I’ve been thinking about what real AI-driven productivity looks like in practice. Not the splashy demos or theoretical futures - but the actual way AI changes knowledge work.
I’m not quite sure what the future is, but I have thoughts on what it looks like and what it doesn’t.
What AI-Driven Productivity Looks Like Today
Manual task automation: It looks like doing things that are easy for humans but that just take time since it’s manual - like transcribing unstructured feedback (comments in /r/chatgptpro).
Custom one-off scripts: Instead of copying snippets from blog posts - same example in the above case, where the commenter was also able to generate a heat map from the journal.
Streamlining knowledge work patterns: Simplify common knowledge worker patterns like sending actionable meeting notes.
- You can dump your thoughts about a meeting and then ask to convert them into meeting notes
- Another one is updating a README in code. There are so many examples that LLMs have a good idea on what to put in a README file.
Building mini automations:
- Write a script that does x - Ex: Re-sync my notes to Google Docs for backup.
Iterative improvements: Instead of saying “fix the issue,” you can say “fix this issue, run this command and if it’s not fixed try in a loop.”
Personalized learning acceleration: I’ve used LLMs to teach me things like a tutor or experienced person would. Example: Understanding a financial statement or earnings report.
“Second brain” for decisions: I dump my thoughts into AI and it’s useful as a sanity check - listing pros/cons or suggesting nuances I hadn’t considered.
Knowledge synthesis: Take 3 papers/blog posts on a topic, compare methodologies and get pros/cons for my specific use case. Saves tons of time.
What It Doesn’t Look Like
Perfect without iteration: My most productive AI use comes when I cycle through versions with it, not expecting perfection on the first try.
Understanding long-term goals: I still need to provide the “why” behind everything, which is fine. But sometimes explaining the ‘why’ takes more time than expected.
One-size-fits-all solution: Over time this’ll get better, but effective AI use is about leveraging it appropriately - similar to how you’d work with different team members with various strengths.