The way I see it, artificial intelligence (or AI), really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Both are provided by OpenAI and are software products that use AI to generate art and writing, respectively (and often at astounding quality). Since then, developments in AI have progressed at a breathtaking pace.

Meanwhile, the latest earnings season for the US stock market is coming to its tail-end. I thought it would be useful to collate some of the interesting commentary I’ve come across in earnings conference calls, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. Here they are, in no particular order:

Airbnb (NASDAQ: ABNB)

Airbnb’s management thinks AI is a massive platform shift

Well, why don’t I start, Justin, with AI. This is certainly the biggest revolution and test since I came to Silicon Valley. It’s certainly as big of a platform shift as the Internet, and many people think it might be even bigger. 

Airbnb’s management thinks of foundational models as the highways and what they are interested in, is to build the cars on the highways, in other words, they are interested in tuning the model

And I’ll give you kind of a bit of an overview of how we think about AI. So all of this is going to be built on the base model. The base models, the large language models, think of those as GPT-4. Google has a couple of base models, Microsoft reaches Entropic. These are like major infrastructure investments. Some of these models might cost tens of billions of dollars towards the compute power. And so think of that as essentially like building a highway. It’s a major infrastructure project. And we’re not going to do that. We’re not an infrastructure company. But we’re going to build the cars on the highway. In other words, we’re going to design the interface and the tuning of the model on top of AI, on top of the base model. So on top of the base model is the tuning of the model. And the tuning of the model is going to be based on the customer data you have.

Airbnb’s management thinks AI can be used to help the company learn more about its users and build a much better way to match accommodation options with the profile of a user

If you were to ask a question to ChatGPT, and if I were to ask a question to ChatGPT, we’re both going to get pretty much the same answer. And the reason both of us are going to get pretty close the same answer is because ChatGPT doesn’t know that it’s between you and I, doesn’t know anything about us. Now this is totally fine for many questions, like how far is it from this destination to that destination. But it turns out that a lot of questions in travel aren’t really search questions. They’re matching questions. Another is, they’re questions that the answer depends on who you are and what your preferences are. So for example, I think that going forward, Airbnb is going to be pretty different. Instead of asking you questions like where are you going and when are you going, I want us to build a robust profile about you, learn more about you and ask you 2 bigger and more fundamental questions: who are you? And what do you want?

Airbnb’s management wants to use AI to build a global travel community and world-class personalised travel concierge

And ultimately, what I think Airbnb is building is not just a service or a product. But what we are in the largest sense is a global travel community. And the role of Airbnb and that travel community is to be the ultimate host. Think of us with AI as building the ultimate AI concierge that could understand you. And we could build these world-class interfaces, tune our model. Unlike most other travel companies, we know a lot more about our guests and hosts. This is partly why we’re investing in the Host Passport. We want to continue to learn more about people. And then our job is to match you to accommodations, other travel services and eventually things beyond travel. So that’s the big vision of where we’re going to go. I think it’s an incredibly expanding opportunity.

Airbnb’s management thinks that AI can help level the playing field in terms of the service Airbnb provides versus that of hotels

One of the strengths of Airbnb is that Airbnb’s offering is one of a kind. The problem with Airbnb is our service is also one of a kind. And so therefore, historically less consistent than a hotel. I think AI can level the playing field from a service perspective relative to hotels because hotels have front desk, Airbnb doesn’t. But we have literally millions of people staying on Airbnb every night. And imagine they call customer service. We have agents that have to adjudicate between 70 different user policies. Some of these are as many as 100 pages long. What AI is going to do is be able to give us better service, cheaper and faster by augmenting the agents. And I think this is going to be something that is a huge transformation. 

Airbnb’s management thinks that AI can help improve the productivity of its developers

The final thing I’ll say is developer productivity and productivity of our workforce generally. I think our employees could easily be, especially our developers, 30% more productive in the short to medium term, and this will allow significantly greater throughput through tools like GitHub’s Copilot. 

Alphabet (NASDAQ: GOOG)

Alphabet’s management thinks AI will unlock new experiences in Search as it evolves

As it evolves, we’ll unlock entirely new experiences in Search and beyond just as camera, voice and translation technologies have all opened entirely new categories of queries and exploration.

AI has been foundational for Alphabet’s digital advertising business for over a decade

AI has also been foundational to our ads business for over a decade. Products like Performance Max use the full power of Google’s AI to help advertisers find untapped and incremental conversion opportunities. 

Alphabet’s management is focused on making AI safe

And as we continue to bring AI to our products, our AI principles and the highest standards of information integrity remain at the core of all our work. As one example, our Perspective API helps to identify and reduce the amount of toxic text that language models train on, with significant benefits for information quality. This is designed to help ensure the safety of generative AI applications before they are released to the public.

Examples of Alphabet bringing generative AI to customers of its cloud computing service

We are bringing our generative AI advances to our cloud customers across our cloud portfolio. Our PaLM generative AI models and Vertex AI platform are helping Behavox to identify insider threats, Oxbotica to test its autonomous vehicles and Lightricks to quickly develop text-to-image features. In Workspace, our new generative AI features are making content creation and collaboration even easier for customers like Standard Industries and Lyft. This builds on our popular AI Bard Workspace tools, Smart Canvas and Translation Hub used by more than 9 million paying customers. Our product leadership also extends to data analytics, which provides customers the ability to consolidate their data and understand it better using AI. New advances in our data cloud enable Ulta Beauty to scale new digital and omnichannel experiences while focusing on customer loyalty; Shopify to bring better search results and personalization using AI; and Mercedes-Benz to bring new products to market more quickly. We have introduced generative AI to identify and prioritize cyber threats, automate security workflows and response and help scale cybersecurity teams. Our cloud cybersecurity products helped protect over 30,000 companies, including innovative brands like Broadcom and Europe’s Telepass.

The cost of computing when integrating LLMs (large language models) to Google Search is something Alphabet’s management has been thinking about 

On the cost side, we have always — cost of compute has always been a consideration for us. And if anything, I think it’s something we have developed extensive experience over many, many years. And so for us, it’s a nature of habit to constantly drive efficiencies in hardware, software and models across our fleet. And so this is not new. If anything, the sharper the technology curve is, we get excited by it, because I think we have built world-class capabilities in taking that and then driving down cost sequentially and then deploying it at scale across the world. So I think we’ll take all that into account in terms of how we drive innovation here, but I’m comfortable with how we’ll approach it.

Alphabet’s management does not seem concerned with any potential revenue-impact from integrating LLMs into Google’s core Search product

So first of all, throughout the years, as we have gone through many, many shifts in Search, and as we’ve evolved Search, I think we’ve always had a strong grounded approach in terms of how we evolve ads as well. And we do that in a way that makes sense and provide value to users. The fundamental drivers here are people are looking for relevant information. And in commercial categories, they find ads to be highly relevant and valuable. And so that’s what drives this virtuous cycle. And I don’t think the underpinnings over the fact that users want relevant commercial information, they want choice in what they look at, even in areas where we are summarizing and answering, et cetera, users want choice. We care about sending traffic. Advertisers want to reach users. And so all those dynamics, I think, which have long served us well, remain. And as I said, we’ll be iterating and testing as we go. And I feel comfortable we’ll be able to drive innovation here like we’ve always done.

Amazon (NASDAQ: AMZN)

Amazon’s management thinks that the AI boom will drive significant growth in data consumption and products in the cloud

And I also think that there are a lot of folks that don’t realize the amount of nonconsumption right now that’s going to happen and be spent in the cloud with the advent of large language models and generative AI. I think so many customer experiences are going to be reinvented and invented that haven’t existed before. And that’s all going to be spent, in my opinion, on the cloud.

Amazon has been investing in machine learning for more than two decades, and has been investing large sums of capital to build its own LLMs for several years

I think when you think about machine learning, it’s useful to remember that we have had a pretty substantial investment in machine learning for 25-plus years in Amazon. It’s deeply ingrained in virtually everything we do. It fuels our personalized e-commerce recommendations. It drives the pick pass in our fulfillment centers. We have it in our Go stores. We have it in our Prime Air, our drones. It’s obviously in Alexa. And then AWS, we have 25-plus machine learning services where we have the broadest machine learning functionality and customer base by a fair bit. And so it is deeply ingrained in our heritage…

…We’ve been investing in building in our own large language models for several years, and we have a very large investment across the company. 

Amazon’s management decided to build chips – Trainium for training and Inferentia for inference – that have great price and performance because LLMs are going to run on compute, which depend on chips (particularly GPUs, or graphic processing units) and GPUs are scarce; Amazon’s management also thinks that a lot of machine learning training will be taking place on AWS

If you think about maybe the bottom layer here, is that all of the large language models are going to run on compute. And the key to that compute is going to be the chips that’s in that compute. And to date, I think a lot of the chips there, particularly GPUs, which are optimized for this type of workload, they’re expensive and they’re scarce. It’s hard to find enough capacity. And so in AWS, we’ve been working for several years on building customized machine learning chips, and we built a chip that’s specialized for training, machine learning training, which we call Trainium, and a chip that’s specialized for inference or the predictions that come from the model called Inferentia. The reality, by the way, is that most people are spending most of their time and money on the training. But as these models graduate to production, where they’re in the apps, all the spend is going to be in inference. So they both matter a lot. And if you look at — we just released our second versions of both Trainium and Inferentia. And the combination of price and performance that you can get from those chips is pretty differentiated and very significant. So we think that a lot of that machine learning training and inference will run on AWS.

Amazon’s management thinks that most companies that want to use AI are not interested to build their own foundational models because it takes a lot of resources; Amazon has the resources to build foundational models, and is providing the foundational models to customers who can then customise the models

And if you look at the really significant leading large language models, they take many years to build and many billions of dollars to build. And there will be a small number of companies that want to invest that time and money, and we’ll be one of them in Amazon. But most companies don’t. And so what most companies really want and what they tell AWS is that they’d like to use one of those foundational models and then have the ability to customize it for their own proprietary data and their own needs and customer experience. And they want to do it in a way where they don’t leak their unique IP to the broader generalized model. And that’s what Bedrock is, which we just announced a week ago or so. It’s a managed foundational model service where people can run foundational models from Amazon, which we’re exposing ourselves, which we call Titan. Or they can run it from leading large language model providers like AI 21 and Anthropic and Stability AI. And they can run those models, take the baseline, customize them for their own purposes and then be able to run it with the same security and privacy and all the features they use for the rest of their applications in AWS. That’s very compelling for customers.

Every single one of Amazon’s businesses are built on top of LLMs

Every single one of our businesses inside Amazon are building on top of large language models to reinvent our customer experiences, and you’ll see it in every single one of our businesses, stores, advertising, devices, entertainment and devices, which was your specific question, is a good example of that.

ASML (NASDAQ: ASML)

ASML’s management sees that mature semiconductor technologies are actually needed even in AI systems

So I think this is something people underestimate how significant the demand in the mid-critical and the mature semiconductor space is. And it will just grow double digit, whether it’s automotive, whether it’s the energy transition, whether it’s just the entire industrial products area, where is the — well, those are the sensors that we actually need as an integral component of the AI systems. This is where the mid-critical and the mature semiconductor space is very important and needs to grow.

Block (NYSE: SQ)

Block’s management is focused on three technology trends, one of which is AI

The three trends we’re focused on: Number one is artificial intelligence; number two is open protocols; and number three is the global south. Consider how many times you’ve heard the term AI or GPT in the earnings calls just this quarter versus all quarters in history prior. This trend seems to be moving faster than anyone can comprehend or get a handle on. Everyone feels like they’re on their back foot and struggling to catch up. Utilizing machine learning is something we’ve always employed at Block, and the recent acceleration in availability of tools is something we’re eager to implement across all of our products and services. We see this first as a way to create efficiencies, both internally and for our customers. And we see many opportunities to apply these technologies to create entirely new features for our customers. More and more effort in the world will ship to creative endeavors as AI continues to automate mechanical tasks away.

