to the internet via proxy (somewhere down the industrial pipeline).

🔥NVIDIA CEO Jensen Huang: “Everybody will have to use AI because if you don’t use AI you’re gonna lose your job to somebody who does.”

@sama Insane how much compute OpenAI has. CODEX is good, but to get more enterprise users, it has to be flawless with uptime.

codex is the best AI coding product and we want to make it easy to try. for the next 30 days, we are giving companies that want to try switching over two months of free codex usage.

The faster AI scales, the faster confidence in it erodes For nine years Stanford Human-Centered AI has tracked where AI actually stands and suggests where it’s heading across academia, industry, and government. The 2026 report is out. Here's what stood out. Adoption is accelerating. Confidence is eroding. 70% of organizations now use AI in at least one business function. But look one layer deeper: 🔹 Among orgs that experienced incidents, those facing 3-5 per year jumped from 30% to 50% 🔹 "Excellent" incident response self-ratings fell from 28% to 18% Deployment is accelerating. Confidence in handling what breaks is not. Agentic AI is stuck – and the blocker isn't capability. 🔹 62% cite security as #1 barrier to scaling agentic AI – outpaces #2 by 24 percentage points 🔹 Scaled agent use sits in single digits across virtually every business function 🔹 Only exception: tech sector at 24% in software engineering, 22% in IT, 21% in service ops Organizations aren't waiting for better models. They're waiting for infrastructure they can trust. Medical AI hits the same wall – from a different angle. Medical AI is ready to move into live clinical deployment. Prospective trials grew 28.5% year-over-year (417 → 536 in 2025). The pipeline is there. But the data isn't: 🔹 Medical imaging training data is roughly 100x smaller than non-medical AI datasets 🔹 Fragmentation across institutions further limits the development of large-scale medical foundation models The models are ready. The environment to run them on real data is not there yet. Three sectors. Three blockers. One root cause: the gap between how fast AI is being deployed and the infrastructure needed to actually trust what it does. Trust is a vulnerability – and it cannot be legislated away. The policies are already multiplying faster than anyone can implement them – and fragmented regulations across jurisdictions don't provide the technical enforceability that sensitive workloads demand. It demands proof that you can independently verify, automatically enforce, and continuously audit. That is exactly what Super Swarm provides. It bridges the gap by delivering cryptographic proof of what actually ran, on which data, and across independently verified infrastructure. Super Swarm makes verifiable confidentiality an architectural guarantee – not a contractual promise.

@unusual_whales It's felt everywhere in one way or another. We've been so focused on software, tech, and other industries but the real constraints lie in energy, minerals, and manufacturing.

@icanvardar I remember when encyclopedias were a thing and how expensive some were. Now we literally get a global library on our phones.

people 10 years ago would kill to have the tools we have today instant access to AI infinite learning resources global distribution cheap infrastructure open source everything and people still hesitate to build crazy times

Saudi Aramco's CEO Amin Nasser: Energy supply shock is the largest ever experienced

@a16z it's really hard to pinpoint whether this is holistically true or if there are very specific nuances as to where the demand exists.

@kimmonismus To their credit, they gained a lot of their market share really fast. It's hard to predict that kind of growth.

Ask a hospital to run AI on their patient data. The answer is always the same. A hospital, a GPU provider, and a medical AI vendor. Everyone has what the others need and none of them can just hand it over. The hospital won't send data to infrastructure they don't control. The vendor won't expose their model. The GPU provider can't take on liability for what runs on their hardware. The model never runs. The patient never benefits. This is the real reason healthcare AI moves slowly. Not the models. Not the regulations. Trust is a vulnerability. Super Swarm solves it structurally. In this demo we used a model from the @ProjectMONAI Model Zoo – open source, anyone can take it. The data is another story. MONAI, originally started by @nvidia and @KingsCollegeLon, is the open-source framework for medical imaging AI. Used at Siemens Healthineers, Mayo Clinic, and beyond. Millions of downloads worldwide. We deployed one of those models on Super Swarm. The app segments the spleen from a CT scan, calculates volume and area, and returns the results. What makes it different is the execution environment and the verifiable proof it leaves behind. The computation runs inside a hardware-protected TEE. Patient data is processed within that sealed environment and never exposed to anyone – including us. Whether the infrastructure is public cloud, on-prem, or hybrid. No policy makes that guarantee. The hardware does. At deployment, Super Swarm generates Deployment Evidence – a cryptographic proof of what code is running, in what environment, on what hardware. No compliance reports. No trust agreements. Access is granted only when the proof matches. Ask a hospital to run AI on their patient data. With Super Swarm, the answer changes – wherever you run it. 👉 Scan to watch the full demo, or find the link in the comments.

