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Sam Tucker1
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Thanks for your reply and for clarifying your perspective. I do agree that the most harmful applications of PLF technology we’re currently seeing are driven by machine learning and deep learning, rather than generative AI. When I refer to AI in factory farming, I’m using the term in its broader sense to include these technologies as well—beyond just large language models specifically.

On the main point, I think campaigns for restrictions or bans on AI in factory farming can actively strengthen the push for transparency, rather than being at odds with it.

Broadly speaking, transparency campaigns without accompanying pressure tend to fail across cause areas. Companies are unlikely to willingly share data unless there’s significant public scrutiny or regulatory threat. Calls for a ban increase that scrutiny by raising public awareness about the risks AI poses to animals, highlighting the need for accountability and uniting broad coalitions that increase political power.

The risk, if the movement focuses solely on promoting “positive” uses of PLF, is that we create an environment where welfare washing and complacency thrive. Companies will only adopt welfare improvements where they align with profitability, and even then, these measures are often incidental rather than intentional. In many cases, welfare "improvements" serve to entrench factory farming further, creating the illusion of progress whilst masking systemic harm. For example, technologies that reduce disease outbreaks may allow producers to justify increasing stocking densities, leading to even greater overall suffering, despite the initial appearance of progress.

To meaningfully challenge these systems, we need radical counterpressure—calls for bans or restrictions. Without this counterbalance, we increase the probability that AI will cement factory farming's dominance rather than dismantle it. History shows us that meaningful action—particularly changes that hurt industry interests—rarely happens without radical demands to push the boundaries of what’s politically acceptable.

Campaigns for bans aren't in opposition with calls for transparency, they're a strategic neccessity in achieving them. They apply the pressure needed to drive reforms, expose harmful practices, and keep the ultimate goal—fighting factory farming—at the center of the conversation. Without this pressure, transparency risks becoming toothless, co-opted as a tool for welfare-washing or superficial improvements that merely serve industry interests. Coupling bold demands for bans with transparency-focused efforts ensures that any improvements are not only genuine and accountable, but also prevent the illusion of progress from entrenching the very systems we aim to dismantle.

In this way, the two strategies can complement each other: bold calls for bans provide the pressure and visibility needed to make transparency campaigns more effective.

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by Wladimir J. Alonso, cynthiaschuck
5 min read 10

21

AI safetyAnimal welfareFarmed animal welfareAnimal welfare regulationOpinion
Frontpage

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. 

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saulius5
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Interesting points. Starting with 27:45, there are two talks here that claim that AI will probably be bad for farmed animals. @Sam Tucker urges in his talk to work on banning AI in animal farms. There is also discussion on it at 58:27 where Sam says that he is 99% sure that AI in farms will be bad for animals, if I understood him correctly, partly because it might allow factory farming to stay around for longer. Perhaps you should discuss this issue with Sam.

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Wladimir J. Alonso3
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Hi,

Thanks for the comments and the links.

I had the opportunity to share a panel with Sam, and I really like his work. That said, it’s true that we have differing perspectives on the role of AI for farmed animals. As described in this article, we don’t believe the impact of AI will be net negative—in fact, it could have positive aspects in certain areas.

For instance, we are aware of a company using AI to detect signs of mistreatment or illness by analyzing images of carcasses at processing plants. With this information, they plan to address issues with suppliers whose animals exhibit these problems. Such an approach should create significant incentives to tackle welfare abuses or neglect. While the overall conditions for animals may remain far from ideal, these improvements could represent meaningful progress in certain aspects of their welfare.

As for suggestions to ban or limit the use of AI in these systems, while I understand the reasoning behind them, I believe such measures are logistically and politically unfeasible. It would be akin to attempting to ban computers or the internet in the animal production sector when those technologies first emerged.

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Sam Tucker1
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Hi @Wladimir J. Alonso and @saulius,

First of all, I want to emphasise that I see value in both approaches—advocating for the abolition of AI in factory farms and pushing for welfare-oriented reforms. These strategies are not contradictory, rather, they can complement each other to achieve broader progress. Radical proposals shift the Overton window, making moderate reforms appear more reasonable, while moderate approaches secure practical wins and build momentum for more ambitious goals.

That said, I remain skeptical that AI in factory farms will have a net-positive impact on animals. The gap between technological development in academia and its real-world implementation in industry has historically favoured profit maximisation at the expense of welfare. For example, CRISPR gene editing was initially intended to address genetic defects but has instead enabled selective breeding that exacerbates welfare issues—like chickens bred to grow so quickly their bodies cannot support their weight.

