Encryption AI has been shut out by Nvidia - why is it difficult for encryption projects to gain mainstream recognition?

Written by: Luke, Mars Finance

In the wave of artificial intelligence (AI), crypto AI projects are like a dark horse, trying to subvert the steady pace of traditional AI with blockchain and token economy. However, NVIDIA's Inception Program, the "temple" of AI start-ups, has erected an invisible threshold for "cryptocurrency"-related projects. Although projects such as Hive Intelligence broke through barriers with AI cores in April 2025, it is still difficult for encrypted AI as a whole to reach NVIDIA's elegant hall. What is it that keeps these projects blocked?

Crypto AI vs Traditional AI: A Clear Distinction in the Race

Traditional AI is the backbone of technology, driving transformations from medical diagnostics to autonomous driving. It focuses on algorithm optimization, model training, and enterprise applications, with funding primarily from venture capital or corporate partnerships, a relatively mature regulatory environment, and a clear development path. In contrast, Crypto AI takes a different approach by integrating AI with blockchain or cryptocurrencies, exploring decentralized computing, optimized crypto transactions, or token incentives. For example, ai16z supports AI Agent development through a DAO, Bittensor builds a decentralized model marketplace, and Fetch.AI focuses on decentralized machine learning networks. These projects often rely on token sales or DeFi fundraising, carrying the passion for innovation but also the burden of market volatility and regulatory pressures.

The differences between the two are as stark as day and night. Traditional AI reigns supreme with technology, steadily advancing towards enterprises and scientific research; while crypto AI meanders between the dreams of decentralization and the shadows of speculation, with the volatility of its token economy making it seem "not orthodox enough." This division lays the groundwork for Nvidia's selection criteria.

NVIDIA's Startup Acceleration Program: The Harsh Gatekeeper of AI

NVIDIA's Inception Program is a beacon for AI startups, providing cutting-edge GPU resources, technical guidance, and industry connections to help companies take off in generative AI, data science, and more. The selection criteria may seem loose – only a registered company and a business plan are required, but in fact the combination of technological innovation and market potential is preferred. Rumors that Nvidia has set limits on "cryptocurrency" projects stem from the statement of its chief technology officer Michael Kagan in 2023: cryptocurrencies are "of no value to society" and are far less transformative than AI. This view seems to cast a shadow over crypto AI, but the selection of projects such as Flux (2023) and Hive Intelligence (2025) shows that Nvidia is not rejecting all of them, but is selective.

Nvidia's caution is not without reason. The cryptocurrency boom in 2017 led to a shortage of GPUs, causing complaints from gamers; in 2022, the SEC fined Nvidia $5.5 million for failing to disclose mining revenue. These lessons have made Nvidia particularly sensitive to the risks associated with crypto-related projects, preferring to focus on the "orthodox" path of AI.

Why is encrypted AI hard to gain favor? The multiple considerations behind it.

Nvidia's distancing from crypto AI is not a simple rejection, but rather a comprehensive consideration based on strategy, legality, and reputation.

Strategic Preference: Nvidia is committed to driving breakthroughs in AI in fields such as healthcare and autonomous driving, while the stable output of traditional AI aligns more closely with its vision. If crypto AI focuses on token speculation or crypto trading, it may deviate from this core objective.

Legal Risks: Token issuance may touch upon securities regulations, triggering regulatory scrutiny. Nvidia's experience with SEC fines makes it wary of similar risks, while the compliance of traditional AI is more appealing.

Reputation concerns: The cryptocurrency industry is often plagued by negative images of scams and high-energy-consuming mining. As a technology giant, Nvidia must maintain the purity of its transformative brand and approach potentially controversial projects with caution.

Resource allocation: The startup acceleration program has limited resources, and Nvidia prioritizes supporting AI projects that deeply collaborate with the GPU ecosystem, such as generative AI, rather than crypto projects that rely on token economies.

Nevertheless, the selection of Hive Intelligence indicates that if crypto AI is technology-centric and blockchain serves as an auxiliary, there is still a possibility of knocking on the door.

Limitations of Crypto AI Projects: Examples of ai16z, Bittensor (TAO), and Fetch.AI (FET)

To gain a deeper understanding of the challenges posed by crypto AI, the following analysis examines the shortcomings and limitations of three representative projects—ai16z, Bittensor (TAO), and Fetch.AI (FET), revealing their bottlenecks in technology, market, and ecology.

ai16z: Ecological Dependence and Community Governance Risks

Project Overview

ai16z is an investment DAO (Decentralized Autonomous Organization) that combines AI and blockchain, supporting AI Agent development through its Eliza framework, with a market capitalization of $AI16Z breaking $2.55 billion by early 2025. Its ecosystem includes tokens like $DEGENAI and $ELIZA, attracting developers and investors through community-driven and airdrop mechanisms. ai16z is seen as a leader in crypto AI, akin to "iOS" in the era of smartphones.

Limitation Analysis

Ecological Complexity and Dependence:

The success of ai16z relies on the network effects of its Eliza framework, but the abundance of tokens within the ecosystem (such as $ELIZA and $METAV) increases coordination difficulties. The open-source nature of the framework attracts developers but may also lead to fragmentation, making it hard to establish unified standards. Predictions for 2025 indicate that new protocols may challenge its position through low-latency inference or no-code tools, and ai16z needs to continue innovating to maintain its lead.

Governance and Speculative Risks:

As a DAO, ai16z's decision-making relies on community voting, but its high market value and airdrop mechanism may attract short-term speculators instead of long-term developers. Users on platform X have already questioned the transparency of token distribution for similar projects, and ai16z needs to strengthen governance to avoid manipulation by interest groups.

