Open Questions About AI

🌈 Abstract

The article discusses various questions and topics related to the current state and future developments in the field of large language models (LLMs) and AI infrastructure. It covers areas such as AI inference clouds, LLM observability, hardware optimization, model architectures, AI agents, large action models, and the competitive landscape among tech giants and startups in the AI space.

🙋 Q&A

[01] Software Synthesis and AI Questions

1. Questions related to the content of the section?

• How do AI inference clouds differentiate themselves and expand their product suites beyond the race to the bottom in GPU-as-a-service?
• Will LLM observability spend be captured by existing ML observability tools, and how can LLM observability vendors become best-of-breed for enterprises?
• How is hardware optimization for LLM training and inference evolving, and which companies are emerging as frontrunners?
• Will the development of transformer-specific infrastructure cement the dominance of transformer models, or will alternative architectures like State Space Models attract sufficient capital to generate their own ecosystem of tooling?
• Which architecture will prevail between Dense and Mixture of Experts (MoE)?
• Will agents distributed by Big Tech solidify their position as the Aggregators of the internet, and how will this impact APIs and websites?
• Are 'large action models' a distinct subset of foundation models, or are their capabilities stemming from post-training using RL data and a reward model?
• How do companies competing in voice, video, audio, etc. maintain their performance edge once the large research labs channel their R&D towards a specific modality?
• Will closed-source model providers need to move upstream into applications if open source models commoditize frontier model capabilities and AI inference clouds abstract away development complexity?
• How will Amazon ensure they can capture spend at the application layer as the AI app ecosystem consolidates?
• How will horizontal AI apps differentiate themselves, and are there any fundamental differences to the Application SaaS paradigm?
• Is the excitement in vertical AI mostly centered around AI-native companies acquiring data before databases can offer AI capabilities?

[02] External References

1. Questions related to the content of the section?

• What are the key insights from the referenced articles, such as "The AI Supply Chain Tug of War" by Sequoia, "The Genius of PostHog Marketing" by Battery Ventures, and "Things that Used to be Impossible, but are Now Really Hard" by Tomasz Tunguz?
• What are the main points made in the excerpt from the article "Flox" regarding the potential for low-powered intelligence to become pervasive around us?