What is Generative AI?
Generative AI is one of the most talked-about technologies in recent memory—but what is it, really? And why is it having such a profound impact on the way we work, communicate, and create?
Here, we aim to give you a clear, grounded understanding of what Generative AI is, how it works, and what it means for individuals, organisations, and society at large. Whether you’re just getting curious or already using tools like ChatGPT in your workflow, this is the starting point for understanding the bigger picture.
15 min read
Estimated time.
Part 1
What is AI, really?
Artificial Intelligence, or AI, refers to computer systems designed to carry out tasks we typically associate with human intelligence—like recognising patterns, learning from experience, making decisions, or processing language.
Beyond the sensational headlines, AI is both simpler and more fascinating than it might seem. At its heart, AI is built on instructions, known as algorithms, that help computers recognise patterns in data. But it’s important to remember that AI simulates intelligence—it doesn’t possess it. These systems don’t understand in the way humans do. They detect, predict, and generate based on data—not on awareness, reasoning, or intent.
💡 A bit of history
AI development began in the 1950s with rule-based systems designed for specific tasks. Over time, as computing power increased and more data became available, AI’s capabilities have expanded. Today, AI is embedded in everyday life, powering everything from streaming recommendations to fraud detection. Progress hasn’t been straight forward though—it’s moved in waves, with bursts of innovation followed by slower periods referred to as ‘AI Winters’. Read more about the history of AI →
Innate vs. Artificial Intelligence
There’s a difference between intelligence and information processing. Humans think, reflect, and adapt with purpose. That’s what we call innate intelligence—the kind that operates even without formal knowledge or training. It’s contextual. It’s intuitive. It’s capable of insight.
AI doesn’t have this. It doesn’t “know” anything. What looks like insight is often high-quality pattern recognition. What sounds like understanding is often probability, dressed in language. That distinction matters. Especially when AI starts sounding human.
Types of AI
AI falls along a spectrum of capability, from narrow to theoretical:
Narrow AI performs well-defined tasks like detecting spam, translating languages, or recommending products. It’s useful, but inflexible—it can’t learn new things beyond its programming.
Artificial General Intelligence (AGI) is a conceptual goal, a system that could perform any intellectual task a human can. We’re not there yet, and we may never get there.
Superintelligent or Conscious AI is entirely speculative. It would require self-awareness, emotion, and independent thought. No existing system comes close.
Some researchers also classify AI based on how it functions:
Reactive AI responds to inputs with pre-set behaviours.
Limited Memory AI learns from past data, temporarily.
Theory of Mind AI is still in the research phase—focused on interpreting human beliefs and emotions.
Self-aware AI remains theoretical.
Some of the most capable systems today—like ChatGPT, Claude, and Gemini—are built on what are known as foundational models. These are large, general-purpose AI models trained on a wide variety of data, across many domains. They’re designed not for one task, but for many: writing, coding, translating, reasoning etc.
They’re called foundational because they serve as the base layer. Developers can adapt them—by fine-tuning or prompting—for more specific uses, from customer service bots to scientific research assistants.
But breadth doesn’t mean depth. These models are flexible and powerful, but they still lack true understanding. They’re “foundational” in capability—not in consciousness.
📌 A note on terminology
It’s easy to confuse general-purpose AI with general intelligence—but they’re not the same. Foundation models like ChatGPT are general-purpose in the sense that they can be applied to many tasks. But they are not examples of Artificial General Intelligence (AGI)—the kind of human-level, flexible reasoning system that can think across domains. That kind of AI doesn’t exist yet, and may never. Today’s models are versatile but still narrow in how they operate. They simulate language, not thought.
AI is a Multidisciplinary Field
AI isn’t the product of one discipline. It draws from:
Computer science, which defines how it’s built.
Mathematics and statistics, which shape how it learns.
Psychology and linguistics, which inform how it processes language and behaviour.
Philosophy and ethics, which guide how it should (or shouldn’t) be used.
This matters because AI isn’t just technical—it’s cultural. It reflects choices, values, and assumptions at every layer.
