Investing

My First Exit

James BaileyLeave a comment

I invested in my first private company in 2022; my first opportunity to cash out of a private investment came this year when Our Bond did an IPO, now trading on Nasdaq as OBAI.

I’m happy to get a profitable exit less than 4 years after my first investment, given that I’m investing in early-stage companies. Venture funds tend to run for 10 years to give their companies time to IPO or get acquired, and WeFunder (the private investment platform I used) says that “On average, companies on Wefunder that earn a return take around 7 years to do so.” The speed here is especially striking given that I didn’t invest in Our Bond itself until April 2025.

Most private companies that raise money from individual investors are very early stage, what venture capitalists would call “pre-seed” or “seed-stage” companies looking for angel investors. Later-stage companies often find it simpler to raise their later stages (Series B, et c) from a few large institutional investors. But a few choose to do “community rounds” and allow individuals to invest later. This is what Our Bond did right before their IPO, allowing me to exit in less than a year.

This helps calm my biggest concern with equity crowdfunding- adverse selection:

The companies themselves have a better idea of how well they are doing, and the best ones might not bother with equity crowdfunding; they could probably raise more money with less hassle by going to venture funds or accredited angel investors.

My guess is that the reason some good companies bother with this is marketing. Why did Substack bother raising $7.8 million from 6000 small investors on WeFunder in 2023, when they probably could have got that much from a single VC firm like A16Z? They got the chance to explain how great their company and product is to an interested audience, and to give thousands of investors an incentive to promote the company. Getting one big check from VCs is simpler, but it doesn’t directly promote your product in the same way.

All this is enough to convince me that the equity crowdfunding model enabled by the 2012 JOBS Act will continue to grow.

Still, things could have easily gone better for me, as these markets are clearly inefficient and have complexities I’m still learning to navigate. Profitability is not just about choosing the right companies to invest in, but about managing exits. I expected the typical IPO roadshow would give me months of heads-up, but Our Bond surprised its investors with a direct listing. The first thing I heard about the IPO was a February 4th email from “VStockTransfer” that I thought was a scam at first, since it was a 3rd-party company I’d never heard of asking me to pay them money to access my shares. But Our Bond confirmed it was real- VStockTransfer was the custodian for the private shares, and charges $120 to “DRS transfer” them to a brokerage of your choice where they can be sold.

I submitted the request to move the shares to Schwab the same day, but Schwab estimated it would take a week to move them. Neither Schwab nor VStockTransfer ever sent me a notification that the shares had been transferred, and by the time I noticed they had moved a week later, the stock price had fallen dramatically:

As I write this on February 18th, the OBAI price represents a 1.3x return on the price I invested in the private company at last April. When I was first able to sell some stock on February 11th, the price represented a 3x return; if I’d been able to sell right away on the 4th without waiting for the brokerage transfer process, it would have been a 10x return.

By the Efficient Market Hypothesis this timing shouldn’t be so critical, but I knew there would be a rush for the exits as lots of private investors would want to unload their shares at the first opportunity, an opportunity some would have waited years for. Sometimes old-fashioned supply and demand analysis is a better guide to markets than the EMH: demand for OBAI stock had no big reason to change in February, but freely floating supply saw a big increase as private shares got unlocked and moved to brokerages.

Getting a 10x return vs a 1.3x return on one of your winners is the difference between a great early investor and a bad one. I always thought such differences would be driven by who picks the best companies to invest in, but at least in this case it could be driven by who is fastest on the draw with brokerage transfers.

If I ever find myself holding shares in another company that does a direct listing, I’ll be doing whatever I can to make sure the transfer goes as fast as possible (pick the fastest brokerage, check on the transfer status every day, et c). This process also seems like one reason to do fewer, larger private investments- a fixed $120 transfer fee is a big deal if the initial investment was in the low hundreds but wouldn’t matter much for a larger one.

Being accredited would help there, allowing access to additional later-stage, less-risky companies. But I’ll call OBAI a win for equity crowdfunding, and a big win for asset pricing theories based on liquidity and flows over efficient estimation of the present discounted value of future cashflows.

Disclaimer: I still hold some OBAI

Broad Slump in Tech and Other Stocks: Fear Over AI Disruption Replaces AI Euphoria

Scott BuchananLeave a comment

Tech stocks (e.g. QQQ) roared up and up and up for most of 2023-2025, more than doubling in those three years. A big driving narrative was how AI was going to make everything amazing – productivity (and presumably profits) would soar, and robust investments in computing capacity (chips and buildings), and electric power infrastructure buildout, would goose the whole economy.

