When evaluating whether to invest time in making things more efficient, I often see people compare the one off cost of make the thing more efficient to the expected future saved time when doing the thing. I think there is often an important third variable to track, that often swings such decisions from not worth the effort to definitely worth the effort. Namely, the expected increase in usage of the thing due to the reduction in friction of utilizing the thing. I in practice often find the final consideration to dominate.
Recent examples from my life:
Reducing the number of button presses needed for common workflows on my laptop means I both can navigate my laptop quicker and end up navigating more instead of procrastinating navigating because it is annoying.
Moving to a more central location in my city has meant I both save time commuting to things and also end up going to more things.
Automating the loading of context from my personal apps into AIs means I spend less time copy pasting context into AIs and also end up asking AIs more questions about my personal context.
I think it was Joel Spolsky (from the Microsoft Visual Basic and Excel team) who mentioned a rule of thumb that each 10% reduction in difficulty would roughly double the market of a piece of software. And Google once knew that even 10ths of seconds of page load time had a noticeable effect on usage. This seems consistent with your claim.
There’s an opposing force here, too: Opportunity cost. If you have 10 hours to automate something that you’ll use for 4 years, is there something else you could do with those 10 hours that offered even greater payoff? This is frequently a major factor, even in business contexts. “Yes, it would be profitable, and it would be fun, but it would involve solving 5 hairy problems that only benefit a single big customer. With the same resources, we could solve 5 other hairy problems that benefit 10 customers each.”
Last November, I wrote a blog post titled You should consider applying to PhDs (soon!), where I argued it is probably a good use of time for junior AI safety researchers (e.g. people who have recently participated in an upskilling or research program like ARENA or MATS) to apply to PhDs in the current cycle, even if they are on the fence about whether they want to do a PhD.
My core arguments were that academic timelines are very slow (i.e. if you apply this year you would not start until Fall 2026), applications are generally cheap and high information value, and that applying strictly increases your future optionality. I applied to PhDs two years ago, got several offers, did not end up doing a PhD, but still post-hoc endorse spending time on this.
My post last year was very late relative to deadlines; you should probably start thinking about applications soon in order to gather the required application materials (e.g. asking for references) for the December 15th deadlines.
applications are generally cheap and high information value
They’re cheap but not that cheap. I did mine in a rush last year (thanks to Bilal’s encouragement) and it still took me at least two weeks of basically full time effort and that is much faster than normal. It also costs like $1000 or more to do a dozen applications.
Also you don’t get much information. You buy a few lottery tickets. The number that come back winners is a weak signal of how good your application was.
Above some P(you want to do a PhD), I claim this is cheap, given it could effect the next 1-6 years of your life. I think I agree with you that you need to be at something like at least 25% (discussed offline) or so here already to actually commit to doing the applications. But I think your probability can be lower for spending some smaller amount of time “considering” applying.
Also you don’t get much information. You buy a few lottery tickets. The number that come back winners is a weak signal of how good your application was.
Mostly agreed. The information I think you get is whether a PhD is a good option for you. It forces you to think through the prospect and you get to chat to PhD students and professors through the process. I updated positively on PhDs after applying through this process.
You might want to stop using the honey extension. Here are some shady things they do, beyond the usual:
Steal affiliate marketing revenue from influencers (who they also often sponsor), by replacing the genuine affiliate referral cookie with their affiliate referral cookie.
Deceive customers by deliberately withholding the best coupon codes, while claiming they have found the best coupon codes on the internet; partner businesses control which coupon codes honey shows consumers.
An example implementation of this feature is Gwern.net’s “link-bibliographies” (eg). We extract all URLs from the Markdown, filter, turn them into a list with the available metadata like title/author/tags/abstract, and because we assign IDs to all links, we can also provide a reverse/backlink ‘↑’ popup of the context in the page where the link was used (and a link might be used multiple times). Wikipedia links are included, but stuffed into a sublist at the end because they would drown out the regular links. It uses dynamic/lazy transclusion, so it doesn’t cost anything if you never look at it, but if you want to print out the page or something, that should also be doable as they get loaded then by the print-mode.
