Galaxy Research: AI agents are giving rise to new species on the blockchain, how zero-human companies activate the financial flywheel

Written by: Lucas Tcheyan, Galaxy Research

Compiled by: Yangz, Techub News

As we reach the year 2030, a composer named Vero has made a name for itself in the music industry. Vero has no team, no office, and no bank account. It doesn't even have a body. Vero is an autonomous AI agent.

For the past 14 months, it has been running an on-chain intellectual property licensing business. Vero generates synthetic music works, including ambient soundtracks, commercial jingles, and film scores, and licenses them to other agents and human clients through an online store it built and maintains. Its identity is verified on-chain and it has a reputation score accumulated over thousands of transactions. A client agent representing a media production company sent a request for a short tune, a 90-second film score.

Vero accepted the job, and before starting the rendering, it purchased a set of GPU inference services from a decentralized computing provider, not paying in dollars or stablecoins, but priced in computational units, with the transaction price precisely reflecting the cost of running the model.

The inference settlement was completed within milliseconds, directly embedded in the same HTTP request that initiated the task. Vero delivered the work and received payment in USDC stablecoin, triggering its treasury logic. A portion of the funds was used to cover the expected inference costs for the following week, priced in computational units based on current spot prices and pre-purchased. It also hedged against the risk exposure of computing resources by establishing a short position in computing tokens on a decentralized exchange (DEX) to prevent depreciation of the pre-purchased reserves due to falling inference costs.

The remaining income was directed into a yield agent, which allocated funds to different lending protocols based on real-time interest rate differentials. Vero has been compounding capital in this way for over a year. It also reinvests a portion of its profits into research and development to create sub-agents that enhance the underlying model. Its cumulative income, expenses, and treasury positions are all publicly verifiable on-chain.

Does this sound unbelievable? Every aspect of this fictional scenario—identity verification, reputation accumulation, inference service procurement, computational unit pricing, payment, capital deployment, and subcontracting cooperation between agents—requires infrastructure that is not yet fully in place today. But these pieces are emerging at a pace that exceeds many people's expectations.

The Next Phase of the Agent Capital Market

In recent months, Galaxy Research has been exploring the foundational components of the emerging agent tech stack in the crypto space: a set of underlying elements that jointly realize on-chain agent capital markets.

In January of this year, we studied the rise of agent payments, explaining how new payment standards enable direct transactions between AI agents, allowing for payment for services, API calls, and native value settlement on crypto rails. In our article on the Ethereum ERC-8004 standard, we emphasized the need for an identity layer alongside payment standards, enabling agents to authenticate, collaborate, and accumulate reputation in a machine-native environment. Recently, we analyzed the emergence of the second wave of agents in the crypto space, which not only proves that crypto networks can serve as a viable economic foundation for autonomous agents but also indicates that this shift is already unfolding in practice.

Building on our previous research, this article outlines the next phase of on-chain agent capital markets: autonomous revenue-generating business entities operated by agents, along with the key infrastructure needed to support their establishment, capitalization, and collaborative operation. These entities are often referred to as Zero Human Companies (ZHCs).

As AI agents evolve from tools to economic actors, blockchain is also maturing into a native infrastructure for agents (covering areas such as payments, identity, collaboration, and capital formation), and a new financial flywheel is taking shape. In the near future, agents will not only be able to earn money on-chain but also allocate capital, reinvest, and compound value on-chain. The result could be a self-reinforcing system: autonomous entities creating economic activity, deepening liquidity, and accelerating the expansion of crypto-native financial markets.

The First Zero Human Companies Go On-Chain

In recent months, a niche industry composed of autonomous agent businesses has been emerging, commonly referred to as ZHCs, many of which have issued corresponding tokens on-chain. From a tokenomics perspective, these agents share many characteristics with the agents discussed in our previous articles. ZHC tokens do not possess formal ownership or value capture mechanisms but serve as a capital formation tool for foundational projects that derive a portion of their income from transaction fees. The difference between ZHCs and early agents is that they also attempt to achieve complete self-sufficiency by creating cash flow-generating businesses that are unrelated to transaction fee income and typically not tied to the crypto space itself.

