TLDR: Michael I. Jordan, a prominent AI researcher from UC Berkeley and Inria Paris, has unveiled an 11-page academic paper proposing a ‘collectivist, economic perspective’ for artificial intelligence development. This new framework challenges the prevailing individualistic approach in Silicon Valley, emphasizing social welfare as a foundational element rather than an afterthought, and aims to address issues like consumer trust and sustainable digital economies.
A groundbreaking academic paper by leading artificial intelligence researcher Michael I. Jordan of UC Berkeley and Inria Paris is set to redefine the discourse around AI development. Published on July 14, 2025, and submitted on July 8, 2025, Jordan’s 11-page research, funded by the European Union’s ERC-2022-SYG-OCEAN program and released through arXiv, directly challenges the Silicon Valley paradigm of AI, advocating for a ‘collectivist, economic perspective’ that prioritizes social welfare.
Jordan argues that the current individualistic focus of AI systems overlooks a fundamental aspect of human nature. ‘Humans are social animals, and much of our intelligence is social and cultural in origin,’ the paper states, highlighting a critical gap in how AI is currently conceived and deployed. Instead of merely enhancing individual cognitive capabilities, Jordan’s framework suggests that AI development should focus on system-level designs where ‘social welfare is a first-class citizen.’
The timing of this publication is particularly pertinent, coinciding with significant industry shifts. Recent Mediaocean research indicates a rapid adoption of generative AI in marketing, with rates climbing from 55% in 2023 to 75% in 2024. However, Jordan’s work implies that these implementations might be missing crucial social dimensions, potentially leading to ongoing challenges in AI adoption and consumer trust.
Consumer sentiment data from July 2025 further underscores the urgency of Jordan’s proposal. Research revealed that 59% of European consumers oppose the use of their data for AI training, and a striking 62% feel they have ‘become the product’ within existing digital ecosystems. Jordan’s collectivist framework offers potential solutions to these concerns, suggesting models where platforms could sell anonymized data to third parties while maintaining formal privacy guarantees through ‘contractual noise levels.’ The paper even posits leaving the choice of noise levels to platforms, rather than solely government regulation, to foster innovation while protecting privacy.
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This research also directly challenges the industry’s intense focus on artificial general intelligence (AGI). Jordan posits that developing collectivist perspectives on information technology ‘can be just as exciting intellectually as AGI, and at least as promising for the future of the species.’ By shifting the focus from individual AI capabilities to broader societal well-being, the paper aims to pave the way for more ethical, trustworthy, and sustainable AI implementations across various sectors, particularly in digital marketing and technology.
