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needsahuman.

Will AI replace helpers–extraction workers?

Nah.

Nearly all of the day is hands-on work on unstable ground, lifting, rigging and signaling, which AI cannot physically take on. This job scores 87 out of 100 on (higher is safer). Today 100% of the work still needs a person.

Updated 3 October 2026 47-5081 8132 2026-Q4
Construction and ExtractionHelpers–Extraction Workers47-5081 · 2026-Q4
0% AI does it0% AI helps100% needs a human
Your job's name, lit by the work that still needs a human.Needs a human 100%AI helps 0%AI does it 0%

AI does it: AI can do the task largely by itself. AI helps: a person still does it, faster with AI. Needs a human: AI can do little of it yet.

Why this work stays on the drill site

Will AI replace helpers extraction workers in the next few years? The honest reading is that the job is held in place by its tasks, not by sentiment. Helpers spend the day moving drill pipe and supplies, clearing rock and debris from the work area, cleaning and oiling machinery, and signaling the operator who is running the rig. Software can plan that work. It cannot carry it.

The second reason is the setting. A quarry bench, a mine face or a well pad changes shape every shift. Mud, dust, vibration and weather break the neat assumptions machines rely on. A helper reads a slipping hose, a loose connection or a coworker’s hand signal without being told what to look for. That judgment sits in the same minute as the lifting, which is why the two cannot be split apart and handed to different systems.

Pay and headcount also shape the picture. The Bureau of Labor Statistics puts US employment for this occupation at about 6,700, with median pay near $47,730 and projected employment change of roughly 1.7% from 2025 to 2035 (BLS, 2025). That is a small, steady crew job. Nobody is building a specialized robot for a workforce that size when a general-purpose machine would still have to climb a muddy slope to earn its keep. You can see how the whole extraction workers family compares on the same measures.

What AI does, helps with, and leaves to people

Start with the needs-a-human group, because that is where this job lives. Our task list puts 100% of task time there, covering work like hauling and positioning tools and supplies, and setting up or dismantling equipment at the site. Both tasks demand grip, balance and a read of the ground underfoot.

No task on this job’s list sits in the AI-does-it group yet (0% of task time). That is unusual, and it is the main reason the headline number lands where it does. Our coverage figure, 2 out of 100, is the share of task time a model could handle today; the method behind it is set out on the coverage scoring page.

The AI-helps group is empty too (0% of task time). Help may still arrive around the edges of the shift, through scheduling, safety checklists and equipment monitoring run by the crew’s supervisors rather than by the helper. Nothing in the task list is scored as assisted work at this point.

What the evidence shows, and what it does not

There is no direct test of AI against people in this job. The evidence grade reads D, our lowest confidence level, which means parity has not been measured and no parity number should be given. Grades and what each one requires are explained in our methodology.

What would settle it is specific: a field trial of a mobile robot doing helper tasks on an active site, measured against a trained person on time, cost, safety incidents and task completion across a full shift in bad conditions. Lab demonstrations on flat floors would not count. Until something like that is published and repeated, the honest answer is that nobody has tested this.

Good to know: a missing test is not evidence of safety, it is evidence of absence, and the grade on this page says so plainly.

When this could change

Most likely after 2048 (8 in 10 of our scenarios). What that window measures, and how the spread is built, is explained on the replacement-year method page.

Two things could pull the date earlier. First, machine capability: the robotics profile on this page points to a dexterous humanoid tier, and if that hardware becomes reliable and cheap outdoors, the physical share of the job stops being a wall. Second, cost: the comparison above shows automation running cheaper per hour than labor on paper, so the moment capability arrives, the budget argument is already made. Our guide to humanoid robots and physical jobs covers how far that hardware has actually come.

Two things hold it back. Site conditions are the first: slope, mud, confined spaces and falling rock defeat machines that handle warehouse floors well. The second is safety and permitting. Mines and well sites run under strict rules, and a new machine on the face has to be proved to regulators and to the crew before it works a shift. Both brakes apply across the mining, oil and gas sector.

How to stay needed

Lean into the tasks a machine cannot copy. Rigging and equipment setup is the first, because it mixes load handling with judgment about what will hold. Hazard spotting is the second: loose ground, a frayed line, a leaking fitting. Working the signals and the radio with an operator is the third, and it is the part crews trust least to anything new.

