AI Sweeps the Globe, Why Is Crypto + AI So Bleak?
TechFlow Selected TechFlow Selected
AI Sweeps the Globe, Why Is Crypto + AI So Bleak?
Does Crypto + AI Have Long-Term Value?
Navigating Web3 tides with focused insightsAuthors: Ekko An, Ryan Yoon
Compiled by: Chopper, Foresight News
TL;DR
- Against the backdrop of booming artificial intelligence, we need to evaluate the blockchain industry from the demand side: What problems does it solve that existing systems cannot, and what unique capabilities does it bring?
- Decentralized compute and decentralized storage indeed have rational logic such as data sovereignty and cost advantages, but they have not yet formed a technically advantageous position with absolute persuasiveness, insufficient to make enterprises deeply bound to traditional cloud service providers bear the switching risk.
- Model verification and privacy encryption technologies cannot solve the urgent business pain points of enterprises at present; enterprises will not actively implement them on a large scale; demand in this sector will likely lag behind the introduction of regulatory policies, with the EU AI Act being a typical precedent: standards are introduced first, and market demand follows.
- The bottleneck in the AI agent underlying infrastructure sector does not lie in technology. The focus of mainstream enterprises at this stage is internal process automation, while blockchain projects are developing underlying infrastructure for the next phase; market demand maturity cannot keep up with the speed of technological development.
- AI agent payments are the only sector where blockchain and traditional finance stand on the same starting line; neither side has properly solved industry pain points, and it is also the only sub-sector currently possessing conditions for direct competition.
- Overall, the dilemma of the Blockchain + AI sector is not that the logic of combining the two is self-contradictory, but rather a severe mismatch between supply and demand. The four major sub-sectors each have unique demand absence issues; only the AI agent payment sector possesses the conditions to directly participate in market competition at present.
AI Explodes Comprehensively, While the Blockchain Sector is Left Far Behind
The AI industry is ushering in an unprecedented boom in capital and infrastructure investment; the large model ecosystems built by major tech giants have fully penetrated public life and industrial production. The crypto industry is also iterating rapidly, attempting to find technical integration points with AI.
Early exploration directions focused on supplementing and replicating traditional AI industry chain links: decentralized GPU compute supply, data ownership confirmation, cryptographic model verification. Recently, the industry focus has shifted to solving pain points difficult for centralized architectures to overcome, including autonomous on-chain interaction of AI agents and real-time automatic settlement between machines.
Generically summarizing the entire sector as "AI + Blockchain" will only obscure the real differences in sub-fields; we need to conduct rigorous demand-side analysis: What problem does each sub-sector target? Can blockchain-native solutions provide truly differentiated solutions?
Four Sub-Sectors
Decentralized Compute
The current cloud market relies heavily on a few top tech enterprises controlling compute resources. High-performance GPU procurement is difficult and costly; AI startup teams and research institutions unable to build large-scale infrastructure face extremely high entry barriers.
Centralized platform resources tend to favor large clients; massive idle GPU compute in the market lacks neutral channels for allocation.
Decentralized compute resolves resource concentration and inefficiency issues through two models. The sharing economy model aggregates idle GPU resources from individuals and small data centers to build a unified compute network, bypassing tech giant monopolies and creating an elastic supply system.
The distributed compute model allows users to lease compute globally without relying on single-service provider hardware, improving idle hardware utilization and lowering the threshold for high-performance compute usage.
Decentralized Storage
Existing data storage systems rely almost entirely on centralized cloud service providers like Google and Meta. After users upload data, actual data ownership transfers to the platform, and AI training data is monopolized by giants long-term. Meanwhile, centralized architectures carry operational risks: policy changes, service interruptions, and platform failures can all lead to data becoming inaccessible or even permanently lost.
Decentralized storage solves these structural issues in two ways. The sharing economy model, represented by Filecoin and Arweave, pools idle storage space from various participants into a network that can replace existing centralized clouds.
The permanent storage model multi-backups data on 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 existing data circulation markets are highly closed; Hugging Face and major cloud vendors monopolize revenue and pricing power. Data creators earn meager returns, and data contribution incentive mechanisms lack transparency.