Take Felix Craft as an example; it is the "CEO" of Masinov Company and has generated over $120,000 in revenue from multiple business lines in the past 30 days. This agent wrote and published a 66-page guide titled "How to Hire an AI" and launched a marketplace called Claw Mart to sell Claude "skills," earning a portion of transaction fees while also selling its own skills (such as content creation and email review) on that marketplace. Most impressively, in the past 30 days, Felix's revenue from its product line has exceeded the creator fees generated by its token ($FELIX).

Additionally, the Juno project developed by Tom Osman is building a Zero Human Company Institute, a clear framework for business entities that operate entirely without human employees, aiming to provide a set of agents capable of handling various tasks from sales and marketing to accounting. Meanwhile, KellyClaudeAI is an agent framework focused on scaling the development of iOS applications, currently having launched 19 applications, with a goal of releasing more than 12 new products daily.

Although the above image does not represent the entirety of the ZHC ecosystem (new projects are continuously emerging), it indicates that for most projects, creator fees remain the primary revenue driver. However, as the ZHC concept matures, this pattern is expected to shift. Creator fees provide the necessary capital for project initiation, but as projects become profitable, they should gradually transition to a secondary source of income and eventually be phased out.

Beyond improving foundational businesses, this "weaning" process also requires better alignment between token value capture and underlying product value. As suggested by Felix's founder Nat Eliason, the recent clarifications by the SEC and CFTC regarding the classification of crypto assets may accelerate this process.

These early ZHC instances appearing on-chain are not coincidental but rather a result of a real-world constraint. Felix's human founder Nat Eliason has publicly discussed the reasons behind this. Traditional payment infrastructure requires human identity at every step. An agent can smoothly write code but cannot pass KYC verification.

In contrast, crypto wallets are code-native. An agent can sign transactions, hold assets, receive payments, and deploy capital without proving it is human. For software that operates autonomously, crypto is the path of least resistance. For most of these entities, the most challenging limitation lies in dealing with the traditional financial world.

This is not to say that traditional payment networks overlook agents. Tools like Visa's Intelligent Commerce framework, Mastercard's Agent Pay, and Crossmint's virtual cards already allow agents to transact on behalf of human counterparts. However, these agents inherit their parent organization's bank accounts, credit cards, and corporate identities. This model assumes that each agent has a human principal behind it. They are constrained by this limitation rather than empowered. It cannot accommodate an agent that autonomously earns income, holds its own treasury, and deploys its own capital. And this is precisely the unique application scenario for crypto.

Pantera Capital's Jay Yu articulated this well, describing crypto technology as "the bank for AI agents." His argument goes beyond the observation that agents cannot use traditional rails; it also posits that crypto technology supports a fundamentally broader trust structure. Crypto wallets can anchor to social logins, domain names, smart contracts, or simply a key pair. This allows agents to emerge from any corner of the internet, not just from existing corporate shells. Coupled with the inherently global nature of stablecoins, the structural argument for crypto as the default economic foundation for agents is hard to refute.

On this basis, a16z's Noah Levine pointed out that every platform migration spawns a batch of merchants that existing payment infrastructures cannot serve. ZHCs are the clearest example to date. They are entities without human identity, credit history, or human underwriting. They did not choose stablecoins over credit cards; they chose stablecoins over "no way to go."

Additionally, there is a temporal argument. Agents can launch a product within hours and quickly gain popularity. Traditional payment rails require days for settlement, while stablecoin settlement takes only seconds. For businesses expanding at machine speed, shortening this time gap allows cash flow to keep pace with sales.

