One Saturday night, Thomas sat down with Gemini and asked, “What will make me the happiest person in the world?” Over the course of the next few hours, he got some fascinating results. All of this is part of the story of AI in our lives today, but there is so much more. This conversation is a small reflection of where we are with AI and why we should embrace its benefits, learning as much as we can with careful curiosity.
“What do I do with my horse-riding skills now that the car has been invented?”
With this statement, Thomas reminds us that mega shifts in our human experience is historically normal, and a reflection of the human mind’s brilliance. The AI Shift is just another technological step change.
AI is replacing ‘commodity tasks’ - those which are repetitive, standardised processes, providing us with more time to lean into creativity. We become the navigator whilst the more mundane jobs could be taken over by AI.
Traditional search engines try to match words whereas modern AI systems match meaning. When you search for trousers for instance, AI systems can use images and semantic understanding to infer style, intent, and context rather than just scanning for the keyword ‘pants or trousers.’
Large language models (LLMs) such as Gemini, ChatGPT, Claude, Perplexity, and so on, predict the most likely next word, turning colossal amounts of data into fluent conversation, explanation, and even advice based solely on statistical probability of word patterns. We don’t even need to invent the perfect query as they can also predict this.
Used well, AI is more like a creative collaborator: a brainstorming partner that proposes alternative angles, structures, and prompts. For small businesses, it can become an extra “virtual team”, generating draft podcasts, social posts, or marketing visuals that can then be curated and refined. But all the while, it remains the human who sets the objectives and the required tone.
This also lends itself to the possibility of many people becoming autonomous, single-person businesses.
When you give an AI tools and sub-tasks, it can orchestrate them toward a goal. One agent might create images; another might check whether those images match the brief (e.g. ‘sunny landscape, not rain’); together, they negotiate improvements until the output fits what you asked for. Even non-technical people can use early agent-like products. NotebookLM, for instance, lets you upload documents, then:
A recurring complaint in companies is: “Our data is too messy to do AI.” That is partly true for training bespoke models: bad data in, bad model out, but paradoxically, AI is also very good at cleaning data in the first place. You can literally give such a tool a messy folder of information and ask to make sense of it.
Since AI understands patterns in addresses, email formats, names, and categories, AI can, for example:
For an individual drowning in paperwork, this is transformative: scan, upload, and ask the AI to pre-fill or summarise, then you simply review and sign.
You do not need to be a computer scientist to get real value from AI. A good starting sequence for a normal day could include:
People in Luxembourg working across languages can also benefit from live translation and dubbing: tools already exist that let you speak in German and be heard in French or English in your own voice, with a slight delay, in meetings or recorded content.
AI is reshaping the job market. In the UK, one study found that companies using AI had eliminated 11% of previous roles and left another 12% unfilled, while creating 19% new roles, which is a net loss of 4% overall, with the UK faring worse than the US on the balance between jobs lost and created. That reality naturally fuels both excitement and anxiety.
What AI targets first are commodity tasks: copy-pasting, routine classification, basic template writing, or standardised analysis. The more your work relies on unique human context, judgment, empathy, and rapport, from live concerts to therapy and even parenting, the harder it is to replace. The opportunity, and pressure, is to climb the value chain: stop being the engine that moves the data and become the navigator who decides where to go.
As AI systems get better at imitating voices and faces, distinguishing fake from real becomes a societal survival skill. Voice scams already exploit cloned speech to convince parents their child is in danger, and manipulated images can travel faster than fact‑checks.
Two layers of protection are emerging:
You do not need to be a prompt engineer, but a few habits make a big difference. First, describe what you do want rather than only what you do not want: “Keep the face unchanged and brighten the background” works better than “Don’t change the face.”
Second, you can use AI to improve your own prompts:
Over time, this becomes a self-teaching loop: the AI drafts the prompt, you tweak and observe the output, and your intuitive sense of what to ask for gets sharper.
Some people now confide in chatbots as if they were friends or therapists. In one late-night experiment, Thomas asked Gemini to interview him and figure out what would make him “the happiest person in the world”; the system eventually pointed out contradictions in his answers and nudged him toward deeper reflection.
That shows how AI can mirror back patterns in your own thinking and ask probing questions. But it still lacks the embodied empathy, nuanced perception, and ethical responsibility of a trained human therapist, who reads not just words but tone, pauses, posture, and history. AI can supplement support; it should not replace serious care.
Paradoxically, Thomas’s biggest fear is not that AI will take over, but that people will be left behind because they are too afraid to try it. Like refusing to learn to drive when everyone else has moved to cars, opting out of AI entirely risks shrinking your options just as the toolset explodes.
The most practical stance is curious, critical use: test it, set boundaries, keep the human touch at the centre, and let the machines handle the drudgery.
