The latest developments in artificial intelligence they are no longer limited to research laboratories. They are beginning to reshape the foundations of white-collar employment, especially at the entry level.

From producing reports and analyzing spreadsheets to writing code and handling customer inquiries, tasks that were once the backbone of junior roles are increasingly being performed by AI systems developed by companies such as OpenAI, Google and Microsoft.

What was initially seen as an increase in productivity, some experts now see as a structural change in the way knowledge work itself is organized.

Market veteran Ajay Bagga recently noted the scale of the disruption in a post on X (formerly Twitter), citing warnings from the AI industry

“Dario Amodei, who is probably the most security-focused CEO in the AI ​​industry, has publicly predicted that AI will eliminate 50% of entry-level white-collar jobs within one to five years. And many in the industry believe he is being conservative,” Bagga wrote.

“Given what the latest models can do, the capacity for massive disruption could be here by the end of this year. It will take time to propagate through the economy, but the underlying capacity is coming now.”

According to Bagga, this technological wave differs fundamentally from previous episodes of automation.

“AI is not replacing a specific skill. It’s a general replacement for cognitive work. It’s getting better at everything at once,” he said.

“When factories became automated, a displaced worker could retrain as an office worker. When the Internet disrupted retail, workers moved into logistics or services. But AI doesn’t leave a convenient gap to move into. Whatever it’s retraining for, it’s getting better, too.”

Different types of automation

Earlier technological changes tended to replace defined functions or industries. AI, on the other hand, is oriented towards cognitive tasks in several sectors at the same time.

Entry-level analysts, paralegals, junior programmers, content writers, and front-line customer service staff (roles traditionally based on repetition, documentation, and structured problem solving) are particularly exposed. AI tools can now digest legal briefs, generate financial models, scrape marketing campaigns and produce functional software code in seconds.

The change is already influencing recruitment strategies. Instead of recruiting large cohorts of fresh graduates, many companies are equipping smaller teams with AI tools that dramatically amplify output.

In this new model, the entry-level professional is expected to validate AI-generated work, integrate automated workflows, and apply judgment, not simply execute instructions.

The interruption can come suddenly

Bagga warned that the transition may not be gradual.

“The experience tech workers have had over the past year, seeing AI go from ‘useful tool’ to ‘doing my job better than I can,’ is the experience everyone else is about to have,” he wrote.

“Law, finance, medicine, accounting, consulting, writing, design, analytics, customer service, not in 10 years. People who build these systems say one to five years. Some say less.”

Investor sentiment already reflects this anxiety. According to Bagga, markets recently wiped out nearly $1 trillion in value from the software sector in one week as investors recalibrated expectations about artificial intelligence-driven productivity gains, workforce reductions and shifting margins.

The problem of learning

Beyond the immediate job losses, economists and business leaders grapple with a deeper structural concern: the erosion of entry-level roles as training grounds.

For decades, junior positions functioned as apprenticeships where employees learned through repetition before advancing to higher-value responsibilities. If artificial intelligence absorbs much of this fundamental work, the pipeline producing future managers, specialists and partners could shrink.

Not all professions face the same risk. Roles that require negotiation, relationship management, ethical judgment and regulatory responsibility remain the most difficult to automate. But routine cognitive workload, long the gateway to these careers, is clearly under pressure.

A new set of survival skills

For young professionals, adaptation is becoming urgent. Fluency in AI tools is quickly becoming a core requirement rather than a niche advantage. Equally important is the ability to interpret, question and refine machine-generated results.

Judgment, synthesis, and communication—skills once expected to develop over years on the job—may now be prerequisites for getting hired.