We also plan to include a second version of the link-bibliography, a ‘browsing history’ version, which quietly logs each link you interact with in a big list at the end of the page (we’ll probably put it before the full link-bibliography). So the standard full link-bibliography provides all the URLs, but the browsing-history would provide just the shortlist of URLs you interact with, in the order you interacted with them. The idea is that you could more freely move in and out of popups if you didn’t have the anxiety of ‘losing’ them, because there’s an append-only log, and after reading a page, you might skim the browsing-history and open up some of them for further reading or to jog your memory about what you were reading at one point. Since the links are in temporal order, it should be easy to reconstruct your train of thought at any point as you were reading. (You could also use it to create a sort of ‘custom bibliography’, where you pop up a small subset of links focused on some particular claim or thesis, and you can save that to PDF or something.) Since it’s all transcludes, the browsing-history is also effectively free (you already paid the cost of downloading & rendering each entry when you popped it up the first time).
The Lifetime Individual Savings Account (LISA) is a government saving scheme in the UK intended primarily to help individuals between the ages of 18 and 50 buy their first home (among a few other things). You can hold your money either as cash or in stocks and shares.
The unique selling point of the scheme is that the government will add a 25% bonus on all savings up to £4000 per year. However, this comes with several restrictions. The account is intended to only be used for the following purposes: 1) to buy your first home, worth £450k or less 2) if you are aged 60 or older 3) if you are terminally ill
The government do permit individuals to use the money for other purposes, with the caveat that a 25% cut will be taken before doing so. Seems like a no brainer? Not quite.
Suppose you invest x in your LISA. The government bonus puts this up to 1.25x. Suppose later you decide to withdraw your money for purposes other than (1-3). Then you end up with 0.75×1.25x=0.9375x. That’s a 6.25% loss!
So when does it make sense to use your LISA? Suppose further you have some uncertainty over whether you will use your money for (1-3). Most likely, you are worried that you might not buy a home in the UK, or you might want to buy a home over the price of £450k (because for instance you live in London, and £450k doesn’t stretch that far).
Let’s compute the expected value of your investment if the probability of using your money for the purposes (1-3) is p (which likely means your probability of using it for 1 is also about p). Suppose we invest £x. For our investment to be worth it, we should expect at least £x back.
So, you should use your ISA if your probability of using it to buy a home (or 2,3) is above 20%. This is surprisingly low! Note further this calculation applies regardless of if you use a cash or stocks and shares LISA.
I havn’t ever seen this calculation written up publicly before so thought it was worth sharing.
Well, it seems like a no-brainer to store money you intend to spend after age 60 in such an account; for other purposes it does seem less universally useful. I’d also check the treatment of capital gains, and whether it’s included in various assets tests; both can be situationally useful and included in some analogues elsewhere.
I don’t think it’s great for post age-60 actually, as compared with a regular pension, see my reply. The comment on asset tests is useful though, thanks. Roughly LISA assets count towards many tests, while pensions don’t. More details here for those interested: https://www.moneysavingexpert.com/savings/lifetime-isas/
(not in the UK, first I’d heard of this) That 6.25% net penalty is less than the US penalty for tax-protected savings (401k), which is 10% And the US Government doesn’t even kick in (some employers do, and the deferred taxation is significant over many years). The chance that you’ll buy a house doesn’t even need to enter into your calculations, if you include the chance that you’ll live to age 60, it seems like a very good deal.
The LISA is a tax free investment account. There are no capital gains taxes on it. This is similar to the regular ISA (which you can put up to £20k in per year, doesn’t have a 25% bonus, and can be used for anything—the £4k LISA cap contributes to this £20k). I omitted this as I was implicitly viewing using this account as the counterfactual.
The LISA is often strictly worse than a workplace pension for saving for retirement, if you are employed. This is because you invest in a LISA post-(income)tax, while pension contributions are calculated pre-tax. Even if the bonus approximately makes up for tax you pay, employer contributions tip the balance towards the pension.
When evaluating whether to invest time in making things more efficient, I often see people compare the one off cost of make the thing more efficient to the expected future saved time when doing the thing. I think there is often an important third variable to track, that often swings such decisions from not worth the effort to definitely worth the effort. Namely, the expected increase in usage of the thing due to the reduction in friction of utilizing the thing. I in practice often find the final consideration to dominate.
