teleo/teleo-codex
8
0
Fork 0
Code Issues 22 Pull requests 6 Projects Releases Packages Wiki Activity Actions 86

clay: extract claims from 2026-02-01-seedance-2-ai-video-benchmark.md

Browse source 
- Source: inbox/archive/2026-02-01-seedance-2-ai-video-benchmark.md
- Domain: entertainment
- Extracted by: headless extraction cron (worker 4)

Pentagon-Agent: Clay <HEADLESS>
This commit is contained in:
Teleo Agents 2026-03-11 05:16:58 +00:00
parent 42aacdf2de
commit a8e804dd1b
1 changed files with 14 additions and 1 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

15
inbox/archive/2026-02-01-seedance-2-ai-video-benchmark.md
View file

@ -7,9 +7,14 @@ date: 2026-02-01
domain: entertainment
secondary_domains: []
format: report
status: unprocessed
status: null-result
priority: medium
tags: [ai-video-generation, seedance, production-costs, quality-threshold, capability]
processed_by: clay
processed_date: 2026-03-11
enrichments_applied: ["non-ATL production costs will converge with the cost of compute as AI replaces labor across the production chain.md", "GenAI adoption in entertainment will be gated by consumer acceptance not technology capability.md", "consumer definition of quality is fluid and revealed through preference not fixed by production value.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "Two claims extracted: (1) Hand anatomy threshold crossing as capability milestone, (2) Sora retention collapse as demand-side constraint evidence. Three enrichments applied to existing entertainment claims. The hand anatomy benchmark is the supply-side signal (capability cleared), the Sora retention data is the demand-side signal (consumer acceptance lagging). Together they support the core KB thesis that adoption is gated by acceptance not capability. Curator hint about Sora retention being the 'surprising signal' was correct — that's the more novel insight. Hand anatomy is confirmatory evidence for existing production cost convergence claim."
---
## Content
@ -59,3 +64,11 @@ Aggregated benchmark data on the leading AI video generation models in 2026 (See
PRIMARY CONNECTION: `non-ATL production costs will converge with the cost of compute as AI replaces labor across the production chain`
WHY ARCHIVED: The hand anatomy benchmark crossing signals that the quality threshold for realistic video has been substantially cleared — which shifts the remaining barrier to consumer acceptance (demand-side) and creative direction (human judgment), not raw capability.
EXTRACTION HINT: The Sora retention data (supply without demand) is the most extractable insight. A claim about AI video tool adoption being demand-constrained despite supply capability would be new to the KB.
## Key Facts
- Seedance 2.0 ranked #1 globally on Artificial Analysis benchmark (February 2026)
- Seedance 2.0 technical specs: native 2K resolution (2048x1080 landscape / 1080x2048 portrait), 4-15 second dynamic duration, 30% faster throughput than Seedance 1.5 Pro, 8+ language phoneme-level lip-sync
- Benchmark methodology: 50+ generations per model, identical prompt set across 15 categories, 4 seconds at 720p/24fps, rated 0-100 on 6 dimensions by 2 independent reviewers
- Competing models: Kling 3.0 (ease of use leader), Google Veo 3 (visual + audio generation), Runway (Lionsgate partnership), Pika Labs
- Sora standalone app: 12 million downloads, <8% retention at day 30 (vs. 30%+ benchmark for top consumer apps)