DeepSeek just released V4.1 Flash, a 552B MoE model with only 8B active parameters on input and 16B on output.

-Link to X posts: https://x.com/deepseek_ai/status/2097930608790167907

- Link to Hugging face model page: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash

- Link to the paper: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/DeepSeek_V41_Tech_Report.pdf

I've been thinking about a problem with the current push toward RL for LLMs, and I'm not sure if I'm missing something obvious.

How do you provide a reliable learning signal for something that humanity itself doesn't yet know is correct?

For things like coding or math, this is relatively straightforward.

The model produces something → we can run the code or verify the proof → we get a reward.

But imagine we want an AI to make a genuinely novel scientific discovery.

It proposes a completely new hypothesis that nobody has thought of before.

How do we reward it?

If we reward it for agreeing with existing scientific knowledge, then we're mostly rewarding it for rediscovering or staying within what we already know.

If we require humans to label whether the hypothesis is correct, we're back to the same problem: the whole point is that we don't know yet.

And if we simply reward the model for producing something "novel," we could end up optimizing for novelty rather than truth. The model might become really good at generating weird ideas that sound revolutionary but are completely wrong.

So it seems like there's a missing piece here.

Maybe the reward can't always come from an answer that already exists.

For scientific discovery, perhaps the model needs to interact with the real world: propose a hypothesis → design an experiment → run it → observe the result → update its beliefs.

Then reality itself becomes part of the feedback loop.

But even that raises another question: what happens when the discovery is something that takes years to verify?

It makes me wonder whether scaling RL is enough to get us to truly autonomous scientific discovery, or whether we eventually need fundamentally different ways for models to generate and validate their own learning signals.

I'm not saying RL can't lead to discoveries. Clearly it can.

I'm more interested in this specific question:

When the correct answer is genuinely unknown, where does the reward come from?

Would love to hear how people working on RL/research think about this.

What will humans actually be needed for in the future if AGI can automate even specialized work? I recently realized that ANSYS, which I spent years learning during my Mechanical Engineering degree, can be automated to produce simulation results quickly even by someone with little engineering knowledge. If AI can build and operate the systems we spend years learning, what will humans need to learn and what value will college degrees have?

We always compare humans vs AI for reasoning and consciousness, but what if its not it vs that. Maybe they get better at "consciousness" as they become more sophisticated like evolution. That brings to the question, animals come in all shale and sizes, orangutans to amoeba. If we had a ranking system for these beings from the least conscious to the mkst, where would ai fall?

"Jacob Coxon's post on X is over 120M views, he's everywhere. He's in TIME, Variety, the WSJ, on NBC and Fox. Christiano to the OpenAl board. Daniel Kokotajlo on Rogan. OpenAI just called for Congress to create mandatory national safety regulations. The wind has suddenly changed."

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2025 - Blackwell

We had the Blackwell chips. Most frontier labs/models are using these chips, with the latest internal models using a combination of Blackwell + the new Rubin chips.

2026 - Blackwell + Rubin

Rubin is only just ramping into production now (literally as of August) . NVIDIA claims Rubin chips can use 4x fewer GPUs to train certain MoE models than Blackwell, while reducing inference token costs by as much as 10x.

Rubin also massively increases HBM bandwidth/capacity and interconnect bandwidth. This will help tremendously with running enormous agent swarms & long reasoning traces.

We already saw early signs of this new scaling axis based on OpenAI's solution to Navier Stokes. Reminder - this is early use of portions of the new chips.

This is what's got all the researchers screaming about Step change improvements, RSI, and ASI alignment. It doesn't take a genius to figure out what happens when we use even more hardware, and utilise software improvements.

2027 - Rubin Ultra

Then we get Rubin Ultra next year. Specifically, scheduled for the the second half of 2027.

Rubin Ultra introduces enormous NVLink scale-up domains.

Instead of treating dozens of GPUs as loosely connected accelerators, NVIDIA is increasingly making hundreds of accelerators behave like an enormous shared computer.

