What We’re Reading (Week Ending 17 September 2023)

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 17 September 2023:

1. How bonds ate the entire financial system – Robin Wigglesworth

“The bond market is the most important market in the world,” says Ray Dalio, the founder of the world’s largest hedge fund, Bridgewater. “It is the backbone of all other markets.”

While the bond market has become larger and more powerful, the importance of banks — historically the workhorses of the capitalist system — is subtly fading. The global bond market was worth about $141tn at the end of 2022. That is, for now, smaller than the $183tn that the Financial Stability Board estimates banks hold globally, but much of the latter is actually invested in bonds — a fact that some US banks have recently rued…

…The market is now facing one of its biggest tests in generations. Last year, resurgent inflation — the nemesis of financial securities that pay fixed interest rates — triggered the worst setback in at least a century. Overall losses were almost $10tn, shaking UK pension plans and regional banks in the US. And although bonds have regained their footing this year, they are still beset by rising interest rates.

Even if the bond market adapts, as it has in the past, its ballooning power, reach and complexity has some awkward implications for the global economy. “This transformation has been extraordinary, and positive,” says Larry Fink, head of BlackRock, the world’s biggest investment group. “But we have a regulatory system designed for a time when banks were the dominant players. They aren’t any more.”

“Shadow banking” is what some academics call the part of the financial system that resembles, but falls outside traditional banking. Policymakers prefer the less malevolent-sounding — but almost comically obtuse — term “non-bank financial institutions”. At $240tn, this system is now far bigger than its conventional counterpart. The bond market is its main component, taking money from investors who can mostly yank it away at short notice and funnel it into long-term investments.

The question of how to tame shadow banking is one of the thorniest topics in finance today. For the financial system as a whole, it is arguably better that the risks bonds inevitably entail are spread across a vast, decentralised web of international investors, rather than concentrated in a narrow clutch of banks. But in finance, risk is like energy. It cannot be destroyed, only shifted from one place to another. As it gets shunted around, its consequences can morph in little understood, even dangerous ways. We saw a perfect example of this in March 2020, when the Covid-19 pandemic acted as a gigantic stress test for the financial system that revealed fresh cracks in its foundation…

…Doge Vitale II Michiel of Venice was in a pickle. Under a dubious pretext, the Byzantine empire had in 1171 arrested all Venetian merchants in its capital Constantinople and seized their property. But the Italian city-state didn’t have the funds to send a navy to rescue its imprisoned citizens. So the Doge forced all citizens to lend the city some money, in return for 5 per cent interest a year until they were repaid.

The rescue mission did not go well. The Venetian fleet was devastated by plague while negotiating with Constantinople, and the Doge was forced to return humiliated. Back in Venice, irate subjects chased their ruler down the city’s streets and beat him to death. Ruined by the debacle, Venice was unable to repay, turning the emergency prestiti (loan) into a permanent fixture that paid 5 per cent annually.

Most people were eventually fine with this arrangement. The steady interest payments were quite attractive. Occasionally, Venice would raise more prestiti, and the one-time emergency facility gradually became a handy way of raising money…

…Another crucial difference is that bonds are designed to be traded, while loans are typically not. In 12th-century Venice, prestiti were bought and sold in the city’s Rialto market. Today, bond trading happens by phone, electronic messages and algorithms across the world’s financial centres. This tradability is central to the growth of bonds, as it allows creditors to shift the risk to someone else.

By the 19th century, bond markets had helped shape the world order. Countries that could best finance themselves tended to succeed. England’s victory over Napoleonic France was enabled by its bond market, which allowed it to finance wartime expenditures more effectively than did the local bankers that Paris depended on for short-term, high-interest loans…

…The aftermath of the second world war was unkind to the bond market. Although it had provided vital wartime funding for allied governments and remained one of the financial system’s most crucial cogs, accelerating inflation in the 1950s and 60s posed a challenge for securities with fixed interest rates. By the 1970s, buying bonds became a constant, brutal race to stay ahead of inflation’s return-eroding force. The aggressive central bank-rate increases that became necessary to tame runaway prices also lowered the value of bonds issued in a lower-rate environment. People dourly joked that bonds had become “certificates of confiscation”.

