// signal reports · published

Signal Reports

Applied Zorthex analyses on specific emerging phenomena. Each report maps the diffusion lag empirically across three independent sources — for investors, companies, and policy makers who need to act before the crowd notices.

SIGNAL REPORT #002 · MAY 2026

Post-Quantum Cryptography

Cybersecurity · Regulatory trigger · NIST August 2024

91 mo

Lag (L)

Months above threshold

48/100

Peak score

35/100

Current · May 2026

■ STRUCTURAL

From 2017 to 2024, PQC existed in near-total public obscurity. The NIST final standard publication was the trigger — not a quantum attack. 91 months of lag closed in weeks. The clearest documented case of regulation as an attention trigger in the Zorthex dataset.

↓ DOWNLOAD REPORT #002 (PDF)

SIGNAL REPORT #001 · MAY 2026

mRNA Cancer Vaccines

Biotech · Health emergency trigger · Aug 2025 breakout

~126 mo

Lag (L)

Months above threshold

100/100

Peak score

17/100

Current · May 2026

◉ OBSERVATION

9 of 12 consecutive months above threshold confirmed. May 2026 dropped below 25/100 — critical inflection point. Whether this is a temporary dip or the beginning of a structural decline will determine if this technology enters mainstream consciousness or becomes a Structural Bubble.

↓ DOWNLOAD REPORT #001 (PDF)

SIGNAL REPORT #003 — IN PREPARATION · Q3 2026

PREPRINT PUBLISHED PREPRINT PUBLISHED — ZENODO — v1.1#8212; ZENODO PREPRINT PUBLISHED — ZENODO — v1.1#8212; v1.2 ↗ DOI: 10.5281/zenodo.20270575

Public Attention
Diffusion Lag

We tried to find a universal law of how the world discovers new things.
We didn't find it. We found something more interesting.

"The explored data show a broad distribution of diffusion times (L) across emerging technologies, with strong heterogeneity between domains. The biotech_medical subset presents the lowest variance (CV = 0.222), while the AI domain shows high heterogeneity (CV = 0.452). Results are limited by selection bias, small sample size, and possible censoring effects. Version 1.1 introduces a 12-month sustained attention window and the Structural Bubble category."

// framework v1.2

Three Locked Definitions

t_start

First public emergence

Seminal paper OR documented commercial launch OR recognized founding event. Must be justified and documented.

t_peak

First structural consolidation

First window of ≥12 consecutive months with Google Trends ≥25/100. Updated from 3 to 12 months in v1.1 to distinguish structural attention from hype.

L = t_peak − t_start (months)

Empirical indicator of public attention diffusion. Not a physical constant. Not a universal law. Represents the preparation window available before mainstream attention arrives.

// results v1.2 // results v1.1 — 11 structural technologies#8212; 12 structural technologies

Domain Distribution

L BY CLUSTER L BY CLUSTER — v1.1 dataset (12-month window)#8212; v1.2 dataset (12-month window)

biotech_medical

CV 0.222

n=2

consumer_tech

CV 0.438

n=4

AI/scientific

CV 0.452

n=3

financial_tech

CV 0.292

n=2

Bar length proportional to mean L. Lower CV = more stable distribution within domain.

Technology	Cluster	t_start	L (months)	Classification	Current Status
mRNA vaccine	biotech_medical	Jan 2015	70	structural	receding
CRISPR	biotech_medical	Jun 2012	51	structural	receding
LLM	AI/scientific	Jun 2017	69	structural	active
Deep Learning	AI/scientific	Sep 2012	112	structural	active
Autonomous Vehicle	AI/scientific	Oct 2010	176	outlier	receding
Virtual Reality	consumer_tech	Jan 2010	71	structural	receding
iPhone	consumer_tech	Jan 2007	29	structural	active
Cloud Computing	consumer_tech	Mar 2006	31	structural	active
Facebook	consumer_tech	Feb 2004	59	structural	receding
Bitcoin	financial_tech	Oct 2008	146	structural	receding
Cryptocurrency	financial_tech	Jan 2013	96	structural	active
Post-Quantum Cryptography	security_tech	Jan 2017	91	structural	active
NFT	structural bubble	—	N/A	bubble	dormant
Metaverse	structural bubble	—	N/A	bubble	dormant
Clubhouse	structural bubble	—	N/A	bubble	dormant
Nanotechnology	censored	—	N/A	censored	—

// limitations

What This Is Not

Survival Bias

Only technologies that reached mainstream are included. Technologies that failed to diffuse are not represented.

