[1]Madeleine I. G. Daepp

1]Microsoft Research

# The Emerging AI Divide in the United States

<EMAIL_ADDRESS>Scott Counts<EMAIL_ADDRESS>[

###### Abstract

The digital divide describes disparities in access to and usage of digital
tooling between social and economic groups. Emerging generative artificial
intelligence tools, which strongly affect productivity, could magnify the
impact of these divides. However, the affordability, multi-modality, and
multilingual capabilities of these tools could also make them more accessible
to diverse users in comparison with previous forms of digital tooling. In this
study, we characterize spatial differences in U.S. residents’ knowledge of a
new generative AI tool, ChatGPT, through an analysis of state- and county-
level search query data. In the first six months after the tool’s release, we
observe the highest rates of users searching for ChatGPT in West Coast states
and persistently low rates of search in Appalachian and Gulf states. Counties
with the highest rates of search are relatively more urbanized and have
proportionally more educated, more economically advantaged, and more Asian
residents in comparison with other counties or with the U.S. average. In
multilevel models adjusting for socioeconomic and demographic factors as well
as industry makeup, education is the strongest positive predictor of rates of
search for generative AI tooling. Although generative AI technologies may be
novel, early differences in uptake appear to be following familiar paths of
digital marginalization.

Posted April 19, 2024
Updated April 30, 2024

###### keywords:

digital divide, artificial intelligence, large language models, digital
inclusion

##  1 Introduction

Generative Artificial Intelligence (AI) is a general-purpose technology, an
innovation that will affect many sectors of the economy [1]. Previous general-
purpose technologies took decades to centuries to achieve widespread adoption
[2, 3]. By contrast, many of the newest generative AI models have been made
available within months of development, to anyone, anywhere in the world, at
low to no cost, so long as they have access to a device with a stable internet
connection. Hypothetically, then, a plurality of people should benefit from
access to these new tools. But adoption could also be subject to the “digital
divide”, differences between those who do and do not benefit from digital
tooling that follow historical patterns of social marginalization [4]. As
DiMaggio et al. [5] emphasize, the digital divide frequently exists not only
at the level of access to devices and bandwidth, but also at a second
level—whether access is converted into actual use [6]—and, at a third level,
whether use leads to beneficial versus damaging outcomes [7]. While access to
generative AI will necessarily be constrained by first-level divides, effects
at the second and third levels remain unclear.

Early differences in access to and use of new generative AI tooling could have
important and long-lasting social implications. Researchers have spent decades
working to develop “artificial intelligence”, or computer systems that can
match or outperform humans in cognitive capacities like reasoning and
sensemaking [8, 9], but even technical experts have been surprised by the
speed of recent advances in generative AI systems [10]. Generative AI refers
broadly to models that can create new content without explicit programming
beyond initial guidelines (prompts) [11]. These include generative large
language models, neural networks that process sequential data including but
not limited to natural language [12], which have gone from scoring in the
bottom 10% on the legal bar exam to the top 10% within a year
[13].111Reevaluations suggest that GPT-4’s score may be closer to the 68th
percentile [14]—still an impressive improvement in performance over the
previous near-random scores of smaller models. Generative image diffusion
models, neural networks trained to emulate the inverse of a process where an
image decays into noise, can now not only generate but also edit images and
even video [15]. Recent advances are made possible by emergent properties of
scale: As compute power, model parameters, and training data sizes increase,
models exhibit qualitatively different abilities from their capabilities at
smaller scales, and from the capabilities that their objective functions
entailed.222 Wei et al. [16] show that Transformer language models trained
with a next- or missing-word objective function, for example, begin to succeed
at modified arithmetic, unscrambling words, and other simple tasks at just
over 10 billion parameters ($2*10^{22}$ training FLOPs); to solve questions
based on diverse domain knowledge as size approaches 100 billion parameters
($3-5*10^{2}3$ training FLOPs); and to exhibit a jump in truthfulness at 280
billion parameters ($5*10^{2}3$ training FLOPs, Gopher model only).

