'Pretty Crazy' Token Usage Is Testing Bosses' Bet on AI A Silicon Valley software maker and an ecommerce company reveal to WIRED how they are navigating the emerging challenge of "tokenomics." At the software company 8x8, employees are using Anthropic's Claude to draft emails, analyze customer feedback, and write code, but so far, their growing reliance on the artificial intelligence chatbot hasn't troubled the finance team. While other Silicon Valley companies, such as Meta, Uber, and Salesforce, have publicly expressed concerns about the growing cost of generative AI tools and have begun introducing usage caps in some cases, 8x8 says it finds itself in the black. Over the past 18 months, the company estimates it has saved about $5 million in annual costs by canceling subscriptions to dozens of software and educational tools it deemed unnecessary in part because Claude could provide similar capabilities. So far, 8x8's annualized bill for Claude is "well below" that figure, says Joel Neeb, the company's chief transformation and business operations officer.

I'm testing smart home gear and high-tech party gadgets at every holiday party I host and attend. Here's how to automate the little things so you can actually enjoy the seasonal vibes this year. It's that time of year when calendars are full of Friendsgiving events and white elephant parties . If you're looking for holiday party ideas that will actually make hosting and enjoying your next holiday party easier, this is the guide for you. It's no small task to host a great party, and as a deeply type B person who's more likely to make another Partiful invite than she is to start cooking a recipe on time, I'm always looking for ways to streamline my hosting.

Restrepo (2025) develops a framework for economic growth in which Artificial General Intelligence (AGI) can perform any human task given sufficient computational resources. In his model, all economically essential "bottleneck" work is eventually automated, wages converge to the computational cost of replicating human work, and labor's share of GDP approaches zero as computational resources expand. This note relaxes one of his assumptions: that all task types have uniform automation costs. Drawing on Moravec's Paradox [1]--the observation that tasks humans find effortless (perception, mobility, manipulation) often require enormous computational resources, while tasks humans find difficult (mathematics, logic) require relatively modest computation--we extend his model to allow for differential automation costs across cognitive and physical tasks.

No one doubts that our future will feature more automation than our past or present. The question is how we get from here to there, and how we do so in a way that is good for humanity. Sometimes it seems the most direct route is to automate wherever possible, and to keep iterating until we get it right. Here's why that would be a mistake: imperfect automation is not a first step toward perfect automation, anymore than jumping halfway across a canyon is a first step toward jumping the full distance. Recognizing that the rim is out of reach, we may find better alternatives to leaping--for example, building a bridge, hiking the trail, or driving around the perimeter. This is exactly where we are with artificial intelligence. AI is not yet ready to jump the canyon, and it probably won't be in a meaningful sense for most of the next decade. Rather than asking AI to hurl itself over the abyss while hoping for the best, we should instead use AI's extraordinary and improving capabilities to build bridges.

Many countries are exploring how artificial intelligence might help with everything from processing taxes to determining welfare benefits. But a survey shows citizens are not as enthusiastic as their governments – and this can create real risks for democracy. "Focusing only on short-term efficiency gains and shiny technology risks triggering public backlash and contributing to a long-term decline in democratic trust and legitimacy," says Alexander Wuttke at the Ludwig Maximilian University of Munich in Germany. Wuttke and his colleagues asked around 1200 people in the UK to share their feelings about government actions where either a human or an AI handled the task. These hypothetical scenarios included processing tax returns, approving or rejecting welfare applications and making risk assessments about whether defendants should be eligible for bail. Some people were only told about how AI could improve government efficiency – but others learned about both AI-related benefits and risks.

The latest artificial intelligence models are not only remarkably good at software engineering--new research shows they are getting ever-better at finding bugs in software, too. AI researchers at UC Berkeley tested how well the latest AI models and agents could find vulnerabilities in 188 large open source codebases. Using a new benchmark called CyberGym, the AI models identified 17 new bugs including 15 previously unknown, or "zero-day," ones. "Many of these vulnerabilities are critical," says Dawn Song, a professor at UC Berkeley who led the work. Many experts expect AI models to become formidable cybersecurity weapons.

On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch. "This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I'm just burning tokens," Yegge says, referring to the cost of generating chunks of text with a large language model (LLM). Learning to code has long been seen as the ticket to a lucrative, secure career in tech.

Affiliations: 1. Cedars Sinai Medical Center, Los Angeles, California 2. University of California Los Angeles, California Keywords: vision transformer, 3D convolutional neural network, assessment tool, suturing skill, video analysis Key information: 1. Research question: Can we automate the end-to-end assessment of suturing expertise, and what benefits would it offer? MANUSCRIPT Introduction We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills. While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes.