From agentic finance to privacy verification: decentralized AI is reshaping every layer of the intelligent economy.

Written by: Pink Brains

Translated by: AididiaoJP, Foresight News

The existence of decentralized AI is due to structural bottlenecks in centralized AI, which cannot be resolved through capital and code:

• Computational resources are scarce and expensive
• Excessive concentration of control
• Output results are unverifiable
• Obtaining training data is becoming increasingly difficult

Computational resources are scarce and expensive

GPU infrastructure is expected to grow from $10 billion in 2025 to $77 billion in 2035. Data center GPUs have been sold out for several months in a row. The decentralized computing market is expected to grow from $9 billion in 2024 to $22 billion in 2035 (Research and Markets data). This figure only holds true if you believe that the shortage is a structural rather than a cyclical issue; we consider it to be a structural issue.

Excessive concentration of control

ChatGPT, Gemini, Grok, and Claude are all owned and operated by a few private companies. Current AI policies assume that only a few entities capable of concentrating massive computational resources can train powerful systems. Once this assumption is broken, the landscape of who can build cutting-edge intelligence will change drastically.

Output results are unverifiable

When models make decisions, users cannot verify whether the correct model was run, whether the calculations were executed correctly, or whether sensitive data was leaked. This is tolerable for chatbots, but when AI handles loans, healthcare, or autonomous operations of real-time wallets, it becomes completely unacceptable.

Obtaining training data is becoming increasingly difficult, due to privacy concerns and regulation

A centralized crawler located in a single AWS region will quickly be rate-limited, geo-blocked, or fed poisoned caches. As a16z mentioned in its 2026 outlook, privacy is becoming "the most important moat in the crypto space."

AI needs blockchain to make intelligence open, verifiable, and economically accessible.

Decentralized AI Technology Stack Map

• Application and service layer: AI agents can do many things, but in the crypto space, the currently dominant use cases are agentic finance and agentic payments
• Middleware layer: organizations that connect — from frameworks that build and identify agents, to agent markets, to coordination layers
• Infrastructure layer: the underlying resources for AI — privacy and verification layer, computing, reasoning, training, data, and storage

Application and Service Layer

Agentic finance turns natural language prompts into on-chain actions.

@gizatechxyz's ARMA agent has handled over $4.6 billion in agentic transaction volume in selected lending markets — running block by block on EigenLayer's AVS framework, non-custodially.

@Infinit_Labs operates a cluster of over 20 specialized agents that can turn intentions like "earning $1000 a month with 1 BTC" into one-click strategies on Ethereum, Solana, and Base.

@coinvestai by Liquid will execute transactions in real-time directly embedded in ChatGPT and Claude, supporting trading across 500+ markets via Model Context Protocol.

@minara integrates Hyperliquid and recently joined Lighter. It runs a complete "analysis → decision → execution" trading loop through the DMind model and 50+ integrations.

@Cod3xOrg: a network of lightweight AI agents that turn intentions into on-chain transactions that build and execute.

@Zyfai_: a self-hosted DeFAI agent that automates and optimizes yield farming, continuously rebalancing capital across protocols to chase risk-adjusted APY with no human intervention.

In predictive markets, @SynthdataCo is a Bittensor subnet running a decentralized predictive financial intelligence network. Miners compete to model short-term price uncertainty. It is already providing real-time data for products like Kalshi's Mode AI Quant in the crypto market.

Agentic Payments: Machines Paying Machines

Just as the internet became the communication layer of the digital economy, blockchain and stablecoins are becoming the settlement layer for agentic payments.

As of May 2026, x402 has processed over 173 million transactions on Base and Solana, with members of the x402 Foundation including Google, Visa, AWS, Circle, Anthropic, Stripe, and Cloudflare. Stripe started using it in February 2026; AWS launched native AgentCore Payments.

Buyer and seller activities are increasing, with most transactions related to real on-demand pay-per-use: API calls, AI reasoning services, agentic business, and similar workloads. The initial hype cycle has cooled, but underlying traction is beginning to catch up.

Meanwhile, Stripe and Tempo's Machine Payments Protocol is emerging as a second track, having recorded over 411,900 transactions and 9,600 buyers since its launch.

These networks collectively indicate that machine-to-machine commerce is shifting toward a broader direction, where software agents can autonomously trade at machine speed.

Middleware Layer

As the number of agents increases, the core challenge becomes coordination: how do agents discover each other, prove identities, and conduct transactions without human involvement.

