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This article spotlights five high-revenue, yet un-tokenized Decentralized Perpetual Exchanges (Perp DEXs), focusing on their protocol revenue, technical features, and growth potential. These projects demonstrate genuine profitability amidst intense competition in the sector. edgeX: The High-Performance Contender edgeX set a new revenue record for Perp DEXs in September 2025, with cumulative revenue reaching $49.47 million, solidifying its position as the second-highest revenue generator in th...
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Finding the Next Aster: 5 High-Revenue, Un-Tokenized Perp DEXs
This article spotlights five high-revenue, yet un-tokenized Decentralized Perpetual Exchanges (Perp DEXs), focusing on their protocol revenue, technical features, and growth potential. These projects demonstrate genuine profitability amidst intense competition in the sector. edgeX: The High-Performance Contender edgeX set a new revenue record for Perp DEXs in September 2025, with cumulative revenue reaching $49.47 million, solidifying its position as the second-highest revenue generator in th...
Can PoL v2 Ignite a BeraChain Rally?
1. Core Breakthrough: From Mercenary Liquidity to Value Feedback Loop In a post-yield-farming world, the only question that matters is “how does a chain manufacture its own organic demand?” Berachain’s answer is to make the native token the first beneficiary of every unit of growth. Proof-of-Liquidity (PoL) v2 flips the old script. Instead of letting ETH/SOL-style gas tokens watch from the sidelines while DeFi protocols pocket the upside, v2 reroutes 33 % of all DApp-bribe incentives from BGT...
Can DeepSeek Stay Hot?
DeepSeek is set to face more pressure and challenges in the future. The race towards a universal model has just begun, and who will ultimately win depends on continuous investment in funding and technological iteration. · A headhunter responsible for sourcing high-end tech talent in the large model field told The Paper Technology that DeepSeek's hiring logic is not much different from that of other companies in the large model sector. The core label for talent is "young and high-potential," m...
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With the explosion of the AI industry this year, Crypto x AI has rapidly emerged. Teng Yan, a researcher specializing in Crypto x AI, has made 10 predictions for 2025. Below are the details of these predictions.
1. Total Market Value of Crypto AI Tokens Reaches $150 Billion
Currently, the market value of Crypto AI tokens accounts for only 2.9% of the altcoin market value, but this ratio will not last long. AI covers a wide range of areas, from smart contract platforms to memes, DePIN, and Agent platforms, data networks, and intelligent coordination layers. Its market position is undoubtedly comparable to DeFi and memes.
Why is there such confidence?
Convergence of Two Powerful Technologies: Crypto AI is at the intersection of two of the most powerful technologies.
AI Hype Trigger Events: An event like the IPO of OpenAI could trigger a global frenzy for AI. Meanwhile, Web2 capital has already 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, only AI is indeed changing the world.
2. Bittensor Renaissance
Bittensor (TAO), a decentralized AI infrastructure that has been live for years, is a veteran project in the Crypto AI field. Despite the popularity of AI, its token price has remained at the level of a year ago.
However, Bittensor's Digital Hivemind has quietly achieved a leap: more subnets with lower registration fees, superior performance in actual metrics such as inference speed compared to Web2 peers, and EVM compatibility that introduces DeFi-like functionality to Bittensor's network.
Why hasn't the TAO token surged? A steep 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 prices of these tokens will determine how emissions are allocated.
3. The Compute Market Is the Next "L1 Market"
The current obvious trend is the endless demand for computing. NVIDIA CEO Huang Renxun once said that inference demand will grow by a "billion times." This exponential growth will disrupt traditional infrastructure plans, and new solutions are urgently needed.
Decentralized compute layers provide raw computing power for training and inference in a verifiable and cost-effective manner. 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 sharply.
4. AI Agents Will Dominate Blockchain Transactions
By the end of 2025, 90% of on-chain transactions will no longer be executed by humans clicking "send," but by AI agents that constantly rebalance liquidity pools, allocate rewards, or execute micro-payments based on real-time data feedback.
This shift sounds plausible. Everything built over the past seven years (L1s, rollups, DeFi, NFTs) has quietly paved the way for a world where AI operates on-chain.
Why will this shift happen?
No Human Error: Smart contracts execute exactly as coded. In turn, AI agents can process vast amounts of data faster and more accurately than humans.
Micro-payments: These agent-driven transactions will become smaller, more frequent, and more efficient. Especially with transaction costs trending downward on Solana, Base, and other L1/L2s.
Invisible Infrastructure: Humans will gladly relinquish direct control if it reduces hassle.
