Lately, I’ve been thinking that most of the conversation around AI and work is still missing the point.

A lot of the focus is on what AI can do faster. But the more important shift is what it’s doing to the value of work itself. Some kinds of work are becoming more useful and more leveraged, while other kinds are quietly getting cheaper.

That’s why some people are pulling ahead so quickly. They’re using AI to reduce the time they spend on execution and putting more of their energy into judgment, prioritization, and decisions. Others are still working hard, but in areas where the value is starting to fall.

So today, I want to break down what’s actually changing, what the data says, what top performers are doing differently, and how to make sure you’re building the kinds of skills that matter more in an AI-first job market.

What we’ll explore today

— Naseema Perveen

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Last month, 48% of visitors to documentation sites across Mintlify were AI agents—not humans.

Claude Code, Cursor, and other coding agents are becoming the actual customers reading your docs. And they read everything.

This changes what good documentation means. Humans skim and forgive gaps. Agents methodically check every endpoint, read every guide, and compare you against alternatives with zero fatigue.

Your docs aren't just helping users anymore—they're your product's first interview with the machines deciding whether to recommend you.

That means:
→ Clear schema markup so agents can parse your content
→ Real benchmarks, not marketing fluff
→ Open endpoints agents can actually test
→ Honest comparisons that emphasize strengths without hype

In the agentic world, documentation becomes 10x more important. Companies that make their products machine-understandable will win distribution through AI.

Make Your Docs Agent-Ready

The most important idea to understand right now is this:

When execution becomes abundant, it stops being valuable on its own.

For years, companies rewarded people for producing output. If you could execute quickly and reliably, you were seen as high-performing because execution itself was the constraint.

That constraint is now breaking.

AI is removing friction from tasks that used to require time, effort, and experience. As a result, the value is no longer in producing work, but in deciding what work should be done and how it should be done.

As execution becomes easier, the bottleneck shifts upward into areas that are harder to automate.

It now sits in:

This is why two people using the same tools can produce very different results. One generates more output, while the other produces better decisions that actually move the business forward.

AI is compressing large parts of knowledge work that used to take hours into minutes. Tasks that once required specialized skills are now accessible to a much broader set of people.

This includes:

This shift does not eliminate these tasks, but it reduces their cost and increases their supply. When supply increases, value drops unless it is paired with something more scarce.

If your role is primarily execution-heavy, you may start to feel:

But if your role involves decision-making, context, and ownership, your leverage increases.

That is the structural shift happening underneath the surface.

Several major reports are pointing to the same pattern.

That last point is especially important.

The constraint is no longer access to AI. It is the ability to integrate it into real workflows and decision-making.

That is where the opportunity sits.

The people pulling ahead right now are not just using AI tools. They are changing how they approach work at a fundamental level.

They are shifting their focus from producing outputs to improving outcomes.

Instead of spending most of their time executing, they:

This changes how they spend their time.

They produce less manually, but contribute more strategically.

Traditional operator:

AI-first operator:

That difference compounds quickly over time.

Most advice suggests using AI more often. A better framing is to understand how it changes your role.

AI changes how you allocate effort.

Instead of being primarily responsible for producing work, you become responsible for shaping it.

Old model:

AI-first model:

The key shift is this:

You move from being the producer of work to the editor of decisions.

That is where leverage increases.

To stay relevant, it helps to think in layers, not just skills.

Some skills help you keep up. Others are what actually make you more valuable as AI gets woven into more of the work.

This is the starting point. It means knowing how to work with AI well enough to make your day-to-day work faster and better.

That includes writing clear prompts, giving useful context, iterating on outputs, and checking results before you use them. In other words, it is the ability to get something useful out of the tool, not just something fast.

This layer matters, but it is quickly becoming expected. AI fluency is no longer the advantage by itself. It is becoming the new baseline, much like being good with spreadsheets or presentations.

In practice, strong AI fluency means you can take a vague task and turn it into a useful prompt. It means you compare outputs instead of taking the first answer, and you can tell when something sounds polished but is actually weak.

That matters. But it only gets you so far.

This is where the real separation starts.

Once everyone has access to similar tools, the gap shifts from who can generate faster to who can think better. The question stops being, “Can you produce something quickly?” and becomes, “Can you make good calls with what the tool gives you?”

That shows up in a few ways:

This is the layer where speed turns into impact. Two people can use the same AI tool and still produce very different outcomes. One creates more output. The other creates something more useful.

A simple test is this: if everyone on your team had access to the same AI tools, would your work still stand out? If the answer is yes, your edge is probably sitting in judgment, not just execution.

This is the layer that drives long-term growth.

It includes influencing decisions, aligning teams, building trust, and navigating uncertainty. These are the capabilities that help good thinking actually turn into action inside a company.

AI can help with execution. It can draft, summarize, analyze, and speed up parts of the workflow. What it cannot do is create alignment around a decision, earn trust from a team, or carry a group through ambiguity.

That is why this layer compounds. The more your value depends on judgment, influence, and clarity, the harder you are to replace.

Put simply:

AI fluency increases speed.
Decision quality increases impact.
Human leverage increases scale.

That is the stack that matters.

One of the biggest risks right now is staying stuck in coordination-heavy work and mistaking it for leverage.

This usually looks like managing updates, organizing meetings, tracking progress, and summarizing information. It often feels important because it keeps things moving, and in the short term, it usually does.

