The core concepts of which emerged in the 50s are what you need for self-driving cars and other applications. Training models need to be similar to the human brain. This requires a large amount of compute power, which is provided by the TPUs or GPUs. The cost of this compute power is out of reach for most artificial intelligence developers who rent it from cloud computing platforms. What should be done?
One approach is taken by Gensyn. It took the idea of the distributed computing power of older projects such as SETI@ home and the COVID-19 focused Folding@ home and applied it in the direction of this desire for deep learning amongst developers.
Gensyn raised a $6.5 million seed led by a Web3 VC. Also participating in the round are some of the people who created some of the protocols. The pre-seed investment of $1.1m was led by 7percent and Counterview Capital.
In a statement, Harry Grieve, co-founder of Gensyn, said: “The ballooning demand for hardware – and fat margins – is why the usual names like AWS and Azure have fought to command such high market share. The result is a market that is expensive and centralized…. We designed a better way – superior on price, with unlimited scalability, and no gatekeepers.”
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