As the housing market stalls, Zillow's CEO sees AI as "an ingredient rather than a threat" that can both help the company protect its turf and reinvent how people search for homes. This will not be a banner year for the real estate app Zillow. "We describe the home market as bouncing along the bottom," CEO Jeremy Wacksman said in our conversation this week. Last year was dismal for the real estate market, and he expects things to improve only marginally in 2026. "The way to think about it is that there were 4.1 million existing homes sold last year--a normal market is 5.5 to 6 million," Wacksman says.

Rather than offering a revolutionary new approach to gig work, RentAHuman is filled with bots that just want me to be another cog in the AI hype machine. I'm not above doing some gig work to make ends meet. In my life, I've worked snack food pop-ups in a grocery store, ran the cash register for random merch booths, and even hawked my own plasma at $35 per vial. So, when I saw RentAHuman, a new site where AI agents hire humans to perform physical work in the real world on behalf of the virtual bots, I was eager to see how these AI overlords would compare to my past experiences with the gig economy. Launched in early February, RentAHuman was developed by software engineer Alexander Liteplo and his cofounder, Patricia Tani.

"We're going to get into a period of potentially unprecedented I.P.O. "But we are confident they're executable given the scale of these companies and the investor interest." These listings could create an enormous bonanza for Wall Street and Silicon Valley after years of lackluster offerings. They could set off a feeding frenzy among public market investors who have been waiting to get a piece of the A.I. boom, and Wall Street banks stand to make hundreds of millions facilitating the listings. That is stoking more excitement for the A.I. boom as it enters its fourth year, even as the question of a bubble intensifies.

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.

Manually generating catchy descriptions and names is labor intensive and a slow process for retailers. Although generative AI provides an automation solution in form of Vision to Language Models (VLM), the current VLMs are prone to factual "hallucinations". Siloed, single task models are not only inefficient but also fail to capture interdependent relationships between features. To address these challenges, we propose an end to end, multi task system that generates factually grounded textual listings from a single image. The contributions of this study are two proposals for the model architecture. First, application of multi task learning approach for fine tuning a vision encoder where a single vision backbone is jointly trained on attribute prediction such as color, hemline and neck style and price regression. Second, introduction of a hierarchical generation process where the model's own predicted attributes are embedded in a prompt and fed to the text decoder to improve factual consistency. The experiments demonstrate the superiority of this architecture. The multi tasking approach outperforms both the independent price regression, with a 3.6% better R2 Value and attribute classification, with a 6.6% improvement F1 score. Critically, the hierarchical generation process proves highly effective, slashing the factual hallucination rate from 12.7% to 7.1%, a 44.5% relative reduction, compared to a non hierarchical ablation. The hierarchical approach also reduces the latency of the autoregressive text generation process by a factor of 3.5 when compared to direct vision to language model of similar size. One minor caveat is that the model does perform 3.5% worse than direct vision-to-language model on ROUGE-L score.

Fake video walk-throughs, a magically expanding loft, and stair hallucinations are just some of the new AI-generated features house hunters are coming across. As you're hunting through real estate listings for a new home in Franklin, Tennessee, you come across a vertical video showing off expansive rooms featuring a four-poster bed, a fully stocked wine cellar, and a soaking tub. It looks perfect--maybe a little too perfect. Everything in the video is AI-generated . The real property is completely empty, and the luxury furniture is a product of virtual staging.

Online shopping giant Temu has agreed to work with the greeting card industry to remove copied designs from its site more quickly. Designers told the BBC the process for getting the plagiarised listings removed has been like the fairground game'whack-a-mole' with copied products re-appearing within days. Temu said protecting intellectual property was a top priority and that it was encouraging sellers to join the trial of a new takedown process specifically for the greetings card industry. Amanda Mountain, the co-founder of York-based Lola Design, discovered the catalogue of designs she had built up over a decade had nearly all been copied. She found the images she had created had been lifted and were being advertised by other sellers on cards and other products like t-shirts.

We propose a method for image categorization and retrieval that leverages graphs and a graph attention network (GAT)-based autoencoder. Our approach is representative-centric, that is, we execute the categorization and retrieval process via the representative models we construct for the images and image categories. We utilize a graph where nodes represent images (or their representatives) and edges capture similarity relationships. GAT highlights important features and relationships between images, enabling the autoencoder to construct context-aware latent representations that capture the key features of each image relative to its neighbors. We obtain category representatives from these embeddings and categorize a query image by comparing its representative to the category representatives. We then retrieve the most similar image to the query image within its identified category. We demonstrate the effectiveness of our representative-centric approach through experiments with both the GAT autoencoders and standard feature-based techniques.

Metropolis The Housing Market Is Already Terrible. A.I. Is Making It Even Worse. While digital staging is nothing new to real estate, bot-made listings are forcing homebuyers and professionals to ask themselves if this is a straight-up deceptive practice. DeAnn Wiley was on the hunt for a new rental in Detroit earlier this month when she had the displeasure of arriving at a property that looked nothing like what was advertised online. "The photos made the home look brand new, only to get there and see the usual wear and tear and the old'landlord special,' " she told Slate.

We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the presence of a high-dimensional confounder depends on the performance of estimators of conditional expectation functions. We show that predictions made by generative models trained on historical data can be used to improve the performance of these estimators relative to approaches that solely rely on adjusting for embeddings extracted from these models. We argue that the historical knowledge and reasoning capacities associated with these generative models can help overcome curse-of-dimensionality problems in causal inference problems. We consider a case study using a small dataset of online jewelry auctions, and demonstrate that inclusion of LLM-generated guesses as predictors can improve efficiency in estimation.