Article
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.
Continue reading for free
Enter your email to keep reading for free. This also subscribes you to my monthly newsletter. No spam, unsubscribe anytime.
Summary
Common questions on this article's topic
What will have value in the AI era?
Which jobs will survive artificial intelligence?
Will AI replace my whole job or only part of it?
Which skills are worth developing in the AI era?
Do I have to follow every piece of AI news?
Where are people irreplaceable compared with AI?
If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.
LinkedIn
Related articles
One of my family members described a firm that has its data in several different tools, and its employees still join it by hand, in spreadsheets. That is the ordinary state of firms. When somebody tells such people not to do it by hand and to use AI, they will not understand. Most firms do not even have the basic connectors between the tools they use. These firms need help with AI transformation.
Employees have the best AI models available at work, the connectors are hooked up, and colleagues already use them. And they still do not. AI adoption fails even when the tools are already there. It is not about time or AI skills. It is the inertia of old thinking.
I have Heidegger and my notebook beside me. I am asking where all of this is heading, where artificial intelligence is taking us.
More articles
The same task, two models. Fable 5 against Opus 4.8. On paper Fable is the better model, with a larger context and stronger specs. And still it lost. Opus handled the task with a single round of checking for 721,000 tokens, while Fable needed nine rounds and burnt through 2.78 million tokens. The difference was not in the model, but in how I set the task. And I know it, because I measured it.
A few weeks ago I installed a small local AI model on my laptop that watches a live camera feed. I turned the webcam on in the dark, and in near total darkness it recognised me and the objects in the room. That such things exist, I have known for a long time. What opened my eyes was the accessibility. I installed it in one prompt, free, and it runs entirely on my machine, sending data nowhere.
I once wrote about building my own privacy-friendly analytics tool. It had bot detection from the first version, yet it was not enough. Direct visits took a strangely high share of my traffic. When someone claims that 20% of their visits are bots and 80% are humans, I used to think the same. Today I would say the opposite ratio is closer to the truth. This is how I got there.
Seventy per cent. That is where the first AI output begins, even when you give it the full company context and the best examples from the past. We are talking about the kind of output that cannot be defined programmatically. It is more complex. Often it is creative work. On one repeated type of output I reached eighty per cent within a week. Every further percentage point is harder than the one before.
For a long time we treated the internet as the main road. The place where work and relationships happen. Yet most of what we see on it today is, or soon will be, AI-generated: text, images, profiles and comments. The internet is turning into an online game full of bots, where you cannot be sure that a human is on the other side of anything. So I ask: was the online world the main road, or only a temporary detour that part of us will return from, back offline?
A few days ago I interviewed a senior marketer. An experienced man, years of practice. I asked him about AI. He said he barely uses it. He had one bad experience with the output and decided he was too senior for it to add value when it is not perfect. I know the other side too: professionals who automate everything that can be automated.
Europe does not have the capacity to face a full-scale, mass drone war of the kind we see in Ukraine. Three dependencies weaken it: China supplies the physical material for defence systems, the United States supplies capabilities Europe does not have, and twenty-seven states cannot agree how fast, or who pays. Rearmament plans exist, but they are being carried out slowly.
AI produces the graphic, the newsletter and the product page faster than a person. What is left for the one who used to do it is the judgement, knowing whether the output is good. But most people have worse judgement than AI. And whoever cannot judge quality cannot delegate either. How do you tell whether yours is the judgement a company relies on, or the kind it can replace?
In April, in the first part of this series, I wrote about an AI prediction system I had started building on my own machine. At the time the software was a few hours old and the prediction record was empty. The record since then has shown one thing: the system does not yet understand the market it is being asked to forecast. It can pull macro context, book value, earnings. But it cannot put those together into something that helps it understand the price.
Four days in Catalonia. No computer, no AI, almost no social media. I bought this notebook so that I could write down what I would think about, and what I would come across and learn on the trip.