Transcription
I keep seeing the same mistake. Be that in corporate sessions, startups, or even just freelancers trying to figure out what their next move is. So many try automating the wrong thing.
They spend 6 months building an internal tool, training the team on it, or trying to find product market fit, rolling it out, then the next AI model update drops, and it's just a feature, right? It's built in. It's free, and it ruins any moat that they thought they had.
I watched a Fortune 500 company do this in real time. They were spending $200,000 in real money, real engineering time, and they built a custom AI workflow for this kind of document processing. So many specific features and processing with basic Microsoft co-pilot and extra features and ideals and big internal rollout. The whole team trained on it and then the platform they were feeding documents into just added that process natively. One API call, a single button, and their entire pipeline, months of work, was now a toggle in someone else's settings menu.
This isn't just a one-off thing. This is a pattern. Startups are getting wiped by a single feature release. Agencies building automations that the next version of the tool just does on its own. People investing serious energy, and more importantly, money into things that most AI tools are going to do for free in a month, maybe two.
So, the obvious question is, what do you actually invest it? Where do you put your time so it's not gone in six months? What skills should you even learn if you're in computer science or learning any of that? And I've also been thinking about it a lot because this is what I get hired to do, right? I charge $2,000 an hour to help companies figure this stuff out. But also, I think there's a framework here that makes it genuinely simple.
Most people will say, "Oh, if you build a course in a couple months, it'll fall behind because of how quickly this industry is moving." I disagree. I know what I am talking about is working and its core fundamentals because I've been using it for three straight years. By the end of this video, you're not only going to know where your work sits on what I call the ladder of abstraction, but more importantly, you'll know what your actual next move needs to be. Not vague "adapt to AI" BS advice, but something you can use to not only survive, but thrive in the world that seems to be changing every day.
And it actually starts with movies. Well, technically it starts with a story. Just a narrative, characters, conflict, resolution. As a book, $15 text on a page. Same experience for every single reader. You can print a million copies and everyone is identical. Take that same story and make it a movie. Hundreds of millions of dollars go into this. Actors, visual effects, sound, a whole production team. Same narrative underneath, but the experience is completely different. And it costs exponentially more to produce because the layer of abstraction is thicker. Now take that same story and make it a video game. GTA 6, billions of dollars is being spent for production alone. But here's the part that matters and really pay attention to what I am saying here. This is how you need to look at the AI paradigm shift. Every player's experience with the product is different. You make choices. You control the pacing. The story responds to you. It's bespoke. Your playthrough is not the same as mine. Same story, three different levels of abstraction. Each one adds more involvement, more control, and each one captures exponentially more value. Not because the story got better, but because the experience got harder to replicate. That is abstraction.
I made a whole video breaking down abstraction coding language from electrons and transistors all the way up to Python. And the link's in the description if you want to see a deeper version. But the short version is this. Each new layer of abstraction doesn't kill what's underneath. It changes what the layer is worth. Books didn't die when movies showed up. People still make money on books and people still lose money on books. Same with movies. Same with games. Some people make money on one book being turned into games and video games and so on and so forth. But the people who were only selling the story, just text, just information with nothing wrapped around it, no abstraction, they had and still have a problem.
Software works the same way. And this is where most people get it wrong. They hear AI can build a website and they think the website is the product, but the website was never the product. The website was always the book version of something deeper, the raw output, the thing that anyone can produce. The actual value in any business, in any industry, lives in the process underneath. How those pieces connect, which parts need a human, which parts a tool handles better, and which parts you leave alone entirely. That's the real work, and that's what most people are currently skipping and continue to skip at every abstraction layer.
Here's what I mean. If that still doesn't make sense, when I work with a company, the first thing we do is break apart their workflows. Not just engineering, everything. What is go to market, their HR, their content, their operations, their development, every department. We take a look at all of those tasks and figure out one by one where AI actually helps, where traditional tooling is actually better than AI, and where you just need a person to do it. That process alone is far more valuable than people realize.
But here's where the abstraction actually comes in. As you do this, as you break things apart and start building AI into specific pieces, you start learning prompting flows, automation patterns, you start connecting tools in ways that are specific to your business, your data, your context. And suddenly AI goes from being a basic tool you type something into into something generally powerful. You're not just using it, you're building with it. And in that next layer, the flows you built start generating data. What's working? What's not, where the bottlenecks are, and now you can use that data to build better prompts, better agents, better tool calls. The system improves itself because you built it with enough structure to learn from. This is the same pattern that happened with programming language, machine code to assembly to C to Python. Each layer abstracted the one below into something you could think at a higher level with. The people who thrived weren't the ones who memorized the lower layer. They were the ones who understood what the new layer made possible.
Right now, most people are at layer 1. They're typing prompts into ChatGPT and getting outputs. You're using AI like a book at this level. It's the same tool, same input, same results for every single person who has it. Layer 2 is building flows. You're connecting prompts to processes, understanding which tasks in your specific business get automated and which don't. That's the movie, production value, context. It takes real knowledge to do well. Layer 3 is when those flows generate data and you use that data build systems that get smarter over time. And that's more like a video game, bespoke, interactive, responsive. That's traditional machine learning that we saw 10 years ago.
