The global economy is bracing for major job disruption as **artificial intelligence (AI)** advances and spreads across industries. Experts have been warning about this shift for years, and fiercely debating whether the benefits of an AI revolution will outweigh the cost of mass displacement in the workforce.

Few sectors expose this tension as clearly as **agriculture**. Pressure on farming is intensifying. Global food demand is projected to rise by **35–56%** by 2050, driven by population growth, urbanisation and changing diets.

This helps explain why AI is increasingly promoted as a **productivity solution** to produce more food with fewer inputs, under more volatile conditions.

Yet on farms, enthusiasm for AI is often tempered by caution. And that caution is not simply about whether jobs will disappear. A deeper concern is **risk**, and who bears responsibility if the technology fails.

## Technological Change

Agriculture is not a controlled environment. Farming is biological, dynamic and deeply context-dependent, shaped by weather, soils, ecosystems and animal behaviour. Because of this complexity, AI is (and will continue to be) rarely used to replace people outright. Instead, it automates specific tasks.

**Automation** has been a big part of the farming story for decades, long before AI arrived on the scene. From mechanised harvesting and GPS-guided tractors to automated milking systems and variable-rate fertiliser application, technology has gradually changed how farms operate.

But AI is different. Rather than replacing farmers, AI is mainly being used to support **decision-making** in environments that are too complex, variable and context-dependent to be fully automated.

Most current uses of AI support **monitoring and optimisation**: detecting crop stress from satellite imagery, predicting irrigation needs, tracking livestock behaviour or flagging disease and weed risks. Farmers and farm workers still interpret the information and decide how to respond.

![A horse-drawn seed drill at a farm in New South Wales in 1926. Technology has dramatically transformed agriculture over the past century.](https://images.theconversation.com/files/716742/original/file-20260206-56-nn912l.jpg?ixlib=rb-4.1.0&q=45&auto=format&w=754&fit=clip)

## AI is Automating Tasks, Not Whole Jobs

Our previous research with colleagues from CSIRO’s Data61 explored the future of Australia’s agricultural workforce, showing how digital and automated technologies are changing how farm work is done rather than removing the need for people. Demand is growing for skills in **decision-making, oversight and technology management**, particularly as labour shortages persist. However, adoption of advanced technology in farming remains limited, especially among small producers.

It’s a similar story internationally. For example, in the United States, only around **25%** of farms used advanced technology by 2019, with adoption of automatic steering and machinery guidance systems more common on larger operations. These technologies automate specific tasks and can reduce fatigue, but they do not eliminate farm operators.

Across other industries, evidence from the International Monetary Fund (IMF) shows about **60% of jobs** in advanced economies are exposed to AI.

Separate findings from the OECD indicate AI exposure is primarily at the task level, with only about **27–28%** of employment currently in occupations at high risk of full automation.

## Uneven Gains

The productivity promise of AI and other types of digitalisation in agriculture is genuine. In practice, however, these gains are uneven.

Evidence shows adoption and benefits vary widely by farm size, crop type, region, and access to capital, data and skills. It also risks leaving some farmers behind due to poor connectivity and limited digital access, constraining their use of data-driven and AI-enabled tools.

## Risk and Reward

This is where the core tension lies. When AI-supported decisions succeed, efficiency improves. When they fail, humans carry the consequences.

For example, if an irrigation system mistimes watering, the farmer bears the yield loss or soil damage. If a particular crop disease is missed, a whole season’s income may be wiped out.

AI systems do not absorb financial loss, regulatory scrutiny or reputational damage. Farmers and advisers do. This dynamic is central to our research through the Australian government’s Soil CRC program on how easy it is for farmers to actually adopt these new technologies.

That work shows farmers assess technologies not just on technical performance, but on how they affect business risk, autonomy and accountability.

## The Future of Farming

AI will continue to reshape Australian agriculture. The most important question is not whether it replaces farm jobs, but who carries the risk when AI becomes part of everyday decisions.

If AI is designed to genuinely support human judgement, backed by shared accountability and proper assurance, it can make farming safer, more resilient and more skilled.

