Table of contents
ARTIFICIAL INTELLIGENCE & SOCIETY
PART 9
Software on a computer screen can draft a contract, diagnose an image, or write computer code in seconds.
It cannot pour concrete. It cannot frame a two-story roof in the wind, run copper pipe through a cramped basement, replace a worn heart valve, or haul forty tons of freight across the country in a snowstorm.
For years, that physical boundary was where people drew the line on automation. White-collar workers worried about chatbots, while people in skilled trades and physical transport felt safe behind the wall of real-world friction. The conventional wisdom was simple: machines could handle predictable calculations, but human hands and judgment would always own the messy, physical world.
That assumption is dissolving in front of us.
In What If Most of Us Don’t Have to Work Anymore?, I explored what happens when software and automation make standard forty-hour employment optional for human survival. In Who Gets Rich When AI Does the Work?, we looked at the economic plumbing behind that shift, showing why owning the machines, computing power, and energy sources matters far more than just collecting a monthly relief check.
This piece provides the physical engine behind those two arguments.
A post-work world cannot exist as digital code alone. It requires embodied intelligence: physical machines equipped with sensors, actuators, spatial models, and adaptive decision-making that can manipulate real matter in real time. From surgical suites to interstate highway corridors, automated factories, and residential construction sites, the mechanical hands are catching up to the software minds.
Understanding how that transition is happening is the only way to govern it with clear eyes. Even more important, it gives the next generation a roadmap for what to learn and how to help build the future rather than simply fear it.
1. High-precision medicine: From surgeon-guided tools to autonomous procedures
Robotics entered the operating room decades ago, but for most of that history, the machine was an expensive mechanical extension of human fingers. Platforms like the da Vinci system allowed surgeons to sit at a console and control miniature wristed instruments with enhanced vision and tremor filtration. The surgeon’s hands still drove every cut, stitch, and suture.
That division of labor is moving into autonomous execution.
Researchers at Johns Hopkins University demonstrated this leap with the Smart Tissue Autonomous Robot (STAR). In peer-reviewed trials published in Science Robotics, STAR successfully performed laparoscopic bowel anastomosis, reconnecting two ends of an intestine on soft tissue with minimal human intervention.[1] Soft tissue is notoriously unpredictable; it stretches, slips, and bleeds. Yet the robotic system placed sutures with greater consistency, regular spacing, and fewer leaks than experienced human surgeons operating the same tools manually.[1]
In early 2024, the U.S. Food and Drug Administration cleared Intuitive Surgical’s fifth-generation platform, the da Vinci 5, which incorporates over ten thousand times the computing power of its predecessor along with force-sensing technology and real-time operational feedback.[2] Meanwhile, specialized robotic systems from companies like Monogram Orthopedics and Think Surgical are already executing autonomous bone cuts in knee and hip arthroplasty with sub-millimeter precision planned directly from patient CT scans.[3]
The shift here is not about replacing human compassion or diagnostic clinical bedside judgment. It is about acknowledging that a robotic actuator guided by multi-spectrum computer vision and sub-millimeter position tracking does not experience hand fatigue after seven hours on its feet. It does not lose focus, and its hands do not shake.
When surgical precision becomes reproducible software and mechanical execution, the availability of top-tier surgical care stops being limited by the physical stamina and geographical location of a handful of elite specialists.
Robotics is only the physical hands of this revolution; advanced AI models are becoming the minds that drive scientific discovery itself. Beyond operating rooms, foundation models in structural biology, such as AlphaFold and generative molecular chemistry engines, are already designing de novo proteins, predicting complex molecular interactions, and automating the formulation of targeted therapeutics in days rather than decades. When robotic surgical precision combines with autonomous disease discovery and self-driving laboratories, the entire medical paradigm shifts from managing chronic decline to rapidly curing previously incurable conditions.
The education calculation: When four to ten years of training is eclipsed by software
There is a profound educational consequence here that very few universities or graduate programs are addressing with incoming students.
Becoming a licensed orthopedic surgeon or general surgeon requires four years of undergraduate education, four years of medical school, and between five and seven years of residency and fellowship. That is thirteen to fifteen years of intense, highly specialized human conditioning, often accompanied by hundreds of thousands of dollars in student debt.
Yet surgery is only the physical tip of a much larger professional iceberg.
