Why Current Distributed Compute Marketplaces Are Broken

Eighteen Months and a Million Dollars: Part 2

Excerpts from the Whitepaper

Key Takeaways

• Many distributed computing marketplaces fail for structural reasons, not just execution issues
• Underutilization + fiat-denominated costs create unsustainable tokenomics; supply-side economics without demand doesn't hold
• Web3 compute differs from Web2: node consent reshapes pricing and scheduling
• Duplicated marketplaces waste resources and likely consolidate into a few prominent solutions
• Marketplace-as-moat strategies (fees, token lock-in) hamper adoption; competitive dynamics push toward no-fee and token-agnostic designs
• Compute markets without built-in storage and bandwidth capabilities will fall behind those that integrate the full stack

The graveyard is full of good ideas

The aggregation of compute power is becoming its own industry. But the path to decentralized compute markets is littered with failures.

In our last post, we listed a set of structural problems with distributed computing marketplaces. Many projects have run into those problems and died. The graveyard is full of protocols with solid technical foundations, talented teams, and genuine vision. They failed anyway.

This is not about execution. The obstacles are not surface-level bugs or marketing missteps. They are architectural. Below is a brief, partial exploration of the dominant failure modes described in the whitepaper.

The broken flywheel

Traditional supply-side economics in Web3 compute tends to follow a predictable loop:

First, a protocol subsidizes supply through token emissions. The pitch is simple: "Bring your GPUs, earn tokens."

Second, node count grows quickly. The network looks large on paper.

Third, utilization rates remain low. Capacity exists, but demand does not materialize at the pace needed to justify the supply.

Fourth, the underlying costs of hardware, networking, and electricity remain denominated in fiat. With low utilization, token prices often depend on external buy pressure the system itself does not generate.

Fifth, node operators start exiting. Token price declines. More operators leave. The flywheel runs in reverse.

This cycle has repeated across a number of protocols. The names differ. The shape doesn't.

Why demand never came

Economic models that attempt to onboard large amounts of computing power through supply-side economics are increasingly becoming outdated; their insufficiencies are evidenced by the lack of demand to counterbalance the supplies they provide.

In practice, the obstacles that most developers and users face in adopting DCNs and DePINs often do not even touch verifiable computing. The integration and operational friction is high enough that verifiability does not even enter the conversation.

Some of that friction is self-inflicted. Many marketplaces were built for humans: dashboard-first workflows, sign-in flows, and manual approval steps, and in some cases KYC. Even where APIs exist, they often assume a human operator. That blocks the machine-native path where agents discover, negotiate, and execute jobs autonomously.

Demand for verifiability will likely increase as popularity, and thus incentive to exploit DCNs, grows (this is also true of highly in-demand traditional compute providers), but it is unclear at what pace this trend will progress.

And when verification does matter, it doesn't scale cleanly. Proof systems can be slow or costly, TEEs have well-known attack surfaces, and optimistic schemes face collusion and dispute overhead. Many protocols pick one approach and force it on everyone, instead of letting participants choose the risk, latency, and cost tradeoff per job.

Outside the whitepaper, a useful contrast is the non-Web3 "neocloud" pattern: pick a narrow initial customer segment and build to meet its needs end-to-end before scaling supply. Many Web3 protocols tried to bootstrap generalized supply first and expected demand to catch up.

The duplication problem

Most Web3 distributed computing networks and DePINs are creating relatively complex marketplaces from scratch. This duplication results in a massive waste of resources, especially since much of the infrastructure is open source and easily forked. Due to competitive dynamics and without substantial changes, the industry will likely consolidate into a small number of prominent solutions.

Many protocols build their own:

• Matching engine
• Pricing algorithms
• Collateral schemes
• Dispute resolution
• Payment channels
• Reputation systems
• Hardware provisioning
• Networking stack

This is wasteful. Open-source marketplace infrastructure should be shared, not reinvented. Differentiation should be in what you are trading and how you verify it, not in rebuilding the entire trading infrastructure from first principles.

Even worse, collateral rules tend to be bespoke and rigid. They are expensive to modify, hard to extend to new deal types, and hard to reason about as requirements change.

Open source should make reuse easy. In practice, it often doesn't, because the marketplace was seen as the moat.

Marketplace as moat doesn't work

Since developers, companies, and others must believe in the long-term success of these protocols before committing, and are unlikely to have large stakes in these protocols, having the marketplace as a moat (via token lock-in or fee-based structures) is more likely to hamper adoption than reinforce it.

Competitive dynamics may push marketplaces toward no-fee and token-agnostic designs over time, especially as the ecosystem trends toward chain-agnosticism and account abstraction.

For these reasons, Arkhai aspires to build the primitives for exchange as a public good: no fees, no token lock-in, token-agnosticism, and multi-chain compatibility.

The consent problem

In Web2, the client controls the nodes on which the compute is being run. In Web3, compute nodes have to consent to having computations run on them.

This changes the nature of scheduling, because now it is necessary to incorporate the price of a job from the perspectives of both client and compute node. While bid-based scheduling has been used in distributed computing protocols before, introducing actual money into the system changes how the scheduling problems need to be approached.

First, pricing and scheduling are intertwined. Deadlines, prices, verification requirements, and collateral constraints all shape whether a node will accept a job and whether a client should submit it.

Second, the mechanisms must treat nodes as utility-maximizers, not obedient workers. If consent is treated as a minor implementation detail, you can end up with thin liquidity and inconsistent service: nodes will decline jobs that don't meet their constraints, and reliability becomes an emergent side effect rather than a design property.

The fragmented stack problem

Compute marketplaces that don't have built-in capabilities for handling storage and bandwidth will fall behind those that do.

A compute job needs more than compute. Where does the input data come from? Where do results go? How does data move between client, node, and storage? How do jobs that span multiple nodes communicate?

Protocols that only provide compute expect developers to integrate separate storage protocols, bandwidth solutions, and coordination layers, each with its own tokens, APIs, and trust models.

The integrated stack matters not because it is impossible to assemble pieces from different protocols, but because the integration complexity becomes a barrier to adoption. Every additional protocol is another potential failure point, another interface to learn, and another place incentives can break.

What this means for you

If you're building infrastructure that needs compute capabilities, the failure modes of previous protocols offer a clear path forward:

• Start with demand. Pick a narrow initial customer segment and meet its needs end-to-end before scaling supply.
• Avoid rebuilding basic marketplace infrastructure. Use modular components that already exist. Differentiate on verification, node network quality, matching, and reliability.
• Avoid token lock-in as a default. Let people try your product without economic commitment. Make it easy to integrate, easy to test, and easy to adopt.
• Design for consent. Your nodes are independent actors with their own incentives. Pricing and scheduling are the same problem viewed from different angles.
• Integrate the stack. Compute alone is not enough. Storage and bandwidth need to be part of the solution, not afterthoughts.

The compute marketplace failures of the past five years were not bad luck. They were predictable outcomes of structural decisions. Different decisions lead to different outcomes.

Next in the series

We've looked at what failed and why. In our next post, we'll explore what worked: the architectural decisions that led from three primitives to two, and the introduction of Alkahest, the programmable escrow system that emerged from unifying bundle exchange and credible commitments into a single, more powerful abstraction.

---

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.