Artificial intelligence (AI) and non-fungible tokens (NFTs) are two rapidly developing fields of technology. Their intersection has the ability to unlock untapped value, or create entirely new functionality for NFTs.

This intersection can take many forms; AI could be used to create NFTs or use its immutable properties to develop AI models or secure the underlying data lakes the AI tools leverage.

This article examines AI and NFTs, predicting the direction of the entwined technologies.

• What is AI?

• Intersect Between AI and NFTs

• Future Predictions

• Closing Remarks

Artificial Intelligence (AI) is a field of computer science that aims to create intelligent machines that can perform tasks that typically require human intelligence. For example:

• Learning.

• Perception.

• Problem-solving.

• Decision-making.

AI has been applied in many areas, including image recognition, natural language processing, and robotics.

AI has roots in academia dating back to 1956. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful.

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. This form of self-teaching has raised many concerns over the existential threat it may bring to the human race, if unchecked.

We’ve seen an explosion of interest in recent months, most stemming from OpenAI’s ChatGPT platform. It is estimated to have reached 100 million monthly active users in January, just two months after launch. Comparably, TikTok took nine months to reach the same level of adoption. There has been a myriad of use cases for AI technology:

• Chat Assistant: ChatGPT (OpenAI)

• Music: JukeBox (OpenAI)

• Visual: Midjourney or Dalle-E (OpenAI)

All of these applications will have varying degrees of utility with NFTs.

The intersection of AI and NFTs can take many forms, such as using AI to create NFTs or using NFTs to train AI models. But this isn’t where it ends, the possible use cases and overlap are enormous.

Let’s take a closer look at some examples.

AI algorithms can be trained to create unique digital art pieces, which can be minted as NFTs and sold to collectors.

We’ve seen the explosion of generative art, but generative art using machine learning to create an output has received less media exposure. At least until now.

Brain Drops

Brain Drops has recently caught the attention of both collectors and traders. It is an NFT platform and community for AI-generated art. It is building functionality for the art to be generated, customized, and curated “on-the-fly” by the minters. An educational arm is also being developed to help spread awareness.

Life in West America (Brain Drops Project)

‘Life in west America’ is Brain Drop’s most hyped collection. This 500-piece project by Roope Rainisto was minted on February 8, 2023 for 0.1 ETH (160 USD).

The floor has multiplied by 120x to 12 ETH ($19.9k USD) creating a collection floor market capitalization valuation of 6,000ETH ($10m USD).

The largest holder, Studio 137, owns 12 NFTs from the collection. Other assets in their portfolio suggest they focus on collections with historical significance with a generative art niche. Some examples in their portfolio include:

• 3 Autoglyphs

• 3 CryptoPunks

• 4 LightYears

• 2 Deaf Beef

• Rarepass: Genesis

It’s unsurprising that “Life in West America” is hitting new all-time highs with ‘Westworld’ recently selling for 16 ETH (26.6k USD). If renowned collectors are accumulating the pieces then it’s likely the provenance will drive a high asking price.

https://twitter.com/mooncat2878/status/1626498176009048064?s=20

Pindar Van Armen

We’ve also seen Pindar Van Armen’s collections emerge as cutting-edge in the AI Art revolution. Congrats to SuperRare Genesis pass holders who received this piece!

Van Armen uses robots to paint the pieces before converting them to NFTs. He intricately trains his robots using:

“deep learning neural networks, artificial intelligence, feedback loops and computational creativity”.

Claire Silver

Similarly, Claire Silver’s “Pieces” recently sold for 52.69 ETH ($87k USD) and “Internet” sold for 40 ETH ($67.6K USD).

https://twitter.com/nftnow/status/1626370676138381314?s=20

AI and NFTs aren’t new though. For instance, "AI-Generated Nude Portrait #1" by Robbie Barrat sold for 100 ETH (over $112,000) on January 5, 2021.

While AI-generated art NFTs are the obvious crossover, there is a myriad of other alternative overlapping utilities.

AI algorithms can be trained to create original music pieces, which can be sold as NFTs.

For instance JukeBox by OpenAI allows you to create music with AI.

Sound bites can then be used and clipped into an NFT, similar to what Dreamloops were back in 2021. The Dreamloop NFTs featured pixel art-based objects with playable retro music saved on each of the NFTs.

There’s no reason this music couldn’t be AI-generated and stored as an NFT to benefit from added security.

AI algorithms can be used to analyze the demand for certain types of NFTs and recommend them to buyers. They can also be used to price NFTs based on market trends and historical data.

