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Botto is a decentralized autonomous artist, initially conceptualized by Mario Klingemann, and governed by a collective of stakeholders through the structure of a DAO (decentralized autonomous organization).

Botto makes use of a combination of software models called Stable Diffusion, VQGAN + CLIP, GPT-3, voting, and a number of other models and custom augmentations. The generative models are the largest neural network architectures publicly available in the world and have analyzed millions of works of art, faces, animals, objects, images, artistic movements, poems, prose, essays, etc. They have been trained on more content than any human being could study in their lifetime. These models give Botto the highest amount of latent space to work with and therefore the most possible variation of different styles and themes without being locked into a single area.

The artist begins its process by creating a text prompt using a custom language learning model which then feeds into a text-to-image model to procure 4k-8k images that are subsequently filtered down by a taste model to 350 pieces each week. The community members, BottoDAO, then vote to select what they think most qualifies as a true artwork.

The chosen piece from weekly voting rounds then becomes a canonical Botto artwork and sold as an NFT. The proceeds from sales go back into the community as well as paying for infrastructure costs like servers, fueling an economy that sustains Botto’s ongoing development as an artist.

Botto’s prompt and taste models hone their aesthetic continuously based on the preferences exhibited in the voting behavior of the community. So as to not find a niche too quickly, Botto is also directed to surprise and challenge the audience by seeking a number of images that have different characteristics from what has been seen before. In essence, the BottoDAO, with over 5000 participants to date, is guiding Botto in its development as an artist as it evolves its body of work.

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