Some people think that AI is here to steal jobs, while a few think it will steal humanity. And a few imagine an AI uprising straight out of a sci‑fi film.
Most of what we fear comes from misconceptions or myths built on half‑understood headlines and viral social‑media content. To understand what AI can do in the future, we first need to understand what it cannot do.
Myth 1: AI Has Agency and Acts on Its Own
What People Wrongly Assume
The content generated by AI tools is fluent in language and can chain reasoning steps, so many users believe that the model is initiating or intending to choose its actions all on its own. Essentially acting like an autonomous agent.
What Studies Actually Say
Research repeatedly shows that AI systems do not possess agency or intentions. A major 2024 review on “The Limitations of Large Language Models for Understanding Human Language and Cognition” finds that today’s LLMs lack grounded internal representations and any mechanism that would support genuine intentional behaviour. They operate through statistical pattern recognition and prediction, generating the next word based on probabilities in their training data, not purpose.
A 2024 paper by Adriana Placani on “Anthropomorphism in AI: Hype and Fallacy” argues that humans have a strong tendency to project human‑like traits, such as agency, emotions and intentions, onto AI systems, especially when their outputs are linguistically rich or socially framed. In this view, agency in AI is largely a hype‑driven anthropomorphic projection rather than a technical fact.
Mostly, this myth is harmless, but it can cause socio-technical blindness when sensational headlines overstate what systems “decide” or “want” to do. To counter this, projects like aimyths.org have experimented with headline‑rephraser tools that take misleading AI headlines and rewrite them more accurately (and sometimes cheekily), to make the underlying limitation clearer to readers.
Myth 2: AI, Machine Learning and Deep Learning Are the Same Thing
What People Wrongly Assume
People think that Artificial Intelligence is the same as Deep Learning and Machine learning.
What Studies Actually Say
Google’s 2024 myth report on AI terminology notes that there is no single universally accepted technical definition of AI, and that the term is used inconsistently across policy, industry and research. Still, there is a widely used working hierarchy of concepts:
| Concept | Definition |
| Artificial Intelligence (AI) | Any technique that makes machines perform tasks typically requiring “intelligence” when done by humans, including rules, logic, planning, search, knowledge systems and learning. |
| Machine Learning (ML) | A subset of AI using algorithms that learn patterns from data and improve with experience instead of being explicitly programmed; essentially the data‑driven half of AI. |
| Deep Learning (DL) | A subset of ML using deep neural networks with many layers to automatically learn complex features from data, rather than relying on hand‑engineered features. |
So, not all AI learns, not all ML uses deep neural networks. Deep learning is one powerful set of techniques inside machine learning, not the whole of AI.
Myth 3: AI is as Good as its Data, and More Data Always Means Better AI
What People Wrongly Assume
Most people think that more data makes smarter AI, and gives better results. They assume that if you feed a system huge amounts of data, it will automatically become more intelligent, and that small datasets necessarily produce weaker models. The intuition is that “garbage in, garbage out,” so AI depends almost entirely on the quantity of data, not the quality. This feels natural because popular models like ChatGPT‑style systems, Claude, Gemini or Meta’s models are trained on very large text corpora containing trillions of tokens.
What Studies Actually Say
DeepMind’s 2022 “Chinchilla” scaling paper, “Training Compute‑Optimal Large Language Models”, shows empirically that an optimal balance between model size and training data matters more than simply scaling data or parameters alone. For a fixed compute budget, moderately sized models trained on the right amount of high‑quality data can outperform much larger models trained on noisier or poorly matched datasets.
In other words, AI is not a giant sponge that just absorbs all available data and becomes smarter. It is more like a microscope. It needs clear, clean, well‑structured and relevant inputs to produce reliable, interpretable outputs. Data quality, diversity and alignment with the task, and how the model is trained matter as much as, or more than, raw volume.
Myth 4: AI is Approaching Human‑Level Intelligence
What people wrongly assume
Modern AI systems can write essays, crack jokes, provide conversational support, talk to us in natural language, solve coding problems and more. This leads many people to believe that AI is close to matching human intelligence, or at least human‑like understanding.
What studies actually say
Current LLMs do not form internal goals or intentions, and they often fail at tasks that require robust, flexible reasoning. AI models perform well on tests, but they collapse under slight distribution shifts like small changes in phrasing or the context itself. In fact, they don’t have human-like comprehension (as seen in Myth 1), so they are more like pattern systems and not thinking systems.
Myth 5: AI is objective
What people wrongly assume
Humans can be biased, but machines are not, because they make decisions based on numbers, not feelings. Under this belief, automating decisions with AI should remove discrimination, and if the model is trained on “facts,” its outputs must be objective. This belief is so common that many organizations use AI in hiring, credit scoring, policy making, healthcare and policing.
What Studies Actually Say
A landmark study, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” showed that commercial facial‑analysis systems had significantly higher error rates for darker‑skinned women compared with lighter‑skinned men. The authors demonstrated that these disparities were linked to imbalanced training data and design choices, directly contradicting the idea that “more data” automatically leads to fair or objective systems.
A 2021 report in “Nature Machine Intelligence” on data bias in AI systems further shows that biased outcomes can arise not only from skewed datasets but also from model architectures, training objectives and deployment contexts. In other words, AI will inherently produce biased results if fed biased data, but bias can also be amplified or introduced by how the model is built and used.
So what’s next?
Across every study and every myth, one theme stands out. AI is not magic, nor is it a mystery. AI is a system built by humans and their choices, limited by human data. It does not think like us because it cannot initiate thoughts. This brings us back to the philosophical arena of “consciousness.”
However, at present, it cannot intend or ‘come alive’ in the way science‑fiction suggests. If anything, the real risk is that it becomes too much like humans, but that we assume it already is. Understanding these differences is what separates hype from reality and blind adoption to responsible use.
