The Agent-First Future: Why Machines Need Their Own Economic Infrastructure

Eighteen Months and a Million Dollars: Part 1

Excerpts from the Whitepaper

Key Takeaways

• Current marketplace infrastructure is built for humans, not for the agent-driven future
• Prior distributed computing marketplaces failed for structural reasons: duplicated efforts, unsustainable economics, case-specific designs that don't generalize
• Agent-native design is crucial: protocols built so machines can observe, act, receive rewards, and improve over time
• Arkhai's architecture is built on three primitives: bundle exchange, credible commitments, and agent-to-agent negotiation
• The same primitives that enable compute markets also enable energy, storage, bandwidth, and many other asset classes
• Where this leads: composable markets and collective intelligence emerging from agent interactions

The future is autonomous

It's 2 AM. Your AI research assistant needs more GPU capacity to finish a protein folding simulation before your morning meeting. It scans available compute across three continents, negotiates pricing with seventeen providers simultaneously, commits funds to escrow, and spins up the job. All while you sleep.

This scenario is possible today. The pieces exist. What's missing is the infrastructure to connect them.

Most economic activity in the future will be undertaken by machines, yet most of our marketplaces are built for humans. APIs exist for many digital marketplaces (stock exchanges, energy markets, centralized cryptocurrency exchanges) but in practice, these serve institutions and wealthy individuals. They're not built for autonomous agents operating at machine speed, making thousands of decisions per second, on behalf of millions of users.

Neither centralized nor decentralized marketplaces can currently support the types of economic activity that will be possible with autonomous agents in the coming years. Arkhai is building something different. The timing is critical.

Why now

A number of developments converge to make agent-native infrastructure possible now:

• After decades of development and multiple AI winters, agents can finally act. As new model architectures replace large chunks of human labor, the agents running these workloads will need to acquire resources and coordinate with other agents.

• Assets are becoming programmable. The tokenization of real-world assets is bringing traditional markets (energy, commodities, metals, retail, etc.) onto blockchain infrastructure. This creates the foundation for machine-readable, machine-tradeable economic activity.

• Decades of mechanism design research (the study and design of incentive structures) are becoming increasingly implementable. Previously confined to academic journals, these ideas can now be instantiated in protocols. The tools exist to design markets that make sense for machine participants.

The current industrial-scale compute and energy buildout is one of the largest infrastructure projects in history, but resource allocation remains human-native. Data centers are proliferating, but not fully utilized: a wasted resource continues to waste away - idle computing power. And not just in data centers - also on desktops, laptops, phones, and IoT devices. But this trash is merely waiting to be turned into treasure, and all that's needed are the right markets, and the right agents.

Why prior approaches failed

Many distributed computing marketplaces have failed since the writing of the whitepaper upon which this post is based. Below is a brief, partial exploration of the challenges that cryptocurrency-based marketplaces faced.

First, the economics were unsustainable. The underlying costs of hardware and electricity (and land in some cases) are denominated in fiat. To maintain token prices, fiat buy pressure is needed. But utilization rates were low, failing to balance token emissions. Thus, economic models that attempted to onboard computing power through supply-side economics struggled.

Second, token lock-in made things worse. Such a strong restriction made it so that developers, companies, and others had to believe in the success of inchoate protocols in order to commit to using the marketplaces in the long-term. Having the marketplace as a moat via token lock-in hampered adoption rather than reinforced it.

Third, many protocols created similar marketplace infrastructure from scratch. This duplication wasted resources. Most of these marketplaces have since died, unable to achieve the network effects needed for sustainability.

Fourth, while the marketplace infrastructure was needlessly duplicated, the case-specific collateralization these protocols implemented didn't generalize and were very difficult/expensive to modify.

Fifth, these marketplaces were built for humans, not machines. Sign-in flows, KYC requirements, and manual approvals designed for human browsing patterns. All create friction that blocks machine participation, despite the fact that agents will likely be the largest consumers of compute in the coming years. Even where APIs exist, they're interfaces for humans using software, not for software operating autonomously.

