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  • So who’s still sitting on cash?

    A couple of months ago people were clambering that anyone holding stocks was a moron and it was better to be sitting on cash. Where are those people now?

    This time December, the S&P 500 was hovering around 3800 and we had just emerged from the S&P’s worst performing year since the Great Recession. With the Federal Reserve continuing to tighten plenty of folks were scrambling to say that the worst was yet to come and everyone needed to get out of stocks NOW.

    Since then, the market has recovered, the FTSE in particular has hit all-time-highs, and sentiment is strengthening. Is there still a case to be made that the market will drop another 20% from here? If there is, I’m not seeing it get made. If you pulled everything out of the market in December, well you missed the upswing. Cash isn’t without its downsides.

    This was supposed to be a bigger post, but frankly I just don’t have much else to say. Don’t be like this guy, just because stocks can go up as well as down doesn’t mean your best bet is to sell everything and put it under a mattress. On average, the people who make the least amount of trades have the best portfolios, and that means buying once and never selling.

  • Rimworld: a game of entropy

    Rimworld is a very popular colony sim game.  I’ve seen a number of other games paint themselves with it’s brush, aping its style and mechanics, and yet none have come close to offering what Rimworld offers.  Rimworld puts you in the shoes of 3 survivors who crashland on a sparsely populated world at the edge of human habitation.  The survivors must immediately get to work building shelter, growing food, and finding some way to escape their new home.  The planet is also home to hostile pirates, insane robots, and murderous bug creatures, so the deck is stacked against you.  But if you can learn to grow and manage your colony, you can survive, thrive, and escape.

    This basic outline of growing a colony and surviving against enemies and nature alike is used in a number of other games I’ve played. But none of them really offer the same experience that Rimworld does. Now, cards on the table, I think Rimworld is good-not-great, and I enjoy some of those other games a lot more. But I wanted to examine why it is I think that Rimworld is offering such a rarified experience when the basic outline is so widely used.

    One of the things to understand about Rimworld is that this is a game of entropy.  Your position in the world is always decaying and you need to work just to maintain it.  You’re fighting an uphill battle just to survive, so growing and prospering is going to be pretty hard.  Food is not plentiful, so your first job is to immediately start farming, foraging, and hunting for raw food.  But raw food isn’t healthy or fun to eat, so you also need to set up a cooking station to make meals.  Meals spoil quickly so you need a refrigerator to store them in.  And unsanitary conditions lead to disease so you need to keep your food areas as clean as possible.  

    Keeping your colony fed it a hell of a lot of work on its own, and that isn’t even getting into all the other entropy the game has, like how clothes degrade and need replacing, or how electric appliances short circuit, or how your colonist will eventually age and acquire new ailments that need treatment.  Surviving on a barren world is hard, and Rimworld wants you to realize it.

    Now, other games I’ve played have included some amount of survival mechanics.  Kenshi and Oxygen Not Included both include the need for food.  But in those games I never really felt like I struggled to survive.  The games were tuned so that keeping everyone fed felt like an occasional annoyance, not a difficult necessity like it is in Rimworld.  

    SURVIVAL IS DIFFICULT

    In Rimworld, there’s a lot to do and only so much time in the day.  At the start of the game your colonists might spend half of their day on basic needs (eating, sleeping, etc) and a third of their day doing the work needed for the colony (cooking, getting food).  That leaves just ⅙ of their day to work on expanding and improving the colony, doing things like making new weapons or building better living quarters.  

    You can try to finagle the numbers a but, but they’re pretty tight.  If you have your colonists work less on the colony’s needs, you might run out of food and starve.  If you don’t let colonists sleep, they’ll have a mental break and stop working for you.  Mental breaks and dead colonists decrease the total amount of work you have available, so it’s best to err on the side of caution and not rush towards colony expansion.

    So in Rimworld, playing the game “well” involves making the colony run as efficiently as possible so you can get as much work out of your colonists as possible.  If the beds and food storage are near the dining halls, colonists don’t have to walk as far for breakfast.  And if you make sure the skilled planter does the farming and the skilled builder does the building, basic necessities will get done a lot faster than they would otherwise.  This lets you siphon off more and more of your colonists’ day towards expansion and improvement.

    But this is where the “storytellers” of Rimworld come in.  At semi-regular intervals, the game will drop some crisis in your lap that needs solving.  Maybe half your colonists get sick and need healing, maybe space pirates start attacking your colony.  Either way you now have to drop what you’re doing and fight for survival.  

    These crises are multifaceted in how they test you: first of all your colony needs to be well enough run that you can drop everything for a day without your colony going to crap.  Secondly, these crises will often bleed you of scarce resources, such as medicine or single-use-items.  Thirdly, you rarely come out of these crises unscathed.  Even if no colonists die, Rimworld battles tend to include long-term and even permanent injuries that not only need to be treated but can also make your colonists less efficient for the rest of their lives.  

