When AI experts are asked whether AI will make work better, most of them are positive about it, with 73% saying yes. But the general public does not seem to feel the same way, as only 23% agree. This big difference shows that the people building AI and the people who will actually use it in everyday work are looking at the technology in very different ways. As a student who will soon enter a workplace where AI is becoming more common, I wanted to understand this gap better. These five charts help explore whether people’s concerns are justified, where optimism is growing, and why some countries seem more confident about AI than others.
For this article, I used two major sources of recent global opinion data: the Stanford AI Index 2026 and the Ipsos AI Monitor. These reports include responses from thousands of people across many different countries, which makes them useful for comparing how people feel about AI around the world. Together, they show where people are hopeful, where they are worried, and how attitudes toward AI can change depending on country, experience, and trust in the technology.
People around the world seem to be becoming more open to AI. Each year, more people say that AI’s benefits are greater than its drawbacks. However, this does not mean people feel completely comfortable with it. At the same time, nervousness about AI has also increased. This shows that people are not simply becoming more positive or more negative about AI. Instead, their feelings are becoming stronger in both directions. Many people are hopeful about what AI can do, but they are also worried about the risks it may bring.
The global average does not tell the full story, because people’s views on AI are very different depending on where they live. In countries like China, Indonesia and Thailand, most people see AI as something that can bring more benefits than harm. But in places like Canada, the United States and the Netherlands, people are much less convinced. This is interesting because the countries that are usually seen as more technologically advanced are not always the most optimistic about AI.
This chart compares the share of people in selected countries who say AI products and services have more benefits than drawbacks. Source: Stanford AI Index 2025 using Ipsos data.
This is where the biggest difference becomes clear. When AI experts and the general public are asked the same questions, their answers are very far apart. For example, 73% of experts believe AI will improve the way people work, but only 23% of the public agree. A similar gap appears when people are asked about AI’s impact on the economy and healthcare. This suggests that experts, who understand the technology more closely, are much more confident about AI than the people who may have to deal with its effects in everyday life.
One interesting question is why people in countries like Indonesia seem much more positive about AI than people in countries like the Netherlands. The data shows a surprising pattern: wealthier countries are often more cautious about AI. When national optimism is compared with income per person, it suggests that people in richer countries may be less enthusiastic and more sceptical. In other words, higher prosperity does not always lead to greater confidence in AI.
The benefits of AI are not being experienced equally by everyone. The data shows that younger workers and higher-earning workers are more likely to say AI has already helped them save time. As a Gen Z student, this feels especially relevant to me because I belong to the group that may benefit from AI early, but also the group whose future career may change the most because of it. This shows that AI is already creating real advantages, but those advantages are not reaching everyone in the same way.
This chart shows which workers are most likely to say AI saved them time at work, split by generation and income group. Source: Ipsos AI Monitor 2026.
Overall, the findings show that the real issue is not whether AI is completely good or completely bad. Experts may feel more hopeful because they understand the technology better, while many ordinary people may feel anxious because they are unsure how AI will affect their jobs and future. This means the gap needs to be addressed honestly, without exaggerating the benefits or ignoring the risks. For students like me, the best response is not to reject AI or depend on it completely, but to learn how to use it carefully and confidently while the opportunities are still growing.
The charts in this article are based on large opinion surveys conducted across multiple countries. The Ipsos AI Monitor is an annual survey that has been running since 2022 and includes more than 20,000 adults from up to 32 countries. I used its results from 2022 to 2026 to show the global trend in whether people think AI’s benefits outweigh its drawbacks, as well as the figures on workers saving time through AI.
The comparison between AI experts and the general public comes from the Stanford AI Index 2026, which compares the views of AI experts with a nationally representative sample of the US public. The income data used in the wealth comparison comes from the World Bank’s 2024 GDP per capita figures, adjusted for purchasing power parity. All percentages used in the charts were taken directly from the original published reports.
This article was designed with accessibility in mind, using the WCAG “POUR” principles discussed in the course. I made sure that colour is not the only way information is shown. Each chart also uses direct labels, position or length so the values can still be understood without relying only on colour. The colour palette used for regions is colour-blind-safe, and the article uses clear headings and descriptive links. I also explained the main figures from each chart in the surrounding text, so readers can still follow the story even if they cannot fully see the visuals.
I used generative AI tools to support parts of this assignment, mainly for research guidance, coding support and reviewing my written explanation. Claude was used to help identify possible open datasets, structure parts of the R code and create an early draft of the narrative. ChatGPT was used to review the plan, improve clarity and check that the figures matched the source reports. I rewrote the final narrative in my own words, checked the data against the original sources, and made the final decisions about the charts, design and story myself.