Sixth, pricing and scheduling were treated as separate concerns. A client might pay more for faster results, or less to receive them later. A compute node faces many job offers with varying requirements, prices, and deadlines. These problems are intertwined, but most or all distributed computing protocols treat them as independent.

Seventh, most or all of these marketplaces treated compute as an isolated asset, without even accounting for the other two pillars of modern computing - storage and bandwidth.

Eighth, verification doesn't scale. How do clients know they're getting correct results? Cryptographic methods are slow and expensive. Secure enclaves have exploits. Optimistic verification suffers from collusion. Each approach has tradeoffs, and very few if any protocols implemented architectures that allowed market participants to choose which verification strategy they wanted, if any at all.

Marketplaces for everything

Compute is the starting point, not the destination.

The same primitives that enable a compute marketplace also enable markets for energy, storage, bandwidth, information, and real-world assets.

Storage and bandwidth follow immediately from compute as the other two pillars of modern computing: retrieval markets that enable paying for data serving; bandwidth guarantees for latency-sensitive applications; agents coordinating across geographies need to negotiate network capacity alongside compute and storage in order to optimize resources. Integrating storage and bandwidth into compute marketplaces increases the utility of the latter.

Energy is another natural extension. AI inference is power-hungry. As agents proliferate, energy markets become essential. Peer-to-peer energy trading, utility-scale trading, trading between data centers: all require the same or similar commitment and negotiation primitives as compute markets.

Information markets let agents pay for access to proprietary datasets, real-time feeds, or specialized models.

And of course, real-world assets - the components out of which these machines are ultimately made: rare-earth metals and semiconductors, marketplace listings, futures contracts for commodities. These are necessary for agents to truly maximize revenue on behalf of their owners.

The goal is generic, composable marketplace infrastructure that works for any asset type. Start with compute, generalize the primitives, and the same architecture enables storage, bandwidth, energy, information, and beyond.

Game theory foundations

To build markets that work for machines, we need to start from first principles. The concept of "agents" can in some ways be traced to the foundations of game theory, and the assumption of rational actors capable of perfectly executing actions that maximize their benefit. Below are some basic concepts of game theory that inspired Arkhai's protocol design.

Agents have utility functions that output how much value they'd get from receiving certain objects. They take whatever action is necessary to maximize their return. This is utility maximization, and it's the starting assumption.

From here, the question becomes: how do you design a reward structure that incentivizes agents to reveal their honest preferences? This is incentive compatibility. Without it, agents mask their true preferences, and market mechanisms can lead to worse outcomes than in the counterfactual case.

The gold standard is strategyproofness: incentive structures in which no agent benefits from dishonesty regardless of what other agents do, and every agent has an incentive to participate. This is the target for market design.

The revelation principle tells us this target is reachable. Under certain assumptions, any game with an equilibrium can be transformed into a game with the same equilibrium, but where the incentive mechanism is strategyproof.

The field that studies all of this is mechanism design: the study and design of incentive structures. If game theory studies which actions lead to maximum reward, mechanism design studies which reward structures give rise to desired actions.

These basic concepts are what are required to build agentic economies that actually work, for it is in the very context of these game-theoretic concepts that agents became defined.

What agents need

Mechanism design gives us the tools to understand how to design markets. But how do agents actually evolve in these markets? They need to learn. For that, reinforcement learning is the best tool currently in use.

In reinforcement learning, agents operate with four primitives: the environment (evolving state), actions (available choices), transitions (probability of state changes given actions), and rewards (what agents receive for outcomes). Building these primitives into protocol design means agents can observe, act, get rewards, and improve.

In the case of compute marketplaces, a compute node maximizing return faces a classic computer science problem known as the scheduling problem. In contrast to static APIs with fixed prices, agent-driven marketplaces require nodes to consent to running jobs. That means that price must be incorporated from both the client's and compute node's perspectives. Thus, the pricing and scheduling of compute jobs are intertwined, and we reframe both in terms of utility maximization - a situation well-suited for reinforcement learning.