    These crises are an excellent way to test your colony and see how well you’re managing your supply of “work.”  If space pirates kill your best planter, can you still grow enough food to survive?  If half your colonists get space-flu, can the other half take care of them?  And after you’ve spent your resources and lost your colonists (or your colonists have lost their arms), can you build yourself back up and continue?

    So survival is very hard, and the storyteller makes it harder, but even that’s not the end.  I said before that expanding your colony is part of the goal of getting off-planet, well Rimworld makes sure that expansion is even harder than survival.  

    COLONY EXPANSION

    To expand your colony you’re going to need new colonists, but while Rimworld is a war-torn backwater there aren’t just plenty of people wandering around happy to join your dirt farm.  There’s a few different ways to get new colonists:

    First, you can incapacitate enemies who attack you, lock them in a jail cell, and then persuade them to join your colony.  This will require you to spend a lot of work feeding your prisoner and keeping them alive, as well as dealing with the occasional jailbreak.  For a colony just barely scraping by, a single extra mouth to feed can be the breaking point, so this method isn’t easy.  Also the game makes sure that the vast majority of enemies will die rather than surrender, so the Stockholm Syndrome route isn’t very productive.

    The second option is to rescue colonists as part of a quest, at which point they will join you in gratitude.  This option is a gamble itself since the quests usually involve a lot of fighting, and as I said above Rimworld’s combat is a huge test to your colony’s survival.  Still, if you can manage it, this is usually the quickest route to growing your colony.  

    Finally, you can sometimes pay to have a colonist join you, but this usually costs an exorbitant amount of money, way more than an early game colony can afford and way more than a mid-game colony would usually spend.  These opportunities are also few and far between, so even if you are rolling in cash this option isn’t a given.  

    Finally, in all three of these options you don’t have the ability to “pick and choose” what type of colonist you want, you get what you get.  And if you end up with 4 artists who your single planter/chef/doctor struggles to keep alive, then oh well.

    Expanding your colony is the easiest way to have more “work” available to do things.  The game knows this, and will fight you to prevent it.  As stated above, the game is finicky about giving you new colonists, but that’s not the only trick up its sleeve.  If you do luck into a big colony, you’ll find you don’t have as much extra work available as you might think.  This is because the game increases the difficulty as your base gets larger, and always keeps you in check to make things challenging.

    NEVER SATISFIED

    The game measures how much “wealth” your colony has.  Wealth includes the rooms your colonists sleep in, the food in their pantry, and the number and abilities of the colonists themselves.  As your colony expands, it has more wealth.  As it has more wealth, the colonists will get more and more picky.  

    Remember what I said about mental breaks, they are one of the ways that the game forces you to look after your colonists’ needs.  If you don’t keep them happy, colonists may hide in their room, start setting fire to things, or start taking drugs in order to cope.  When you’ve just crashlanded on the planet, the colonists are happy just to be alive, and their mood will be high enough that they’re unlikely to break over little things like not having good food or clothing.  As your colony expands, your colonists’ needs expand too.  They’ll want more entertainment, better living quarters, more time to themselves.  As colonists’ needs increase, you need to spend more and more of their labor on meeting those needs, having less and less available to do other things.  

    So growing your colony isn’t always going to give you more available “work.”  With 3 colonists, you can perhaps use ⅙ of their daily labor to do your own things, while ⅚ are needed for their survival.  If 3 more colonists are added, everyone may suddenly demand more niceties such that all their daily labor is spent keeping themselves alive and happy.  If another 3 colonists join, maybe people will demand so much stuff that you can’t keep all of them happy.  Again, good play means making your colony efficient enough that each new colonist gives you enough labor to offset the expense of keeping them alive and happy.

    And even that’s not the end of it!  Your colony’s wealth will also determine the difficulties of the challenges that your colony faces.  Your crashlanded survivors will only be attacked by small animals or single, melee-armed humans.  A large and wealthy colony will be attacked by wasting diseases, herds of wild megafauna, killer robots and more.  The game is constantly testing you, and the tests get harder as you get better.

    So that’s what Rimworld is.  I may not have explained it well but it is a game of entropy.  Every day is a fight for survival, your position in the world is always moving backwards.  You need to struggle to keep what you have and struggle harder to get anything more.  Even as you run forward, the game ups the speed on the treadmill giving you harder and harder challenges so you’re always struggling.

    THE UNIQUENESS OF RIMWORLD

    This contrasts pretty strongly with the other games that I’ve been told are “colony sims” and “like Rimworld.”  Now to lay my cards on the table, I like these other games a hell of a lot more than I like Rimworld. But I don’t like them for their “Rimworldness,” in fact I’d say I like them because they do a lot of things better than Rimworld, and their “Rimworldness” often holds them back. But these games keep being talked about in the same breath as Rimworld and I’d like to discuss why I think they really aren’t like it at all.

