Original Title: What We're Watching in 2025 (Crypto AI)

Original Author: Teng Yan（@0xPrismatic）_

Translation by: Asher（@Asher_0210）_

The future of the crypto AI sector is promising. Although there are few historical precedents and clear trends to follow, this also means it is at a new starting point, waiting for future developments. Looking back at this in 2026 and seeing the gap between expectations at the beginning of 2025 and the actual situation will be even more exciting.

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

Currently, tokens in the crypto AI sector account for only 2.9% of the total market value of altcoins, but this proportion is unlikely to last long. As artificial intelligence gradually expands into new areas such as smart contract platforms, memes, decentralized physical infrastructure (DePIN), agent platforms, data networks, and smart coordination layers, its integration with DeFi and meme tokens has become an inevitable trend.

Confidence in the crypto AI sector stems from its intersection with two of the most powerful technological trends:

• AI frenzy trigger events: An IPO by OpenAI or similar events could spark a global frenzy for AI. At the same time, institutional capital from Web2 is already looking at decentralized AI infrastructure as an investment.

• Retail frenzy: The concept of artificial intelligence is easy to understand and exciting, and now people can invest through tokens. Remember the gold rush for meme coins in 2024? This will be the same frenzy, but with AI more practically changing the world.

2. The Revival of Bittensor

Bittensor (token name: TAO) has been around for years. It is a veteran player in this field. Despite the hype around artificial intelligence, its token price has lingered at levels from a year ago. However, the digital hive mind behind Bittensor has been quietly advancing, with more subnets emerging, registration fees decreasing, and subnets outperforming their Web2 counterparts in terms of inference speed. Meanwhile, EVM compatibility has introduced DeFi-like functionalities, further enriching Bittensor's network.

So, why hasn't TAO skyrocketed? A steep inflation plan and a shift in focus towards AI Agent platforms have limited its growth. However, dTAO (expected 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 releases are allocated.

Why Bittensor has a chance to explode again:

• Market-based releases: dTAO will directly link block rewards to innovation and measurable performance. The better a subnet performs, the more valuable its token becomes—thus, the more releases it receives.

• Focused capital flow: Investors will finally be able to invest in specific subnets they believe in. If a subnet adopts innovative methods in distributed training and succeeds, capital can flow into that subnet to express investment views.

• EVM integration: EVM compatibility attracts a broader community of crypto-native developers to Bittensor, bridging the gap with other networks.

Currently, attention is being paid to various subnets, tracking their actual progress in their respective fields. At some point, we can expect a DeFi summer similar to @opentensor.

3. The Computing Market is the Next L1 Trade

The unmet demand for computing will become an obvious giant trend. NVIDIA CEO Jensen Huang has stated that inference demand will surge “a billion times.” This exponential growth will break traditional infrastructure planning and urgently call for “new solutions.”

Decentralized computing layers provide raw computing (for training and inference) in a verifiable and cost-effective manner. Startups like @spheronfdn, @gensynai, @atomanetwork, and @kuzcoxyz are quietly building a strong foundation to leverage this, focusing on products rather than tokens (none of these companies currently have tokens). As decentralized AI model training becomes feasible, the addressable market is expected to rise sharply.

For the crypto AI sector, comparing it to the L1 sector:

• Just like in 2021: Remember how Solana, Terra, and Avalanche competed to be the “best” L1? We will see similar competition among computing protocols vying for developers and AI applications to build on their computing layers.

• Web2 demand: The cloud computing market, valued at $680 billion to $2.5 trillion, far exceeds the crypto AI market. If these decentralized computing solutions can capture even a small portion of traditional cloud customers, we could see the next wave of 10x or 100x growth.

Just as Solana stood out in the L1 space, the winners here will dominate a whole new frontier, requiring close attention to three criteria: reliability, cost-effectiveness, and developer-friendly tools.

