The AI circle has no time for blockchain.

Author: Ekko An, Ryan Yoon; Source: Tiger Research; Compiled by: BitpushNews

The artificial intelligence industry continues to surge ahead with no signs of cooling down. However, the situation in the "blockchain AI" space is quite different. Why has it failed to attract comparable attention?

Core Highlights

• Amid the AI boom, the blockchain industry needs to examine from a demand-side perspective: What problems does it solve that existing systems cannot, and what unique capabilities does it bring?

• Decentralized computing and storage indeed have a logical rationale in terms of data sovereignty and cost competitiveness. The obstacle is that neither has yet demonstrated a sufficiently compelling technical advantage to make customers already locked into existing cloud infrastructure willing to bear the migration risk.

• The problems addressed by model verification and privacy technologies have not yet reached a level of urgency that would prompt enterprises to take proactive action. Such demand is more likely to follow regulatory requirements rather than precede them. The EU AI Act is a typical model: standards come first, markets follow.

• In the category of agent frameworks, the bottleneck is not technical. Mainstream enterprises are still focused on internal workflow automation, while blockchain projects are already building the infrastructure layer for the next phase. Demand takes time to catch up with technology.

• Agent payments are the only field where blockchain stands on the same starting line as traditional finance. Neither side has solved this problem yet, making it the only category where both face the same challenge simultaneously.

• Overall, the blockchain AI industry is struggling not because the combination lacks rationality, but because of a mismatch: four categories each face different reasons why demand has not yet materialized, and only agent payments currently have the conditions to compete on equal footing.

1. Blockchain Projects Forgotten by the AI Boom

The artificial intelligence industry is experiencing unprecedented capital and infrastructure investment. The large language model ecosystem, dominated by big tech companies, has become a standard feature of daily life and industrial operations. Amid this rapid expansion, the cryptocurrency industry is also evolving quickly, seeking technical intersections with AI.

Early efforts focused on supplementing or replicating segments of the traditional AI value chain: decentralized GPU supply, data ownership restoration, and cryptographic verification. Recently, the focus has shifted to filling gaps that centralized architectures struggle to address, including on-chain autonomous activities of AI agents and real-time machine-to-machine (M2M) settlement.

Describing this field broadly as "AI plus blockchain" masks its complexity. A rigorous demand-side analysis is needed: What problem does each segment target? Does the blockchain-native solution offer a truly differentiated solution?

2. Functions of Each Category

2.1. Decentralized Computing

Today's cloud computing market structurally relies on a few large tech companies that control computing resources. High-performance GPUs are both scarce and expensive, creating steep barriers for AI startups and research teams that cannot access large-scale infrastructure.

Centralized systems concentrate resources among the largest buyers, and there is no neutral channel in the market to redistribute large amounts of idle GPU capacity.

Decentralized computing addresses this centralization and inefficiency in two ways:

• Sharing economy model: Projects aggregate idle GPU resources held by individuals and small data centers into a unified network, creating a more flexible supply chain outside the existing tech monopoly.

• Distributed computing model: Users can lease computing resources globally without relying on any single provider's infrastructure, thus improving hardware utilization and lowering the barrier to high-performance computing.

2.2. Decentralized Storage

Current data storage architecture relies almost entirely on centralized cloud infrastructure operated by companies like Google and Meta. When users upload data to these platforms, ownership effectively transfers to the platform, consolidating its monopoly control over AI training data. Centralized infrastructure also introduces operational risks: policy changes, service outages, or platform failures can cut off data access or lead to data loss.

Decentralized storage addresses these structural issues in two ways:

• Sharing economy model: Examples include Filecoin and Arweave, which pool participants' idle storage space into a network that can replace existing centralized cloud services.

• Permanent storage model: Data is replicated distributively across nodes to ensure data persistence independent of any single server's operational status, reducing reliance on any single platform.

2.3. Data Markets

AI developers need training data, but the current data distribution market operates in a closed manner, with large platforms like Hugging Face and cloud service providers capturing the economic benefits and controlling pricing. Data creators receive almost no compensation, and reward mechanisms for data collection and contribution lack transparency.

