Laureate Education, Inc. (LAUR)
Universities in Mexico and Peru. Read October 2, 2026 from the annual report for the period ending December 31, 2025, filed February 19, 2026. Ranked 11 of the 31 Consumer Defensive companies read so far, by the share within reach now.
Within reach now
21%
End of 2028
50%
Employees, annual report
33,900
Leading staffing pattern
Colleges, Universities, and Professional Schools
Share of the working time within reach, long-run pace, four times in five
- Now21%
- End of 202741%
- End of 202850%
- End of 203058%
What changes for its people by the end of 2028, by its industries' figures
- Stays with people 47%
- Becomes checking a model's work 36%
- Handed over with lighter checks 10%
The filing states more than 33,900 employees, 18,340 of them academic staff, at five institutions in Mexico and Peru, so one college pattern carries the whole workforce (c).
The figures on this page come from the company's own annual report and from BLS staffing patterns for its industries, never from a roster. They are estimates with a stated method. Nothing here says what the company will do: within reach is not a forecast of jobs lost or of savings.
What follows from the reading
Share of working time within reach of AI, on the long-run pace at four in five: 21% now, 41% at the end of 2027, 50% at the end of 2028, 58% at the end of 2030. By the patterns, the work is about 59% desk work, 25% people work and 17% body work. These are the site's industry estimates weighted by the shares below: estimates with a stated method and range, not a forecast of what the company will do. By the same patterns, at the end of 2028 about 47% of the working time stays with people (conversation, in-person and hands-on work), 36% becomes checking a model's work and 10% could be handed over with lighter checks, from the industries' own figures, not the company's roles.
Headcount
33,900 employees as of December 31, 2025 (stated). The institutions are in Mexico and Peru; the filing states no count by country.
The filing: “Our students are supported by a workforce of more than 33,900 employees, including 18,340 academic staff.”
The workforce as industry patterns
| Pattern | Share | Reason, from the filing |
|---|---|---|
| Colleges, Universities, and Professional Schools | 100% | Five degree-granting institutions in Mexico and Peru with 18,340 academic staff among 33,900 employees (NAICS 6113); one pattern, applied abroad under rule 7 because the work is the same; by description (c). |
What the filing says about the workforce: More than 33,900 employees, including 18,340 academic staff; employees get an average of about 80% of tuition paid at the company's institutions.
What the filing says about AI
16 sentences in the filing name AI. The filing names data science and AI among the operational best practices shared across its institutions, and competitors' use of artificial intelligence in online education among its risks.
- “Some of the factors that we believe could affect our results include: the risks associated with operating our portfolio of degree-granting higher education institutions in Mexico and Peru, including complex business, political, legal, regulatory, tax and economic risks; our ability to maintain and, subsequently, increase tuition rates and student enrollments in our institutions; our ability to...”
- “Through collaboration across our institutions, best practices for key operational processes, such as digital marketing, data science/AI, scheduling, retention management, market research, campus design, faculty training, student services and recruitment, are identified and then rolled out to all of our institutions.”
- “In addition, our efforts may be materially adversely affected by increased competition in the online education market and our competitors' increasing use of artificial intelligence ("AI") and machine learning or because of problems with the performance or reliability of our online program infrastructure.”
- “We continually seek to maintain and improve the content of our existing academic programs and develop new programs in order to meet changing market needs, including through the use of AI and machine learning.”
- “Even if our institutions are able to develop acceptable new programs and adapt to new technologies (such as AI and machine learning), our institutions may not be able to begin offering those new programs and technologies as quickly as required by prospective students and employers or as quickly as our competitors begin offering similar programs.”
