Why do the most valuable NFTs look old-school and how will they look in the future?

Every byte needs a good reason to exist

The computer that took men to the moon had 69.12kB of memory. To get to this capacity, half a mile of wires were manually threaded through small magnetisable rings, with each ring giving 1 bit of storage. This was crazy expensive, time-consuming and error-prone. Back then, if you were a byte and you wanted to go to the Moon, you better have had a very good reason to be in that memory! Each byte counted, each one was precious.

Rope core memory as the one used in the Apollo 11
The Prince of Persia on Apple II. Pixel art was a thing, and there were plenty of smart tricks involved in the creation of these images.
Note, the y-axis is logarithmic and the year of the Apollo mission (1969) is not on the graph. Graph from Komorowski 2014 (mkomo.com).

Why do the most valuable NFTs look old-school?

Well, this was before the blockchain had arrived. After years of disrespect and frivolous use, today bytes are precious again! At the time of writing (10/11/2021), every byte stored on the Ethereum blockchain costs around $0.3 to store. So, if you want to immortalise a 2 MB photo of your cat that will be $600 000 please.

(Left) The most expensive Blitmap sold to date (222 eth or 1.05 million dollars). (Right) Noun 63. Sold for 160 eth (730k dollars).
People could combine two original blitmaps to create another one: with the patterns of one parent and the colour palette of the other parent.

What does the future hold?

In the last years, where bytes were cheap, we’ve learned a whole lot about images, information, perception and compression. We can now use modern tools to search for the art with the highest beauty to bytes ratio.

An example of a breeding mechanism, the polygons of the original are replaced by their inscribing circle. The creator of the original gets paid for the breeding.
A breeding mechanism similar to blitmaps’, one parent gives the polygons and the other gives the colours.
Any mathematical transformation can be applied to the polygons to create interesting animations.



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João Abrantes

João Abrantes

AI researcher into reinforcement learning, complex systems and collaboration tech.