The other day I noticed a tweet from Justin Duke which outlined a plan to get his company’s codebase ready for Devin—a programming focused generative AI product. While many are skeptical about AI taking over coding tasks, progress happening quickly and it seems likely that these tools will help software engineers, though maybe not replace the job outright).
If we think AI can positively impact many domains, the question becomes, how can we position ourselves today to take advantage of it in the future?
Writing more is almost certainly one way. Large language models operate on content. That content is mostly text. If you want to have a future co-pilot that can help you build useful and unique knowledge (and potentially avoid knowledge collapse), it’s reasonable to believe you need to be writing a heck of a lot so that AI can draw from what you already know. For example, in a video of Reid Hoffman interviewing himself as an AI, the AI was trained on every book Reid has written and every talk he’s given.
When it comes to code, writing comments explaining what the code is doing and why that’s important seems like it would help generative AI make better contributions that need less supervision. After all, reading someone else’s code is hard enough! I would love to read some research on performance improvements not just on writing code but about comprehension of business logic and how documentation helps or hurts.
Running a business, building a culture where writing is thinking and the primary way teams work asynchronously will amplify every future investment in AI. When Notion rolled out their AI-powered search, a new hire at Mosey started asking it questions about projects, tools, processes, and terms he didn’t understand—the responses helped him get up to speed quickly. FAQs don’t work, but being able to respond to almost any question however it’s formulated sure does!