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    <pre class="document-text">## Money

Real costs come from:
	— initial preprocessing (names + indicators)
	— storing the small non-deterministic layers
	— serving tiles globally
	— occasional indicator + naming rebuilds (epochs)
	— coordinating shared volunteer compute
	— running paid tools (Maps + What if)




### Serving eight-hundred trillion unique hexagons

	deterministic backbone
		Level 4–6 generated on demand
			cached briefly
			discarded
		oceans, deserts, ice, tundra:
			use deterministic filler dictionaries
			no storage, no special vocab consumption
		community overrides:
			names, traces, metadata
			small and sparse

	stored layers
		Level 0–3 names, themes, indicators, metadata
		L4–L5 only when overridden
		L6 almost entirely virtual
		epochs stored as snapshots (rare)

	estimated storage
		L0–L3 indicators + maps + metadata: ~200–400 GB
		L4–L6 overrides + name shards: ~60–160 GB
		epoch archives: +50–100 GB/epoch
		total baseline (active epoch + history): ~300–500 GB

	storage cost
		object store (R2/S3): $7–15/month
		growth slow + predictable


		— deterministic hex generation
		— shallow levels only stored
		— tiny tiles + high cache ratio
		— empty regions use filler dictionaries
		— community layers light + sparse
		— indicator rebuilds cheap and infrequent

	result
		public layer (names, grid, indicators, epochs) stays free
		running costs remain tens → low hundreds per month even at scale







	hexagons.world defines hex0–hex6 everywhere on Earth: from ~1,000 km down to ~1 m. In theory that’s on the order of 10¹⁵ cells. In practice, almost all of them are deterministic. Themes and wordlists are derived from open data (UNESCO, ecoregions, land-use, climate, species) plus a coordinate-based seed.

	A hex only stops being “purely deterministic” when a group of people override something – a local name, a tag, an indicator edit, metadata, or a photo. Even if we gave every inhabited 1 km and 100 m cell special attention, that’s roughly a billion hexes out of hundreds of trillions – about one in a million. Everything else is maths, not storage.

		tile generation
			10–50 KB tiles
			deterministic maths at the edge
			L4–L6 tiles regenerated on demand
			&gt;95–99% cache hit ratio

		request patterns
			casual: 30–80 tiles/month
			heavy: 300–500/month
			research: 1,000+/month

		cost per million requests
			Workers + KV: ~$0.15–0.30/million

		rounded monthly cost
			10k MAU → ~$10–20
			100k → ~$20–60
			1M → ~$80–150
			10M → ~$300–600
			100M → ~$2,000–4,000

		keeps cheap because:
			— tiny tiles
			— high cache ratios
			— deterministic deep layers



#### Pre-processing

		initial ingest (~200–300 GB):
			UNESCO, ecoregions, species ranges, OSM, climate, terrain, admin data

		name assignment
			combine biome + terrain + species + settlement
			LLM filtering to remove bad fits
			assign L0–L3 globally; L4–L5 only where inhabited
			spot compute total: ~$250–550

		indicator build
			preprocess + modelling + QA
			total statistical build: **~$50–70**

		epochs
			only when world/methods change
			recompute names + stats: ~$40–60/epoch
			rare, not monthly



#### Ongoing updates

		community naming
			sparse rows only (L4–L6 mostly untouched)
			reviewer time = human cost
			storage impact minimal

		events + facilities
			small structured records keyed by hex
			few GB even with millions of items










### Maps ($1/month)

Maps is inherently useful in a way that doesn’t need much explanation. For many people, there are specific reasons to keep it — safer routes at night, reliable local information, or something that feels worth supporting, even at a small cost.

The free tier can support much larger adoption than the paid tier. Everyday use can remain free:

	- base maps
	- ordinary directions
	- offline local tiles
	- events and facilities
	- predictable, private defaults

The paid tier is for heavier compute and deeper personalisation:

	- well-lit routes at night
	- shade-first routing
	- multi-stop errands
	- comfort / safety / energy-aware routing
	- recomputation and advanced personal layers

Maps operates as a system whose raw per-request and platform costs are extremely low and predictable; so, no matter the amount of people pay, it's more than enough to keep the Maps and API layers free and calm for everyone, and fund a subtantial surplus into direct, local work (food, housing, shade, mental health) without ever touching advertising or surveillance.

This means payment and adoption should be treated separately.


couple of factors to motivate paid adoption

#### Frictionless Agree Amount in Local Currency

			we will charge one dollar a month
	    		and feels like one dollar everywhere in the world
	       			Frictionless-agree amount ≈ 0.5%–1% of a typical daily wage
	       				rounded to a common small denomination.
	       				plus market clustering
	       				- one euro
	       				- one pound
	       				- one *(five halalas as the feeling)* riyal
	       				- 10k rupiah
	       				- 50k peso
	       				- 10 lira




#### Relaying a month to someone

			relay a month to someone
			allows pooled payments in cash
				(useful for people without debit cards) licenses sharable:
				— irl via QR
				— online via a short encoded phrase
				users privately see the country and flag their current license came from
					(not public; relies on honesty)


			Relay/gifting does two things at once:

				It increases trial: people can be handed a working license instantly (QR / phrase), so they can adopt before they’ve even decided to pay.
				It increases paid-month coverage: even if a user never pays digitally, the system can still collect the dollar (parents → kids, friends → friends, donors buying stacks, cash pooling).

			So adoption is not “blocked” by payments. Payment becomes a social flow, not a checkout funnel. That matters because it lets MAU rise faster than traditional “subscription conversion” would allow.





#### Who might opt-in to support the map?

Paid adoption here shouldn't be thought of as a normal premium subscription funnel.

Most people won't be making a hard, analytical decision about “whether the advanced tier is worth it” in the way they might for business software or entertainment subscriptions.

Instead, support emerges inside a particular environment:

	- the map is already useful in ordinary life
	- the free layer is generous
	- the paid layer solves heavier or more personal problems
	- the operator is visibly non-extractive
	- Peaceful Foundation gives the whole thing a wider moral shape.

People aren't primarily responding to features. They're responding instead to whether the tool feels trustworthy, whether it fits into daily life, and if giving a dollar feels natural rather than coerced.

So, paid adoption is best understood as the combined effect of three reinforcing conditions, not one isolated conversion lever. In practice, people tend to support when a few simple things line up:

	- the amount feels negligible
	- the product keeps proving useful
	- and the surrounding story feels clean enough to trust.

