AI, Talent, and the Future of Animal Advocacy

Tl;dr: AI is likely to weaken the economic case for junior roles, leading to fewer entry-level positions and thinner talent pipelines.^[1] This may result in:

• Increased reliance on external mid-level and senior hires from sectors like tech and consulting, which will require better context absorption infrastructure
• Engagement without employment (e.g., fellowships, communities, structured volunteering) becoming core infrastructure, requiring deliberate investment in coordination and mentorship.
• A shift in how early-career advocates are evaluated, prioritizing judgment, strategic thinking, and effective human-AI collaboration over enthusiasm or credentials.
• The emergence of a smaller core of experienced people, facing higher burnout and workload concentration.
• Greater importance for retention, job quality, and management/mentorship to support the fewer, more highly-leveraged staff.

I conclude that the movement must respond deliberately with near-term experiments, such as professionalizing coordination, designing dedicated learning roles, and funding management capacity. This could help us avoid a future with a fragile, slow-to-renew movement.

Acknowledgements: Thanks to Kevin Xia and Jason Levy Møller for your valuable feedback on this post!

Introduction

AI is usually discussed in animal advocacy in terms of productivity: faster research, cheaper communications, leaner operations. Much less attention is paid to how it may reshape talent pipelines.

Outside our movement, this question is already widely discussed. Across tech, consulting, policy, and the non-profit sector, people are debating whether AI could compress entry-level work, change how juniors are trained, and disrupt the traditional path from junior contributor to experienced professional.

This post is an attempt to pull that discussion into the animal advocacy context.

Reading Thomas Manandhar-Richardson’s writing on AI and the junior talent pipeline made this feel immediate to me. It prompted me to think more systematically about what might happen to movement health if entry-level roles shrink or change meaningfully over the next few years. I am not making predictions. I am outlining plausible pressures and trade-offs that seem worth thinking about now.

AI may also create new entry points, apprenticeships, and learning pathways if organisations and funders choose to invest in them deliberately. The concern here is not that positive redesign is impossible, but that without intentional effort, pipeline erosion may happen faster than replacement pathways emerge.

In this post, I focus on animal advocacy, since it’s the cause area I know best. Some of the dynamics I describe may well apply to other cause areas too, especially those with lean teams and high context requirements.

A note on uncertainty

What follows is not a set of predictions. It is a set of potential implications, under significant uncertainty, that seem worth taking seriously now.

This post is not about a future world where AI replaces most human labour. It is about the next 2 to 5 years, in a world where organisations, movements, and human judgment still matter, but where AI meaningfully changes how much entry-level work is available and how people are trained.

If AI disrupts society so radically that these structures stop functioning, talent pipelines will not be the binding constraint we need to worry about. Short of that level of disruption, however, the pressures described below seem more likely to intensify than disappear.

The quiet shift: fewer juniors, thinner pipelines

Traditionally, junior roles in animal advocacy have served three functions at once:

• Getting work done affordably
• Training future mid-level and leadership staff
• Socialising people into the movement

AI weakens the economic case for the first of these. Once that weakens, the other two weaken as side effects.

If a small team can use AI tools to draft content, run analyses, or manage operations with fewer people, it may become rational to:

• Hire fewer juniors
• Expect higher autonomy from anyone hired
• Prioritise immediate impact over long ramp-up periods

This creates a potential pipeline problem.

If we hire increasingly fewer juniors, we should not be surprised if we struggle to find mid-level people with movement experience later.

We have already seen parallel shifts in other sectors. Policy and research organisations increasingly recruit people who trained elsewhere and arrive with transferable skills, rather than expecting to train people from scratch. AI may accelerate this pattern by further reducing the amount of low-risk, low-stakes work available for learning on the job.

External hiring may become more common

As junior hiring declines, organisations may rely more on mid-level and senior hires trained elsewhere.^[2] This is already happening to some extent. AI could accelerate it.

Increasingly, animal advocacy may recruit from adjacent sectors such as:

• Tech
• Consulting
• Policy
• Philanthropy
• Communications agencies
• Operations-heavy roles in other fields

This is not inherently bad. Many people entering from these backgrounds bring strong skills and professional maturity.