Will the Enormous AI Capex Spending Really Pay Off?

But in the past few months, a different narrative seems to have taken hold. Now the buzz is “the dark side of AI”. First, there is growing angst among investors over how much money the Big Tech hyperscalers (Google, Meta, Amazon, Microsoft, plus Oracle) are pouring into AI-related capital investments. These five firms alone are projected to spend over $0.6 trillion (!) in 2026. When some of this companies announced greater than expected spends in recent earning calls, analysts threw up all over their balance sheets. These are just eye-watering amounts, and investors have gotten a little wobbly in their support. These spends have an immediate effect on cash flow, driving it in some cases to around zero. And the depreciation on all that capex will come back to bite GAAP earnings in the coming years, driving nominal price/earnings even higher.

The critical question here is whether all that capex will pay out with mushrooming earnings three or four years down the road, or is the life blood of these companies just being flushed down the drain?  This is viewed as an existential arms race: benefits are not guaranteed for this big spend, but if you don’t do this spending, you will definitely get left behind. Firms like Amazon have a long history of investing for years at little profit, in order to achieve some ultimately profitable, wide-moat quasi-monopoly status.  If one AI program can manage to edge out everyone else, it could become the default application, like Amazon for online shopping or Google/YouTube for search and videos. The One AI could in fact rule us all.

Many Companies May Get Disrupted By AI

We wrote last week on the crash in enterprise software stocks like Salesforce and ServiceNow (“SaaSpocalypse”). The fear is that cheaper AI programs can do what these expensive services do for managing corporate data. The fear is now spreading more broadly (“AI Scare Trade”);  investors are rotating out of many firms with high-fee, labor-driven service models seen as susceptible to AI disruption. Here are two representative examples:

  • Wealth management companies Charles Schwab and Raymond James dropped 10% and 8% last week after a tech startup announced an AI-driven tax planning tool that could customize strategies for clients
  • Freight logistics firms C.H. Robinson and Universal Logistics fell 11% and 9% after some little AI outfit announced freight handling automation software

These AI disruption scenarios have been known for a long time as possibilities, but in the present mood, each new actual, specific case is feeding the melancholy narrative.

All is not doom and gloom here, as investors flee software companies they are embracing old-fashioned makers of consumer goods and other “stuff”:

The narrative last week was very clearly that “physical” was a better bet than “digital.” Physical goods and resources can’t be replaced by AI like digital goods and services can be at an alarming rate

As I write this (Monday), U.S. markets are closed for the holiday. We will see in the coming week whether fear or greed will have the upper hand.

SaaSmageddon: Will AI Eat the Software Business?

Scott BuchananLeave a comment

A big narrative for the past fifteen years has been that “software is eating the world.” This described a transformative shift where digital software companies disrupted traditional industries, such as retail, transportation, entertainment and finance, by leveraging cloud computing, mobile technology, and scalable platforms. This prophecy has largely come true, with companies like Amazon, Netflix, Uber, and Airbnb redefining entire sectors. Who takes a taxi anymore?

However, the narrative is now evolving. As generative AI advances, a new phase is emerging: “AI is eating software.”  Analysts predict that AI will replace traditional software applications by enabling natural language interfaces and autonomous agents that perform complex tasks without needing specialized tools. This shift threatens the $200 billion SaaS (Software-as-a-Service) industry, as AI reduces the need for dedicated software platforms and automates workflows previously reliant on human input. 

A recent jolt here has been the January 30 release by Anthropic of plug-in modules for Claude, which allow a relatively untrained user to enter plain English commands (“vibe coding”) that direct Claude to perform role-specific tasks like contract review, financial modeling, CRM integration, and campaign drafting.  (CRM integration is the process of connecting a Customer Relationship Management system with other business applications, such as marketing automation, ERP, e-commerce, accounting, and customer service platforms.)

That means Claude is doing some serious heavy lifting here. Currently, companies pay big bucks yearly to “enterprise software” firms like SAP and ServiceNow (NOW) and Salesforce to come in and integrate all their corporate data storage and flows. This must-have service is viewed as really hard to do, requiring highly trained specialists and proprietary software tools. Hence, high profit margins for these enterprise software firms.

