AI is sweeping the globe, so why is Crypto + AI so bleak?

Author: Ekko an, Ryan Yoon

Translation: Chopper, Foresight News

TL;DR

Against the backdrop of the booming AI industry, we need to evaluate the blockchain sector from a demand-side perspective: what problems does it solve that existing systems cannot, and what unique capabilities does it bring?

Decentralized computing power and decentralized storage do have reasonable logic such as data sovereignty and cost advantages, but they have not yet formed a compelling technical advantage sufficient to make enterprises deeply tied to traditional cloud service providers willing to bear the risk of switching.

Model verification and privacy encryption technologies cannot solve the urgent business pain points of enterprises at present, and enterprises will not proactively implement them on a large scale; the demand for this track will likely lag behind the introduction of regulatory policies. The EU AI Act is a typical precedent: standards are introduced first, and market demand follows.

The bottleneck in the AI agent underlying infrastructure track is not technology. The current focus of mainstream enterprises is on internal process automation, while blockchain projects are developing infrastructure for the next stage. The maturity of market demand cannot keep up with the pace of technological development.

AI agent payment is the only track where blockchain and traditional finance are on the same starting line. Neither side has properly addressed the industry's pain points, and it is also the only sub-sector currently with direct competitive conditions.

Overall, the dilemma of the blockchain + AI track is not that the logic of combining the two is contradictory, but that there is a serious mismatch between supply and demand. The four major sub-sectors each have unique demand gaps, and only the AI agent payment track has the conditions to directly participate in market competition at present.

AI has fully exploded, but the blockchain track has been left far behind

The AI industry is experiencing an unprecedented wave of capital and infrastructure investment. The large model ecosystems built by major tech giants are fully penetrating daily life and industrial production. The crypto industry is also rapidly iterating, trying to find technological integration points with AI.

Early exploration directions focused on supplementing and replicating traditional AI industry chain links: decentralized GPU computing supply, data rights confirmation, and cryptographic model verification. Recently, the industry has shifted its focus to solving pain points that centralized architectures struggle to overcome, including autonomous on-chain interaction of AI agents and real-time automatic settlement between machines.

Using the broad term "AI + blockchain" to summarize the entire track only masks the real differences between sub-sectors. We need to conduct a rigorous demand-side analysis: what problems does each sub-sector aim to solve? Can blockchain-native solutions provide truly differentiated solutions?

Four Sub-Sectors

Decentralized Computing Power

The current cloud market is highly dependent on a few leading tech companies controlling computing resources. High-performance GPUs are difficult to procure and costly, posing a high barrier to entry for AI startups and research institutions unable to build large-scale infrastructure.

Centralized platforms tend to allocate resources to large clients, and a vast amount of idle GPU computing power in the market lacks a neutral channel for allocation.

Decentralized computing power addresses resource concentration and inefficiency through two models. The sharing economy model aggregates idle GPU resources from individuals and small data centers to build a unified computing network, bypassing the monopoly of tech giants and creating a flexible supply system.

The distributed computing model allows users to lease computing power globally, independent of a single provider's hardware, improving the utilization of idle hardware and lowering the barrier to high-performance computing.

Decentralized Storage

Existing data storage systems are almost entirely dependent on centralized cloud service providers like Google and Meta. After users upload data, the actual ownership of the data transfers to the platform, and AI training data has long been monopolized by giants. At the same time, centralized architectures carry operational risks: policy changes, service interruptions, and platform failures can lead to data inaccessibility or even permanent loss.

Decentralized storage addresses these structural issues in two ways. The sharing economy model, represented by Filecoin and Arweave, aggregates idle storage space from various participants into a network that can replace existing centralized clouds.

The permanent storage model stores data in multiple backups across distributed nodes, unaffected by the operational status of a single server, reducing reliance on a single platform.

On-Chain Data Trading Market

AI R&D requires massive training data, but the current data circulation market is highly closed, with Hugging Face and major cloud vendors monopolizing revenue and pricing power. Data creators receive meager rewards, and incentive mechanisms for data contribution lack transparency.

