AI Job Postings Surged 134% While Overall Tech Roles Stay Flat: What This Split Market Means for Your Hiring Strategy
One statistic captures the entire tech hiring market right now: by the end of 2025, US job postings mentioning AI were about 134% above their February 2020 level, while total postings across the economy were just 6% higher (Indeed Hiring Lab, 2026). Two numbers, the same period, moving in opposite directions.
The problem for anyone planning a team is that the headline “tech hiring is flat” is dangerously misleading. It is not one market — it is two, running in parallel. If you read the flat top-line and conclude it is a buyer’s market, you will be blindsided when you try to hire anyone with real AI skills and find a fierce, expensive scramble. Plan against the average and you plan against a market that does not exist.
This article breaks down the two-speed tech hiring market: what the 134% surge actually shows, why AI demand broke away from everything else, and — most importantly — what it should change about where UK leaders spend their hiring budget and how they build an AI-ready team without overpaying.
One Headline, Two Markets
The divergence is stark once you separate the two trends.
From February 2020 to early 2022, AI-related postings nearly tripled, then fell back in line with everything else. But from late 2023 the two trends split apart: AI postings surged back while overall postings kept cooling. By end-2025, AI postings sat about 134% above the 2020 baseline against just 6% for total postings (Indeed Hiring Lab, 2026). Inside tech specifically, roughly 45% of all postings now mention AI in some form.
Meanwhile the broader picture was genuinely weak. Total US tech postings spent much of 2025 well below both pre-pandemic and 2022-peak benchmarks — the story of tech postings stuck below pre-pandemic levels that dominated headlines (Indeed Hiring Lab, 2025). Early 2026 brought a rebound, with CompTIA reporting postings hitting a three-year high (CompTIA, 2026), but the shape of demand had already changed.
That is the two-speed tech hiring market in one line: AI-related roles booming, everything else flat to soft. The average tells you almost nothing useful.
Why AI Demand Broke Away From the Pack
The split is not random. It reflects where companies now believe value is created.
As firms clarified their AI strategies through 2025 and into 2026, hiring shifted toward people who could build, deploy, and manage AI systems. The result is intense demand for AI skills in 2026 even while routine roles stagnate. Employers are not simply hiring more — they are swapping skill profiles, trading roles tied to manual or easily automated work for AI-adjacent ones.
The money confirms it. AI/ML roles carry a clear AI skills premium: AI staff engineers earn roughly 18.7% more than non-AI peers, and published AI/ML salary ranges sit well above general software engineering (Levels.fyiQ3 2025; Robert Half, 2026). Strong machine learning engineer demand and a thin supply of proven talent push those numbers up.
For UK leaders the implication is direct. The talent you most need is the talent everyone else is also chasing — and paying a premium for. The flat overall market offers little comfort when you are competing for the exact roles driving the surge.
What the Split Means for Where You Spend
A two-speed market changes budgeting. Spreading spend evenly, as if all tech roles were equally easy or hard to fill, wastes money on the soft side and underfunds the fierce side.
The first move is to map your roles against the split. Which of your open roles are AI-adjacent and genuinely scarce, and which sit in the flatter, easier-to-fill part of the market? Your decision about where to invest your tech hiring budget should follow that map — concentrate premium budget and effort on the scarce roles, and be more cost-disciplined on the rest.
The second move is to be realistic about cost and time on the scarce side. A sound tech hiring strategy 2026 builds in the AI premium and longer search times for specialist roles, rather than being surprised by them. Underbudgeting here is how roadmaps slip.
The third move is to question whether every AI-adjacent need must be a permanent UK hire at all. A flexible UK tech hiring strategy keeps the option to source specialist skills differently — which leads to the final, most practical question.
Building an AI-Ready Team Without Overpaying
You do not have to win every bidding war to build an AI-ready team. There are three levers, and the smartest leaders pull all three.
First, upskill from within. Many AI-adjacent capabilities can be developed in your existing engineers, who already know your systems and domain. Upskilling existing developers is often faster and cheaper than hiring a scarce specialist cold — and it improves retention.