Technical barrier:

The Eliza framework is very popular on GitHub, but its complexity may limit the participation of developers without a crypto background, narrowing the potential user base. Compared to standardized tools in traditional AI, the blockchain integration of ai16z increases the learning curve.

Improvement direction

ai16z needs to simplify the ecological structure, launch more user-friendly development tools, and strengthen governance transparency to balance speculation and innovation.

Bittensor (TAO): Inflation Pressure and Market Attention Diversion

Project Overview

Bittensor (TAO) is a decentralized AI model marketplace, where the token $TAO incentivizes miners to provide models and validators to assess quality. Its parachain (subnet) architecture supports tasks such as images and text, with a market capitalization expected to reach $3.9 billion by 2024, ranking among the top in the cryptocurrency market. The dTAO mechanism set to launch in the first quarter of 2025 will introduce independent tokens for subnets, optimizing incentive distribution.

Limitation Analysis

Inflation and Token Economy:

The high inflation plan of TAO has diluted the value of the token, and its price has not surged with the AI boom in 2024, lingering at levels from a year ago. Users on platform X criticize the lack of transparency in the initial allocation of over 5 million TAO, where a 70% high staking rate conceals an actual circulating market value of only 600 million USD, and the inflated market value issue undermines investor confidence.

Market attention diversification:

Bittensor's subnetworks (such as Nineteen.ai) surpass Web2 in inference speed, but market attention has shifted to AI Agent platforms, and TAO's "digital hive" model has not gained widespread recognition. Predictions for 2025 indicate that dTAO may bring a turning point, but subnetworks need to demonstrate actual application value.

Technical Complexity:

The subnet architecture of Bittensor is flexible yet complex, requiring miners and validators to stake TAO and have knowledge of blockchain, which limits the participation of non-crypto developers. Compared to traditional AI cloud computing platforms, its decentralized model is slightly inferior in terms of usability.

Improvement direction

Bittensor should optimize its inflation mechanism, accelerate the implementation of dTAO to enhance the attractiveness of subnets, and develop a more user-friendly interface to lower the technical barriers.

Fetch.AI (FET): Ecological Integration and Competitive Pressure

Project Overview

Fetch.AI (FET) supports scenarios such as DeFi and transportation through a decentralized machine learning network. In 2024, it will merge with SingularityNET and Ocean Protocol to form the ASI Alliance, with the token $ASI doubling in value. FET features an open and permissionless network, attracting developers and enterprise users.

Limitation Analysis

Merging Integration Challenges:

The ASI alliance integrates third-party resources aimed at accelerating decentralized AGI, but token migration and ecological integration increase complexity. Some users are concerned that the merger may weaken the independence of FET, and uneven resource allocation in the short term may affect development progress.

Competitive pressure:

In 2024, there are over 600 crypto AI projects, with a market value exceeding $70 billion. Fetch.AI faces competition from rivals such as Bittensor and NEAR Protocol. The AI Agent projects within the Solana ecosystem threaten its market share with low costs and high throughput, and FET needs to further optimize its performance and costs.

Insufficient application implementation:

Although FET has applications in DeFi and transportation, its actual adoption rate is lower than expected. Compared to traditional AI mature enterprise cases, FET's decentralized network still needs breakthroughs in commercialization and requires more flagship use cases to demonstrate its value.

Improvement direction

Fetch.AI needs to accelerate the integration of the ASI alliance, optimize network performance to meet competition, and promote the landing of landmark applications to enhance market recognition.

The Common Bottlenecks of Crypto AI and Nvidia's Examination

The limitations of ai16z, Bittensor, and Fetch.AI reveal common issues in crypto AI:

Token speculation: inflation or opaque distribution (such as TAO) undermines trust, and speculation obscures technological value.

Technical barriers: Blockchain integration increases development difficulty and limits non-crypto users' participation.

Market fragmentation: New protocols are emerging one after another, and existing projects need to continuously innovate to maintain competitiveness.

Regulatory Risk: Token economics may trigger scrutiny under securities regulations, increasing uncertainty.

These issues are directly related to Nvidia's screening logic. The acceleration program favors technology-driven, low-risk projects, while the speculative and complex nature of crypto AI may be seen as "not elegant." However, the community vitality of ai16z, the subnet innovation of Bittensor, and the open network of FET also show potential, and if optimized, there is still a possibility for breakthrough.

Knocking on the Door of NVIDIA: The Breakthrough Path of Crypto AI

If crypto AI wants to win the favor of NVIDIA, it needs targeted improvements:

Focus on AI core: highlight algorithm innovation or GPU-intensive applications, downplay token speculation.

Lower the threshold: develop user-friendly tools to attract traditional AI developers and expand the user base.

Transparent Governance: Optimize token distribution and community decision-making to enhance trust.

Real-world application: Create flagship use cases to demonstrate the commercial value of decentralized AI.

The success case of Hive Intelligence indicates that Nvidia has an open attitude towards technological innovation. Crypto AI needs to take AI as its banner and blockchain as an auxiliary, breaking free from the "speculative" label.

Conclusion: From being turned away at the door to breaking through the door.

NVIDIA's startup acceleration program is a testing ground for the intersection of AI and crypto, and its cautious approach to crypto AI reflects the game of innovation and risk. The robustness of traditional AI contrasts sharply with the disruptive nature of crypto AI, while the limitations of ai16z, Bittensor, and Fetch.AI—speculation, complexity, and competitive pressure—explain why they struggle to gain mainstream acceptance. However, the potential of crypto AI cannot be overlooked. The dream of decentralization and the integration of technology and economics are driving the industry forward. NVIDIA's "being shut out" is not the end, but a challenge. If crypto AI can wield technology as a sword and trust as a shield, it may open the door and write a new chapter in the dance between AI and blockchain.