I think it’s worth spending 10h/week even if you expect to get less than 10h/week in productivity boost right now, because it does take a while to get good at using these systems
I am aware of this argument. Counterpoint: models get increasingly easier to use as they get more powerful – better at inferring your intent, not subject to entire classes of failure modes plaguing earlier generations, etc. – so the skills you’ll learn by painstakingly wrangling current LLMs will end up obsoleted by subsequent generation.
Like, inasmuch as one buys that LLMs are on the trajectory to becoming absurdly powerful, one should not expect to need to develop intricate skillsets for squeezing value out of them. You’re not gonna need to prompt-engineer AGIs and invent custom scaffolds for them, they will build the scaffolds for themselves and your cleverest prompts will be as effective as “just talk to them the obvious way”. (Same for ad-hoc continuous-memory setups and context-management hacks et cetera: if the AGI labs crack architectural continuous learning, it’ll all be obsoleted overnight.)
On the other hand, inasmuch as you don’t believe that LLMs are going to be getting increasingly easier to use, you essentially don’t believe that they’re on the trajectory to become absurdly powerful AGIs. If so, you should downgrade your expectation of how much value their future generations will bring you, and accordingly downgrade how much you should be investing in them now.
Oh, by the way: I saw you saying that you’re observing much more software downstream of LLMs. Any chance you can elaborate on that, provide some examples? This is the sort of thing I’m very interested in tracking, and high-quality information sources are hard to come by.
It’s clear to me that the product velocity of things like Cursor, Claude Code and Codex is much higher than I’ve seen for basically any other product. This is what I meant by saying most of the software I’ve seen has been for software developers themselves.
We are now starting to see this trickle out. Internally at Lightcone more of my staff can now build software solutions to problems where they previously needed support from a software engineer (a random example of this is building Airtable automations with script blocks). My guess is if you surveyed Hacker News you would also see that more things on there are small applications that someone built that previously would have taken prohibitively long to build. This is a random example of one such project: https://www.ismypubfucked.com/
The improvements in thinking quality of the models doesn’t address one of the main causes of downlift, which is the breaking up of deep work by regularly (and sometimes surprisingly) having 1-10 min periods where you are no longer able to do productive work because the LLM is executing a task, and so you lose cognitive context, and tend toward shallower decision-making. This is something that continues to plague me, often causing me to waste a lot of time (both in the individual chunks and when summing my decision-making over a day).
Not convinced this isn’t a temporary artefact of the current time horizons. Like, in the future, I think it’s plausible that the two categories of tasks you’d be delegating would be either (a) the sort of shallow tasks the future models would be able to complete instantly, or (b) the sort of deep tasks that’d take future models hours to complete.
Fair enough, though, maybe this counts. But is there really a rich suite of skills like that, and would they really take that long to learn by the time learning them does become immediately net-positive?
I think it’s fairly likely I need to re-orient my entire workflow around constantly (but somewhat surprisingly) having heavy-tail distributions of time where I can’t do productive work on my main work. This is not a small deal. I suspect that many people will deal with it very differently.
Here are some possible responses:
Build a practice of having multiple parallel LLM projects you can work on simultaneously (I have not found this cognitively trivial)
Build up a backlog of simple low-context tasks you can do, and figure out how to turn your lower-importance work into that kind of task
Learn how to identify tasks that aren’t worth it because of the downlift, even though you know an AI could do it.
The first two really sound quite complex, and the third sounds genuinely hard. I suspect other people will find other solutions...
I am aware of this argument. Counterpoint: models get increasingly easier to use as they get more powerful – better at inferring your intent, not subject to entire classes of failure modes plaguing earlier generations, etc. – so the skills you’ll learn by painstakingly wrangling current LLMs will end up obsoleted by subsequent generation.
Like, inasmuch as one buys that LLMs are on the trajectory to becoming absurdly powerful, one should not expect to need to develop intricate skillsets for squeezing value out of them. You’re not gonna need to prompt-engineer AGIs and invent custom scaffolds for them, they will build the scaffolds for themselves and your cleverest prompts will be as effective as “just talk to them the obvious way”. (Same for ad-hoc continuous-memory setups and context-management hacks et cetera: if the AGI labs crack architectural continuous learning, it’ll all be obsoleted overnight.)
On the other hand, inasmuch as you don’t believe that LLMs are going to be getting increasingly easier to use, you essentially don’t believe that they’re on the trajectory to become absurdly powerful AGIs. If so, you should downgrade your expectation of how much value their future generations will bring you, and accordingly downgrade how much you should be investing in them now.
Oh, by the way: I saw you saying that you’re observing much more software downstream of LLMs. Any chance you can elaborate on that, provide some examples? This is the sort of thing I’m very interested in tracking, and high-quality information sources are hard to come by.
It’s clear to me that the product velocity of things like Cursor, Claude Code and Codex is much higher than I’ve seen for basically any other product. This is what I meant by saying most of the software I’ve seen has been for software developers themselves.
We are now starting to see this trickle out. Internally at Lightcone more of my staff can now build software solutions to problems where they previously needed support from a software engineer (a random example of this is building Airtable automations with script blocks). My guess is if you surveyed Hacker News you would also see that more things on there are small applications that someone built that previously would have taken prohibitively long to build. This is a random example of one such project: https://www.ismypubfucked.com/
The improvements in thinking quality of the models doesn’t address one of the main causes of downlift, which is the breaking up of deep work by regularly (and sometimes surprisingly) having 1-10 min periods where you are no longer able to do productive work because the LLM is executing a task, and so you lose cognitive context, and tend toward shallower decision-making. This is something that continues to plague me, often causing me to waste a lot of time (both in the individual chunks and when summing my decision-making over a day).
Not convinced this isn’t a temporary artefact of the current time horizons. Like, in the future, I think it’s plausible that the two categories of tasks you’d be delegating would be either (a) the sort of shallow tasks the future models would be able to complete instantly, or (b) the sort of deep tasks that’d take future models hours to complete.
Fair enough, though, maybe this counts. But is there really a rich suite of skills like that, and would they really take that long to learn by the time learning them does become immediately net-positive?
I think it’s fairly likely I need to re-orient my entire workflow around constantly (but somewhat surprisingly) having heavy-tail distributions of time where I can’t do productive work on my main work. This is not a small deal. I suspect that many people will deal with it very differently.
Here are some possible responses:
Build a practice of having multiple parallel LLM projects you can work on simultaneously (I have not found this cognitively trivial)
Build up a backlog of simple low-context tasks you can do, and figure out how to turn your lower-importance work into that kind of task
Learn how to identify tasks that aren’t worth it because of the downlift, even though you know an AI could do it.
The first two really sound quite complex, and the third sounds genuinely hard. I suspect other people will find other solutions...