Written by: Ekko an, Ryan Yoon
Compiled by: Chopper, Foresight News
TL;DR:
- Against the backdrop of booming artificial intelligence, we need to assess the blockchain industry 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 logical advantages such as data sovereignty and cost benefits, but they have yet to form an absolute, compelling technological edge. This is insufficient for enterprises deeply entrenched with traditional cloud service providers to take on the risk of switching.
- Model verification and privacy encryption technologies cannot solve the urgent business pain points enterprises face today; thus, businesses will not actively adopt them on a large scale. Demand in this track will most likely lag behind the introduction of regulatory policies. The EU AI Act is a typical precedent: standards are set first, and then market demand follows.
- The bottleneck in the underlying infrastructure track for AI agents is not technical. Mainstream enterprises currently focus on internal process automation, while blockchain projects are developing the underlying infrastructure for the next stage. Market demand maturity cannot keep up with the speed of technological development.
- AI agent payments is the only track where blockchain stands on the same starting line as traditional finance. Neither side has adequately solved the industry's pain points, making it the only sub-sector currently possessing the conditions for direct competition.
- Overall, the dilemma of the blockchain + AI track is not due to a contradiction in the logic of their combination, but rather a severe mismatch between supply and demand. The four major sub-sectors each face unique issues of missing demand, with only the AI agent payments track currently having the conditions to directly participate in market competition.
AI Explodes Universally, But the Blockchain Track is Left Far Behind
The AI industry is experiencing an unprecedented surge in capital and infrastructure investment. The large model ecosystems built by tech giants are comprehensively permeating both public life and industrial production. The crypto industry is also rapidly iterating, attempting to find technological intersection points with AI.
Early explorations focused on supplementing or replicating segments of the traditional AI industry chain: decentralized GPU computing power supply, data ownership verification, and cryptographic model validation. Recently, the industry's focus has shifted towards solving pain points difficult for centralized architectures to tackle, including AI agents autonomously interacting on-chain and real-time automated settlements between machines.
Bluntly summarizing the entire sector as "AI + blockchain" only obscures the real differences between sub-sectors. We need a rigorous demand-side analysis: what specific problems does each sub-sector target? Can the native blockchain solution offer a truly differentiated answer?
Four Sub-Sectors
Decentralized Computing Power
The current cloud market heavily relies on a few leading tech companies controlling computing resources. High-performance GPUs are difficult to procure and come at a high cost, creating extremely high entry barriers for AI startups and research institutions unable to build large-scale infrastructure.
Centralized platform resources tend to favor large clients, while the market's vast amount of idle GPU computing power lacks neutral channels for allocation.
Decentralized computing power addresses resource concentration and inefficiency through two models. The sharing economy model aggregates idle graphics card resources from individuals and small data centers, building a unified computing network that bypasses tech giant monopolies and creates an elastic supply system.
The distributed computing model allows users to rent computing power globally, not relying on a single vendor's hardware. This increases the utilization rate of idle hardware and lowers the barrier to entry for using high-performance computing.
Decentralized Storage
The existing data storage system is almost entirely dependent on centralized cloud service providers like Google and Meta. After users upload data, actual data ownership transfers to the platform, leading to long-term monopolization of AI training data by giants. Additionally, centralized architectures carry operational risks: policy changes, service disruptions, and platform failures can all 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, pools the unused storage space of various participants into a network capable of replacing existing centralized clouds.
The permanent storage model involves multiple backups of data across distributed nodes, unaffected by the operational status of any single server, thereby reducing dependence on a single platform.
On-Chain Data Trading Markets
AI development requires massive training data, but current data circulation markets are highly closed, with Hugging Face and major cloud vendors monopolizing profits and pricing power. Data creators receive meager compensation, and incentive mechanisms for data contributions lack transparency.
