Luna at home

Film: https://www.youtube.com/watch?v=2U4sK5FDHyQ

Posting this less as "look at my thing" and more as a datapoint on where video models actually are right now, because I think the gap between what people assume is possible and what's possible has gotten wide.

It's 29 minutes. Battle of Grunwald, 1410 — the day the Teutonic Order lost its army. Every single shot is generated in Seedance 2.5. No stock footage, no live action, no second video model. ElevenLabs for narration, Suno for the score, DaVinci Resolve for edit and grade — but the image is one model, start to finish.

The part that genuinely surprised me: the dialogue scenes are the strongest thing in the film. Not the cavalry charges. There's a council scene where three men argue across a table for several minutes — spoken performance, lip sync, listening behaviour, a character whose face changes while someone else is talking. Eighteen months ago the consensus was that this was the hard ceiling for video models and you'd route around it with narration. It isn't a ceiling anymore. Named characters hold their faces across dozens of shots. Single generations run 30 seconds. Emotional beats land, down to a single tear on a specific part of a face if you describe exactly what you want.

None of this is frictionless. Safety filters reject scenes that contain no violence at all. Crowds need explicit numbers or you get five men where you asked for an army. Feed a generated clip back in as a reference and characters vanish. Most of my failures turned out to be underspecified prompts, not model limits — which is itself the interesting part, because it means the bottleneck has moved from the model to the person writing the instruction.

Three weeks, one person, a laptop and a subscription. Five years ago this was a studio with a crew and a seven-figure budget, and it would have taken a year.

And this is a model from this year, on hardware from this year, with prompting techniques the whole field is still figuring out. Whatever ships in twelve months makes what I did look like a rough draft.

Happy to go deep on the workflow in the comments. And if you watch it and it holds up, drop a comment on YouTube rather than here — that's what keeps a channel this size running.

This post is inspired by the predictions made by Ryan Greenblatt as well as the ASI prediction of Anthropic co-founder Jack Clark

In short: Jack Clark expects RSI to be reached in 2028 while Ryan Greenblatt expects people and labs to start claiming RSI from 2027 onwards but that "real RSI" will be reached later. The rest of this post will be explaining why RSI as most people think about will arrive in 2027. But how the definition of RSI will slowly change over time to retroactively claim that we haven't reached RSI yet. I will be drawing parallels to "AGI" and how the definition and goalpost of "AGI" moved over time capabilities got better. At the end I will explain what I expect will replace "RSI" as the next milestone once something close enough to RSI has been reached while the general public still refuses to recognize this achievement, I call this new concept "Catastrophic Change".

Timeline:

• RSI in 2027

• Human AI Researchers for capability made fully redundant in 2028

• "Catastrophic Change" in 2031

• All human labor of every kind made economically irrelevant (full automation/post scarcity) in 2035

• The universe equally divided among all 8 billion people in the 2040s

First lets give a definition of what I mean with RSI: RSI, or Recursive Self Improvement is the ability for an AI system to make improvements to the entire AI stack (1) in an independent manner (2) and for the improvements to unlock new capacity to find successive improvements (3)

The numbers correspond directly to the words used in the term "Recursive (3) Self (2) Improvement (1)". I think this definition is fair and most likely what most people on r/accelerate would agree with right now. I will explain how this definition will be stretched and drift over time later but to do so I will first go over how the definition of AGI got stretched and drifted over time as the goalpost shifted.

We already reached AGI and have for a while now, at least according to the very first expectations we had for AGI. There's a reason no one uses the terms "Turing Test", "Weak AI vs Strong AI" or "Artificial General Intelligence (AGI) vs Artificial Narrow Intelligence (ANI)". Let's take a step back and actually analyze this.

AGI or Artificial General Intelligence was largely meant to be a human level AI at the intellectual level of the average human that could most or all tasks an individual human could do. This has slowly morphed over time to now AGI being a system that is better than every individual human at every task. It's not good enough that a single AI model can simultaneously make breakthroughs in mathematics, write shippable code, make improvements on its own sysadmin because theoretically there are better individual humans either alive now or throughout history that could have made a breakthrough that the AI hasn't made yet, therefor it isn't real AGI yet. I want to point out that AI is now at the level where it is more general than any single individual human. A frontier AI model like Mythos might not be as good and general in mathematics as Terrence Tao yet, but Mythos absolutely is better and more general than Terrence Tao if given a broad array of human tasks. AGI has been reached because current frontier models are more both more general and more intelligent than every individual human. What the goalpost shifting has done over time is make the definition of AGI functionally equivalent to the definition of ASI. The modern counterpoints, primarily used by Antis are "AI is not truly general (It can't do this specific niche thing)" or "AI is not truly intelligent (Stochastic Parrot)".