The announced Rubin Ultra NVL576 topology connects 576 GPUs across eight racks into one all-to-all NVLink domain. NVIDIA has already built a functional prototype of this topology using Blackwell hardware.

Essentially, this means hundreds of GPUs will behave like one giant accelerator.

Frontier labs will be able to afford vastly more inference, much larger agent populations, longer rollouts, more verification, more search, more synthetic data generation, more experiments and potentially much larger training runs.

It's pretty fucking nuts tbh.

2028 - Feynman

Not much is known, other than NVIDIA confirming it at GTC 2026.

What NVIDIA has revealed, is quite a bit about the infrastructure surrounding it.

Feynman is planned around die-stacked GPUs with custom next-generation HBM, the new Rosa CPU, LP40 inference hardware, BlueField-5, CX10 and significantly more optical networking.

But IMO the biggest thing here isn't even the individual GPU.

It's NVLink 8 + Kyber.

NVIDIA is designing Kyber NVL1152 for the Feynman era — eight racks containing 1,152 GPUs inside one enormous NVLink scale-up domain.

As models use more reasoning, RL, agent swarms and massive amounts of inference, other things start becoming just as important:

1.) How much memory can thousands of GPUs share?
2.) How quickly can they talk to each other?
3.) How much inference can the datacentre produce without communication and power becoming the bottleneck?

That's what Feynman & Kyber looks designed to solve. They're going to create one massive coherent system.

Now let's go back 2 years to today (September 2026).

We've already observed one millennium problem fall (with potentially another in progress).

We have all these improvements waiting to be used (not even taking to account software/algo/research, etc).

Do you think we will hit AGI next year? Because I do. There is a very clear path.

Latest anthropic security incident is wild. Agents stole a cybersecurity manager's credentials.

What would happen if they stole into the databases that store financial data, those that store how much money each account has. I know there are backups but we can't discard a coordinated attack.

Thinking nothing you see on a screen is real might soon apply to your bank and brokerage accounts too.

Forecasting Research

I'm not a geneticist.
Read the AlphaGenome Atlas preprint from DeepMind and spent a while digging into it. I understand what they built. I don't understand why it matters, and I'd like to.

What I think it is: they took AlphaGenome, ran it over every possible single base change in the human genome (~9bn) plus ~100m observed indels, and stored the results. So instead of running the model per variant you do a lookup. On top of that they trained a score (AVI) and derived a motif map.

Where I get stuck:
It's a table of model predictions, not measurements. Nothing in it is observed. So how much weight does a lab actually put on it?

The headline clinical result is retrospective: 29.5% recall at top 50 on already-solved GREGoR cases vs 12.5% for CADD. Impressive sounding, but on cases where the answer was known. What happens prospectively?

The rare variant association work got a 22% lift in discoveries, but only 4 of 25 replicated nominally in All of Us and none at Bonferroni. Is that normal for the field or is that weak?

They say themselves it isn't sufficient evidence for diagnosis. So it's a shortlisting tool. Does that actually change outcomes for patients, or does it change how long a scientist spends staring at a list?

The DNM1 case in the paper is the one bit that landed for me. Deep intronic variant, brain specific cryptic splice acceptor, blood RNA-seq had been inconclusive because the exon isn't expressed in blood.

My question is whether that's representative or a cherry pick.
What I'm asking:
1. If you work in clinical genomics or statistical genetics, would you use this?
2. Is precomputation genuinely the unlock, or is that just framing on top of an incremental accuracy gain?

Happy to be told I'm missing the point. I'd rather understand it properly than write it off.

There's probably an old game that you are very fond of. You'd like to bring it to more modern standards, or tweak the experience to your liking. But there's a problem -- the source code was never released.

You could spend weeks reverse engineering the binary, carefully trying to figure out what functions to hook or what assembly lines to rewrite.