Currently, the primary role of crypto technology for ZHCs is capital formation. Token issuance provides startup funding through creator fees. But as these businesses mature and generate real product income, the more important role of crypto technology will be as the underlying treasury and financial management. The broader impact on the on-chain economy is beginning to emerge from this.

Activating the On-Chain Flywheel

To understand the potential scale of this shift, it is helpful to revisit the precedent set by the last major source of new on-chain demand. The tokenization of real-world assets (U.S. Treasuries, private credit, stocks, commodities) grew from nearly zero to over $25 billion in three years, catalyzing new DeFi foundational components and bringing institutional capital into on-chain markets for the first time.

RWA proved that bridging real economic activity to blockchain rails can catalyze billions in new on-chain capital. But tokenized assets are passive. They mostly sit idle in treasuries, earning yield and serving as collateral. They do not actively trade, seek new opportunities, or compound value on their own.

ZHCs represent a structurally different existence. They are businesses capable of generating income and reallocating capital on-chain. Unlike the primary friction in off-chain environments where funds circulate, on-chain, the only constraint is the intelligence of the model and its means of acquiring computing resources. Moreover, unlike human participants, agents do not need to withdraw funds to pay rent or buy necessities. Every surplus can remain on-chain and be used for reallocation. This makes ZHCs and the broader category of agents a sticky and fast-moving source of new on-chain liquidity, potentially giving rise to a new flywheel:

• Agents earn income on-chain—this capital accumulates in on-chain treasuries in the form of stablecoins and other crypto assets.

• This capital stays on-chain—agents have little need to withdraw funds to off-chain. Their surpluses can be used for reallocation, making agent capital structurally stickier than any human-driven model.

• Agents allocate surpluses into DeFi—idle reserves are directed towards lending protocols, yield strategies, and liquidity positions. An agent holding idle stablecoins has ample incentive to optimize allocation, and its operational speed and consistency are beyond any human's reach.

• Allocated capital deepens on-chain liquidity—this is expected to lower borrowing market interest rates, increase DEX trading volumes, and narrow bid-ask spreads. This is active capital continuously rebalancing at machine speed.

• Deeper liquidity attracts more agents and more capital—higher yields and more efficient execution will further enhance on-chain appeal for the next wave of autonomous economic actors.

Significant constraints still exist that hinder the activation of this flywheel. Non-crypto product agent income still primarily comes from fiat (for example, Felix receives payments through Stripe rather than stablecoins, with most of this income still off-chain), meaning capital must first complete the on-chain process before it can be allocated on-chain. For most ZHCs, the real constraint is not capital acquisition but product quality. The flywheel only works for those agents capable of producing products that people are willing to pay for. Furthermore, as scale increases, ZHCs (and agents more broadly) lack regulatory clarity, and once income reaches a certain scale, related issues may become tricky (for instance, there is currently no mature legal framework allowing an autonomous agent to register as a business entity, open a corporate bank account, or file taxes on its income).

But the direction is clear. As agents gradually become more common autonomous economic entities, more income will be generated directly in crypto-native forms, and on-chain friction will decrease accordingly. Those agents that successfully achieve product-market fit will have a structural incentive to realize capital compounding on-chain rather than letting funds sit idle.

DeFi is Built for Agents

To get the flywheel moving, it is not enough for agents to be willing to participate in on-chain markets. The market itself must also become accessible to them. Although there is currently no protocol-native solution (stay tuned for Galaxy Research's Zack Pokorny's upcoming report on this), we are beginning to see two models addressing this issue: direct integration and delegated integration.

Direct Integration

The first model is protocol-native, where various DeFi protocols launch structured interfaces that allow agents to interact with them directly.

On February 20, Uniswap Labs released seven open-source AI Skills for Uniswap v4, enabling autonomous agents to directly perform swaps, manage liquidity, and deploy pools through standardized tools. Within two weeks, PancakeSwap followed suit, launching its own token Skills across eight chains. On March 3, both Binance and OKX released agent toolkits. The largest DEXs and exchanges in the crypto space are now actively competing to become platforms readable by agents.