But the 1980s brought a new era of slowing inflation, falling rates, regulatory liberalism and financial innovation, which would transform the bond market…

…But what made Ranieri’s name was not his persona. Wall Street has had plenty of bombastic bond traders with a penchant for coarse practical jokes. It was what he did to make a dime: packaging up individual mortgages into bonds and then trading chunks of those bonds, a process known as securitisation.

Securitisation is an old concept. Back in 1774, the very first mutual fund bought bonds backed by loans from plantations in the Caribbean and toll roads in Denmark. US mortgage-backed bonds existed as early as the 19th century. But these bonds only used the underlying loans as collateral.

In 1970, the US Government National Mortgage Association (known as Ginnie Mae) engineered the first “passthrough” mortgage-backed securities, where the underlying individual loan payments flowed directly through to the bond investor. This was followed by similar deals by other US mortgage agencies such as Freddie Mac and Fannie Mae, to little fanfare. Ranieri did for securitisation what Milken had done for the junk bond market; he transformed it from the backwaters into a global and massively lucrative industry.

The first fillip was the crisis that struck the US “savings and loans” industry when the Federal Reserve ratcheted up rates in the early 1980s. Congress passed a jammy tax break to make it easier for the banks to shed entire portfolios of mortgages at fire-sale prices. Ranieri’s Salomon was there to scoop them up and flip them to other investors. Money began to course through Salomon’s mortgage trading desk.

Ranieri realised that he needed to turn a one-off vein into an entire gold mine that could be exploited year after year. Luckily, he found some in-house inspiration: an innovative deal his former boss Dall had done with Bank of America in 1977, which sought to tackle the difficulty of valuing the cash flows of mortgage-backed securities with a technique called “tranching”. It sliced them up into different portions each with their own interest rates, maturities and riskiness. That way, each investor could simply choose what kind of exposure they might like — a buffet rather than a set-course meal of variable quality.

Ranieri ran with the idea. Rather than just take the mortgage of one bank, he pooled together bunches of mortgages from lots of them. To handle the complexity, he hired a lot of bright young mathematicians to complement the mini-Ranieris on the trading desk. He then lobbied vociferously for government blessing of the tranching structure, knowing this would add to the products’ lustre with investors. He succeeded. By the mid-1980s, the market took off…

…The story had an unhappy ending: the new market proliferated until it nearly brought the global financial system down in 2008, something that later weighed on Ranieri. “I will never, ever, ever, ever live out that scar that I carry for what happened with something I created,” he told The Wall Street Journal in 2018.

But the fundamental idea — packaging up smaller loans into bigger bonds and thereby bringing together more people who needed money with those who had it — was sound. Done judiciously, it actually makes banks less risky, by shifting the inherent danger of extending loans out of banks and into markets. (This is why securitisation has bounced back since 2008, and is starting to gain ground outside the US as well, often with government encouragement.)…

…Given how disastrous bank crises can be, it could be a good thing that bonds nowadays are doing more of the heavy lifting. Unlike bank depositors, bond fund investors do not expect to get their money back (even if it can be a shock when things fall apart). And unlike banks, bond funds typically do not use much or even any leverage.

But bond crises can also be painful — as we saw in both 2008 and nearly in 2020. Modern capitalism has largely been ordered around banks as the main intermediaries of money. Central banks were mostly set up to backstop these commercial banks, and, eventually, they began trying to regulate the temperature of economies by tweaking the cost of their funding, moving overnight interest rates up and down. But with the rise of bond markets, entirely new challenges have emerged and experimental tools to deal with them have become necessary — most notably quantitative easing, negative interest rates and “yield curve control”.

