Categories
technical world

AI Predictions

It’s always terrifying to write down your AI predictions, for they rarely age well. And so I’m not writing this post because I have strongly-held views of the future. In fact, I have not felt as uncertain about the future for a long, long time. I’m writing this because, as a purely personal matter, I wish to take a snapshot of myself at this moment in time. And to keep myself honest, I’ll put it up on my pseudonymous blog. As such, to save myself the embarrassment, please do not share this, even (especially) if you know my real name. These are not carefully thought out numbers. And they will likely not be the same by the next time we meet.


Serious pullback in AI (beginning of “winter”) in 2025: 15%
Humanity is extinct before 2030: 0.01%
AI directly causes catastrophe where 10,000+ people die before 2030: 10%
AI can do {95% of non-physical labor weighted by 2024 US GDP} by 2030: 1%
AI solves a millennium prize problem by 2030: 50%
Median year that AI-generated social media content overtakes human-generated content: 2027
Median year the average American spends over an hour a day with an AI companion: 2028
Median year most US major metropolitan areas have self driving cars: 2029

The claims below are that each topic becomes one of the “icons” of AI in 2025, not that they happen or that progress is made. For calibration, I would probably say that 2024 (so far) is the year of open source, investment, and post-training. Keep in mind though that the year is not yet over. 

2025 is the year of agents: 55%
2025 is the year of multimodal: 75% 
2025 is the year of reasoning: 65% 
2025 is the year of test time compute: 35%
2025 is the year of 1T+ parameter models: 15%
2025 is the year of small models: 35%
2025 is the year of AI products: 90%
2025 is the year of open source: 10%
2025 is the year of sub-quadratic attention: 5%
2025 is the year of interpretability: 5%
2025 is the year of robotics: 10%
2025 is the year of pre-training: 20%
2025 is the year of post-training: 40%
2025 is the year we get rid of this garbage pre/post naming convention: 50%
2025 is the year of RL: 70%
2025 is the year of winter: 15% [repeated from above]
2025 is the year of government: 10%
2025 is the year of war: 5%
2025 is the year of situational awareness: 1%

This era of scaling laws has ended: cannot see through the mist
Humanity is extinct before 2050: cannot see through the mist
Median year that AI will solve cancer: cannot see through the mist
Median year that non-physical labor mostly becomes reviewing AI outputs: cannot see through the mist
First year US GDP doubles (2023 growth rate was 2.5%): cannot see through the mist
Our descendants will spread across the stars: cannot see through the mist


For those of you who have talked much to me about AI, you will recognize that one of the numbers is an enormous aberration from my previous beliefs. It’s that human extinction by 2030 is sitting at maybe 0.01% right now. I am not even remotely close to confident in this number. But my current point estimate that varies wildly by the day (in log space no less) is that 1/10000 worlds rolled out from this moment may result in human extinction by 2030. For the first time in my life, I acknowledge that we are perhaps only one jump away from AGI. Of course, we might actually be far, far away. Once again, one can never see all that clearly through the mist. I still do not believe that scaling up next token prediction will lead us to AGI as many proclaim to believe (though only a select few actually believe). Given that I assign a 15% probability of the onset of AI winter in 2025 and a 0.01% probability to extinction before 2030, it’s also kind of hard to classify me as “pilled”. But I am semi-pilled, or as you might say, I have begun to feel the AGI.

For I have glimpsed the future. Terawatt clusters costing more than the GDP of a small country will soon pouring out concrete foundations. And once built, they will not be torn down even if the markets tremble. Not even if the investors turn the Transformer 8 into curse words. They may not build new ones, but once built, they are here to stay — at least, until the hardware depreciates in a few years. And so for the first time in human history, an idea can go from the scrap of a napkin to using more compute than all the world had access to not long ago, in a matter of weeks or months, perhaps even days. In 2015, Andrej Karpathy wrote The Unreasonable Effectiveness of Recurrent Neural Networks. It was a simple next-token predictor trained on probably a million times less compute than the LLMs of today. I read it and thought it was a cute trick — nothing more. Karpathy himself did not really believe it, as he declared a decade later, having not pursued that line of research in the time since. Once again, this is the bitter lesson at work. Not that you can scale up any idea. No. But that when you find the right idea, the right thing to scale — you can shake the world before you.


For calibration, my 2024 predictions went reasonably OK all told? Among some of the best for people outside the frontier labs, though the bar is really on the floor there for both informational and competence reasons. I did well enough that I could see it on a few people’s faces that they thought I was getting serious leaks. Ironically, I think I mostly outperformed the leakers simply because there is so much poison out there that leak dependents get duped far more often than they hit gold.

Things I got right:

– Synthetic data
– Inference time compute
– Competitive coding

This section is self explanatory given my choices, which I will not describe or else reveal my actual name. Given my name, you can find out why I was dead on quite easily.