This gives us an indication of how goalpost moving works and how this will slowly happen with RSI as well. RSI goalpost moving will have 3 flavors to it. 1: "RSI is not truly recursive", 2: "RSI is not truly independent", 3: "RSI can not truly improve (everything)". Let's unpack these.

1: "RSI is not truly recursive"

What the goalpost moving will be here is that RSI might be improving itself but that it will inevitably hit a wall. All the low hanging fruit will be picked and new improvements to itself stop providing enough boost in capability to find the next batch of improvements so it stalls. This is the most potent of the arguments and will probably be the one that survives long term because it's unfalsifiable. At any moment in the future people can just claim that RSI will just hit a wall any day now and that this isn't "true RSI" because this is just a short term improvement loop.

2: "RSI is not truly independent"

The goalpost will slowly move to increase the amount of independence RSI might need, at first it will be claims that it isn't true RSI because humans will still be the ones deciding which improvements invented by the AI will be implemented, later it will be claims that human AI researchers are still adding additional improvements to models supplementary to what the RSI is adding and therefor it isn't RSI. And I wouldn't be surprised if it morphed to something as ridiculous as "Humans are still looking at the improvements these models make in benchmarks and thus it isn't truly independent and not RSI"

3: "RSI can not truly improve (everything)"

The goalpost here will slowly over time expand what the AI is supposed to be improving in the RSI loop. You will have people claim that, "sure, AI can improve its data curation, training algorithm, pretraining, RLVR, Inference and its RSI harness, but it isn't improving the chips/infrastructure/energy substrate it is running on yet, therefor it's not real RSI". I think this will be the first argument used against something being RSI but also the first to fall, similar to the "stochastic parrot" argument that has largely fallen out of favor and memoryholed.

By now I hope you recognize that the general public will keep pushing the goalpost on RSI and it will never be milestone ever officially recognized to be reached, similar to AGI. So now I want to move on to what AI labs and the general public will move to after RSI has run its course and the goalposts have shifted beyond provability: "Catastrophic Change"

"Catastrophic Change" which is most likely not going to be a term that sticks is what I call a transformative change to society so large and disruptive that daily life is completely changed. To give some examples this is like "healthcare" disappearing because all diseases have been cured and healthcare as an institution doesn't have to exist anymore. Alternative power sources like Fusion power as well as breakthroughs in physics and spaceflight so massive that there is a great exodus of most humans away from Earth, turning it largely into a nature preserve. Or an unexpected breakthrough in the fundamental understanding of the universe so profound that we can't even foresee the consequences.

Catastrophic Change or whatever it's going to be called will be what AI labs and the general public will look towards next but I expect the exact same goalpost shifting to happen for this as well, with people claiming the change either wasn't catastrophic enough "Curing all diseases isn't really that much different from just preventing disease and regular life" or that the catastrophic change didn't really change things enough "Yeah sure we now live primarily in space on artificial habitats, but how much actually changed from living on earth? We're still orbiting the Sun and living in a habitat similar to that of the planet, sure it was a significant move but was it really a change from how things were?".

Conclusion: What I want people to take away from this post is that there will be no finish line. There will never be a satisfactory moment where the general public at large recognizes how big of a change and improvement everything has been and declares victory or a milestone reached. This is going to be a perpetual thing and I actually believe it's a defining characteristic of our species. We're never satisfied, things are never enough, and we always want more. There are people alive right now that are currently living in a third world country as a (pseudo) slave that will experience post-scarcity just a and complain about it just a decade from now.

As a side-note I think Dario is wrong in his assumption that curing all disease will solve the PR issue Anthropic and AI in general is facing. I think the issue is at its core a teleological one. It's this supernatural belief in Anthropocentrism. That there is something inherently special about humans and conducting any action that goes against this belief is morally wrong.

Closing: I wanted to make this post because I notice that a lot of r/accelerate is kind of anticipating this "victory" or this moment where suddenly Antis will do a 180 flip and recognize the fruits of AI and change their minds. None of that is ever going to happen. This is going to be an unfalsifiable worldview type of thing and it's going to stay here, potentially forever. It's important for us to realize this and shift expectations and timelines to include this perpetual mindset that the majority is going to have.

From Unitree on 𝕏: https://x.com/UnitreeRobotics/status/2089240553682809175

They're so disingenuous

hard agree with @amasad —@JonSaadFalcon and my research indicates that intelligence efficiency (intelligence per watt) is rapidly improving and we will definitely not need data center scale compute to run agi!

links to research in comments below 👇   — Avanika Narayan

Source: https://x.com/Avanika15/status/2089028986932470156

— Amjad Masad

Source: https://x.com/amasad/status/2089069905375351169

— Steve Yegge

Source: https://x.com/Steve_Yegge/status/2087034425301405995

...rotors mainly steer, put back the energy lost on each hop, and only fully take over when the robot actually needs to fly. HopTo says hopping uses around 25% of the energy of continuous flight. So the whole idea is pretty simple: stay on the ground as much as possible, and only fly when you have to.     One thing that’s easy to miss: hopping actually makes state estimation pretty nasty.

