Will AI replace Ophthalmic Laboratory Technicians?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Ophthalmic Laboratory Technicians (United States, SOC 51-9083), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 3–9, 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 “Ophthalmic Laboratory Technicians” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Examine prescriptions, work orders, or broken or used eyeglasses to determine specifications for lenses, contact lenses, or other optical elements” (33/100, low); “Select lens blanks, molds, tools, and polishing or grinding wheels, according to production specifications” (19/100, minimal); “Inspect, weigh, and measure mounted or unmounted lenses after completion to verify alignment and conformance to specifications, using precision instruments” (10/100, 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 “Ophthalmic Laboratory Technicians” 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: “Control equipment that coats lenses to alter their reflective qualities” (0/100, minimal); “Immerse eyeglass frames in solutions to harden, soften, or dye frames” (0/100, minimal); “Lay out lenses and trace lens outlines on glass, using templates” (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 “Ophthalmic Laboratory Technicians” 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 Ophthalmic Laboratory Technicians 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 18 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

Ophthalmic Laboratory Technicians

mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting, mounting, securing and examining prescriptions. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting 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 5 out of 100 (3–9 allowing for uncertainty): minimal exposure, across 18 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 ophthalmic laboratory technicians 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: the paper around the work, not the work.

Your week, as this page understands it

Cut, grind, and polish eyeglasses, contact lenses, or other precision optical elements. Assemble and mount lenses into frames or process other optical elements. Includes precision lens polishers or grinders, centerer-edgers, and lens mounters. The job title says “ophthalmic laboratory technicians”. The real job is the part underneath: mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting. 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 ophthalmic laboratory technicians is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting, 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.

2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
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

18 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.

Mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting
This work happens in the physical world: lens blanks, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Mount and secure lens blanks or optical lenses in holding tools or chucks of cutting, polishing, grinding, or coating machines.” (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: Mounting blanks into machine chucks is manual setup.
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.
Examining prescriptions, work orders or broken or used eyeglasses to determine specifications for lenses, contact lenses or other optical elements
This work happens in the physical world: prescriptions, work orders or broken or used eyeglasses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Examine prescriptions, work orders, or broken or used eyeglasses to determine specifications for lenses, contact lenses, or other optical elements.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Turning a prescription into lens specifications is a documented calculation, though damaged glasses must be examined in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting lens blanks to detect flaws
This work happens in the physical world: lens blanks, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect lens blanks to detect flaws, verify smoothness of surface, and ensure thickness of coating on lenses.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging flaws and surface smoothness means holding the blank and looking at it under light.
The five ratings: output a model can produce 1/4 · 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.
Cleaning finished lenses and eyeglasses
This work happens in the physical world: finished lenses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Clean finished lenses and eyeglasses, using cloths and solvents.” (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 lenses with cloths and solvents is straightforward 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 2/4.
Shaping lenses appropriately so that they can be inserted into frames
This work happens in the physical world: lenses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Shape lenses appropriately so that they can be inserted into frames.” (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: Shaping a lens to fit a frame changes a physical object, which software cannot do.
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.
Mounting, securing and align finished lenses in frames or optical assemblies, using precision hand tools
This work happens in the physical world: align finished lenses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Mount, secure, and align finished lenses in frames or optical assemblies, using precision hand tools.” (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: Fitting and aligning lenses in frames with precision hand tools is entirely manual.
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.
Setting up machines
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set up machines to polish, bevel, edge, or grind lenses, flats, blanks, or other precision optical elements.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Setting up grinding and polishing machines means physically configuring them at the workbench.
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 3/4.
Adjusting lenses and frames to correct alignment
This work happens in the physical world: lenses, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Adjust lenses and frames to correct alignment.” (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: Bending frames and seating lenses back into alignment is done with fingers and pliers.
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.
Assembling eyeglass frames and attaching shields
This work happens in the physical world: eyeglass frames, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Assemble eyeglass frames and attach shields, nose pads, and temple pieces, using pliers, screwdrivers, and drills.” (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: Assembling frames with pliers, screwdrivers and drills is hand 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 2/4.
Inspecting, weighing
This work happens in the physical world: mounted, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect, weigh, and measure mounted or unmounted lenses after completion to verify alignment and conformance to specifications, using precision instruments.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Verifying a finished lens takes hands on precision instruments, even though the result is a pass-or-fail figure.
The five ratings: output a model can produce 1/4 · 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 3/4.

