{"slug":"ai-adapt-crypto-white-vvv-20261106","entry":{"slug":"ai-adapt-crypto-white-vvv-20261106","title":"Crypto AI Adaptation Research Report — VVV (Venice Token) · WhiteSwan Crypto constituent","symbol":"VVV","kind":"ai-adapt-crypto","name":"Venice Token","book":"AMF-WHITESWAN-CRYPTO","direction":"long","verdict":"whiteswan","run_date":"2026-11-06","narrative":"deterministic","level":3,"aas":69.6,"date":"2026-11-06","generated_at":"2026-11-06T00:15:41+00:00","description":"VVV is a WhiteSwan constituent on scoring run 2026-11-06: level 3 — AI-native — because AI is the protocol itself, with verifiable evidence behind the claim.","algorithm_release":"1.1.0","rank":2,"composite":71.943,"market":76.68,"weight":0.2,"pinned":false},"url":"/marr/ai-adapt-crypto-white-vvv-20261106","markdown":"# Crypto AI Adaptation Research Report — VVV (Venice Token) · WhiteSwan Crypto constituent\n\n*EventTrader research desk · generated 2026-11-06 00:15 UTC · report ai-adapt-crypto-white-vvv-20261106 · built from the public-data evidence pack by the platform*\n\n| | |\n|---|---|\n| **Project** | VVV — Venice Token · CoinGecko venice-token |\n| **Book** | AMF-WHITESWAN-CRYPTO (long) — WhiteSwan Crypto |\n| **Composition algorithm** | Swan Composite v1.1.0 — 67% AI Adaptation Score + 33% market verdict versus BTC, conviction-weighted |\n| **Level** | 3 — AI-native · model-confirmed |\n| **AI Adaptation Score** | 69.6 / 100 · run 2026-11-06 |\n| **Place in the book** | rank 2 of 6 · composite 71.9 · market verdict 76.7 · weight 20.0% |\n\n## Verdict\n\n- **VVV is a WhiteSwan constituent** on scoring run 2026-11-06: level 3 — AI-native — because AI is the protocol itself, with verifiable evidence behind the claim [S1].\n- Its crypto AI Adaptation Score is 69.6 / 100 (the highest tail of the AI-claiming universe) [S1].\n- Level confirmation: a model read confirmed the level [S1].\n\n## What the project says about AI\n\n- Claim layer: AI in the name no; AI category on CoinGecko no (Farm); AI markers on the homepage 8 [S1].\n- Substance: 27 AI sentences across 14 fetched documents (14,935 words): 1 describe AI as the protocol, 0 state an AI origin (built from the ground up), 0 proprietary models / data, 2 bolt-on feature language, 5 name a third-party AI vendor, 0 deployment, 2 with numbers [S1].\n\nVerbatim from the project's own materials:\n\n> \"Claude Claude Google Google DeepSeek DeepSeek OpenAI OpenAI Mistral Mistral Meta Meta Qwen Qwen Grok Grok Kimi Kimi Black Forest Labs Black Forest Labs NVIDIA NVIDIA Claude Claude Google Google DeepSeek DeepSeek OpenAI OpenAI Mistral Mistral Meta Meta Qwen Qwen Grok Grok Kimi Kimi Black Forest Labs Black Forest Labs NVIDIA NVIDIA ElevenLabs ElevenLabs Runway Runway Bytedance Bytedance MiniMax MiniMax GLM GLM Gemma Gemma Kling Kling PixVerse PixVerse Vidu Vidu ElevenLabs ElevenLabs Runway Runway Bytedance Bytedance MiniMax MiniMax GLM GLM Gemma Gemma Kling Kling PixVerse PixVerse Vidu Vidu View\" — homepage [S3]\n> \"Try Free Today Monthly Yearly Save 10% Pro $18 /mo $216 Save $36 Full access to all AI models and features Get Pro All AI models (Pro & Advanced) Unlimited text prompts 1,000 images per day $1 in credits per month Hi-res upscaling and watermark removal Character creation API access Pro+ Most Popular $68 /mo $816 Save $81 Everything in Pro, plus massive credit allocation Get Pro+ Everything in Pro $75 in credits per month 10% credit bonus vs. retail 2-month credit banking Video generation via credits Higher API limits Max $200 /mo $2,400 Save $240 Maximum power for creators and enterprises Get\" — homepage [S3]\n> \"View all endpoints → ​ Agents Private inference, MCP tools, and wallet-funded workflows for messaging, coding, and onchain agents. ​ Agent apps OpenClaw Run Venice on WhatsApp, Telegram, Discord, iMessage, and Slack.