Within that setting, three factors matter most.


##### Factor A — $1 Is Psychologically Not a “subscription”

			$1/month:

			    is below most people’s “should I cancel this?” threshold
			    is often rounded away mentally
			    feels more like:

			        a tip
			        a membership
			        a contribution
			        a token of support


			Especially when:

			    it’s framed as “one dollar everywhere”
			    it can be gifted
			    it’s visibly keeping something good alive

			So churn behaves very differently.





##### Factor B — Maps' Usefulness Is Local and Recurring

			Maps is not a “once a month” app.

			People use maps when they:

			    walk
			    commute
			    run errands
			    meet others
			    explore
			    travel
			    help someone

			Even light usage creates habitual presence, which supports retention.


				Subset of above, but stronger:

					organisers
					frequent walkers
					safety-conscious users (especially women)
					delivery / navigation-heavy users

					They:

					feel real value repeatedly
					anchor retention
					rarely churn

					This lifts the overall average.



##### Factor C — Opt-in Is Shaped by Framing

			At $1/month, conversion becomes mostly a function of:

			    Trust (charity + open source + no ads)
			    Default positioning (“this is your normal map”)
			    A moral story that doesn’t guilt
			    Gifting/relay (people cover others)


				A charity-run, open-source replacement:

				    reduces fear of enshittification
				    reassures people it won’t “turn evil later”
				    lowers switching anxiety
				    encourages long-term commitment

				This creates identity-aligned retention, not feature-aligned retention.

				People like this:

				    tolerate minor imperfections
				    don’t churn on small annoyances
				    feel a sense of participation, not consumption


so therefore,
	price ≈ negligible
	trust ≈ high
	usage ≈ recurring
	framing ≈ contribution, not extraction

Adoption can become very large even if only a minority of users ever pay.

So paid conversion comes from the subset of people who either rely on the map more heavily, or feel comfortable supporting something they use.



##### Who is donating towards the map?


		There are three payer modes, not one.

			1. Self-supporters

				People who:

				    use the app
				    like it
				    pay $1
				    forget about it
				    stay subscribed for a long time

				This is the base.

				They:

				use the map
				feel neutral-to-positive
				don’t think much about the payment

				Because $1 is frictionless: This is already higher than SaaS because:

				no pricing pressure
				no feature gating
				no extraction feeling



			2. Gifters

				People who:

				    buy months for others
				    relay access
				    pool cash
				    support people without cards

				This is not marginal — it increases effective revenue without increasing MAU.

					Because:

						price is tiny
						ethos is pro-social
						gifting is culturally natural

						This creates:
							revenue &gt; MAU conversion
							Not by much, but meaningfully.
							Not everyone gifts — but enough do.



			3. Institutional spillover (indirect)

				Some people pay because they:

				trust Peaceful Foundation
				participate in campaigns
				see Maps as shared infrastructure

				So they pay not because of maps alone, but because:

				Trust the operator, like the ethos, and are more willing to leave $1 on even without heavy feature use.

				“this is part of something I’m already in”




Payment should be modelled as opt-in support, not as a normal premium subscription.

The rate depends on:

	- who the retained users are
	- how often they rely on the map
	- how embedded the map is in everyday life
	- how strongly they trust Peaceful Foundation and the product’s non-extractive design

As such, conversion differs by scenario.


#### Minimal

		the map is chosen deliberately
		~0.25% of global maps users
		~6–8 million users

			adoption is values-driven
			the map is mostly secondary
			trust is high

			most users still treat it as a secondary map
			there are few social or environmental reinforcements
			Peaceful Foundation exposure exists,
				but is still limited

		reasonable direct self-support conversion
			~1.5–2.7%

		reasonable effective conversion
			after gifting and spillover
			~1.8–3.2%

			around: ~110k – 260k people

		this is a small but unusually aligned user base

			these are exactly the sorts of people
				who care about:
					privacy
					calm tools
					open source
					non-corporate infrastructure

			they are more willing than average
				to support something morally clean

			that lifts trust
				and makes support more natural than in an ordinary niche app

			but the cap is still clear

				most do not rely on the map daily
				most still keep another map as default
				the map is chosen on principle,
					more than on embedded daily usefulness

		so the band lands in the low single digits

			below this
				you are underweighting the unusually trust-aligned audience

			above this
				you are pretending niche values users already behave like default-map users

		working intuition

			this is:
				“a meaningful minority of a values-heavy niche”



#### Reasonable

		the map is now used repeatedly
		~0.5–1.0% of global maps users
		~12–28 million users

			it becomes a second default for many
			and a primary task-specific map for some

			retention matters more than discovery
			word-of-mouth and local visibility start to matter

			Peaceful Foundation exposure is now more present,
				and no longer just a light adjacency

		reasonable direct self-support conversion
			~2.2–3.8%

		reasonable effective conversion
			after gifting and spillover
			~2.6–4.5%

			around: ~310k – 1.26M people

		now the map is no longer being supported mainly
			by people who simply like the idea

		it is being supported by people
			who keep finding themselves using it

			for:
				campus use
				walking
				transit
				offline reliability
				work navigation

			these are repeated use cases,
				not abstract preferences

		that changes the feel of payment

			the average user is now not just aligned,
				but repeatedly relying on the map in specific situations

			this lifts habitual willingness to support,
				because the map has started to earn its place through ordinary use

			most support still comes from:
				self-supporters first
				then a growing amount of gifting
				then a noticeable Peaceful Foundation spillover tail

		the free tier remains sufficient for many users

			the map is often a second default,
				not yet a universal primary one

			use is still situational for many,
				not fully embedded across everything

		so the band rises,
			but not explosively

			below this
				you are underweighting a retained base
				that now uses the map repeatedly in ordinary life

			above this
				you are pretending a useful second default
				already behaves like a socially assumed shared map

		working intuition

			this is:
				“a useful recurring second default with strong trust”



#### Ambitious

		the map is now used repeatedly across communities
		~1.0–1.8% of global maps users
		~30–80 million users

			adoption is socially reinforced

			the map is repeatedly encountered across:
				campuses
				local events
				Peaceful Foundation projects
				everyday local activity

			in some environments,
				it becomes expected or recommended

		reasonable direct self-support conversion
			~2.8–4.8%

		reasonable effective conversion
			after gifting and spillover
			~3.4–5.8%

			around: ~1.0M – 4.6M people

		by this stage,
			the map is no longer spreading mainly one person at a time

		it starts spreading through places

			the question is no longer just:
				“do I personally find this useful?”