Things I got wrong:

– General reasoning progress went faster than I expected. This is quite ironic given my dead on guesses. The crux is that while I correctly guessed the most important research directions, I incorrectly guessed how many other people also thought this was the most important direction. I think this actually happens more than people expect.

– I did not expect GPT-4 models to get commoditized. This was a huge question mark in my head and I leaned towards it not being likely to happen. Obviously, many people confidently predicted this, and they predicted it right. I was not one of them. Ironically, as much as people like to rag on VCs, they got this one dead on. It turns out there is no magic for this class of models — just flops.

– Multimodal did not take off as much as I expected. I still(?) think it’s directionally correct, so I’m shifting my date to 2025 with the updated prediction.

– The importance of electricity.

Note the date. I saw this when it first came out, but it took until mid 2024 before I had any idea of what roon was talking about here. Ironically, a year later, I still think the tweet is wrong, though at least I think(?) I understand what he means now. 

Categories
life technical

Thoughts on AI And Related Topics

I believe all the below but make no guarantees that what I say is true. And even if it were all true, remember that there is perhaps nothing more misleading than the truth. Read at your own peril.

  • There are almost no contrarian views in AI right now. Say that humanity will be extinct in ❤ years, you will find a room where someone argues that it’s actually 2. Say that AGI won’t happen this century, you’ll find someone arguing that AGI has probability 1e-40. Every possible opinion under the sun is held by someone, and moreover, someone of note and importance.
  • Whenever something is uniformly praised and yet very rarely done, there are usually one of two explanations. The first is that it is a very good thing, it is just something that is hard to do, and you should expend tremendous effort into doing it if possible. The classic example of this is limiting your social media usage. The second explanation is that it is actually a terrible thing to do, and that that most people telling you to do it are simply virtue signaling. The classic example of this is asking stupid questions. As I have just stated, AI is full of contrarian views, so much to the point that there is almost nothing that everyone agrees on. One notable exception is that everyone loves evals! Evals, evals, evals. If you go around to an SF AI party and tell people you are working on a new eval, everyone will sing praises on your name. Of course, once your eval is completed, you might find an entirely different reaction that is suspiciously dependent on who tops your results table. As someone who worked (works?) on evals, I cannot actually tell you which of the two categories evals is in. I will say though, that evals is 90% political, 9% logistical, and 1% technical. I did not realize this when I started, and despite my precautions, I suspect I survived only by pure luck.
  • One problem with evals is that with the rise of LLMs, any particular eval, can likely be beaten by collecting enough training data on that specific domain. But that very different from the claim that we will have general intelligence. I do not know how to solve this problem. But I think that if you bet on a particular eval to not be solved, you should pick a very, very, very hard domain. ARC evals will be solved within the year.
  • People are terrified about model builders training on user data / chat logs. I thought so too, but no longer. Researchers from OpenAI, Google, and Meta have all told me that they did some experimenting with user data and concluded that it was sh**. This could be a coordinated stunt, but strikes me as quite plausible. If they told me they declined to train on this data out of the goodness of their heart, I wouldn’t believe them. If they told me that they fell to temptation (as all do) but realized the fruit was sour all along, I do believe them.
  • H100s are renting below market price, partly due to the first wave of model builders going under (Stability, Inflection, Adept, etc.) and partly because B100s are coming around the corner. From the perspective of DRAGON, our cloud provider took advance payment and yet refused to deliver any goods …. until around a month ago when they frantically called us asking if they could deliver THE ENTIRE SHIPMENT in a few hours and start counting that against our clock. So that is how we went from 0 to [redacted] GPUs in a single afternoon. Ask around and you’ll hear similar stories. Remember, there was never a shortage not followed by a glut.
  • Nassim Nicholas Taleb is the voice I trust the most, in everything. That being said, some of what he says is absolute nonsense. The other parts pay it back a hundredfold though.
  • I cannot sample the median OpenAI employee, but I have had the privilege of chatting with ~60 researchers and the rumors say the research org is ~600, so I have a reasonable sample, even if it’s not random. My findings are 1) There is no personal feud between Anthropic and OpenAI. The fight is at the org / leadership level, not at the employee level. 2) They do not work 100 hour weeks every week. 3) While some of them are worried about near term AGI, the majority of people I talk to are absolutely unconcerned. The above was all a huge surprise to me, so perhaps pardon my naïveté. My understanding was that everyone there was omega-pilled, grinding their lives away — for who wouldn’t if the end was nigh, and that each had glimpsed the face of GOD in the yet-unreleased models hidden behind closed doors. I’m now quite certain that this narrative is false.
  • Three facts, I present for your consideration. 1) OpenAI employees have fairly attractive partners. 2) Many of these couples have been dating since before OpenAI strut into the limelight. 3) OpenAI employees themselves look pretty good. [redacted] hypothesizes that being good looking is an underrated part of success in AI. I don’t have a strong theory, so this is just me noticing some confusion.
