Technology

The earliest adopters and the hardest hit. Technology companies embraced AI content tools before any other sector. The irony is structural: the companies building these tools have been most transformed by them.

−14.2pts
Sector median shift from baseline
74%
Mean intra-sector similarity (was 35%)
10 organisations4,892 documents7 countries3 languages2015–2025

The one-minute story

Three findings from the Technology sector corpus. Each carries statistical evidence from the full PRISM™ analysis.

−14.2pts

Joint-highest drift with Luxury, but from moderate baselines. Technology never had the linguistic heritage of Luxury, making the loss harder to reverse.

PRISM™ TEMPORAL · sector median drift
2020

The inflection point arrived two years before ChatGPT. Internal AI writing tools were already deployed across content teams by 2020.

PRISM™ L1 · Generic Phrases
48 → 72

Generic phrase density rose from 48 to 72. The largest absolute increase of any dimension in any sector in the corpus.

PRISM™ L1 · Generic Phrases

Where Technology organisations stand today

Current PLI scores versus 2015–2019 baseline. Sorted by drift magnitude.

PLI scores by organisation

Current PLI score versus 2015–2019 baseline. Scores with fewer than 10 documents in the current window are excluded. ⓘ Ordering rationale

OrganisationCurrent PLIBaseline PLICoverage
ASML58.473.8
Dassault57.272.1
SAP56.872.4
Infineon56.471.8
Ericsson55.671.2
Amadeus55.170.5
STMicro54.870.2
Nokia54.270.8
Capgemini53.869.4
Logitech53.268.8

Organisations are ranked by PLI score. The confidence badge reflects the number of scored documents in the current window: High (≥ 30 documents), Medium (10–29), Low (fewer than 10). Low-confidence scores are included in the ranking and should be read alongside the document count.

Technology PLI over time

Sector median with interquartile range (shaded). Corpus median shown as dashed reference line.

Which dimensions collapsed

Ten PRISM™ dimensions. Baseline (dashed) versus current (solid). Sorted by drift in the table.

DimensionBaselineCurrentΔ
Cultural Markers66.044.0-22.0
Idiom Density62.042.0-20.0
Perplexity68.054.0-14.0
Burstiness64.050.0-14.0
Lexical Diversity70.056.0-14.0
Readability72.060.0-12.0
Citation Density60.048.0-12.0
Jargon Load74.068.0-6.0
Hedging Language58.066.0+8.0
Generic Phrases48.072.0+24.0

Technology's convergence zones

Cross-sector linguistic similarity. Higher scores mean Technology is beginning to sound like other sectors.

Technology Automotive
68%
Cross-sector linguistic similarity. Was 24% in 2015–2019 (+44 points)
Technology Retail
65%
Cross-sector linguistic similarity. Was 22% in 2015–2019 (+43 points)
Technology Luxury
58%
Cross-sector linguistic similarity. Was 22% in 2015–2019 (+36 points)
Technology Finance
54%
Cross-sector linguistic similarity. Was 20% in 2015–2019 (+34 points)

Corpus health for this sector

Quality metrics for the Technology sector corpus used in this analysis.

4,892
Documents accepted
84%
Acceptance rate
2015–25
Year coverage
3
Archive sources
Sector data last updated February 2026 · Methodology version 2.1 · Full method · How to cite