We’ll get a first look Wednesday at the state of the US job market as 2026 kicked off, as well as a clearer picture of hiring in 2025.

The Bureau of Labor Statistics is set to release the January jobs report at 8:30 am ET Wednesday. The crucial employment snapshot is slightly delayed because of the brief government shutdown and will show whether the trajectory improved for the US labor market, which has been stuck in a low-hire and low-fire lull.

Last year, the economy posted its weakest year of job gains outside of a recession since 2003.

The year ended with the economy adding 50,000 jobs in December (roughly matching the average monthly gain for the year) and unemployment dipping to 4.4%, the BLS reported.

“Many workers feel stuck in their careers or feel frozen out of the job market,” said Daniel Zhao, chief economist at employment site Glassdoor.

The all-important churn needed for a healthy labor market has slowed considerably, and there are more people searching for jobs than are available.

The January jobs report will also include a series of critical revisions (namely, the annual benchmark revision) and statistical modeling adjustments that not only will provide a fuller look at past employment trends but also could very well shape our current and future view of the labor market.

There’s going to be a lot to unpack Wednesday, so here’s a cheat sheet to help get up to speed:

## What are the expectations for hiring and unemployment in January?

In short: Expect to see more of the same. Heading into this year, economists said monthly job gains could hover around that 50,000-a-month range.

The recent batch of labor market data (both public and private) indicated that there’s a high likelihood that job growth was tepid, unemployment remained subdued and that health care remained a primary driver of overall hiring.

There’s a possibility that seasonal and weather-related factors could result in a stronger-than-expected reading for January: Weaker holiday hiring has meant fewer post-holiday layoffs, and unseasonably warm weather during the early part of last month could have bolstered employment in industries like construction.

Economists’ consensus estimates are for job gains of 80,000 last month and for the jobless rate to stay at 4.4%, according to FactSet.

## Why has job growth been so weak?

There is a combination of factors at play.

On the supply side, Baby Boomers are aging and retiring, population growth has slowed, and there’s been a sharp reduction in immigration and an increase in deportations.

On the demand side: Large employers are whittling down their ranks after over-hiring during the pandemic; high levels of uncertainty – particularly around the Trump administration’s sudden, shifting and sweeping domestic policy changes – have clouded businesses’ decision-making and stifled hiring; firms have shifted some investments away from hiring and toward equipment and technology (including artificial intelligence) to shore up productivity; and a high-cost environment alongside steep tariffs, federal funding cutbacks and aggressive immigration enforcement have negatively affected some businesses.

Joe Brusuelas, senior economist at RSM US, highlighted a few of those factors when pushing back on White House economic adviser Kevin Hassett’s claim Monday that subdued job gains are primarily the result of lower population figures and higher productivity.

“The idea that slower hiring is simply a function of long-term demographics is both unsatisfactory and an attempt to distract from immigration and trade policies – see the 72,000 decline in manufacturing jobs last year that will likely look worse following the upcoming benchmark revision,” Brusuelas said in a statement.

## What is the benchmark revision?

Federal data is fluid and frequently subject to change as more detailed and accurate information becomes readily available. The BLS’ monthly jobs report is meant to provide a higher-frequency look at employment trends, but that timeliness comes with some cost to accuracy.

To get the monthly employment snapshot, the BLS surveys about 121,000 US employers, accounting for 631,000 work sites (covering more than a quarter of overall employment). Those respondents are given three opportunities to report their payroll gains and losses for any given month.

Every year, the BLS undertakes a process aimed at providing a near-complete employment county by squaring up the monthly survey estimates with data drawn from the Quarterly Census of Employment and Wages program, which covers about 95% of US jobs.

The QCEW provides a more comprehensive and accurate read on the number of businesses, employees and wages throughout the country because that data is derived from state unemployment insurance tax records most employers are required to file. Given that process, the QCEW comes with a significant lag: The data for the third quarter of last year won’t be released until next month.

## What did the preliminary benchmark revision show?

That was the preliminary benchmark revision, an annual first-look estimate that coincides with the release of the first-quarter QCEW data.

In September, the preliminary revision inferred that the US economy likely added about 911,000 fewer jobs than the jobs reports initially estimated for the 12-month period running from April 2024 through March 2025.

Spread out, that’s about 76,000 fewer jobs per month. If the preliminary estimate were to pan out, it would essentially cut the posted job gains for that period in half.

## Is this revision unusual?

No, the process of benchmarking and these significant adjustments to past employment are not some proof of nefarious data-related activity as has been unjustifiably claimed by President Donald Trump and others.

In fact, they’re the exact opposite.

For one, this is a process that’s been conducted by the BLS in some shape or form for 90 years. To quote former BLS commissioner Erica Groshen, it’s “not a bug; it’s a feature.”

These and other revisions are a reflection of how a transparent and rules-driven organization accounts and adjusts for new information as it becomes available, said Groshen and other former BLS officials in past interviews with CNN.

If it were to hold – and history has shown that the final revision is smaller – it would be the biggest downward revision on record (which go back to 1979), BLS data shows.

## What is the final benchmark revision expected to be?

Economists expect that the final adjustment could be a downward revision of 700,000 jobs.

This time last year, the final annual benchmarked figure for the 12 months ending in March 2024 was a negative 589,000 jobs seasonally adjusted (-598,000 not accounting for seasonality).

That’s significantly narrower than the preliminary estimate of -818,000 jobs, which still seems to stick in some minds, even though it was not the final figure.

The final tally of nearly negative 600,000 was the largest downward revision since March 2009 (which was previously the largest on record, at minus 902,000) and steeper than the downward revision of 489,000 jobs for the 12-month period ended March 2019 (during Trump’s first term).

By the way, for context, the adjustments amount to a sliver (tenths of a percentage point) of overall employment.

Still, such large swings, positive or negative, typically occur in times when the economy is suddenly changing in volumes that seemingly well-tuned models aren’t able to pick up as quickly.

The factors likely contributing to the upcoming downward revision include: declining survey response rates; the BLS’ modeling of business creation (called the birth-death model) being thrown out of whack by the pandemic and overestimating job gains; and immigration-related measurement gaps.

“This has been a half-decade with enormous changes to the economy – both the pandemic and the beginning and end of the immigration surge,” said Jed Kolko, an economist and former Under Secretary of Commerce for Economic Affairs during the Biden administration.

## And what are statistical model adjustments?

The benchmark revision – which ultimately affects 21 months of not-seasonally adjusted data from April of the prior year through the following year’s December – isn’t the only adjustment in this upcoming release.

Because the BLS establishment surveys existing employers, they miss businesses that just opened or closed. BLS crafted the “birth-death” model to capture these dynamics.

BLS has fine-tuned its birth-death model and will adjust past data produced under the prior model.

The agency also traditionally updates its seasonal adjustment models with each benchmark revision, resulting in the past five years of seasonally adjusted data being affected.

We’ll get a first look Wednesday at the state of the US job market as 2026 kicked off, as well as a clearer picture of hiring in 2025.

2026 US Job Market Report: What to Expect from the First Jobs Data of the Year

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.

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