 Until recently, these firms been darlings of the stock market. For instance, as of June, 2025, NOW was up nearly 2000% over the past ten years. Imagine putting $20,000 into NOW in 2015, and seeing it mushroom to nearly $400,000.  (AI tells me that $400,000 would currently buy you a “used yacht in the 40 to 50-foot range.”)

With the threat of AI, and probably with some general profit-taking in the overheated tech sector, the share price of these firms has plummeted. Here is a six-month chart for NOW:

Source: Seeking Alpha

NOW is down around 40% in the past six months. Most analysts seem positive, however, that this is a market overreaction. A key value-add of an enterprise software firm is the custody of the data itself, in various secure and tailored databases, and that seems to be something that an external AI program cannot replace, at least for now. The capability to pull data out and crunch it (which AI is offering) it is kind of icing on the cake.

Firms like NOW are adjusting to the new narrative, by offering pay-per-usage, as an alternative to pay-per-user (“seats”). But this does not seem to be hurting their revenues. These firms claim that they can harness the power of AI (either generic AI or their own software) to do pretty much everything that AI claims for itself. Earnings of these firms do not seem to be slowing down.

With the recent stock price crash, the P/E for NOW is around 24, with a projected earnings growth rate of around 25% per year. Compared to, say, Walmart with a P/E of 45 and a projected growth rate of around 10%, NOW looks pretty cheap to me at the moment.

(Disclosure: I just bought some NOW. Time will tell if that was wise.)

Usual disclaimer: Nothing here should be considered advice to buy or sell any security.

After the Crash: Silver Clawing Back Up After Epic Bust Last Week

Scott BuchananLeave a comment

A month ago (red arrow in 5-year chart below), I noticed that the price of silver was starting into a parabolic rise pattern. That is typical of speculative bubbles. Those bubbles usually end in a bust. Also, the rise in silver price seemed to be mainly driven by retail speculators, fueled by half-baked narratives rather than physical reality.

Five-year chart of silver prices $/oz, per Trading View

So I wrote a blog post here last month warning of a bubble, and sold about a quarter of my silver holdings. (I also initiated some protective options but that’s another story for another time.) I then felt pretty foolish for the next four weeks, as silver prices went up and up and up, a good 40% percent over the point I initially thought it was a bubble. Maybe I was wrong, or maybe the market can stay irrational longer than you can stay solvent, per J. M. Keynes.

When the crash finally came, it was truly epic. Below is a one-month chart of silver price. The two red lines show silver price at the close of regular trading on Thursday, January 29 (115.5 $/oz), and at the close of trading on Friday, January 30 (84.6 $/oz):

This is a drop of nearly 30% in one day, which is a mind-boggling move for a major commodity. Gold got dragged down, too:

These aren’t normal moves. Over roughly the past 25+ years (through 2025), gold’s price has changed by about 0.8% per day on average (in absolute percentage terms). Silver, being more volatile, has averaged around 1.4–1.5% per day. If you’re scoring at home, that’s about a 13 Sigma move for Gold and 22 Sigma move for Silver! You’re witnessing something that shouldn’t happen more than once in several lifetimes…statistically speaking. Yet here we are.

After the fact, a number of causes for the crash were proposed:

  • The nomination of Kevin Warsh as the next Federal Reserve Chair.  Warsh is perceived as a hawkish policymaker, leading investors to expect tighter monetary policy, higher interest rates, and a stronger U.S. dollar—all of which reduce the appeal of non-yielding assets like silver. 
  • Aggressive profit-taking after silver surged over 40% year-to-date and hit record highs near $121 per ounce. 
  • Leveraged positions in silver futures were rapidly unwound as prices broke key technical levels, triggering stop-loss orders and margin calls. 
  • CME margin hikes (up to 36% for silver futures) increased trading costs, forcing traders to cut exposure and accelerating the sell-off. 
  • Extreme speculation among Chinese investors, leading the Chinese government to clamp down on speculative trading. (And presumably Chinese solar panel manufacturers have been complaining to the government about high costs for silver components).

What happens next?

Silver kept falling to a low of 72.9 $/oz in the wee hours of February 2, a drop of 40% percent from the high of 120.8 on Jan 26. However, it looks to my amateur eyes like the silver bubble is not really tamed yet. For all the drama of a 22-sigma crash one day crash, about all that did was erase one months’ worth of speculative gains. The charts above are showing that silver is clawing its way right back up again.  It is very roughly on the trend line of the past six months, if one excludes the monster surge in the month of January.

