Fri Sep 22 2023

Foundation Models Becoming Commoditized:

• Companies with as few as seven employees are creating models comparable to those of OpenAI and Anthropic for certain use cases.
• Model size will influence factors such as cost and latency, but smaller models may be better.

Generative AI Landscape:

• There is hype surrounding generative AI, but there is also fundamental innovation and disruption in the field.
• Creative industries are particularly well-suited for adopting generative AI.
• Financial services and retail/CPG companies have been quick to adopt generative AI due to their data maturity.

Data vs. Model Size:

• The value of data outweighs that of model size, with data accounting for over 90% of the value.
• Smaller models fine-tuned for specific purposes can often produce better results than generic models.

Incumbents vs. Startups & Open vs. Closed Source:

• Incumbents with access to large amounts of data are best positioned to win in the AI space.
• However, startups focusing on core technology innovations still have opportunities for success.
• Both open source and closed source approaches have their merits, but hosted cloud services provided by incumbents are likely to dominate.

Transition Between Models:

• The ability to transition between different models easily is crucial for long-term success in the AI industry.
• Startups should build optionality into their systems and leverage multiple models based on specific use cases.

Challenges in Adoption of New Models:

• Concerns about correctness, security, privacy, and rights to answers pose challenges in the adoption of new models.
• Platforms that allow running models close to the data without moving or copying it offer potential solutions.

Cost of Training:

• The cost of training models is expected to decrease over time due to advancements in compute efficiency and new approaches like model compression.

Longevity of Models:

• Specific versions of current models may not be used a year from now, but variations and refinements of those models will continue to emerge.

Impact of AI on Society:

• AI is expected to significantly boost productivity across various industries, leading to positive impacts on GDP.

Role of UI in the Age of AI:

• The importance of user interfaces (UI) may reduce in certain use cases with the increasing prominence of AI.
• However, for other use cases, richer interaction and UIs can still be valuable alongside AI capabilities.

Importance of Technical Skills for Product Managers:

• Deep technical skills are generally necessary for success as a product manager in today's AI-driven landscape.

Leadership Lessons from Satya Nadella and Frank Slootman:

• Both leaders possess clarity of thought and vision, simplifying decision-making processes.
• Confidence in decision-making evolves over time, allowing for more top-down influence when needed.

Balance between Internal Debate and Speed of Execution:

• Balancing internal debate and speed of execution depends on the nature of the technology or product being developed.
• Critical components may require more thorough consideration, while less critical aspects can benefit from faster iterations.

Value Accrual in the Next Decade:

• Incumbents with access to data are likely to accrue significant value in the next decade due to their ability to leverage data for competitive advantage.