  • I am very annoyed by this culture of over celebrating fundraising rounds. You should absolutely celebrate it a little — it’s hard to raise a round! But not too much! Historically, big rounds came AFTER business success. And so people associated raising big rounds with having a successful business. In the modern day, big rounds frequently come BEFORE creating a product, let alone revenue or profit. And yet people don’t make any sort of distinction. This has led to the exact sort of degeneracy that you would expect. Getting a round of funding should be viewed as a blessing to take a shot. Celebrating too early is like celebrating that your team has made the NBA playoffs — if you take your foot off the gas, you’ll be eliminated before you know it.
  • If you think Twitter is unimportant, you are gravely mistaken. If you think Twitter is everything though, you are also mistaken. I do not know the balance. My feeling is that if you can get 10K followers on AI twitter without resorting to social media degeneracy, you have obtained a vector of influence which should neither be ignored nor placed in the highest regard. But if you succumb to degeneracy, even hundreds of thousands of followers are completely useless. Ironically, millions are likely useful regardless of how you came about them, including if they are bots.
  • If you haven’t read it, reading Situational Awareness might be the single most important thing you can do this week. And this is coming from someone who would say that he largely does not believe it. At the present, I do not agree with the timelines he presents. But the direction is 100% right. Also, I think it is worth reading this piece, which is nominally a piece of fiction. If you ask the average person whose not super tuned in ML to describe the history of 2022 – 2024 right now in 2024, many of them will do far, far, far worse than Daniel Kokotajlo who described this in 2021. 
  • LLMs will plateau. Multimodal models are just getting started. This makes me wonder if I’m even working on the right thing. But do not bet against multimodal models. Sure, they suck now, but those motherf***ers haven’t even begun to scale, let alone go through post-training. And I cannot say whether there will be a crash or not, but from a technological perspective, keep betting on them until people start complaining about the “good old days” when social media was “real” and full of actual humans. For I say unto you, we have not even begun to see what will happen when we scale up video and audio generation. 
  • There are logistical, social, financial, reputational costs to switching too often. For f***’s sake, people criticized me for leaving Google, despite the fact that I was there FOR AN INTERNSHIP. I brushed that off, but the dagger was felt regardless. With research, you must plan your direction of attack long in advance, and once you set it, you cannot shift on a moment’s notice. This is very tough and something that is harder for outsiders to appreciate. As a researcher — sure, of course, you care about whether this works in the long run. You need to trend in the direction of things that work in the long run for anything to matter. But you also care very much about whether it works in the short run. There is a very large difference for you personally if you miss the window by a few months. OK sure, MCTS and LLMs will work eventually BUT WILL THEY WORK BEFORE MY PROJECT GETS AXED? And even if it does work, can you personally build it? Ironically, finance people understand completely.
  • I am very disappointed that the injection of a relatively low amount of capital into the AI system warped things so massively. It makes me truly wonder what will happen when the fate of the world is at stake. The problem is not really the capital but the resulting hysteria. AI researchers literally chose this motherf****ing field over finance, where we easily could have made our paydays. So why, when the payday distribution end up matching finance (everyone makes six, lots of people make 7, some people make 8, a few people make 9+) did everything go berserk? Literally EVERYONE who entered AI early made the conscious decision to not pursue a finance career! I, of course, know the answer: that an injection of capital changes the culture and that change itself changes the culture too. No matter, the one and only job of the AI researcher is to adapt, and adapt we will. 
  • Historically, I have held very strong opinions about the direction of the world. While far from perfect, with hindsight, I think I can say that many of these opinions were actually pretty good. Nevertheless, it is with much gnashing of teeth and regret that I say that failed to follow through on most of these beliefs, mostly due to lack of conviction in myself. Talk is cheap. It is action — being the man in the arena — that determines success. I did not bet on my beliefs, and I did not win. Much of my anger with my parents is really a thinly veiled anger with myself for not having more conviction and a jealously of those who had someone who believed in them early. Ironic then, now that I have finally started to muster up the courage to believe in myself because I have begun to believe in people who believe in me, that I stand more confused than ever about which direction to run in.
Categories
ideas speedwrite technical

Thoughts on Decentralized Stablecoins

4 hours 23 minutes, 3,891 words.

Writer’s note. I’m pretty happy with this piece. One of the goals of speedwrite November was to write a full fleshed out thought in one sitting. This is the first time I think I’ve actually done it. Of course, I’d been thinking about this for a while, but the whole thing was written, from scratch, today. Had to be crypto. AI is too high stakes for me — I get too nervous that it’s not perfectly correct. But of course, the ultimate goal is to write my thoughts on AI in one sitting. This is the first practice run. Also, since this piece is so long, it’s going to count for the next five days. I come back on the 23rd. 

As someone who studied Economics in undergrad but was actually a (not so) secret CS/AI person and then went on to join the EconCS group at Harvard, it shouldn’t come as a huge surprise that I’ve been quite interested in stablecoins for a while. 