\" — docs [S6]\n> \"Get an API key → Get started curl TypeScript Python curl https://api.venice.ai/api/v1/chat/completions \\ -H "Authorization: Bearer $VENICE_API_KEY " \\ -H "Content-Type: application/json" \\ -d '{ "model": "zai-org-glm-5-1", "messages": [{"role": "user", "content": "Build without permission."}] }' import OpenAI from "openai" ; const client = new OpenAI ({ apiKey: process . env .\" — docs [S6]\n> \"VENICE_API_KEY , baseURL: "https://api.venice.ai/api/v1" , }); const res = await client . chat . completions . create ({ model: "zai-org-glm-5-1" , messages: [{ role: "user" , content: "Build without permission." }], }); import os from openai import OpenAI client = OpenAI( api_key = os.environ[ "VENICE_API_KEY" ], base_url = "https://api.venice.ai/api/v1" , ) res = client.chat.completions.create( model = "zai-org-glm-5-1" , messages = [{ "role" : "user" , "content" : "Build without permissi\" — docs [S6]\n> \"Kimi K3 Moonshot AI 1M context Private kimi-k3 Claude Fable 5 Anthropic 1M context Anonymized claude-fable-5 GLM 5.2 Z.ai 1M context Private zai-org-glm-5-2 390+ models Text, image, audio, and video Browse the catalog → ​ Tools Parse documents, generate embeddings, search the live web, and pull onchain data.\" — docs [S6]\n> \"Learn More OpenAI SDK LangChain Vercel AI CrewAI Claude Code Codex CLI ElizaOS OpenClaw | Copy Snippet Capabilities Uncensored AI chat, images, video, and more Text, image, video, audio, code, and search in one place, all private or anonymous.\" — homepage [S3]\n> \"Minimum balance to call inference is $0.10.\" — readme [S10]\n\n## What can be verified\n\n- Agent card (A2A / MCP) on the project's own domain: none; llms.txt: found; embedded AI support widget: none [S1].\n- Repositories inspected: 3; ML frameworks in dependency manifests: openai; last push 0 days ago; stars 116; commits in the last 4 weeks unknown [S1].\n- Verified-evidence points: 4 (ML code, agent card, widget, llms.txt, README depth, deployed products with numbers) [S1].\n\n## Score and level\n\n- The crypto AI Adaptation Score weighs the level (0–4) at 45%, greenfield (built with AI from the ground up, level 3+) at 20%, credibility at 15%, trajectory at 12% and developer activity at 8%, the last two relative to the crypto universe [S1].\n- VVV: level 33.8, greenfield 12.0, credibility 12.0, trajectory 5.2, developer activity 6.6 points → 69.6 [S1].\n- Model read: level 3 (native), confidence 0.86; sub-scores product 9.0, code evidence 6.0, data moat 4.0, credibility 8.0, greenfield 6.0 (each 0–10) [S1].\n- Model summary: \"Venice is a private AI inference platform: VVV/DIEM staking buys daily inference allowance across 390+ models, with a live API, agent/MCP tooling, and llms.txt. AI IS the protocol, not a bolt-on. Verified facts (openai manifest, active repos, llms.txt) back it, though no agent card and no genesis/origin statement confirming full greenfield inception.\" [S1].\n> \"Access leading AI models with your privacy in mind Create text, image, video, code, build agents, and more using fully private or anonymized models from leading AI providers.\" — model evidence, homepage [S1]\n> \"Stake DIEM or VVV once and earn a fixed inference allowance every day, with no per-call charges.\" — model evidence, docs [S1]\n> \"Agents Private inference, MCP tools, and wallet-funded workflows for messaging, coding, and onchain agents.\" — model evidence, docs [S1]\n> \"390+ models Text, image, audio, and video Browse the catalog →\" — model evidence, docs [S1]\n> \"Minimum balance to call inference is $0.10.