Alex K. Chen

jvgf jgh jmh,g jhg


Sorted by New


How often do you check this forum?

You know, I barely checked LW from 2015 to 2020, and now I check it like, every time I feel like I need some novelty refresh, almost as much as I do Twitter... It has definitely improved since a few years ago

Are index funds still a good investment?

The dot-com crash was also preceded by an extremely obvious and unique bubble that has not been seen since - diversifying/rebalancing during a massive/obvious bubble doesn't take that much special skill or awareness, and we're more aware of bubble dynamics now than 2000.

Are index funds still a good investment?

Throughout the last decade (or last 15 years, really), FAANG stocks (and QQQ) have consistently overperformed the market/index funds, with roughly comparable maximum drawdowns relative to even the S&P. It was clear to many of us technophilic early adopters even in the late 2000s that Amazon/Google were going to take over the world (though I'd replace Netflix with NVIDIA as NVIDIA is just more innovative), and their returns have massively outperformed the market, with much smaller drawdowns. COVID only accelerated the returns from FAANG - however - with their monopolization (and penetration into all markets, reducing what upside risk there is left), I'm not sure if FAANG has as much market capture, going forward, as there was 5-10 years ago. I know some have said that it is safe to invest in "singularity stocks" like the cloud - ones that have non-zero chance of precipitating the singularity (or feeding into the data-heavy thesis that accelerationism happens when you have more data/compute power/better algorithms, and only tech-heavy companies have really embraced this trend - some even liken Tesla's valuation to one that you can only understand if it were a "tech stock"). 

This year has pretty much accelerated the growth of all "new technology" stocks too (eg everything and anything to do with "new tech" exploded in value => to be fair ALL the "meme stocks" performed well), but many are now  that they're overvalued and the upside risk to them is not as high as they used to be. Ark Invest is the closest thing there is to a "hedge fund" that tries to understand "new technology" (even traditional hedge fund people like Bill Ackman and Ray Dalio aren't technophiles or well-versed in "technology" => it's known that hedge fund people tend not to outperform the market on long timescales, but a surprisingly small percent of them are like, technophilic), and it has had amazing market-beating returns over the last few years (where you don't have to spend that much time paying attention to it). Also, despite technophilia, the ARKK funds haven't really beat index funds pre-COVID (similarly to solar ETFs, which somehow exploded post-COVID for who-knows-what reason)

You have to look at companies with managers who constantly keep up with new technology/trends (rather than dig into what has always worked for them) and who can be expected to never stagnate.

I'm a little concerned about post-COVID overvaluations across the "tech sector" (especially given the "stagnation hypothesis" that many, particularly the Thielosphere, is concerned about), but I would still put some into QQQ, as QQQ has vastly outperformed index funds (but QQQ may be in a bubble itself). John Hussman has sounded the alarm for years, but if you were paying attention to him, you would have lost out on the returns over the last 5 years. I've observed that most of the high-profile companies that have recently IPO'd (especially in the tech sector) and which have rapidly-growing userbases have had much higher returns than most other companies - just look at how far up Twilio and Slack and Spotify and Cloudflare have gone up. Many recent biotech companies that have targeted CRISPR have also gone way up but they're more at risk of a sudden catastrophic drop if a clinical trial doesn't pan out.

Many I know are bullish in cryptocurrency [particularly BTC\ethereum] again, esp given the prevalence of money printing/devaluation as a response to the COVID crisis (and perhaps as an easier/more politically feasible way to "get money into the economy" than is higher taxation ), and since BTC is near ATH and still nowhere at risk of being at a bubble.

A heuristic I might use: What products/technologies are the smartest/most innovative people (eg those at DeepMind) using? [eg note how viciously smart people in AI have massive salaries and actually, like, use their salaries on smg] Their resourcefulness + financial resources will only improve with time, and them having higher ability to have frictionless workflows (minimizing the amount of time they spend on unnecessary logistical things such as upgrading their PC/changing homes/buying a new car/backing up data/preventative medicine/etc)+ collect data + store energy for data centers + make use of these massive datasets depends on them having access to certain resources (be it energy, speed, technology, SSDs, advanced materials). Think of what they will be like 10 years in the future, and of the materials they will use to maximize their ability to make money-time (or money-time-energy) tradeoffs. Sufficiently resourceful companies will never saturate - they will figure out how to create demand in areas where previous demand was not thought of as existing (kind of like how if you create enough products and advertise them to people, you may convince them that they have a "need" they might not have thought out themselves). If you use this valuation in itself, you wouldn't be surprised at the massive increases in NVIDIA/AMD/GOOGLE/LRCX/GLW/cloud stocks/whatever..