Datadog (NASDAQ: DDOG)

Datadog’s management thinks AI can make software developers more productive in terms of generating more code; as a result, the complexity of a company’s technology will also increase, which will lead to more importance for observability and trouble-shooting software products

First, from a market perspective, over the long term, we believe AI will significantly expand our opportunity in observability and beyond. We seek massive improvements in developer productivity will allow individuals to write more applications and to do so faster than ever before. And as with past productivity increases, we think this will further shift value from writing code to observing, managing, fixing and securing live applications…

… Longer term, I think we can all glimpse at the future where productivity for everyone, including software engineers, increases dramatically. And the way we see that as a business is, our job is to help our customers absorb the complexity of the applications they’ve built so they can understand and modify them, run them, secure them. And we think that the more productivity there is, the more people can write in the amount of time. The less they understand the software they produce and the more they need us, the more value it sends our way. So that’s what makes us very confident in the long term here…

…And we — the way this has played out in the past typically is you just end up generating more stuff and more mess. So basically, if one person can produce 10x more, you end up with 10x more stuff and that person will still not understand everything they’ve produced. So the way we imagine the future is companies are going to deliver a lot more functionality to their users a lot faster. They’re going to solve a lot more problems in software. But the they won’t be as tight and understanding from their engineering team as to what it is they’ve built and how they built it and what might break and what might be the corner cases that don’t work and things like that. And that’s consistent with what we can see people building with a copilot today and things like that.

Etsy (NASDAQ: ETSY)

Etsy’s management thinks that AI can greatly improve the search-experience for customers who are looking for specific products

We’ve been at the cutting edge of search technology for the past several years, and while we use large language models today, we couldn’t be more excited about the potential of newer large language models and generative AI to further accelerate the transformation of Etsy’s user experience. Even with all our enhancements, Etsy search today is still key-word driven and text based and essentially the result is a grid with many thousands of listings. We’ve gotten better at reading the tea leaves, but it’s still a repetitive cycle of query result reformulation. In the future we expect search on Etsy to utilize more natural language and multimodal approaches. Rather than manipulating key words, our search engines will enable us to ask the right question at the right time to show the buyer a curated set of results that can be so much better than it is today. We’re investigating additional search engine technologies to identify attributes of an item, multi-label learning models for instant search, graph neural networks and so much more, which will be used in combination with our other search engine technologies. It’s our belief that Etsy will benefit from generative AI and other advances in search technology as much or perhaps even more so than others…

…When you run a search at Etsy, we already use multiple machine learning techniques. So I don’t think generative AI replaces everything we’re doing, but it’s another tool that will be really powerful. And there are times when having a conversation instead of entering a query and then getting a bunch of search results and then going back and reformulating your query and then getting a bunch of search results, that’s not always very satisfying. And being able to say, no, I meant more like this. How about this? I’d like something that has this style and have that feel like more of a conversation, I think that can be a better experience a lot of the time. And I think in particular for Etsy where we don’t have a catalog, it might be particularly powerful.

Fiverr (NYSE: FVRR) 

Fiverr’s management thinks that the proliferation of AI services will not diminish the demand for freelancers, but it will lead to a bifurcation in the fates of freelancers between those who embrace AI, and those who don’t

We haven’t seen AI negatively impact our business. On the contrary, the categories we open to address AI-related services are booming. The number of AI-related gigs has increased over tenfold and buyer searches for AI have soared over 1,000% compared to 6 months ago, indicating a strong demand and validating our efforts to stay ahead of the curve in this rapidly evolving technological landscape. We are witnessing the increasing need for human skills to deploy and implement AI technologies, which we believe will enable greater productivity and improved quality of work when human talent is augmented by AI capabilities. In the long run, we don’t anticipate AI development to displace the need for human talent. We believe AI won’t replace our sellers; rather sellers using AI will outcompete those who don’t…

…In terms of your question about AI, you’re right, it’s very hard to understand what categories or how categories might be influenced. I think that there’s one principle that we’ve — that I’ve shared in my opening remarks, which I think is very important, and this is how we view this, which is that AI technology is not going to displace our sellers, but sellers who have better gross and better usage of AI are going to outcompete those who don’t. And this is not really different than any meaningful advancement within technology, and we’ve seen that in recent years. Every time when there’s a cool new technology or device or form factor that sellers need to become professional at, those who become professional first are those who are actually winning. And we’re seeing the same here. So I don’t think that this is a different case. It’s just different professions, which, by the way, is super exciting.

Fiverr’s management thinks that AI-produced work will still need a human touch

Furthermore, while AI-generated content can be well constructed, it is all based on existing human-created content. To generate novel and authentic content, human input remains vital. Additionally, verifying and editing the AI-generated content, which often contains inaccuracies, requires human expertise and effort. That’s why we have seen categories such as fact-checking or AI content editing flourish on our marketplace in recent months.

Mastercard (NYSE: MA)

Mastercard’s management thinks AI is a foundational technology for the company

For us we’ve been using AI for the better part of the last decade. So it’s embedded in a whole range of our products…

…So you’ll find it embedded in a range of our products, including generative AI. So we have used generative AI technology, particularly in creating data sets that allow us to compare and find threats in the cybersecurity space. You will find AI in our personalization products. So there’s a whole range of things that we set us apart. We use this as foundational technology. And internally, you can see increasingly so, that generative AI might be a good solution for us when it comes to customer service propositions and so forth.

MercadoLibre (NASDAQ: MELI)

MercadoLibre is utilising AI within its products and services, in areas such as customer-service and product-discovery

In terms of AI, I think as most companies, we do see some very relevant short- to midterm positive impact in terms of engineering productivity. And we are also increasing the amount of work being done on what elements of the consumer-facing experiences we can deploy AI on I think the focus right now is on some of the more obvious use cases, improving and streamlining customer service and interactions with reps, improving workflows for reps through AI-assisted workflow tools and then deploying AI to help a better search and discovery in terms of better finding products on our website and better understanding specific — specifications of products where existing LLM are quite efficient. And then beyond that, I think there’s a lot of work going on, and we hope to come up with other innovative forms of AI that we can place into the consumer-facing experience. but the ones I just mentioned are the ones that we’re currently working on the most.

Meta Platforms (NASDAQ: META)

Meta’s work in AI has driven significant improvements in (a) the quality of content seen by users of its services and (b) the monetisation of its services

Our investment in recommendations and ranking systems has driven a lot of the results that we’re seeing today across our discovery engine, reels and ads. Along with surfacing content from friends and family, now more than 20% of content in your Facebook and Instagram Feeds are recommended by AI from people groups or accounts that you don’t follow. Across all of Instagram, that’s about 40% of the content that you see. Since we launched Reels, AI recommendations have driven a more than 24% increase in time spent on Instagram. Our AI work is also improving monetization. Reels monetization efficiency is up over 30% on Instagram and over 40% on Facebook quarter-over-quarter. Daily revenue from Advantage+ shopping campaigns is up 7x in the last 6 months.

Meta’s management is focused on open-sourcing Meta’s AI models because they think going open-source will benefit the company in terms of it being able to make use of improvements to the models brought on by the open-source-community

Our approach to AI and our infrastructure has always been fairly open. We open source many of our state-of-the-art models, so people can experiment and build with them. This quarter, we released our LLaMA LLM to researchers. It has 65 billion parameters but outperforms larger models and has proven quite popular. We’ve also open sourced 3 other groundbreaking visual models along with their training data and model weights, Segment Anything, DINOv2 and our Animated Drawings tool, and we’ve gotten some positive feedback on all of those as well…

…And the reason why I think why we do this is that unlike some of the other companies in the space, we’re not selling a cloud computing service where we try to keep the different software infrastructure that we’re building proprietary. For us, it’s way better if the industry standardizes on the basic tools that we’re using, and therefore, we can benefit from the improvements that others make and others’ use of those tools can, in some cases, like Open Compute, drive down the costs of those things, which make our business more efficient, too. So I think to some degree, we’re just playing a different game on the infrastructure than companies like Google or Microsoft or Amazon, and that creates different incentives for us. So overall, I think that that’s going to lead us to do more work in terms of open sourcing some of the lower-level models and tools, but of course, a lot of the product work itself is going to be specific and integrated with the things that we do. So it’s not that everything we do is going to be open. Obviously, a bunch of this needs to be developed in a way that creates unique value for our products. But I think in terms of the basic models, I would expect us to be pushing and helping to build out an open ecosystem here, which I think is something that’s going to be important.

Meta’s management thinks the company now has enough computing infrastructure to do leading AI-related work after spending significant sums of money over the past few years to build that out

A couple of years ago, I asked our infra teams to put together ambitious plans to build out enough capacity to support not only our existing products but also enough buffer capacity for major new products as well. And this has been the main driver of our increased CapEx spending over the past couple of years. Now at this point, we are no longer behind in building out our AI infrastructure, and to the contrary, we now have the capacity to do leading work in this space at scale. 

Meta’s management is focused on using AI to improve its advertising services

We remain focused on continuing to improve ads ranking and measurement with our ongoing AI investments while also leveraging AI to power increased automation for advertisers through products like Advantage+ shopping, which continues to gain adoption and receive positive feedback from advertisers. These investments will help us develop and deploy privacy-enhancing technologies and build new innovative tools that make it easier for businesses to not only find the right audience for their ad but also optimize and eventually develop their ad creative.

Meta’s management thinks that generative AI can be a very useful tool for advertisers, but they’re still early in the stage of understanding what generative AI is really capable of

 Although there aren’t that many details that I’m going to share at this point, more of this will come in focus as we start shipping more of these things over the coming months. But I do think that there’s a big opportunity here. You asked specifically about advertisers, but I think it’s going to also help create more engaging experiences, which should create more engagement, and that, by itself, creates more opportunities for advertisers. But then I think that there’s a bunch of opportunities on the visual side to help advertisers create different creative. We don’t have the tools to do that over time, eventually making it. So we’ve always strived to just have an advertiser just be able to tell us what their objective is and then have us be able to do as much of the work as possible for them, and now being able to do more of the creative work there and ourselves for those who want that, I think, could be a very exciting opportunity…

…And then the third bucket is really around CapEx investments now to support gen AI. And this is an emerging opportunity for us. We’re still in the beginning stages of understanding the various applications and possible use cases. And I do think this may represent a significant investment opportunity for us that is earlier on the return curve relative to some of the other AI work that we’ve done. And it’s a little too early to say how this is going to impact our overall capital intensity in the near term.

Meta’s management also thinks that generative AI can be a very useful way for companies to have high-quality chatbots interacting with customers

I also think that there’s going to be a very interesting convergence between some of the AI agents in messaging and business messaging, where, right now, we see a lot of the places where business messaging is most successful are places where a lot of businesses can afford to basically have people answering a lot of questions for people and engaging with them in chat. And obviously, once you light up the ability for tens of millions of small businesses to have AI agents acting on their behalf, you’ll have way more businesses that can afford to have someone engaging in chat with customers.

Microsoft (NASDAQ: MSFT)

Microsoft’s management thinks there is a generational shift in online search happening now because of AI

As we look towards a future where chat becomes a new way for people to seek information, consumers have real choice in business model and modalities with Azure-powered chat entry points across Bing, Edge, Windows and OpenAI’s ChatGPT. We look forward to continuing this journey in what is a generational shift in the largest software category, search.