Sadly, post LUNA/TERRA collapse (and now FTX), saying that public opinion has soured on stablecoins (and crypto in general) is a fairly large understatement. In hindsight, most of the “stablecoins” or stable coin variants were a bit too heavy on the Ponzi and too light on the stable coin, in my opinion. But in some sense, at the heart of it, they were correct on some level. Here are some speculations on what I think(?) the Platonic ideal of what they were after looks like. If I were to build a stablecoin (not likely), here is what I would do. 

General Framework

There are two fundamental parts to a stablecoin. First is the circulating supply. These are the coins that everyone runs around spending. When you issue circulating supply, you incur liabilities in that you need to have 1 USD backing every coin out there. Second is the reserves. These are the things that you keep to back up the circulating supply. Ideally, reserves balance (and greatly exceed) the circulating supply. In the olden days of America, the circulating supply was the US dollar and the reserves were literal gold bars held in vaults all around the country. Nowadays, it’s a bit more complicated. But certainly, there is no gold backing anymore.

The basic framework I think that a decentralized stablecoin should follow is analogous to the gold standard for classic money. In the last few years, people skipped a few too many steps. It’s kind of hard to initiate a currency as a floating currency based on supply and demand. The non-crypto world spent thousands of years on the gold standard before they were ready to jump off of it. The crypto world is probably faster, but not quite ready for unbacked stablecoins though. Maybe one day, long in the future. Of course, you don’t base a crypto stablecoin off of gold. You base it off the crypto gold equivalent: Bitcoin.

The Race Dynamic

In my mind, there is a race. This race is not a Ponzi style race, as Matt Levine thinks it must be (though to be fair, most practical implementations were Ponzis). It is a race to see if your reserves can hit critical mass before you go bust.

Let me describe this in more detail. Imagine that your stablecoin starts when people start depositing BTC (gold) and getting an analogous amount of tokens (dollars) out. Additionally, every time someone spends your stablecoin, you collect a small fee (sales tax) and add it to the reserves. There are two possible outcomes in “equilibrium” (note that equilibrium may not be reached for a long time, and in fact, there might not be an “equilibrium” in the static sense). 

If at ANY point, the value of your reserves is less than the circulating supply of your token, you are insolvent and at risk of market liquidation. However, at some point, if you manage to survive without getting liquidated and people use your token, the transaction fees (and/or growth of BTC if you are bullish) will eventually mean that your reserves will be FAR more valuable than the liabilities that you incurred. And if you survive long enough that your emergency fund contains far more than the to circulating supply of your USD token, then you are safe. This system is like playing a late game carry like Vayne in League of Legends. If you make it to late game, then you win, and the system is stable. But you could very well fail early. 

Doesn’t this kind of sound like the Ponzi stableshitcoins that blew up this past year? In some sense, yes! That’s part of my claim. Projects like OlympusDAO, Wonderland, or LUNA were in some sense, degenerate reflections of this vision, but actually mostly Ponzis. But there is some version of this that might work!

[second writer’s note: I’m not building this, so feel free to unload on me if you think I’m completely wrong.  In fact, in general if you think that what I’m doing is BS, crypto or not, I’d rather hear it from you than after it’s too late]

I want to emphasize that this idea is not “new” any more than the idea that scaling AI systems to make them more powerful is “new.” One way or another, I think everyone in the space (and some people like me not in it), have thought of some form of this, in one way or another. The hard part is finding out how to get there.

If there is one thing that the previous projects did wrong (though to be fair, saying that they did this “wrong” presumes that they wanted to make a stablecoin and not, you know, get rich and run. It’s not clear that they actually wanted that in hindsight), it is covering up this race dynamic with smoke and mirrors. I think to do it right, you need to put the primary risk front and center. The race dynamic makes or breaks the coin. Everything else, the staking, algorithmic balancing, (3, 3), LUNATICS, is all smoke and mirrors. Plain and simple, the one real dynamic is the race between your reserves and your circulating supply. Expose that at the heart of it for everyone to see.

Let me describe a procedure that explains what I’m thinking. Alas, when I first pictured this, it was very simple and then complexity added itself as it always does. Nevertheless, I think it’s still a relatively simple system and will be described in a simple blog post. I don’t want to say the numbers don’t matter, because the numbers are crucial on whether you hit the critical point for one equilibrium versus the other. But the following numbers are made up without much though and just used to make things concrete. Here goes.

Overview

There are two tokens: COIN and STOCK. COIN is the stable coin. You can redeem it anytime for 1 USD worth of BTC at market price (details described later). Anytime you spent COIN, you pay a 1% transaction fee (lots of details which will be discussed later).

STOCK is not stable. A portion of the transaction fees will be distributed to holders of STOCK. This is the only utility of the STOCK token. In typical corporate lingo, COIN is debt (senior claims to the reserve) and STOCK is stock (expectations of future revenue flows). Unlike a company though, we aren’t trying to maximize the value of STOCK and don’t really mind that much if it is pretty close to zero.

