AI futures for animals hold enormous opportunity – as well as risk. The direction this and other transformative technologies take will require concerted efforts from animal advocates, technical experts, researchers, policymakers, and more. Over the next eight weeks, we will engage with high-stakes issues from the present day (e.g. the emerging use of AI in factory farming) to the far future (e.g. developments across animal communication, genetic engineering, and beyond). Our goal is to equip you with the knowledge and network to steer these powerful technologies towards positive outcomes for all sentient beings.

This all starts today. In this session, you’ll get to know your facilitator and fellow learners. We'll walk through the course structure and themes, with plenty of time for your questions. We're so glad you're here.

AI is already shaping the lives of many animals today – sometimes directly (e.g. use in factory farming), and sometimes indirectly (e.g. speciesist bias in chatbots and image generation models). This session maps the current and near-future landscape where AI and animal lives intersect, exploring major benefits and risks for animals– as well as key leverage points for advocates.

🧩 Central questions

1. AI benefits: How might current and emerging AI technologies lead to significant improvements in animal welfare?
2. AI risks: How might the very same technologies lead to massive suffering and increased exploitation of animals
3. Leverage points: As advocates, how can we effectively influence the trajectory of these powerful new technologies?

🧭 Learning objectives

1. Understand: Identify current and emerging AI technologies with significant implications for animals.
2. Assess: Evaluate the potential benefits and harms of specific, real-world AI applications. Pinpoint strategic opportunities for intervention (e.g. through research, policy, or technical work) across AI development and deployment.
3. Reason: Apply evidence and principles to the prioritisation of cause areas and approaches within the AI×Animals frontier.
4. Next steps: Identify key resources, organisations, and individuals for further learning and discover opportunities for impact (e.g. jobs, volunteer roles, etc.).

Precision livestock farming (PLF) is a reality for many farmed animals today, and its use is likely to accelerate in coming years. Systems that are already being rolled out in factory farms track many aspects of farmed animals’ health, such as feed consumption, weight, lameness, and disease prevalence. This data is then processed through machine learning algorithms to identify patterns, enabling producers to more efficiently manage livestock.

Will PLF usher in a new era of welfare transparency across animal agriculture (“precision welfare”), or are we witnessing the takeoff of never-before-seen levels of fully automated animal exploitation?

🧩 Central questions

1. How PLF works: How do sensor technology and machine learning come together in PLF?
2. Defining and measuring welfare: How is the welfare of farmed animals defined in the context of PLF, and how does this relate to the data that is collected?
3. Risks/Benefits: How might PLF lead to improvements in farmed animal welfare, and how could it lead to greater suffering and exploitation?
4. Welfare vs. profit: Is there a tension between farmed animal welfare and profit? Could PLF be leveraged to optimise for both?
5. Steering: What are the most effective intervention points – from the initial data infrastructure and algorithm design to industry regulation – for ensuring PLF systems genuinely prioritize the interests of farmed animals?

🧭 Learning objectives

1. Understand: Explain how PLF works, and identify major benefits (e.g. improved transparency and accountability) and risks (proxy drift, welfare washing).
2. Assess: Anticipate potential tensions between animal welfare and economic efficiency, drawing connections to outcomes for animals. Compare and contrast different conceptions of farmed animal welfare, highlighting assumptions and risks.
3. Reason: Formulate evidence-based arguments about whether PLF is likely to be net positive or negative for farmed animals.
4. Next Steps: Identify strategic recommendations and key intervention points for steering PLF toward animal-positive outcomes.

Machine learning is driving rapid progress in “decoding” animal communication – with the potential to radically transform our relationships towards animals. This session explores the ethical and legal implications of such breakthroughs. Should we talk to animals at all, and if so, what ethical considerations should guide our interactions? Which animals are we giving voice to? How might our world change if breakthroughs allowed us to understand animals on their own terms? Are there limits to how much we can understand one another? And perhaps most importantly: Are we ready to listen?

