You said communicating with AI to solve business problems will be a highly leveraged skill, but since it's easy to learn, there will be lots of competition. What are your thoughts?
It's easy to get started with AI communication because the companies building these models intentionally make them accessible so anyone can get decent results without being a prompt engineering expert. However, the real advantage comes from skill: due to a power‑law relationship, improving your prompting ability from the 90th to the 99th percentile can roughly double the quality of the AI’s output. In other words, small gains in skill produce large gains in results. While the basics are simple, mastering AI prompting is hard, and because AI is a meta‑tool that enhances all other tools, becoming proficient at it makes you effective across many domains.
No, I think AI automation is the concert of several skills. To be a very good automator you need strong attention to detail, which is a skill—not something you're born with. You also need good systems and process thinking. To be a good AI automation specialist you must understand business needs and problems. Many people enter this field from a technical side, deeply understanding systems and first‑principles thinking, but then they spend hours on tiny academic edge cases that businesses don’t care about. Businesses reward solving real problems, not perfect academic cases. So AI automation really is a blend of technical and business skills. I lean heavily toward the business side, which is why I make more money than many technically gifted people in my community. If you extend the idea to running an AI automation company, the owner needs some copywriting, video editing, ads, or SEO knowledge—being a jack of all trades is better than mastering just one. In short, an AI automation company’s value isn’t just in the automation itself; it’s in how the automation works with your business. You need a money‑generating process or flow first, then you add automation to speed it up. I discuss this in yesterday’s video.
The best way to implement AI in a digital business is not to start with the back‑office tasks like fulfillment, accounting, HR, or other tedious, time‑intensive processes. Those are low‑value automation opportunities that only save you money you already make. Instead, focus AI on the front‑end of the business – growth, reach, acquisition, conversion, and lifetime‑value expansion. Think of AI as a tool that helps you get more impressions, improve ad and organic reach, boost conversion from eyeballs to leads, speed up payment systems, proposals, and follow‑ups, and ultimately increase upsells and product differentiation. One hour spent automating admin might net you $50, whereas one hour spent building a system that improves a company’s ability to grow could generate $5,000. Even with modest margins, that’s a 25× multiple. So prioritize AI projects that directly impact revenue generation rather than pure cost‑cutting.
To survive and thrive in the automation and agency space in 2026‑2027, master business skills: learn to communicate business needs and requirements, speak in consulting terms, understand core consulting concepts like driver trees and a four‑step framework, grasp foundational business principles (revenue, cost, profit, valuations, risk conceptualization akin to an 80/20 view of a business degree), and then get in front of customers to practice selling and communicating—exactly what Maker School teaches.
If I were you and thinking long‑term, I’d ask where everything is heading. Everything is moving toward AI and automation over the next five years; what happens after that is uncertain, but my honest advice is to choose the AI automation business model. I’ve found the most success with it and have run it four times. The main reason is leverage: AI and automation offer far more leverage than traditional marketing agencies. Traditional marketing agencies are declining—this model peaked around 2017‑2019 and has been falling since, partly due to market saturation and the rise of SaaS tools that automate tasks like scheduled posting, brand strategy, and marketing strategy. In contrast, the AI automation model is still early; it really took off around 2021‑2022 with ChatGPT, and I predict it will continue growing until about 2027‑2028, when AI agents could double the market and demand. After that, demand may dip slightly as agents become more advanced. If you're in Saudi Arabia, you're essentially seeing a delayed version of this curve, giving you a few extra years to ride the upward trend. So, if I were in your place, I’d go all‑in on AI automation.