Will AI replace Fiberglass Laminators and Fabricators?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fiberglass Laminators and Fabricators (United States, SOC 51-2051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 2 out of 100 (range 1–6, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Fiberglass Laminators and Fabricators” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Select precut fiberglass mats, cloth, and wood-bracing materials as required by projects being assembled” (13/100, minimal); “Check all dies, templates, and cutout patterns to be used in the manufacturing process to ensure that they conform to dimensional data, photographs, blueprin…” (13/100, minimal); “Release air bubbles and smooth seams, using rollers” (0/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Fiberglass Laminators and Fabricators” stay human?

About 100% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Trim cured materials by sawing them with diamond-impregnated cutoff wheels” (0/100, minimal); “Inspect, clean, and assemble molds before beginning work” (0/100, minimal); “Apply lacquers and waxes to mold surfaces to facilitate assembly and removal of laminated parts” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Fiberglass Laminators and Fabricators” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Fiberglass Laminators and Fabricators calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Fiberglass Laminators and Fabricators

releasing air bubbles and smooth seams, patting or pressing layers of saturated mat or cloth into place on molds and inspecting, cleaning and assembling molds before beginning work. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: releasing air bubbles and smooth seams is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 0%
staying human: 100%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 2 out of 100 (1–6 allowing for uncertainty): minimal exposure, across 16 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for fiberglass laminators and fabricators is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is the routine end of the work. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Laminate layers of fiberglass on molds to form boat decks and hulls, bodies for golf carts, automobiles, or other products. The job title says “fiberglass laminators” or “fabricators”: officially one job, two names. The real job is the part underneath: releasing air bubbles and smooth seams. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But fiberglass laminators and fabricators is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is releasing air bubbles and smooth seams, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 0%
staying human: 100%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

0 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Changing shape

0 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

16 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Releasing air bubbles and smooth seams
This work happens in the physical world: air bubbles, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Release air bubbles and smooth seams, using rollers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Rolling out air bubbles and smoothing seams is hand work on the part.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Mixing catalysts into resins and saturate cloth and mats with mixtures, using brushes
This work happens in the physical world: catalysts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Mix catalysts into resins, and saturate cloth and mats with mixtures, using brushes.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Mixing resin and wetting out cloth by brush is hands on.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Patting or pressing layers of saturated mat or cloth into place on molds
This work happens in the physical world: layers of saturated mat, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Pat or press layers of saturated mat or cloth into place on molds, using brushes or hands, and smooth out wrinkles and air bubbles with hands or squeegees.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Pressing wet mat into a mold and working out wrinkles is done by hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Checking completed products for conformance to specifications and for defects by measuring with rulers or micrometers
This work happens in the physical world: completed products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Check completed products for conformance to specifications and for defects by measuring with rulers or micrometers, by checking them visually, or by tapping them to detect bubbles or dead spots.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Measuring and tapping a finished part to find defects means handling it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Selecting precut fiberglass mats, cloth and wood-bracing materials as required by projects
This work happens in the physical world: precut fiberglass mats, cloth and wood-bracing materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Select precut fiberglass mats, cloth, and wood-bracing materials as required by projects being assembled.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Reading the job spec is easy for software, but picking the mats and bracing means being at the racks.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Bonding wood reinforcing strips to decks and cabin structures of watercraft
This work happens in the physical world: wood reinforcing strips, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Bond wood reinforcing strips to decks and cabin structures of watercraft, using resin-saturated fiberglass.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Bonding wood strips into a hull is physical assembly work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Spraying chopped fiberglass, resins and catalysts onto prepared molds or dies using pneumatic spray guns with chopper attachments
This work happens in the physical world: chopped fiberglass, resins and catalysts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Spray chopped fiberglass, resins, and catalysts onto prepared molds or dies using pneumatic spray guns with chopper attachments.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Spraying resin and chopped fiberglass onto a mold is physical work with a spray gun.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Inspecting, cleaning and assembling molds before beginning work
This work happens in the physical world: molds before beginning work, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect, clean, and assemble molds before beginning work.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning and assembling molds is hands on preparation.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Curing materials by letting them set at room temperature
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Cure materials by letting them set at room temperature, placing them under heat lamps, or baking them in ovens.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Curing parts means moving them to heat lamps or ovens.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Masking off mold areas not
This work happens in the physical world: mold areas not, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mask off mold areas not to be laminated, using cellophane, wax paper, masking tape, or special sprays containing mold-release substances.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Masking off areas of a mold is hand work with tape and film.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