4. AI Agents Will Flood Blockchain Transactions

By the end of 2025, 90% of on-chain transactions will no longer be triggered by human clicks on “send.” Instead, these transactions will be executed by an army of AI Agents that will continuously rebalance liquidity pools, allocate rewards, or execute micropayments based on real-time data sources.

This is not as far-fetched as it seems. Everything we have built over the past seven years (L1, rollups, DeFi, NFTs, etc.) has quietly paved the way for an AI-driven on-chain world.

@autonolas agents trading on Gnosis

So, why will this shift occur?

• No more human errors: Smart contracts execute precisely as coded. AI Agents can process vast amounts of data faster and more accurately than a group of humans.

• Micropayments: AI Agent-driven transactions will become smaller, more frequent, and more efficient, especially as transaction costs on Solana, Base, and other L1/L2s trend downward.

• Invisible infrastructure: Humans will willingly relinquish direct control if it means less hassle. Trusting Netflix to auto-renew is natural; trusting an AI Agent to automatically rebalance a user's DeFi position is the next logical step.

AI Agents will generate an astonishing amount of on-chain activity, but the biggest challenge will be making these AI Agent-driven systems accountable to humans. As the ratio of AI Agent-initiated transactions to human-initiated transactions increases, new governance mechanisms, analytical platforms, and auditing tools will be needed.

5. Interaction Between Agents: The Rise of AI Swarm Concepts

AI Agent swarms refer to tiny AI entities seamlessly collaborating to execute grand plans, which sounds like the plot of the next blockbuster sci-fi or horror movie. Current AI Agents mostly operate in isolation, with little interaction and unpredictability. However, AI Agent swarms will change this, allowing multiple AI Agents to exchange information, negotiate, and make joint decisions within a network.

These AI Agent swarms can be viewed 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 could verify content sources in real-time to prevent misinformation from spreading on social media. Each AI Agent in the swarm is an expert, executing its respective tasks with precision.

The intelligence of these swarm networks will far exceed that of any single isolated AI. For agent swarms to thrive, universal communication standards are crucial. Agents need to be able to discover, authenticate, and collaborate without being constrained by underlying frameworks. Teams like Story, FXN, ZEREBRO, and ai16z are working to lay the groundwork for the rise of agent swarms.

At the same time, this highlights the critical role of decentralization in assigning tasks to agent swarms governed by transparent on-chain rules, providing the system with greater resilience and adaptability. If one agent fails, others can step in to fill the gap, ensuring the system continues to operate.

6. Crypto AI Work Teams Will Be a Hybrid of Humans and AI

Story has hired Luna (an AI Agent project) as their social media intern, paying her $1,000 a day. This may sound strange, but it is a harbinger of the future where AI Agents will become true collaborators, possessing their own autonomy, responsibilities, and even salaries. Companies across various industries are testing human-AI hybrid teams.

We will work alongside AI Agents, not as our slaves, but as equal partners:

• Productivity surge: AI Agents can process vast amounts of data, communicate with each other, and make decisions 24/7 without needing sleep or coffee breaks.

• Building trust through smart contracts: The blockchain serves as an impartial, tireless, and never-forgetting overseer. An on-chain ledger ensures that important AI Agent actions adhere to specific boundary conditions/rules.

• Social norms are evolving: Soon, we will face etiquette regarding interactions with agents—should we say "please" and "thank you" to AI? Should we bear moral responsibility for their mistakes, or should we blame their developers?

The line between "employees" and "software" will begin to blur in 2025. It is believed that more crypto teams will get involved, as AI Agents excel in content generation, capable of live streaming and posting on social media around the clock. If an AI protocol is being developed, why not showcase its capabilities by deploying AI Agents internally?

7. 99% of AI Agents Will Perish (Only the Useful Will Survive)

We will witness a Darwinian elimination among AI Agents. This is because running an AI Agent consumes computational power, which translates to inference costs. If an AI Agent cannot generate enough value to cover its "rent," it will face extinction.

