The Future of AI and Crypto: Can This Tech Duo Deliver on Its Promises?

The Future of AI and Crypto: Evolution or Illusion?

It has been said that the next great thing in technology will be the combination of crypto and artificial intelligence. The market capitalization of AI cryptocurrency tokens has surpassed $1 billion in recent years. However, a matching surge in user demand has not yet materialized, despite the enormous interest from investors.

If you ask the typical AI user what application they use daily, they’ll probably say something like ChatGPT. Few, if any, will mention utilizing a blockchain-based system or crypto protocol in their daily routines. So, can blockchain and artificial intelligence converge at this point? In other words, can blockchain technology succeed in revolutionizing AI? Or is this just false hype?

Decentralized Blockchain Projects Could Solve AI’s GPU Shortage Crisis

Today’s centralized cloud computing systems just cannot meet the demand for graphical processing units (GPUs), which AI developers are in dire need of, according to Guarav Sharma, chief technology officer of AI project IO (IO). This presents a chance for decentralized blockchain projects.

Sharma worked in the hotel sector before this project, creating AI models that assisted in predicting which hotels a user would most likely book and how much to charge. However, he requested enough GPUs from Amazon to train his model, and they allegedly said they didn’t have enough stock to meet his needs.

We went to Amazon to be honest, like we first thought of buying it. We couldn’t buy it. Then we went to the cloud. We didn’t find it there also, and we just had to wait for months to get this inventory from the AWS itself at that time.

Sharma

He believes that by establishing a market for GPU power, decentralized protocols like IO can address this issue. Customers can visit the site to locate servers, and providers can list their GPUs there as well, so clients can locate GPUs from any source. This is the only effective method of connecting consumers and sellers, especially since the demand for GPUs is increasing due to the growing popularity of AI applications.

Nevertheless, Sharma acknowledged that certain AI teams are not contributing much to the field of AI in general and blockchain AI in particular. Some teams assert that they can produce the next big model with just three or five members, but in practice, a significantly larger team is required. Some have engineers who have worked for big businesses but don’t have any portfolios to present to potential investors.

Blockchain and AI Convergence: Tokenizing Models for Next-Gen Funding

Kartin Wong, a co-founder of ORA, claims that in the future, blockchain prediction markets will require AI, which will require the two technologies to combine. Wong cited Polymarket’s growth as evidence of this necessity. Despite being based on a blockchain, Polymarket lacks an oracle to handle and decide who won a wager. Rather, it typically relies on human judgment. However, if this occurred on the Internet, blockchain AI might produce oracles that would provide answers to anything.

Additionally, he made the case that tokenization can help AI model funding. By introducing the concept of an “initial model offering,” ORA made it possible for tokens to be launched using untrained AI models. The model’s training, which is quite costly and GPU-intensive, can be covered with the money raised. Wong did concede, though, that certain blockchain AI ventures—or AI projects in general—are fraudulent. Some models might say they are utilizing AI to deliver results, but in reality, they might be using humans to review the model’s output, which could render it unnecessary.

Inference Labs Co-Founder: Blockchain Enables Verifiable AI Autonomy

According to Ron Chan, co-founder of the blockchain AI protocol Inference Labs, blockchain is the only means to build genuinely autonomous AI. Therefore, without it, humanity will not be able to survive in the future. According to Chan, centralized AI is created with the enterprise’s objectives in mind. Decentralized AI meets a different demand, even though this has a place in the world. Its growth is driven by the speed and engagement of market demand, which encourages human-centered innovation that may overcome significant challenges.

According to him, decentralized AI will create tools for proof of inference, or the capacity to demonstrate that a specific response originated from a specific AI model. He claimed that the sector has an immediate need for this. According to Chan, the solution is to grant the AI complete authority, verifiable autonomy, and irrevocable delegation. Furthermore, it must be impossible for humans to reclaim control of the account once it has been given to the AI. Then and only then will we be able to verify that everything it does is actually started by the AI model and not by a human operating in the background.

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