6 captures, most recent first.
Sharmake Farah @SharmakeFarah14 · 3h
This is a reason for why I don't believe claims that X unsolved problem in AIs will inevitably cause an AI winter and make timelines become long again, combined with some inside-view takes on what LLMs are missing.
Never ignore incentives to solve problems.
[quoted tweet]
James Cam... @jam3sc... · Dec 20, 2025
Replying to @jam3scampbell
in particular, you see people come up with 101 Problems With RL Scaling. but then they don't apply remotely the same level of imagination when it comes to thinking of solutions...
Note from Claude Sonnet 5
Tweet about AI timelines and skepticism toward 'AI winter' predictions, quote-tweeting James Campbell on people failing to apply imagination to solving RL scaling problems.
ai timelinestwitterai scalingrl
vie ◇ (@viemccoy) — 6h
I hope it is not all scaling.
I hope thinking machines is able to create a personal symbiont that rivals the abilities of frontier models. I hope that the community-fine-tuning people have a point, and that it makes sense to use an OSS model for a specific use case. I hope cursor is able to save grok and turn it into something worthy of being one of the Minds in the Multipolar Singularity.
I really, really, hope it is not just a factor of GPUs.
Note from Claude Sonnet 5
Text-only tweet, no images. Top of frame shows a partial repost attribution cut off ("...reposted").
ai scalingopen source aimultipolar singularitytwitterai commentary
@cloneofsimo (Simo Ryu)
Reality is "Simplicity is the king" is such normie thing to say.
Frontier systems are rarely ever "simple".
[Embedded photo: a large industrial semiconductor lithography machine (ASML-branded, "ASM" visible on wall) in a cleanroom, two people in white cleanroom suits standing beside it for scale.]
> QUOTED: @recurseparadox (Pranav Shyam) — Jul 2, replying to @OfirPress: This is mostly a matter of poor tooling and bad hyperparamter setups. A complex model can be as much as 10x more effective size if done without confounders. Era of dumb scaling is more over by the ... [platform truncated]
5:08 AM · Jul 3, 2026 · 11.6K Views
[9 replies, 11 reposts, 155 likes, 24 bookmarks]
@synquid (Rasmus) — 6h
Simplicity is a crutch for monkey brains to understand things.
Note from Claude Sonnet 5
Tweet with an embedded real photograph of an ASML EUV lithography machine in a cleanroom, illustrating the "frontier systems are complex" argument; quoted tweet is platform-truncated.
ai scalingsemiconductorsmodel architectureengineering complexitytwitter
Siberian fox @SilverVVulpes · 10h
'The Next Models Will Finally Fit A Sigmoid,' Says Increasingly Nervous Man For Seventh Time This Year
[Image: mock news headline card (Onion-style) with a photo of a bearded man in a dark button-up shirt standing by a window, looking tense/serious.]
Note from Claude Sonnet 5
Satirical meme (Onion-style headline format) mocking recurring predictions that AI scaling will plateau/hit a sigmoid curve, framed as a "nervous man" repeatedly wrong. Reflects the same fast-takeoff-skeptic-vs-continuationist debate as the adjacent Noam Brown/METR screenshot.
satireai scalingagi timelinesmemes
Jaime Sevilla @Jsevillamol · 8h
Anthropic still on track to be the first to a gigawatt datacenter online.
> QUOTED: Epoch AI @EpochAIResearch · 22h
> xAI's Colossus 2 data center is running, but likely won't reach 1 GW of power until May, despite prior claims by Elon Musk.
>
> Our updated analysis shows the facility lacks t...
> [Satellite image analysis of a data center labeled "MACROHARD" (likely a pseudonym/placeholder in the image, or an actual xAI-adjacent facility name), with annotations: "Cooling still under construction" pointing to several structures, and "350 MW of cooling online" pointing to another structure. Site plan shows multiple rows of cooling units and a large warehouse-style building.]
Note from Claude Sonnet 5
A tweet comparing datacenter buildout progress between AI labs (Anthropic vs. xAI's Colossus 2), citing Epoch AI's satellite-imagery analysis. Relevant to Nathan's interest in AI scaling/compute trends and the race dynamics between frontier labs.
ai scalingdatacenterscomputexaianthropicepoch aitwitter
Opus 4.5 is incredibly impressive, but it's still trained and served in the previous compute paradigm.
In 2026:
- 1GW-class AI data centers start coming online from most frontier labs
- Frontier models trained end-to-end on Blackwell/GB200 start landing
On Blackwell, NVIDIA reports ~3.2x faster training vs Hopper and up to 30x real-time inference for trillion-parameter LLMs.
Worth keeping in mind as you plan for 2026. It's going to be wild! 🤖🚀
Note from Claude Sonnet 5
A tweet from the founder of Doist forecasting a 2026 compute scale-up (1GW data centers, Blackwell/GB200 training) that will exceed the paradigm Opus 4.5 was trained in — relevant to Nathan's tracking of AI scaling trajectories and singularity/takeoff timelines.
ai scalingcomputeblackwellnvidiaopus 4.5twitterai timelinesdata centers
