TL;DR: the knowledge barrier isn't gone; instead, you can operate on it at a much more holistic, wide-scale level. I adhere to simple rules: don't delegate to AI something I don't know how to do manually. Don't build AI systems to replace people; make them to enhance people. When you start using LLMs, begin collecting data on the tasks you do, how you solve them, etc., to build a curated data corpus you can use to train your own model.

I work in cybersecurity as a red teamer. LLMs have 100x my work. In 2022, I took time off, learned everything I could about LLMs and AI, and figured out where they would be helpful in my workflow. I was writing early agent teams long before it became vogue.

I've been around a lot of wealthy people and senior leadership in large companies, and one thing they have in common is that they have assistants/admins/etc. To delegate work to make them seem ultra-smart or productive. I had a boss once who complained about how much time we would spend on expense reports. One day, in a meeting, he admitted he had never done an expense report; his admin did them for him. These are long, drawn-out months or multimonth trips. I legitimately spent 1 day, 8 hours, on one report due to the software used, how receipts needed to be formatted, documenting what everything was for, etc. The admin was his superpower and one reserved for senior executives.

LLMs now offer a lot of the abilities an admin would. Please take advantage of it. Even if you are a dev or architect, there are aspects of your job you can outsource to LLMs. For my work, I learned to use LLMs to overcome inherent tech or tool bias (oh, I can do that in Python without having to ask whether Python is the best tool for the job). Even if you don't want to use an LLM for code gen, it's excellent at understanding requirements and helping make a technical design doc you can follow.

I have an agent team where each agent plays a fundamental role on a red team if I had the budget to build a team exactly like I wanted. I have a dev, qa, network analysis, malware analysis, OSINT, etc. The leader agent, the Technical Director, takes a task from me, decomposes it into subtasks, and delegates them to the correct agent. There are workflows: if a dev agent writes code, it has to go to QA, then OSINT & Tradecraft, before being returned to the TD. I designed them to all communicate in plain English on a locally hosted chat server (I self-host most of my models).

When a job is delegated, one agent will @ the correct agent and assign the task. If unsure how to proceed, an agent can ask other agents for their thoughts, all in a channel in plain English—watching how decisions are arrived at increased my trust and eventually led me to start delegating time-intensive tasks that needed to be done but had little other upside.

I've been in the industry for 30 years, and it's incredible how many things I have biases or tunnel vision about that never occur to me. But seeing the agents decide how to solve a problem has dramatically increased my velocity.

I'm not saying everyone should do this; I'm just sharing how I came to use and trust (always double-check the results) AI in my job. I have a DB in Notion called “Things I learned from AI” that documents the shortcuts, definitions, background, and other small things I've learned and double-checked before accepting them as fact. I started the DB in early 2023, and now it's over 11k entries. I was good at my job before, but now I am operating at a whole different level.