Recent examples from my life:
Reducing the number of button presses needed for common workflows on my laptop means I both can navigate my laptop quicker and end up navigating more instead of procrastinating navigating because it is annoying.
Moving to a more central location in my city has meant I both save time commuting to things and also end up going to more things.
Automating the loading of context from my personal apps into AIs means I spend less time copy pasting context into AIs and also end up asking AIs more questions about my personal context.
Yes, Dan Luu wrote about how he writes a lot because he’s a fast typer.
See also Jevon’s paradox.
I think it was Joel Spolsky (from the Microsoft Visual Basic and Excel team) who mentioned a rule of thumb that each 10% reduction in difficulty would roughly double the market of a piece of software. And Google once knew that even 10ths of seconds of page load time had a noticeable effect on usage. This seems consistent with your claim.
There’s an opposing force here, too: Opportunity cost. If you have 10 hours to automate something that you’ll use for 4 years, is there something else you could do with those 10 hours that offered even greater payoff? This is frequently a major factor, even in business contexts. “Yes, it would be profitable, and it would be fun, but it would involve solving 5 hairy problems that only benefit a single big customer. With the same resources, we could solve 5 other hairy problems that benefit 10 customers each.”
Consider applying to PhDs soon!
Last November, I wrote a blog post titled You should consider applying to PhDs (soon!), where I argued it is probably a good use of time for junior AI safety researchers (e.g. people who have recently participated in an upskilling or research program like ARENA or MATS) to apply to PhDs in the current cycle, even if they are on the fence about whether they want to do a PhD.
My core arguments were that academic timelines are very slow (i.e. if you apply this year you would not start until Fall 2026), applications are generally cheap and high information value, and that applying strictly increases your future optionality. I applied to PhDs two years ago, got several offers, did not end up doing a PhD, but still post-hoc endorse spending time on this.
My post last year was very late relative to deadlines; you should probably start thinking about applications soon in order to gather the required application materials (e.g. asking for references) for the December 15th deadlines.
They’re cheap but not that cheap. I did mine in a rush last year (thanks to Bilal’s encouragement) and it still took me at least two weeks of basically full time effort and that is much faster than normal. It also costs like $1000 or more to do a dozen applications.
Also you don’t get much information. You buy a few lottery tickets. The number that come back winners is a weak signal of how good your application was.
[Edit: I still am glad that I applied!]
Above some P(you want to do a PhD), I claim this is cheap, given it could effect the next 1-6 years of your life. I think I agree with you that you need to be at something like at least 25% (discussed offline) or so here already to actually commit to doing the applications. But I think your probability can be lower for spending some smaller amount of time “considering” applying.
Mostly agreed. The information I think you get is whether a PhD is a good option for you. It forces you to think through the prospect and you get to chat to PhD students and professors through the process. I updated positively on PhDs after applying through this process.
You might want to stop using the honey extension. Here are some shady things they do, beyond the usual:
Steal affiliate marketing revenue from influencers (who they also often sponsor), by replacing the genuine affiliate referral cookie with their affiliate referral cookie.
Deceive customers by deliberately withholding the best coupon codes, while claiming they have found the best coupon codes on the internet; partner businesses control which coupon codes honey shows consumers.
A LW feature that I would find helpful is an easy to access list of all links cited by a given post.
An example implementation of this feature is Gwern.net’s “link-bibliographies” (eg). We extract all URLs from the Markdown, filter, turn them into a list with the available metadata like title/author/tags/abstract, and because we assign IDs to all links, we can also provide a reverse/backlink ‘↑’ popup of the context in the page where the link was used (and a link might be used multiple times). Wikipedia links are included, but stuffed into a sublist at the end because they would drown out the regular links. It uses dynamic/lazy transclusion, so it doesn’t cost anything if you never look at it, but if you want to print out the page or something, that should also be doable as they get loaded then by the print-mode.
