The look-then-leap rule is an optimal stopping strategy: spend a defined phase gathering information (look), then commit when a threshold is reached (leap). It comes from the secretary problem and related optimal search literature. The idea is to resist committing too early — before you've seen enough options or signals — and to avoid searching forever. There is a right moment to stop looking and act.
In the classic secretary problem, you see n candidates one by one, must accept or reject each immediately, and want to maximise the chance of picking the best. The optimal policy is to reject the first n/e candidates (the "look" phase), then choose the first candidate who is better than all previous ones (the "leap"). That policy gives a probability of 1/e ≈ 37% of selecting the best — far better than random, and provably optimal under the problem's constraints. The rule generalises: when you face a sequence of options and must decide in real time with no recall, reserve a fraction of the "budget" for looking, then leap when you see something that clears a threshold.
In practice, look-then-leap appears in hiring (interview many, then hire when you see someone above a calibrated bar), in dating and partnership, in venture (see many deals before writing cheques), and in product (explore before doubling down). The mistake is leaping too soon (first acceptable option) or looking too long (never committing). The discipline is setting the look phase and the leap threshold in advance, then following them.
The rule also clarifies when not to use it. If you can batch options and compare them (e.g. receive 50 CVs and rank them), you're not in a sequential, no-recall setting — use comparison, not look-then-leap. If the cost of rejecting a candidate is low (you can re-open the search), the optimal policy may be more conservative (look longer). The model is most valuable when options arrive over time, you must accept or reject each, and you cannot go back.
Section 2
How to See It
Look for situations where you receive a sequence of options or signals over time and must accept or reject each without going back. When there's a natural "sample first, then commit" structure and a risk of either premature commitment or endless search, the look-then-leap logic applies.
Business
You're seeing Look-Then-Leap when a hiring manager decides to interview at least 20 candidates before making an offer, then hires the first person who clears a fixed bar (e.g. better than the best so far). The look phase calibrates the bar; the leap happens when the bar is cleared.
Technology
You're seeing Look-Then-Leap when a team runs a fixed number of experiments or A/B tests before committing to a direction. They "look" at the data from the first k experiments, then "leap" to the option that beats a threshold (e.g. best so far, or statistically significant winner).
Investing
You're seeing Look-Then-Leap when an investor sets a rule to see 50 deals in a sector before writing the first cheque, then invests in the first deal that exceeds a quality bar. The look phase builds pattern recognition; the leap is triggered by a threshold, not by a calendar.
Markets
You're seeing Look-Then-Leap when a buyer (e.g. acquirer) evaluates a set of targets in sequence and commits to the first one that meets a pre-set criterion (e.g. strategic fit and price below X). The look phase establishes the market; the leap is rule-based.
Section 3
How to Use It
Decision filter
"When facing a sequence of options with no recall, define the look phase (how many to sample or how long to explore) and the leap rule (what threshold triggers commitment). Then follow the rule. Avoid committing to the first acceptable option; avoid never committing because the next might be better."
As a founder
Use look-then-leap for hiring, partnerships, and strategic bets. Set a minimum look phase (e.g. N candidates, N weeks of exploration) so you don't leap on the first plausible option. Set a clear leap rule (e.g. first candidate better than the best we've seen, or first channel that clears ROI threshold). Revisit the rule when the environment changes (e.g. market speed, scarcity of options).
As an investor
Deal flow is a sequence. Allocate a look phase — e.g. the first 30–50 meetings in a new theme — to calibrate the bar. Then leap when a deal clears that bar (e.g. best in class founder-market fit, or first to meet a checklist). The mistake is writing the first cheque too early (before the bar is set) or never writing because the next deal might be better.
As a decision-maker
For any one-way door decision that comes as a sequence (vendors, locations, partners), set the look budget and the leap threshold in advance. Write them down. When the threshold is hit, commit. When the look phase is over and nothing has cleared the bar, either extend the look (with a new end) or explicitly lower the bar — don't drift.
Common misapplication: Using look-then-leap when options are not sequential or when recall is possible. The rule assumes you can't go back. If you can batch options and compare them, do that instead; the secretary problem doesn't apply. Use look-then-leap for true sequential, no-recall settings.
Second misapplication: Confusing the look phase with "research forever." The rule requires a defined end to looking. If you never set n or never leap, you've abandoned the framework. The optimal policy always has a leap phase.
Bezos's "disagree and commit" and "two-way door vs one-way door" framing align with look-then-leap for big bets. For one-way doors, Amazon gathers information (look) before committing. The principle: don't leap on the first strategy; run a deliberate look phase (e.g. pilots, data), then commit when the threshold is met. Reversible decisions get less look; irreversible ones get a structured look and a clear leap rule.
Sequoia's approach to sectors involves looking at many companies before backing one. The look phase builds pattern recognition and sets the bar; the leap happens when a company clears that bar. Moritz has emphasised the importance of seeing enough of the landscape before committing — a look-then-leap discipline in venture.
Section 6
Visual Explanation
Look-Then-Leap — Reject the first r ≈ n/e options (look), then accept the first option better than the best seen so far (leap). The look phase sets the bar; the leap is triggered by crossing that bar.
