Your Title Used to Say What You Did. Not Anymore.

For years, job titles described, at least roughly, a person’s place in an organization and the work they were responsible for.

Engineering, design, analysis, product, sales…

The boundaries were never perfect, but they were legible. You could look at a business card and get a reasonable sense of where someone stood within the company.

Now those boundaries are blurring.

Because AI is not eliminating entire professions, but it is taking on an increasing share of the tasks that define them.

It writes code. Performs analysis. Prepares drafts. Scans data. Creates presentations. Conducts research.

The business card still says the same thing.

But the work it describes is changing.

That leaves a question:

As machines take over what can be done, where will human value come from?

Two recent developments offer an interestingly similar answer.

First sign: Less about your title, more about the momentum you bring

Boris Cherny, the creator of Claude Code, describes how conventional professional boundaries are becoming increasingly meaningless in his own working environment.

Engineering, product, design, data science…

People no longer work only inside their own boxes. Someone in product can write code; a technical person can participate in product decisions.

From this, he develops a different framework.

Think about people not only by their titles, but by the kind of movement they bring to the work.

He defines five archetypes:

The Prototyper makes an idea tangible quickly.

The Builder turns it into a robust, functioning product.

The Sweeper clears away complexity and simplifies.

The Grower expands the product in the market.

The Maintainer keeps a mature system secure, fast, and sustainable.

Here is what is interesting:

None of these belongs to a particular profession.

A designer can be a Prototyper.

An engineer can be a Sweeper.

A product manager can act like a Builder.

The question is gradually shifting away from ‘What do you do?’ toward something else:

When you join a piece of work, what do you move forward?

This is more than a small change in language.

Because the same person can create extraordinary value at one stage of a product and be the wrong person at another.

At the prototype stage, constant concern with scale, security, and perfection can slow the work down. In a mature system, those very concerns keep it running.

The talent has not changed.

The context has.

So value does not come merely from being good.

It comes from bringing the right kind of contribution at the right time.

Second sign: From the desk into the customer’s world

At the same time, another role is rapidly gaining visibility:

Forward Deployed Engineer — FDE.

Neither a conventional consultant.

Nor a salesperson.

Nor simply an engineer developing a product at a desk.

They enter the customer’s real problem. See the data, understand the workflow, integrate the system, build the solution, and make it work.

The role is not new. Palantir pioneered this approach years ago.

What is new is why it has become so valuable again in the AI era.

Enterprise AI often does not work as simply as the traditional software logic of ‘buy, install, use.’

Every company’s data is different.

Its workflows are different.

Its exceptions are different.

Its authority boundaries are different.

Its compliance and security requirements are different.

Buying a model is becoming easier.

Connecting the model to real work is becoming harder.

This is precisely where the FDE’s value emerges.

They do not simply leave the product with the customer.

They close the gap between the product and the customer’s actual work.

The model works. Where is the business outcome?

MIT Project NANDA’s 2025 study of enterprise GenAI highlighted a similar problem.

Most of the initiatives examined had not produced a measurable financial impact.

That did not mean AI did not work.

It meant something more uncomfortable:

Technology working and a company creating value from it are not the same thing.

A model can generate excellent text.

It can make an employee’s work faster.

It can look impressive in a pilot meeting.

But if it does not affect the company’s revenue, costs, cash flow, risk, or decision quality, business value is being lost elsewhere.

Often, that loss is not inside the model.

It is in the distance between the model and real work.

In data integration.

In the workflow.

In the feedback mechanism.

In ownership.

And in the failure to design where people should step in.

Two separate developments tell the same story

Cherny’s archetypes look inward at the team.

The FDE looks inside the customer’s business.

At first glance, they are different worlds.

But their messages are remarkably similar:

As machines do more of the work, human value concentrates less in the task performed and more in how the work is moved forward.

Who defined the problem?

Who simplified the disorder?

Who connected two separate worlds?

Who recognized where human judgment was needed?

Who tracked whether the solution actually worked?

And who owned the work until it produced a result?

None of these is a job title on its own.

The business card and the fingerprint

Perhaps it is easier to see it this way.

A title is like a business card.

It can be printed.

Standardized.

Held by thousands of people in the same sector.

What you actually do is more like a fingerprint.

It emerges only through action.

In how you see a problem.

In what you notice before others.

In how you simplify something complex.

In where you pick up a piece of work and how far you take it.

The business card will not disappear in the AI era.

But the fingerprint will become more valuable.

As the cost of standardized tasks falls, what cannot easily be copied gains importance.

Judgment.

Context.

Ownership.

Choosing the right problem.

The ability to connect things.

The capacity to deliver an outcome.

For organizations, the implications are greater

This change is not only about employees’ careers.

It also changes how companies organize people.

Many organizations still start by drawing boxes:

Finance.

IT.

Marketing.

Product.

Operations.

Then they put people into those boxes.

But AI is increasingly breaking through the boundaries between functions.

Management’s question must change too.

Instead of ‘What is this person’s title?’

‘What kind of human contribution do we need at this stage of this problem?’

Perhaps someone who can build a prototype quickly.

Perhaps someone who can strengthen the system.

Perhaps someone who can enter the customer’s world and connect technology to the real problem.

Or perhaps someone who can design where and in what sequence all of these things should happen.

This is where a technology issue becomes a decision-architecture issue.

Who steps in, and where.

What AI does.

What the human takes on.

Which decisions are automated.

Which decisions undergo human validation.

And whether the entire structure leads to a real business outcome.

The back of the business card

One day, when you hand someone your business card, they will still read your title.

But that title will probably tell them less about you than it once did.

The real story will be on the side that is not printed.

Which complex problem did you solve?

At what point did you enter the picture?

What did you see in a way others could not?

Which piece of work did you own to the end?

And what remained after you left?

Perhaps the hardest career question in the AI era is now this:

When machines take over what can be done, what mark will you leave?

If the answer is only your title, that is exactly where the problem begins.

Sources

• Boris Cherny / Claude Code — The Prototyper, Builder, Sweeper, Grower, and Maintainer archetypes; observations on the blurring of traditional functional boundaries in the AI era.

• MIT Project NANDA — The GenAI Divide: State of AI in Business 2025 — measurable business impact, the learning gap, and workflow integration challenges in enterprise GenAI applications.

• Aishwarya Srinivasan (2026) — The renewed rise of the Forward Deployed Engineer role at AI companies and the need to connect products with customers’ real business problems.

• Palantir — One of the early, institutionalized examples of the Forward Deployed Engineer approach.