# Decentralized AI的所有文章

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Why Does 'AGI Godfather' Ben Goertzel Believe the Future of AI Relies on Blockchain?

Ben Goertzel, known as the "AGI Godfather," argues that the future of Artificial General Intelligence (AGI) must be built on blockchain to prevent its control by a few corporations or venture capital firms. He believes the core AGI code should be free and open-source, but that this alone is insufficient without a decentralized infrastructure to run it affordably. His blockchain project, SingularityNET, and the broader Artificial Superintelligence Alliance aim to create a user-owned, decentralized network for hosting and deploying AGI, contrasting with the closed models of companies like OpenAI and Anthropic. Goertzel criticizes the shift of other labs from open to closed development. He argues that while a closed path is simpler, an open, decentralized model—akin to Linux and the internet—is both possible and ultimately better for humanity. He envisions an "Agent economy" where individuals orchestrate teams of AI agents to perform tasks, including transactions, on an open network rather than corporate clouds. While his current model relies on cryptocurrency, plans include offering paid AI services to businesses with the decentralized blockchain as the backend. Goertzel predicts human-level AGI could arrive by 2029 and warns that a gap in understanding and access to AGI could drastically worsen inequality. The first test of his decentralized approach will be the upcoming release of the Agent Omega Claw.

Foresight News06/22 12:10

Why Does 'AGI Godfather' Ben Goertzel Believe the Future of AI Relies on Blockchain?

Foresight News06/22 12:10

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

**The 2026 Landscape of Decentralized AI: Why Blockchain is the "Cure" AI Cannot Ignore** Decentralized AI addresses fundamental bottlenecks of centralized AI: scarce and expensive computational resources, excessive control concentration, unverifiable model outputs, and increasing difficulty in acquiring training data due to privacy and regulation. Blockchain offers a path to make intelligence open, verifiable, and economically accessible. The technical stack comprises three layers: 1. **Applications & Services**: The main crypto use cases are "Agentic Finance" (converting natural language into on-chain actions) and "Agentic Payments" for machine-to-machine commerce. Projects like Giza, Infinity Labs, Coinvest AI, and x402 (handling 173M+ transactions) are key players. 2. **Middleware**: This coordination layer enables agents to discover, identify, and transact. Notable projects include Gokite AI (specialized L1), Virtuals (an OS for the agent economy), and especially Bittensor—a network of specialized subnets forming competitive AI micro-economies. 3. **Infrastructure**: The capital-intensive layer providing raw resources. It includes decentralized compute (Akash, Render, Aethir), verifiable inference (Venice AI, OpenGradient), distributed training (Prime Intellect, Templar AI), decentralized storage (Filecoin, Walrus), and privacy/verification layers (Nillion, Arcium, Phala Network) using technologies like ZKPs, MPC, and TEEs. The outlook for 2026-2027 indicates AI demand outpacing infrastructure, with AI agents as a primary growth engine. Computation is becoming an asset class, with on-chain markets as its financial layer. Tokenomics is emerging as a structural advantage for coordinating capital, compute, and data in decentralized AI networks. While still early—with adoption uneven and revenue often trailing token incentives—projects like Bittensor, NEAR, and Virtuals demonstrate a shift from speculative narrative to a new model for coordinating intelligence.

marsbit06/12 02:40

2026 Landscape of Decentralized AI: Why is Blockchain the Inevitable "Antidote" for AI?

marsbit06/12 02:40

The 2026 Landscape of Decentralized AI: Why Blockchain is the Inevitable 'Antidote' for AI?

