Although generative art can be created by any kind of autonomous system, most modern generative art is created using algorithms. Generative artists write a computer program to produce the art using a set of rules combined with a degree of randomness. In this way, the end piece is a collaborative work containing input from both the artist and the system.
Generative art can take many forms, including visual imagery, music, written work (like poetry), or architectural designs.
In the crypto ecosystem, generative art combined with blockchain-based smart contracts enables artists to produce collections of art comprising potentially thousands of unique pieces, each tagged to its own NFT with a unique cryptographic hash. In this way, the NFT acts as an attestation to the uniqueness or scarcity of the work, which would otherwise be infinitely reproducible as a straightforward digital file.
History of generative art
The history of generative art has strong connections with the development of modern computing.
Prior to modern computing, generative art constituted niche experiments using complex mechanical equipment. One example is the work of Swiss kinetic artist and sculptor Jean Tinguely, who worked at the convergence of art and mechanics. In the 1950s, Tinguely created machines that could generate random abstract drawings with a pen and paper.
The earliest pioneers of generative art using computers emerged during the 1960s. Many artists, including German artists Frieder Nake and Georg Nees, and British-born Harold Cohen, were academics and scientists with access to computing laboratories where they could experiment.
One notable exception is French-Hungarian artist Vera Molnár, who trained in classical art in the 1940s. In the 1960s, she learned computer code and set up several research groups in the field of generative art in the 1960s. At the time, Molnár could only access a computer via a research group in Paris.
Over the following decades, as computing developed and created more opportunities for generative artists, it became a more recognized discipline. Musicians such as Brian Eno and John Cage popularized generative music in the 1990s.
In 2010, generative art expanded to other disciplines. For example, architect Michael Hansmeyer designed an algorithm that could produce architectural patterns autonomously. The White Tower, a 29-meter, 3D-printed concept building located in the Swiss Alps, is one example of his algorithmic design brought to life.
Modern generative art
Over recent years, it’s become possible for anyone to create generative art thanks to the emergence of artificial intelligence, which powers tools such as AI programs Midjourney and Dall-E 2. With these tools, the user simply enters their keywords, and the underlying AI creates an image based on reference data.
Generative art and NFTs
Before NFTs, there was no means of assigning ownership to a given piece of digital generative art. A digital work that can be reproduced indefinitely by anyone with ease has little value, and there was no significant market for generative art prior to the existence of NFTs.
Since the launch of the ERC-721 non-fungible token standard on Ethereum in 2018, the market for NFTs has grown exponentially, and generative art has been a substantial component of this growth.
Generative NFT art is issued as collections based on themes specified by artists, so that anyone can identify an individual piece as being from a collection. Within a collection, the artist defines traits that the algorithm can customize to make each piece individual. For instance, in the case of profile picture artwork, each NFT could have a different combination of hair styles and colors, glasses, clothing, or eye color to create a potentially massive number of individually unique characters that all look thematically similar. Traits can also be assigned a weighting so that some are rarer than others.
NFT technology provides a medium for a new generation of digital artists who can create marketable pieces of work, but it is also allowing pre-NFT era works of generative art to achieve their full value on the open market as unique pieces. For instance, one piece by generative art pioneer Vera Molnár fetched over £100,000 ($120,000) in an NFT sale by UK art auctioneer Phillips.
Modern generative artists
Given the growth in popularity of generative art NFTs, there are several artists that have gained prominence in the sector with high-profile collections.
Pak is the pseudonymous creator best known for the work “Merge,” which sold for a record-breaking $91.8 million in December 2021. Pak also released the Lost Poets NFT collection, where each piece was created by an AI robot that was designed for the project.
Matt Kane achieved initial success in September 2020 with his first sale titled “Right Here, Right Now” which fetched the equivalent of $100,000. His 2021 collection, Gazers, was a series of 100 generative art NFTs released on the popular ArtBlocks platform, each of which changes every 24 hours based on lunar cycles.
Dmitri Cherniak is another ArtBlocks contributor using generative art to create NFTs. One example is his collection “Ringers” which depicts 1000 unique ways that strings can be wrapped around pegs, each created by an algorithm.
Generative art essentials
- Generative art involves creating artistic output via an autonomous system, most typically an algorithm.
- Generative art emerged as a discipline in the second half of the twentieth century, but the advent of NFTs has turned generative art into a marketable asset.
- Well-known generative artists now using NFTs include Pak, Matt Kane, and Dmitri Cherniak.