Cryptocurrency-based AI Market and AI Financial Analysis – Cointelegraph Magazine

We’ll be publishing real-world use cases for AI and cryptocurrency every day this week. This includes why you shouldn’t believe the hype. Today, get two things for the price of one: a blockchain-powered AI marketplace and financial analytics.

Mixing AI and cryptocurrency may not seem like the most exciting use case, but both Illia Polosukhin, co-founder of Near, and Vance Spencer, founder of Framework Ventures, say blockchain-based marketplaces for sourcing and computing data for AI are top choices.

AI is an incredibly fast-growing industry that requires increasingly more computing power. Microsoft was the only one reported. Invest Invest $50 billion in data center infrastructure in 2024 to handle demand. AI also requires huge amounts of raw and training data sorted into categories by humans.

Polosukhin believes that a decentralized blockchain-based marketplace is the ideal solution to help crowdsource the necessary hardware and data.

“(Blockchain) allows us to build more equal and effective markets,” he told Magazine, explaining that currently AI projects must negotiate with one or two large cloud providers, such as Amazon Web Services. Nonetheless, the lack of Nvidia’s A100 graphics processing units makes it difficult to access the required capacity.

Spencer also cites a blockchain-based marketplace for AI resources as a top use case right now.

“The first thing is sourcing the actual GPU chips,” he says. “How do you source chips in places where there is a huge shortage of GPU chips without having a network to actually source and serve and bootstrap the market?”

Spencer highlights the Akash Network, which provides a distributed computing resource marketplace on Cosmos, and the Render Network, which provides distributed GPU rendering.

“At this point, there are some pretty successful companies that are actually doing the protocol.”

Another example of a decentralized marketplace providing cloud computing for AI is Aleph.im. Token holders of the project have access to computing and storage resources to run the project.

Aleph.im runs Libertai.io, a distributed large language model (LLM). You might think that decentralization would make AI too slow to function, but Aleph.im founder Moshe Malalawach explains that’s not the case.

“This is the problem. For one user, the entire inference (when using a model to generate data) runs on a single computer. Decentralization comes from the fact that the network uses random computers. However, it is centralized for the time you request. So it can happen quickly,” he said.

SingularityNET, another blockchain-based AI marketplace, offers a variety of AI services that users can plug into models or websites, from image creation to colorizing old photos.

An emerging blockchain-based AI market that Spencer is very excited about is tokenizing and trading AI models. Framework has invested in AI Arena, a fighting game like Super Smash Brothers where users train AI models to fight each other. Models are tokenized into non-fungible tokens and can be bought, sold or rented. “I think it’s really cool,” he says. “It’s not just the cryptocurrency-based monetization, but also the ownership of these models that is interesting.”

“I think at some point, probably some of the most valuable models, some of the most valuable assets on-chain, will be tokenized AI models. At least that’s my theory.”

Don’t believe the hype: You can currently source components, data, and compute through the existing Web2 marketplace.

Bonus use cases: financial analysis

Anyone who has tried to interpret the ocean of data generated by on-chain financial transactions knows that having immutable and transparent records is one thing, being able to analyze and understand them is another.

AI analytics tools are perfectly suited to summarizing and interpreting patterns, trends and anomalies in data, potentially suggesting strategies and insights to market participants.

For example, Mastercard’s CipherTrace Armada platform recently partnered with AI company Feedzai to use the technology to analyze, detect and block cryptocurrency transactions related to fraud or money laundering across 6,000 exchanges.

Elsewhere, GNY.io’s machine learning tool attempts to predict the volatility of the top 12 cryptocurrencies, and Range Report uses ChatGPT-4 to analyze trends and buy/sell signals.

But can AI help traditional markets as well? That’s what Bridgewater hopes to do next year, when it plans to launch a fund from its new Artificial Investment Association (AIA) Institute, which aims to analyze patterns in financial markets and make predictions that can help investors generate profits.

Previous attempts to do this have yielded lackluster results. The EurekaHedge Index, comprised of 12 AI-based funds, underperformed the broader hedge fund index by about 14 percentage points over the five years through 2022.

This is primarily due to the challenges associated with providing the large amounts of accurate information required.

Ralf Kubli, Director of the Casper Association, believes that AI can revolutionize traditional finance. But this is only possible if AI combines blockchain records with rigorous standards to ensure that the information fed to models is comprehensive and accurate.

For years, he has advocated for the financial industry to adopt the Algorithmic Contract Types Universal Standard (ACTUS), which was created in the wake of the global financial crisis. This was partly due to complex derivatives where no one understood the liabilities or cash flows involved. He believes that on-chain standardized data is essential to ensure trust and transparency in model output.

“Basically, we believe that without blockchain, AI will disappear completely,” he told the Magazine. “Imagine you are looking to invest in an AI company and you receive updates on your LLM progress every three months. Yes? “If we can’t see what they’re putting into the model, there’s no way to know what progress they’re making.”

He describes those who police blockchains against companies manipulating the results. “And in the past (…) there was so much money that they would manipulate what was happening.”

“I don’t think AI will be effective going forward without this layer of assurance in the blockchain – what happened, when and where it was used.”

He says combining the two will give rise to new predictive capabilities.

“The hope for AI in the future is that predictive models will become much more powerful and be able to predict behavior much better,” he said, citing credit scores as an example.

“AI used in the right way can potentially lead to much more powerful predictive models. This means that certain creditworthy people who are currently unable to obtain credit will be able to do so. That’s something I feel very passionate about.”

Don’t believe the hype: AI’s predictive capabilities have so far been shown to be poor at best, and trusted data not recorded on the blockchain could be a useful input for AI analytics.

Also read:

#1 Real-World AI Use Case in Cryptocurrency: The Best Money for AI Is Cryptocurrency

Real-world AI use case in cryptocurrency No. 2: AI can run DAOs.

Real-World AI Use Case in Cryptocurrency No. 3: Smart Contract Auditing and Cybersecurity

Real-world AI and cryptocurrency use case #4: Fighting AI fakes through blockchain

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Andrew Fenton

Andrew Fenton, based in Melbourne, is a journalist and editor covering cryptocurrency and blockchain. He has worked as a national entertainment writer for News Corp Australia, a film journalist for SA Weekend and The Melbourne Weekly.

Follow the author @andrewfenton