			it becomes:
				“this is part of how things work around here”

		that shifts all three payer modes upward

			self-support rises
				as more users treat it as a genuine default

			gifting rises
				as usage becomes socially visible

			spillover rises
				as the map begins to feel like shared infrastructure

		the average user is now encountering the map
			not just through personal use,
			but through their surrounding environment

		that makes support feel less like
			paying for a feature

		and more like
			leaving a small amount on
			for something good that is becoming part of local life

		the ceiling is still real

			incumbents still dominate globally
			adoption remains uneven
			the map is strong in pockets and regions,
				but not yet universal

		so the band moves up,
			but does not explode

			below this
				you are underweighting the shift
				from individual retention
				to social reinforcement

			above this
				you are pretending every socially visible utility
				converts like a hard paywall SaaS

		working intuition

			this is:
				“a map that feels normal in some places”



#### Optimistic

		the map is now used repeatedly across regions and networks
		~2–4% of global maps users
		~50–150 million users

			it becomes a “third default”

			it is widely recognised even by non-users
			normal across multiple regions and demographics

			social norms reinforce usage
			families,
			friend groups,
			campuses,
			and neighbourhood contexts
			begin to standardise around it

		reasonable direct self-support conversion
			~3.0–5.0%

		reasonable effective conversion
			after gifting and spillover
			~3.8–6.5%

			around: ~1.9M – 9.8M people

		here the map starts to feel familiar,
			not just admirable

		many people encounter it
			through something useful first

				a cheap meal poster
				a campus noticeboard
				a friend sharing a hex name
				a parent installing it for safety

		that strengthens:
			trust
			default-use
			gifting behaviour

		the average user is now often surrounded by other users

			this lifts support again,
				because the map has crossed into:
					household logic
					group logic
					regional habit

			most support still comes from:
				self-supporters first
				then a materially stronger amount of gifting
				then a larger Peaceful Foundation spillover tail

		but the free product still remains enough
			for a great many people

			many users still sit happily on the free tier

			only some users need:
				deeper safety routing
				deeper personalisation
				heavier compute

		so the band rises again,
			but still stays well short
			of mass consumer payment behaviour

			below this
				you are underweighting a map
				that has become culturally normal in some environments

			above this
				you are assuming mass consumer payment behaviour
				across the whole global base

		working intuition

			this is:
				“a socially normal, low-friction public utility in many environments”


#### Reasonable, visionary and optimistic

		the map is now used repeatedly as infrastructure
		~1.0–1.5 billion main users
		plus another ~1.0–2.0 billion secondary or situational users

			it is no longer mainly behaving
				as a consumer replacement app

			it functions as:
				an addressing layer
				a shared reference layer
				a neutral civic substrate

		at this stage,
			one global conversion band stops being the cleanest model

		the better question is:
			what kinds of places does the map live inside,
			and how does support behave there?

##### A) Full-stack Zones

			here the map is woven into daily life

			people keep encountering it
				through:
					youth posters
					food systems
					mutual-aid chats
					local campaigns
					campus spaces

			the map is not just useful here

				it is part of how things work

			reasonable direct self-support conversion
				~6–10%

			reasonable effective conversion
				after gifting and spillover
				~8–12%

				around: ~80M – 130M people

			this feels less like:
				buying an app upgrade

			and more like:
				keeping shared local infrastructure alive


##### B) Partial-stack Zones

			here the map is clearly useful
				and often kept

			but it is not yet ambient everywhere

			people often arrive through one or two strong needs:
				cheap food
				safe routes
				hex names

			the map matters,
				but it is not yet the whole surrounding environment

			reasonable direct self-support conversion
				~3–5%

			reasonable effective conversion
				after gifting and spillover
				~4–7%

				around: ~12M – 28M

			this feels like:
				a strong local layer

			not yet:
				full infrastructure


		(c) outer zones

			here the map still spreads more individually

			people come because they need:
				addressing
				safety
				offline use
				a calmer alternative

			the surrounding environment does less of the work

			reasonable direct self-support conversion
				~1–2.5%

			reasonable effective conversion
				after gifting and spillover
				~1.5–3.5%

				around: ~0.45M – 3.2M

			this still produces real support

				but it behaves more like:
					replacement-app adoption

				than:
					locally assumed infrastructure


		overall,
			conversion should be derived from zone mix,
				not assumed globally

			a plausible blended rate is:
				~4–8%

			or around:
				~90M – 160M people

		working intuition

			this is:
				“support for shared infrastructure”

			not:
				“a global subscription product”



| Scenario                       | Users (MAU)       | Conversion (effective) | Supporters (opt-in) |
|--------------------------------|-------------------|------------------------|---------------------|
| Minimal                        | 6–8 million       | 1.8–3.2%               | 110k – 260k         |
| Reasonable                     | 12–28 million     | 2.6–4.5%               | 310k – 1.26M        |
| Ambitious                      | 30–80 million     | 3.4–5.8%               | 1.0M – 4.6M         |
| Optimistic                     | 50–150 million    | 3.8–6.5%               | 1.9M – 9.8M         |
|                                |                   |                        |                     |
| Infrastructure (full-stack)    | 1.0–1.1 billion   | 8–12%                  | 80M – 130M          |
| Infrastructure (partial-stack) | 300–400 million   | 4–7%                   | 12M – 28M           |
| Infrastructure (outer zones)   | 30–90 million     | 1.5–3.5%               | 0.45M – 3.2M        |
|                                |                   |                        |                     |
| Infrastructure (total)         | 1.33–1.59 billion | ~4–8% (blended)        | 90M – 160M          |








#### Revenue for Maps

The Maps revenue case should be understood as opt-in support rather than a normal premium subscription funnel.

Most users can still use the public map freely. Paid coverage comes from people who either use the map heavily, receive a gifted month, want advanced routing and personalisation, or simply trust the project enough to keep a tiny recurring contribution active.

| Scenario                       | Users (MAU, average) | Conversion (effective, average) | Supporters (opt-in, average) |
| ------------------------------ | -------------------: | ------------------------------: | ---------------------------: |
| Minimal                        |            7 million |                            2.5% |                         185k |
| Reasonable                     |           20 million |                           3.55% |                         785k |
| Ambitious                      |           55 million |                            4.6% |                         2.8M |
| Optimistic                     |          100 million |                           5.15% |                        5.85M |
| Infrastructure (total)         |         1.46 billion |                     ~6% blended |                         125M |

So the important number is not just “conversion”. It is how many people are covered for a paid month through self-payment, gifting, pooled cash, institutional spillover, and local relay.

