What Will Have Value in the AI Era

When you think, or hear from others, that artificial intelligence still does not deliver good enough output, that it is unusable for certain tasks, it is worth realising that this may no longer be true today. Often it is only a matter of using the right tools in the right way. And even if it were true, the most intelligent minds on this planet are working intensively on making AI deliver better output and become more useful.

Anyone who works with artificial intelligence in practice, who goes into it in detail, sees this. Anyone who does not often sees only the mass view from social media, where people rate AI output badly, or say that at work it causes them more trouble than benefit.

The debate about whether AI is useful or not, whether it is replacing and will replace work activities or not, no longer makes sense. That question is settled.

Most of the work activities people do today will be automated. I am not saying all of them. And I am not talking about whole positions. I am talking about the individual tasks a person carries out during the day.

The barrier is not whether something is done on a computer today. A computer, and with it AI, can be attached to something that has worked without one until now. I write about that in I Ran Object Detection on My Laptop, and Saw Everything Is Possible.

At the same time I do not claim that this will replace every work activity fully today or tomorrow, and certainly not on the first attempt. I write about that in Which Work Will AI Not Replace?

Following everything written about AI is impossible, and it is not necessary

More is published about AI than anyone can read. No AI summariser solves that. In my view it is not even necessary. Whole days spent watching videos about AI will not make you better at this unless you try it yourself. Watch one good hour of video instead, then go and try what it describes. While you try it, you will see further possibilities worth testing. The practice will hold your interest more than the next video.

To understand where this is heading, alongside trying AI models in practice, some questions have to be asked. Those questions lead us to what we can do with AI and where to put our energy.

Three questions

What will have value in the coming years?

Where will people be replaceable and where not?

Where will it be valuable to have expertise and where not at all?

I have been looking for the answers for a long time and I write about them.

What will have value

I ask myself this question very often. I ask it, for instance, from the side of human needs: what people will actually need in ten years. Water, food, safety, housing, energy, care for the old and the sick. And to live a good life they will need some meaning, and beyond that freedom, mental health, the ability to tell the essential from the irrelevant, community, and a chance to take part in public affairs. From the answer to what people will need, I would work out what will have value. This is of course the optimistic version of the future, one where the needs of people still matter.

The second part of the answer is simpler. Whatever is limited and needed will hold its value. I do not know whether Google or Facebook will exist in 2040. I know that whoever leads in technology then will need strategic raw materials. A robot, a chip and a battery have to be made of something.

An equally important question is what will not have value. It helps to imagine what the supply will look like, because when supply approaches infinity, the price goes to zero. What was, or still is, expensive today because there is little of it may be as cheap as a calculator in a few years.

Inside companies, I think value will belong to the guide to company transformation. Someone who walks into a firm that holds its data in five tools and whose employees join it by hand in spreadsheets. Someone who maps the processes and can automate them as well.

Online, value will belong to the primary source. To do something and write about it. To find something out and write about it. A unique experience combined with expertise in the subject is something no machine and no algorithm will replace any time soon. It is connected to whom we are more likely to trust. Sooner the person who signs the work with their own name, expertise and experience, than a large brand.

Where people are replaceable and where not

Value belongs to human judgement, but not any judgement. Most people in many fields have worse judgement than AI today. Whoever cannot assess quality cannot delegate to AI either, because to delegate is to stay responsible for quality and to be able to check it.

The work that supplies AI tools with company context is also irreplaceable today. The founder of a medium-sized firm often carries an idea of what a good product and good marketing look like, in a level of detail never written down anywhere. That context has to be handed over to the AI tool. Records of internal meetings are rarely enough. It has to be worked through.

Where expertise makes sense

Expertise makes sense where the machine on its own does not reach the right result, even when it has all the data.

After one hundred and fifty practical interviews I know what the best solutions had in common. Good mathematics combined with logic, attentiveness and common sense. AI does not have that combination today. Not to the degree that brings the best possible result. The strongest solution I have ever received came from a mathematics teacher without any model. She noticed what mattered, what agreed with common sense, and did not focus in isolation on the question in the task. That was the difference.

So can this approach be put into an AI model? To a large extent, yes. What matters is that a person who has this approach and then uses AI gains a large advantage. They can solve a problem many times better, or solve a problem many times harder. For someone who already has the right approach and good judgement, AI is a far bigger advantage than for someone who does not.

The right approach shows in prompting. The quality of the prompt matters more than the quality of the model. I have this from my own experience. The stronger model lost to the weaker one, because I gave the weaker one a better prompt. I told it to verify facts at the input, not at the output, and to do it systematically. What the cheaper model did with that prompt was many times more efficient than without the rule.

Without a good prompt, a good brief, we go round in circles. I have tried it. Nine rounds of corrections each began with me looking at a number and saying it was wrong. That was an inefficient process. Choosing the best process for working with AI is valuable expertise.

Expertise makes sense in interpreting information and data. AI often handles it at seventy per cent, or at ninety with a good process, but an expert can still find the extra ten per cent, which is often the part that decides. The expertise lies in knowing what the data says and being able to place it in a wider context, in connections that are not obvious at first sight, not even to AI.

To keep the upper hand over AI models, I think a person has to disconnect from them sometimes. That is why I work in two extremes. Either I solve everything through AI agents, or I am completely offline. The middle path, always a little in the digital world, with the AI helper always on the phone, has stopped working for me.

I deliberately keep something I do on my own, without help. Not because the machine cannot do it, but because it is the thing that keeps me in shape and makes me more skilled. Blogging is one example.

At work I do the opposite. Everything that can be automated, I automate, and I try to push the boundary of what is possible and use AI to the maximum.