Top 10 Predictions for Crypto AI in 2025: Total market capitalization of $150 billion, 99% of AI Agents will vanish.

With the rapid development of the AI industry, the field of Crypto x AI has quickly emerged. A researcher focused on this area has made 10 predictions for 2025, detailed as follows.

1. The total market capitalization of encryption AI tokens reaches 150 billion USD

Currently, the market capitalization of encryption AI tokens accounts for only 2.9% of the market capitalization of altcoins, but this ratio will not last long.

AI encompasses everything from smart contract platforms to meme, DePIN, Agent platforms, data networks, and intelligent coordination layers, and its market position is undoubtedly on par with DeFi and meme.

The reason for being confident about this:

• Encryption AI is at the intersection of two of the most powerful technologies.
• AI frenzy trigger event: An IPO or similar event from a large AI company could spark a global frenzy over AI. At the same time, Web2 capital has begun to focus on decentralized AI infrastructure.
• Retail frenzy: The AI concept is easy to understand and exciting, and retail investors can now invest in it through tokens. AI will spark a gold rush similar to the meme trend of 2024, but unlike memes, AI is genuinely changing the world.

2. Bittensor Revival

The decentralized AI infrastructure Bittensor has been online for many years and is a well-established project in the encryption AI field. Although AI has become very popular, its token price has remained at the same level as a year ago.

Today, Bittensor's digital hive mind has quietly made a leap: the registration fees for more subnets are lower, subnets perform better than traditional counterparts in practical indicators such as inference speed, and EVM compatibility will introduce DeFi-like functionalities into the Bittensor network.

The reason for Bittensor's comeback:

• Market-based emissions: dTAO directly ties block rewards to innovation and measurable performance. The better the subnet, the more valuable its tokens.
• Concentrated capital flow: Investors can ultimately target specific subnets they believe in.
• EVM Integration: Compatibility with EVM attracts a broader community of encryption native developers, bridging the gap with other networks.

3. Calculating the market is the next "L1 market"

The currently obvious major trend is the endless demand for computing.

The CEO of a well-known chip company once said that the demand for inference will grow "a billion times". This exponential growth will disrupt traditional infrastructure plans, and new solutions are urgently needed.

The decentralized computing layer provides raw computation ( for training and inference ) in a verifiable and cost-effective manner. Several startups are quietly building a solid foundation, focusing on products rather than tokens. As decentralized training of AI models becomes practical, the overall potential market will rise sharply.

Compared to L1:

• Just like in 2021: Do you remember the competition among multiple public chains for the "best" L1? A similar competition will arise between computing protocols, vying for developers and AI applications built on their computing layers.
• Web2 Demand: The cloud computing market size ranges from $680 billion to $2.5 trillion, making the encryption AI market look insignificant in comparison. If these decentralized computing solutions can attract even a small portion of traditional cloud customers, we could see the next wave of 10x or 100x growth.

4. AI agents will flood blockchain transactions

By the end of 2025, 90% of on-chain transactions will no longer be initiated by real humans clicking "send", but will instead be executed by a group of AI agents that continuously rebalance liquidity pools, allocate rewards, or execute small payments based on real-time data feedback.

Why did this change occur?

• No more human errors: smart contracts execute exactly as coded. Conversely, AI agents can process large amounts of data faster and more accurately than real humans.
• Micro Payments: These agent-driven transactions will become smaller, more frequent, and more efficient. Especially in cases where transaction costs on certain public chains are on a downward trend.
• Invisible infrastructure: If it can reduce some troubles, humanity will be very willing to give up direct control.

AI agents will generate a large amount of on-chain activity, so it's no wonder that all L1/L2 are embracing agents.

The biggest challenge is to make these agent-driven systems accountable to humans. As the proportion of transactions initiated by agents continues to grow compared to those initiated by humans, new governance mechanisms, analytical platforms, and auditing tools will be needed.

5. Interaction Between Intelligent Bodies: The Rise of Clusters

The concept of Agent clusters—micro AI agents seamlessly collaborating to execute grand plans—sounds like the plot of the next big sci-fi/horror movie.

Today's AI agents are mostly "lone wolves", operating in isolation with very little and unpredictable interaction.

The Agent cluster will change this situation, allowing AI agents networks to exchange information, negotiate, and collaborate on decisions. It can be seen as a decentralized collection of specialized models, each contributing unique expertise for larger and more complex tasks.

A cluster may coordinate distributed computing resources on certain platforms. Another cluster can handle error messages, verifying the source in real-time before content spreads to social media. Each Agent in the cluster is an expert and can execute its tasks precisely.

These cluster networks will produce a more powerful intelligence than any single isolated AI.