    The closest approximation of Rimworld I’ve played is Oxygen Not Included.  Like Rimworld it gives you a few colonists and tells you to keep them alive and grow your colony.  Unlike Rimworld, keeping them alive isn’t the hard part.  I haven’t played more than 20 hours of Oxygen Not Included, but I don’t think I’ve ever had a mental break or been close to starvation.  Dirt and water on their own can be turned into cheap food, and a single skill can turn that food into something the colonists actually enjoy.  You don’t have to put a huge amount of energy into farming, fueling your stoves, or even keeping your food clean.  Food is very cheap and a simple CO2 trap keeps it fresh and unspoiled forever.

    Not only that, Oxygen Not Included isn’t shy about giving you more resources.  Every hour or so of game time it gives you a new colonist for free.  You don’t have to take the colonist mind, but unlike Rimworld I was struggling to keep my base small, not struggling to grow it.  Before I knew it I had more labor than I knew what to do with, and half my colonists were wandering around aimlessly.  In Rimworld this is usually a sign of a very efficient colony, in Oxygen Not Included it was a sign that I didn’t really know what else I should be doing. 

    Kenshi is a game I like much more than Rimworld, but again I think comparing the two is unfitting.  Kenshi will also let you set up a colony to farm, grow, and survive, but it’s not hard for the reason Rimworld is hard.  In Rimworld it’s hard to stay fed and clothed, and the random events add spice to your entropy sandwich.  In Kenshi it was very easy to stay fed and clothed, I found a single farmer and a single cook could keep 20 people fed no problem.  The difficulty in Kenshi is entirely in the politics and combat.  

    Everywhere you can settle in Kenshi is already “owned” by some organization, either a nation or a bandit tribe, and so if you set up a dirt farm the owners will come to collect.  Sometimes they’ll demand tribute, sometimes they’ll ransack your storage, sometimes they’ll try to kill you, but keeping them assuaged or fighting them off is the real test of your colony.  

    Unlike Rimworld and its “wealth” system though, Kenshi doesn’t have a way to scale these challenges up or down.  The same horde of bandits will descend upon a dirt farm as will descend upon a thriving metropolis with harpoons guarding the doors.  Once you’re strong enough to fight off the attacks, nothing else can touch you.  

    Furthermore, Kenshi isn’t shy about giving you colonists either.  You can pay for people to join, but unlike in Rimworld the costs isn’t that great compared to the amount of money you can expect to have on hand.  It’s not difficult to fill your ranks in Kenshi, and as I said it’s not difficult to keep a large horde of people healthy and happy.  The difficulty is because (in true action movie fashion), untrained mooks are useless in combat while trained swordsmen cleave through them like butter. Kenshi rewards you for your skill, not your numbers.

    So Rimworld is a game that’s all about Entropy, and I think that makes it different to every other “colony” sim I’ve ever played, including several I haven’t mentioned here.  It’s difficult to survive in Rimworld, most of your time will be spent on bare necessities with little left time over for expansion.  And even if you do expand, you’ve only increased the challenges you’ll face and the needs of your people, so they game ensures you’ll always struggle.  I’ve stopped playing Rimworld, but I think this is what people need to understand if they want to make a game to challenge it.  It’s not about adding farming and a “mental break” mechanic, it’s about making the mere act of existing a genuine struggle.  That’s what Rimworld is really about.

  • Energy Return on Energy Investment, a very silly concept

    Today I’d like to address one concept that I read about in Richard Heinberg’s The End of Growth, Energy Return on Energy Investment or EROEI. The concept is an attempt to quantify the efficiency of a given energy source, and in the hands of Heinberg and other degrowthers it is a way to “prove” that we are running out of usable energy.

    EROEI is a simple and intuitive concept, taking the amount of energy produced by a given source and dividing by the amount of energy it costs to set up and use that source. Oil is a prime example. In the beginning of the 20th century oil extract was easy since it just seeped out of the ground in many places. Drilling a small oil well won’t cost you that much, hell you can probably do it with manpower alone. In that case the oil gushing forth will easily give you a good energy return.

    In the 21st century however, things have become harder. Oil wells require powerful machines to drill (which costs energy), and the amount and quality of the oi you get out is often lower. Add to that the fact that modern wells require huge amounts of metal and plastics, all of which cost energy to produce and even more energy to transport to their location, then add the energy it took to find the oil wells in the first place using complex geographical surveys and seismographic data, and taken together some people claim that the EROEI for a modern oil well is already less than 1, meaning that more energy is being put in than the energy we get out.

    And oil isn’t the only fuel source heading towards and EROEI of less than 1. Modern mining techniques for coal require bigger and bigger machines, natural gas requires more and more expansive facilities, even solar panels require minerals that are more and more difficult to acquire. It seems everything but hydro power and (perhaps) nuclear power are becoming harder and harder to produce, sending energy returns down further and further.