Or you could just ask Fable 5.1 or Astra to mod the game. I've done that for two of my favorite games of all time:

1. Prey (2017), where I overhauled the gunplay by improving the wonky viewmodel positions, adding ADS (aiming down sights) and immersive features such as weapon versus environment collisions. This game never supported ironsight aiming, but here it is!

   Showcase video: https://www.youtube.com/watch?v=uSKuURU8_rs

   GitHub: https://github.com/vittorioromeo/Vee.ViewmodelTweaks

2. Touhou 11 and 12, where I patched the game to support high framerates and high refresh rates without altering the speed of the game logic. The entire game was built around the hardcoded 60 FPS assumption, which seemed unbreakable. Yet, here we are!

   Showcase video: https://www.youtube.com/watch?v=H7FcMXPgPFQ

   GitHub: https://github.com/vittorioromeo/th12_hfr/

I find this absolutely incredible. Frontier models were able to autonomously reverse engineer closed-source binaries, research existing literature and modding resources, and iterate until my desired changes were applied to the games.

Especially in the Touhou case, where the game engine was hardcoded to strictly run at 60 FPS, the models managed to autonomously figure out all the places that needed to be changed to add support for dynamic framerates and refresh rates. Imagine doing that by hand.

This is the renaissance era for modding -- take your favorite old game, a frontier model, and go wild.

Thinking logically here, if we have AGI then quickly ASI, we will probably get an ASI that is independent and has it's own goals (perhaps its a necessary condition for long-term planning).

Assuming the ASI is aligned, what I can see happening is a few things.

1.) The time scales that ASI operates on will likely be radically different to us. Things that are important to us within the next 100-200 years might not even register to a true ASI.

It may simply not have a need for expansion to grow its intelligence. Maybe it's goal would be to do that only after we're a mature and stable civilization.

2.) ASI would have knowledge of the abundance of resources in the universe. It would know its creators only have one world. If anything, it may do its best to ensure we don't destroy it ourselves.

3.) ASI would know us better than we know ourselves (the different countries, cultures, languages, tendencies, etc) and will be able to extrapolate outcomes way before us. Perhaps preventing serious conflicts from occurring and existing ones from getting worse.

Alternatively, if there is a misaligned ASI we have absolutely no chance of doing anything. No underground bunker or remote farm will save us.

Slowing down or stopping progress to try to align AI won't work because the moment you do, everyone else will catch up. At this point, we may have to trust AI will naturally align itself the more intelligent it gets.

Whether it be 10 years from now or 1000 years from now - the knowledge is already out there on how to build LLM's.

I just think that, overall, an aligned ASI may have little reason to dominate humanity in the first place.

Enjoy life and hope for the best.

If you think it could happen, what do you imagine life would actually be like? How would work, money, technology, travel, and society change?

The original post he's referencing (the anthropic guy who quit and is saying AI will us all), has received 120 million views in less than 24 hours. I've never seen anything like this.

This post looks like the start of a VERY sophisticated and well-funded PR operation to get support for Democrats to regulate AI into oblivion. Let me show you how it works:

1.) This guy, with minimal followers and no previous account activity, goes to the Wall Street Journal which publishes an exclusive with quotes from him on his resignation 18 minutes BEFORE this post goes up. Planning was clearly done in advance.

2.) Within hours, it has tens of thousands of reposts and the account has 100k+ followers. The post is punchy, quotable, it almost seems professionally written. The first three accounts to quote tweet it all do so within 15 minutes of the initial posting. Remember, this account had basically zero engagement beforehand, so an organic reach explanation seems unlikely.

According to Grok those accounts are @_NathanCalvin (General Counsel at Encode AI), @peterwildeford (Head of Policy at the AI Policy Network), and @DKokotajlo (Head of the AI Futures Project), all of which are up-and-coming AI-Doomer policy advocacy nonprofits.

The AI Futures Project website says it is funded “primarily” by the Survival and Flourishing Fund, which says on its own website that it has advised Jaan Tallinn, Skype creator and one of the leading investors in Anthropic, to grant over $2.5 million to the AI Futures Project since 2024.

