I also believe the AI we have now has deep unmined depths and uses , and my point of view is that we should take time tomlearn how to use and more importantly manage what AI can do now before rushing ahead and making it bigger, faster, stronger.
Especially when it comes at such a huge ecological cost at a time where clean water availability is shrinking in shocking leaps and bounds.
I do wonder if these datacenter planners have asked ai how to make it environmentally friendly and cost effective, or if they just decided that reusable cooling gel is too expensive and haven't even bothered looking at alternatives 🤷
Very interesting summary of some presently available AI capabilities people may be unaware of-but your overall thesis that we shouldnt worry about “future” capabilities ignores the fact that according to the leading experts in AI that “future” and the possibility of catastrophic harm is no longer hypothetical or a worry for down the road - its arrived, its here - so its essential to immediately try to deal with that reality
As a marketer, I relate so much to what must be expertly discarded. Much of my focus with AI is preparing it to produce the best work it can on round 1, then cumulatively learning from what I choose to cut. Anyway, love your thinking on this.
Your jagged edge framing might be useful for discussing taste as well.
A couple of years ago I was helping a startup get into the AI consulting space for asset and wealth managers. My pitch to prospective clients was that we had the technology, talent and taste to be their preferred partner. And of the three, taste should be considered our real differentiator. All of senior leadership had decades of experience in their respective niches, and well-honed senses of what good looks like.
The emphasis on taste had the virtue of being both true and comforting. Prospects appreciated the idea there would still be competent humans-in-the-loop, and we liked feeling that our experience—nuance and secret sauce not captured by the models—was still worth something.
A lot of people feel this way. I read my fair share, and a common theme is that “taste” is our collective safe harbor. I used to nod along, but had an uneasy sense. If AI can learn what we know, why not what we like? The words needed to finish the thought escaped me until recently.
Taste is pattern matching.
The patterns are what we like or don’t like. What’s true for me might not be true for you, but that’s beside the point. As long as one can articulate their preferences, and leave enough breadcrumbs along the way, AI can learn to be a good enough arbiter most of the time. If AI is good at anything it is pattern matching.
The agency piece is critical- in academic fields next to nobody is using these tools at all, and if they are, only at a very basic level. I have yet to meet anyone in my field who didn’t give me a blank stare when I asked if they were using a harness.
I try to build things that are unrelated to my actual work just to learn from my failures and get better at prompting- because with each new release, all of a sudden an area of my field that AI didn’t really get is suddenly extremely proficient
I found this post very helpful in thinking about the recent calls for oversight in AI development and what that might look like. I began thinking about my experience in oversight of an evolving technology which had both potential great risks and advantages when I served on the NIH’s Recombinant DNA Advisory several decades ago. This oversight activity used members who had varying amounts of deep and wide knowledge, different tastes(viewpoints and beliefs) and varying degree of agency. However we decide to approach the oversight of AI these will be critical issues in choosing those that execute that oversight.
"GPT-6 Astra and Fable 5.1 are already enough to change how large parts of the economy work"
I think if AI frontier work stopped right not (I'm sure it won't, and I'm not sure I think it should), we would be able profitably to mine present AI's value for decades. It's so easy to end a day with AI with more items on your to-do list than you started with, and it can soak up every minute you give it.
How does your home lab look Ethan - I was wondering do you have a separate set of computers to run experiments like the blender one - do you have VMs sitting on a box somewhere, or a rack of servers over in the laundry?
I love everything you write about AI. And it’s genuinely useful. You think deeply about all these issues and communicate so clearly. I also like the calm nature of your tone. There is so much fear mongering going on right now. There are real risks, no doubt about it, AND I share your optimism that we humans will figure it out and everything will turn out ok.
I too noticed that the LLMs seem to be expertish at everything, but not make many spontaneous connections between fields the way a human generalist / renaissance person might. I wonder if this has something to do with the mixture of experts architecture, according to GPT 6 Pro the answer is not clear.
I also believe the AI we have now has deep unmined depths and uses , and my point of view is that we should take time tomlearn how to use and more importantly manage what AI can do now before rushing ahead and making it bigger, faster, stronger.