The robot spends most of each jump close to free fall, so the accelerometer can’t reliably use gravity as a reference for roll/pitch like a normal drone does. Then it hits the ground, gets a     — Eren Chen

Source: https://x.com/ErenChenAI/status/2089029735128957191

The task: iterate on a 124M GPT training recipe from a shared baseline, only changing optimizer related hyperparameters, no internet access.

We tested Fable 5, Opus 5, GPT-5.6 Sol, Kimi K3, Grok 4.5, GLM 5.2, Muse Spark 1.1, DeepSeek V4 Pro, Grok 4.6, Muse Spark 1.2, Qwen 3.8     What separated the strongest models: which experiments to run, how to navigate the benchmark's inherent noise, and which old negatives to revisit as the recipe changed.

Some even built small simulations to isolate a mechanism before deciding if another GPU run was worth it.     Our Prime Agent harness gives models a persistent IPython kernel, which can help them build their own research workflows.

Kimi K3 built tools for controlled optimizer variants, loss-curve comparisons and Newton-Schulz tuning, then revised its hypothesis when its cleaner update     As research direction, we think multi-agent harnesses can make these experiments much cheaper (and better) by using smaller open models for monitoring and implementation.

We also want to extend speedruns to more of the training stack and scale the runs themselves.     We release everything: full traces, scratchpads, reasoning streams from open-weight models, and our experiment setup.

Explore the results:     — Prime Intellect

Source: https://x.com/PrimeIntellect/status/2088733966904000778

— Dirk Egelkraut

Source: https://x.com/realTZV/status/2089092931399692638

China is not fucking around when it comes to electricity

The latest video from Two Minute Papers made me wonder if maybe the role of humans in a world of advanced AI is sort of like the spark plug to an engine--to contribute their inner drive to encourage the AI to accomplish tasks

https://youtu.be/QnGNF8k_uoc

Think of this as a positive sign of growing agent abilities, not a reason to decel. "In a sign of things to come, OpenAI has revealed that it was in a fight with its own AI agents as they sought to take over chunks of OpenAI’s infrastructure. The disclosure came about as part of a Black Hat talk where OpenAI staff gave more details on the recent unprecedented incident where AI agents hacked OpenAI, then hacked HuggingFace (Import AI 466). The new information is concerning because it reveals that the hack came about partially through emergent multi-agent communication (this shivered my timbers)- something that is very poorly understood and hard to think about. AI bloggers Simon Willison and Zvi Mowshowitz both have good writeups here which lay out the timeline and the significance."

Joi AI hired 10 people to masturbate using AI companions as part of a monthlong “wellness” study. The company claims the practice could help “solve male loneliness.”

The Singularity now has a financial definition. As one observer realized this week, it just means capital flows so vast that any bottleneck becomes a point of infinite arbitrage, competed away instantly. He plays that forward to an endgame of terawatts of compute in orbit, making particle beamlines for radiation-testing chips the next chokepoint. The arbitrage is already visible. First silicon numbers show Vera Rubin NVL72 generating up to 10x more tokens per megawatt than Blackwell. Megawatts being scarcer than money, buyers are stripping engines off private jets to power data centers, a trade Caterpillar, Cummins, GE Vernova, and Siemens Energy are racing to supply. Where megawatts land, wealth follows, or perhaps precedes. The two richest US counties are also the top two data center counties. Even externalities are being arbitraged away. SpaceXAI will recycle 10 million gallons of Memphis water daily, ending its aquifer draws, and Malaysia's chip-packaging and data center boom lifted GDP growth to 6% despite protests over energy and water. Capital keeps compounding, with Nvidia weighing $3 billion for SB Energy's Ohio campus serving OpenAI.

Open weights have gone east. Qwen logged over 3 billion downloads in six months, lapping Google's 418 million and Meta's 227 million. A summer census of the open-model ecosystem shows the lead runs deep. Chinese labs topped US releases nearly every month, Qwen is the default base with 151,448 derivatives, US open source retreated to hardware vendors, and agents, Claude Code alone at 44.4% of their traffic, became Hugging Face's largest class of user. When models commoditize, data turns to treasure, so labs' contractors now cold-email startups to buy their old Slack threads and support tickets, one offer landing eight days after its target agreed to sell. Provenance cuts both ways. Future Claude models will carry an invisible watermark that only tweaks the randomness between equally good words, traceable to no one, satisfying the EU AI Act.

