WIRED spoke with Bloomberg's chief technology officer about the big, chatbot-style changes coming to the iconic platform for traders. For its famous intractability, the Bloomberg Terminal has long inspired devotion, bordering on obsession . Among traders, the ability to chart a path through the software's dizzying scrolls of numbers and text to isolate far-flung information is the mark of a seasoned professional. But as a greater mass of data is fed into the Terminal--not only earnings and asset prices, but weather forecasts, shipping logs, factory locations, consumer spending patterns, private loans, and so on--valuable information is being lost. "It has become more and more untenable," says Shawn Edwards, chief technology officer at Bloomberg.

'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. 'There is no guaranteed outcome with any job,' said Shola West, 25, a media consultant. Working for yourself at least allows you some control over your fate. Facing AI and a tough job market, gen Z turns to entrepreneurship: 'I have to prove myself' When Ashley Terrell graduated from the University of Hawaii in 2024, she planned to find a job in marketing, maybe for a tech company.

When you purchase through links in our articles, we may earn a small commission. Investigation: RAM prices are falling. A few price dips don't mean the memory crisis is over -- AI demand, tight supply, and a jittery market could keep PC upgrades expensive. Rising prices are the biggest tech story of 2026 . Well, the biggest tech story, anyway -- the biggest story in a broader sense is "AI" in general.

Why did Trump fire Pam Bondi? Trump's US Fed nominee Warsh vows independence, says he's no'sock puppet' Kevin Warsh, United States President Donald Trump's pick to lead the Federal Reserve, has addressed concerns about his independence pending his appointment to the bank amid fears that Trump could sway his decisions on monetary policy. On Tuesday, Warsh -- who served on the central bank's Board of Governors from 2006 to 2011 -- faced waves of criticism during a confirmation hearing of the Senate Banking Committee where Democrats voiced concerns about the Fed's independence should he be appointed to lead the organisation. "I do not believe the operational independence of monetary policy is particularly threatened when elected officials -- presidents, senators, or members of the House -- state their views on interest rates," Warsh said. "Monetary policy independence is essential. Monetary policymakers must act in the nation's interest . . . Warsh, 56, also called for "regime change" at the US central bank, including a new approach for controlling inflation and a communications overhaul that may discourage his colleagues from saying too much about the direction of monetary policy. Warsh blamed the central bank for an inflation surge after it slashed interest rates to nearly zero in the wake of the COVID-19 pandemic, a move that continues to hurt US households. Concerned by the implications of artificial intelligence for jobs - expected to increase productivity - and prices, he said he would move quickly to see if new data tools could provide better insight on inflation, and would also discourage policymakers from saying too much about where interest rates might be heading. "What the Fed needs are reforms to its frameworks and reforms to its communications," the former Fed governor said. "Too many Fed officials opine about where interest rates should be That is quite unhelpful." Warsh has also long been an advocate for shrinking the Fed's $6.7 trillion balance sheet. In the Tuesday hearing, he said any such plans would take time and must be publicly discussed well in advance. Jai Kedia, a research fellow at the Center for Monetary and Financial Alternatives at the libertarian Cato Institute, told Al Jazeera that there were many "encouraging" signs in Warsh's candidacy. "Warsh is presenting himself as a regime change candidate at a time when the Fed needs serious reform," Kedia noted. "Particularly encouraging was his understanding of the negative effects of QE and his focus on reducing the balance sheet.

Ashlin McCourt clocks up 60 hours a week working as a civil servant, a waitress and a baker because life's so expensive, she says. The UK unemployment rate stands at 4.9% - however, increasing numbers of those in work are juggling more than one job. While working in multiple jobs and side hustles has long been a needs must for many households to manage the cost of living, there are now a record 1.35 million adults working at least two jobs. It is mostly Gen Z - adults aged up to 29 - driving this poly-employment trend - according to Deputy, a global workforce management platform, which analysed more than 20 million shifts done by over 300,000 UK workers. For 28-year-old Ashlin from Northern Ireland, having more than one job seems normal.

