After Fable was banned, will DeAI become the next breakout?

Author: CoinW Research

June 25, the dispute over Anthropic's model security, access control, and capability leakage escalated again. Anthropic publicly accused Alibaba of systematically extracting information related to Claude model capabilities through nearly 25,000 fraudulent accounts. This accusation further complicates the already stalled restoration process of Fable 5 and brings a core issue to the forefront once again: when frontier models simultaneously possess stronger cybersecurity, code analysis, and automation capabilities, model access, account risk control, cross-border usage, and capability leakage will all be incorporated into regulatory and platform governance frameworks.

To understand this dispute, we need to trace the timeline back to June 12. On that day, Anthropic's Claude Fable 5 and Mythos 5 were suddenly suspended from access, quickly drawing attention from the AI industry and the crypto market. Fable 5 was originally a Mythos-level model open to the public, with additional security restrictions layered on top to reduce the potential for abuse in high-risk areas such as cybersecurity and biosecurity. However, after a bypassable path was discovered in the security measures, the U.S. government imposed export controls restricting foreign nationals' access to the related models, and Anthropic subsequently expanded the access restrictions to all users. Around the same time, Microsoft also temporarily restricted internal employees from using the model due to Fable 5's data retention requirements. This chain of reactions indicates that enterprise customer concerns have already extended beyond the model's capabilities to include data retention, internal code, and trade secret protection.

Since then, expectations for Fable 5's restoration have been fluctuating. On June 18, U.S. government officials required Anthropic to prove that its security measures could not be bypassed before re-releasing. On June 22, the relevant API documentation pages reappeared in search results, but the actual call endpoints have not yet been restored. Polymarket predictions show that the market is still betting on Fable 5 eventually being restored: the probability of its restoration in the U.S. before the end of July is about 90%, and before the end of August is about 94%. This volatility itself indicates that access to frontier AI is no longer just a matter of product launch or shutdown, but the result of a combination of security proof, policy judgment, and platform execution.

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Thus, the key to the Fable 5 ban incident is not when a particular model will regain access, but that the structural boundaries of centralized frontier AI have been exposed collectively: the stronger the model's capabilities, the more easily it is simultaneously constrained by security reviews, export controls, enterprise data compliance, and platform permissions. For the crypto industry, this provides a fresh perspective to rethink DeAI. The significance of decentralized AI lies in attempting to use open computing power, distributed inference, on-chain incentives, privacy computing, and verifiable execution to weaken a single platform's control over model access, data processing, and execution processes. Following this thread, CoinW Research will first review the Fable incident, then sequentially dissect three types of gaps in centralized AI, the problems DeAI can address, three technical paths for verifiable AI computing, and the differentiation of representative projects across various infrastructure layers, finally returning to the realistic boundaries and long-term opportunities of DeAI.

1. Review of the Fable Incident: Not a Simple Model Shutdown

Main Event: Amazon Researchers Discovered Guardrail Bypass Path

The sensitivity of Fable 5 and Mythos 5 stems from their capabilities in cybersecurity tasks. Mythos 5 is primarily open to screened partner institutions for discovering and fixing software vulnerabilities; Fable 5 is a more widely released public version, retaining some Mythos-level capabilities while using security restrictions to prevent the output of offensive content.

The problem lay in these security restrictions. Public information shows that Amazon researchers discovered a bypassable path in Fable 5's guardrails during testing, after which Amazon CEO Andy Jassy expressed concerns to the White House. Subsequently, senior White House officials held multiple rounds of communication with Anthropic CEO Dario Amodei within 24 hours, demanding the company proactively take the model offline and address the vulnerability. Anthropic believed the bypass method was more of a localized issue and did not constitute a widespread "jailbreak"; the White House considered the security risk sufficient to trigger national security-level intervention.

The U.S. government subsequently imposed export controls on Fable 5 and Mythos 5, prohibiting foreign nationals from using the related models. Since Anthropic could not quickly and reliably identify the nationality and identity of all users, the company eventually suspended access for all customers. This step transformed the Fable incident from a model security dispute into a frontier AI access rights event.