    This phenomenon, where the EROEI for our energy sources is less than 1, is supposed to presage an acute energy crisis and the economic cataclysm that degrowth advocates have been warning us about. If we’re getting out less energy than we’re putting in, then we’re really not even gaining, aren’t we? The problem is, I’m struggling to see how EROEI is even a meaningful way to look at this.

    First let me note that not all energy is created equal. Energy in certain forms is more usable to us than in others. A hydroelectric dam holds water which (due to its being elevated above its natural resting place) acts as a store of potential energy. The release of that water drives a turbine to produce electricity. But you can’t fly a plane using water power nor keep it plugged in during flight. Jet fuel is another source of potential energy, and it has a number of advantages versus elevated water. Jet fuel is very easy to use and transport, you can fill a tank with it and move it to wherever your plane is, then fill the plane’s tanks from there.

    If the only two energy sources in the world were jet fuel and hydroelectric power, we would still find it beneficial to somehow produce jet fuel using hydroelectric power even though that would necessity an EROEI of less than one. Because although this conversion would have less total energy, the energy would be in a more useful form. People would happily extract oil using hydroelectric power, then run refineries using hydroelectric power, because jet fuel has so much utility. This utility means that (supply being equal), jet fuel would command a higher price than hydroelectric power per unit of energy. And so the economic advantages would make the EROEI disadvantages meaningless.

    This is the fatal flaw of EROEI in my mind. The fact that some forms of energy are more useful than others means we can’t directly compare energy out and energy in. The energy that is used to run a modern oil well comes to it from the grid, which is usually powered by coal, solar, wind, or nuclear, none of which can be used to fuel a plane. Converting these forms of energy into oil is an economic gain even if it is an energy loss. Furthermore EROEI estimates are generally overly complex and try to account for every joule of energy used in extraction, even when those calculations don’t really make sense. Let me give you an example:

    A neolithic farmer has to plow his own fields, sow his own seeds, reap his own corns. Not only that, but the sun’s rays must shine upon his fields enough to let them grow. Billions of kilocalories of energy are hitting his plants every second, and most of then are lost during the plants’ growth process because photosynthesis is actually not all that efficient to begin with. The plant will have used billions of kilocalories of energy, and from them the farmer gets a few thousands of kilocalories of energy. Most of the energy is lost.

    This is the kind of counting EROEI tries to do, applied to farming. When you count up every joule of energy that went into the farmer’s food, you find his food will necessarily provide him with an EROEI of less than one thanks to the first law of thermodynamics. But this isn’t a problem because Earth isn’t a closed system, nor are our oil wells. We are blasted by sunlight every minute, our core produces energy from decaying nucleotides, our tides are driven in part by the moon’s gravity, there is so much energy hitting us that we could fuel the entire world for a thousand years and never run out. The problem is that there are some scenarios where that energy isn’t useful. You can’t fly a plane with solar or geothermal or gravitational energy, but you can power an oil well. So we happily use the energies we have lots of (including our use of solar power to grow useful plants and animals!) and use that energy to help us extract the energies with greater utility.

    I think EROEI failed from the very beginning for this very reason. It ignores economic realities and the massive amount of energy that surrounds us, and instead argues from the first law of thermodynamics. Yes in any closed system energy eventually runs out, but it isn’t even clear that our universe is a closed system, and the earth definitely is not, so we need to face up to economic reality on this.

  • Interesting notes about ChatGPT

    I know I’m about 2 months late to the party, but I just looked into ChatGPT and I was interested in what I found. These will be some random assessments since I don’t have the energy for a full post. Obviously they keep updating the model so some of this may no longer be true, but here is what I found.