Selection Bias

Technologies chosen for historical prominence, not random sampling. Results cannot be generalized.

Proxy Limitation

Google Trends measures search interest, not understanding or adoption. Biased toward English-speaking populations.

Small Sample

n=12 structural total. No robust statistical inference possible. Target for v2.0: n≥20 per cluster.

t_start Subjectivity

Identification of the founding event involves judgment. v2.0 will introduce confidence bands based on alternative t_start candidates.

Operational Window

L is measurable only after t_peak. In real time, historical sector L is used as a probabilistic estimate, not a certainty.

// sector trends · may 2026

Sector Overview

Average diffusion lag and current attention state by domain. Based on verified dataset v1.2.

BIOTECH MEDICAL · n=2

~60 mo

avg diffusion lag · 5 years

2 Receding 0 Active

mRNA vaccine · CRISPR
CV 0.222 — most stable domain

AI / SCIENTIFIC · n=3

~119 mo

avg diffusion lag · 10 years

2 Active 1 Receding

LLM · Deep Learning · Autonomous Vehicle
CV 0.452 — highest heterogeneity

CONSUMER TECH · n=4

~47 mo

avg diffusion lag · 4 years

2 Active 2 Receding

iPhone · Cloud · Facebook · Virtual Reality
CV 0.438 — fastest adoption domain

FINANCIAL / SECURITY TECH · n=4

~105 mo

avg diffusion lag · 8.7 years

2 Active 2 Receding

Bitcoin · Cryptocurrency · PQC · RWA*
CV 0.273 — institutional adoption pattern

* RWA (Real World Asset Tokenization) is under analysis for inclusion in v1.3. Current app estimate: L=72 months, STRUCTURAL Active. Manual verification pending.
Sector averages are descriptive only. Small n per cluster limits statistical inference. See framework limitations for details.

// current status · legend

Current Status

Once acquired, the STRUCTURAL classification is permanent. Current Status tracks today's attention level independently from historical classification.

Active

Current score ≥ 25/100

Sustained attention confirmed. The topic is still present in mainstream public discourse.

Receding

Current score 10–24/100

Attention declining. Topic present but no longer dominant in public discourse.

Dormant

Current score < 10/100

Attention returned to technical baseline. STRUCTURAL classification still holds historically.

// custom reports · for organizations

Custom Reports

Custom Reports are verified products — real data, documented sources, no AI estimates. Built for organizations that need certainty, not exploration.

ESSENTIAL

€500–1.000

Worldwide analysis · Single topic

→ t_start documented with primary source
→ Real Google Trends CSV verified
→ Monthly curve validated — no gaps
→ STRUCTURAL / Bubble / Censored classification
→ Current Status: Active / Receding / Dormant
→ Operational section for your sector
→ Explicit limitations declared

PREMIUM

€2.000–3.000

Everything in Essential, plus:

→ Geographic granularity — L for 3 countries/regions
→ Robustness check — versions B and C
→ Proprietary 4th source — domain-specific
→ Quarterly update included for 12 months
→ Results presentation call included

The 4th source is selected based on the domain and category of the topic analyzed. Declared explicitly in every report.

To request a Custom Report:

→ zorthex.official@gmail.com

// open source

Replicate This Work

All code, data, and methodology are publicly available. If you follow the pipeline and obtain different results — that is a contribution, not a problem.

→ Preprint v1.2 on Zenodo → v1.0 (archived) → View on GitHub ← Back to Zorthex

Cite as (v1.2):
Santi, R. (2026). ZORTHEX v1.2 — Public Attention Diffusion Lag: A Replicable Framework for Measuring Technology Diffusion (1.2). Zenodo.
https://doi.org/10.5281/zenodo.20270575

Cite all versions:
https://doi.org/10.5281/zenodo.20049068