🧩 Central questions

1. The ethics of contact: Just because we can talk to animals, should we? If so, what ethical considerations might govern how we communicate with them?
2. Which animals are we giving voice to? How does our relationship with an animal – as a companion, a commodity, or a wild being – shape the ethics of communicating with them? Could advances in interspecies communication benefit some types of animals but not others?
3. The ethics of listening: How might our moral and legal obligations towards animals change if we could truly understand them? Does an animal's capacity for complex communication change what we owe them, or does it simply make it harder to ignore the duties we already have?
4. Societal transformation: How would our legal systems and moral codes need to evolve in a world where we could truly understand what animals think and feel? What are the most plausible positive and negative outcomes that could result from complex interspecies communication?

🧭 Learning objectives

1. Understand: Explain how machine learning is accelerating research into animal communication.
2. Assess: Anticipate how human society might adapt to advances in interspecies communication, and identify positive and negative outcomes for animals, including backfire risks.
3. Reason: Explore ethical frameworks and key considerations for interspecies communication (e.g. major implications for human society). Compare and contrast implications for different animal groups (e.g. companion vs. farmed vs. wild animals).
4. Next steps: Identify priorities across research, policy, and other domains to ensure the ethical use of interspecies communication.

Many traits are significantly influenced by genetic factors. Genetic modification is nothing new — humans have been selectively breeding animals and plants for thousands of years. However, recent developments, including gene editing (e.g. CRISPR) and gene drives – as well as the potential of AI to accelerate further research – raise pressing questions about the welfare implications of these tools. These questions are not simple: genetic interventions may reduce animal suffering in some contexts and exacerbate it in others, and their effects often interact with broader systemic factors — including farming conditions, regulatory oversight, and the viability of non-genetic alternatives. This week, we survey the current landscape of genetic modification in animals, examine the evidence for and against various applications, and develop a principled framework for evaluating when — and whether — genetic interventions are warranted.

🧩 Central questions

1. Advances in genetic modification: How do the different major techniques of genetic modification work, and what are their advantages and disadvantages?
2. Genetic vs. non-genetic approaches to improve animal welfare: Which considerations might favour genetic interventions to improve welfare over non-genetic strategies (e.g. improving factory farming conditions; food system transformation)?
3. The ethical gradient: What key considerations govern the acceptability of genetic editing across farmed animals, wild animals, and humans?

🧭 Learning objectives

1. Understand: Explain in basic terms how both genetic and environmental factors contribute to trait development in sexually reproducing organisms, and differentiate major methods of genetic modification, including selective breeding, somatic vs. germline gene editing, and gene drives.
2. Assess: Critically interrogate personal intuitions and apprehensions about genetic interventions to improve welfare. Evaluate core ethical arguments for and against genetic interventions across different animal populations (e.g. farmed, wild).
3. Reason: Develop a principled stance on the most promising and hazardous applications of gene editing in animals. Compare and contrast genetic vs. non-genetic strategies to improve animal welfare.
4. Next steps: Identify key organizations, contacts, and areas for further investigation into genetic welfare.

Alternative proteins aim to do what decades of advocacy have not: make animal agriculture obsolete by offering products that are tastier, cheaper, and more convenient. Plant-based meat, cultivated meat, and precision fermentation each take a different route to this goal — and each faces different bottlenecks in manufacturing, regulatory approval, and consumer receptiveness. AI is increasingly being applied across all three pillars, but how much of this is real progress and how much is hype? Which constraints can machine learning actually solve, and which are fundamentally physical, financial, or political? And with investment down sharply from its 2021 peak, how should advocates think about timing, priorities, and the risk that these technologies stall before reaching the consumers who matter most?

🧩 Central questions

1. The strategic case for replacement: What makes alternative proteins a high-leverage intervention for animal welfare and climate – and what are the strongest objections?
2. Pillar bottlenecks: What is the single most binding constraint to mainstream adoption for each technology pillar (plant-based, cultivated, precision fermentation) – and is that constraint the kind AI can help solve?
3. Evidence vs. hype: Where does AI/ML already deliver validated results in alternative protein development, and where is it mostly promise? How should advocates and funders distinguish between the two?
4. Non-technical constraints: What role do consumer acceptance, regulation, data sharing, industry standards, and political economy play – and can AI help with any of these?
5. Lock-in and backfire: If alternative proteins succeed, what gets locked in – and what risks emerge (corporate concentration, uneven global diffusion, regulatory capture, energy intensity)?