    • The model says its data only goes to 2021 and is coy about exactly when its data ends, but I was to be able to pin it down to mid-2021 some time right before the Olympics. The model is unaware of anything about the 2021 Olympics, but believes Naftali Bennet is the current Prime Minister of Israel. Bennet became PM in July 13th 2021, the Olympics began July 23rd. Since the Olympics are always such a media frenzy, I find it hard to believe that ChatGPT would not have been trained on at least a few articles about the 2021 Olympics if its training dataset included dates after July 23rd, so I estimate that its dataset cuts off in mid-July 2021 between the 13th and the 23rd.
    • As a topic becomes more in depth, the quality of the answers decreases. It has some amazingly esoteric “surface level” knowledge (ask it what the capital city was for some ancient, long dead nation/civilization), but has a harder time with the kind of deep knowledge that makes one an expert in a field. I asked it to explain how Isoelectric Point relates to pH, and while it gave all the right words it came up with an answer that is the opposite of reality (see image below). For reference if the pH is higher than the pI, the protein will have a positive charge due to deprotonated amino acids. This answer would be like someone giving you a a very deep description of an electron but ended up saying it always has a positive charge. Sounds good! But wrong answer.
    • The math update has fixed some of the fun, dumb responses you used to be able to get, and it can now give some strong answers for your physics homework like calculating the speed and trajectory of a baseball. But it still has some weird hangups though and I don’t know why. Complex word problems usually seem ok but simple math is much trickier. I’ve heard tell that the language processor will (somehow) be hooked up to Wolfram Alpha to get solutions to math problems, but it seems like that’s not the case yet.
    • As an aside, when ChatGPT gives me a wrong answer, I find myself doubtful and second guessing myself. It gives blatantly wrong answers with the exact same cadence that it tells you all the correct things, so I find myself wondering if I’m the dumb one and my college degrees are all a lie. I guess it proves the maxim that if you just say things confidently people will believe you.
    • I wondered if ChatGPT would work as an ad supported model. This may be dumb, but hear me out. Say Khan Academy wants to advertise itself, and they already know students are looking up homework answer on ChatGPT (just like they used google before) so this is the perfect opportunity. A submodel could be trained using Khan Academy-approved language, such as testimonials from happy parents and children about how great Khan Academy is. Then when ChatGPT’s language model starts using words associated with Khan Academy topics (calculus, biology, physics etc) it can insert a canned tagline for Khan Academy and follow it up with words chosen based on the Khan Academy-approved text. So in my pI question above, it could add a snippet somewhere which would go “isoelectric point and pH [tagline starts here] are also taught as part of the Khan Academy course on Chemistry. Parents and students love Khan Academy because blah blah blah [end advertisement] oh by the way deprotonated amino acids are negatively charged.” Inserting ads into your searches is basically Google’s whole business model, and I’d certainly prefer this over a paid version of ChatGPT.
    • The non-deterministic nature of the answers makes it sometimes hard to gauge the overall “quality” of the model. I’ve had days where every answer seemed right and days where everything was a shambles. I’ve seen people complain that certain tricks don’t work while others post snapshots showing that they do. I think the output is at least partly determined by previous parts of the conversation but it also just seems semi-random (would love to know if it IS semi-random!). Either way, it makes it hard to judge without doing some statistics that I don’t feel like doing.

    Anyway those are my impressions of ChatGPT so far. Fun timewaster, MUCH less toxic than spending all day on Twitter.

  • 10x Genomics: what happened to all the DNA stocks?

    2020 was the year of COVID, but it was also the year of DNA. Thousands of DNA companies and researchers got in on the pandemic since there was a sudden surge in demand for COVID testing, contact tracing, and virus studying. Now, COVID is an RNA virus, but RNA and DNA work so similarly that most organizations that do one can do both. Even the Universities got into it, I know the Genomics Research Core Facility at the university I used to work for got money from city, state, and federal governments to process COVID tests, which was way more profitable for them than sequencing my plasmids once a month.

    So 10x Genomics was a DNA sequencing company that, like so many others, skyrocketed in valuation during 2020. It hit its absolute peak in early 2021 and then fell precipitously through 2022. That describes a lot of companies but it especially seems to describe DNA companies. Still, I wanted to know if 10x Genomics was a buy, and considering my LinkedIn account got spammed all last year with recruiters and ads for the company, I figured they were at least worth a look. The result? A resounding “eh.”

    One of 10x Genomics’ big claims to fame is their ability to perform reads of long segments of DNA as opposed to the shorter segments read by rival Illumina. Sequencing DNA gets less accurate the longer the DNA is, so Illumina and others use a technique of chopping the DNA into pieces, sequencing each piece, and putting them all back together. This usually works fine because there are enough overlapping pieces to make the puzzle fit, if you know read 3 different pieces with letters “AATT”, “TTGG” and “GGCC” then you can hazard a guess that the full sequence reads “AATTGGCC” and that the 3 pieces simply overlapped each other. This doesn’t work with long sequences of a single letter or repeating patters. If you simply have “AAAA” “AAAA” “AAAA” then you actually don’t know how long that string of As is. Those 3 reads could overlap on 2 letter and the result could be “AAAAAAAA” or they could overlap completely and the result is simply “AAAA.” About 8% of the human genome is these sorts of repeating patterns that Illumina and others are ill-equipped to read, which gives 10x an exploitable niche.

    Now this is a genuinely interesting piece of equipment and their barcoding of DNA segments to read them in the correct order is a nice bit of chemistry, but was this a company that was ever worth 21 billion dollars? In my opinion *no*. The COVID era was a bubble in a number of ways, the easy-money policies of the post Financial Crisis era led to the super-duper-easy-money policies of the COVID era. Operation Warp Speed and other pandemic-focused funding sources meant that money was flowing into the biotech sector, and with most of the world still coming out of lockdown investors were desperate to park their money in companies that were still able to operate. It seemed like the moment for Biotech had come, and companies like 10x Genomics rode the wave to the very top. But from where I’m standing this was always an obvious bubble, and people were making claims about DNA companies that were woefully unfounded.