A common operation in crypto is “locking” your tokens for some period of time. In our case, Locked COIN cannot be redeemed for BTC until the time is up. Let’s say the protocol starts with the founder / initial backer locking in 100M of BTC for ten years in exchange for minting 100M Locked COIN and 100M STOCK. After release, periodically, people are allowed to add BTC to the reserves in exchange for minting a proportional amount of COIN and STOCK. Why do they want to do this? Well, if COIN is actually stable at 1 USD and people are using it, then STOCK will be worth some positive value because of the transaction fees. And thus, you gain positive value from depositing your BTC there to compensate the risk of losing the race. There are a lot of tricky details here that will be described later.

As promised, this highlights the race dynamic described above. If you survive the 1) hackers 2) fluctuations in the markets 3) pressure to grow too fast and at some point, the reserves say, become 50x the value of the circulating supply, I think it is fairly safe to say that you have successfully crated a decentralized stable coin. Matt Levine claimed that all stablecoins had no endgame. And to be fair, he was right! But you could imagine a stablecoin which had an endgame like this. The endgame is not Ponzi style where people don’t redeem COIN any more than you would redeem USD. To be fair, that can come way, way, way later, long after people feel comfortable with a BTC-standard system and are ready to switch into a floating system. The (near-term) endgame is that your reserves are far larger than your liabilities, so you don’t care if people redeem or not.

Let’s talk about implementation details and main failure modes in more detail.

Fees

So what are the fees concretely. The fee structure will simply be that every time you send COIN to a new address, 1% of the transfer is sent to the reserve / STOCK holders.

Earlier, I claimed that 50x is a good tipping point for safety of reserves. Some might argue that it’s too much, but it’s better to be safe than sorry here. You want to be robust to black swans. And I think if you are robust to 98% dips in price, you are pretty safe. Until you hit this 50x critical mass, 90% of the fees go to the reserve (i.e. they are burned) and 10% get distributed proportionally to STOCK holders. After critical mass, you can flip it (90% to STOCK, 10% to reserve). 

STOCK/COIN is NOT part of the reserves. LUNA blew up because it relied on the value of TERRA (its sister token). FTX blew up because it relied on the value of FTT (its own token). Sending COIN to the reserve is just burning it. This is because COIN represents a claim of 1 USD on the reserves. If you have a 1 USD claim to yourself, it’s a no-op.

In terms of numbers, 1% is a number I pulled out a hat. On one hand, it seems kind of high. That being said, credit cards, in effect, charge 2.5% on each transaction (the merchant pays the fee and passes it onto you via higher prices) and sales tax varies, but is over 10% in California. On the other hand, the point is to escape the system not to create a new one. I think after though after you hit a critical mass, you can anneal the rate down to 0.1%. But it has to be relatively high in the beginning so the protocol can survive. 

Oracle Attacks.

I initially thought minting was simple, and then I realized I was completely wrong. I bet that I didn’t catch all the edge cases here, but here is a flavor of why it is hard. The general idea though is that people can put in BTC and get out an equivalent amount of COIN. There is a common attack in the crypto world known as the “oracle” attack. Alameda / FTX / SBF was famous for running variations of this strategy and it’s one (of many) reasons that SBF ticked off CZ and which led to his own downfall. Here is a simplified scenario. 

Say you take out a loan for 1M, secured by say, 2M worth of BTC. This loan has a condition that if the value of your collateral drops to say only 1.5M, then it will sell your BTC on the open market until you have paid back your loan. How does it know what the value of BTC is? It has to check an “oracle” or a trusted source of truth. You can now probably guess what the attack is. Perhaps you notice that the oracle doesn’t have much liquidity. You decide to sell a large amount of BTC on the oracle and crash the price of BTC to say, 1 cent for a few seconds. During that time, the original loan is liquidated in your favor. Immediately after, you buy up a lot of BTC on the oracle and recover most of your attack cost. Whether or not this attack is profitable depends on the details of a given contract. In the real world, these attacks have centralized defenses. For example, on the stock exchange, if the price moves too fast for no reason, trading is halted. This happened, notoriously, multiple times a day during the GME saga. Crypto exchanges have no such problems. Most “hacks” you have heard about in recent years in crypto are instances of this, in various degrees of sophistication. See a contract that queries some oracle for a value. Realize that you might be able to manipulate the value temporarily to your benefit, with the cost of manipulation much less than the gain from the attack.