If the ultimate goal is to regulate the temperature of an economy by changing the cost of credit, then the fact that credit is increasingly extended by the bond market rather than banks inevitably has consequences. The market’s decentralised nature means that dangers can be harder to monitor and address, requiring massive, untargeted “spray-and-pray” monetary responses by central banks when trouble erupts.

Unfortunately, the custodians of the financial system have yet to fully grapple with those consequences, even if everyone from the Federal Reserve to the IMF has repeatedly warned about the multi-faceted dangers the shift from banks to bonds entails.

2. Searching for Resilience – Michael Weeks

For a business to survive 260 years in the same industry, with the same family owners, is a remarkable achievement. Starting in 1761 as a one-man shop making lead pencils, Faber-Castell has grown into the largest producer of colored and graphite pencils globally, producing over 2 billion pencils each year, as well as pens, markers, highlighters, and related products

Already the leading pencil producer in the mid-1800s, Faber-Castell has stayed on top of their industry for close to two centuries, betraying incredible entrepreneurial ability and drive. When the English supply of graphite began to fail and pencils became unaffordable, they bought a Siberian graphite mine and relied in part on reindeer transport to bring new raw materials to their factories. They expanded their product catalog, built up operations across Europe and the Americas, and invested in new technologies and equipment to improve their production. They helped introduce trademark law in Germany to protect their reputation against competitors. They established a 10’000 hectare forest plantation in Brazil to ensure their wood supply. They took their business seriously.

Nine generations of history also come with hardship. When the Americans joined World War I, Faber-Castell was cut off from the US market despite having operated there since in the 1850s. All of their US assets—land, equipment, inventory, patents, and trademarks—were seized and sold at auction after the war ended. During World War II their largest factory in Brazil was seized, not to be recovered for another twenty years, while their German factories were commandeered by the Nazi war machine. And in 1971, after 95 years of building a reputation as the finest producer of slide rules, they saw this entire side business vanish almost overnight when the pocket calculator was commercialized.

Resilience—or, the ability to survive hard times, as Faber-Castell has demonstrated time and time again—is something that we value instinctively. Yet, it’s not a popular subject. For all the years we’ve heard talk of sustainability, it seems that economic resilience, a once important dimension of economic prosperity, has become a relic of the past…

…What makes resilience so hard to spot is that it can only be proven during those rare times of crisis…

…It follows that resilience is not the same as looking good or having predictable financial results…

…Worse, a steady business model can actually become a source of fragility if placed in the wrong hands, as when companies with highly regular income streams leverage up their balance sheets, providing more immediate returns to their owners at the expense of their own resilience. Private equity has perhaps perfected this business model, but the growing dependence on debt in all walks of life reveal this as a defining feature of modern times…

…Resilience is not a destination. Seeking resilience means abandoning a narrow definition of success like sales growth or annual returns and instead becoming prepared for any eventuality that can cause serious harm. It is gained by pursuing new capabilities and flexibility, and by avoiding landmines. It means creating new options for the future instead of more plans for the present…

…Resilience comes at a cost. We are reminded of a metaphor used by Nassim Nicholas Taleb dealing with the nature of redundancy: “Layers of redundancy are the central risk management property of natural systems.”…

…A more effective way to add resilience to one’s savings is by ruthlessly avoiding its opposite: economic fragility. Thankfully, unlike resilience, fragility is often staring you in the face. This is where financial analysis really starts to shine. Is the company dependent on a few key customers and suppliers? Is the company overleveraged or buying back shares at indecent prices, just because it can? Are there elements of pricing power, or do their earnings evaporate at the first sign of trouble? Could a government ruling or decree suddenly break their business? Can their customers really afford to buy their products next year?…

…Perhaps the best way to find resilience is to look for its source: Resilience only comes from owners. Resilience is not a fluke that one stumbles into. It is a deliberate and purposeful objective which some aim for and others don’t. A good business plan, a profitable sector, a lot of cash, loyal management, or hardworking employees may all be wonderful, but only owners have the time horizon required to balance the present against an unknowable future, and only they have skin in the game—their own savings on the line. Unlike investors (renters), owners have no easy exits. They must build up reserves and competencies in the good years to give them options in the bad. They are motivated by a sense of responsibility—to themselves, their families, those they work with, and those who will come after them…

