Written by James Richards

Introduction

When OpenAI released GPT-4 in 2023, artificial intelligence (AI) became the most talked-about technology on the planet. Previously the preserve of data scientists and science-fiction enthusiasts, it seemed that AI was suddenly everywhere: in people’s homes, pockets and workplaces. And the AI revolution shows no sign of slowing down.

Businesses and governments are vying to demonstrate their integration of AI as an efficiency tool and productivity enabler, sometimes leading to inflated claims, while the tech companies who develop these systems are waging intense arms races to launch the next best model. In this article, we take a broad view of AI, explaining the technology, the major applications, the big players, the downsides, and where it all might be heading.

What is artificial intelligence?

At the very top level, artificial intelligence is the practice of making machines behave in ways that appear to be intelligent. Machine learning, a subset of AI, refers to computer algorithms that learn to predict outcomes based on finding patterns in data. Other approaches to AI that are not machine learning involve the use of rules, search, and logic. The distinction matters. Much of what people casually call AI today is in fact machine learning.

A voice assistant that recognizes speech, or a recommendation engine that suggests the next film you might enjoy, is a statistical pattern recognizer trained on examples. These systems are not intelligent in the human sense. They recognize patterns and generate outputs that fit their training distribution, rather than reasoning about the world. For clarity we will continue to use AI as the umbrella term, since this is common usage, but it is helpful to remember that most of today’s practical systems are ML.

Neural networks and deep learning

In the late twentieth century, machine learning research led to the development of neural networks: processing systems inspired by the human brain, in which data flows through interconnected nodes. Early types were known as shallow because they only used a few layers of nodes.

As computational power increased and larger datasets became available, the number of layers grew, and deep neural networks began to outperform older approaches. The release of AlexNet in 2012 was a seismic moment in machine learning. The model dramatically outperformed previous approaches on the ImageNet benchmark and triggered a surge of investment and research in deep learning, along with huge interest in big data, the large information sets required to train modern networks.

It is helpful to place that moment in a longer story. The roots of AI stretch back to the 1950s, when symbolic AI and expert systems attempted to encode knowledge in rules and facts. These systems could be impressive in narrow domains, yet they were brittle, and the cost of maintaining the rules soon outweighed the benefits. Expectations ran ahead of reality, investment dried up, and the field entered so called AI winters.

The emergence of deep learning marked a decisive shift because it offered a method that improved with scale rather than collapsing under complexity.

Transformers and NLP tasks

The next major milestone was the development of transformers in 2017. Transformers are deep neural network architectures that process data by focusing on the relationships between all elements in a sequence at the same time, such as across a long passage of text.

They use a mechanism called self-attention to decide which parts of the input are most important when making predictions, and they scale efficiently. Because transformers made it possible for models to handle much longer context and capture meaning across larger bodies of text, they paved the way for major advances in Natural Language Processing, the branch of AI focused on human language.

For decades NLP had been a holy grail, the promise that we could communicate with machines effectively using human language. Language is central to intelligence. If machines could understand and generate language reliably, they could do swathes of human work, from writing to coding and customer support. With transformers, that prospect moved from speculative to practical.

Large Language Models

These developments in NLP, built on decades of progress in machine learning, reached their most visible expression in the rise of Large Language Models. LLMs are transformer based AI models that generate and interpret human language. Familiar examples include ChatGPT from OpenAI, Claude from Anthropic, Perplexity for research oriented querying, Grok from xAI, and BLOOM, an open source multilingual model. LLMs are called large for a reason.

They are trained on enormous datasets comprising hundreds of billions to trillions of tokens sourced from books, articles and websites. Tokens are small chunks of text, sometimes a whole word and sometimes part of a word, that give the model a granular way to represent language. During training, the model learns the probability distribution over tokens and becomes skilled at predicting the next token in a sequence. That is what enables the remarkable fluency of modern systems.

It is also what explains their limits. LLMs are powerful statistical engines. They arrange words in an order that is most probable given their training data, not because they possess understanding in the human sense. This is why they can write convincingly about tulips or envy without having any conception of either. It also explains why they sometimes produce confident but false answers that readers must check.