Let’s apply that here. When you deposit BTC, the contract needs to know the price of BTC to know how much COIN to give to you. If you use the current price on any given exchange, you are always vulnerable to manipulations. If you use the average price, you are vulnerable to arbitrage. Both drain the reserve, which is very bad. I think the solution here is that when you deposit BTC, the protocol doesn’t give you your COINS immediately. Instead, it picks some (cryptographically secure) random time in the next week and gives you the coins based on the price at that moment. After all, if you can manipulate the price for a whole week, it’s not clear that it is manipulation :D. This is a solution that works here, but not in general. In trading, you cannot wait very long. Here though, there is really no rush. There are some considerations here, especially if the protocol gets big. But on the whole, proof of work (Bitcoin again!) is very good at generating randomness. 

Market Fluctuations

OK, I admit it. I have been procrastinating about the central question of stablecoins. For good reason though! I needed to describe the details before I could actually discuss the important bits. There is one central question about decentralized stablecoins which do not hold USD one-to-one. What happens in a market crash? After all, Bitcoin crashes by double digits all the time.

Let’s say you have 1B dollars of BTC deposits and 1B of liabilities in terms of COIN. What happens if BTC crashes by 20%? COIN stays the same because it is a stablecoin. But your reserves have lost 20%. You are now insolvent. This is more or less what people thought happened to FTX last week. This week …. 

There are two main defenses to this. Recall that the founder / initial backer locked in 100M and is not allowed to redeem for 10 years. Why is this important? A cold start donor is not strictly required, but in practice, I think likely necessary. If you have no donor, any tiny fluctuation is basically instant death. You are running a protocol with tiny margins and if at any point, say your reserves are only 99% of deposits. This might trigger a bank run! No one wants to be in the last 1% and get nothing. The locked donor is your backstop. Until the time period runs out, he is the last one out. The locking means that for the first decade, you have a 100M dollar buffer whereupon the founder cannot conduct a bank run. Thus, your reserves only have to be at least 100M less than the reserves for the first ten years to survive.

Why might someone do this? Well, some institutions are quite interested in seeing a good stablecoin. Perhaps you can incentivize them by offering a larger than usual amount of STOCK token. Finally, an idea that I’ve been mulling around with but am still very unsure about is that this might be a one angel can save a million sinners equilibria problem. If you jumpstart the protocol for a relatively small amount, perhaps you can drastically shift the equilibrium even if everyone else behaves selfishly. That is, past attempts stablecoins are obviously unstable equilibriums, even while they survived. But maybe not having a stablecoin is also an unstable equilibrium in the sense that if someone jumpstarts it at relatively small cost to themselves, the whole ecosystem can benefit.

OK, but this is just a temporary bandage. The second defense is controlling your growth rate. Most things in crypto (and in tech in general) want to blitzscale. I think you actually don’t want to here. Let me make things super clear. Let’s say you have 500M circulating supply and 1B in reserves. A 2-to-1 ratio is pretty safe right! People think you are ultra-safe and put in 1T dollars of deposits. Now you have 1.001T in reserves and 1.0005T in liabilities. A 1% change in the market prices means that you are dead, dead, dead, dead, dead. You are probably dead with a 0.1% change in market prices. 

So you don’t want to grow too fast. Thus, you want to only allow in deposits that allow you to maintain some buffer. I think a good rule of thumb is that you should only allow new deposits in so far as you can maintain a 2-to-1 ratio between reserves and unlocked COIN. This parameter might be too loose, but if you can hit it, you seem to be relatively safe. No one can do a bank run, because even if everyone redeemed, you have enough reserves to pay off your (unlocked) creditors.

What if too many people want to deposit? Auction it off I guess. Since I doubt people will want to lose money, perhaps you can auction off the amount of time people are willing to lock the money for. You only have room for 10M of deposits this month with 100M of interest in depositing? Have people bid for how many months/years they are willing to lock their token for. If not enough people want to deposit, that’s arguably even better. After all, the entire goal is to get to reserves that are far larger than liabilities. The ideal growth rate is super unclear. Perhaps as some sort of reference. USDT moves on average, 48 times a year. If the reserves get 0.9% (90% of the 1% fee) every time, they grow by 1.009^48 = >50%/yr. Not great, but I doable I suppose. 

Since we are making up numbers, let’s make up some more! Tether has a 66B market cap. If COIN starts with a 100M initial deposit and manages to raise 1B total relatively early, it will take about 10 years for the reserve to fully catchup up from transaction fees alone (1.5^10 = 70). While that may sound like a long time, it seems likely to me that growth would probably happen via locking / depositing BTC rather than fees. I suspect the phases will look roughly like this:

So there is a double race in a sense. First, you will grow. And in this growth phase, your reserve ratio will be roughly constant because any excess reserve will be diluted with new deposits. And then after you hit equilibrium in the growth sense, THEN you need to hit equilibrium where your reserves outpace your liabilities.

Growth Phase. Mainly driven by institutions/protocols that lock large amounts of capital in because they benefit from a stablecoin. The reserve ratio is low but risk of bank run is also relatively low because these locked institutions backstop everyone else.

Stable Phase. Eventually the locking stops and the trust is converted from knowing that you can get out before the big capital holders can liquidate to seeing that the reserves dwarf the liabilities.