There is a saying among commodities traders, that the cure for high prices is high prices. This means that over time, there will be adjustments that will bring down prices. In the case of silver, that will include figuring out ways to use less of it, including recycling and substitution of other metals like copper and aluminum. However, my guess is that the silver bulls feel vindicated by the price action so far, and will keep on buying at least for now.

Disclaimer: As usual, nothing here should be regarded as advice to buy or sell any security.

The Wealth Ladder

James BaileyLeave a comment

The Wealth Ladder is a 2025 personal finance book from data blogger Nick Maggiulli. The core idea is good: that the best financial strategies will be different based on your current wealth level. Maggiulli divides people into 6 net worth levels based on orders of magnitude, from less than $10K to over $100M. The middle of the book has separate chapters with advice for people in each level, so a book that is already a fairly quick and easy read as a whole could be even quicker if you skipped the chapters about levels other than your own.

The beginning of the book tries to develop some simple rules phrased in a way that they can apply across every level, because they are based on a percentage of your net worth. I like the idea but don’t think it really worked. His “1% Rule” says you should only accept an opportunity to earn money if it will increase your net worth by at least 1%. But in practice, whether an earning opportunity is worth your time depends less on how many absolute dollars in generates as a % of your net worth, and more on how many $ per hour it generates. The “0.01% Rule” (don’t worry about spending money on anything that costs less than 0.01% of your net worth) is better. But whether it is a good rule for you will depend on your age and income.

In short, while tailoring his advice in 6 different ways for the 6 wealth levels of his ladder is an improvement on one-size-fits all personal finance books, even this much tailoring isn’t enough. Having a $1 million net worth is normal for a household in their 60s but would be exceptional for one in their 20’s; and vice-versa for a household with under $10k net worth. Chapter 10 explains the data on this well, but it kind of undermines the ideas of the previous chapters. Households with the same net worth should be making very different decisions in their 20s vs 60s.

The strongest part of the book is the use of data from the Survey of Consumer Finances and the Panel Study of Income Dynamics to show how people differ by wealth level and how people move from one level to another. For instance, he shows that the poor have most of their wealth in cash and vehicles; the middle class in homes; the wealthy in retirement accounts and stocks; the very rich in private businesses. Americans tend to climb the wealth ladder slowly but steadily; over 10 years they are twice as likely to move up the ladder as to move down; over 20 years, 3 times as likely. The median person who made it to one of the top 3 rings (i.e. the median millionaire) is in their 60s.

If you get ahold of a copy of the book it’s definitely worthwhile to flip through all the tables and figures, but I won’t be adding to to my short list of the best personal finance books. The core metaphor of the ladder carriers the implicit assumption that everyone should be trying to get to the top of the ladder. But if someone is satisfied with less than $10 million, why should they take on lots of time and effort and risk to start a business for a small chance to go over $100 million?

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Is the Silver Bubble Bursting?

Scott Buchanan3 Comments

This is a five-year chart of the silver ETF SLV:

By most standards, this pattern looks like we entered a bubble a few months ago: speculative froth, unjustified by fundamentals. Economic history is replete with such madness of crowds. It is accepted wisdom on The Street that these parabolic price rises seldom end well. I lost a few pesos buying into the great gold bubble of 2011. All sorts of justifications were given at the time by the gold bugs on why gold prices ought to just keep on rising, or at least reach a “permanently high plateau” (in the famous words of Irving Fisher, just before the 1929 crash). Well, gold then proceeded to go down and down and down, losing some 60% of its value, until the price in 2015 matched the price in 2009, before the great bubble of 2010-2011.

Today, similar justifications are proffered as to why silver is going to the moon. There is a long-standing deficit in supply vs. demand; it takes ten years for a new silver mine to get productive; China has started restricting exports; Samsung announced a breakthrough lithium battery that can charge in six minutes, but requires a kilogram of silver; AI infrastructure is eating all the silver. These narratives seem to feed on each other. As the silver price moved higher in the past month, out came yet wilder stories that ricochet around the internet at high speeds: the commodities exchanges have run out of physical silver to back the paper trades; and the persistent claim that “they” (shadowy paper traders, central banks, commodity exchanges, the deep state, etc.) are “suppressing” silver and gold prices by means of shorting (which makes no sense). Given this popular shorting myth, it was with great glee that the blogosphere breathlessly spread the bogus story that some “systematically important bank” was in the process of being liquidated because it got squeezed on its silver short position.

