Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams

Xiaoshn Nee; Haobo Zhong; Xiaomin Ni

cs.DLSep 13, 2026

Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams

Authors: Xiaoshn Nee, Haobo Zhong, Xiaomin Ni

Organizations: Independent researcher · HSBC Business School, Peking University, Shenzhen City, Guangdong 518055, China · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology, Shenzhen, China

Abstract

Generative artificial intelligence raises a central question for scientific training and organization. Is research shifting from deep specialization toward broad individual knowledge? We examine this proposition across papers, cited knowledge, contributor histories, and teams using 47,959 articles from six fields over 2010-2025, 51,736 resolved cited works, and chronologically reconstructed prior publication histories for 1,754 randomly selected index contributors. From 2010 to 2022, team size increased by an estimated 37.3% (95% confidence interval [34.4%, 40.3%]), while paper topic breadth declined by 0.0144 on a 0-1 hierarchical distance scale. Cited knowledge was stable to modestly broader, revealing a divergence between focused outputs and the reach of knowledge inputs. Established contributors' prior breadth increased by 0.0190 [-0.0078, 0.0459] by 2019-2022, within a +/-0.05 equivalence bound assessed in sensitivity analysis. In mature citation windows, one standard deviation of focal depth was associated with 8.2% higher 1 + FWCI [1.9%, 14.9%]; average breadth and interaction associations were smaller under the specified equivalence bounds. Post-2022 deviations from earlier trends were not systematic, and recent changes did not vary clearly with baseline AI intensity across 83 subfields. The findings support a differentiated structure of scientific expertise in which focused individual accumulation coexists with expanding collaboration and sustained access to diverse knowledge inputs.

Figures & tables

Figure 1: Linked levels of scientific knowledge and capability. The article sample supplies two complementary branches, one following cited knowledge and the other reconstructing contributor histories. Citation returns link prior individual profiles to focal articles published through 2022, allowing the complete four year window used by OpenAlex’s field weighted citation impact (FWCI). Arrows indicate data linkages. The reference branch includes 3,360 sampled articles, of which 3,215 meet the coverage criteria. The contributor branch contains 1,755 index events for 1,754 unique authors; 1,316 events meet the history and identity criteria, with 959 complete cases in the return model.

Layer	$N$	Inclusion and measurement	Target quantity
Articles	47,959	Random sample of 500 OpenAlex articles in each of six fields and 16 publication years (2010–2025), excluding retracted and paratext records	Field adjusted trends in topic breadth and team size
Cited knowledge	3,215	Thirty-five papers per field and year with at least five references; up to 20 references sampled per paper; at least five and 70% with a classified primary topic	Breadth of the knowledge inputs actually cited
Index contributors	1,316	One contributor selected uniformly from teams of 1–20 authors; 3–300 lifetime works; at least three prior works spanning two years; stable author identity	Revealed five year prior breadth and focal subfield depth
Citation returns	959	Model-complete index-contributor events published through 2022 with OpenAlex FWCI	Conditional citation associations with breadth, depth, and their interaction
AI intensity analysis	83 subfields	At least 30 baseline papers (2010–2018) and 15 post-2022 papers; exposure fixed before the event window	Differential post-2022 change by prior AI intensity

Table 1: Study layers, analytic samples, and target quantities. Counts are after the exclusions shown unless otherwise stated.

Figure 2: Long term changes in breadth and team size. (a) Each breadth series is centered on its field specific 2010–2014 mean and then averaged across six fields; bars show 95% intervals based on variation across fields. Topic breadth uses the primary exactly three topic metric. Cited knowledge breadth uses the expanded reference sample. (b) Team size is expressed as the percentage change from each field’s 2010–2014 mean. Dashed lines mark the start of the post-2022 period.

Figure 3: Direct comparison of earlier and recent research profiles. Blue open circles show 2010–2014, orange diamonds show 2023–2025, and gray arrows connect the two means for each field. (a) Team size and paper topic breadth. (b) Paper topic breadth and cited knowledge breadth. Each coordinate averages the annual field means with equal year weights. Team size uses the full article sample, topic breadth uses articles with exactly three topics, and cited breadth uses the eligible reference sample. Field labels identify the recent endpoints.

Figure 4: Adjusted changes in individual capability. (a) Prior topic breadth. (b) Accumulated depth in the focal article’s subfield, measured on a logarithmic scale. Points are weighted period contrasts relative to 2010–2014, with HC3 95% confidence intervals. The baseline is fixed at zero. Both measures use publications strictly before the focal article, a five year lookback, and a five year exponential half-life. The dashed line separates the recent 2023–2025 period.

Figure 5: Earlier and recent distributions of individual expertise. Blue shows 2010–2014 and orange shows 2023–2025. (a) Prior topic breadth, with 306 and 332 observed contributor events. (b) Focal depth on its logarithmic scale, with 311 and 343 events. The filled shapes show weighted kernel densities, small points show every observed event, diamonds mark weighted medians, thick segments span the interquartile ranges, and thin segments span the 10th–90th percentiles. Each field receives equal total weight within each period, with sampling weights retained within fields. Both periods use the same smoothing bandwidth within each panel.

