“That’s cool, but I have no idea if I’d like that.”

A rising senior at a Pennsylvania public high school, describing the moment he stood on a college campus holding a pamphlet, reading the list of majors.

The case at a glance

A rising senior, strong in math and science, arrived with a list of majors and no sense of what any of them would feel like to do. Over nine hours he worked through thirty activities drawn from real professional work: ten he loved, sixteen he tolerated, four he disliked. He came in thinking forensics, and left with computer engineering as the strongest fit — a field he had ruled out because the only kind of engineering he could picture was building bridges.

01The student, before we startedA strong senior with campus visits underway — and no way to find out what any of those majors would actually be like to do.

He brought the pamphlet up himself, unprompted, in our third session. Then he kept going:

“I’d kind of want to know more about what’s in that major, than if I just hear the word finance, or if I just hear the word like whatever, something else. Like I don’t totally know what that entails, you know what I mean?”

Strong student. Campus visits underway. Nobody has failed him — and he is sharp enough to see the gap.

It is a familiar problem at this age, and it is not really a shortage of information. It is that the name of a major carries no information about the work.

Looking back. The student, on how he had been choosing before Decade Ahead.

02What he did for nine hoursNine work simulations, one per workstyle, each built from real professional work. No questionnaire, at any point.

Nobody can hand a seventeen-year-old a career to test-drive. But a career is made of pieces, and the pieces are small enough to try in an hour.

So we broke professional work into thirty activities across nine workstyles, and built one simulation for each. He did all nine. No questionnaire, at any point.

Perform
Operate a complex system, then troubleshoot when it fails.
Underwrite
Decide whether an investment is worth backing, and at what price.
Create
Design a solution for users with real constraints.
Lead
Steer a team through a crisis and keep it together.
Care
Assess a person’s situation, weigh the options, deliver a plan.
Sell
Choose the right buyer, then make the case live.
Research
Design a test, run it, and work out what the data says.
Teach
Teach someone a skill until they can do it alone.
Engage AI
Direct an AI through a task, check its work, make the final call.

After every task, one question: did that pull you in, or wear you down?

03What he said while he was doing itTen minutes into the systems case he stopped and corrected himself: “I like troubleshooting. Honestly, I’ll say I loved it.”

All of this is his, verbatim, from the session recordings.

Ten minutes into the systems case, offered an easier route and a harder one:

“I’ll try the expert field, why not?”

Reflecting on it afterwards, correcting himself mid-sentence:

“I like troubleshooting. Honestly, I’ll say I loved it.”

On the teaching case — one he expected to be neutral about:

“I actually enjoyed building the actual storyboard, thinking about what icons to help them remember.”

And, on the same case, ninety seconds later:

“I don’t really love my success being based off of other people. That’s very open to their willingness to learn.”

On selling, having enjoyed choosing which buyer to target:

“Constantly trying to go from a new angle. It was kind of just making me like, okay, what do I say now? And I was like, I don’t know.”

On the care case, after delivering a plan:

“I would want to help someone, but I wouldn’t want to be the person with that role.”

Asked which moment in the AI case felt most like him:

“It definitely happened within the auditing the AI — being able to see all of those differences, and then being like, okay, cool, now I can start finding all the flaws.”

None of that is available to a seventeen-year-old on a survey. “I want to help people” and “I want to be the person carrying it” are the same answer on an interest inventory. They are different answers once he has done the work and said out loud how it felt.

04The turning pointHe told us he leaned away from engineering. Minutes later he took it back — the only kind of engineering he could picture was building bridges.
THE LAST SESSION

“I lean away from — I don’t know if this is ironic or contradictory to what I’ve done. But I lean away from engineer. Engineering doesn’t stick out to me, honestly.”

He volunteered that. Nobody asked. And he flagged the contradiction himself before anyone else could — nine hours of his own reactions pointed one way, and the word in his head pointed the other.

Asked to say more, he narrowed it on his own: “Maybe I should say this: I’m not drawn to structural engineering.” The only engineering he could picture was bridges.

So his guide walked him through the shapes engineering actually takes — operating a complex system, modeling the numbers that decide an outcome, building and debugging software — and tied each one back to a task he had already done and already said he loved.