However, context is a big bottleneck when it comes to hiring from outside the movement (e.g. see this post). Hiring from outside the movement will be most effective if we have a strong context-absorption infrastructure.^[3]

It may also change the shape of the movement:

• Entry becomes more lateral than linear
• “Fresh graduates getting their first job in advocacy” becomes rarer
• Movement experience may need to be accumulated alongside a different primary career

That has consequences for who enters, who stays, and who feels able to belong.

Engagement without employment may become core infrastructure

If fewer people can enter the movement through paid junior roles, engagement without employment may become core infrastructure. This could help people build context before they start their jobs and keep them engaged with the movement while they work elsewhere.

This could include:

• Fellowships
• Online communities
• Conferences, regular events and retreats
• Ongoing mentorship programmes
• Structured, time-bounded volunteering
• Project-based collaborations with clear learning goals

But this only works if organisations invest in coordination.

Engagement does not scale on goodwill alone. As the number of semi-engaged people grows, the need for intentional coordination grows with it.

Volunteer and community coordination is skilled work: scoping tasks, matching people to the right opportunities, setting expectations, giving feedback, and closing loops. Without this, volunteer energy is wasted, senior staff are distracted, and people churn, feeling underused or ignored.

If junior roles shrink but coordination capacity does not grow, the movement risks losing one of its main pathways for developing future mid-level talent.

Volunteering may not replace the entry-level pipeline

Skilled volunteering already plays an important role in helping people gain experience in an increasingly competitive job market. However, it seems unlikely that AI replaces junior staff work while leaving volunteer work untouched.

If AI absorbs low-skill, repeatable staff tasks, it is likely to absorb low-skill, repeatable volunteer tasks too. This means volunteer opportunities may become scarcer, more specialised, and more skill-gated, rather than expanding to compensate for fewer entry-level jobs.

If volunteering is to substitute for some of what junior roles used to provide, it likely needs:

• Clear scope
• Skilled volunteer coordinators
• Defined skill outcomes
• Time limits
• Feedback and evaluation

Without these guardrails, volunteering is unlikely to function as a credible training pipeline.

There is also an equity risk here. Volunteer-heavy pipelines tend to favour people with time, financial slack, and existing social capital. If unpaid or loosely structured work becomes a primary entry point, the movement may narrow who is able to enter and stay. Even small stipends, fellowships, or time-bounded paid learning roles could make a meaningful difference to who gets to build experience.

How juniors are evaluated may change

As entry-level roles become scarcer, juniors may face a different signalling environment.

Enthusiasm and credentials may not be enough.

What could matter more (see research from Cornell on this):

• Evidence of good judgment
• Strategic thinking
• Moral reasoning
• The ability to explain decisions, not just execute tasks
• The ability to set up, manage, and critically evaluate AI systems

This shifts signalling away from job titles and degrees, toward:

• Agency
• Analysis
• Thoughtful critiques
• Demonstrated learning from failure
• Effective human–AI collaboration

Historically, many people developed these skills gradually, on the job. Increasingly, students and early-career advocates may need to find ways to build them earlier and more deliberately.

A smaller core of deeply experienced people may emerge

Another possible outcome is a smaller group of very experienced advocates who entered early and stayed for a long time.

This group may:

• Hold disproportionate institutional memory
• Shape culture and norms
• Become harder to replace

There are upsides to this. There are also risks:

• Increasing workload concentration
• Higher burnout costs
• Greater exit risk
• Narrower intellectual and strategic diversity

In this scenario, retention and renewal become more important.

Retention, job quality, and pay may matter more

When people are harder to replace, organisations may need to take retention more seriously.

That could include:

• Clear growth paths
• Respect for expertise
• Sustainable workloads
• Psychological safety

It may also mean paying more competitively for roles that carry high context and responsibility, though this may interact differently with more radical AI-driven futures.

The movement may end up with fewer roles per unit of output and higher investment in the people who hold the most responsibility.

Management and mentorship may become more decisive

AI can automate many tasks. It is much less likely to replace management and mentorship any time soon, largely because these rely on trust, judgment, and human relationships.

If organisations have:

• Fewer juniors
• More external hires
• Faster onboarding expectations

Then, management quality may become decisive.

Poor feedback, unclear expectations, and weak people management become far more costly when fewer people carry more responsibility.

With fewer internal pipelines, the movement may also need to hire more managers from outside. That increases the importance of strong onboarding, leadership training, and explicit cultural transmission.

