Network Effects and Conclusion

How each becomes a network effect

Brevis network effect

Compute infrastructure improves with:

• better proving economics

• faster provers

• better SDKs

• more circuits and integrations

Glyph network effect

Glyph compounds harder because the asset isn’t compute. It’s the user graph.

Every integration improves:

• wallet linkage coverage

• behavioral signal quality

• policy accuracy

• portability of identity claims

That creates a category moat:

Glyph becomes the default “user layer” for Web3.

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The category distinction (public framing)

If Brevis helps contracts answer:

“Did this wallet do X?”

Glyph helps apps answer:

“Who is this user, what do they want, and can we trust them — without exposing them?”

That is why Glyph isn’t “another ZK coprocessor.”

It is a new primitive:

The Consent Rail for Web3 Growth.

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Comparison Table (Public Summary)

Dimension	Brevis	Glyph
What it is	ZK coprocessor for historical chain data	User coprocessor for identity + intent + reputation
Primary job	Prove computations over tx/receipts/storage	Prove portable user claims across wallets/chains
Returns	Verified computation result	Verified identity/behavior claims (minimal disclosure)
Best for	Protocol automation, on-chain intelligence	Anti-sybil, personalization, retention, targeting
Integration model	Developers implement computations/circuits	Developers adopt policies + claims
Unit of value	Historical truth + correctness	User continuity + trust + growth outcomes
Privacy default	Depends on app design	Default privacy + consent + minimal claims
Attack surface	Mostly compute correctness	Identity is adversarial, policies must resist gaming
Moat	Proving network + compute economics	User graph + policy standards + portable claims
“North Star”	Smarter contracts	Apps that understand users without surveillance