Thoughts on AIGC for Non-AI Industries

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1. AIGC is a paradigm shift from goal-oriented problem solving to free-form interactive engineering. It is time to expand our imagination to products that can talk and draw with the customers, on top of being able to completing its own tasks with these interactions. Your fridge can help to order groceries when asked, but can also answer generic questions like ChatGPT does. No reason to limit the AI to do what its shell product is designed to do. For manufacturers of these products, it means better customer stickiness.


2. The entire AIGC economy is in its infancy because right now the paying customers are the tech-savvy people who can afford a few tens of bucks in subscription fees every month. To make it really ubiquitous in every product and every place, the AI model serving cost must be reduced by multiples of thousands of times. When that is achieved, products like GitHub copilots might just be free like Bing search. At that time, every product that is capable of accessing the Internet will have a chance to tap in some AIGC capability. The good news is that the AI industry and academic world is making very fast progress on serving costs.


3. The real business barrier for AIGC is use case and its accompanying data. The technology cannot be the business barrier as long as open source AI development is happening at its current pace. OSS will out-pace most AIGC technology providers, and eventually we will just end up with a few that are really good from the hundreds of new companies we are seeing now. However, if you are a non-tech company that has your own products, be aware that the AIGC uses cases of that product are the true business barriers of the future. Do not send data to any AIGC service provider that doesn’t promise that they will not use your data to train their own models (yes, cuing OpenAI and Google here).


4. The development of AIGC has to be tightly coupled with direct user feedback. There are 2 reasons for this: 1) initial deployment of any AIGC model cannot be the best solution because there is not sufficient use case data to fine-tune the models; 2) because AIGC models operate in free form, users can frequently teach us how to use generative models even if we are creators. It is impossible to think of AIGC as a product that we can purchase once and deploy somewhere without change — it has to be a joint iterative development process with both the product and the users.


5. In spite of its paradigm-shifting capabilities, AIGC did not come from nowhere. Human kind find new scientific theories and technologies when physical scales of experiments are pushed to their extremes. For example, at planet-scale observation we proved the general theory of relativity, and quantum mechanics were discovered when we dig deep into particle scales. For computer science, the physical scale is computational power. It is actually quite natural that this generative AI boom came about when people started training generative models at much larger scales than before. There is no myth.

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