ASI Alliance Review (FET, AGIX, CUDOS)

Introduction

The ASI Alliance Review explores one of the most ambitious transformations in the decentralized AI landscape: the unification of Fetch.ai, SingularityNET, and CUDOS under a single coordinated ecosystem aimed at accelerating the path toward Artificial General Intelligence (AGI) and, ultimately, Artificial Superintelligence (ASI). This alliance forms the largest open‑source, decentralized collective in AI research and development, challenging the dominance of Big Tech and offering an alternative pathway for building ethical, transparent, and community‑governed AI systems.

At the core of the initiative is the belief that advanced AI including agents, generative systems, neural‑symbolic models, and large‑scale compute networks should not be controlled by centralized corporations. Instead, the ASI Alliance Review highlights how this partnership combines years of technical innovation, research depth, and decentralized infrastructure into a single interoperable ecosystem. With ASI: Create, Learn, Train, Data, Compute, and Zero, the alliance aims to deliver a full-stack AI DePIN architecture capable of powering industries ranging from drug discovery and finance to climate modeling and autonomous systems.

The merger of tokens, technologies, and teams positions the ASI Alliance as a force capable of driving open AI development at global scale. This ASI Alliance Review examines the problems centralized AI faces today and how this new decentralized AI superstructure aims to solve them.

Problem Statement

• 1. AI Development Is Dominated by Centralized Big Tech Models: Today’s AI landscape is controlled by a handful of corporations that determine access, pricing, training data, and model usage. This centralization limits innovation and restricts user autonomy.
• 2. Lack of Open, Collaborative Infrastructure for AGI Development: Most AI systems are built in silos, preventing researchers, developers, and communities from contributing to a shared ecosystem capable of advancing AGI.
• 3. High Costs of Compute Limit AI Innovation: Training models, running inference, or deploying agents requires expensive GPU clusters, forcing smaller teams and independent developers to rely on centralized cloud providers that set restrictive terms.
• 4. Data Access, Provenance, and Monetization Are Fragmented: AI systems rely on high‑quality data, but traditional data marketplaces lack transparency, provenance, and tools for tokenizing and tracking data usage end‑to‑end.
• 5. Limited Interoperability Between AI Agents and Networks: AI agents struggle to interact across different ecosystems, limiting multi‑agent workflows, composability, and coordination at scale.
• 6. No Unified Stack for Decentralized AI: AI development requires compute, models, datasets, agents, reasoning systems, and interoperability standards but until now, no decentralized platform offered all layers in a coordinated architecture.

Solutions Provided by the ASI Alliance

• 1. A Decentralized, Open‑Source AI Ecosystem: Fetch.ai, SingularityNET, and CUDOS combine to create the largest decentralized AI research and development ecosystem, offering an alternative to closed Big Tech AI infrastructures.
• 2. Collaborative AGI‑Oriented Development via ASI’s Multi‑Layer Stack: Through platforms like ASI: Create, Train, Learn, and Data, the alliance provides a unified framework for building and deploying agents, models, and knowledge systems collectively.
• 3. Affordable, Decentralized Compute Through ASI: Compute: CUDOS contributes bare‑metal hardware, GPUs, storage, and decentralized cloud infrastructure, lowering the barrier to training and running AI models at scale.
• 4. Data Provenance, Tokenization, and Exchange with ASI: Data: Data scientists can buy, sell, trade, and tokenize data with traceability, enabling transparent compute‑to‑data operations and prediction models.
• 5. Multi‑Agent Interoperability with ASI’s Agent Protocols: Fetch.ai’s multi‑agent tools enable agents to compose workflows, register services, interact across networks, and perform cognitive tasks in a decentralized environment.
• 6. A Complete AI DePIN Stack (Create, Train, Learn, Data, Compute, Zero): The ASI Alliance delivers an end‑to‑end architecture from LLMs and neural‑symbolic systems to compute and ledgerless interoperability enabling scalable, ethical AI development.

Problem–Solution Overview

ASI Alliance Technology & Architecture

ASI Alliance Tokenomics

• Unified Token: FET
• Utility: Staking, governance, compute payments, agent execution, data marketplaces, model training, and platform access.
• Ecosystem Role: Powers all ASI platforms Create, Learn, Train, Data, Compute, Zero.
• Incentives: Rewards for compute providers, data contributors, model developers, and agent creators.

The unified ASI token aligns incentives across every layer of the decentralized AI stack.

Initial Token Distribution of ASI

The Artificial Superintelligence Alliance (ASI) token, originally under the FET ticker (with a planned rename to ASI), was formed through the merger of Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (OCEAN) in 2024, with later integrations like CUDOS. The initial total supply post-core merger (FET, AGIX, OCEAN) was set at 2,630,547,141 tokens. This distribution was designed to fairly allocate tokens based on the valuations and holder bases of the merging projects, using specific conversion ratios to ensure equitable transitions for all participants.

