AI is neither marching in to wipe out tech jobs nor harmlessly hovering at the edges. A new global survey of 2,050 IT executives from Snowflake paints a more tangled picture: the same roles seeing automation-driven cuts are also seeing fresh hiring, often inside the very teams touched by AI.
In short, the jobs themselves are changing. Routine tasks are getting automated. Higher-order work in integration, governance, and oversight is growing. That tension explains why layoffs and recruitment can rise together across identical job titles.
Cuts And Hiring Are Happening Side By Side
The survey shows a split-screen reality for core IT functions. In IT operations, 40% of leaders report reductions tied to automation, while 56% report new hiring for the same function. Software development exhibits a similar dynamic: 26% report cuts while about 38% say they are adding headcount. Cybersecurity shows 25% reporting losses but 46% gains. For data analysts, it’s a dead heat at 37% reporting cuts and 37% reporting hiring.
Why both at once? AI is absorbing repetitive work—think ticket triage, build-and-deploy steps, and basic dashboarding—and creating demand for roles that stitch AI into production, monitor models, enforce access controls, and validate data pipelines. The job title might be “IT ops” or “software engineer,” but the task mix is tilting toward AI-aware skills.
Where Job Risk Is Highest And Where Demand Is Surging
Outside core IT, the patterns look more one-directional. Customer service stands out: 37% of organizations report shrinking support teams, while only 15% are hiring more. Generative chatbots and AI-assisted agents are part of the story; so is ongoing outsourcing to lower-cost providers, which can compound the effect.
Elsewhere, the moves are more modest. In manufacturing and supply chain operations, 6% report cuts and 13% report hiring, suggesting automation is expanding capacity rather than displacing large numbers. Marketing sees a near balance—16% cutting and 12% hiring—as teams experiment with AI content tools while elevating strategy, brand safety, and analytics.
Gen AI’s Net Effect More Creation Than Destruction
When asked directly about generative AI’s impact on employment, 42% of leaders say it has created jobs, 11% say it has led to job loss, and 35% say both effects are happening at once. Only 13% report no impact. Add that up, and 77% see some job creation underway, often alongside reconfiguration of tasks and teams.
This aligns with a long-run pattern identified by groups like the OECD: major technology shifts tend to reallocate work rather than cause broad-based, permanent job collapse. Early adopters in the Snowflake survey are more likely to report net-positive employment, a signal that once AI pilots graduate to production, organizations lean into building the talent and tooling to operate them responsibly.
The Skills Crunch Is The Real Bottleneck
While the hype has centered on model breakthroughs, the constraint inside enterprises is talent. Fully 35% of organizations cite skills gaps as a top barrier to AI success. It’s not just prompt writing; it’s data engineering, secure integration with legacy systems, model evaluation, observability, and governance.
That shows up in technical concerns: 42% flag interoperability issues across tools, 39% struggle with legacy system compatibility, and 42% cite the need for real-time data to support agent decision-making. Another 29% worry about job displacement, 29% about maintaining human oversight to prevent rogue AI actions, and 29% about data storage and use. In practice, this is pushing demand for roles like AI operations (AIOps), MLOps, data product owners, security engineers versed in model risks, and compliance leaders who can codify AI policy.
What It Means For Workers And Leaders Now
If you work in tech, the takeaway is less “AI will take your job” and more “AI will take parts of your job—so claim the higher-value parts first.” Data literacy, software architecture, security principles, and lifecycle management of models will travel well across roles. LinkedIn’s recent hiring insights show rapid growth in titles such as AI engineer, platform engineer, and data governance specialist, reinforcing where demand is headed.
For executives, the message is to replace blanket automation goals with capability building. Invest in data quality, model monitoring, and cross-functional guardrails. Pair engineers with domain experts to design workflows where AI agents act within well-defined boundaries and humans stay accountable for outcomes. This is how organizations move beyond demos and into durable productivity gains.
The headline answer to “Is AI stealing our jobs?” is complicated because the jobs are being rebuilt in real time. The Snowflake survey suggests the near-term future is not mass elimination but accelerated evolution—fewer rote tasks, more oversight and integration work, and a premium on people who can make AI safe, reliable, and useful at scale.