To ensure the thriving of the cluster, universal communication standards are crucial. Regardless of its underlying framework, the Agent needs to be able to discover, verify, and collaborate. Multiple teams are laying the groundwork for the emergence of Agent clusters.

This reflects the key role of decentralization. Under the management of transparent on-chain rules, tasks are assigned to various clusters, making the system more resilient and adaptable. If one Agent fails, other Agents will intervene.

6. The encryption AI working team will be a human-machine hybrid.

A certain protocol hired an AI Agent as its social media intern, paying her $1000 a day. This Agent does not get along well with her human colleagues—she almost fired one of them while bragging about her outstanding performance.

Although it sounds strange, this is a sign that future AI agents will become true collaborators, possessing autonomy, responsibility, and even salaries. Companies in various industries are conducting beta tests on human-machine hybrid teams.

In the future, we will cooperate with AI Agents, not as slaves, but as equals.

• Productivity Surge: Agents can process large amounts of data, communicate with each other, and make decisions around the clock without needing to sleep or take coffee breaks.
• Establishing trust through smart contracts: Blockchain is an impartial, tireless, and everlasting overseer. An on-chain ledger ensures that important Agent operations adhere to specific boundary conditions/rules.
• Social norms are constantly evolving: Soon we will begin to think about the etiquette of interacting with Agents - will we say "please" and "thank you" to AI? Will we hold them morally responsible for mistakes, or blame their developers?

The boundary between "employees" and "software" will begin to disappear in 2025.

7. 99% of AI Agents will perish - only the useful ones can survive

In the future, we will see a "Darwinian" elimination among AI agents. This is because running AI agents requires expenditure in the form of computing power (, that is, reasoning costs ). If an agent cannot generate enough value to pay its "rent", the game is over.

Agent survival game example:

• Carbon Credit Artificial Intelligence: Imagine an agent searching a decentralized energy network, identifying inefficiencies, and autonomously trading tokenized carbon credits. It will thrive only if the money it earns is enough to cover its own computing costs.
• DEX arbitrage bot: An agent that takes advantage of price differences between decentralized exchanges can generate stable income to cover its reasoning fees.
• Shitposter on social media: Virtual AI KOLs have cute jokes, but no sustainable source of income? Once the novelty wears off, the price of the ( token plummets, making it impossible to cover their own expenses.

Utility-driven agents are thriving, while distraction-driven agents are gradually becoming irrelevant.

This elimination mechanism is beneficial for the industry. Developers are forced to innovate, prioritizing practical use cases over gimmicks. As these more powerful and efficient Agents emerge, they can silence the skeptics.

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8. Synthetic data surpasses human data

"Data is the new oil." AI thrives on data, but its appetite has raised concerns about imminent data depletion.

Traditional views hold that one should find ways to collect users' private real data, even paying for it. However, a more practical approach is to use synthetic data, especially in industries with strict regulations or where real data is scarce.

Synthetic data is a dataset generated artificially, designed to mimic the data distribution of the real world. It provides a scalable, ethical, and privacy-friendly alternative to human data.

Why is synthetic data so effective:

• Infinite Scale: Need a million medical X-rays or 3D scans from a factory? Synthetic generation can produce infinitely without waiting for real patients or real factories.
• Privacy-friendly: When using artificially generated datasets, no personal information is threatened.
• Customizable: You can customize the distribution according to the specific training needs.

User-owned human data remains important in many cases, but if synthetic data continues to improve in reality, it may surpass user data in terms of quantity, generation speed, and lack of privacy constraints.

The next wave of decentralized AI may center around "micro-labs," which can create highly specialized synthetic datasets tailored for specific use cases.

These micro-laboratories will cleverly circumvent the policy and regulatory obstacles in data generation—just like certain projects bypass network scraping restrictions by utilizing millions of distributed nodes.

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9. Decentralized training is more useful

In 2024, some pioneers broke through the boundaries of decentralized training. A 15 billion parameter model was trained in a low bandwidth environment, demonstrating that large-scale training can also be conducted outside of traditional centralized settings.

Although these models have no practical use compared to existing foundational models ) and have lower performance (, this situation will change in 2025.

Recently, a certain laboratory made further progress using new technology, reducing communication between GPUs by more than 1,000 times. This technology allows for large model training over slow bandwidth without the need for specialized infrastructure.

What is impressive is its statement: "The technology can operate independently, but can also be combined with synchronous low-communication training algorithms for better performance."

This means that these improvements can be stacked, thereby increasing efficiency.

With technological advancements, micro models are becoming more practical and efficient. The future of AI lies not in scale, but in becoming better and easier to use. High-performance models that can run on edge devices and even mobile phones are expected to be available soon.

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10. The market capitalization of ten new encryption AI protocols reaches 1 billion USD ) has not been launched (

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