There’s been rapid development of NFT analytical and trading tools since the 2021 NFT boom. Unsurprisingly, the next iteration could include machine learning to optimize pricing, sniping and the underlying algorithms.

One possible application of AI in NFT trading is using machine learning algorithms to analyze large datasets of past sales to identify patterns and predict future market trends.

NFTs can be used to represent virtual assets in video games or virtual worlds, for example, weapon skins in CS Go.

https://twitter.com/RealJonahBlake/status/1593790820716613632?s=20

AI algorithms can be used to create or modify these assets, maintaining unique and scarce properties as the game evolves.

The algorithm could automatically update the metadata on these assets to rebalance scarcity within a game or prevent power creep by rebalancing weapon statistics based on damage outputs.

AI can be used to verify the authenticity of NFTs.

AI algorithms could also be trained to identify fraudulent NFTs by analyzing their metadata, transaction history, and other factors, which could help protect buyers and sellers from scams.

This automation removes the need to manually check the authenticity of the digital asset.

Using NFTs to incentivize and reward AI development. For example, some projects are exploring the use of NFTs as a way to reward AI developers for creating useful or innovative AI models. These NFTs could represent a share in the profits generated by the AI model or could be used to unlock access to additional resources or tools.

Alternatively, the NFT could be used as proof of ownership with royalties for the underlying AI intellectual property flowing back to holders.

The limitation here is related to whether it is construed as a security. There may be an expectation of future profits, meaning more stringent reporting compliance and reporting requirements.

AI and NFTs could be used together to create new types of interactive and immersive experiences.

For example, an AI-powered virtual assistant could be programmed as an NFT to interact with players in a virtual world, creating a unique and personalized experience for each user.

The NFT can then be extracted and inserted, similar to a cartridge with metadata containing machine learning. A dynamic NFT consistently adapts and learns based on the environment it is embedded within.

NFTs could also be used to represent digital assets that are controlled by AI, such as a virtual pet or another type of AI-powered avatar. This lends itself to having a more advanced NPC in gaming.

NFTs can be used to create synthetic datasets that can be used to train AI models.

These datasets can be used to generate images, videos, or text, which can be sold as NFTs. A cartridge that can be plugged into a deep learning machine to upgrade the robot.

We are only just scratching the surface of machine learning. The recent surge in interest and breakthroughs with OpenAI has led to an explosion of attention in the AI sector. A clear overlap arises with blockchain technology.

There’s no reason these technologies should be thought of in isolation. Eventually, AI will be integral to many aspects of society, with NFTs as an alternative complementary form of technology.

Three months to gain 100m users is the fastest rate of adoption for any technology in history. The current daily active users are also increasing for applications like ChatGPT, according to ArkInvest.

We can predict that this rate will continue as it becomes more widely available. With interconnected networks and exponential machine learning, a secure data source will be imperative. NFTs and blockchain provide this utility.

We are experiencing an explosion in interest for AI visuals from Dall-E 2 or Midjourney. These are being immortalized as NFTs, but it’s likely this will create a trend for many new collection launches, leading to market saturation. Similar to open editions and Check VV derivatives.

Those who pioneered the art movement will likely hold value, while others may pump but likely trend to zero in the long term. If you want to create an AI NFT that holds value, do something that hasn’t been done before.

It’s inevitable that the use of AI will be entwined with NFTs, albeit to varying degrees depending on the purpose.

Before we conclude, out of curiosity I asked the ChatGPT and Midjourney AI applications about NFTs. Here were the outcomes.

ChatGPT provided a relatively balanced answer on the future of NFTs. Suggesting the NFT asset bubble and environmental challenges were headwinds.

While MidJourney visualized us being at a crossroads with the door to enter the next phase of the technological revolution. Or at least that’s how I interpret it. What do you think?

Both tools appear to have uncertainty embedded in their response. This leads me to conclude that AI-generated outputs are inherently dependent on the inputs. If we feed them with data that can be construed as biased, the outputs will likely be tentative, ambiguous or provide subjective outputs.

Regardless, the AI boom has increased the need for NFTs. Proof of ownership of underlying IP, capturing datasets to use as cartridges for deep learning robots or simply AI-generated artwork are all emerging intersections. It will be unsurprising that the variety of use cases will expand as AI develops further.

Origins Analytics

At OriginsNFT we leverage data-driven decision-making, educational resources, and proprietary analytics to remain ahead of the curve with respect to blockchain tech, specifically NFTs. To find out more, please visit our website or Twitter.

To purchase a pass, please visit our Opensea page.

