Two-Thirds of Your Engineers Are Learning AI: What Does This Mean for the Team You’re Building Next?
There were two data points from the 2025-26 NTP Market Insights & Salary Guide that on first glance look to be contradictory.
- The first is that software development and engineering hiring, while still the largest category of technical hiring in Australia, fell 23% year-on-year.
- The second is that 68% of tech professionals are actively upskilling in AI and machine learning, the highest figure for any skill category in our survey, and well ahead of the next-largest skill category (leadership, at 28%).
In the same year, 67% of Australian tech leaders named AI as the top trend for 2025, and the Tech Council of Australia projects AI adoption will inject approximately A$200 billion per year into the Australian economy between 2025 and 2030, while generating around 150,000 new jobs.
These findings only look contradictory if you read AI as a binary: either it replaces engineers, or it doesn’t. Neither of those is what’s actually happening. The useful read is more specific, and it has direct implications for how you’ll build tech teams in the next two years.
What’s actually happening
The 23% year-on-year drop in software hiring intent doesn’t tell us that companies are cutting engineering teams. It tells us that the rate of growth in software engineering hiring is moderating, and that companies are choosing, where they can, to extract more output from existing headcount.
The 68% upskilling figure, read alongside this, tells us where that extra output is expected to come from. AI and machine learning tools are large productivity levers and the community is adapting accordingly. Two-thirds of engineers aren’t upskilling in AI because it’s a passing trend. They’re upskilling because the baseline expectation of what a senior engineer can ship in a quarter is changing, and the ones without AI fluency will be on the wrong side of that shift.
Meanwhile, the categories where hiring is still growing fastest—data analytics and data science (+8% year-on-year), cybersecurity, AI/ML engineering specifically, and cloud infrastructure—tell us where the team composition is moving. Companies aren’t replacing engineers with AI. They’re rebalancing the shape of their tech teams around AI.
The shift in team composition
If you map the survey findings against the placements we’ve made over the last twelve months, a pattern emerges in how modern tech teams are being structured.
- The mid-level “generalist developer” seat is getting squeezed.
Companies are still hiring juniors (in part because they’re cheap to train into AI-augmented workflows from day one) and still hiring seniors (because experience and judgment compound under AI tools rather than being diluted by them). The seat that’s getting harder to justify is the mid-level generalist whose output is now readily achievable by a senior with AI assistance. - Data and ML engineering roles are replacing some of that volume.
The infrastructure around AI (data pipelines, feature stores, MLOps, model serving) is where a lot of the new engineering work lives. Companies that were hiring three mid-level full-stack engineers a year ago are often hiring one ML engineer, one data engineer, and one senior full-stack this year. - Cybersecurity is growing in lockstep with AI adoption.
Every AI use case a company deploys expands the attack surface, and the survey bears this out, cybersecurity sits high on both hiring intent and upskilling lists. Security Analysts, Governance, Risk & Compliance Specialists, Security Architects, and CISOs are among the fastest-growing roles in the Australian market. - Leadership roles around AI are being invented in real time.
Head of AI, Head of Data, AI Product Managers, and AI Engineering Managers are roles that barely existed two years ago and are now being searched for by serious scale-ups and enterprises. The talent pool is shallow and competitive.
The ethics undercurrent
When we asked technical professionals what they see as the most significant challenges facing the tech industry in the next three to five years, the ethical implications of emerging technologies, AI and automation specifically, came in highest, named by 24% of respondents.
This matters for hiring in two ways.
First, it’s a signal about where the best AI engineers want to work. Senior AI and ML engineers are, on average, more discerning than the broader tech population about the organisations they’ll join. A role at a company deploying AI thoughtfully with proper governance, human-in-the-loop design, and honest external communication is materially more attractive than a comparable role at a company treating AI as a way to quietly cut headcount. The talent market reads employer behaviour.
Second, it’s an early indication of where regulation is going. Governments globally are moving toward AI accountability frameworks, and Australia is following. Companies that are building AI capability without building the governance layer around it are accumulating a regulatory liability that will arrive on the balance sheet within the next two to three years. The senior AI hires you make now should be able to operate credibly inside that governance context, not just ship models.
What this means for how you hire
A few practical implications follow.
- Rethink the mid-level brief before you post it.
Before hiring another mid-level full-stack engineer, it’s worth asking whether the role is better structured as a senior-with-AI-tooling, or as a data engineer, or as a split between a senior and a graduate who can grow into AI-augmented workflows. The default shape of a 2024 engineering team isn’t necessarily the right shape of a 2026 engineering team. - Screen for AI fluency without overindexing on AI hype.
A senior engineer who uses AI tools meaningfully in their day-to-day work is dramatically more valuable than one who either avoids AI entirely or treats it as magic. The bar for senior engineering is quietly being raised, and the screening process needs to catch the difference. - Invest in AI-specific leadership early.
The companies that will extract real value from AI over the next three years are the ones that have a senior voice in the room whose job is to make the strategic calls: what to build, what to buy, what to regulate internally, what to communicate externally. This role is much harder to hire into reactively. Start earlier than feels necessary. - Don’t confuse upskilling with ready-to-ship.
68% upskilling is a real signal, but it doesn’t mean 68% of the market can ship production AI systems tomorrow. The gap between “doing AI courses” and “has shipped a reliable ML pipeline in production” is significant, and specialist recruiters earn their fees in telling the difference.
The data is pointing at a specific conclusion that’s easy to miss amid the louder AI narratives. Companies aren’t replacing engineers with AI. They’re building smaller, more senior, more specialist teams, augmented by AI, oriented around data and security, and led by people who can navigate the ethical and regulatory terrain that comes with the technology.
The team you hire next year shouldn’t look like the one you hired last year. The best hires won’t either.
NTP Talent’s specialist practice covers AI, data, cybersecurity, and senior engineering hiring across Australia’s east coast. If your 2026 team shape is different from your 2025 team shape, we can help you build it.
Talk to our team