Scaling

For years it was assumed that highly sophisticated generative AI systems would require revolutionary breakthroughs in algorithms or new insights into human cognition. Instead, many of the biggest leaps came from scaling up models. Engineers increased computational power, expanded datasets, and built models with many more parameters. As these ingredients grew, performance improved, often in surprisingly predictable ways. That growth is not cost free.

Training large models consumes significant electricity and water, and the cloud time required is expensive. Analysts estimate that training a model in the GPT 3 class consumed more than one thousand megawatt hours of electricity, enough to power around one hundred and twenty US homes for a year. These figures are a reminder that scaling is a strategic choice as well as a scientific one.

Some researchers, including Oxford’s Toby Ord, have suggested that the rate of improvement is less impressive on close inspection, because each new generation demands far more compute and data. Practical limits also loom. Chip supply, access to high quality data, and energy costs place constraints on how far brute force scaling can go. The frontier will continue to advance, but algorithmic efficiency, better data, and new architectures will matter more over time.

Human thought, human training

LLMs do not understand language like a human being. They are statistical prediction machines that excel at finding plausible continuations. That means they sometimes produce answers that are factually wrong, misleading, or simply not what a user wants. To bridge that gap, developers use techniques that incorporate human feedback. Reinforcement learning from human feedback is one prominent approach.

Human reviewers rank candidate answers, and the model learns a policy that prefers responses that people judge more helpful, safe, or polite. This does not grant understanding. It does help align behaviour with human preferences and norms. There is a further challenge on the horizon. High quality human written text is finite, and a great deal of web content is duplicated or low value.

As models absorb the available data, the risk grows that new systems will be trained on synthetic content written by earlier models, which can cause quality to degrade. A philosophical question sits in the background - if a system is trained on human written data and operates according to human defined rules, is it truly intelligent or just a sophisticated machine learning algorithm? The conservative answer is that today’s systems do not think as humans do, even if their outputs look convincing.

The practical answer is that they are already useful tools when used with care.

The major applications of AI today

Generative AI beyond LLMs

While LLMs generate and understand text, they are just one branch of generative AI, the family of models that can create new content on demand. Other generative systems have been developed for images, video, music and code, and many use architectures that differ from language models. The headline tools will be familiar.

In text, ChatGPT, Claude, Perplexity and Grok are widely used. For images, DALL·E, Midjourney and Stable Diffusion have become part of the creative toolkit. For video, research systems like Sora and commercial tools like Runway are moving rapidly. In audio and music, there are systems such as Jukebox, AudioLM and Suno. For code, developers rely on models such as Code Llama, GitHub Copilot and Replit.

Gemini and Claude offer multimodal capabilities that allow users to mix text, images and documents in one workflow. The creative impact is significant. Designers iterate on concepts visually before committing resources. Marketers produce campaign variants in days rather than weeks. Teachers draft lesson plans at different reading levels. Individuals with disabilities combine text to speech and image description to improve access.

Creative workers, coders and software designers are among the most intensive users of generative AI today, but the tools are moving quickly into consumer products as well.

Assistants in everyday life

Generative AI assistants have already penetrated everyday life. ChatGPT popularised the chat interface as a general knowledge and writing companion. Apple’s Siri and Amazon Alexa remain the best known voice assistants, and both are adopting more capable language models.

The humble website chatbot has evolved from a decision tree to a free form conversation partner that can retrieve answers, draft messages and complete simple tasks. A common misconception is that assistants learn automatically from each conversation and improve without intervention. In reality, most improvements come from periodic model upgrades and optional personalisation settings. Users can choose to share data to enhance future responses, but that is not the default in many products.

This distinction matters because it sets realistic expectations for privacy, and it helps teams decide what information to share with digital tools.

Healthcare and science

Although many generative tools are designed with the average consumer in mind, the applications for AI in specialist sectors are expanding quickly. In healthcare, models analyse patterns in patient histories and medical images to help clinicians flag potential risks earlier.

Predictive models support resource planning, helping hospitals allocate beds, staff and equipment more efficiently. In pharmaceutical research, AI has accelerated discovery. DeepMind’s AlphaFold predicted protein structures for nearly all known human proteins and then expanded to hundreds of millions across species. These predictions do not remove the need for experiments, yet they narrow the search space and reveal promising paths.

