Hello everyone! Today I want to pause and take apart one very topical and pressing question. For many years, more than twenty years, I have been working in people management, and now more than ever I hear people's fear about their own relevance — about whether AI will replace me, whether I will be left without a job, what career opportunities I will have at all. I have a personal observation: sometimes the specialists who are more afraid are the ones who use AI both more deeply and more often, because, in my view, they understand all the capabilities of the tool much better.
But that is only my hypothesis, so I wanted to look at the forecasts, at the real figures in general. Today I want to take the US market as an example. I take the US not because it is the whole world or because its figures can somehow be transferred to the markets of Asia, Russia or Europe, but purely because changes around AI show up there quickly, and there are quite a lot of fresh sources, some released even in the first week of September, that we can rely on. Today is September 2026, and in a quarter or half a year these figures may well change, some trends may change.
Today I want to suggest we work out where we are now in terms of labour markets: do we see any mass layoffs connected with AI and around AI, given how much people talk about it now and how full of it the headlines are. To work out what is happening with hiring, what vacancies are appearing, what skills stand behind those vacancies, and what new career opportunities all of us can discover and see for ourselves. So, we will ask each figure different questions. First of all: who counted it, over what period, on which market, a percentage of what number?
Because, as always, the devil is in the details. The first scary figure. Why is it scary? Because it sounds quite frightening, and it is very easy to put into a screaming headline. Since the beginning of the year we have seen 116,175 announced job cuts connected with AI. So, this figure is taken from the Challenger, Gray & Christmas report. It is an American company that for decades has been tracking public statements, public announcements by employers about job cuts and hiring plans.
It is an important source — not official statistics, but essentially an important source that is quite well regarded in labour markets. So, Challenger records the reason the way the employer itself formulated it. And for January–August 2026 employers named AI as the reason in 116,175 announced job cuts. And that is about 21% of all the job cuts in the Challenger database for the period. That is, 20% of all the job cuts the research company recorded for January to August 2026 are cuts connected with AI in one way or another.
In fact, there is a lot of detail inside this. If we go inside — what does a job cut connected with AI actually mean? It can be connected with AI, and with restructuring, and with cost cutting, and so on. But at the same time, such a figure exists, and you can come across it quite often in various sources. And the overall market, the overall market in that same database, looks very calm.
Because if you take all the announced job cuts for the eight months of 2026, that is 41% fewer than in 2025 over the same period. If you exclude the public sector from that sample, it is 15% fewer. Sometimes such details are very important so that we understand the real dynamics, the real movement. So I think it really makes sense to include such comments, so that we understand everything. Technology companies need to be looked at separately. Here the picture is much more tense, since Challenger was talking about the whole commercial market.
If we talk about technology companies, then since the beginning of 2026 they have announced 155,126 job cuts for all reasons. That is 52% more than a year earlier, and roughly 29% of all the job cuts in the Challenger database. But the technology market is the employer's industry, not a person's profession. And a technology company employs not only developers but also HR, marketers, finance people, sales and lawyers. So job cuts in tech do not mean, and are not always equal to, cuts of developers alone.
A logical question arises: how much of the total number of job cuts in the technology market fell on cuts connected specifically with AI? What percentage is that? Unfortunately, we cannot say for certain, because I did not find exactly that figure and exactly that breakdown, so I would rather leave this gap than invent percentages. But let's look at what this figure — 116,175 job cuts because of AI in 2026 on the US market — means overall, and how large it is for the whole economy.
To get a feel for the scale, I compared this figure with… this figure of announced cuts over eight months with the 159,075,000 jobs in the American employer payroll statistics for August. And it comes to roughly 0.07 percent, or about seven positions for every 10,000 jobs. And this is not the probability of being laid off, nor the share of people who really lost their jobs. We are dividing an eight-month flow of announcements by the stock of jobs at one moment in time. It is just a rough ruler that I wanted to use so that we can understand and assess how big this figure is — or, perhaps, how considerable.
I do not mean to say that the fear is made up because of this: after all, if you are inside that zone, then for you it stops being statistics — for you it becomes a real event. But this big figure, one way or another, does not describe the risk for each person. And before being frightened by a scary headline, before being frightened by trends that are quite alluring today and by all this information noise, it is always better to work it out and understand what you are relying on.
My next observation, from studying and diving into various figures, is that the market seems to have frozen a little. And what led me to that observation? There is the US Bureau of Labor Statistics, a federal statistical agency that tracks all vacancies, actual hires, voluntary quits and separations initiated by the employer. There is no AI reason there. But what is interesting is that in July 2026 there were 7,271,000 open vacancies. That is 2.6% more than a year earlier. But actual hires were only a little over five million.