Pricing is locally weighted, so the effective monthly amount is not always exactly USD $1. In wealthier regions it may sit close to $1. In lower-income regions it may be lower, because the point is that the payment should feel similarly small everywhere.

***people who end up covered for a paid month*** × ***the amount that feels like one dollar there*** × ***how much we actually keep after fees***

The estimate below uses average supporter counts from the adoption table, plus midpoint assumptions for local pricing and retained revenue.

Minimal
	~185k supporters × ~$0.95 × ~92.5%
	→ ~$163k / month
	→ ~$2.0M / year


Reasonable
	~785k supporters × ~$0.90 × ~88.5%
	→ ~$625k / month
	→ ~$7.5M / year


Ambitious
	~2.8M supporters × ~$0.825 × ~84%
	→ ~$1.94M / month
	→ ~$23.3M / year


Optimistic
	~5.85M supporters × ~$0.75 × ~80%
	→ ~$3.51M / month
	→ ~$42.1M / year


Infrastructure
	~125M supporters × ~$0.75 × ~81.5%
	→ ~$76.4M / month
	→ ~$917M / year



**total revenue** = ***effective paid coverage*** × ***local calm price*** × ***collection efficiency***





#### × ***effective paid coverage***

effective paid coverage = self-support + gifting + relay + spillover

(this is calculated in the section above)




#### × ***local calm price***

local calm price
= the locally rounded “feels like one dollar” amount

	it is:
		- roughly ~0.5–1% of a typical daily wage
		- rounded to a normal local denomination
		- clustered to simple, familiar values

	examples:
		£1
		€1
		10k rupiah
		50 peso
		10 lira

	the goal is:
		it feels tiny,
		normal,
		and not worth thinking about




| Region               | Median daily wage band | Local denomination cluster | Local calm price | Rail quality | Relay friendliness | Collection efficiency |
|----------------------|------------------------|----------------------------|------------------|--------------|--------------------|-----------------------|
| America              | medium–high (wide)     | 1 / 5 / 10 / 50 / 100      | ~$1 equiv        | medium–high  | medium–high        | ~80–95%               |
|                      |                        |                            |                  |              |                    |                       |
| Atlantic Europe      | high                   | €1 / £1                    | €1 / £1          | high         | medium             | ~90–97%               |
| Continental Europe   | medium–high            | €1                         | €1               | high         | medium             | ~90–97%               |
| Mediterranean Europe | medium                 | €1                         | €1               | medium–high  | medium             | ~85–95%               |
| Nordic Europe        | high                   | 10–20 local units          | ~$1–2 equiv      | high         | medium             | ~90–97%               |
| Eurasian Plains      | low–medium             | 20 / 50 / 100              | ~$0.5–1 equiv    | medium       | medium–high        | ~75–90%               |
|                      |                        |                            |                  |              |                    |                       |
| Continental Asia     | low–medium             | 10 / 20 / 50 / 100         | ~$0.5–1 equiv    | medium       | high               | ~70–90%               |
| Monsoonic Asia       | low                    | 5k / 10k / 20k style       | ~$0.3–0.8 equiv  | medium       | high               | ~70–90%               |
| Oceanic Asia         | medium                 | 10k / 50 / 100 style       | ~$0.8–1 equiv    | medium–high  | high               | ~75–92%               |
|                      |                        |                            |                  |              |                    |                       |
| Oceania              | high                   | 1 / 2 local units          | ~$1–2 equiv      | high         | medium             | ~90–97%               |
|                      |                        |                            |                  |              |                    |                       |
| Mediterranean Africa | low–medium             | 5 / 10 / 20                | ~$0.5–1 equiv    | medium       | high               | ~70–90%               |
| Saharan Africa       | low                    | 5 / 10 / 20                | ~$0.3–0.8 equiv  | low–medium   | high               | ~60–85%               |
| Monsoonic Africa     | low                    | 500 / 1k / 2k style        | ~$0.3–0.7 equiv  | low–medium   | high               | ~60–85%               |
| Highland Africa      | low                    | 5 / 10 / 20                | ~$0.3–0.7 equiv  | low–medium   | high               | ~60–85%               |
| Southern Africa      | low–medium             | 5 / 10 / 20                | ~$0.5–1 equiv    | medium       | medium–high        | ~70–90%               |
|                      |                        |                            |                  |              |                    |                       |
| Arabia               | medium–high            | 1 / 5 / 10                 | ~$1 equiv        | medium–high  | medium–high        | ~80–95%               |




#### × ***collection efficiency***


// more considerations here

collection efficiency
= the share actually retained after practical payment friction

where possible, minimise fees transparently

	use direct bank transfer where possible
	use Wise or similar low-friction rails
	avoid app-store cuts where possible
	avoid routing everything through card processors when local banking works fine

so in practice:

	*High efficiency*
		direct transfer easy
		Wise or similar works
		low-fee rails available
		relay easy

		Keep rate: ~90–97%


	*Medium efficiency*
		some friction
		some card reliance
		some wallet fragmentation
		relay still workable

		Keep rate: ~80–90%


	*Lower efficiency*
		fragmented rails
		higher fee dependency
		more difficult direct settlement
		relay helps, but collection still leaks more

		Keep rate: ~65–80%




| Region                 | Local calm price (USD-equiv) | Collection efficiency | Net revenue per covered month |
|------------------------|------------------------------|-----------------------|-------------------------------|
| America                | 1.00                         | 0.90                  | 0.90                          |
| Atlantic Europe        | 1.00                         | 0.95                  | 0.95                          |
| Continental Europe     | 1.00                         | 0.94                  | 0.94                          |
| Mediterranean Europe   | 0.90                         | 0.90                  | 0.81                          |
| Nordic Europe          | 1.20                         | 0.95                  | 1.14                          |
| Eurasian Plains        | 0.60                         | 0.82                  | 0.49                          |
| Continental Asia       | 0.70                         | 0.82                  | 0.57                          |
| Monsoonic Asia         | 0.50                         | 0.78                  | 0.39                          |
| Oceanic Asia           | 0.80                         | 0.85                  | 0.68                          |
| Oceania                | 1.10                         | 0.95                  | 1.05                          |
| Mediterranean Africa   | 0.70                         | 0.80                  | 0.56                          |
| Saharan Africa         | 0.45                         | 0.72                  | 0.32                          |
| Monsoonic Africa       | 0.40                         | 0.72                  | 0.29                          |
| Highland Africa        | 0.40                         | 0.72                  | 0.29                          |
| Southern Africa        | 0.65                         | 0.82                  | 0.53                          |
| Arabia                 | 0.90                         | 0.90                  | 0.81                          |


From there, the scenario revenues become easier to read.

