{"id":449116,"date":"2026-02-02T12:49:16","date_gmt":"2026-02-02T12:49:16","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/449116\/"},"modified":"2026-02-02T12:49:16","modified_gmt":"2026-02-02T12:49:16","slug":"will-be-a-total-loss","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/449116\/","title":{"rendered":"‘Will be a total loss’"},"content":{"rendered":"

\t\t\t\t\t\t\t\t\t\t\"Yann<\/p>\n

Photo Credit: Getty Images\n<\/p>\n

Yann LeCun, Meta’s longtime chief artificial intelligence scientist, said last November that he planned to leave the company after more than a decade. In later interviews, including one referenced by Futurism<\/a>, he openly criticized Meta’s approach to AI and the direction the company was taking.<\/p>\n

LeCun joined Meta in 2013, when the company prioritized open-ended research over rapid product launches. For years, he had unusual freedom to explore ideas without pressure to turn them into revenue. As he later told<\/a> the Financial Times, “Money was clearly not going to be a problem.”<\/p>\n

That changed after the release of ChatGPT in late 2022. The sudden surge in competition pushed Meta \u2014 along with Google and OpenAI \u2014 toward faster development, tighter secrecy, and large-scale commercial deployment. LeCun has warned that this shift favors speed over caution, increasing the risk of unreliable systems, misuse, and unintended consequences.<\/p>\n

While much of the debate around AI focuses on ethics and jobs, LeCun’s exit also highlights something more tangible: AI’s growing physical footprint.<\/p>\n

Tech companies use enormous amounts of electricity and water<\/a> to train and operate large AI models. That demand often concentrates in data centers that draw power from local grids, creating added strain that can ripple out to nearby communities through higher costs or infrastructure pressure.<\/p>\n

At the same time, AI can also help energy systems operate more efficiently, such as by analyzing long-term weather patterns to improve solar output forecasting<\/a> and reduce waste. Whether AI becomes a burden or a benefit depends less on the technology itself and more on how deliberately companies choose to deploy it.<\/p>\n


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In a LinkedIn post, LeCun said he is launching a new startup, Advanced Machine Intelligence, focused on advanced machine learning, a research program he has pursued for years. He wrote<\/a>: “The goal of the startup is to bring about the next big revolution in AI: systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.”\u00a0<\/p>\n

The update made clear that his focus now lies with that research direction and with AMI’s broader ambitions.\u00a0<\/p>\n

Online reaction has been more skeptical. One comment compared Meta’s recent AI investment to past high-risk bets, writing<\/a>: “This is going to be like Zuckerberg’s investment into Oculus; Scale AI investment will be a total loss on the books soon.”<\/p>\n

The contrast underscores the uncertainty of AI’s rapid expansion \u2014 and whether today’s race for scale will deliver real value or leave costly consequences in its wake.<\/p>\n

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