The AI is here, and by passing the 2025 I faced, everyone is realizing that the AI is good at completing tasks such as reading emails, organizing them, writing letters, and generating images. I have created the pictures for my blog using AI, and they are impressive. If I think about it, it is good to maintain my pattern.
So, why am I running away from AI in 2026 if the hype is still there, the race to acquire the best AI is still ongoing in our world, and models are continually improving? At some point, it becomes counterintuitive and feels like a bad decision if you think about it, especially given the news that the AGI could possibly be raised by 2030, which is the goal.
Everything started with the illusion of thinking. I read that paper, and something resonated with me. Then, I read "The Illusion of the Illusion of Thinking," which takes the opposite stance, explaining why Apple Papers feel bad and what they did wrong in their research.
Both papers are extraordinary in understanding the key point of AI.
The real truth about the system
After reading the book The Art of Doing Science and Engineering: Learning to Learn, and making an LLM prototype by myself, I realized the pattern is still clear, we have a super models that basically run from the same pattern, connecting dots based on the prompts by generating the most credible idea based on the most probable answer any user is looking for.
So that is why if I ask any LLM:
What is the command to display where my folders are mount
I will get this answer below:
Which is correct and saves me the time to Google it. There is, however, a paradox: creating the illusion that I know how to use the terminal, and perhaps I do; it may also be creating an illusion that I understand something I do not clearly comprehend. I am not saying that we should learn all the commands by memory.
Imagine now an application made by someone that does not know how to code or create applications, it is some requirements as software engineer you need to follow but how to get all those requirements within the code to then create an application, that is tricky behavior it is not only code the lines of code or modeling the system requirements is synergy of everything and understand what you are doing what every you are doing.
The path is missing out.
At this point, it is clear why I'm moving away from LLMs. I feel I need to buy something to be super fast and feel productive by just paying to be smart. We are buying the power of thinking, or at least that's what it seems like.
It is the same feeling on video games 20 years back and nowadays, where you clearly need to buy the best skin instead of demonstrating your best ability. We are, in fact, showing who is wealthier than others, generally speaking.
The feeling of being extraordinary without being extraordinary feels real, and we must remember that all these products are maintained by companies, so they have to generate revenue in the end, right? We should think about how we utilize the new technology added to our lives to maximize its benefits to our advantage.
What's going to be my 2026
In fact, I won't stop using AI tools at all, but I won't spend too much money on learning and practicing with them. After building my own LLM, where I felt I could understand much better how the AI works and what the AI is by removing all the hype.
I will follow my own pattern of learning. I will still be using AI to code if I know the language, after all. Once I am on an LLM loop, I need to be able to write the code myself. It is okay if I don't remember a command line; I can ask an LLM or look it up on Google. Though I will remember it for next time, I will write it down in a notebook to reinforce my own learning.
If I need to write something valuable or feel the need to convey an idea, I will draft it myself and figure out how to make it sound better. At the end, I will just use an LLM to correct grammar and congruency rather than simply typing a prompt to create something that will make me feel good about it. Perhaps I don't have the idea about.
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