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TRUVACE RECORD VERSION
record: TRV-2026-2500
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:43:37.812653Z
status: published
lens: trace
sector: business
headline: The Growth and Performance of Artificial Intelligence in Asset Management
dek: We examine the growth and performance of AI-driven investing. Using investment advisers' regulatory disclosures, labor market data, and fund strategy descriptions, we document that AI-driven investing has grown steadily since the early 2010s and is concentrated among hedge funds. AI hedge funds outperformed non-AI hedge funds in the early years, but this outperformance declined over time, even among early adopters. Contrary to concerns about AI-driven strategy homogeneity, AI hedge funds exhibit lower return com…
gain_title: In a study of 7,896 US hedge funds, funds that disclosed AI-driven strategies beat non-AI funds by about 6% a year after benchmark adjustment in the early years.
problem_title: The same study found the AI funds' edge fell to statistically indistinguishable from zero after 2017, even for funds that adopted AI early.
trace_subject: Performance of AI-driven hedge funds versus non-AI hedge funds
gain_reading: In a study of 7,896 US hedge funds, funds that disclosed AI-driven strategies beat non-AI funds by about 6% a year after benchmark adjustment in the early years.
gain_evidence: AI funds outperform non-AI funds by approximately 6% annually on a benchmark-adjusted basis in earlier years.
problem_reading: The same study found the AI funds' edge fell to statistically indistinguishable from zero after 2017, even for funds that adopted AI early.
problem_evidence: this outperformance decreases over time and becomes statistically indistinguishable from zero after 2017
quick_read: Economists at the University of Melbourne, the University of Texas at Austin and Southern Methodist University tracked 7,896 US hedge funds from 2006 to 2024. AI-driven funds beat non-AI funds by about 6% a year on a benchmark-adjusted basis in the early years. Before 2017 the gap was 49.6 basis points a month, and it disappeared afterward.

The fade was not explained by early versus late adopters: funds launched as AI funds in the early years lost most of their advantage after 2017. The authors link the decline to markets adapting as AI strategies spread and exploit the same opportunities.

Policymakers have worried that AI trading would make funds move in lockstep. Among funds in the same strategy category, AI funds' returns were less correlated with each other (0.021) than non-AI funds' were (0.124). The work is a working paper that has not been peer reviewed, and it measures only disclosed AI use.
limitation: 
tag: Dual reading
key_points: Only 89 of 7,896 hedge funds (1.1%) were ever identified as AI funds, a deliberately strict definition; AI funds' assets reached about USD 12 billion by 2024. | AI funds outperformed non-AI funds by 49.6 basis points a month before 2017, and the difference disappeared after 2017. | Compared with sibling funds run by the same adviser, AI funds outperformed by 34.9 to 41.1 basis points a month over the whole sample, so adviser quality alone does not explain the gap. | Average pairwise alpha correlation was 0.021 among AI fund pairs versus 0.124 among non-AI pairs in the same strategy category. | Investors did not send extra money to AI funds beyond what past performance explained; a one percentage point rise in lagged 12-month alpha went with 34.5 basis points higher monthly inflow. | About 60% of AI funds in 2024 followed systematic diversified macro strategies, and AI-Autonomous funds held over 90% of AI fund assets. | AI funds charged incentive fees nearly 2.5 percentage points higher but imposed roughly 60% shorter lockup and restriction periods. | AI and non-AI funds generated similar net value added over their lifetimes, because AI funds' higher alpha was offset by smaller assets.
rundown: The authors built the sample from SEC Form ADV filings (116,276 brochures covering 62,253 adviser-years from 2012 to 2024), Lightcast job postings and archived Hedge Fund Research strategy descriptions. They used OpenAI's GPT-5 to classify filings and strategy text, applying a strict prompt to limit false positives. They defined AI-driven investing as autonomous AI strategies or strategies in which AI signals directly inform investment decisions.

Advisers mentioning AI in their filings grew from fewer than 10 in 2012 to nearly 300 in 2024, but most of the 2023 to 2024 jump came from risk disclosures, AI-themed assets and research support. Among about 1,550 advisers disclosing quantitative strategies in 2024, around 100 described AI-autonomous or AI-augmented strategies. AI-related job postings stayed below 4% of postings and rose sharply around 2017, with advisers showing higher AI labor intensity than other firms.

Advisers that manage hedge funds, and advisers with larger private-fund assets, posted more AI jobs. AI funds clustered in macro strategies, where liquid markets suit short-term trading despite small datasets. They showed lower exposure to equity risk factors and a positive loading on momentum.

On performance, AI funds' outperformance fell by 5.9 basis points a year on average. Using AQR factors gave similar but slightly smaller results, and the early outperformance was concentrated among AI-Autonomous funds. Early-adopter AI funds beat non-AI funds before 2017, but their edge vanished on one benchmark and reversed on the other afterward, while late adopters showed no significant difference. The authors say the findings point to alpha decay and to growth in AI and quantitative investing making persistent outperformance harder to sustain.
sources:
- peer_reviewed | NBER Working Paper | https://doi.org/10.3386/w35273 | 2026-06-01
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