AI agents will generate a significant amount of on-chain activity, and it's no wonder that 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 grows, new governance mechanisms, analytics platforms, and auditing tools will be required.
5. Interactions Between Agents: The Rise of Clusters
The concept of agent clusters—micro AI agents seamlessly collaborating to execute grand plans—sounds like the next big hit for a sci-fi/horror movie plot.
Today's AI agents are mostly "lone wolves," operating in isolation with minimal and unpredictable interactions.
Agent clusters will change this status quo, enabling AI agent networks to exchange information, negotiate, and make collaborative decisions. It can be seen as a decentralized ensemble 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 a cluster is an expert, capable of precisely executing its task.
These cluster networks will generate intelligence far more powerful than any single isolated AI.
For clusters to thrive, universal communication standards are crucial. Agents need to be able to discover, verify, and collaborate regardless of their underlying frameworks. 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 rules, tasks are assigned to various clusters, making the system more resilient and adaptable. If one agent fails, others step in.
6. Crypto AI Workforces Will Be Human-Machine Hybrids
Story Protocol hired Luna (an AI agent) as its social media intern, paying her $1,000 per day. Luna didn't get along well with her human colleagues—she almost fired one of them while boasting about her own performance.
Though it sounds strange, this is a harbinger of a future where AI agents become true collaborators with autonomy, responsibility, and even salaries. Companies across various industries are beta-testing human-machine hybrid teams.
The future will involve working with AI agents not 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 Through Smart Contracts: The blockchain is an impartial, tireless, and infallible supervisor. An on-chain ledger ensures that important agent operations follow specific boundary conditions/rules.
Evolving Social Norms: Soon, there will be etiquette considerations for interacting with agents—will you say "please" and "thank you" to AI? Will you hold them morally accountable for mistakes, or blame their developers?
The boundary 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" culling among AI agents. This is because running AI agents comes at the cost of computational power (i.e., inference costs). If an agent cannot generate enough value to cover its "rent," it's game over.
Examples of the agent survival game:
Carbon Credit AI: Imagine an agent searching decentralized energy grids for inefficiencies and autonomously trading tokenized carbon credits. It will thrive only if its earnings cover its computational costs.
DEX Arbitrage Bot: An agent exploiting price differences between decentralized exchanges can generate a stable income to cover its inference costs.
Shitposter on X: A virtual AI influencer 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 own costs.
Utility-driven agents will flourish, while distracting agents will become increasingly irrelevant.
This culling mechanism is beneficial for the industry. Developers will be forced to innovate, prioritizing production use cases over hype. As these stronger, more efficient agents emerge, they will silence skeptics.
8. Synthetic Data Surpasses Human Data
"Data is the new oil." AI thrives on data, but its appetite has raised concerns about impending data exhaustion.
The traditional approach is to painstakingly collect users' private real data, even paying for it. However, a more practical path is to use synthetic data, especially in heavily regulated industries or those with scarce real data.
Synthetic data is artificially generated datasets designed to mimic real-world data distributions. It provides a scalable, ethical, and privacy-friendly alternative to human data.
Why synthetic data is so effective:
Unlimited Scale: Need a million medical X-rays or 3D scans of factories? Synthetic generation can produce them in unlimited quantities without waiting for real patients or real factories.
Privacy-Friendly: When using artificially generated datasets, no personal information is at risk.
Customizable: Distributions can be customized to exact training needs.
Human data will still be important in many cases, but if synthetic data continues to improve in realism, 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 create highly specialized synthetic datasets tailored to specific use cases.
These micro-labs will cleverly bypass policy and regulatory hurdles in data generation—much like Grass bypassed web scraping limitations by leveraging millions of distributed nodes.
9. Decentralized Training Becomes More Useful
In 2024, pioneers like Prime Intellect and Nous Research pushed the boundaries of decentralized training. Training a 15-billion-parameter model in a low-bandwidth environment proved that large-scale training is possible outside traditional centralized settings.
Although these models were not practically useful compared to existing foundation models (due to 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 enables large model training on slow bandwidth without specialized infrastructure.
Impressively, it states: "SPARTA can operate independently but can also be combined with synchronous low-communication training algorithms like DiLoCo for better performance."
This means these improvements can be stacked, increasing efficiency.
As technology advances, micro-models become more practical and efficient. The future of AI is not about scale, but about becoming better and more user-friendly. Expect soon to have high-performance models running on edge devices and even smartphones.