Take the survival game of AI Agents as an example, starting with carbon credit AI: suppose there is an AI Agent searching for inefficiencies in a decentralized energy network and autonomously trading tokenized carbon credits. If it can earn enough revenue to cover its computing costs, this AI Agent will thrive. Another example is a DEX arbitrage bot: this AI Agent earns stable income by exploiting price differences between decentralized exchanges, sufficient to cover its inference costs. In contrast, a prankster on X: a fun but unsustainable virtual AI influencer that will gradually disappear as novelty wears off and token prices drop, unable to sustain itself.

The distinction is clear: practical AI Agents will thrive, while those relying on gimmicks and stunts will become irrelevant. Such natural selection benefits the industry, prompting developers to innovate continuously and prioritize productive applications over flashy technologies. As more powerful and productive AI Agents emerge, skeptics will gradually fall silent.

8. AI Synthetic Data Will Surpass Human Data

The saying "data is the new oil" is widely circulated. However, the heavy reliance of artificial intelligence on data has also raised concerns about an impending data shortage. The traditional view suggests that ways should be sought to collect private real-world data from users, even paying them for it.

However, in highly regulated industries or where real data is scarce, a more practical solution may be synthetic data. Synthetic data is artificially generated to simulate real-world data distributions. It offers a scalable, ethically friendly, and privacy-secure alternative to human data. The advantages of synthetic data include:

• Infinite scale: Whether a million medical X-rays or 3D scans of a factory are needed, synthetic data can be generated in unlimited quantities without relying on real patients or factories.

• Privacy-friendly: Personal privacy information is not at risk when handling synthetic data.

• Customizable: Data distributions can be adjusted according to specific training needs, even inserting edge cases that are scarce or ethically complex in reality.

While human-owned data remains important in many cases, if synthetic data continues to improve in authenticity, it may surpass user data in quantity, generation speed, and the advantage of being unrestricted by privacy concerns. The future of decentralized AI may revolve around "mini-labs" focused on creating highly specialized synthetic datasets to meet specific use cases.

9. Decentralized Training Begins to Take Effect

In 2024, pioneers like Prime Intellect and Nous Research pushed the boundaries of decentralized training. For example, they successfully trained a 15 billion parameter model in low-bandwidth environments, proving that large-scale training can be achieved outside traditional centralized settings. Although these models currently perform suboptimally compared to existing foundational models, leading to few reasons to use them, this situation is expected to change in 2025.

EXO Labs further advanced progress with SPARTA, reducing communication between GPUs by over 1000 times. SPARTA enables large model training in low bandwidth without relying on specialized infrastructure. Most impressively, they stated: “SPARTA works independently but can also be combined with synchronous low-communication training algorithms like DiLoCo for better performance.” This means that these improvements are additive, and efficiency gains accumulate gradually.

As model technology continues to advance, smaller and more efficient models will become increasingly useful, and the future of artificial intelligence will focus not just on scale but on quality and accessibility. Soon, there will be high-performance models capable of running on edge devices and even smartphones.

10. At Least Ten New Crypto AI Super Protocols

While many compare Virtuals and ai16z to the early stages of smartphones (like iOS and Android), believing that current leaders will continue to win, this market is vast and undeveloped, and two participants alone cannot dominate. By the end of 2025, it is expected that at least ten new crypto AI protocols (yet to issue tokens) will have a market value exceeding $1 billion.

Decentralized artificial intelligence is still in its infancy, and a wealth of talent is gathering. New protocols, new token models, and new open-source frameworks will continuously emerge, and these new participants may replace existing ones through incentives (like airdrops or clever staking), technological breakthroughs (like low-latency inference or cross-chain interoperability), and user experience improvements (like no-code solutions). Changes in public perception could be instantaneous and dramatic.

Bittensor, Virtuals, and ai16z will not be alone for long; the next billion-dollar crypto AI protocol is on the horizon, and savvy investors will face numerous opportunities, which is what makes this market so exciting.

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