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Ground Zero programme

AI Sweet Spots

5 authored topic chapters · 7 studies with linked canonical stories.

Topics

Core Curves

Core Curves brings together Core Curves (ASS-1) and Population Profiles. Within the AI Sweet Spots Theme, these studies examine a shared problem from different human, technical and institutional perspectives. The Topic makes their relationships explicit so that methods, risks, discoveries and unresolved questions can be interpreted as one research programme rather than isolated studies.

  • SUB-0001 — Core Curves (ASS-1): Which regions of the assistance intensity curve maximise combined performance, learning and autonomy?
  • SUB-0002 — Population Profiles: How do AI sweet-spot locations differ across population subgroups?

Multi-Site Validation

Multi-Site Validation brings together ASS-2 — Extended/Multi-Site Validation. Within the AI Sweet Spots Theme, these studies examine a shared problem from different human, technical and institutional perspectives. The Topic makes their relationships explicit so that methods, risks, discoveries and unresolved questions can be interpreted as one research programme rather than isolated studies.

Social Skills and Relational Intelligence

Social Skills and Relational Intelligence brings together SS-A — Social Skills Baseline and SS-B — Altered States Social Skills. Within the AI Sweet Spots Theme, these studies examine a shared problem from different human, technical and institutional perspectives. The Topic makes their relationships explicit so that methods, risks, discoveries and unresolved questions can be interpreted as one res

Assessment and Navigation

Assessment and Navigation brings together GAIN — Global AI Navigation Validation. Within the AI Sweet Spots Theme, these studies examine a shared problem from different human, technical and institutional perspectives. The Topic makes their relationships explicit so that methods, risks, discoveries and unresolved questions can be interpreted as one research programme rather than isolated studies.

Brain Capital and Longitudinal Capability

Brain Capital and Longitudinal Capability brings together Brain Capital Study and Index. Within the AI Sweet Spots Theme, these studies examine a shared problem from different human, technical and institutional perspectives. The Topic makes their relationships explicit so that methods, risks, discoveries and unresolved questions can be interpreted as one research programme rather than isolated stu

Studies & stories

GZ-STORY-001

Core Curves (ASS-1)

The dashboard was green, but Mara Chen did not trust it. The team had finished faster with AI, yet nobody could say whether they had learned more, understood less, or simply moved the verification wor

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GZ-STORY-002

Population Profiles

At 8:10 on Monday morning, Noah Williams watched two people receive the same AI advice and move in opposite directions. One became more capable. The other became quieter, less certain and increasingly

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GZ-STORY-018

ASS-2 — Extended/Multi-Site Validation

The meeting began with a number everyone liked and a question nobody could answer. Output had risen. Complaints had fallen. But Dr Arun Bose asked what had happened to judgement, agency and the abilit

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GZ-STORY-019

SS-A — Social Skills Baseline

The dashboard was green, but Claire Dubois did not trust it. The team had finished faster with AI, yet nobody could say whether they had learned more, understood less, or simply moved the verification

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GZ-STORY-020

SS-B — Altered States Social Skills

At 8:10 on Monday morning, Marcus Lee watched two people receive the same AI advice and move in opposite directions. One became more capable. The other became quieter, less certain and increasingly de

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GZ-STORY-024

GAIN — Global AI Navigation Validation

The meeting began with a number everyone liked and a question nobody could answer. Output had risen. Complaints had fallen. But Fatima Al-Sayed asked what had happened to judgement, agency and the abi

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GZ-STORY-025

Brain Capital Study and Index

The dashboard was green, but Dr Naomi Sato did not trust it. The team had finished faster with AI, yet nobody could say whether they had learned more, understood less, or simply moved the verification

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