In materials science, similar techniques help identify candidates for better batteries and more efficient catalysts. Used responsibly, these tools can speed up science, although careful validation and oversight are essential.

FinTech and investment

Finance has been fast to adopt AI. Deep learning models now power credit scoring and fraud detection, and they help banks meet compliance obligations at scale.

Customer service teams use AI to triage messages and draft replies, with human agents handling exceptions. On the buy side, investment teams use machine learning to source and analyse alternative data, from satellite imagery and shipping records through to social sentiment. Language models parse earnings calls and regulatory filings at scale, turning hours of reading into a shortlist of points to investigate.

Reinforcement learning and predictive models power trading strategies and execution algorithms. Operations teams apply AI to reconcile transactions, monitor risk and detect anomalies before they become losses.

Environmental action and energy systems

Scientists hope AI can be used to fight climate change by finding patterns in the enormous datasets used in this field, for example historical weather and temperature information. AI can also help optimise power systems.

Grid operators use forecasting to match renewable generation to demand. Smart building systems reduce waste by adjusting heating and cooling in real time. Materials discovery shortens the time to new clean energy technologies. There is a countervailing pressure. Training models and serving them at scale consumes electricity and water, and it requires large data centres. Estimates suggest that a single query to a large language model uses several times more energy than a conventional web search.

As usage grows, efficiency becomes as important as capability. Techniques such as model distillation and quantisation reduce the cost per query, and specialised chips can perform the same work with less energy. The goal is to capture the environmental benefits while limiting the footprint of the tools themselves.

AI’s big players and pioneers

Most of the most visible generative models have been developed and operated by US based technology companies.

OpenAI builds ChatGPT and the underlying GPT models. Google integrates research across DeepMind and Google Research into products such as Gemini. Meta releases open source models such as Llama and Code Llama. Anthropic develops the Claude series with a focus on helpfulness and safety. xAI develops Grok with an emphasis on real time information access. Outside the US, China’s Alibaba, Huawei and Baidu offer significant large language models, although AI is not always their core business.

Europe is building momentum. Stability AI in the United Kingdom developed Stable Diffusion. France hosts Hugging Face, the leading platform for sharing models and datasets, and Mistral, a fast growing model company. Germany’s Aleph Alpha develops large models with a focus on enterprise deployment. This European ecosystem matters for our audience because it offers alternatives and collaboration opportunities closer to home. Hardware is a critical layer.

AI relies heavily on advanced microchips known as graphics processing units. NVIDIA is the market leader. The company designs chips but relies primarily on TSMC in Taiwan for fabrication. NVIDIA’s market value has risen dramatically in recent years, reflecting how central GPUs are to the AI economy. Competitors are investing, and new architectures are emerging, yet most cutting edge training still runs on NVIDIA hardware today.

Risks, challenges and debates

Hallucinations, brittleness and bias

Despite the obvious power of AI, concerns and limitations remain. One of the trickiest problems facing LLMs is hallucination. A generative model can produce a reply that sounds confident but is false or misleading. It is worth remembering that LLMs, despite their outstanding abilities, do not know things in the way humans do.

They lack the rational filters that would detect absurd claims without explicit checks, and they can mislead users who are not vigilant. Another issue is brittleness. A model can be capable on one set of tasks but struggle with others, especially when questions fall outside the distribution of its training data. If the response is delivered with confident tone and no caveats, the effect can be misleading. Bias is a more sensitive topic.

If models are trained on material that contains bias, they can reproduce it. A model might assume a Western holiday is universal or reflect stereotypes that harm under represented groups. There is also a political dimension. In some jurisdictions, rules require models to align with official ideology, which shapes what they can say. There is a final human factor. People naturally anthropomorphise chatbots, projecting intention and emotion onto systems that generate text fluently.

This can lead to misplaced trust. Some newer assistants intentionally lean into an artificial persona, for example a character that is clearly not human, to reduce that risk.