Its annual reports, year by year
Sentences naming AI in each annual report, counted by one rule, beside the median report in the Index that year. Each count links its filing. Every company, by year and sector
- Fiscal 20211median 0
- Fiscal 20221median 0
- Fiscal 20231median 1
- Fiscal 202416median 4
- Fiscal 202516median 10
Headcount by filing year
As reported in each year's annual report, with the filing. A year marked not read had no figure the reading could take from the report's text.
| Period | Employees | Filed | Filing |
|---|---|---|---|
| 2025-12-31 | 33,900 | 2026-02-19 | 10-K |
| 2024-12-31 | 31,800 | 2025-02-20 | 10-K |
| 2023-12-31 | 28,900 | 2024-02-22 | 10-K |
| 2022-12-31 | 35,000 | 2023-02-23 | 10-K |
| 2021-12-31 | 1,800 | 2022-02-24 | 10-K |
| 2020-12-31 | 3,000 | 2021-02-25 | 10-K |
| 2019-12-31 | 50,000 | 2020-02-27 | 10-K |
| 2018-12-31 | 60,000 | 2019-02-28 | 10-K |
| 2017-12-31 | 9,000 | 2018-03-20 | 10-K |
| 2016-12-31 | 9,000 | 2017-03-29 | 10-K |
| 2006-12-31 | 23,000 | 2007-03-01 | 10-K |
| 2005-12-31 | 22,800 | 2006-03-16 | 10-K |
| 2004-12-31 | 17,534 | 2005-03-11 | 10-K |
| 2003-12-31 | 13,374 | 2004-03-15 | 10-K |
| 2002-12-31 | 16,200 | 2003-03-31 | 10-K |
| 2001-12-31 | 13,300 | 2002-03-28 | 10-K |
| 2000-12-31 | 14,200 | 2001-04-02 | 10-K |
| 1999-12-31 | 7,437 | 2000-03-29 | 10-K |
| 1998-12-31 | 6,300 | 1999-03-31 | 10-K |
| 1997-12-31 | 3,600 | 1998-03-31 | 10-K |
| 1996-12-31 | 2,850 | 1997-03-31 | 10-K |
Limits of this reading
- The workforce is in Mexico and Peru; the U.S. staffing pattern for colleges is applied abroad under rule 7.
- One pattern by description (c); the academic staff count is recorded in the notes.
Source: the company's annual report on EDGAR, accession 0001628280-26-009479, the filing. Read by Claude Fable 5.1 under rubric version 1, by the method. A company can write to info@stratussc.com to have its reading checked against its filing.
Questions about this company
- How much of Laureate Education, Inc.'s working time is within reach of AI?
- An estimated 21% now and 50% by the end of 2028, on METR's long-run pace at four in five, from the workforce Laureate Education, Inc.'s own annual report describes and BLS staffing patterns for its industries, never from a roster. It is an estimate with a stated method. It is not a forecast of jobs lost or of savings, and it says nothing about what the company will do.
- How many people does Laureate Education, Inc. employ?
- 33,900 as of December 31, 2025, as its annual report for the period ending December 31, 2025 states.
- What kinds of work make up Laureate Education, Inc.'s workforce?
- Read from its annual report as a blend of 1 industry staffing pattern, the largest Colleges, Universities, and Professional Schools; by those patterns the work is about 59% desk work, 25% people work and 17% body work. Each share has a one-sentence reason on this page that can be checked against the filing.
- What does Laureate Education, Inc.'s annual report say about AI?
- 16 sentences in the filing name AI. Its annual reports for fiscal 2021 to 2025 name it in 1, 1, 1, 16 and 16 sentences, year by year. They are counted and quoted on this page, never judged: a count says how much a filing talks about AI, not what the company does with it.
- How is this estimated?
- Claude Fable 5.1 read the annual report under the Index's written rubric (version 1) on October 2, 2026: the stated headcount, the share in the U.S. when stated, and the workforce as a blend of industry staffing patterns. The estimates are the site's industry estimates weighted by those shares, from O*NET tasks with estimated lengths and METR's measurements of how long a task AI models can finish. A company can write to info@stratussc.com to have its reading checked against its filing.
Embed these figures on your site
A small card with the share of working time within reach now and at the end of 2027, 2028 and 2030, the date and a link back. Free under CC BY 4.0; the card carries the credit.