Non-fungible tokens (NFT) are becoming one of the most important trends in the crypto ecosystem. The first generation of NFTs has focused on key properties such as ownership representation, transfer, automation as well as building the core building blocks of the NFT market infrastructure.

The hype in the NFT market makes it relatively hard to distinguish signal versus noise when even the most simplistic form of NFTs are able to capture incredible value. But, as the space evolves, the value proposition of NFTs should go from static images or text to more dynamic and intelligent collectibles. Artificial intelligence (AI) is likely to have an impact in the next wave of NFTs.

Jesus Rodriguez is the CEO of IntoTheBlock, a market intelligence platform for crypto assets. He has held leadership roles at major technology companies and hedge funds. He is an active investor, speaker, author and guest lecturer at Columbia University in New York.

We are already seeing manifestations of NFT-AI convergence in the form of generative art. However, the potential is much bigger. Injecting AI capabilities into the lifecycle of NFTs opens the door to forms of intelligent ownership that we haven’t seen before.

Today, NFTs remain mostly digital manifestations of the offline word in areas such as art or collectibles. While compelling, that vision is quite limited. A more intriguing way to think about NFTs is as digital ownership primitives. Ownership representations have much wider applications than collectibles. While in the physical world ownership is mostly represented as static records, in the digital on-chain world ownership can be programmable, composable and, of course, intelligent.

With intelligent digital ownership the possibilities are endless. Let’s illustrate this in the context of collectibles that remain one the best-known applications of NFTs.

Read more: Jesus Rodriguez - When DeFi Becomes Intelligent

Imagine digital-art NFTs that could converse in natural language answering questions to explain the inspiration behind their creation and adapt those answers to a specific conversation context. We could also envision NFTs that could adapt to your feelings, mood and provide an experience that is constantly fulfilling. What about intelligent NFT wallets that, as they interact with a website, could decide which ownership rights to present in order to improve the experience for a given user?

Echoing William Gibson’s famous quote, “The future is already here, it’s just not very evenly distributed,” we should think about the intersection of intelligent digital ownership as something that is possible with today’s AI and NFT technologies. NFTs are likely to evolve as a digital ownership primitive and intelligence should definitely be part of it.

To understand how intelligent NFTs can be enabled with today’s technologies, we should understand what AI disciplines have intersection points with the current generation of NFTs. The digital representation of NFTs relies on digital formats such as images, video, text or audio. These representations map brilliantly to different AI sub-disciplines.

Podcast: How Erick Calderon Turned NFT Squiggles Into a $6M Funding Round

Deep learning is the area of AI that relies on deep neural networks as a way to generalize knowledge from datasets. Although the ideas behind deep learning have been around since the 1970s, they have seen an explosion in the last decade with a number of frameworks and platforms that have catalyzed its mainstream adoption. There are some key areas of deep learning that can be incredibly influential to enable intelligence capabilities in NFTs:

Computer vision: NFTs today are mostly about images and videos and, therefore, a perfect fit to leverage the advancements in computer vision. In recent years, techniques such as convolutional neural networks (CNN), generative adversarial neural networks (GAN) and, more recently, transformers have pushed the boundaries of computer vision. Image generation, object recognition, scene understanding are some of the computer vision techniques that can be applied in the next wave of NFT technologies. Generative art seems like a clear domain to combine computer vision and NFTs.

Natural language understanding: Language is a fundamental form to express cognition, and that includes forms of ownership. Natural language understanding (NLU) has been at the center of some of the most important breakthroughs in deep learning in the last decade. Techniques such as transformers powering models such as GPT-3 have reached new milestones in NLU. Areas such as question answering, summarization and sentiment analysis could be relevant to new forms of NFTs. The idea of superposing language understanding to existing forms of NFTs seems like a trivial mechanism to enrich the interactivity and user experience in NFTs.

Read more: Jesus Rodriguez - 3 Factors That Make Quant Trading in Crypto Unique

Speech recognition: Speech intelligence can be considered the third area of deep learning that can have an immediate impact in NFTs. Techniques such as CNNs and recurrent neural networks (RNN) have advanced the speech intelligence space in the last few years. Capabilities such as speech recognition or tone analysis could power interesting forms of NFTs. Not surprisingly, audio-NFTs seem like the perfect scenario for speech intelligence methods.

The advancements in language, vision and speech intelligence expand the horizon of NFTs. The value unlocked at the intersection of AI and NFTs will impact not one but many dimensions of the NFT ecosystem. In today’s NFT ecosystem, there are three fundamental categories that can be immediately reimagined by incorporating AI capabilities:

AI-generated NFTs: This seems to be the most obvious dimension of the NFT ecosystem to benefit from recent advancements in AI technologies. Leveraging deep learning methods in areas such as computer vision, language and speech can enrich the experience for NFT creators to levels we haven’t seen before. Today, we can see manifestations of this trend in areas such as generative art but they remain relatively constrained both in terms of the AI methods used as well as in the use cases they tackle.

In the near future, we should see the value of AI-generated NFTs to expand beyond generative art into more generic NFT utility categories providing a natural vehicle for leveraging the latest deep learning techniques. An example of this value proposition can be seen in digital artists like Refik Anadol who are already experimenting with cutting edge deep learning methods for the creation of NFTs. Anadol’s studio have been a pioneer in using techniques such as GANs, and even dabbling into quantum computing, trained models in hundreds of millions images and audio clips to create astonishing visuals. NFTs have been one of the recent delivery mechanisms explored by Anadol.

Read more: Designer Eric Hu on Generative Butterflies and the Politics of NFTs

NFTs’ embedded-AI: We can use AI to generate NFTs but that doesn’t mean that they will be intelligent. But what if they could? Natively embedding AI capabilities into NFT is another market dimension that can be unlocked by the intersection of these two fascinating technology trends. Imagine NFTs that incorporate language and speech capabilities to establish a dialog with users, answer questions about its meaning or interact with a specific environment. Platforms such as Alethea AI or Fetch.ai are starting to scratch the surface here.

AI-first NFT infrastructures: The value of deep learning methods for NFTs won’t only be reflected at the individual NFT level but across the entire ecosystem. Incorporating AI capabilities in building blocks such as NFT marketplaces, oracles or NFT data platforms can prepare the foundation to gradually enable intelligence across the entire lifecycle of NFTs. Imagine NFT data APIs or oracles that provide intelligent indicators extracted from on-chain datasets or NFT marketplaces that use computer vision methods to make smart recommendations to users. Data and intelligence APIs are going to become an important component of the NFT market.

AI is changing the landscape of all software and NFTs are not the exception. By incorporating NFT capabilities, the NFTs can evolve from basic ownership primitives to intelligent, self-evolving forms, or ownership that enable richer digital experiences and higher utility for NFT creators and consumers. The era of intelligent NFTs does not require any futuristic technical breakthroughs. The recent advancements in computer vision, natural language understanding or speech analysis combined with the flexibility of NFT technologies already offered a great landscape for experimentation to bring intelligence to the NFT ecosystem.