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Rob Wiblin (retweeted by @alexolegimas on 2026-08-07) lists seven AGI-relevant…

2026-08-07 · 17:02 UTC ·@alexolegimas ·1 min read

Brief

Rob Wiblin summarizes seven 2026 developments—exploding GPU rental revenue, METR horizon saturation, a 'Mythos' jump, early RSI sparks, business weaknesses, math breakthroughs, and cheap inference scaling—and says they shorten his AGI timelines by about one year since December 2025. He highlights four unresolved questions that split AGI bulls and bears.

Why it matters

Rob Wiblin (retweeted by @alexolegimas on 2026-08-07) lists seven AGI-relevant updates this year: exploding revenue and GPU rental $, METR time horizons saturating, the Mythos 'jump', sparks of RSI, AI still sucks at business, AI makes some math breakthroughs, and inference scaling remaining cheap.

Key details

  • Wiblin reports his AGI timelines have contracted by about one year since December 2025 after accounting for these developments.
  • He names four questions that most split AGI bulls from bears: (1) what skills are needed for recursive self-improvement (RSI), (2) whether AI lacks crucial skills in low-feedback-density domains, (3) whether RLVR will generalize to messy domains, and (4) whether RSI will create a positive feedback loop.
Source evidence

Being on X can feel like whiplash, with vibes completely changing by the day. This is an excellent summary of the meaningful, significant changes in the last few months.

Rob Wiblin (@robertwiblin)

I see 7 actual updates relevant to AGI timelines this year:

  1. Exploding revenue and GPU rental $
  2. METR time horizons saturate
  3. The Mythos 'jump'
  4. Sparks of RSI
  5. AI still sucks at business
  6. AI makes some math breakthroughs
  7. Inference scaling remains cheap

I wanted to understand whether these things truly show what people think they show, and how they should affect my timelines, so I made this video.

On balance my timelines have contracted about 1 year since last December.

IMO these are the 4 topics that most split AGI bulls from bears:

  1. What skills are needed for RSI
  2. Whether AI still lacks crucial skills in areas with poor feedback density
  3. Whether RLVR will generalise to messy domains
  4. Whether RSI will produce a positive feedback loop

It's no surprise people disagree about this stuff as there's little public evidence to settle them either way.

On the 80,000 Hours Podcast, links below. Enjoy!

Video

— https://nitter.net/robertwiblin/status/2085387015286095931#m