CANONICAL RESEARCH / LUNARTULIP DEEP DIVE
COMPANY DEEP DIVES / 03
Each object fixes its as-of date, Claim IDs, Evidence Ledger, valuation scenarios and forward falsifiers. New evidence advances the version without overwriting the original information set.
Commercial revenue, TCV and margin accelerated together in Q2, and that much is settled. What still needs proving is where the money sits. Innovation budgets get cut in a downturn. Operating budgets get defended.
Read the research →The 1,700% figure establishes that the traffic arrived. It says nothing about revenue per machine action: Cloudflare discloses no attributable revenue, no paid-request mix and no cross-product attach rate. The variable to underwrite is monetization density.
Read the research →FY2026 revenue grew 26%; subscription ARR grew 23%. That gap is worth interrogating. Collaboration software has always billed headcount, and AI coding compresses headcount while manufacturing work state.
Read the research →THEME STUDIES / 02
Theme Studies test one shared question across companies, using control cases to distinguish industry narrative from attributable economic value.
PLTR, NET and TEAM sit at three points on the same path: budget confirmed, billing unit unproven, billing basis itself in question. DDOG and APP serve as controls, showing what completed transmission looks like in the financials.
Read the theme study →The second derivative of spot is rising while the second derivative of contract prices is falling. Structural memory profit lands first with the constraint owners, NAND amplifies the cycle, and the Chinese module makers amplify inventory timing. In optics the reported numbers are still strong and the margin for error has thinned. The tidy version — memory takes the baton, optics hands it over — is not what the global reporting season produced.
Read the theme study →Use these Deep Dives to evaluate Lunartulip’s work on companies, industry transmission, expectation gaps and risk boundaries. Point-in-time context and later corrections help separate the original information set and thesis from evidence that emerged afterward.
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