Select Page

So what is AI and how will it influence our future?

Technology

AI has been described as the biggest technology shift since the birth of the internet. It might end up being bigger. Whatever it is, it’s no longer something happening in a research lab or a Silicon Valley demo. It’s already in your phone, your inbox, your search results, your kids’ homework, and your customer service calls. The question isn’t whether to engage with it anymore. It’s how to engage with it well.

Concerns about job displacement, ethics, privacy, and the unpredictability of autonomous systems are all real and worth taking seriously. So is the genuine excitement about what AI can already do for individuals, businesses, and communities. Both can be true at once.

A short detour into history

In 1950, the British mathematician Alan Turing published a paper called Computing Machinery and Intelligence. In it, he proposed what’s now known as the Turing Test. The idea was simple. If you sit a person at a screen and let them have a text conversation with another entity, and they can’t reliably tell whether they’re talking to a human or a machine, then in some meaningful sense the machine has crossed a threshold.

For decades, the Turing Test was a thought experiment. Then, somewhere in the last few years, it quietly became unremarkable. Whether or not the test has been formally “passed” is still debated, but most people interacting with modern AI tools could be forgiven for thinking it’s no longer the right question.

We’ve crossed a line, and we’re still figuring out what’s on the other side.

What AI actually is

Stripped of the hype, AI is software that can do things that used to require human intelligence. Learning. Reasoning. Recognising patterns. Self-correcting.

There’s a useful distinction between narrow AI (systems trained for specific tasks like image recognition or language translation) and general AI (systems that could in theory perform any intellectual task a human could). Almost everything you’ve actually encountered is narrow AI, even when it feels remarkably general.

Two terms worth knowing. Machine learning is the broad category, algorithms that improve through exposure to data. Deep learning is a particular technique using layered neural networks, which is what’s behind most of the recent breakthroughs.

The applications are everywhere. Healthcare diagnostics. Financial fraud detection. Self-driving systems. Translation. Content creation. Customer service. The list keeps growing.

Why now?

If AI has been around as a concept since the 1950s, why has it exploded into public consciousness in the last few years?

A few converging reasons.

  • Computing power. Over the last two decades, processing power has grown at a rate that’s hard to grasp. Things that were computationally impossible in 2004 are now happening on phones in 2026.
  • Data. AI systems learn from data, and the modern world produces unimaginable amounts of it. Every social media post, every sensor reading, every transaction adds to the pool.
  • Better algorithms. Breakthroughs in deep learning, particularly the architectures that power modern language models, made things possible that simply weren’t before.
  • Industry pull. Once businesses realised what was possible, the demand pulled investment, talent, and innovation forward at a remarkable pace.
  • Investment. The money following AI right now is on a different scale to anything seen in tech in decades.

To put the speed in perspective: Netflix took just over three years to reach a million users. Spotify took five months. ChatGPT took a week.

What it can already do for you

Beyond the corporate applications, AI is genuinely useful for individuals. A few of the most practical ways it’s already helping people:

  • Recommendations that learn. Personalised suggestions for content, products, and services that get sharper the more you use them.
  • Time back. Routine admin, scheduling, drafting, summarising, organising, all things that used to chew through your week.
  • Health support. Personalised wellness tools, early-warning monitoring, support for managing long-term conditions.
  • Learning that adapts to you. Tutoring systems and educational tools that meet you where you are and adjust to how you actually learn.
  • Genuine accessibility. For people with disabilities, AI is opening up real and meaningful ways to interact with the digital world.
  • Creative collaboration. Tools that help draft, refine, suggest, and build, whether you’re writing, designing, composing, or coding.

What to be careful about

The other side of the conversation matters too. A few risks worth taking seriously.

  • Bias. AI inherits the biases in the data it was trained on. If the data was uneven, the outputs will be uneven, often in ways that are hard to spot.
  • Privacy. Most AI systems need data to work. That raises real questions about what’s being collected, where it’s stored, and who can see it.
  • Security. AI systems can be attacked, manipulated, or fooled in ways that aren’t always obvious to the people relying on them.
  • Jobs. Some roles will change profoundly. Some will go away. Some new ones will appear. The transition isn’t going to be evenly distributed.
  • Ethics. Who’s accountable when an AI system makes a bad decision? When AI is used for surveillance, or warfare, or political influence, who decides where the lines are?
  • Over-reliance. AI is impressive enough that it’s tempting to stop checking its work. That’s a mistake. Treat its outputs as drafts, not verdicts.

Being aware of these isn’t pessimism. It’s just being a sensible adult about a powerful tool.

Who’s funding the future

One of the largest individual investors in AI research is Stephen Schwarzman, the American businessman who co-founded Blackstone. In 2018 he donated $350 million to MIT to establish the Schwarzman College of Computing, one of the largest gifts in MIT’s history. In 2019 he donated £150 million to the University of Oxford for the Schwarzman Centre for the Humanities, which deliberately includes work at the intersection of AI and the humanities.

That intersection matters more than the headlines suggest. The future of AI isn’t going to be decided purely by engineers. It’s going to need philosophers, ethicists, theologians, and ordinary people willing to ask difficult questions about what we want this technology to actually do for us.

The future is being shaped less by traditional politics and economics and more by who has the technological capacity to lead. Nations and companies are investing accordingly.

Some places to start

If you’re new to all this and want to get hands-on, here are ten tools worth trying. Most have free tiers.

If you’re trying to find the right AI tool for a specific job, two directories worth knowing about are Open Tools and TinyWow.

A human-centred approach

The temptation with any powerful new technology is either to embrace it uncritically or reject it entirely. Both miss the point. The interesting work is in the middle. How do we use this well? What does responsible adoption look like? What guardrails matter? What gets enhanced, and what gets protected from being eroded?

The answers aren’t going to be solved by engineers alone, or by regulators alone, or by individuals alone. They’re going to require all of us paying attention.

So as AI becomes more woven into your daily life, what are you doing to make sure it’s expanding what you’re capable of, rather than slowly replacing the parts of you that matter most?