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The unintended consequences of large language models as a labor-augmenting technology in science

As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.

The unintended consequences of large language models as a labor-augmenting technology in science

As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.

The Paper Factory

How can large language models (LLMs) contribute to social science research, and what parts of research remain stubbornly human? Building on existing LLM tools, we offer a multi-agent workflow capable of producing a full quantitative social science paper from an initial prompt. The workflow relies on researchers codifying their heuristics for doing data analysis, and we suggest some core design principles for researchers interested in building on this scaffolding. Using this case, we also examine what current LLM capabilities reveal about the organization of research. LLM agents can lower the cost of pursuing high-risk ideas, expand robustness and transparency, reduce concerns about the scientific file drawer, and force scholars to articulate the heuristics that create valuable work. But they also pose challenges, both in terms of the quality of papers and in the adequacy of scientific institutions to adapt. Meeting these challenges will require new institutional norms that make use of these tools observable, auditable, and accountable.

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).

Towards Automating Scientific Review with Google's Paper Assistant Tool

Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT benchmark. Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.

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