The golden era of the tech industry is dead--leaving 1.2 million laid-off workers like me scrambling in a job market that no longer wants us. On Feb. 10, 2025, at 7:32 a.m., the dreaded email hit my inbox. After nearly six years at Meta as a content strategist, one total company rebrand, and three previous mass layoffs, I got the axe. My time was bound to come. I often joked darkly that I was a cat with only so many lives left.

Time series graphical models have recently received considerable attention for characterizing (conditional) dependence structures in multivariate time series. In many applications, the multivariate series exhibit variable-partitioned blockwise dependence, with distinct patterns within and across blocks. In this paper, we introduce a new class of time series Gaussian chain graph models that represent contemporaneous and lagged causal relations via directed edges across blocks, while capturing within-block conditional dependencies through undirected edges. In the frequency domain, this formulation induces a cross-frequency shared group sparse plus group low-rank decomposition of the inverse spectral density matrices, which we exploit to establish identifiability of the time series chain graph structure. Building on this, we then propose a three-stage learning procedure for estimating the undirected and directed edge sets, which involves optimizing a regularized Whittle likelihood with a group lasso penalty to encourage group sparsity and a novel tensor-unfolding nuclear norm penalty to enforce group low-rank structure. We investigate the asymptotic properties of the proposed method, ensuring its consistency for exact recovery of the chain graph structure. The superior empirical performance of the proposed method is demonstrated through both extensive simulation studies and an application to U.S. macroeconomic data that highlights key monetary policy transmission mechanisms.

Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives such as clicks, applications, or hires, rather than job seekers' welfare. We develop a job-search model with an application stage in which the value of a vacancy depends on two dimensions: the utility it delivers to the worker and the probability that an application succeeds. The model implies that welfare-optimal RSs rank vacancies by an expected-surplus index combining both, and shows why rankings based solely on utility, hiring probabilities, or observed application behavior are generically suboptimal, an instance of the inversion problem between behavior and welfare. We test these predictions and quantify their practical importance through two randomized field experiments conducted with the French public employment service. The first experiment, comparing existing algorithms and their combinations, provides behavioral evidence that both dimensions shape application decisions. Guided by the model and these results, the second experiment extends the comparison to an RS designed to approximate the welfare-optimal ranking. The experiments generate exogenous variation in the vacancies shown to job seekers, allowing us to estimate the model, validate its behavioral predictions, and construct a welfare metric. Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark. Our results show that embedding predictive tools within a simple job-search framework and combining it with experimental evidence yields recommendation rules with substantial welfare gains in practice.

This paper develops a unified framework for measuring concentration in weighted systems embedded in networks of interactions. While traditional indices such as the Herfindahl-Hirschman Index capture dispersion in weights, they neglect the topology of relationships among the elements receiving those weights. To address this limitation, we introduce a family of topology-aware concentration indices that jointly account for weight distributions and network structure. At the core of the framework lies a baseline Network Concentration Index (NCI), defined as a normalized quadratic form that measures the fraction of potential weighted interconnection realized along observed network links. Building on this foundation, we construct a flexible class of extensions that modify either the interaction structure or the normalization benchmark, including weighted, density-adjusted, null-model, degree-constrained, transformed-data, and multi-layer variants. This family of indices preserves key properties such as normalization, invariance, and interpretability, while allowing concentration to be evaluated across different dimensions of dependence, including intensity, higher-order interactions, and extreme events. Theoretical results characterize the indices and establish their relationship with classical concentration and network measures. Empirical and simulation evidence demonstrate that systems with identical weight distributions may exhibit markedly different levels of structural concentration depending on network topology, highlighting the additional information captured by the proposed framework. The approach is broadly applicable to economic, financial, and complex systems in which weighted elements interact through networks.

AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.