He sat with it. The conversation moved on. Then, minutes later, he came back to it himself:

“I do want to recant what I said. I actually am really interested in aspects of engineering. I just honestly had no idea what kinds of engineering there are.”

He had ruled out, on one word, the field his own evidence pointed to most strongly. Not from dislike. From not knowing what was inside it.

05His results, activity by activityAll thirty activities marked by how they actually felt to do: ten he loved, sixteen he could tolerate, four drained him.

Every activity he tried, colored by how it actually felt to do it. Thirty specific pieces of work, not a type or a score.

Loved Tolerated Disliked

Perform

ENERGIZER
  • Understand how a system works
  • Follow a precise process
  • Troubleshoot problems

Underwrite

ENERGIZER
  • Model the key drivers and optimize
  • Manage risks & trade-offs
  • Negotiate deal terms

Create

ENERGIZER
  • Gather user needs
  • Prioritize features and design a solution
  • Get feedback and adjust
  • Create a message that sparks interest

Lead

ENERGIZER
  • Select candidates for roles
  • Coordinate planning
  • Inspire people
  • Resolve conflicts and align people

Engage AI

SUPPORTING
  • Direct AI
  • Check AI outputs
  • Make the final call

Care

NEUTRAL
  • Diagnose or assess human needs
  • Create and justify a care plan
  • Share with empathy

Sell

DRAINER
  • Learn product details
  • Find the right customers to target
  • Make a persuasive case to an audience

Research

DRAINER
  • Read technical or scientific content
  • Design a research approach
  • Analyze complex data
  • Share recommendation with evidence

Teach

DRAINER
  • Learn a concept
  • Create teaching aids
  • Adapt explanation and teach

Ten loved. Sixteen tolerated. Four disliked.

06Why the detail mattersHe disliked selling, teaching and research overall. Inside each one there was still a part he loved — and a job title would have thrown all three away.

Every one of them still contains something he loved.

Sell

He disliked pitching live. He enjoyed working out which buyer to target — “I did enjoy kind of figuring out which buyer I wanted to sell to.”

Teach

He disliked the teach-back. He enjoyed building the teaching aid — the storyboard, the icons, the design of the explanation.

Research

He disliked the reading. He enjoyed the analysis — “you can really figure out, just simply with math, what is the most important to build on.”

Three of his four hard dislikes are the same underlying thing — landing something in another person’s head in real time — showing up in three unrelated cases, including one he enjoyed overall.

Any tool that answers at the level of a job title throws all three keepers away. It tells him he is not a salesperson, not a teacher, not a researcher, and loses the customer analytics, the instructional design and the data work along with them.

07The careers his results pointed toMore than 400 occupations ranked by how much of the daily work he had already enjoyed doing. The three strongest: computer engineering, software, and quantitative finance.

More than 400 occupations in the U.S. Department of Labor’s O*NET database, scored by one rule: how much of each role’s core work draws on what he loved, minus what drained him.

The three that came back strongest are not a verdict. They are the places where the largest share of the day-to-day work is made of things he loved doing.

Computer engineering

Computer Engineering, Electrical Engineering

Troubleshooting · understanding how a system works · precise execution · checking outputs

Computing & software

Computer Science, Software Engineering

Debugging · building toward a working result · finding the flaw · designing a solution

Quantitative & financial

Finance, Applied Math, Actuarial Science

Modeling the drivers that decide an outcome · analyzing data · optimizing against constraints

Two of the three sit inside the field he had dismissed in the previous session.

08What he said at the endHis last words in the final session, plus two short videos recorded afterwards — the student, and his father.

His last words in the final session, unprompted:

“I really enjoyed this. It’s like very unique. I’ve never really done something like this before, so I actually really did enjoy this.”

Whatever he settles on from here, he is settling on it with evidence — nine hours of it, about himself, in his own words. Two weeks earlier it was a word on a brochure.

He is one student. There are hundreds more in a graduating class, most of them facing the same question: what’s my major, and why?

AFTERWARDS

The student and his father, in the two videos below. Both recorded after the program had finished.

Know what you’re building toward.

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