By Stuart Kerr, Technology Correspondent, LiveAIWire

The professions AI will create are no longer a forecasting exercise. LinkedIn’s Jobs on the Rise 2026 report, based on hiring data from millions of its own users, found that AI engineer is now the single fastest-growing job title in the United States, with AI consultant and strategist in second place and data annotator in fourth. Four of the top five fastest-growing roles on the entire list are tied directly to AI. This is not a projection of what hiring might look like in five years. It is what has already happened in the three years LinkedIn’s data covers.

The scale behind that ranking is concrete. The World Economic Forum, citing LinkedIn’s Economic Graph data, put a number on it in January: roughly 1.3 million new positions, including AI engineers, forward-deployed engineers and data annotators, have been created globally in the past two years, alongside more than 600,000 new AI-enabled data centre jobs. The Forum’s own Future of Jobs Report 2025 puts the longer-run figure at 170 million new roles created worldwide by 2030, against 92 million displaced, for a net gain of 78 million jobs. The professions AI will create are already visible in the hiring data. What is less visible, and more useful, is what those jobs actually require and who is positioned to get them.

The Roles That Did Not Exist Three Years Ago

AI engineer, the number-one role on LinkedIn’s list, did not function as a distinct job category before large language models became commercially deployable at scale. The role now has an identifiable skill signature: LangChain, retrieval-augmented generation and PyTorch are the three most common skills LinkedIn found among people holding the title, concentrated heavily in San Francisco, New York and Dallas. Most of the people moving into it are transitioning from software engineering, data science and full-stack development roles that already existed, which tells you something important about the professions AI will create: they are drawing talent from adjacent technical fields rather than inventing an entirely new labour pool from scratch.

Data annotator, fourth on LinkedIn’s list, is a different kind of new profession entirely. It exists because every AI model, however sophisticated, still needs humans to label, correct and evaluate its outputs before and after deployment. Forward-deployed engineer, a title barely used before 2024, describes technical staff embedded directly with enterprise clients to customise and troubleshoot AI systems in production, a hybrid of consulting and engineering that did not need to exist when software was simpler to configure off the shelf. Neither role is a rebrand of an old job. Both are professions AI will create in the literal sense that the underlying task did not exist until the technology that requires them did.

Where the Professions AI Will Create Are Concentrated

Our own reporting on what the 2026 jobs data actually shows about AI displacement found that AI-related job postings have grown roughly 143 percent year over year, concentrated overwhelmingly in a handful of technology hubs and large enterprises. The professions AI will create are not spreading evenly across the labour market the way earlier general-purpose technologies eventually did. They are concentrated in specific metropolitan areas, specific company sizes, and specific prior career paths, which means the same AI transition that is creating 1.3 million new roles is also creating a geography of winners that maps closely onto where the technology industry already had capital and talent concentrated before AI arrived.

That concentration cuts against the more optimistic framing of the jobs-created number on its own. A net gain of 78 million jobs globally by 2030 is a real, well-evidenced projection, not a talking point. It is also an aggregate figure that says nothing about whether the person losing a role in administrative support has the training, location or capital to move into the AI engineering or data annotation roles being created in a different city entirely.

Our analysis of whether AI is helping or hurting workers found the same pattern from the labour economics side: augmentation and replacement are both genuinely happening, in different occupations, and the professions AI is creating do not automatically absorb the workers displaced from the professions it is replacing. Our reporting on how AI is reshaping professional identity and expertise found the same tension inside individual careers, not just across the labour market: the professions AI will create are, for many workers, arriving faster than the professional identity built around the ones being displaced can adapt.

The Roles Beyond the Engineering Core

The professions AI will create are not limited to people building the models themselves. LinkedIn’s wider list includes AI consultants and strategists, who help organisations without in-house technical capacity decide what to build and buy, and a rapidly growing set of specialist titles inside existing functions: AI trainers who work directly with model behaviour and evaluation, and governance-focused roles that did not exist as standalone jobs three years ago. Chief AI officer, a title that barely registered on corporate organisation charts before 2023, is now common enough at large companies that the World Economic Forum tracks Head of AI and Director of AI postings as a distinct hiring category across multiple countries.

The common thread across almost all of these new professions is that none of them are purely technical. LinkedIn’s data on AI consultants found the largest single group transitioning into the role came from product management, not software engineering, and the World Economic Forum’s framing of what it calls the “new-collar era” describes exactly this pattern: work that blends applied technical fluency with judgement, communication and domain expertise that AI itself cannot supply. The professions AI is creating, in other words, reward people who can translate between what a model can do and what a specific business or client actually needs, a skill that sits closer to consulting and product thinking than to pure engineering.

What This Means for Anyone Planning a Career Move

The practical lesson from the current data is not that everyone should retrain as an AI engineer. It is that the professions AI will create are disproportionately reachable from adjacent roles rather than from a standing start. Software engineers, data scientists and product managers are moving into the fastest-growing new titles because their existing skill base sits one step away from what the new roles require, not because they started from zero.

For someone assessing where to invest time now, the more useful question is not “which brand-new job title should I chase” but “which of the skills those new roles actually use, RAG pipelines, model evaluation, translating business needs into AI deployment decisions, can I start building from where I already stand.” That question, more than any single job title, is the honest starting point for anyone trying to get ahead of the professions AI will create rather than react to them after the fact.

The professions AI will create over the next five years will keep expanding as the technology matures, and the 78 million net new jobs the World Economic Forum projects by 2030 are a real opportunity rather than a rounding error. But the roles being created right now reward proximity to the technology and to the industries adopting it fastest, which means the honest advice for most workers is to get close to how AI is actually being deployed in their own field, rather than wait for a title to appear that did not exist yet.

About the Author

Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, emerging technology, and their impact on business, society, and everyday life. LiveAIWire publishes original AI journalism every weekday at liveaiwire.com.