If AI is addictive, where does the responsibility lie – with big tech or its users?

2026-06-08 · Source: Artificial intelligence (AI) – The Conversation · Field: Legal & Regulatory — Regulatory Affairs & Government Relations, Compliance & Risk Management, Artificial Intelligence & Machine Learning · Depth: Novice, short

Summary

Generative AI tools are showing signs of problematic use and potential addictive properties, with data indicating heavy chatbot engagement can lead to neural patterns and behaviors associated with addiction, including emotional dependency and loss of real-world connections. This raises questions about responsibility, especially following Meta's and YouTube's recent legal defeat in a social media addiction trial. While medical evidence is still being gathered, researchers suggest strong evidence for generative AI's addictive potential. Drawing parallels with historical precedents like tobacco and gambling, the article identifies four key stakeholder groups responsible for addressing this challenge: governments and regulators, big tech companies, academic researchers, and civil society organizations. It stresses the need for collaboration among these parties, noting that individual user moderation alone is insufficient, and highlights the lack of structured debate on responsibilities.

Key takeaway

For policy makers and regulators evaluating AI governance, you must proactively establish frameworks for generative AI, recognizing its potential addictive properties. Your focus should be on requiring clear labeling, restricting advertising, and applying liability law to big tech companies. Collaborate with industry, researchers, and civil society to develop common rules, drawing lessons from tobacco and social media precedents, to shape acceptable AI use for the future.

Key insights

Generative AI exhibits addictive properties, necessitating a multi-stakeholder approach to responsibility and mitigation.

Principles

• AI addiction parallels tobacco and social media.
• Responsibility for harm requires collective action.
• User data is key to understanding AI addiction.

In practice

• Require AI product labeling and advertising restrictions.
• Fund research into AI's addictive features.
• Establish early-warning structures for problematic use.

Topics

• Generative AI Addiction
• AI Governance
• Regulatory Frameworks
• Stakeholder Responsibility
• Social Media Addiction
• Public Health Policy

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, Legal Professional, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence (AI) – The Conversation.