CairnCareers vs Career Mirror.
Career Mirror describes itself as career intelligence for people who are growing, switching, or building. CairnCareers is narrower on purpose: students and recent graduates choosing a first path. The biggest difference is not the audience, though. It is whether you can see where the numbers came from.
| Feature | Career Mirror | CairnCareers |
|---|---|---|
| Built for | People who are growing, switching, or building, according to its homepage.5 | College students and recent graduates choosing a first path. |
| What it does | Helps you understand your skills, map your path, and navigate what comes next.5 | Scores each path you are weighing for AI exposure, shows pay and growth as context, and hands you a term-by-term roadmap toward a first job.3 |
| Where the numbers come from | We did not find a methodology page or a list of data sources on careermirror.ai on the date checked.5 | O*NET occupational task data, the Eloundou et al. (2024) exposure framework in Science, and METR's* capability trend. Every source is named and linked on the methodology page.1 |
| Can you check a number | Not that we could find. | Yes. Every number on the dashboard carries a numbered footnote with a source link, and pay and growth data are kept separate from the exposure score.1 |
| The plan | A mapped path.5 | A term-by-term roadmap, each term closing one evidence gap, plus resume, LinkedIn, network, and interview modules built from the same records.3 |
| Invented numbers | Not stated. | Never. The coach asks for the figure and shows where it belongs, and falls back to an honest scope description if you do not have one. |
| Price | See careermirror.ai for current plans.5 | Free tier. Pro at US$6 a month. Premium at US$11 a month.2 |
When Career Mirror is the better fit
- You are mid-career, switching fields, or building something, and you want a broad read on your skills rather than a first-job plan.
- You do not need to show anyone the source behind the result.
When CairnCareers is the better fit
- You are a student or recent graduate and the plan needs to fit an academic calendar.
- You want an AI-exposure score you can defend, with every source one click away.
- You want the record you enter once to show up as a resume bullet, a LinkedIn line, and an interview answer.
Does Career Mirror publish a methodology?
We did not find one. On September 21, 2026, we found no methodology page and no list of data sources on careermirror.ai.5 That does not mean the tool has none, only that a reader cannot check it from the public site.
It matters because a career score is a claim about your future. Without the sources, you cannot tell whether it came from task data, a survey, or a guess, and neither can the adviser or recruiter you repeat it to.
How is the AI-exposure score calculated?
It averages how exposed each task in an occupation is to current AI, weighted by how much of the work each task takes up, and puts the result on a 0 to 100 scale.1 The tasks come from O*NET. Each one gets an exposure value from the Eloundou et al. (2024) framework: 0 if today's AI cannot meaningfully do it, 0.5 if AI can do it with the right tools, and 1.0 if AI can do it on its own.1
A worked example: half of an occupation's work is a task AI can do on its own, a quarter is a task AI can do with tools, and a quarter is a task AI cannot touch. That gives (0.5 x 1.0) + (0.25 x 0.5) + (0.25 x 0) = 0.625, or about 63 out of 100.1
What does the AI-exposure score not tell you?
It does not predict that a job will disappear, and it does not predict anything about you.1 It measures which tasks current AI can do, which is one input to a career choice, not the whole choice.
It also cannot see your employer, your skill, your judgment, or the people you know.1 Pay and job growth from the U.S. Bureau of Labor Statistics are shown next to it and never move it, so a well-paid path can still score high on exposure.1
How can you tell whether a career score is worth trusting?
Ask four things of any score, ours included. Does it name its sources? Can you open them? Does it say what a value means? Does it say what it does not measure?
For CairnCareers the answers are on the methodology page. The sources are O*NET for tasks, Eloundou et al. (2024) in Science for exposure, and METR* for the capability trend, each linked. A task scores 0, 0.5, or 1.0 depending on whether current AI cannot do it, can do it with the right tools, or can do it alone. The page also lists what the score cannot see.1 For Career Mirror, we could not answer the first two questions from its public site on the date checked.5 If Career Mirror answers them for you directly, hold its score to the same four.
Why does CairnCareers not give a single future percentage?
Because nobody can honestly pin one down. Next to the current score, the methodology gives a direction of travel instead: rising, steady, or already at the ceiling.1
That direction comes from METR's* finding that the length of task an AI can complete with 50 percent reliability has been doubling roughly every seven months.1 The methodology uses that long-run trend rather than the faster 2026 estimates, because those come from a software task suite near saturation, carry very wide confidence intervals, and do not justify stretching across every occupation.1 A single figure for a named future year would claim a precision the research does not have.
Is CairnCareers useful if you are switching careers?
It is built for college students and recent graduates choosing a first path, so a mid-career switcher is not who it was designed around. The roadmap does work without a school calendar: month one starts the day you decide, an online course or workshop stands in for a class, and the breaks become catch-up weeks.3
The part that helps a switcher most is the exposure score. Matching the path you are considering to a standardized occupation and scoring its tasks works the same at any age or career stage.1 If what you want is a broad read on the skills you already have, Career Mirror describes itself as built for people who are growing, switching, or building, and it may be the closer fit.5
Why score occupations instead of job titles?
Because the same title can mean different work at different employers, and AI changes tasks, not titles. Every path you enter is matched to a standardized occupation first, and it is that occupation's task list that gets scored.1
The tasks are also weighted by how much of the working week each one takes up, so the tasks that fill most of the job move the number most.1 This is why two paths that sound alike can land far apart: the score follows the mix of work inside each occupation, not the name on the outside. You still see the plain occupation name and how close the match is, so you can tell when a path was matched loosely.1
Sources
- CairnCareers methodology page: the three sources behind the score (O*NET, Eloundou et al. 2024 in Science, METR) and the 0 to 100 scale.*
- CairnCareers pricing: Free, Pro at US$6 a month, Premium at US$11 a month, on the pricing section of the homepage.
- CairnCareers sample roadmap: the term-by-term plan, one evidence gap per term.
- Career Mirror homepage, as described there on the date checked, with no methodology or data-source page found on the site on that date.
Statements about Career Mirror describe its public pages on the date checked and may have changed since, so if something here is out of date, tell us and we will correct it.
* This projection uses METR's established long-run time-horizon trend, not the faster 2026 estimates. Those newer figures come from a task suite near saturation, carry very wide confidence intervals, and measure software tasks specifically, so extrapolating them across all occupations is not yet reliable. See MIT Technology Review, "This is the most misunderstood graph in AI," February 5, 2026, https://www.technologyreview.com/2026/02/05/1132254/this-is-the-most-misunderstood-graph-in-ai/