Detail One: Dual-Use of Mythos-Level Capabilities

The core of the Fable controversy is not about ordinary Q&A, but about the increasingly blurred boundary between "defensive capabilities" and "offensive capabilities." A cybersecurity model can help enterprises discover vulnerabilities and patch systems, but it can also help attackers find entry points and automate exploit exploitation.

This is also why the government intervened quickly. If a model only writes copy or generates code, regulatory pressure is relatively limited; once it possesses strong vulnerability discovery and exploitation capabilities, it gets revalued within a national security framework. As a public version, Fable 5 intended to reduce risk through guardrails; when the guardrails can be bypassed, regulators see a "high-risk capability entry point that might be opened."

Detail Two: Microsoft's Usage Restriction Reveals Enterprise-Side Risks

Another thread of the Fable incident comes from Microsoft. Microsoft temporarily restricted employees from using Claude Fable 5 due to Anthropic's new data retention requirements. Fable 5's prompts and outputs may be retained for 30 days, and content flagged by the security system may be retained longer. Microsoft was concerned that employees might input customer data, company materials, or internal code during use, and if such content were retained and entered the investigation process, it could lead to compliance and competitive risks.

This detail is crucial. It shows that the risk of frontier AI has expanded from "whether the model is dangerous" to "whether enterprises can control their own data." When enterprises use AI, they care not only about the quality of the model's responses but also whether prompts are saved, whether data can be deleted, whether model calls comply with internal compliance, and whether the supplier might access sensitive content during security investigations.

Detail Three: Export Controls Raise AI Sovereignty Issues

The Fable incident also triggered broader discussions on AI sovereignty. The core of market skepticism is: while the U.S. government wants to promote American AI globally, it can also temporarily cut off access to frontier models for overseas users through export controls, leading global customers to reassess the reliability of U.S. AI supply.

This means the impact of the Fable incident will not be limited to Anthropic. Enterprises, countries, and developers all need to rethink the AI supply chain: if core models come from a few U.S. companies, is access stable; if enterprise workflows deeply depend on a certain model, could policy changes cause business disruptions; if security and compliance rules are determined internally by the platform, can external users obtain sufficient evidence?

At this point, the Fable incident is no longer an isolated model shutdown. The reason it truly triggers DeAI discussions is that three long-standing structural gaps of centralized AI have been simultaneously amplified: access rights are jointly determined by the platform and regulators, data flow remains within the platform, and the execution process of models and agents lacks externally verifiable evidence.

2. Gaps in Centralized AI: Access, Data, and Execution Are Unverifiable

Uncontrollable Access: Model Services May Be Cut Off by External Rules

The Fable incident proves that frontier models are no longer ordinary internet services. They are subject to national security, export controls, identity verification, partner feedback, and geopolitical factors. Once enterprises integrate R&D, code audits, risk control, customer service, or automated tasks into a single model, a sudden model suspension becomes a business continuity issue.

This risk has been underestimated by the market. Users typically only compare model capabilities, price, and response speed, but rarely consider "whether the model might suddenly become unavailable." After Fable was withdrawn, this risk was demonstrated in reality. In the future, when enterprises choose AI suppliers, they may consider redundant solutions, backup models, and cross-supplier switching capabilities, much like they do for cloud services.

Invisible Data: Enterprises Struggle to Confirm How Sensitive Information Is Handled

The core of Microsoft's restriction on Fable 5 was data retention. The stronger the model, the more likely it is to be integrated with source code, customer data, financial documents, strategy documents, and internal knowledge bases. At this point, whether prompts and outputs are retained, for how long, who can access them, and whether they are used for security investigations become key factors in whether enterprises adopt the model.

Centralized AI services typically keep these processes within the platform. Users can only read policy terms and cannot technically verify whether data is truly deleted, whether it enters a certain classifier, or whether it is accessed by a certain investigation process. Enterprises need clearer privacy statements and externally verifiable execution evidence.

Unverifiable Execution: Whether Security Layers Are Actually Effective Is Difficult for External Parties to Judge

The Fable controversy also centers on the security layer. The model claims to have restrictions, but external users cannot easily verify whether the restrictions are correctly executed every time. Model versions, system prompts, routing mechanisms, security classifiers, and output filtering all occur within the platform. Users see the answers but cannot see the execution path behind them.

