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TEXXR

Chronicles

The story behind the story

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Sources: blockchain AI companies SingularityNET, Fetch.ai, and Ocean Protocol are discussing merging their tokens into ASI, with a ~$7.5B fully diluted value

Three artificial intelligence companies that operate on blockchain are in talks to merge their crypto tokens, a move aimed at helping them develop a decentralized AI platform.

Bloomberg Michelle F Davis

Context & Ripple Effects

The three projects are part of a blockchain-AI cohort that uses decentralized infrastructure to support AI research and challenge the concentration of AI capabilities in large technology companies. Combining their token economies would put that shared objective behind one network asset rather than three separate ones.

The proposal also fits a broader AI-sector drive to combine complementary assets into larger platforms, later visible in the combination of xAI and X. Here, the integration is centered on tokens, governance, and network incentives rather than a conventional corporate merger.

First-order effects

  • If completed, SingularityNET, Fetch.ai, and Ocean Protocol holders would transition from separate token ecosystems to ASI, concentrating the network's implied fully diluted value at about $7.5B.
  • The projects would need to align token migration, governance, and incentives around a single decentralized AI platform, replacing independent economic structures with a shared one.

Second-order effects

  • A combined token could make it easier to present the three projects' AI, data, and agent infrastructure as one offering to developers and users, while making execution risk more visible in a single asset.
  • Other blockchain-AI projects may face pressure to demonstrate comparable scale or interoperability rather than relying on standalone tokens and communities.

Third-order effects

  • If similar combinations persist, decentralized AI may organize around fewer, broader protocol stacks whose token design functions as both financing mechanism and coordination layer.
  • That model could sharpen the trade-off at the heart of decentralized AI: consolidation can improve usable scale, but may also concentrate governance within networks created to counter centralized AI control.

The trend: Blockchain-AI projects are consolidating token and infrastructure layers to pursue platform scale against more integrated AI ecosystems.