\" — model evidence, readme [S1]\n- Market: cap $1.07B, 24 h volume $56.9M, rank 72 (CoinGecko, as of the run) [S1].\n\n## Risks to this view\n\n- An AI-native protocol's token can still fall with the market; the score measures adaptation, not price [S1].\n- Origin statements are the project's own; the greenfield sub-score is scored only from them and could be marketing [S1].\n## Place in the book\n\n- Rank 2 of 6 in AMF-WHITESWAN-CRYPTO (Swan Composite v1.1.0, scoring run 2026-11-06): the book holds the highest composites [S1].\n- Composite: 0.67 × 69.6 (AAS) + 0.33 × 76.7 (market) = 71.9 [S1].\n- Market verdict 76.7: over the last 90 days VVV rose more and fell less than BTC (down-capture 0.66, up-capture 1.73, L = 1.07; 45 down / 43 up days) — the Failure-To-Adapt fund's selection signal, 50 = moves with BTC [S1].\n- Weight: rank factor 1.80 (rank 2 of 6) × market factor 1.27, normalised across the book and clamped to 5–20% → 20.0% [S1].\n\n## Sources\n\n- **S1** — EventTrader crypto AI Adaptation Score run 2026-11-06\n- **S2** — profile — https://defillama.com/protocol/venice — \n- **S3** — homepage — https://venice.ai/ — \n- **S4** — llms_txt — https://venice.ai/llms.txt — \n- **S5** — llms_txt — https://docs.venice.ai/llms.txt — \n- **S6** — docs — https://docs.venice.ai/ — \n- **S7** — repo — https://github.com/veniceai/api-docs — \n- **S8** — repo — https://github.com/veniceai/venice-cli — \n- **S9** — repo — https://github.com/veniceai/venice-mcp-server — \n- **S10** — readme — https://github.com/veniceai/venice-mcp-server#readme — \n- **S11** — manifest — https://github.com/veniceai/venice-mcp-server/blob/HEAD/package.json — \n- **S12** — readme — https://github.com/veniceai/api-docs#readme — \n- **S13** — manifest — https://github.com/veniceai/api-docs/blob/HEAD/package.json — \n- **S14** — readme — https://github.com/veniceai/venice-cli#readme — \n- **S15** — manifest — https://github.com/veniceai/venice-cli/blob/HEAD/package.json — \n\n## Method and disclaimer\n\nThe crypto AI Adaptation Score reads each project's PUBLIC materials only: its CoinGecko profile (description, categories, links, genesis date, developer data), its homepage (text, AI markers, embedded AI widgets), A2A / MCP agent cards and llms.txt on its own domain, its whitepaper and docs, and its GitHub repositories (push recency, READMEs, the ML frameworks named in dependency manifests). Deterministic features set a level on a 0–4 rubric (no mention · AI in name only · bolt-on · AI-native · greenfield, built with AI from the ground up); a model read of the same evidence confirms the level and scores credibility and greenfield; the score combines level, greenfield, credibility, trajectory and developer activity, with greenfield carrying the highest reward. The books are composed by Swan Composite v1.1.0: each coin's composite is 67% AI Adaptation Score + 33% market verdict — the market verdict is the Failure-To-Adapt fund's selection signal, the signed down/up capture ratio of the coin against BTC over 90 days (a coin that falls more and rises less than BTC scores below 50). AMF-BLACKSWAN-CRYPTO holds the ten lowest composites among coins that claim AI that pass the liquidity screen, the new-listing gate and the crowded-short gate, and AMF-WHITESWAN-CRYPTO the ten highest, weighted by conviction (rank and market agreement, each leg 5–20%), recomposed on every scoring run. Every quote is the project's text verbatim; every factual sentence names its source; a document the platform could not reach is reported as missing.\n\n*This report is information, not investment advice. Figures come from the cited sources as of their dates. Nothing here is a recommendation to buy, sell or hold any asset.*\n","html":"

