Masters of AI

Generative AI: A Creative New World

A powerful new class of large language models is making it possible for machines to write, code, draw and create with credible and sometimes superhuman results.
Humans are good at analyzing things. Machines are even better at creating things.
Up until recently, machines had no chance of competing with humans at creative work-they were relegated to analysis and rote cognitive labor.
Now, machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI.”

The Chronology of generative AI

Earlier, relatively small models excel at analytical tasks and become deployed for jobs from delivery time prediction to fraud classification, but are not expressive enough for general-purpose generative tasks
Between 2015 and 2020, the compute used to train these models increased by 6 orders of magnitude and their results surpass human performance benchmarks in handwriting, speech and image recognition, reading comprehension and language understanding

As applications get more user data, they can fine-tune their models to:

improve model quality/performance for their specific problem space
Decrease model size/costs
Applications are great at spitting out multiple different ideas to get the creative process going
Most AI demos are one-and-done: you offer an input, the machine spits out an output, and you can keep it or throw it away and try again. Increasingly, the models are becoming more iterative.

Generative AI is still very early
The platform layer is just getting good
We don’t need large language models to write a Tolstoy novel
Models are good enough today to write first drafts of blog posts and generate prototypes of logos and product interfaces
There is a wealth of value creation that will happen in the near-to-medium-term

Compute gets cheaper
New techniques, like diffusion models, shrink down the costs required to train and run inference
Developer access expands
Applications begin to bloom
Killer apps emerge (Now)

Generative AI companies can generate a sustainable competitive advantage by executing relentlessly on the flywheel between user engagement/data and model performance.
The platform layer of AI is just getting good, and the application space has barely gotten going.

Text is the most advanced domain.
Code generation is likely to have a big impact on developer productivity in the near term as shown by GitHub CoPilot
Image models have gone viral
Speech synthesis is just getting good for consumer and enterprise applications
Other domains: There is fundamental model R&D happening in many fields, from audio and music to biology and chemistry
Media/Advertising: Imagine the potential to automate agency work and optimize ad copy and creative on the fly for consumers
Applications: The short form and stylized nature of the verbiage combined with the time and cost pressures on these teams should drive demand for automated and augmented solutions