From the hash-bound evidence releases
Recorded claims
Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.
The paper explains why general-purpose technologies can coexist with weak measured productivity during a period of complementary invention, process redesign, diffusion, and learning, producing a J-curve between investment and measured return.
Scope: General-purpose AI and historical analogies as of 2017; mechanism boundary for current claims.
The productivity-paradox mechanism predicts that purchasing a technology without complementary organizational capital may yield little measured return during the transition, so tool adoption is not an adequate proxy for becoming AI-native.
Scope: Mechanism-level implication; it does not quantify a current firm's lag or guarantee later gains.
Structured relationships
In-corpus citations
Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.
Cites (0)
No in-corpus outgoing citation is available in the current enrichment coverage.
Cited by (0)
No in-corpus incoming citation is available in the current enrichment coverage.
Boundaries
Limitations & independence
Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.
Recorded limitations
- Published before modern generative AI
- Conceptual/historical rather than a current field experiment
- Macro measurement and lag mechanisms cannot identify one firm's outcome
Source independence
Academic economic synthesis predating current generative AI; not vendor evidence.