New Market Entry

SET IN — THE CAPACITY RACE, A GPU CLOUD COMPUTE MARKET

Students run GPU compute providers deciding how much capacity to install each round — a decision that can also shape whether a new entrant ever shows up at all. Separately, students play the role of that new entrant, deciding whether, and when, to step into a market someone else already occupies.

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The academic idea

Entry timing, commitment, and installed capacity

A market is already running when a new firm has to decide whether to step into it — and that decision isn't just yes or no, it's a question of how large a position to commit to. Meanwhile, the incumbents already inside face their own version of the same tension: how much capacity to commit to before knowing whether a new entrant will show up and reshape the market they're building for. Incumbents aren't purely reactive, either — their own behavior can deter entry altogether, keeping a new competitor out before it ever arrives. Students feel both sides of that trade-off, often in the same session.

The simulation supports several different configurations of that tension, without switching tools:

  • Sunk vs. flexible installed capacity. Whether installed capacity is a one-way ratchet — once committed, it can never be reduced, only added to — or fully flexible, freely raised or lowered each round with cost based only on whatever's currently installed, can be configured. The same market can teach very different lessons about commitment depending on which is chosen.
  • One-shot vs. repeated entry. Whether a firm that declines to enter is locked out for good, or free to reconsider in any later round, can be configured — isolating how much of the "first-mover" story depends on entry actually being irreversible.
  • Single vs. multiple entrants. More than one prospective entrant can compete for the same market at once, turning the entry decision into a race rather than a solo call — and giving incumbents more than one rival's behavior to react to.
The scenario

The Capacity Race

Each team runs a GPU compute provider, renting out processing power to AI companies that need it. Every round, incumbent teams decide how much GPU capacity to install; price falls as total installed capacity across every active provider rises. Starting from a configurable round, a new entrant can choose whether to step in and compete too — reshaping the market for everyone already there.

Installed capacity can be configured as a sunk investment — once committed, it can only go up, never down. Build too much, and you're stuck paying for it whether or not the market ends up rewarding it, exactly like the real over-build risk playing out in this industry today.

A vast data center hall with endless rows of illuminated server racks and a single engineer walking the central aisle

How a session runs

Every simulation follows the same shape — what changes is the strategic decision at its core and the industry context around it.

SET UP

Configure the conditions

Before class, the teacher sets the conditions: how many markets, firms per market, and the specific market conditions — e.g., demand, cost, entry, exit, investment/acquisition opportunities, etc — so the same core idea can be taught several different ways.

PLAY

Teams compete live

Students join their respective teams and make real decisions, round after round, watching prices and payoffs move in response to everyone's choices — including their own.

DEBRIEF

Discuss the real data

Every round is logged automatically, and the full results can be downloaded by teacher and students alike. With real numbers on the table instead of a hypothetical, the class can dig into the strategic logic behind what happened — discussed in more depth, and grasped more readily, because everyone has already felt the pros and cons of their own choices firsthand.

Ready to enter a session?

Enter the simulation →