People really hate AI, so why can’t they get enough?
Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI….
Public opinion about artificial intelligence (AI) is deeply divided, reflecting a strong love-hate relationship. While many people express concern or outright dislike for AI, usage of AI tools continues to grow rapidly. According to the Pew Research Center, more adults in the US believe AI will have a negative impact on them personally and on society than those who expect positive effects. This pessimism is particularly pronounced among younger people. Globally, over half of respondents report feeling nervous about AI products and services, as shown in a Stanford University report. Despite these negative feelings, AI adoption is soaring: ChatGPT reached one billion monthly users in May, while Google DeepMind’s Gemini had 950 million users in July. Additionally, half of US adults now say they use a chatbot, more than double the number from 2023, and one in four use them daily. Across 38 OECD countries, over a third of adults have used generative AI tools in the last three months. This contradiction highlights a paradox where people both dislike and heavily rely on AI.
Public opposition to AI is growing in measurable ways. A Gallup poll in May 2024 found that 71% of US adults would oppose the construction of a new AI data center in their area, a higher percentage than the 53% who opposed a new nuclear power plant. In a March 2024 NBC poll, AI was ranked as less popular than ICE (Immigration and Customs Enforcement), indicating strong resistance. The Global North, where AI adoption is highest, tends to have more negative opinions about the technology, while the Global South, with lower adoption rates, shows more optimism. This suggests a correlation between usage and dissatisfaction, though the relationship may not be causal. The opposition reflects broader concerns about the rapid and unchecked deployment of AI technologies by companies and governments.
The primary source of public discontent with AI is not the technology itself but the aggressive push by companies to integrate AI into every aspect of life. Many people feel overwhelmed by the relentless drive to normalize AI, which is often framed as an inevitable force that will cause significant social and economic upheaval. This mirrors past dynamics seen with social media platforms like Facebook and Twitter, where billions of users became dependent despite growing backlash against the companies behind them. Unlike social media, however, AI offers more opportunities for regulatory intervention and consumer choice. All 50 US states have either passed or proposed laws governing AI development and deployment, creating over 2,100 bills across the country—a tenfold increase in three years. Additionally, open-source alternatives to major AI providers are emerging, potentially increasing competition and giving users more options.
The current landscape of AI regulation and alternatives presents a unique opportunity for public influence over the technology’s future. Politically, there is unprecedented momentum for AI regulation, with all 50 US states actively involved in crafting laws. This regulatory push contrasts with the early days of social media, where little oversight existed. Economically, the rise of open-source AI models—such as those developed by startups like Springboards—offers users alternatives to the dominant commercial AI systems from Google, OpenAI, and Anthropic. These alternatives could foster market competition and give consumers more control. For example, the CEO of Springboards, a startup building a large language model (LLM) designed to generate more varied responses, admitted that his company is part of the AI industry despite reservations about the technology. This underscores the idea that while AI’s development may be unstoppable, its direction can still be shaped.
The future of AI development may hinge on whether companies prioritize transparency and realistic expectations over hype. Many experts argue that current AI systems, including large language models (LLMs), are often overstated in their capabilities. For instance, claims about *artificial general intelligence* (AGI)—hypothetical AI that matches or exceeds human intelligence—are frequently debunked under scrutiny. The article highlights that AI systems today lack the creativity and innovation required for genuinely open-ended research. Instead of positioning AI as a world-changing force, companies could focus on clear communication about what AI can and cannot do. This approach would align with public sentiment, which favors more cautious and grounded AI applications rather than revolutionary claims.