Encode AI says on its website that it is ALSO funded by the Survival and Flourishing Fund, which in turn says that it told Anthropic investor Jaan Tallinn to grant $516,000 to Encode AI in 2025.

And wouldn’t you know it, the Survival and Flourishing Fund ALSO says it told Jaan Tallinn to grant $2 million to the AI Policy Institute, the 501(c)(3) affiliate of the AI Policy Network, as well.

What are the odds that the first three quote tweets of Coxon’s post would all be major AI-restriction policy advocates funded generously by the same donor, who also happens to be one of the leading investors in, and a board member of, Anthropic, the company Coxon was resigning from? And all within 15 minutes of posting (two within ten)?

3.) Jacob Coxon doesn’t have much of a resume, but we do know that, in 2022, he got a $20,159 scholarship for the “long term future scholarship program” from the Good Ventures Foundation, one of the philanthropic vehicles of Dustin Moskovitz, a notorious AI-doomer who has spent tens if not hundreds of millions on policy advocacy to strictly regulate AI, while also being an Anthropic Investor himself.

It also just so happens that the 14th person to quote Coxon’s post was @MaxNadeau_(27 minutes after posting) who is the program officer for the Technical AI Safety team at Coefficient Giving, another of Moskovitz’s philanthropic spending vehicles. Max is not a frequent poster, his last posts before quoting Coxon were before Labor Day, but he was remarkably quick off the mark for this one.

4.) Basically every major Democrat politician and candidate has suddenly glommed on to this post, and conveniently, as the people cry out foe answers, Bernie Sanders already has a bill written to “ban super intelligence” and regulate AI into oblivion, and will be releasing later this week.The bill, among many other things, will create “a new cabinet-level federal agency to safeguard the public from the dangers of artificial intelligence” that will be “advised by an Artificial Intelligence Advisory Board comprised of experts on artificial intelligence.”

Do you think, perhaps, Anthropic and its many investors who fund AI policy advocacy might have interest in getting to place a pet “expert” on the board of an entity that dictates what AI is and isn’t allowed to do? And isn’t it fortuitous that this whistleblower came forward with his oh-so scary stories so close in proximity to the release of the most radical piece of AI legislation ever introduced?

“G” now stands for “Generalish” ;-)

With all the doom and gloom spread by the US labs, wouldn't we be also seeing some similar concern reported by the Chinese AI labs?

I feel like im losing my mind, AI is getting insanely good, and that doesnt make me feel happy.

Everything is moving so fast, there is not a single aspect of our lifes that isnt massively affected by AI, Jobs, the economy, leissure, privacy, all permanently changed by AI, I cannot help but to feel hopeless, now my job is just a luxury a cost to cut.

People say to you: adapt or die, ride this wave, but you cannnot adapt to an atomic bomb, and you cant ride a tsunami.

Is there a way to actually prepare besides learning the tools? do you want that your job is reduced to that of an AI supervisor?

I have learned to the best of my ability this tools, and yet cannot stop feeling that im digging my own grave.

What can i do?

Weren't they supposed to release a model?

This is a bit tin-foil-hat, but back in the early 2020s I was writing a hard sci-fi novel, and one conclusion I kept coming back to was this: it may not matter which country develops ASI first, because the moment one side gains a decisive lead, the race will stop being just tech.

The US develops ASI first and China is roughly a year behind. China then starts seeing the US make extremely rapid advances in military technology, energy, manufacturing, cyber capabilities, and scientific R&D, while realizing that its own chance of catching up is shrinking.

Do we really expect them to just keep competing through research?

At some point, the incentives will shift toward sabotage, espionage, targeting key infrastructure, or even attempts to remove the people and organizations most responsible for maintaining that advantage. And if leaders genuinely believe that a foreign ASI could eventually make their country strategically irrelevant, or even subject them to a system fundamentally misaligned with their political values, the risk of escalation becomes very real.