💡 Artificial vs. Augmented Intelligence
Artificial Intelligence often aims to automate decisions or actions that humans would otherwise take, whereas Augmented Intelligence uses the same tools to support and extend human capability.
Part 2
What is Generative AI?
Generative AI, or GenAI, refers to systems that can produce new content—text, images, sound, even code—based on what they’ve learned from massive datasets. These AI models don’t just recognise patterns. They use them to create something new.
That marks a significant shift. Earlier forms of AI could detect spam, recommend products, or classify images. GenAI can write essays, compose music, draft arguments, and even brainstorm strategies. It engages with human language, logic, and structure at a level that, not long ago, seemed out of reach.
But they come with important limitations. GenAI doesn’t understand its outputs in a human sense. It works by identifying patterns in training data and predicting what’s most likely to come next.
Large language models, for example, are trained on vast amounts of human-generated text—books, forums, research papers, transcripts etc. From this, they learn how language flows, how ideas connect, and how arguments are structured. When generating a response, they predict the most likely next word—not because they understand it, but because they’ve seen that pattern before.
But despite convincingly simulating conversation, GenAI lacks self-awareness and genuine understanding. It doesn’t reason. It doesn’t have beliefs or awareness. It doesn’t know what’s true or false unless it has been explicitly trained to mimic a source that does. That’s why it’s essential not to confuse fluency with intelligence. GenAI can sound authoritative, but that doesn’t mean it’s right.
But GenAI changes more than workflows. It changes who gets to create. It lowers the barrier to producing high-quality writing, images, code, and strategy. It amplifies individual output. It compresses time. It accelerates ideas.
It also challenges old assumptions. What does it mean to “write” if a model can draft for you? What does it mean to “know” something if a model can summarise it better than you can recall it? How do we measure originality, when everything is built from what came before? These are not philosophical side notes. They’re live questions—for education, for work, for culture.
GenAI doesn’t “know” facts in the way people do—it’s drawing from patterns, not memory. And while it can support creativity, boost productivity, and enhance problem-solving, it cannot, and should not, replace human judgement or context.
Used well, generative AI is a multiplier. But it still needs direction. It doesn’t know what you’re trying to do. It doesn’t know what’s worth saying. It doesn’t know what you care about. That’s your role. The more intentional you are, the more effective these tools become.
A common question is whether GenAI is conscious, or capable of thinking for itself. The answer, for now—and possibly forever—is no. While it can convincingly mimic human language and interaction, it has no self-awareness, beliefs, or emotions.
Whether AI could ever achieve consciousness remains an open question—but today’s systems aren’t even close.
Part 3
A brief insight into how it works
To understand how GenAI functions, it helps to start with two key concepts: Natural Language Processing (NLP) and Large Language Models (LLMs).
NLP: Making Language Machine-Readable
Natural Language Processing represents one of AI's most significant achievements. It's the technology that allows machines to work with human language in all its messy, ambiguous glory.
Think about the challenge. Human communication involves not just words, but context, tone, cultural references, and implied meaning. NLP breaks this complex process into manageable steps.
First, NLP systems analyze the structure of language, identifying parts of speech, sentence structure, and grammatical relationships. Then they work to extract meaning by connecting words to concepts and tracking relationships between ideas.
What makes this technology remarkable isn't just that it can process text and speech. It's that it can navigate the nuances that make human language so challenging for machines. From detecting sentiment to summarising documents, NLP continues to narrow the communication gap between humans and technology.
For generative AI specifically, NLP provides the foundation that allows these systems to both understand your prompts and generate coherent responses that follow linguistic patterns.
LLMs: Prediction at Unimaginable Scale
Large Language Models represent a quantum leap in AI capability. Unlike traditional systems built with specific language rules, LLMs discover patterns by processing enormous datasets of human text.
The scale is staggering. These models absorb billions of books, articles, websites, and conversations during training, allowing them to capture the subtle patterns in how humans express ideas.
What makes LLMs revolutionary is their self-guided learning approach. They aren't told what's "correct" but instead predict what words should come next in a sequence. Through billions of predictions and adjustments, they build a statistical map of language relationships.