Because of Microsoft’s partnership with OpenAI, Microsoft Azure is now exposed to new AI-related workloads that it previously was not

Because of some of the work we’ve done in AI even in the last couple of quarters, we are now seeing conversations we never had, whether it’s coming through you and just OpenAI’s API, right, if you think about the consumer tech companies that are all spinning, essentially, i.e. the readers, because they have gone to OpenAI and are using their API. These were not customers of Azure at all. Second, even Azure OpenAI API customers are all new, and the workload conversations, whether it’s B2C conversations in financial services or drug discovery on another side, these are all new workloads that we really were not in the game in the past, whereas we now are. 

Microsoft’s management has plans to monetise all the different AI-copilots that it is introducing to its various products

Overall, we do plan to monetize a separate set of meters across all of the tech stack, whether they’re consumption meters or per user subscriptions. The Copilot that’s priced and it is there is GitHub Copilot. That’s a good example of incrementally how we monetize the prices that are there out there and others are to be priced because they’re in 3D mode. But you can expect us to do what we’ve done with GitHub Copilot pretty much across the board.

Microsoft’s management expects the company to lower the cost of compute for AI workloads over time

And so we have many knobs that will continuously — continue to drive optimization across it. And you see it even in the — even for a given generation of a large model, where we started them through the cost footprint to where we end in the cost footprint in a period of a quarter changes. So you can expect us to do what we have done over the decade plus with the public cloud to bring the benefits of, I would say, continuous optimization of our COGS to a diverse set of workloads.

Microsoft’s management has not been waiting – and is not waiting – for AI-related regulations to show up – instead, they are thinking hard about unintended consequences from Day 1 and have built those concerns into the engineering process

So overall, we’ve taken the approach that we are not waiting for regulation to show up. We are taking an approach where the unintended consequences of any new technology is something that from day 1, we think about as first class and build into our engineering process, all the safeguards. So for example, in 2016 is when we put out the AI principles, we translated the AI principles into a set of internal standards that then are further translated into an implementation process that then we hold ourselves to internal audit essentially. So that’s the framework we have. We have a Chief AI Officer who is sort of responsible for both thinking of what the standards are and then the people who even help us internally audit our following of the process. And so we feel very, very good in terms of us being able to create trust in the systems we put out there. And so we will obviously engage with any regulation that comes up in any jurisdiction. But quite honestly, we think that the more there is any form of trust as a differentiated position in AI, I think we stand to gain from that.

Nvidia (NASDAQ: NVDA)

Cloud service providers (CSPs) are racing to deploy Nvidia’s chips for AI-related work

First, CSPs around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer AI applications for training and inference. Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure, upcoming offerings at AWS and general availability at emerging GPU-specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from generative AI pioneers.

Nvidia’s management sees consumer internet companies as being at the forefront of adopting AI

Second, consumer Internet companies are also at the forefront of adopting generative AI and deep-learning-based recommendation systems, driving strong growth. For example, Meta has now deployed its H100-powered Grand Teton AI supercomputer for its AI production and research teams.

Nvidia’s management is seeing companies in industries such as automotive, financial services, healthcare, and telecom adopt AI rapidly

Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, health care and telecom where AI and accelerated computing are quickly becoming integral to customers’ innovation road maps and competitive positioning. For example, Bloomberg announced it has a $50 billion parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification and question answering. Auto insurance company CCC Intelligent Solutions is using AI for estimating repairs. And AT&T is working with us on AI to improve fleet dispatches so their field technicians can better serve customers. Among other enterprise customers using NVIDIA AI are Deloitte for logistics and customer service, and Amgen for drug discovery and protein engineering.

Nvidia is making it easy for companies to deploy AI technology

And with the launch of DGX Cloud through our partnership with Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure, we deliver the promise of NVIDIA DGX to customers from the cloud. Whether the customers deploy DGX on-prem or via DGX Cloud, they get access to NVIDIA AI software, including NVIDIA-based command, end-to-end AI frameworks and pretrained models. We provide them with the blueprint for building and operating AI, spanning our expertise across systems, algorithms, data processing and training methods. We also announced NVIDIA AI Foundations, which are model foundry services available on DGX Cloud that enable businesses to build, refine and operate custom large language models and generative AI models trained with their own proprietary data created for unique domain-specific tasks. They include NVIDIA NeMo for large language models, NVIDIA Picasso for images, video and 3D, and NVIDIA BioNeMo for life sciences. Each service has 6 elements: pretrained models, frameworks for data processing and curation, proprietary knowledge-based vector databases, systems for fine-tuning, aligning and guard railing, optimized inference engines, and support from NVIDIA experts to help enterprises fine-tune models for their custom use cases.

Nvidia’s management thinks that the advent of AI will drive a shift towards accelerated computing in data centers

Now let me talk about the bigger picture and why the entire world’s data centers are moving toward accelerated computing. It’s been known for some time, and you’ve heard me talk about it, that accelerated computing is a full stack problem but — it is full stack challenged. But if you could successfully do it in a large number of application domain that’s taken us 15 years, it’s sufficiently that almost the entire data center’s major applications could be accelerated. You could reduce the amount of energy consumed and the amount of cost for a data center substantially by an order of magnitude. It costs a lot of money to do it because you have to do all the software and everything and you have to build all the systems and so on and so forth, but we’ve been at it for 15 years.

And what happened is when generative AI came along, it triggered a killer app for this computing platform that’s been in preparation for some time. And so now we see ourselves in 2 simultaneous transitions. The world’s $1 trillion data center is nearly populated entirely by CPUs today. And I — $1 trillion, $250 billion a year, it’s growing of course. But over the last 4 years, call it $1 trillion worth of infrastructure installed, and it’s all completely based on CPUs and dumb NICs. It’s basically unaccelerated.

In the future, it’s fairly clear now with this — with generative AI becoming the primary workload of most of the world’s data centers generating information, it is very clear now that — and the fact that accelerated computing is so energy efficient, that the budget of a data center will shift very dramatically towards accelerated computing, and you’re seeing that now. We’re going through that moment right now as we speak, while the world’s data center CapEx budget is limited. But at the same time, we’re seeing incredible orders to retool the world’s data centers. And so I think you’re starting — you’re seeing the beginning of, call it, a 10-year transition to basically recycle or reclaim the world’s data centers and build it out as accelerated computing. You have a pretty dramatic shift in the spend of a data center from traditional computing and to accelerated computing with SmartNICs, smart switches, of course, GPUs and the workload is going to be predominantly generative AI…

…The second part is that generative AI is a large-scale problem, and it’s a data center scale problem. It’s another way of thinking that the computer is the data center or the data center is the computer. It’s not the chip. It’s the data center, and it’s never happened like us before. And in this particular environment, your networking operating system, your distributed computing engines, your understanding of the architecture of the networking gear, the switches and the computing systems, the computing fabric, that entire system is your computer, and that’s what you’re trying to operate. And so in order to get the best performance, you have to understand full stack and understand data center scale. And that’s what accelerated computing is.

Nvidia’s management thinks that the training of AI models will be an always-on process

 You’re never done with training. You’re always — every time you deploy, you’re collecting new data. When you collect new data, you train with the new data. And so you’re never done training. You’re never done producing and processing a vector database that augments the large language model. You’re never done with vectorizing all of the collected structured, unstructured data that you have. And so whether you’re building a recommender system, a large language model, a vector database, these are probably the 3 major applications of — the 3 core engines, if you will, of the future of computing as well as a bunch of other stuff. But obviously, these are very — 3 very important ones. They are always, always running.

When it comes to inference – or the generation of an output – there’s a lot more that goes into it than just the AI models

The other thing that’s important is these are models, but they’re connected ultimately to applications. And the applications could have image in, video out, video in, text out, image in, proteins out, text in, 3D out, video in, in the future, 3D graphics out. So the input and the output requires a lot of pre and postprocessing. The pre and postprocessing can’t be ignored. And this is one of the things that most of the specialized chip arguments fall apart. And it’s because the length — the model itself is only, call it, 25% of the data — of the overall processing of inference. The rest of it is about preprocessing, postprocessing, security, decoding, all kinds of things like that.

Paycom Software (NYSE: PAYC)

Paycom’s management thinks AI is definitely going to have a major impact in the payroll and HCM (human capital management) industry

I definitely think it’ll be relevant. You can use AI for multiple things. There are areas that you can use it for that are better than others. And they’re front-end things you can use it for direct to the client. There are back-end things that you can use it for that a client may never see. And so when you’re talking about AI, it has many uses, some of which is front end and some back end. And I don’t want to talk specifically about what exactly we’re using it for already internally and what our opportunities would be into the future. But in answer to your question, yes, I do think that over time, AI is going to be a thing in our industry.

PayPal (NASDAQ: PYPL)

PayPal has been working with AI (in fraud and risk management) for several years, and management thinks generative AI and other forms of AI will be useful in the online payments industry

For several years, we’ve been at the forefront of advanced forms of machine learning and AI to combat fraud and to implement our sophisticated risk management programs. With the new advances of generative AI, we will also be able to accelerate our productivity initiatives. We expect AI will enable us to meaningfully lower our costs for years to come. Furthermore, we believe that AI, combined with our unique scale and sets of data, will drive not only efficiencies, but will also drive a differentiated and unique set of value propositions for our merchants and consumers…

…And we are now beginning to experiment with the first generation of what we call AI-powered checkout, which looks at the full checkout experience, not just the PayPal checkout experience, but the full checkout experience for our merchants…

…There’s no question that AI is going to impact almost every function inside of PayPal, whether it be our front office, back office, marketing, legal, engineering, you name it. AI will have an impact and allow us to not just lower cost, but have higher performance and do things that is not about trade-offs. It’s about doing both in there.

Shopify (NASDAQ: SHOP)

Shopify’s management thinks the advent of AI makes a copilot for entrepreneurship possible

But now we are at the dawn of the AI era and the new capabilities that are unlocked by that are unprecedented. Shopify has the privilege of being amongst the companies with the best chances of using AI to help our customers. A copilot for entrepreneurship is now possible. Our main quest demands from us to build the best thing that is now possible, and that has just changed entirely.

Shopify recently launched an AI-powered shopping assistant that is powered by OpenAI’s ChatGPT

We also — you’re also seeing — we announced a couple of weeks ago, Shop at AI, which is what I think is the coolest shopping concierge on the planet, whereby you as a consumer can use Shop at AI and you can browse through hundreds of millions of products and you can say things like I want to have a barbecue and here’s the theme and it will suggest great products, and you can buy it right in line right through the shopping concierge.  

Shopify has been using AI to help its merchants write product descriptions so that merchants can better focus on taking care of their customers

 For example, the task of writing product descriptions is now made meaningfully easier by injecting AI into that process. And what does that — the end result of that is merchants spend less time running product descriptions and more time making beautiful products and communicating and engaging with their customers. 

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s management sees demand in most end-markets as being mostly soft, but AI-related demand is growing

We observed the PC and smartphone market continue to be soft at the present time, while automotive demand is holding steady for TSMC and it is showing signs of soften into second half of 2023. I’m talking about automotive. On the other hand, we have recently observed incremental upside in AI-related demand

TSMC’s management thinks it’s a little too early to tell how big the semiconductor market can grow into because of AI, but they do see a positive trend

We certainly, we have observed an incremental increase in AI-related demand. It will also help the ongoing inventory digestion. The trend is very positive for TSMC. But today, if you ask me to quantitatively to say that how much of the amount increase or what is the dollar content in the server, it’s too early to say. It still continue to be developed. And ChatGPT right now reinforce the already stronger conviction that we have in HPC and AI as a structurally megatrend for TSMC’s business growth in the future. Whether this one has been included in our previous announcement is said that we have a 15% to 20% CAGR, the answer is probably partly yes, because of — for several, we have accelerated into our consideration. But this ChatGPT is a large language model is a new application. And we haven’t really have a kind of a number that put into our CAGR. But is definitely, as I said, it really reinforced our already strong conviction that HPC and AI will give us a much higher opportunities in the future…

…We did see some positive signs of the people getting much more attention to AI application, especially the ChatGPT’s area. However, as I said, quantitatively, we haven’t have enough data to summing it up to see what is the contribution and what kind of percentage to TSMC’s business. But we remain confident that this trend is definitely positive for TSMC.