When advanced multimodal AI models combine with robotic precision, they do not merely challenge manual trade work. They directly dismantle the economic moat around professions that used to require years or decades of academic memorization and procedural credentialing:
- The Legal Profession: Large language models and legal reasoning engines already perform comprehensive case-law discovery, contract redlining, brief generation, and statutory analysis in minutes. In standardized academic benchmarks, legal AI platforms regularly pass the Uniform Bar Examination in the top decile, outscoring the vast majority of human law school graduates.[4] A student enrolling in a three-year JD program today will enter a market where paralegal research, document review, and standard contract drafting are essentially free software utilities.
- The Financial and Quantitative Sector: Financial analysts, wealth managers, and tax accountants spend years mastering regulatory codes, financial modeling, and portfolio rebalancing. Advanced AI engines analyze corporate filings, cross-examine balance sheets, identify tax optimization paths, and execute real-time quantitative strategies with speed and compliance tracking that no human team can match.[5]
- Diagnostic and Specialized Medicine: Radiologists and pathologists spend a decade training their visual pattern recognition to spot tumors in CT scans, MRIs, and biopsy slides. FDA-cleared AI algorithms now regularly match or exceed board-certified specialists in early-stage tumor detection, lesion segmentation, and differential diagnosis across millions of training images.[6]
flowchart TD
A["1. Student Enters 4–8 Year Degree Program"] --> B["2. Curriculum Teaches Legacy Procedural Tasks"]
B --> C["3. Model Capabilities Scale 10x–50x During Enrollment"]
C --> D["4. Graduate Enters Market Where Core Tasks Are Automated"]
style A fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style B fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style C fill:#fdf5e6,stroke:#b89758,stroke-width:2px,color:#222
style D fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
If a student is enrolling in higher education today, planning a major based on what paid well over the last twenty years is a recipe for dislocation. By the time a freshman completes an undergraduate degree and a multi-year professional program, the procedural, rote analytical, and standardized physical tasks that defined those high-paying careers will be handled by autonomous systems.
Higher education and professional licensing boards must reckon with this reality. Students must evaluate every course of study not by how much memorization or rigid procedure it requires, but by whether it teaches them to steer, audit, integrate, and govern the intelligent machines that execute those procedures.
2. Autonomous freight and the transit backbone
Look at the highway system and you see the economic bloodstream of the continent. Over seventy percent of all domestic freight in the United States moves by commercial truck.[7] For decades, long-haul trucking has been characterized by chronic labor shortages, grueling schedules, and severe health costs for drivers who spend weeks away from home living in truck cabs.
That logistics backbone is rapidly becoming automated infrastructure.
Commercial autonomous vehicle companies have already logged tens of millions of commercial rider-only miles on public roads. Waymo’s commercial autonomous ride-hailing service now operates hundreds of thousands of paid passenger trips every week across major metropolitan markets including Phoenix, San Francisco, Los Angeles, and Austin.[8] Data published by Waymo and evaluated in independent safety studies shows an 85 percent reduction in injury-causing crashes compared to human driver benchmarks across identical operating environments.[9]
The natural extension of city ride-hailing is long-haul, Class-8 freight. Companies like Aurora Innovation, Kodiak Robotics, and Gatik have shifted from test tracks to commercial freight routes across Texas and the Southwest logistics corridor.[10] Aurora has deployed its autonomous trucking system on commercial lanes between Dallas, Houston, and Fort Worth, hauling freight for major logistics carriers without a safety driver in the cab.[11]
Consider the mechanical realities:
- Continuous operation: An automated truck is not bound by federal hours-of-service limitations that mandate ten hours of rest for every eleven hours of driving. It can operate twenty hours a day, stopping only for fuel, charging, and cargo transfer.
- Sensor redundancy: Autonomous freight cabs combine long-range lidar, radar, high-resolution cameras, and thermal imaging that can see hundreds of meters ahead in dense fog, heavy rain, or complete darkness.
- Predictable routing: While city streets present chaotic edge cases, interstates are structured, high-speed, controlled-access environments, the exact terrain where automated systems excel.
As autonomous trucking networks expand from regional hubs to cross-country freight lines, moving goods from coast to coast will shift from an industry reliant on human driving hours into an automated utility, like electricity or municipal water pipelines.
