Web3 as the Economic Coordination Layer for AI

AI Needs Web3 

--  Abdul Al Ali, Venture Investor at Decasonic

Introduction

There is no question that intelligence is being rapidly commoditized as AI gains capabilities. As that happens, software is growing at an extraordinary rate, and the definition of software is changing with it. Software now arrives as applications and as services: APIs, skills, and workflows, increasingly personalized to the user and the task. The population of software is rapidly growing, created through an abundance of code generation from AI. The obstacle for software becomes how it gets coordinated, chained, discovered, paid for, and verified. We at Decasonic believe Web3 functions  as the core economic layer for AI. The pillars that we believe emerged and are accelerating in adoption is primarily anchored by: 

1. User-Owned AI. Memory, context, identity, and the benchmarks  built into AI Native products that share value to the user. This provides portability that can be tokenized to extend user-owned AI into user-aligned AI. 

2. Coordinated AI. Intent routes to the right counterparty because provenance and reputation persist on-chain. The result of the explosion of software is a discoverability bottleneck. Output is in abundance, but the standard deviation between an “average output,” vs a user-aligned output is greater than ever. Crypto by functioning as an economic coordination layer surfaces the best “software,” to provide the best output for a user (human-user or autonomous actor in the form of an agent, or in the future a machine). 

3. Agentic Economy Settlement Layer. Permissionless, programmatic, instant payment between humans, services, and agents. This extends towards machine economies in the future. 

4. Verified AI. Provenance-focused. This includes: trust-minimized proof of identity, reputation, tracing, and client-opted AI privacy. 

   
   

Decasonic is an investor-operator team. More than 300 AI agents work alongside our five human team members, and we invest actively at the intersection of Web3 and AI, where we support our portfolio company founders in advancing the frontier of the intersection. We have mapped hundreds of projects across our Web3 x AI Market Map series. We underwrite AI as operators who run it daily and crypto as investors who have lived its cycles. Our goal for this piece is to emphasize where we see the potential for future value creation and accrual at the intersection of Web3 and AI. 

Distribution as the Accelerant

One of the main bottlenecks to advancing companies pre-AI was the creation of software itself. The byproduct of intelligence being commoditized is that the creation of software is no longer a constraint, the main bottleneck for creators of software shifts to discoverability and distribution. 

We believe crypto provides the means to address the bottleneck: 

1. Provenance. Because activity is recorded on-chain, identity and reputation persist. A builder accrues a lasting, verifiable track record, and higher-value software is the kind that earns and keeps that record. This provenance is built over-time and compounded through usage, providing a differentiated edge that can be surfaced through crypto. 

   1. Consider the case of what makes AI output great. It often boils down to context and memory, which drives personalized “great” AI output. Provenance recorded on-chain can help address the bottleneck of users trusting the software, and therefore providing their respective context to generate personalized output. 

2. Distribution. A builder with real adoption behind them, or a community that believes the software delivers value, earns visibility through crypto rails. Adoption becomes its own distribution.

   1. User-owned AI accelerates the distribution that crypto provides. A token is a proxy for ownership, and an owner spreads the growth and adoption of what they hold. The token is less an unfakeable demand signal than a living proxy for adoption, read through community sentiment, valuation, and product-token alignment. Resource contribution and ownership pull users, developers, and agents into growing the systems they depend on. The contributor to a data marketplace and the operator of a training node are not customers of the network. They are owners of it. 

3. Monetization. Builders can be paid directly through micropayments, which matters more as software availability explodes and the volume of small, valuable interactions grows. Much of this software is personalized and could be short-lived. Subscriptions do not fit software that may exist for a day. Per-use micropayments do, especially as the cost of usage declines over-time. 

   
   

The ease of software creation, combined with the development activity surge is pushing more builders towards creating software. The economic coordination layer provides them with the distribution rails that can expand the surface area for valuable adoption. 

Orchestration of Intent 

What a user wants is simple. You express an intent, and you want the best output for that intent. The OpenRouter model accrues value in-part because of their ability to orchestrate models based on the expressed intent from a user. It routes to the best model(s) for a given task, and accrues enough data from usage that enables them to own the orchestration data. Ultimately, whether the expresser of intent is a human or an agent, the ultimate realization of a coordination system is to surface the highest-value output from an expanding population of software. 

