in the space of aging (or models in bioscience research in general), you should contact Alexey Guzey and Jose Ricon and Michael Nielsen and Adam Marblestone and Laura Deming. You’d particularly click with some of these people, and many of them recognize the low number of independent thinkers in the area.
I think you have a kind of thinking that almost everyone else in aging I know seems to lack (If I showed your writing to most aging researchers, they’d most likely glare over what you wrote), so writing a good way to, say, put a physical principles framework to aging can result in a lot of people wanting to fund you (a la Pascal’s wager—there are LOTS of people who are willing to throw money into the field even if it doesn’t have a huge chance of producing results—and a good physical framework can make others want you to make the most out of your time, especially as many richer/older people lack the neuroplasticity to change how aging research is fine). Many many many papers have already been written on the field (many by people making guesses as to what matters most) - a lot of them being very messy and not very first-principles (even JP de magalhaes’s work, while important, is kind of “messy” guessing at the factors that matter).
Are you time-limited? Do you have all the money needed to maximize your output on the world? (note for making the most out of your limited time, I generally recommend being like mati roy and trying to create a simulation of yourself that future you/others can search, which generally requires a lot of HD/streaming—though even that is not that expensive).
It seems that you can understand a broad range of extremely technical fields that few other people do (esp optimization theory and category theory), and that you get a lot out of what you read (the time investment of other people reading a technical textbook may not be as high as that of you reading one) - thus you may be more suited for theoretical/scaleable work than you are for work that’s less generalizeable/scaleable (one issue with bioscience research is that most people in bioscience research spend a lot of time on busywork that may be automated later, so most biologists aren’t as broad or generalizeable as you are, and you can put together broad frameworks that can improve the efficiencies/rigor of future people who read you, so you should optimize for things that are highly generalizeable.)
[you also put them all in a clear/explainable fashion that makes me WANT to return back to reading your posts, which is not something I can say for most textbooks].
There are tradeoffs between spending more time on ONE area vs spending time on ANOTHER area of academic knowledge—though there are areas where good thinking in one area can transfer to another (eg optimization theory ⇒ whole cell modeling/systems biology in biology/aging). Building general purpose models (if described well) could be an area you might have unique comparative advantage over others in, where you could guide someone else’s thinking on the details even if you did not have the time to look at the individual implementations of your model on the system at hand.
If you become someone who everyone else in the area wants to follow (eg Laura Deming), you can ask question and get pretty much every expert swarming over you, wanting to answer your questions.
You seem good at theory (which is low-cost), but how much would you want to ideally budget for sample lab space and experiments? [the more details you put in your framework—along with how you will measure the deliverables, the easier it would be to get some sort of starter funding for your ideas]. Doing some small cheap study (and putting all the output in an open online format that transcends academic publishing) can help net you attention and funding for more studies (it certainly seems that with every nascent field, it takes a certain something to get noticed, but once you do get noticed, things can get much easier over time, particularly if you’re the independent kind of person). Wrt biology, I do get the impression that you don’t interact much with other biologists, which might make the communication problems more difficult for now [like, if I sent your aging posts as is to most biologists I know, I don’t think they would be particularly responsive or excited].
Wealth is a measure of your ability to do what you would like to do, when you would like to do it—a measure of your breadth of immediately available choice. Therefore your wealth is determined by the resources you presently own, as everything requires resources.
Generally speaking, due to aging [and the loss of potential that comes with it] most people’s wealth decreases with age (it’s said that the wealthiest people are really those that are born) - however, your ability to imagine what you can do with wealth (within an affordance space—or what you can imagine doing over the next year if given all the resources you can handle—framework) can increase over time. Mental models are only wealth inasmuch as they actively work to improve people’s decision-making on the margin relative to an alternative model (they are necessary for innovation, but there are now so many mental models that taking time to understand one reduces the amount of time one has to understand another mental model) - I do believe that compressible mental models (or network models) that explain a principle elegantly can offload the time investment it takes to use a model to act on a decision (eg superforecasters use elegant models that others believe and can act on—thus knowing when to use the expertise of superforecasters can help decision-making). Not many people can create an elegant mental model, and fewer can create one that is useful on top of all the other models that have been developed (useful in the sense that it makes it more useful for others to read your model than all the confusing model renditions used by others) - obviously there is vast space for improvement on this front (as you can see if you read quantum country) as most people forget the vast majority of what they read from textbooks or from conversations with others. Presentism is an ongoing issue as more papers/online content is published than there are total eyeballs to read them (+all the material published in the past)
The best kind of wealth you can create, in this sense, is a model/framework/tool that everyone uses. Think of how wealth was created with the invention of a new programming language, for example, or with Stack Exchange/Hacker News, or a game engine, or the wealth that could be created with automating tedious steps in biology, or the kind that makes it far easier for other people to make or write almost anything. The more people cite you, the more wealth and influence (of a certain kind) you get. This generalizes better than putting your entire life into studying a single protein or model organism, especially if you find a model/technique that is easily-adoptable and makes it easy to do/automate high-throughput “-omics” of all organisms and interventions at once (making it possible for others to speed up and generalize biology research where it used to be super-slow). Bonus points if you make it machine-readable and put in a database that can be queried so that it is useful even if no one reads it at first [as amount of data generated is higher/faster than the total mental bandwidth/capacity of all humans who can read it].
