Will AI Take Over the World?

What Does "AI Taking Over the World" Actually Mean?

"Taking over the world" usually bundles three different claims together: AI doing more economic work than humans, AI systems making more decisions that affect people's lives, and a science-fiction scenario where AI pursues its own goals against human interests. Only the third one is what "takeover" literally means.

The first claim, AI doing more of the work, is already true and mostly boring: spreadsheets, drafts, code, customer replies. The second claim, AI making more decisions, is also happening: loan approvals, hiring screens, content moderation, and it deserves real scrutiny because a biased or broken model making decisions at scale is a genuine harm, not a hypothetical one. The third claim, the one people usually mean when they ask this question, is about autonomy: a system that sets its own objectives and acts on them regardless of what a human wants. That is the claim with almost no evidence behind it today, and it is the one this piece focuses on.

What Do AI Researchers Actually Think?

The largest survey of AI researchers ever conducted found a median 5% probability that AI causes human extinction or a similarly permanent catastrophe. More than half of respondents gave at least a 5% chance to that outcome, and the average estimate, at 9%, was nearly double the median.

AI Impacts surveyed 2,778 researchers who had published at top AI venues for its 2023 Expert Survey on Progress in AI, led by Katja Grace. Asked to weigh the long-run impact of high-level machine intelligence, the median respondent put 5% odds on an "extremely bad, e.g. human extinction" outcome, the same figure as the prior year's survey. The distribution has a long tail: 57.8% of respondents gave at least a 5% chance to that outcome, and the same survey found the median aggregate forecast for a 50% chance of human-level AI moved up to 2047, thirteen years earlier than the 2022 estimate. Researchers who work on this every day are not dismissing the risk. They also are not treating it as likely.

Why Do Experts and Superforecasters Disagree on AI Risk?

AI domain experts put a median 3% probability on AI causing human extinction by 2100. Superforecasters, people with a documented track record of accurate predictions, put that number at 0.38%, nearly eight times lower. Months of structured debate between the two groups did not close the gap.

The Forecasting Research Institute ran the most rigorous version of this question anyone has attempted: the Existential Risk Persuasion Tournament, which put 80 domain experts and 89 superforecasters through months of research, argument, and rebuttal on AI and other long-run risks. On AI specifically, the two groups landed further apart than on any other risk category they studied, and staying apart is itself the finding. The tournament's own researchers called it the most puzzling result: incentivized to persuade each other, given months and thousands of forecasts, the two groups barely moved. Read that as evidence that this is a genuinely hard question, not one where either group is obviously right and the other is obviously wrong.

Why Is Artificial Intelligence Bad? The Risks That Are Real Right Now

The documented risks are concentration, misuse, and reliability, not robot uprisings. A small number of companies control the compute and infrastructure much of the world now runs on, bad actors already use AI for fraud and cyberattacks, and models still fail in ordinary, expensive ways that have nothing to do with scheming for power.

Start with concentration: a handful of labs and cloud providers control the compute, data, and model access that everyone else builds on, which means outages, price changes, or policy shifts at one company can ripple through an entire economy. Add misuse: AI already lowers the cost of phishing, deepfakes, and automated scanning for software vulnerabilities. Then add reliability, the least dramatic and most common failure: the AI Incident Database tracked 362 documented AI-related incidents in 2025, up from 233 in 2024, according to Stanford's AI Index Report. Nearly all of that growth is mundane stuff: wrong outputs shipped as fact, biased screening tools, chatbots giving bad advice. None of it required a system with its own agenda. It required a system that was wrong and nobody caught it in time.

Why Is Artificial Intelligence Good? What It Actually Delivers

AI experts rate the technology's likely impact far more positively than the public does, and the gap is largest on personal benefit. AI already drafts, codes, catches errors, and handles repetitive work at a scale no equivalent team of humans could match at the same cost. The realistic upside is compounding productivity, not sentience.

In Pew's 2025 survey of 1,013 U.S.-based AI experts and 5,410 members of the public, 56% of experts said AI will have a positive effect on the United States over the next 20 years, versus 17% of the public. That gap is worth sitting with: it is not that experts are naive about risk, it is that they spend their days watching the technology do useful things the public mostly does not see. We see the same pattern from the inside. The honest upside of AI right now is narrow and real: faster first drafts, fewer dropped tasks, more work done per person, not a general intelligence taking on the world's problems on its own initiative.

What Does the Public Think About AI?

Half of Americans say they are more concerned than excited about AI in daily life, up from 37% in 2021, and only 10% say they lean toward excited. AI experts feel almost the opposite: 47% are more excited than concerned, and just 15% lean toward worried.

Pew has tracked this since 2021, and the trend line only moves one direction: worry is up, excitement is flat. More than half of Americans, 57%, rate AI's societal risks as high, against 25% who rate its benefits as high. On jobs specifically, only 23% of Americans think AI will have a positive impact on how people do their work, per Pew's 2026 tracking. That is a public that has clearly decided AI is a big deal. It is not a public that is worried about a robot uprising. It is worried about control, jobs, and whether the people running these systems are accountable to anyone.

Can AI Become Self-Aware?

No credible research has demonstrated self-awareness or consciousness in a deployed AI system, and there is no established test that could even detect it if it existed. Current models predict text and select actions. They do not have persistent goals, a body, or any stake in their own continuation between conversations.

It is worth separating two things people conflate here. A model can appear to reason, hold a consistent persona across a conversation, and describe its own "thinking" in plain language, and none of that requires consciousness, any more than a thermostat needs consciousness to "decide" to turn on the heat. Whether any information-processing system can be conscious at all is a contested question in philosophy of mind that predates AI by decades, and nothing about current transformer-based models has settled it. What we can say from running these systems daily: they have no memory between sessions unless we explicitly give them one, no body, and no persistent objective that survives past the task in front of them. "Self-aware" is doing a lot of science-fiction work in that question, and the systems in production today do not do any of it.

Will AI Replace Humans?

AI replaces tasks, not people, so far. It is already displacing routine work in translation, first-draft writing, and boilerplate code, while creating new work like agent oversight and prompt design. Some jobs shrink, some grow, and almost every job changes what a normal day of work looks like.

We wrote a full breakdown of this by profession in our companion report on AI and jobs, so we will not duplicate it here. The short version: the public is more pessimistic than optimistic on this question, and Pew's data backs that up, with only about a quarter of Americans expecting AI to help how they do their jobs. The pattern we see building these systems for clients matches that caution more than it matches either extreme. AI takes over specific, bounded tasks well. It does not take over judgment, accountability, or the messy parts of a job that involve reading a room.

What's Our Take, Building AI Agents Every Day?

The failures we see in production are mundane: a scraper that misreads a date, an agent that retries the wrong step, a model that is confidently wrong about a phone number. None of that resembles a system scheming for power. The gap between "got the timezone wrong" and "took over the world" is the entire story.

That does not mean the frontier labs get a pass on scale risk. The concentration and misuse problems above are real and they are getting more attention, not less: the International AI Safety Report, led by Yoshua Bengio and backed by more than 30 countries and over 100 independent experts, tracks exactly this ground every year, and its most recent update found the number of companies publishing formal frontier AI safety frameworks more than doubled in 2025. That is the right kind of response to a technology this consequential: independent, international, and focused on what labs actually do rather than what they promise. Our own working rule, after shipping agents into real businesses week after week, is simpler and less dramatic than either the doom framing or the hype framing: today's systems fail like software, not like adversaries. Reliability, oversight, and who controls the infrastructure are the risks worth your attention right now. A system plotting against you is not.