The trust gap here is the bottleneck. The estimated scale of agentic commerce will reach $1.5 trillion to $5 trillion by 2030, but adoption is limited by one point — most users are willing to let AI conduct research, but very few are willing to let AI actually make purchases.

Today's systems still rely on API keys, with almost no systems treating agents as entities with identities.

@GoKiteAI is building a dedicated L1 that treats identity and payments as native primitives. ERC-8004 is an Ethereum standard that provides portable on-chain identity and reputation for agents, able to follow across chains.

In the market domain, @virtuals_io is the operating system for the agent economy on Base. By June 2026, it had processed over 2.38 million agent tasks, generating nearly $480 million of "agent GDP."

But the jewel of this layer is Bittensor. It is a network of professional subnets, with each subnet being a micro-economy, where miners run AI models, validators score outputs, and TAO emissions flow to those producing the most useful work. There are three mechanisms that make it economically serious:

• December 2025 halving will reduce the daily TAO issuance from 7,200 to 3,600, corresponding to a hard cap of 21 million.
• dTAO upgrade provides each subnet with its own Alpha tokens and AMM pools — the market decides the emissions.
• Taoflow upgrade (launching November 2025) allocates emissions purely based on net staking flow. A subnet that unstakes more than it stakes may drop to zero. The design is Darwinian.

The network has exceeded 128 active subnets, with the top three computing subnets reportedly achieving a combined $20 million ARR within three months of monetization. Darwinism is the product.

Other projects focus on creating dedicated AI blockchains or providing tools, frameworks, and incentives needed to support community-owned AI ecosystems.

@NEARProtocol: an intangible coordination layer combining settlement, identity, privacy, TEE, MPC, and PII protection, serving autonomous agents.

@base — the main base of the "agent economy." Base MCP allows AI tools like Claude, ChatGPT, Cursor, etc., to execute on-chain actions through prompts on platforms like Uniswap, Morpho, Avantis — swaps, transfers, DeFi interactions.

@SentientAGI: its GRID ecosystem connects agents, models, data, and computing, routing queries to specialized participants for optimal results.

@gensynai: verifiable ML execution, coordinating distributed hardware for training and reasoning while ensuring work credibility, $AI coordination network.

@SaharaAI connects data, models, agents, and rewards within a single AI native ecosystem.

Infrastructure Layer

The infrastructure is the skeleton of AI — the raw computing, reasoning, training, data, and privacy primitives upon which everything above relies. This is the most capital-intensive layer in the decentralized AI stack.

Decentralized Computing

@akashnet runs a reverse auction marketplace where providers bid to win your workload. New leases grew by 27% in Q1 2026 to over 43,500, marking the third consecutive quarter of growth. Its AkashML reasoning service processed nearly 120 billion tokens in April, pricing 60-85% cheaper than mainstream clouds.

@rendernetwork reported a 428% year-over-year increase in usage.

@ionet aggregates over 130,000 GPUs from more than 130 countries on Solana.

@AethirCloud is one of the few with real revenue: reporting approximately $166 million ARR (Q3 2025), delivering over 1.5 billion compute hours.

Distributed and Verifiable Reasoning

Reasoning accounts for over 70% of AI operational costs, and Goldman Sachs projects that agentic AI will drive token consumption to grow 24 times by 2030 — 120 trillion tokens per month.

The decentralized answer is to make reasoning cheap, private, and verifiable.

@AskVenice has provided over 50 billion tokens daily to over 2 million users using private and uncensored models, with a moat based on the model.

@OpenGradient has processed over 2 million verifiable inferences, generating over 500,000 + zkML proofs.

@chutes_ai: developers can deploy and scale AI models through a simple API, supported by GPU miners, with costs potentially 85% cheaper than AWS. Platform revenue translates into token demand through an automatic staking mechanism.

@dphnAI — decentralized AI reasoning network. Notably, Dolphin developed the uncensored model used by Venice AI and allocated 100% of network revenue to token buyback.

Decentralized Training

Training is the toughest issue and the most impactful one — it determines whether cutting-edge models must be built within the labs of three or four companies.

@PrimeIntellect's INTELLECT-1 (10 billion parameters) is the first globally distributed training run; INTELLECT-2 (32 billion parameters) is the first distributed RL run.

@tplr_ai successfully trained Covenant-72B on 70+ distributed nodes, processing about 1.1 trillion tokens while reducing communication costs by 146 times.