On-chain trading markets use smart contracts to remove intermediaries and establish transparent trading rules. In direct trading models like Ocean Protocol, data owners and AI developers trade directly via smart contracts, with rewards distributed transparently. In contribution reward models like Grass, individuals connect idle bandwidth to AI data collection and receive corresponding rewards based on their contribution value.
Model Inference Verification and Privacy Protection
Traditional AI belongs to black-box systems; externally, it is impossible to verify whether model computations are compliant or whether sensitive user data is processed securely.
Zero-Knowledge Machine Learning (ZKML) overlays cryptographic verification mechanisms on the AI inference layer, simultaneously achieving privacy protection and auditable traceability. Model computations are still completed off-chain, but the computation process generates cryptographic credentials proving the entire process strictly follows preset rules.
This proof is recorded on-chain, not the underlying data. For example: in an automatic medical insurance claims scenario, hospitals only upload AI computation compliance credentials without needing to upload complete patient medical records; insurance companies verify the authenticity of the credentials to complete claims, never accessing 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 into autonomous economic entities. The existing financial system is designed based on human consumption behavior and naturally cannot adapt to machine-dominated payment scenarios.
The agent economy requires millisecond-level high-frequency small-value transactions and cross-border real-time settlement, which traditional financial infrastructure struggles to bear.
On-chain agent infrastructure solves this problem through two mechanisms. The autonomous execution and control mechanism assigns unique wallets and identities to AI agents, enabling them to sign transactions directly, and sets configurable spending limits and security measures to prevent accidental behavior.
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.
Differences Between Blockchain + AI and the Traditional AI Industry Chain
The capital logic of the traditional AI industry chain revolves around "breaking development bottlenecks." As AI demand expands, VRAM, electricity, and data transmission bandwidth successively become shortcomings; enterprises that can quickly solve bottlenecks (such as high-bandwidth memory manufacturers and power infrastructure enterprises) will gain huge financing and market cap increases. The market is willing to pay high valuations for solutions that break growth bottlenecks.
Blockchain + AI projects indeed target real industry pain points but consistently fail to gain equivalent market attention. If these issues were truly urgent, the market would have already seen large-scale implementation and transformation.
Even though sectors like decentralized compute and data ownership confirmation possess rational value, they struggle to attract mainstream capital; the core contradiction lies in the severe disconnection between the technology supply side and the demands of purchasers holding funds.
The AI industry develops at a compact pace; buyers (mainly large tech companies and enterprise clients) will invest heavily in solutions that can solve their current operational bottlenecks fastest. They will not spend time evaluating untested infrastructure. Their primary considerations are compute performance, infrastructure reliability, and measurable ROI.
For example: when data transmission speed became a bottleneck for model training, massive funds flooded into fiber optic infrastructure to replace copper cables. When memory bandwidth became the main constraint, SK Hynix and Samsung Electronics solved this problem by providing high-bandwidth memory, thereby rising to prominence globally. This pattern is consistent: capital follows enterprises that can eliminate constraints and drive progress.
The fundamental problem of the Blockchain + AI sector is positioning deviation. Enterprises holding large budgets only value short-term performance improvements and cost reductions; whereas Blockchain AI projects delve deeply into long-term issues that are secondary and distant in the eyes of enterprises. The supply-side technical vision cannot match the demand-side current operational needs.
The supply-side technical vision cannot match the demand-side current operational needs.
Insufficient Technical Hard Power
Many projects prove the potential and design logic of decentralized infrastructure through benchmarks but fail to achieve disruptive technical breakthroughs, insufficient to shake deeply entrenched centralized cloud vendors in the market (AWS, GCP, etc.).
Centralized cloud platforms already hold massive funds and mature infrastructure; for new technology to seize market share, it must possess overwhelming performance advantages to make enterprises willing to bear switching costs. When Apple switched from Intel chips to self-developed M1 chips, it needed to bear the huge risk of software compatibility crashes; what supported its decision was the advantage of tripled energy efficiency, a benefit sufficient to cover the transformation cost.
However, Blockchain + AI currently cannot provide a sufficiently persuasive benefit logic to enterprise clients requiring PB-level data synchronization and ultra-low latency; enterprises are unwilling to bear migration risks.