[given current tech valuations, I feel that materials science is the sector that has the most potential to improve/advance, and I am somewhat invested in LRCX/MU/AMAT, but I feel like there still isn't, like, an equivalent to "big tech" for the materials science sector - there isn't a huge market [yet] for photonic or neuromorphic computing, for instance. This is overdue, and it's possible that "AI" can catalyze a massive shift in materials science innovation that could lead to fast AI takeoff]. Also, with quantitative easing and other novel financial instruments (such as potentially cryptocurrency fintech), we may be able to more quickly "manufacture ourselves" out of recessions/crises than before, without being overly dependent on politics or which party wins the white house and dictates tax policy => this flexibility is also why a great depression a la 1929 is unlikely to happen ever again). 

[Linkpost] AlphaFold: a solution to a 50-year-old grand challenge in biology

Does knowing the structure of a protein help with simulating how it responds to any arbitrary/unknown protein/molecule/agonist/antagonist/superagonist? [it seems that even with all the protein structures that we do know well, that finding appropriate agonists of the protein with the desired action is still a huge unsolved problem]. Is simulation a much more difficult problem than "folding"?

This allows us to design "efficient" proteins (proteins designed "intelligently" often do tend to be smaller, less "messy" and "bulky" than naturally-evolved proteins [which also cross over at the most pedagogically unhelpful sites ever], and with protein folding solved, it may be easier for us to design proteins that are less complicated/more amenable to simulation than the natural set of proteins that exist => not to mention that it may be possible to find a specific transferase protein that is able to precisely add a methyl or carboxyl group to any molecule at any location, or a ligase that is able to split a molecule at any arbitrary location). We may also be able to design them based on properties like how easy it is to introduce them into the cell via mRNA (the genes for many natural proteins are not easy to introduce into the cell via CRISPR or AAV, but as protein design-space is so large, you can probably design another protein that carries out the same function that can be delivered into cells via mRNA or CMV-based vectors, without needing to force the corresponding gene at the right location at the cell's nucleus). 

Anyhow, designing proteins for industrial chemistry (eg properly degrade polyethylene plastics in the ocean) [and also those with a specific physical property rather than those that perform a very specific function] is a much easier problem than, say, figuring out how to make an extremely particular histone acetyltransferase or DNA methyltransferase or chaperone enzyme [often those at the center of hub networks and whose evolved messiness naturally evolves due to the necessity of needing to have other extremely precise interactions with other proteins that have also evolved to become messy bloated behemoths] localize/diffuse at the locations where it can precisely do the right things at {X} sites and not do the wrong things at the {Y} other sites. 

Also, this helps us develop a "periodic table of protein function" where you can design proteins that can carry out X function if you change certain motifs to it, and it will turn out as much cleaner/more organizeable/more predictable than the natural super-messy [and hard to organize] set of protein motifs we find in the wild. I think this is especially relevant for manufacturing and industrial chemistry - proteins that broadly carry out functions sort of similar to zymogen. 

The whole field of structural biology was 95% useless anyway.

As long as it produces machine-interpretable output, it's useful for training new algorithms, even if the vast majority of humans are unable to properly interpret protein structure.

^Anyhow, this post was replying to the idealized version. Protein folding is still far from solved, as explains. It's an exciting advance to be sure. I think this allows us to better figure out what a stable system of ultrastructural scaffolds is first before figuring out what precise things can be built USING those ultrastructural scaffolds.

How do you assess the quality / reliability of a scientific study?

Is there an online way to better tag which studies are suspect and which ones aren't - for the sake of everyone else who reads after?

When Money Is Abundant, Knowledge Is The Real Wealth

So, two years ago I quit my monetarily-lucrative job as a data scientist and have mostly focused on acquiring knowledge since then. I can worry about money if and when I know what to do with it.

Also this knowledge only matters if you do something useful with that knowledge, which I'm convinced that you are, for instance. many other people are not able to create useful knowledge and thus may be better suited for earning2give.

Gears-Level Models are Capital Investments

Do you think that applying black box models can result in "progress"? Say, molecular modeling/docking or climate modeling or whole-cell modeling or certain finite-element models? [climate models kind of work with finite element analysis but most people who run them don't understand all the precise elements used in the finite element analysis or COMSOL]? It always seems that there are many many more people who run the models than there are people who develop the models, and the many people who run the models (some of whom are students) are often not as knowledgeable about the internals as those who develop them - yet they still can produce unexpected leads/insights  [or stories - which CAN be deceiving, but which in an optimal world helps others understand the system better even if they aren't super-familiar with the GFD equations of motions that run inside climate models or COMSOL] that might be better than chance.

When Money Is Abundant, Knowledge Is The Real Wealth

>In line with John’s argument here, we should develop a robust gears-level understanding of scientific funding and organization before assuming that more power or more money can’t help.

How about a metaculus/prediction market for scientific advances given an investment in X person or project? (where people put stake into the success of a person or project?) is this susceptible to bad incentives?

When Money Is Abundant, Knowledge Is The Real Wealth

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

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.

Load More