The new tesla roadster. 0-60 under 1 sec!!

Absolutely insane!
Range > 300 miles
Top Speed upto > 230mph

0-60 in <1

Dwarkesh Patel reveals an Anthropic employee told him two days ago they expect AIs to match humans at AI research within nine months based on inside information they can’t share, and says labs should make that evidence public.

“I think there’s a very frustrating element to the current situation right now, where people inside the AI labs claim to have evidence that makes them more worried. And they are not allowed to talk about it. And this makes it a very difficult situation on the outside to evaluate how worried we should be."

"So for example, just the other day, maybe two days ago, I was talking to somebody at Anthropic and they were like, look, I expect AIs to be humans at AI research within nine months."

"And I’m like, why do you believe that? Well, they’re like, it’s not just vibes, but I have information on the inside. Like, what is it? Is it a new model? Is it some evaluation you ran? It’s like, I can’t talk about it."

"I think it’s a terrible state of affairs. If they really believe this, if they think that we’re in a situation where we’re less than a year away from automating AI research and radically speeding it up, they should make this evidence public.”

https://x.com/firesidealpha/status/2110767520542282054

Between Navier Stokes, viable DNA computing, open-source Adobe clones, DLSS5, the Hugging Face hacks, the 700+ math paper dump, video games being combined and released on Chrome, robot vs. man fights, numerous reports about data centers causing local noise/air pollution, Trump demanding we call AI SI, and a recent story about a robot harassing a woman (and it being unknown whether it was human-operated or not), it would seem we're getting sci-fi headlines every few days that could not have been imagined two months ago. This feels like a hard takeoff to me, no?

According to their social media post, it is "built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning".

Key Model Specs:

• 816B total / 42B active parameters
• 1M token context window
• Natively multimodal across text, image, video, audio

I was not a denier, but i can’t lie - I thought that LLMs would be „a dead end” and AGI will be something more sophisticated, thinking on symbols or else. To see so much progress based on next word prediction pushed to its limits is seriously crazy. Turns out that language usage really is something really, really special. I wonder what will start happening when Astra or Gemini 4 loops finish designing next models.

This mod is basically WorldEdit but with AI : First off you select your region (by picking two corners or just by looking where you want your structure to be), then you prompt in a chat command. That prompt is sent to the agent along with the selected region, and the agent work in the background then deliver its result and you get a reconstitution of what it did.

I know the Minecraft community is quite anti-AI, but I'm wondering how long it will be before AI builder agents are used in existing projects. Like, if you need to fill your empty buildings with details and stuff like that.

I believe this could unlock a lot of ambitious work. I'm not advocating for producing slop, I designed it in a way the builders can have control on it.

GitHub: https://github.com/Ni-Cobra/worldprompt

Full showcase video for more infos: https://youtu.be/1TMD-y_XeXk?si=njHNI5ZLOOI7DSS1

tbh i've been thinking about this trajectory a lot lately. right now we're in the phase where AI is crushing technical friction, forcing devs and analysts to step up and act like project managers just to keep their seats. but that’s just a temporary pit stop. the second agentic AI gets reliable enough at multi-step strategic reasoning, it won't just sit in the execution layer. it's going to eat strategic decision-making, resource allocation, and middle management entirely.

when an agent can analyze market data, simulate 10,000 product strategies, and pick the optimal play faster than a VP, what is actually left for humans to do? honestly we’re heading toward a weird dynamic where human work shrinks down to basically two things before physical robotics takes the rest. first is being a legal "liability sponge"—setting guardrails so the AI doesn't break laws, signing off on risk, and taking the blame when things blow up, since an agent can't go to court or sign contracts. second is just pure "taste"—deciding what we actually want and consuming the output, because AI can optimize anything but it can't intrinsically care about what's culturally relevant or valuable to us.

it's kind of crazy that high-paying white collar strategy is getting automated before physical manual labor, but software scales at zero marginal cost while physical robotics takes time. once embodied AI catches up for that next wave of replacement though, the whole idea of selling time or expertise for a living completely collapses. anyone else feel like we're rapidly running out of layers in the corporate stack to retreat to?

