What even is AI?
Everywhere you look, something is labeled AI. I see it every day in stores, on apps, in ads. But here’s the problem: a lot of it isn’t really AI at all. And that raises the real question: do we even know what AI actually is, or how much of it we really want in our lives? UK research shows public trust in actual AI is declining: concerns are rising, like using AI to assess welfare eligibility, where worry jumped from 44% to 59% in just two years.
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This comes from the Ada Lovelace Institute’s nationwide survey, looking at how people in the UK feel about AI, what they understand, and where their trust is shifting. One big problem with AI research is that it often treats AI like a single thing, when in reality it depends completely on the context. To close that gap, the survey asked people about awareness of eight different AI applications, and what they saw as the main benefits and concerns for each one.
So what actually is AI? Because if trust is falling, the least we can do is separate the hype from the reality.
The survey gave respondents this definition of AI:
“ Artificial intelligence (AI) is a term that describes the use of computers and digital technology to perform complex tasks commonly thought to require human reasoning and logic. AI systems typically analyse large amounts of data to draw insights or patterns and achieve specific goals. They can sometimes take actions autonomously, that is without human direction. These systems can also be used to generate content like text, images, music or videos.”
Most people know about AI in the big headline areas — 93% have heard of driverless cars and 90% know about facial recognition in policing. But awareness drops off when it comes to everyday uses: only 18% realise it’s used to assess welfare benefits, and just 24% know about robotic care assistants or loan tools. And that gap matters, because when people don’t really know where AI is being used, it’s easy for companies to throw the label around and cash in on the hype.
Welcome to Is this AI?, the game show where you guess what’s actually artificial intelligence or just marketing hype.
Driverless cars?
That’s AI. They use sensors and machine learning to make split-second driving decisions.
Amazon self-checkout?
Nope! That’s people in India watching you get your oat milk, not AI.
Spam filters in your email?
Yes! They learn patterns to keep junk out of your inbox.
Dog ears IG filter?
Nope! The app just detects a face with computer vision and applies the overlay.
Final question: Netflix recommending what to watch next?
Yes, that’s algorithms predicting what you would like.
And that’s the game. The truth is, real AI often hides in everyday tools we don’t even think about, while some things that look high-tech are just simple code. For most people who don’t know how coding works, it’s easy to believe both are ‘AI,’ because the term is used so broadly.
And companies take advantage of that confusion. Some of the biggest names have sold visions of AI that turned out to be far less intelligent than advertised.
Amazon’s so-called AI-powered grocery shops looked like the future: walk in, grab your items, and walk out, no checkout needed. It seemed like exactly the kind of efficient, convenient use of AI that surveys show people want. But in reality, it wasn’t algorithms running the show, it was people.
Reports revealed that more than a thousand workers, mostly based in India, were reviewing footage and labeling sales, with one insider saying around 70% of transactions in 2022 needed human review. Amazon denied it, but it shows the gap between the AI we’re promised and the reality.
And it’s not just Amazon. Builder.ai is one of the clearest examples of AI hype gone wrong. They launched Natasha, a virtual assistant that promised to handle 80% of app development, with the bold slogan ‘building an app should be as easy as ordering a pizza.’ Investors loved it, they raised over $400 million, had Microsoft’s backing, and reached a $1.5 billion valuation.
But insider reports later showed most of the coding was done by human developers in India and Ukraine, with Natasha only handling small pieces of UI work. Investigations also uncovered inflated revenue numbers and shady ‘round-trip’ billing deals. By May 2025, the company collapsed into bankruptcy as creditors seized funds. What looks like cutting-edge AI is often just clever branding covering up ordinary human labor.
It’s not all hype. There are also places where AI is quietly making a real difference.
Take automatic translation. On YouTube, videos can now be dubbed into multiple languages almost instantly, which means creators can reach audiences far beyond their own language, and viewers can access information, entertainment, or education they’d never have been able to before.
Another great example is Samsung’s interpreter mode on their flip phones. Imagine traveling abroad, meeting someone who doesn’t speak your language, and being able to hold a real-time conversation without needing a translator. The phone listens, translates, and speaks back, helping people connect and get their point across.
But AI isn’t just making our daily lives easier, it’s also pushing the boundaries of science in ways that could change the future. Here’s a good example of AI actually living up to the promise. Scientists at UPenn used AI to scan through the global microbiome, basically the DNA of microbes from all over the world. Normally, analyzing that amount of data would take decades. But with AI, they sifted through 150,000 genomes and discovered nearly one million potential new antibiotics.
They then tested 100 of those, and dozens worked against drug-resistant bacteria — even in live mice. That’s huge, because antibiotic resistance is one of the biggest threats to modern medicine. And here’s the perk: without AI, this kind of discovery simply wouldn’t be possible at this speed or scale. It shows how AI can cut through mountains of data and find solutions that humans alone would never get to in time.
It’s important to be clear on what’s actually AI and what’s not. Otherwise, people just see it as a buzzword and don’t get the real value. The more transparent we are about how it works, the easier it is for the public to understand the potential, adapt to it, and see the benefits in the long run.
That’s exactly what the Ada Lovelace Institute found in their 2025 survey: most people are positive when they can clearly see the benefit, like AI spotting cancer early, but they’re a lot more skeptical with things like driverless cars or autonomous weapons. So if we want people to trust and actually use AI, we need to show them clearly what it is, how it works, and why it matters, not just throw the label “AI” on everything.
So how can you tell if something is really AI, or just hype? Here are a few things to look out for:
Look at whether the system is really learning or just following simple rules. For example, a toy ball that rolls to random spots might seem clever, but it isn’t learning from your cat’s behavior or getting better over time. It’s just repeating pre-programmed movements.
Ask for transparency. Find out what kind of AI the system is using, such as machine learning, natural language processing or deep learning, and understand why. Then ask yourself if that type of AI is truly essential for the product to work or if it is just being used as a marketing term. Most importantly, does it actually make the product better?
Look for evolving insights instead of static outputs. Real AI adapts and changes recommendations over time. For example, if you start watching one K-drama like Crash Landing on You on Netflix, the system will notice and keep adjusting by recommending more K-dramas until you finish their whole library.
Check for evidence. Some companies add the term “AI-driven” to describe what is really just simple automation. If there is no clear technical documentation or explanation of how AI is actually being used, that is a red flag.
At the end of the day, AI isn’t magic, it’s a tool. And if we know what to look for, we can spot the difference between real innovation and just another buzzword.
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