[btw, attention also correlates with wealth, and money/attention/wealth is competitive in a way that knowledge is not (wisdom may be which knowledge to read in which order—wisdom is how you can use knowledge to maximize the wealth that you can use with that knowledge)]
[Shaping people’s framework by causing them to constantly refer to your list of causes, btw, is another way to create influence/wealth—but this may get in the way of maximizing social wealth over a lifetime if your frameworks end up preventing people from modeling or envisioning how they can discover new anomalies in the data that do not fit within those frameworks—this is also why we just need a better concrete framework with physical observables for measuring aging rate, where our ability to characterize epigenetic aging is a local improvement. ]
In the area of aging already there is too much “knowledge” (though not all of it particularly insightful), but does the sum of all aging papers published constitute as knowledge? Laura Deming mentions on her twitter that she thinks about what not to read, rather than what to read, and recommends students study math/CS/physics rather than biochemistry. There can be a way to compress all this knowledge into a more organized physical principles format that better helps other people map what counts as knowledge and what doesn’t count—but at this moment the sum of all aging research is still a disorganized mess, and it may be that the details of much of what we know now will become superseded by new high-throughput papers that publish data/meta-data rather than as papers (along with a publicly accessible annotation service that better guides people as to which aging papers represent true progress and which papers will simply obsolete quickly.). Guiding people to the physical insight of a cell is more important for this kind of true understanding of aging, even though we can still get things done through rudimentary insight-free guesses like more work on rapamycin and calorie restriction.
in the space of aging (or models in bioscience research in general), you should contact Alexey Guzey and Jose Ricon and Michael Nielsen and Adam Marblestone and Laura Deming. You’d particularly click with some of these people, and many of them recognize the low number of independent thinkers in the area.
I think you have a kind of thinking that almost everyone else in aging I know seems to lack (If I showed your writing to most aging researchers, they’d most likely glare over what you wrote), so writing a good way to, say, put a physical principles framework to aging can result in a lot of people wanting to fund you (a la Pascal’s wager—there are LOTS of people who are willing to throw money into the field even if it doesn’t have a huge chance of producing results—and a good physical framework can make others want you to make the most out of your time, especially as many richer/older people lack the neuroplasticity to change how aging research is fine). Many many many papers have already been written on the field (many by people making guesses as to what matters most) - a lot of them being very messy and not very first-principles (even JP de magalhaes’s work, while important, is kind of “messy” guessing at the factors that matter).
Are you time-limited? Do you have all the money needed to maximize your output on the world? (note for making the most out of your limited time, I generally recommend being like mati roy and trying to create a simulation of yourself that future you/others can search, which generally requires a lot of HD/streaming—though even that is not that expensive).
It seems that you can understand a broad range of extremely technical fields that few other people do (esp optimization theory and category theory), and that you get a lot out of what you read (the time investment of other people reading a technical textbook may not be as high as that of you reading one) - thus you may be more suited for theoretical/scaleable work than you are for work that’s less generalizeable/scaleable (one issue with bioscience research is that most people in bioscience research spend a lot of time on busywork that may be automated later, so most biologists aren’t as broad or generalizeable as you are, and you can put together broad frameworks that can improve the efficiencies/rigor of future people who read you, so you should optimize for things that are highly generalizeable.)
[you also put them all in a clear/explainable fashion that makes me WANT to return back to reading your posts, which is not something I can say for most textbooks].
There are tradeoffs between spending more time on ONE area vs spending time on ANOTHER area of academic knowledge—though there are areas where good thinking in one area can transfer to another (eg optimization theory ⇒ whole cell modeling/systems biology in biology/aging). Building general purpose models (if described well) could be an area you might have unique comparative advantage over others in, where you could guide someone else’s thinking on the details even if you did not have the time to look at the individual implementations of your model on the system at hand.