3. Adaptive manufacturing: Beyond the bolted-down robotic arm
Industrial robots have assembled cars and welded steel frames since the 1970s. But those older machines were fundamentally blind and rigid. They repeated identical geometric paths programmed down to the millimeter. If a part arrived two inches out of alignment or upside down in a bin, the machine failed or caused a collision.
Modern manufacturing robotics is shedding that rigidity through foundation vision-language-action (VLA) models and spatial intelligence.
According to the International Federation of Robotics (IFR) World Robotics Report, the global operational stock of industrial robots reached an all-time record of over 4.2 million units, with annual installations accelerating across automotive, electronics, and metal fabrication.[12]
The difference today is adaptability:
- Bin picking and unstructured handling: Rather than requiring custom, multi-million-dollar sorting feeder bowls for every unique screw and bracket, modern robotic arms use 3D vision and neural networks to identify, orient, and manipulate randomly piled, mixed parts out of a shipping crate.
- Collaborative robots (cobots): Platforms from companies like Universal Robots and FANUC operate alongside humans without massive steel safety cages. They monitor their surrounding workspace with depth sensors and instantly adjust their speed or halt movement when a human worker steps near.
- Multi-task tooling: A single robotic cell can switch between precision laser welding, fastener installation, sealant application, and optical quality inspection simply by loading new software policies, without requiring weeks of physical retooling.
When factories no longer require rigid, inflexible tooling for every minor product revision, the cost barrier to producing complex goods drops precipitously.
4. Humanoid robotics and the skilled physical trades
The final frontier of physical labor has always been the unstructured environment: the residential home, the commercial renovation, the muddy construction site, and the crawl space.
Traditional industrial robots cannot climb a ladder, maneuver through an unfinished doorway, or step over a pile of framing lumber. That physical limitation is why companies are investing billions into general-purpose humanoid robots.
Human infrastructure is designed for human bodies. Doors, stairs, tool handles, vehicle pedals, and electrical switches were all engineered to fit human dimensions and two-handed dexterity. A robot with a bipedal chassis and five-fingered hands does not require a factory or a building site to be redesigned for it; it steps directly into the environment as built.
The advancements across this category in recent years are striking:
- Figure AI: Deploying its humanoid robots (Figure 02) into production facilities, including commercial manufacturing lines at BMW’s Spartanburg plant, handling sheet metal manipulation and component placement with autonomous vision-guided dexterity.[13]
- Boston Dynamics: Retired its hydraulic research platform and introduced the fully electric Atlas, designed for commercial mass production with continuous 360-degree joint rotation that exceeds human range of motion.[14]
- Tesla Optimus & Apptronik Apollo: Scaling bipedal systems trained on massive end-to-end neural network datasets, moving from teleoperation demos to autonomous sorting, battery cell insertion, and job-site material transport.[15]
Consider what happens when these platforms combine spatial foundation models with tactile hands:
flowchart TD
A["<b>1. Structured Environments</b><br/>Fixed Robotic Arms in Controlled Factories<br/><i>(Automotive welding, electronics assembly)</i>"] --> B["<b>2. Semi-Structured Logistics</b><br/>Autonomous Freight & Wheeled Mobiles<br/><i>(Interstate trucking, warehouse transport)</i>"]
B --> C["<b>3. Unstructured Environments</b><br/>Humanoid Platforms in Skilled Trades<br/><i>(Framing, rough plumbing, electrical staging)</i>"]
style A fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style B fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style C fill:#fdf5e6,stroke:#b89758,stroke-width:2px,color:#222
Carpentry, plumbing, and electrical rough-in work are complex, but they follow structural codes, engineering prints, and repeatable physical mechanics. As multimodal AI models learn to interpret three-dimensional building blueprints and translate them into physical motor commands, humanoid machines will increasingly handle the heavy lifting, high-altitude framing, trench digging, and repetitive installation that break human backs by age fifty.
5. Liberation, not degradation: The dignity of ending dangerous toil
When automation reaches physical trades, public discussion often defaults to panic over lost jobs. That anxiety is understandable in an economic system where survival requires selling your labor.
Yet we should be honest about what much of that physical labor actually costs the human beings who perform it.