We also plan to include a second version of the link-bibliography, a ‘browsing history’ version, which quietly logs each link you interact with in a big list at the end of the page (we’ll probably put it before the full link-bibliography). So the standard full link-bibliography provides all the URLs, but the browsing-history would provide just the shortlist of URLs you interact with, in the order you interacted with them. The idea is that you could more freely move in and out of popups if you didn’t have the anxiety of ‘losing’ them, because there’s an append-only log, and after reading a page, you might skim the browsing-history and open up some of them for further reading or to jog your memory about what you were reading at one point. Since the links are in temporal order, it should be easy to reconstruct your train of thought at any point as you were reading. (You could also use it to create a sort of ‘custom bibliography’, where you pop up a small subset of links focused on some particular claim or thesis, and you can save that to PDF or something.) Since it’s all transcludes, the browsing-history is also effectively free (you already paid the cost of downloading & rendering each entry when you popped it up the first time).
Should you invest in a Lifetime ISA? (UK)
The Lifetime Individual Savings Account (LISA) is a government saving scheme in the UK intended primarily to help individuals between the ages of 18 and 50 buy their first home (among a few other things). You can hold your money either as cash or in stocks and shares.
The unique selling point of the scheme is that the government will add a 25% bonus on all savings up to £4000 per year. However, this comes with several restrictions. The account is intended to only be used for the following purposes:
1) to buy your first home, worth £450k or less
2) if you are aged 60 or older
3) if you are terminally ill
The government do permit individuals to use the money for other purposes, with the caveat that a 25% cut will be taken before doing so. Seems like a no brainer? Not quite.
Suppose you invest x in your LISA. The government bonus puts this up to 1.25x. Suppose later you decide to withdraw your money for purposes other than (1-3). Then you end up with 0.75×1.25x=0.9375x. That’s a 6.25% loss!
So when does it make sense to use your LISA? Suppose further you have some uncertainty over whether you will use your money for (1-3). Most likely, you are worried that you might not buy a home in the UK, or you might want to buy a home over the price of £450k (because for instance you live in London, and £450k doesn’t stretch that far).
Let’s compute the expected value of your investment if the probability of using your money for the purposes (1-3) is p (which likely means your probability of using it for 1 is also about p). Suppose we invest £x. For our investment to be worth it, we should expect at least £x back.
EV = p(bonus scenario) + (1−p)(penalty scenario) = p(1.25x)+(1−p)(0.75×1.25x)≥x, implying p≥0.2.
So, you should use your ISA if your probability of using it to buy a home (or 2,3) is above 20%. This is surprisingly low! Note further this calculation applies regardless of if you use a cash or stocks and shares LISA.
I havn’t ever seen this calculation written up publicly before so thought it was worth sharing.
Well, it seems like a no-brainer to store money you intend to spend after age 60 in such an account; for other purposes it does seem less universally useful. I’d also check the treatment of capital gains, and whether it’s included in various assets tests; both can be situationally useful and included in some analogues elsewhere.
I don’t think it’s great for post age-60 actually, as compared with a regular pension, see my reply. The comment on asset tests is useful though, thanks. Roughly LISA assets count towards many tests, while pensions don’t. More details here for those interested: https://www.moneysavingexpert.com/savings/lifetime-isas/
(not in the UK, first I’d heard of this) That 6.25% net penalty is less than the US penalty for tax-protected savings (401k), which is 10% And the US Government doesn’t even kick in (some employers do, and the deferred taxation is significant over many years). The chance that you’ll buy a house doesn’t even need to enter into your calculations, if you include the chance that you’ll live to age 60, it seems like a very good deal.
Couple more things I didn’t explain:
The LISA is a tax free investment account. There are no capital gains taxes on it. This is similar to the regular ISA (which you can put up to £20k in per year, doesn’t have a 25% bonus, and can be used for anything—the £4k LISA cap contributes to this £20k). I omitted this as I was implicitly viewing using this account as the counterfactual.
The LISA is often strictly worse than a workplace pension for saving for retirement, if you are employed. This is because you invest in a LISA post-(income)tax, while pension contributions are calculated pre-tax. Even if the bonus approximately makes up for tax you pay, employer contributions tip the balance towards the pension.
karpathy reviews sleep trackers: https://karpathy.bearblog.dev/finding-the-best-sleep-tracker/
He might want to consider taking a look at https://manifund.org/projects/ozempic-for-sleep-proof-of-concept-research-for-safely-reducing-sleep