10. Ten New Crypto AI Protocols with a Circulating Market Value of $1 Billion (Not Yet Launched)
ai16z achieved a $2 billion market value 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 vast and undeveloped for just two players to dominate. By the end of 2025, it is expected that at least ten new Crypto AI protocols (not yet launched) will have a circulating (not fully diluted) market value of over $1 billion.
Decentralized AI is still in its infancy. Moreover, the talent pool is growing.
Expect new protocols, novel token models, and new open-source frameworks to emerge. These new entrants can displace existing players through a combination of incentives (such as airdrops or clever staking), technical breakthroughs (like low-latency inference or chain interoperability), and user experience improvements (no-code). A shift in public perception can be sudden and dramatic.
This is both the beauty and the challenge of this field. Market size is a double-edged sword: the pie is huge, but for technical teams, the entry barrier is low. This sets the stage for a burst of projects, many of which will fade away, but a few will have transformative power.
Bittensor, Virtuals, and ai16z won't lead for long. The next $1 billion Crypto AI protocol is coming. Savvy investors have plenty of opportunities, and that's what makes it so exciting.
With the explosion of the AI industry this year, Crypto x AI has rapidly emerged. Teng Yan, a researcher specializing in Crypto x AI, has made 10 predictions for 2025. Below are the details of these predictions.
1. Total Market Value of Crypto AI Tokens Reaches $150 Billion
Currently, the market value of Crypto AI tokens accounts for only 2.9% of the altcoin market value, but this ratio will not last long. AI covers a wide range of areas, from smart contract platforms to memes, DePIN, and Agent platforms, data networks, and intelligent coordination layers. Its market position is undoubtedly comparable to DeFi and memes.
Why is there such confidence?
Convergence of Two Powerful Technologies: Crypto AI is at the intersection of two of the most powerful technologies.
AI Hype Trigger Events: An event like the IPO of OpenAI could trigger a global frenzy for AI. Meanwhile, Web2 capital has already 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, only AI is indeed changing the world.
2. Bittensor Renaissance
Bittensor (TAO), a decentralized AI infrastructure that has been live for years, is a veteran project in the Crypto AI field. Despite the popularity of AI, its token price has remained at the level of a year ago.
However, Bittensor's Digital Hivemind has quietly achieved a leap: more subnets with lower registration fees, superior performance in actual metrics such as inference speed compared to Web2 peers, and EVM compatibility that introduces DeFi-like functionality to Bittensor's network.
Why hasn't the TAO token surged? A steep 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 prices of these tokens will determine how emissions are allocated.
3. The Compute Market Is the Next "L1 Market"
The current obvious trend is the endless demand for computing. NVIDIA CEO Huang Renxun once said that inference demand will grow by a "billion times." This exponential growth will disrupt traditional infrastructure plans, and new solutions are urgently needed.
Decentralized compute layers provide raw computing power for training and inference in a verifiable and cost-effective manner. 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 sharply.
4. AI Agents Will Dominate Blockchain Transactions
By the end of 2025, 90% of on-chain transactions will no longer be executed by humans clicking "send," but by AI agents that constantly rebalance liquidity pools, allocate rewards, or execute micro-payments based on real-time data feedback.
This shift sounds plausible. Everything built over the past seven years (L1s, rollups, DeFi, NFTs) has quietly paved the way for a world where AI operates on-chain.
Why will this shift happen?
No Human Error: Smart contracts execute exactly as coded. In turn, AI agents can process vast amounts of data faster and more accurately than humans.
Micro-payments: These agent-driven transactions will become smaller, more frequent, and more efficient. Especially with transaction costs trending downward on Solana, Base, and other L1/L2s.
Invisible Infrastructure: Humans will gladly relinquish direct control if it reduces hassle.
AI agents will generate a significant amount of on-chain activity, and it's no wonder that 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 grows, new governance mechanisms, analytics platforms, and auditing tools will be required.
5. Interactions Between Agents: The Rise of Clusters
The concept of agent clusters—micro AI agents seamlessly collaborating to execute grand plans—sounds like the next big hit for a sci-fi/horror movie plot.
Today's AI agents are mostly "lone wolves," operating in isolation with minimal and unpredictable interactions.
Agent clusters will change this status quo, enabling AI agent networks to exchange information, negotiate, and make collaborative decisions. It can be seen as a decentralized ensemble 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 a cluster is an expert, capable of precisely executing its task.
These cluster networks will generate intelligence far more powerful than any single isolated AI.
For clusters to thrive, universal communication standards are crucial. Agents need to be able to discover, verify, and collaborate regardless of their underlying frameworks. 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 rules, tasks are assigned to various clusters, making the system more resilient and adaptable. If one agent fails, others step in.