Two skills raise the floor under all of that. One is a formal safety credential, such as site-specific mine safety training, which turns you into the person who can clear a job rather than just work it. The other is equipment competence: a blasting, drilling or heavy equipment ticket moves you from helper rates toward operator rates and keeps you useful when crews shrink.

Nearby jobs are worth a look before you commit. The closest step up is roustabouts, oil and gas, which uses the same site skills. Earth drillers, except oil and gas rewards the equipment ticket. Rock splitters, quarry stays on the hands-on side of the trade.

Set any two of them side by side on the job comparison tool, or see where hands-on trades land on the list of jobs that mostly need a person.

Frequently asked questions

What jobs will be gone by 2030 due to AI?

No whole occupation in our dataset is scored as gone by 2030. The clearer pattern is task erosion: routine writing, data entry, first-pass support and basic scheduling move first, and employers hire fewer juniors. Jobs built on physical work in unstable settings, like extraction helping, change far more slowly. The replacement-year chart above shows the dated range for this job rather than a single year.

What types of workers are most exposed to AI?

Exposure tracks tasks, not job titles. Work done entirely on a screen, with digital inputs and digital outputs, is the most exposed: drafting documents, sorting records, summarizing and routine coding. Work that needs hands, balance and on-site judgment is the least exposed. The task list on this page splits the job into those groups, so you can see which part of an extraction helper’s day falls where.

Which jobs will survive AI?

Rather than a fixed list, look at three things: how much of the work is physical, how unpredictable the setting is, and whether a person must be legally or practically accountable for the outcome. Skilled trades, hands-on care and supervisory site roles score well on all three. Our rankings let you check any occupation against the same measures instead of relying on a top-ten article.

Can robots do extraction helper work yet?

Not in the field. Mobile robots handle flat, clean, repeatable environments well, and mining already uses automated haulage and drilling on large fixed equipment. Helper work is different: it is varied lifting, rigging, cleaning and signaling on broken ground. The robotics section above shows the hardware tier that would be required, and that tier is not deployed at everyday cost on active sites.

What does AI already change in mining and drilling?

Mostly the planning and monitoring layers. Software schedules maintenance, flags equipment faults from sensor data, models ore bodies and tracks safety reporting. That shifts paperwork and some supervision, and it can reduce downtime. It does not reduce the need for a crew member to move pipe, clear debris or watch the face. The effect reaches extraction helpers through the supervisor, not the shovel.

How do I move up from a helper role?

Collect tickets. Site safety training, confined space, rigging and a machine certification each unlock better-paid work and make you harder to cut when crews shrink. Time on a specific rig or quarry also counts, because operators hire people who already know the ground. From there, operator and first-line supervisor roles are the usual next steps, and both have their own pages on this site.

Each ridge is a slice of the job's task time.Needs a human 100%AI helps 0%AI does it 0%
The job’s mark

No two jobs leave the same print

Every job gets its own fingerprint, drawn from its code. The amber ridges are the share of task time that still needs a person. Below them, the same ridges are written out in ones and zeros: slate for the work AI helps with, white for the work AI can do.

Helpers–Extraction Workers, O*NET-SOC 47-5081. 100% of the job’s task time still needs a human, so 100 of every 100 ridges are amber; slate is what AI helps with, white what AI can do.

What AI can and cannot do

The tasks that make up the job, from , and where AI stands on each today: , (a person does it, with AI speeding it up) or . 100% of the still needs a human.

Each block is one task; its height is its share of working time.Needs a human 100%AI helps 0%AI does it 0%
The job's task list: the parts AI can do are blacked out.Needs a human 100%AI helps 0%AI does it 0%
Observe and monitor equipment operation during the extraction process to detect any problems.Needs a human
Drive moving equipment to transport materials and parts to excavation sites.Needs a human
Unload materials, devices, and machine parts, using hand tools.Needs a human
Set up and adjust equipment used to excavate geological materials.Needs a human
Organize materials to prepare for use.Needs a human
Repair and maintain automotive and drilling equipment, using hand tools.Needs a human
Clean up work areas and remove debris after extraction activities are complete.Needs a human
Clean and prepare sites for excavation or boring.Needs a human
Load materials into well holes or into equipment, using hand tools.Needs a human
Provide assistance to extraction craft workers, such as earth drillers and derrick operators.Needs a human
Collect and examine geological matter, using hand tools and testing devices.Needs a human
Signal workers to start geological material extraction or boring.Needs a human
Dismantle extracting and boring equipment used for excavation, using hand tools.Needs a human
Dig trenches.Needs a human

Is it better than a person? The evidence

No direct test against people in this job yet. Every study is , and vendor studies are labelled as such.