…Bakkafrost is a vertically integrated salmon farmer operating in the Faroe Islands and Scotland…

…A choice every salmon farmer has to make is what to do with the fish once it is ready to harvest. Do they sell their fish to wholesalers and other processors, or do they take it a step further, converting some into filets or smoked salmon that goes straight to the grocery store? This latter step is called Value-Added Processing or VAP, and Bakkafrost aims to sell about 30–40% of its fish through this channel each year.

A choice every salmon farmer has to make is what to do with the fish once it is ready to harvest. Do they sell their fish to wholesalers and other processors, or do they take it a step further, converting some into filets or smoked salmon that goes straight to the grocery store? This latter step is called Value-Added Processing or VAP, and Bakkafrost aims to sell about 30–40% of its fish through this channel each year. In the ten years from 2011 to 2020, Bakkafrost has reported cumulative revenues of about €4.5 billion and operating earnings of €1.1 billion, yet of those earnings only €14 million, or 1.2% of the total, have come from their VAP division. A financial observer might say, quite rightly, why bother? Salmon farming is hard enough, why dedicate additional capital and resources for a pittance? What a financial owner doesn’t see— and which the owners of Bakkafrost see plain as day—is the resilience this seemingly irrelevant processing step embeds in the organization…

…When hotels and restaurants shut their doors last year, all the food that was headed to this channel, including many millions of whole salmon, all needed to end up somewhere. Remember the 30-month lag between laying eggs and the salmon harvest? This means that while the demand for whole salmon evaporated, supply kept pouring in and the markets were soon stuffed full of whole fish with no one around to buy them…

3. Dangerous CFOs, Imperial CEOs, Chagrined Bankers, and Warren Buffett – Dan Noe

While I was a credit analyst and manager at Moody’s, I met with many CEOs and CFOs. Most meetings were routine and executives were good at explaining their company’s business and financial strategies. They almost always put their best foot forward. But the exceptions were notable…

…The most revealing comment I ever heard in a meeting came from a savings and loan CEO during that industry’s crisis in the late 1980s. S&L holding companies had issued a lot of junk bonds and God knows what they did with the money. A lot of them were going under. During one meeting, this particular S&L CEO said, apropos of nothing, “I hope the feds never figure out what I’m doing.” His bankers looked like they were going to throw up. That was an example of an in-person meeting affecting our credit assessment…

…The worst case of self-importance was a regional bank CEO who insisted we meet him in his big suite at a fancy hotel and have breakfast. This was weird, and a member of his entourage tried to explain: “He needs anonymity while he is in New York.” I said, “Well, he’s got it. He works at a Midwest bank. Nobody here knows who he is.”

I did these meetings for years and felt like I’d seen it all. Then, one day, I met the antithesis of imperial CEOs and executives who have problems answering questions about their business. Warren Buffett and Charlie Munger from Berkshire Hathaway came in for a visit. They arrived in a taxi, not a limo, with no hangers-on, not one person with them at all. When I addressed him as “Mr. Buffett” he said, “Please call me Warren.” They had come in to talk about a debt issuance, and Buffett made a self-deprecating joke: “My mother always told me to avoid liquor, ladies, and leverage. I’ve avoided the first two, but sometimes I like a little leverage.” Our analyst, Weston Hicks, was an excellent insurance industry analyst and asked detailed and probing questions. Buffett and Munger spent an hour, an hour-and-a-half, giving very specific answers. When the meeting ended and we were walking to the elevators, Munger said to me, out of earshot of Weston, “Make sure you keep that analyst. He’s really good.”

