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Career switch in a third world country
Hey guys! I'm not sure if this is the place to post it, but I'd love to hear your thoughts on the matter. I am from Argentina and I'm based there. I work remotely in an American startup (I don't want to relocate), and I consider myself a really good software engineer. I got into tech because I thought I could have a huge impact! After reading the 80k blog a thousand times I started considering a career switch. I'm not sure anymore about how much impact I can have as a software engineer. I'm 23, and I've been considering a career in academia. I discarded AI, so I'm thinking about a completely different field (economics).Assume I enjoy both the same and I'm equally good: can you think of any scenario in which I can have more impact as a software engineer?
Pros for switching:
- As a researcher, I'd graduate from the best (or second-best) university in Latinamerica.
- I know I can get into research kind of easily (it's pretty common as an undergrad if you show interest).
- At least based on 80k, I can have way more impact.
- I discarded AI as a field because I'm not sure about the impact I can have from a third world country (again, relocating is out of the table).
- I also discarded AI because the best program in the country was too much for me. I couldn't stand working full time and calculus
Pros for staying on software engineering:
- I suffer from impostor syndrome as anyone else, but I've been regarded as highly competent in every job I've had. I've also been regarded as someone who can both design the bigger picture while implementing every tiny detail.
- I already have a ton of career capital.
- A low salary in the US or Europe is a high salary in Argentina, so I could work for EA organizations while donating, saving, and investing a lot.
- There aren't many researchers in the country regarding AI, and most of them end up leaving the country. I don't know how it could be worth it though, given that Argentina will be one of the last countries in the world to implement any AI technology.
Curated and popular this week
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Advanced AI could unlock an era of enlightened and competent government action. But without smart, active investment, we’ll squander that opportunity and barrel blindly into danger.
Executive summary
See also a summary on Twitter / X.
The US federal government is falling behind the private sector on AI adoption. As AI improves, a growing gap would leave the government unable to effectively respond to AI-driven existential challenges and threaten the legitimacy of its democratic institutions.
A dual imperative
→ Government adoption of AI can’t wait. Making steady progress is critical to:
* Boost the government’s capacity to effectively respond to AI-driven existential challenges
* Help democratic oversight keep up with the technological power of other groups
* Defuse the risk of rushed AI adoption in a crisis
→ But hasty AI adoption could backfire. Without care, integration of AI could:
* Be exploited, subverting independent government action
* Lead to unsafe deployment of AI systems
* Accelerate arms races or compress safety research timelines
Summary of the recommendations
1. Work with the US federal government to help it effectively adopt AI
Simplistic “pro-security” or “pro-speed” attitudes miss the point. Both are important — and many interventions would help with both. We should:
* Invest in win-win measures that both facilitate adoption and reduce the risks involved, e.g.:
* Build technical expertise within government (invest in AI and technical talent, ensure NIST is well resourced)
* Streamline procurement processes for AI products and related tech (like cloud services)
* Modernize the government’s digital infrastructure and data management practices
* Prioritize high-leverage interventions that have strong adoption-boosting benefits with minor security costs or vice versa, e.g.:
* On the security side: investing in cyber security, pre-deployment testing of AI in high-stakes areas, and advancing research on mitigating the ris
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Summary
In this article, I estimate the cost-effectiveness of five Anima International programs in Poland: improving cage-free and broiler welfare, blocking new factory farms, banning fur farming, and encouraging retailers to sell more plant-based protein. I estimate that together, these programs help roughly 136 animals—or 32 years of farmed animal life—per dollar spent. Animal years affected per dollar spent was within an order of magnitude for all five evaluated interventions.
I also tried to estimate how much suffering each program alleviates. Using SADs (Suffering-Adjusted Days)—a metric developed by Ambitious Impact (AIM) that accounts for species differences and pain intensity—Anima’s programs appear highly cost-effective, even compared to charities recommended by Animal Charity Evaluators.
However, I also ran a small informal survey to understand how people intuitively weigh different categories of pain defined by the Welfare Footprint Institute. The results suggested that SADs may heavily underweight brief but intense suffering. Based on those findings, I created my own metric DCDE (Disabling Chicken Day Equivalent) with different weightings. Under this approach, interventions focused on humane slaughter look more promising, while cage-free campaigns appear less impactful. These results are highly uncertain but show how sensitive conclusions are to how we value different kinds of suffering.
My estimates are highly speculative, often relying on subjective judgments from Anima International staff regarding factors such as the likelihood of success for various interventions. This introduces potential bias. Another major source of uncertainty is how long the effects of reforms will last if achieved. To address this, I developed a methodology to estimate impact duration for chicken welfare campaigns. However, I’m essentially guessing when it comes to how long the impact of farm-blocking or fur bans might last—there’s just too much uncertainty.
Background
In
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In my opinion, we have known that the risk of AI catastrophe is too high and too close for at least two years. At that point, it’s time to work on solutions (in my case, advocating an indefinite pause on frontier model development until it’s safe to proceed through protests and lobbying as leader of PauseAI US).
Not every policy proposal is as robust to timeline length as PauseAI. It can be totally worth it to make a quality timeline estimate, both to inform your own work and as a tool for outreach (like ai-2027.com). But most of these timeline updates simply are not decision-relevant if you have a strong intervention. If your intervention is so fragile and contingent that every little update to timeline forecasts matters, it’s probably too finicky to be working on in the first place.
I think people are psychologically drawn to discussing timelines all the time so that they can have the “right” answer and because it feels like a game, not because it really matters the day and the hour of… what are these timelines even leading up to anymore? They used to be to “AGI”, but (in my opinion) we’re basically already there. Point of no return? Some level of superintelligence? It’s telling that they are almost never measured in terms of actions we can take or opportunities for intervention.
Indeed, it’s not really the purpose of timelines to help us to act. I see people make bad updates on them all the time. I see people give up projects that have a chance of working but might not reach their peak returns until 2029 to spend a few precious months looking for a faster project that is, not surprisingly, also worse (or else why weren’t they doing it already?) and probably even lower EV over the same time period! For some reason, people tend to think they have to have their work completed by the “end” of the (median) timeline or else it won’t count, rather than seeing their impact as the integral over the entire project that does fall within the median timeline estimate or
Is there any plan to translate Doing Good Better to other languages? Spanish in particular. I think it would be the best introductory material to EA.