Plus: Tesla isn't the AI powerhouse Elon Musk says it is.
Signal President Meredith Whittaker said AI agents pose a "profound" security risk, speaking at the SXSW 2025 Conference in Austin.For all the hype they’ve gotten, AI agents— software that can browse the web, operate multiple apps on your device and perform tasks on your behalf— are “haunted” by real security and privacy risks, Signal presidentat tech conference SXSW in Austin this week.
Whittaker said these systems were like putting your “brain in a jar,” as AI agents need to be given access to sensitive data such as credit card information or phone contacts and also require access to use apps on your behalf. There currently aren’t encrypted models to send and receive that data, which has to be processed in the cloud, she said., which it claims can perform tasks like screening resumes, searching for properties and analyzing stock, mostly on its own. The AI system isdeveloped by other companies such as San Francisco-based Anthropic, rather than being trained from scratch. It doesn’t seem like there’s much substance to the hype, asreported. Meta, whose family of AI models called Llama reportedly crossed 600 million monthly users last year, is far from the only player designing its own silicon.labor that goes into developing artificial intelligence models. And as the models themselves get better and more capable at advanced tasks, so do the qualifications of those training them. $14 billion-valuedis increasingly looking for domain experts–including people with PhDs–within the USreported. The shift is part of CEO and billionaire Alex Wang’s new “America first” philosophy under the Trump administration.an AI company—if you believe CEO Elon Musk.is exactly what Tesla needs to train its cars to operate without any human assistance, a goal that’s core to Musk's vision for the future of Tesla. But there’s a problem:Building AI that can drive a car as well as a human is a challenge dramatically different than building a natural language processing chatbot like ChatGPT, which was trained on billions of words scraped from the internet. While the goal with ChatGPT and competing systems like xAI’s Grok is to use pattern recognition to provide reliable information and answers to questions, the results oftenDriving a car is a very different proposition with many more variables — driving conditions, weather, construction, changing traffic patterns, how other vehicles are moving. Successfully wrangling all those variables, and being ready to react to unexpected developments, is at the crux of autonomous driving AI. Training it on endless videos of people driving down highways doesn’t do much to help the AI learn how to handle what it most needs to: the edge cases that cause collisions or other dangerous scenarios. Yann LeCun, Meta’s chief AI scientist and a professor of computer science at New York University, also isn’t convinced that Tesla’s data gives it a competitive advantage. “The impact of data is generally overstated: as you get more data, performance improves, but there are diminishing returns,” he said. “A doubling of data volume brings marginal improvements that are still far from human reliability.” Even with massive amounts of data, no company has developed so-called Level-5 autonomy, the point at which a vehicle can drive itself in all the circumstances a human can.” said LeCun. “This tells you that current AI architectures are missing something big in their ability to understand the world and to learn from limited amounts of data or trials.’“Having access to unique data feeds is certainly some kind of advantage,” said computer scientist Alex Ratner, CEO of Snorkel AI, which makes software to help automate labeling of raw data., which pasted experts from 10 random articles into chatbots and asked them to identify the publisher, article and publication date.
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