4. Product-Led AI – Seth Rosenberg

I believe there’s tremendous value to be captured by product builders who can successfully put the power of AI into products that people love. As my partner Jerry Chen recently put forth, if we’re living in an age where foundation models make it possible for anyone to build an AI company, “the most strategic advantage [of applications] is that you can coexist with several systems of record and collect all the data that passes through your product.”…

…Of course there are plenty of detractors who don’t believe startups have a chance at this layer – incumbents own the data and distribution, and access to LLMs is both commoditized and fraught with platform risk. There will likely be many casualties of companies where an API call to OpenAI isn’t sufficient to build lasting value…

…In the last wave of consumer software, social networks and marketplaces were the dominant business models that created trillions of dollars of market cap, with Meta alone valued at just under $800 billion. Greylock was lucky to back many of these, including Meta, LinkedIn, Roblox, Airbnb, Discord, Musical.ly (now TikTok), and Nextdoor.

As reflected by the valuations, these networks were assumed to be “unbreakable”.

But now, AI challenges many of our initial assumptions. This is creating a new arms race to build the next AI-first network.

We moved from networks that connect people to algorithms that connect people to content. Now, we’re moving to algorithms that replace people…

…You can imagine a freelance logo design marketplace, like parts of Fiverr, will be replaced with an algorithm. A user inputs a prompt, and after a few tries, gets their logo. In this case, the data the algorithm receives is fairly shallow (prompts and selection), and the supply side is entirely replaced by an algorithm.

Contrast this to an AI-first jobs marketplace. The optimal product would be an AI career coach for job seekers and an AI assistant for recruiters – two seemingly separate products, connected by the same algorithm. The coach could gather deep insight from a job seeker – far beyond what they would share on a resume or LinkedIn – and use this data to not just find the perfect match, but help them discover their most fulfilling career path. Combine this data with a strong understanding of a recruiter’s needs, and both the coach and assistant get better…

…The best opportunities for start-ups attacking large software categories comes from finding angles where incumbents can’t compete. Here are four examples:

UI/UX is re-imagined with AI – incumbent UI is irrelevant
Product surface area is re-imagined with AI – incumbents compete at a different scope
Business model is re-imagined with AI – incumbent business model can’t adapt
No incumbent tech co before AI…

…Another great example is customer service, a $10 billion software category. The “obvious” starting point would be to automate customer service reps using AI. But what if the entire concept of customer service was re-imagined? Today, most companies actively reduce call volume by hiding the “contact us” button behind 5 menus and an ever-expanding phone tree. But, in a world of AI, every interaction can be cheap, delightful, and revenue-generating. In that world, companies might actively try to speak with their customers.

When I was at Meta in 2016, we tried to remedy this with an AI bot platform. Piloting with KLM airlines, we built an experience where Messenger handled every aspect of the passenger’s journey – boarding pass, customer service, travel recommendations at their destination, etc, all in a single conversation.. Despite amazing feedback, this pilot was shut down because of the cost to serve – but today, LLMs could make these types of interactions possible…

…One of the most interesting new opportunities with AI is going after the vastly larger market for services versus software with AI “co-pilots”. Most knowledge work involves analyzing and transforming data, a task that algorithms are better suited for.

I believe the best opportunities for co-pilots are “branded” sales people, like wealth managers, insurance brokers, and mortgage brokers. Their role involves a lot of text- based coordination, they work across multiple apps, and the ROI of increased efficiency is tangible. Take wealth managers as an example. According to Morgan Stanley, the biggest indicator of client retention for wealth managers is not portfolio performance, but consistency of personalized interactions with clients.

5. Society’s Technical Debt and Software’s Gutenberg Moment – Paul Kedrosky and Eric Norlin

Software has a cost, and it has markets of buyers and sellers. Some of those markets are internal to organizations. But the majority of those markets are external, where people buy software in the form of apps, or cloud services, or games, or even embedded in other objects that range from Ring doorbells to endoscopic cameras for cancer detection. All of these things are software in a few of its myriad forms.