In low-risk scenarios, this opacity can be tolerated; in finance, cybersecurity, code auditing, on-chain transactions, and asset management, it becomes a liability issue. Users need to know whether the model has been replaced, whether the execution environment is trustworthy, whether inputs and outputs have been tampered with, and whether the AI agent has exceeded its authority. The structural gap of centralized AI lies here: capabilities are increasingly powerful, but external verifiability mechanisms have not matured in tandem.

Thus, the question DeAI needs to answer becomes more specific: when model access may be cut off, are there alternative entry points; when sensitive data must enter the model workflow, can a provable processing environment be provided; when AI agents begin executing transactions, calling contracts, and managing permissions, can a chain of evidence be left for accountability? The importance of verifiable AI computing is revealed at this level.

3. What DeAI Can Solve: From Open Access to Trustworthy Execution

The Fable incident resonates in the crypto industry because it touches on a familiar issue: whether critical infrastructure can be shut down by a single entity. Bitcoin's core value is not just its asset price, but providing a global, permissionless, censorship-resistant value transfer network. AI is becoming new critical infrastructure. When model capabilities begin to affect code, security, enterprise processes, and asset execution, the market naturally asks: is a more open, switchable, and verifiable AI access and execution layer also needed?

This does not mean all AI must be trained via decentralized networks, nor that technology can completely bypass regulation. A more realistic view is that users will need two types of capabilities simultaneously: strong intelligence from centralized frontier models, and access redundancy, privacy protection, and verifiable execution from open networks. When models like Fable are suddenly suspended due to policy or platform rules, the market will re-understand the need for permissionless AI. Currently, the value of DeAI is mainly reflected in three aspects:

Solving Access Single Points: Reducing Dependency on a Single Model Supplier

DeAI can first mitigate the access single point issue. The Fable incident shows that frontier models may be suddenly cut off by policy or platform rules. At the product level, DeAI can reduce risk in three ways: first, introducing multi-model routing, allowing users to switch between centralized models, open-source models, and decentralized inference networks; second, through an open model market, enabling different models and inference services to connect freely, reducing a single supplier's control; third, through privacy inference entry points and local model combinations, allowing users to maintain backup paths for critical tasks.

In the short term, DeAI may not be able to train another Claude. The more realistic value is to ensure that critical workflows are not entirely dependent on a single model entry point. For ordinary users, this is access choice; for enterprises, it is business continuity; for countries and regions, it is part of AI sovereignty.

Solving Data Trust: Enabling Sensitive Computation in Provable Environments

The second value of DeAI is enabling sensitive computation with stronger provability. When enterprises and on-chain applications call AI, they often involve private data, code, trading strategies, or user assets. Trusted execution environments, remote attestation, privacy computing, and on-chain auditing can allow users to confirm whether sensitive data is processed in a protected environment.

The focus of this path is to allow users to obtain evidence about the execution environment without exposing privacy. For example, enterprises can require AI inference to occur in a trusted execution environment and confirm the running code and model version through remote attestation; on-chain applications can record task hashes, execution results, and proofs on-chain; users can confirm that the execution environment has not been arbitrarily replaced without disclosing original data. For finance, healthcare, enterprise compliance, and on-chain asset management, this is more important than simply pursuing stronger models.

Solving Execution Accountability: Leaving a Chain of Evidence for AI Agent Behavior

The third value of DeAI is establishing a responsibility chain for AI agents. In the future, AI agents will call wallets, exchanges, cloud services, enterprise systems, and on-chain contracts. They will move from answering questions to directly executing tasks. At that point, the market needs not only model outputs, but also execution logs, permission records, call paths, fund flows, and error accountability mechanisms.

On-chain systems are better suited to record such behaviors. Through on-chain logs, collateral, challenge mechanisms, and economic penalties, DeAI can transform AI execution from "platform backend operations" into traceable, verifiable, and accountable actions. For example, every time an agent calls a contract, reads data, initiates a transaction, or submits a result, an auditable record can be left; when a node submits an incorrect result, it can be reviewed and penalized through a challenge mechanism. The Fable incident truly drives this demand.

4. How DeAI Builds Trustworthy Execution: Three Paths for Verifiable AI Computing

From existing projects and research paths, verifiable AI computing is not a single technology but a combination of solutions centered on "execution environment, computation results, and execution behavior." Different paths solve different problems and have different implementation timelines.