Show the other 8 tasks

Selecting lens blanks, molds, tools and polishing or grinding wheels, according to production specifications
staying human
This work happens in the physical world: lens blanks, molds, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Select lens blanks, molds, tools, and polishing or grinding wheels, according to production specifications.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Matching blanks, molds and wheels to a written specification is a lookup, but the parts still get picked off the shelf.
The five ratings: output a model can produce 3/4 · 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 3/4.
Setting dials and starting machines to polish lenses or hold lenses against rotating wheels to polish them manually
staying human
This work happens in the physical world: dials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set dials and start machines to polish lenses or hold lenses against rotating wheels to polish them manually.” (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: Setting dials and holding lenses against polishing wheels is bench work at the machine.
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.
Repairing broken parts, using precision hand tools and soldering irons
staying human
This work happens in the physical world: broken parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Repair broken parts, using precision hand tools and soldering irons.” (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 broken parts with hand tools and a soldering iron needs a person at the bench.
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.
Positioning and adjusting cutting tools to specified curvature
staying human
This work happens in the physical world: tools, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Position and adjust cutting tools to specified curvature, dimensions, and depth of cut.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Positioning cutting tools to a set curvature and depth means physically adjusting the machine.
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 3/4.
Removing lenses from molds and separate lenses in containers for further processing or storage
staying human
This work happens in the physical world: lenses, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Remove lenses from molds and separate lenses in containers for further processing or storage.” (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: Taking lenses out of molds and sorting them into containers is manual handling.
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.
Laying out lenses and trace lens outlines on glass
staying human
This work happens in the physical world: lenses, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Lay out lenses and trace lens outlines on glass, using templates.” (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: Tracing lens outlines onto glass with a template is done by hand on the material itself.
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.
Immersing eyeglass frames in solutions
staying human
This work happens in the physical world: eyeglass frames, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Immerse eyeglass frames in solutions to harden, soften, or dye frames.” (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: Dipping frames in hardening or dye baths is a physical handling job.
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.
Controlling equipment that coats lenses to alter their reflective qualities
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Control equipment that coats lenses to alter their reflective qualities.” (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: Running the coating equipment means loading lenses and working the machine on the shop floor.
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.

What this job pays, and how many people do it

Median pay: $39,460a 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: 18,660in 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 lens blanks, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting and examining prescriptions. 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: mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting 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 18 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 lens blanks 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 mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting. 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 ophthalmic laboratory technicians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was opticians, dispensing: only about 14% of its durable work is work you already do. And on the numbers you do not need one. This job scores 5/100 here, with only 0% of the task list in the top band, and “mount and secure lens blanks or optical lenses in holding tools or…” 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.

Opticians, Dispensing
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean finished lenses and eyeglasses, using cloths and solvents, and their equivalent is to assemble eyeglasses by cutting and edging lenses, and fitting the lenses into frames. Across both published task lists that is about 15% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 15% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Tool Grinders, Filers, and Sharpeners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already select lens blanks, molds, tools, and polishing or grinding wheels, according to production…, and their equivalent is to dress grinding wheels, according to specifications. 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.
Look at that job’s page anyway →
Computer Numerically Controlled Tool Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already mount, secure, and align finished lenses in frames or optical assemblies, using precision…, and their equivalent is to mount, install, align, and secure tools, attachments, fixtures, and workpieces on machines, using…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% 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 18 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 mounting and securing lens blanks or optical lenses in holding tools or chucks of cutting 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 Florists 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

The other groups this work is counted across:

Precision instrument makers and repairers

In UK official statistics this job is counted as Florists and Precision instrument makers and repairers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

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 ophthalmic laboratory technicians launches. Nothing else.

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No Space for ophthalmic laboratory technicians 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.

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Questions people ask about this job

Will AI replace Ophthalmic Laboratory Technicians?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Ophthalmic Laboratory Technicians (United States, SOC 51-9083), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 3–9, 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 “Ophthalmic Laboratory Technicians” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Examine prescriptions, work orders, or broken or used eyeglasses to determine specifications for lenses, contact lenses, or other optical elements” (33/100, low); “Select lens blanks, molds, tools, and polishing or grinding wheels, according to production specifications” (19/100, minimal); “Inspect, weigh, and measure mounted or unmounted lenses after completion to verify alignment and conformance to specifications, using precision instruments” (10/100, 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 “Ophthalmic Laboratory Technicians” 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: “Control equipment that coats lenses to alter their reflective qualities” (0/100, minimal); “Immerse eyeglass frames in solutions to harden, soften, or dye frames” (0/100, minimal); “Lay out lenses and trace lens outlines on glass, using templates” (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 “Ophthalmic Laboratory Technicians” 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 Ophthalmic Laboratory Technicians 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 18 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.
2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
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.