Just noting for my own future edification this LW exchange between David Manheim (who argues that infinite ethics is irrelevant to actual decisions, per paper above) and Joe Carlsmith (who argues the opposite, per essay above), which only increased my conviction that Manheim and Anders Sandberg were right.

FWIW here's Claude Sonnet 3.5 attempting to first steelman Carlsmith's essay and then being neutral about which stance is more right: 

Steelman of Carlsmith's key arguments:

1. Even if infinite value/disvalue isn't currently accessible, we should have non-zero credence in possibilities that would make it accessible (e.g. discovering our physics is wrong in specific ways). Given expected value theory, a non-zero credence in infinite value should dominate our calculations.
2. We face decisions with potentially infinite consequences even under current physics through acausal influence - if we accept certain decision theories and cosmological models suggesting infinite copies of us exist, our choices could influence an infinite number of copies.
3. Even if we can't access infinities in practice, ethics shouldn't fall silent when considering infinite cases. We have strong intuitions about choices like "infinite heaven vs infinite hell" that a complete ethical theory needs to address.
4. The mathematical/philosophical challenges infinite ethics raises reveal important limitations in our ethical frameworks that we should take seriously, even if just to better understand finite ethics.

Manheim/Sandberg's key counterarguments:

1. Under our best physics, the accessible universe is fundamentally finite in space, time, and computational capacity.
2. Any decision theory allowing infinite value requires rejecting either current physics, rational preferences, or established decision theory in problematic ways.
3. We can represent supposedly "infinite" values (like lexicographic preferences) with very large but finite numbers while preserving all decision-relevant distinctions.
4. Ethics should focus on actionable choices that make a difference to actual moral patients in our universe.

My analysis: While Carlsmith makes interesting theoretical points, Manheim/Sandberg seem more right for practical purposes. The key considerations:

1. Their physics arguments about fundamental finitude seem solid and would need extraordinary evidence to overturn.
2. Even if we assign some credence to infinite possibilities, we can't operationalize that credence in a way that helps real decision-making.
3. The practical ethical questions we face (e.g. existential risk, animal welfare, global poverty) don't require solving infinite ethics.
4. Time spent on infinite ethics likely trades off against work on pressing finite problems where we can make concrete progress.

So while infinite ethics raises fascinating theoretical questions, I lean towards Manheim/Sandberg's view that it's not practically relevant for "doing good better." That said, I appreciate Carlsmith's rigorous exploration of the theoretical challenges, which may yield insights for finite ethics.

The prudent path seems to be: Note the theoretical challenges infinite ethics raises, remain humble about our ethical frameworks, but focus our efforts on tractable finite problems where we can clearly help actual moral patients.

Just wanted to copy MacAskill's comment here so people don't have to click through: 

Though I was deeply troubled by the poor meater problem for some time, I've come to the conclusion that it isn't that bad (for utilitarians - I think it's much worse for non-consequentialists, though I'm not sure).

The basic idea is as follows. If I save the life of someone in the developing world, almost all the benefit I produce is through compounding effects: I speed up technological progress by a tiny margin, giving us a little bit more time at the end of civilisation, when there are far more people. This benefit dwarfs the benefit to the individual whose life I've saved (as Bostrom argues in the first half of Astronomical Waste). Now, I also increase the amount of animal suffering, because the person whose life I've saved consumes meat, and I speed up development of the country, which means that the country starts factory farming sooner. However, we should expect (or, at least, I expect) factory farming to disappear within the next few centuries, as cheaper and tastier meat substitutes are developed. So the increase in animal suffering doesn't compound in the same way: whereas the benefits of saving a life continue until the humanity race (or its descendants) dies out, the harm of increasing meat consumption ends only after a few centuries (when we move beyond farming).

So let's say the benefit to the person from having their live saved is N. The magnitude of the harm from increasing factory farming might be a bit more than N: maybe -10N. But the benefit from speeding up technological progress is vastly greater than that: 1000N, or something. So it's still a good thing to save someone's life in the developing world. (Though of course, if you take the arguments about x-risk seriously, then alleviating global poverty is dwarfed by existential risk mitigation).

I missed that, thanks for pointing it out Vasco! And always appreciate the thoroughness and transparency of your estimates. 