🧭 Learning objectives

1. Understand: Explain, at a functional level, how plant-based meat, cultivated meat, and precision fermentation work. Identify at least one concrete, published AI/ML application for each pillar.
2. Assess: Distinguish AI applications that are evidenced in peer-reviewed or industry work from those that are speculative or early-stage. Evaluate when constraints are primarily informational (AI-suited) versus physical, financial, or regulatory (AI-limited).
3. Reason: Develop a considered position on whether AI is net-positive for farmed animals through alternative proteins, taking seriously the dual-use risk that the same tools could optimize factory farming. Surface backfire risks and lock-in dynamics.
4. Next steps: Identify concrete intervention points — in research, policy, advocacy, standards, or career — where an individual could have high leverage at the AI×Alternative proteins intersection.

What we do today may shape the lives of animals for billions of years to come. According to longtermism, positively influencing the far future is a key moral priority of our time. In this session, we will examine the implications of longtermism for animal advocacy, including how lock-in scenarios limit future pathways, uncertainty about the outcomes of our actions over increasingly long timescales, backfire risks, and a potential strategic reorientation: towards the welfare of wild animals.

🧩 Central questions

1. Why future beings matter: What is the case for prioritizing the needs and interests for future beings?
2. Present vs. future: How do we balance today’s urgent suffering against the vast, uncertain needs of the future? Which actions might benefit both present and future beings?
3. Lock-in: Certain developments could permanently and irreversibly shape the future of animal welfare for the better – or for the worse. How can we anticipate and navigate lock-in scenarios?
4. Negotiating cluelessness: With so much uncertainty, what actions can we take that are robustly positive for animals? How can we preempt and limit backfire risks?
5. Shifting priorities: How does adopting a longtermist perspective shift advocacy priorities? What does impact for wild animals look like?

🧭 Learning objectives

1. Understand: Clarify the core motivations, claims, and assumptions of longtermism. Define related concepts like lock-in, cluelessness/uncertainty, and backfire risks. Explain the importance of wild animal welfare, identifying major causes of suffering.
2. Assess: Critically evaluate arguments for prioritizing the far future. Evaluate wild animal welfare as a longtermist priority.
3. Reason: Weigh scale against uncertainty, backfire risks, and other considerations. Explore and develop strategies which are robust across different assumptions.
4. Next steps: Identify key organizations, thinkers, and research areas in longtermist animal advocacy.

The emergence of Transformative Artificial Intelligence (TAI) may fundamentally change the world as we know it – perhaps even sooner than you think. Imagine 100 years of scientific and technological progress, coupled with explosive economic growth – all in the span of 10 years. While it’s hard to predict what exactly this means for animals, one thing’s for certain: unless advocates adapt, our current business-as-usual strategies risk becoming obsolete. This session explores how best to future-proof our movement by shifting priorities toward interventions that remain robust across a wildly different technological and political landscape.

🧩 Central questions

1. TAI timelines: What evidence supports the claim that AI progress is accelerating and unpredictable, making TAI a near-term possibility?
2. Not in Kansas: In what ways might the post-TAI world fundamentally differ from the world we know today?
3. Strategic obsolescence: Which existing advocacy strategies (e.g. consumer boycotts, legislation) are least likely to be effective across different post-TAI scenarios?
4. Futureproofing: Which advocacy startegies might be robustly positive across different post-TAI scenarios?

🧭 Learning objectives

1. Understand: Define Transformative AI (TAI) and explain the evidence for rapid, unpredictable technological development.
2. Assess: Identify key institutional, economic, and otherwise structural assumptions of current advocacy strategies, and evaluate their plausibility across different TAI scenarios, while noting areas of uncertainty.
3. Reason: Apply your reasoning to advocacy theories of change as well asto your own impact trajectory.
4. Next steps: Adapt your own impact trajectory (e.g. focus, career plan, research agenda) accordingly.

