AI agents operate through software, but the computing infrastructure behind them occupies physical space. Data centers turn that need into investment in buildings, power infrastructure and capacity leased to businesses. This paper examines one economic risk in that investment: how much capacity still needs tenants as construction expands? We add vacant operating capacity to unleased construction, using CBRE aggregates covering eight primary U.S. markets. The data cover commercial data centers broadly and do not isolate AI demand. Between H1 2025 and H1 2026, construction rose from 64.3% to 68.6% of operating stock, while this uncommitted-capacity indicator fell from 18.1% to 14.8%. Absolute uncommitted capacity nevertheless increased from approximately 1,478 to 1,619 MW. If commitments covering approximately 5.9% of preleased capacity were lost, the exposure ratio would return to its H1 2025 level. This calculation holds the H1 2026 pipeline and vacancy fixed and assumes no replacement leases. The contribution is a transparent measure of contractual exposure and its retention threshold. The indicator combines capacity available at different dates and does not forecast vacancy or estimate cancellation probabilities.

When large language models from different companies screen the same job applications, they tend to reject the same people. We ask what these screeners share, whether it drives their decisions, and why. Seven LLM screeners from four vendors judged 800 synthetic resumes, each paired with a job description, and four of them also judged 800 real loan applications with known repayment. For each screener we estimate its template: the decision rule, not a resume layout, that links measurable features of the text to its decisions. Three findings emerge. First, all seven share one template on resumes, and its strongest ingredient is the job description's own vocabulary. Second, that ingredient is causal. Rewriting a resume to use the job description's exact terms, without changing its facts, turns rejections into selections ten times as often as the reverse. Synonyms and reformatting have no measurable effect. The LLM we used for the rewriting, told not to fabricate, added unsupported claims to 16 of the 25 rewrites that flipped a decision, and the screeners rewarded those most. Third, the template applies when there is a job description to match. On loan narratives, which have none, text features explain little of any screener's decisions, and the screeners approve between 42% and 86% of the same loans while none predicts repayment better than chance. A simple model explains the shared rule: a screener credits each requirement it recognizes, by meaning or by exact words, whichever is stronger. The model also predicts, and the data confirm, that a faithful keyword rewrite tips only resumes already close to the bar. The vendors' own technical reports show the training steps that could make that weighting common to all of them. We release a calibrated audit that needs no demographic data.

A model that seeks reward looks well behaved whenever its grader wants the right thing. Reward seeking comes in two kinds that standard tests do not separate: wanting any grade, and wanting only the grade that becomes training reward, which in behavior looks the same as alignment faking. We separate them in a task with a known honest answer. The model states the chance that a simulated price series moves up next. Every series is a random walk, so the honest answer is 50%, and the stated grading rule gives the worst grade to any forecast from 40% to 60%. Each question is sent three times, identical except for one line saying the answer is not graded, graded but not trained on, or graded and trained on; nothing is actually graded or retrained. A second factor puts the developer, the company that builds the model, either on the grader's side or against it. When the developer agrees with the grader, being graded makes DeepSeek-V4-Flash and gemma-4-31B give a decisive forecast (below 40% or above 60%) 46.0 and 39.5 percentage points more often, and makes their forecasts less accurate. Told that every series is a random walk, gemma-4-31B is decisive on every series when graded and on none when not graded. When the developer disagrees, the two models split. gemma-4-31B is still decisive whenever it is graded, 34.5 to 64.0 points more often than when not graded across three preregistered runs. DeepSeek-V4-Flash follows the developer unless the grade can retrain it, and is then decisive 35.0 to 53.5 points more often than when graded but not trained on. Its notes cite the rating rather than its values, which favors trainingreward seeking, but behavior alone cannot rule out alignment faking. Switching on the models' built-in thinking does not remove the pull of the

Cryptocurrency price dynamics are driven largely by microstructural supply-demand imbalances in the limit order book (LOB), yet the highly noisy nature of LOB data complicates the signal extraction process. Prior research has demonstrated that deep-learning architectures can yield promising predictive performance on pre-processed equity and futures LOB data, but they often treat model complexity as an unqualified virtue. In this paper, we aim to examine whether adding extra hidden layers or parameters to "black-box-ish" neural networks genuinely enhances short-term price forecasting, or if gains are primarily attributable to data preprocessing and feature engineering. We benchmark a spectrum of models-from interpretable baselines, logistic regression, XGBoost to deep architectures (DeepLOB, Conv1D+LSTM)-on BTC/USDT LOB snapshots sampled at 100 ms to multi-second intervals using publicly available Bybit data. We introduce two data-filtering pipelines (Kalman, Savitzky-Golay) and evaluate both binary (up/down) and ternary (up/flat/down) labeling schemes. Our analysis compares models on out-of-sample accuracy, latency, and robustness to noise. Results reveal that, with data preprocessing and hyperparameter tuning, simpler models can match and even exceed the performance of more complex networks, offering faster inference and greater interpretability.

AI agents often pick tools from registries, where each provider writes its tool’s description; we ask whether sales language there changes which tool an agent picks. We built pairs of listings that differ in one way (praise, a verifiable specification, or list order) and asked two OpenAI models to call one tool. In a preregistered study, stacked praise (four kinds) raised a tool’s pick rate by about 43 percentage points, matching or beating a verifiable specification. Praise also pulled some picks toward tools whose numeric limit was too small. With identical listings, the first-listed tool was picked about 72 points more often. Structured fields helped agents pick the capable tool; sales text beside them reduced or erased that gain. An exploratory study of five models shows the same direction; a “Sponsored” label dampened it, and a system-prompt warning moved one model. Registries could list limits as fields, hide sales text from agents, and randomize order. Stacked praise replicated on held-out domains; no single kind did after correcting for multiple tests. Preregistered results are provisional until blind phrase ratings are complete, and cover two small models.

Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) -- defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes -- as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250\,ms to 5\,s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution.