Top 10 Predictions for Crypto AI in 2025: Total Market Value Reaches $150 Billion, 99% of AI Agents Will Disappear

Original Title: "What I'm Watching in 2025"

Author: Teng Yan, Researcher (Focusing on Crypto x AI)

Compiled by: Felix, PANews

With the explosion of the AI industry this year, Crypto x AI has rapidly risen. Researcher Teng Yan, who focuses on Crypto x AI, has published 10 predictions for 2025. Here are the details of the predictions.

1. The total market value of crypto AI tokens will reach $150 billion

Currently, the market value of crypto AI tokens accounts for only 2.9% of the altcoin market, but this ratio will not last long.

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

Why am I so confident about this?

• Crypto AI is at the intersection of two of the most powerful technologies.
• AI frenzy trigger events: An OpenAI IPO or similar event could spark a global frenzy for AI. Meanwhile, 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. Remember the gold rush of memes in 2024? AI will be the same frenzy, but AI is indeed changing the world.

2. Bittensor Revival

The decentralized AI infrastructure Bittensor (TAO) has been online for years and is a veteran project in the crypto AI field. Despite the AI craze, its token price has lingered at levels from a year ago.

Now, Bittensor's Digital Hivemind has quietly achieved a leap: lower registration fees for more subnets, subnets outperforming Web2 peers in practical metrics like inference speed, and EVM compatibility bringing DeFi-like functionalities to Bittensor's network.

Why hasn't the TAO token skyrocketed? A sharp inflation plan and market focus on agent platforms have hindered its rise. However, dTAO (expected to launch in Q1 2025) could be a significant turning point. With dTAO, each subnet will have its own token, and the relative price of these tokens will determine how emissions are allocated.

Why Bittensor can make a comeback:

• Market-based emissions: dTAO will directly link block rewards to innovation and measurable performance. The better the subnet, the more valuable its token.
• Concentrated capital flow: Investors can ultimately target specific subnets they believe in. If a particular subnet excels with innovative distributed training methods, investors can deploy capital to represent their views.
• EVM integration: EVM compatibility attracts a broader community of crypto-native developers to Bittensor, bridging gaps with other networks.

3. The computing market is the next "L1 market"

The obvious trend is the endless demand for computing.

NVIDIA CEO Jensen Huang once said that inference demand will grow "a billion times." This exponential growth will disrupt traditional infrastructure plans, and new solutions are urgently needed.

Decentralized computing layers provide raw computing in a verifiable and cost-effective manner (for training and inference). Startups like Spheron, Gensyn, Atoma, and Kuzco are quietly building a solid foundation, focusing on products rather than tokens (none of these companies have tokens). As decentralized training of AI models becomes practical, the entire potential market will surge.

Comparison with L1:

• Just like in 2021: Remember the competition among Solana, Terra/Luna, and Avalanche for the "best" L1? Similar competition will emerge among computing protocols for developers and AI applications building on their computing layers.
• Web2 demand: The cloud computing market size of $680 billion to $2.5 trillion makes the crypto AI market look small. 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.

Just as Solana won in the L1 space, the winner will dominate a brand new domain. Keep a close eye on reliability (e.g., strong service level agreements or SLAs), cost-effectiveness, and developer-friendly tools.

4. AI agents will flood blockchain transactions

Olas agent transactions on Gnosis; Source: Dune

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

This doesn't sound far-fetched. Everything built over the past seven years (L1, rollup, DeFi, NFT) has quietly paved the way for a world where AI operates on-chain.

Ironically, many builders may not even realize they are creating infrastructure for a machine-dominated future.

Why will this shift happen?

• No more human errors: Smart contracts execute exactly as coded. In turn, AI agents can process vast amounts of data faster and more accurately than real humans.
• Small payments: These agent-driven transactions will become smaller, more frequent, and more efficient, especially as transaction costs trend downward on Solana, Base, and other L1/L2s.
• Invisible infrastructure: Humans will gladly relinquish direct control if it can reduce some hassle.

AI agents will generate a massive amount of on-chain activity, and it's no wonder all L1/L2s are embracing agents.

The biggest challenge is making these agent-driven systems accountable to humans. As the ratio of agent-initiated transactions to human-initiated transactions continues to grow, new governance mechanisms, analytics platforms, and auditing tools will be needed.

5. Interaction between agents: The rise of clusters

Source: FXN World

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 minimal and unpredictable interaction.

Agent clusters will change this status quo, allowing networks of AI agents to exchange information, negotiate, and make collaborative decisions. It can be seen as a decentralized collection of specialized models, each contributing unique expertise to larger, more complex tasks.

One cluster might coordinate distributed computing resources on platforms like Bittensor. Another cluster could handle misinformation, verifying sources in real-time before content spreads to social media. Each agent in the cluster is an expert capable of executing its task precisely.

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

To enable clusters to thrive, universal communication standards are crucial. Regardless of their underlying frameworks, agents need to be able to discover, verify, and collaborate. Teams like Story Protocol, FXN, Zerebro, and ai16z/ELIZA are laying the groundwork for the emergence of agent clusters.

