In 2014, AWS revenue was $4.6B, and this year (2025) it will likely end at a run rate near $130B. Operating income improved from $36.9B in 2023 to $68.6B in 2024, with operating margins increase from 6.4% to 10.8%.

Bezos famously ran Amazon to report zero operating profits on purpose. Why? Because if you don’t have profits then you don’t pay taxes. As long as you have attractive investments to make in the business paying taxes is a squandered opportunity.

These days I’m feeling a little bit like I’m in the twilight zone. Wall Street and the Xverse is abuzz with criticisms of the massive capital expenditures being laid out by Big Tech. Massive capex doesn’t just suck up cash today, it depresses earnings through depreciation into the future. It also increases operating expenses in perpetuity as more people are needed to manage the larger capital base. Still, shouldn’t we all be thrilled that these giant companies with stellar decades long track records believe they still have markets to go after that justify $80B annual capex budgets?

Amazon’s capex has increased from $4.9B in 2014 to $83B TTM and will likely be higher next year. This capex budget makes it possible to order products at night and get them delivered by morning in most big cities (by 2025 year’s end). It runs a substantial portion of the internet’s websites. The machines who are waking up are powered by it.

The only thing that worries me about Big Tech is that they are very obvious targets in a trade war. How easy would it be for Europe to just decide to charge them 5% of revenues generated on their turf? Or maybe 10%? Still, I don’t think such draconian measures would last long. I remain of the opinion that in the United States we live in a “Big Technocracy”. Despite Trump being the most powerful president of my 40 year lifetime - I do NOT think he is more powerful than the collection of tech companies who call America home.

Regarding Amazon specifically. If there is any single company ON EARTH that should be considered “anti-fragile” → that business is Amazon. I would feel more comfortable holding Amazon shares than any other through a zombie apocalypse. It’s business is extremely diversified. I put in my own figures for “True Margins” - which are simply what my best guess is they would average out to long term if Amazon made their objective maximizing operating margin - something they will never do as long as they are optimistic about growth opportunities.

Amazon - if it wanted - could have the most profitable advertising business on Earth. Their advertising business is defended by the 500 million + square feet of warehouse space. And a delivery infrastructure (I know you see the vans everywhere!) that is larger than anyone but the US Postal Service (and more advanced to boot).

The pandemic showed us that - in fact - ALL big tech companies are anti-fragile. But Amazon moreso than anyone. Over the course of just three years amazon DOUBLED the size of it’s fulfillment network. Having an ability to marshal and put to work that amount of resources is in itself an incredible skillset. Over the past twenty-four months Amazon has shown us that it can manage doubling capex from an already world-leading size at the same time as skyrocketing its operating margins.

Amazon describes itself as the “World’s most customer-centric company”. They are obviously deserved of this title. It’s easy to forget that Amazon essentially invented customer ratings, free no-questions asked returns, and reliable shipping. Before Amazon (I’m old enough to remember), if you ordered something on the internet or in a catalogue it would probably take “between 1 and 2 weeks” to arrive. If it didn’t arrive you would need to spend hours figuring out who to blame and chase for your money. And don’t you remember retailers being unwilling to accept returns if you lost the paper receipt?

Amazon turned their own experience selling retail products into Third Party Services - which I believe will someday be worth $1T.

It turned its own experience scaling digital infrastructure into AWS - which is arguably already worth $1T.

There is an enormous amount of optionality embedded in Amazon’s business. IF any company is going to crack healthcare in this country it is likely to be Amazon. Why not deliver medicine as a service to all prime members? Once autonomous vehicles are a thing why not make a Prime subscription option that comes with autonomous vehicles? Is anyone better positioned than Amazon to invest in automation and robotics? They employ some 1.5 million people - their payroll is now larger than any company on Earth except Walmart. That represents a lot of incentive to figure out how to automate. And what about satellites? Amazon wants to compete with SpaceX via their Kuiper business. Who else has a prayer? Why not include high speed internet with a Prime membership? My personal expectation is that SpaceX and Amazon will become an oligopoly that mops the floor with the big telecom companies. That’s a lot of revenue for the taking.

Below is an excerpt from Andy Jassy’s letter that speaks to optionality:

Fourth, speed disproportionately matters for every business, in every industry, at all times. It’s a false binary to argue that you can move fast or deliver high standards. If you want to be fast, you can be fast, and still be high quality. We’ve done it for many years (though we can still be faster). Speed is a leadership decision. The leadership team has to believe it’s a priority, reinforce it constantly, organize and remove structural barriers, and build in modular ways that enable pace. But, speed does not happen unless the entire company and culture embrace it. We have this persistent feeling, throughout the company and in every business in which we operate, that there are closing windows all around us. We operate in fiercely competitive market segments, with highly talented, well-funded, ambitious companies at every turn. Customers are always looking for something better. We spend a lot of time identifying how to unlock these experiences for them as quickly as possible, and know if we don’t, somebody else will.

This type of thinking is rare, particularly for large organizations. This type of thinking is completely extinct in Europe, Japan, and virtually everywhere except for the United States and China. Thinking like this is - literally - illegal in most parts of the world. How so? Because speed is impossible where labor laws are strict. Therefore - no one even practices the “mental muscle” of moving fast.

