DOI: 10.5061/dryad.xpnvx0ksf
title: Who expands the human creative frontier with generative AI: Hiveminds or masterminds?

description: Artists are rapidly integrating generative text-to-image models into their
 workflows, yet how this human–AI collaboration affects creative discovery
 remains unclear. Leveraging large-scale data from an online art platform,
 we compare AI-assisted creators to matched non-adopters to assess novel
 idea contributions. Initially, generative AI increases novelty among a
 concentrated subset of artists, driven primarily by substantial
 productivity gains; however, marginal novelty per artifact declines
 post‑adoption, reflecting a shift toward high‑volume, incremental
 exploration, ultimately yielding a greater aggregate of novel artifacts by
 AI adopters. We observe no evidence of a human–AI complementarity effect
 beyond productivity-driven gains. Notably, the release of open-source
 Stable Diffusion accelerates novel contributions across a broader, more
 diverse group, suggesting that text‑to‑image tools facilitate exploration
 at scale, initially enabling persistent breakthroughs by a select
 “mastermind” group, driven by substantial volume increases, and
 subsequently enabling widespread novel contributions from an emergent
 “hivemind” of artists.
description: # Who expands the human creative frontier with generative AI: Hiveminds or
 masterminds? Dataset DOI:
 [10.5061/dryad.xpnvx0ksf](10.5061/dryad.xpnvx0ksf) "Leveraging
 large-scale data from an online art platform, we compare AI-assisted
 creators to matched non-adopters to assess novel idea contributions."
 The dataset consists of a main folder, Hiveminds-Masterminds.zip. ## Data
 **All data has been anonymized (userid, postid) to protect user privacy.**
 The repository contains the following datasets. 1. **`posts`**: Post-level
 data containing artwork performance metrics and embeddings. This dataset
 describes the performance of individual artworks and associated feature
 representations. * **userid**: anonymized user identifier * **postid**:
 anonymized post identifier * **published_time**: year-month-date time *
 **umap**: UMAP dimensionality-reduced embedding 2. **`users`**: User-level
 data containing creator performance metrics, treatment assignment, and
 propensity scores. This dataset characterizes individual creators,
 including their performance metrics, assignment to a treatment or control
 group, and calculated propensity scores. * **userid**: anonymized user
 identifier * **num_posts**: total number of posts published *
 **favorites**: total number of favorites received * **comments**: total
 number of comments received * **pageviews**: total number of profile page
 views received * **profile_comments**: total number of profile page
 comments received * **watchers**: total number of profile followers *
 **treatment**: binary indicator for treatment/control cohort *
 **avg_posts_prior**: average number of posts published per period prior to
 AI release 3. **`baseline`**: Idea embeddings external to creators in the
 sample; used as the universal baseline in the convex hull computation.
 These embeddings provide a reference point for comparison, representing
 ideas generated outside of the studied creator population. * **postid**:
 auto-indexed identifier for baseline set of artifacts * **umap**: UMAP
 dimensionality-reduced embedding 4. **`matched_cohorts`**: All users
 composing the matched treatment and control cohorts for each epoch from
 running `convex_hull.py`. This dataset specifies the composition of the
 cohorts used in convex hull computations across different time periods. *
 same as **2.** **users** * **ps**: propensity score estimates for
 selection into adoption based on user-level engagement metrics upon which
 matching is performed * **epoch**: bootstrapped iteration to which the
 user was matched 5. **`convex_hulls`**: Estimated convex hull metrics
 using the default configuration in `convex_hull.py`. These metrics
 represent the boundaries of the performance achieved by the treatment and
 control cohorts. * **period**: year-month-date * **volume**: convex hull
 volume * **area**: convex hull surface area * **num_vertices**: number of
 vertices that make up the convex hull * **num_unique_vertex_users**:
 number of unique creators who own a vertex point on the convex hull *
 **group**: {treatment, control, all}, denoting the cohort treatment
 assignment * **epoch**: bootstrapped iteration * **{treatment,
 control}_n**: number of treatment units making up the cohort 6.
 **`first_count_{treatment, control}_heatmaps`**: User-month panel of
 creators' first contribution counts for treatment and control units.
 This dataset visualizes the timing and frequency of creators' first
 contributions, separated by treatment group. * **userid**: anonymized user
 identifier * **rank**: user ranking based on the total number of vertex
 contributions over the entire data period * {**vertex periods**}: number
 of vertex points owned in each period 7. **`convex_hulls-prodeq`**: Same
 as `convex_hulls` but with productivity-equalized treatment cohort. This
 dataset is identical to `convex_hulls` but uses a cohort construction
 method that adjusts for differences in creator productivity. * Same
 features as **5. convex_hulls** 8. **`{#}-prodeq_{True/False}-{mastermind,
 hivemind, hetero-low, hetero-high}_hulls`**: All convex hull
 configurations for cohort analyses with and without productivity
 equalization. These represent a series of experiments exploring how cohort
 composition and productivity equalization affect the resulting convex hull
 metrics. `{mastermind, hivemind, hetero-low, hetero-high}` refer to
 different grouping strategies. `{True/False}` indicates whether
 productivity equalization was applied. * Same features as 5. convex_hulls
 9. **`masterminds-hiveminds-u`**: User IDs for the top 20%/bottom 80%
 vertex contributors. This dataset identifies users based on their
 contribution levels within the network. * **userid**: anonymized user
 identifier * **treatment**: binary indicator for treatment/control cohort
 * **rank**: user ranking based on the total number of vertex contributions
 over the entire data period * **percentile**: percentile ranking 10.
 **`exploration-u`**: User IDs for low/high exploration users prior to
 adopting AI. This dataset identifies users based on their level of
 exploratory activity before the intervention. * **userid**: anonymized
 user identifier * **max_cdf**: within-user empirical cumulative
 distribution function value for maximum average embedding distance prior
 to AI release * **low**: binary indicator for assignment to low/high
 pre-AI exploration cohort ## Scripts The repository contains the following
 Python scripts and Jupyter notebooks: 1. **`convex_hull.py`**: Iteratively
 matches treatment and control units to produce unique cohorts, then
 computes convex hulls for cohorts over time. Running this script with the
 default configuration will replicate the main results. 2.
 **`convex_hull-falsification.py`**: Same as `convex_hull.py`, but allows
 for control group contamination up to a user-specified level (default
 30%). This script allows for a controlled level of
 "contamination" or overlap between treatment and control groups.
 3. **`1-did_analysis.ipynb`**: Continuous difference-in-differences
 estimation with visualizations. Performs a Difference-in-Differences (DID)
 analysis to estimate the treatment effect and presents the results
 visually. 4. **`2-vertex_analysis.ipynb`**: Generates data for decile
 contribution heatmaps and market share analyses and corresponding
 visualizations. Analyzes contribution patterns and market share,
 generating heatmaps to visualize these trends. 5.
 **`3-scm_analysis.ipynb`**: Synthetic Control Method estimation
 (robustness check) with visualizations. Utilizes a Synthetic Control
 Method to provide a robustness check on the DID analysis, providing an
 alternative estimate of the treatment effect. 6.
 **`4-vertex_discovery.ipynb`**: Descriptive and bootstrapping analyses for
 the productivity effect. Provides further investigation and statistical
 robustness through bootstrapping methods to assess the effect of the
 intervention on creator productivity.