AI is taking the world by storm, what is missing from Crypto+AI?
Written by Ekko an, Ryan Yoon
Compiled by Chopper, Foresight News
Original title: AI is taking the world by storm, why is Crypto + AI bleak?
TL; DR
In the context of booming artificial intelligence, we need to evaluate the blockchain industry from a demand-side perspective: what problems does it solve that existing systems can't solve, and what unique capabilities does it bring?
Decentralized computing power and decentralized storage do have reasonable logic such as data sovereignty and cost advantage, but they have not yet developed absolutely convincing technical advantages, which is not enough for enterprises that are already deeply tied to traditional cloud service providers to bear the risk of switching.
Model verification and privacy encryption technology cannot solve the company's current pressing business pain points, and the company will not actively implement it on a large scale; demand on this racetrack will probably lag behind the introduction of regulatory policies. The EU AI Act is a typical precedent: standards are introduced before market demand is followed up.
Technology is not the bottleneck in the underlying infrastructure circuit for AI agents. Mainstream companies are focusing on internal process automation at this stage, while blockchain projects are developing the next stage of low-level facilities, and the maturity of market demand cannot keep up with the pace of technological development.
AI smart payments are the only racetrack where blockchain and traditional financial platforms run on the same line. Neither side has properly solved the pain points of the industry, and it is currently the only segment with conditions for direct competition.
Overall, the blockchain+AI circuit's dilemma is not a logical contradiction between the two, but rather a serious mismatch between supply and demand. Each of the four major segments has a unique lack of demand. Only the AI smart payment circuit has the conditions to directly participate in the current market competition.
AI has exploded, yet the blockchain circuit has been far removed
The AI industry has ushered in an unprecedented boom in capital and infrastructure investment. The large-scale model ecosystem built by major tech giants has fully penetrated public life and industrial production. The crypto industry is also rapidly iterating, trying to find technical integration points with AI.
Early exploration focused on supplementing and replicating traditional AI industry chain links: decentralized GPU computing power supply, data authorization, and cryptographic model verification. Recently, the industry's focus has shifted to solving pain points that are difficult to overcome with centralized architectures, including autonomous on-chain interaction of AI agents and real-time automatic settlement between machines.
The general use of “AI+ blockchain” to summarize the entire circuit will only mask the real differences in the segmented field. We need to conduct a rigorous demand-side analysis: What issues does each segmented track target? Can blockchain native solutions provide truly differentiated solutions?
Four subdivided tracks
Decentralized computing power
Currently, the cloud market is highly dependent on a few leading technology companies to control computing power resources. High-performance GPUs are difficult and expensive to procure, and AI startup teams and research institutions that are unable to build large-scale infrastructure face extremely high entry barriers.
Centralized platform resources will be skewed towards large customers, and the massive amount of idle GPU computing power in the market lacks neutral channels for allocation.
Decentralized computing power solves the problems of resource concentration and inefficiency through two models. The sharing economy model aggregates idle graphics card resources from individuals and small data centers, builds a unified computing power network, circumvents the monopoly of tech giants, and creates a flexible supply system.
The distributed computing power model allows users to rent computing power globally without relying on hardware from a single service provider, improving the utilization rate of idle hardware, and lowering the threshold for using high-performance computing power.
Decentralized storage
The existing data storage system is almost entirely dependent on centralized cloud service providers such as Google and Meta. After users upload data, actual data ownership is transferred to the platform, and AI training data has been monopolized by giants for a long time. At the same time, centralized architectures have operational risks: policy changes, service interruptions, and platform failures can all cause data to become inaccessible or even permanently lost.
Decentralized storage addresses these structural problems in two ways. The sharing economy model, represented by Filecoin and Arweave, brings together the idle storage space of each participant into a network that can replace the existing centralized cloud.
The permanent storage model backs up data multiple times in distributed nodes, is not affected by the operating status of a single server, and reduces dependency on a single platform.
On-chain data exchange market
AI research and development requires massive training data, but the current data circulation market is highly closed, and Hugging Face and major cloud vendors have a monopoly on revenue and pricing power. Data creators earn very little, and the incentives for data contributions lack transparency.
