I agree this cannot replace donation-based interventions! It is still feels potentially underrated and underconsidered.

I do agree that management and structure are the hardest parts. I do imagine many EA orgs have solved harder problems in the past.

I think automatic dubbing services have become good enough to make English fluency not be a hard requirement anymore for many potential jobs.

Here is a super hacky fermi-gpt estimate of a headcount of potentially hireable global workers:

"""

hacky fermi estimate — internet users → elite tail

definitions (clean + explicit):

• population: total population (≈2024–2025)
• internet users: people using the internet (any device)
• final pool (÷8000): internet users filtered by three independent 95th-percentile criteria
  • high cognitive ability (≈95th percentile)
  • hardworking (≈95th percentile)
  • ethical / trustworthy (≈95th percentile)
    combined ⇒ (1 / (20×20×20) ≈ 1 / 8000)

interpretation: this is a very conservative lower bound on people who could plausibly do high-quality remote cognitive work using tools like chatgpt (incl. translation). this is not a hiring claim; it’s an order-of-magnitude sanity check.

hacky fermi table

key takeaway:
even after filtering to internet users only and then applying an extremely harsh 95%×95%×95% filter, many countries still have thousands to tens of thousands of plausible high-quality contributors. at global scale, talent supply is not the bottleneck; coordination, tooling, and trust are.

"""

(I know this estimation relies on some independence assumptions. Regardless, it is meant to be illustrative, not authoritative.)

An underexplored alternative to donation is hiring people from low-income contexts to do paid work on meaningful problems.

Here's a rough estimate of "happy" hourly rates for remote intellectual manual labor (data labeling, checking, summarization, interpretability grunt work), in USD:

Estimated Happy Rates ($/h)

There exists a very large supply of people who are both willing and happy to do careful cognitive work at rates that are trivial by rich-country standards, if the work is structured and paid.

Some reasons this possibility can be quite good and interesting:

• It allows money to be converted into actual work on impactful tasks, even if that work is initially "intellectual manual labor" (labeling, checking, summarizing, auditing, interpretability grunt work, etc.).
• It treats people as participants rather than recipients. Receiving payment for work tends to be more humanizing than receiving aid, because it encodes agency, skill, reciprocity, and contribution.
• It onboards people into the global intellectual labor market: deadlines, quality standards, tooling, communication norms. Those skills compound and transfer.
• It can operate without heavy intermediary organizations, which reduces overhead and incentive distortion and keeps the causal chain legible: money → work → output → learning.

A lot of important research and analysis is not bottlenecked on genius so much as on coordination, paradigms, and tooling. Once those exist, large amounts of careful human attention can be usefully applied in parallel.

My usual joke is "GPT-2 has 12 attention heads per layer and 48 layers. If we had 50 interns and gave them each a different attention head every day, we'd have an intern-day of analysis of each attention head in 11 days."

This is bottlenecked on various things:

• having a good operationalization of what it means to interpret an attention head, and having some way to do quality analysis of explanations produced by the interns. This could also be phrased as "having more of a paradigm for interpretability work".
• having organizational structures that would make this work
• building various interpretability tools to make it so that it's relatively easy to do this work

— Buck's comment on "How might a herd of interns help with AI or biosecurity research tasks/questions?", EA Forum

https://www.encultured.ai/ might be somewhere of your interest? i'd be curious to hear what they think