So this is, of course, not the conversion of any one vacancy, but the combination looks like caution on the part of employers. Why do I say the market seems to have frozen? Vacancies are opening, hiring plans are growing, but it is as if they do not plan to hire: they hold back a little and look for the right person. They watch what will happen on the market tomorrow, the day after, in a month, they take stock and somehow adjust the opportunities they offer on the labour market to a wide range of specialists.
One more point to add to this picture of the market: employees have started leaving on their own less often. And this is very interesting analytics, because this very bureau tracks precisely people leaving to change jobs — for another role, another position, another company. So, there have been fewer such moves. Which says that people have started relying more on their current employer, taking a wait-and-see position, wanting to make the right decisions in this crisis — I would even say not crisis, but rather uncertain — period of time.
Separations initiated by the employer are 6% fewer than a year earlier. So my interpretation of all these figures is this: the market seems to have frozen a little, and employers are holding vacancies and hiring more cautiously. I repeat: people are leaving on their own a bit less often, and a general wave of layoffs — the so-called layoffs around AI or not connected with AI — is not something we are seeing at the moment. So I do connect part of this caution with AI, with a lack of understanding of the future model of work, but at the same time that is my own personal observation.
I think there are, of course, colleagues who share it and those who do not. Your opinion is interesting here — what do you think? Share it in the comments. And I am sure we will find plenty of reasons to reflect on these topics in our next episodes.
So what does AI actually do to headcount inside a company? What happens there? There is probably one more interesting source here. A working paper by the Center for Economic Studies at the US Census Bureau. So, this research unit used the answers — it surveyed businesses — the answers of more than 117,000 unique companies. The data was collected from November 2025 to February 2026. And among the companies that used AI in their work, 95.7% reported that over the previous six months AI had not changed their total headcount.
2.3% reported a rise in headcount and 2% a decline. So the conclusion for the question we posed for today's podcast is this: across a broad mass of companies, AI so far still changes the work inside the organisation much more often — we will talk about that in a moment, and it is, it seems to me, a really great link to all our previous episodes and to all the topics on our channel in general — than it changes the total number of people. This does not contradict Challenger. Challenger, after all, sees the large public announcements. The survey of companies shows a more everyday picture.
But these are, so to speak, different views of reality, of what is happening on the market today. How much to be afraid, where to look, where to run. Today's market and the influence of AI cannot be extended to every person losing a job, just as they cannot be extended to every person gaining one.
In general, I think there are different levels to rely on if we are talking about such figures and want to appeal to them. One level is the employer's industry. Right at the start I gave an example of how much more the current changes affect the technology market. The second is the person's profession, because even inside the technology market professions differ very much. The third is the specific task inside the profession. And obviously there are tasks today that potentially — let's call it that — we can replace more. And there are tasks that, in general, we probably cannot replace, or it is impossible to.
So before we say that AI is destroying all jobs, let's say which industry we are talking about, which profession we are talking about and which task.
Now I suggest moving on to the second part of our story. We have taken things apart and talked them through with you, and found some footholds in reality. What is happening on the market in terms of this hiring and firing, in terms of the fact that… We talked a little about how the market has frozen, about that fear that AI will replace everyone. But I also want to look here at what new things the market is starting to buy. Because within the technology contour it is probably somewhat easier to quickly formulate groups, some new groups.
Today we see vacancies on the market for those who create AI. For example, the position of AI engineer. We see vacancies for people, for engineers, who embed AI into the product and the process. These may be so-called AI strategists. Or there is a vacancy today that is growing very strongly: the Forward Deployed Engineer, an engineer for the applied deployment of AI, who works with the client, understands the business problem, connects the model to a real process and brings it to a working result.
Within the technology contour, specialists are needed who ensure the reliable operation of systems and who carry these changes through the company. So it would seem that these new positions arising in the AI era are, well, somewhat more obvious. But the most interesting part, the most interesting part for me, begins not only inside the technology professions: I looked at real vacancies today and analysed them in marketing, sales, finance, HR, recruiting, support and administrative work.
The same pattern repeats everywhere there, and I want to talk about it now. The company is looking not only for a person who performs some function with the help of AI; it is looking for a person who improves that way of working. I want to go through several positions right here. In marketing, for example, they are looking for a Marketing AI Operations Manager — a head of AI operations in marketing. Not a person who simply makes content: they have to own the whole technology system of marketing and, on top of that, manage internal AI agents.