If not, it risks quietly increasing exposure for those already operating at the edge of uncertainty.

Productivity gains are possible. But they will only be realised and socially accepted when AI systems are designed not just to optimise outcomes, but to protect the people who live with the consequences.

Pressure on farming is intensifying. AI promises some relief through productivity gains – but could create other problems.

AI in Agriculture: Not Replacing Farmers, But Redistributing Risk - What You Need to Know

Want to understand how **artificial intelligence** could change your job? Look to radiology as a clue.

Radiology has become a recent talking point in the AI race. It was mentioned multiple times last month by tech executives at the World Economic Forum in Davos as well as in a White House whitepaper about AI and the economy.

Radiology is far from being the only occupation impacted by AI, which is gradually integrating into the work of software engineers, teachers, and even plumbers, among many others. If widely adopted, Goldman Sachs estimates that advancements related to AI could displace 6 to 7% of the US workforce, although the technology is expected to create new jobs too.

But the radiology field has become a **case study for how AI could enhance, and not replace, jobs**. The type of work in radiology is also ideal for AI assistance, said Dr. Po-Hao Chen, a doctor specializing in diagnostic radiology at the Cleveland Clinic.

Radiology has plenty of available data for AI research and applications, which need copious amounts of data for training. AI can parse through troves of data much more quickly than human workers can, and it is already helping to speed up certain processes in radiology—for example, figuring out which scans need immediate attention.

But human physicians are still required to do the bulk of the work—like making diagnoses, physically examining patients, and writing reports. And radiology jobs are projected to grow faster than roles in other areas as the field continues to embrace the tech.

"(AI) is not only not replacing those workers, but it’s actually increasing the amount of work they can do and increasing demand for their services," said Jack Karsten, a research fellow at Georgetown’s Center for Security and Emerging Technology. "That’s sort of a bright future that the tech industry can point to as far as this is AI doing good in the economy."

AI is very good at analyzing images and spotting patterns in data, both critical to radiology. And the field has been digitized for years, meaning there is an abundance of data, according to Chen.

"There are smaller use cases that are analogue still, but in the US for the most part, every X-ray, every CT (scan), every MRI, can be available as zeros and ones," Chen said.

Today, radiologists are using AI to help figure out which scans to prioritize, enhance image quality, and assist with summarizing reports, according to Dr. Chen and two other radiology experts who spoke with CNN.

"It’s something that doesn’t replace anyone, that just makes our job more efficient and more meaningful," said Dr. Shadpour Demehri, who works in interventional radiology at Johns Hopkins Medicine.

René Vidal, a professor in engineering and radiology at the University of Pennsylvania’s Penn Engineering department, views AI as particularly useful for capturing high-quality MRI scans with fewer measurements. That speeds up the process and allows more patients to be seen in the same amount of time.

Other applications are being explored in research, such as using AI to measure the volume of a tumor or automatically populate reports, although they’re likely still far out, said Vidal.

AI tools must be approved by the US Food and Drug Administration for medical use, which could take around eight years considering the development process and clinical testing, Vidal said. But those approvals are certainly happening: Of the 1,357 AI-enabled medical devices currently with FDA approval, 1,041 are for radiology.

At the same time, radiology jobs seem to be growing. The Bureau of Labor Statistics projects employment in radiology will grow 5 percent from 2024 to 2034, which is higher than the average of 3% across all occupations. Data from Indeed provided to CNN also indicates there were more radiology jobs in 2025 compared to five years ago.

Demand for imaging during the medical diagnosis process, along with an increased aging population, is likely driving the need for more radiology services, say the radiology experts who spoke with CNN.

But that wasn’t always the thinking. Nobel Prize-winning computer scientist Geoffrey Hinton, also referred to as the godfather of AI, said in 2016 that "people should stop training radiologists now" because deep learning—a subset of AI that models how the human brain learns—would handle the job better in five to 10 years.

Hinton said in an email to the New York Times last year that he spoke too broadly in those 2016 comments.

