The wait equation is totally irrelevant here. Where the wait equation has relevance is the fact that a generation ship, once launched, is likely unable to reconfigure itself to take advantage of new technologies invented while it is underway. This is key to why there is an incentive to wait, and there is no parallel in the idea of investing in AI. Someone who chooses to invest in AI does get access to tech that develops in the mean time. Indeed it is that investment which generates the the technology.

This completely misses the concept that the technology doesn’t just create itself without investment. SOMEONE has to invest for that progress to be made. It’s why I always hated the application of this to tech.

Reminds me of the classic paper "The Effects of Moore's Law and Slacking on Large Computations": https://arxiv.org/abs/astro-ph/9912202

There's a fun application of this argument to the bit in the Hitch Hiker's Guide to the Galaxy books, where a civilization runs a 7.5m-year-long computer program to calculate the answer to the question of life, the universe and everything.

Someone pointed out that they would have been better off waiting for about 40 years of Moore's law to happen, then building a computer and running the same calculation in about 2 years.

I think with AI it's a bit of a different question then space travel. The flip side you have a limited time to add value before the activity is no longer valuable due to AI.

In general once a machine can do something (and AI is a machine) it quickly becomes no longer a highly valuable activity.

For example, being a portrait painter around the time cameras were invented.

So if there's a project that you think AI will do for you, just keep in mind that by the time AI gets there the effective value of that activity will have greatly diminished and you are unlikely to get out of it what you would if you do it today.

If you wait, you may find you've been reduced from highly trained master of fine art to just another guy who says "say cheese" and pushes a button.

The value of problems is not fixed.

The space version is an interesting comparison though, because while the value of space exploration would increase with speed of travel (due to being able to make use of resources across greater distances), the value of any technological accomplishment decreases as they become easier.

What I find interesting about this are the things the specific example leaves out. Many engineers consider code reviews a real drag, but they aren't just busywork or wasted time or compliance checkboxes. Done well, they are a learning tool, a teaching opportunity, and a form of communication between engineers working in different areas of the code at different skill levels and knowledge levels. All of those things have value and add to team productivity in the long term. The "cost" in terms of hours spent doing code reviews is not wasted. But if you turn that job over to AI (even if you could), you lose almost all the benefits, adding a drag on your future productivity as knowledge gaps grow and communication declines.

On the other hand, if you're doing code review poorly, and it's just a waste of everyone's time, then you're far better off just dropping them altogether than spending money on an AI system to do poor code reviews for you.

There is a neglected aspect here, which is internal expertise. By choosing to build, even if ultimately you end up choosing another tool from a vendor, your team picked up many important skills to be able to evaluate costs, architecture and performance. They also might be able to better customise the tool to your application. This expertise might end up saving money in the end.

I think this analogy is best suited for training models. Even if you had access to OpenAIs datasets right now, I don't think it would make sense for you to train them, except if you are a 500B+ company. Training costs will likely go down with time though, so at some point this might change.

Someone needs to start building the Linux of spaceships now if we're ever going to have one. In terms of AI, the difference is that advances in AI can self adapt new advances. So the question is, at what point do you invest in AI given we haven't reached that stage yet - because by then you'll have waited too long. So, in reality, any time is the best time to invest in AI as long as it's before that line.

Most AI investment is going to be applying off the shelf stuff to a new industry, so you might as well get started now to get the customers then when your wholesaler (OpenAI, AWS etc.) stocks the latest product you can wrap it up in a nice bow and sell it to your customers.

AI investment in SOTA though I have no idea how you time that but I am guessing early is better.

I sometimes think of a bigger version of this question. Would it be better if rennaisance and scientific revolution had come way later than it did. Maybe humans would have been more evolved and ready for the changes and can make better decisions. Progress is inevitable, but the rapidity of change might be our downfall.

It's like saying "If you can predict the future, you'll know whether to invest or not!"

Well duh, but first find all those coefficients and then come back and we'll talk yeah?

>C_b = (T_s - T_v) * ROC * 1/52

I have so many problems with this line

Makes me think of this xkcd: https://xkcd.com/989/