As such, the stablecoin is largely functioning very early on and can rise to meet demand, but doesn’t become truly “stable” until a decade or so in. That feels like it’s fine to me. Nevertheless, if at ANY point, the value of your reserves falls below the value of your total outstanding COIN tokens, you are at risk of a bank run. And the fact that everything is completely in the public view means that you can’t rely on FTX/Binance/Tether style obfuscations where you claim you have the assets and you actually don’t.

Final Thoughts

Of course, an important question is: if it all goes bust, who is left holding the bag? In terms of seniority, the claims go (unlocked) COIN > Locked COIN > STOCK. STOCK holders get zeroed out first if the protocol goes bust, because there will be no transaction fees if no one is using the protocol. But that’s OK. We never claimed STOCK was stable. Locked COIN cannot redeem until their timer is up, so the seniority is clear. The order at which you unlock is the order in which you can redeem your claims. In terms of marketing, I think it’s probably pretty important to emphasize that Locked Coin is not stable until it is “unlocked.” But assuming nothing terrible happens, unlocked COIN seems like it should be fairly safe. After all, you are supposed to maintain the 2-to-1 ratio and if it goes bust, because the protocol is not growing / being used, I suppose that is indeed a failure of the system, albeit one you can see from a mile away as the reserves are trackable in real time. In practice, if reserves are about to crash too low, the unlocked COIN holders can see 1) the total reserve and 2) when the next Locked coin unlocks, and so they should always be able to exit first. There is a secondary benefit too. Once people cash out, the ratio of reserves to remaining capital (assuming it was larger than 1 originally) will increase, making the system safer for the remaining people. Of course, if you end up below 1 in terms of ratio of unlocked COIN to reserves, the system is toast.

In my mind, this is the way things should go. The founder is locked for 10 years — likely longer than anyone else. At that point, it should be abundantly clear whether the protocol failed or not. So in a sense, this is very much in line with the spirit of Taleb: ultimate skin in the game.

LUNA was a Ponzi and sort of proud of it. FTX was a Ponzi though no one (on the outside) knew it until it was too late. I think this is not a Ponzi. But you might be skeptical, and you should be. It does sort of sound like a Ponzi and I’m only mostly convinced myself that it’s not.

But I would claim that it doesn’t have the core Ponzi property in that early backers get fantastically rich at the expense of people late to the game. Perhaps you can offer more STOCK to early backers, but honestly, STOCK should not be worth very much anyways relative to the reserves, since its valuation should just be some small multiple on flows. Certainly there are no 100,000% APRs here (not a typo).

And most importantly, it’s not a Ponzi because the endgame isn’t hoping no one pulls out. The endgame you actually get what was promised: a fully functioning decentralized stablecoin backed by more than sufficient reserves. It’s just the road to get there which is straight and narrow. 

Categories
technical

On Tight Bounds

A random thought I had about “tight” bounds in mathematics, which I will illustrate using a silly example. Let’s say I show that the positive integers are lower bounded by 1. This bound is tight, in the sense that, since the smallest possible positive integer is 1, a lower bound of the form 1 + \epsilon would be false, for any positive \epsilon. But now consider a different claim that a positive integer X is lower bounded by X. In some sense, this bound even tighter than my previous so called “tight” bound because for any positive integer X, the gap between this new bound and X is at least as small as lower bound of 1 and usually much smaller. So what happened here? How did I make an already tight bound tighter?

This is a degenerate version of an actual experience of mine recently. I found a bound, convinced myself it was tight because I found an example where it held exactly, then woke up the next day and found a bound that tightened the old bound (in other areas) by using some information that the previous bound had not incorporated. 

Believe it or not, I was actually confused for a while and tried to figure out what exactly went wrong with my thought process or if I had made a mistake somewhere. Eventually, I realized that a bound is “tight” with respect to a certain class of improvements that you are allowed to consider. In the example above, the lower bound of 1 is tight if you are not allowed to use which integer you are bounding. The lower bound of X is tight with respect to all possible information you could use. Another way of phrasing this is that your bound may be “tight” because you are ignoring other possible information you could use about the structure of your problem. This means that one of the first questions you should yourself when you encounter a tight bound is: with respect to what class of bounds? Thus, as opposed to dusting your hands and thinking that you are done when you find a single example where your bound holds exactly, a better approach is to figure out if you have maximally used all the relevant information in your problem.

Categories
ideas technical

On Wealth Inequality

An apparent paradox of the twenty-first century: though economic opportunities abound everywhere, class mobility is pretty close to an all time low [1]. It just occurred to me that perhaps this should not actually be all that surprising given how the world currently works.