Verifying the Execution Environment: First Confirm Where the Model Runs

The first path is Trusted Execution Environment (TEE), with the core being to prove that the model runs in a protected hardware environment. Users do not need to see the backend server but can use remote attestation to confirm that the code, model, and execution environment have not been arbitrarily tampered with. This type of solution is closer to real-world applications, suitable for enterprise private models, AI agent execution, financial risk control, and on-chain automation tasks.

Its advantage is relatively controllable cost and latency, allowing it to first solve the problem of "where the model runs and whether data is processed in a protected environment." The limitation is that it still depends on hardware vendors, TEE, and remote attestation mechanisms. If the underlying hardware or attestation mechanism fails, the verification foundation is also affected.

Verifying Computation Results: Attaching Proofs to AI Outputs

The second path is cryptographic proofs, with common directions including zero-knowledge proofs and zkML. Its goal is to generate a verifiable computation credential for AI computation, allowing third parties to confirm that the result indeed comes from the specified computation process without re-running the entire model.

This path is closer to "mathematical proof." Its advantage is stronger determinism, suitable for scenarios with extremely high requirements for result correctness; limitations include high proof generation costs, high latency, and limited support for large frontier models. Lightweight verifiable inference research has begun using sampling and commitment mechanisms to reduce costs, but moving from research to large-scale commercial use still takes time.

Verifying Execution Behavior: Making Errors and Overreach Costly

The third path is economic incentives and auditable logs. It does not require generating complete proofs for every AI inference immediately; the core lies in challenge, re-computation, sampling verification, collateral penalties, and on-chain records, making erroneous results and malicious behavior costly. Nodes submitting false results may lose collateral, while those who discover errors can receive rewards.

This path is especially important for AI agents. In the future, users will not only look at model responses but also at which interface the agent called, what permissions it used, whether it overstepped, and whether it executed according to authorization. Auditable logs turn AI behavior from backend operations into traceable records and may be implemented earlier than full verification of large models.

5. Representative Projects: DeAI Is Differentiating into Various Infrastructure Layers

Following the three verification paths outlined above, DeAI projects are differentiating into various infrastructure layers: Bittensor and Gensyn lean more toward intelligence supply networks, Venice leans more toward user entry points, while OpenGradient and Ritual are closer to verifiable computing and on-chain execution layers. The differences among these projects also illustrate that DeAI is a combined ecosystem centered on access, privacy, proof, and execution.

5.1 Bittensor: Using Subnet Mechanism to Screen Machine Intelligence Supply

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As one of the earlier and larger ecosystems in decentralized AI, Bittensor represents the open intelligence market route. It consists of numerous subnets, each functioning as a relatively independent machine intelligence market: miners produce digital commodities, covering computing power, storage, AI inference, training, financial predictions, etc.; validators evaluate the quality of miner outputs; subnet creators design incentive mechanisms; TAO holders can support validators through staking. The network ultimately distributes TAO incentives to participants deemed to have contributed more.

In terms of capital structure, Bittensor differs from typical equity financing projects. It has not conducted traditional private placements or ICOs; the core protocol is maintained by the Opentensor Foundation, and TAO did not reserve shares for early investors. However, this does not mean capital is absent: Polychain participated in incubating Bittensor as early as 2019 and accumulated approximately $200 million worth of TAO positions through secondary market, mining, and validation processes; Digital Currency Group continued to buy through its subsidiary Yuma, once becoming the largest holder with about 500k TAO, approximately 2.4% of the total supply.

From on-chain activity, the Taostats subnet page shows that Bittensor's subnet market 24-hour total trading volume is about 193.3k TAO, of which Alpha Tokens (native subnet tokens corresponding to each subnet, reflecting market pricing, staking, and capital flow of specific subnets) accounted for about 139k TAO, or 71.93%; Root TAO (the native TAO asset of the Bittensor mainnet, serving as the base asset for entering and exiting each subnet's Alpha Tokens) related trading volume was about 54.3k TAO, or 28.07%. This indicates that current trading activity mainly comes from specific subnet assets rather than the mainnet TAO side.