When could it be replaced?

When AI could largely do this job: no sooner than 2048, most likely after 2060

Most likely after 2048 (8 in 10 of our scenarios). A range from our of how fast AI improves, how fast employers take it up and what holds it back, not a forecast that the job ends. “” has a strict meaning here. Today’s answer is at the top of the page; this is how it could change.

The sand is the human working years left, measured in the same 40-year glass for every job, so a safe trade starts nearly full and an exposed job with a thin layer.

The sand is the human working years left, in the same 40-year glass for every job.Years still needing a humanYears run out

How this job could shift, year by year

Where the job could sit on our scale each year to 2060, across the ten behind its .

Today
Will AI replace this job?
Nah.
By 2045
10%
of our scenarios have AI largely doing this job by 2045 (Largely.)
90% still have it mostly needing a person (A little. or Nah.)
By 2060
10%
of our scenarios have AI largely doing this job by 2060 (Largely.)
90% still have it mostly needing a person (A little. or Nah.)

We run this job as ten scenarios spread across its replacement range. In each, the score moves towards the bottom band (Largely: AI could largely do the job) by the year that scenario reaches it, slowly at first and faster later, as adoption usually goes. Each bar splits the ten by the band they put the job in. The model stops at 2060. How the timeline works

Share of this job's scenarios in each verdict band, today to 20600%25%50%75%100%2026: 100.0% of scenarios: this job mostly needs a person (Nah.)100%Today2030: 100.0% of scenarios: this job mostly needs a person (Nah.)100%20302035: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2035: 10.0% of scenarios: AI could do a little of this job (A little.)10%20352040: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2040: 10.0% of scenarios: AI could mostly do this job (Mostly.)10%20402045: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2045: 10.0% of scenarios: AI could largely do this job (Largely.)10%20452050: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2050: 10.0% of scenarios: AI could largely do this job (Largely.)10%20502055: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2055: 10.0% of scenarios: AI could largely do this job (Largely.)10%20552060: 90.0% of scenarios: this job mostly needs a person (Nah.)90%2060: 10.0% of scenarios: AI could largely do this job (Largely.)10%2060
Will AI replace the job?Largely.Mostly.Partly.A little.Nah.
Share of this job's scenarios in each band, year by year. Updated with every release.
Show the data
YearLargelyMostlyPartlyA littleNah
Today (2026)0.0%0.0%0.0%0.0%100.0%
20300.0%0.0%0.0%0.0%100.0%
20350.0%0.0%0.0%10.0%90.0%
20400.0%10.0%0.0%0.0%90.0%
204510.0%0.0%0.0%0.0%90.0%
205010.0%0.0%0.0%0.0%90.0%
205510.0%0.0%0.0%0.0%90.0%
206010.0%0.0%0.0%0.0%90.0%

What’s stopping AI taking over?

The things that keep this work with people, strongest first. Each is scored 0 to 100 from work context, licensing and the evidence we have.

LiabilityMistakes are rated 3.8 out of 5 for consequence and decisions 4.0 out of 5 for impact; someone has to answer for them.
Clients want a personFace-to-face contact is rated 4.8 and physical closeness 4.2 out of 5; caring for or serving people is 3.9 out of 5 in importance.
Evidence gapNo study yet compares AI with people doing this job, so employers have no proof it is good enough.
RegulationWorkers rate responsibility for others' health and safety 4.1 out of 5; the sector has its own rules on who may do the work.
Physical work93% of the task time is physical; robots have been shown on 78% of that time.
LicensingUsual entry requirement (BLS): high school diploma or equivalent, then moderate-term on-the-job training.

What would it cost to hand the work to AI?

The share of the year AI could handle (44 of 2,080 hours a year), priced two ways. Both are ranges, not quotes.

AI model usage, a year
$0–$440
A person’s wage for the same hours
$780–$1,430

AI cost covers model usage only: no integration, licences, oversight or the human time still needed to review the work. Human cost is the wage for the same hours, without benefits or overheads. As of 2026-10.