Figure 6: Conditional citation returns to prior capability. (a) Transformed coefficients

100[\exp(\hat{\beta})-1]

for breadth, focal depth, and their interaction, with HC3 95% confidence intervals. Breadth and depth are standardized within field; each main coefficient is evaluated at the other variable’s mean. The interaction is the coefficient of their product. Dotted lines mark the sensitivity bounds

\pm\log(1.10)

, transformed to –9.1% and 10.0%. (b) Fitted geometric FWCI values at the observed lower and upper quartiles of standardized breadth and depth, obtained by averaging fitted log outcomes over the observed covariate distribution and applying

\exp(\cdot)-1

. Bars summarize fitted profiles.

Test	Estimate	95% CI	Raw $p$	Holm $p$	$H_{0}$ at 5%
Annual paper topic breadth trend	-0.0012	[-0.0017, -0.0007]	$<0.001$	$<0.001$	Reject
Annual $\log(1+\mathrm{team\ size})$ trend	0.0174	[0.0160, 0.0188]	$<0.001$	$<0.001$	Reject
Individual breadth, 2019–2022 vs. 2010–2014	0.0190	[-0.0078, 0.0459]	0.165	0.494	Do not reject
Individual depth, 2019–2022 vs. 2010–2014	-0.0274	[-0.1451, 0.0902]	0.648	1.000	Do not reject
Breadth $\times$ depth return on log FWCI	0.0069	[-0.0504, 0.0641]	0.814	1.000	Do not reject
Mean post-2022 forecast residual	-0.0108	[-0.026, 0.004]	0.116	0.463	Do not reject

Table 2: Six primary tests with familywise adjustment. The family was specified after data construction and before estimating capability and outcome associations.

Quantity	Estimate	95% CI	Scale
Paper topic breadth, cumulative 2010–2022	-0.0144	[-0.0199, -0.0089]	distance
Cited knowledge breadth, cumulative 2010–2022	0.0273	[0.0002, 0.0545]	distance
Team size, cumulative 2010–2022 (auxiliary model)	37.3	[34.4, 40.3]	%
Individual breadth, 2019–2022 vs. baseline	0.0190	[-0.0078, 0.0459]	distance
Individual breadth, 2023–2025 vs. baseline	0.0327	[0.0044, 0.0610]	distance
Return to breadth per within field SD	1.8	[-4.7, 8.8]	%

Table 3: Effect sizes and equivalence analyses.

Figure 7: Recent patterns relative to earlier trajectories and baseline AI intensity. (a) Mean 2023–2025 paper breadth minus the forecast from each field’s selected 2010–2018 trend model. Bars show 1.96 standard errors of the residuals across the three recent years; the pooled test in the text uses the six field means. (b) Continuous difference in differences coefficients per standard deviation of pre-2019 AI intensity within field, with 95% confidence intervals clustered by subfield. The outcomes are paper topic breadth, cited knowledge breadth, and

\log(1+\text{team size})

. Coefficients retain each outcome’s measurement scale.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A1 : Topic and cited knowledge breadth within fields. Blue solid lines with circular markers show paper topic breadth, and orange solid lines with square markers show cited knowledge breadth. Each series is centered on its own 2010–2014 field mean. Black horizontal lines indicate zero change from this baseline, and gray vertical dashed lines mark the start of 2023. Panels correspond to (a) arts and humanities, (b) computer science, (c) engineering, (d) medicine, (e) physics and astronomy, and (f) social sciences.

Specification	Annual slope	95% CI	$p$	$N$
Primary score weighted, exactly 3 topics	-0.00120	[-0.00166, -0.00074]	$<0.001$	29,992
Exactly 3 topics, SE clustered within field and year cells	-0.00120	[-0.00190, -0.00050]	$<0.001$	29,992
All papers, topic count adjusted	-0.00099	[-0.00136, -0.00061]	$<0.001$	38,976
Maximum topic distance, exactly 3 topics	-0.00098	[-0.00161, -0.00036]	0.002	29,992
Unweighted pair distance, exactly 3 topics	-0.00117	[-0.00173, -0.00061]	$<0.001$	29,992
Any cross-field topic (linear probability)	-0.00268	[-0.00409, -0.00127]	$<0.001$	29,992

Appendix

Table A1 : Robustness of the 2010–2022 paper topic breadth trend.

Specification	Breadth	Depth	Breadth $\times$ depth	$N$
Five year main	0.018 (0.598)	0.079 (0.010)	0.007 (0.814)	959
Ten year history	0.011 (0.729)	0.072 (0.015)	0.007 (0.790)	970
Last ten works	-0.003 (0.942)	0.074 (0.035)	0.018 (0.599)	685
ORCID linked authors	0.049 (0.270)	0.136 (0.002)	0.026 (0.521)	601
Unweighted	0.007 (0.811)	0.108 ( $<0.001$ )	-0.011 (0.653)	959
Strict primary subfield depth	0.018 (0.583)	0.093 (0.002)	0.003 (0.924)	959

Appendix

Table A2 : Robustness of capability and citation estimates. Entries are log-coefficients with two sided

p

-values in parentheses.