These powerful technical capabilities translate into real-world benefits.
Access to generative AI can have large effects on productivity [17, 18, 19],
creativity [20, 21, 22, 23], and performance [24]. In an experimental study of
computer programmers, for example, use of the large language model-based tool
ChatGPT increased lines of code written by 56%, benefitting the least-skilled
workers the most[17]. A real-world deployment with call center agents
similarly found that use of a generative AI-based conversational agent
increased overall productivity and reduced productivity gaps between novices
and experts [18]. Although the tools are still new, impaired access already
has consequences. Italy’s nationwide ban on ChatGPT was associated with a
short-term but large drop in code production before methods for bypassing the
restrictions enabled a rebound [25]. However, several studies offer important
caveats: first, a set of field experiments showed that access to a high-
performing large language model led to significant improvements in creative
ideation—again with the largest improvements for low performers—its use
reduced the uniqueness of ideas overall (a finding also observed in [26] and
[23]) and actually impaired performance in business problem solving [21];
second, an experiment on the impacts of ChatGPT access with the UK working-age
population did not find reductions in performance inequality between education
or age groups, though it did continue to find evidence of performance
improvements overall [27]. Although the value these tools create for their
users is likely to increase economic growth in general, differences in
adoption could also magnify the already considerable harms of digital divides.

There is thus a pressing need to evaluate the extent to which digital divides
are evident in emerging patterns of generative AI usage. This study
characterizes emerging spatial and social patterns in knowledge of generative
AI tooling across the United States. We focus on a single generative AI tool,
ChatGPT, a simple chat interface atop a powerful large language model that saw
unprecedented rapid uptake after its initial public release on November 30,
2011 [28], leveraging a large-scale search log database to characterize the
fraction of searches for ChatGPT by county over the first six months since the
tool’s release. Our analysis surfaces three observations. First, interest in
chatGPT, as proxied by searches, is spatially clustered, with the highest
rates of search in West Coast states and persistently low rates of search in
Appalachia and Gulf states. Second, counties with the highest rates of search
for ChatGPT are proportionally more urbanized, more educated, and have more
technology and creative sector jobs in comparison with the U.S. overall.
Finally, in fully adjusted hierarchical models, education emerges as the
strongest predictor of interest—a finding that is consistent with past
literature on the second-level digital divide. Although generative AI tools
may be new, user interest follows the well-worn patterns of established
digital divides.

##  2 Methods

###  2.1 Data

Our dataset comprises deidentified search interactions from Microsoft’s Bing
search engine. We analyze billions of searches collected between December 1,
2022 and May 31, 2023 in the United States, including both desktop and mobile
query search. All data were deidentified, aggregated to zip code and then to
the county level, and securely stored, and analyzed only after aggregation to
preserve user privacy and in accordance with Bing’s privacy policy. The study
was reviewed and approved by the Microsoft Research Institutional Review Board
(protocol ID 10590) prior to research activities.

To construct our analytic dataset, we first calculated the counts of searches
for ChatGPT as well as total search counts and user counts by county. Each
search interaction includes the search query string as well as a randomly
generated client ID, a timestamp, and the associated ZIP code and state. We
added an indicator of whether the query referred to ChatGPT by a case-
insensitive search for the terms “chatgpt” or the similarly common “chat gpt”,
and then aggregated total counts and unique user counts by zip code. The ZIP
code linkage is provided via a proprietary location inference engine that,
though still subject to error, uses contextual and historical information to
improve upon accuracy over standard reverse IP lookup databases. We linked ZIP
codes with counties using the HUD-USPS ZIP Code crosswalk files [29]. For any
ZIP code linked with multiple counties, we allocate queries to counties
proportionally to the ratio of all addresses in the ZIP-county overlap to the
total number of all addresses in the ZIPcode. Finally, we suppress data from
county-query cells with fewer than 50 unique users to preserve anonymity. We
also aggregate counts of overall and ChatGPT queries by state and month; at
these levels of aggregation, all cells meet the threshold for inclusion. We
use Bing because of its broad coverage across the United States as well as our
ability to examine finer spatial geographies than those available using
comparable, publicly available datasets, following Suh et al. [30]. However,
as a robustness check, we also run all analyses using the finest available
geographies (metropolitan areas) from publicly available Google Trends query
data [31].

Finally, we obtain county-level socioeconomic, demographic, and industry
makeup from the 5-year American Community Survey for 2016-2020 using the
National Historical Geographic Information System [32]. We use these data to
construct socioeconomic variables including: percent college educated,
calculated as the fraction of the population over 25 with a college education
or higher; percent rural, or the fraction of the population living in a rural
area; median household income in the past 12 months; and the unemployment
rate, or the fraction of the civilian labor force 16 years and over that is
unemployed. To characterize demographic makeup, we first calculate Hispanic or
Latino as a fraction of the total population; for the other three demographic
groups (White, Black, and Asian) we calculate the percent identifying with
that group only as a fraction of the total population. Finally, we calculate
industry makeup as the percent of all people in the adult employed civilian
population who are employed in technology, arts, finance, or service sector
jobs.

