Let's consider Web3 Lean Canvas as an upgrade for tokenized systems.

This is just a continuation of my last week's essay about reasons why Lean Canvas and Business Model Canvas can be very useful. Different audiences and different situations, but great time-savers at the end.

If you haven't read this primer, check it out here before diving into Web3 adaptation which replaces several Lean Canvas blocks with Web3-specific elements. If you're already familiar with Lean Canvas, you can jump right into its adapted blocks.

Adapted blocks are, namely: token model, governance assumptions, demand generation, liquidity requirements, and regulatory/design constraints.

What fundamental pain are you solving — and do not say “we add tokens”?

Define the core job-to-be-done and the broken status quo.

Include:

• What is inefficient / overpriced / insecure today?

• What is impossible without decentralization?

• What is artificially restricted by middlemen?

In Web3, users are not one monolith.

Specify each participant type:

• End users / consumers

• Liquidity providers

• Delegators

• Node operators / validators

• Integrations

• DAO contributors

• Partners (protocols, wallets, infra)

Each has different incentives.

Include:

• Web2 incumbents

• Competing protocols

• Manual or off-chain solutions

• “Do nothing / stay centralized”

• User hacks + spreadsheets

• Forked versions of similar protocols

What is the non-obvious, protocol-native differentiation?

Examples:

• “Pay-per-use, censorship-resistant storage” (Irys)

• “High quality local weather data with economic incentives” (WeatherXM)

• “Self-sovereign identities with verifiable proofs” (Humanode/Cheqd)

Sketch the minimum viable protocol:

• MVP smart contracts

• Oracles

• Frontend

• Key flows (deposit → borrow → repay, etc.)

• Governance hooks

• Token gating or roles

• What can be off-chain for v0

Replace “Unfair Advantage” or “Channels” in the classic Lean Canvas.

This is Web3-specific and essential.

Think in terms of:

• Utility (what can you do with the token?)

• Sink mechanisms (what burns or locks it?)

• Demand loops (who needs it and why?)

• Speculative bootstrap effects

• Future accrual mechanisms (fees, buybacks, staking rewards)

Token design must be grounded in real protocol usage — not vibes.

How decentralized should the protocol be at:

• Launch

• PMF

• Scale

• Maturity

Clarify:

• What decisions will be on-chain?

• What decisions stay with the core team for now?

• What is the progressive decentralization roadmap?

• How will contributors be coordinated?

Decentralization is not a feature — it's a maintenance strategy.

Think about leading indicators, not lagging vanity metrics.

Examples:

• Number of active wallets, not just “addresses”

• Repeat transaction ratio

• Liquidity depth & retention

• Protocol revenue from real usage

• Ratio of sybil to genuine users

• Time-to-first-transaction

• Token velocity vs. hoarding

• Governance participation health

• Cost-to-acquire-liquidity (CAL)

This replaces the classic “Channels.”

Web3 depends on:

• Developer adoption channels (SDKs, templates, integrations)

• Protocol partnerships (money legos, RFQs, AMMs, LST/LRT partners)

• Liquidity mining / bootstrapping programs

• Airdrop mechanics (when, how, to whom, why)

• Incentive design for early usage and for continuous usage

• Community-led onboarding

• CEX/DEX listing strategy

• Grants → hackathons → aggregator integrations

• Key opinion/power users (LRT farmers, MEV searchers, DA collectors)

This is where 90% of Web3 projects fail.

Separate on-chain and off-chain:

On-chain costs:

• Gas fees / calldata fees

• Oracle costs

• Keeper costs

• Slashing penalties

• RPC/infra

Off-chain costs:

• Team, auditors, BD, ops

• Security reviews

• Running validators

• Liquidity incentives

Protocols don’t “make money” — they capture value created by usage.

List clear streams:

• Protocol fees

• MEV recapture

• Spread capture

• Staking yields

• Premium features

• API metering

• Data sales

• Liquidity-as-a-service earnings

• Rebates from partners

Define:

• Who pays?

• When?

• Is revenue organic or artificially incentivized?

Three buckets:

• Does anyone want this?

• Are users willing to switch?

• Is the onboarding friction too high?

