DeepMind’s New AI Found A Strange New Way To Think
Credibility score: 57/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
Claims analyzed
DeepMind's AlphaProof Nexus solved 95.7% of 350 Erdős problems — Unverifiable (50/100)
95.7% of 350 Erdős problems solved — name one solved problem or it’s just a flex
Sources: Unveiling the Genius Behind DeepMind’s AlphaProof Nexus – Frank's World of Data Science & AI, Google Deepmind's AlphaProof Nexus solves decades-old math problems for a few hundred dollars, Google DeepMind's AlphaProof Nexus solves 9 Erdős problems and proves 44 sequence conjectures
These problems sat unsolved for decades until this AI — Dubious (45/100)
Calling them "decades-old unsolved" sounds dramatic — without naming the problems we can't verify nobody cracked them before.
ELO scoring system turns unreliable AI outputs into reliable formal proofs — Solid (75/100)
ELO tournament on LLM outputs is real and the paper is public — just not magic.
Iterating from best-wrong solution until validator accepts creates formal proof — Solid (78/100)
The loop is correctly described; New Scientist and VentureBeat both confirm the process.
Reliable system emerges from repeatedly running lying AI — Opinion (50/100)
The framing is hype, but the underlying filtering effect is documented.
DeepMind released the full research openly and for free — Verified (85/100)
GitHub link in the video description confirms open release — rare and appreciated.
Future AI progress comes from better harnesses, not smarter models — Opinion (50/100)
Classic pivot from capability to scaffolding — still just one paper's approach.
DeepMind only tested 350 of 1200 Erdős problems due to selection bias — OK (65/100)
Speaker admits they cherry-picked easier problems — but calls it fine. Fair callout, weak defense.
Selection bias on 350 problems isn't an issue because you have to start somewhere — Opinion (50/100)
Downplays cherry-picking by saying "start somewhere" — ignores that easier problems don't prove the hard ones are solvable.
Smaller models solved zero of these math problems — Unverifiable (50/100)
Claims smaller models got literally zero — no specific model sizes or benchmark numbers given to check.
AI solved 9 unsolved Erdős problems for a few hundred dollars each — Dubious (45/100)
Nine problems solved sounds huge — but web sources don't confirm any DeepMind paper announcing solutions to open Erdős problems.
AI progressed from basic addition to solving 56-year-old open math problems in just four years — Sketchy (35/100)
From "can't add" to open-problem solver in four years sounds dramatic — but the unsolved problems part lacks confirmation.
Weights & Biases Weave is the best LLM toolkit — Sponsored (50/100)
Straight sponsor read — "It is the best" with zero comparison data.
See the full analysis with sources and timestamps →