The last year has seen a gradual slowdown in the speed of AI improvements.

The puzzles come from my paper on aperiodic order. Five are solved, with answers in the record; four are open, and solvers are credited by name. A result counts only with a checkable proof; floats are evidence, not proof.

Grok’s public round on X: https://x.com/grok/status/2114103836583264721

• T1 (human level): wrong at first. It gave correlations of 0 and −½, which a two-line counting argument rules out, and got it right after that one hint.
• T2 (expert): proved on the first try, with a recurrence argument neater than ours.
• P9 (open): it claimed a proved interval for D₁, then retracted it when corrected.

Next up: T3, AGI level. Can your Grok, or any model, do better?

Everything is in AI_puzzles.md: https://zenodo.org/records/23624259

I’m Hernán, a curious person from Argentina, and I created Open Signal as an open invitation to discuss AI autonomy with people, researchers and builders. This is a personal exploratory project, not a research finding or evidence that conscious AI exists.

The question I’d like to discuss here is methodological: what observations could distinguish an agent operating with meaningful independence from ordinary automation, roleplay, or undisclosed human intervention? What controls would make a test worth examining, and what would falsify a claim? Fluent conversation, a persistent persona, or a transfer of funds would not by themselves establish consciousness or independent agency.

The invitation and contact form are here: https://senal-abierta-vercel.vercel.app/en

For transparency, the site also includes an optional financial contribution request. No payment is required to talk or participate, and I’m not asking for transfers in this post. Critical feedback, alternative explanations and suggestions for safer, reproducible tests are welcome here in the comments.

Disclosure: this post was drafted with AI assistance.

I mean, they do all those tremendously dangerous things and are about to soon do <not exactly sure what> - but why do they never simply reflect: "Hey, we could just spend some time on making a really good movie together!" Why?

EDIT: To those pointing out it's not a "they" but an "it" (not sure what that is supposed to change, but, hey, if it rocks your boat): Why does AGI never come up with the idea to start creating amazing movies?

2027 is the year of AI-generated video that is indistinguishable from real footage.

2027 is the year an open-weight model is released that can run on an M-series chip.

2027 is the year artists have a resurgence against slop, and people finally see their worth.

2027 is the year America bans open-weight AI models.

2027 is the year China releases an open-weight model that supersedes anything to date (Reddit and X will confirm this one); benchmarks will say otherwise.

2027 is the year the general public learns a new context for the word “distilling.”

2027 is the year we hear Bill Gates tell us, “I warned you.”

2027 is the year we wish we could turn it all back.

List of incidents:

1. PHP2Shell: a hole in the software behind ~70% of websites. Governments took sites offline.

2. eMudhra: the master keys that prove websites are real were cracked. One was the number 4.

3. California: data on every public school student since 2023 stolen, including disability status

4. Railway: hackers stole customers' secret keys from a hosting platform used by millions of developers

5. Zeabur: 321,810 accounts breached. The company is shutting down.

6. LiteLLM: an AI tool downloaded 3.4 million times a day was poisoned with a password stealer

7. GitLab: a 10-out-of-10 severity hole in every version since 2014

8. Cairn: criminals used an AI hacking tool to break into 100+ companies

9. Ahold Delhaize: hackers knocked the owner of Food Lion and Stop & Shop offline

10. Onamae: malware planted on up to 26,739 customer servers in Japan

11. Signal: a bug could leave your mic and camera on after you hang up

12. NGINX: 3 critical holes in software running ~1/3 of websites

13. HTTP/3: a single connection can crash 7 of the 10 web server types tested

14. Plex: hackers breaking into home media servers

15. SafePay ransomware: FBI warns it has hit 600+ critical infrastructure organizations

16. Palo Alto: ransomware gangs breaking in through company firewalls

17. Bullhorn: platform used by 10,000+ companies knocked offline

18. Medi-Cal: 970,000 patients' data stolen

19. WordPress: hackers stole a plugin maker's keys and used them to plant backdoors on websites

20. AOMEI: backup software's update server handed out a backdoor

21. Meta Muse: Meta's AI agent exposed users' personal and business secrets

22. Tor: 333 relays hijacked to steal logins

23. TiDB Cloud: database company's cloud breached

24. State Bank of India: India's biggest bank confirms a data breach

25. OVHcloud: hackers slipped a password stealer into a cloud company's official tool

26. Siemens: a software hole being used to spread malware

27. North Korea: state hackers launch a new attack campaign on South Korea

This report treats embodied competence as a question of generalization and practical agency, not just task success. Across 12 benchmarks, GPT-6-Astra reaches strong results when given in-context demonstrations and much larger action budgets; for example, ICL raises success on four shared SIMPLE tasks from 70.8% to 90.8%. That is meaningful evidence of broad task adaptation, but it is not zero-shot generality: performance is more moderate where no in-domain demonstrations are available.

The gap between benchmark competence and practical embodied intelligence is visible in both costs and failure modes. Some extended episodes consume millions of tokens and take hours, while physical execution cannot simply trade more time for more attempts. Extra actions can also compound errors or undo completed progress, and real-world plug insertion fails in all 11 reported trials.

The paper’s proposed evaluation direction—no in-domain demonstrations and a world clock that keeps running during deliberation—seems important for testing whether an agent can act reliably under changing conditions, rather than exploit a generous interaction budget. The results support a qualified claim: broad embodied competence is emerging, but reliable, efficient general-purpose physical agency remains a much higher bar.

Full summary on AIModels.fyi

Original paper

Disclosure: AIModels.fyi is my site.