Another vacancy in marketing is Agentic Marketer — I would translate it as a marketer who builds and uses AI agents. Again. There the candidate is directly asked to show a working marketing agent, for example one built on n8n and Claude Code. And yet, right alongside that, the requirements still include four-plus years of experience in digital or growth marketing, SEO, an understanding and knowledge of how conversion funnels are built, strategy, campaigns, client communication and so on.
In sales a new title appears — Go-To-Market Engineer, an engineer of commercial processes and of the go-to-market system. For example, combined with automation. That is, a company is looking, for example, for a head of automation of the go-to-market system. That is, the person owns the CRM as a product and automatically assembles a map of companies, people and signals, and creates AI agents for finding clients, enriching data and developing accounts. Or — there are quite a lot of vacancies in sales in general today. I found such go-to-market engineers, specialists who connect Salesforce and other systems, automate account scoring, bolt on AI to understand and assess all the intent signals, to assess and analyse the work and the effectiveness of sales-department specialists, and so on.
But in parallel with that, these people are still expected to have five to eight years of work in… in revenue operations, working with revenue, working in sales operations, the ability to work with SQL and BI, to understand the funnel, to have commercial planning skills, and so on. So such a role at the intersection is becoming very strong. In finance, in particular, even Apple — well, not 'even Apple', it is just one of my examples — Apple is looking for a Product Manager, AI Finance Transformation, that is, a manager of an AI product for transforming finance.
Together with finance leaders they must see the real business pain, redesign the process, choose the right AI scenario, translate it into requirements and, of course, define what data the AI agent receives and how — how explainable the result is, and how to measure accuracy and effect. In financial planning specifically, I found a great many vacancies for an AI transformation analyst. In HR and recruiting the same thing is happening. Today there are vacancies in Automation and AI HR Transformation.
A product owner for AI automation in HR transformation. Such a person, again, defines certain behaviour for AI agents, restrictions, points of mandatory human checking in the process. They are looking for Recruiting Operations and AI Automation — heads of recruiting operations and AI automation. A person who has command of and understands what an applicant tracking system is, who embeds AI into sourcing, embeds AI into communication with candidates. In the same way, of course, especially in an area as sensitive — very sensitive — to the use of AI as HR and recruiting, such a person understands those points of human control, of human management inside the process.
In support and administrative work, standard requests can be automated more and more often, and what is highly valued is working with such exceptions, understanding confidentiality, knowing when automation must be stopped. So in all these functions — I am not even retelling the companies I went through — in all these functions I see what seems to be one and the same formula.
That is, essentially: doing the work is required — doing the work and rebuilding the way it is done. That is, a kind of new demand appears. And it is not at all about AI taking a vacancy or AI taking a job; rather, genuinely new layers of the professional profile are being formed. And in the end I really arrived at several pillars, so to speak, that we can rely on. This is purely my interpretation, and it may or may not resonate. Share what you think about it. How do you see these changes happening specifically in your professional field? Because I listed a very limited number of professions.
So, the first layer, which is the basic one and has not disappeared from a single set of requirements, is domain expertise. You need to know your function so well that you understand where that AI can be bolted on at all, or not — whether anything can be automated or not. That deep, detailed understanding of your area, the ability to form professional judgements correctly, the ability to deeply see and perhaps anticipate, highlight all the risks, an understanding of how all the details in your specific domain are interconnected.
The second layer — well, let's call them layers — is process architecture. From that deep understanding of domain expertise it follows that a specialist must be able to break their work down very clearly into processes, into components, into tasks, to understand, in each process, to see what result they want to get, what trigger starts the process, what stages the process has, who takes part, who is responsible, what tools are used, what deadlines are inside, what exceptions there are.
That is, to break your work down into details and into separate tasks. And that, after all, is possible only with a deep understanding and with depth of your professional judgement in general. The third layer is to assemble some working solution on the basis of the first and the second, right? What else struck me in all the examples I gave earlier is that, essentially, each specialist is also asked to be a bit of an engineer. There are very few places where there is that contact, and the ability to formulate a task for some technical specialist.