Section 7
Connected Models
Look-then-leap is a concrete instance of optimal stopping and explore-exploit. The models below either reinforce it (option value, threshold rule), create tension (sunk cost, reversible decisions), or extend to related tradeoffs (explore-exploit, Fermi).
Reinforces
Explore-exploit Tradeoff
Explore-exploit is the tradeoff between gathering more information and acting on what you know. Look-then-leap is a formal explore-then-exploit policy for sequential options: explore in the look phase, exploit when you leap. The reinforcement: both say don't commit too early and don't search forever; look-then-leap adds an optimal rule for the sequential, no-recall case.
Reinforces
Option Value
Option value is the value of keeping a choice open. In the look phase, you're preserving the option to see better candidates. The leap happens when the option value of continuing drops below the value of committing (the first candidate who beats the bar). Look-then-leap operationalises option value in a stopping rule.
Tension
Sunk Cost Fallacy
Sunk cost fallacy is continuing because you've already invested. In look-then-leap, you deliberately "waste" the look phase — you reject good options to set the bar. The tension: the rule requires accepting that the look phase is sunk; the fallacy would be continuing to look because you've already looked a lot. The rule says stop looking and leap when the threshold is hit; don't extend look just because you've invested in it.
Tension
Reversible vs Irreversible Decisions
Reversible decisions can be undone; irreversible ones cannot. Look-then-leap is most valuable for irreversible or costly-to-reverse decisions (hire, acquire, commit to a strategy). For reversible decisions, a shorter look or no formal rule may be better. The tension: applying a heavy look-then-leap process to every decision is wasteful; reserve it for one-way doors.
Section 8
One Key Quote
"The optimal strategy for the secretary problem is to reject the first r applicants and then accept the first one who is better than all preceding ones. For n applicants, the optimal r is approximately n/e, and the probability of selecting the best is 1/e."
— Thomas Bruss, 'Sum the Odds to One and Stop' (2000)
The quote captures the exact optimal rule. The practical takeaway: when you face a sequence of options with no recall, reserve about 37% of the "budget" (candidates, time, or trials) for looking, then commit to the first option that beats the best you've seen. The 1/e result is a benchmark; in real settings, the principle (look phase + threshold leap) matters more than the precise fraction.
Section 9
Analyst's Take
Faster Than Normal — Editorial View
Set the look phase explicitly. Most people either leap too early (first decent option) or look forever (fear of missing something better). Write down: we will look at N options or for N weeks, then we will leap when X is true. N and X should be set before you see the first option.
Use it for one-way doors. Hiring, acquisition, major partnership, strategic pivot — these are high-cost to reverse. For two-way doors, a lighter process is fine. Reserve look-then-leap for decisions where the cost of a bad leap is high.
Calibrate the bar in the look phase. The leap threshold should be "best so far" or a pre-set bar that was informed by the look. If you haven't seen enough to know what "good" is, you're not ready to leap. The look phase is what makes the bar meaningful.
Avoid drift. If the look phase ends and nothing cleared the bar, decide explicitly: extend the look (with a new end date or count) or lower the bar. Don't keep looking indefinitely without updating the rule. The discipline is committing to a rule and then following it.
Section 10
Test Yourself
Is this mental model at work here?
Scenario 1
A VC decides to meet 40 seed-stage companies in a new vertical before investing. After 35 meetings, she meets a company that is clearly stronger than any of the previous 34. She offers a term sheet.
Scenario 2
A founder interviews 3 candidates for a critical role and hires the third because 'they were the best of the three.'
Scenario 3
A company sets a rule: we will evaluate 20 vendors, then choose the first one that beats the best score we've seen so far on our criteria.
Scenario 4
An investor has looked at 60 deals in a new theme and passed on all. The next deal is good but not clearly the best. She passes again, saying she'll know the best when she sees it.
Section 11
Summary & Further Reading
Summary: The look-then-leap rule is an optimal stopping strategy for sequential, no-recall decisions: spend a defined look phase gathering options and calibrating a bar, then leap (commit) when the first option clears that bar. It comes from the secretary problem (reject first n/e, then take the first best-so-far). Use it for hiring, investing, partnerships, and any one-way door choice that arrives as a sequence. Set the look phase and leap threshold in advance; avoid leaping on the first acceptable option or never leaping. Connected to explore-exploit, option value, and threshold rules.
Accessible treatment of optimal stopping, secretary problem, and look-then-leap in life and work. Practical guidance on when and how to apply the rule.
Reinforcement learning treatment of explore-exploit. Look-then-leap is a simple form of explore-then-exploit; the book extends to more general settings.
Leads-to
Threshold Rule
Threshold rules say "act when X exceeds Y." Look-then-leap is a threshold rule where Y is "best seen in the look phase" and the action is "accept." The connection: once you adopt threshold rules, look-then-leap tells you how to set the threshold when you have a sequential, no-recall process.
Leads-to
Fermi Problem
Fermi problems are order-of-magnitude estimates to bound unknowns. In look-then-leap, you often need to set n (how many options) or r (how long to look) without perfect data. Fermi-style reasoning — how many candidates will we see? What fraction should we look? — helps set the look phase in practice.