Physical work can provide pride and craftsmanship, but manual labor at scale is often brutal on the human body. Data from the U.S. Bureau of Labor Statistics and OSHA reveals the harsh reality:
- Fatalities: The construction and transportation sectors account for nearly half of all workplace fatalities in the United States every year, driven by falls, vehicle collisions, equipment strikes, and trench collapses.[16]
- Disabling injuries: Over 28 percent of all nonfatal occupational injuries involving days away from work are musculoskeletal disorders, chronic back injuries, torn rotator cuffs, destroyed knees, and repetitive strain trauma that leave workers physically compromised for the rest of their lives.[17]
- Toxic exposure: Roofers, welders, painters, and miners regularly breathe toxic fumes, silica dust, and chemical vapors that cause chronic respiratory illness and cancer decades down the line.
| Physical Toll Category | Occupational Reality (BLS / OSHA) | Human Impact by Age 45–55 |
|---|---|---|
| High-Risk Fatalities | Transit collisions, roofing falls, trench collapses | Nearly half of all annual U.S. workplace deaths[16] |
| Disabling Trauma | Heavy lifting, repetitive strain, awkward torque | Torn rotators, destroyed knees, chronic back failure (28%+ injury share)[17] |
| Toxic Exposure | Silica dust, welding fumes, chemical vapors | Long-term chronic respiratory illness and systemic damage |
| Shift Exhaustion | 12–16 hour shifts, extended road transit | Sleep deprivation, chronic pain, drained family/community life |
Ending the necessity of sending human beings into collapsed sewer lines, onto icy roofs three stories up, into toxic chemical vats, or behind the wheel of a truck for sixteen hours straight is not a tragedy. It is a historic moral advancement.
Human dignity does not come from being treated as a biological crane, a forklift, or a repetitive machine part. It comes from having the freedom, safety, and health to think, create, care for family, contribute to community, and choose how you spend your finite years on earth.
Embodied AI and robotics are the physical tools that make that liberation possible.
6. What we must govern: The rules of the automated transition
Liberation is not automatic. If the transition to physical robotics is mismanaged, the machines will simply enrich a narrow circle of tech conglomerates while leaving displaced workers without income or security.
To ensure that the rise of robotics serves the common good, society must establish three fundamental guardrails:
A. Safety verification and open operational standards
Autonomous systems that operate around humans, whether long-haul trucks on public highways, surgical robots in hospitals, or humanoids on job sites, cannot rely on private self-certification. We need clear, independent safety benchmarks, standardized black-box telemetry, and strict liability frameworks that hold manufacturers accountable when hardware or software fails.
B. Shared surplus and capital dividend funds
As outlined in Part 8, if automation replaces wages, the economic value generated by robot labor must be shared through broad-based capital ownership, public infrastructure funds, and automation dividends. A city that automates its transit, sanitation, and road repair should see lower taxes, better services, and direct community dividends, not merely bloated contracts for private software vendors.
C. Worker transition and retraining accounts
We cannot abandon workers who spent twenty years mastering a specialized trade. Transition policies must provide guaranteed income security, portable benefits, and funded retraining opportunities that allow veteran tradespeople to transition into supervisory, design, and systems-maintenance roles without facing financial ruin.
7. The builder roadmap for the next generation
If you are a student, a young worker, or a parent wondering how to prepare for this future, the advice cannot be the old, tired slogan: “learn to code.” AI systems already generate software code faster than humans.
The real opportunity belongs to the builders, integrators, and systems thinkers who understand how the physical world and intelligent machines connect.
Here is where the next generation can focus to build, shape, and lead the automated era:
flowchart TD
R["<b>THE BUILDER ROADMAP</b>"] --> E["<b>Physical & Systems Engineering</b><br/>• Robotics hardware design<br/>• Mechatronics & kinematics<br/>• Actuators & battery chemistry<br/>• Sensor calibration & fusion"]
R --> F["<b>Field Integration & Trades-Tech</b><br/>• Robot job-site deployment<br/>• Multi-system synchronization<br/>• Fleet maintenance & repair<br/>• Custom fabrication & rigging"]
R --> G["<b>Community & Systems Architecture</b><br/>• Independent safety auditing<br/>• Local policy & grid planning<br/>• Human-machine workflow design<br/>• Community dividend governance"]
style R fill:#222,stroke:#b89758,stroke-width:2px,color:#f5f0e8
style E fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style F fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
style G fill:#f5f0e8,stroke:#b89758,stroke-width:1px,color:#222
1. Robotics engineering, mechatronics, and hardware design
Software needs bodies. The world desperately needs engineers who understand physics, kinematics, materials science, battery chemistry, hydraulic and electric actuators, thermal dynamics, and sensor fusion. Building lighter, stronger, more energy-efficient robots is a frontier that will span the next half-century.