That is, essentially, the very task of working on AI automation of one's own processes becomes a requirement precisely for that deep domain specialist. That is, each of us becomes a little bit of an engineer. And any strong specialist today must be able to assemble that small prototype or describe the logic to another engineer so precisely, with all the constraints and results, as to be responsible not just for some idea, for what they see can be done, but for the result. The next layer — moving on. It is about quality, always about quality, because some… we must always understand — we have talked about this a lot on our channel — regarding quality, and with any bolting-on of AI, with any automation, we must always understand: what result did we get, what did we earn, what did we save, what did we achieve?
How much better has our process become? How do we even understand where a human check is needed? When should we stop the system? Which errors are acceptable? Well, this is a very multi-faceted, multi-component layer. And the last layer that I single out for myself in all the vacancies, in fact, in all today's requests, is implementation — yes, the implementation layer. Because you can be a very strong specialist, you can understand things and cut the elephant into pieces. You can be perfectly able to assemble some engineering solution and understand its result, but it still has to be carried through the business, through information security, through the lawyers.
Today this is, it seems to me, a really huge topic — the whole subject of artificial intelligence security in businesses. You have to train the team, you have to work through the cognitive resistance, which, you know, probably never seems to disappear — it just moves over time, during this AI era, from one block to another. But the most important thing is that the base of this whole chain is professional expertise and an understanding of the process. In essence, even the technical part — that third layer and the engineering solution — can very often be assembled, and you can find out what to assemble, by asking AI itself.
That is where a certain paradox lies. But that professional depth — domain depth, professional depth — and an understanding of what you do and what your work is built on is exactly what lets you ask the right question in order to find the right solution. So the way I see it, we do not need some identical list of courses for everyone.
And if we simply answer the question of what to invest in now and how to deal with this fear of ours that AI will replace us, then it seems to me extremely important now to deepen your profession, whatever industry you work in and whoever you work as. To learn to describe processes, at least to understand their components, to build small AI solutions, to try it yourself on some small part, on the most understandable process — perhaps to describe just one working process, build that AI solution on it and see what changed, what data we got, and try to implement that process inside your company, inside your own business perhaps, inside some personal project of yours.
That is, essentially, you can start not with a new position, not with some ten tools, but with exactly what you do in your work every day. I think they definitely pay attention to it. And it seems to me we have touched on this many times too — well, repeatedly, one way or another — that this requirement of at least basic AI literacy,
of understanding your tasks and where you can use AI to strengthen yourself, to strengthen the quality of your work, is already becoming a default requirement, the way knowledge of Microsoft Office used to be, say, on a CV. But that is the first part of the answer to the question. Obviously, yes. The second is that, as we have said more than once, there is in general a shift in terms of whom a particular business is looking for and how well a person's previous experience matches this new vision — what we have been talking about today.
And there is definitely a mismatch between how much the employer's requirements and what the business is looking for have changed, and what the person offers, because very often they rely on their previous experience, very often on their previous knowledge, in which, after all, they were successful, they were in demand, and they do not always see those areas for their own development, including with the use of artificial intelligence. While companies have already moved ahead: they already understand that they absolutely have to cover new areas.
So this one — this is an answer for a separate podcast, because there are a great many components built into it: we need to work out whom companies are actually looking for, and exactly which people, with which competences, in which professions cannot find work. That, you know, Olya, is such a good example. By the way, I also came across it while I was searching for various figures just now. There are positions that seem not to be cut deliberately. But people leave, and the positions are not reopened, because businesses understand internally that these tasks have become very easy to redistribute and replace.
After all, there is another such layer, which we simply have not talked about. Such tasks have sort of stayed a little under the bonnet. They have not shown up in any statistics, because nobody was fired. And we do not see anyone having stopped being hired; it is simply that in some segment, certain tasks… A person left — for one reason or another, left the company. Snap! And those particular actions were distributed among different employees, or certain actions were handed, for example, to a model.
So there is no answer — simply no single answer — to this question. Yes, we have already listed three components. I think the list can be continued. So my answer is this: to the question "will AI replace us?" — no. In some tasks — yes.
The figures we looked at, using the US market as an example, do not show any general wave of mass layoffs because of AI. They show and highlight a more complex process: caution in hiring, the cutting of some roles and the non-replacement of some, the cutting of some roles and their replacement with new ones, the change of tasks within a profession, and perhaps, here and there, the caution of people in this uncertain reality. If you would be interested in taking apart similar figures for the European market or the Russian market, or Asia, please write in the comments — we will definitely do it.
But in essence, today we talked about the market starting to look for specialists who understand their work so well that they can decide, overall, how that work — how it should, how it can be changed, which process can exist and which cannot, which part of it to hand to AI and where to keep the human. An amazing time. We live in interesting times.