We’re Gonna Need a Bigger Moat

🌈 Abstract

The article discusses the recent developments in the field of large language models (LLMs), including the implications for the SaaS industry and the broader technology landscape. It covers the following key points:

🙋 Q&A

[01] The Significance of Transformers and LLMs

1. What makes Transformers and LLMs so significant?

• Transformers are a simple yet powerful mathematical construct that can extract meaning from language, similar to how the Fourier transform extracts frequencies from noise.
• As Transformers are scaled up to "Trillion-parameter space", they develop surprising higher-order capabilities like visualization, multi-step reasoning, and a theory of mind.
• Transformers exhibit biological-like properties with distinct processing regions and mysterious connections, making them more akin to "The Matrix" than a typical software system.

2. Why is the Transformer invention compared to the invention of fire?

• The Transformer is a powerful and dangerous technology that can be shaped and used to create many remarkable things, but if a large one gets loose, it can spread like wildfire.
• The ability of Transformers to learn from each other through techniques like Low Rank Adaptation (LoRA) means that knowledge can spread rapidly, similar to how fire can spread quickly.

[02] The Commoditization of LLMs

1. What happened that led to the rapid commoditization of LLMs?

• The open-sourcing of Meta's LLaMA model, which was roughly competitive with GPT, allowed researchers and tinkerers to access a GPT-class LLM architecture.
• The subsequent leaking of LLaMA's model weights on Discord made a GPT-class LLM accessible to anyone with a laptop and PyTorch.
• Techniques like LoRA enabled rapid fine-tuning and knowledge transfer between LLMs, leading to a proliferation of high-performing, low-cost LLM clones.

2. What are the implications of this commoditization for the LLM industry?

• The LLM-as-a-moat model is disappearing, as the performance of open-source LLMs is catching up to the proprietary models of big tech companies.
• This means that companies can no longer rely solely on their LLM models as a competitive advantage, and will need to focus on building other types of moats, such as data moats.
• The big tech companies like Google and OpenAI may be scrambling to respond to this rapid commoditization of LLMs.

[03] Implications for SaaS Builders

1. What opportunities does the commoditization of LLMs present for SaaS builders?

• SaaS builders can now leverage high-performing, low-cost LLMs to power their products, without being dependent on the big tech companies.
• Fine-tuning LLMs on domain-specific data can provide competitive performance, as long as the SaaS builder has a strong data moat.
• Enterprise customers are increasingly setting up GPU infrastructure to develop their own custom AI models, creating opportunities for SaaS builders to provide tools and services.

2. How does Cody, the coding assistant, fit into this landscape?

• Cody is still reliant on the big tech companies' LLM models for now, as the open-source alternatives do not yet match their performance out-of-the-box.
• However, Sourcegraph's platform, which powers Cody, provides a strong moat in the form of its comprehensive code understanding and manipulation capabilities.
• This moat may become increasingly important as the LLM landscape becomes more commoditized, and SaaS builders need to differentiate themselves beyond just the LLM capabilities.