Artificial intelligence (AI) and non-fungible tokens (NFTs) are two rapidly developing fields of technology. Their intersection has the ability to unlock untapped value, or create entirely new functionality for NFTs.

This intersection can take many forms; AI could be used to create NFTs or use its immutable properties to develop AI models or secure the underlying data lakes the AI tools leverage.

This article examines AI and NFTs, predicting the direction of the entwined technologies.

• What is AI?

• Intersect Between AI and NFTs

• Future Predictions

• Closing Remarks

Artificial Intelligence (AI) is a field of computer science that aims to create intelligent machines that can perform tasks that typically require human intelligence. For example:

• Learning.

• Perception.

• Problem-solving.

• Decision-making.

AI has been applied in many areas, including image recognition, natural language processing, and robotics.

AI has roots in academia dating back to 1956. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful.

Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. This form of self-teaching has raised many concerns over the existential threat it may bring to the human race, if unchecked.

We’ve seen an explosion of interest in recent months, most stemming from OpenAI’s ChatGPT platform. It is estimated to have reached 100 million monthly active users in January, just two months after launch. Comparably, TikTok took nine months to reach the same level of adoption. There has been a myriad of use cases for AI technology:

• Chat Assistant: ChatGPT (OpenAI)

• Music: JukeBox (OpenAI)

• Visual: Midjourney or Dalle-E (OpenAI)

All of these applications will have varying degrees of utility with NFTs.

The intersection of AI and NFTs can take many forms, such as using AI to create NFTs or using NFTs to train AI models. But this isn’t where it ends, the possible use cases and overlap are enormous.

Let’s take a closer look at some examples.

AI algorithms can be trained to create unique digital art pieces, which can be minted as NFTs and sold to collectors.

We’ve seen the explosion of generative art, but generative art using machine learning to create an output has received less media exposure. At least until now.

Brain Drops

Brain Drops has recently caught the attention of both collectors and traders. It is an NFT platform and community for AI-generated art. It is building functionality for the art to be generated, customized, and curated “on-the-fly” by the minters. An educational arm is also being developed to help spread awareness.

Life in West America (Brain Drops Project)

‘Life in west America’ is Brain Drop’s most hyped collection. This 500-piece project by Roope Rainisto was minted on February 8, 2023 for 0.1 ETH (160 USD).

The floor has multiplied by 120x to 12 ETH ($19.9k USD) creating a collection floor market capitalization valuation of 6,000ETH ($10m USD).

The largest holder, Studio 137, owns 12 NFTs from the collection. Other assets in their portfolio suggest they focus on collections with historical significance with a generative art niche. Some examples in their portfolio include:

• 3 Autoglyphs

• 3 CryptoPunks

• 4 LightYears

• 2 Deaf Beef

• Rarepass: Genesis

It’s unsurprising that “Life in West America” is hitting new all-time highs with ‘Westworld’ recently selling for 16 ETH (26.6k USD). If renowned collectors are accumulating the pieces then it’s likely the provenance will drive a high asking price.

https://twitter.com/mooncat2878/status/1626498176009048064?s=20

Pindar Van Armen

We’ve also seen Pindar Van Armen’s collections emerge as cutting-edge in the AI Art revolution. Congrats to SuperRare Genesis pass holders who received this piece!

Van Armen uses robots to paint the pieces before converting them to NFTs. He intricately trains his robots using:

“deep learning neural networks, artificial intelligence, feedback loops and computational creativity”.

Claire Silver

Similarly, Claire Silver’s “Pieces” recently sold for 52.69 ETH ($87k USD) and “Internet” sold for 40 ETH ($67.6K USD).

https://twitter.com/nftnow/status/1626370676138381314?s=20

AI and NFTs aren’t new though. For instance, "AI-Generated Nude Portrait #1" by Robbie Barrat sold for 100 ETH (over $112,000) on January 5, 2021.

While AI-generated art NFTs are the obvious crossover, there is a myriad of other alternative overlapping utilities.

AI algorithms can be trained to create original music pieces, which can be sold as NFTs.

For instance JukeBox by OpenAI allows you to create music with AI.

Sound bites can then be used and clipped into an NFT, similar to what Dreamloops were back in 2021. The Dreamloop NFTs featured pixel art-based objects with playable retro music saved on each of the NFTs.

There’s no reason this music couldn’t be AI-generated and stored as an NFT to benefit from added security.

AI algorithms can be used to analyze the demand for certain types of NFTs and recommend them to buyers. They can also be used to price NFTs based on market trends and historical data.

