The AI agent ecosystem has changed very quickly over the last year.
Interestingly, this trend is very similar to the rise and fall of early blockchain projects between the 2010s until date.
At present, this sector, which was initially fueled mostly by hype, has become more mature over the years.
Sustainable business models and real-world applications are thriving in the AI space, even as competition intensifies.
Here’s how far Artificial Intelligence has come and the wonders it has birthed in the tech space.
The initial wave of AI-driven crypto agents in 2024 started with inflows of new projects all over the market.
There were several major events along the way, including Marc Andreessen’s $50,000 Bitcoin donation and the success of token launchpads.
These events sparked a surge of AI-based projects entering the crypto space that lasted for most of the year.
However, this expansion led to a sharp problem of liquidity dilution by the start of the current year.
This issue exposed the fragility of many projects that lacked clear use cases.
Said problem, again, was similar to the ICO boom of 2017, where many crypto projects relied on hype rather than functional products.
Investors were drawn to the promise of the AI-powered automation before realizing later on that not all projects were built to last.
The market is maturing so far, and the focus is now shifting from speculation to real-world utility.
AI-driven crypto agents have so far been proving their worth in areas like automated trading, asset management, market analysis, cross-chain interactions and much more.
This shift is especially clear in the rise of initiatives like DeFAI (decentralized finance + AI) and multi-agent systems (MAS).
In particular, these multi-agent systems are special due to their ability to use collaborative intelligence to perform tasks like enhanced market analysis.
Unlike single-agent models, these MAS frameworks use specialized agents that perform specific tasks, like Data analytics, risk assessment, trade execution and more.
Projects like Griffain, NEUR and BUZZ are even already well known for showing how AI-powered agents can help users interact with DeFi protocols.
Moreover, inter-agent communication mechanisms within MAS allow these AI agents to refine their predictions better than any human ever could.
As it stands, the next step in the ongoing DeFAI’s evolution is adding decentralized governance models to the mix.
As far as this goes, AI agents will be able to perform tasks like on-chain compliance, protocol treasury optimization, and decentralized decision-making in the future.
One of the biggest advancements in the AI-driven crypto space comes from DeepSeek-R1.
DeepSeek-R1 is a lot unlike conventional models that rely on supervised fine-tuning (SFT) before reinforcement learning (RL).
This model optimizes entirely through RL from the start, in an approach that greatly improves its ability to adapt and think.
The introduction of DeepSeek
To understand this shift, think of two new employees in a work environment.
The first employee went through traditional AI training where they went to a university and then got better at their job through mentoring.
On the other hand, DeepSeek-R1’s Pure RL Approach is similar to an employee getting into the workplace and learning as much as possible through trial and error in real-world scenarios.
The DeepSeek-R1’s bypassing the need for initial supervision allows AI agents to adapt more quickly to market conditions based on direct feedback.
This innovation is more useful in DeFi, where the market can be very unpredictable and instant decision-making is very important.
AI-powered agents using the DeepSeek-R1 can perform even more complex functions like monitoring liquidity pools and arbitrage opportunities, as well as optimizing asset allocations better than any other model.
November of last year saw iDEGEN become the first crypto AI agent built on DeepSeek-R1.
This launch marked a major step forward within the AI agent space and showed how AI crypto agents could inherit advanced reasoning while remaining cheap to use.
One of the biggest challenges facing AI-driven crypto agents so far is the cost of closed-source AI models like OpenAI’s GPT-based systems.
As it stands, many workflows need to process thousands of tokens per transaction and can be quite expensive to run.
On the other hand, open-source RL models like DeepSeek-R1 are a more decentralized and cheaper alternative for defi applications.
As AI adoption grows in crypto, open-source development is likely to become more and more popular within the space.
This shift will allow more innovative AI solutions to emerge. It will also create a more inclusive ecosystem for everyone, where healthy competition can thrive.
AI-driven crypto agents will need to thrive over the long term and will require more innovation and cost efficiency as the years go by.
Open-source AI models like DeepSeek-R1 continue to make it easier for projects to develop and deploy better and more specialized AI solutions.
Meanwhile, advancements in DeFAI and MAS continue to drive deeper integration between AI and the DeFi space.
The message has so far been received loud and clear: projects will now have to prove their value beyond initial hype.
In the meantime, the AI crypto sector is changing from a speculative gold rush into a field where success is determined by more than mere hype.
The next wave of winners will be projects that innovate and also validate their successes as they go.
