Goodbye White Collar Jobs. Hello AI Tasks

TL;DR: AI didn’t peak with ChatGPT. It has crossed into non-human intelligence, where systems plan, reason, and execute processes on their own. Eric Schmidt and the San Francisco consensus warn of runaway risks, geopolitical conflict, and economic shocks. For white-collar workers, this plays out as jobs unbundling into tasks with AI consuming the repeatable parts and leaving humans to prove value in what remains.

 
The Moment We Missed

In 2016, an AI program called AlphaGo made a move no human had ever seen in 2,500 years of the game of Go. It wasn’t just clever. It was alien — and it won.

For Eric Schmidt, then Google’s CEO, this was the quiet moment the ground shifted. A machine had not only learned from humans but invented something new. That was the first crack in the door to non-human intelligence.

Most people missed it. They still think ChatGPT is the big story. It wasn’t. 

What’s Really Happening 

The shift is that AI no longer predicts and imitates. It now reasons and plans. These systems chain tasks together, set goals, and execute them without waiting for step-by-step input. What began as a tool for writing emails and content now looks more like a machine grad assistant that can run a business process end-to-end. It is a CFO's dream...

The limits aren’t technical, they’re physical and political. Training frontier AI already consumes the power of whole cities (500K Kwh is the estimate - so the same as a Boeing 747 from Sydney to Johannesburg in power use and carbon).

US officials estimate that keeping pace could require the equivalent of ninety nuclear plants. Meanwhile, the race between the US and China for chips, data centres, and open-source dominance raises the risk of conflict. Schmidt has even suggested that data centres themselves may be treated as strategic targets if one side pulls too far ahead.

And then there’s the slope. The San Francisco consensus (yes, all those types that live and run our lives over there) is clear: progress compounds. A slight lead becomes a gulf. A country, a company, or even a single team that learns faster can lock everyone else out. Open-source speeds the cycle, fuelling rapid innovation, but also handing dangerous capability to anyone who picks it up.

What It Means for Your Work

Yes even you, you MBA, CFA, CPA, LLB big shot...

Schmidt frames this as a civilisation-scale change, not a product, tech or channel launch. The consensus inside the labs is that AI is moving faster than institutions, companies, or citizens can absorb. And this is true...even for those in or around it...it is moving to fast to build real domain expertise.

Systems that self-learn beyond our control.

Models that replicate themselves across the internet.

AI reaching for tools and weapons without a human gatekeeper.

These are scenarios tracked seriously, not dismissed as science fiction.

Even if we avoid runaway systems, the economic shock is the big problem. Productivity gains of 30 percent a year would smash every existing economic model. Add in falling birth rates with fewer workers supporting more retirees — and AI becomes possibly the only cheap way to keep welfare and pretty useless white collar societies functioning.

That shock lands first on white-collar work. Amazon’s cuts were not on factory floors but on analysts and support roles where AI could replicate the task list. For graduates, the first rung of the ladder has already broken. The stage of “learning by doing” has been replaced by AI outputting in seconds what used to take you years to master. Yet...you are expected to exercise judgement immediately.

For those mid-career, the change is quieter but just as brutal. Jobs are being unbundled into their component tasks. The repeatable parts: reporting, tracking, producing slides, are absorbed by machines. What remains is the messy work of making trade-offs, deciding under uncertainty, and carrying accountability when things go wrong.

Work itself is coming apart. The danger isn’t only unemployment, but being the professional left holding only the task oversight scraps once bundled into a role.

How to Prepare 

Schmidt’s advice is blunt: adopt AI, or become irrelevant. But adopting doesn’t mean handing your role over. It means learning how these systems cut, combine, and accelerate the work around you — and then using them to extend your reach.

• Teachers can harness AI tutors to personalise learning.
  
• Doctors can use AI to diagnose faster and translate medical knowledge.
  
• Businesses can automate repetitive admin, freeing managers to focus or be sacked
  

 It’s a continuous skill. You need to understand the basics, test systems in your own work, and keep experimenting as they evolve. The real danger is not bad actors or rogue code. It’s actually 'you' (not in the netflix series way) who stand still until they discover their tasks are being stripped away. And make no mistake - any CFO accountant who sees a 10% decline in quality but a 3000% increase in throughput with that flow through hitting the bottom line...is going to choose AI enhanced over you. No doubt. 

The arrival of non-human intelligence is dismantling work at the seams. Jobs are no longer bundles of tasks, they’re fragments left behind after machines strip away the predictable pieces. The pressure is simple: prove your value where systems can’t, or watch your role dissolve.

Fluency is the only defence. You don’t need to become an engineer, but you do need to understand how AI operates and where it fits. That’s why I run the AI Fundamentals Masterclass, built for professionals who need clarity fast. And for those ready to go deeper, the AI 5-Day Bootcamp gives practical, applied experience so you can work with these systems instead of competing against them.

Work will keep unbundling whether you act or not. 

FAQ

Q: What does “non-human intelligence” mean?
A: It refers to AI inventing new ways of doing things, not just copying human output — like AlphaGo’s move that no human had conceived.

Q: Why are white-collar jobs hit first?
A: Because AI thrives on information-heavy, repeatable work: analysis, reporting, scheduling, drafting. Physical jobs still rely on presence and hands.

Q: What about creative work?
A: AI already generates drafts for campaigns, designs, and scripts. The human role is shifting to editing, directing, and deciding.

Q: What happens to new graduates if entry-level roles disappear?
A: Move to Thailand and party like it is 1999. No. They’re expected to act like decision-makers from the start. The “learn by repetition” phase is gone. How do you think that is going to work out?

Q: Why does energy and geopolitics matter to me?
A: Because access to AI is shaped by who controls the power, chips, and infrastructure. It decides whether your employer — or a rival abroad — gets the advantage.

Q: How can I prepare without being technical?
A: Start small: learn what AI can and cannot do, apply it to your own tasks, and build fluency in its use. The skill gap is in adoption, not coding. So you do not need to know Python to know how to speak to the C suite.