Second, buy specialist depth flexibly. For genuinely specialist needs, staff augmentation lets you bring in AI skills from South-East Asia and Europe, adding vetted senior talent quickly without committing to a permanent premium salary you may not need in a year. Many engineers in these markets have invested heavily in exactly the AI tooling fluency that is scarce in the UK.
Third, hire permanently where it compounds. Reserve full-time AI hires for the roles that sit at the core of your product and need long-term ownership. That focuses your premium budget where it genuinely pays back.
Pull all three and you ride the surge without letting it set fire to your budget — building real AI capability while staying disciplined on cost.
Conclusion
The 134% surge against a flat overall market is not a contradiction — it is the single most important fact about tech hiring in 2026. There is no “tech market” to plan against any more. There is a booming, expensive market for AI-related skills, and a flatter, softer one for everything else, hiding behind the same headline.
For UK leaders, the response is to stop planning against the average. Map your roles to the split, fund the scarce side properly, and be disciplined on the rest. Then build AI capability with all three levers — upskilling your own people, augmenting with flexible specialist talent, and reserving permanent premium hires for the roles at the heart of your product.
The companies that thrive will not be the ones who panic at the surge or relax at the flat line. They will be the ones who see both markets clearly and resource each on its own terms.
Ready to scale your tech team? Get in touch with ThoughtGears — we’d love to hear about your project.
FAQs
What does the 134% AI job posting surge actually measure?
It measures how far US job postings mentioning AI rose above their February 2020 level by the end of 2025, according to Indeed Hiring Lab. Over the same period, total postings across the economy were only about 6% above that baseline — which is why the two figures together reveal a split market.
Is overall tech hiring actually shrinking?
It cooled significantly through 2025, with total tech postings below pre-pandemic and 2022-peak levels for much of the year, before rebounding to a three-year high in early 2026 per CompTIA. The recovery is real, but demand has concentrated heavily in AI-related roles.
What is a “two-speed” or “split” tech hiring market?
It describes a market where one segment — AI and AI-adjacent roles — is booming while the rest stays flat or soft. The danger is that the averaged headline hides both realities, leading leaders to misjudge how hard specific roles will be to fill.
Why are AI roles in so much more demand than other tech roles?
As companies clarified their AI strategies, they shifted hiring toward people who can build, deploy, and manage AI systems, while trimming routine roles. Demand is high and proven talent is scarce, which is why AI/ML roles also command a clear salary premium.
How big is the AI skills pay premium?
AI staff engineers earn roughly 18.7% more than non-AI peers, and published AI/ML salary ranges sit well above general software engineering (Levels.fyi, 2025; Robert Half, 2026). The premium tends to widen at senior levels where proven experience is rarest.
How should this change where I spend my hiring budget?
Map your open roles to the split. Concentrate premium budget, effort, and longer timelines on the scarce AI-adjacent roles, and stay cost-disciplined on roles in the flatter part of the market. Spreading spend evenly over- and under-funds the wrong things.
Do I have to pay the AI premium to build AI capability?
Not for everything. You can upskill existing engineers, who already know your systems, for many AI-adjacent needs. Reserve premium permanent hires for core roles, and use flexible specialist talent for the rest rather than winning every bidding war.
How does staff augmentation help in a two-speed market?
It lets you add vetted senior AI-skilled engineers quickly — often from South-East Asia or Europe, where AI tooling fluency is strong — without locking in a permanent premium salary. That is useful when demand is spiky or you need depth fast.
Should I still hire permanent AI engineers at all?
Yes, for the roles at the core of your product that need long-term ownership and institutional knowledge. The point is to focus your scarce premium budget there, rather than trying to make every AI-adjacent need a permanent senior hire.
What’s the single biggest mistake leaders make in this market?
Planning against the average. Reading “tech hiring is flat” and assuming it is easy to hire leads to nasty surprises when you try to fill an AI-adjacent role. Treat the two segments as separate markets and resource each on its own terms.
Disclaimer
This article is for general guidance only and reflects analysis based on sources available at the time of writing. ThoughtGears is not a legal, financial, employment, or tax adviser. Always seek qualified professional advice before making hiring, investment, or compliance decisions.