###  2.2 Analyses

We first map logged rates of search for ChatGPT by state. At this level of
aggregation, we are also able to evaluate trends over time, examining rates
over each month of observation. Mapping is conducted using the sf [33] and
usmap [34] packages in R 4.1.1 [35].

We next map county-level rates, testing for spatial autocorrelation using the
Moran’s I statistic [36]. That is, given $n$ counties, we calculate

$I=\frac{n}{W}\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}(x_{i}-\bar{x})(x_{j}-\bar{x})}{\sum_{i=1}^{n}(x_{i}-\bar{x})^{2}}$
(1)

where $x_{i}$ is the rate of search for ChatGPT in county $i$, $x_{j}$ is the
corresponding value for county $j$, and $\bar{x}$ is the overall mean across
counties. We set $w_{i,j}$ as the spatial weight between two counties $i$ and
$j$, using queen’s contiguity weights where $w_{i,j}=1$ if counties $i$ and
$j$ share a border and zero otherwise.333We exclude counties with no neighbors
or with no neighbors with unsuppressed data from our calculation. W is the
aggregate of the spatial weights, $W=\sum_{i=1}\sum_{j=1}^{n}w_{i,j}$. The
Moran’s I statistic is normalized so that values fall between -1 and 1, with
positive values indicating that neighboring features tend to be both smaller
or both larger than the mean (clustering) and negative values indicating that
features higher than the mean tend to be located next to features with low
scores (dispersal). We can use this statistic to test, using a one-sided test
at the 5% level, whether there is more spatial clustering of observed values
relative to what would be expected if values were distributed randomly.

Given evidence of global spatial clustering, we examine local clustering
patterns. We calculate the Getis-Ord G* statistic [37] as follows:

$G_{i}^{*}=\frac{\sum_{j}^{n}w_{i,j}x_{j}-\bar{x}\sum_{j}^{n}w_{i,j}}{S\sqrt{\frac{n\sum_{j=1}^{n}w_{i,j}^{2}-(\sum_{j=1}^{n}w_{i,j})^{2}}{n-1}}}$
(2)

That is, for county of interest $i$, $w_{i,j}$ is the spatial weight for
county $i$ and county $j$444We set $w_{i,j}=1$ if county $j$ is a contiguous
neighbor of county $i$ or if $j$ = $i$ and 0 otherwise., $n$ is again the
total number of counties, $\bar{x}$ is the overall average, $x_{j}$ is the
outcome value for county $j$, and

$S=\sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}}{n}-\bar{x}^{2}}$ (3)

The result is a z-score, which can be used to test the null hypothesis that
neighboring counties have rates no more similar to one another than would be
expected by random change. In contrast with the Moran’s I, however, which is a
global score across all counties, we are able to calculate the G* statistic
for each county individually. We can thus map results using a two-sided 5%
cutoff, identifying hotspots as counties where the G* statistic $>$ 1.96 and
coldspots as those counties where the G* statistic $<$ -1.96. We conduct both
our global and local clustering analyses in R using the spdep package [38].

Next, we examine raw associations between rates of search for ChatGPT and
county-level socioeconomic, demographic, and industry makeup. Because county-
level correlates have skewed and non-normal distributions, we examine logged
rates of search for ChatGPT in comparison with counties’ percentile ranks for
each variable. We further calculate medians and interquartile ranges (IQRs) of
search rates in each percentile rank bin. We calculate these estimates,
weighted by population, in R using the HMisc package [39].

Finally, we fit a series of negative binomial models to predict the rate of
searches for ChatGPT. We use a multilevel specification, allowing a random
effect by state to account for the spatial autocorrelation in our data.
Likelihood ratio tests confirm a significant improvement in fit from the use
of a multilevel specification, as well as significant improvements from the
use of negative binomial rather than simpler poisson models to account for
overdispersion; we also observe large reductions in AIC and BIC values for the
multilevel negative binomial specification in comparison with simpler
approaches.