The on-chain trading market uses smart contracts to remove intermediaries and establish transparent trading rules. Under direct transaction models such as Ocean Protocol, data owners and AI developers directly transact through smart contracts, and rewards are distributed in a transparent manner. In contribution reward models such as Grass, individuals connect idle bandwidth to artificial intelligence data collection and receive corresponding rewards based on the value of their contributions.
Model inference verification and privacy protection
Traditional AI is a black box system, and it is impossible to externally verify whether model computation is compliant or whether sensitive user data is handled safely.
Zero-knowledge machine learning (ZKML) uses AI inference to layer cryptographic code verification mechanisms, while achieving privacy protection and audit traceability. The model calculation is still completed off-chain, but the computation process will generate cryptographic certificates to prove that the entire process strictly follows the pre-set rules.
This proof is recorded on-chain, not the underlying data. For example, in an automated medical insurance claim scenario, hospitals only upload AI computing compliance certificates; there is no need to fully upload patient medical records; insurance companies can complete claims by verifying the authenticity of the documents, and cannot access the original private medical data throughout the process.
AI agent framework
AI agents are gradually becoming the core of traffic and value creation, evolving from tools to autonomous economic agents. The existing financial system is designed based on human consumption behavior, and is naturally unable to adapt to machine-dominated payment scenarios.
The smart economy requires millisecond high-frequency microtransactions and real-time cross-border settlement, making traditional financial infrastructure difficult to carry.
The on-chain agent infrastructure addresses this issue through two mechanisms. The autonomous execution and control mechanism assigns a unique wallet and identity to the AI body, enabling it to directly sign transactions, and set configurable spending limits and security measures to prevent unexpected behavior.
The protocol-based settlement mechanism uses stablecoin payment protocols (such as x402) to settle microtransactions and high-frequency payments in real time, bypassing currency conversion and approval processes.
The difference between blockchain + AI and traditional AI industry chains
The capital logic of the traditional AI industry chain revolves around “breaking development bottlenecks.” As demand for AI expands, video storage, electricity, and data transmission bandwidth have successively become shortcomings, and companies that can quickly solve card points (such as high-bandwidth memory vendors and power infrastructure companies) will reap huge amounts of financing and rising market capitalization. The market is willing to pay high valuations for solutions that break growth bottlenecks.
The blockchain+AI project did target real industry pain points, but it never received the same market attention. If these problems were really imminent, the market would have already experienced large-scale implementation transformation.
Even if decentralized computing power, data authorization, etc. have reasonable value, it is difficult to attract mainstream capital. The core contradiction is that there is a serious disconnect between the needs of technology suppliers and purchasers with funds.
The AI industry is developing at a rapid pace, and buyers (mainly large technology companies and enterprise customers) will invest on a large scale in solutions that can solve their current operational bottlenecks as quickly as possible. They don't spend time evaluating untested infrastructure. Their primary considerations are computational performance, infrastructure reliability, and measurable return on investment.
For example, when data transmission speed became a bottleneck in model training, large amounts of money poured into fiber infrastructure to replace copper cables. When memory bandwidth became a major constraint, SK Hynix and Samsung Electronics solved this problem by providing high-bandwidth memory, thereby gaining worldwide fame. The model remains the same: capital follows companies that remove constraints and drive progress.
The fundamental problem with the blockchain+AI circuit is positioning bias. Companies with large budgets only value short-term performance improvements and cost reductions; while blockchain AI projects are deep-seated, they are all secondary and long-term issues in the eyes of enterprises. The supply-side technical vision cannot match the current operational requirements on the demand side.
The supply-side technical vision cannot match the current operational requirements on the demand side.
Technical hardware and strength are insufficient
Many projects have proven the potential and design ideas of decentralized infrastructure through benchmarking, but they have failed to achieve disruptive technological breakthroughs, which is not enough to shake the market's deep-rooted centralized cloud vendors (AWS, GCP, etc.).
Centralized cloud platforms already have massive capital and mature infrastructure. If new technologies want to seize market share, they must have overwhelming performance advantages, so that enterprises are willing to bear the cost of switching. When Apple switched from an Intel chip to a self-developed M1 chip, it needed to bear the huge risk of software compatibility collapse. What supports its decision is the advantage of increasing energy efficiency by three times. This benefit is enough to cover the cost of transformation.
However, blockchain+AI is currently unable to provide sufficient persuasive revenue logic to enterprise customers requiring petabyte-level data synchronization and ultra-low latency, and enterprises are unwilling to take the risk of migration.