On-chain markets eliminate intermediaries through smart contracts and establish transparent transaction terms:

• Direct trading model: For example, Ocean Protocol, where data owners and AI developers trade directly via smart contracts, with compensation distributed transparently.

• Contribution reward model: For example, Grass, where individuals connect idle bandwidth for AI data collection and receive compensation proportional to their contribution value.

2.4. Model and Inference Verification/Privacy

Traditional AI systems operate like "black boxes," with no external means to verify whether the model is running correctly or whether sensitive user data is being handled securely.

Zero-Knowledge Machine Learning (ZKML) introduces a cryptographic verification layer for AI inference, enabling privacy protection and auditability. In this architecture, the model runs off-chain in the traditional way, but the computation generates a cryptographic proof that the process was executed correctly according to established rules.

What is recorded on-chain is this "proof," not the underlying data. For example: In an automated medical insurance claims service, a hospital only needs to submit a proof that the AI model ran correctly, without sharing the full medical records. The insurance company can verify the claim's legitimacy without accessing the original data.

2.5. AI Agent Frameworks

As AI agents become the primary core of traffic and value creation, they are evolving from tools into autonomous economic participants. Existing financial systems are designed around human consumption patterns and are structurally incompatible with machine-centric payment environments.

The agent economy requires micro-payments, high-frequency settlement, and millisecond-level cross-border payments that existing financial infrastructure cannot accommodate.

On-chain agent infrastructure addresses this through two mechanisms:

• Autonomous execution and control mechanism: Assigns unique wallets and identities to AI agents, enabling them to sign transactions directly, with spending limits and protections against unintended behaviors.

• Protocol-based settlement mechanism: Uses stablecoin payment protocols like x402 to settle micro-transactions and high-frequency payments in real time, bypassing currency conversion and approval processes.

3. Why Blockchain AI Has Strayed from the AI Value Chain

The AI value chain has formed around "sequential bottleneck elimination." As AI demand grows, memory shortages emerge, and power and data transmission capacities become constrained. Companies that quickly solve these problems (e.g., HBM manufacturers and power infrastructure providers) attract huge capital and market appreciation. The market clearly values solutions that remove hurdles to growth.

Although blockchain AI projects have identified real problems, they have not received comparable market attention. If these problems were as urgent as claimed, they should have already driven significant market shifts.

The reason blockchain AI projects fail to attract mainstream capital while promoting reasonable goals such as "reducing GPU centralization" and "restoring data sovereignty" is the huge gap between the priorities of technology suppliers and those of buyers controlling capital allocation.

The AI industry operates on competitive timelines, and buyers (mainly big tech companies and enterprise customers) invest massively in technologies that most quickly solve their immediate operational bottlenecks. They do not spend time evaluating unproven infrastructure. Their priorities are computational performance, infrastructure reliability, and demonstrable 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 solved this critical problem through High Bandwidth Memory (HBM), gaining global recognition. The pattern is consistent: capital follows those who remove constraints on progress.

The fundamental problem with blockchain AI is "positioning." Buyers with large capital budgets focus only on near-term performance improvements and cost reductions. In contrast, blockchain AI addresses issues that buyers consider "secondary" or "future state." The technical ambition on the supply side does not align with the immediate operational requirements on the demand side.

3.1. Technical Limitations

Some projects have demonstrated the potential and design philosophy of decentralized infrastructure using benchmarks. But the more fundamental issue is that this work has not yet produced the decisive technical leap needed to displace the established giants in the mainstream market.

For a new technology to take share from centralized cloud providers like AWS or GCP, which already have massive capital and infrastructure, it must offer a huge performance advantage so large that the gap with existing giants becomes irrelevant.

When Apple switched from Intel chips to M1 chips (taking the huge risk of breaking software compatibility), the rationale was a threefold improvement in power efficiency—a gap significant enough to make the switch worthwhile.

For enterprise buyers who require PB-level data synchronization and ultra-low latency as baseline conditions, blockchain AI has not yet provided a sufficiently clear case for them to accept the switching risk.