Our final models have the following form

$\displaystyle\log(\text{SearchesChatGPT}_{ij})$
$\displaystyle=X_{ij}\beta+u_{j}+\text{offset}(\log(\text{SearchesOverall}_{ij}))$
(4) $\displaystyle\text{SearchesChatGPT}_{ij}$
$\displaystyle\sim\text{NegativeBinomial}(\mu_{ij},\theta)$

Where we model the count of searches, $\text{SearchesChatGPT}_{ij}$ for county
$i$ in state $j$, as a function of county-level covariations $X_{ij}$ and a
state-level random effect $u_{j}$, with the total count of searches at the
county level, $\text{SearchesOverall}_{ij}$, included as an offset. We fit
models with the sets of socioeconomic, demographic, and industry makeup
variables separately; we then fit a full specification including all
covariates. We standardize and scale covariates both to stabilize the model
fit and to facilitate comparison across coefficients. After observing large
changes in the full model in comparison with the separate
specifications—consistent with confounding—we iteratively remove individual
regressors to evaluate the change across other covariates associated with each
variable’s exclusion. Finally, we assess the fit of the final model by mapping
the predicted and residual values and again using the Moran’s I statistic to
test for residual spatial autocorrelation. Models are fitted using the glmmTMB
package [40], with residuals assessed using the DHARMa package [41], again
using R.

To test the robustness of our results to the limitations of our search data
set with respect to coverage and data suppression, we also re-run all of our
analyses with the Google trends data set. Although these data are the gold
standard for research using internet search data given their disproportionate
coverage relative to all search queries [42], the finest available level of
disaggregation is at the metro level. This limits our ability to examine fine-
grained differences between percentile groups with respect to economic and
demographic correlates; nevertheless, we are able to reproduce our main
analyses at this level as a means of testing the robustness of our results to
limitations of the choice of dataset.

##  3 Results

Over the six months of observation, we observe a median rate of approximately
6.2 searches for ChatGPT per 10,000 total searches (IQR 4.5 - 7.7). Figure 1
shows rates of search for ChatGPT by state. Rates of search are highest in
Washington and California and lowest in states in the Gulf (Louisiana,
Alabama, Mississippi) and Appalachia (West Virginia, Kentucky and Tennessee),
as well as New Mexico and South Dakota.

Figure 1: Rates of Search for ChatGPT by State. Colors indicate the fraction
of all searches that included reference to ChatGPT in each state-period
observation across the first six months since the initial public release of
the generative artificial intelligence tool.

These spatial differences emerge early and persist throughout the observation
period. Figure 2 shows state-level search rates disaggregated by month. The
west-coast states rank in the top 10% of all state-period observations
beginning in February, and remain among the highest-ranked regions through
May. Although rates of search for ChatGPT do increase in Gulf and Appalachian
states over this period, these states persistently rank in the bottom 10
states with respect to interest over each month of the six-month period of
observation.555The remaining two places in the top five with respect to search
rates are the District of Columbia and Utah.

Figure 2: Average monthly rates of searches for ChatGPT by state. West coast
states with the highest rates of search are highlighted in green; states with
persistently low rates of search are highlighted in purple (Louisiana,
Alabama, Mississippi) and blue (Tennessee, West Virginia, Kentucky).

Examining rates of search at the county level, we observe a moderate but
statistically significant level of spatial clustering (Moran’s I = 0.26, p $<$
0.001). This suggests that counties with higher rates of search for ChatGPT
are likely to be geographically clustered. The estimate is robust to the
exclusion of values from outlier counties: examining winsorized rather than
raw values, we see a similar result (Moran’s I = 0.27, p $<$ 0.001).

Figure 3: Detection of hot- and coldspots with the Getis-Ord G* Statistic. Red
indicates the presence of a statistically significant hotspot (Getis-Ord G*
$>$ 1.96) and blue indicates the presence of a statistically significant
coldspot (Getis-Ord G* $<$ -1.96).

Figure 3 shows clusters of neighboring counties with more elevated rates of
search (hotspots) or lower rates of search (coldspots) than would be expected
if rates were distributed randomly; we note that the hotspots coincide with
major metropolitan areas in the West (Seattle, Portland, San Francisco and
Silicon Valley, Los Angeles); in the Mountain West (Salt Lake City and
Denver); on the East coast (Boston, New York City to Philadelphia, and
Washington D.C); and in Texas (Houston, Austin, and Dallas); as well as
Atlanta and Minneapolis. Coldspots are predominantly observed in more rural
areas including the northern reaches of Wisconsin and Michigan, and in West
Virgina, South Carolina, and Georgia. We also observe notable urban coldspots
surrounding Memphis, TN, Baton Rouge, LA, and Jackson, MS. No coldspots are
observed in the three West Coast states.

