Operate pumps connected to high-pressure hoses.[2]
Where your edge is
Time Machine
Firefighters
Scrub through 300 years of this role's history — from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
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2026
Known today as Firefighters (BLS SOC 33-2011)
US Employment
333K
BLS OEWS May 2023 estimate for SOC 33-2011: approximately 332,800 career firefighters. The median annual wage was approximately $56,780. The steady growth trajectory since 2019 reflects sustained EMS call volume and continued wildland fire response expansion. NFPA 2023 data documents approximately 364,000 career firefighters in NFPA survey respondents (including some categories BLS may classify differently); the BLS figure is used as the authoritative OEWS-consistent number.
Median Annual Wage
$56,780
Source: BLS-OEWS
Tool of the era · AI thermal overlays + wildland drones (Skydio/DJI/Parrot) + IoT fire detection + AI dispatch
The 2017-2018 California wildfire seasons — Tubbs (October 2017, 36 deaths, 5,643 structures), Camp Fire (November 2018, 85 deaths, the deadliest in California history, 18,804 structures) — put wildland firefighting and fire-adjacent technology on an accelerated adoption track. The core challenge of wildland fire: situational awareness over thousands of acres of terrain, with fire behavior changing by the hour and firefighter safety requiring real-time intelligence about fire position and progression. Drones solved part of this. The FAA's 2016 Part 107 rules enabled commercial and public-safety drone operations; CAL FIRE and US Forest Service began integrating fixed-wing and rotary-wing unmanned aerial systems into incident management. By 2022, multiple wildland fire agencies were deploying DJI Matrice and Parrot Anafi thermal drones for active fire scouting — launching a drone from the IC (incident command) post to map fire perimeter and spot hotspots in real time without risking a scout's life. Skydio's autonomous drone systems (Skydio X2, Skydio X10) capable of obstacle-avoiding autonomous flight in complex terrain entered CAL FIRE and municipal fire department evaluation programs. The thermal overlay — AI software processing the drone's thermal camera feed to auto-highlight active fire zones and predict spread — moved from research prototype to operational tool by 2022-2023. In commercial and high-rise buildings, the IoT and SCADA (Supervisory Control and Data Acquisition) layer had transformed fire detection. Modern buildings have addressable fire alarm systems that report individual sensor location to the fire alarm control panel (and increasingly to a cloud dashboard), sprinkler systems with pressure monitoring, and smoke control systems integrated with building management platforms. By the time a fire company arrives at a commercial high-rise, the building system can tell dispatch which floor the alarm originated on, whether the elevator lobby smoke detectors have triggered, and whether the sprinkler flow alarms indicate activation. AI-enhanced dispatch systems (from vendors including Priority Dispatch and Motorola Solutions) began incorporating predictive resource allocation — routing the right number and type of apparatus for an alarm type based on historical response data. For structure fires, AI thermal cameras mounted on apparatus or worn on helmets (Bullard FX1, 2017; FLIR K-series) moved from monochrome heat maps to color-coded AI overlays that highlight victim silhouettes, classify heat severity by zone, and alert to rapid temperature increases indicating flashover risk — the sudden transition from smoldering compartment fire to an engulfed room that kills firefighters.
Drone and AI tools augment situational awareness without eliminating the need for ground crews — wildland fire suppression still requires hand crews with drip torches for prescribed burn, hose lays, and direct attack. The technology extends what each crew can safely know about fire behavior; it does not replace the crew. In dispatch, AI routing optimization can improve efficiency of resource deployment without reducing the staffing levels required for a working fire. The net effect is augmentation within a labor model that physical life-safety requirements keep human-anchored.
Projection cone · present → 2034
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread you see below is the range of futures credible sources project for this role.
Climate-change wildfire demand scenario
→ 2034
+10%
USDA Forest Service and CAL FIRE have both significantly expanded their permanent and seasonal firefighting workforces in response to the intensification of wildfire seasons since 2017. The USFS employed approximately 11,000 firefighters in 2021 and announced plans to add 1,000 permanent positions in 2022 under the Bipartisan Infrastructure Law's $600 million in wildland firefighter workforce funding. CAL FIRE's 2022-23 budget included $2.3 billion for fire suppression and emergency response — the largest in state history. If wildfire seasons continue to intensify through the 2030s as climate projections suggest (NOAA and IPCC both project increasing fire weather conditions in western North America), sustained expansion of the permanent wildland and WUI firefighter workforce would push total SOC 33-2011 employment substantially above the BLS baseline +4%. This optimistic-tail scenario projects +10% from 2023 baseline if federal and state wildland expansion accelerates as projected.
BLS Occupational Outlook 2023–33
→ 2033
+4%
BLS Employment Projections 2023-33 cycle. Published employment change for firefighters (SOC 33-2011): approximately +4%, described as about as fast as the average for all occupations. BLS cites continued population growth and community need for fire and emergency response services, including the growing demand for wildland firefighting in the expanding wildland-urban interface (WUI). The +4% figure is the BLS central estimate; approximately 17,200 annual openings are projected, with most openings resulting from replacement need (retirements, departures) rather than net new positions.
Eloundou et al. — 'GPTs are GPTs' (2023)
→ 2028
-2%
GPT-4 task-by-task LLM exposure labeling on O*NET tasks. Firefighters have very low LLM exposure because the core tasks — fire suppression, victim rescue, emergency medical response, operating apparatus, conducting building searches — are physical tasks an LLM cannot perform. The estimated -2% represents the conservative ceiling of LLM substitution: AI dispatch routing and report-writing tools (analogous to Axon Draft One for police) could marginally reduce administrative burden, but the hands-on emergency response function has no LLM-addressable component. Eloundou et al. classify firefighters as very low exposure, consistent with F&O's physical-presence bottleneck analysis.
Municipal fiscal stress scenario
→ 2030
-3%
Municipal fire departments — which employ the majority of career firefighters — are funded through property tax and general fund revenues. Municipal fiscal stress from rising pension obligations, declining assessed values in some regions, or federal fiscal austerity has historically translated into fire department staffing cuts in smaller and mid-sized cities. The 2008-09 recession produced documented layoffs in some departments. This scenario models a modest headcount contraction if municipal fiscal pressure exceeds the EMS and fire demand growth, particularly in declining-population Rust Belt and mid-sized cities that face both fiscal strain and stagnant call volume. It is the pessimistic tail of the uncertainty cone, not the central projection.
Frey & Osborne (2013)
→ 2033
-5%
Gaussian-process classifier on O*NET task features. Frey & Osborne assigned Firefighters a probability of computerization of approximately 0.17 — placing them in the lowest third of the 702-occupation dataset, i.e., low automation risk. The bottleneck factors F&O identified: high manual dexterity and physical strength requirements, 'cramped work positions,' high social intelligence requirements (public emergency response, victim interaction), and the irreducibly embodied nature of fire suppression and rescue. The -5% figure represents a conservative displacement estimate if the F&O probability were partially realized through AI dispatch optimization and drone-assisted reconnaissance reducing some first-responder runs. F&O did not predict this magnitude; the figure represents a pessimistic interpretation of their low risk score over a decade. In practice, BLS projects growth, not decline.
Today, in this role
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting — and what stays yours.
Assess fires and situations and report conditions to superiors to receive instructions, using two-way radios.[2]
Where your edge is
Collaborate with police to respond to accidents, disasters, and arson investigation calls.[2]
Where your edge is
Present-day sources
Sources
Every claim on this page traces back to one of the following. Updated 2026-05-30.
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