This approach gives LLMs their remarkable versatility. The same underlying model can write essays, answer questions, summarise documents, and even generate code without being specifically programmed for each task.
But LLMs don't truly understand meaning as humans do. Their apparent comprehension comes from recognising patterns in how words appear together. When an LLM generates a thoughtful-seeming response, it's predicting the most likely sequence of words based on similar patterns in its training data.
When you prompt a GenAI model, it’s not retrieving information or understanding your intent. It’s drawing on its training to predict the most statistically appropriate response, one word at a time. It doesn’t “know” what it’s saying. But the results are often so coherent, so contextually tuned, that it feels like it does. That’s the power of scale and structure—not consciousness, but computation.
This isn’t just clever programming. It’s a new way for machines to work with language—not by following rules, but by learning patterns from the world. That shift—from hard coding to probabilistic learning—is what makes generative AI so flexible, and so impressive.
Of course, this is a high-level overview. Beneath the surface are layers of mathematical models, engineering trade-offs, and evolving architectures that keep pushing the boundaries of what’s possible. But for now, what matters is this: GenAI doesn’t “think”. It predicts. And that prediction, scaled to billions of examples, is what makes it so powerful—and so easy to misread.
Part 4
Why all the attention lately?
You might be wondering why GenAI is everywhere all of a sudden. The answer lies in a perfect storm of technology, data, and timing.
First, we now have the computing power to train and run models at unprecedented scale. Second, we have access to massive datasets—enough to teach machines the subtleties of human language, tone, and structure. Third, breakthroughs in model architecture have made these systems faster, cheaper, and more capable than ever before.
Then came the tipping point: accessibility.
Tools like ChatGPT and Midjourney made GenAI usable by anyone with an internet connection. No setup. No coding. Just a blank box and a blinking cursor. And that’s the shift. That’s what changed it from research to routine. From engineering to everyday thought.
The tools didn’t just get better. They got closer.
Part 5
Open vs closed models
Not all GenAI models are built the same way. Some are open source—meaning their code and training weights are freely available to inspect, adapt, or build on. Others are closed— developed and maintained by private companies who keep the inner workings under wraps.
Open models promote transparency, experimentation, and shared progress. Developers can tailor them to specific tasks or industries. Communities can extend and audit them. This openness often fuels faster innovation—and, sometimes, deeper insight. But openness has trade-offs. Open models offer less control over how they’re used—which creates space for innovation, but also risk. There’s less control over how the model is deployed, and more reliance on the judgment of the user.
Closed models are more tightly managed. Companies like OpenAI limit direct access to their models’ internals to prevent misuse, maintain quality, and protect brand integrity. This allows for smoother user experiences—and stronger guardrails. But what’s gained in control is often lost in transparency. When the model is a black box, it becomes harder to question its outputs, trace its logic, or identify its flaws.
Both approaches have their place. Open models fuel experimentation and innovation. Closed models prioritise safety, branding, and ease of use. But this isn’t just a software question. It’s a governance question. Who decides how intelligence is shaped? Who gets to build with it, question it, or critique it? Whose values are embedded in the outputs—and can they be changed?
As GenAI becomes more embedded in the tools we use and the decisions we make, these distinctions will matter more—not less.
Part 6
Intellectual Technology
Some tools change how we use our bodies—like the wheel, the engine, or the touchscreen. Others change how we use our minds. These are intellectual technologies. Tools that enhance how we think, communicate, and solve problems.
Writing let us preserve memory. The calculator handled logic at speed. The internet connected thought across distance. Now, generative AI joins that lineage. So, what makes a technology “intellectual”?
Intellectual technologies don’t replace human thought. They extend it. They reduce friction. They scale ideas. They give shape to what we’re trying to express. GenAI does this by reflecting back language, logic, and tone, at the speed of a prompt. Used well, it supports exploration, accelerates iteration, and helps people move from draft to decision.