We next examine the associations between rates of search for chatGPT and
socioeconomic, demographic, and industry makeup respectively. In Figure 4, we
see a strong positive association between the fraction college-educated or
median incomes and logged rates of search. In particular, in counties in the
top 10% with respect to the fraction of residents who are college-educated,
population-weighted median rates of search are approximately 7.7 per 10k
searches (IQR 6.8 - 10.4) versus 2.8 (IQR 2.4-3.3) for the bottom 10% if
counties. By contrast, more rural counties have relatively lower rates of
search (3.0, IQR 2.4-3.9 for the bottom 10%) in comparison with less rural
counties (7.1, IQR 6.0 - 8.7 for the top 10%).

Figure 4: Searches for ChatGPT in relation to socioeconomic makeup. Blue
points indicate raw observations; black points are medians and 75% inter-
quartile ranges. Observations and estimates are weighted by county population.

Figure 5 offers evidence of an inverse association with the fraction of the
population that is non-Hispanic White, with counties with the highest rates of
search characterized by proportionally large Hispanic or Latino and Asian
populations. The association with the fraction Asian is particularly strong:
counties in the top percentiles with respect to fraction Asian have rates of
search greater than 10 per 10k searches (IQR 7.8 - 14.8) versus 2.6 (2.2 -
3.3), 5.8 (3.7 - 5.9), and 5.6 (4.1 - 7.3) for the top percentiles of White,
Black, and Hispanic or Latino populations, respectively. These associations
should be interpreted with caution, as they are likely confounded by the
strong associations of region or urbanicity and ChatGPT search rates.

Figure 5: Searches for ChatGPT in relation to demographic makeup. Blue points
indicate raw observations; black points are medians and 75% inter-quartile
ranges. Observations and estimates are weighted by county population.

Finally, Figure 6 examines the association of search rates for ChatGPT with
the fraction of jobs in different sectors. As might be expected, there is a
strong and increasing association between the percentile of fraction tech
sector jobs in a given county and its rates of search for ChatGPT. Counties in
the lowest ten percentiles of arts sector jobs also have lower rates of search
(3.0, IQR 2.5 - 3.7) in comparison to other counties (6.3, 4.6 - 7.8), though
the trend flattens across the upper quantiles. With respect to finance sector
jobs, we observe an increasing trend, though the gradient is not as steep as
that observed for the tech sector. No similar gradient is observed for service
sector jobs.

Figure 6: Searches for ChatGPT in relation to industry makeup. Blue points
indicate raw observations; black points are medians and 75% inter-quartile
ranges. Observations and estimates are weighted by county population.

Table 1 shows rate ratios from adjusted models with state-level random
effects. We first examine the adjusted assocations between socioeconomic
factors and rates of search for ChatGPT (columns 1 and 2). We continue to see
negative associations of fraction rural and rates of search for ChatGPT after
adjusting for median income (Column 1); however, after including education as
a covariate, the fraction rural is no longer statistically significant and
median income is actually inversely associated with rates of search for
ChatGPT (Column 2).

We next examine adjusted associations with demographic makeup. Table 1 column
3 shows a strong positive association with the fraction Asian-a county that
has 1SD higher fraction Asian would have approximately 1.3 times the rate of
search for ChatGPT of a comparable county in the same state with the same
Black and Hispanic fractions (implying a reduction in fraction White, which is
excluded). This association remains positive and significant, though it is
attenuated, after the inclusion of education as a covariate (Column 4).
Notably, there is a significant and negative association with the fraction
Black in single-level models as well as in robustness checks with Google
Trends data (Table 2), but this association is no longer significant in the
main specifications after the inclusion of state-level random effects.

Columns 5 and 6 offer evidence that education confounds the association of
industry makeup and search rates. In column 5, we observe a significant and
positive effect of fraction jobs in the tech sector, a smaller but still
significant and positive association with arts sector jobs, and a small but
significant inverse association with the share of jobs in the service
industry. However, after adjusting for fraction college educated, associations
are attenuated or even inverted (Column 6). Using the Google Trends data, the
fraction of jobs in the technology sector has a large and significant positive
association with rates of search, but the association is attenuated with the
inclusion of education (Table 2 Columns 5-6).

