AIORG-W001 Working papers

AI-Native Firms

HBS Working Paper 26-090 · SSRN 6905079

working paper abstract

Recorded claims
2
coded from the inspected source
Evidence classes
1
scholarly-metadata
Recorded limitations
3
stated, never hidden

From the wave-0 evidence ledger

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.

In the inspected working-paper abstract, firms classified as AI-native were about 25% smaller while allocating about 13% more of their workforce to engineering; they had roughly 15% fewer entry-level employees and managers and were about half a seniority level flatter, despite comparable reported valuations.

confidence: medium scholarly-metadata

Scope: YC W20-F24 and comparable US venture-backed firms founded 2020-2024; structural associations, not a universal organization template.

The reported structural differences were stronger when AI was part of what a firm sold than when AI appeared only in worker tool-use signals, suggesting that product architecture and organizational architecture covary more strongly than generic tool adoption in this sample.

confidence: medium scholarly-metadata

Scope: Early venture-backed firms and the paper's text-based AI-native classifications.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Young US venture-backed startup sample
  • AI-native classification and entry selection can confound structure comparisons
  • Working paper may be revised

Source independence

Academic working paper using YC, venture, and workforce-profile data; no focal-firm outcome audit.