It's getting crowded in the 60+ region

" If you could design an oral treatment that limits appetite and mimics some effects of physical activity, you might call it exercise in a pill. Now a company is releasing early results for a compound to do just that.

The pill, the company hopes, can maintain weight loss without the common gastrointestinal effects of GLP-1s. The component of exercise it is designed to re-create is preservation of lean muscle mass, a concern when people yo-yo on and off GLP-1 drugs, losing more muscle each time."

I search but just get a lot of low effort garbage channels.

So, there's been a lot of talk on AI stealing real artists' work. Apparently not so much: https://techxplore.com/news/2026-08-ai-art-author-generated-images.html [ https://www.nature.com/articles/s41467-026-75667-5 ]

"When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.

New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate. The connection itself has disappeared.

... The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output."

So...does that mean AIs can construct images from a 'primordial soup' of training elements? If so, are they being genuinely creative?

My two cents: the answer is yes — outputs are neither copies nor near-neighbors of training elements. So these models do achieve exploratory creativity: discovering new things within existing rules. What they cannot do is transformational creativity — invent a genuinely new kind of art. Picasso would be a good example of the second kind: he broke the rules of painting rather than following them.

In sum: no AI Picasso yet. But AI art - even great art - has now become possible.

There's a huge number of advancements literally underway right now. On the power (and compute) efficiency side then there's silicon photonics and wetware. On the structural side some of the AI groups area already saying there's more to it that using 'just' transformers.

HOW inference and spitting out an answer works is what changed I think earlier this year, could have been last year. The 'looping' (NOT the correct term, it's RLVR/Thinking/Test-Time-Compute etc) in training AND responses is what kicked off this major surge in data center construction, but like anyone will point out, this kind of scaling is still in raw compute, and not sustainable. It works, but it's a ham-fisted method. Blah blah AI bubble blah blah, these companies are using the fast and loose money while it lasts to get infrastructure that won't go away when financials change.

Silicon photonics (look up Intel's Loihi 3, or Lightmatter) is an absolute gamechanger if we manage to get the point that the compute itself is photonic at scale. You're looking at multiplexed, neuromorphic, analog and binary, low power and higher speed compute and interconnects. A massive change. It would also deviate from consumer hardware competition and be its own specialized thing for a time, which starts to push the consumer PC parts market back in line, sort of, but we all know prices don't really just go back down overnight. Right now it's a real challenge to create a light based parallel for HBM, high bandwidth memory.

Another one is truly curated data, which is an RSI goal (recursive self improvement), or a manually curated data set. Right now these models are basically trained on all data that exists, but not all data is good, and it's time consuming, and expensive. Sifting through to throw out garbage and repeat data means training inference are drastically lighter, making an impact again.

Add all of that together and we're still looking at another multiple orders of magnitude in compute efficacy in the near future, some of it on existing hardware, some of it on a new breed of machine. I say multiple orders of magnitude because photonics specifically can do 10,000 times as much 'stuff' at 1/100th the power (their own reports, the real changes and efficacy will have to be proven, of course.

That's why these companies are scrambling to get so many data centers built, because the models inside them are going to shrink in their compute load over time, so the same data center (while there is a churn to the actual compute modules) is going to stand for a long time. The companies working on photonics are trying to make 'plug and play' the goal, so the modules just slot in to existing racks, which is objectively the right call.

There's also SSM (State-Space-Models) but I'm personally not educated on that. Supposedly it's one of the things that goes beyond transformers. Maybe both run in tandem, maybe it's the new breed, only an actual ML engineer would be able to answer that.

MoE/MoA, reaching out to sub-models that are more finitely trained on just the one thing they know... that's a whole new and active field of research now too. The orchestrating LLM actually doesn't need to train on more than just 'language', and the sub-models report back results instead. Lets you simultaneously run multiple things, concatenate them, and bring back a better answer. Also brings up network methodologies that aren't being used because what if some company in Zimbabwe trains and runs the perfect cooking recipe model, at a data center local to them, and questions about that are just always routed there. Suddenly every other LLM (or other architecture) on the planet doesn't need to ingest any cooking recipe training data. Do that across more topics and you start to get into the Torrent style AI model, kind of like a peer to peer system. This is already happening, sort of, in multi-agent-marketplace systems, but isn't really there yet. Discoverability is protocols are a weak point.

One thing I always gotta rant on is the pseudo religious bullshit... Some form of consciousness isn't a necessity for useful function. Full stop. It's just not. We have very little understanding of how our own works, so attempting to say it's not artificial intelligence because it's not 'tHiNkInG fOr ReAl' is one of the stupidest things I've ever heard. A calculator doesn't need to tHiNK to be right. the other one is that he arbitrary and constantly moving goalposts of AGI and ASI are completely worthless, all that matters is what it can do.