Finally, fully adjusted models follow a similar pattern. When education is
excluded, we see positive assocations with median income, fraction asian, and
arts share, and negative assocations with fraction rural and fraction
Hispanic. After accounting for education, we continue to see positive
associations with fraction Asian and fraction Hispanic, but associations with
median income, tech share, and finance share are negative and other
associations are no longer significant. Notably, the association with
education is both significant and large: holding other factors constant, a
county with 1 SD higher fraction college educated would have approximately 1.4
times more searches for ChatGPT as a fraction of total searches. This result
is also robust to the use of Google Trends rather than Bing Search data (Table
2 Column 8).

| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
---|---|---|---|---|---|---|---|---
College Educated |  | 1.39∗∗∗ |  | 1.31∗∗∗ |  | 1.44∗∗∗ |  | 1.43∗∗∗
|  | 0.02 |  | 0.01 |  | 0.02 |  | 0.02
Median Income | 1.11∗∗∗ | 0.93∗∗∗ |  |  |  |  | 1.05∗∗∗ | 0.96∗∗∗
| 0.01 | 0.01 |  |  |  |  | 0.02 | 0.01
Rural | 0.86∗∗∗ | 0.98 |  |  |  |  | 0.93∗∗∗ | 1
| 0.01 | 0.01 |  |  |  |  | 0.01 | 0.01
Unemployed | 1 | 1.01 |  |  |  |  | 0.98 | 1.01
| 0.01 | 0.01 |  |  |  |  | 0.01 | 0.01
Black |  |  | 0.99 | 1 |  |  | 0.99 | 0.99
|  |  | 0.01 | 0.01 |  |  | 0.01 | 0.01
Hispanic |  |  | 0.99 | 1.04∗∗∗ |  |  | 0.98∗∗∗ | 1.04∗∗∗
|  |  | 0.01 | 0.01 |  |  | 0.01 | 0.01
Asian |  |  | 1.28∗∗∗ | 1.06∗∗∗ |  |  | 1.16∗∗∗ | 1.08∗∗∗
|  |  | 0.02 | 0.01 |  |  | 0.02 | 0.01
Tech Share |  |  |  |  | 1.16∗∗∗ | 0.94∗∗∗ | 1.03 | 0.92∗∗∗
|  |  |  |  | 0.01 | 0.01 | 0.02 | 0.01
Arts Share |  |  |  |  | 1.14∗∗∗ | 1 | 1.1∗∗∗ | 1
|  |  |  |  | 0.01 | 0.01 | 0.01 | 0.01
Finance Share |  |  |  |  | 1.01 | 0.96∗∗∗ | 0.99 | 0.97∗∗∗
|  |  |  |  | 0.01 | 0.01 | 0.01 | 0.01
Service Share |  |  |  |  | 0.96∗∗∗ | 1.03∗∗∗ | 1 | 1.02
|  |  |  |  | 0.01 | 0.01 | 0.01 | 0.01
AIC | $35782$ | $35143$ | $35735$ | $35149$ | $35756$ | $35113$ | $35535$ | $35046$
BIC | $35817$ | $35183$ | $35769$ | $35190$ | $35797$ | $35159$ | $35610$ | $35127$
Log Likelihood | $-17885$ | $-17564$ | $-17861$ | $-17567$ | $-17871$ | $-17548$ | $-17754$ | $-17509$
Var: STATE (Intercept) | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$
Table 1: Results are exponentiated coefficients and standard errors from
multilevel negative binomial models. Models predict the count of searches for
ChatGPT, with the total count of searches by county included as an offset. N =
2,397 counties across 51 states. ${}^{***}p<0.001$; ${}^{**}p<0.01$;
${}^{*}p<0.05$

Residual tests show that observations deviate significantly from the
distribution expected given the model, but we see no significant evidence of
over- or underdispersion or of outliers. Spatial autocorrelation in the
residuals is attenuated relative to that observed for the raw values (Moran’s
I = 0.18) though still statistically significant (p $<$ 0.001).

##  4 Discussion

This study characterizes the digital divide with respect to knowledge of
generative AI in the United States. Examining county- and state-level rates of
search for a popular new technology, ChatGPT, we make three observations.
First, we observe a strong geographic divide, with the highest rates of search
on the West coast and lower rates of search along the Gulf Coast through
Appalachia. Second, we show how spatial differences are consistent with
socioeconomic divides. In particular, we observe that counties with the
highest rates of search for ChatGPT are relatively more urbanized, higher
income, more educated, more Asian, and have more technology jobs relative to
other counties. Finally, education emerges as the strongest predictor of
interest in fully adjusted models.