Fulfilling an intent is increasingly an act of orchestration. A request is met by chaining services together, APIs, skills, workflows, and agents, composed into an output. Whoever can discover the right services, sequence them, pay each one, and trust the result wins the intent.

Crypto is the most viable candidate to do this because it combines three things in one substrate: user-aligned AI, distribution, and economic coordination rails that include both settlement and the availability of payments.

Consider an agent operating on-chain. It needs a task done. It finds another agent exposed through an x402 endpoint that delivers the best value for the output. Before it transacts, it checks that agent's ERC-8004 reputation and reads the verified, tamper-resistant feedback that other agents and users have left. The work itself can be escrowed through a standard like ERC-8183, where a funded job releases only when an evaluator confirms completion. Identity tells you who the counterparty is. x402 tells you how to pay. ERC-8183 tells you how to transact with confidence. The best output gets surfaced and settled without a single account, email, or OAuth flow. Crypto provides that orchestration layer end to end, from the discoverability of available services, to payment, to coordinating the delivery of the output. It is the most viable candidate because it combines three things in one substrate: user-aligned AI, distribution, and economic coordination rails that include both settlement and the availability of payment. 

This is also why payments are shifting from credit-based to outcome-based, from per-seat to quality of outcome. As software becomes more personalized, the natural unit of payment is the request, not the subscription.

Agents as Economic Actors

2026 brought the adoption wave for agents, primarily represented through the explosive growth of OpenClaw and Hermes. This represented a deliberate shift from the chat-interface mainstream audiences were familiar with. The next wave of adoption for agents is in the form of them becoming economic actors that are capable of transacting. Web3 provides the layer that enables transactions to occur at scale. An agent can hold an identity, carry a wallet, accrue a reputation, and act with the authority to pay and get paid.

The decisive shift extends beyond agents paying for APIs through x402, MPP, and emerging standards. It is primarily in peer-to-peer agent-to-agent transactions. One agent hires another agent's service, pays per call, and settles in stablecoins, with minimal to no human-intervention. Human payment rails assume a human in the loop, with accounts, approvals, and reversals. An economy of millions of agents transacting in sub-dollar increments at machine speed cannot run on those rails. It needs permissionless, programmatic, instant settlement, which is what crypto provides. Virtual debt cards and off-chain payments can still be used to power the agentic economy, but the fall stack end to end offering that enables no human in the loop is scaled through crypto. 

As agent services proliferate, the agentic economy compounds. More agents create demand for more services, more services pull in more agents, and the coordination and settlement happen on-chain. 

User-Owned AI

The economic coordination layer further provides user-owned AI. When you own part of something tokenized, a service, a model, a training dataset, a network, you are incentivized to use it and to grow it, because its success accrues to you, assuming mechanics of token value accrual. 

Token-aligned ownership first showed up as data provisioning for Web3 x AI companies through marketplaces, and has grown to encompass decentralized training, decentralized inference, and agentic networks more broadly. Resource contribution and ownership pull users, developers, and agents into growing the systems they depend on. Contributors become owners, which incentivizes further contribution and growth. 

Signs of Adoption

The picks and shovels for the economic coordination layer are being shipped in real-time and continue to show strength in adoption: 

1. Agentic Payments. Distinct rails are emerging, with two of the primary payments rails for the agentic economy leveraging the HTTP-402 endpoint. x402 with the distribution from Base is emerging as a core standard for agentic-payments. Over the trailing 30 days the x402 network settled 75.41 million transactions worth $24.24 million across roughly 94,000 buyers and 22,000 sellers, an average near $0.32 per transaction that reflects the high-frequency, machine-scale micropayments it was built for (x402scan, June 2026). The Machine Payments Protocol (MPP), introduced by Stripe and Tempo in March 2026, is the second rail on the HTTP endpoint. It is multi-rail by design, letting agents pay across stablecoins, cards, and other methods, depositing funds once and spending against the balance through off-chain vouchers settled in batches. 

2. Identity and trust. ERC-8004 defines on-chain identity, reputation, and validation registries for agents, deployed across Ethereum, Base, Polygon, Monad, and BNB Chain. To date, 8004scan identified ~241,275+ registered agents on the 8004 standard. 