The argument that factory farming cannot get worse through further optimisation strikes me as overly optimistic. AI is already contributing to worsening conditions:

Historically, unforeseen consequences of new technologies—like antibiotics enabling extreme overcrowding—have harmed animals, and it’s unrealistic to assume that future AI breakthroughs won’t follow similar patterns. This research paper outlining 12 harms caused by precision livestock farming provides a useful starting point for thinking through some of these concerns.

On the question of bans, I agree that such campaigns are unlikely to succeed in the near term in the U.S., but the political dynamics in other regions—like Europe, Australia, and New Zealand—are different. In these contexts, smaller political parties (e.g., animal justice and green parties) hold influence and could plausibly campaign for bans or significant restrictions on AI in factory farms.

Importantly, campaigns for a ban have value beyond their immediate outcomes. They can:

  • Build broader coalitions across political divides, uniting meat industry labor unions (concerned about job losses) with environmental and animal welfare advocates.
  • Drive moderate reforms by setting ambitious demands, which create space for compromise and incremental progress.
  • Establish global precedents that legitimise concerns about AI and animal welfare, shifting the political landscape toward stronger regulations and, eventually, abolition.

In short, while a ban may not be immediately tractable, campaigning for one could yield significant benefits: building coalitions, achieving meaningful reforms, and paving the way for larger victories down the line. Ultimately, both approaches—welfare reforms and abolition—can reinforce one another, driving the systemic change we all aim for.

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Wladimir J. Alonso1
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Hi Sam,
Thank you for sharing your thoughts. You mentioned that AI is already contributing to worsening conditions, but I’m not fully convinced that the examples you provided support this claim. Both examples seem to reflect broader trends of technological intensification, rather than generative AI specifically (which wasn’t available at the time those developments occurred). My focus is on generative AI, while other forms like machine learning and deep learning are already deeply embedded in industry practices.

That said, my main point remains: other things being equal, and acknowledging that factory farms are, unfortunately, a current reality, I hold an optimistic view of AI’s introduction into the industry: AI can monitor and address key production factors that overlap with welfare concerns, such as body scores, heat stress at the individual level, and the detection of injuries or diseases, far more effectively than traditional methods.

Rather than advocating for the abolition of AI in factory farming, I believe we should focus on campaigning for transparency. Specifically, the data gathered by AI and other monitoring technologies should be made accessible to independent stakeholders. This would create greater accountability and improve oversight.

Transparency-focused legislation is more plausible than bans on AI across an entire sector. It’s difficult to argue against the idea that the food industry should be transparent about its non-proprietary practices, particularly when animal welfare is concerned. While I’m not naive about existing challenges, such as ag-gag laws and potential loopholes, the chances of passing transparency laws are higher than prohibiting the use of technology outright.

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Sam Tucker1
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Thanks for your reply and for clarifying your perspective. I do agree that the most harmful applications of PLF technology we’re currently seeing are driven by machine learning and deep learning, rather than generative AI. When I refer to AI in factory farming, I’m using the term in its broader sense to include these technologies as well—beyond just large language models specifically.

On the main point, I think campaigns for restrictions or bans on AI in factory farming can actively strengthen the push for transparency, rather than being at odds with it.

Broadly speaking, transparency campaigns without accompanying pressure tend to fail across cause areas. Companies are unlikely to willingly share data unless there’s significant public scrutiny or regulatory threat. Calls for a ban increase that scrutiny by raising public awareness about the risks AI poses to animals, highlighting the need for accountability and uniting broad coalitions that increase political power.

The risk, if the movement focuses solely on promoting “positive” uses of PLF, is that we create an environment where welfare washing and complacency thrive. Companies will only adopt welfare improvements where they align with profitability, and even then, these measures are often incidental rather than intentional. In many cases, welfare "improvements" serve to entrench factory farming further, creating the illusion of progress whilst masking systemic harm. For example, technologies that reduce disease outbreaks may allow producers to justify increasing stocking densities, leading to even greater overall suffering, despite the initial appearance of progress.

To meaningfully challenge these systems, we need radical counterpressure—calls for bans or restrictions. Without this counterbalance, we increase the probability that AI will cement factory farming's dominance rather than dismantle it. History shows us that meaningful action—particularly changes that hurt industry interests—rarely happens without radical demands to push the boundaries of what’s politically acceptable.

Campaigns for bans aren't in opposition with calls for transparency, they're a strategic neccessity in achieving them. They apply the pressure needed to drive reforms, expose harmful practices, and keep the ultimate goal—fighting factory farming—at the center of the conversation. Without this pressure, transparency risks becoming toothless, co-opted as a tool for welfare-washing or superficial improvements that merely serve industry interests. Coupling bold demands for bans with transparency-focused efforts ensures that any improvements are not only genuine and accountable, but also prevent the illusion of progress from entrenching the very systems we aim to dismantle.

In this way, the two strategies can complement each other: bold calls for bans provide the pressure and visibility needed to make transparency campaigns more effective.