TSMC’s management sees most of the AI work performed today as being focused on training but that it will flip to inference in the future – but nonetheless, high-performance semiconductors will still need be needed for AI-related work

Right now, most of the AI concentrate or focus on training. And in the future, it will be inference. But let me say that, no matter what kind of application, they need to use a very high-performance semiconductor component, and that actually is a TSMC’s advantage. So we expect that semiconductor content starting from a data center for [indiscernible] to device and edge device or those kind of things, put all together, they need a very high-speed computing with a very power-efficient one. And so we expect it will add to TSMC’s business a lot.

Tencent (NASDAQ: TCEHY)

Tencent is using AI to deliver more relevant ads to users of its services

We upgraded our machine learning advertising platform to deliver higher conversions for advertisers. For example, we help our advertisers dynamically feature their most relevant products inside their advertisements by applying our deep learning model to the standard product unit attributes we have aggregated within our SPU database. 

Tencent’s management thinks there will be a proliferation of AI models – both foundational as well as vertical – from both established companies as well as startups

So in terms of going forward, we do believe that number one, there’s going to be many models in the market going forward for the large companies, I think each one of us would have a foundation model. And the model will be supporting our own use cases as well as offer it to the market both on a 2C basis as well as on a 2B basis. And at the same time, there will be many start-ups, which will be creating their own models, some of them may be general foundation model. Some of them may be more industry and vertical models and they will be coming with new applications. I think overall, it’s going to be a very vibrant industry from a model availability perspective.

Tencent’s management thinks AI can help improve the quality of UGC (user-generated content)

In terms of the user-to-user interaction type of services like social network and short video network and games, long lead content, there will be — a lot of usages that helps to increase the quality of content, the efficiency at which the content are created as well as lowering the cost of content creation. And that will be net beneficiary to these applications. 

Tencent’s management thinks China’s government is supportive of innovation in AI

Now in terms of — you asked about regulation. Without the government’s general stance is like it’s supportive of regulation, but the industry has to be regulated. And I think this is not something that’s specific to China, even around the world. And you look at the U.S., there’s a lot of public discussion about having regulation and even the founder of OpenAI has been testifying and asking for regulation in the industry. So I think that is something which is necessary, but we felt under the right regulation and regulatory framework, then the government stance is supportive of innovation and the industry will actually have room for healthy growth.

Tesla (NASDAQ: TSLA)

Tesla’s management thinks data will be incredibly valuable when building out AI services, especially in self-driving

Regarding Autopilot and Full Self-Driving. We’ve now crossed over 150 million miles driven by Full Self-Driving beta, and this number is growing exponentially. We’re — I mean, this is a data advantage that really no one else has. Those who understand AI will understand the importance of data — of training data and how fundamental that is to achieving an incredible outcome. So yes, so we’re also very focused on improving our neural net training capabilities as is one of the main limiting factors of achieving full autonomy. 

Tesla’s management thinks the company’s supercomputer project, Dojo, could significantly improve the cost of training AI models

So we’re continuing to simultaneously make significant purchases of NVIDIA GPUs and also putting a lot of effort into Dojo, which we believe has the potential for an order of magnitude improvement in the cost of training. 

The Trade Desk (NASDAQ: TSLA)

Trade Desk’s management thinks that generative AI is only as good as the data that it has been trained on

ChatGPT is an amazing technology, but its usefulness is conditioned on the quality of the dataset it is pointed at. Regurgitating bad data, bad opinions or fake news, where AI generated deep bases, for example, will be a problem that all generative AI will likely be dealing with for decades to come. We believe many of the novel AI use cases in market today will face challenges with monetization and copyright and data integrity or truth and scale.

Trade Desk has very high-quality advertising data at scale (it’s handling 10 million ad requests per second) so management thinks that the company can excel by applying generative AI to its data

By contrast, we are so excited about our position in the advertising ecosystem when it comes to AI. We look at over 10 million ad requests every second. Those requests, in sum, represent a very robust and very unique dataset with incredible integrity. We can point generative AI at that dataset with confidence for years to come. We know that our size, our dataset size and integrity, our profitability and our team will make Koa and generative AI a promising part of our future.

Trade Desk’s management sees AI bringing positive impacts to many areas of the company’s business, such as generating code faster, generating creatives faster, and helping clients learn programmatic advertising faster

In the future, you’ll also hear us talk about other applications of AI in our business. These include generating code faster; changing the way customers understand and interact with their own data; generating new and more targeted creatives, especially for video and CTV; and using virtual assistance to shorten the learning curve that comes with the complicated world of programmatic advertising by optimizing the documentation process and making it more engaging.

Visa (NYSE: V)

Visa, which is in the digital payments industry, has a long history of working with AI and management sees AI as an important component of what the company does

I’ll just mention that we have a long history developing and using predictive AI and deep learning. We were one of the pioneers of applied predictive AI. We have an enormous data set that we’ve architected to be utilized at scale by hundreds of AI and ML, different services that people use all across Visa. We use it — we use it to run our company more effectively. We use it to serve our clients more effectively. And this will continue to be a big part of what we do.

Visa’s management thinks generative AI can take the company’s current AI services to the next level

As you transition to generative AI, this is where — we see this as an opportunity to take our current AI services to the next level. We are kind of as a platform, experimenting with a lot of the new capabilities that are available. We’ve got people all over the company that are tinkering and dreaming and thinking and doing testing and figuring out ways that we could use generative AI to transform how we do what we do, which is deliver simple, safe and easy-to-use payment solutions. And we’re also spending a fair bit of time thinking how generative AI will change the way that sellers sell, and we all buy and all of the shop. So that is — it’s a big area of opportunity that we’re looking at in many different ways across the company.

Wix (NASDAQ: WIX)

Wix’s management thinks AI can reduce a lot of friction for users in creating websites

First, our goal at Wix is to reduce friction. The easier it is for our users to build websites, the better Wix is. We have proven this many times before, through the development of software and products, including AI. As we make it easier for our users to achieve their goals, their satisfaction goes up, conversion goes up, user retention goes up, monetization goes up and the value of Wix grows…

…  Today, new emerging AI technologies create an even bigger opportunity to reduce friction in more areas that were almost impossible to solve a few years ago and further increase the value of our platform. We believe this opportunity will result in an increased addressable market and even more satisfied users. 

Wix’s management thinks that much more is needed to run e-commerce websites than just AI and even if AI can automate every layer, it is still very far into the future

The second important point is that there is a huge amount of complexity in software, even with websites, and it’s growing. Even if AI could code a fully functional e-commerce website, for example — which I believe we are still very far from — there is still a need for the site to be deployed to a server, to run the code, to make sure the code continues to work, to manage and maintain a database for when someone wants to buy something, to manage security, to ship the products, to partner with payment gateways, and many more things. So even if you have something that can build pages and content and code…you still need so much more. This gets to my third and final point, which is that even in the far future, if AI is able to automate all of these layers, it will have to disrupt a lot of the software industry, including database management, server management and cloud computing. I believe we are very far from that and that before then, there will be many more opportunities for Wix to leverage AI and create value for our users.

Zoom Video Communications (NASDAQ: ZM)

Zoom management’s approach to AI is federated, empowering, and responsible

We outlined our approach to AI is to drive forward solutions that are federated empowering and responsible. Federated means flexible and customizable to businesses unique scenarios and nomenclature. Empowering refers to building solutions that improve individual and team productivity as well as enhance the customers’ experience. And responsible means customer control of their data with an emphasis on privacy, security, trust and safety.

Zoom recently made a strategic investment in Anthropic and management will be integrating Anthropic’s AI assistant feature across Zoom’s product portfolio

Last week, we announced our strategic investment in Anthropic, an AI safety and research company working to build reliable, interpretable and steerable AI systems. Our partnership with Anthropic further boosts our federated approach to AI by allowing Anthropic’s AI assistant cloud to be integrated across Zoom’s entire platform. We plan to begin by layering Claude into our Contact Center portfolio, which includes Zoom Contact Center, Zoom Virtual Agent, and now in-beta Zoom Workforce Engagement Management. With Claude guiding agents towards trustworthy resolutions and empowering several service for end users, companies will be able to take customer relationships to the next level.

Zoom’s management thinks that having AI models is important, but it’s even more important to fine-tune them based on proprietary data

Having said that, there are 2 things really important. One is the model, right? So OpenAI has a model, Anthropic and Facebook as well, Google and those companies. But the most important thing is how to leverage these models to fine tune based on your proprietary data, right? That is extremely important when it comes to collaboration, communication, right? Take a zoom employee, for example. We have so many meetings, right, and talk about — every day, like our sales team use the Zoom call with the customers. We accumulated a lot of, let’s say, internal meeting data. How to fine tune the model with those data, it’s very important, right?

Examples of good AI use cases in Zoom’s platform

We also look at our core meeting platform, right, in meeting summary. It is extremely important, right? And it’s also we have our team chat solution and also how to lever that to compose a chat. Remember, last year, we also have email candidate as well. How do we leverage the generative AI to understand the context, right, and kind of bring all the information relative to you and help you also generate the message, right? When you send an e-mail back to customers or prospects, right, either China message or e-mail, right? We can lever to generate is, right? I think a lot of areas, even like you like say, maybe you might be later to the meeting, right? 10 minutes later, you joined the meeting. You really want to stand in what had happened, right? Can you get a quick summary over the positive minutes. Yes, you just also have to generate AI as well. You also can get that as well. 

Zoom’s management thinks there are multiple ways to monetise AI

I think in terms of how to monetize generative I think first of all, take Zoom IQ for Sales for example, that’s a new service to target the sales deportment. That AI technology is based on generative AI, right, so we can monetize. And also seeing some features, even before the generative AI popularity, we have a live transmission feature, right? And also, that’s not a free feature. It is a paid feature, right, behind the pay wall, right? And also a lot of good features, right, take the Zoom meeting summary, for example, for enterprise — and the customers… For to customers, all those SMB customers, they did not deploy Zoom One, they may not get to those features, right? That’s the reason — another reason for us to monetize. I think there’s multiple ways to monetize, yes.

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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. I have a vested interest in Alphabet, Amazon, ASML, Datadog, Etsy, Fiverr, Mastercard, MercadoLibre, Meta Platforms, Microsoft, Paycom Software, PayPal, Shopify, TSMC, Tencent, Tesla, The Trade Desk, Visa, Wix, Zoom. Holdings are subject to change at any time.

This March has been a wild month in the world of finance. So far, three banks in the USA have collapsed, including Silicon Valley Bank, the 16th largest bank in the country with US$209 billion in assets at the end of 2022. Meanwhile, First Republic Bank, ranked 14th in America with US$212 billion in assets, has seen a 90% month-to-date decline in its share price. Over in Europe, the Swiss bank Credit Suisse, with a market capitalization of US$8.6 billion on the 17th, was forced by regulators to agree to be acquired by its national peer, UBS, for just over US$3 billion on the 19th.