Demehri recalls there being a sense of anxiety in the radiology field about AI replacing human roles around the 2015 and 2016 timeframe. Now, the technology is seen as a "second set of eyes," he said.

Still, there are risks around bias and potential overreliance on AI, according to Chen. Unlike human radiologists, for example, AI can accurately predict a person’s race based on an X-ray, according to a 2022 MIT study, raising concerns about bias in diagnoses.

Chen says he also worries about the temptation to make staffing decisions—such as replacing a doctor with a nurse or a subspecialist radiologist with a primary care doctor—if AI becomes advanced enough. That might work in some cases, but not for the majority of conditions that radiology is used for, like detecting cancer or deadly infections.

"We have to understand that a lot of the performance of (the) algorithm comes from the fact that the automation output is reviewed by an expert," he said. "And together, this collaboration, if you will, between the machine and the expert is what makes the improvement real."

Radiology has come up multiple times as an example of a field that’s been impacted by AI without replacing the need for human workers.

Why AI Won't Replace Radiologists: The Ultimate Case Study in Human-AI Collaboration

Leave it to the leading artificial intelligence (AI) chatbot to recommend its own tech base as the future of side hustles. However, with AI seizing jobs in every industry, ChatGPT’s predictive skills are spot on when it comes to upcoming trends in the gig economy.

For many Americans, the days of relying on a single job for financial independence and creative freedom are long gone. More workers are choosing side hustle work than ever, fueled by the passion and skills they bring to the table.

We asked ChatGPT to predict the next big side hustle, and here’s what it said about opportunities gaining serious traction in 2026 and beyond.

## AI-Powered Hustles and Microservices

The potential for successful AI **side hustles** is limitless, but some gigs have much more earning power than others.

According to NetCredit data, business, influencer and social media marketing strategy; content and video editing; and website, landing page and UX design are AI-related side hustles that are in demand and growing. Basically, any job that can uniquely apply AI to existing industries to do things faster, cheaper and with a personal touch will be highly sought-after.

## Mobile and Convenience-Based Services

Speaking of a personal touch, when new technology begins to mesh with everyday life, people are drawn to preserve vital purpose-driven human connections. For example, the AI platform stated that search interest in mobile car washes has grown 276% recently.

Keep your eyes and ears open to “real world” services like pet sitting, mobile bike repair and home organization, along with curated experiences that help build community bonds.

## Digital Product-Based Income

All side hustles that involve AI-created digital content will be a boon to every small business and industry giant.

This can include things like editing and template design, as well as anything involving embedding integrations (fusing AI in embedded systems to allow devices to process data and make decisions autonomously) and stable diffusion art (a deep learning model used for converting text to images).

## Micro-Consulting and Freelance Services

Targeted, specialized side hustles are increasingly popular as consumers seek personalized experiences from people with actual experience. “This form of freelance consulting focuses on niche markets and small-scale projects, and it’s quickly emerging as a popular way for individuals to leverage their expertise and profit from their experience,” according to the entrepreneur supporters at Founder Club.

ChatGPT suggested virtual sessions for resume building and marketing campaigns, as well as voiceover and audio branding, as examples.

## Sustainability and Niche Markets

Both businesses and job seekers place a high importance on sustainability in today’s job market. The need for workers with “green skills” is growing as a result of government laws, investment trends and the push for accountability by younger generations. “Vintage, thrift resale, plant-based or organic creations, [and] green consulting all tap into rising eco-conscious consumers,” ChatGPT explained.

Likewise, ChatGPT suggested that opportunities will be plentiful for those providing niche services to under-resourced communities, like tech support for the elderly and culturally focused content. AI has been supporting these high-loyalty, less competitive areas for years and will continue to expand into new applications — and side hustle opportunities — in the future.

Cooking up the next big side hustle won’t include a hidden recipe. It will use new technology to solve old problems, foster real-world connections in a digital era and market personal experiences to audiences through AI application. This is good news for observant and open-minded entrepreneurs who stand to thrive in the perpetually developing hustle economy.

ChatGPT’s predictive skills are spot on when it comes to upcoming trends in the gig economy.

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