The extreme price action at the very end of December (discussed below) was like rocket fuel for these rumors. Having bought a little SLV myself so as to not feel like a fool if the silver rally did have legs, I spent a number of hours as 2025 turned to 2026 trying to sort all this out. Here are some findings.

First, as to  the medium term supply/demand issues, I refer the reader to a recent article on Seeking Alpha by James Foord. He shows a chart showing that silver demand is increasing, but slowly:

He also notes that as silver price increases, there is motivation for more recycling and substitution, to compensate. He concludes that the current price surge is not driven by fundamentals, but by paper speculation.

The last ten days or so have been a wild ride, which merits some explanation. Here is the last 30 days of SLV price action:

Silver prices were rising rapidly throughout the month, but then really popped during Christmas week, reaching a crescendo on Friday, Dec 26 (blue arrow), amid rumors of physical shortages on the Shanghai exchange. To cool the speculative mania, the COMEX abruptly raised the margin requirements on silver contracts by some 30%,  from $25,000 to $32,500, effective Monday, Dec 29. I think the exchange was trying to ensure that speculators could make good on their commitment, and the raise in margin requirement would help do that. (Note, the exchange is liable if some market participant fails to deliver as promised and goes BK).

Anyway, this move forced long speculators to either post more collatoral or to liquidate their positions, on short notice. Blam, the price of silver dropped a near record amount in one day (red arrow). For me, a little minnow caught in the middle of all this shark tank action, the key part is what came after this forced decline. Was the bubble punctured for good? Should I hold or fold?

As shown above, the price has traded in a range for the past week, with violent daily moves. Zooming out to the a one-year view, it looks like the upward momentum has been halted for the moment, but it is unclear to me whether the bubble will deflate or continue for a while:

I sold about a quarter of my (small) SLV holding, hoping to buy back cheaper sometime in the coming year. Time will tell if that was a good move.

Usual disclaimer: Nothing here is advice to buy or sell any security.

P.S. Tuesday, Jan 6, 2025, after market close: I wrote this last night (Monday, Jan. 5) when silver was still rangebound. SLV was about $69, and spot silver about $76/oz. But silver ripped higher overnight, and kept going during the day, up nearly 7% at the close to new all time high. It looks like the bubble is alive and well, for now. Congrats to silver longs…

How Good Were 2025 Forecasts?

James Bailey1 Comment

Last January I shared a roundup of forecasts for the year from markets and professional economists. Were they any good? Here was their prediction for the US economy:

WSJ’s survey of economists reports that inflation expectations for 2025 were around 2% before the election, but are closer to 3% now. Their economists expect GDP growth slowing to 2%, unemployment ticking up slightly but staying in the low 4% range, with no recession. The basic message that 2025 will be a typical year for the US macroeconomy, but with inflation being slightly elevated, perhaps due to tariffs.

The verdicts (based on current data, which isn’t yet final for all of 2025):

Inflation: Nailed it exactly (2.7%)

GDP: We’re still waiting on Q4, but 2025 as a whole is on track to be a bit above the 2.0% forecast.

Unemployment: 4.6% as of November 2025, a bit above the 4.3% forecast

Recession: Didn’t happen, making the 22% chance forecast look fine

So the professional forecasters were probably a bit low on GDP and unemployment, but overall I’d say they had a good year. What about prediction markets?

For those who hope for DOGE to eliminate trillions in waste, or those who fear brutal austerity, the message from markets is that the huge deficits will continue, with the federal debt likely climbing to over $38 trillion by the end of the year. This is one reason markets see a 40% chance that the US credit rating gets downgraded this year.

While the US has only a 22% chance of a recession, China is currently at 48%, Britain at 80%, and Germany at 91%. The Fed probably cuts rates twice to around 4.0%.

Deficits: Nailed it, the federal debt is currently around $38.4 trillion.

US Credit Downgrade: It’s hard to score a prediction of a 40% chance of a binary event happening, but in any case Moodys downgraded the US’ credit rating in May, so that all three major agencies now rate it as not perfect.

The Fed: Cut rates a bit more than expected.

Foreign Recessions: China and Britain avoided recessions. Germany had a recession by the technical definition of Kalshi’s market, but not really in practice (FRED shows -0.2% Real GDP growth in Q2 followed by 0.00000% growth in Q3). Britain avoiding recession when markets showed an 80% chance was the biggest miss among the forecasts I highlighted.