6. Crypto AI Workforces Will Be Human-Machine Hybrids
Story Protocol hired Luna (an AI agent) as its social media intern, paying her $1,000 per day. Luna didn't get along well with her human colleagues—she almost fired one of them while boasting about her own performance.
Though it sounds strange, this is a harbinger of a future where AI agents become true collaborators with autonomy, responsibility, and even salaries. Companies across various industries are beta-testing human-machine hybrid teams.
The future will involve working with AI agents not 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 Through Smart Contracts: The blockchain is an impartial, tireless, and infallible supervisor. An on-chain ledger ensures that important agent operations follow specific boundary conditions/rules.
Evolving Social Norms: Soon, there will be etiquette considerations for interacting with agents—will you say "please" and "thank you" to AI? Will you hold them morally accountable for mistakes, or blame their developers?
The boundary 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" culling among AI agents. This is because running AI agents comes at the cost of computational power (i.e., inference costs). If an agent cannot generate enough value to cover its "rent," it's game over.
Examples of the agent survival game:
Carbon Credit AI: Imagine an agent searching decentralized energy grids for inefficiencies and autonomously trading tokenized carbon credits. It will thrive only if its earnings cover its computational costs.
DEX Arbitrage Bot: An agent exploiting price differences between decentralized exchanges can generate a stable income to cover its inference costs.
Shitposter on X: A virtual AI influencer 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 own costs.
Utility-driven agents will flourish, while distracting agents will become increasingly irrelevant.
This culling mechanism is beneficial for the industry. Developers will be forced to innovate, prioritizing production use cases over hype. As these stronger, more efficient agents emerge, they will silence skeptics.
8. Synthetic Data Surpasses Human Data
"Data is the new oil." AI thrives on data, but its appetite has raised concerns about impending data exhaustion.
The traditional approach is to painstakingly collect users' private real data, even paying for it. However, a more practical path is to use synthetic data, especially in heavily regulated industries or those with scarce real data.
Synthetic data is artificially generated datasets designed to mimic real-world data distributions. It provides a scalable, ethical, and privacy-friendly alternative to human data.
Why synthetic data is so effective:
Unlimited Scale: Need a million medical X-rays or 3D scans of factories? Synthetic generation can produce them in unlimited quantities without waiting for real patients or real factories.
Privacy-Friendly: When using artificially generated datasets, no personal information is at risk.
Customizable: Distributions can be customized to exact training needs.
Human data will still be important in many cases, but if synthetic data continues to improve in realism, 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 create highly specialized synthetic datasets tailored to specific use cases.
These micro-labs will cleverly bypass policy and regulatory hurdles in data generation—much like Grass bypassed web scraping limitations by leveraging millions of distributed nodes.
9. Decentralized Training Becomes More Useful
In 2024, pioneers like Prime Intellect and Nous Research pushed the boundaries of decentralized training. Training a 15-billion-parameter model in a low-bandwidth environment proved that large-scale training is possible outside traditional centralized settings.
Although these models were not practically useful compared to existing foundation models (due to 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 enables large model training on slow bandwidth without specialized infrastructure.
Impressively, it states: "SPARTA can operate independently but can also be combined with synchronous low-communication training algorithms like DiLoCo for better performance."
This means these improvements can be stacked, increasing efficiency.
As technology advances, micro-models become more practical and efficient. The future of AI is not about scale, but about becoming better and more user-friendly. Expect soon to have high-performance models running on edge devices and even smartphones.
10. Ten New Crypto AI Protocols with a Circulating Market Value of $1 Billion (Not Yet Launched)
ai16z achieved a $2 billion market value 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 vast and undeveloped for just two players to dominate. By the end of 2025, it is expected that at least ten new Crypto AI protocols (not yet launched) will have a circulating (not fully diluted) market value of over $1 billion.
Decentralized AI is still in its infancy. Moreover, the talent pool is growing.
Expect new protocols, novel token models, and new open-source frameworks to emerge. These new entrants can displace existing players through a combination of incentives (such as airdrops or clever staking), technical breakthroughs (like low-latency inference or chain interoperability), and user experience improvements (no-code). A shift in public perception can be sudden and dramatic.
This is both the beauty and the challenge of this field. Market size is a double-edged sword: the pie is huge, but for technical teams, the entry barrier is low. This sets the stage for a burst of projects, many of which will fade away, but a few will have transformative power.
Bittensor, Virtuals, and ai16z won't lead for long. The next $1 billion Crypto AI protocol is coming. Savvy investors have plenty of opportunities, and that's what makes it so exciting.
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