The social model tells us how many people end up covered.
The regional model tells us roughly what each covered month is worth on average.

That gives us a grounded way to estimate revenue under each adoption pattern.


| Scenario       | Avg supporters | Avg monthly value | Avg retained/payment capture | Estimated monthly revenue | Estimated yearly revenue |
| -------------- | -------------: | ----------------: | ---------------------------: | ------------------------: | -----------------------: |
| Minimal        |           185k |             $0.95 |                        92.5% |                    ~$163k |                   ~$2.0M |
| Reasonable     |           785k |             $0.90 |                        88.5% |                    ~$625k |                   ~$7.5M |
| Ambitious      |           2.8M |            $0.825 |                          84% |                   ~$1.94M |                  ~$23.3M |
| Optimistic     |          5.85M |             $0.75 |                          80% |                   ~$3.51M |                  ~$42.1M |
| Infrastructure |           125M |             $0.75 |                        81.5% |                   ~$76.4M |                   ~$917M |

#### Minimal

	effective paid coverage
		~110k – 260k people covered

		there is enough support here
			for the map to clearly stand on its own

		the early base forms where:
			trying alternative software is culturally legible
			small recurring payments are easy
			and the moral shape of the project is immediately understood

		this naturally concentrates early support
			in higher-trust, lower-friction environments

		so the average net revenue per covered month stays relatively high

	average net revenue per covered month
		~$0.82 – $0.92

	monthly revenue
		~$90k – $240k

	annual revenue
		~$1.1M – $2.9M

		what this feels like

			not mass adoption

			a small but real layer of loyal support
				strong enough to keep the map calm,
				free,
				and maintained

			at this level,
				the system has already proved
				that a morally clean map can survive



#### Reasonable

	effective paid coverage
		~310k – 1.26M people covered

		the map now starts to earn support
			not only because people like the idea,
			but because they keep finding themselves using it

		the support base broadens

			early high-trust environments still anchor it

			but repeated everyday usefulness now matters more:
				walking
				transit
				campuses
				reliability
				local coordination

		this begins to pull in support
			from a wider mix of places
			where the map fits into ordinary life

		so the average net revenue per covered month softens slightly
			even as total coverage rises much faster

	average net revenue per covered month
		~$0.74 – $0.86

	monthly revenue
		~$230k – $1.08M

	annual revenue
		~$2.8M – $13.0M

		what this feels like

			the map has moved beyond curiosity

			it is now part of ordinary life
				for a real minority of people

			not everyone pays

			but enough people rely on it often enough
				that support begins to feel routine



#### Ambitious

	effective paid coverage
		~1.0M – 4.6M people covered

		by this stage,
			the map is no longer spreading mainly one person at a time

		it begins to spread through places

			campuses
			friend groups
			local scenes
			repeated public encounters

		the support base becomes structurally broader

			some places contribute more per person
				because payment is simple and consistent

			others contribute more people
				because the map solves stronger coordination problems

		this is where:
			relay
			gifting
			and pooled support
			start to matter more

		so the average net revenue per covered month falls a little further

			not because the model weakens

			but because it is becoming more global in behaviour

	average net revenue per covered month
		~$0.64 – $0.78

	monthly revenue
		~$640k – $3.6M

	annual revenue
		~$7.7M – $43M



#### Optimistic

	effective paid coverage
		~1.9M – 9.8M people covered

		now the map is carried
			by a visibly mixed world

		it is no longer strongest only
			where payment is easiest

		it is strongest
			where usefulness,
			trust,
			and social reinforcement overlap

		this creates a layered support base

			some places contribute more
				because collection is clean and reliable

			others contribute more
				because the map has become too useful
				not to be part of ordinary life

		this pushes coverage much higher,
			even while the average net revenue per covered month softens again

	average net revenue per covered month
		~$0.58 – $0.74

	monthly revenue
		~$1.1M – $7.3M

	annual revenue
		~$13M – $88M

			the map is no longer just useful
				it is familiar

			people hear about it through:
				friends
				family
				campuses
				neighbourhood habits
				something useful they needed anyway



#### Reasonable, Visionary and Optimistic



##### A) Full-stack Zones

		effective paid coverage
			~80M – 130M people covered

		these are places where the map is not merely used

			it is woven into local life

			addressing
			food
			campus spaces
			safe routes
			and shared reference points
			all reinforce each other

		support behaves less like paying for software
			and more like keeping infrastructure alive

		that is why coverage is so large

			and why the average net revenue per covered month
			can be lower than earlier scenarios
			while total revenue becomes enormous

		average net revenue per covered month
			~$0.52 – $0.72

		monthly revenue
			~$42M – $94M

		annual revenue
			~$500M – $1.1B


##### B) Partial-stack Zones

		effective paid coverage
			~12M – 28M people covered

		these are places where the map is clearly useful
			but not yet ambient everywhere

		people often arrive through one strong wedge:
			safe routes
			food
			addressing
			local reliability

		the system is important,
			but not yet the whole environment

		this is why the average net revenue per covered month
			sits slightly higher than in full-stack zones

			while total scale remains smaller

		average net revenue per covered month
			~$0.60 – $0.78

		monthly revenue
			~$7M – $22M

		annual revenue
			~$85M – $260M


##### C) Outer Zones

		effective paid coverage
			~0.45M – 3.2M people covered

		here the map is still spreading more individually

			people come because they need:
				better addressing
				offline use
				safety
				a calmer alternative