Decentralized AI 2026 Landscape: Why Blockchain is AI's Essential "Antidote" Centralized AI faces structural bottlenecks—expensive compute, concentrated control, unverifiable outputs, and difficult data access—that cannot be solved by capital or code alone. Blockchain offers a path to make intelligence open, verifiable, and economically accessible. The decentralized AI stack comprises: * **Infrastructure:** The foundation with compute, verifiable inference, distributed training, data/storage, and privacy/verification layers. Projects like Akash, Render, and Filecoin provide cheaper, decentralized alternatives for raw resources. * **Middleware:** The coordination layer for agent discovery, identity, and commerce. Key players include Bittensor (a network of specialized AI subnets), Virtuals (an agent economy OS), and frameworks providing agent identity and tooling. * **Applications & Services:** Dominated by Agentic Finance (AI agents executing on-chain actions based on natural language) and Agentic Payments (machine-to-machine transactions using blockchain as a settlement layer). Projects like Giza, Infinit Labs, and x402 are enabling these use cases. Key trends for 2026-2027 show AI demand outgrowing infrastructure, compute becoming an asset class, and tokenomics emerging as a structural advantage for coordinating capital, compute, and data. While still early—with adoption uneven and revenue often trailing token incentives—projects like Bittensor, NEAR, and Venice demonstrate decentralized AI is evolving from a narrative into a new model for coordinating intelligence.

Foresight News06/11 10:02

The 2026 Landscape of Decentralized AI: Why Blockchain is the Inevitable 'Antidote' for AI?

Foresight News06/11 10:02

The Real Progress and Investment Opportunities of Decentralized AI Computing Power Networks in 2026

In 2026, the AI compute market is marked by centralized GPU consolidation and a significant GPU shortage for smaller players. In this context, Decentralized Physical Infrastructure Networks (DePIN), valued at $9.4B+, have emerged as a viable, revenue-generating alternative. Leading protocols like Aethir ($150M ARR), io.net (130k+ GPUs), Akash, Bittensor, and Render are carving out distinct niches, moving beyond hype to deliver verifiable income primarily from non-crypto-native clients. The key advantage of decentralized GPU networks lies in serving latency-tolerant, cost-sensitive workloads like AI inference, fine-tuning, data preprocessing, and agent operations, offering substantial cost savings (45-80%) compared to major cloud providers. However, reliability variance, lack of robust SLAs, and fragmented tech stacks remain significant adoption hurdles. The sector is maturing with critical 2026 shifts: 1) Evolution of tokenomics towards demand-driven, revenue-linked models (e.g., Render's BME, io.net's IDE), and 2) Clearer enterprise adoption pathways, with traditional firms integrating decentralized compute. For new entrants, opportunities are now concentrated in specialized tooling layers (orchestration, verification, SLA management), vertical applications (e.g., bio-med, content generation), and innovative token designs tied to real usage, rather than generic GPU aggregation. The convergence with the emerging AI Agent economy presents a significant future growth vector.

marsbit05/25 08:01

The Real Progress and Investment Opportunities of Decentralized AI Computing Power Networks in 2026

marsbit05/25 08:01

The TAO Subnet Team Praised by Jensen Huang Has Parted Ways with the Founder Amidst a Fallout

Nvidia CEO Jensen Huang recently praised the decentralized AI project Bittensor (TAO) during a podcast, specifically highlighting a 72-billion-parameter Llama model trained collaboratively by a subnet team called Covenant AI. This endorsement initially boosted TAO's price, but the situation deteriorated rapidly when Covenant AI's founder, Sam Dare, publicly announced the team's departure from the Bittensor network. Covenant AI accused Bittensor and its key figure, Jacob Steeves (known as Const), of centralization and abuse of power, contradicting Bittensor’s decentralized ethos. The team claimed that Const exercised unilateral control by halting subnet emissions, removing administrative rights, discarding infrastructure, and using token sales to pressure the team. They argued that Bittensor’s governance is effectively centralized under Const, despite claims of distributed control. As a result, Covenant AI decided to leave, intending to continue its work on decentralized AI training elsewhere. The exit has sparked significant concern within the Bittensor community, raising doubts about the network’s decentralization narrative, technical future, and token value. TAO’s price fell sharply following the news. Const responded vaguely on social media, suggesting the event would push Bittensor toward more decentralized, “headless” subnets, but has not addressed the specific allegations in detail. The incident has damaged Bittensor’s reputation while raising Covenant AI’s profile.

Odaily星球日报04/10 03:08

The TAO Subnet Team Praised by Jensen Huang Has Parted Ways with the Founder Amidst a Fallout

Odaily星球日报04/10 03:08

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