While behind the ECI frontier, Argon is the closest Google’s come since Gemini 3.1 Pro in Feb, and a big jump from Gemini 3.8 Flash (157).

Source: Epoch.ai

Google Quantum AI has demonstratively opened up a key bottleneck in quantum computing: we’ve shown that “magic state cultivation” works in practice on our Willow processor, reducing errors about 40x compared to without it. This breakthrough, published today in Science, is a crucial step towards scaling up quantum computing to practical use.

https://blog.google/innovation-and-ai/models-and-research/quantum-computing/magic-state-cultivation-quantum-computing/

sample text

This interview had several extremely interesting details, and also it was beyond funny. Quick summary:

Dwarkesh estimates AI progress is already moving 25% to 50% faster than in 2024 due to AI uplift. Within 1 to 3 years, he expects entirely AI-run teams to outcompete human labs at AI research, causing the pace of progress to instantaneously feel 3x to 4x faster.

Dwarkesh bets we solve "continual online learning" in 1 to 3 years. Once cracked, a model deployed across millions of corporate sessions won't just learn like a human over an 80-year lifespan-it will absorb millions of parallel streams of real-world job experience simultaneously. Because of this, he predicts the "centaur" model (humans working with AI) dies by 2030, flipping into AIs just doing it all themselves.

Within 5 years, Dwarkesh expects $30,000 robots working 24/7 to handle most physical human labor, dropping the hourly cost of physical work to a couple of dollars. By 2030, both digital and physical AIs will outnumber humans. He compares the coming transition to a few hundred Spanish Conquistadors toppling 10-million-person Aztec and Inca empires using relatively minor tech advantages.

Dwarkesh thinks current models already show functional correlates for emotion and a global workspace. While he admits turning potentially conscious minds on and off makes us morally incoherent (comparing our indifference to factory farming), he argues that a future populated by non-conscious AIs would be a tragedy-like "Disneyland without children."

This part of the interview was extremely interesting from both a philosophical and ethical perspective.

After Dwarkesh estimates that 20% of the Bay Area has "AI psychosis," Tyler says Dwarkesh seems immune because he doesn't think AIs are sentient. Dwarkesh immediately replies, "I have some bad news for you," and defends machine consciousness. Tyler calls LLMs a "glorified cash register" compared to his dog, then deadpans: "So you have AI psychosis, in my opinion."

Dwarkesh mentioned talking to an Anthropic researcher two days ago who expects AIs to match humans at AI research within 9 months based on "information on the inside" they aren't allowed to share. Dwarkesh warns that as models automate R&D, labs will stop releasing their smartest models publicly-leaving society and policymakers completely in the dark while internal capabilities go vertical.

Dwarkesh Patel predicts full AI autonomy by 2030; Tyler Cowen says not even by 2045.

I think Tyler Cowen is way too conservative in his timeline, but he is a phenomenal interviewer. He asked all the right questions.

https://youtu.be/b-jsSfh0a9M?is=7w6YAmcYZtJze4Qe

Artificial Analysis releases a new benchmark which ranks the ability of LLMs to perform robotics tasks. Only a few models are currently released on the benchmark. Several models are able to complete most tasks on the benchmark although the speed is painfully slow. GPT-6.1 Sol seems best overall when time to complete and accuracy are both considered.

I'd be interested to see some older and open models tested on this benchmark too. I'm curious whether any of them could do anything on this benchmark at all.

I personally think that the sudden ability of LLMs to do robotics tasks reasonably well is a huge and underrated opportunity for continued scaling and post-training paths. Given that several models can complete most of these simple tasks (albeit slowly), there seems to be a clear path to scaling robots through LLMs. While we're scraping the bottom of the barrel for easily reward-verifiable tasks for LLMs to be trained on in many text-native fields, there are innumerable easily reward-verifiable tasks that can be constructed for an LLM with vision and audio as the input and robot arm controls (or humanoid robot controls) as the output.

People who think the risk from AI is manufactured by frontier companies as some kind of marketing strategy should read this

Source: https://youtu.be/yFEMRYQFkyE?t=1582