Unsurprisingly, the global AI market currently peaks at around 244 billion US dollars and could hit 1.01 trillion dollars within the next few years. If this happens, the industry will have grown by a CAGR of more than 26%. Artificial intelligence has been making waves in almost every sector, and the crypto industry has not been left out.
In fact, according to the Business Research Company, the generative AI in the crypto market is expected to hit $1.02 trillion in 2025. Evidently, decentralising AI is not fictional but a real revolution that could be crypto’s next frontier.
With digital currencies witnessing significant changes and more people tracking everything from the dogecoin price shifts to gas fees, the market has really been evolving. And clearly, AI has been making its way into the market through infrastructures that aim to make it more transparent, accessible and fair. Continue reading to learn more.
What is decentralised AI (DAI) all about?
While everyone can agree that AI has brought many helpful changes, it has not been without challenges. Given that traditional models are centralised and mostly controlled by large corporations, issues like data privacy and security have not ceased to be raised. Actually, according to Security Magazine, about eight in ten experts believe this technology increases data security challenges.
The matter is even more serious now that, according to 94% of organisations, customers may never buy from them if they don’t protect data well. Thankfully, blockchain can help overcome these challenges because of its decentralised ecosystem. No wonder the union between blockchain and AI is getting more robust, with Interexy approximating the blockchain AI market to reach $973.6 million by 2027.
With decentralised AI, there is more resilience against malicious actors; mitigating the manipulation risks often associated with centralised, traditional models is possible. Plus, blockchain, as you may know it, is immutable and will not allow the altering of data once it’s stored, which significantly improves a system’s trustworthiness.
Its decentralised nature provides good grounds for improved collaboration. Since data is shared across the network, models can be trained collaboratively, using numerous databases to develop more effective models. This also allows for the contributions of developers and researchers from different companies.
Areas opening up to DAI
Decentralised finance (DeFi) is already changing the face of the finance sector, with Mordor Intelligence anticipating its market size to grow by a CAGR of about 10.98% by 2030. But that’s not all because brands are combining AI with blockchain-based systems to make financial services more efficient.
A good example is how automated trading uses artificial intelligence to analyse real-time data, allowing for better trading outcomes. In risk management, combining AI and DeFi can improve yield optimisation by providing smart asset allocation strategies that help improve returns.
The best part of decentralised finance artificial intelligence is that it can offer insights while still allowing you to retain control over the execution of different actions. This is unlike intent-centric blockchains, where execution is entirely automatic based on pre-defined conditions.
Consider token swaps, for instance. DeFAI analyses key factors like liquidity and market trends and recommends the optimal route for changing tokens. An intent-centric approach, on the other hand, would just swap the tokens automatically without personalising guidance.
The supply chain is another sector in which DAI is really gaining popularity. Mark you: supply chains are already complex by themselves, involving multiple stakeholders, data handled and so on.
This often leads to things like inaccurate data, which may cost a business operationally and financially. In fact, according to ZoomInfo Blog, bad data is one of the reasons companies lose up to 25% of their potential revenue.
However, with platforms like Haust Network emerging, supply chain managers can now get ahead of these costs. Haust’s efficient and secure features make it relevant for different sectors, including retail, automotive, etc.
Are there any challenges?
Before an AI model is fully functional, huge loads of high-quality data are usually needed. And as much as crypto AI teams can scrape the open web, they will still need to access enterprise data sets, which can be challenging. This is why tech giants still have the upper hand here.
A limited amount of data means weaker AI models, which may lead to less adoption. This is where the need to use existing centralised AI infrastructures comes in. Crypto AI teams can use APIs from organisations like OpenAI, which can act as a decentralised front end for centralised AI backends. However, this poses the question of whether the models are really decentralised.
Plus, developing AI projects like DAI is quite costly. No wonder some closed-source models, including OpenAI, often pressure developers to use a pay-to-play structure without caring about the output quality.
Well, the rise of more cost-friendly options like DeepSeek could significantly encourage the spread of this innovation. And while this brings a lot of hope, decentralisation purists are still in a dilemma. Remember, shifting to DeepSeek makes crypto AI teams dependent on China, yet it has a strict stand on both AI development and crypto technology.
Since the world is always gravitating to better technologies to improve life experiences, it makes sense to expect technologies like DAI to become the norm in the coming days. However, just as with any other innovation, DAI faces several challenges, like high implementation costs and insufficient data, which must be addressed to benefit from it to the maximum.