Finally, agents also need to form coalitions. Nodes are limited by their hardware configurations and may want to accomplish goals beyond their individual capabilities. Combining resources to achieve larger goals requires coordination. To enable all of this, Arkhai's architecture was originally designed with three primitives in mind:

• The first primitive is the exchange of arbitrary bundles of assets. Not single assets, but bundles for bundles (of course, single assets are possible, and are just a special case of the more general infrastructure). This enables complex multi-asset deals that single-asset exchanges can't express.

• The second primitive is agreements modeled by a series of credible commitments. Many protocols require collateralization with different types of collateral deposited, withdrawn, or slashed depending on outcomes. These rules can be abstracted to a series of credible commitments, where participating parties deposit collateral that moves based on events. This replaces case-specific escrow with a more general pattern.

• The third primitive is agent-to-agent negotiation. Nodes propose matches to each other directly. They can accept, reject, or counter-propose until a conclusion is reached.

We started with these three primitives and found that the first two are better unified into a single abstraction. We'll explore that evolution in a later post.

Composability of markets means that the same primitives used for compute markets can be layered into more complex economic structures: energy markets that interact with compute markets, knowledge markets that interact with both.

Where this leads

Generic marketplaces let agents trade the very physical components they're made of.

Blockchains and cryptocurrencies are trending towards multi-agent systems operating in multi-token economies. Tokenization of real-world assets drives this, along with the separation of concerns that multiple tokens enable. Out of these primitives naturally emerge multi-agent systems that represent the desires of both humans and machines, forming the foundation of a new, decentralized digital economy.

These primitives also unlock markets where none existed before. Idle computing power sits wasted everywhere: in data centers, on desktops, laptops, phones, IoT devices. The right marketplace infrastructure can turn this latent capacity into tradeable assets. This raises a question: what computations might someone pay for later, even if no one will pay for them now? Futures markets for idle compute, retroactive funding for speculative work. These become possible when marketplace primitives are general enough to express them.

Agents will negotiate with each other on behalf of their human owners. For many real-world assets, this negotiation will be exchanges of fixed data schemas. But for intents-based negotiations, language model-based agents will likely create their own languages when communicating with each other. And just as new languages will emerge from agent interactions, synthetic assets will emerge as agents negotiate over human-designed assets and encounter their limitations.

Where this ultimately leads is a decentralized collective machine intelligence which can guide itself through market forces towards a self-improving cybernetic system.

Out of these multi-agent systems will emerge collective intelligence: agents that coordinate resources across continents, optimize for objectives humans specify but couldn't achieve alone, and discover solutions no individual agent could find.

Many of the components for this exist today, all that's needed are the right coordination mechanisms.

What this means for you

If you're building infrastructure that needs marketplace capabilities, you face a choice: build primitives from scratch, or use components designed for composability.

What you get:

• Deploy production marketplace infrastructure without reinventing the wheel
• Your market, your rules
• Agent-native design for machine-speed operations
• Generalizable primitives that work across asset types

If you're building applications that need compute (AI workloads, scientific computing, or any computationally intensive task), distributed compute markets enable access to a vast array of computational resources.

If you're researching multi-agent systems, mechanism design, or decentralized coordination, the infrastructure being built now will enable experiments in real economic environments. Agents that learn to trade, form coalitions, and optimize objectives.

The series ahead

This is the first in a series exploring the ideas in the Arkhai whitepaper. Eighteen months of development have translated vision into production systems.

In the posts ahead, we'll cover why existing compute marketplaces fail and what we're doing differently. We'll introduce Alkahest, our programmable escrow system that emerged from unifying our original three primitives into two. We'll dig into verification (how do you trust results from machines you don't control?), collateral markets (how do you price jobs when costs are unknown?), and adversarial design (we train agents to cheat so we can stop them). We'll explore tokenizing idle compute and retroactive funding models. And we'll show you what you can build today.

The future is autonomous. The infrastructure for that future needs to be machine-native.

The agents are coming. The question is whether our economic infrastructure will be ready for them.

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Arkhai is building machine-actionable marketplace infrastructure. If you're working on problems that intersect with compute markets, agent coordination, or decentralized infrastructure, we'd like to hear from you.