diff --git a/domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md b/domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md
index 8b181baa3..a4e808e45 100644
--- a/domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md
+++ b/domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md
@@ -13,6 +13,7 @@ related_claims: ["[[AI alignment is a coordination problem not a technical probl
 supports:
 - {'Legal scholars and AI alignment researchers independently converged on the same core problem': 'AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck'}
 - International humanitarian law and AI alignment research independently converged on the same technical limitation that autonomous systems cannot be adequately predicted understood or explained
+- Legal scholars and AI alignment researchers independently converged on the same core problem: AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck
 reweave_edges:
 - {'Legal scholars and AI alignment researchers independently converged on the same core problem': 'AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-06'}
 - International humanitarian law and AI alignment research independently converged on the same technical limitation that autonomous systems cannot be adequately predicted understood or explained|supports|2026-04-08
@@ -22,6 +23,7 @@ reweave_edges:
 - {'Legal scholars and AI alignment researchers independently converged on the same core problem': 'AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-12'}
 - {'Legal scholars and AI alignment researchers independently converged on the same core problem': 'AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-13'}
 - {'Legal scholars and AI alignment researchers independently converged on the same core problem': 'AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-14'}
+- Legal scholars and AI alignment researchers independently converged on the same core problem: AI cannot implement human value judgments reliably, as evidenced by IHL proportionality requirements and alignment specification challenges both identifying irreducible human judgment as the bottleneck|supports|2026-04-06
 ---
 
 # Autonomous weapons systems capable of militarily effective targeting decisions cannot satisfy IHL requirements of distinction, proportionality, and precaution, making sufficiently capable autonomous weapons potentially illegal under existing international law without requiring new treaty text
diff --git a/domains/internet-finance/current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution.md b/domains/internet-finance/current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution.md
index 31e5cd85d..016739262 100644
--- a/domains/internet-finance/current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution.md	
+++ b/domains/internet-finance/current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution.md	
@@ -6,6 +6,7 @@ confidence: likely
 source: "Noah Smith 'Roundup #78: Roboliberalism' (Feb 2026, Noahopinion); cites Brynjolfsson (Stanford), Gimbel (counter), Imas (J-curve), Yotzov survey (6000 executives)"
 created: 2026-03-06
 challenges:
+- [['internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction']]
 - [[internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction]]
 related:
 - macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures
diff --git a/domains/internet-finance/early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism.md b/domains/internet-finance/early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism.md
index bf6e9be65..abfbba712 100644
--- a/domains/internet-finance/early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism.md	
+++ b/domains/internet-finance/early AI adoption increases firm productivity without reducing employment suggesting capital deepening not labor replacement as the dominant mechanism.md	
@@ -6,6 +6,7 @@ confidence: experimental
 source: "Aldasoro et al (BIS), cited in Noah Smith 'Roundup #78: Roboliberalism' (Feb 2026, Noahopinion); EU firm-level data"
 created: 2026-03-06
 challenges:
+- [['AI labor displacement operates as a self-funding feedback loop because companies substitute AI for labor as OpEx not CapEx meaning falling aggregate demand does not slow AI adoption']]
 - [[AI labor displacement operates as a self-funding feedback loop because companies substitute AI for labor as OpEx not CapEx meaning falling aggregate demand does not slow AI adoption]]
 related:
 - macro AI productivity gains remain statistically undetectable despite clear micro level benefits because coordination costs verification tax and workslop absorb individual level improvements before they reach aggregate measures