Hi friends 👋

A few months ago, I came across a McKinsey chart that I couldn’t stop thinking about.

It showed that by 2030, nearly 30% of current work hours could be automated — not by the robots we imagine, but by AI tools we already use. That’s one in every three tasks you or I do today.

At first glance, it felt like another overhyped headline.
But the more I read, the more unsettling the pattern became.

The real shift isn’t that jobs are disappearing.
It’s that they’re thinning.

AI isn’t replacing entire professions — it’s eroding them from the inside out.
First, it automates a few tasks.
Then, it changes what “doing the job” means.
And finally, it reduces the need for as many people to do it.

No headlines. No mass layoffs.
Just quiet compression.

And that’s what I want to explore today — the reality behind the data, and what it means for anyone building a career, a product, or a company in this AI-driven decade.

We’ll unpack:

Let’s get into it.

— Naseema Perveen

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If you read McKinsey, Goldman Sachs, and OpenAI’s most recent research side by side, one thing stands out immediately:

They don’t actually predict mass unemployment.
They predict a massive redistribution of tasks.

Let’s look at the core findings:

All three studies agree:
AI’s impact is task-first, not job-first.

Source

That distinction matters because it explains why this shift feels invisible.
Most of us won’t wake up one morning jobless.

We’ll just notice that what once took a day now takes an hour — and that fewer people are needed to do it.

This is how jobs quietly shrink.

One of the biggest misunderstandings about AI at work is that it’s a yes-or-no question. As in, will this job survive or not. But that isn’t how change is showing up in real teams. What’s actually happening is far more gradual and much more practical.

AI slips into the workflow one task at a time. Nothing dramatic. No big moment. Just tiny shifts that stack up until the job feels different than it did a year before.

Here are the shifts that come up over and over in teams I talk to:

Most roles have quiet loops everyone forgets to count. Drafting updates, summarizing calls, rewriting the same content, logging notes, cleaning data. These are the first to move to AI. People don’t lose the job. They lose the parts of the job they never liked doing anyway.

Tools like Notion AI and Copilot let one person handle what used to require a handful of people. Status tracking, planning, follow-ups. The work still gets done. It just concentrates around fewer people because the coordination overhead drops.

What used to require three different specialists can often be handled by one person with a few well-chosen AI tools. Writer plus analyst plus designer becomes a single person with solid taste and a few reliable copilots.

And this leads to the part most people miss:

Titles stay the same for a long time. The job underneath them slowly shifts.

The marketing manager still exists, but most of their “creating” now happens through tools. The operations lead still exists, but their dashboards surface issues before they ever look for them. The analyst still exists, but they start with an AI-generated draft instead of a blank sheet.

If you only look at job titles, nothing seems to be changing. If you look at the work inside the job, everything is.

That’s the real story: not replacement, but redesign. A quiet, steady rewiring of how work gets done.

If you zoom out, AI-driven change unfolds in three layers.
Each layer makes a role slightly less human — and slightly easier to replace.

This is the visible layer.
It’s when tools like ChatGPT or Copilot take over your repetitive work — generating drafts, notes, summaries, replies.

You still do the job, but you spend less time thinking through it.
It feels efficient — until it starts feeling shallow.

As AI copilots become embedded in tools, teams need fewer people per function.

Marketing goes from ten specialists to three hybrid operators.

Finance goes from seven analysts to one analyst with a suite of GPTs.

This is where job shrinkage happens silently.

Eventually, a role that’s been automated for too long becomes too cheap to justify.
The company doesn’t fire anyone — it just stops hiring replacements.
The work still gets done. The career path disappears.

This erosion doesn’t happen overnight.
It happens project by project, feature by feature, until the ladder you’ve been climbing quietly vanishes.

Let’s look at which job clusters face the highest exposure over the next three years.
This isn’t speculation — it’s drawn directly from McKinsey, Goldman, and OpenAI’s task-based models.

Scheduling, reporting, data entry, and document prep — all rule-based, repetitive, and pattern-heavy.
AI excels here.
By 2028, McKinsey expects automation in administrative support to reach 70% task-level coverage.

Voice AI is maturing fast.
Deepgram’s 2025 report shows that 97% of enterprises already use voice automation in some form.
The result: a single rep can now manage 4–5x more cases.

Generative models are handling ad copy, email sequences, and SEO drafts.
AI can produce first-draft creative at scale; humans are moving up to strategy and refinement.

AI can already summarize 10K-word reports, build financial models, and find insights in unstructured text.
Goldman’s estimate: 46% of analyst tasks can be automated today.

Testing, validation, reconciliation, and process-checking are rule-based and easily learned by AI agents.
This is the first frontier for full automation.

These categories all share one pattern:
Structured inputs + predictable outputs.
That’s the recipe AI eats for breakfast.

When people ask, “Is my job safe?”, the most useful way to answer is through a framework I call The Exposure Matrix.

It’s simple: map your work by type of task and level of judgment required.

The key takeaway:
If your daily work lives mostly in the first column, your risk isn’t job loss — it’s career stagnation.

Because as AI handles more execution, organizations will need fewer doers and more translators — people who can bridge between data, tools, and human intent.

If jobs are being redesigned rather than replaced, the obvious question is: what actually keeps someone valuable in this new environment?

It’s easy to assume the answer is technical skills. Learn to code. Master prompt engineering. Build agents. But the data from McKinsey, LinkedIn, and dozens of enterprise teams points in a different direction.

The people rising fastest aren’t the most technical. They’re the ones who understand how to work with AI as a partner instead of a tool. Their edge comes from “meta-skills” — abilities that compound across every system they use.

Here are the five that matter most:

This is quickly becoming the new baseline skill in modern work. It’s the ability to take a messy, fuzzy business need and turn it into something structured that a system can act on.