It is generally accepted that most people’s utility functions are concave on money [2]. If you are homeless, a thousand dollars means the world to you. If you are a billionaire, not so much. Among other things, this means that humans are generally risk-averse. Most people would rather have a billion dollars rather than a coinflip chance between nothing and two billion. On the flip side (by definition), expected value is risk neutral. The expected value of a pot of money worth one billion dollars and the coin flip deal above is the same. (Strict) concavity means that your risk-aversion approaches zero as you get more and more money. For example, Jeff Bezos would likely be indifferent between the coin flip and the pot of money. His net worth probably fluctuates by over a billion dollars every day. 

Imagine that because our world is full of opportunity, everyone naturally receives a bunch of low to medium probability, high expected value options (e.g. a desire/talent for a risky venture, such as running for political office, scaling a small business, proving a famous theorem, starting a movement, etc.). Unfortunately, people start off with very different amounts of money, and thus have very different risk preferences.

If you’re a leftist, you see this and immediately say: hey, what if we redistributed some of the money from the wealthiest people to everyone else. They would barely mind (concavity to the rescue again) and everyone would be much happier! For better or for worse, the world does not quite work that way. The actual trade that happens in the real world is much closer to what classical economics would predict in this scenario. Rich people buy the high expected value options off poorer people with guaranteed cash. Instead of starting your own [venture / campaign / movement / project] (exercising the option), you sell your time and work at someone else’s [venture / campaign / movement / project] (selling the option). Moreover, the more opportunity (especially if it takes the form of very low probability but very high expected value options) around, the worse wealth inequality gets because the rich people can claim all the surplus (there are fewer rich people, so supply and demand favors the buyers here).

I’m not immune to this criticism. Someone recently challenged me by asking why I decided to go to graduate school instead of starting my own AI lab? I don’t quite have (any of) the expertise, money, or connections to pull that off yet. But nevertheless, it was an interesting thing to think about. And in 5 years, who knows?

This is obviously a highly simplified model of reality, but I think it raises some pretty deep questions about inequality. In particular, it suggests that even if you gave everyone ample opportunity, people would still be incentivized to sell their options away for a secure form of income as a fundamental corollary of concave utility curves. Usually, when I hear fragments of this idea discussed, it is usually uttered by defeatists who throw up their hands and say: “what can we do?” or from hardcore libertarians who use this idea to justify that poor people actually deserve to be poor (because it’s their own choice). I’m not sure that either one is entirely correct.

My two takeaways from this thought exercise: 1) giving someone an opportunity is not enough if they are not in a position to take it. 2) I’m increasingly in favor of a universal basic income. 3) manage your risk and your location on your utility curve so that you can swing at as many of good chances as you can afford to.

[1]: I did an (admittedly very poor) Oxford tutorial on the subject and got the impression that economists really liked to overclaim when in fact the explanation was still very unclear. The evidence that class mobility had gone down since 1970 is very strong, of course. The evidence for any particular explanation of why is significantly less so. One can immediately see the beginnings of this observation by noting that every economist has their own *different* reason and instead of proving each other wrong, simply say: “it’s a combination of many factors.”

[2]: The poorer you are, the more each dollar means to you. I wrote this down as a general intuition then realized that it wasn’t just an intuition: it’s equivalent to the actual definition of concavity.

Categories
ideas technical unfinished

Predicting Chaos

Here is a proposal to predict deterministic chaotic systems, assuming they are continuous and reversible (you can “undo” the chain).

Setup

Assume your chaotic system evolves according to some function f and that your initial state a_0 is drawn uniformly from the set of all possible states. Denote each future state a_t = f(a_0, t) where t can be any positive real. Since our chain is reversible, define g such that a_0 = g(f(a_0, t), -t) for all positive real t. Assume that every unit of time (denoted by the positive integers) we measure the state of our chaotic system. Because our tools are imperfect, our measurement x_t is sampled from \mathcal{N}(a_t, \sigma^2), the normal distribution centered at the true state with variance \sigma^2. Assume \sigma is known.

Prediction

After collecting observations from T time steps, we can use our knowledge of how the system evolves and our imperfect measurements to narrow down the possibilities of what the true state is. For each possible true state a_T, we compute a likelihood score based on how consistent that state would be with our measurements x_1 \dots x_T. Because we p(x_1 \cdots x_T) is unknown, our likelihood score results in an unnormalized energy distribution. Even so, using standard MCMC methods, we can draw (semi-correlated) samples from the distribution and use that to estimate future trajectories. 

The assumption that this chain is continuous means that for any fixed time horizon \delta, getting a better estimate of the true state a_T means we get better at predicting f(a_T, T+\delta). The assumption that the chain is reversible allows us to calculate the probability of each value of a_T as opposed to only being able to deduce the value of a_0, which is much less valuable for predicting the future. Because of these two properties, we should be able to come up with a relationship along the lines of: accurately predicting N timesteps into the future with Y\% confidence is guaranteed if we observe at least H(N, Y) previous time steps, though as [redacted] pointed out, there is a possibility that this will asymptote at some point.


Follow up: can this method predict a double pendulum?

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