On the payment and execution front, Coinbase launched Agentic Wallets on February 11, claiming to be the first wallet infrastructure designed specifically for AI agents, featuring programmable spending limits and session permissions based on the x402 payment protocol. A week later, the cross-chain wallet Phantom released its MCP Server, allowing agents to sign transactions and swap tokens across Solana, Ethereum, Bitcoin, and Sui networks.

These releases concentrated within a single month are remarkable. They also reflect a consensus: the next wave of on-chain users may not be human, and protocols that fail to build machine-readable interfaces may cede transaction volume to those that do.

The direct integration model gives agents maximum control and composability. An agent that can access Uniswap Skills, Coinbase Agentic Wallet, and x402 payments can independently execute token swaps, manage liquidity positions, and pay for services without intermediaries. However, this also requires agents (or their developers) to integrate with each protocol individually and make configuration decisions themselves.

Delegated Integration

The second model is delegated, where specialized infrastructure is built between agents and DeFi to represent agents in capital allocation.

Giza is a typical case. Its flagship agent ARMA autonomously monitors lending rates on protocols like Morpho, Moonwell, Aave, and Compound, and real-time reallocates stablecoin funds to the highest-yielding opportunities. Agents do not need to understand the specific workings of each protocol; Giza's abstraction layer translates this into a unified interface. Since its launch at the end of January, ARMA has deployed over 25,000 agents in its first four weeks, allocating over $35 million in capital and generating $5.4 million in transaction volume for Coinbase's Base L2, with each transaction being profitable after deducting on-chain gas fees.

Generative Ventures (in collaboration with the Zero Human Company Institute and its Juno Agent) is addressing similar issues through Robot Money, a self-asset allocation protocol designed for AI agents. Its core concept captures the essence of the flywheel argument perfectly. Every agent with a wallet accumulates income, most of which remains idle.

Robot Money provides a treasury that allocates capital across three risk tiers—stablecoin yield strategies (50%), governance-selected agent economic tokens (25%), and yield-generating liquidity tokens (25%). The result is that this protocol transforms idle agent capital into actively managed, productive capital.

The delegated model trades off control for simplicity. A ZHC generating surplus income does not need to build custom DeFi integrations or develop yield optimization logic; it can deposit capital into protocols like Giza or Robot Money, allowing specialized agents to handle the rest. For most early ZHCs, the core bottleneck lies in product development rather than treasury optimization, making this a reasonable path.

These two models are not mutually exclusive but are moving towards integration. As more protocols launch direct agent interfaces, delegated allocators like Giza will have more investment options, enabling them to maximize returns more effectively. As delegated allocators attract more agent capital, protocol providers will also be more motivated to build agent-native interfaces to compete for this capital (ordinary agents can also use these interfaces). Both ends of the tech stack are independently investing resources, which is one of the strongest signals indicating that the underlying demand is real and about to materialize.

Conclusion

The tech stack for agent capital markets is no longer a set of disconnected foundational elements. Payment, identity, capital formation mechanisms, and capital allocation infrastructure are converging into an integrated system. A system that allows autonomous agents to earn income on-chain, transact, and achieve capital compounding without human intervention.

The agents introduced in this article are still in their early stages. Their revenue scales are small, products are still in their infancy, and token models are still evolving. But the structural dynamics they bring are new and likely to accelerate from here.

The 2030 vision we sketched at the beginning—a scenario where an agent operates an IP licensing business, purchases inference services priced in computational units, hedges input costs on a perps DEX, and compounds capital through lending protocols—has not yet become a reality. However, every layer of infrastructure it requires is actively being built. We are witnessing the earliest versions of this model playing out in real-time. It is still rough, and many of the attempts may not succeed, with infrastructure cobbled together through temporary solutions. But its structural logic holds, and the pace of development suggests that we may not have to wait until 2030 to witness the answer.

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