I thought it'd be helpful to improve comparability with LChamberlain's answer on Sinergia Animal by being a bit more object-level: 

• Saulius previously estimated that a dollar donated to support corporate cage-free commitments historically helped free somewhere between 9 to 120 hens from cages, with a mean of 42 hens; since their average lifespan is 1.1–1.5 years that's 54 years of improved life per dollar. Vasco then discounts this by -80% going forward to incorporate Open Phil's thinking that "we’ve covered many of the strongest opportunities in this space, and we think that current marginal opportunities are considerably weaker" to get ~8.4 hens freed per dollar donated
• From LChamberlain's answer, ACE estimates Sinergia's work to free 53.5 hens per dollar, which is >6x Vasco's estimate for (presumably) cage-free campaigns in general and slightly above Saulius' average

I'd be curious to understand how much of this ~6x difference is a 'best charity implementing an intervention' thing (which I'm guessing is what Omnizoid asked for? Akin to how cost per person treated is the most important consideration for MH treatment interventions), and how much is due to differences in methodology, considerations included/excluded (e.g. OP's "current marginal opportunities are considerably weaker"), inputs, etc.

I appreciated these parts of Jason's article, and am curious if others have a different take:

What to do?

The article recommends that humanity avoid creating mirror bacteria, even as a scientific experiment, no matter how tight the biosecurity around it (which can never be perfect). Funders should not fund such research; governments should even ban it.

This is a simple cost-benefit calculation. On the cost side, the threat is plausible, and the potential damage incalculable. Thus, the risk is immense. On the benefit side, there is no crucial goal for humanity that is known to be enabled by mirror life. Restricting this research would not fundamentally impede progress in biology or bioengineering generally.

Not all forms of mirror biology would even need to be restricted. For instance, there are potential uses for mirror proteins, and those can be safely engineered in the lab. The only dangerous technologies are the creation of full mirror cells, and certain enabling technologies which could easily lead to that (such as the creation of a full mirror genome or key components of a proteome).

In short, by pruning off a relatively small branch of the tech tree, we can avoid a true existential risk.

The article also recommends research to develop surveillance and countermeasures, in case humanity ever does encounter mirror bacteria. This research can be advanced significantly without creating full mirror cells.

We have time to react

Given that the threat is relatively distant, no immediate action is needed. We have time to discuss it thoroughly, among a wider set of participants. The article released today is meant to be the beginning of that wider conversation, not a call to urgent action.

I like Austin Vernon's idea for scaling CO2 direct air capture to 40 billion tons per year, i.e. matching our current annual CO2 emissions, using (extreme versions of) well-understood industrial processes.  

The proposed solution may not be the cheapest out there. Other ideas like ocean seeding or olivine weathering might be less expensive. But most of the science is understood, and it can scale quickly. I'd guess 100,000 workers could build enough sites to capture our 40 billion tons goal in a decade. The capital expenditure rate would be between $1 trillion and $5 trillion yearly, or 1% to 5% of global GDP. That cost and deployment speed take doomer scenarios off the table. Say something scary like melting permafrost threatens runaway warming. You can target the area with a few years of sulfur cooling while a tiny portion of the global economy builds carbon capture devices. It is nothing like a wartime mobilization.

The most disruptive aspect would be energy usage. We'd need to ramp output up at double-digit rates because each ton of CO2 requires 2-3 MWh of energy for removal. Thankfully low-grade heat is easy to come by. There is enough energy near coal mines in Wyoming or natural gas fields in SW Pennsylvania at less than $5/MWh. Other places might use solar, hydro, or geothermal steam if they lack fossil fuel reserves. The key is to put the facilities at the energy sources instead of trying to move the energy. Cheap energy makes the operating costs <1% of global GDP. Many clean energy proponents have fretted about how to keep fossil fuel reserves in the ground. Burning them to run carbon capture equipment kills two birds with one stone!

The takeaway is that we could completely turn around the carbon dioxide problem within a few years with a similar spending rate as rich world COVID relief. There won't be a scenario where we've waited too long to act.

I am admittedly perhaps biased to want moonshots like Vernon's idea to work, and for society at large to be able to coordinate and act on the required scale, after seeing these depressing charts from Assessing the costs of historical inaction on climate change:

Pretty funny CGD blog post by Victoria Fan and Rachel Bonnifield: If the Global Health Donors Were Your Parents: A (Whimsical) Comparative Perspective. Quoting at length (with some reformatting):

Navigating the global health funding landscape can be confusing even for global health veterans; there are scores of donors and multilateral funding mechanisms, each with its own particular structure, personality, and philosophy. For the uninitiated, PEPFAR, GAVI, PMI, WHO, the Global Fund, UNITAID, and the Gates Foundation can all appear obscure and intimidating. But if your head is spinning from acronym-induced vertigo, fear not! We are here to help you make sense of it all. How, you ask? With a clear method for donor identification: comparing the donors to your parents. So what would happen if the donors were your parents and you asked them for a new car? 