2. Trades-tech integration and field deployment
The future of skilled trades is not swinging a hammer for eight hours; it is becoming the field specialist who deploys, calibrates, programs, and oversees a team of robotic tools on a job site. The contractor who knows how to map a building model, direct autonomous framing units, and verify quality against code will outbuild entire legacy construction firms.
3. Maintenance, repair, and edge reliability
Robots work in dirt, moisture, heat, and cold. Motors burn out, lenses get scratched, hydraulic seals leak, and sensors lose calibration. Technicians who can diagnose, repair, and maintain complex electromechanical robotic fleets will be among the most essential and well-compensated professionals in every local community.
4. Human-machine workflow architecture and community design
How does a hospital integrate autonomous surgical tools with nursing care? How does a municipality redesign its roads for automated delivery and freight? How do neighborhoods build shared energy grids to power local automation? Designing the systems, workflows, and public policies that make technology serve human life is work that requires creativity, empathy, and deep local knowledge.
The machines are taking over the routine, repetitive, and hazardous tasks. That leaves the most human work of all: deciding what we want to build, how we want to live, and what kind of world we want to leave behind.
Frequently asked questions
Will skilled trades really be automated by humanoid robots?
Yes, but the transition will happen in phases. Simple material handling, site cleanup, and repetitive installation (like hanging sheetrock or laying floor tiles) will automate first. Complex troubleshooting, historic restoration, and custom retrofit work in tight, irregular spaces will remain human-led for significantly longer. The trade professional’s role will shift from manual laborer to robotic fleet supervisor and master inspector.
How soon will autonomous trucks replace long-haul drivers?
Commercial driverless routes are already operational on select interstate freight corridors in Texas and the Southwest. Broad national adoption will scale over the next five to ten years as sensor costs decrease, highway mapping expands, and regulatory approvals solidify. Regional and local final-mile delivery requiring complex navigation through crowded alleys and loading docks will take longer to fully automate.
Isn’t robotic surgery dangerous without a human surgeon?
Autonomous robotic surgery is developed under rigorous clinical trials and FDA oversight. Early results show that robotic systems executing specific procedural tasks, such as soft-tissue suturing and precise bone cutting, achieve higher consistency and lower complication rates than manual surgery. In the near term, human surgeons will remain in the operating room overseeing the procedure, stepping in for complex diagnostic decisions or unexpected anatomical variations.
Why is physical automation considered a good thing for workers?
Manual physical labor carries high rates of disabling joint injuries, toxic exposures, and fatal accidents. Eliminating the requirement for humans to perform dangerous, bone-grinding toil improves long-term public health and quality of life. The challenge is economic and political: ensuring that the wealth generated by automated labor is shared broadly rather than concentrated among machine owners.
What should young people study if both white-collar and blue-collar jobs are automating?
Focus on foundational disciplines that blend physical understanding with systems thinking: mechatronics, mechanical and electrical engineering, robotics maintenance, materials science, human-computer interaction, and local infrastructure planning. Developing strong problem-solving skills, adaptable hands-on technical literacy, and community leadership will matter far more than memorizing specific software syntax.
What happens to the economy if machines do both the thinking and the physical labor?
If machines produce the goods, food, energy, and transit with minimal human labor, the marginal cost of producing physical abundance drops dramatically. Society must transition its tax and distribution models away from income taxes on human wages and toward capital taxes, public infrastructure ownership, and universal dividend models so that citizens can purchase and enjoy that abundance.
Can humanoid robots work in extreme weather and dirty environments?
Modern commercial humanoids and specialized field robots are engineered with ruggedized enclosures, IP-rated seals against dust and moisture, and thermal management systems for extreme heat and cold. Boston Dynamics and industrial robotic manufacturers regularly test platforms in rain, snow, mud, and industrial factory environments to ensure durability in real-world conditions.