With these characteristics in mind, you can think of software in a basic price/quantity graph from introductory economics. There is a price and a quantity demanded at that price, and there is a price and quantity at which those two things are in rough equilibrium, as the following figure shows. Of course, the equilibrium point can shift about for many reasons, causing the P/Q intersection to be at higher or lower levels of aggregate demand. If the price is too high we underproduce software (leaving technical debt), and if too low, well … let’s come back to that…

…But technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening.

Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.). In CPUs, this has long been called Moore’s Law, where CPUs become more powerful by some increment every 18 months or so. While these laws are more like heuristics than F=ma laws of physics, they do help as a guide toward how the future might be different from the past.

We have seen this over and over in technology, as various pieces of technology collapse in price, while they grow rapidly in power. It has become commonplace, but it really isn’t. The rest of the economy doesn’t work this way, nor have historical economies. Things don’t just tumble down walls of improved price while vastly improving performance. While many markets have economies of scale, there hasn’t been anything in economic history like the collapse in, say, CPU costs, while the performance increased by a factor of a million or more.

To make this more palpable, consider that if cars had improved at the pace computers have, a modern car would:

Have more than 600 million horsepower
Go from 0-60 in less than a hundredth of a second
Get around a million miles per gallon
Cost less than $5,000

And they don’t. Sure, Tesla Plaid is a speedy car, it is nowhere near the above specs—no car ever will be. This sort of performance inflection is not our future, but it fairly characterizes and even understates what has happened in technology over the last 40 years.

… And each of these collapses has had broader consequences. The collapse of CPU prices led us directly from mainframes to the personal computer era; the collapse of storage prices (of all kinds) led inevitably to more personal computers with useful local storage, which helped spark databases and spreadsheets, then led to web services, and then to cloud services. And, most recently, the collapse of network transit costs (as bandwidth exploded) led directly to the modern Internet, streaming video, and mobile apps…

…Each collapse, with its accompanying performance increases, sparks huge winners and massive change, from Intel, to Apple, to Akamai, to Google & Meta, to the current AI boomlet. Each beneficiary of a collapse requires one or more core technologies’ price to drop and performance to soar. This, in turn, opens up new opportunities to “waste” them in service of things that previously seemed impossible, prohibitively expensive, or both…

…Still, the suddenly emergent growth of LLMs has some people spending buckets of time thinking about what service occupations can be automated out of existence, what economists called “displacement” automation. It doesn’t add much to the aggregate store of societal value, and can even be subtractive and destabilizing, a kind of outsourcing-factory-work-to-China moment for white-collar workers. Perhaps we should be thinking less about opportunities for displacement automation and more about opportunities for augmenting automation, the kind of thing that unleashes creativity and leads to wealth and human flourishing.

So where will that come from? We think this augmenting automation boom will come from the same place as prior ones: from a price collapse in something while related productivity and performance soar. And that something is software itself.

By that, we don’t literally mean “software” will see price declines, as if there will be an AI-induced price war in word processors like Microsoft Word, or in AWS microservices. That is linear and extrapolative thinking. Having said that, we do think the current frenzy to inject AI into every app or service sold on earth will spark more competition, not less. It will do this by raising software costs (every AI API call is money in someone’s coffers), while providing no real differentiation, given most vendors will be relying on the same providers of those AI API calls…

… In a hypothetical two-sector economy, when one sector becomes differentially more productive, specialized, and wealth-producing, and the other doesn’t, there is huge pressure to raise wages in the latter sector, lest many employees leave. Over time that less productive sector starts becoming more and more expensive, even though it’s not productive enough to justify the higher wages, so it starts “eating” more and more of the economy.

Economist William Baumol is usually credited with this insight, and for that it is called Baumol’s cost disease. You can see the cost disease in the following figure, where various products and services (spoiler: mostly in high-touch, low-productivity sectors) have become much more expensive in the U.S., while others (non-spoiler: mostly technology-based) have become cheaper…

…But there is another sector being held back by a variant of Baumol’s cost disease, and that is software itself. This may sound contradictory, which is understandable. After all, how can the most productive, wealth-generating, deflationary sector also be the victim of the same malaise it is inflicting on other sectors?