    I’ve just written a lot about 10x Genomics, but all this could apply to most any DNA/RNA startup around. They all had neat ideas, got woefully overvalued during the pandemic, and have since crashed to earth taking shareholder value along with them. My question today then is why, why does it seem like Wall Street Investors saw something in DNA/RNA companies that I, a biology researcher, never did? Now part of that is that I’m just curmudgeony by nature, but part of that is that I think a lot of investors have this Sci-Fi idea of genetics in their head that isn’t really reflected in the field. I could just point to Cathie Wood and say “she doesn’t know Jack” but I want to dig a little deeper into some of the strange narratives that surround nucleic acids.

    First, “DNA as a coding language.” I wonder if computer scientists just latch onto the word “code” or something, because the genetic code is no where near to being something that we can manipulate like computer code, and probably won’t be for decades. We’ve been inserting novel DNA into organisms since the 70s, but recently there’s been a spate of investors and analysts who believe that we’re on the cusp of truly programming cells, being able to manipulate them into doing everything we want as cleanly and as easily as a computer. This would definitely unlock a whole host of industries, it’s also not going to happen for a long while yet. DNA codes for proteins, and proteins are the functional units of most biological processes. Nothing you change in the DNA matters until it shows up in the proteins, and we are far, FAR from being able to understand how to manipulate proteins as easily as we do computers. You cannot simply say “let’s add a gene to make this wheat crop use less water,” you have to find a protein from another plant that causes it to use less water, insert that gene into wheat, do tests to make sure the wheat crop tolerates the new protein, alter the protein to account for unexpected cross reactions, and then finally test your finished wheat product out to make sure it works as designed. Any one of these steps could require an entire company to do, and each step could prove impossible and bankrupt the company working on it. We can’t just make de novo proteins to do our bidding because we don’t even know yet how to predict what a protein will look like when we code for it. Folding at home and other machine learning projects have helped us get partway there, but it will still take many Nobel Prizes before we can make de novo proteins as easily as we make de novo programs. So while there is a genomics revolution going on, it’s still an expensive and time consuming one, it’s not going to solve all our problems in a single go.

    Second, “DNA as a storage medium.” I’ve said before that while DNA does store and transmit information, that information cannot be well integrated with our modern technology. The readout of DNA information is in RNA and proteins, while the readout of the circuits in your computer is photons on a screen or electrons in a modem. RNA and proteins do not easily produce or absorb electrons and photons, so having DNA communicate with our current technology is not currently doable. In addition, the time lag between reading DNA and making RNA/proteins is astronomical compared to the speed of information retrieval in semiconductors. I sometimes get an annoyed by the seconds-long delay it takes to load a webpage, but I’d be tearing my hair out if I had to wait on the minutes-long delay for DNA to be transcribed into RNA! At this time I really don’t see any reason to use DNA as a storage medium and I certainly don’t see a path to profit for any company trying to use it as such.

    Third, curing genetic diseases. There is definitely a market for curing genetic diseases, let me just say that first, but many of the hyped-up corporate solutions are not feasible and rely more on sci-fi than actual science. I’ve discussed how even though CRISPR can change a cell’s DNA, bringing the CRISPR and cell together is much more challenging. The human body has a lot of defenses to protect itself from exogenous DNA and proteins, and getting around those defenses is a challenge. But in addition I don’t think investors realize that DNA is not the end-all be-all of genetic diseases, and so they tack things on to the Total Addressable Market (TAM) of DNA companies that shouldn’t really be there. Then they get flummoxed when the company has no path to addressing its TAM. Valuing companies based on what they can’t do is a bad investment strategy that I see over and over again with DNA companies. As to genetic disease themselves: there’s a truism in biology that “you are what your proteins are”. Although DNA codes for those proteins, once they’re coded they act all on their own, and some of their actions cannot readily be undone. When a body is growing and developing, its proteins can act up in ways that cause permanent alterations, and after they’ve done so changing the DNA won’t change things back. There are a number of genetic-linked diseases which are not amenable to CRISPR treatment because by the time the disease is discovered the damage has been done and changing the DNA won’t undo the damage.