3.2. Demand Mismatch

In decentralized computing, some projects have introduced "Service Level Agreements" (SLAs) as risk mitigation mechanisms, but enterprise buyers remain unconvinced. The reason is structural, not contractual. Large cloud providers offer controlled, dedicated data centers. Blockchain networks rely on scattered, anonymous node participation.

If a node goes offline and interrupts a model training task worth hundreds of millions of KRW, no token compensation or financial reimbursement can recover the opportunity cost and time loss. For enterprise buyers operating on competitive timelines, system stability is not a negotiable parameter. Even with hedging mechanisms, most buyers have no incentive to bear the residual uncertainty risk.

3.3. Demand Has Not Yet Materialized

Blockchain agent frameworks are designed for complex ecosystems (i.e., multiple AI agents collaborating autonomously), but there is a maturity gap between this vision and the current state of the mainstream market.

Enterprise adoption of AI agents is accelerating under the leadership of companies like Microsoft and Salesforce, but the current focus is firmly on "workflow automation" within controlled internal networks. The infrastructure that blockchain projects are building targets the next phase: independent AI agents operating autonomously on external networks beyond any organizational boundaries. Most enterprises are still focused on establishing the stability and ROI of their deployed AI systems. Multi-agent collaboration across external networks is not yet a priority on enterprise infrastructure roadmaps.

The limited demand at this stage reflects "timing" rather than "technical failure." This should be understood more as a long-term infrastructure investment for the future agent economy rather than a near-term revenue opportunity.

3.4. Regulatory Prerequisites

Zero-knowledge proofs and privacy-preserving technologies are core solutions for building trust in AI, but in the early stages of AI adoption, enterprises have limited practical demand for privacy infrastructure. Voluntary enterprise adoption is unlikely to drive meaningful adoption; the more likely path is that regulatory standards create demand, and then technology follows.

The increasing specificity of global regulatory frameworks, including the EU AI Act, is a positive development in this regard. As legal requirements regarding data provenance and security become concrete, blockchain's advanced verification capabilities are expected to become compliance requirements in enterprise deployments rather than optional features.

Regulatory developments in this area are best understood as catalysts for market formation, not constraints. Clear regulatory standards reduce market uncertainty and thereby create a stable path for blockchain AI to build mainstream demand within an institutional framework.

3.5. Lack of Sufficient Use Cases

These structural factors combine to create a more fundamental problem: a lack of a "defining success case" that proves value at scale. The traditional AI industry established its current position through the adoption flywheel triggered by ChatGPT, leveraging a specific and widely visible product to attract the capital and talent needed to sustain further growth.

Blockchain AI projects have not yet produced equivalent product-market fit evidence at scale. Beyond early community enthusiasm, no project has demonstrated a level of adoption in enterprise operations or everyday consumer life sufficient to attract serious attention from mainstream capital. The lack of convincing reference cases remains the biggest obstacle to attracting conservative institutional investment that could accelerate broader adoption.

4. Is the Combination Valuable?

Blockchain AI has not yet found a foothold in the mainstream AI value chain. But does that mean the combination is meaningless?

Not at all.

The fundamental reason blockchain AI projects are currently overlooked is not that the combination logic is contradictory, but rather that in every segment, there is a misalignment between the requirements of the established industry and the direction offered by the technology.

The priorities of the traditional AI industry are clear: near-term performance, cost optimization, and rigorous infrastructure reliability. Many current blockchain AI proposals focus on data ownership, computational transparency, and decentralization.

These issues are not considered "immediate bottlenecks" by established industry players, and pursuing these goals often requires accepting performance degradation that is too costly relative to the benefits.

Before the AI boom, power infrastructure companies were widely classified as mature, low-growth enterprises. The surge in data center-driven power demand changed this, and they have since attracted significant market attention. The current coolness toward blockchain AI may reflect a similar lag—a transition period before a new paradigm creates conditions that reveal its value.

During this transition, the key lies in how the industry responds to real market demand.

The road ahead branches in two directions: one is to actively adapt to the existing AI value chain standards, bridging the immediate performance gap as quickly as possible; the other is to persist with current capabilities while continuing to build the infrastructure needed for the next generation of AI deployment.

The final outcome will depend on which choice aligns better with where demand is heading next.