If you become someone who everyone else in the area wants to follow (eg Laura Deming), you can ask question and get pretty much every expert swarming over you, wanting to answer your questions.
You seem good at theory (which is low-cost), but how much would you want to ideally budget for sample lab space and experiments? [the more details you put in your framework—along with how you will measure the deliverables, the easier it would be to get some sort of starter funding for your ideas]. Doing some small cheap study (and putting all the output in an open online format that transcends academic publishing) can help net you attention and funding for more studies (it certainly seems that with every nascent field, it takes a certain something to get noticed, but once you do get noticed, things can get much easier over time, particularly if you’re the independent kind of person). Wrt biology, I do get the impression that you don’t interact much with other biologists, which might make the communication problems more difficult for now [like, if I sent your aging posts as is to most biologists I know, I don’t think they would be particularly responsive or excited].
BTW—regarding wealth—fightaging has a great definition at https://www.fightaging.org/archives/2008/02/what-is-wealth/
Generally speaking, due to aging [and the loss of potential that comes with it] most people’s wealth decreases with age (it’s said that the wealthiest people are really those that are born) - however, your ability to imagine what you can do with wealth (within an affordance space—or what you can imagine doing over the next year if given all the resources you can handle—framework) can increase over time. Mental models are only wealth inasmuch as they actively work to improve people’s decision-making on the margin relative to an alternative model (they are necessary for innovation, but there are now so many mental models that taking time to understand one reduces the amount of time one has to understand another mental model) - I do believe that compressible mental models (or network models) that explain a principle elegantly can offload the time investment it takes to use a model to act on a decision (eg superforecasters use elegant models that others believe and can act on—thus knowing when to use the expertise of superforecasters can help decision-making). Not many people can create an elegant mental model, and fewer can create one that is useful on top of all the other models that have been developed (useful in the sense that it makes it more useful for others to read your model than all the confusing model renditions used by others) - obviously there is vast space for improvement on this front (as you can see if you read quantum country) as most people forget the vast majority of what they read from textbooks or from conversations with others. Presentism is an ongoing issue as more papers/online content is published than there are total eyeballs to read them (+all the material published in the past)
The best kind of wealth you can create, in this sense, is a model/framework/tool that everyone uses. Think of how wealth was created with the invention of a new programming language, for example, or with Stack Exchange/Hacker News, or a game engine, or the wealth that could be created with automating tedious steps in biology, or the kind that makes it far easier for other people to make or write almost anything. The more people cite you, the more wealth and influence (of a certain kind) you get. This generalizes better than putting your entire life into studying a single protein or model organism, especially if you find a model/technique that is easily-adoptable and makes it easy to do/automate high-throughput “-omics” of all organisms and interventions at once (making it possible for others to speed up and generalize biology research where it used to be super-slow). Bonus points if you make it machine-readable and put in a database that can be queried so that it is useful even if no one reads it at first [as amount of data generated is higher/faster than the total mental bandwidth/capacity of all humans who can read it].
[btw, attention also correlates with wealth, and money/attention/wealth is competitive in a way that knowledge is not (wisdom may be which knowledge to read in which order—wisdom is how you can use knowledge to maximize the wealth that you can use with that knowledge)]
[Shaping people’s framework by causing them to constantly refer to your list of causes, btw, is another way to create influence/wealth—but this may get in the way of maximizing social wealth over a lifetime if your frameworks end up preventing people from modeling or envisioning how they can discover new anomalies in the data that do not fit within those frameworks—this is also why we just need a better concrete framework with physical observables for measuring aging rate, where our ability to characterize epigenetic aging is a local improvement. ]
In the area of aging already there is too much “knowledge” (though not all of it particularly insightful), but does the sum of all aging papers published constitute as knowledge? Laura Deming mentions on her twitter that she thinks about what not to read, rather than what to read, and recommends students study math/CS/physics rather than biochemistry. There can be a way to compress all this knowledge into a more organized physical principles format that better helps other people map what counts as knowledge and what doesn’t count—but at this moment the sum of all aging research is still a disorganized mess, and it may be that the details of much of what we know now will become superseded by new high-throughput papers that publish data/meta-data rather than as papers (along with a publicly accessible annotation service that better guides people as to which aging papers represent true progress and which papers will simply obsolete quickly.). Guiding people to the physical insight of a cell is more important for this kind of true understanding of aging, even though we can still get things done through rudimentary insight-free guesses like more work on rapamycin and calorie restriction.