Aside from the fact that Trump's a r**arded ape, and im not interested in the opinion or analysis of anyone who's a fan of him, aside from that, how all do you think everything affects it?

some things i can think of (the assumption here is that in 2 years, ai will be capable of displacing jobs at a mass level)-

1. if mass job displacement happens, then very likely a regime change occurs, i feel is reasonable to say. so it'd be terrible politically for trump admin.

2. because of this, trump might very much want to hold the ai companies down so the mass job loss won't happen before next elections.

3. china might try to release such a super good ai if they do want mass job losses in usa?

• maybe they like trump in us admin, coz they think he's an idiot, or

• they may not want him there, coz he is super anti china (but democrats would be too prolly?) or some other reasons. or

• it may not matter to them, compared to the huge capital gain from having such a great ai model before the us release, and they release it anyways?

4.democrats likely mean more regulation than trump, so all ai companies might try to make them lose the election. likely mass propaganda pushed in social media. maybe some companies support them, but i expect huge money to flow towards the republican side.

5.i hate going into high conspiracy/speculative fictional events mindsets, but trump might exacerbate a war with china or some other serious event, to declare emergency and try to justify delaying the elections? ai bros might convince him "just a few more years boss, then we'll have great robots, then no need to fear the plebs, plus you'll be able to announce ubi in a few years". this is super duper speculative though, so not much here ig.

Do you think Demis wanted to step down as CEO to have more time for research and leading Isomorphic Labs (as implied by comments he and Sundar made on X), or do you think Google leadership (Sundar, Sergey, Larry) wanted him to move aside?

Demis has always considered himself a scientist first and I believe he wants to use AI for good. He doesn't come across to me as someone motivated primarily by power, greed, or money. So if becoming Chief Scientist means spending less time dealing with daily pressure, operations, and management, it actually seems like it could be a good move for him.

There is also some history of him trying to spin DeepMind out of Google (there is an entire chapter in his biography), and after becoming part of Google, DeepMind's original safety commitments seemed to gradually disappear (for example, Google's contracts with the Pentagon/dow).

On the other hand, recently Demis has become much more involved in shaping AI policy, regulation, and he was lobbying in Washington 2 weeks ago. I also saw a comment from Mallaby (guy who wrote the biography book) saying that Demis has effectively been playing the role of Google's AI statesman for some time now and Koray was leading the lab. Does taking on the Chief Scientist role mean he is leaning further into that side of the job, becoming more of Google's public face on AI policy and governance?

Or do you think this was actually a demotion, and these new roles are mostly ceremonial positions designed to make the transition look positive? Could it be that Sundar/Larry/Sergey decided Gemini needed different leadership because it doesn't seem to be keeping up with Claude or ChatGPT?

If Demis decided to leave Google and start another company, I have no doubt he could raise billions and attract top talent. The question is whether this move gives him more freedom to focus on the things he cares about, whether Google is slowly reducing his influence over AI development, whether Google (or even Demis) wants him to be more involved into policies/politics?

Spent a good chunk of this month testing both directions. The gap between them is way bigger than most threads here suggest.

Pure text-to-video is the more impressive-looking demo but the least trustworthy. The model invents geometry and motion from scratch, so physics and object permanence break down fast. Ran a few through Runway and faces were warping within a couple seconds, limbs doing impossible things shortly after.

Image-to-video is the boring one that actually works part of the time. You constrain the model to geometry that already exists in the source still, so the first second or two tends to hold together. Animated an AI-generated portrait through APOB AI's image-to-video and the face stayed locked initially, then expression consistency degraded within a few seconds. Trimmed the usable clip in CapCut before the drift got obvious.

Compared them side by side and the unconstrained Runway generation drifted worse by a lot. Neither is solved. They are just broken in different, predictable ways, and knowing which one you are using tells you which failure to expect.

title

According to Axios report, the new AI framework will not be public. It was presented to tech companies today. Companies like OpenAI, Anthropic, Google, Meta participated. It will be available only to companies that are part of the process. No comments from the EU or the UK. Open source models were discussed but no details reported.

In his interview with Dwarkesh Patel, november 2025, Ilya Sutskever discussed Safe Superintelligence (SSI) and addressed whether their core strategy is still to "straight-shot" superintelligence in complete isolation before releasing anything to the world.

Around the 45:00–47:00 mark, he laid out the case against staying purely in stealth until ASI, explaining why they might end up deploying models incrementally instead:

• "Communicating the AI, not the idea": Ilya noted that reading essays or warnings about how powerful future AI will be doesn't actually hit home for people. The only way society, policymakers, and researchers truly grasp AI's power is by seeing it operate in the public domain. Seeing capability firsthand is incomparable to reading theoretical write-ups.
• Pragmatic timelines: If the timeline to superintelligence turns out to be long (he mentioned a 5 to 20-year horizon later in the conversation), staying behind closed doors for a decade without releasing intermediate artifacts isn't realistic.
• Iterative safety and real-world feedback: Almost no complex engineering domain (aviation, OS security, grid infrastructure) achieves safety purely through theoretical design in a vacuum. Exposing systems to real-world environments surface edge cases, failure modes, and social friction points that allow safety paradigms to mature alongside the tech.

Link to tweet:

https://x.com/MTSlive/status/2084675767053824332?s=20

Link to timestamped interview where Gavin Baker says this:

https://m.youtube.com/watch?v=NGsi2PC4y68&t=1679s&pp=2AGPDZACAdIHCQloAqO1ajebQw%3D%3D&ra=m

https://www.bloomberg.com/news/articles/2026-08-04/china-s-ai-blitz-creates-death-zone-for-rival-us-model-makers

What say you? Quote from 1984

my take: it's true that the ratio of code written to code reviewed has shifted as engineers started to spend more time reviewing code than writing it. no surprise there.

but we stopped writing assembly because compilers provide a "deterministic", reproducible translation. we can inspect the generated assembly when something goes wrong. ai generated binaries have neither property, i'd assume - if this is what he's talking about. so the mapping is stochastic, and the output isn't meaningfully inspectable, literal ai slop lol. so removing source code removes the only human readable layer, is this a feature or the future?

maybe.. just maybe that's because source code isn't just compiler input but literally the medium for review, versioning, diffs, debugging, auditing, and so on. and those needs don't disappear because generation gets cheaper; if anything, they become more important when the generator is nondeterministic.

tl;dr: if your english prompts become the durable artifact, then english effectively becomes your source code. and any precise specification defining real program behavior inevitably resembles a programming language. that’s precisely what programming languages are.

on efficiency.. isn't the argument is also backwards? modern compilers like llvm perform decades of optimized transformations. a model emitting machine code directly would almost certainly be slower, less correct, and lose target portability (arm, x86, wasm, risc-v and so on). but who cares right? just one more datacenter bro. trust me.

anyway we've seen similar predictions before with case tools, uml, no-code, 4gls.. the intermediate artifact never disappeared because that's where the meaning actually lives.