Motivated by real-life supply chain management, we study a repeated newsvendor problem in which the learner is a mediator that facilitates trades between suppliers and retailers in a sequence of supplier/retailer interactions. At each time step, a new supplier and retailer join the mediator's platform with a private production cost and utility function, respectively, and the platform proposes a unitary trading price. The supplier accepts the proposed price if it meets or exceeds their unitary production cost and communicates their decision to the platform; simultaneously, the retailer decides the quantity to purchase at the proposed trading price based on their private utility function and sends their decision to the platform. If the supplier accepts the trading price, the transaction proceeds, and the retailer purchases their chosen quantity of units, paying the product of this quantity and the trading price to the supplier. The mediator's objective is to maximize social welfare. We design an online mediator's pricing strategy that features sharp regret rates under some natural assumptions, and we investigate the necessity of these assumptions, proving that relaxing any of them leads to unlearnability.

Millions of people can get discounts on their bills - here's how Water, phone and broadband companies are willing to give millions of people discounted deals on their bills. Social tariffs - sometimes known as essential, or basic, tariffs - can reduce bills for people on various benefits. Generally, you only need to ask your supplier to get on one. Importantly, they are not price promotions designed to attract customers, but lower bills for the same service for those who would otherwise struggle to pay. Most people who have fallen behind on paying their bills are unaware this help is available, a major report has suggested.

A labour rights organisation claims it has found evidence of worker exploitation in a Chinese factory that makes the viral Labubu dolls. China Labor Watch (CLW), a US-based non-governmental organisation, alleges that its investigation found that one of Pop Mart's suppliers made employees work excessive overtime shifts, sign blank or incomplete contracts and did not give them paid leave. The furry Labubu dolls have surged in popularity around the world in recent years and are best known for selling toys in blind boxes, which hide its content from buyers until it is opened. Pop Mart told the BBC that it is investigating the claims. The Beijing-based toy retailer said it appreciated the details from the review and that it will firmly require companies making its toys to correct their practices if the allegations are found to be true.

Octopus Energy to spin off $8.65bn tech arm Kraken Octopus Energy is set to spin off its Kraken Technologies arm as a standalone company after a deal to sell a stake in the platform valued it at $8.65bn (£6.4bn). The energy giant, Britain's biggest gas and electricity supplier, has sold a $1bn stake in the AI-based division to a group of investors led by New York-based D1 Capital Partners. The move paves the way for Kraken to be demerged from Octopus, and for a potential stock market flotation for the business in the future. Octopus founder and chief executive Greg Jackson told the BBC there was every chance Kraken would list its shares in the medium term, with the location of the flotation between London and the US. Kraken uses AI to automate customer service and billing for energy companies and can manage when customers use energy, rewarding them for reducing consumption at peak times. It was initially built for use by Octopus but has since picked up a raft of other utilities clients, including EDF, E.On Next, TalkTalk and National Grid US.

It used to be just a sci-fi nightmare scenario, but today, AI phishing is real, and it's costing companies millions. We've already touched upon this one, but the Hong Kong phishing scam that targeted an employee at Arup deserves a deeper dive. The employee was tricked by deepfake versions of her CFO and colleagues into transferring HK 200 million across 15 transactions. The case has been widely reported and confirmed by the Hong Kong police. Every face and voice was AI-generated.

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or high-margin products to incorporate. The system applies demand forecasting, supplier selection optimization, multi-agent negotiation and continuous learning. We apply a prototype to a setting in the store of a middle scale mart, test its performance on three conventional and artificial data tables, and compare the results to the base heuristics. Our findings indicate that there is a decrease in stockouts, a reduction of inventory holding costs, and an improvement in product mix turnover. We address constraints, scalability as well as improvement prospect.

Much of the US economy rests on AI's future. On this episode of podcast, cohost Joe Weisenthal breaks down why AI's impact on finance goes beyond billion-dollar investments. If you read any of WIRED's recent AI edition, you know that lots of people are spending lots of time talking about how the technology is revolutionizing pretty much everything--from coding to writing to accounting. You've also probably heard by now, from us or somebody else, that we might very well be in an economic bubble of AI origin, one wherein the billions and billions of dollars being funneled into the industry is creating an untenable economic scenario that could turn catastrophic. Of course, you may also have read that I'm really sick of being asked about AI . I'm still not sick, though, of asking other people about it--especially when they're much smarter about this stuff than I am. Enter Joe Weisenthal, the cohost of Bloomberg's fantastic podcast, and a former coworker of mine. Trust me: As someone who spent a year listening to Joe lose his mind in the office--loudly!--anytime the economy hiccuped, few people think more about our country's, and our planet's, financial circumstances than Joe does. And right now, Joe's concerns aren't strictly about what happens if or when that AI bubble bursts. His worries are more focused on what's going right and wrong with the US economy writ large. For this week's episode of, Joe and I talked about weird market indicators, US competition with China, and whether or not we should all prepare for an AI economic apocalypse. Nice to see you again. We were just talking about how [you] and I worked together--what was that, like nine years ago? I think you were there 2014, 2015, so maybe 10 years ago or something? Yeah, I worked at Bloomberg. I lasted about a year. But Joe, you were there, you were loud, you were proud, you were always very excited about the economy.

Taking the time to make a use case for AI will propel companies further and improve the return on investment in this fast-changing technology. The market is officially three years post ChatGPT and many of the pundit bylines have shifted to using terms like "bubble" to suggest reasons behind generative AI not realizing material returns outside a handful of technology suppliers. In September, the MIT NANDA report made waves because the soundbite every author and influencer picked up on was that 95% of all AI pilots failed to scale or deliver clear and measurable ROI. McKinsey earlier published a similar trend indicating that agentic AI would be the way forward to achieve huge operational benefits for enterprises. At's Technology Council Summit, AI technology leaders recommended CIOs stop worrying about AI's return on investment because measuring gains is difficult and if they were to try, the measurements would be wrong. This places technology leaders in a precarious position-robust tech stacks already sustain their business operations, so what is the upside to introducing new technology?

The first day of September should have marked the beginning of one of the busiest periods of the year for Jaguar Land Rover. It was a Monday, and the release of new 75 series number plates was expected to produce a surge in demand from eager car buyers. At factories in Solihull and Halewood, as well as at its engine plant in Wolverhampton, staff were expecting to be working flat out. Instead, when the early shift arrived, they were sent home. The production lines have remained idle ever since.