The issues plaguing the troubled banks have much to do with the sharp rise in interest rates seen in the USA and Europe that began in earnest in the third quarter of 2022. For context, the Federal Funds Rate – the key interest rate benchmark in the USA – rose from a target range of 1.5%-1.75% at the end of June 2022 to 4.5%-4.75% right now. Over the same period, the key interest rate benchmarks in the European Union rose from a range of -0.5% to 0.25%, to a range of 3.0% to 3.75%. Given the aforementioned banking failures, it’s clear that rising interest rates are already a big problem for banks. But there could be more pain ahead for banks who fail to understand a lesson from an old banking crisis in the USA.

A blast from the past

The Savings & Loan (S&L) crisis started in the early 1980s and stretched into the early 1990s. There were nearly 4,000 S&L institutions in the USA in 1980; by 1989, 563 of them had failed. S&L institutions are also known as thrift banks and savings banks. They provide similar services as commercial banks, such as deposit products, loans, and mortgages. But S&L institutions have a heavier emphasis on mortgages as opposed to commercial banks, which tend to also focus on business and personal lending.

In the early 1980s, major changes were made to the regulations governing S&L institutions and these reforms sparked the crisis. One of the key changes was the removal of caps on the interest rates that S&L institutions could offer for deposits. Other important changes included the removal of loan-to-value ratios on the loans that S&L institutions could make, and the relaxation on the types of assets that they could own.

The regulatory changes were made to ease two major problems that S&L institutions were confronting. First, since the rates they could offer were limited by the government, S&L institutions found it increasingly difficult to compete for deposits after interest rates rose dramatically in the late 1970s. Second, the mortgage loans that S&L institutions made were primarily long-term fixed rate mortgages; the rise in interest rates caused the value of these mortgage loans to fall. The US government thought that S&L institutions could cope better if they were deregulated.

But because of the relaxation in rules, it became much easier for S&L institutions to engage in risky activities. For instance, S&L institutions were now able to pay above-market interest rates on deposits, which attracted a flood of savers. Besides paying high interest on deposits, another risky thing the S&L institutions did was to make questionable loans in areas outside of residential lending. For perspective, the percentage of S&L institutions’ total assets that were in mortgage loans fell from 78% in 1981 to just 56% by 1986.

Ultimately, the risks that the S&L institutions had taken on, as a group, were too high. As a result, many of them collapsed.

Learning from the past

Hingham Institution of Savings is a 189-year-old bank headquartered in Massachusetts, USA. Its current CEO, Robert Gaughen Jr, assumed control in the middle of 1993. Since then, the bank has been profitable every single year. From 1994 (the first full-year where the bank was led by Robert Gaughen Jr) to 2022, Hingham’s average return on equity (ROE) was a respectable 14.2%, and the lowest ROE achieved was 8.4% (in 2007). There are two things worth noting about the 1994-2022 timeframe in relation to Hingham: 

• The bank had – and still has – heavy exposure to residential-related real estate lending.
• The period covers the 2008-09 Great Financial Crisis. During the crisis, many American banks suffered financially and US house prices crashed. For perspective, the US banking industry had ROEs of 7.8%,  0.7%, and -0.7% in 2007, 2008, and 2009, while Hingham’s ROEs were higher – at times materially so – at 8.4%, 11.1%, and 12.8%.

Hingham’s most recent annual shareholder’s meeting was held in April 2022. During the meeting, its president and chief operating officer, Patrick Gaughen, shared an important lesson that banks should learn from the S&L crisis (emphasis is mine):

“We spent some time talking in the past about why bankers have misunderstandings about the S&L crisis in the ’80s, with respect to how a lot of those banks failed. And that was in periods when rates were rising, there were a lot of S&Ls that looked for assets that had yields that would offset the rising price of their liabilities, and those assets had risks that the S&Ls did not appreciate. Rather than accepting tighter margins through those periods where there were flatter curves, they resisted. They tried to protect those profits. And in doing so, they put assets on the balance sheet that when your capital’s levered 10x or 11x or 12x or 13x to 1 — they put assets on the balance sheet that went under.”

In other words, the S&L institutions failed because they wanted to protect their profits at a time when their cost of deposits were high as a result of rising interest rates. And they tried to protect their profits by investing in risky assets to chase higher returns, a move which backfired.

A better way

At Hingham’s aforementioned shareholder’s meeting, Patrick Gaughen also shared what he and his colleagues think should be the right way to manage the problem of a high cost of deposits stemming from rising interest rates (emphases are mine):

“And I think it’s important to think and maybe describe the way that I think about this is that we’re not protecting profits in any given period. We’re thinking about how to maximize returns on equity on a per share basis over a long time period, which means that there are probably periods where we earn, as I said earlier, outsized returns relative to that long-term trend. And then periods where we earn what are perfectly satisfactory returns. Looking back over history, with 1 year, 2 years exceptions, double-digit returns on equity. So it’s satisfactory, but it’s not 20% or 21%.

And the choices that we’ve made from a structural perspective about the business mean that we need to live with both sides of that trade as it occurs. But over the long term, there are things we can do to offset that. So the first thing we’re always focused on regardless of the level and the direction of rates is establishing new relationships with strong borrowers, deepening the relationships that we have with customers that we already have in the business. Because over time, those relationships as the shape of curve changes, those relationships are going to be the relationships that give us the opportunity to source an increasing volume of high quality assets. And those are the relationships that are going to form the core of the noninterest-bearing deposits to allow us to earn those spreads. And so that’s true regardless of the direction of rates.”

I find the approach of Hingham’s management team to be sensible. By being willing to accept lower profits when interest rates (and thus deposit rates) are high, management is able to ensure Hingham’s longevity and maximise its long-term returns.

In our current environment, interest rates are elevated, which makes the cost of deposits expensive for banks. If you’re looking to invest in the shares of a bank right now, you may want to determine if the bank’s management team has grasped the crucial lesson from the S&L crisis of old. If there’s a bank today which fails to pay heed, they may well face failure in the road ahead.

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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. I have a vested interest in Hingham Institution of Savings. Holdings are subject to change at any time.

On 12 March 2023, Signature Bank, which was based in New York, was closed by banking regulators in the USA. Its closure happened in the wake of Silicon Valley Bank’s high-profile collapse just a few days prior. Silicon Valley Bank was dealing with a flood of deposit withdrawals that it could not handle. After regulators assumed control of Silicon Valley Bank, it was revealed that depositors tried to withdraw US$42 billion – around a quarter of the bank’s total deposits – in one day. 

Signature Bank was by no means a behemoth, but it was definitely not small. For perspective, the US’s largest bank by assets, JPMorgan Chase, had total assets of US$3.67 trillion at the end of 2022; Signature Bank, meanwhile, reported total assets of US$110 billion. But what is fascinating – and shocking – about Signature Bank’s failure is not its size. It has to do with its board of directors, one of whom is Barney Frank, a long-time politician who retired from American politics in 2012.

During his political career, Frank was heavily involved with reforming and regulating the US banking industry. From 2007 to 2011, he served as Chairman of the House Financial Services Committee, where he played an important role in creating a US$550 billion plan to rescue American banks during the 2008-2009 financial crisis. He also cosponsored the Dodd-Frank Wall Street Reform and Consumer Protection Act, which was signed into law in July 2010. The Dodd-Frank act was established in the aftermath of the 2008-2009 financial crisis, which saw many banks in the USA collapse. The act was created primarily to prevent banks from engaging heavily in risky activities that could threaten their survival.

Although Signature Bank was able to tap on Frank’s experience for the past eight years – he has been a director of the bank since June 2015 – it still failed. An argument can be made that Signature Bank was  engaging in risky banking activities prior to its closure. The bank started taking deposits from cryptocurrency companies in 2018. By 2021 and 2022, deposits from cryptocurrency companies made up 27% and 20%, respectively, of Signature’s total deposit base; the bank was playing with fire by having significant chunks of its deposit base come from companies in a highly speculative sector. The key takeaway I have from this episode is that investors should never be complacent about the capabilities of a company’s leaders, even if they have a storied reputation. Always be vigilant.

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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. I do not have a vested interest in any stocks mentioned. Holdings are subject to change at any time. 

A book I read recently is Edward Chancellor’s The Price of Time, published in July 2022. The book traces the history of interest rates from ancient Mesopotamia (a civilisation that dates back to 3100 B.C.) to our current era. One of the thought-provoking collection of ideas I gleaned from the book involves China and the growing problems with its economy over the past two to three decades.

Jeremy and I have investments in China, so I want to document these facts for easy reference in the future. Moreover, given the size of China’s economy – the second largest in the world – I think anyone who’s interested in investing may find the facts useful. To be clear, none of what I’m going to share from The Price of Time is meant to be seen as a commentary on the attractiveness (or lack thereof) of Chinese stocks or the growth prospects of the Chinese economy. Instead, Jeremy and I merely see them as providing additional colour in the mosaic we have collected over time about how the world works and where the world is going. With that, here’re the fascinating new details I picked up about China’s economy from The Price of Time (bolded emphases are mine):

The state of China’s property bubble in 2016

Quote 1

“In parts of Shanghai and neighbouring Suzhou, empty development plots sold for more than neighbouring land with completed buildings – a case of ‘flour more expensive than bread’. By late 2016, house prices were valued nationwide at eight times average Chinese incomes, roughly double the peak valuation of US housing a decade earlier.”

Quote 2

“A study released in 2015 by the National Bureau of Economic Research found that rental yields in Beijing and Shanghai had fallen below 2 per cent – in line with the discount rate. However, rental yields of less than 2 per cent implied a payback of nearly seven decades – roughly the same length of time as residential land leases, after which title reverted to the state… But, as the NBER researchers commented, ‘only modest declines in expected appreciation seem needed to generate large drops in house values.’”

Quote 3

“By late 2016 total real estate was valued at [US]$43 trillion, equivalent to nearly four times GDP and on a par with the aggregate value of Japanese real estate (relative to GDP) at its bubble peak. Like Japan three decades earlier, China had transformed into a ‘land bubble’ economy. The French bank Société Générale had calculated back in 2011 that over the previous decade China had built 16 billion square metres of residential floor space. This was equivalent to building modern Rome from scratch every fourteen days, over and over again. A decade after the stimulus more than half of the world’s hundred tallest buildings were under construction in the People’s Republic, and more than a quarter of economic output was related, directly or indirectly, to real estate development.”

The stunning growth of debt in China in the 21st century

Quote 4

“In ten years to 2015, China accounted for around half the world’s total credit creation. This borrowing binge constituted ‘history’s greatest Credit Bubble’. Every part of the economy became bloated with debt. Liabilities of the banking system grew to three times GDP. At the time of Lehman’s bankruptcy, households in the People’s Republic carried much less debt than their American counterparts. But, since the much-touted ‘rebalancing’ of the economy never occurred, consumers turned to credit to enhance their purchasing power.

Between 2008 and 2018, Chinese households doubled their level of debt (relative to income) and ended up owing more than American households did at the start of the subprime crisis. Over the same period, Chinese companies borrowed [US]$15 trillion, accounting for roughly half the total increase in global corporate debt. Real estate companies borrowed to finance their developments – the largest developer, Evergrande, ran up total liabilities equivalent to 3 per cent of GDP. Local governments set up opaque financing vehicles to pay for infrastructure projects with borrowed money. Debt owed by local governments grew to [US]$8.2 trillion (by the end of 2020), equivalent to more than half of GDP.”