These findings offer evidence of a new, emerging set of disparities in
generative AI access and use that follows very similar patterns to the more
general digital divide in access and use of internet tooling [4, 6, 43]. It is
important to note, however, that the strong observed associations with area-
level racial makeup, urbanicity, income, and industry makeup are confounded by
education, which emerges as the strongest positive predictor of rates of
search for ChatGPT. This finding is consistent with prior research, which
finds that education is among the strongest predictors of differences in
internet use [44, 45, 46, 47] and, in one case [45] documents a similar
attenuation of other associations after accounting for education. Our study is
novel, however, in documenting this association for emerging generative AI
tooling as well as in highlighting the spatial as well as socioeconomic
patterning in observed differences.

Our study is subject to several limitations. First, our data are from a major
search provider. While this affords us the benefit of scale, with a large
enough sample size that we can examine finer-grained geographic patterns
(counties) relative to those available using other data sources, the data may
be subject to selection bias due to the set of people who choose to use the
particular search provider from which we obtain our data. Moreover, to protect
the privacy of search users, data are suppressed for a relatively large
faction of counties. To assess the sensitivity of our results to these
challenges, we revisit all analysis with data from the google trends database,
a search database with the highest rates of coverage nationally [42], using
data at the smallest available spatial unit—metropolitan areas—without data
suppression. We obtain similar results to those described in our primary
analyses.

Second, we recognize that search is a crude proxy for actual usage, and one
that may be subject to confounding over time if users use search only for
initial discovery and then gradually select out of the data set. We mitigate
this challenge by examining a completely novel tool (ChatGPT), and by limiting
our analyses to only the first six months after it became publicly available—a
period for which searches are among the best available early indicators of
interest. Moreover, we note that the confounding problem would lead us to
underestimate disparities as users in the highest-interest areas selected out
of the dataset while more people in other areas continued to rely on search
for initial discovery; similarly, because we examine differences in rates of
search over time, our results describe only differences in use (second-level
divides) and are likely exacerbated by fundamental differences in internet
access (the first-level divide). The differences we observe should thus be
treated as a lower bound on actual disparities. As users increasingly access
these tools directly rather than relying on internet search discovery, there
is a need for further research that examines actual rates of usage either
using the relevant (proprietary) usage datasets or individual-level surveys.

Third, we examine spatially aggregated outcomes that may be subject to the
modifiable areal unit problem in which aggregate patterns may not correspond
directly with individual-level relationships [48]. However, our findings are
consistent with early individual-level studies. For example, in a Pew Research
poll conducted in March of 2023—-four months after the initial release of
ChatGPT—adults with postgraduate degrees were almost twice as likely to have
heard about ChatGPT in comparison with adults with high school degrees or
less, and Asian adults had notably higher awareness of the tool in comparison
with Black, Hispanic, or White adults [49].

Finally, our findings are not causally identified and focus exclusively on a
Global North context. To our knowledge, however, this study is the first
national-scale characterization of geographic differences in uptake of tooling
with large potential impacts on productivity and performance [18, 17, 19, 21,
22, 23, 24]. The stylized facts presented here support further research to
better characterize and isolate relevant processes of social and spatial
diffusion producing these aggregate patterns. Moreover, regardless of the
underlying processes, the stark differences we observe across places should be
of serious concern to policymakers given the evidence of strong economic
advantages for places where novel technologies are adopted early [50].

This study offers an important early warning of an emerging digital divide in
generative AI, the likely next general-purpose technology [1]. While these
findings are not surprising given their consistency with prior digital divides
[43], generative AI has unique characteristics that should make possible a
leapfrogging rather than lagging of the most disadvantaged places with respect
to its use. For example, multi-lingual capabilities make these tools more
widely accessible to non-english speakers in comparison with existing digital
tooling [51], though early research shows that these elements must be
incorporated early in design with active effort required to mitigate
differences in cost and quality between high- and low-resource languages [52].
Similarly, the multimodality of advanced models shows promise to support
general-purpose interfaces that make traditional tooling accessible by voice,
text, or other diverse forms of interaction [53]. While these characteristics
have the potential to enable broadly accessible tooling, our research suggests
that more active intervention may be necessary. Mitigating divides in computer
and internet access continues to require mass campaigns, with workshops and
free resources at public libraries and active interventions across schools
[54]; a similar effort may be called for in the context of the new generative
AI divide.

