AI-related tech has been progressing for well over a decade now. But in 2025, we’ve entered a supercycle for AI developments, as innovations emerge at an unprecedented pace and become integrated into every single industry. This rapid progress is largely reducing the bottleneck that has existed in the AI development industry for a long time.
Before 2024, a small number of tech giants have completely dominated the development and deployment of AI technologies. Companies like Google, Microsoft, and Amazon control the cloud infrastructure, proprietary AI models, and data sets that fuel artificial intelligence. Their tight grip has created a bottleneck, restricting how and when users engage with AI.
This centralized control comes with consequences. When a handful of corporations dominate a field, user agency shrinks. Boardrooms make decisions about what AI can or can’t do, not the broader public. Such consolidation is efficient at first but prone to stagnation, inefficiency and resistance to change over time.
The question now is whether decentralization can break this stranglehold.
Decentralized AI (DeAI) Defined
Instead of relying on central servers or proprietary data hubs, decentralized AI (DeAI) uses distributed networks. By distributing AI development across nodes, it becomes more resilient, adaptable and fair.
Centralization Is Always the Enemy of AI Innovation
The tech giants’ control extends beyond software. These companies dominate the hardware that powers AI, including the chips used for model training and inference. A select few providers control cloud computing, which is a fundamental pillar for training large AI models. The most significant choke point, however, is apparent in the data itself, which is critical for training and refining AI models.
Data is often locked away in silos controlled by a few corporations. These silos prevent smaller players from accessing valuable resources and stifle collaborative opportunities, limiting the diversity and quality of AI systems.
When only a few entities dictate the rules, systemic risks arise. Centralized AI systems often fail to adapt to diverse regional or cultural needs. For instance, a single global model might overlook the nuances of local languages or customs. Worse, this consolidation opens the door for manipulation and bias. Decentralized AI disrupts the traditional approach.
AI Developments Can’t Scale Without Decentralization
Instead of relying on central servers or proprietary data hubs, DeAI uses distributed networks. By distributing AI development across nodes, it becomes more resilient, adaptable and fair.
Platforms like Ocean Protocol and SingularityNET are already demonstrating what this future could look like. Ocean Protocol facilitates secure, decentralized data sharing, allowing individuals and organizations to contribute data sets without losing control over them. SingularityNET acts as a marketplace where anyone can buy, sell, or collaborate on AI models. These platforms remove barriers, giving startups and researchers tools previously locked behind corporate walls.
AI Agents Are Transforming the Web3 Economy
Autonomous AI agents were one of the biggest web3 narratives of 2024 and are now accelerating the shift toward decentralization. These agents function independently, analyzing real-time data to make decisions without human intervention. As of January 2025, more than 10,000 AI agents operate in the Web3 ecosystem, managing tasks like crypto staking, on-chain trading and governance in decentralized autonomous organizations (DAOs).
How are they transforming web3? AI agents have been designed to make real-time decisions, analyze vast amounts of data, and adapt strategies dynamically without any human oversight. This allows AI agents to execute decisions autonomously.
Take blockchain staking, for example. Traditionally, staking required constant oversight to maximize rewards and minimize risks. Autonomous agents streamline this process: They monitor network conditions, adjust strategies dynamically, and optimize participation. This is critical as blockchain networks grow in complexity and transaction volumes increase.
The same applies to DAOs. These organizations rely on collective decision-making, which can be slow and prone to bottlenecks. AI agents automate fund allocation, proposal voting, and governance monitoring, ensuring that DAOs remain efficient and unbiased.
AI Agents Are Reshaping Blockchain
One of the biggest reasons why AI agents are becoming notably efficient today is because of the progress made in large language models (LLMs). If we look at the development cycle of OpenAI’s ChatGPT alone, it’s astonishing how GPT’s LLM models have evolved in just two years. Unlike earlier AI systems that relied on predefined rules, modern LLMs can reason, adapt and learn in real time. This shift makes AI agents capable of handling complex tasks in dynamic environments.
One example is Andy Ayrey’s Terminal of Truths, also called Truth Terminal. This AI agent operates semi-autonomously. The agent has been managing its own social presence on X and creating content without any human intervention. Key features of the Truth Terminal are generating content, cultural engagement with real social media users, and most importantly, financial operations — the chatbot received 1.93 million Goatseus Maximus (GOAT) tokens after promoting it on X. Truth Terminal became the first AI crypto millionaire.
With more than 250,000 followers on X, it showcases how AI agents can integrate with public platforms to drive engagement and decision-making.
These advancements aren’t just theoretical. AI agents are already driving blockchain activity. They optimize trades, manage liquidity pools, and reduce latency in transactions. The economic impact is already visible. AI-agent-based cryptocurrencies already have a market cap of nearly $16 billion. This entire market segment barely existed a year ago.
Even niche sectors like meme coins are feeling the influence. For example, the meme coin GOAT reached a valuation of $937 million, driven in part by the influence of autonomous agents like the Terminal of Truths. This demonstrates how these agents can shape market behavior, making them indispensable in the changing digital economy.
By 2025, it’s predicted that more than 80 percent of blockchain transactions will involve autonomous agents, marking a fundamental shift in how decentralized networks operate. And the market value of AI agents in blockchain ecosystems could surpass $47 billion.
Challenges of Decentralization
Although the promise of DeAI is exciting, several hurdles must be addressed for this technology to thrive and achieve wider adoption. Security remains a significant concern. In 2024 alone, blockchain hacks resulted in losses exceeding $2 billion. Introducing autonomous agents increases the attack surface, making strong safeguards essential.
Scams are another issue. The hype around AI agents has attracted bad actors who exploit the narrative for fraudulent projects. One of the most common scams involves fake AI agents with fake tokens and no real utility — they often lead to rug pulls.
Ethical considerations also come into play. As agents take on more responsibilities, ensuring their actions align with societal norms and user intentions becomes crucial since they make autonomous decisions around financial operations, content moderation, and asset management.
Also, in order to thrive, DeAI needs an infrastructure capable of handling millions of transactions per second. To achieve the scalability demands of DeAI, for instance, companies and developers would have to take a multi-layered approach — combine high-throughput layer-two scaling solutions with modular blockchain architectures like Celestia and Cosmos, and allow off-chain AI processing with on-chain validation.
For example, decentralized networks like Render and Akash can provide the processing power for DeAI platforms while Ethereum or Cosmos chains validate the results via Zero-Knowledge proofs like ZKSNARKs to ensure integrity without overwhelming the blockchain.
The Path Forward for DeAI
For DeAI to realize its full potential, a few key areas require attention. First, clear regulatory frameworks by government agencies and financial regulators can build trust and reassure users, encouraging adoption. Transparency in how AI agents operate is equally important. Users need to know how decisions are made and who is accountable when things go wrong. Aiming for transparency would mean that developers need to open-source critical components of AI models or allow independent third-party audits. In addition, AI agents would need to have human-readable explanations for their decisions.
Most importantly, using public blockchains could also help make AI operations crystal clear due to the technology's decentralized, immutable, and transparent nature.
The current infrastructure must evolve to handle the demands of autonomous agents. This includes faster and more scalable networks like Arbitrum or Optimism, better interoperability between the AI platforms and blockchains, and enhanced computational capabilities to be able to run complex AI models as they require massive computational power.
Most importantly, the ethical dimensions of DeAI must be front and center. Developers need to make sure that AI agents act responsibly. From the initial development cycle, there needs to be a greater emphasis on user privacy and societal values. Collaboration between technologists, policymakers, and ethicists will be critical to managing these challenges since these issues can potentially undermine the sector’s authority.