Key allocations from the core merger:

• FET Holders: Received 1,152,997,575 ASI tokens, representing approximately 43.8% of the initial supply. The conversion ratio was 1 FET = 1 ASI, meaning FET tokens were directly swapped on a 1:1 basis.
• AGIX Holders: Allocated 866,700,367 ASI tokens, about 33% of the supply. The conversion ratio was 1 AGIX = 0.433350 ASI.
• OCEAN Holders: Given 610,849,199 ASI tokens, roughly 23.2% of the supply. The conversion ratio was 1 OCEAN = 0.433226 ASI.

Subsequent mergers, such as with CUDOS, increased the total supply to 2,714,384,546 tokens (capped max supply), with new tokens minted and distributed via fair conversion rates and vesting schedules to promote long-term alignment and prevent immediate dumps. Vesting typically involved linear releases over periods like 3 months for CUDOS.

Ocean’s Exit and Its Effects on Token Allocation and Distribution

Ocean Protocol officially exited the ASI Alliance on October 9, 2025, due to disagreements over vision, governance, token management, and operational issues, including accusations of unauthorized conversions and market impacts. This withdrawal partially unwound Ocean’s integration but did not fundamentally alter ASI’s overall supply cap or core mechanics, though it affected the practical distribution and future conversions from the OCEAN segment.

Key effects:

• Partial Unwinding of OCEAN Allocation: Of the original 610.8 million ASI tokens allocated to OCEAN holders, about 81% (equivalent to roughly 661 million OCEAN tokens, including from oceanDAO) had already been converted to approximately 286 million ASI tokens by the exit date. These converted tokens remain fully integrated into the ASI ecosystem and are distributed to holders as initially planned, with no reversals or clawbacks.
• Unconverted OCEAN Tokens: The remaining ~19% (about 270 million OCEAN tokens held by over 37,000 wallets) stays unconverted. The conversion bridge, managed by Fetch.ai, remains open for voluntary swaps to ASI, but liquidity is limited (only ~7 million ASI available as of October 2025), reducing the likelihood of full distribution from this pool without further interventions, which were not pursued amid disputes.
• De-Pegging and Independence: OCEAN was de-pegged from ASI, allowing it to trade as a standalone token again on exchanges like Coinbase, Kraken, Binance US, Uniswap, and SushiSwap. Ocean plans to use profits from its independent technologies for OCEAN buybacks and burns, which could decrease OCEAN’s supply but has no direct impact on ASI’s tokenomics.
• Overall ASI Supply Stability: The total and max supply stays at 2,714,384,546 tokens. Circulating supply adjusted slightly to around 2.35–2.36 billion (from earlier ~2.6 billion estimates), partly due to fewer OCEAN conversions reducing inflationary pressure. Locked/unlocked breakdowns remain similar, with vesting schedules intact for other components.
• Broader Implications: The alliance now focuses more on Fetch.ai, SingularityNET, and CUDOS, with reduced emphasis on data infrastructure. ASI’s utility persists, and future integrations could still expand supply independently. For OCEAN holders, the exit restores autonomy, while converted holders stay with ASI.

Market Performance

ASI Alliance Team

The alliance brings together three influential teams:

• Dr. Ben Goertzel: CEO at Superintelligence Alliance & CEO and Chief Scientist at SingularityNET.
• Humayun Sheikh: CEO of Fetch.ai and Chairman of the Artificial Superintelligence Alliance.
• Janet Adams: COO of SingularityNET, Board Director in the Artificial Superintelligence Alliance Council.
• Matt Hawkins: CEO and Founder of CUDOS

The ASI Alliance brings together expertise in agents, neural-symbolic AI, decentralized compute, token engineering, and advanced research. Together, this creates one of the strongest technical teams in Web3.

ASI Alliance Project Analysis

Comparative Overview

• Vs. OpenAI: ASI is decentralized and open-source, offering community‑governed AI development instead of corporate‑controlled infrastructure.
• Vs. Bittensor: While Bittensor focuses on incentivized model training, ASI delivers a full‑stack AI ecosystem across compute, models, agents, data, and reasoning.
• Vs. Render: Render focuses on GPU supply; ASI offers compute, data, agents, LLMs, and governance in one system.
• Vs. Ocean Protocol: Ocean contributes to ASI: Data, but ASI integrates beyond data into AGI‑aligned development.

Strengths

• Largest decentralized AI alliance in the world
• Full‑stack architecture spanning compute, data, models, knowledge, and agents
• Strong leadership from AGI pioneers
• High potential for enterprise and scientific adoption
• Scalable multi‑platform infrastructure

Challenges

• High execution complexity due to multi‑layer stack
• Competition from centralized AI labs with massive funding
• Regulatory scrutiny as AGI development accelerates
• Need for rapid user onboarding and developer education

Conclusion

The ASI Alliance Review reveals how this coalition of Fetch.ai, SingularityNET, and CUDOS represents one of the most ambitious attempts to democratize Artificial Superintelligence. By merging compute, data, agent frameworks, decentralized infrastructure, and cross-domain AI development, the alliance provides a path toward open, verifiable, and community-governed AI systems.

Compared with centralized AI giants controlling proprietary models and data, ASI pushes for a future where creators, developers, and users participate in value generation rather than being sidelined by corporate silos. The ASI Alliance is still evolving, but its expanding toolset, multi-chain approach, and strong research pedigree position it as a credible contender shaping the next wave of decentralized AI.