There’s been rapid development of NFT analytical and trading tools since the 2021 NFT boom. Unsurprisingly, the next iteration could include machine learning to optimize pricing, sniping and the underlying algorithms.

One possible application of AI in NFT trading is using machine learning algorithms to analyze large datasets of past sales to identify patterns and predict future market trends.

NFTs can be used to represent virtual assets in video games or virtual worlds, for example, weapon skins in CS Go.

https://twitter.com/RealJonahBlake/status/1593790820716613632?s=20

AI algorithms can be used to create or modify these assets, maintaining unique and scarce properties as the game evolves.

The algorithm could automatically update the metadata on these assets to rebalance scarcity within a game or prevent power creep by rebalancing weapon statistics based on damage outputs.

AI can be used to verify the authenticity of NFTs.

AI algorithms could also be trained to identify fraudulent NFTs by analyzing their metadata, transaction history, and other factors, which could help protect buyers and sellers from scams.

This automation removes the need to manually check the authenticity of the digital asset.

Using NFTs to incentivize and reward AI development. For example, some projects are exploring the use of NFTs as a way to reward AI developers for creating useful or innovative AI models. These NFTs could represent a share in the profits generated by the AI model or could be used to unlock access to additional resources or tools.

Alternatively, the NFT could be used as proof of ownership with royalties for the underlying AI intellectual property flowing back to holders.

The limitation here is related to whether it is construed as a security. There may be an expectation of future profits, meaning more stringent reporting compliance and reporting requirements.

AI and NFTs could be used together to create new types of interactive and immersive experiences.

For example, an AI-powered virtual assistant could be programmed as an NFT to interact with players in a virtual world, creating a unique and personalized experience for each user.

The NFT can then be extracted and inserted, similar to a cartridge with metadata containing machine learning. A dynamic NFT consistently adapts and learns based on the environment it is embedded within.

NFTs could also be used to represent digital assets that are controlled by AI, such as a virtual pet or another type of AI-powered avatar. This lends itself to having a more advanced NPC in gaming.

NFTs can be used to create synthetic datasets that can be used to train AI models.

These datasets can be used to generate images, videos, or text, which can be sold as NFTs. A cartridge that can be plugged into a deep learning machine to upgrade the robot.

We are only just scratching the surface of machine learning. The recent surge in interest and breakthroughs with OpenAI has led to an explosion of attention in the AI sector. A clear overlap arises with blockchain technology.

There’s no reason these technologies should be thought of in isolation. Eventually, AI will be integral to many aspects of society, with NFTs as an alternative complementary form of technology.

Three months to gain 100m users is the fastest rate of adoption for any technology in history. The current daily active users are also increasing for applications like ChatGPT, according to ArkInvest.

We can predict that this rate will continue as it becomes more widely available. With interconnected networks and exponential machine learning, a secure data source will be imperative. NFTs and blockchain provide this utility.

We are experiencing an explosion in interest for AI visuals from Dall-E 2 or Midjourney. These are being immortalized as NFTs, but it’s likely this will create a trend for many new collection launches, leading to market saturation. Similar to open editions and Check VV derivatives.

Those who pioneered the art movement will likely hold value, while others may pump but likely trend to zero in the long term. If you want to create an AI NFT that holds value, do something that hasn’t been done before.

It’s inevitable that the use of AI will be entwined with NFTs, albeit to varying degrees depending on the purpose.

Before we conclude, out of curiosity I asked the ChatGPT and Midjourney AI applications about NFTs. Here were the outcomes.

ChatGPT provided a relatively balanced answer on the future of NFTs. Suggesting the NFT asset bubble and environmental challenges were headwinds.

While MidJourney visualized us being at a crossroads with the door to enter the next phase of the technological revolution. Or at least that’s how I interpret it. What do you think?

Both tools appear to have uncertainty embedded in their response. This leads me to conclude that AI-generated outputs are inherently dependent on the inputs. If we feed them with data that can be construed as biased, the outputs will likely be tentative, ambiguous or provide subjective outputs.

Regardless, the AI boom has increased the need for NFTs. Proof of ownership of underlying IP, capturing datasets to use as cartridges for deep learning robots or simply AI-generated artwork are all emerging intersections. It will be unsurprising that the variety of use cases will expand as AI develops further.

Origins Analytics

At OriginsNFT we leverage data-driven decision-making, educational resources, and proprietary analytics to remain ahead of the curve with respect to blockchain tech, specifically NFTs. To find out more, please visit our website or Twitter.

To purchase a pass, please visit our Opensea page.