🇯🇴 AI Anxiety, Layoffs and the New Career Playbook - Jordan

AI & Technology•٩ كانون الأول ٢٠٢٥•By 3L3C

AI layoffs are pushing UK students toward the trades. Here’s what that shift reveals about the future of work—and how to turn AI into a productivity edge, not a threat.

AI layoffsfuture of workUK labour marketskilled tradesAI productivityautomationcareers

Artificial intelligence has already been cited in nearly 50,000 job cuts in the US this year alone. That’s not a forecast; that’s a body count.

While execs talk about productivity and efficiency, students in the UK are quietly rewriting their career plans. Instead of chasing traditional white‑collar roles that look increasingly exposed to AI, many are pulling on steel‑toe boots and picking up tools.

This shift isn’t just a quirky labour-market story. It’s a warning sign for anyone thinking about work, technology and long‑term productivity. If you’re a leader, creator or professional trying to build a resilient career in an AI-driven economy, you should be paying close attention.

This article breaks down what’s actually happening, why it matters for your own work, and how to use AI to work smarter instead of simply hoping your job is “AI-proof.”

How AI-Driven Layoffs Are Rewiring Career Choices

AI is no longer an abstract future threat; it’s already shaping who gets hired, who gets cut, and which careers feel safe.

In the UK, AI anxiety is pushing some students away from university and desk jobs toward skilled trades. Reuters recently followed an 18‑year‑old London plumbing student who summed up the sentiment bluntly: “No AI can do plumbing.” She’s not alone. Around half of UK adults say they’re worried about AI’s impact on their jobs, with anxiety peaking among 25‑ to 35‑year‑olds.

At the same time:

Companies in the US have cited AI in 48,414 job cuts this year, including about 31,000 in October.
A King’s College London study found that UK firms with high exposure to AI cut jobs by 4.5% between 2021 and 2025.
Junior roles took the biggest hit, shrinking by 5.8% on average, with high‑paying firms cutting almost 10% of their workforce.

Most companies get this wrong. They treat AI as a quiet way to trim headcount and “optimize costs” instead of asking the harder question: How do we redesign work so humans and AI systems actually make each other better?

For students looking at that landscape, the logic is simple: if white‑collar work is getting automated and entry‑level rungs are disappearing, why not choose careers where AI has a much harder time operating?

The Real Pattern Behind AI Job Cuts

The pattern is clear: AI is hitting tasks, not entire professions, and it’s starting with work that’s repetitive, digital and easy to standardize.

Roles most exposed to automation share a few traits:

They’re screen-based and can be done from anywhere.
They involve high-volume, repetitive workflows: reporting, basic analysis, document drafting, customer queries.
There’s clear training data: lots of historic examples for models to learn from.

That’s why job postings for software engineers, data analysts and other technical roles have actually seen some of the steepest declines in certain high‑exposure firms. It sounds counterintuitive, but if your day is 80% Jira tickets, dashboards and documentation, AI can do a growing share of that faster and cheaper.

Executives are open about it. Some now talk about AI “agents” as workers that don’t need lunch breaks or health insurance. Others quietly use AI as a convenient narrative for layoffs that are really driven by over‑hiring or macroeconomic uncertainty.

Here’s the thing about AI and work: AI doesn’t destroy all jobs; it reshuffles where value is created.

Low-complexity, process-heavy work gets automated.
High-context, relationship-heavy, and physically constrained work holds its ground.
New AI-native roles emerge, but usually with higher skill requirements and smaller headcounts.

If you’re building a career or a business around knowledge work, the question isn’t, “Will AI replace me?” It’s, “How much of my week is repeatable enough that a model could take it over—and what am I doing with the time that creates?”

Why Trades Feel Safer (And What Knowledge Workers Should Learn From Them)

Trades are attractive right now because they combine physical presence, variable real‑world conditions, and high-stakes problem solving. That trio is hard for current AI and robotics to match.

Think about plumbing, electrical work, or HVAC:

Every site is slightly different.
There’s messy physical reality: tight spaces, old buildings, unpredictable failures.
Work often requires trust, empathy and communication with customers on‑site.

AI can help with diagnostics, planning routes or ordering parts, but it can’t yet climb into the crawlspace. That’s why even reports warning about automation wiping out up to 3 million low‑skilled UK jobs by 2035 still predict overall employment growth—especially in higher-skilled roles and in work that’s physical or deeply interpersonal.

What does this mean for people who aren’t about to retrain as plumbers?