Crypto AI Adaptation Research Report — VVV (Venice Token) · WhiteSwan Crypto constituent

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EventTrader research desk · generated 2026-11-06 00:15 UTC · report ai-adapt-crypto-white-vvv-20261106 · built from the public-data evidence pack by the platform

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ProjectVVV — Venice Token · CoinGecko venice-token
BookAMF-WHITESWAN-CRYPTO (long) — WhiteSwan Crypto
Composition algorithmSwan Composite v1.1.0 — 67% AI Adaptation Score + 33% market verdict versus BTC, conviction-weighted
Level3 — AI-native · model-confirmed
AI Adaptation Score69.6 / 100 · run 2026-11-06
Place in the bookrank 2 of 6 · composite 71.9 · market verdict 76.7 · weight 20.0%
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Verdict

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    \n
  • VVV is a WhiteSwan constituent on scoring run 2026-11-06: level 3 — AI-native — because AI is the protocol itself, with verifiable evidence behind the claim [S1].
    • \n
  • Its crypto AI Adaptation Score is 69.6 / 100 (the highest tail of the AI-claiming universe) [S1].
    • \n
  • Level confirmation: a model read confirmed the level [S1].
    • \n
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What the project says about AI

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    \n
  • Claim layer: AI in the name no; AI category on CoinGecko no (Farm); AI markers on the homepage 8 [S1].
    • \n
  • Substance: 27 AI sentences across 14 fetched documents (14,935 words): 1 describe AI as the protocol, 0 state an AI origin (built from the ground up), 0 proprietary models / data, 2 bolt-on feature language, 5 name a third-party AI vendor, 0 deployment, 2 with numbers [S1].
    • \n
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Verbatim from the project's own materials:

\n
\n

\"Claude Claude Google Google DeepSeek DeepSeek OpenAI OpenAI Mistral Mistral Meta Meta Qwen Qwen Grok Grok Kimi Kimi Black Forest Labs Black Forest Labs NVIDIA NVIDIA Claude Claude Google Google DeepSeek DeepSeek OpenAI OpenAI Mistral Mistral Meta Meta Qwen Qwen Grok Grok Kimi Kimi Black Forest Labs Black Forest Labs NVIDIA NVIDIA ElevenLabs ElevenLabs Runway Runway Bytedance Bytedance MiniMax MiniMax GLM GLM Gemma Gemma Kling Kling PixVerse PixVerse Vidu Vidu ElevenLabs ElevenLabs Runway Runway Bytedance Bytedance MiniMax MiniMax GLM GLM Gemma Gemma Kling Kling PixVerse PixVerse Vidu Vidu View\" — homepage [S3]\n\"Try Free Today Monthly Yearly Save 10% Pro $18 /mo $216 Save $36 Full access to all AI models and features Get Pro All AI models (Pro & Advanced) Unlimited text prompts 1,000 images per day $1 in credits per month Hi-res upscaling and watermark removal Character creation API access Pro+ Most Popular $68 /mo $816 Save $81 Everything in Pro, plus massive credit allocation Get Pro+ Everything in Pro $75 in credits per month 10% credit bonus vs. retail 2-month credit banking Video generation via credits Higher API limits Max $200 /mo $2,400 Save $240 Maximum power for creators and enterprises Get\" — homepage [S3]\n\"View all endpoints → ​ Agents Private inference, MCP tools, and wallet-funded workflows for messaging, coding, and onchain agents. ​ Agent apps OpenClaw Run Venice on WhatsApp, Telegram, Discord, iMessage, and Slack.\" — docs [S6]\n\"Get an API key → Get started curl TypeScript Python curl https://api.venice.ai/api/v1/chat/completions \\ -H \"Authorization: Bearer $VENICE_API_KEY \" \\ -H \"Content-Type: application/json\" \\ -d '{ \"model\": \"zai-org-glm-5-1\", \"messages\": [{\"role\": \"user\", \"content\": \"Build without permission.\"}] }' import OpenAI from \"openai\" ; const client = new OpenAI ({ apiKey: process . env .\" — docs [S6]\n\"VENICE_API_KEY , baseURL: \"https://api.venice.ai/api/v1\" , }); const res = await client . chat . completions . create ({ model: \"zai-org-glm-5-1\" , messages: [{ role: \"user\" , content: \"Build without permission.\" }], }); import os from openai import OpenAI client = OpenAI( api_key = os.environ[ \"VENICE_API_KEY\" ], base_url = \"https://api.venice.ai/api/v1\" , ) res = client.chat.completions.create( model = \"zai-org-glm-5-1\" , messages = [{ \"role\" : \"user\" , \"content\" : \"Build without permissi\" — docs [S6]\n\"Kimi K3 Moonshot AI 1M context Private kimi-k3 Claude Fable 5 Anthropic 1M context Anonymized claude-fable-5 GLM 5.2 Z.ai 1M context Private zai-org-glm-5-2 390+ models Text, image, audio, and video Browse the catalog → ​ Tools Parse documents, generate embeddings, search the live web, and pull onchain data.\" — docs [S6]\n\"Learn More OpenAI SDK LangChain Vercel AI CrewAI Claude Code Codex CLI ElizaOS OpenClaw | Copy Snippet Capabilities Uncensored AI chat, images, video, and more Text, image, video, audio, code, and search in one place, all private or anonymous.\" — homepage [S3]\n\"Minimum balance to call inference is $0.10.\" — readme [S10]

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What can be verified

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    \n
  • Agent card (A2A / MCP) on the project's own domain: none; llms.txt: found; embedded AI support widget: none [S1].
    • \n
  • Repositories inspected: 3; ML frameworks in dependency manifests: openai; last push 0 days ago; stars 116; commits in the last 4 weeks unknown [S1].
    • \n
  • Verified-evidence points: 4 (ML code, agent card, widget, llms.txt, README depth, deployed products with numbers) [S1].
    • \n
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Score and level