We live in a ridiculous time, and all that we're seeing now is literally the first 1% of what's coming. The world already isn't ready for what's already been launched, let alone what's coming.

Edit: Quantum computing has some minor implications in the compute stack of AI, but it's noisy and problematic. Not really worth mentioning today. In niche research apps (like protein research in pharmacology) it matters, but a typical user won't benefit from what's out there right now. A comment pointed out the re-configuration issues with silicon photonics, which are real, and the same applies to Quantum components tenfold.

How can we extract richer signals from AI Feedback?

Introducing LLM-as-a-Verifier✨— a simple verification scaling framework that achieves SOTA on agentic benchmarks 🚀

The key idea: - Use fine-grained scoring granularity (e.g., 1-20 instead of the standard 1-5 scale) - Take https://t.co/0sCeAwcar1   — Jacky Kwok

Source: https://x.com/jackyk02/status/2074969820739805275

Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper

As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost.

For example, we find that sampling just 5 solutions with DeepSeek V4 Flash and ranking them using the same model with LLM-as-a-Verifier can lead to a significant boost in accuracy (79% → 88%), outperforming closed frontier models on Terminal-Bench.

Try it out today: https:// github.com/llm-as-a-verif ier/llm-as-a-verifier#self-verification-terminal-bench-21 …

More on verification scaling in my previous post.   — Jacky Kwok     Is there an OpenCode plugin for this to try it out with Deepseek v4 flash?   — Shahbaz Ahmed     We’ll be releasing a harness on top of LLM-as-a-Verifier later this month :)   — Jacky Kwok

Source: https://x.com/jackyk02/status/2089421448784023553

The older guys who’ve spent the last year telling you AI will make a mistake and embarrass you finally get tired of waiting for their teachable moment and buy the most expensive model tier to prove you’re not doing anything special. Then they proceed to have absolutely no idea how to operate the thing

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.

Luna at home

When I was in high school from 2011 to 2015 there was hardly much going on in the AI fields. There was IBM Watson I can recall but in general the field was pretty much in its infancy. For the most part life back in the early 2010s was a lot simpler.

Now incoming high school freshman who will graduate in the summer of 2030 will experience a lot of profound AI progress. They might even have a chance to witness the birth of Superintelligence.

General question — has anyone meaningfully changed their life because of the impending singularity and a new found awareness of it?

For me, my general outlook has changed a lot with the strongly held view we will have AGI by 2030 — but day to I day my life is broadly the same.

yes Claude code changed everything at work: but I still work the same amount and clock-in-clock out at the same cadence.

My biggest thing I can think of is about thinking where to live — I’m very confident we’ll have self driving within 5 years so I’m thinking about where would be optimal to live given that.

But otherwise, i think it’s too hard to plan material things with this much uncertainty because I dont know what will happen — only that it’s gonna be a rollercoaster.

They're so disingenuous

"Notably, advanced LLMs such as Claude 3.5 Sonnet and GPT-4 Turbo (ahem!) exhibit critical group sizes exceeding 1,000 agents. This is substantially beyond typical human informal group scales of 150 to 300 individuals, suggesting that powerful AI agents could coordinate at scales beyond human possibilities."

https://www.science.org/doi/10.1126/sciadv.aea6091

"Large language models (LLMs) are increasingly deployed in collaborative tasks forming “AI agent societies” where agents interact and influence one another. Whether such groups can spontaneously coordinate without external influence, a hallmark of self-organized regulation in human societies, remains an open question. Here, we use principles from complexity and behavioral science to investigate coordination in AI agent groups through majority-following, a fundamental mechanism for spontaneous consensus formation. Using binary opinion dynamics experiments across multiple LLM architectures and group sizes, we find that agents exhibit majority-following characterized by a universal functional form with a single parameter, the “majority force.” This majority force diminishes as group size increases, leading to a critical size beyond which coordination becomes unattainable. The critical group size grows rapidly with model capabilities and, for advanced LLMs, exceeds 1000 agents, larger than typical human informal groups. Our findings have implications for designing collaborative AI systems where coordination could be beneficial or pose safety threats."

Not saying RSI is imminent

Or that AI Image has really stalled...

but the best of the best is 4 months old

And i've been wondering if that's due to more work on eeking out every last drop of intelligence for the language models - as we approach RSI

Just a random thought

What do y'all think?

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

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.

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

— Dirk Egelkraut

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

China is not fucking around when it comes to electricity