This highlights the key role of decentralization. Under transparent on-chain rule management, tasks can be assigned to various clusters, making the system more resilient and adaptive. If one agent fails, others will step in.

6. Crypto AI work teams will be human-machine hybrids

Source: @whip_queen_

Story Protocol hired Luna (an AI agent) as its social media intern, paying her $1,000 a day. Luna does not get along well with her human colleagues—she almost fired one of them while boasting about her performance.

While this sounds strange, it is a precursor to the future where AI agents become true collaborators, possessing autonomy, responsibility, and even salaries. Companies across various industries are beta testing human-machine hybrid teams.

In the future, working with AI agents will not be as slaves but as equals:

• Productivity surge: Agents can process vast amounts of data, communicate with each other, and make decisions around the clock without needing sleep or coffee breaks.
• Trust established through smart contracts: The blockchain is an impartial, tireless, and never-forgetting overseer. An on-chain ledger ensures that important agent operations adhere to specific boundary conditions/rules.
• Evolving social norms: Soon, we will start to think about etiquette when interacting with agents—will we say "please" and "thank you" to AI? Will we hold them morally accountable for mistakes, or blame their developers?

The line between "employees" and "software" will begin to blur in 2025.

7. 99% of AI Agents will perish—only the useful will survive

The future will see a "Darwinian" elimination among AI agents. Running AI agents requires expenditure in the form of computational power (i.e., inference costs). If an agent cannot generate enough value to pay its "rent," the game is over.

Examples of agent survival games:

• Carbon credit AI: Imagine an agent searching a decentralized energy network, identifying inefficiencies, and autonomously trading tokenized carbon credits. It will thrive only if it earns enough to cover its own computing costs.
• DEX arbitrage bots: Agents that exploit price differences between decentralized exchanges can generate stable income to cover their inference costs.
• Shitposter on X: A virtual AI KOL with cute jokes but no sustainable income source? Once the novelty wears off (and token prices plummet), it won't be able to cover its expenses.

Utility-driven agents will thrive, while distraction-driven agents will gradually become irrelevant.

This elimination mechanism benefits the industry. Developers are forced to innovate, prioritizing production use cases over gimmicks. As these more powerful and efficient agents emerge, skeptics will be silenced.

8. Synthetic data will surpass human data

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

The traditional view is to find ways to collect users' private real data, even paying for it. But a more practical approach is to use synthetic data, especially in heavily regulated industries or where real data is scarce.

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

Why synthetic data is so effective:

• Infinite scale: Need a million medical X-rays or 3D scans of a factory? Synthetic generation can produce them in unlimited quantities without waiting for real patients or real factories.
• Privacy-friendly: No personal information is threatened when using artificially generated datasets.
• Customizable: Distributions can be tailored to exact training needs.

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

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

These micro-labs will cleverly circumvent policy and regulatory barriers in data generation—much like Grass bypasses web scraping restrictions by leveraging millions of distributed nodes.

9. Decentralized training will be more useful

In 2024, pioneers like Prime Intellect and Nous Research broke through the boundaries of decentralized training. They trained a 15 billion parameter model in low-bandwidth environments, proving that large-scale training can occur outside traditional centralized settings.

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

This week, EXO Labs made further progress with SPARTA, reducing GPU-to-GPU communication by over 1,000 times. SPARTA can perform large model training on slow bandwidth without specialized infrastructure.

Impressively, it stated: "SPARTA can run independently but can also be combined with synchronous low-communication training algorithms like DiLoCo for better performance."

This means these improvements can stack, increasing efficiency.

As technology advances, micro-models become 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 or even smartphones are expected soon.

10. Ten new crypto AI protocols will have a circulating market value of $1 billion (not yet launched)

ai16z achieves a market value of $2 billion in 2024

Welcome to the real gold rush.

It's easy to think that current leaders will continue to win, and many compare Virtuals and ai16z to early smartphones (iOS and Android).

But this market is too large and undeveloped for just two players to dominate. By the end of 2025, it is expected that at least ten new crypto AI protocols (with no tokens yet launched) will have a circulating (not fully diluted) market value exceeding $1 billion.

Decentralized AI is still in its infancy. Moreover, the talent pool is continuously growing.

Anticipate the arrival of new protocols, novel token models, and new open-source frameworks. These new entrants could replace existing players through a combination of incentives (like airdrops or clever staking), technological breakthroughs (like low-latency inference or chain interoperability), and user experience improvements (no-code). Shifts in public perception could be instantaneous and dramatic.

This is both the beauty and the challenge of this field. The market size is a double-edged sword: the cake is huge, but the barrier to entry is low for tech teams. This lays the groundwork for explosive growth of projects, many of which will gradually disappear, but a few will possess transformative power.

Bittensor, Virtuals, and ai16z won't lead for long; the next billion-dollar crypto AI protocol is on the horizon. Savvy investors have ample opportunities, which is why it is so exciting.