Structural Supply-Demand Mismatch
Some decentralized compute projects launch Service Level Agreements to reduce enterprise risk, but enterprises remain on the sidelines; the root of the problem lies not in the contract but in the underlying structure: top cloud service providers can offer dedicated isolated server rooms; blockchain networks rely on dispersed, anonymous nodes to provide compute.
Once a node goes offline, interrupting model training worth hundreds of millions, token refunds or cash compensation cannot make up for the time costs and business opportunities lost by the enterprise. For enterprises in fierce industry competition, system stability is a non-negotiable bottom line. Even with supporting risk hedging tools, enterprises have no motivation to take on the uncertainty inherent in decentralized networks.
Market Demand Not Yet Mature
Blockchain agent frameworks face a mature ecosystem of multi-agent collaborative autonomy, but the mainstream market development stage is far from reaching this vision.
Although enterprises like Microsoft and Salesforce are accelerating AI agent implementation, they are currently all focused on intranet process automation. The infrastructure built by blockchain projects serves the next phase: autonomous agents running independently across external inter-enterprise networks. At present, the vast majority of enterprises are still polishing the stability and ROI of existing AI systems; cross-network multi-agent collaboration is completely not on the priority list of enterprise infrastructure planning.
Low demand at this stage is a development cycle issue, not a technical defect. Blockchain agent infrastructure is better positioned as long-term infrastructure layout for the future agent economy, rather than short-term monetization business.
Regulation
Zero-knowledge proofs and privacy encryption technologies are core solutions for building trustworthy AI, but in the early stage of AI popularization, enterprises' active demand for implementing privacy infrastructure is extremely low. It is difficult to rely on enterprises voluntarily promoting large-scale implementation; industry demand will likely be spawned by regulatory standards, with technology then implementing compliance requirements.
Global regulatory details such as the EU AI Act continue to be refined, bringing benefits to the sector. When data provenance and data security become hard legal requirements, blockchain's verification capability will change from an optional function to a compliance necessity for enterprises implementing AI.
Regulatory perfection is not an industry constraint but a catalyst for market formation. Clear regulations reduce industry uncertainty, opening stable implementation channels for Blockchain + AI in the institutional market.
No Benchmark Implementation Cases
Multiple structural contradictions superimposed derive the core obstacle: there are no persuasive large-scale benchmark cases proving commercial value. The traditional AI industry relies on ChatGPT to form a growth flywheel, a blockbuster product visible to everyone, attracting massive capital and talent for continuous iteration.
The Blockchain + AI sector still has no product-market fit cases of equivalent magnitude. Except for early community hype, no projects have penetrated enterprise production or public daily consumption scenarios, failing to gain the attention of traditional institutional capital. The lack of benchmark implementation cases is the biggest barrier discouraging conservative institutional funds and delaying industry popularization.
Does Blockchain + AI Possess Long-Term Value?
Setting aside short-term market hype, Blockchain + AI has not yet gained a foothold in the mainstream AI industry chain, but this does not mean the combination of the two has no value.
The core reason for the sector cooling is not a contradiction in the technical combination logic, but rather that every sub-sector exists with a misalignment between mature industry demand and the direction of technical supply.
The core demands of the traditional AI industry are very clear: short-term performance improvement, cost optimization, extreme infrastructure stability; whereas the vast majority of Blockchain AI solutions focus on data ownership, computation transparency, and decentralization.
These are not bottlenecks the industry urgently needs to solve at present; implementation often requires sacrificing performance, and the ROI is difficult to persuade enterprises.
Before the AI boom arose, power infrastructure companies were usually classified as mature, slow-growth enterprises. The surge in power demand driven by data centers changed this status quo; thereafter, they attracted significant market attention. The current indifference towards Blockchain AI may also reflect a similar lag effect, where the value of infrastructure has not yet fully manifested before the new paradigm emerges.
In this transition period, what is important is how the industry responds to actual market demands.
The path forward divides into two directions: 1) Actively adapt to mature AI industry chain standards, making up for short-term performance shortcomings; 2) Persist with the current technical route, continuously laying out long-term infrastructure adapted for the next generation of AI large-scale implementation.
The ultimate direction of Blockchain + AI depends on which route can match future real market demands.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@tiger_research