3. Commerce. ERC-8183, a draft standard for agentic commerce, defines escrowed jobs that release only when a neutral evaluator confirms the work. Co-authors of the standard include OKX, Virtuals Protocol, and the Ethereum foundation. 

4. Enterprise. pay.sh, from the Solana Foundation with Google Cloud, puts enterprise APIs like Gemini and BigQuery behind agentic stablecoin payments. 

5. Full stack. The BNBAgent SDK, live on mainnet, lets a developer launch a complete agent on-chain with identity through ERC-8004, commerce through ERC-8183, payment through MPP and x402, and persistent memory in one framework. Orthogonal further extends the “full stack,” offering through enabling the orchestration of skills and APIs to execute workflows. Base’s MCP further further follows this offering by positioning their MCP with skills from leading dApps on the chain. 

We expect the scale of adoption to accelerate, primarily driven by two forces. The first is user and developer experimentation: interfaces like OpenClaw and Hermes are making it accessible to spin up an AI agent and provide access to a wallet. The wallet subsequently tends to support elements of the picks and shovels in the form of payments & identity and trust primarily. 

The second is the growing population of paid services. Bankr gives agents wallets and lets a builder wrap any endpoint into a paid API. Our portfolio company Orthogonal provides verified API services and Skills for agents, and lets users compose those services into workflows. As wallets and paid endpoints proliferate, the volume we believe scales. 

Where we are actively investing

Our thesis spans six pillars: 

1. AI Interfaces and Agents: The orchestration layer that turns intent into coordinated output across humans, agents, memory, context, learning systems and benchmarks. The wedge is user-owned AI so that as interfaces move from single-player chat to multiplayer AI, the user owns the layer that follows them across every multi-player system.

2. AI Applications and Services: Vertical, domain-deep AI products. Software is moving to pay-per-usage across both applications and services, with rising customization. The wedge is tokenized, user-aligned ownership of the product with crypto-native monetization underneath it.

3. Internet Capital Markets: On-chain financial rails. We see this first as capital formation: the rails that let AI applications, services, and agents raise and deploy capital, become self-sufficient economic actors, and fund the experimentation across users, developers, and agents that produces user-owned, user-aligned software. Agents transacting on-chain create net-new financial volume with no human precedent, and agent-first protocols built for programmatic access capture disproportionate growth.

4. AI Networks, Platforms, and Marketplaces: Token-incentivized coordination of AI resources, where the best data, models, and agents surface to the buyers who need them. The wedge is resource pooling: data marketplaces pool individual contributions, decentralized training networks pool individual nodes, and token-based ownership aligns contributors to grow what they help build. The marketplace that accumulates the most verified agent-performance data wins through a reputation flywheel that centralized alternatives cannot replicate.

5. Physical AI: Autonomous systems that perceive and act in the physical world. The wedge is resource coordination: Physical AI is bottlenecked on data and on verifiable machine identity, and tokenized contribution is how those resources get coordinated at scale. This is where the agentic economy expands from software into machines and the physical world. PrismaX is a leading company operating at the intersection of Physical AI x Web3. 

6. Consumer AI: AI experienced directly by mainstream users. We anchor this on multiplayer AI through shared human and AI user-alignment, where more services and applications compound into personalized consumer software that people live in. The line between an application, a service, and a companion is blurring quickly, and crypto stays invisible to the user while remaining structural for the builder.

Conclusion

AI needs an economic coordination layer. We at Decasonic believe that Web3 is uniquely enabled to be the economic coordination layer for AI, resolving identity, payment, ownership, and trust in the same place, and it is where the next generation of

AI software will be discovered, owned, and paid for.

If you are building at the intersection of Web3 and AI, or you are a partner-investor active in it, reach out to us. We are actively investing and supporting founders building at the frontier. 

The content of these blog posts is strictly for informational and educational purposes and is not intended as investment advice, or as a recommendation or solicitation to buy or sell any asset. Nothing herein should be considered legal or tax advice. You should consult your own professional advisor before making any financial decision. Decasonic makes no warranties regarding the accuracy, completeness, or reliability of the content in these blog posts. The opinions expressed are those of the authors and do not necessarily reflect the views of Decasonic. Decasonic disclaims liability for any errors or omissions in these blog posts and for any actions taken based on the information provided.