Not “write me a report,” but “compare these three datasets, pull anomalies, and generate a one-page summary for the leadership team.”

People who can translate problems into clear instructions end up shaping the work instead of reacting to it.

As AI becomes more capable, knowing when to not use it becomes a strategic advantage. Automating everything usually creates more problems than it solves.

Great operators are the ones who can spot exceptions, edge cases, and moments where human interpretation matters more than speed.

This isn’t anti-automation. It’s intelligent automation.

3. Systems thinking
When the tools get powerful, the bottleneck moves from execution to design.
Most teams don’t fail because someone wrote a bad prompt. They fail because no one mapped the workflow.

If you can see how inputs move through a system, where things break, and how to redesign the flow, you’re instantly more valuable than any point solution.

4. Narrative communication
AI can generate words at scale. What it can’t do is create meaning, stakes, or direction.
Every team still needs someone who can turn AI generated outputs into a story that aligns people, persuades them, or moves a project forward.

The ability to craft narrative is becoming a quiet superpower because it’s what makes information actionable.

5. Human coordination
Even the most automated environment still relies on humans choosing to work together.
Empathy, alignment, expectation setting, and coaching are becoming more important as the technical friction drops.

When tools handle the tasks, the scarce skill becomes getting people to trust each other and move in the same direction.

Put simply: if AI makes information abundant, interpretation becomes the scarce resource.

These are the skills that make someone indispensable in a world where the work shifts faster than the job title.

Source

Let’s visualize what’s actually happening inside most companies right now:

Notice the pattern?
Every new role sits on top of AI systems, not next to them.
The fastest-growing jobs aren’t replacing humans — they’re redefining what “productive” means.

The question comes up in almost every conversation about automation: what if we simply run out of jobs? It’s a fair concern. But history gives us a different story. Every major wave of technology — from steam engines to software — has reshaped labor markets without permanently shrinking them. Jobs don’t vanish. They evolve.

Over time, economies adjust to new technologies. Roles that fade are replaced by entirely new ones. McKinsey’s analysis suggests that by 2030, roughly 8–9% of total labor demand could come from jobs that don’t exist today. The challenge isn’t the number of jobs, but the timing — people often struggle to transition fast enough as work changes around them.

If economic growth, innovation, and investment continue at healthy levels, new job creation can more than offset automation’s impact. But this outcome isn’t guaranteed everywhere. Advanced economies with slower growth or limited investment may need additional support to prevent job shortages.

The real danger lies in how quickly workers can adapt. Without the right reskilling programs and mobility pathways, countries could face higher unemployment or stagnant wages even if jobs exist on paper. Managing this transition — not fighting automation — will decide who benefits from AI.

The pace and pressure of automation will look different depending on four key factors:

In short, there will likely be enough work — but not the same kind of work, and not evenly distributed. The next decade won’t be about whether jobs exist. It’ll be about whether people, companies, and governments can evolve fast enough to meet them.

Here’s a practical, four-step approach you can apply immediately:

List what you do in a week.
Mark each task as: routine, analytical, or strategic.
You’ll often find 40–60% of your week in “routine” — that’s your automation opportunity.

Build small copilots for yourself.
If you write reports, create an AI template.
If you research data, build a search agent.
Owning your automation makes you irreplaceable.

Don’t just upskill — cross-skill.
Pair your domain with AI literacy.
Example: a marketer who learns analytics, or a designer who learns data visualization.

Document your new workflows publicly — inside your team, on LinkedIn, or in a personal portfolio.
Hiring managers increasingly look for evidence of adaptation.

Here’s the part few people mention in these conversations:
AI doesn’t just automate tasks — it automates growth opportunities.

Think about early-career employees.
They used to learn by doing grunt work — building decks, summarizing calls, organizing data.
Now, AI does that.

That means fewer reps, fewer small mistakes, fewer quiet lessons.
And without those, expertise doesn’t form.

The long-term risk isn’t unemployment.
It’s underdevelopment.
An entire generation could rise through roles that feel productive but teach little.

The antidote?
Seek friction.
Choose projects that stretch your judgment, not just your output.
If AI makes work easier, make learning harder.

If we fast-forward three to five years, here’s what the corporate landscape will likely look like:

Every company will effectively become an AI organization, whether it plans to or not.
And every employee will operate at one of two levels:
either with AI, or through AI.

The difference is massive.
Working “with” AI means using it as a tool.
Working “through” AI means designing systems that others rely on.

If you want to stay relevant, aim for the second.

We’ve seen this movie before — with spreadsheets, the internet, and CRMs.
Each time, jobs didn’t vanish; they reorganized.

When Excel arrived, it didn’t kill accountants.
It made great accountants more valuable — and average ones unnecessary.
When Photoshop came, it didn’t destroy design — it exposed taste as the new differentiator.

AI will do the same.
It won’t remove marketers; it’ll expose those who can’t connect data to narrative.
It won’t remove managers; it’ll expose those who rely on oversight instead of insight.

The future belongs to people who can layer human sense-making on top of automation.

When you strip away the noise, the story isn’t about AI taking over.
It’s about AI reshaping the definition of valuable work.

Every role is becoming a portfolio of tasks.
Some will be automated.
Some will be amplified.
And the rest — the parts that require judgment, empathy, or synthesis — will define your actual career trajectory.

The next decade won’t belong to coders, or even to “AI experts.”
It’ll belong to translators — people who can bridge what machines do with what humans need.

If AI could handle every repetitive part of your job tomorrow,
what would be left that’s still uniquely you?

Would it be enough to keep you indispensable?

👇 I’d love to hear your take:
Do you think AI will erase roles, or just erode their meaning first?

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

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