The rise of generative AI platforms is similar to the early days of machine learning, where centralized tooling is necessary for scaling and managing resources effectively.

General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.

The emergence of generative AI has led to more collaborative and expansive applications, requiring new approaches to testing and evaluation.

Generative AI tools will not only enhance existing workflows but also enable the creation of entirely new types of content and interactions.

The rise of generative AI presents opportunities to create deep, complex applications that can provide long-term value, unlike simpler applications that may not be defensible in the long run.

The growth in computational power over the last decade has been astounding, enabling the development of more complex AI models and applications.

Generative AI is just another step in the journey of using machine learning technologies in finance.

Generative AI is an exciting new area that is currently smaller in scale but is expected to grow significantly in the next few years due to increased developer interest and venture capital investments.

The core of generative AI relies on supervised learning to predict the next word in a sequence, enabling the creation of coherent text.