Written by: Ekko an, Ryan Yoon
Compiled by: Chopper, Foresight News
TL;DR
- Against the backdrop of thriving artificial intelligence, we need to evaluate 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 indeed have logical rationales like data sovereignty and cost advantages, but they have yet to form absolute and convincing technical superiority, which is insufficient for enterprises deeply integrated with traditional cloud service providers to take the risk of switching.
- Model verification and privacy encryption technologies cannot solve the urgent business pain points enterprises face currently; enterprises will not proactively deploy them on a large scale. Demand in this track is highly likely to lag behind the introduction of regulatory policies, with the EU AI Act being a typical precedent: standards come first, then market demand follows.
- The bottleneck for the underlying infrastructure track of AI agents is not technical. The current focus of mainstream enterprises is on internal process automation, while blockchain projects are developing infrastructure for the next stage; market demand maturity cannot keep up with the speed of technological development.
- AI agent payment 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 direct competitive conditions.
- Overall, the predicament of the blockchain + AI track is not due to a logical contradiction in their combination, but to a severe mismatch between supply and demand. The four major sub-tracks each have unique issues of missing demand; only the AI agent payment track currently possesses the conditions for direct participation in market competition.
AI Explodes Globally, Yet the Blockchain Track Falls Far Behind
The AI industry is experiencing an unprecedented boom in capital and infrastructure investment, with ecosystems built around large models by major tech giants thoroughly penetrating everyday life and industrial production. The crypto industry is also iterating rapidly, trying to find technological integration points with AI.
Early explorations focused on supplementing or replicating segments of the traditional AI industry chain: decentralized GPU computing power supply, data provenance, cryptographic model verification. Recently, the industry's focus has shifted to addressing pain points that centralized architectures struggle to overcome, including autonomous on-chain interaction for AI agents and real-time automated settlement between machines.
Vaguely categorizing the entire field as 'AI + blockchain' only obscures the real differences within sub-sectors. We need rigorous demand-side analysis: What problem is each sub-track targeting? Can the blockchain-native solution provide a truly differentiated one?
Four Sub-tracks
Decentralized Computing Power
The current cloud market heavily relies on a few major tech companies controlling computing resources. The high difficulty and cost of procuring high-performance GPUs create significant 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 to build a unified computing network, bypassing tech giant monopolies and creating an elastic supply system.
The distributed computing model allows users to lease computing power globally, not reliant on a single provider's hardware, improving idle hardware utilization and lowering the barrier to using high-performance computing power.
Decentralized Storage
The current data storage ecosystem is almost entirely dependent on centralized cloud service providers like Google and Meta. After users upload data, actual data ownership transfers to the platform, with AI training data long monopolized by giants. Simultaneously, centralized architectures carry operational risks: policy changes, service outages, or 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, pools the idle storage space of various participants into a network capable of replacing existing centralized clouds.
The permanent storage model replicates data across multiple distributed nodes, unaffected by the operational status of any single server, reducing dependence on a single platform.
On-chain Data Trading Market
AI development requires massive training datasets, but the existing data circulation market is highly closed, with Hugging Face and major cloud vendors monopolizing profits and pricing power. Data creators receive minimal returns, 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 transact directly via smart contracts, with compensation distributed transparently. In contribution reward models like Grass, individuals connect idle bandwidth for AI data collection and receive rewards proportional to their contribution's value.
Model Inference Verification & Privacy Protection
Traditional AI is a black-box system; it's impossible to externally verify if model operations are compliant or if sensitive user data is handled securely.
Zero-Knowledge Machine Learning (ZKML) overlays cryptographic verification mechanisms on the AI inference layer, achieving both privacy protection and auditability. Model computations still occur off-chain, but the process generates cryptographic proofs demonstrating strict adherence to preset rules.
These proofs are recorded on-chain, not the underlying data. For example, in an automatic health insurance claims scenario, a hospital only uploads a proof of compliant AI operation without sharing complete patient records; the insurer verifies the proof's authenticity to process 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 into autonomous economic entities. Existing financial systems are designed for human consumption behavior and are inherently ill-suited for machine-dominated payment scenarios.
The agent economy requires millisecond-level, high-frequency microtransactions 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 safety measures to prevent unintended actions.
Protocol-based settlement mechanisms use stablecoin payment protocols (e.g., x402) to settle microtransactions and high-frequency payments in real-time, bypassing currency conversion and approval processes.
Blockchain + AI vs. Traditional AI Industry Chain
The capital logic of the traditional AI industry chain revolves around 'removing development bottlenecks.' As AI demand expands, memory, power, and data transmission bandwidth sequentially become constraints. Companies that can quickly resolve these bottlenecks (e.g., high-bandwidth memory manufacturers, power infrastructure firms) receive massive funding and market cap increases. The market is willing to pay high valuations for solutions that remove growth barriers.
Blockchain + AI projects do target real industry pain points but consistently fail to garner comparable market attention. If these issues were truly urgent, large-scale adoption and transformation would have already occurred.