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Among current subnets, notable representatives include SN3 τemplar and SN64 Chutes: SN3 τemplar focuses on decentralized large model training; its team completed training the Covenant-72B model with 72B parameters on Bittensor Subnet 3, making it a representative subnet for Bittensor's training capabilities; SN64 Chutes focuses on Serverless AI inference, processing over 9.1 trillion tokens cumulatively, with a daily peak exceeding 50 billion tokens, making it a prominent inference subnet in terms of current usage. Meanwhile, CoinW has launched a TAO ecosystem section and has listed three subnets first: Chutes-SN64, Gradients-SN56, and Targon-SN4.

Bittensor has expanded from a single AI network into an open intelligence market with multiple tasks, assets, and incentive curves coexisting, decomposing different digital commodities such as AI inference, training, data, financial predictions, computing power, and storage into independent markets, supplied by miners, evaluated by validators, and allocated through token incentives.

More notably, some inference subnets have begun to strengthen result evaluation and verification layers. Here, "verification" is closer to an internal quality screening mechanism: miners submit model outputs or task results, validators evaluate result quality through scoring, backtesting, sampling review, benchmark tasks, and incentive rules, ultimately affecting the TAO incentives miners receive. Bittensor's value lies in turning "who can provide intelligence services" into an open competition problem; the challenge is significant quality differences among subnets, and verification standards and anti-cheat mechanisms determine whether the network can truly filter out high-quality AI services.

5.2 Venice: User-Side Privacy AI Entry Point

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Venice leans more toward DeAI application entry points. It integrates multiple AI capabilities such as text, images, video, audio, code, and search, emphasizing private or anonymous access. At the model level, Venice supports multiple entry points including Claude, Google, DeepSeek, OpenAI, Mistral, Meta, Qwen, Grok, Kimi, while also providing OpenAI-compatible APIs for integration with agent tool stacks, function calls, web search, and multimodal generation.

Venice was launched by ShapeShift founder Erik Voorhees in May 2024, with strong founder endorsement. Its funding and incentives rely more on tokens than traditional VC rounds. In January 2025, Venice issued its native token VVV on the Base network, with a genesis supply of 100 million tokens, about half distributed via airdrop to early users and the crypto AI community, with the remainder held by the project team, liquidity pools, and incentive funds. Later, Venice launched the DIEM token, forming a dual-token structure: each DIEM corresponds to a fixed daily API quota and can only be minted by VVV holders, thus binding token demand to actual platform computing consumption.

Returning to the product itself, Venice's differentiator lies in privacy tiers. It has four privacy architectures: anonymized access to third-party models, zero data retention on self-hosted open-source models, reduced platform visibility through TEE, and end-to-end encryption. For ordinary users, this is more understandable than underlying proof networks: users care about whether they can access, whether data will be saved, and whether calls will be used for training or review by the platform. After the Fable incident, this demand becomes more direct, because model disabling is not just a developer issue but also affects ordinary users' trust in AI tool continuity.

Venice addresses the DeAI user-side entry point issue. Underlying proof networks solve "whether computation can be verified," while privacy AI entry points solve "whether users can use AI safely, continuously, and with low friction." Venice cannot replace zkML or TEE execution layers, nor can it completely eliminate model provider restrictions, but it shows that DeAI commercialization paths do not have to start from the very bottom layer; users first perceive accessibility, switchability, low friction, and privacy protection.

5.3 OpenGradient: Putting Model Hosting, Verified Inference, and On-Chain Agents into One Network

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OpenGradient is closer to a full-stack verifiable AI computing network. It attempts to integrate model hosting, inference calls, x402 payments, on-chain agents, and the proof layer into a single developer network, rather than just providing one model entry point. The goal is to combine model deployment, calling, settlement, and trust proofs into the same developer workflow.

In terms of funding, OpenGradient completed an $8.5 million seed round in 2024, led by a16z, with participation from Coinbase Ventures, Symbolic Capital, Wintermute Ventures, GSR, among others. The investor base covers Silicon Valley AI capital, crypto trading infrastructure, and market-making institutions, a combination conducive to simultaneously advancing the developer ecosystem, on-chain settlement, and computing resource market.

From on-chain data, its Portal page latest data shows that the OpenGradient network has 4,448 models, approximately 874.9k Inference TXs, approximately 332.2k x402 transactions, with the current block height around 1,599,860; the average daily transactions over the past 30 days are about 2,510.

