The company is reducing pressure on workers to use artificial intelligence tools while encouraging them to experiment with Hatch, its most advanced AI project yet. Meta is formally ending what amounted to a tokenmaxxing incentive program for employees. In an internal announcement this week, the social media giant told workers that their performance evaluations would no longer be dependent upon how much they used AI tools, three employees who received the message tell WIRED. But as Meta workers simultaneously begin testing a new agentic AI tool known as Hatch, they say their token consumption continues to surge. Almost a year ago, Meta had said workers would be graded on their AI-driven impact," which in practice meant evaluating the extent to which they used chatbots and agents to improve their day-to-day work, Business Insider reported at the time . Usage correlated with employees receiving labels such as "AI Native," "AI First," or "AI Enabled," according to a lawsuit filed in July by about two dozen employees arguing that Meta violated US antidiscrimination laws when laying them off in May. New guidance for performance reviews unveiled this week replaces references to evaluating employees based on criteria like "usage of AI" and their "AI Native" designation, with looser wording, which caveats that "these outcomes can be supported by AI or other means." It restores Meta's focus to grading employees on their impact, with less regard to how it is achieved. Some Meta employees tell WIRED the changes are subtle but welcome, freeing them from feeling pressured to use AI in situations in which it doesn't make sense. Meta spokesperson Tracy Clayton tells WIRED that the updates this week aim to emphasize what has always been the case--Meta evaluates employees based on their contributions. He adds that labels such as "AI Native" were never used for performance evaluation. Engineers across the company were told this week that the company "will not use AI adoption dashboards or token counts to evaluate impact," The Information reported on Wednesday. "Are you a current or former Meta employee who wants to talk about what's happening?

From killing your chatbots to optimizing your prompts, here are the best ways to go full AI native and conquer the new world. Sam Liang is appalled as I confess my technique for recording an interview: running the Voice Memos app on an iPhone and transferring the transcript manually to a Google Doc. The CEO of Otter, a transcription service for analyzing meetings, looks at me as if I tried to call into our video chat using a rotary phone. He believes, naturally, that I should switch to Otter. Time-saving productivity tools like next-gen note-takers, task-based agents, and chatty inbox assistants are exploding in popularity as they invade every nook and cranny of our digital lives.

Children take part in an extracurricular club about coding and AI in Cambridge. Children take part in an extracurricular club about coding and AI in Cambridge. Generation AI: fears of'social divide' unless all children learn computing skills In a Cambridge classroom, Joseph, 10, trained his AI model to discern between drawings of apples and drawings of smiles. "AI gets lots of things wrong," he said, as it mistakenly identified a fruit as a face. He set about retraining it and, in a flash, he had it back on track - instinctively understanding the inner nature of artificial intelligence and machine learning in a way few adults do.

Artificial intelligence (AI) is unlocking use cases that are transforming industries across a wide swath of the world's economy. From infrastructure that "self-heals" to radically reimagined (and touchless) customer service and experience; from large scale hyper-personalization to automatically created marketing messages and images leveraging Generative AI tools like ChatGPT--it is all a reality today. These AI solutions can powerfully augment and sometimes radically outperform most traditional business roles. This article is a collaborative effort by Joshan Abraham, Jorge Amar, Yuval Atsmon, Miguel Frade, and Tomás Lajous, representing views from McKinsey's Technology, Media & Telecommunications Practice. The impact from these solutions is becoming evident.

Since most companies are early in their AI journey, a key obstacle is to rack up early wins that validate the business value of AI projects. For enterprises this means convincing business executives that AI can ultimately deliver billions of dollars in value to the company. Clearly it's critical to pick problems that are big and contribute meaningfully to the business. One of the more common mistakes is actually from AI projects that are halted prematurely. Focusing on a big problem allows you to ask for time for things to play out.