Three years ago this month, the world lost Bernard “Bernie” E. Rollin, PhD, a philosopher and pioneer in the field of animal ethics. Rollin wrote and lectured extensively on the profound changes technology has brought to animal agriculture, particularly its role in breaking the historical connection between animal welfare and productivity. In traditional farming, productivity and welfare were closely linked: animals that were healthy and well-cared-for produced more. However, industrial agriculture broke this connection. What Rollin called technological “sanders”—tools like antibiotics and air-changing systems—allowed animals to survive and produce in environments that would once have been fatal.

Given this history, it is not surprising that artificial intelligence (AI), the latest technological revolution, is being met with skepticism regarding its potential role in animal welfare. While AI’s ability to enhance productivity raises concerns that it could worsen the poor conditions endured by billions of animals in intensive farming systems, its potential for improving welfare merits close examination. This analysis explores AI’s impact on animal welfare by first assessing the inherent limitations of intensive animal farming and comparing them with AI’s transformative potential for the animal welfare movement.

Intensive Animal Industry: Marginal Gains through AI

The intensive animal farming industry operates under strict resource constraints, such as limited space, feed, energy, and other essentia physical inputs. While AI may help optimize processes and increase efficiency of operations, it has limited room to overcome physical and biological needs inherent to biological systems. For example, there’s little room to reduce space use in intensive operations further, or to improve feed conversion efficiencies beyond what has already been achieved, or can be achieved, by non-AI technologies.

Biological Limits on Productivity

 While there are concerns that AI could revolutionize genetic engineering and dramatically boost productivity, several factors suggest its impact will be incremental rather than transformative. First, genetic modifications still face fundamental biological constraints - animals need time to grow, metabolize nutrients, and maintain basic physiological functions. Second, most major productivity gains have already been achieved, or can be achieved, without the use of AI. Decades of selective breeding, high-energy feed, and controlled environments have maximized growth rates, milk yields, and egg production to the point where further gains are constrained by the animals’ physiology.While AI might help identify new genetic modifications or optimize breeding programs, these are likely to yield marginal improvements rather than dramatic breakthroughs. When biological limits are reached, further productivity gains often come at the cost of animal viability itself. 

Resource Constraints

The industry also operates under finite resource constraints, including feed, water, energy, and land—all of which are influenced by global markets and environmental factors. Take feed, for example: while AI can optimize how much feed each animal receives, it cannot create more land to grow soy and maize, nor can it eliminate the competition with human food and biofuel production. Similarly, for energy use: AI can make ventilation systems more efficient, but farms still need a baseline amount of energy to maintain temperature, lighting, and air quality. These physical resource requirements - whether land, water, or energy - represent hard limits that efficiency improvements alone cannot overcome.

Opportunities for Welfare-Oriented Improvements

Interestingly, AI’s most impactful applications in intensive farming may align with welfare improvements, particularly in addressing the severe limitations of human monitoring in commercial facilities. Good stockmanship is fundamental for animal welfare, yet current industrial systems often operate with just one stockperson responsible for thousands of animals - ratios that make proper individual monitoring nearly impossible. Under such constraints, even skilled stockpeople cannot adequately observe and respond to the needs of each animal. Advanced monitoring systems, powered by AI, can track indicators of animal health and behavior in real-time, identifying signs of illness, stress, or injury earlier than what would be possible by human observation alone. While AI cannot replace good stockmanship, it can extend the observational capabilities of farm workers, helping identify which animals need attention. For instance, automated systems can monitor feeding patterns, movement, vocalizations, and physiological indicators across thousands of animals simultaneously, alerting staff to potential problems that might otherwise go unnoticed.AI can also optimize environmental conditions, such as ventilation, temperature, and humidity, reducing problems like heat stress and respiratory issues. However, welfare challenges emerging from genetics for fast-growth and productivity, as opposed to management, like chronic hunger due to feed restriction in breeders, most probably will remain unresolved by these advancements.

Growing Pressure from Public Scrutiny

The industry faces increasing public scrutiny as consumers demand greater transparency and humane practices. Many routine practices—such as the use of gestation crates, overcrowding, and mutilations like tail docking and beak trimming—remain hidden from public view. AI’s ability to process and analyze vast amounts of data could expose these practices to greater scrutiny. For instance, AI can analyze farm footage or supply chain data to document inhumane practices, empowering advocacy groups and creating pressure for reform. AI could enhance independent welfare monitoring by analyzing video footage from farms and slaughterhouses, tracking animal-based welfare indicators, and processing supply chain data to verify compliance with standards. Businesses failing to adapt to these demands risk reputational damage and financial losses, while those embracing transparency could gain a competitive edge. 

AI as a Powerful Ally for the Animal Welfare Movement

In contrast to the resource-driven farming industry, the animal welfare movement thrives on information and advocacy. AI is uniquely suited to support this mission in several ways:

First, AI can gather, analyze, and share information more effectively than ever before. The animal welfare movement depends on raising awareness and encouraging ethical choices. AI can connect data on animal welfare directly to consumer behavior, helping people make informed decisions that reduce suffering.

Second, AI enhances the ability to measure and document animal suffering. Tools like the Hedonic-Track Custom GPT from the Welfare Footprint Project exemplify how AI can scientifically quantify pain and suffering, shedding light on issues that are often ignored or hidden. This data is essential for identifying the most critical welfare challenges and crafting effective solutions.

Third, AI can dramatically improve resource efficiency. Many animal welfare organizations operate on tight budgets, particularly in low-resource settings. By automating tasks like data analysis, strategy development, operations, and outreach, AI allows these organizations to achieve more with less. This efficiency is particularly important for smaller advocacy groups and countries with limited funding.

Fourth, AI holds enormous potential to accelerate the development of cruelty-free alternatives such as lab-grown meat and plant-based products. By improving production processes, reducing costs, and aligning products with consumer preferences, AI can help make ethical alternatives more accessible and appealing.

Finally, AI can support better animal farming practices in systems that prioritize welfare. Precisely because AI can offer tailored solutions to specific challenges, it can help for instance smaller-scale, cage-free producers optimize their operations or assist in managing slower-growing, locally adapted breeds - even in remote or underserved areas. This capability democratizes access to intellectual and technical resources, making advanced support available regardless of the scale of the business, the uniqueness of the problem, or its geographical location.

Conclusion

In our analysis we stand optimistic that this technological revolution will, different from the one that so much troubled Rollin, disproportionately favour the welfare of animals. While the intensive animal industry will likely derive only marginal incremental improvements from this technology, the AI’s potential to transform animal welfare is vast and multifaceted.

 Like any disruptive technology, AI must be developed and deployed responsibly. Ethical safeguards are crucial to ensure it benefits animals and society as a whole. However, its positive potential in this case cannot be ignored.