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What is AI Graveyard?

AI Graveyard is a public database that tracks artificial intelligence tools and services that have shut down or been discontinued. Rather than promoting new AI products, this tool documents which ones have failed, been acquired, or pivoted away from their original purpose. It serves as a historical record of the AI landscape and helps users understand the mortality rate of AI startups. The database is useful for investors, researchers, and product builders who want to learn from failed ventures, understand market trends, and avoid repeating mistakes. By cataloguing defunct AI tools with details about their closure, you gain insight into what works and what doesn't in the competitive AI space.

Key Features

Database of discontinued AI tools

Browse a searchable list of AI products that have shut down, been acquired, or ceased operations

Closure reasons documented

Each entry includes information about why the tool failed, was acquired, or pivoted

Timeline tracking

See when tools launched and when they closed, helping identify patterns in AI market viability

Categorisation by industry

Filter defunct tools by sector such as writing, image generation, code, or general productivity

Community submissions

Users can contribute information about deceased AI tools to expand the database

Pros & Cons

Advantages

  • Provides valuable historical context about AI market failures and consolidation
  • Helps identify trends in which AI categories struggle versus flourish
  • Free access to useful market research data without paywalls
  • Offers lessons for founders and investors evaluating AI business viability

Limitations

  • Information accuracy depends on community submissions and may not be exhaustive or always current
  • Limited practical advice for users simply looking for working AI tools to use today
  • Database may lag in capturing very recent closures or become outdated quickly as the landscape changes

Use Cases

Researching the failure rate of AI startups before investing in or founding a competitor

Identifying acquisition patterns to understand which companies acquire AI tools most frequently

Learning what features or business models led to tool discontinuation

Tracking the consolidation of the AI market over time

Teaching case studies about AI product failures and market dynamics