The AI×Animals future is in your hands. This final session distills all that we have learned over the past seven weeks into a concrete impact plan for you to carry forward after the fellowship. You will develop and receive targeted feedback on your project pitches and career strategies. Together, let’s build better AI futures for all sentient beings.

🧩 Central questions

1. Major issues: Which specific challenges at the AI×Animals frontier are currently the most important, tractable, and neglected?
2. Personal fit and synergy: How can individuals from diverse professional backgrounds (tech, policy, science, advocacy, etc.) uniquely leverage their existing expertise to advance positive AI-animal outcomes? What opportunities are there for high-impact transdisciplinary collaboration?
3. Organizations: Who are the key organizations currently working on these high-stakes, high-leverage issues, and what defines their core strategy?
4. Gaps and opportunities: Where are the most critical gaps and opportunities in this space?
5. Resources: What are the most valuable informational resources, funding sources, connections, and events necessary to enter and succeed in this area?

🧭 Learning objectives

1. Understand: Identify important, tractable, and neglected problems. Map the relevant ecosystem of organizations and stakeholders working on these issues, and pinpoint critical gaps and opportunities.
2. Assess: Evaluate your own personal fit for working in this area through quick and easy tests for fitness. Identify areas of uncertainty and anticipate backfire risks.
3. Reason: Apply evidence and principles to build a personalised post-fellowship impact plan (project pitch and/or career strategy).
4. Next Steps: Execute the first steps of your impact plan. Establish strategic connections, explore leads, and learn more. Leverage the Sentient Futures Slack community for ongoing collaboration and support.

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✅ Qualifications

📆 Time frame and commitment

Apart from the weekly facilitated sessions (1h45m), facilitators will also be required to prepare for the sessions (including completing the readings; 2-3h) as well as to complete a short training module (3h) prior to the start of the fellowship.

Experienced facilitators may take on up to 2 groups (~20 learners).

Summary of key details

We offer our programs for free.

On average, running our fellowships costs us about $568.32 USD per learner. In order to be able to offer all of our fellowships free of charge, we rely on donations. If you would like to support our work at Sentient Futures, please donate to us.

• We would be grateful for any donation size, big or small. If you're not able to donate, but would like to volunteer for us, please reach out.
• Whether or not you donate to Sentient Futures does not affect your chances of being accepted for our fellowships.

The application forms are located above.

• The application forms are separated by role, not by course. There is one application form for learners and one for facilitators.
• Click the buttons below to jump to the applications. They may take a few moments to load.
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Jump to the learner application

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Jump to the facilitator application

Yes, you are welcome to apply as a learner to multiple fellowships if you will have the time & space to follow multiple fellowships. If you are unsure, then we recommend only applying to one fellowship. Given the additional commitments & responsibilities of the facilitator role, we are only accepting facilitator applications for one fellowship at a time.

There is no required project portion, but we strongly encourage participants to undertake new projects, where they receive feedback during Week 8 of the AI×Animals fellowship. We also periodically host a project incubator where you can work closely with an experienced mentor on a concrete project exploring AI’s impact on animals, artificial sentience, post-AGI futures, and other future-focused topics. There are both mentor-led projects and mentor support for projects proposed by the participants. Projects can span research, field-building, or real-world action. More information on the project incubator can be found here.

The exact days & times for the fellowships will be determined based on the availability of the accepted participants. At this time, what we can say is that the spring 2026 semester of AI×Animals will run from @April 6, 2026 to @Monday (tentative dates). There will be 10 cohorts meeting at different times during the week.

We’ve prepared a comprehensive document to prepare you for our fellowships:🎓Learner guide

We strongly recommend that you have a stable Internet connection, quiet space from which to take the call, and a quality microphone.

Yes, the curriculums for AI×Animals and Prioritizing Neglected Animals are available open access.

We currently plan on offering the AI×Animals fellowship in the future.