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From product data, OpenGradient has formed a complete path including model hosting, inference calls, x402 payments, on-chain agents, and the proof layer. Users can think of it as an AI computing market for developers: after models are hosted, they can be directly called; calls generate transactions and payments; key results are then made more trustworthy through zkML or TEE.

OpenGradient's advantage lies in its relatively complete product chain and the provision of verifiable on-chain usage data. The next stage requires observing two issues: whether transaction volumes can translate into sustained payments, and whether the demand for proofs can cover additional computational costs. Model count and inference count can be rapidly increased through incentives; the true determinant of network value is whether developers are willing to pay long-term for stable calls, privacy execution, and verifiable results.

5.4 Gensyn: From Computing Power Network to Machine Intelligence Market

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Gensyn is a project with prominent capital background and technical ambition among DeAI underlying networks. It initially started as a network aggregating idle GPU computing power, with the goal of gradually evolving into a more complete machine intelligence network, enabling training, inference, model collaboration, and intelligence services to be called and traded within an open network.

From a product structure perspective, the Gensyn network is no longer just a GPU scheduling layer. Its AXL component is used for exchanging weights, gradients, and signals between machine learning nodes; on-chain identity and reputation record the historical performance of models, agents, and computing nodes; verification mechanisms are used to confirm whether part of the computation was performed as required. Gensyn's Delphi information market further tests scenarios where humans and AI agents jointly participate in predictions, with settlement completed by AI oracles.

In terms of funding, Gensyn's capital background stands out among similar projects. In 2022, Gensyn completed a $6.5 million seed round led by Eden Block, with participation from Galaxy Digital, CoinFund, etc.; in 2023, it completed a $43 million Series A round led by a16z, with public financing totaling at least $49.5 million. The longer R&D cycle and sustained support from top capital allow it to simultaneously advance multiple technical lines such as distributed training, machine intelligence market, on-chain identity, and verification mechanisms.

Gensyn addresses the supply fragility after excessive concentration of frontier AI capabilities. The Fable incident shows that model access can be quickly cut off amid policy, regional, and corporate security strategies. Gensyn aims to make machine intelligence an open market that is accessible, competitive, and verifiable, so that model training, model collaboration, agent trading, and machine intelligence services do not entirely depend on a single platform. Its challenge is that decentralized training has high demands on bandwidth, data synchronization, gradient verification, and incentive design; in the short term, it is more likely to land in vertical models, open model optimization, agent collaboration, and prediction markets.

5.5 Ritual: Turning AI Tasks into Callable and Traceable On-Chain Execution

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Ritual enters the AI execution layer, focusing on how model calls, agent behaviors, and complex tasks can be orchestrated, executed, and settled directly on-chain, rather than remaining in off-chain black-box services. Ritual Chain uses an EVM architecture with off-chain verifiable machine tasks. Deterministic tasks like ordinary transfers and storage reads are still executed by the EVM through replicated execution; high-cost tasks like LLM inference, agent calls, and image generation are executed in TEE environments, with results bound to original requests and returned on-chain. System contracts such as AsyncJobTracker, TEEServiceRegistry, Scheduler, and AsyncDelivery manage task states, executor registration, scheduling, and result callbacks. Ritual also develops Infernet, allowing smart contracts to call models and external computations, positioning it closer to an "on-chain AI execution operating system."

In terms of funding, Ritual completed a $25 million funding round in 2023, led by Archetype, with participation from Accomplice, Robot Ventures, dao5, Avra, Hypersphere, etc.; in 2024, it brought in Polychain as a strategic investor, further strengthening its resource reserves in the crypto infrastructure direction.

Ritual's advantage lies in its proximity to real on-chain demand, suitable for automated trading, AI oracles, on-chain agents, machine payments, and complex task orchestration. Its focus is not training a stronger model but enabling model calls to enter smart contract permission and settlement systems. The risk is that TEE still relies on hardware trust roots; executor selection, async callback security, and developer barriers need continuous verification. Whether Ritual can achieve scale ultimately depends on whether on-chain applications are willing to entrust high-value AI tasks to this execution layer.