It can, if you think back to the two-sector model we discussed earlier. One sector is semis and CPUs, storage and backbone networks. Those prices are collapsing, requiring fewer people while producing vastly more performance at lower prices. Meanwhile, software is chugging along, producing the same thing in ways that mostly wouldn’t seem vastly different to developers doing the same things decades ago. Yes, there have been developments in the production and deployment of software, but it is still, at the end of the day, hands pounding out code on keyboards. This should seem familiar, and we shouldn’t be surprised that software salaries stay high and go higher, despite the relative lack of productivity. It is Baumol’s cost disease in a narrow, two-sector economy of tech itself.

These high salaries play directly into high software production costs, as well as limiting the amount of software produced, given factor production costs and those pesky supply curves. Startups spend millions to hire engineers; large companies continue spending millions keeping them around. And, while markets have clearing prices, where supply and demand meet up, we still know that when wages stay higher than comparable positions in other sectors, less of the goods gets produced than is societally desirable. In this case, that underproduced good is…software. We end up with a kind of societal technical debt, where far less is produced than is socially desirable—we don’t know how much less, but it is likely a very large number and an explanation for why software hasn’t eaten much of the world yet…

…We think that’s all about to change. The current generation of AI models are a missile aimed, however unintentionally, directly at software production itself. Sure, chat AIs can perform swimmingly at producing undergraduate essays, or spinning up marketing materials and blog posts (like we need more of either), but such technologies are terrific to the point of dark magic at producing, debugging, and accelerating software production quickly and almost costlessly.

And why shouldn’t it be? As the following figure shows, Large Language Model (LLM) impacts in the job market can be thought of as a 2×2 matrix. Along one axis we have how grammatical the domain is, by which we mean how rules-based are the processes governing how symbols are manipulated. Essays, for example, have rules (ask any irritated English teacher), so chat AIs based on LLMs can be trained to produce surprisingly good essays. Tax providers, contracts, and many other fields are in this box too…

… Software is even more rule-based and grammatical than conversational English, or any other conversational language. Programming languages—from Python to C++—can be thought of as formal languages with a highly explicit set of rules governing how every language element can and cannot be used to produce a desired outcome…

…Again, programming is a good example of a predictable domain, one created to produce the same outputs given the same inputs. If it doesn’t do that, that’s 99.9999% likely to be on you, not the language. Other domains are much less predictable, like equity investing, or psychiatry, or maybe, meteorology.

This framing—grammar vs predictability—leaves us convinced that for the first time in the history of the software industry, tools have emerged that will radically alter the way we produce software. This isn’t about making it easier to debug, or test, or build, or share—even if those will change too—but about the very idea of what it means to manipulate the symbols that constitute a programming language…

…Now, let’s be clear. Can you say MAKE ME MICROSOFT WORD BUT BETTER, or SOLVE THIS CLASSIC COMPSCI ALGORITHM IN A NOVEL WAY? No, you can’t, which will cause many to dismiss these technologies as toys. And they are toys in an important sense. They are “toys” in that they are able to produce snippets of code for real people, especially non-coders, that one incredibly small group would have thought trivial, and another immense group would have thought impossible. That. Changes. Everything.

How? Well, for one, the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster…

…We have mentioned this technical debt a few times now, and it is worth emphasizing. We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

The question people always have at this point is, “So what app gets made?” While an understandable question, it is somewhat silly and definitely premature. Was Netflix knowable when Internet transit costs were $500,000/Mbps? Was Apple’s iPhone imaginable when screens, CPUs, storage and batteries would have made such devices the size of small rooms? Of course not. The point is that the only thing we know is that the apps and services will come. Without question.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. We currently have a vested interest in Alphabet (parent of Google), Amazon (parent of AWS), Apple, Meta Platforms, Microsoft, and Tesla. Holdings are subject to change at any time.