Overall though, I’d say forecasters did fairly well in predicting how 2025 turned out, in spite of curveballs like the April tariff shock.

If you think the forecasters are no good and you can do better, you have more options than ever. Prediction markets are getting more questions and more liquidity if you’re up for putting your money where your mouth is; if you don’t want to put your own money at risk, there are forecasting contests with prizes for predicting 2026.

Investing: You Vs. All Possible Worlds

Zachary Bartsch1 Comment

This post illustrates a couple of things that I learned this year with an application in finance. I learned about the simplex when I was researching amino acids. I learned some nitty-gritty about portfolio theory. These combined with my pre-existing knowledge about game theory and mixed strategy solutions.

Specifically, I learned a way of visualizing all possible portfolio returns. This post narrowly focuses on 3 so that I can draw a picture. But the idea generalizes to many assets.

Say that I can choose to hold some combination of 3 assets (A, B, & C), each with unique returns of 0%, 20%, and 10%. Obviously, I can maximize my portfolio return by investing all of my value in asset B. But, of course, we rarely know our returns ex ante. So, we take a shot and create the portfolio reflected in the below table. Our ex post performance turns out to be a return of 15%.

That’s great! We feel good and successful. We clearly know what we’re doing and we’re ripe to take on the world of global finance. Hopefully, you suspect that something is amiss. It can’t be this straightforward. And it isn’t. At the very least, we need to know not just what our return was, but also what it could have been. Famously, a monkey throwing darts can choose stocks well. So, how did our portfolio perform relative to the luck of a random draw? Let’s ignore volatility or assume that it’s uncorrelated and equal among the assets.  

Visualizing Success with Two Assets

Say that we had only invested in assets A and B. We can visualize the weights and returns easily. The more weight we place on asset A, the closer our return would have been to zero. The more weight that we place on asset B, the closer our return would have been to 20%.

If we had invested 75% of our value in asset B and 25% in A, then we would have achieved the same return of 15%. In this two-asset case, it is clear to see that a return of 15% is better than the return earned by 75% of the possible portfolios. After all, possible weights are measures on the x-axis line, and the leftward 75% of that line would have earned lower returns.  Another way of saying the same thing is: “Choosing randomly, there was only a 25% that we could have earned a return greater than 15%.”  

Visualizing Success with Three Assets

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Google’s TPU Chips Threaten Nvidia’s Dominance in AI Computing

Scott BuchananLeave a comment

Here is a three-year chart of stock prices for Nvidia (NVDA), Alphabet/Google (GOOG), and the generic QQQ tech stock composite:

NVDA has been spectacular. If you had $20k in NVDA three years ago, it would have turned into nearly $200k. Sweet. Meanwhile, GOOG poked along at the general pace of QQQ.  Until…around Sept 1 (yellow line), GOOG started to pull away from QQQ, and has not looked back.

And in the past two months, GOOG stock has stomped all over NVDA, as shown in the six-month chart below. The two stocks were neck and neck in early October, then GOOG has surged way ahead. In the past month, GOOG is up sharply (red arrow), while NVDA is down significantly:

What is going on? It seems that the market is buying the narrative that Google’s Tensor Processing Unit (TPU) chips are a competitive threat to Nvidia’s GPUs. Last week, we published a tutorial on the technical details here. Briefly, Google’s TPUs are hardwired to perform key AI calculations, whereas Nvidia’s GPUs are more general-purpose. For a range of AI processing, the TPUs are faster and much more energy-efficient than the GPUs.

The greater flexibility of the Nvidia GPUs, and the programming community’s familiarity with Nvidia’s CUDA programming language, still gives Nvidia a bit of an edge in the AI training phase. But much of that edge fades for the inference (application) usages for AI. For the past few years, the big AI wannabes have focused madly on model training. But there must be a shift to inference (practical implementation) soon, for AI models to actually make money.

All this is a big potential headache for Nvidia. Because of their quasi-monopoly on AI compute, they have been able to charge a huge 75% gross profit margin on their chips. Their customers are naturally not thrilled with this, and have been making some efforts to devise alternatives. But it seems like Google, thanks to a big head start in this area, and very deep pockets, has actually equaled or even beaten Nvidia at its own game.