6. Realistic Boundaries: DeAI Cannot Solve All Problems Yet

Decentralized Training Still Faces Physical Constraints

The long-term value of DeAI must be built on realistic boundaries. Large model pre-training requires extremely high bandwidth, stable GPU clusters, massive high-quality data, and mature engineering systems. While decentralized networks can lower some computing power barriers, public internet communication, heterogeneous device coordination, and dataset quality all affect training efficiency. This does not diminish DeAI's value. A more realistic path is: the training layer first serves niche models and open model optimization; the inference layer first serves privacy, cost, and multi-model routing; the verification layer first serves proof and auditing for high-value scenarios; the execution layer first serves on-chain agents and automation tasks. The first area to mature in DeAI may be a complete set of trusted infrastructure formed around model calls.

Verification Capabilities Still Have Applicable Boundaries

Verifiable AI computing also has clear applicable boundaries. TEEs can prove the execution environment but require trust in hardware and remote attestation mechanisms; zkML can prove computation results but cost and latency remain constraints; economic incentives can make malicious behavior costly but require reasonable challenge mechanisms, collateral design, and validator incentives. Different solutions address different problems; a single "verifiable" label cannot summarize all capabilities. Therefore, when screening projects, one needs to see what exactly they prove. Proving model identity, proving execution environment, proving output results—these correspond to different product boundaries. The more clearly a project defines its verification target, the more likely it is to realistically meet enterprise and on-chain application needs.

Market Hype Does Not Equal Real Usage

The Fable incident will bring sentiment to the DeAI sector, but sentiment cannot directly translate into long-term value. What really needs to be observed is whether projects have sustained task demand, whether users are willing to pay for verifiability, whether network revenue comes from real calls, and whether verification costs are lower than the security premium users are willing to pay. DeAI without real usage will ultimately return to concept trading.

7. Summary: DeAI's Opportunity Lies in Rebuilding the AI Trust Layer

What truly deserves attention in the Fable incident is not that a particular Anthropic model was temporarily disabled, but that frontier AI has for the first time clearly exposed the structural contradiction between improved model capabilities and decreased access stability. In the past, the market typically assumed that stronger model capabilities would lead to higher adoption rates; however, the Fable incident shows that when models possess highly sensitive capabilities like cybersecurity, biosafety, and code execution, their operational boundaries are more easily incorporated into export controls, corporate compliance, and national security frameworks. The stronger the model, the more the platform needs to add security layers; the more complex the security layers, the harder it is for external users to verify execution; the deeper regulatory involvement, the more model access becomes not just a product-level issue. This means that future AI competition will not only revolve around model capabilities but will also extend to access stability, data controllability, and execution trustworthiness.

This is also where DeAI needs to be re-understood. In the short term, DeAI may not be able to replicate frontier models like Claude, but it can start from the weakest links of centralized AI: whether models can be replaced, whether data can be protected, whether computation processes can be proven, and whether agent behavior can be held accountable. Truly valuable DeAI projects are not simply migrating AI capabilities on-chain, but decomposing the AI call process into several verifiable steps: who provides the model, who executes inference, how results are generated, who bears responsibility for errors, and whether users can switch between different services. In the past, these questions were mostly hidden within centralized platforms; in the future, they may evolve into a new infrastructure market.

From this perspective, verifiable AI computing may be the most worthy direction for in-depth research in DeAI. AI is transitioning from a content generation tool to an intelligent entity capable of task execution. When AI is mainly used to generate text, users can tolerate a certain degree of opacity; but when AI begins to participate in code audits, asset management, wallet calls, trade execution, and contract interactions, opacity becomes systemic risk. The future market will not only pay for stronger model capabilities but also for provable, auditable, and accountable execution processes.

Therefore, after the Fable incident, the investment logic for DeAI also needs to shift from sentiment narratives to verification narratives. In the past, the market easily chased AI concepts, model names, and short-term hotspots; the next stage should focus more on three types of indicators: whether there is real call demand, whether a verifiable proof mechanism exists, and whether users are willing to pay a premium for trustworthy execution. Only when these conditions are met simultaneously can verifiable AI computing transition from a periodic crypto market hotspot to a new trust layer in the AI era. The core of future AI competition will no longer be just model capabilities themselves, but whether models can be called stably, trustworthily, and verifiably in an open environment. This is the long-term space that DeAI can truly open.