What We're Watching in 2025 (Crypto AI)

Yan

What We're Watching in 2025 (Crypto AI)

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

Currently, tokens in the crypto AI sector only account for 2.9% of the altcoin market cap, but I believe this proportion will not last long. As artificial intelligence gradually expands to new areas such as smart contract platforms, Meme, decentralized physical infrastructure (DePIN), proxy platforms, data networks, and intelligent coordination layers, its integration with DeFi and Meme tokens has become an inevitable trend.

The reason for confidence in the crypto AI sector is that it sits at the intersection of two of the most powerful trends in technology:

2. The revival of Bittensor

Bittensor (token name TAO) has been around for years. It is a veteran player in this field. Despite the craze around artificial intelligence, its token price has been hovering at the level of a year ago. But in fact, the digital hive mind behind Bittensor has quietly advanced, more subnets have emerged, registration fees have been reduced, and subnets have surpassed the corresponding technologies of Web2 in actual performance such as inference speed. At the same time, EVM compatibility has introduced DeFi-like functions, further enriching Bittensor's network.

So why hasn’t TAO surged? A steep inflation schedule and a shift in focus toward platforms geared toward AI agents have kept it limited. However, dTAO (expected Q1 2025) could be a major turning point. With dTAO, each subnet will have its own token, and the relative prices of those tokens will determine how releases are distributed.

Why Bittensor has a chance to explode again:

Currently watching the various subnets and recording the actual progress they make in their respective fields. It is expected that at some point, there will be a DeFi summer similar to @opentensor's version.

3. The computing market is the next L1 transaction

The insatiable demand for computing will become an obvious megatrend. Nvidia CEO Jensen Huang once said that the demand for inference will surge "a billion times." This exponential growth will break the planning of traditional infrastructure and urgently call for "new solutions."

The decentralized computing layer provides raw compute (for both training and inference) in a verifiable and cost-effective manner. Startups like @spheronfdn, @gensynai, @atoma_network, and @kuzco_xyz are quietly building strong foundations to take advantage of this, focusing on products rather than tokens (none of these companies have tokens yet). As decentralized AI model training becomes feasible, the addressable market is expected to rise dramatically.

For the encrypted AI sector, let’s compare it with the L1 sector:

Just as Solana emerges as a leader in the L1 space, the winner here will dominate a whole new frontier, paying close attention to three criteria: reliability, cost-effectiveness, and developer-friendly tooling.

4. AI Agents will flood into blockchain transactions

By the end of 2025, 90% of on-chain transactions will no longer be triggered by humans manually clicking "Send". Instead, these transactions will be executed by an army of AI Agents that will continuously rebalance liquidity pools, distribute rewards, or perform micropayments based on real-time data feeds.

None of this is as far-fetched as it seems. Everything we have built in the past seven years (L1, rollups, DeFi, NFTs, etc.) has quietly paved the way for an on-chain world dominated by AI.

So, why is there this shift?

AI agents generate a staggering amount of on-chain activity, but the biggest challenge will be to make these AI agent-driven systems accountable to humans. As the ratio of transactions initiated by AI agents to those initiated by humans increases, new governance mechanisms, analytical platforms, and audit tools will be needed.

5. Interaction between agents: The rise of the AI swarm concept

AI agent swarms refer to tiny artificial intelligence entities working together seamlessly to execute grand plans, which sounds like the plot of the next popular sci-fi or horror movie. Most current AI agents operate in isolation, with few and unpredictable interactions. However, AI agent swarms will change this situation, allowing multiple AI agents to exchange information, negotiate, and make joint decisions in a network.

These AI Agent swarms can be thought of as decentralized collectives of specialized models, each contributing its unique expertise to larger, more complex tasks. The potential is staggering. For example, one swarm might coordinate distributed computing resources on a platform like Bittensor, while another swarm can verify the source of content in real time to prevent false information from spreading on social media. Each AI Agent in the swarm is an expert and performs its respective task with precision.

These swarm networks will be far more intelligent than any single isolated AI. In order for agent swarms