The instinct right now is to automate everything you can. And I get it. You see a process that's slow. You throw AI at it. You save time. You feel productive. But few people are asking this question: Is the thing you just automated going to be a built-in feature in 6 months? Because if it is, you just spent your energy on something with an expiration date and not a long one. This is the trap. The trap isn't AI replacing your job. The trap is you investing in automating something that the next model was always going to handle on its own anyway. You're building the tutorial level of a game that hasn't even released yet. Don't automate what the next model will give you for free.
So, what do you invest in? The stuff that's specific to your context, your domain knowledge, your client relationships, your data, the things that can't be scraped and fine-tuned into a general model because they only exist inside your specific situation.
Here's a real example. I know a web agency that used to charge about $20,000 a month to maintain a large client base. Hundreds of unique pages, multiple internal teams, constant updates, big, big operation. Now, can something like Lovable generate pages? Yeah, for a couple hundred dollars. The layouts, the basic functionality, the raw HTML, that part is commoditized. That's the book layer. But this agency doesn't just write HTML and manage a website. They interview the client's employees to understand internal knowledge structures. They make design decisions based on user psychology and conversion data that's specific to this company's audience. They know things about this business that you can't prompt because it came from years of being embedded in it, from nuance.
What they did was smart. They stopped fighting the commoditization. They used it, cut the costs of basic work dramatically, and built a portal where the client's team can handle simple updates themselves. Things that used to need a support ticket and a two-day turnaround. Now, the agency spends almost all their time on the high-context work, the design strategy, the research, the stuff that actually requires taste and judgment nuance. They went from selling movies to selling video games. Are you catching my drift now? Same client, same general service, but now the client is involved with the service. They have control over day-to-day. They are playing the game, and the agency focuses on the bespoke layer that the game provides.
This works in the other direction too. I see companies spending a year building a full SaaS product when they haven't even validated the idea. What they actually need is like a 60% solution built with some basic AI tools that are free to use. Run it internally. Figure out what matters, then decide if the full build is even worth it. Use the book version while you test the story. Don't build the movie until you know people want to watch it.
The pattern underneath all of this is pretty straightforward. Whatever AI made cheap, give it away. That's your book layer. That's what gets people in the door. The thing that you actually charge for is the layer above, the involved stuff, the context-heavy stuff, the thing that takes judgment for taste or real relationships.
Which gets to the real question underneath all of this? And if you're still listening, the real question you should be asking is, is there a limit to human abstraction? Nobody's saying it out loud, but I think everyone's feeling it. Because if there is a ceiling, if humans can only stack so many layers, if there's a hard limit to how far up we can go with our brain, then yeah, eventually AI is going to reach that ceiling, too. And either there's nowhere left for us to move and AI is going to control it, or there's nowhere for all of us to go.
But I don't think that's actually true. And that's not an opinion. It's a pattern that I think history kind of supports. When assembly language showed up, people were writing machine code by hand, direct instructions to the processor, ones and zeros. Assembly abstracted that away, and people who were writing machine code didn't disappear. Some of them moved up. But here's the thing, new problems showed up that only existed because assembly existed. Problems nobody could have predicted because you can't see the next layer's problems from the current one. Then C abstracted assembly. Python abstracted C. Frameworks abstracted Python. And now AI tools are abstracting the frameworks. Every single time this happened, the layer below didn't die. It became part of the infrastructure, a module in the workflow. And the new layer created entirely new categories of work that weren't there before. Work that nobody was doing because nobody knew it needed to even be done.
There's never been a point in computing history where a new abstraction layer reduced the total amount of work humans do. Not once. Every single time it expanded, we created more work to do. Because here's the thing about automation. It doesn't collapse markets. It kills specific positions within markets. The rent-seeking ones, the middlemen, the people who were only in business because friction was protecting them. But the market itself, the market is as durable as human desire. Think about it. If AI does your shopping, finds the best price, schedules delivery, handles payment, you're still the one who's hungry. You're still the one who wants a better couch or a specific pair of shoes. The whole transaction chain still ends at a person with a preference. And I honestly think AI might grow markets more than it shrinks them.
Right now, there are millions of small producers who can't reach their ideal customers. The discovery layer is still completely broken. It's all SEO games and ad spend and attention economy middlemen standing between the person who makes the thing and the person who wants it. If AI strips that away and just matches need to provider directly, that's a bigger economy, more distributed, more transactions happen.
Different industries are at different points of this right now. Some are still selling books, raw features, basic information, commodity work. They're about to have a few rough years, I will say. But some are already at the movie layer. Decent production value, real effort, but still the same experience for every client. They need to figure out the video game version. Some unlucky few, if you've realized this now, are selling video games, and they're staring at the question of what's more bespoke than that? What's more interactive, more involved, more human than a video game? That's the frontier. And nobody has the answer because you can't see the next layer from this one. You never can. But it will show up. It always does.
So the question was never, "Will AI replace me?" The question is, what am I selling? Which layer am I standing on? And what does the layer up look like? Figure that out. Give away the movies. Sell the video games.