		the surrounding environment does less of the work

			so support is thinner,
			and collection is less consistent

		but even here,
			a real minority still supports it

			because the map solves something tangible

		average net revenue per covered month
			~$0.42 – $0.62

		monthly revenue
			~$0.2M – $2.0M

		annual revenue
			~$2.5M – $24M


	total

		effective paid coverage
			~90M – 160M people covered

		monthly revenue
			~$49M – $117M

		annual revenue
			~$590M – $1.4B

	the map has crossed the line from useful product to quiet infrastructure




| Scenario                              | Covered people       | Avg. net revenue / covered month | Monthly revenue      |
|---------------------------------------|----------------------|----------------------------------|----------------------|
| Minimal                               | 110k – 260k          | $0.82 – $0.92                    | $90k – $240k         |
| Reasonable                            | 310k – 1.26M         | $0.74 – $0.86                    | $230k – $1.08M       |
| Ambitious                             | 1.0M – 4.6M          | $0.64 – $0.78                    | $640k – $3.6M        |
| Optimistic                            | 1.9M – 9.8M          | $0.58 – $0.74                    | $1.1M – $7.3M        |
|                                       |                      |                                  |                      |
| Infrastructure — full-stack zones     | 80M – 130M           | $0.52 – $0.72                    | $42M – $94M          |
| Infrastructure — partial-stack zones  | 12M – 28M            | $0.60 – $0.78                    | $7M – $22M           |
| Infrastructure — outer zones          | 0.45M – 3.2M         | $0.42 – $0.62                    | $0.2M – $2.0M        |
|                                       |                      |                                  |                      |
| Infrastructure — total                | 90M – 160M           | blended                          | $49M – $117M         |


Within different people's lives, this feels like:

* A 19-year-old in Jakarta is meeting up for potato with her friends tonight, and walks there along a shaded, well-lit route, and sees her campus hexagon slowly filling with tiny traces of good
* She shares a hexagon name in a WhatsApp chat so a friend can find the right gate in a maze of streets.
* Her aunt in São Paulo uses the free tier just to see which local market is cheapest this week and where the nearest community garden is.
* A municipal worker in Barcelona reads the tree-cover indicator by hexagon and uses What-if to test where to plant another 1,000 trees.

They’re all using the same map, for different reasons, without being asked to “join a platform”. It’s just infrastructure that quietly helps.

At that point, large numbers stop feeling too surprising. They simply reflect that many people have found the map useful and worth keeping around.









### Hexagons API


    100k/month free
    generous limits


// running costs
	// static

// aim is to not operate at a loss


why an API will be useful
and for whom

public health researchers
environmental science departments
poverty / development economics
urban planning
psychology / loneliness studies
social policy programmes
climate modelling labs
student projects

Who uses the API:

    researchers
    students
    NGOs
    civic hackers
    journalists
    small startups





APIs are about embedding hexagons.world as a standard, not profit.



#### Usage tiers and pricing

		generous API limits
		100k/mo for free


- 100k API calls per month free
- public-interest use encouraged
- open-source, student, journalist, researcher, and civic projects treated kindly
- no surprise bills
- no lock-in

A student project, small civic app, or local journalist should be able to use the API without thinking about cost.

A company, institution, or high-volume service should pay enough that their usage cannot quietly become a burden on the commons.

Free
    100k calls/month
    public indicators
    basic hex lookup
    basic tiles / metadata
    rate-limited bulk access
    attribution required

You get a generous free tier. Once you go over it, you move into a fixed monthly band that is priced from the actual cost profile we already defined.

then you pay



#### Costs

As most of the map is deterministic and cache-heavy, the marginal cost per normal API call should be tiny.

raw costs
$0.00000030 / per api call
or, 0.30 / per million requests


practical loaded cost per API call:
$0.000002


1M calls
raw cost: $0.30/month
loaded cost: $2/month

10M calls
raw cost: $3/month
loaded cost: $20/month

100M calls
raw cost: $30/month
loaded cost: $200/month





```

monthly_api_cost =
  fixed_api_cost
  + (api_calls × marginal_cost_per_call)
  + (heavy_jobs × marginal_cost_per_heavy_job)
  + support/admin_cost
  ```
The aim isn't to make the API expensive, but to make sure each extra layer of usage pays for itself.


#### Modelling

API profit should be modelled as a linear function of usage, where the fixed platform cost is already mostly covered by the public map infrastructure, and the marginal cost is mostly requests, bandwidth, storage reads, and any heavier indicator/statistical queries.

	as a function


revenue

```
monthly_api_revenue =
  paid_api_calls × price_per_call
  + paid_heavy_jobs × price_per_heavy_job
  + institutional/API plans
```

profit

```
monthly_api_profit =
  monthly_api_revenue - monthly_api_cost```

```











### What if

Public What if should stay generous for most people.

	Explorer — free
		Can create unlimited drafts.
		Can run a few small public scenarios per month on the shared pool.
		Can fork public scenarios and share links.
		No team features.
		No guaranteed scheduling.
		Good enough for students, curious locals, and journalists trying things out.

	Builder — about $5/month
		More monthly runs.
		Can schedule recurring public scenarios.
		Can compare multiple versions side by side.
		Can save personal libraries and templates.
		Gets slightly higher queue allowance, but still no “fast track.”
		This tier is about reducing friction, not buying power.




Most users stay on Explorer, the free tier lets people play, learn, share scenarios, and build the habit of thinking in public indicators.

Paid adoption comes from regular users who hit recurring friction.


	workloads:
		choose compute mode:
			— volunteer-first (slow, extremely cheap)
			— datacentre-only (fast, guaranteed)
			— mixed

		billing:
			datacentre compute charged at near-cost
			volunteer jobs almost free

	Workspace
		Something light like $10/month for a shared public workspace with permissions, templates, shared scenario libraries, and comments. This is for small NGOs, student groups, local councils testing the waters, and media teams.




	use cases:
		shade impact, bus network changes, food waste, pollution checks, local scenarios





| Scenario  | Civic monthly users | Student monthly users | Practitioner monthly users | Total monthly users |
| --------- | ------------------: | --------------------: | -------------------------: | ------------------: |
| Low       |                ~83k |                  ~85k |                       ~89k |               ~258k |
| Middle    |               ~817k |                 ~781k |                      ~723k |              ~2.32M |
| Ambitious |              ~4.43M |                ~4.83M |                     ~3.40M |             ~12.67M |



#### Builders

Builder is the individual paid tier.

Builder is attached to an individual person, giving them more runs, saved templates, comparison views, recurring public scenarios, and a smoother personal workflow.