Show the other 6 tasks

Checking all dies
staying human
This work happens in the physical world: all dies, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Check all dies, templates, and cutout patterns to be used in the manufacturing process to ensure that they conform to dimensional data, photographs, blueprints, samples, or customer specifications.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Comparing tooling against drawings is checking work, but it means measuring the dies and patterns themselves.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Trimming excess materials from molds
staying human
This work happens in the physical world: excess materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Trim excess materials from molds, using hand shears or trimming knives.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Trimming excess material with shears or a knife is hand work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Repairing or modifying damaged or defective glass-fiber parts
staying human
This work happens in the physical world: damaged, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Repair or modify damaged or defective glass-fiber parts, checking thicknesses, densities, and contours to ensure a close fit after repair.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Repairing a damaged fiberglass part is physical rework.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Applying layers of plastic resin to mold surfaces prior to placement of fiberglass mats
staying human
This work happens in the physical world: layers of plastic resin, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply layers of plastic resin to mold surfaces prior to placement of fiberglass mats, repeating layers until products have the desired thicknesses and plastics have jelled.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Laying resin onto mold surfaces layer by layer is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Applying lacquers and waxes to mold surfaces to facilitate assembly and removal of laminated parts
staying human
This work happens in the physical world: lacquers, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply lacquers and waxes to mold surfaces to facilitate assembly and removal of laminated parts.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Waxing and lacquering mold surfaces is done by hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Trimming cured materials by sawing them with diamond-impregnated cutoff wheels
staying human
This work happens in the physical world: cured materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Trim cured materials by sawing them with diamond-impregnated cutoff wheels.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Sawing cured material with a cutoff wheel is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $46,880a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 16,170in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on air bubbles, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: releasing air bubbles and smooth seams and mixing catalysts into resins. The parts that are changing are the paperwork and the tools around the job, not the middle of it, which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: releasing air bubbles and smooth seams is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 0% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 100% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. The routine end of the work is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to the routine work, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether air bubbles are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near releasing air bubbles and smooth seams. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to fiberglass laminators and fabricators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was paperhangers: only about 8% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 2/100 here, with only 0% of the task list in the top band, and “release air bubbles and smooth seams, using rollers” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Paperhangers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already release air bubbles and smooth seams, using rollers, and their equivalent is to smooth strips or sections of paper with brushes or rollers to remove wrinkles…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 1,570 of those jobs against 16,170 of yours (OEWS May 2025), 10% as many seats.
Look at that job’s page anyway →
Bakers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already cure materials by letting them set at room temperature, placing them under heat…, and their equivalent is to set oven temperatures, and place items into hot ovens for baking. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $37,160 against your $46,880, 20.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Molders, Shapers, and Casters, Except Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already trim excess materials from molds, using hand shears or trimming knives, and their equivalent is to trim or remove excess material, using scrapers, knives, or band saws. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like releasing air bubbles and smooth seams arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Plastics process operatives is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Plastics process operatives. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for fiberglass laminators / fabricators launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

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No Space for fiberglass laminators / fabricators yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for fiberglass laminators / fabricators existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for fiberglass laminators / fabricators launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Fiberglass Laminators and Fabricators?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fiberglass Laminators and Fabricators (United States, SOC 51-2051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 2 out of 100 (range 1–6, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Fiberglass Laminators and Fabricators” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Select precut fiberglass mats, cloth, and wood-bracing materials as required by projects being assembled” (13/100, minimal); “Check all dies, templates, and cutout patterns to be used in the manufacturing process to ensure that they conform to dimensional data, photographs, blueprin…” (13/100, minimal); “Release air bubbles and smooth seams, using rollers” (0/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Fiberglass Laminators and Fabricators” stay human?: About 100% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Trim cured materials by sawing them with diamond-impregnated cutoff wheels” (0/100, minimal); “Inspect, clean, and assemble molds before beginning work” (0/100, minimal); “Apply lacquers and waxes to mold surfaces to facilitate assembly and removal of laminated parts” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Fiberglass Laminators and Fabricators” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Fiberglass Laminators and Fabricators calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.