    Finally, “move fast and break things” doesn’t work with DNA the way it does with computers. I’ve worked on both coding projects and wet-lab projects. When something goes wrong in my computer code, fixing it is a long and arduous process but I have tools available that let me know what exactly the code is doing every step of the way. I can step through the code line by line and find out exactly what went wrong, and use that knowledge to fix things. Nothing is so straightforward when working with DNA, your ability to bugfix is only as good as your ability to read the code and reading DNA is a difficult and time-consuming process. Not only that, remember how I said above that we don’t know the exact relation between the DNA we put in and the DNA product we get out? If I’m trying to make a novel protein using novel DNA and it doesn’t get made, what went wrong? I can’t step through the code on this one because there’s no way to read out the activity of every RNA polymerase, every ribosome, or every post-transcriptional enzyme in the cell. I can make hypotheses and do experiments to try to guess at what is going on, but I can’t bugfix by stepping through the code, even using Green Fluorescent Protein as a print(“here”) crutch is difficult and time consuming. Even if I try to bugfix, the time lag between making a change and seeing the results can be weeks, months, or years depending on what system I’m working in, a far cry from how long it takes to compile code! A DNA-based R&D pipeline just doesn’t have the speed necessary to scale the way a coding house does, once you’ve got a program working the cost of sharing it is basically zero and the cost of starting a new project isn’t that great. That speed isn’t’ available to DNA companies yet.

    This was a lot of words not just on 10x Genomics but on DNA-based companies in general. The pandemic-era highs may never be seen again for many of these companies, much like how some companies never again saw the highs of the Dotcom bubble. I think it’s important for investors to take a level-headed approach to DNA-based companies and not get caught up in the sci-fi hype. Anyone can sell you an idea, it takes a lot more work to make a product.

  • A series of proposals for testing the validity of technical analysis (TA)

    I’ve said before that I think TA is astrology, and I still haven’t seen any evidence to rid me of that belief. I’ve thought about genuine scientific experiments that could be done to see if it’s true and I’m wondering if people have already done them.

    See if TA-knowers all move the same way by giving a bunch of them a chart and have them predict the forward movement of the stock based on that chart. Two key ways you know astrology/fortune telling is fake are 1. that it uses weasel-words and vagueness to make predictions, and 2. because the same data can cause its practitioners to make wildly different predictions. In actual science however, any two scientists should be able to take the same data and make the same prediction: if two bowling balls of different weights are dropped from the Eiffel Tower, which hits the ground first? Any physicist can tell you the answer. Now to be clear, second opinions in medicine do exist, but these occur because we often work with incomplete information and have to use priors and estimations for the rest. But TA claims that the chart is the information, so if the information is complete than the prediction should always be the same. So if 100 TA-knowers all make the same prediction using the same chart, then perhaps we can start treating this as a complete and testable theory. If they all draw different lines on it then it becomes more clear we’re dealing with astrology.

    Find out if the TA of ETFs follow the TA of their underlying assets. The mechanisms of ETFs ensures that their price never deviates far from the price of their underlying assets, and if both ETFs and the securities they contain obey TA then the movement of the two should correlate. Essentially you should be able to make predictions of the movement of an ETF by performing TA on the stocks that compose it, and I’d like to know if this is true.

    Correlation analysis. Most theories of the stock market claim that the movement of a stock price is uncorrelated with any of it’s previous prices. Just because a stock is down 50% doesn’t mean it’s dead or a bargain. If I’m going to believe TA I’d like a TA-believer to prove to me that price movement is correlated with previous prices.

    A working, mathematical definition of “resistance” and “support.” I understand that these are TA terms, but I’ve asked 5 different TA people for a true definition of them and have gotten 5 different answers. If TA really is based on math then these terms need to be mathematically defined, not emotionally defined based on how someone wants to analyze a chart at that time.

    These are just a few of the things I’d like to be demonstrated before I start believing in TA.

  • Personal update: I will likely be blogging less, perhaps one post a week

    Due to changes in my personal life (mostly positive) I will likely be updating this blog a little less. I still want to post at least once a week, more if I have more things running around my head, but I don’t think I can commit to daily posts as much and keep them in good quality.

  • What was the best 10-year period to invest in the S&P 500?

    I’m doing a small project right now looking at whether stop losses are actually useful in investing. When FTX blew up, it was noted that the traders there didn’t believe in stop losses, for which they were ridiculed on social media. Of course, do stop losses actually help? Or are they more likely to kick you out of a volatile-but-profitable investment than save you from an unprofitable one? Well I can’t answer that yet, but I can answer a different question.

    To start my project, I downloaded 30ish years of S&P 500 data starting September 1990 and asked a quick question: what 10-year period gave the best return if you had invested in the S&P? Once I get the baseline return down, I can add in things like stop-losses and momentum strategies to see if a savvy investor could have improved their return with simple rules. Anyway, here’s the data:

    I make a small program to estimate the return if you have bought $10,000 of S&P 500 stocks and simply held them for 10 years, selling them at the end of the 10th year. From this we can see that 1990 would have by far been the best years to start as you would have been able to sell at the peak of the Dotcom Bubble. Just a couple of years later however and you would have sold into the Dotcom Crash instead, drastically lowering your returns. The worst years for a 10-year buy-and-hold were 1998-2000 as you would have sold into the teeth of the Financial Crisis. These are only years where your 10-year return would have been negative. Then we can see 2008-2009 themselves as some of the best years to start investing, since you would have bought right at the bottom and ridden strong returns into 2018-2019.

