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Animal Scientists

studying nutritional requirements of animals and nutritive values of animal feed materials, advising producers about improved products and techniques that could enhance their animal production efforts and conducting research concerning animal nutrition. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: conducting research concerning animal nutrition is work software can't reach.

Your move: what you can actually do about this ↓

What shifts is studying nutritional requirements of animals and nutritive values of animal feed materials: the paper around the work, not the work.

Your week, as this page understands it

Conduct research in the genetics, nutrition, reproduction, growth, and development of domestic farm animals. The job title says “animal scientists”. The real job is the part underneath: conducting research concerning animal nutrition. 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 animal scientists is not one task. It is 9 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conducting research concerning animal nutrition, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
0%
changing shape
60%
staying human
40%

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

Whole-job exposure score 41 out of 100 (34–48 allowing for uncertainty): partial exposure, across 9 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 animal scientists 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.

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.

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

5 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.

  • Studying nutritional requirements of animals and nutritive values of animal feed materials

    The software now makes the first pass at nutritional requirements of animals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 5 · Core
    Source: “Study nutritional requirements of animals and nutritive values of animal feed materials.” (O*NET task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Feed composition and nutritional requirements are heavily documented, so software can pull together and compare the evidence well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Writing up or orally communicate research findings to the scientific community

    The software now makes the first pass at or orally communicate research findings, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Write up or orally communicate research findings to the scientific community, producers, and the public.” (O*NET task statement)
    How this row was scored

    Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Writing up results is something AI does well, though presenting to farmers and the public still benefits from a person.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Developing improved practices in feeding

    The software now makes the first pass at improved practices, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Develop improved practices in feeding, housing, sanitation, or parasite and disease control of animals.” (O*NET task statement)
    How this row was scored

    Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: AI can suggest known best practice, but improving it takes trials in real barns and herds.

    The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Studying effects of management practices

    The software now makes the first pass at effects of management practices, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Study effects of management practices, processing methods, feed, or environmental conditions on quality and quantity of animal products, such as eggs and milk.” (O*NET task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Comparing how practices affect milk or egg quality is data analysis software handles well once results are collected.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Determining genetic composition of animal populations and heritability of traits

    The software now makes the first pass at genetic composition of animal populations, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 3 · Supplemental
    Source: “Determine genetic composition of animal populations and heritability of traits, using principles of genetics.” (O*NET task statement)
    How this row was scored

    Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Genetics calculations on population data are exactly the kind of analysis software does well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

4 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.

  • Advising producers about improved products and techniques that could enhance their animal production efforts

    The value here is that a specific person handles producers and stands behind it. That is earned, not computed.

    importance 4 · Core
    Source: “Advise producers about improved products and techniques that could enhance their animal production efforts.” (O*NET task statement)
    How this row was scored

    Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: The advice can be drafted, but producers act on it because they trust an adviser who knows their operation.

    The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

  • Conducting research concerning animal nutrition

    This work happens in the physical world: research concerning animal nutrition, in a real place. Software cannot follow it there.

    importance 4 · Core
    Source: “Conduct research concerning animal nutrition, breeding, or management to improve products or processes.” (O*NET task statement)
    How this row was scored

    Exposure score: 29 out of 100 (22–36 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: The analysis and write-up are within AI’s reach, but the animals, feeding trials and measurements are real and physical.

    The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Researching and controlling animal selection and breeding practices to increase production efficiency and improving animal quality

    This work happens in the physical world: animal selection, in a real place. Software cannot follow it there.

    importance 4 · Core
    Source: “Research and control animal selection and breeding practices to increase production efficiency and improve animal quality.” (O*NET task statement)
    How this row was scored

    Exposure score: 29 out of 100 (22–36 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: Selection decisions can be modeled, but running a breeding program means working with real animals over years.

    The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Crossbreeding animals with existing strains or cross strains to obtain new combinations of desirable characteristics

    This work happens in the physical world: animals, in a real place. Software cannot follow it there.

    importance 3 · Supplemental
    Source: “Crossbreed animals with existing strains or cross strains to obtain new combinations of desirable characteristics.” (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: Crossbreeding animals is hands-on work with living animals.

    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
$68,940a 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
3,100in 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. Most of what is shifting here is reading one thing and writing another: nutritional requirements of animals in, a record out. The rows above are exactly that shape: studying nutritional requirements of animals and nutritive values of animal feed materials. What it cannot do is be there in the room, and that is still where research concerning animal nutrition gets done. 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: conducting research concerning animal nutrition 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, 60% in rows that change shape rather than disappear, and 40% in rows it is nowhere near. That is the position, measured across 9 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. Studying nutritional requirements of animals and nutritive values of animal feed materials 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 nutritional requirements of animals, 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 research concerning animal nutrition is 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 studying nutritional requirements of animals and nutritive values of animal feed materials. 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 animal scientists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was animal caretakers: only about 3% of its durable work is work you already do and it pays 48.7% less. Your own job splits about 0/100: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “study nutritional requirements of animals and nutritive values of animal feed materials”, and let the exposed end go.

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.

  • Animal Caretakers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already study nutritional requirements of animals and nutritive values of animal feed materials, and their equivalent is to feed and water animals according to schedules and feeding instructions. Across both published task lists that is about 3% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $35,360 against your $68,940, 48.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Biological Technicians

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already study nutritional requirements of animals and nutritive values of animal feed materials, and their equivalent is to feed livestock or laboratory animals. 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. It is a pay cut, in those words: $57,510 against your $68,940, 16.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Animal Control Workers

    Why it looked obvious: It came up as a near neighbour on the overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.

    Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. It is a pay cut, in those words: $45,660 against your $68,940, 33.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    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 9 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 conducting research concerning animal nutrition 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 Biological scientists 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:

In UK official statistics this job is counted as Biological scientists, Other researchers, unspecified discipline, Natural and social science professionals n.e.c. and Biochemists and biomedical scientists. 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.

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 animal scientists launches. Nothing else.

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

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for animal scientists 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 animal scientists existed, with researched problems, courses and people in the same boat, would you want in?

We use your email address for one thing: to tell you if a Space for animal scientists 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 Animal Scientists?
Not as a job, but it is already doing parts of the work. Across the 9 official task statements scored for Animal Scientists (United States, SOC 19-1011), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 41 out of 100 (range 34–48, band: partial). 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 “Animal Scientists” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Study nutritional requirements of animals and nutritive values of animal feed materials” (56/100, partial); “Study effects of management practices, processing methods, feed, or environmental conditions on quality and quantity of animal products, such as eggs and milk” (56/100, partial); “Determine genetic composition of animal populations and heritability of traits, using principles of genetics” (56/100, partial). 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 “Animal Scientists” stay human?
About 40% 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: “Crossbreed animals with existing strains or cross strains to obtain new combinations of desirable characteristics” (0/100, minimal); “Research and control animal selection and breeding practices to increase production efficiency and improve animal quality” (29/100, low); “Conduct research concerning animal nutrition, breeding, or management to improve products or processes” (29/100, low). 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 “Animal Scientists” do about AI?
Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 40% 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 Animal Scientists 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 9 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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Using these figures?

Cite this

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 . 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.