@elonmusk Which is why an ever-agreeing LLM is extremely dangerous. Makes people even more confident to double down on lies.

@Nate_Esparza seems like their TAM is universal.

SpaceXAI will be the best company not only on earth but the universe 🚀

Dario&Daniela Amodei Interview: The 80x growth reportedly caught them completely off guard, and that's the reason for the compute constraint. The compute deal with SpaceX is the first attempt to address the shortage and continues to search for solutions.

Narrative violations abound: - Demand for software engineers is rising - Software devs are rising as a share of new jobs - AI exposed industries are seeing above-trend wage growth - Open PM jobs haven't been higher since 2022 More from a16z's David George on the "AI job apocalypse" myth: a16z.news/p/the-ai-job-a…

Accurate

@zerohedge Very unique position in the market. As long as they keep Mac Mini M4s in the market, they'll do great.

*APPLE PLANS TO LET USERS CHOOSE RIVAL AI MODELS ACROSS IOS 27 *APPLE TO OFFER MORE AI FLEXIBILITY WITH IOS 27 DUE THIS FALL

@bridgemindai It's been very token efficient so far. If you're really efficient with prompts/instructions, instant is good enough to handle mid-level tasks/queries.

GPT 5.5 Instant just dropped. OpenAI is not slowing down. GPT 5.5 xHigh is already the most intelligent model on the market. Now they're releasing a faster, lighter version for everyday use. While Anthropic is rate limiting and Google is teasing 3.2 Flash, OpenAI is shipping model after model. This is the fastest model cycle in AI history. OpenAI is pulling away.

@zerohedge Bidding for energy gets brutal, and AI wins since they have consistent/predictable growth demand.

The system works – until you try to automate it. The trust domain spans every infrastructure, every organization. Data never leaves its sealed environment. Nobody depends on anyone else’s goodwill. And then the product team asks: can we automate this? AI agents are already operating on behalf of organizations – querying data, calling models, chaining actions across boundaries. Not one request at a time. Thousands per hour. A bank deploys a fraud detection agent. It needs to cross-reference transaction patterns across three partner institutions in real time. Each request takes milliseconds. Each approval takes days. The fraud happened. The access request is still pending. The verification model still applies. Sealed hardware. Cryptographic proof. A trust domain that spans every cloud and every data center. But the decision about who gets access can't wait for a human to review it. No administrator can keep up. No approval queue moves fast enough. The same rigor that made the first collaboration work becomes the bottleneck that makes the next hundred impossible. This is Problem #4 of 4. The Access Problem. Super Swarm solves this with policy-driven access. Each data owner defines their conditions once: what code, what configuration, what hardware qualifies to touch their data. When an agent requests access, it presents a cryptographic proof of its runtime environment – the same proof a human would review manually. The system checks it automatically. Match – execution is allowed. No match – nothing happens. No human in the loop. No delay. The data owner’s role is simple: define the policy once. The system enforces it at whatever speed the agents operate. A hospital might set conditions as narrow as a specific model, a specific partner, a specific project. Or as broad as any application running inside verified secure hardware with a certified diagnostic framework. The policy reflects their risk tolerance – not the system’s limitations. Hardware nobody can see into. Proofs that verify in milliseconds. Infrastructure that spans every cloud and every data center. Policies that govern access at the speed AI actually operates. Each piece exists because the one before it made it necessary. None of them works on its own. That’s the system. That’s Super Swarm. Trust. Control. Scale. Access. How they connect – link in the comments.👇