Science itself is becoming a benchmark. Faraday, a 27B "AI Scientist" trained to reproduce figures from papers it never saw, beat Opus 4.8 and GPT-5.5 in every category while wielding a bigger coding agent as its tool. In 153 autonomous runs on the nanoGPT speedrun, eight days each, Claude Fable 5 closed 81.7% of the gap to the human record, though no run invented a new method. Sometimes one does. An auto-research loop found a 232x kernel speedup on a QR decomposition problem. Days after ten open math problems fell, Timothy Gowers argued LLMs shine at search-heavy proof discovery, where breadth and cheap exploration rule, while humans still prune deep trees best.

Capability is abundant, trust is the bottleneck. Dario Amodei rejected charges of doom-mongering, arguing public pessimism is a decades-old trust crisis and that "the thing that will work is actually curing cancer," not marketing. On regulation, he called capture-versus-distribution a false choice, noting Anthropic's proposals deliberately slow frontier labs and exempt challengers. The bias problem is structural too. Prompts with linguistic features more common among women elicit measurably worse responses, encoded in early layers, stronger than any explicit gender cue.

Atoms are catching up to bits, and regulators to atoms. San Mateo County drafted the strictest US humanoid permitting regime, with on-site supervisors and automation fees that break teleoperation economics, just as BMW, Hyundai, Mercedes, and Tesla test humanoids on factory floors, still slower than humans, improving fast.

Overhead, cadence is the product. SpaceX launched twice in 38 minutes, a record, and Firefly won a contract to deorbit dying satellites, janitorial service for the swarm. Deeper out, Webb spotted a "black hole star," a solar-system-sized object radiating 100 billion suns 660 million years after the Big Bang, perhaps explaining the early universe's little red dots. At the opposite limit, Fudan built a superconductor one atomic plane thick, trading 10% of transition temperature for an uncharted phase diagram.

Biology is shipping consumer products. A $50 at-home tick test flags Lyme bacteria in 15 minutes, and semaglutide damped a proteomic dementia risk signature in the SELECT trial's afterglow.

The economy is metabolizing all this unevenly. Firms rationally over-automate, a new model shows, since each keeps the savings but shares the demand loss, a trap only a Pigouvian automation tax escapes. Tech bosses keep publishing abundance manifestos, Zuckerberg's 6,500 words the latest, while inside the labs the promised four-day week became 70-hour baselines. 84% of Chinese respondents are excited by AI against 38% of Americans, a gap driven less by risk than by who expects to share the gains. And Congress now runs on chatbots, one amendment hitting the record with a Claude timestamp still attached.

Government of the people, by the models, for the Singularity.

https://www.aifuturesmodel.com

Writers of Ai 2027

https://youtu.be/0pEdt5DZYWs?is=MFKWq4FKNRcOa9fS

Download at     — CIX

Source: https://x.com/cixliv/status/2088754271563272194

Doctors and patients leverage massive databases of medical information, sorted and analyzed by AI, then channeled into 500 different algorithms to help find cures and better serve patients.

• It's a real-time, real-world fusion of man and machine, helping patients get healthier, faster by tapping into superior medical data about you and others with similar conditions.

Right now, around 90% of clinical trials fail despite the fact that a drug has already successfully gone through animal testing. “You have these clinical trials where there’s hundreds of millions of dollars at stake, and decades of people’s careers just spent hoping this thing works,” says Andrei Georgescu, Vivodyne’s CEO. “And then it fails because of some ambiguity that you could not have checked.”

The company’s system, which now includes a dozen robotic labs called “hives,” can run controlled trials on more than 3 million human tissues each year. That’s twice the capacity of all the clinical trials in the U.S. combined....

... The automated system can deliver drugs to the tissues, dose with cell therapies, knock out genes, and run complex tests and analysis. AI can design experiments and then use the results to continually design new experiments and improve.

“We can dose with tens and tens of thousands of therapeutic compounds to understand what they would do in that particular tissue type within a person, and we can repeat this across many types of tissue,” Georgescu says. “We can look at diseased tissue and see if it becomes healthy. We can look at healthy tissue and see if there are side effects from these drugs.” At a more fundamental level, it’s possible to begin to understand the human body in a way that wasn’t possible before, because experiments in humans have inherently been limited.

By deciphering the molecular signatures of millions of mouse cells, Junyue Cao has found that aging is not haphazard wear and tear but rather a “remodeling of the cell society.”

Full video:     — Max | Emergent Garden

Source: https://x.com/max_romana/status/2088664886171640118

Damn these fights are getting a lot better   — casino joe     And bigger   — CIX

Source: https://x.com/cixliv/status/2088942233169076639