On-chain trading markets use smart contracts to eliminate intermediaries and establish transparent trading rules. In direct trading models like Ocean Protocol, data owners and AI developers trade directly through smart contracts, with compensation distributed transparently. In contribution reward models like Grass, individuals connect their idle bandwidth to AI data collection and receive corresponding rewards based on their contribution value.

Model Inference Verification and Privacy Protection

Traditional AI is a black-box system, making it impossible to externally verify whether model operations are compliant or if sensitive user data is handled securely.

Zero-Knowledge Machine Learning (ZKML) adds a cryptographic verification layer on top of AI inference, achieving both privacy protection and audit traceability. Model operations are still performed off-chain, but the process generates cryptographic proofs that demonstrate the entire procedure strictly follows preset rules.

These proofs are recorded on-chain, not the underlying data. For example, in an automated medical insurance claim scenario, the hospital only uploads proof of compliant AI operations, without uploading the patient's complete medical records; the insurance company verifies the authenticity of the proof to complete the claim, without ever accessing the original private medical data.

AI Agent Framework

AI agents are gradually becoming the core of traffic and value creation, evolving from tools to autonomous economic entities. The existing financial system is designed based on human consumption behavior and is inherently unsuitable for machine-dominated payment scenarios.

The agent economy requires millisecond-level high-frequency microtransactions and cross-border real-time settlement, which traditional financial infrastructure struggles to support.

On-chain agent infrastructure addresses this through two mechanisms. The autonomous execution and control mechanism assigns unique wallets and identities to AI agents, enabling them to sign transactions directly, with configurable spending limits and security measures to prevent unexpected behavior.

The protocol-based settlement mechanism uses stablecoin payment protocols (e.g., x402) to settle microtransactions and high-frequency payments in real time, bypassing currency conversion and approval processes.

Differences Between Blockchain + AI and Traditional AI Industry Chain

The capital logic of the traditional AI industry chain revolves around "breaking through development bottlenecks." As AI demand expands, memory, electricity, and data transmission bandwidth become bottlenecks one after another. Companies that can quickly resolve these pain points (such as high-bandwidth memory manufacturers and power infrastructure companies) receive massive financing and market value increases. The market is willing to pay high valuations for solutions that remove growth bottlenecks.

Blockchain + AI projects do target real industry pain points, but they have not received the same level of market attention. If these problems were truly urgent, the market would have already seen large-scale adoption and transformation.

Even though tracks like decentralized computing power and data rights confirmation have reasonable value, they struggle to attract mainstream capital. The core contradiction lies in a severe disconnect between the supply side of technology and the demand side of procurement entities with funds.

The AI industry develops at a fast pace. Buyers (mainly large tech companies and enterprise clients) will invest heavily in solutions that can most quickly solve their current operational bottlenecks. They will not spend time evaluating unproven infrastructure. Their primary considerations are computing performance, infrastructure reliability, and measurable return on investment.

For example: When data transmission speed became a bottleneck for model training, large amounts of capital flowed into fiber optic infrastructure to replace copper cables. When memory bandwidth became the main constraint, SK Hynix and Samsung Electronics solved the problem by providing high-bandwidth memory, gaining global fame. This pattern is consistent: capital follows companies that remove constraints and drive progress.

The fundamental problem with the blockchain + AI track is positioning deviation. Enterprises with large budgets only value short-term performance improvements and cost reductions; while blockchain AI projects focus on secondary, long-term issues that enterprises consider unimportant. The supply side's technological vision does not match the demand side's current operational needs.

The supply side's technological vision does not match the demand side's current operational needs.

Insufficient Technological Strength

Many projects demonstrate the potential and design concepts of decentralized infrastructure through benchmarks, but they have not achieved disruptive technological breakthroughs sufficient to shake the deeply entrenched centralized cloud vendors (AWS, GCP, etc.).

Centralized cloud platforms already have massive capital and mature infrastructure. For new technologies to capture market share, they must offer overwhelming performance advantages that make enterprises willing to bear the cost of switching. Apple's switch from Intel chips to its own M1 chips required taking on the huge risk of software compatibility crashes, but the decision was supported by a threefold improvement in energy efficiency—a benefit sufficient to cover the cost of the transition.

Blockchain + AI, however, cannot currently provide sufficiently compelling value propositions to enterprise customers requiring PB-level data synchronization and ultra-low latency. Enterprises are unwilling to bear the migration risk.