	``Builder subscribers = monthly users × Builder conversion``

| Demographic   | Builder conversion | Why                                                                                                                                       |
| ------------- | -----------------: | ----------------------------------------------------------------------------------------------------------------------------------------- |
| Civic         |                 4% | Some regular local organisers and civic users want saved templates, recurring public scenarios, and comparison views, but many stay free. |
| Students      |                 3% | Lots of usage, but more price-sensitive. Some pay because they use it repeatedly for projects, organising, or coursework.                 |
| Practitioners |                 8% | Strongest individual paid fit. They use it for reports, workshops, explanations, planning, and repeated public-facing scenarios.          |



Civic users pay when they are regularly testing local ideas, comparing public scenarios, or keeping small campaign templates.
	Some regular local organisers and civic users want saved templates, recurring public scenarios, and comparison views, but many stay free.

Students pay less often, but some pay when What if becomes part of coursework, campus organising, student journalism, or repeated project work.
	Lots of usage, but more price-sensitive. Some pay because they use it repeatedly for projects, organising, or coursework.

Practitioners pay most often because What if helps them explain things to other people: in workshops, reports, grant applications, planning sessions, and public briefings.
	Strongest individual paid fit. They use it for reports, workshops, explanations, planning, and repeated public-facing scenarios.


| Scenario  | Civic Builders | Student Builders | Practitioner Builders | Total Builders |
| --------- | -------------: | ---------------: | --------------------: | -------------: |
| Low       |          ~3.3k |            ~2.6k |                 ~7.1k |           ~13k |
| Middle    |         ~32.7k |           ~23.4k |                ~57.8k |          ~114k |
| Ambitious |          ~177k |            ~145k |                 ~272k |          ~594k |

At ~$5/month:

| Scenario  | Builder revenue / month |
| --------- | ----------------------: |
| Low       |                   ~$65k |
| Middle    |                  ~$570k |
| Ambitious |                 ~$2.97M |




#### Workspace

Workspaces are used for easily working with others.

One person, group, organisation, teacher, organiser, or practitioner pays for the room. The people inside might be Builders working on the workspace, or explorer (free) users with read-only access. Workspace is attached to a shared group space. One person or organisation pays for the workspace, and multiple people can join it.

| Demographic   | Workspace-involved rate | Avg users per workspace | Why                                                                                           |
| ------------- | ----------------------: | ----------------------: | --------------------------------------------------------------------------------------------- |
| Civic         |                      8% |                       6 | Local groups, campaigns, civic teams, neighbourhood projects.                                 |
| Students      |                      6% |                       8 | Student project groups, campus organisers, class teams. Bigger groups, lower payer intensity. |
| Practitioners |                     12% |                       6 | NGO teams, journalists, researchers, council-adjacent staff, public planning teams.           |

- Civic workspaces are for local groups, neighbourhood projects, public campaigns, and civic teams.
- Student workspaces are for class projects, campus groups, student organisers, and research groups.
- Practitioner workspaces are for NGO teams, journalists, researchers, council-adjacent workers, public health people, planners, and people who need to explain a scenario with others.

	``Workspace subscriptions = monthly users × Workspace participation rate ÷ average people per workspace``

We don't treat workspace members as extra individual paid users, since workspace participation is not the same as workspace payment. A share of regular users participate in group-shaped work, and those group-shaped users produce a smaller number of paid workspaces.

| Scenario  | Civic Workspaces | Student Workspaces | Practitioner Workspaces | Total Workspaces |
| --------- | ---------------: | -----------------: | ----------------------: | ---------------: |
| Low       |            ~1.1k |               ~640 |                   ~1.8k |            ~3.5k |
| Middle    |           ~10.9k |              ~5.9k |                  ~14.5k |           ~31.3k |
| Ambitious |             ~59k |               ~36k |                    ~68k |            ~163k |

At ~$10/month:

| Scenario  | Workspace revenue / month |
| --------- | ------------------------: |
| Low       | ~$35k                     |
| Middle    | ~$313k                    |
| Ambitious | ~$1.63M                   |


#### Revenue

The simple read is that making things free creates culture, the builder assists enables individual use, and workspace assists collaboration.

Civic users pay when they are trying to keep local scenarios alive.
Students pay when the tool becomes part of a project.
Practitioners pay when the tool becomes part of their work.


| Scenario  | Builder revenue | Workspace revenue | Total monthly revenue | Total annual revenue |
| --------- | --------------: | ----------------: | --------------------: | -------------------: |
| Low       |           ~$65k |             ~$35k |                ~$100k |               ~$1.2M |
| Middle    |          ~$570k |            ~$313k |                ~$883k |              ~$10.6M |
| Ambitious |         ~$2.97M |           ~$1.63M |                ~$4.6M |                ~$55M |






### What if? - Private


	non-government organisations
	companies
	small companies
	local governments



	how many people and organisations might be interested?


		- who do geospacial already
		- who could be demographics who might be interested


| Segment                       | Users universe | Likely payer                                                | Adoption style                    |
| ----------------------------- | -------------: | ----------------------------------------------------------- | --------------------------------- |
| Institutions                  |     ~30M users | councils, universities, NGOs, public health, planning teams | slower adoption, higher retention |
| Operational local businesses  |     ~31M users | logistics, trades, agriculture, services, property          | practical, ROI-driven             |
| Retail &amp; hospitality          |     ~27M users | chains, markets, hospitality groups, local retailers        | lighter usage, local planning     |
| Consultants / scenario people |     ~12M users | consultants, analysts, grant writers, planners              | high influence per user           |




#### Costs

		system administration
		support staff
		training



		hosting costs
			cloud providers typical pricing





#### Pricing

$20 for a workspace + $5 per active contributor
	(viewers are free)

	For researchers, civic hackers, NGO staff, and consultants using only public data.
	Larger scenario scopes, more active jobs, better exports, better collaboration, more workspace features.
	Still on public/shared compute unless they move into private.


At $5/user, a 10-person team costs $50/month. That feels like nothing for an organisation, and still meaningful for Peaceful Foundation at scale. More importantly, nobody thinks too hard about inviting the intern, the volunteer, the researcher, the local organiser, the comms person, or the student group. That matters because What if only becomes valuable when a bunch of different people are thinking together.


