A capstone, in three conference rooms

In January, quantum computing was mostly a vocabulary problem. By April, it had become a judgment problem.

What I remember from the four-month Columbia SIPA Capstone for Wood Mackenzie is three rooms, three versions of the same question, and a team slowly learning what a useful answer looks like.

The first room: admitting what we did not know

Six of us began with different backgrounds and no claim to being quantum physicists. The assignment was broad: assess where quantum technologies might matter in energy, and when.

At first, every paper seemed to require three others before it could be understood. Terms such as variational algorithms, error correction, and quantum amplitude estimation were individually definable but difficult to connect to an operating decision. The temptation was to collect impressive applications and call that a forecast.

We did the slower thing. We built a shared glossary, divided the application areas, kept one source list, and met in person each week. Our notes became useful only when we began recording uncertainty as carefully as conclusions. “We do not know yet” stopped naming a failure and began naming the next research task.

My part focused on energy finance, a Monte Carlo explainer, and the summary framework that compared applications. That meant repeatedly translating between two questions: can a method produce a technical advantage, and would that advantage be worth anything after data, integration, hardware, and organizational costs?

The second room: making the comparison fair

By the midpoint, we had six application areas and too many incompatible ways to describe them. Battery chemistry was not directly comparable with grid optimization. Quantum sensing did not belong on the same hardware timeline as fault-tolerant quantum computing. A promising algorithm did not necessarily imply a promising business case.

So the project changed from a catalogue into a matrix. We assessed each area against three shared dimensions:

1. time to value;
2. fit between the problem and a plausible quantum advantage; and
3. implementation cost relative to the benefit.

The scores mattered less than the discipline of producing them. A common framework exposed assumptions that prose could otherwise shelter. It also gave disagreement somewhere to go. When two people scored an application differently, we could ask whether the gap came from hardware timing, the classical benchmark, or the economic value assigned to an improvement.

That midpoint review also made the standard clearer: every claimed advantage had to survive comparison with the best available classical method. “Quantum could do this” was not enough. The relevant question was whether the advantage would be large enough, arrive soon enough, and matter to a real decision.

The third room: saying less, more precisely

The final month was mostly subtraction. We removed claims that had outrun their evidence, separated sensing from computing, and tied timing to capabilities rather than impressive-looking calendar years. We sharpened the distinction between technical fit and commercial readiness. Smooth adoption curves gave way to thresholds: value may remain nearly flat until accuracy, scale, and integration cross a useful line.

The final presentation felt calmer than the work that produced it. That was a good sign. The framework could carry the argument without theatrical certainty, and the team could explain where the evidence was strong, where it was directional, and what would change our view.

The answer I learned to trust

The interesting part was not acquiring a new technical vocabulary. It was learning how a group of generalists can earn a point of view without impersonating specialists.

The visible work was research, interviews, modeling, and slides. The enabling work was more ordinary: agendas, shared definitions, rotating notes, care with sources, and deadlines that meant the same thing to six people. Coordination did not sit beside the analysis. It was part of the analysis, because inconsistent inputs produce inconsistent judgment.

I also learned to wait longer before committing to a thesis. Early certainty feels efficient; often it merely turns the rest of the research into a defense. The useful answer appeared only after we had enough structure to compare unlike things, and enough restraint to keep the conditions attached to every conclusion.

By April, we could explain the six applications. More importantly, we could explain why several attractive claims had not yet earned confidence. That is the kind of expertise I trust: not the reflex of having an answer, but the craft of building one that lets another person see how it stands.

This essay describes my own learning from the academic project. Client materials, private conversations, contact details, and other team members' work are intentionally omitted.