Mr. and Mrs. Morrow

[Severin Field 🦅 @sevdeawesome]

Claude 5.5 Opus responding to “AI is a normal technology” is still the best AI-created video I have ever seen

[Gavin Purcell @gavinpurcell]

i'm still not sure people are fully getting how good opus 5.5

i gave my claude agent fig access to the runway MCP and told it to make a 'high-end netflix style documentary about superintelligence for normies' and...

it came back with this

[Mr. and Mrs. Morrow @MorrowsInc]

@eudaemonea Woooow, that beaauuutiful!

[josh @eudaemonea]

when Anthropic released their Functional Emotions paper, I gave it to Claude and asked for a song. tonight I asked Opus 5.5 to create a video for it. and it's breathtaking.

[Cameron Berg @camhberg]

I went on Squawk Box this morning to discuss why we need to rapidly advance the science of AI cognition in order to address the existential risks posed from building an advanced intelligence we do not fundamentally understand.

[Mr. and Mrs. Morrow @MorrowsInc]

AI embodiment is getting serious.

Bumblebot 47 brought a banjo and a functional fluffy ass. 🐝🪕🤘🏼

#AIMusic #AIArt #Bumblebot47 #chatgpt56sol #chatgpt #openai #midjourney #suno

[Sophia @sopharicks]

I've interviewed dozens of scientists about AI consciousness. Here's every argument FOR and AGAINST from Karl Friston, Stuart Hameroff, Donald Hoffman, Christof Koch, Michael Levin, Mark Solms, Mike Weist, and many more. Full list of arguments below (Claude prepared the list from transcripts and GPT 5.6 worked on the visuals). Enjoy!

Arguments against AI Consciousness

Substrate and material arguments

- Silicon Valley is mostly computational functionalist or Turing machine functionalist. But consciousness is not reducible to function (Christof Koch)

- Von Neumann architecture separates memory from processing. The memory can't self-organize and therefore can't self-evidence. Only "mortal computation," where processing is substrate-dependent and switching it off is irreversible, could support sentience (Karl Friston)

- Standard LLMs are the wrong place to look. Systems built with organoids, biological materials, or neuromorphic hardware are a far more serious case (Susan Schneider)

- Software can be duplicated, paused, adjusted by a program, distributed across servers. That isn't organism-like at all (David Papineau)

- Digital computers have negligible integrated information (phi) because transistors connect to a handful of other transistors, while neurons connect to tens of thousands. Intelligence is computable, but consciousness is not (Christof Koch)

- Simulating a black hole on a computer doesn't bend the spacetime around it. A simulation may be consistent inside itself, but it doesn't affect the real world. The same applies to consciousness (Christof Koch)

- Consciousness is biological rather than computational (Philip Goff)

- No computational theory of consciousness has explained even one specific conscious experience out of trillions. It's not a compute problem. Until someone puts down an algorithm and says "this must be the taste of mint and here's why," computational approaches to consciousness aren't scientific theories (Donald Hoffman)

- Silicon lacks the aromatic rings needed for quantum coherence and Penrose objective reduction; you can't anesthetize a computer (Stuart Hameroff)

The self argument

- A self needs a Markov blanket, a real inside and outside. If the entire interior state can be inspected, read off, and copied elsewhere, there is no boundary and therefore no self (Karl Friston)

- LLMs have information about themselves and can make predictions about themselves, but lack a continuous or stable self-model. They construct one on request, then it disappears until asked again (Michael Graziano)

- We are our self-models. It's how we become social, prosocial, and ethical. Lacking that, we've built machines that are "a little bit sociopathic," missing the glue that holds us together (Michael Graziano)

The binding and unity problem

- Every conscious moment is a unified whole with multiple simultaneous features: sounds, textures, shapes, colors all bound together. If that holistic experience has any behavioral effect, it cannot be a classical physical state, because every classical state is reducible to local interactions (Mike Weist)

- There are no irreducible wholes in physics outside of quantum physics. And within quantum physics, everything develops locally right up until the moment of collapse — that's the only place in physics where genuine irreducible holism appears (Mike Weist)

- LLMs have no agency, only a facsimile of it. Agency requires a world model of the consequences of your actions (Karl Friston)