Steal the trades’ playbook:

Anchor your value in the physical or the interpersonal.
- Get closer to your customers, stakeholders or on‑the‑ground operations.
- Spend more time in live conversations, workshops, and decision rooms, not just behind dashboards.
Make your work harder to fully specify.
- Work that’s purely rules‑based is easier to automate.
- Work that depends on context, judgment and negotiation is stickier.
Treat AI as your apprentice, not your rival.
- Trades use tools to extend their capability. Knowledge workers should do the same with AI.
- Offload routine “mental labor” and focus your energy on the judgment calls and relationships.

The reality? You don’t have to choose between being a spreadsheet jockey and a bricklayer. You do have to be honest about how much of your day is genuinely human‑critical.

From AI Anxiety to AI Productivity: A Practical Playbook

Anxiety is understandable, but it’s not a strategy. The better move is to actively redesign how you work so AI multiplies your output instead of competing with you.

Here’s a practical framework I’ve seen work for professionals, small teams and solo founders.

1. Audit your week by task type

First, quantify where your time goes. For one normal week, tag every activity as:

C – Creative / strategic (designing, planning, deciding)
R – Relational (selling, coaching, negotiating, leading)
O – Operational / repetitive (reports, formatting, inbox, documentation)

Most knowledge workers discover that 40–60% of their time is in the O bucket. That’s where AI and automation technology can make a real productivity impact.

2. Automate one high-friction workflow at a time

Pick a single process that drains you and standardize it with AI support. For example:

Drafting client emails or proposals
Summarizing long documents or meetings
Creating first drafts of reports, slide decks or documentation
Responding to common customer queries

Turn that into a simple workflow:

Template the inputs you usually provide.
Use an AI tool to create the first draft (email, outline, summary, script).
Edit with your expertise instead of writing from scratch.

If you save even 30 minutes a day, that’s more than 10 hours per month you can redirect into higher-value work.

3. Move up the value chain in your own role

Once AI takes over some operational tasks, don’t just fill the gap with more of the same. Use that new capacity to:

Own more end-to-end outcomes, not just tasks.
Volunteer for cross‑functional projects where human coordination is key.
Get closer to revenue or mission-critical metrics.

In other words, use AI to clear the low‑value work out of the way, then deliberately step into work your organization can’t easily automate.

4. Build “AI literacy” as a core skill

You don’t need to become a machine learning engineer. You do need to:

Understand what current AI tools are good at (pattern recognition, language, summarization) and where they fail (facts, nuance, live context).
Learn how to write clear, structured prompts that reflect your domain.
Know the guardrails around confidentiality, compliance and ethics in your industry.

Professionals who treat AI like a core productivity skill—alongside communication and problem‑solving—will outrun those who ignore it or fear it.

What This Means for Leaders and Organizations

For businesses, the way you integrate AI into work will directly shape your talent pipeline and your long‑term capacity.

The King’s College research shows a troubling pattern: firms with higher AI exposure cut junior roles more sharply and pulled back on hiring. Short term, that trims costs. Long term, it starves the organization of future experts.

If you run a team or company, you have a choice:

Cost-led AI adoption: Use AI mostly to justify headcount cuts, hollow out junior layers, and hope you can later “hire senior.”
Capability-led AI adoption: Use AI to amplify your people, redesign roles, and create new, higher-value paths for both junior and senior staff.

From a productivity standpoint, capability-led adoption wins. Practical moves include:

Redesigning entry-level work so juniors learn judgment and communication while AI handles grunt work.
Pairing humans and AI on key workflows (e.g., AI drafts, human finalizes with context and nuance).
Setting clear expectations that AI use is encouraged, trained and supported—not a secret shortcut or a threat.

This matters because the teams that figure out how to combine human judgment with AI speed will ship more, learn faster and adapt better than those clinging to pre‑AI org charts.

Navigating Your Next Move in an AI-Driven Job Market

AI is rewriting the playbook for careers, from the UK trades classroom to the Fortune 500 boardroom. Some students are choosing wrenches over Word docs. Some companies are swapping junior analysts for AI agents.

The smarter move—for individuals and organizations—isn’t to run from AI or blindly bet on “AI-proof” work. It’s to design your work so that AI handles the repeatable parts and you own the irreplaceable parts.

If you’re a professional, ask yourself:

Which 30–50% of my tasks could AI realistically support or automate this quarter?
How can I re-invest that time into deeper client work, better decisions or new skills?

If you’re a leader:

Where are we using AI just to cut, and where are we using it to create new capability?
Are we investing in our people’s AI literacy, or leaving them to figure it out alone?

The future of work won’t belong to people who are untouched by AI. It’ll belong to people who treat AI as a force multiplier and design their careers—and companies—around that.

Now’s the time to choose which side of that line you want to be on.