Reply
Wladimir J. Alonso1
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Hi Sam,

It’s clear we agree on the most important point: that promoting transparency is an urgent and essential step toward achieving significant welfare gains.

Where we differ is in strategy. While you believe that demanding technological-blocking legal restrictions on AI technology will create the necessary pressure to achieve transparency, I believe that directly advocating for something widely supported in society—namely transparency in food production—will better align us with a greater portion of society, increasing the chances of achieving this transparency much sooner.

I would focus our energy not only on demanding transparency but also on ensuring that it is thorough. There are many potential alignments with public health, environmental, and workers’ rights advocates, who could become powerful allies in this effort.

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CB🔸3
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The post is interesting and well argued, but I am not sure I agree - one example I have in mind is Microsoft using AI to double the productivity of a shrimp farm, likely by increasing density.

Regarding this : "The industry also operates under finite resource constraints, including feed, water, energy, and land" It is also possible that AI, by increasing economic growth and developing better energy sources, can indirectly increase animal consumption by giving more resources to people.

I agree that animal welfare activists should use AI to boost their outreach, however.

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cynthiaschuck6
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Hi CB, thanks a lot for your comment, I think it represents a main concern of many people. I'll break my thoughts in two parts

(1) AI use in shrimp farming and similar situations.

In this case, I understand what AI-monitoring did was to enable farmers to optimize feed use enormously (shrimp grew larger, mortality was reduced, and feed was not wasted), as well as water quality monitoring. This could be seen as negative for welfare, as it facilitates farming in high stocking densities, makes shrimp farming more profitable and could reduce prices, though this price effect is complex since the same AI technologies will likely make alternative proteins cheaper too, making the net effect on consumption less certain.

However, consider the actual conditions shrimp face. Without AI, feed distribution was uneven, leading to competition, stress, malnutrition and starvation for a large fraction of animals (mortality without AI was higher), as well as longer exposure times to poor water quality, and higher incidence of toxicities (hence respiratory distress, gill damage, skin damage) that come associated with it. This leads to suffering and higher mortality rates. So it's possible (though this should be measured) that even in higher-density environments, AI use can maintain better welfare than lower-density farms with poor feed and water quality management. Importantly , if shrimp feed relies on fishmeal and fish oil, optimizing feed reduces the number of wild fish needed, so each pound of shrimp has a smaller welfare footprint in terms of wild fish captures.

The industry trajectory also matters. Aquaculture is already moving toward higher-density and intensified farming with or without AI. So I believe the relevant comparison isn't between AI farming and a low-density or extensive scenario, but between AI-farming and conventional (intensive) high-density farming without AI. 

(2) On AI leading to greater income/prosperity, potentially increasing consumption of animal foods.

I see greater incomes and prosperity as extremely positive to reduce human suffering, but animal suffering as well. While rising incomes historically increased meat consumption, the relationship is not linear, in that as societies become more prosperous (on top of being an extraordinary thing in itself), they often can afford being more concerned with environmental and ethical issues. It's particularly in wealthier nations that we see a trend towards reduced meat consumption, stronger welfare legislation, increased interest in plant-based alternatives, and the means needed for the development of innovations like cultivated meat and other substitutes of animal protein. And again, the same technologies making animal farming more efficient are simultaneously making alternatives more competitive and affordable. I believe that the key isn't if AI increases income (something to be celebrated), but how to channel greater incomes toward ethical food systems.

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CB🔸3
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Interesting, thank you.

On the second point this reads like very optimistic (the way animals are treated in rich countries is just very bad). I agree that it's maybe easier to appeal to ethical values and develop alternatives now but it's hard to know if this will be enough to offset all the negative stuff associated by 'more power and money = easier to buy animal products'. But I won't have much time to engage and it's not that important since we can't change this part of the trajectory.

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SummaryBot1
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Executive summary: While AI's impact on industrial animal farming will likely be limited by biological and resource constraints, it has transformative potential for animal welfare advocacy through enhanced monitoring, documentation, and development of alternatives, suggesting this technological revolution may primarily benefit animal welfare rather than intensive farming practices.

Key points:

  1. Industrial farming's ability to benefit from AI is fundamentally limited by biological constraints and physical resource limitations.
  2. AI can enhance animal welfare monitoring in existing farms but cannot solve core genetic welfare issues.
  3. The technology offers powerful tools for advocacy through improved data analysis, documentation of suffering, and development of alternatives.
  4. Increasing public scrutiny and AI-powered transparency create new pressure for industry reform.
  5. The extent of AI's impact on actual industry reform remains uncertain despite improved monitoring capabilities.
  6. Resources should be directed toward developing AI tools for advocacy and welfare monitoring rather than industrial farming optimization.

 

 

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

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