	Honey
		for heavier compute

		honey is priced in a transparent structure



#### Uptake



		from hexagons people section



| Scenario                | Rounded active users |
| ----------------------- | -------------------: |
| Very conservative       |                ~450k |
| Middle                  |                ~1.4M |
| Ambitious               |                ~3.9M |
| Global / infrastructure |                 ~10M |




| Region    | Very conservative |    Middle | Ambitious | Global / infrastructure |
| --------- | ----------------: | --------: | --------: | ----------------------: |
| Americas  |               72k |      233k |      630k |                   1.61M |
| Europe    |               70k |      221k |      592k |                   1.50M |
| Asia      |              240k |      768k |     2.10M |                   5.49M |
| Oceania   |                4k |       12k |       32k |                     81k |
| Africa    |               41k |      132k |      354k |                    903k |
| Arabia    |               21k |       66k |      178k |                    452k |
| **Total** |          **448k** | **1.43M** | **3.89M** |              **10.04M** |


speed of uptake within different regions

#### Revenue






















#### Regions

Different regions also adopt through different institutional shapes.

This is important because What if? isn't only a modelling product. It's also a coordination layer between local knowledge, operational planning, and shared indicators. So the shape of adoption depends partly on how decisions already move through a region


| Region    | Rough labour force | Plausible decision-maker share | Decision-maker universe |
| --------- | -----------------: | -----------------------------: | ----------------------: |
| Americas  |              ~520M |                         1.5–4% |                 ~8M–21M |
| Europe    |              ~350M |                           2–5% |                 ~7M–18M |
| Asia      |             ~2.15B |                           1–4% |                ~22M–86M |
| Oceania   |               ~18M |                         2.5–5% |             ~0.45M–0.9M |
| Africa    |              ~520M |                       0.5–2.5% |                 ~3M–13M |
| Arabia    |              ~140M |                       1.5–4.5% |                  ~2M–6M |
| **Total** |          **~3.7B** |                                |           **~42M–145M** |

Even this is still conservative in one sense: it only captures people directly making operational decisions themselves. In reality, many more people influence those decisions indirectly through reports, proposals, dashboards, grant applications, campus planning, logistics coordination, or local advocacy work.

So the true “influence surface” around place-based modelling is larger than the direct paid seat count. What if? spreads partly because one person running a scenario often affects many other people nearby.


| Region    | Institutions | Operational local businesses | Retail &amp; hospitality | Consultants |
| --------- | -----------: | ---------------------------: | -------------------: | ----------: |
| Americas  |         4.6M |                         4.6M |                 3.9M |        2.3M |
| Europe    |         6.0M |                         2.7M |                 2.7M |        2.0M |
| Asia      |        14.4M |                        20.2M |                17.3M |        5.8M |
| Oceania   |         0.3M |                         0.2M |                 0.1M |        0.1M |
| Africa    |         3.0M |                         1.7M |                 2.6M |        1.3M |
| Arabia    |         1.9M |                         1.1M |                 0.9M |        0.4M |
| **Total** |    **30.3M** |                    **30.4M** |            **27.4M** |   **11.9M** |

To avoid overcomplicating things, we'll use 100 million people as the working global decision-maker universe for What if – Private, split roughly into 30M institutional users, 31M operational local-business users, 27M retail/hospitality users, and 12M consultants or scenario people.














### Additional costs running paid offerings

	Developers
		web
		android
		ios
		secure software

	engineers
		software
		geospacial
		data
		//

	legal and compliance
		lawyer
		paralegals

	administration staff
		billing and financial
		project managers
		//

	Domain experts
		physics
		statistics
		//



2. Missing cost buckets (important gaps)

You’ve covered compute and storage very well. What’s missing are human and operational costs, which is where most “cheap” platforms fail.
A. People costs (not optional)

Even with volunteers, you will need paid continuity roles.

Minimum realistic baseline (very lean):
Role	Why	Rough cost (monthly, USD)
1 core engineer	keeps infra alive	$6k–10k
0.5 geospatial/data engineer	indicators, pipelines	$3k–5k
0.5 product/generalist	maps, APIs, What-If UX	$3k–5k
0.5 ops/admin	billing, emails, orgs	$2k–3k
Bare minimum		$14k–23k / month

You can defer this early, but not indefinitely once:

    universities

    NGOs

    enterprises
    start depending on the system.

👉 This is the real floor your paid offerings must cover.
B. Trust, governance, and liability

Not huge costs, but must be named:

    Legal review (privacy, disclaimers, liability)

    Data governance docs (especially for What-If)

    Occasional insurance (orgs will ask)

Ballpark:

    $3k–10k/year initially

    grows slowly, not exponentially

C. Maps-specific hidden costs

Not infra-heavy, but support-heavy.
3. Who actually pays (this is the key missing clarity)



3.5 What-If Private / Enterprise

This is slower, but very high leverage.

Even:

    5 enterprise clients

    at $120k/year
    = $600k/year

You do not need many.

This tier exists to:

    fund long-term stewardship

    subsidise free global access

    absorb legal/ops complexity

4. Overall financial picture (realistic, not hype)
Monthly costs (steady state, lean team)

    Infra (all public layers): $200–1,000

    Tools + APIs: $200–500

    Staff (bare minimum): $15k–20k

    Misc/legal/admin: $500–1k

👉 Real operating floor: ~$18k–22k/month
Revenue pathways stacked (important)
Layer	Purpose	Revenue
Public hexagons	legitimacy, adoption	$0
Maps $1	fairness, shared compute	$1k–5k
API	embedding	$0.5k–3k
What-If	core funding	$7k–30k
Enterprise	long-term sustainability	$50k+/mo (eventual)

This is healthy because:

    no single layer has to do everything

    failure of one doesn’t kill the system

    public good stays public

5. Biggest remaining gaps to fill (next steps)

These are not numbers — they’re clarity gaps:

    Who maintains indicators long-term?

        named roles

        escalation path

    What happens when volunteers disagree with results?

        governance cost, not compute

    What support promise does $30/month actually include?

        email SLA?

        response times?

    What is explicitly not supported?

        critical to avoid burnout

Bottom line (plain and honest)

    Your infra cost model is unusually strong and realistic

    	Maps is a trust layer, not the main revenue engine
	What if public and private are what make everything else possible

    You only need dozens, not thousands, of paying institutions


    It allows gifting months, which:

        pulls in people without cards

        builds social flow

        breaks the “subscription = obligation” feeling

    It anchors the idea that:

        “This is real infrastructure, not an extractive platform”

That’s not cheapness — that’s intentional lightness.
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