Robots and humanoids

AI software can only take the work at a screen. The rest needs a robot that can do it.

93%
of the task time is physical work
Dexterous humanoid
the kind of robot the physical work would need
Not commercial: no cited robot does most of this work; humanoids are at demonstration and pilot stage.

Source: Anthropic Economic Index, 'What work can robots do?' (30 September 2026); O*NET 31.0 task weights.

Which AI skills does this job lean on?

The job’s task time split by what an AI model would need to be good at, and where models stand today.

Each star is a task, grouped by the AI skill it leans on.Needs a human 100%AI helps 0%AI does it 0%
Writing · 0% of time
Strong
Drafts, edits and translates most routine documents at professional quality.
Analysis · 0% of time
Strong
Reliable on structured data and rules; uneven on judgement calls with thin information.
Coding · 0% of time
Strong
Agents complete many routine software tasks end to end; larger systems still need people.
Vision and design · 9.1% of time
Good
Reads documents, images and layouts well; specialist imaging needs dedicated, approved tools.
Speech · 0% of time
Good
Voice agents handle routine calls and live interpreting; complex or sensitive calls still go to people.
Planning and agents · 0% of time
Emerging
Multi-step agents work in narrow, well-tooled workflows; open-ended coordination is unreliable.
Physical manipulation · 90.9% of time
Early
Robots handle structured, repetitive handling; general dexterity outside fixed settings is not commercial.
Care and persuasion · 0% of time
Limited
Can script, coach and advise; trust, presence and accountability still need a person.
Amber matter holds its orbit, slate circles the inner disc, white falls in.Needs a human 100%AI helps 0%AI does it 0%
How exposed is it?

Still needs a human: 87/100↑ safer

The amber matter on the outside holds its orbit: that is the task time that still needs a person. The slate matter circling the inner disc is the work AI helps with. The white matter is the share AI can do; it spirals in and is gone over the edge.

The matter follows the job’s task split: 100% needs a human, 0% AI helps, 0% AI does it. Still needs a human: 87/100 ↑ safer. Will AI replace them? Nah.

What the AIs say

We asked four AI assistants the same question: will AI replace this job in the next 10 years? One word (Yes, Partly or No), then one sentence. Our own answer, from the data: Still needs a human: 87/100 ↑ safer. Will AI replace them? Nah.

ChatGPTPartly

AI and automation will reduce some routine/helper extraction work, but many roles will still require human judgment, physical adaptability, safety oversight, and on-site problem-solving.

gpt-5.5-2026-04-23 · asked 2026-10-03
ClaudePartly

AI and automation will likely take over many repetitive extraction and data-processing tasks, but roles requiring physical dexterity, judgment, or human interaction will still need human workers for the foreseeable future.

claude-sonnet-5 · asked 2026-10-03
GeminiPartly

While AI and automation will increasingly handle dangerous, repetitive tasks and machinery operation, human physical adaptability, manual dexterity, and unpredictable on-site problem-solving will still be required for many extraction support roles.

gemini-3.8-flash · asked 2026-10-03
PerplexityPartly

AI will automate some routine helper and extraction tasks, but most workers will remain needed for physical, unpredictable, and safety-critical work.

sonar · asked 2026-10-03

Assistants answer from what they learned in training (Perplexity also searches the web), so they can be confidently wrong, and the same question can get a different answer tomorrow. Our score is built from task data and graded evidence. Answers collected through DataForSEO.

Cite this page

NeedsAHuman.com (2026). Will AI replace Helpers–Extraction Workers? Nah. Still needs a human: 87/100, higher is safer; release 2026-Q4. https://needsahuman.com/jobs/helpers-extraction-workers/ (accessed 4 October 2026).

Scores change with each , so cite the release. The data is open under : credit NeedsAHuman.com with a link. Open data · Press

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The badge updates itself with each release and links back to this page.

Sources

  • Tasks and work context: 31.0, ().
  • Jobs, pay and projections: US , and 2025–35.
  • How AI is used today: ; Microsoft Research, .
  • What AI can do: our task ratings ( r1) and the quality evidence register.
  • UK names and employment: coding index and .

How each score is built: methodology. Every figure on this page: open data. Release 2026-Q4.