• PEPFAR: Ok, we’ll buy you a new car, but we’re going with you to the dealership and it must be American-made. At least one seat must be devoted to abstinence and the delay of sexual debut. Before you drive the car, you must promise not to support prostitution. Each quarter, you must report how many miles you’ve driven with how many passengers, with a target of 1000 passenger-miles per month.
• President’s Malaria Initiative: We’ve made it very clear that we only support four proven, cost-effective interventions for child rearing: food, clothing, health care, and education. What, do you think money in the Malaria family just grows on trees? Just because HIV/AIDS has a shiny new car doesn’t mean we can afford it.
• UNITAID: We’ve identified pediatric vehicles as a niche market which is currently underserved by the major transport providers. By buying cars for you and all our other children, we are helping to create a pediatric automotive market with new and superior transportation commodities. Prior to our innovative entry into the pediatric vehicle market, most of our potential beneficiaries were getting around using lower-quality forms of transportation, such as bicycles, buses, and walking.
• GAVI: We will purchase and a deliver a car for you from a particular GAVI-approved dealership. However, you must co-finance the purchase with wages from your part-time job. Gas and insurance will require separate applications.
• WHO: Sorry, we haven’t had a car budget in ten years. But we DO have a new set of guidelines on best practices for safe car driving, and a box full of old carfax vehicle reports that you’re welcome to look at any time. Please let us know right away if you experience any engine trouble; regular and reliable reporting allows us to maintain an up-to-date transmission failure surveillance system. And don’t forget to celebrate Vehicle Safety Day on May 11!
• Gates Foundation: Of course, darling, we gave your boarding school plenty of money to buy a car. And since we’re on the Board, we’ll make sure they buy the right car. And you can drive it any time you want…as long as one of us is in the passenger seat to make sure you’re going the right way.
• Global Fund: We’ve reviewed your proposal for a Range Rover and according to Consumer Reports it is a technically capable car for city driving. Here is a $70,000 check for you to go and buy the Range Rover, as discussed in your proposal.

Oliver Kim's How Much Should We Trust Developing Country GDP?, a review of Morten Jerven's 2013 book Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It, makes the same point about GDP as well. Improving data collection in underresourced areas in general seems like a cross-cutting 'cause X'.

Some quotes: 

Hollowed out by years of state neglect, African statistical agencies are now often unable to conduct basic survey and sampling work. Jerven writes:

In 2010, I returned to Zambia and found that the national accounts now were prepared by one man alone… Until very recently he had had one colleague, but that man was removed from the National Accounts Division to work on the 2010 population census. To make matters worse, lack of personnel in the section for industrial statistics and public finances meant that the only statistician left in the National Accounts Division was responsible for these data as well. (pg. x)

Without the staff to collect and analyze survey data, statistical agencies are usually forced to improvise, guessing the size of the economy from population figures, which are themselves extrapolated from censuses that are decades-old.

... I’m haunted by the words of the lone Zambian statistician, sitting in his empty office, who asks Jerven plaintively: “What happens if I disappear?”

Many African states are failing at the basic task of knowing how many people live in their borders—let alone accurately measuring their economic activity. The vast, unobserved informal sector (which includes subsistence farming, and something like 60% of working people) is usually estimated just as a direct function of population.⁵ Lacking direct harvest yields, estimates of agricultural output are often produced using FAO models based on planting-season rainfall data.⁶ Even the minimal task of measuring the goods traveling across borders—in theory, the easiest thing for a sovereign state to accomplish—is occasionally beyond the reach of statistical agencies. Until 2008, landlocked Uganda only collected trade data on goods that eventually passed through the Kenyan port of Mombasa, ignoring the four other countries on its borders.⁷

In the absence of good underlying data, the prevailing approach for GDP in developing Africa can be summarized as:

Income estimates… derived by multiplying up per capita averages of doubtful accuracy by population estimates equally subject to error. (p. 39)

Poor Numbers came out in 2013, attracting a wave of scholarly and policy attention (including by Bill Gates). Once you’ve heard its arguments, it’s virtually impossible to look at a GDP statistic the same way again.

But what actual progress has been made in the statistical capacity of nations?

Seemingly, not much. In late 2014, perhaps in response to Jerven’s book, the World Bank relaunched its website for its Statistical Capacity Indicator—a metric on a 0-100 scale which scores countries based on the strength of their “Methodology”, “Source Data”, and “Periodicity and Timeliness”. But even by this clunky internal metric, progress has been glacially slow: in 2004, the average score for African countries was 58.2; in 2019, it was just 61.4.

Moreover, over six years of an economics PhD, I have never heard of any economist using this statistic. Poor Numbers is well-cited and well-read (at least by Africa specialists), but its lessons about the fundamental unreliability of statistics have largely not been absorbed in how we actually do economics.