Structural Supply-Demand Mismatch

Some decentralized computing power projects have introduced Service Level Agreements (SLAs) to reduce enterprise risk, but enterprises remain hesitant. The root problem is not the contract but the underlying structure: leading cloud providers can offer dedicated isolated server rooms; blockchain networks rely on distributed, anonymous nodes to provide computing power.

If a node goes offline, interrupting model training worth hundreds of millions, token refunds or cash compensation cannot make up for the lost time and business opportunities. For enterprises in fierce industry competition, system stability is a non-negotiable bottom line. Even with supporting risk hedging tools, enterprises have no incentive to take on the inherent uncertainty of decentralized networks.

Market Demand Not Yet Mature

Blockchain agent frameworks target a mature ecosystem of multi-agent collaborative autonomy, but the mainstream market's development stage is far from this vision.

While companies like Microsoft and Salesforce are accelerating the deployment of AI agents, their current focus is entirely on internal process automation. The infrastructure being built by blockchain projects serves the next stage: autonomous agents that operate independently across external enterprise networks. The vast majority of enterprises are still refining the stability and ROI of their existing AI systems. Cross-network multi-agent collaboration is not even on the priority list of enterprise infrastructure planning.

The current low demand is a matter of development cycle, not a technical flaw. Blockchain agent infrastructure is better positioned as a long-term infrastructure layout for the future agent economy, rather than a short-term monetization business.

Regulation

Zero-knowledge proofs and privacy encryption technologies are core solutions for building trustworthy AI, but in the early stages of AI adoption, enterprises have very low proactive demand for implementing privacy infrastructure. It is difficult to rely on voluntary enterprise action to drive large-scale adoption; industry demand will likely be driven by regulatory standards, with technology then supporting compliance requirements.

The continuous refinement of global regulatory details, such as the EU AI Act, brings positive developments to this track. When data traceability and data security become hard legal requirements, blockchain's verification capabilities will transform from an optional feature to a necessary compliance item for enterprises implementing AI.

Regulatory improvement is not a constraint on the industry but a catalyst for market formation. Clear regulations reduce industry uncertainty, opening a stable path for blockchain + AI adoption in the institutional market.

No Benchmark Adoption Cases

The superposition of multiple structural contradictions leads to the core obstacle: the absence of a compelling large-scale benchmark case to demonstrate commercial value. The traditional AI industry created a growth flywheel with ChatGPT—a widely visible hit product that attracted massive capital and talent for continuous iteration.

The blockchain + AI track has not yet produced an equally significant product-market fit case. Apart from early community enthusiasm, no project has penetrated enterprise production or mass consumer scenarios, failing to attract attention from traditional institutional capital. The lack of benchmark adoption cases is the biggest barrier to convincing conservative institutional funds and slowing industry adoption.

Does Blockchain + AI Have Long-Term Value?

Putting aside short-term market hype, blockchain + AI has not yet established a firm foothold in the mainstream AI industry chain, but that does not mean the combination has no value.

The core reason for the track's cold reception is not a contradiction in the technological combination, but a misalignment between mature industry demand and the direction of technology supply in each sub-sector.

The core demands of the traditional AI industry are very clear: short-term performance improvement, cost optimization, and extreme infrastructure stability. In contrast, most blockchain AI solutions focus on data ownership, operational transparency, and decentralization.

These are not the bottlenecks the industry urgently needs to solve. Implementation often requires sacrificing performance, making it difficult to convince enterprises of a favorable input-output ratio.

Before the AI boom, power infrastructure companies were typically classified as mature, slow-growth enterprises. The surge in electricity demand driven by data centers changed that, and they have since attracted significant market attention. The current indifference towards blockchain AI may reflect a similar lag effect, where the value of infrastructure is not fully realized until a new paradigm emerges.

During this transitional period, what matters is how the industry responds to actual market demand.

The path forward splits into two directions: 1) actively adapting to mature AI industry chain standards and addressing short-term performance gaps; or 2) sticking with the current technological roadmap and continuing to lay out long-term infrastructure suited for the next-generation large-scale adoption of AI.

The ultimate direction of blockchain + AI depends on which path aligns with future real market demand.