Outcome	Post $\times$ AI intensity	95% CI	$p$	$N$	Subfields
Paper topic breadth	0.0000	[-0.0047, 0.0047]	1.000	16,591	83
$\log(1+\mathrm{team\ size})$	0.0127	[-0.0166, 0.0419]	0.396	20,393	83
Cited knowledge breadth	0.0047	[-0.0127, 0.0220]	0.596	1,360	83
Individual prior breadth	0.0235	[-0.0005, 0.0476]	0.055	661	77
Individual focal depth	-0.0467	[-0.1719, 0.0785]	0.465	674	77

Appendix

Table A3 : Exploratory post-2022 heterogeneity by pre-2019 AI intensity.

Figure A2 : Reference metadata coverage by field and publication year. Lines show the mean share of sampled reference identifiers resolved to a work with a primary topic. Coverage is calculated for each focal article before averaging within field and year.

Field	2010–2014, $n/N$ (%)	2023–2025, $n/N$ (%)
Arts and Humanities	2/2,500 (0.08)	0/1,499 (0.00)
Computer Science	2/2,497 (0.08)	2/1,488 (0.13)
Engineering	2/2,500 (0.08)	0/1,500 (0.00)
Medicine	10/2,500 (0.40)	27/1,497 (1.80)
Physics and Astronomy	44/2,500 (1.76)	57/1,499 (3.80)
Social Sciences	1/2,500 (0.04)	1/1,500 (0.07)

Appendix

Table A4: Team coverage and sensitivity of the paper topic trend.

Outcome	Definition	Estimate	95% CI	$p$	$N$
Paper topic breadth	Broad	$0.000000$	$[-0.004670,0.004671]$	1.000	16,591
	Legacy core	$-0.000004$	$[-0.004688,0.004679]$	0.999	16,591
$\log(1+\mathrm{team\ size})$	Broad	$0.0127$	$[-0.0166,0.0419]$	0.396	20,393
	Legacy core	$0.0128$	$[-0.0163,0.0418]$	0.389	20,393
Cited knowledge breadth	Broad	$0.0047$	$[-0.0127,0.0220]$	0.596	1,360
	Legacy core	$0.0053$	$[-0.0116,0.0222]$	0.539	1,360

Appendix

Table A5: Sensitivity of recent AI intensity contrasts to the topic vocabulary.

Bibliometric Analysis

Bibliometric analysis uses publication, citation, collaboration, and text data to map and measure the structure and evolution of research.

See all 35 papers

Stronger artificial intelligence breakthroughs hasten their own exhaustion
Oct 19, 2026Beichen Lu, Honglin Bao, Qi Huang +2
School of Public Policy and Management, Tsinghua University, Beijing, China. · Knowledge Lab, University of Chicago, Chicago, IL, USA. · Data Science Institute, University of Chicago, Chicago, IL, USA. +2
Scientific exploration, collaboration and labor division in the large language model era
Jul 23, 2026Xiang Zheng, Xi Hong, Jialin Liu +1Scientific Discovery AI-Assisted Scientific Research
Information School, University of Wisconsin–Madison, Madison, WI, USA.
Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams
Apr 24, 2026Haoyang Wang, Mingze Zhang, Yi Bu +2AI-Assisted Scientific Research
School of Information, University of Texas at Austin, Austin, TX 78701, USA · National Science Library, Chinese Academy of Sciences, Beijing 100190, China · Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China +4

Oct 19, 2026J/K move · Enter open · S save

Stronger artificial intelligence breakthroughs hasten their own exhaustion

Beichen Lu, Honglin Bao, Qi Huang, Qingguo Meng+1

School of Public Policy and Management, Tsinghua University, Beijing, China. · Knowledge Lab, University of Chicago, Chicago, IL, USA. · Data Science Institute, University of Chicago, Chicago, IL, USA.+2

Open paper AskPDF arXiv

Progress in artificial intelligence is widely assumed to scale with effort: more researchers, papers and investment should accelerate discovery. Theories of technological growth instead locate the difficulty of discovery in the space of ideas. Here we show that neither scale nor space is enough: breakthroughs depend on diverse attention, which collective concentration can deplete. Across 36 years of computer science, we analyze 7.2 million papers, 73,089 peer reviews and 3.7 million researchers and their topic shifts. The field expanded while its attention narrowed sharply after the Transformer. Over half of topic-shifting researchers moved into AI, especially mid-career ones, and reviewer confidence fell by a full point on a 5-point scale. Each successive breakthrough, from AlexNet to large language models, raised the field's carrying capacity for ideas but was followed by steeper exhaustion of those ideas. Bigger breakthroughs capture attention more completely, depleting the diversity their successors may draw upon. Structural biology shows the same cycle after cryo-EM and AlphaFold. Simulations show how well-intentioned and widely used policies such as reciprocal reviewing empower prolific authors and further narrow what the field accepts. We call for institutions that protect diverse attention in artificial intelligence rather than reward concentration on the winning paradigm.

Abstract

Bibliometric Analysis

Explore similar work

Stronger artificial intelligence breakthroughs hasten their own exhaustion

Scientific exploration, collaboration and labor division in the large language model era

Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams