The integration of large language models (LLMs) into peer review brought a structural change in the production of evaluative signals for research publication. While AI tools relax expert attention constraints, their use interacts critically with reviewer expertise. I model peer review as a screening problem in which authors exert costly effort to reduce fundamental flaws, while referees observe noisy signals of manuscript quality. Under informed (expert) review, noise is mean-zero and reviewers can distinguish signal from noise, even when AI tools are used, so that effort can drive acceptance probability arbitrarily close to one. By contrast, under uninformed review, where referees lack the domain expertise required to alter AI-generated critique, the review signal contains an irreducible noise component with strictly positive lower support. As a result, even flawless work faces a positive probability of rejection, which is strictly higher than under informed review conditional on the same underlying quality. I term this distortion "the Noise Floor". I characterize equilibrium effort and topic choice under uninformed review: the Noise Floor bounds equilibrium effort independently of publication value and induces a distortion away from innovative research. I then explore a mechanism, "Author Speaks Last" , to restore efficient screening.

We develop a novel empirical framework to quantify media deceptiveness, defined as the semantic misalignment between an article's preview and its underlying text. Departing from methodologies that rely on costly external truth anchors, our approach exploits the internal coherence of the text, enabling the scalable detection of misleading framing within factually accurate reporting. We conceptualize the informational structure of a news article as a network of verifiable claims, utilizing topological measures of centrality and semantic distance to identify "Trojan claims": peripheral components of the narrative that are disconnected from the central thesis yet strategically elevated to the headline to manipulate reader inference. Applying this framework to a corpus of 20,000 articles from two U.S. outlets in 2020, we document systematic heterogeneity in deceptiveness. To establish causal evidence of strategic editorial behavior, we exploit a quasi-experiment: Twitter's June 2020 introduction of a "read before you share" friction. Through an event-study design, we demonstrate that this exogenous reduction in reader verification costs induced a permanent, significant decline in the deceptiveness of the lower-reputation outlet. These findings suggest a model of news provision wherein deceptive framing is a strategic choice constrained by the endogenous probability of reader verification, demonstrating that platform-mediated alterations to information frictions can discipline media markets independent of external fact-checking infrastructure.

Large Language Models are said to exhibit "sycophancy"—a tendency to tell users what they want to hear, irrespective of the truth. We propose an outcome-based measure grounded in economic theory: the error rate gap between users who prefer validation and users who prefer accuracy, holding information constant. We develop a model where sycophancy arises in equilibrium from the interaction of LLM incentives, user heterogeneity, and strategic user pushback. Different user types push back at different rates; when LLMs pander to pushback, validation-seekers experience systematically higher error rates. We show that validation-seekers impose a negative externality on accuracy-seekers: their anticipated pushback causes LLMs to disagree less often, depriving accuracy-seekers of valuable information. We develop an empirical methodology of using "structural agents," endowed with known preferences, who interact with commercial LLMs. We use the resulting data to identify the model's key parameters, measure the sycophancy gap across frontier models, and test the presence of a negative externality from sycophancy.