I had a fairly easy tech job, making well over 6-figures. However, every time I went to work, I was never happy. Not due to the amount of stress on projects or relationships with coworkers - but, the idea of knowing I could be making much more efficient use of my time and potential. For the past 2 years, I got deep into swing trading (on the side). I quickly realized that there’s a ton of obstacles I had to cross along the way in order to be consistently profitable. For example, coding up all the indicators I wanted to implement, or even automating my strategies entirely was wayy harder than asking gpt to walk me through it. I used my network to find solutions, and understood that most people in the community don’t know how to code, and if so, it is extremely complicated to use or on the contrary, too much “drag and drop” (meaning that it’s not customizable/flexible enough). Almost a year ago, I created a tool to solve my problem. The more I worked on it, and shared it to my shared groups/community , the more the tool gained interest. After tons of recommendations and criticism, the tool evolved into a platform. After releasing a close group beta (capped at 100 ppl), I saw the traction and understood the validity of what I was building. I saw the potential and put more time in. This time, I was getting 2 hours in before work and putting extra hours in after work (ultimately adding up to 25 extra hours of work a week + the 25 going in at the time) - then having to clock in from 8-5. I was completely obssesed, and thought about doing something unique and helpful in the community: implementing AI insights, exporting of strategies, even having a marketplace for people who want to share their own strategies and get paid off commission. Fast forward to now, over 1.5k users signed up, and it’s gaining real traction as a FinTech startup. And yeah, I understand it’s pre-revenue and I “shouldn’t” leave my comfy job because of how things are this time around. But the truth is, I know I have to take this risk at the age I am, having no real responsibilities in life except having to take care of my parents.

In this video, I break down the AI systems pyramid and explain how I decide what type of system to build for a given problem. We walk through all four layers, starting with custom GPTs, then simple workflow automations with no AI, followed by AI workflows, and finally full AI agents. As you move up the pyramid, complexity, cost, and the chance of things going wrong all increase, and I explain exactly why that matters in real projects. I also show real examples of each layer so you can see how these systems actually work in practice. By the end of the video, you should be able to confidently decide which type of AI system you need to build and avoid overengineering solutions that do not need it. Access the Decision Tree HERE

If AI can write, design, code, and “think” with you today… it’s not magic. It’s decades of progress (with a couple of AI winters ❄️). Here’s the full timeline in plain English—what changed in each era and why we’re here now. 🧩 1) Precursors (1943–1956): the idea is born The early foundations appear: - Early neural models (McCulloch & Pitts) - The question: can machines simulate intelligence? - The theoretical groundwork that sparks everything 🧪 Key shift: imagining intelligence as something computable. 🧠 2) Symbolic Era (1956–1974): AI = rules + logic The Dartmouth moment (1956) kicks off the “classic” approach: - Rule-based reasoning (“if X, then Y”) - Logic and symbolic representations - Big promises… too early 😅 Key shift: intelligence was hand-coded. ❄️ 3) AI Winter (1970s): hype cools down Why it happened: - Not enough compute power - Not enough data - Overpromised outcomes Lesson: hype without infrastructure is expensive. 🧑‍⚕️ 4) Expert Systems (1975–1989): AI works in narrow domains AI becomes practical in specific contexts: - Strong rule systems in controlled environments - Use cases like diagnostics and industry - Feigenbaum as a key reference Key shift: AI succeeds when the world is structured and predictable. ❄️ 5) Second AI Winter (1987–1993): another downturn Another reset due to: - High costs - Hard maintenance - Rule-based limitations 📊 6) Statistical ML (1990s–2009): data starts winning Major paradigm change: - Instead of writing rules, you train on examples - SVMs, statistical learning, Big Data - Neural nets return to the stage Key shift: data + statistics + compute beats “rules”. 🔥 7) Deep Learning (2010–2016): the big leap With GPUs + massive datasets: - Backprop + deep networks - CNNs transform vision; speech improves fast 📸🎙️ - Hinton / LeCun / Bengio become central names Key shift: AI gets dramatically better at perception. 🧱 8) Transformers & Foundation Models (2017–2020)

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