When It All Burns: Fighting Fire in a Transformed World
Jordan Thomas
2025 NATIONAL BOOK AWARD FINALIST FINALIST FOR THE LA TIMES BOOK PRIZE ONE OF PUBLISHERS WEEKLY'S TOP 10 BOOKS OF 2025 NAMED A BEST NONFICTION BOOK OF THE YEAR BY KIRKUS “Exceptional. . . . When It All Burns is one of those books that immerses the reader in the nuances of a world most of us know only through the lens of tragedy and destruction. Thomas’ visceral, crystalline prose only adds fuel to the fire.” —Los Angeles Times A gripping firsthand account of a record-setting fire season, from a cultural anthropologist who spent a year working as a hotshot firefighter, exploring the history and future of fire in America Eighteen of California’s largest wildfires on record have burned in the past two decades. Scientists recently invented the term “megafire” to describe wildfires that behave in ways that would have been nearly impossible just a generation ago, burning through winter, exploding in the night, and devastating landscapes historically impervious to incendiary destruction. In When It All Burns, wildland firefighter and anthropologist Jordan Thomas recounts a single, brutal six-month fire season with the Los Padres Hotshots—the special forces of America’s firefighters. Being a hotshot is among the most difficult jobs on earth. Thomas viscerally renders his crew’s attempts to battle flames that are often too destructive to contain.
Award History
0 wins · 3 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| Los Angeles Times Book Prize for Current InterestMajor | 2025 | Finalist | Official source |
| Los Angeles Times Book Prize for Science and TechnologyMajor | 2025 | Finalist | Official source |
| National Book Award for NonfictionMajor | 2025 | Finalist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
None extracted with sufficient confidence.
02 Central places
None extracted with sufficient confidence.
These suggestions did not meet the normal display threshold and are more likely to be wrong.
- California55%
- Los Padres Hotshots45%
03 Suggested argument
No argument inferred with sufficient confidence.
04 Reading orientation
30 / 100 academicSerious trade
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 62%.