Litepaper

State of the Art AI

Today’s state of the art AI systems come as a result of many improvements over the last few years. Some examples are advanced dataset collection and AI cleanup algorithms (AI used to train AI), Reinforcement Learning from Human Feedback (RLHF), and hardware advancements such as the release of the NVIDIA A100/H100 GPUs. These improvements have led ChatGPT to be one of the fastest growing products ever released, with massive user growth expected to continue.

These AI systems have caught the attention of the public because they are novel and useful. They are seemingly able to answer any question from scratch before the user's eyes. They have an established business and entertainment use case.

Open Source AI

A recently leaked Google Memo highlights and confirms the closed sourced AI producers' concerns. In a matter of days, the open source community was able to take a leaked LLM from Meta and improve it in a way that took Google and OpenAI months.

The initial assumption of creating models which have a) prohibitive training costs b) 2-3 month training times c) massive datasets in the size of PBs, would inherently create datamoats may be incorrect. Open Source initiatives are currently training models on consumer grade desktop hardware with iteration cycles of days instead of months while achieving 90% of the closed source model utility. Community members are creating datasets from the larger proprietary models and other sources. This is achieved with spending pennies on the dollar in comparison to the larger corporations.

Monolithic AI

Today’s companies which deliver AI solutions operate in all parts of the AI value chain. Everything from dataset collection and data cleaning, training and operating infrastructure, to delivery of products and services (such as API integrations) are often developed and offered in house.

As these companies and the industry overall matures, the structure will become less monolithic. Naturally some companies will emerge as more performant on certain parts of the AI value chain than others and will come integrated and reliant on more external parties. The same trend has been previously observed in all other established value and supply chains.

AI and Web2

Many products and services have been built in the web2 space around AI technology. Everything from AI Assistants, Companions, Trading Algorithms and Funds, to AI Video Creation has evolved well past tech demonstrations and has

AI and Web3

AI and Web3 will create entirely new forms of businesses and opportunities. As the AI industry grows, the common use case we see today of humans interacting with AI will become matched with machines interacting with machines. This can already be seen today with the rise of tools such as AutoGPT, an open source project, in which users create a bot to achieve any arbitrary goal by using ChatGPT.

Payment settlement layers will become more important as machine to machine AI interaction becomes more prevalent. How can machines agree AI is executed correctly without the need for human confirmation or intervention? How can one qualify the output of an AI system? How can a node prove it will offer value without disclosing specifics upfront? Blockchain and trustless technology is the obvious answer, offering guarantees to both parties where traditional payment systems don’t.

Conclusion

Proprietary AI systems had a head start due to stockpiles of resources, developer talent and well defined business objectives. It was quickly realized with a fraction of the resources, an internet based community of people working together can outpace and outperform large technology conglomerates. In the long term, Open Source will prevail.