A rare case where expectations and outcomes fully align.

It is possible to rationally prioritise between causes without engaging deeply on philosophical issues

The best cause isn’t the one that wins the argument. It’s the one that survives contact with the world.

Hi Max and team 
Thank you for this beautifully framed and data-rich piece. As someone new to the EA community, I found the structure of “The world is much better. The world is awful. The world can be much better.” both emotionally honest and strategically motivating.

I have a question about using the European Union’s child mortality rate (0.41%) as an aspirational benchmark. I found it inspiring to see what’s already possible in real-world conditions, and it powerfully illustrates the scale of preventable child deaths globally.

That said, I was curious if you’ve considered how this benchmark might be interpreted in light of demographic differences. The EU has a relatively small and aging population, with significantly lower birth rates compared to regions like Sub-Saharan Africa or South Asia. So while matching that mortality rate globally is desirable, achieving it would require disproportionately larger systemic improvements in more populous, high-birth-rate regions.

I would love to hear your thoughts, or if there are existing posts or models that explore this demographic nuance in greater depth. 

Thanks again for your work

Thank you for this thoughtful and comprehensive piece.

After reading it, I was grateful for the authors' presentation of the prioritization landscape. It got me thinking: 

What if some of the challenges they describe aren’t just analytical, but reflect more profound questions of governance and institutional design?

As someone new to Effective Altruism but with a background in operations and systems governance, I found the framework of “types of prioritization” helpful and surprisingly familiar in a different context.

What stood out most was the idea that prioritization functions not only as analysis but also as a form of governance. In organizational settings I’ve worked in, prioritization often acts less like a single decision point and more like an ongoing structure, shaping whose voices are heard, what counts as valid evidence, and how (or whether) feedback loops are built in. In that sense, prioritization becomes embedded in institutional infrastructure: it influences how systems adapt, not just what they focus on.

So I wondered: How much of the prioritization in EA is a design question about institutional learning, decision rights, and oversight?

From that angle, the current emphasis on within-cause work doesn’t just feel like a strategic imbalance; it may also reflect what’s easier to operationalize within existing organizational structures. This raises the question of how cross-cause or cause-level prioritization could be scaffolded intellectually and institutionally through clearer decision ownership, more deliberate feedback systems, or periodic governance reviews.

I don’t have a strong view here; I'm just curious from a governance perspective: If prioritization were treated as infrastructure, not just analysis, how might that reshape how EA allocates resources and decision-making power?

Grateful for the clarity of this post, and looking forward to learning more.

Note: I’m still relatively new to EA, and I’m sharing these reflections from a systems and governance lens. They’re offered with curiosity and I welcome clarifications from those thinking deeply about these dynamics.