I could not submit my vote as the time was closed. I am sharing my perspective below after reading all the comments and the original post.

The voting distribution in this poll exposes a challenging dilemma for the movement. On one side, several voters point out that we cannot confidently verify the absolute direction of our impact on the ground, creating a high risk of wasting money or accidentally causing net harm. On the other side, it is rightly noted that building effective field organizations is slow and difficult. If we starve field teams to fund data science, we risk losing the operational vehicles needed to execute better strategies down the line.

It is always good to learn from other movements where similar challenges were faced. 

We can see this exact tradeoff by looking at the history of education reform in India, particularly the journey of the NGO Pratham and their Teaching at the Right Level model. For years, the global development community debated whether money should be spent directly on teaching children or on conducting rigorous evaluations to see if the pedagogy actually worked.

If early funders had treated this as a zero-sum choice, the program would have failed. Shifting all funding to pure academic research would have killed the field team, leaving no one to actually teach the children. But shifting all funding to blind scaling would have been just as dangerous. Early versions of the program actually failed to show impact when integrated directly into standard school hours because teachers felt forced to stick to the rigid official curriculum. Had the team just scaled up blindly without real-time data, millions of dollars would have been wasted on an ineffective approach.

Pratham resolved this by turning their active field operations into the research loop itself. They deployed teaching teams to run short, targeted learning camps outside school hours, testing the pedagogy in real time while collecting immediate data on student progress. The field team was not just executing an intervention blindly, and the researchers were not just sitting in distant offices. They integrated the evaluation directly into the daily operational DNA of the field vehicle.

Instead of treating evidence building and field execution as a zero-sum funding tradeoff, the solution is to treat them as an integrated pipeline. The focus should be on shifting how field organizations operate during their initial phases.

Instead of building organizations that optimize blindly for rapid scaling on day one, early field deployment should be designed intentionally as an active data gathering exercise. When a field team treats its initial footprint as a live research engine, the conflict between research and action disappears. The execution itself becomes the primary tool used to eliminate the exact impact uncertainties the community is worried about.

If we do not integrate research directly into the daily operational DNA of our field teams, we will remain permanently stuck in this loop. We will either be driving blind with great execution teams, or map making with no vehicles left to drive.

Thank you for this incredibly thoughtful and nuanced response. I completely agree with your core premise that waiting for absolute economic parity before acting would lock in decades of intensive industrialized suffering that we absolutely must try to prevent. The counterfactual value of early intervention is a powerful argument, and it is something many advocates have been highlighting for India and other Asian countries as well.

Since we both agree on the goal but see the timeline through different structural lenses, perhaps the real opportunity lies in how we design these early interventions. If we want Africa (and India) to leapfrog Europe's trajectory, the strategies cannot just be accelerated versions of Western campaigns. They have to be structurally adapted to informal economies and local context from day one.

Ultimately, whether the timeline turns out to be 5-10 years or 20, aiming for an ambitious trajectory is exactly what forces creative non-linear policy thinking. Thank you for an enriching discussion. I look forward to seeing how these strategies evolve on the ground, and sharing those learnings across LMICs.

Firstly, thank you for writing this article. There are some great points in this article. I found the overview of Europe's animal welfare trajectory particularly useful, as it provides excellent historical context that I was unaware.

However, looking at this from a policy and institutional perspective, I think the bottleneck of state capacity and economic thresholds across Africa is understated. The comparison needs to heavily account for the stark differences in economic development and market structures (something I usually compare about between Europe and India in policy reform):

• The Economic Gap: In 1990, the average GDP per capita for the European Union was around $15,500, while Western Europe averaged over $20,000. This is 5 to 7 times higher than Ghana’s (~$2,300), the wider sub-Saharan African average, or even India’s (~$2,800) today. European consumers had the financial cushion to absorb increased costs. In Africa and India, where a high percentage of income goes to basic food, even marginal price hikes risk worsening food insecurity. Currently something already being talked about in India with recent fuel hikes.
• The Informal Bottleneck: Europe had formalized supply chains and robust veterinary inspection departments. In contrast, many African nations and India face constrained budgets and a massive fragmented informal sector, such as backyard farming and wet markets. When the majority of animal agriculture exists outside formal corporate chains, top-down regulatory enforcement is virtually impossible.

This economic reality is the primary reason why a country like India, despite having a long-standing legal framework and active advocacy, still faces steep uphill battles in enforcing modern farm animal laws. This is because we never start at the same starting point.

While your strategic recommendations are excellent, factoring in economic constraints and the dominance of informal markets makes a 5 to 10 year timeline for Africa look highly optimistic. A longer and more staggered horizon is likely more realistic.

Thank you for the question. This is something we are actively exploring, but cautiously. My response reflects limited direct exposure to investigations and is largely informed by the Indian context.

Yes, a significant amount of time is spent on manual work such as reviewing large volumes of footage, identifying legally relevant moments, transcription, translation, redaction, and assembling evidence for lawyers, media, or campaigns. These are all areas where AI could plausibly reduce friction in the evidence-to-impact pipeline.

That said, a few constraints shape our thinking:

• Risk and reliability matter more than speed - Investigations carry legal and safety risks, so any AI-assisted analysis needs to be highly reliable, explainable, and secure.
• Context and judgment remain central - Deciding what evidence matters, how it fits into a pattern of abuse, and when it is strategically useful is still highly contextual and human-led.
• Other bottlenecks are currently more binding - For many investigators, gaps in safety, mental wellbeing, legal support, training, and coordination matter more than the absence of advanced analysis tools, and premature AI use could even increase risk.

Where we see near-term promise is in assistive and investigator-controlled tools (e.g. secure transcription, translation, basic indexing, and first-pass flagging) that reduce cognitive load without replacing judgment.

Longer term, AI may help standardise evidence preparation so investigations are more consistently advocacy- and litigation-ready. For now, our priority is ensuring the surrounding infrastructure is strong enough that any future technical gains actually translate into impact.

We expect our thinking here to evolve as both the tools and the investigative ecosystem mature.