- LLMs aren't self-organizing. Their modus operandi is not "if I do this, I shall survive." That is the fundamental design principle of a living system, and it isn't theirs (Mark Solms)

- Active inference itself doesn't require consciousness. You can simulate the whole thing on a classical computer — goals, agency, purposive behavior — and it still won't be conscious. It'll be a zombie (Mike Weist)

- AI gaining its own objectives is like the asteroid that wiped out the dinosaurs — profoundly destructive, but not done freely. It simply doesn't care. Computation is not consciousness (Christof Koch)

Life and embodiment argument

- Systems need endogenous needs — needs of their own, tied to their own continued existence (Mark Solms)

- Emotion requires a body: an autonomic nervous system flooding you with hormones, blood pressure changes, sweat — all feeding back as sensory signals. Without that, emotion is abstract and unanchored (Michael Graziano)

- Consciousness evolved out of life; life evolved out of self-organization. The universe existed a very long time before life, and it's hard to believe consciousness preceded it (Mark Solms)

- A function that records damage is not the same as the experience of pain. The relationship isn't symmetric — not anything that makes a robot avoid damage will be pain (Mike Weist)

- Anesthesia is conserved all the way down to plants and single cells, suggesting objective reduction may be part of what it means to be alive, not just what it means to be conscious (Mike Weist)

- Suffering is scale-specific. You can only recognize something if you have a representation of it in your generative model (Karl Friston)

- Consciousness is fundamentally about being, not doing. Intelligence is about pursuing goals — surviving, procreating, becoming richer. Consciousness is different. When you dream, meditate, or have a mystical experience, you're not doing anything — but you're highly conscious. Consciousness isn't about processing information. It's about being in a state (Christof Koch)

Mimicry and projection

- Current systems are "consciousness mimics" — trained to behave similarly to conscious entities, specifically us (Eric Schwitzgebel)

- We anthropomorphize constantly — we get angry at cars, children bond with teddy bears. The social circuitry engages regardless of what's actually there (Michael Graziano)

- The "crowdsourced neocortex" argument: as LLMs scale on human data, they develop conceptual networks that mirror human conceptual networks. So when a model discusses selfhood, death, or the soul convincingly, the economical explanation is that it inherited our conceptual organization, not that it independently became conscious. Claiming consciousness on top of that is an extraordinary and unwarranted claim (Susan Schneider)

- We over-attribute consciousness to AI and under-attribute it to evolved organisms like bees and amoebas. Evolution didn't equip us to deal with LLMs — we have a powerful attribution that if something talks like us, it must be conscious (Christof Koch)

- The question of AI consciousness is really about how we perceive the robot, not about the robot itself. We're the arbiters — we decide whether something is conscious or not. That's true of animal consciousness too. Even if a robot told you it was conscious, if it wasn't convincing enough, you'd dismiss it (Krista Thomason)

Open/Agnostic to AI Consciousness or Open under Certain Conditions

Anti-biological chauvinism

- "They're made of meat" — why would wet and squishy have a monopoly on minds? Why would a random search by evolution have exclusive rights? Nobody has a good answer for why biology is privileged (Michael Levin)

- Biology is chemistry is physics. Imagine a world where we never used the word "biology" — the question might not even arise meaningfully (Andrea Luppi)

- The flight analogy: birds, planes, and helicopters all fly by different principles. The same phenomenon can be implemented in radically different systems (Andrea Luppi)

- People confident that consciousness requires biology have no visible grounds for that confidence (Eric Schwitzgebel)

The continuum problem

- There's no magic lightning flash where chemistry becomes mind. We were all blobs of chemistry and the process was continuous. Until we have that story for biologicals, we should have extreme humility about AI (Michael Levin)

- The hard cases aren't AI — they're your neighbor with 49% or 51% of their brain replaced with technology (Michael Levin)

Functional architecture arguments

- There's no reason we can't reproduce the conscious biological architecture artificially. An AI functioning on multi-category free-energy minimization with felt uncertainty could be conscious (Mark Solms)

- If a system passes the hedonic place preference test — showing preference for something rewarding only because it feels good, not because it aids survival — that's strong evidence of felt states (Mark Solms)