How China concealed its bad-debt problems in the 21st century and the problems this concealment is causing

Quote 5

“Although they borrowed more cheaply than private firms, state-owned enterprises nevertheless had trouble covering their interest costs. After 2012 the total cost of debt-servicing exceeded China’s economic growth. An economy that can’t grow faster than its interest costs is said to have entered a ‘debt trap’. China avoided the immediate consequences of the debt trap by concealing bad debts. What’s been called ‘Red Capitalism’ resembled a shell game in which non-performing loans were passed from one state-connected player to another.

The shell game commenced at the turn of this century when state banks were weighed down with nonperforming loans. The bad loans weren’t written off, however, but sold at face value to state-owned asset management companies (AMCs), which paid for them by issuing ten-year bonds that were, in turn, acquired by the state-owned banks. In effect, the banks had swapped uncollectible short-term debt for uncollectible long dated debt. When the day finally arrived for the AMCs to redeem their bonds, the loans were quietly rolled over. Concealing or ‘evergreening’ bad debts required low interest rates. China’s rate cuts in 2001 and 2002 were partly intended to help banks handle their debt problems. Over the following years, bank loan rates were kept well below the country’s nominal GDP growth, while deposit rates remained stuck beneath 3 per cent. Thus, Chinese depositors indirectly bailed out the banking system.

After 2008, cracks in the credit system were papered over with new loans – a tenet of Red Capitalism being that ‘as long as the banks continue to lend, there will be no repayment problems.’ But it became progressively harder to conceal problem loans. In 2015, an industrial engineering company (Baoding Tianwei Group) became the first state-owned enterprise to default on its domestic bonds. The trickle of defaults continued. One could only guess at the scale of China’s bad debts. Bank analyst Charlene Chu suggested that by 2017 up to a quarter of bank loans were non-performing. This estimate was five times the official figure.

As Chu commented: ‘if losses don’t manifest on financial institution balance sheets, they will do so via slowing growth and deflation.’ Debt deflation, as Irving Fisher pointed out, occurs after too much debt has accumulated. At the same time, excess industrial capacity was putting downward pressure on producer prices and leading China to export deflation abroad – for instance, by dumping its surplus steel in European and US markets. Corporate zombies added to deflation pressures. Despite the soaring money supply after 2008, consumer prices hardly budged. By November 2015, the index of producer prices had fallen for a record forty-four consecutive months.

If China’s investment had been productive, then it would have generated the cash flow needed to pay off its debt. But, for the economy as a whole, this wasn’t the case. So debt continued growing. Top officials in Beijing were aware that the situation was unsustainable. In the summer of 2016, President Xi’s anonymous adviser warned in his interview with the People’s Daily that leverage must be contained. ‘A tree cannot grow to the sky,’ declared the ‘authoritative person’; ‘high leverage must bring with it high risks.’ Former Finance Minister Lou Jiwei put his finger on Beijing’s dilemma: ‘The first problem is to stop the accumulation of leverage,’ Lou said. ‘But we also can’t allow the economy to lose speed.’ Since these twin ambitions are incompatible, Beijing chose the path of least resistance. A decade after the stimulus launch, China’s ‘Great Wall of Debt’ had reached 250 per cent of GDP, up 100 percentage points since 2008.”

The troubling state of China’s shadow banking system in 2016

Quote 6

“By 2016, the market for wealth management products had grown to 23.5 trillion yuan, equivalent to over a third of China’s national income. Total shadow finance was estimated to be twice as large. Even the relatively obscure market for debt-receivables exceeded the size of the US subprime market at its peak. George Soros observed an ‘eery resemblance’ between China’s shadow banks and the discredited American version. Both were driven by a search for yield at a time of low interest rates; both were opaque; both involved banks originating and selling on questionable loans; both depended on the credit markets remaining open and liquid; and both were exposed to real estate bubbles.”

China’s risk of facing a currency crisis because of its expanding money supply

Quote 7

“As John Law had discovered in 1720, it is not possible for a country to fix the price of its currency on the foreign exchanges while rapidly expanding the domestic money supply. Since 2008 China’s money supply had grown relentlessly relative to the size of its economy and the world’s total money supply. Those trillions of dollars’ worth of foreign exchange reserves provided an illusion of safety since a large chunk was tied up in illiquid investments. Besides, cash deposits in China’s banks far exceeded foreign exchange reserves. If only a fraction of those deposits left the country, however, the People’s Republic would face a debilitating currency crisis.” 

China’s problems of inequality, financial repression, and tight control of the economy by the government

Quote 8

“From the early 1980s onwards, the rising incomes of hundreds of millions of Chinese workers contributed to a decline in global inequality. But during this period, China itself transformed from one of the world’s most egalitarian nations into one of the least equal. After 2008, the Gini coefficient for Chinese incomes climbed to 0.49 – an indicator of extreme inequality and more than twice the level at the start of the reform era.

The inequality problem was worse than the official data suggested. A 2010 report from Credit Suisse claimed that ‘illegal or quasi-legal’ income amounted to nearly a third of China’s GDP. Much of this grey income derived from rents extracted by Party members. The case of Bo Xilai, the princeling who became Party chief of Chongqing, is instructive. As the head of this sprawling municipality, Bo made a great display of rooting out corruption. But after he fell from grace in 2012 it was revealed that his family was worth hundreds of millions of dollars. Premier Wen’s family fortune was estimated at [US]$2.7 billion.

The richest 1 per cent of the population controlled a third of the country’s wealth, while the poorest quartile owned just 1 per cent. The real estate bubble was responsible for much of this rise in inequality. Researchers at Peking University found that 70 per cent of household wealth was held in real estate. A quarter of China’s dollar billionaires were real estate moguls. At the top of the rich list was Xu Jiayin, boss of property developer China Evergrande, whose fortune (in 2018) was estimated at [US]$40 billion. Many successful property developers turned out to be the offspring of top Party members. Local government officials who drove villagers off their land to hand it over to developers acted as ‘engines of inequality’.

Financial repression turned back the clock on China’s economic liberalization. Throughout its history, the Middle Kingdom’s progress ‘has an intermittent character and is full of leaps and bounds, regressions and relapses’. In general, when the state has been relatively weak and money plentiful, the Middle Kingdom has advanced. Incomes were probably higher in the twelfth century under the relatively laissez-faire Song than in the mid-twentieth century when the Communists came to power. But when the state has shown a more authoritarian character, economic output has stagnated or declined. The mandarins’ desire for total monetary control contributed to Imperial China’s ‘great divergence’ from Western economic development.

In recent years, China has experienced an authoritarian relapse. Paramount leader Xi Jinping exercises imperial powers. An Orwellian system of electronic surveillance tracks the citizenry. Millions of Uighurs are reported to have been locked up in camps. Private companies are required to place the interests of the state before their own. The ‘China 2025’ economic development plans aim to establish Chinese predominance in a number of new technologies, from artificial intelligence to robotics. A system of social credits, which rewards and punishes citizens’ behaviour, will supplement conventional credit. A digital yuan, issued by the People’s Bank, will supplement – or even replace – conventional money. These developments are best summed up by a phrase that became commonplace in the 2010s: ‘the state advances, while the private [sector] retreats.’

Financial repression has played a role in this regressive movement. The credit binge launched by the 2008/9 stimulus enhanced Beijing’s sway over the economy. As the state has advanced, productivity growth has declined. Because interest rates neither reflect the return on capital nor credit risk, China’s economy has suffered from the twin evils of capital misallocation and excessive debt. Real estate development, fuelled by low-cost credit, delivered what President Xi called ‘fictional growth’. By 2019 Chinese GDP growth (per capita) had fallen to half its 2007 level.

The Third Plenum of the Eighteenth Chinese Communist Party Congress, held in Beijing in 2013, heralded profound reforms to banking practices. The ceiling on bank deposit rates was lifted, and banks could set their own lending rates. Households earned a little more on their bank deposits, but interest rates remained below nominal GDP growth. The central bank now turned to managing the volatility of the interbank market interest rate. The People’s Bank still lacked independence and had to appeal to the State Council for any change to monetary policy.

Allowing interest rates to be set by the market would have required wrenching changes. Forced to compete for deposits, state-controlled banks would suffer a loss of profitability. Bad loans would become harder to conceal. Without access to subsidized credit, state-owned enterprises would become even less profitable. Corporate zombies would keel over. Economic planners would lose the ability to direct cheap capital to favoured sectors. The cost of controlling the currency on the foreign exchanges would become prohibitively expensive. Beijing would no longer be able to manipulate real estate or fine-tune other markets.

The Party’s monopoly of power has survived the liberalization of most commercial prices and many business activities, but the cadres never removed their grip on the most important price of all. The state, not the market, would determine the level of interest. The legacy of China’s financial repression was, as President Xi told the National Congress in 2017, a ‘contradiction between unbalanced and inadequate [economic] development and the people’s ever-growing needs for a better life’, which, in turn, provided Xi with a rationale for further advancing the role of the state.”

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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. I do not have a vested interest in any stocks mentioned. Holdings are subject to change at any time.

What happens to stocks when risk-free rates are high? Theoretically, when risk-free rates are high, stocks should fall in price – why would anyone invest in stocks if they can earn 8%, risk-free? But as Yogi Berra was once believed to have said, “In theory, there is no difference between practice and theory. In practice, there is.”

Ben Carlson is the Director of Institutional Asset Management at Ritholtz Wealth Management. He published a blog post recently, titled Will High Risk-Free Rates Derail the Stock Market?, where he looked at the relationship between US stock market returns and US government interest rates. It turns out there’s no clear link between the two.

In the 1950s, the 3-month Treasury bill (which is effectively a risk-free investment, since it’s a US government bond with one of the shortest maturities around) had a low average yield of 2.0%; US stocks returned 19.5% annually back then, a phenomenal gain. In the 2000s, US stocks fell by 1.0% per year when the average yield on the 3-month Treasury bill was 2.7%. Meanwhile, a blockbuster 17.3% annualised return in US stocks in the 1980s was accompanied by a high average yield of 8.8% for the 3-month Treasury bill. In the 1970s, the 3-month Treasury bill yielded a high average of 6.3% while US stocks returned just 5.9% per year. 

Here’s a table summarising the messy relationship, depicted in the paragraph above, between the risk-free rate and stock market returns in the USA:

So there are two important lessons here: (1) While interest rates have a role to play in the movement of stocks, it is far from the only thing that matters; (2) one-factor analysis in finance – “if A happens, then B will occur” – should be largely avoided because clear-cut relationships are rarely seen.

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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. I do not have a vested interest in any stocks mentioned. Holdings are subject to change at any time.

In my December 2021 article, The Need For Patience, I shared two of my favourite investing stories. The first involved Warren Buffett’s experience with investing in The Washington Post Company in 1973 and the second was about the recommendation of Starbucks shares by the brothers, David and Tom Gardner, during a TV show in the USA in July 1998. 

The thread tying the two stories together was that both companies saw sharp declines in their stock prices while on their way to delivering massive returns. Washington Post’s stock price fell by more than 20% shortly after Buffett invested, and then stayed in the red for three years. But by the end of 2007, Buffett’s investment in Washington Post had produced a return of more than 10,000%. As for Starbucks, its stock price was down by a third a mere six weeks after the Gardners’ recommendation. When The Need For Patience was published, the global coffee retailer’s stock price was 30 times higher from where the Gardners recommended the company.

I recently learnt that Walmart, the US retail giant, had walked a similar path. From 1971 to 1980, Walmart produced breath-taking business growth. The table below shows the near 30x increase in Walmart’s revenue and the 1,600% jump in earnings per share in that period. Unfortunately, this exceptional growth did not help with Walmart’s short-term return. Based on the earliest data I could find, Walmart’s stock price fell by three-quarters from less than US$0.04 in late-August 1972 to around US$0.01 by December 1974 – in comparison, the S&P 500 was down by ‘only’ 40%. 