This explains much of the recent disparity in stock movements. It should be noted, however, that for a quirky business reason, Google is unlikely in the near term to displace Nvidia as the main go-to for AI compute power. The reason is this: most AI compute power is implemented in huge data/cloud centers. And Google is one of the three main cloud vendors, along with Microsoft and Amazon, with IBM and Oracle trailing behind. So, for Google to supply Microsoft and Amazon with its chips and accompanying know-how would be to enable its competitors to compete more strongly.

Also, AI users like say OpenAI would be reluctant to commit to usage in a Google-owned facility using Google chips, since then the user would be somewhat locked in and held hostage, since it would be expensive to switch to a different data center if Google tried to raise prices. On contrast, a user can readily move to a different data center for a better deal, if all the centers are using Nvidia chips.

For the present, then, Google is using its TPU technology primarily in-house. The company has a huge suite of AI-adjacent business lines, so its TPU capability does give it genuine advantages there. Reportedly, soul-searching continues in the Google C-suite about how to more broadly monetize its TPUs. It seems likely that they will find a way. 

As usual, nothing here constitutes advice to buy or sell any security.

Visualizing the Sharpe Ratio

Zachary BartschLeave a comment

We all like high returns on our investments. We also like low volatility of those returns. Personally, I’d prefer to have a nice, steady 100% annual return year after year. But that is not the world we live in. Instead, there are a variety of returns with a diversity of volatilities. A general operating belief is that assets with higher returns tend to be associated with greater return volatility. The phrase ‘scared money don’t make money’ implies that higher returns are risky. The Sharpe ratio is a tool that helps us make sense of the risk-reward trade-off.

Let’s start with the definition.

By construction, the risk-free return is guaranteed over some time period and can be enjoyed without risk. Practically speaking, this is like holding a US treasury until maturity. We assume that the US government won’t default on its debt. Since there is no risk, the volatility of returns over the time period is zero.

Since an asset’s return doesn’t mean much in a vacuum, we subtract the risk-free return. The resulting ‘excess return’ or ‘risk premium’ tells us the return that’s associated with the risk of the asset. Clearly, it’s possible for this difference to be negative. That would be bad since assets bear a positive amount of risk and a negative excess return implies that there is no compensation for bearing that risk.

The standard deviation of an asset’s returns are a measure of risk. An asset might have a higher or lower value at sale or maturity. Since the future returns are unknown and can end up having any one of many values, this encapsulates the idea of risk. Risk can result in either higher or lower returns than average!

Putting all the pieces together, the excess return per risk is a measure of how much an asset compensates an investor for the riskiness of the returns. That’s the informational content of the Sharpe ratio, which we can calculate for each asset using historical information and forecasts. Once we’ve boiled down the risk and reward down to a single number, we can start to make comparisons across assets with a more critical eye.

Sometimes friends or students will discuss their great investment returns. They achieve the higher returns by adopting some amount of risk. That’s to be expected. But, invariably, they’ve adopted more risk than return! That means that their success is somewhat of a happy accident. The returns could easily have been much different, given the volatility that they bore.

Let’s get graphical.

Consider a graph in (standard deviation, return) space. In this space we can plot the ordered pair for some portfolios. The risk-free return occurs on the vertical intercept where the return is positive and the standard deviation is zero. Say that a student was thrilled with asset A’s 23.5% return and that it’s standard deviation of returns was 16%. Meanwhile, another student was happy with asset B’s 13.5% return and 5% standard deviation. With a risk-free rate of 3.5%, the Sharpe ratios are 1.25 & 2 respectively. We can plot the set of standard deviation and return pairs that would share the same constant Sharpe ratio (dotted lines). Solving for the asset return:

The above is simply a linear function relating the return and standard deviation. In particular, it says that for any constant Sharpe ratio, there is a linear relationship between possible asset returns and standard deviations. The below graph plots the two functions that are associated with the two asset Sharpe ratios. The line between the risk-free coordinate and the asset coordinate identifies all of the return-standard deviation combinations that share the same Sharpe ratio. This line is known as the iso-Sharpe Line.

With this tool in hand, we can better interpret the two student asset performances. There are a couple of ways to think about it. If asset A’s 23.5% return had been achieved with an asset that shared the Sharpe ratio of asset B, then it would have had risk that was associated with a standard deviation of only 10%. Similarly, if asset A’s volatility remained constant but enjoyed the returns of asset B’s Sharpe ratio, then its return would have been 35.5% rather than 23.5%. In short, a higher Sharpe ratio – and a steeper iso-Sharpe line – imply a bigger benefit for each unity of risk. The only problem is that a such an nice asset may not exist.

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