How much coding knowledge is required to run an automation agency, and how much of your success do you attribute to coding skills?
I don't think coding skills are irrelevant—they let me get up and running much faster than most people. I had no‑code experience first, then learned coding around 2018‑2020 while experimenting with models, and quickly realized there was more money in no‑code tools and building my own business with them than in pure programming. That shift happened around mid‑2020, after which my one‑second‑copy project took off and I started the automation agency. While some say good coding skills are required, I actually think the value of coding skills is declining month by month because AI tools now let us do most of the work we want in natural language. Consequently, the value of no‑code tool skills is rising, but will eventually fall as well when everything can be done via natural language—it's a zero‑sum game. Coding did give me an unfair advantage early on, but I wouldn't recommend that someone starting today spend five or six months trying to earn a computer‑science‑level depth without a degree. Instead, spend a week learning the basics—JavaScript, JSON, dot notation, functions—enough to read and write simple code. In 2025, tools handle linting and formatting, and if you're using a no‑code platform, the exact syntax matters less; you just need to follow the platform's format. Four hours of fundamentals is enough to do 99% of what I can do; you don't need that extra 1%. That balance of learning and doing is ideal, and I highly recommend giving it a try.
Yes, I still recommend learning tools like Make and N8N. The simplest way to get work as a total newbie is to sign up on platforms like Upwork and apply for jobs that require those tools. Learning N8N instantly opens a massive pool of jobs—there are dozens posted every hour. The same goes for Make.com, and even Claude Code has a market. Once you know the basics, you can pitch N8N, Make, or any other automation in your proposals. It doesn’t take long to become fluent; a 4‑to‑6‑hour course can get you up to speed. Think of it like unlocking new areas in a video game—the fog of war lifts after you spend a few hours studying the visual workflow. These tools are visual, so your brain can grasp them faster than raw code, and they also train you to think in systems, which later helps you prompt AI models like Claude. You don’t need a CS degree—just a few focused hours and you’ll have a marketable skill that feeds directly into AI agency work.
The market is never simply open or closed; it's always a matter of degree of difficulty. With your strong technical background, AI engineering involves building systems from scratch, while an AI agency uses existing systems and stitches them together at a higher level of abstraction—letting you launch faster and deliver value quicker, but you won't use all your ML skills. The trade‑off is that you'll also need to learn business ownership, which brings risk and downside; I went broke for most of my early adulthood learning that lesson. Your family’s advice to take a safe job comes from a place of protection, and over the next year or two they might be right, especially since software engineering roles are being deprecated. However, the top demand remains for people with real AI/ML engineering experience. If I were you, I’d use that experience to build your own model—perhaps a transformer—and create a few portfolio pieces (e.g., a nano‑GPT‑style project) to apply for AI/ML engineering jobs. In your spare time, learn no‑code basics with my tutorials so you can fall back on building with Make or NADN if job hunting stalls.
First, I’d join a community like Maker School—not just to use my own products but because membership gives you deep discounts (often a 10x return) on AI platform credits and payment‑processing fees. With those savings I’d start by learning a visual drag‑and‑drop automation builder (such as the n platform) for about four to six hours; that teaches you the fundamentals of systems, workflows, JSON, HTTP requests, etc., which accelerates everything else. Next I’d spend another four hours on a Claude‑code course that covers the technical setup, agent communication, model selection, and so on—roughly ten hours total. After that foundation, I’d devote virtually all remaining time to acquiring customers; the first 90 days would be less than 1% spent on learning and the rest on outreach. You need a customer to actually enter the arena, so I’d use Maker School’s day‑by‑day accountability to hit call and outreach targets, then loop back to upsell from any lower tier to higher ones as the relationship grows.
I started with Harvard CS50, then taught myself computer science from the ground up, excluding languages and compilers, distributed systems, and operating systems. I completed nanotech to Tetris, building a computer from scratch. After that, I learned AI from scratch via a course that just listed introductory material, then I began training models—first a TensorFlow model called Stalan 2 that generated faces, then Gwarn's 'This Wu Does Not Exist' which I turned into an anime face generator, which I found hilarious and turned into a business called One Second Painting. I scaled that to about $3,500 per month by selling AI-generated art, but Midjourney's release destroyed that business. I had been selling NFTs, which gave me hands‑on AI experience. I then used that experience to freelance write while aiming for $10k/month, employing GPT‑3 for content. I also spent six months to a year locked in my room learning make.com and Zapier, building automations for my One Second Painting business and later for a second venture with a business partner. Eventually the money came from disproportionate time invested, leading to a compressed business model: get your first customer quickly, then worry about everything later. I realized most of the low‑level AI skills I learned aren’t used today, but the practical experience with automation platforms like make.com and Zapier, plus building info products, allowed me to scale to $100k/month through a blend of an AI automation agency and information products.