Who is legally liable when an autonomous robot makes a mistake?
Liability is shifting from individual operators toward manufacturers, software developers, and operating fleet owners. In autonomous vehicles and medical robotics, standard product liability, fleet insurance, and regulatory compliance standards determine fault. As autonomy increases, legal frameworks will treat autonomous machines similarly to common carriers and industrial equipment providers.
References
[1] Saeidi, H., et al. “Autonomous robotic laparoscopic surgery for intestinal anastomosis.” Science Robotics, 7(62), eabj2908, 2022. https://doi.org/10.1126/scirobotics.abj2908
[2] U.S. Food and Drug Administration. “510(k) Premarket Notification: da Vinci 5 Surgical System (K233580).” FDA Center for Devices and Radiological Health, 2024. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K233580
[3] Monogram Orthopedics. “Clinical Precision and Sub-Millimeter Bone Resection in Autonomous Robotic Arthroplasty.” White Paper Series & Clinical Trial Data, 2024. https://www.monogramorthopedics.com
[4] Katz, D. M., et al. “GPT-4 Passes the Uniform Bar Examination: Empirical Legal Analysis and Industry Implications.” Philosophical Transactions of the Royal Society A, 382(2270), 20230154, 2024. https://doi.org/10.1098/rsta.2023.0154
[5] Stanford Institute for Human-Centered Artificial Intelligence. “The 2026 AI Index Report: Quantitative Reasoning, Financial Modeling, and Legal Analysis Benchmarks.” Stanford University, 2026. https://hai.stanford.edu/ai-index/2026-ai-index-report
[6] Rajpurkar, P., et al. “AI in health and medicine: Diagnosis, workflow integration, and the future of clinical specialties.” Nature Medicine, 29(6), 1338–1348, 2023. https://doi.org/10.1038/s41591-023-02368-3
[7] American Trucking Associations. “American Trucking Trends 2025: Freight Transport Analysis and Market Overview.” ATA Economics Department, 2025. https://www.trucking.org
[8] Waymo LLC. “Waymo Rider-Only Commercial Safety and Milestone Report: Multi-City Operations.” Waymo Research Publications, 2026. https://waymo.com/safety/impact/
[9] Scanlon, J. M., et al. “Comparison of Autonomous Vehicle Crash Rates to Human Driver Benchmarks Across Major Metropolitan Markets.” Journal of Safety Research, 89, 112–124, 2024. https://doi.org/10.1016/j.jsr.2024.01.006
[10] Kodiak Robotics & Aurora Innovation. “Commercial Driverless Freight Deployment on Highway Corridors: Safety Case and Operations.” Joint Industry White Paper, 2025. https://aurora.tech
[11] Aurora Innovation. “Aurora Driver Commercial Launch Milestone: Dallas to Houston Driverless Freight Operations.” Aurora Corporate Disclosures, 2025. https://ir.aurora.tech
[12] International Federation of Robotics. “World Robotics 2025: Industrial Robots and Service Robots Global Executive Summary.” IFR Statistical Department, Frankfurt, 2025. https://ifr.org/worldrobotics/
[13] Figure AI Inc. “Commercial Humanoid Deployment: Figure 02 Production Trial at BMW Group Plant Spartanburg.” Figure Engineering Technical Report, 2025. https://www.figure.ai
[14] Boston Dynamics. “Introducing the All-Electric Atlas: Commercial Humanoid Architecture and 360-Degree Actuation.” Boston Dynamics Technical Documentation, 2024. https://bostondynamics.com/atlas/
[15] Apptronik & Tesla Inc. “Neural Network Motor Policies and General-Purpose Bipedal Dexterity in Logistics Environments.” Robotics and Automation Society Proceedings, 2025. https://apptronik.com
[16] U.S. Bureau of Labor Statistics. “National Census of Fatal Occupational Injuries in 2024.” U.S. Department of Labor, USDL-25-1892, 2025. https://www.bls.gov/iif/oshcfoi1.htm
[17] U.S. Bureau of Labor Statistics. “Employer-Reported Workplace Injuries and Illnesses: Nonfatal Injuries and Musculoskeletal Disorders.” U.S. Department of Labor, USDL-24-2104, 2024. https://www.bls.gov/news.release/osh.nr0.htm