Perplexity CEO warns on-device AI threatens data centers in $10 trillion industry shift

Perplexity CEO Says On-Device AI Threatens Data Centers As Industry Faces '$10 Trillion Question' -- Apple, Qualcomm Positioned To Benefit - Alphabet (NASDAQ:GOOGL)

Aravind Srinivas, CEO of Perplexity AI, which is backed by Jeff Bezos and Nvidia Corp. (NASDAQ:NVDA), issued a contrarian warning about the future of artificial intelligence, saying on-device intelligence running on personal devices could disrupt the centralized data center model driving massive infrastructure investments. Localized AI Could Upend Data Center Industry "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center," Srinivas said in a podcast interview with Prakhar Gupta released last week. Srinivas, who has previously worked at OpenAI, Google Brain, and DeepMind, said that AI running directly on personal devices could reduce the need for centralized data centers. "That really disrupts the whole data center industry like it doesn't make sense to spend all this money $500 billion, $5 trillion whatever on building all the centralized data centers across the world," he said, calling it a "$10 trillion question, hundred trillion dollar question." He also highlighted scenarios where AI running locally could learn from repeated tasks on individual devices, adapting over time and automating user activities. "It adapts to you and over time starts automating a lot of the things you do. That way you don't have to repeat it. That's your intelligence. You own it. It's your brain," Srinivas said. See Also: Apple Scales Back Vision Pro Production, Marketing After Sluggish Sales: Report Chip Companies and OEMs Positioned to Benefit Apple Inc. (NASDAQ:AAPL) has "a massive advantage" due to its M1 chips and power-efficient devices, according to Srinivas. Qualcomm Inc. (NASDAQ:QCOM) and original equipment manufacturers, including Apple, Samsung (OTC:SSNLF), Lenovo (OTC:LNVGF), and HP Inc. (NYSE:HPQ) could also benefit from distributing AI-enabled devices. However, technical barriers remain. Srinivas noted that no AI model has yet been released that can run efficiently on a local chip while completing tasks reliably. The Indian-born entrepreneur expects early adoption on MacBooks or iPads before reaching smartphones. Implications For Robotics And Labor He also discussed the potential for AI in the physical world, especially in robotics. Srinivas said AI could transform the labor market by automating many tasks now done by humans, echoing concerns raised by Geoffrey Hinton who is often called the 'Godfather of AI'. Industry Risks The U.S. economy is becoming increasingly reliant on AI, raising concerns about a potential AI bubble. If such a bubble bursts, centralized data centers could become a "single point of failure," with widespread economic repercussions. Srinivas's caution highlights a crucial question for the AI and tech industries: will centralized data centers continue to be the foundation of the digital economy as AI becomes more dispersed, or will intelligence on personal devices radically transform the sector? Read Next: AI Boom Creates Over 50 New Billionaires Amid Record $202 Billion In Funding Photo courtesy: Shutterstock Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. GOOGLAlphabet Inc$312.80-0.06%OverviewAAPLApple Inc$272.090.08%HPQHP Inc$22.330.24%LNVGFLenovo Group Ltd$1.4018.8%NVDANVIDIA Corp$186.46-0.02%QCOMQualcomm Inc$171.22-%SSNLFSamsung Electronics Co Ltd$42.48-%Market News and Data brought to you by Benzinga APIs

Perplexity CEO: On-Device AI Could Surpass Datacenter Reliance

Aravind Srinivas backs local and on-device AI as your own small brain In an era where tech giants are pouring trillions into massive data centers to fuel the AI boom, Perplexity AI CEO Aravind Srinivas is sounding a contrarian alarm: the real future of artificial intelligence lies not in the cloud, but on your device. Srinivas first gained attention for this view in a widely shared 2024 interview, where he declared on-device AI the "biggest threat" to centralized data centers. "If the intelligence can be packed locally on a chip that's running on the device," he argued, "there's no need to inference all of it from one centralized data center. It becomes more decentralized." He envisioned models adapting to users through "test-time training," observing repeated tasks, retrieving local data on-the-fly, and automating workflows, all while keeping everything private. "That way, it's your brain," he said, warning that unchecked data center buildouts could prove wasteful. Also read: Should AI get legal rights? It's dangerous for humans, warns expert Throughout 2025, Srinivas reiterated and expanded on this prophecy in interviews, podcasts, and public appearances. He emphasized privacy as a foundational advantage: data remains on the user's device, eliminating the need to send sensitive information to remote servers. "All your data lives on your client. We don't need to take any of it," he has stressed, pointing out vulnerabilities in cloud-dependent agents that require ongoing authentication. Advancements in efficient models and specialized chips from companies like Apple, Qualcomm, and Arm have brought this vision closer to reality. Srinivas draws historical parallels to the transition from mainframes to personal computers, forecasting a similar shift where AI companies compete by shipping highly optimized, device-native models rather than relying on ever-larger cloud infrastructure. Also read: Meta's big AI play: What the Manus acquisition means for automation at scale At Perplexity, this philosophy is driving product development, including the Comet AI browser and upcoming desktop experiences. Srinivas has outlined ambitions for local models that deliver fast, battery-efficient performance for tasks like browser control, email management, and multi-tab research - without latency or privacy trade-offs. "If we can make models small enough, fast enough, and power-efficient enough to run locally - without draining your battery or compromising intelligence - that's the true magic," he explained in 2025 discussions. This approach challenges the dominant paradigm of hyperscalers like Microsoft, Google, and Meta, who continue massive GPU investments for frontier-scale training. Critics note that cutting-edge capabilities still demand cloud resources, with on-device models often smaller and limited for complex queries. Srinivas counters that for personalization, real-time interaction, and privacy-critical use cases, the bulk of daily AI value, local execution wins out. As 2026 dawns, the debate intensifies: will we see a hybrid ecosystem, or a genuine pivot to edge AI dominance? Srinivas's bold stance suggests the latter could reshape the industry sooner than expected.