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    \n
  • The crypto AI Adaptation Score weighs the level (0–4) at 45%, greenfield (built with AI from the ground up, level 3+) at 20%, credibility at 15%, trajectory at 12% and developer activity at 8%, the last two relative to the crypto universe [S1].
    • \n
  • VVV: level 33.8, greenfield 12.0, credibility 12.0, trajectory 5.2, developer activity 6.6 points → 69.6 [S1].
    • \n
  • Model read: level 3 (native), confidence 0.86; sub-scores product 9.0, code evidence 6.0, data moat 4.0, credibility 8.0, greenfield 6.0 (each 0–10) [S1].
    • \n
  • Model summary: \"Venice is a private AI inference platform: VVV/DIEM staking buys daily inference allowance across 390+ models, with a live API, agent/MCP tooling, and llms.txt. AI IS the protocol, not a bolt-on. Verified facts (openai manifest, active repos, llms.txt) back it, though no agent card and no genesis/origin statement confirming full greenfield inception.\" [S1].
    • \n
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\"Access leading AI models with your privacy in mind Create text, image, video, code, build agents, and more using fully private or anonymized models from leading AI providers.\" — model evidence, homepage [S1]\n\"Stake DIEM or VVV once and earn a fixed inference allowance every day, with no per-call charges.\" — model evidence, docs [S1]\n\"Agents Private inference, MCP tools, and wallet-funded workflows for messaging, coding, and onchain agents.\" — model evidence, docs [S1]\n\"390+ models Text, image, audio, and video Browse the catalog →\" — model evidence, docs [S1]\n\"Minimum balance to call inference is $0.10.\" — model evidence, readme [S1]

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  • Market: cap $1.07B, 24 h volume $56.9M, rank 72 (CoinGecko, as of the run) [S1].
    • \n
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Risks to this view

\n
    \n
  • An AI-native protocol's token can still fall with the market; the score measures adaptation, not price [S1].
    • \n
  • Origin statements are the project's own; the greenfield sub-score is scored only from them and could be marketing [S1].
    • \n
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Place in the book

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    \n
  • Rank 2 of 6 in AMF-WHITESWAN-CRYPTO (Swan Composite v1.1.0, scoring run 2026-11-06): the book holds the highest composites [S1].
    • \n
  • Composite: 0.67 × 69.6 (AAS) + 0.33 × 76.7 (market) = 71.9 [S1].
    • \n
  • Market verdict 76.7: over the last 90 days VVV rose more and fell less than BTC (down-capture 0.66, up-capture 1.73, L = 1.07; 45 down / 43 up days) — the Failure-To-Adapt fund's selection signal, 50 = moves with BTC [S1].
    • \n
  • Weight: rank factor 1.80 (rank 2 of 6) × market factor 1.27, normalised across the book and clamped to 5–20% → 20.0% [S1].
    • \n
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Sources

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Method and disclaimer

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The crypto AI Adaptation Score reads each project's PUBLIC materials only: its CoinGecko profile (description, categories, links, genesis date, developer data), its homepage (text, AI markers, embedded AI widgets), A2A / MCP agent cards and llms.txt on its own domain, its whitepaper and docs, and its GitHub repositories (push recency, READMEs, the ML frameworks named in dependency manifests). Deterministic features set a level on a 0–4 rubric (no mention · AI in name only · bolt-on · AI-native · greenfield, built with AI from the ground up); a model read of the same evidence confirms the level and scores credibility and greenfield; the score combines level, greenfield, credibility, trajectory and developer activity, with greenfield carrying the highest reward. The books are composed by Swan Composite v1.1.0: each coin's composite is 67% AI Adaptation Score + 33% market verdict — the market verdict is the Failure-To-Adapt fund's selection signal, the signed down/up capture ratio of the coin against BTC over 90 days (a coin that falls more and rises less than BTC scores below 50). AMF-BLACKSWAN-CRYPTO holds the ten lowest composites among coins that claim AI that pass the liquidity screen, the new-listing gate and the crowded-short gate, and AMF-WHITESWAN-CRYPTO the ten highest, weighted by conviction (rank and market agreement, each leg 5–20%), recomposed on every scoring run. Every quote is the project's text verbatim; every factual sentence names its source; a document the platform could not reach is reported as missing.

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This report is information, not investment advice. Figures come from the cited sources as of their dates. Nothing here is a recommendation to buy, sell or hold any asset.

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