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You can't build a moat with AI

Apr 11, 2024 - generatingconversation.substack.com
The article discusses the differentiation of AI applications, emphasizing that the key differentiator lies in the data fed into the models, not the models themselves. It argues that while Large Language Models (LLMs) have sparked the current AI hype, their use will likely become commoditized across competitive products, making the data used to build applications more important. The article also dismisses the idea that prompts or prompt templates can serve as a form of differentiation, as any good engineering team can quickly figure out the right changes.

The article further asserts that the data makes a significant difference, especially in enterprise applications that require customer data to drive responses and decisions. It highlights the importance of data engineering, with the author's team at RunLLM spending about 70% of their engineering cycles on data engineering in the last quarter. The article concludes by stating that the moat for AI application today is in the data and data engineering, and that focusing too much on the AI might be detrimental to differentiation.

Key takeaways:

  • The real differentiation in AI applications lies in the data you feed into your models, not just the models themselves.
  • Everyone is using the same model, so the advantage you get by picking the right model is small. Your prompts aren't IP and can be easily replicated by competitors.
  • If the models and prompts are commoditized, the only thing left to differentiate your AI application is the data you feed into your models. The data makes a huge difference.
  • The moat for AI application is in the data and the data engineering today. You don't need to be an AI genius to succeed in building applications today, but rather focus on thoughtful software engineering and customer data.
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