Dan Lewis retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
We’ve raised $300M in Series C funding at a $10.3B valuation from Sequoia, Andreessen Horowitz, Jane Street, Argo, and SK Hynix.
Our mission is to run the world's inference. This round accelerates production of our inference clusters.
We've opened an 80,000-sqft, 10-MW facility 15 minutes from our office to expedite production and prototyping.
This is the hardest working, most focused team. They will make a big mark on the industry! Excited for you @robertwachen and @UbertiGavin
Dan Lewis retweeted
I'm a cardiologist. Something just happened today that I genuinely did not see coming — and it could change the future of preventive medicine more than anything I've written about on this platform.
Midjourney — the AI company that became famous for generating images from text prompts — just announced a medical hardware division and unveiled a working prototype of a full-body scanner unlike anything that's ever existed.
It's called the Midjourney Scanner. And it works like this.
You step into a shallow pool of water. You stand on a platform that slowly descends — about two inches per second — through a ring containing roughly half a million tiny ultrasonic transducers, each the size of a grain of sand. Every one of them acts as both a speaker and a microphone, sending ultrasonic waves through your body from every angle and recording what comes back.
60 seconds later, you step out. The scan is done.
No radiation. No magnets. No claustrophobia. No IV contrast. Just sound, water, and an almost incomprehensible amount of computing power — roughly 2 petaflops processing 17 gigabytes per second of raw acoustic data — reconstructing a 3D map of your entire internal anatomy down to half a millimeter resolution.
Organs. Tissues. Blood vessels. Bones. Muscle. Fat distribution. All segmented by AI in real time.
As a cardiologist who has spent months writing about how the standard screening playbook misses the majority of future heart attacks — this is the technology I've been waiting for without knowing it existed.
Here's why this matters for the future of your heart.
Right now, getting a detailed look inside your cardiovascular system requires either a CT scan (radiation), an MRI (magnets, claustrophobia, 45-60 minutes, $1,000+), or a coronary CT angiogram (radiation, IV contrast, limited availability). These are powerful tools. I order them regularly and they save lives.
But they're reactive. You get them when something is already suspected. They're expensive. They're uncomfortable. And for most people, they happen once — maybe twice — in a lifetime.
Imagine instead: a 60-second scan with no radiation that you could repeat monthly or quarterly. Tracking cardiac structure over time. Watching body composition shift. Detecting changes in organ size, fluid distribution, or vascular architecture before symptoms ever develop. Building a longitudinal dataset of YOUR body that AI can analyze for patterns no single snapshot would reveal.
That's what Midjourney is building toward.
The company plans 50,000 scanners worldwide over six years, with capacity for a billion scans per month. The first location — the "Midjourney Spa" in San Francisco — opens at the end of 2027 with 10 scanners alongside saunas, cold plunges, and a gym. The scan costs a few dollars. The experience is designed to feel like wellness, not medicine.
The technology is built on Butterfly Network's ultrasound-on-chip platform — 40 modules per scanner — combined with Midjourney's own AI segmentation and reconstruction stack. David Holz, the founder, claims the system aims for image quality comparable to MRI in many aspects but at nearly 100x the speed with zero radiation.
Now the caveats — because I'm a physician and the caveats matter enormously.
This is a Gen 1 prototype. About a dozen people have been scanned so far. Current scan time is actually closer to 20 minutes, not 60 seconds — the system is bottlenecked by bandwidth and reconstruction algorithms. The 60-second target is aspirational for future hardware generations.
It is not FDA-cleared for diagnostic use. Midjourney is starting with body composition maps — a category below diagnostic imaging in the regulatory hierarchy. The path from "beautiful 3D body scans" to "clinically validated diagnostic tool that your cardiologist can act on" runs through years of clinical trials, comparative studies against MRI and CT gold standards, and FDA review.
No independent clinical validation has been published. The imaging claims come from Midjourney's own demonstrations. Comparative data against established modalities does not yet exist.
And the privacy implications of full-body internal scans at planetary scale — a billion scans per month — is a conversation that hasn't even started yet.
So I want to be precise. This is not ready for clinical medicine today. It may not be ready for years. Many ambitious medical hardware projects have failed in the gap between prototype and product.
But.
The fact that a working prototype exists — producing real segmented 3D anatomy from sound waves and compute alone — means the physics works. The engineering works. The question is no longer "is this possible" but "how fast can it be validated and scaled."
And if it is validated — if the resolution holds up against MRI, if the AI segmentation proves reliable, if the regulatory path clears — then what we're looking at is the most significant new imaging modality in 50 years.
For my entire career, preventive cardiology has been limited by the fact that seeing inside the body is expensive, slow, uncomfortable, and infrequent. We catch disease late because we image rarely. We image rarely because imaging is hard.
A 60-second, no-radiation, spa-based full-body scan that costs a few dollars would demolish every one of those barriers.
I've written about AI detecting inflamed arteries. About gene editing curing cholesterol. About GLP-1 drugs rewriting metabolic medicine. About cellular reprogramming reversing aging.
This is the missing piece: the ability to see inside every human body, routinely, safely, and affordably — so all of those interventions can be deployed before the disease arrives instead of after.
The company that taught AI to generate images from imagination just built a machine that generates images from the human body.
The future of medicine showed up today from the last place anyone expected.