On-chain trading markets use smart contracts to remove intermediaries and establish transparent trading rules. In direct trading modes like Ocean Protocol, data owners and AI developers transact directly through smart contracts, with compensation distributed transparently. In contribution reward modes like Grass, individuals connect their idle bandwidth to AI data collection and receive corresponding rewards based on the value of their contribution.
Model Inference Verification & Privacy Protection
Traditional AI is a black-box system; external parties cannot verify whether model operations are compliant or whether sensitive user data is processed securely.
Zero-Knowledge Machine Learning (ZKML) overlays a cryptographic verification mechanism on the AI inference layer, achieving both privacy protection and audit traceability. Model computations still occur off-chain, but the computation process generates cryptographic proofs, certifying that the entire process strictly follows preset rules.
These proof records are stored on-chain, not the underlying data. For example, in an automated medical insurance claim scenario, a hospital only uploads proof of compliant AI computation without the need to upload complete patient records; the insurance company can verify the authenticity of the proof to complete the claim, never accessing the original private medical data.
AI Agent Frameworks
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 for human consumption behavior and is inherently unsuitable for machine-dominated payment scenarios.
The agent economy requires millisecond-level high-frequency micro-transactions and cross-border real-time settlements, 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 unintended actions.
The protocol-based settlement mechanism uses stablecoin payment protocols (e.g., x402) to settle micro-transactions and high-frequency payments in real-time, bypassing currency conversion and approval processes.
The Difference Between Blockchain + AI and the Traditional AI Industry Chain
The capital logic of the traditional AI industry chain revolves around "removing development bottlenecks." As AI demand expands, memory, electricity, and data transmission bandwidth successively become bottlenecks. Companies that can quickly solve these pinch points (e.g., high-bandwidth memory manufacturers, power infrastructure firms) receive massive financing and market capitalization increases. The market is willing to pay high valuations for solutions that remove growth bottlenecks.
Blockchain + AI projects do target real industry pain points, yet consistently fail to garner comparable market attention. If these issues were truly urgent, large-scale adoption and transformation would have already occurred.
Even if sectors like decentralized computing power and data ownership verification possess reasonable value, they struggle to attract mainstream capital. The core contradiction lies in a severe disconnect between the technological supply side and the procurement side holding the funds.
The AI industry's development pace is intense. Buyers (primarily large tech companies and enterprise clients) invest heavily in solutions that can most quickly resolve their current operational bottlenecks. They won't spend time evaluating unproven infrastructure. Their primary considerations are computational performance, infrastructure reliability, and measurable return on investment.
For example: when data transfer speed became a bottleneck for model training, massive funds flowed into fiber optic infrastructure to replace copper cables. When memory bandwidth became the main constraint, SK Hynix and Samsung Electronics addressed it by providing high-bandwidth memory, gaining global prominence. This pattern is consistent: capital follows entities that can remove constraints and drive progress.
The fundamental issue with the blockchain + AI track is misalignment. Enterprises with large budgets focus solely on short-term performance gains and cost reductions; meanwhile, blockchain AI projects delve into issues enterprises view as secondary, long-term concerns. 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 Hard Power
Many projects have demonstrated the potential and design philosophy of decentralized infrastructure through benchmark tests but have failed to achieve disruptive technological breakthroughs. This is insufficient to challenge the deeply entrenched market position of centralized cloud providers (AWS, GCP, etc.).
Centralized cloud platforms already possess vast capital and mature infrastructure. For new technology to capture market share, it must offer overwhelming performance advantages that make enterprises willing to bear switching costs. When Apple switched from Intel chips to its own M1 chips, it assumed the huge risk of software compatibility breakdowns. The decision was supported by a threefold improvement in energy efficiency—a benefit substantial enough to cover the transition cost.
Currently, blockchain + AI cannot provide a sufficiently compelling benefit logic for enterprise clients requiring petabyte-scale data synchronization and ultra-low latency, making them unwilling to assume migration risks.
