AI Will Replace White Collar Jobs in 12 Months? The Truth No One Explains

Описание: Is AI about to automate every white collar job in the next 12–18 months? Or is it just an overhyped “probabilistic parrot” dressed up in Silicon Valley marketing?

In this video, we cut through the noise and break down what’s actually happening in Artificial Intelligence from early Large Language Models like GPT-3 to today’s powerful agentic AI systems. You’ve probably heard bold claims about AGI, the Singularity, and the total automation of knowledge work. At the same time, some industry leaders argue AI hasn’t meaningfully increased productivity at all. So… who’s right?

We walk step-by-step through the real evolution of modern AI:

Pure probability models (giant autocomplete engines)

Supervised Fine Tuning (SFT)

Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from AI Feedback (RLAIF)

Chain-of-Thought “reasoning” models

Tool use and function calling

ReAct (Reasoning + Acting) loops

Agentic frameworks orchestrating long-running cognitive work

You’ll see how today’s AI systems combine math, feedback loops, evaluators, and external tools to move from “answering questions” to actually performing complex tasks, writing code, managing documents, executing workflows, and operating 24/7.

Most importantly, we examine recent research showing frontier models sustaining over an hour of deep focused cognitive work with high success rates, comparable to average human white collar productivity. And that capability has been doubling roughly every six months.

This isn’t about hype. It’s about understanding the mechanics behind the curtain.

Is this true intelligence? No.
Is it human consciousness? Not even close.
Is it powerful enough to disrupt IT, engineering, marketing, accounting, finance, management, and legal work?

That’s the real question.

If you work in tech or any computer-based profession, this is a conversation you can’t afford to ignore. Something big is happening and it’s happening right now.


Timestamps:
0:00 Intro
1:20 Original LLM
2:01 Supervised Fine Tuning
2:40 Reinforcement Learning
5:13 Reasoning Models
6:00 Tools
7:34 ReAct Loop
9:10 Agentic Frameworks
11:14 Conclusion


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