I had previously observed that humans are the great interop layer—we are the glue that fits together disparate processes and tools into usable systems. After using large language models, I’m becoming convinced that they can offload a large amount of the interop cost that currently falls to us. In a nut shell, AI can ‘do what I mean not what I say’ pretty darn well.
The latest LLM tools can interpret what we mean far better than before. Natural language interfaces opens up the possibility of sloppy interfaces—ones where not everything needs to be specified precisely. This allows for tolerance in the system making it easier for more things to fit together.
By contrast, APIs are largely a ‘do what I say’ interface. They require precise steps to complete an action. They require documentation to help a human understand how to use them. It takes creativity to implement them in ways that can solve their problem. Now we have LLMs that can figure out how to make API calls (Zapier NLAPI) and map different actions to different methods (langchain tools).
Links to this note
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46% of Developers Code Comes from Github Copilot
A blog post from GitHub says that their code assistance tool, Copilot, is behind 46% of developers' code, up from 27%. For Java, that number is 61%.
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Devin AI Fixes Bugs it Created
The “the first AI software engineer” Devin, from Cognition Labs, was found to be fixing bugs of it’s own doing, solving problems in a roundabout way, and taking a long time, in a debunking video by a human software engineer.
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I want to be able to use generative AI in spreadsheets to solve unique problems. I want to call OpenAI from a cell that passes in a prompt and a value from a column then returns an answer I can easily parse.
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AI Multiplies the Value of Expertise
AI reduces the cost of certain tasks to effectively zero. In doing so, it lowers the barriers to domains that would previously take years to build skills such as writing code, data analysis, and more. This is precisely why AI also increases the value of expertise and experience.
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The Labor Market Is Merging With the Saas Market
What if the entire services industry merges with SaaS when it becomes possible to deliver a service with artificial intelligence?
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LLM Applications Need Creativity
Making the most of practical applications of large language models requires creativity. It’s a blank canvas to be filled in the same way that early mobile application developers faced when a new set of APIs unlocked new possibilities.
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Satya from Microsoft talks about how orchestrating between business applications is the next step for artificial intelligence which will replace business logic with AI.