• Smart contract exploits

• Liquidity extraction attacks

• Governance attacks

• Oracle failures

• Token-incentive gaming

• Custody

• KYC vs. permissionless access

• Token classification

• Geographic restrictions

List the top 3 assumptions that can kill the project.

To fill the canvas for your own project, you can simply cover areas in the list below, or you can jump into Miro and use my template there...

https://miro.com/app/board/uXjVGZndI6A=/?share_link_id=101695725527

Example of 1-page Web3 Lean Canvas for WeatherXM.

If you don't know WeatherXM yet, check their website here or my chat with their founder Manos on Web3Magic Podcast here.

Now you're ready to check out what they do in summarized form aka Web3 Lean Canvas.

• Global weather data is sparse, inaccurate, and dominated by monopolies.

• Microclimate data (cities, farms, coastlines) is largely unavailable.

• Traditional networks lack incentives for expanding coverage.

Supply: station operators, farmers, hobbyists, DePIN contributors, NGOs.

Demand: energy companies, insurers, mutual funds, logistics/drones, agriculture tech, weather apps, governments.

Ecosystem: hardware manufacturers, data integrators, researchers, other protocols, agri-communities, general weather-interested public

• The Weather Company, AccuWeather, national weather systems.

• DIY weather stations (no coordination).

• Web3 DePIN alternatives (limited but existing).

Decentralized, global, hyperlocal weather network that uses cryptoeconomics to incentivize accurate, dense, real-time weather data — owned and expanded by the community.

Hardware: plug-and-play weather stations with secure identity.

Protocol: on-chain data verification + reward system.

Data Layer: unified API + high-resolution forecasts built on top.

Frontends: explorer, dashboards, enterprise portal.

Utility: pay for data, premium access, staking for station reputation.

Sinks: API usage, enterprise data packages, staking locks.

Demand Loops: more stations → better data → more enterprise usage → more token demand.

Now: core team controls updates and models.

Later: DAO governs data pricing, hardware verification, treasury allocation.

End State: a community-run global weather data marketplace.

• Active stations & uptime

• Coverage density by region

• Data-quality scores

• API usage (free vs. paid)

• Enterprise contracts & retention

• Token sinks vs. emissions

• Station activation rate

Supply: hardware distributors, farming communities, NGOs, DePIN builders.

Demand: BD with energy/insurance/logistics; integrations with weather APIs; partnerships with renewable forecasting platforms.

Crypto: CEX/DEX listings, staking, DePIN awareness.

On-chain: data verification, reward distribution, oracle & infra.

Off-chain: hardware manufacturing & logistics, R&D, BD, cloud compute, audits, support.

• API subscriptions & enterprise data fees

• Forecast model licensing

• Custom deployments for renewables

• Token-based data payments

• Treasury accrual + potential burn mechanisms

Market: enterprises reluctant to switch; low usage of token-based payments.

Protocol: data spoofing, hardware failures, insufficient density.

Regulatory: token classification, hardware import rules.

Kill Hypotheses:

1. Enterprise demand doesn’t materialize (ineffective sales);

2. Network fails to reach sufficient density;

3. Hardware rollout bottlenecks.

4. A new competitor will reach scale faster.

I hope this was not only useful but also interesting! Tools are here to help us think better and faster. Obviously, once you've done it a couple of times yourself, you can use any AI of your choice to help.

It's hard to think clearly about AI's outputs when you have never done it before. That's why I would not start with AI but rather with yourself. But what do I know 😉

Let's BUILD BETTER apps and businesses! 🎄

BFG

ICYMI: Essay focused on thinking about business names - with example of my recent strugle between 2094Lab or Lab2094. Read here...

BuildBetter by BFG

Nov 25

2094 - How I Chose Business Name - Psychology of Brand Naming and Recall

I have been naming companies, products, and features so many times that I can say I have a decent understanding of how the psychology behind branding/naming works. This is a summary of thinking and science behind recent company naming exercise.

Connect with me:
- on Farcaster: https://warpcast.com/bfg
- on X: https://twitter.com/aka_BFG
- on TG: https://t.me/BrightFutureGuy 

I still have a LinkedIn in case you're that old.