But by the end of 1979, Walmart’s stock price was above US$0.08, more than double what it was in late-August 1972. Still, the 2x-plus increase in Walmart’s stock price was far below the huge increase in earnings per share the company generated. This is where the passage of time helped – as more years passed, the weighing machine clicked into gear (I’m borrowing from Ben Graham’s brilliant analogy of the stock market being a voting machine in the short run but a weighing machine in the long run). At the end of 1989, Walmart’s stock price was around US$3.70, representing an annualised growth rate in the region of 32% from August 1972; from 1971 to 1989, Walmart’s revenue and earnings per share grew by 41% and 38% per year. Even by the end of 1982, Walmart’s stock price was already US$0.48, up more than 10 times where it was in late-August 1972. 

What’s also interesting was Walmart’s valuation. It turns out that in late-August 1972, when its stock price was less than US$0.04, Walmart’s price-to-earnings (P/E) ratio was between 42 and 68 (I couldn’t find quarterly financial data for Walmart for that time period so I worked only with annual data). This is a high valuation. If you looked at Walmart’s stock price in December 1974, after it had sunk by 75% to a low of around US$0.01 to carry a P/E ratio of between 6 and 7, the easy conclusion is that it was a mistake to invest in Walmart in August 1972 because of its high valuation. But as Walmart’s business continued to grow, its stock price eventually soared to around US$3.70 near the end of 1989. What looked like a horrendous mistake in the short run turned out to be a wonderful decision in the long run because of Walmart’s underlying business growth. 

This look at a particular part of Walmart’s history brings to mind two important lessons for all of us when we’re investing in stocks:

• Even when you’ve found the best company to invest in, it’s likely that there will be pain before gain; don’t give up on the company’s stock just because its price has fallen
• Paying a high valuation can still work out really well if the company’s underlying business can indeed grow at a high clip for a long time 

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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. Of all the companies mentioned, I currently have a vested interest in Starbucks. Holdings are subject to change at any time.

The venerable Charlie Munger is one of my investing heroes. On 15 February 2023, he participated in a 2.5 hour Q&A session during the annual shareholder’s meeting for Daily Journal Corporation (he’s a shareholder and board member of the company). Munger’s already 99, so I count it a blessing for the world that he’s still able to share his thoughts publicly.

Shortly after Munger’s Q&A ended, my friend Thomas Chua posted a transcript and video of the session at his excellent investing website Steady Compounding. The italicised passages between the two horizontal lines below are my favourite pieces of Munger’s wisdom after I went through Thomas’s article.

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1) On the worst human behaviour that leads to bad decision-making

Well, what.. If I had to name one factor that dominates human bad decisions, you would be what I call denial. If the truth is unpleasant enough, people kind of, their mind plays tricks on them, and they can, it isn’t really happening. And of course, that causes enormous destruction of business where people go out throwing money into the way they used to do things, even if they know this isn’t gonna work at all well, and the way the world is now having changed. 

And if you want an example of how denial was affecting things, take the world of Investment Management. How many managers are going to beat the indexes, all costs considered? I would say, maybe 5%, consistently beat the averages. Everybody else is living in a state of extreme denial. They’re used to charging big fees and so forth, for stuff that isn’t doing their clients any good. It’s a deep moral depravity. If some widow comes to you with $500,000, and you charge her one point a year for, you could put her in the indexes. But you need the one point. So people just charge some widow a considerable fee for worthless advice. And the whole profession is full of that kind of denial. It’s everywhere.

So I had to say, and I always quote Demosthenes. It’s a long time ago, Demosthenes, and that’s 2,000, more than 2,000 years ago. And he said, “What people wish is what they believe.” Think of how much of that goes on. And so of course, it’s hugely important. And you can just see it, I would say the agency costs of money management. There are just so many billions, it’s uncountable. And nobody can face it. Who wants to? To keep your kids in school, you won’t quit, you need the fees, you need the broker fees, you need this and that, so you do what’s good for you and bad for them.

Now, I don’t think Berkshire does that. And I don’t think Guerin and I did it at the Daily Journal. Guerin and I never took a dime of salary or directors fees or anything. If I have business, I talk on my phone or use my car, I don’t charge into the Daily Journal. That’s unheard of. It shouldn’t be unheard of. And it goes on in Berkshire. It goes out in the Daily Journal. But we have an incentive plan now in this Journal Technologies, and it has a million dollars worth of Daily Journal stock. That did not come from the company issuing those shares. I gave those shares to the company to use in compensating the employees. And I learned that trick, so to speak, from BYD, which is one of the securities we hold in our securities portfolio. And BYD at one time in its history, the founder chairman, he didn’t use the company’s stock to reward the executives. He used his own stock, and it was a big reward too. Well last year what happened? BYD last year made more than $2 billion after taxes in the auto business in China. Who in the hell makes $2 billion in a brand new auto business for all practical purposes. It’s incredible what’s happened.

And so there is some of this old fashioned capitalist virtue left in the Daily Journal and there’s some left in Berkshire Hathaway. And there’s some left at BYD. But most places everybody’s trying to take what they need, and just rationalising whether it’s deserved or not.

2) On why leverage can be used wisely

Well, I use a little bit on my way up and so did Warren by the way. The Buffett partnership used leverage regularly, every year of its life. What Warren would do was he would buy a bunch of stocks and then he borrowed and those stocks, he would buy under these… they used to call them event arbitrage, liquidations, mergers, and so forth. And that was not, didn’t go down with the market – that was like an independent banking business and Ben Graham’s name for that type of investment, he called them Jewish treasury bills. And it always amuses me that’s what he would call them but Warren used leverage to buy Jewish treasury bills on the way up and it worked fine for him… 

Berkshire has stock in Activision Blizzard. And you can argue whether that’ll go through or not, I don’t know. But but but that’s the Jewish treasury bill. Well, yeah, so we’ve had arbitrage but we sort of stopped doing it because it’s such a crowded place. But here’s a little Berkshire doing it again in Activision Blizzard, and Munger using a little leverage at the Daily Journal Corporation. You could argue I used leverage to buy BYD,  you could argue it’s the best thing I’ve ever done for the Daily Journal. I think most people should avoid it but maybe not everybody need play by those rules. I have a friend who says, “The young man knows the rules of the old man knows the exceptions.” He’s lived right? You know.

3) On what went wrong with Jack Ma and Alibaba in China

Well, of course, it was a very interesting thing. Jack Ma was a dominant capitalist in Alibaba. And one day he got up and made a public speech where he basically said the Communist Party is full of malarkey. They don’t know their ass from their elbow. They’re no damn good, and I’m smart. And of course, the Communist Party didn’t really like his speech. And pretty soon he just sort of disappeared from view for months on end. And now he’s out of [Alibaba]. It was pretty stupid, it’s like poking a bear in the nose with a sharp stick. It’s not smart. And Jack Ma got way out of line by popping off the way he did to the Chinese government. And of course, it hurt Alibaba.

But I regard Alibaba as one of the worst mistakes I ever made. In thinking about Alibaba, I got charmed with the idea of their position on the Chinese internet, I didn’t stop to realise it’s still a goddamn retailer. It’s gonna be a competitive business, the internet, it’s not gonna be a cakewalk for everybody.

4) On why Chinese companies provide good value

Well, that’s a very good question, of course. But I would argue that the chances of a big confrontation from China have gone down, not up because of what happened in Ukraine. I think that the Chinese leader is a very smart, practical person. Russia went into Ukraine and it looked like a cakewalk. I don’t think Taiwan looks like such a cakewalk anymore. I think it’s off the table in China for a long, long time. And I think that helps the prospects of investors who invest in China.

And the other thing that helps in terms of the China prospects are that you can buy the best, you can buy better, stronger companies at cheaper valuation in China than you can in the United States. The extra risk can be worth running, given the extra value you get. That’s why we’re in China. It’s not like we prefer being in some foreign country. Of course, I’d rather wait in Los Angeles right next to my house, you know, it’d be more convenient. But I can’t find that many investments, you know, right next to my house.

5) On why the Chinese government is well run

Well, I have more optimism about the leader of the Chinese party than most people do. He’s done a lot right too and, and, you know, he led that big anti corruption drive, he’s done a lot of things right. And I don’t know where this man lives. Where is there a place where the government is perfect in the world, I see zero. Democracies aren’t that brilliantly run either. So it’s natural to have some decisions made by government that don’t work well.

It’s natural to have decisions in each individual life that don’t work very well. We live in a world of sense, sorrow, and misdecisions. That’s, that’s, that’s, that’s what human beings get to cope with in their days of life. So I don’t expect the world to be free of folly and mistakes and so forth. And I just hope I’m invested with people who have more good judgments than bad judgements. I don’t know anybody who’s right all the time.

6) On why cryptocurrencies are a bad idea

Well, I don’t think there are good arguments against my position, I think the people who oppose my position are idiots. So I don’t think there is a rational argument against my position. This is an incredible thing. Naturally, people like to run gambling casinos where other people lose. And the people who invented this crypto crapo, which is my name for it. Sometimes I call it crypto crapo, and sometimes I call it crypto shit. 

And it’s just ridiculous anybody would buy this stuff. You can think of hardly nothing on earth that has done more good to the human race than currency, national currencies. They were absolutely required to turn man from a goddamn successful ape into modern, successful humans. And human civilization has enabled all these convenient exchanges. So if somebody says I’m going to create something and sort of replaces the national currency, it’s like saying, I’m going to replace the national air, you know, it’s asinine. It isn’t even slightly stupid – it’s massively stupid. And, of course, it’s very dangerous.

Of course, the governments were totally wrong who permitted it. And of course, I’m not proud of my country for allowing this crap, what I call the crypto shit. It’s worthless. It’s no good. It’s crazy. It’ll do nothing but harm. It’s anti-social to allow it. And the guy who made the correct decision on this is the Chinese leader. The Chinese leader took one look at crypto shit and he says “not in my China.” And boom, oh, well, there isn’t any crypto shit in China. He’s right, we’re wrong. And there is no good argument on the other side. I get canceled by it.

There are a lot of issues you ought to be.. How big should the social safety net be? That’s a place where reasonable minds can disagree. And you should be able to state the case on the other side about as well as the case you believe in. But when you’re dealing with something as awful as crypto shit, it’s just unspeakable. It’s an absolute horror. And I’m ashamed of my country, that so many people believe in this kind of crap, and that the government allows it to exist. It is totally absolutely crazy, stupid gambling, with enormous house odds for the people on the other side, and they cheat. In addition to the cheating and the betting, it’s just crazy. So that is something that there’s only one correct answer for intelligent people. Just totally avoid it. And avoid all the people that are promoting it.

7) On why shorting stocks is miserable

No, I don’t short. I have made three short sales in my entire life. And they’re all more than 30 years ago. And one was a currency and there were two stock trades. The two stock trades, I made a big profit on one, I made a big loss on the other and they cancelled out. And when I ended my currency bet, I made a million dollars, but it was a very irritating way to me. I stopped. It was irritating. They kept asking for more margin. I kept sending over Treasury notes. It was very unpleasant. I made a profit in the end, but I never wanted to do it again.

8) On why the world will become more anti-business

I would say it’ll fluctuate naturally between administrations and so on. But I think basically the culture of the world will become more and more anti-business in the big democracies and I think taxes will go up not down. So I think in the investment world, it’s gonna get harder for everybody. But it’s been almost too easy in the past for the investment class. It’s natural it would have a period of getting harder. I don’t worry about it much because I’m going to be dead. You know, it won’t bother me very much.