Even if tracks like decentralized computing and data provenance possess reasonable value, they struggle to attract mainstream capital. The core contradiction lies in the severe disconnect between the needs of technology supply and the capital-holding buyers.
The AI industry's development pace is intense. Buyers (primarily large tech companies and enterprise clients) invest heavily in solutions that 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 transmission speed became a bottleneck for model training, massive capital 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 enterprises that can eliminate constraints and drive progress.
The fundamental issue of the blockchain + AI track is misalignment. Enterprises with large budgets only care about short-term performance gains and cost reductions; whereas blockchain AI projects focus on what enterprises perceive as secondary, long-term future issues. The technological vision on the supply side does not match the immediate operational needs on the demand side.
Insufficient Technical Prowess
Many projects have demonstrated the potential and design philosophy of decentralized infrastructure through benchmarks but have failed to achieve disruptive technological breakthroughs sufficient to challenge the entrenched market dominance 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 justify the switching costs for enterprises. Apple's shift from Intel chips to its own M1 chips carried the huge risk of software compatibility failures. The decision was supported by a threefold efficiency gain—a benefit substantial enough to cover the transition cost.
Currently, blockchain + AI cannot provide a compelling enough value proposition for enterprise clients requiring petabyte-scale data synchronization and ultra-low latency, making them unwilling to bear migration risks.
Structural Supply-Demand Mismatch
Some decentralized computing projects offer service-level agreements to mitigate enterprise risk, but enterprises remain hesitant. The root cause isn't the contract but the underlying structure: leading cloud providers can offer dedicated, isolated data centers; blockchain networks rely on dispersed, anonymous nodes for computing power.
If a node goes offline, interrupting a model training run worth hundreds of millions, neither token refunds nor cash compensation can make up for the enterprise's lost time and commercial opportunities. For enterprises in fierce competition, system stability is a non-negotiable baseline. Even with配套 risk hedging tools, enterprises have no incentive to accept the inherent uncertainty of decentralized networks.
Immature Market Demand
Blockchain agent frameworks target mature ecosystems with multi-agent collaboration and autonomy, but the mainstream market's development stage is far from this vision.
While companies like Microsoft and Salesforce are accelerating AI agent deployment, 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 enterprise networks. Currently, most enterprises are still refining the stability and ROI of their existing AI systems. Cross-network, multi-agent collaboration is not at all on the priority list for their infrastructure planning.
The current low demand is a development cycle issue, not a technical flaw. Blockchain agent infrastructure is better positioned as long-term foundational development 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 minimal proactive demand for deploying privacy infrastructure. It's difficult to rely on voluntary enterprise action to drive large-scale adoption; industry demand will likely be catalyzed by regulatory standards, with technology then implemented to meet compliance requirements.
Global regulatory details like the EU AI Act continuously refine and offer potential benefits for the track. When data traceability and security become hard legal requirements, blockchain's verification capabilities will transition from optional features to mandatory compliance items for enterprise AI deployment.
Regulatory完善 is not an industry constraint but a catalyst for market formation. Clear regulations reduce industry uncertainty, opening stable pathways for blockchain + AI adoption in institutional markets.
Lack of Landmark Adoption Cases
The叠加 of multiple structural矛盾 gives rise to the most critical barrier: the absence of convincing, large-scale landmark cases demonstrating商业 value. The traditional AI industry relied on ChatGPT to create a growth flywheel—a爆款 product visible to all attracted massive capital and talent for continuous iteration.
To date, the blockchain + AI track has no product-market fit案例 of comparable scale. Beyond early community hype, no project has penetrated enterprise production or everyday consumer scenarios, failing to gain the attention of traditional institutional capital. The lack of landmark adoption cases is the biggest barrier deterring conservative institutional funds and slowing industry普及.
Does Blockchain + AI Possess Long-term Value?
Setting aside short-term market hype, blockchain + AI has not yet secured a firm foothold in the mainstream AI industry chain, but this doesn't mean their combination lacks value.
The core reason for the track's cold reception is not a contradictory logic in the技术组合, but a mismatch between mature industry demand and the direction of technology supply within each sub-track.
The core demands of the traditional AI industry are very clear: short-term performance improvement, cost optimization, and极致 infrastructure stability. In contrast, most blockchain AI solutions focus on data ownership, computational transparency, and decentralization.
These are not the bottlenecks急需解决 by the industry at present; their adoption often requires performance trade-offs, making the ROI难以说服 enterprises.
Before the AI boom, power infrastructure companies were typically categorized as mature, slow-growth enterprises. The surge in power demand driven by data centers changed that, after which they attracted significant market attention. The current indifference towards blockchain AI may reflect a similar lag effect, where the value of infrastructure isn't fully recognized until a new paradigm emerges.
During this transitional period, it's crucial how the industry responds to the market's actual needs.
The path forward divides into two directions: 1) Proactively adapt to the standards of the mature AI industry chain, addressing short-term performance shortcomings. 2) Persist with the existing technological路线, continuously developing the远期 infrastructure suited for下一代 AI大规模 adoption.
The ultimate direction of blockchain + AI depends on which route aligns with future真实 market demand.
