04/08/2023
Great thinking from a16z on generative . Here's a summary.
Traditional AI applications have been difficult to scale because they require a lot of data and compute resources.
Generative AI applications, on the other hand, can be scaled more easily because they can be trained on smaller datasets and run on less powerful hardware. This makes generative AI applications more economically attractive, as they can be deployed more widely and at a lower cost.
The economic value of generative AI is likely to be transformative, with a positive correlation between the median wage of an industry and the magnitude of its impact. This is because generative AI can automate tasks currently performed by humans, leading to productivity gains and lower costs.
The authors of the post anticipate that generative AI will significantly impact a wide range of industries, including language education, business operations, and healthcare.
Here are some additional points from the post:
- Define generative AI as "the ability to produce new and original content." They argue that generative AI is different from traditional AI in that it does not require a large amount of data to be trained.
- Generative AI is more scalable than traditional AI, as it can be deployed on less powerful hardware.
Conclusions: they believe generative AI has the potential to be a "transformative technology" with a significant impact on the economy.
With generative AI, we’re already seeing use cases with orders-of-magnitude improvement in time, cost, and performance over previous AI waves.
