The miracle of effortless intelligence, promise of an era where thinking too hard drains your battery.
You too have probably encountered this coach with eyes moist with certainty, ambassador of synergy 4.0 and evangelist of asynchronous cognitive performance, proclaiming on LinkedIn that “the age of engineers is over” and that “in 2025, everyone will know how to code their AI in their underwear from Bali.” A visionary of emotional scaling, convinced that neural disruption triggers with three emojis and an inspiring carousel. It’s as beautiful as a glittery startup nation, it’s catchy, it’s motivating… and it’s about as authentic as the CV of a digital guru who pivoted after crashing an e-shop selling organic celery juice through ethical dropshipping.
Because let’s be clear: learning artificial intelligence is not learning to click on ChatGPT to make it churn out newsletter ideas in yoga-performance mode. It’s not even building a chatbot with three poorly glued APIs. It’s, as they say, a bit more hairy.
Real AI, the kind that doesn’t put on makeup with Canva templates, is an austere branch of computer science born in the 1950s, nourished on formal logic, symbolic computation, linear algebra, graph theory, and a good dose of intellectual discomfort. It smells like chalk and sweat, speaks English, reads papers on arXiv on Sundays, and has never heard of your sales funnel. To tackle it, you need solid foundations in mathematics (statistics, optimization, probability), real programming practice (Python, of course, but not only), understanding of data structures, algorithms, logic, and an insatiable curiosity for complex systems. And above all, you need to love searching long before understanding something. In short, everything your latest LinkedIn carousel carefully avoided mentioning. LLMs? A recent veneer on a cathedral of concepts. Promise, we didn’t wait for Copilot to think up models.
But what does rigor matter when you just need to film yourself standing in your kitchen saying “prompt engineering is life” while the food processor whirs in the background. Don’t laugh, it’s a methodology. It’s called Bullshit-Driven Learning: you speak before learning, you sell before understanding, you pitch while you scroll, and you bill while you hallucinate. All wrapped in disruptive storytelling of scalable personal growth, where you’re certified “AI Expert” from the second slide, as long as you can pronounce “vectorial tokenization” without stuttering. It’s more than a method: it’s an economic model based on cognitive fog and personal branding inherited from reality TV.
It must be said that the era lends itself to it. The word “AI” has become a password to transform any PowerPoint as flat as a flounder into fundraising. You’re promised golden spiral ROIs, autonomous agents to replace your employees (and your common sense), and 80-page PDFs generated in 2 seconds, without worrying that they’re as empty as a ministerial speech on the digital divide.
So let’s set things straight. AI isn’t an app, it’s a continent. Three mountain ranges to climb: the legacy of pioneers (symbolism, logic, constraints), the algorithmic cordillera (SVM, KNN, RNN), and the LLM mirage, which shines bright but doesn’t illuminate everything. And even at base camp, you need maps: math, code, linguistics, a bit of psychology, quite a bit of humility. Without that, your “AI solutions” might well perform worse than Jean-Paul’s old Excel sheet in the quality department.
But here’s the thing, in the enchanted land of slides, we don’t like effort. We prefer myths. That of intelligence without pain. Of success without competence. Of knowledge without method. We gorge ourselves on prompts and wonder why we regurgitate textual mush. It’s not a revolution, it’s karaoke.
It’s time to say it without blushing: no, not everyone can learn AI. Everyone can play with AI, sure, like playing piano by randomly hitting keys. But understanding, mastering, creating, integrating? That’s another song. A complex fugue, with dissonant chords, asymmetric measures, subtle modulations, and quite a few scales to learn in multiple keys. It requires patience, work, and sometimes a form of joyfully laborious obsession. In short, everything a 42-minute webinar promises to help you avoid.
No! AI is not simple. And… that’s a good thing.
Because if it were simple, it would be a mundane product with a TikTok manual and guaranteed success in three reels. AI deserves better. It deserves to be learned, not just used. And above all, it deserves that we stop locking it in the cardboard promises of recyclable coaches.
The next time a cardboard influencer explains to you that “everyone can master AI with my free 12-page ebook“…
Tell them: “Thanks, I prefer learning to think. It’s slower, less Instagrammable, and doesn’t unlock any bonuses in your sales funnel. Because thinking, really thinking, means accepting not understanding everything right away, doubting often, having nothing to post for several weeks. It’s reading a scientific paper without emojis, writing code without copy-pasting, and rejoicing when you find an error in your own certainty. In short, it’s boring, demanding, but surprisingly jubilant. Simply put, thinking has to be earned.”