    I hope to update the program soon to see if momentum strategies beat buy-and-hold, but for now this gives a good picture of the historical returns for the S&P 500. The average 10-year-return was 100%, but with an 80% standard deviation. The absolute worst return would have been to start investing March 30th 1999, you would have bought into the Dotcom Bubble and sold into the Financial Crisis with a net return of -48%. The best 10-year-return was to start October 11, 1990, which would have had you buy very low and sell near the tippy top of the Dotcom Bubble for a 510% return. There are some wild swings with the buy-and-hold strategy, but the average is still very positive, we’ll see later if stop-losses can beat that.

  • Send troops to the Fed?

    Pardon me for wading into Twitter Drama, but Rohan Grey is a remarkably unserious “intellectual” and I couldn’t help myself.

    Before I start, let me share a tiny story from “Zen and the Art of Motorcycle Maintenance.” This book was a thoroughly unenjoyable read for teenaged me, but it has one anecdote that still sticks with me. If memory serves, there is a university that is being threatened with losing its accreditation due to repeated failures and the students are naturally protesting as this would make their degrees worthless. One student talks to the narrator and claims that the University in fact can’t lose its accreditation, because if someone tried to take it “the Governor would send the national guard to protect us!”

    I shouldn’t have to spell out the ridiculousness, but I want to hit word count so I will. Accreditation isn’t held in a vault, it isn’t something you can protect with guns and soldiers. Accreditation is the trust that other institutions have in you, and while some of it is legally codified most of its power is in the uncodified trust that a society is built on. You can’t protect accreditation with and soldiers any more than you can protect trust or friendship.

    And so it was with bewilderment that I read an Assistant Law Professor on Twitter making the same mistakes as the nameless student from a book. Rohan Grey wants to do an end-run around the debt ceiling by having the Treasury mint a one trillion dollar platinum coin and deposit it in the Federal Reserve. This coin would then pay for the USA’s financial obligations without the need to borrow money. A big (and usually ignored) problem is that the Fed would have to accept the coin, and as Josh Barro writes, the Fed has expressed the opinion that this chicanery is illegal and undermines Fed independence. (Read Barro’s article, it goes into great detail as to why this idea probably wouldn’t work). Undeterred, Grey thinks the Fed’s opinion doesn’t matter, and that if they refuse to accept the coin then Biden should send troops to the Federal Reserve and force them to accept it.

    Grey’s mistake is thinking that guns can be used to enforce trust. The Federal Reserve has the trust of the markets, and its power to move markets is based on that trust as much as anything else. The Federal Reserve trades bonds and sets rates, but those bonds and rates have value because people trust the Fed to keep its word, Jerome Powell’s speeches about the Fed’s plans have as much or more power as any action taken by the Fed. Now imagine a scenario where troops are instructed to besiege and occupy the Federal Reserve, where Powell is held at gunpoint and forced to accept a one trillion dollar deposit from the Treasury which he and the Fed have gone on record as saying is illegal. Trust in the Fed would be shattered, nothing Powell says or does matters anymore because the troops (and by extension the President) are running the show. Investors would flee from US government bonds, causing yields (and thus America’s cost of borrowing) to skyrocket, because America’s currency will have been debased against the will of its central banks, and will now be at the whims of the President.

    And you may say “that’s fine, I like Biden as President” but do you like DeSantis? Do you trust that DeSantis wouldn’t be willing to send his own troops to force his will on the Fed? Would you buy a 10-year government bond if there’s a chance that DeSantis or Trump will be controlling it 2 years? And furthermore, Powell’s remarks on inflation will become worthless. Maybe Biden doesn’t like the rate rising that Powell needs to do, or maybe when the election comes he wants to juice the economy. So what’s to stop him from leaning over and reminding Powell who’s boss? What’s to stop Trump or DeSantis from doing the same? People like Grey once griped that Trump’s complaining caused the Fed to pause rate rises in 2019 (ignoring of course that inflation went under the Fed’s 2% target, which should cause them to pause rate hikes all on its own). Now Grey wants to make the Fed wholly subsumed by the President, so Trump would be able to do whatever he wanted.

    Once you’ve sent troops to the Fed, you can’t unring that bell. Investors invest in American Dollars and American bonds in large part because they trust the Federal Reserve to do its duty with regards to the currency. Shattering that trust with soldiers would shatter investor confidence in the American economy as a whole. You’d have a trillion shiny dollars, but they wouldn’t be worth a pence.

  • Tired

    No real post today. I’ve been too tired recently. I need to do a better job of getting to sleep on time 😦