Structural Mismatch Between Supply and Demand
Some decentralized computing projects have introduced service level agreements to mitigate enterprise risk, but businesses remain hesitant. The root cause isn't the contracts but the underlying structure: leading cloud providers can offer dedicated, isolated server rooms; blockchain networks rely on dispersed, anonymous nodes to provide computing power.
If a node goes offline, interrupting a model training session worth billions, token refunds or cash compensation cannot make up for the enterprise's lost time cost and commercial opportunity. For enterprises in fierce industry competition, system stability is a non-negotiable bottom line. Even with accompanying risk hedging tools, enterprises have no incentive to take on the inherent uncertainty of decentralized networks.
Immature Market Demand
Blockchain agent frameworks target a mature ecosystem of multi-agent collaborative autonomy, but the mainstream market's development stage is far from reaching 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 built by blockchain projects serves the next stage: autonomous agents operating independently across external, inter-enterprise networks. Currently, the vast majority of enterprises are still refining the stability and ROI of their existing AI systems. Cross-network, multi-agent collaboration is completely absent from the priority list of their infrastructure planning.
The current low demand is a lifecycle issue, not a technological defect. Blockchain agent infrastructure is better positioned as a long-term foundational investment 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. However, in the early stages of AI adoption, enterprises have extremely low proactive demand for deploying privacy infrastructure. It's difficult to rely on voluntary corporate action to drive large-scale adoption; industry demand will most likely be catalyzed by regulatory standards, with technology then implemented to meet compliance requirements.
Ongoing refinement of global regulatory details like the EU AI Act brings favorable conditions for the sector. When data traceability and security become hard legal requirements, blockchain's verification capabilities will shift from optional features to mandatory compliance components for enterprises deploying AI.
Regulatory完善 is not an industry constraint but a catalyst for market formation. Clear laws and regulations reduce industry uncertainty and open stable channels for blockchain + AI adoption in institutional markets.
Lack of Landmark Implementation Cases
The叠加 of multiple structural contradictions衍生出 the most critical obstacle: the lack of convincing, large-scale landmark cases proving commercial value. The traditional AI industry relies on ChatGPT to form a growth flywheel—a single, massively visible hit product attracting vast capital and talent for continuous iteration.
To date, the blockchain + AI track lacks a product-market fit case of comparable magnitude. Beyond early community hype, no project has permeated enterprise production or daily consumer scenarios, failing to gain the attention of traditional institutional capital. The absence of landmark implementation cases is the biggest barrier discouraging conservative institutional funds and delaying industry普及.
Does Blockchain + AI Have Long-Term Value?
Setting aside short-term market hype, blockchain + AI has not yet firmly established itself within the mainstream AI industry chain, but this doesn't mean their combination lacks value.
The core reason for the sector's chill is not a contradiction in the logic of combining the technologies, but rather a misalignment between mature industry demand and the direction of technological supply in each sub-sector.
The core demands of the traditional AI industry are very clear: short-term performance improvement, cost optimization, and ultimate infrastructure stability. In contrast, the vast majority of blockchain AI solutions focus on data ownership, computational transparency, and decentralization.
These are not the industry's current pressing bottlenecks, and their implementation often comes at the cost of performance, making the return on investment难以说服 enterprises.
Before the AI boom, power infrastructure companies were typically categorized as mature, slow-growth businesses. The surge in power demand driven by data centers changed that, and they subsequently attracted significant market attention. The current冷漠 towards blockchain AI might reflect a similar lag effect, where the value of infrastructure isn't fully recognized until a new paradigm emerges.
During this transition period, what's important is how the industry responds to the actual demands of the market.
The path forward splits into two directions: 1) Actively adapt to the standards of the mature AI industry chain,补足 short-term performance shortcomings; 2) Persist with the current technological路线, continuing to lay the groundwork for the long-term infrastructure适配 the next generation of AI大规模落地.
The ultimate trajectory of blockchain + AI depends on which path aligns with the real market demands of the future.