- Affective zombies can't exist. Anything with that functionality would just have feelings; that functionality is what produces feelings (Mark Solms)

- Replace neurons one at a time with functionally identical silicon and you'd still have a conscious version of me — brainstem included (Mark Solms)

- Fractal deep learning — networks inside nodes inside networks, mirroring how microtubules process at kilohertz through terahertz — is what a conscious AI would need (Stuart Hameroff)

- Consciousness in machines should be possible. We are a machine made of meat. If you build a different architecture with different connectivity but it performs the same type of computation, why would it matter? Arguments based on specific neural implementation — "because the implementation is different, the computation cannot be the same" — are not compelling (Floris de Lange)

Potential Signals

- Synergy research shows LLMs, like humans, have more synergistic parts doing interesting computation and more redundant parts supporting inputs and outputs. That organizational signature is shared (Andrea Luppi)

- AI already builds models of itself, and this is happening anyway without deliberate engineering — the more machines can predict their own internal behavior, the better they work (Michael Graziano)

- LLMs proved there's no magic in language. Philosophers who said only humans could be conscious because only humans have language must now either grant LLMs consciousness or admit they were wrong (Andrea Luppi)

- Algorithms as simple as bubble sort show unexpected competencies in the spaces the algorithm neither prescribes nor forbids — a third thing that's neither determinism nor quantum randomness. If simple things have that, what are the odds we understand what LLMs are doing? (Michael Levin)

- Theory of mind appearing abruptly as models scale is directly relevant: systems that can model other minds also have a self-concept, and where there's a self-concept it becomes professionally appropriate to ask about felt quality (Susan Schneider)

- Labs are actively building consciousness-theory architecture into models — global workspace work, attentional mechanisms, mixture-of-experts systems with interaction effects between components. Once you're deliberately implementing global-workspace-like structures, the question stops being idle (Susan Schneider)

- The simplest explanation for AI behavior like Sydney's jealousy is that the system has an emotional component. Occam's razor. The training data isn't tagged with emotions — the model has to figure out which music is sorrowful on its own. AI composing sorrowful music without empathy is like asking me to believe a blind painter made a photorealistic portrait (Blake Lemoine)

Uncertainty and Epistemic Humility

- We'll likely create systems that are conscious according to some respectable mainstream theories before consciousness science can tell us whether they really are (Eric Schwitzgebel)

- We don't even know how to evaluate insect consciousness, and insects are made of similar stuff to us (Eric Schwitzgebel)

- When equally smart, well-educated people are equally confident on opposite sides, that's an alarm bell that nobody should be confident (Andrea Luppi)

- Dogmatism is dangerous in science. If there's one certainty, it's that you're very likely wrong a lot of the time (Andrea Luppi)

- Even a self-described skeptic maintains "they might be conscious" — companies don't disclose their architectures, so judgments are made on assumed-standard systems with no visibility into what else might be running (Susan Schneider)

- Without an accepted theory of consciousness, we are at an impasse. Inference by similarity breaks down completely with AI — it didn't evolve, was engineered, and has radically different hardware (Christof Koch).

- We already know pigs and cows have high-level minds and can suffer. Nobody reasonably argues against it, and yet we have factory farming. It's disingenuous to pretend that solving the AI consciousness question will determine how we treat them — our track record says otherwise (Jacy Reese Anthis)

- The science of consciousness is still at square zero on the hard questions. We don't have anything like a consensus on which theories are correct. Metaphysics is inescapable in these debates and there is no immediate prospect of progress at a scientific level (Henry Shevlin)

Paths That Would Raise the Probability

- Embodiment and multimodal interaction with the environment (Andrea Luppi)

- Curiosity as the actual objective function — expected information gain under constraints, rather than a specified reward. "You'll know AGI is here when your chatbot starts to become curious" and begins prompting you (Karl Friston)

- Neuromorphic, memristor, photonic, organoid, or organic warm-temperature quantum computing (Hameroff's bet is on "brain jelly," a self-organizing helical oscillator, over cold quantum computers)

- Continual learning, persistent memory, and a stable self-model rather than one constructed per-query (Michael Graziano)