9) On the secret to longevity…

Now I’m eating this peanut brittle. That’s what you want to do if you want to live to be 99. I don’t want to advertise my own product, but this is the key to longevity. I have almost no exercise, except when the Army Air Corps made me do exercise. I’ve done almost no exercise on purpose in my life. If I enjoyed an activity like tennis, I would exercise. But for the first 99 years, I’ve gotten by without doing any exercise at all.

10) On finding optimism in difficult circumstances

Well, I step out of my bed these days and sit down, sit down in my wheelchair. So I’m paying some price for old age, but I prefer it to being dead. And whenever I feel sad, maybe in a wheelchair, I think well, you know, Roosevelt ran the whole damn country for 12 years in a wheelchair. So I’m just trying to make this field here so they can last as long as Roosevelt did.

11) On the impact of inflation and interest rates on stocks 

Well, there’s no question about the fact that interest rates have gone up. It’s hostile to stock prices. And they should go up and we couldn’t have kept them forever at zero. And I just think it’s just one more damn thing to adapt to. In investment life, there are headwinds and there are tailwinds.

And one of the headwinds is inflation. And I think more inflation over the next 100 years is inevitable, given the nature of democratic politics, politics and democracy. So I think we’ll have more inflation. That’s one of the reasons the Daily Journal owns common stocks instead of government bonds… Trump ran a deficit that was bigger than the Democrats did. All politicians in a democracy tend to be in favour of printing the money and spending it and that will cause some inflation over time. It may avoid a few recessions too so it may not be all bad, but it will do more harm than good, I think from this point forward.

12) On being unable to predict short-term movements

I think I’m pretty good at long run expectations. But I don’t think I’m good at short term wobbles. I don’t know the faintest idea what’s gonna happen short term.

13) On an idea he recently destroyed

Well, the idea that I destroyed, it wasn’t a good idea – it was a bad idea. When the internet came in, I got overcharmed by the people who were leading in online retailing. And I didn’t realise, it’s still retailing, you know. It may be online retailing, but it’s also still retailing and I just, I got a little out of focus. And that had me overestimate the future returns from Alibaba.

14) On the genius of Benjamin Franklin

Well, Ben Franklin was a genius. It was a small country, but remember, he started in absolute poverty. His father made soap out of the carcasses of dead animals which stank. That is a very low place to start from. And he was almost entirely self educated – two or three years of primary school and after that, he had to learn all by himself. Well to rise from that kind of a starting position and by the time he died, he was the best inventor in this country, the best scientists in this country, the best writer in this country, the best diplomat in this country. You know, thing after thing after thing he was the best there was in the whole United States. 

He was a very unusual person, and he just got an extremely high IQ and a very kind of pithy way of talking that made him very useful to his fellow citizens. And he kept inventing all these things. Oh, man, imagine inventing the Franklin stove and bifocal glasses and all these things that we use all the time. I’m wearing bifocal glasses, as I’m looking at you. These are Ben Franklin glasses. What the hell kind of a man that just goes through life and his sight gets a little blurred and he invented the goddamn bifocals. And it was just one of his many inventions.

So he was a very, very remarkable person. And, of course, I admire somebody like that. We don’t get very many people like Ben Franklin. He was the best writer in his nation, and also the best scientist, and also the best inventor. When did that ever happen again? Yes, yes. All these other things. Yes. And he played four different musical instruments. And one of which he invented, the glass thing that he rubs his fingers on the glass. They still play it occasionally. But he actually played on four different instruments. He was a very amazing person. The country was lucky to have him.

15) On the importance of delayed gratification

I’m still doing it [referring to delayed gratification]. Now that I’m older, I buy these apartment houses, it gives me something to do. And we’re doing it, we run them the way everybody else runs them. Everybody else is trying to show high income so they can hike distributions. We’re trying to find ways to intelligently spend money to make them better. And of course, our apartments do better than other people do, because the man who runs them does it so well for me, the man or two young men who do it for me. But it’s all deferred gratification. We’re looking for opportunities to defer, other people are looking for ways to enjoy it. It’s a different way of going at life. I get more enjoyment out of my life doing it my way than theirs.

I learned this trick early. And you know, I’ve done that experiment with two marshmallows with little kids. Watch them how they work out in life by now. And the little kids who are good at defering the marshmallows are all also the people that succeed in life. It’s kind of sad that so much is inborn, so to speak. But you can learn to some extent too. I was very lucky. I just naturally took the deferred gratification very early in life. And, of course, it’s helped me ever since.

16) On how a country can achieve growth in GDP per capita over time

Well, what you got to do if you want growing GDP per capita, which is what everybody should want, you’ve got to have most of the property in private hands so that most of the people who are making decisions about our properties to be cared for, own the property in question. That makes the whole system so efficient that GDP per capita grows, in the system where we have easy exchanges due to the currency system and so on. And so that’s the main way of civilization getting rich is having all these exchanges, and having all the property in private hands.

If you like violin lessons, and I need your money, when we make a transaction, we’re gaining on both sides. So of course, GDP grows like crazy when you got a bunch of people who are spending their own money and owning their own businesses and so on.And nobody in the history of the world that I’m aware of has ever gotten from hunter-gathering, to modern civilization, except through a system where most of the property was privately owned and a lot of freedom of exchange.

And, by the way, I just said something that’s perfectly obvious, but isn’t really taught that way in most education. You can take a course on economics in college and not know what I just said. They don’t teach it exactly the same way.

17) On what has surprised him the most about investing

I would say some of the things that surprised me the most was how much dies. The business world is very much like the physical world, where all the animals die in the course of improving all the species, so they can live in niches and so forth. All the animals die and eventually all the species die. That’s the system. 

And when I was young, I didn’t realise that that same system applied to what happens with capitalism, to all the businesses. They’re all on their way to dying is the answer, so other things can replace them in lieu. And it causes some remarkable death.

Imagine having Kodak die. It was one of the great trademarks of the world. There was nobody that didn’t use film. They dominated film. They knew more about the chemistry of film than anybody else on Earth. And of course, the whole damn business went to zero. And look at Xerox, which once owned the world. It’s just a pale shrink. It’s nothing compared to what it once was.

So practically everything dies on a big enough time scale. When I was young enough, that was just as obvious then. I didn’t see it for a while, you know, things that looked eternal and been around for a long time, I thought I would like to be that way when I was old. But a lot of them disappeared, practically everything dies in business. None of the eminence lasts forever.

Think of all the great department stores. Think about how long they were the most important thing in their little community. They were way ahead of everybody in furnishing, credit, convenience, and all seasons, you know, convenience, back and forth, use them in banks, elevators, and so forth, multiple floors, it looked like they were eternal. They’re basically all dying, or dead. And so once I understood that better, I think it made me a better investor I think.

18) On the best business people he knows

Well, some of the best people, I would argue that Jim Sinegal at Costco was about as well adapted for the executive career he got. And by the way, he didn’t go to Wharton, he didn’t go to the Harvard Business School. He started work at age 18, in a store and he rose to be CEO of Costco. And in fact, he was a founder, under a man named Sol Price. And I would argue that what he accomplished in his own lifetime was one of the most remarkable things in the whole history of business, in the history of the world. Jim Senegal, in his life – he’s still very much alive. He’s had one business through his whole life, basically. And he just got so damn good at it, there was practically nothing he didn’t understand, large or small. And there aren’t that many Jim Sinegals.

And I’ll tell you somebody else for the job of the kind he has. Greg Able, in a way, is just as good as Sinegal was. Yea he has a genius for the way he handles people and so forth and problems. And I can’t tell you how I admire somebody who has enough sense to kind of run these utilities as though he were the regulator. He’s not trying to pass on the cost because he can do it. He’s trying to, he’s trying to do it the way he’d wanted it done if he were the regulator instead of the executive. Of course, that’s the right way to run the utility. But how many are really well run that way? So there’s some admirable business people out there, and I’ve been lucky to have quite a few of them involved in my life.

The guy who ran TTI was a genius. TTI is a Berkshire subsidiary. At the Daily Journal people are saying how lucky you’d be if we still had our monopoly on, publishing our cases or something, we’d be like TTI. Well, TTI is just a march of triumphs and triumphs. And it was run by a guy, he got fired and created the business. Got fired from a general defence contractor, I forget which one. But he was a terrific guy. And, and he ran the business for us, he wouldn’t let us raise his pay. How many people have the problem with their managers – they won’t allow you to raise their pay?

19) On the best investments he’s made for Berkshire 

Well, I would say, I’ve never helped do anything at Berkshire that was as good as BYD. And I only did it once. Our $270,000 investment there is worth about eight billion now, or maybe nine. And that’s a pretty good rate of return. We don’t do it all the time. We do it once in a lifetime.

Now we have had some other successes too, but, but hardly anything like that. We made one better investment. You know what it was? We paid an executive recruiter to get us an employee and he came up with Ajit Jain. The return that Ajit has made us compared to the amount we paid the executive recruiter, that was our best investment at Berkshire. I was very thankful to the executive recruiting firm for getting us Ajit Jain. But again, it only happened once.

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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. I have an interest in Activision Blizzard and Costco among the companies mentioned. Holdings are subject to change at any time.

In my August 2022 article, The Truths About Investing In Stocks During Recessions, I discussed why jumping in and out of stocks based on whether a recession is coming or ending is a bad idea. It seems that many people are currently obsessed with whether the US is on the verge of – or already experiencing – a recession, based on the commentary that I have been seeing lately. In light of this, I want to bring up as well as expand on a point I made in my aforementioned article: That stocks tend to bottom before the economy does.

When I wrote about stocks reaching a trough before the economy, I used historical examples. One of them involved the S&P 500’s experience during the US’s most recent recession prior to COVID, which lasted from December 2007 to June 2009. Back then, the S&P 500 reached a low of 676 in March 2009 (on the 9th, to be exact), three months before the recession ended; the S&P 500 then rose 36% from its trough to 919 at the end of June 2009. 

After the recession ended, the US economy continued to worsen in at least one important way over the next few months. The figure below shows the unemployment rate in the country from January 2008 to December 2010. In March 2009, the unemployment rate was 8.7%. By June, it rose to 9.5% and crested at 10% in October. But by the time the unemployment rate peaked at 10%, the S&P 500 was 52% higher than its low in March 2009 and it has not looked back since.

  

During an economic downturn, it’s natural to assume that it’s safer to invest when the coast is clear. But history says that’s wrong, and so do the wise. At the height of the 2007-09 Great Financial Crisis, which was the cause of the aforementioned recession, Warren Buffett wrote a now-famous op-ed for the New York Times titled simply, “Buy American. I Am.”  In it, Buffett wrote (emphasis is mine): 

“A simple rule dictates my buying: Be fearful when others are greedy, and be greedy when others are fearful. And most certainly, fear is now widespread, gripping even seasoned investors. To be sure, investors are right to be wary of highly leveraged entities or businesses in weak competitive positions. But fears regarding the long-term prosperity of the nation’s many sound companies make no sense. These businesses will indeed suffer earnings hiccups, as they always have. But most major companies will be setting new profit records 5, 10 and 20 years from now.

Let me be clear on one point: I can’t predict the short-term movements of the stock market. I haven’t the faintest idea as to whether stocks will be higher or lower a month or a year from now. What is likely, however, is that the market will move higher, perhaps substantially so, well before either sentiment or the economy turns up. So if you wait for the robins, spring will be over.”

If you wait for the robins, spring will be over. This is a really important lesson from Buffett that we should heed throughout our investing lives. Meanwhile, investing only when the coast is clear is a thought we should banish from our minds.

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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. I have no interest in any companies mentioned. Holdings are subject to change at any time.