January 6, 2026
The Focus Wars Begin
The skill of the future is not 'AI', but 'Focus' (2025)
Internet screams: 'Attention is all you need' as bosses prep a 'Deep Work' fad
TLDR: An engineer argues focus beats blind reliance on AI tools, warning chatbots push quick answers over real understanding. The crowd fires back with memes, cautionary tales, and a prediction that “Deep Work” will become a twisted office trend, agreeing on one thing: keep humans in charge of thinking.
The piece argues that the next big skill isn’t artificial intelligence but plain old concentration. Writer Antonin says tools like Large Language Models (LLMs—chatbots that generate text and code) are handy, but they can make us lazy, nudge us into shortcut thinking, and erode real problem‑solving if we stop digging into the why. He warns that unlike old-school Google searches, chatbots push instant answers over exploration—bad news for mastering the basics that power complex work. Read the original take here.
Cue the comments section going full gladiator arena. The top meme-crowd chanted the thesis with a wink: “Attention is all you need.” Others backed the nuance—“AI helps, but only if you stay in the driver’s seat.” One pragmatist predicted the corporate remix: Deep Work, the book about sustained focus, will soon be rolled out like Agile meetings—then “weirdly twisted” into yet another management fad. A seasoned engineer shrugged that skill atrophy happens every time we move up the tech ladder anyway. The most vivid confessional came from a coder who said when he lets AI “own” the fix, his brain “goes numb,” but when he owns the problem and uses AI as a sparring partner, he levels up. The vibe: focus monks vs. automation addicts, with memes, side‑eye at bosses, and a collective plea to keep humans at the wheel.
Key Points
- •LLMs can assist engineers by automating tasks, generating code, aiding brainstorming, and helping with debugging.
- •LLM outputs may hallucinate, be inconsistent (including in self-reflection models), and contain biases, requiring careful review.
- •LLMs are trained on existing solutions and can be unreliable on truly novel problems, leaving error detection to engineers.
- •Search engines offered an exploration–exploitation balance, whereas LLMs encourage immediate exploitation, reducing exploration.
- •The author advocates maintaining focus and foundational skills, understanding AI reasoning, and balancing tool use to preserve mastery.