Grendel1209

• Grendel1209 19 Apr 2026 3:59 UTC

  6 points

  1

  in reply to: Noosphere89’s comment on: We need Git for AI Timelines

  I think you’re missing what he’s saying here.

  Pre training was easy to scale in 22, 23 and 24. There was excess capacity. Mythos is likely the first >10b pertained model. The Claude4-4.6 paradigm was likely driven by one pre trained model with RLVF on top. Mythos is the new class of pre trained model and scaling and doubling times will be based on the speed of building RL models on top of Mythos.

• Grendel1209 12 Apr 2026 18:06 UTC

  3 points

  0

  in reply to: lc’s comment on: lc’s Shortform

  I work at a threat intelligence company and my default view is that Mythos is likely under-hyped for SWE and overhyped for cyber.

  People VASTLY underestimate how easy it is to break into the vast majority of organizations. There is no need to design custom 0-Days because you can simply log-in (or run a session replay attack) using credentials/​sessions freely available on the dark web. I would put forward that if Mythos was released tomorrow with standard API guardrails you would not see an explosion in cyberattacks.

  However, the one exception is that organizations that already heavily invest in security (think Google) would have a new major attack surface to cover, and you may see an increase in sophisticated actors attacking other sophisticated actors because it changes offense-defense balance in favor of offense.

• Grendel1209 11 Apr 2026 20:33 UTC

  1 point

  0

  on: Dario prob­a­bly doesn’t be­lieve in superintelligence

  There can be too very distinctive and conceptually important beliefs here that seem to be a crux.

  1. Dario doesn’t believe super-intelligence is intrinsically possible given any architecture (Effectively Francois Chollet’s’ view that humans + tools represent something close to the upper bound of intelligence)

  2. Dario believes current architectures at maximum scale and optimization are likely to top out at extremely smart human intelligence (a country of geniuses in a data center), but super intelligence is theoretically possible with a different architecture.

  From his comments it seems very difficult to disentangle whether he believes 1 or 2.

  It is very easy to believe that he may acknowledge 1. is conceptually possible, but by default he expects 2. given the current state of AI. I tend to lean towards 2. with the caveat that creating a fully automated coder and radically accelerating science may quickly allow iteration super-intelligence via a different architecture (If it doesn’t end the world in a more banal way first)

• Grendel1209 11 Apr 2026 19:33 UTC

  1 point

  0

  in reply to: TsviBT’s comment on: TsviBT’s Shortform

  Where I have doubts about FOOM/​RSI is that LLMs seem to me in many ways a fundamentally different type of intelligence than organic life.

  Psychometrics shows that general intelligence improves human abilities across a broad range of domains. If you take this view and apply it to AI it doesn’t quite work, I leverage AI very very heavily at work, and sometimes it is phenomenal, often it is not, and occasionally it makes mistakes a grade schooler would not (I’m using Opus4.6). The ”intelligence” is very unevenly distributed and skewed towards verifiable domains.

  I tend to see LLMs as a grab bag of heuristics and concepts. And I see general intelligence as effectively pattern matching both within a domain and across domains. RLVF enhances the base models ability to pattern match within a domain (programming) but doesn’t seem to extend evenly outside of it.

  I tend to land with Steve Byrnes that this particular architecture is unlikely to scale to AGI (I use a definition of a system capable enough to serve as a drop in replacement for all remote workers), although it could definitely replace a large percentage of them.

  I do not hold these views with high confidence however, and am always open to having my mind changed.