Capability jumps and rogue AIs

A live debate concerns capability jumps. As models scale, they sometimes display skills that were not obvious at smaller sizes. Some researchers see hints of emergent behaviour. Others argue that such effects reflect measurement choices rather than genuine leaps. Either way, humility is wise.

Progress does not always follow a straight line, and surprises are likely as models gain capability and tools give them more scope to act. Even so, today’s systems are narrow AI. They are trained to excel at specific tasks like working with language. Artificial General Intelligence refers to systems with the flexibility to learn and reason across many domains. Timelines vary widely in expert surveys, with medians often placed decades away.

The potential impact is significant, which is why policymakers and researchers focus on safety, alignment and evaluation methods that work before systems become substantially more powerful.

Legal, moral and societal concerns

Intellectual property and privacy

One of the most contentious issues in the development of AI, especially LLMs, is the data used for training. Models typically learn from vast datasets of books, articles, code, music and images.

Scraping across the public web can capture content that was originally behind paywalls or shared under specific licences. Creators argue that using their work without permission infringes their rights, especially when models can now generate output in the style of living authors, artists and composers within seconds. Developers respond that training is a transformative process that falls under principles such as fair use.

Courts are beginning to decide, and the outcomes will shape how future models are trained and how creators are compensated. Privacy is closely linked. Scraped data can include personal or confidential information. Even if models do not store documents verbatim, they can reproduce distinctive phrases that appeared often in training.

Data minimisation, opt out mechanisms and strong security practices are essential, and regulators are paying attention, particularly in jurisdictions with strict privacy laws.

Social change and job insecurity

With generative AI able to produce large volumes of text, code and images, there is real concern about workforce shifts. Companies under cost pressure are already replacing some tasks with automated systems. The phenomenon is not confined to offices.

Hollywood has debated the arrival of AI generated performers. Fashion and retail brands have experimented with AI generated models in campaigns, and the backlash shows how unsettled audiences are about provenance and authenticity. It is difficult to gauge exactly how labour markets will evolve. Some roles will shrink, new ones will appear, and many will change shape.

The healthy response is to invest in retraining and lifelong learning, to create pathways into new kinds of work, and to support transitions when jobs shift. Without proactive policy, the adjustment could be painful, with knock on effects for welfare, healthcare and social cohesion.

Environmental concerns

Another major social concern relates to the climate impact of AI. Training large models requires vast computing resources, and running them continuously uses energy and water.

Data centres consume electricity for storage and cooling, and rapid hardware refresh cycles create electronic waste. The carbon emissions associated with this activity are non trivial. Training a single frontier model can require energy on the scale of a small town for days or weeks. Each chat style query is more energy intensive than a conventional search. The environmental case for AI will hinge on whether its applications save more energy and emissions than the systems consume.

AI: the road ahead

Regulation

Nations and international bodies have stepped up efforts to create guardrails for AI development in response to the risks outlined above. There is a gap between the speed of innovation and the pace of legislation, which makes coordination difficult. The European Union has taken a more prescriptive approach, passing comprehensive rules that categorise risks and set obligations.

The United States has focused on safety standards, reporting and competition, with one eye on geopolitical dynamics. The United Kingdom hosted a high profile safety summit and has emphasized a pro innovation approach with sector specific guidance rather than a single sweeping law. These approaches reflect different priorities. Europe emphasises consumer protection and fundamental rights. The United States emphasizes innovation and national security.

The United Kingdom emphasizes agility and coordination. Over time, testing regimes, audit frameworks and international standards will mature, and cross border collaboration will be essential because models and data do not stop at borders.

The future is AI

The pace of AI development has created new opportunities for businesses and consumers to increase productivity, learn faster, and do more with fewer resources.

At the same time, the energy required to train and maintain models, the environmental impact, and the legal and moral questions surrounding the distribution of AI point to a dynamic and uncertain path ahead. What is certain is that AI will continue to evolve, reaching further into everyday life with new applications and new features. Whether we embrace it is no longer in question. The real challenge is how we mitigate the risks and how we distribute the benefits fairly.

Many argue that AI is the biggest technological revolution since the rise of the internet, perhaps even comparable to electricity in its reshaping of society. How we choose to guide it will define the next chapter.