Acknowledgments

The authors are grateful for comments and suggestions from Jina Suh, Jake
Hofman, Dan Goldstein, Sonia Jaffe, Daricia Wilkinson, and Siddharth Suri.
This work was also improved by feedback from Scott Page and Seth Spielman.

##  Appendix A Comparison with Google Trends

Figure 7: Map of state-level searches for ChatGPT using the Google Trends index. The inset shows that the Google Trends Index is highly correlated with the rates of search calculated from Bing search data (Pearson’s Correlation = 0.86) Figure 8: Searches for ChatGPT in relation to socioeconomic makeup. Blue points indicate values of the google search trends index; black points are medians and 75% inter-quartile ranges of metro-level ACS data, manually linked to the closest designated market area. Observations and estimates are weighted by metro area population. Figure 9: Searches for ChatGPT in relation to demographic makeup. Blue points indicate values of the google search trends index; black points are medians and 75% inter-quartile ranges of metro-level ACS data, manually linked to the closest designated market area. Observations and estimates are weighted by metro area population. Figure 10: Searches for ChatGPT in relation to socioeconomic makeup. Blue points indicate values of the google search trends index; black points are medians and 75% inter-quartile ranges of metro-level ACS data, manually linked to the closest designated market area. Observations and estimates are weighted by metro area population. | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
---|---|---|---|---|---|---|---|---
(Intercept) | 17.52∗∗∗ | 17.1∗∗∗ | 17.74∗∗∗ | 17.26∗∗∗ | 17.59∗∗∗ | 17.18∗∗∗ | 17.58∗∗∗ | 17.28∗∗∗
(Intercept) | 0.62 | 0.6 | 0.63 | 0.53 | 0.65 | 0.59 | 0.53 | 0.52
College Educated |  | 1.34∗∗∗ |  | 1.28∗∗∗ |  | 1.32∗∗∗ |  | 1.33∗∗∗
|  | 0.05 |  | 0.03 |  | 0.05 |  | 0.05
Median Income | 1.25∗∗∗ | 1.02 |  |  |  |  | 1.01 | 0.93
| 0.04 | 0.04 |  |  |  |  | 0.05 | 0.04
Rural | 0.95 | 0.98 |  |  |  |  | 1.03 | 1.01
| 0.03 | 0.03 |  |  |  |  | 0.04 | 0.03
Unemployed | 1 | 1.04 |  |  |  |  | 0.99 | 1.03
| 0.03 | 0.03 |  |  |  |  | 0.04 | 0.04
Black |  |  | 0.93∗∗∗ | 0.95 |  |  | 0.92∗∗∗ | 0.92∗∗∗
|  |  | 0.03 | 0.03 |  |  | 0.03 | 0.03
Hispanic |  |  | 1.01 | 1.07∗∗∗ |  |  | 1.01 | 1.06
|  |  | 0.03 | 0.03 |  |  | 0.04 | 0.04
Asian |  |  | 1.28∗∗∗ | 1.1∗∗∗ |  |  | 1.14∗∗∗ | 1.12∗∗∗
|  |  | 0.03 | 0.03 |  |  | 0.04 | 0.03
Tech Share |  |  |  |  | 1.3∗∗∗ | 1.08∗∗∗ | 1.22∗∗∗ | 1.04
|  |  |  |  | 0.04 | 0.04 | 0.05 | 0.04
Arts Share |  |  |  |  | 0.99 | 0.96 | 0.98 | 0.97
|  |  |  |  | 0.03 | 0.03 | 0.03 | 0.03
Finance Share |  |  |  |  | 0.98 | 0.97 | 0.99 | 0.98
|  |  |  |  | 0.03 | 0.02 | 0.03 | 0.03
Service Share |  |  |  |  | 0.97 | 1.06 | 1.01 | 1.01
|  |  |  |  | 0.04 | 0.04 | 0.05 | 0.04
AIC | $1273$ | $1213$ | $1264$ | $1193$ | $1258$ | $1211$ | $1242$ | $1200$
BIC | $1293$ | $1236$ | $1284$ | $1216$ | $1281$ | $1237$ | $1285$ | $1246$
Log Likelihood | $-631$ | $-599$ | $-17861$ | $-17567$ | $-17871$ | $-17548$ | $-17754$ | $-17509$
Var: STATE (Intercept) | $0.03$ | $0.03$ | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$ | $0.02$
Table 2: Results are exponentiated coefficients and standard errors from
multilevel negative binomial models. Models predict the searches for ChatGPT
(Google Search Index). N = 198 metro areas across 48 states.
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

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