But that is also what makes it tricky. Coordination work creates a lot of visible activity, which can make it feel more strategic than it really is.

Over time, it does not scale as a strong advantage. AI is getting very good at compressing exactly this kind of work, which means the effort required starts to fall, and eventually the value attached to that effort starts to fall too.

That is the trap. You can stay busy all day and still be building your value in the part of the workflow that is getting cheaper.

This is where the divergence starts. Some professionals stay close to coordination. Others move closer to judgment, prioritization, and system design.

The second group pulls ahead.

A useful question to ask yourself is: Am I mostly moving information, or shaping decisions? The more your work sits in judgment, prioritization, and system improvement, the stronger your position becomes.

What distinctly human skills are becoming more valuable as AI takes over execution-heavy work, and how should professionals build them over the next few years?

We’d love to hear your perspective.

Email your thoughts to: [email protected]
Selected responses will be featured in next week’s edition.

Most career advice around AI is too abstract to be useful.

“Learn AI.”
“Upskill.”
“Adapt.”

That sounds right, but it doesn’t tell you what to do on Monday morning.

So instead, here’s a simple way to think about the next 90 days:

You are not trying to learn everything.
You are trying to move your value up the stack.

Start by writing down everything you do in a typical week.

Then force yourself to categorize each task into one of three buckets:

Most people skip this step, but it’s the most important one.

Because what you think you’re paid for and what you’re actually spending time on are often very different.

Execution

Decision-making

Leverage

If your week looks like:

you’re in a risky position long-term.

The goal is not to eliminate execution completely.

The goal is to reduce it enough that you can operate more in decision and leverage layers.

Now that you see where your time goes, pick 2–3 tasks you do every week.

For each one, ask:

Most people use AI to save time.

High-leverage people use AI to change what they spend time on.

Instead of writing weekly reports from scratch, you move to:

That shift is subtle, but powerful.

You are moving from reporting information to explaining meaning.

This is the highest ROI step.

Because once everyone has access to AI, decision quality becomes the differentiator.

Pick one real problem from your work each week.

Then run this process:

Most people use AI to get answers.

You’re using it to train your judgment.

Over time, you get better at:

That’s what compounds.

This is where you move from being a task executor to a leverage creator.

Instead of doing work repeatedly, you build a system that produces that work.

Pick one recurring workflow and redesign it:

Before

After

The key idea:

AI handles speed.
You handle judgment.

That combination is where leverage comes from.

This is one of the most overlooked career moves right now.

Most people deliver outputs.

Very few explain how they think.

Instead of just sharing results, add:

In an AI-first environment:

So people trust:

This is how you build credibility quickly.

The shift to AI does not affect every role in the same way. But it does create the same pressure for everyone: move closer to judgment, ownership, and leverage.

At this stage, the biggest mistake is becoming great at execution but weak at understanding how decisions get made. AI will increasingly handle parts of the execution layer, so your advantage comes from learning how work connects to real outcomes.

In practice, that means:

A useful goal early on is to become the person who does the work well and also understands what the work is trying to achieve.

This is where the shift becomes much more visible. Many mid-career professionals built their value around being reliable executors or strong coordinators, but those layers are exactly where AI is starting to put pressure.

The move here is to get closer to:

Practically, that means:

This is the stage where you want your role to feel less like “I keep things moving” and more like “I improve how things move.”

The role of a manager is shifting from oversight to leverage creation. In the past, a lot of management value came from tracking execution, checking status, and making sure work was happening.

Now, more of that can be automated or surfaced by tools. So the manager’s job moves upward.

In practice, strong managers should focus on:

The best managers in this environment will be the ones who raise the quality of decisions across the team, not just the volume of output.

For specialists, depth still matters a lot. But depth alone is no longer enough if you cannot translate it into business value.

That is why clarity becomes critical. If you can explain your expertise clearly, connect it to outcomes, and help others act on it, your influence increases significantly.

In practice, that means:

The specialists who pull ahead will not just be the deepest experts. They will be the ones who can turn expertise into decisions, action, and trust.

This is not just a tooling change. It’s a shift in how professional value is defined.

That’s why two people in the same role can now have completely different trajectories.

One is getting faster at execution.

The other is moving into higher-value decisions.

Over time, that gap widens.

AI is not just changing how much work gets done.

It’s changing where value is created inside that work.

For a long time, the advantage came from being able to execute well. If you could produce faster, deliver consistently, and handle more volume, you stood out.

That model is starting to break.

Execution is becoming easier, cheaper, and more widely available. When that happens, the value shifts upward, away from producing work and toward deciding what work matters in the first place.

That’s the part many people underestimate.

Because from the outside, it can look like nothing has changed. The same roles exist. The same tasks are being done. The same outputs are being delivered.

But underneath, the value of those outputs is shifting.

Some people will use AI to produce more. They’ll move faster, complete more tasks, and increase their output.

Others will use AI to think better. They’ll spend less time on execution and more time on framing problems, evaluating options, making tradeoffs, and improving decisions.

That difference is subtle at first.

But over time, it compounds.

The first group becomes more efficient.

The second group becomes more valuable.

And that gap becomes very hard to close, because it’s not just about tools. It’s about how you think, where you focus, and how you define your role in the work itself.

So the real question is not:

“Am I using AI?”

It’s:

“Am I using AI to do more work, or to do more valuable work?”

That’s the shift.

And that’s where the leverage is.

—Naseema

Writer & Editor, AIJ Newsletter

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