You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Artificial Intelligence and the Labor Market | NBER

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Artificial Intelligence and the Labor…

Artificial Intelligence and the Labor Market

Menaka Hampole
,

Dimitris Papanikolaou
,

Lawrence D.W. Schmidt

&
Bryan Seegmiller

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Working Paper
33509

DOI
10.3386/w33509

Issue Date

February 2025

Revision Date

September 2025

We use advances in natural language processing to construct new measures of workers’ task-level exposure to artificial intelligence (AI) and machine learning from 2010 to 2023, capturing variation across firms, occupations, and time. Tasks with higher AI exposure subsequently experience reduced labor demand. To interpret these patterns, we develop a model that separates direct substitution from indirect reallocative effects of labor-saving technologies. Two variables summarize the impact of AI on within-firm labor demand: the mean exposure of an occupation’s tasks, which depresses demand, and the concentration of exposure in a few tasks, which offsets losses by enabling workers to reallocate effort. Using an instrument based on historical university hiring networks, we find causal evidence consistent with these predictions. Despite strong substitution at the task level, overall employment effects are modest, as reduced demand in exposed occupations is offset by productivity-driven increases in labor demand at AI-adopting firms.

Acknowledgements and Disclosures

We are grateful to Matthew Akuzawa, Huben Liu, Weizhe Sun, and Tim Zhang for their excellent research assistance. We also thank Daron Acemoglu, Philippe Aghion, David Autor, Antonin Bergeaud, Tarek Hassan, Wei Jiang (discussant), Yueran Ma, Pascual Restrepo (discussant), and participants at various conferences and seminars for their helpful comments. Dimitris Papanikolaou and Bryan Seegmiller thank the The Financial Institutions and Markets Research Center and the Asset Management Practicum for their generous financial support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Menaka Hampole, Dimitris Papanikolaou, Lawrence D.W. Schmidt, and Bryan Seegmiller, "Artificial Intelligence and the Labor Market," NBER Working Paper 33509 (2025), https://doi.org/10.3386/w33509.

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February 18, 2025

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Future Proofing Your Career In An Era Of AI - Forbes

January 6, 2026

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</reference>

<statements>
1. Emerging and developing economies, in contrast, often exhibit greater adjustment constraints, including limited digital infrastructure, weaker education and training systems, and larger informal sectors, which can magnify displacement risks and restrict access to high-quality AI-complementary employment.
2. Studies focused on specific countries (e.g., Türkiye) using high-frequency online labor market data document shifts in skill demand toward AI-related competencies but also highlight frictions in labor market adjustment, including skills mismatches and uneven access to training.
3. Overall, the literature emphasizes that policy choices will be decisive in determining whether AI’s role in the Fourth Industrial Revolution leads to inclusive growth or deepening inequality.
4. Another gap relates to detailed occupational and regional analyses in emerging and developing economies, where data limitations hinder precise measurement of AI exposure and impacts.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.