What Skills Will Be Needed Tomorrow? What AI Is Already Changing in the Labour Market
Is there really a wave of layoffs because of AI — and if the figures do not show one, what exactly is changing: whom companies are looking for, what requirements have appeared in ordinary vacancies, and what a specialist should invest in to stay in demand?
According to Challenger, Gray & Christmas, in January–August 2026 US employers named AI as the reason for 116,175 announced job cuts — about 21% of the database — while total cuts are 41% below last year's; technology companies announced 155,126 cuts, 52% more, but an industry is not a profession. Against 159 million jobs that is about 0.07%, seven positions in ten thousand. The Bureau of Labor Statistics shows 7,271,000 open vacancies against five million hires, fewer voluntary quits and 6% fewer separations — the market has frozen, not collapsed. A Census Bureau survey of 117,000 companies: for 95.7% of those using AI, headcount did not change in six months. Real vacancies in marketing, sales, finance and HR ask people to do the work and rebuild the way it is done — manage AI agents, build prototypes on n8n and Claude Code, connect Salesforce — with the same five to eight years of experience as before. Anna breaks this into five layers: domain expertise, process architecture, building a solution, quality, implementation — and advises deepening your profession and building small AI solutions on your own process. Her answer: AI will not replace everyone, but it will replace some tasks.
What to watch for
Key takeaways
Challenger, Gray & Christmas records the reason the way the employer named it; for January–August 2026 AI was cited in 116,175 announced cuts. Meanwhile total cuts are 41% below last year's, and 15% below once the public sector is excluded.
A technology company employs HR, marketers, finance people and lawyers, so cuts in tech are not the same as cuts of developers. Anna did not find the share of AI-related cuts and leaves the gap.
116,175 cuts against 159 million jobs — about 0.07%. It is a rough ruler, not a probability of being laid off: the fear is real for those inside the zone, but the figure does not describe anyone's individual risk.
According to the Bureau of Labor Statistics, vacancies are up 2.6% on a year ago, hires are only a little over five million, quits are fewer and separations 6% fewer. Employers hold vacancies, people wait and see.
A Census Bureau survey of 117,000 companies from November 2025 to February 2026: 2.3% reported growth, 2% a decline. AI so far changes the work inside the organisation, not the number of people.
The influence of AI cannot be extended to every person losing or gaining a job. Before saying AI destroys jobs, one must name the industry, the profession and the specific task.
Forward Deployed Engineer is a vacancy growing very strongly: an engineer of applied deployment who understands the client's business problem, connects the model to a real process and brings it to a working result.
Marketing AI Operations Manager, an agentic marketer with an agent on n8n and Claude Code, a go-to-market engineer with CRM and Salesforce, a Product Manager, AI Finance Transformation at Apple, AI automation in HR — and still the same four to eight years of experience.
Every specialist becomes a bit of an engineer: build a prototype or describe the logic to another engineer precisely enough to be responsible for the result. The base of the chain is domain expertise: AI itself can suggest the engineering part, but not the right question.
Not an identical list of courses but one process of your own: describe it, build an AI solution on it, see what changed, implement it. Basic AI literacy is becoming a default requirement, as Microsoft Office once was.
When a person leaves, their tasks are distributed among employees or handed to a model — and this never reaches any statistics. Add the mismatch between employers' changed requirements and candidates' previous experience.
The figures show not a wave of layoffs but cautious hiring, the cutting of some roles and the appearance of others, and changing tasks within professions. The market is looking for those who understand their work well enough to decide what to hand to AI and where to keep the human.
What this episode is about
A solo ToTheMoon episode with Anna Volchek. She has worked in people management for more than twenty years and hears people's fear for their own relevance more often than ever — and notices that those who use AI more deeply and more often are more afraid. To test the hypothesis she goes to US market figures: changes show up there quickly and there are many fresh sources. Every figure gets four questions: who counted it, over what period, on which market, and a percentage of what number.
The first figure is 116,175 announced job cuts connected with AI, from the Challenger, Gray & Christmas report for January–August 2026: about 21% of all the cuts in the database. Inside are the details: the reason is formulated by the employer itself, "because of AI" can also mean restructuring and cost cutting, and the overall market in the same database is 41% calmer than last year. Technology companies announced 155,126 cuts — 52% more — but the employer's industry is not the person's profession, and Anna did not find the share of AI-related cuts within tech, preferring a gap to an invented percentage.
The scale: against 159,075,000 jobs that is 0.07%, seven positions in ten thousand — not a probability of being laid off but a ruler. The fear is not made up, but the big figure does not describe anyone's individual risk. Next comes a market that seems to have frozen: according to the Bureau of Labor Statistics, in July 2026 there were 7,271,000 open vacancies, 2.6% more, while hires were only a little over five million; employees quit less often, and employer-initiated separations are 6% fewer. There is no general wave of layoffs, there is caution, part of which Anna connects with AI and a lack of understanding of the future model of work.
Inside companies the picture is calmer still: a Center for Economic Studies working paper at the Census Bureau — a survey of 117,000 companies from November 2025 to February 2026 — showed that for 95.7% of those using AI headcount did not change, for 2.3% it grew and for 2% it fell. AI so far changes the work inside the organisation, not the number of people. Hence three levels at which replacement should be discussed: industry, profession, specific task.
The second half is about what new things the market is buying. Within the technology contour — AI engineers, AI strategists and the fast-growing position of Forward Deployed Engineer. But Anna found the main thing in ordinary functions: marketing is hiring Marketing AI Operations Managers and agentic marketers who are asked to show a working agent built on n8n and Claude Code; sales — go-to-market engineers who own the CRM and connect Salesforce with other systems; in finance Apple is hiring a Product Manager, AI Finance Transformation; HR — product owners for AI transformation and heads of recruiting operations with AI automation. Everywhere the formula is the same: do the work and rebuild the way it is done — with the same experience requirements as before.
From this come five layers of a professional profile: domain expertise, process architecture, building a working solution (everyone becomes a bit of an engineer), quality, and implementation through the business, security and the lawyers. The base of the chain is expertise and an understanding of the process: AI itself can suggest the engineering part, but the right question comes from domain depth.
Anna's advice is not an identical list of courses but deepening your profession, the ability to describe processes, and small AI solutions on the most understandable process; basic AI literacy is becoming a default requirement, as Microsoft Office once was. She notes the mismatch between employers' changed requirements and candidates' previous experience, and a layer of tasks "under the bonnet": positions that are not cut but are not reopened after a person leaves.
The conclusion: to "will AI replace us?" — no; in some tasks — yes. The figures show not a wave of layoffs but a more complex process: cautious hiring, the cutting of some roles and the appearance of others, changing tasks within professions. The market is looking for those who understand their work well enough to decide which part of it to hand to AI and where to keep the human.
The episode is useful because it does with the fear what headlines rarely do: it puts the source, the period and the denominator next to the figure. 116,175 cuts "because of AI" turn into seven positions in ten thousand jobs, and a "wave of layoffs" into a cautious market with more vacancies than hires. The second conclusion is more practical than the first: ordinary vacancies already spell out what is expected of a specialist — not to use AI, but to rebuild their own work with it while remaining an expert in their function. Anna suggests starting not with courses but with one process of your own.
Episode transcript
The episode is in Russian; below is an English reading guide to the transcript (the full EN transcript is a machine translation). Voice matching applied to 47 segments: 47 identified, 0 mixed, 0 probable, and 0 unresolved.
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