Especially when it comes at such a huge ecological cost at a time where clean water availability is shrinking in shocking leaps and bounds.
I do wonder if these datacenter planners have asked ai how to make it environmentally friendly and cost effective, or if they just decided that reusable cooling gel is too expensive and haven't even bothered looking at alternatives 🤷
Very interesting summary of some presently available AI capabilities people may be unaware of-but your overall thesis that we shouldnt worry about “future” capabilities ignores the fact that according to the leading experts in AI that “future” and the possibility of catastrophic harm is no longer hypothetical or a worry for down the road - its arrived, its here - so its essential to immediately try to deal with that reality
As a marketer, I relate so much to what must be expertly discarded. Much of my focus with AI is preparing it to produce the best work it can on round 1, then cumulatively learning from what I choose to cut. Anyway, love your thinking on this.
Your jagged edge framing might be useful for discussing taste as well.
A couple of years ago I was helping a startup get into the AI consulting space for asset and wealth managers. My pitch to prospective clients was that we had the technology, talent and taste to be their preferred partner. And of the three, taste should be considered our real differentiator. All of senior leadership had decades of experience in their respective niches, and well-honed senses of what good looks like.
The emphasis on taste had the virtue of being both true and comforting. Prospects appreciated the idea there would still be competent humans-in-the-loop, and we liked feeling that our experience—nuance and secret sauce not captured by the models—was still worth something.
A lot of people feel this way. I read my fair share, and a common theme is that “taste” is our collective safe harbor. I used to nod along, but had an uneasy sense. If AI can learn what we know, why not what we like? The words needed to finish the thought escaped me until recently.
Taste is pattern matching.
The patterns are what we like or don’t like. What’s true for me might not be true for you, but that’s beside the point. As long as one can articulate their preferences, and leave enough breadcrumbs along the way, AI can learn to be a good enough arbiter most of the time. If AI is good at anything it is pattern matching.
The agency piece is critical- in academic fields next to nobody is using these tools at all, and if they are, only at a very basic level. I have yet to meet anyone in my field who didn’t give me a blank stare when I asked if they were using a harness.
I try to build things that are unrelated to my actual work just to learn from my failures and get better at prompting- because with each new release, all of a sudden an area of my field that AI didn’t really get is suddenly extremely proficient
I have often considered using ai in my research but right now, I don't know enough about using AI for that to be a wise idea for me.
I suppose it is the difference between someone learning how to use AI to improve what they do and AI replacing what that someone can currently do.
I found this post very helpful in thinking about the recent calls for oversight in AI development and what that might look like. I began thinking about my experience in oversight of an evolving technology which had both potential great risks and advantages when I served on the NIH’s Recombinant DNA Advisory several decades ago. This oversight activity used members who had varying amounts of deep and wide knowledge, different tastes(viewpoints and beliefs) and varying degree of agency. However we decide to approach the oversight of AI these will be critical issues in choosing those that execute that oversight.
"GPT-6 Astra and Fable 5.1 are already enough to change how large parts of the economy work"
I think if AI frontier work stopped right not (I'm sure it won't, and I'm not sure I think it should), we would be able profitably to mine present AI's value for decades. It's so easy to end a day with AI with more items on your to-do list than you started with, and it can soak up every minute you give it.
How does your home lab look Ethan - I was wondering do you have a separate set of computers to run experiments like the blender one - do you have VMs sitting on a box somewhere, or a rack of servers over in the laundry?
I love everything you write about AI. And it’s genuinely useful. You think deeply about all these issues and communicate so clearly. I also like the calm nature of your tone. There is so much fear mongering going on right now. There are real risks, no doubt about it, AND I share your optimism that we humans will figure it out and everything will turn out ok.
I too noticed that the LLMs seem to be expertish at everything, but not make many spontaneous connections between fields the way a human generalist / renaissance person might. I wonder if this has something to do with the mixture of experts architecture, according to GPT 6 Pro the answer is not clear.
Sort of.
Cool. cool. cool. cool.
You are a rational animal. Keep up the good work.
cool