AI’s Future Isn’t Inevitable. So Why Do We Talk About It That Way?

Public concern about the consequences of AI is growing as the technology becomes more powerful and more deeply embedded in our lives. As reports of “rogue” AI systems raise questions about who is in control, the need to understand how people think about AI—and how those understandings shape the solutions they see as possible and demand—has become increasingly urgent.

But concern doesn’t guarantee change. How we talk about AI matters.

If people think about the future of AI as inevitable—and if we don’t meaningfully counter that thinking—we risk creating a fatalistic sense of defeat before we’ve even begun the conversation about what we can do.

We’ve studied how Americans think about AI since 2022. Our research shows that public thinking is caught between two poles:

• “Techno-optimism” – the assumption that AI is inevitable progress that will make life easier and more efficient
• “Techno-dystopia” – the assumption that AI is an autonomous force that threatens humanity, jobs, and social life

Both perspectives obscure the fact that AI is a human-made technology that can be shaped, governed, and used for public benefit. Despite their differences, both mindsets make AI’s trajectory seem inevitable and beyond our ability to control.

The Curse of Inevitability

When AI is framed as an unstoppable force, either for good or ill, our attention shifts away from the people and institutions making decisions about its development and our interest in and motivation to act is sapped. Many policymakers have already referred to these rogue agents as if they are characters from the once seemingly fantastical movie Terminator 2, where robots gain revenge on humans for trying to control them.

If AI is destined to deliver either utopia or apocalypse, why try to shape what happens next? We treat the future as something to brace for rather than something we can influence, which informs the kinds of actions and solutions we see as relevant.

This framing can show up in how we talk about AI systems that act in unexpected ways. Discussions of rogue AI agents, for example, can evoke images of science fiction scenarios in which robots turn against their human creators. These stories may capture people’s attention, but without careful framing, they also reinforce the idea that AI is on a predetermined path toward a future humans can no longer control.

These mindsets and the powerful current of inevitability that runs through them distract us from the people, institutions, and systems responsible for AI’s development. AI technologies are built and deployed by companies led by actual people making human decisions about how they work, where they are used, and who benefits.

Focusing on those decisions brings the conversation back to the people and institutions that can and must be held accountable—and to the policies that can shape what happens next.

Building Better AI Stories

If we want to build public understanding of AI and increase support for governance and other meaningful solutions, we need better ways to talk about the technology. Our research points to several ways to do so.

1. Explain how AI works. Give people a clear, accessible understanding of the technology rather than relying on abstract language or science fiction comparisons. For example: “AI is a computer process that identifies patterns in large amounts of data. Those patterns are then used to generate content, make predictions, or support decisions.”
2. Reveal power. Help people see who has the power to shape AI’s development and who is affected by those decisions. For example: “A handful of powerful companies have significant control over how AI is developed and used. When our country’s leaders do not create or enforce boundaries, those companies prioritize their own interests over the public good.”
3. Reveal the humans behind the technology. Make visible the people making decisions about how AI is developed and used. For example: “People make decisions at every stage of AI development: They choose the data used to train AI systems, design and test the technology, and decide how to apply its results. Those choices shape how AI works and how it affects people’s lives.”
4. Explain bias. Show how the assumptions and choices built into AI systems can produce biased outcomes, rather than treating those outcomes as inevitable features of the technology. For example: “An AI system used to predict where crime is likely to occur may direct more police to Black neighborhoods if it relies on historical arrest data from communities that have been over-policed.”
5. Ground the conversation in consequences. Connect AI to the real-world effects it has on people, communities, workers, and our economy. For example: “When health insurance companies use AI to help determine which patients need additional care, the data they rely on can have serious consequences. If AI uses past health care spending to predict patients’ needs, it may exclude patients who have historically faced barriers to care and spent less on health care, even when they have significant health needs.”
6. Show agency; highlight efficacy. Emphasize that people can work together to shape how AI is developed and used, rather than leaving its future in the hands of a few. For example: “ We can control AI’s effects on society by demanding transparency, including communities affected in decision making, and establishing safeguards to prevent harm. There are things we can do to ensure AI serves the public interest.”
7. Put specific solutions on the table. Name the policies and interventions that can address the problems AI creates. Vague calls to action fuel the sense of inevitability where concrete explanations build a sense of efficacy and an understanding of what can change and how. For example: “Governments can require companies to test AI systems for bias before deploying them, disclose how systems are used in high-stakes decisions, and establish independent oversight to investigate harms. These safeguards can help ensure AI systems are developed and used in ways that serve the public interest.”

Together, these principles help shift public thinking away from inevitability and toward the possibility and importance of meaningful upstream action. They can also help redirect attention from the technology itself to the decisions, incentives, and power structures that shape it.

Shaping the Future of AI

The story we tell about AI can either reinforce the myth of inevitability or build a sense of efficacy and help people see opportunities to shape the future. We need stories that make solutions visible, show that collective action is possible, and build understanding of how to rebalance power over who gets to decide the future of AI.

Terminator 2 doesn’t have to become our reality. The future of AI is still being shaped by human decisions, and our public conversations must make that clear.