It can help someone write when they don’t yet have the words. It can help clarify meaning mid-process. It can widen the space between idea and outcome. And like any intellectual tool, its value depends on how it’s used. GenAI doesn’t know what matters. It doesn’t care what’s true. That part still belongs to us.
Framing GenAI as an intellectual technology helps us focus on what matters. It cuts through hype and fear, and brings the conversation back to human capability. This isn’t about replacing intelligence. But it could threaten it—if we start treating machine output as a substitute for thought, rather than a companion to it.
Used well, these systems support better thinking. But they can just as easily encourage faster shortcuts, shallower reflection, and overconfidence in what’s generated. The goal isn’t to outsource thinking. It’s to build the conditions where better thinking can happen—more clearly, more creatively, and more intentionally.
Part 7
Ethical AI
Like any powerful technology, generative AI doesn’t arrive neutral. It reflects the data it’s trained on, the values of those who build it, and the intentions of those who use it. That means ethics isn’t an add-on—it’s built into every decision, every prompt, every output.
Let’s look at three key areas where ethical awareness is essential.
Bias and Representation
AI systems learn from human data—and that data carries our assumptions, omissions, and systemic biases.
If a dataset underrepresents certain groups, the model will too. When stereotypes appear in training data, they don’t stay hidden. Models trained on biased data don’t just reflect those patterns—they reinforce them. Sometimes subtly. Sometimes unmistakably.
The risk here isn’t just offensive outputs. It’s distortion. Skewed data can reinforce existing inequalities, misrepresent people or ideas, and marginalise voices that are already under heard.
Bias can’t easily be fully removed—but it can be recognised, questioned, and mitigated. That starts with awareness.
Transparency and Accountability
Many GenAI systems operate as black boxes. Even their creators can’t always explain how a particular output was generated. That lack of transparency becomes a problem when these tools are used in sensitive areas—education, hiring, law, healthcare—where trust and explainability matter.
As users, we need to ask:
Can this tool explain its reasoning?
If something goes wrong, who’s accountable?
Are we treating the model’s output as suggestion—or as fact?
Ethical use starts with asking better questions, even when the answers are incomplete.
Privacy and Data Use
Some AI models learn from user input. That creates value—but also risk.
Sensitive information can be accidentally exposed, remembered, or re-generated. Organisations using GenAI need clear policies about what data goes in, where it goes, and who has access to it.
For individuals, the rule is simple: Don’t put anything into an AI tool that you wouldn’t share publicly.
For teams and companies, it’s more complex—but no less important.
A Human Responsibility
GenAI doesn’t make ethical decisions. It reflects the ones we embed in it—and the ones we overlook. The responsibility for thoughtful use doesn’t belong to the model. It belongs to us.
That doesn’t mean avoiding the tools. It means approaching them with clarity, intention, and a willingness to pause and ask: Is this helpful? Is it fair? Is it true?
Used well, GenAI can expand access, surface new ideas, and unlock creativity. Used carelessly, it can distort, mislead, and exclude.
The difference isn’t in the system. It’s in the person using it.
Part 7
Why it matters
Understanding how GenAI works is important. But so is understanding why it matters.
For individuals, it’s reshaping how we work, learn, and express ideas. It changes the tools we use, the skills we value, and the way we approach creative and analytical tasks. Whether you’re writing, coding, designing, or making decisions, GenAI can support your thinking—or distort it. The difference lies in how you use it.
For organisations, it presents both opportunity and responsibility. Faster workflows, new product possibilities, cost efficiencies—yes. But also new ethical demands, new questions about governance, and new expectations around trust. The challenge isn’t just adoption. It’s integration—with clarity, context, and care.
And then there’s the broader picture. GenAI is already touching education, employment, creativity, politics and more. It’s raising new questions about authorship, truth, equity, and access. These aren’t theoretical issues. They affect how knowledge is produced, how decisions are made, and who benefits from progress.
This is more than a new wave of software. It’s a shift in how we relate to information—and how we shape meaning in a world where machines can now generate it at scale. The tools will keep evolving. But we don’t have to follow them blindly. Because it isn’t just about what GenAI can do. It’s what we’re willing to use it for—and what we’re not.