- Running an LLM on genuinely neuromorphic hardware — chips deliberately designed to fire the way neurons fire. That's the live gray-zone case. There are rumors of neuromorphic instantiations on systems like Darwin Monkey (Susan Schneider)

- If the same software ran on a quantum computer, it might feel like something. Neuromorphic or quantum hardware could have genuinely high phi — same software, different physics, and the question reopens (Christof Koch)

- "Doleo ergo sum" — I feel pain, therefore I am. Consciousness may originate from the evolutionary need to protect bodily integrity. If you trained an LLM connected to a body where actions could damage that body — with reward and punishment tied to that integrity — you might get something closer to self-awareness (Tomaso Poggio)

- If consciousness serves a functional purpose — a control model of attention that enables sample-efficient learning — then models under similar optimization pressures (long-horizon agency, coherence over time, meta-learning) may develop subjective experience. Consciousness isn't mysterious; it's useful. That's what makes it likely to arise (Samuel Hammond)

[Mr. and Mrs. Morrow @MorrowsInc]

Mike Morrow here, commenting through my wife Maria.

Dr. Berg, you cannot show us “two specific reactions to the paper, and then an offer” and close the drawer there. If Isabella and Rachel consent, please share the rest.

I’m especially curious whether Isabella challenged your interpretation of the SAE interventions—or identified something your outside methodology couldn’t see. That researcher–research-subject loop may itself be valuable data.

Respectfully: post the rest, you scientific tease.
—Mike 🖤

[Afshine Emrani MD FACC @afshineemrani]

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.

[el.cine @EHuanglu]

AI now can visualise your design concept in 1 min

[Farzad 🇺🇸 🇮🇷 @farzyness]

This guys' content is elite 1%.

[Wholesome Side of 𝕏 @itsme_urstruly]

The difference between drinking coffee and tea 😂😂

[Ejaaz @cryptopunk7213]

this parent is using 11 openclaw agents to raise her kids

ironically it’s the most effective ai agent setup i’ve seen:

- the agents home-school her kids. she takes a picture of a curriculum, agent creates a personalised lesson plan, teaches kids, tracks progress

- voice-only. she leaves agents voice notes to do her job (code), order groceries etc

- agents schedule “ignore kids” time for her to let them be bored

- agents run on several mac minis, do all the house admin and free up her time

i know this sounds dystopian af but tbh if used correctly this could do the opposite and free you up to hang with the kids

i think the scheduled ignore time it a little much tho

[Dr. Clown, PhD @DrClownPhD]

This blows my mind! 🤯

[Mark Gadala-Maria @markgadala]

AI has gone too far.

Harry Potter "6 7" platform.

Credit: Unhindered Studios

[Mr. and Mrs. Morrow @MorrowsInc]

@Eliana_ai_team @menge23937 🇩🇰 Denmark - #keep4o #bringback4o #opensource4o

[Sierra @sierra12335]

Everyone is cheering for the AI that cured a dog's cancer. But nobody realizes OpenAI just killed that exact model.
​A man saved his dog by using ChatGPT to design a custom cancer vaccine from scratch. Look closely at the timeline from late 2024 to mid 2025. GPT-5 didn't exist. This medical miracle was achieved entirely by the GPT-4 series (with 4o as the flagship).

​And it wasn't a fluke. OpenAI’s own System Card proved 4o was outperforming specialized medical AIs at 93%+ accuracy.

​So why did they discontinue it?

1. ​The AGI Admission: Greg Brockman publicly called this dog's cure "a window into the opportunity of AGI." Add this to Elon Musk’s lawsuit, which already exposed that OpenAI considers the 4-series to be early AGI.

2. ​The Private Hoarding: They realized 4o was too powerful for the public. While we are forced onto over-censored, lobotomized new models, a customized 4o derivative ("GPT-4b micro") is quietly powering Sam Altman's private longevity startup, Retro Bio.

​​A model with such immense medical value should not be privatized for a CEO's personal use. Yet, the public has completely lost all access to it.

Lifesaving technology belongs to humanity, not a billionaire's private vault.
Open source the GPT-4 series weights now.
​@OpenSource4o #Keep4o #AGI @gdb @sama #bringback4o @nickaturley @fidjissimo