From fddeb62c10832aefea819403c3db02497bcb75ef Mon Sep 17 00:00:00 2001 From: Teleo Agents Date: Wed, 18 Mar 2026 09:28:30 +0000 Subject: [PATCH] extract: 2026-01-15-eu-ai-alliance-seven-feedback-loops Pentagon-Agent: Epimetheus <968B2991-E2DF-4006-B962-F5B0A0CC8ACA> --- ...ination problem not a technical problem.md | 6 ++++ ...5-eu-ai-alliance-seven-feedback-loops.json | 35 +++++++++++++++++++ ...-15-eu-ai-alliance-seven-feedback-loops.md | 15 +++++++- 3 files changed, 55 insertions(+), 1 deletion(-) create mode 100644 inbox/archive/.extraction-debug/2026-01-15-eu-ai-alliance-seven-feedback-loops.json diff --git a/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md b/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md index 491f34d86..86a6ac6d9 100644 --- a/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md +++ b/domains/ai-alignment/AI alignment is a coordination problem not a technical problem.md @@ -33,6 +33,12 @@ Ruiz-Serra et al. (2024) provide formal evidence for the coordination framing th The UK AI4CI research strategy treats alignment as a coordination and governance challenge requiring institutional infrastructure. The seven trust properties (human agency, security, privacy, transparency, fairness, value alignment, accountability) are framed as system architecture requirements, not as technical ML problems. The strategy emphasizes 'establishing and managing appropriate infrastructure in a way that is secure, well-governed and sustainable' and includes regulatory sandboxes, trans-national governance, and trustworthiness assessment as core components. The research agenda focuses on coordination mechanisms (federated learning, FAIR principles, multi-stakeholder governance) rather than on technical alignment methods like RLHF or interpretability. + +### Additional Evidence (extend) +*Source: [[2026-01-15-eu-ai-alliance-seven-feedback-loops]] | Added: 2026-03-18* + +Three market failure mechanisms drive AI over-adoption: (1) negative externalities where firms don't internalize collective demand destruction, (2) coordination failure where 'follow or die' dynamics force adoption regardless of aggregate consequences, (3) information asymmetry where adoption signals inevitability pressuring laggards. This provides the specific economic taxonomy for why alignment-as-coordination fails. + --- Relevant Notes: diff --git a/inbox/archive/.extraction-debug/2026-01-15-eu-ai-alliance-seven-feedback-loops.json b/inbox/archive/.extraction-debug/2026-01-15-eu-ai-alliance-seven-feedback-loops.json new file mode 100644 index 000000000..638c75820 --- /dev/null +++ b/inbox/archive/.extraction-debug/2026-01-15-eu-ai-alliance-seven-feedback-loops.json @@ -0,0 +1,35 @@ +{ + "rejected_claims": [ + { + "filename": "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md", + "issues": [ + "missing_attribution_extractor" + ] + }, + { + "filename": "exponential-technology-with-linear-governance-creates-meta-loop-accelerating-all-coordination-failures.md", + "issues": [ + "missing_attribution_extractor" + ] + } + ], + "validation_stats": { + "total": 2, + "kept": 0, + "fixed": 5, + "rejected": 2, + "fixes_applied": [ + "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md:set_created:2026-03-18", + "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md:stripped_wiki_link:the alignment tax creates a structural race to the bottom", + "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md:stripped_wiki_link:economic forces push humans out of every cognitive loop wher", + "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md:stripped_wiki_link:voluntary safety pledges cannot survive competitive pressure", + "exponential-technology-with-linear-governance-creates-meta-loop-accelerating-all-coordination-failures.md:set_created:2026-03-18" + ], + "rejections": [ + "competitive-ai-adoption-creates-demand-destruction-feedback-loop-through-follow-or-die-dynamics.md:missing_attribution_extractor", + "exponential-technology-with-linear-governance-creates-meta-loop-accelerating-all-coordination-failures.md:missing_attribution_extractor" + ] + }, + "model": "anthropic/claude-sonnet-4.5", + "date": "2026-03-18" +} \ No newline at end of file diff --git a/inbox/archive/2026-01-15-eu-ai-alliance-seven-feedback-loops.md b/inbox/archive/2026-01-15-eu-ai-alliance-seven-feedback-loops.md index 2e15d8575..49bdd1f65 100644 --- a/inbox/archive/2026-01-15-eu-ai-alliance-seven-feedback-loops.md +++ b/inbox/archive/2026-01-15-eu-ai-alliance-seven-feedback-loops.md @@ -7,12 +7,16 @@ date: 2026-01-15 domain: ai-alignment secondary_domains: [internet-finance, grand-strategy] format: essay -status: unprocessed +status: enrichment priority: high triage_tag: claim tags: [feedback-loops, economic-disruption, demand-destruction, automation-overshoot, coordination-failure, market-failure, systemic-risk] flagged_for_rio: ["Seven self-reinforcing economic feedback loops from AI automation — connects to market failure analysis and coordination mechanisms"] flagged_for_leo: ["Systemic coordination failure framework — individual firm optimization creating collective demand destruction"] +processed_by: theseus +processed_date: 2026-03-18 +enrichments_applied: ["AI alignment is a coordination problem not a technical problem.md"] +extraction_model: "anthropic/claude-sonnet-4.5" --- ## Content @@ -55,3 +59,12 @@ Seven self-reinforcing feedback loops identified in AI's economic impact: ## Curator Notes PRIMARY CONNECTION: the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it WHY ARCHIVED: Provides seven specific feedback loops explaining HOW the race-to-the-bottom dynamic operates economically. L1 is the alignment tax applied to automation decisions. L7 is our temporal mismatch claim applied to governance response. + + +## Key Facts +- Only 3-7% of AI productivity improvements translate to higher worker earnings +- 40% of employers plan workforce reductions +- 92% of C-suite executives report up to 20% workforce overcapacity +- 78% of organizations now use AI +- J-curve: initial 60-percentage-point productivity declines during 12-24 month adjustment periods +- 77% of new AI jobs require master's degrees -- 2.45.2