Dan Lewis retweeted
We raised a $27M Series A to replace the spreadsheets and human duct tape behind $100 trillion in global assets.
Fund administration is the invisible backbone of private equity and venture capital - and it’s broken.
Why?
Financial data is scattered, stale, and locked inside legacy providers. Books take forever to close. Basic questions about your own fund take days to answer.
So we rebuilt the general ledger, waterfall engine, investor portal, and portfolio management from scratch.
One single source of truth for your firm.
Our AI agents read emails, propose journal entries, and extract portfolio updates in seconds. Our CPAs review every output.
Today, we administer $15 billion in assets - and we’re just getting started.
Every fund CFO keeps getting asked: how will you adopt AI?
Now you have an answer.
Run your firm in real-time with @hanoverpark.
–-
Excited to partner with Jake Saper at @emergencecap @peterjhebert at Lux, @chadbyers/@pratyushbuddiga at Susa and CFOs at the largest private equity firms to forge this future.
Dan Lewis retweeted
From a mall booth to a $3B fintech: Matt Oppenheimer’s startup lessons from a 15-year journey with Remitly geekwire.com/2026/from-a-mal… via @GeekWire
Dan Lewis retweeted
The math on this project should mass-humble every AI lab on the planet.
1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output.
The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice.
Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet.
And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.”
This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one.
We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that.
The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.
Dan Lewis retweeted
Gemini 3.1 Pro is now at the top of the APEX-Agents leaderboard. Gemini jumped from 18.4% to 33.5% on Pass@1 in just 90 days.
It also completes 5 tasks that no model has ever been able to do before.
@GeminiApp shows how quickly agents are improving at real knowledge work. It can turn hundreds of documents, spreadsheets, and emails in Google Workspace into a client-ready deliverable.
This rapid progress is evidence that we will be able to saturate any benchmark. The bottleneck is measuring all economic value.
Dan Lewis retweeted
your app's UI has entered the chat.
just register your UI components and your APIs.
build an agent that speaks your interface in minutes 👇
Dan Lewis retweeted
A cool demonstration of physics.
The truck is moving forward at 80 km/h. The guy is catapulted in the opposite direction at 80 km/hr.
Dan Lewis retweeted
if you are thinking about joining a team, ask them for their ai token usage graph. if it's not exponential, reconsider.
Dan Lewis retweeted
build an agent that speaks your UI.
your charts. your forms. your seat maps.
multi-turn, streaming, interactive.
introducing tambo 1.0, the open-source generative UI toolkit for react.
Now available in Copilot Studio for those using our platform to build agents and workflows. @Microsoft
Solves the "but will it work for my business?" problem. One of the best teams out there. Congrats @ypatil125, @rhythmrg, and @lindensli !
Generalists are useful, but it’s not enough to be smart.
Advances come from specialists, whether human or machine.
To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data.
We call this Specific Intelligence.
It's what we're building at Applied Compute.
We unlock the latent knowledge inside a company, use it to train custom models, and deploy an in-house agent workforce that reports to your team.
We work with sophisticated companies that have already captured early gains from general models, like @cognition, @DoorDash, and @mercor. They’re pulling even further ahead with proprietary in-house agents that don’t need to wait for the next public model release.
Together, we are building and validating models and agents in days instead of months, achieving state-of-the-art performance on customer evals.
Our team has high density and low latency. Our founders all worked on different parts of this problem while they were researchers at OpenAI — @ypatil125 as a key member on the agentic software engineer effort (Codex), @rhythmrg as a core contributor to the first RL-trained reasoning model (o1), and @lindensli as a core contributor on ML systems and infrastructure for RL training.
Two-thirds of the team are former founders, and everyone brings a deep technical background, from top AI researchers to Math Olympiad winners.
We are backed by $80M in funding from Benchmark, Sequoia, Lux, Elad Gil, Victor Lazarte, Omri Casspi, and others. With their support, we are growing the team, scaling deployments, and bringing to market the first generation of agent workforces built on specific models.
In short:
1. We are building Specific Intelligence for specific work at specific companies.
2. That will power in-house agent workforces to support their human bosses.
3. That in turn will unlock AI’s full potential through humanity’s greatest engine of progress: thriving corporations in a free market.
Dan Lewis retweeted
We’ve raised our $350M Series C at a $10B valuation from @felicis, @benchmark, and @generalcatalyst.
Just 2 years after starting, Mercor is paying $1.5 million per day to experts in our marketplace.
We’re creating a new category of work in the AI economy, where software engineers, bankers, lawyers, and other professionals earn based on their experience while advancing the frontier of AI.
While most new categories take time to build momentum, we’ve broken every growth record. For comparison, in their first 2 years:
- Uber paid out just over a $1 million to drivers
- Airbnb paid out $10 million to hosts
We are unlocking human potential in the AI economy.
Dan Lewis retweeted
Today we’re expanding Microsoft 365 Copilot with the addition of Anthropic’s Claude models. Customers can now use both OpenAI and Claude — starting in Researcher and Copilot Studio, and coming to more experiences soon.
Our multi-model approach goes beyond choice. It's all about bringing the best AI from across the industry to Copilot, tuned for work and tailored to every business.
Read more: microsoft.com/en-us/microsof…

