If we do nothing

Some plausible outcomes may look like this:

• A small group of highly experienced people carrying a disproportionate load
• Thin or missing mid-level layers, especially in specialised roles where judgment, context, and domain expertise are critical
• Few credible pathways for new talent to grow within the movement
• Increased funding may become harder to absorb, as fewer people are ready to step into roles that require context, judgment, and management capacity
• Increased reliance on external consultants, contractors, or short-term solutions to fill gaps that internal talent pipelines no longer supply

Over time, this raises burnout risk, narrows perspectives, and increases dependence on external consultants or last-minute senior hires.

The movement becomes harder to enter, harder to renew, and slower to learn and act, even if individual organisations look more efficient in the short term.

What might deliberate responses look like?

If even some of the dynamics above are real, then the question is not whether AI reshapes talent pipelines, but whether the movement responds deliberately or by default.

I do not think we have clear answers yet. Many of the relevant choices involve trade-offs, costs, and uncertainty. But that does not mean the movement has to wait for clarity before acting. Some responses can be treated as near-term experiments, tested and adapted over the next 6–12 months, rather than as permanent commitments.

A few design directions seem worth exploring:

1. Explicitly weighing internal training against external recruitment.
   Is it more cost-effective for the movement to invest in training its own talent, even if this is less efficient in the short term, or to rely more heavily on talent trained elsewhere and invest instead in retention, onboarding, and integration? This could be explored through small pilots rather than movement-wide shifts and this could be different for different roles.
2. Separating learning roles from output roles.
   If we decide to invest in training our own talent, what would it look like to design some roles explicitly for learning and pipeline development, even if they are not maximally efficient in the short term? Time-bounded learning roles, fellowships, or apprenticeship-style positions could test whether this produces stronger mid-level capacity over time.
3. Treating coordination and mentorship as core infrastructure.
   If engagement without employment becomes more central, how do we professionalise volunteer coordination, mentorship, and project scoping rather than treating them as side work? One near-term test could be funding dedicated coordination or mentorship capacity, even at a small scale, and evaluating its impact on retention and skill development.
4. Making management and onboarding fundable capacity.
   Should funders explicitly support management, onboarding, and mentorship time, rather than assuming these emerge organically as organisations scale? This could start with a small number of grants that treat management capacity as a first-order output rather than overhead.
5. Creating legible signals of readiness beyond job titles.
   How do we help early-career advocates practice and demonstrate judgment, strategic thinking, and effective human–AI collaboration in the absence of traditional entry-level roles? Shared artefacts, writing, case analyses, or structured project work could serve as early experiments here.
6. Designing mid-level bridge roles intentionally.
   If mid-level layers are thinning, what would it look like to deliberately create roles that combine responsibility, learning, and support, rather than expecting people to arrive “fully formed”? Short-term bridge roles with explicit scope and mentorship could help test this assumption.
7. Prioritise AI adoption where it lowers barriers to entry into the movement, expands access to training and skill-building, and enables advocates to demonstrate real competence without elite credentials or prior organisational status. While it’s important that leading organisations keep up with the best available AI, we also need to make sure that AI progress translates into a broader, more capable, and more geographically diverse pool of effective advocates.

These are not solutions so much as starting points. They imply experimentation rather than certainty. But avoiding the question entirely also has costs.

A note for early-career advocates

I’ll write separately about practical ways to prepare for this environment; here I’m focusing on the structural side.

What do you think?

Which of these dynamics resonate with your experience, and where do you disagree?
What would deliberate pipeline design look like in a world with strong AI?

Hi, I’m Sofia Balderson. I lead Hive, a global community for people working to end factory farming. This is a link post from my Substack, Notes from the Margin to share the messier, more personal reflections that don’t fit in formal updates. If you care about leading, belonging, or building something that matters (especially from the edges), feel free to subscribe here. 

1. ^{^}

   When I talk about “talent pipelines,” I mean the set of pathways through which people enter animal advocacy, build judgment and context over time, and become capable of holding mid-level and senior responsibility.

2. ^{^}

   Similar patterns have been observed in other sectors where entry-level hiring has slowed while demand for experienced hires has increased.

3. ^{^}

   This could take the form of more management and training time in the first 6 months of the role, dedicated mentorship programs, courses, fellowships, etc.