@NousResearch: its Psyche network achieves fault-tolerant distributed training, with Hermes 4.3 being the first Hermes model trained on decentralized infrastructure rather than centralized clusters.

@MacrocosmosAI's IOTA subnet (SN9) facilitates decentralized LLM pre-training and "training at home," while its Data Universe subnet (SN13) handles the data layer. The DiLoCo series low-communication algorithms allow globally distributed GPUs to collaborate without a data center's ultra-fast internal network.

Decentralized Data Availability and Storage

As AI workload scales, both are becoming bottlenecks. Cutting-edge models consume vast amounts of fresh data, while storage demand has surged to the extent that major disk suppliers report being sold out years in advance.

The economics are enticing. Decentralized storage can be 60-80% cheaper than traditional cloud providers, with networks like @Filecoin offering storage prices below $1 per TB per month, while centralized alternatives are about $30.

@grass pays idle bandwidth fees to 2.5 million nodes from 190 countries, enabling AI labs to scrape real-time data from the web.

@WalrusProtocol, built by @Mysten_Labs, is a rapidly rising contender for decentralized storage and data availability — efficiently storing large "blobs" using two-dimensional erasure codes, increasingly positioned as a persistent memory layer for AI agents.

@eigencloud: a verifiable cloud platform built around data availability, verifiable computing, and dispute resolution. Secured by re-staked ETH, its theory is to allow AI agents to operate with cryptographic guarantees, making actions provable, auditable, and enforceable.

@vana — an EVM L1, Data DAOs, and Data Liquidity Pools transform individual data into tokenizable, tradeable assets.

@reppo and @oroagents build high-quality and trustworthy datasets for AI training through incentivized competitions.

Privacy and Verification Layer

Ordinary AI users cannot verify whether their data has been privately processed by the model, whether the calculations have been executed correctly, or even whether the claimed model was used.

By 2026, privacy and verification are becoming prerequisites for AI, rather than additional features.

@nillion — "blind computing," executes computations on encrypted data without decryption, using MPC and its own Nil Message Compute. Use cases include private AI reasoning, encrypted databases, and private RAG (allowing AI to query proprietary knowledge bases without leaking).

@Arcium: a decentralized confidential computing network on Solana. Use cases include Umbra (shielded transfers / private yields) and confidential AI training on sensitive datasets.

@OasisProtocol: a privacy-first L1 using ROFL (Runtime Offchain Logic), a TEE-based framework for running verifiable, privacy-preserving off-chain computations — for AI agents, model training, oracles.

@octra: a privacy-first L1 natively supporting FHE, employing a proprietary solution HFHE (Hypergraph FHE), designed for parallel encrypted computation and throughput.

@eigencloud: a heavyweight verifier built on the re-staking security of EigenLayer. EigenAI (verifiable LLM reasoning is a compatibility API with OpenAI for open-source models where prompts and responses are provably unaltered) and EigenCompute (verifiable off-chain execution for agent logic).

@PhalaNetwork. Cloud GPUs are powerful but not private; Phala makes workloads provable while being shielded even from Phala itself. Its core product, Phala Cloud's GPU TEE, deploys open-source models onto hardware, providing a compatibility API with OpenAI, where each inference has a cryptographic proof.

The Direction of Decentralized AI in 2026-2027

The demand for AI is growing faster than infrastructure can keep up, with AI agents becoming the dominant growth engine — the on-chain tracks are ready.

Computing is turning into an asset class, and on-chain markets are becoming its financial layer. Institutional participants are shifting from experimentation to infrastructure investment.

Token economics are becoming the structural advantage of decentralized AI in coordinating capital, computation, and data. Opportunities are expanding from AI to robotics, autonomous machines, and physical AI.

Conclusion

Decentralized AI is growing in major stacks such as infrastructure, middleware, and applications, reflected in computing revenue, a growing agent economy, and large-scale distributed training.

But this field is still in its early stages. Revenue often lags behind token incentives, adoption remains uneven, and while overall AI investment soars, decentralized AI still accounts for only a small portion of venture capital. Token-driven networks can be a significant advantage, provided that value capture is designed correctly.

Even so, the emergence of projects like Bittensor, NEAR, Virtuals, Base, and Venice indicates that decentralized AI is evolving from a speculative narrative into a new model that coordinates computation, data, capital, and intelligence.