Structural mismatch between supply and demand
Some decentralized computing power projects have introduced service-level agreements to reduce enterprise risk, but companies are still watching. The root cause of the problem is not the contract, but the underlying structure: leading cloud service providers can provide exclusive isolated computer rooms; blockchain networks rely on distributed and anonymous nodes to provide computing power.
Once a node goes offline, model training worth hundreds of millions of dollars is interrupted, and neither token refunds nor cash compensation can make up for lost time costs and business opportunities for the enterprise. For companies in fierce competition in the industry, system stability is an uncompromising bottom line. Even with risk hedging tools, companies have no incentive to take on the uncertainty inherent in decentralized networks.
Market demand is not yet mature
The blockchain agent framework targets a mature ecosystem of multi-agent collaborative autonomy, but the mainstream market development stage is far from reaching this vision.
Although companies such as Microsoft and Salesforce are speeding up the implementation of AI agents, they are currently all focusing on automating intranet processes. The infrastructure built by the blockchain project serves the next stage: autonomous intelligence that operates independently across the enterprise's external network. Currently, the vast majority of enterprises are still refining the stability and return on investment of existing AI systems. Multi-agent collaboration across networks is completely out of the priority list of enterprise infrastructure planning.
The current slump in demand is a development cycle issue rather than a technical flaw. Blockchain smart infrastructure is better positioned as a long-term infrastructure layout for the future smart economy rather than a short-term monetization business.
custodial
Zero-knowledge proof and privacy encryption technology are the core solutions for building trusted AI, but in the early days of AI popularity, the active demand for enterprises to implement privacy infrastructure was extremely low. It is difficult to rely on enterprises to voluntarily promote large-scale implementation; industry demand is likely to be driven by regulatory standards, and technology will support the implementation of compliance requirements.
Continued refinement of global regulations such as the EU AI Act has brought benefits to the racetrack. When data traceability and data security become mandatory legal requirements, blockchain's verification capability will change from an optional function to a compliance requirement for enterprises to implement AI.
Regulatory improvement is not an industry constraint, but a catalyst for market formation. Clear regulations reduce industry uncertainty and open up a stable implementation channel for blockchain+AI in the institutional market.
There are no benchmark implementation cases
The superposition of multiple structural contradictions has given rise to the core obstacle: there is no persuasive large-scale benchmark case to prove commercial value. The traditional AI industry relies on ChatGPT to form a growth flywheel, a popular product that is visible to all, and attracts massive capital and talent to continue to iterate.
The blockchain+AI circuit has so far not had the same level of product market matching cases. Other than the early popularity of the community, no project penetrated enterprise production or the daily consumption scenario of the public, and was unable to gain the attention of traditional institutional capital. Lack of benchmark implementation cases is the biggest barrier to dissuading conservative institutional funding and delaying the popularity of the industry.
Does blockchain + AI have long-term value?
Leaving aside short-term market popularity, blockchain+AI has yet to gain a foothold in the mainstream AI industry chain, but that doesn't mean that the combination of the two is worthless.
The core reason for the cooling of the racetrack is not a logical contradiction in technology combinations, but rather the misalignment between mature industry demand and technology supply direction in every segmented track.
The core demands of the traditional AI industry are clear: short-term performance improvement, cost optimization, and ultimate infrastructure stability; the vast majority of blockchain AI solutions focus on data ownership, transparent computation, and decentralization.
These are not bottlenecks that need to be solved urgently in the industry right now. Implementation often requires sacrificing performance, and the input-output ratio is difficult to convince enterprises.
Prior to the rise of the AI boom, power infrastructure companies were generally classified as mature, slow-growing businesses. The surge in power demand driven by data centers changed this status quo, and since then they have attracted significant market attention. Currently, people's indifference to blockchain artificial intelligence may also reflect a similar lag effect, that is, the value of infrastructure has not been fully demonstrated until the new paradigm appears.
In this transition period, what matters is how the industry responds to the actual needs of the market.
The path forward is divided into two directions: 1) actively adapting to mature AI industry chain standards to make up for short-term performance shortcomings; 2) adhering to existing technology routes and continuing to lay out long-term infrastructure adapted to large-scale implementation of next-generation AI.
The final direction of blockchain+AI depends on which route can match actual future market needs.
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