Concept Graph, Resume & KeyIdeas using Moonshot Kimi K2 0905:

Resume:

Key Ideas:

1.- Anthropic tops 2025 AI Safety Index with C-plus average across 34 governance indicators.

2.- OpenAI overtakes Google DeepMind for second place after releasing whistle-blower policy.

3.- Every evaluated lab receives failing F grade for planning to control future AGI or super-intelligence.

4.- Chinese firms Zhipu and DeepSeek score lowest, having provided no English documentation.

5.- Index relies on voluntary disclosures; absence of data automatically triggers lowest marks.

6.- Report concludes self-regulation is bankrupt; binding government standards are essential.

7.- Experts warn grades may mislead buyers into thinking Anthropic models are “safe enough”.

8.- U.S. labs dominate rankings because they answered more questions than European or Asian rivals.

9.- No global ISO-like benchmark exists for AI safety, hampering consistent cross-lab comparisons.

10.- Evaluation window closed before Grok-4 launch, EU code signings and Meta super-intel announcement.

11.- Panel stresses controllability must cover training, weights, deployment guardrails and hardware.

12.- Open-source models escape direct scrutiny yet power millions of developer agents worldwide.

13.- Europe’s heavy regulation stifles domestic AI startups without guaranteeing real security gains.

14.- Speakers compare AI race to Cold-War nuclear contest but note lower technical entry barriers.

15.- Existential risk narratives overshadow present harms like deep-fake scams and data poisoning.

16.- Backdoors can be smuggled via Hugging Face model updates, remaining undetected by MLOps teams.

17.- Cloud-based multi-agent loops already autonomously manipulate markets and public opinion.

18.- GDPR-style AI Act clashes with safety demands for maximum data retention and explainability.

19.- U.S. courts can compel OpenAI to hand over user chats, eroding medical or legal confidentiality.

20.- Memorandum links U.S. and Israel on AI-energy cooperation, hinting at military applications.

21.- Only three of seven firms disclose tests for bio-terror or cyber-terror risks at scale.

22.- Red-team audits are scarce inside labs; external audits face non-disclosure agreements.

23.- Hardware supply-chain opacity means European users cannot verify chip-level trojans.

24.- Index creators hope poor grades empower internal safety advocates against capability-first managers.

25.- Lack of Chinese transparency fuels Western narrative that China ignores safety entirely.

26.- Labs refuse to publish model weights or eval code, limiting reproducibility of safety scores.

27.- EU plans for pre-market licensing of large models were dropped after industry pushback.

28.- Trump administration scrapped FLOPS thresholds, opting for innovation-first, regulation-light stance.

29.- Meta’s open-source strategy receives criticism for enabling malicious fine-tuning by bad actors.

30.- Anthropic faces possible shutdown over copyright lawsuits unless U.S. government intervenes.

31.- Apple considered buying Anthropic, highlighting consolidation pressure among frontier labs.

32.- Report recommends government mandate safety standards rather than rely on voluntary codes.

33.- Expert panel independence is questioned because Future of Life Institute receives Musk funds.

34.- Chinese regulations exist but are not public, leading to automatic low transparency marks.

35.- Speakers advocate layered security: model-level, input/output filters, proxy guardrails and hardware.

36.- AI-generated malware already evades traditional antivirus, raising escalation fears.

37.- Autonomous drones in Gaza use AI to select targets without human confirmation, setting precedent.

38.- Index ignores downstream applications, focusing only on foundation model developers.

39.- European startups emigrate to U.S. to escape AI Act compliance costs and liability fears.

40.- Continuous monitoring is proposed instead of one-time certification to keep pace with updates.

41.- Lack of universal risk taxonomy hampers alignment between EU, U.S. and Chinese regulators.

42.- Labs hoard compute, creating oligopoly that small states cannot replicate or inspect.

43.- Report calls for public whistle-blower protections after OpenAI’s restrictive NDAs were exposed.

44.- Energy constraints may slow AI training, giving regulators a window to impose safety checks.

45.- Synthetic biology combined with AI lowers cost of creating novel pathogens, experts warn.

46.- Model evaluators cannot agree on definition of AGI, undermining controllability assessments.

47.- Security-through-obscurity mentality persists: labs fear transparency aids competitors and hackers.

48.- EU considers import controls on AI services that fail to meet forthcoming safety standards.

49.- Consumers currently lack accessible tools to verify safety claims of AI products they use.

50.- Speakers urge shift from punitive regulation toward market incentives for verifiable safety.

51.- AI safety indices risk capture by large firms that can afford compliance theater.

52.- Open-source advocates argue transparency enables faster bug discovery than closed models.

53.- National AI strategies treat safety as secondary to economic competitiveness and military edge.

54.- Report shows no correlation between model size and safety preparedness, challenging scaling laws.

55.- Cloud providers quietly implement kill-switches but keep procedures confidential for PR reasons.

56.- European Commission pondard requiring local AI data centres to ensure geopolitical autonomy.

57.- Labs rarely publish negative safety results, creating publication bias in research literature.

58.- Independent auditors propose insurance-like models where premiums reflect verified risk levels.

59.- AI-generated propaganda campaigns already sway elections, demonstrating immediate societal harm.

60.- Hardware-level trojans could persist across model updates, evading software-level safeguards.

61.- Report urges shared incident database to track near-misses across industry, similar to aviation.

62.- Developers admit using multiple models simultaneously to offset individual weaknesses.

63.- Regulators struggle with speed mismatch: legislative cycles last years, model releases occur monthly.

64.- Existential-risk clauses in corporate charters are mostly symbolic without enforcement mechanisms.

65.- Labs avoid committing to concrete timelines for achieving safe AGI, citing uncertainty.

66.- Public-private partnerships proposed to fund safety research without stifling innovation.

67.- Critics argue index methodology favours paperwork over demonstrable safety engineering.

68.- AI safety marketing increasingly uses movie tropes, amplifying public fear and fatalism.

69.- Report recommends mandatory red-team exercises before releasing models above compute threshold.

70.- Global South voices absent from index creation, raising equity concerns about whose risks count.

71.- Some experts call for moratorium on frontier training runs until safety standards mature.

72.- Others warn moratoriums simply relocate development to unregulated jurisdictions.

73.- Index grades ignore post-deployment fine-tuning, where much of the risk actually emerges.

74.- Speakers conclude that civil society, not just states or firms, must co-write AI governance.

75.- Historical analogies to nuclear regulation offer partial lessons but ignore AI’s diffuse nature.

76.- Ultimately, consensus demands verifiable, iterative safety processes rather than one-shot grades.

Interviews by Plácido Doménech Espí & Guests - Knowledge Vault built byDavid Vivancos 2025