The N+1 Rule: Simplest Thing Until It Breaks
Start with the simplest possible policy, then prove by induction it still works at the next headcount.
- Difficulty
- Easy
- Time to result
- ~ongoing to results
- Steps
- 5
- Confidence
- 92%
A policy-design method borrowed from mathematical induction. Rather than designing the policy you think you will need at 500 people, start with the simplest binary rule that works for employee number one, then keep asking: if it works at N, why wouldn't it work at N+1? Run it until it actually breaks, then patch it locally rather than replacing it. HubSpot used this for information transparency, office seating, and even the number of designated insiders at IPO.
Origin
Dharmesh Shah and Brian Halligan at HubSpot, explicitly framed by Shah as mathematical induction ('if it's true for N it's true for N+1'). Applied from the first employee onward.
Core principles
- 01Binary decisions are much easier than non-binary decisions — 'everything or nothing' is far simpler than any threshold.
- 02Start with the simplest possible thing that might work, then add complexity only if forced.
- 03Do not accept 'that won't scale' as an argument before it has actually failed. Investors said seating lottery would break at 15; it worked at 200.
- 04When a simple rule finally strains, patch it with local optimisations rather than abandoning it.
- 05The politics you avoid with a simple rule are incalculable — and invisible, so nobody gets credit for them.
How to run it
- 1
Find the binary version of the decision
When a policy question arises, ask what the everything-or-nothing version looks like. Rather than deciding which files a new employee may access, ask why you would hide anything at all.
Watch out Any threshold rule ('directors and above can see X') carries permanent maintenance cost and permanent politics.
- 2
Apply it at N=1
Implement the simple rule for the first employee. HubSpot gave employee number one access to everything: balance sheet, fundraising terms, whether payroll could be met in six weeks.
Pro tip Carve out only the exceptions you are legally or ethically required to carve out — illegal-to-share information, and salaries (co-owned by the individual, not yours alone to disclose).
- 3
Run the induction
If it works at three people, why not four? If four, why not five? If five, why not fifty? Keep going until it actually breaks — not until someone predicts it will break.
- 4
Patch locally when it strains
When the rule starts creaking, apply the smallest possible optimisation rather than scrapping it. HubSpot's seating lottery gained two patches: engineers seated away from salespeople, and re-shuffles moved from every hire to every quarter.
- 5
Push the induction against external 'rules' too
When told a limit exists, test it. During IPO prep the bankers said the list of designated insiders was 'five or six'; the founders asked for seven, then eight, and discovered there is no legal limit.
Pro tip The bankers' 'five or six' was a norm, not a rule. Most limits you are told about are norms.
Watch out Do not make the extended policy optional — being an insider has real downsides (trading windows). HubSpot made every employee an insider, mandatory, on IPO day.
In the wild
The first HubSpot office had four chairs — two by the window, two not. Rather than founders claiming the window seats, they ran a lottery where the winner chose from remaining seats (since different people value different seats). They re-ran it on every new hire, then every quarter. Investors said it was cute but would never work at 15 or 20 people.
→ It worked at 25, 50, 100 and 200 people, and eliminated an incalculable amount of the office-size, desk-height politics that consume calories at most companies.
HubSpot's absolute transparency (all information shared with everyone) appeared incompatible with going public. In IPO prep, lawyers and bankers asked who the 'designated insiders' would be and suggested five or six. Halligan said seven; Shah said eight. By induction they discovered there was no legal cap.
→ On the day HubSpot went public they designated every single employee an insider — hundreds of people — preserving full financial transparency, a practice they still maintain at 7,000+ employees.
Common mistakes
Designing for the scale you do not have yet
Building the policy you think you will need at 500 people imports complexity you may never need, and locks you out of the enormous simplicity dividend the naive rule would have paid.
Accepting a predicted breaking point as an actual one
Investors confidently predicted the seating lottery would break at 15-20 people. It ran to 200. Wait for the rule to actually fail before replacing it.
Treating a norm as a hard limit
'You can have five or six insiders' sounded like a rule and was merely convention. Probing N+1 revealed there was no legal cap at all.
Is it for you?
Best for
Early-stage founders setting the first policies of a company (access, seating, compensation, information sharing) who want to avoid importing big-company complexity prematurely.
Not ideal for
Regulated, safety-critical, or genuinely legally-constrained domains where the naive simple rule creates real exposure, and where 'run it until it breaks' means a breach.
From the transcript
“like binary decisions are much easier than non-binary decisions”
“we try to start with the simplest possible thing uh that might work and then if necessary add the you add the complexity”
“worked at 25 worked at 50 worked at 100 worked at 200 we updated the algorithm”
“we designated every single employee to be a designated Insider”
“can you imagine the amount of politics we avoided”
From the episode
Zigging vs. zagging: How HubSpot built a $30B company
Dharmesh Shah (co-founder/CTO)