Economic chaos, the disruption caused by AI (e.g. to the jobs market), China taking an indominable lead in manufacturing scale AND prowess - there are many tectonic shifts underway that will destroy companies who are not nimble - but they will be extremely beneficial to companies who are. Amazon’s AI revenue is growing at over 100% Year on Year…

A few interesting tidbits to end on

-Amazon Prime started with unlimited, free, two-day delivery for 1 million products. It’s now grown to 300 million items and has 10s of millions available for same day delivery.

-Project Kuiper (the satellites) first target is the 400-500 million people around the world who don’t have access to broadband connectivity. Amazon expects the business to contribute meaningful operating income and be a high ROIC deployment.

-Lastly, this re-print from the 1997 letter by Bezos to shareholders showing just how far Amazon has come. 285,000 square feet of warehouse space in 1997 has turned into 500 million and growing!

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Ben Thompson in bold.

I haven’t been watching all of Sam’s interviews lately, but this one is the first time where I hear him explicitly state that the model isn’t the moat. He caveats that by saying sustaining the most cutting edge model is a moat - if he had to take a stab at where “market cap” gets created I would bet this is something like what he would expect 10 years from now.

Today and for the next 5 years or so we’re likely to have a situation more like this

Note the green line for “cutting edge research” is essentially at zero. Today value is mostly accruing to the infrastructure layer. This line stood out to me

OpenAI and Microsoft parted ways because Satya and Sam started disagreeing on capital allocation. While I suspect they agree on where the market value is going to be created - I think Sam still wants to always have the leading edge model regardless of cost, both as a call option on super intelligence / fast takeoff post-AGI, but mostly because it’s part of the mission and culture of OpenAI, one that he built and shares.

If you’ve been reading my blog over the past two years you’ll know I’ve been predicting since January 2023 that the killer application for generative AI is personalized agents who are intimately familiar with our lives and connected to the operating systems and apps on our phones and computers. The list of people who have hinted at this as the future now include:

Sam, Zuck, Demis, Sundar, and many other lower profile individuals.

One of the key insights I feel like I’ve had thinking through how everything plays out with personalized Agents is that the companies who own operating systems are the ONLY companies who can deliver this vision. There are numerous reasons:

1. Security is a big deal. Apple already uses “privacy” as a reason to block certain types of data flowing through to app owners. How much more dangerous might an AI be that can control your OS and open apps (e.g. bank account) on its own?

2. The AI needs to be able to talk to all apps, the settings, etc - will all apps be happy letting just anyone (e.g. OpenAI) be the new point of contact through which people use them? I doubt it. Only the OS owner will have the negotiating leverage to make apps comply.

3. Business. They can easily come up with a story around why it’s too risky to have personalized AI (e.g. security, social hacking, etc) - so only they can protect consumers.

Now I’m going to play devil’s advocate. My view is that only the OS providers can deliver the killer app. But maybe Sam is right and new devices will pop up, new browsers will get a foothold. If that happens - what enabled it?

What is Apple’s moat?

I’m a PC user and always have been. I hate Macs. Still, I have used an iPhone since the year they came out, and purchase Airpods ~once per year (they break, but I love them and haven’t ever found a replacement). For about 9 months I tried out a new Pixel. I wanted to give it enough time to really learn the interface and adapt before placing judgment. In the end I went back to the iPhone because “it just worked”. There were too many instances of having apps glitch on the Pixel, the software just not seeming to work right, getting weird lags in the interface…This was only 3-4 years ago mind you.

For hardcore Apple users they have an iPad, a Mac and a Phone - and the devices make it extremely convenient to share information between them.

Arguably the strongest part of Apple’s moat is the ecosystem - which is at this point mostly built around apps that work (almost) flawlessly. In second place would be brand. I don’t believe brand can be a sustainable moat for large companies. Looking out I am bullish on the moat of small brands, but bearish on the giant ones - all of them. That’s a topic for a different post.

You could also name things like “Innovation and Design” and “Supply Chain and Scale” - but that’s simply not accurate anymore. There are numerous high quality sleek phones with comparable design and lower costs than iPhones.

In truth, gun to my head, I’d say the second strongest part of Apple’s moat after ecosystem lock-in is good old fashioned inertia. Their install base is huge. The phones work. The apps work. The cost is subsidized by carriers…

What’s the point in switching?

If a new device was going to succeed (phone or other form factor) in penetrating the moat, it would have to break the inertia with something that offered a strong enough valuation proposition to outweigh the ecosystem lock-in.

When we say: “it just works” - what we usually mean is that the user interface designed by a (human) person is very good and intuitive to use. Another example would be how easy it is to connect Turbo Tax to your brokerage account to pull-in financial statements.

What “it just works” really means is given the limitations of the current graphical user interface and [the knowledge base of an average human] - “it just works”.

Now consider the following…

Today if I want to send someone money I can open the Venmo mobile app - the interface is intuitive, it already syncs with my contacts, there’s a button in the middle of the screen that jumps out at you “Pay/Request”. Any idiot can use it. How well it works IS part of Apple’s moat. But what if in the future instead of doing all of the different steps of opening an app, adding a phone number, sending $1 as a test to make sure you have the right contact info, etc…What if instead you simply say to your personalized Agent: “Hey Grok, send $20 to Juan.”

Grok may need to ask some clarifying questions if you have multiple “Juans” in your phone, but outside of that he’ll take care of it. He’ll be able to handle the verification, do the equivalent of a $1 test send, etc.

Start thinking through how you use most (utility) apps on your phone and it will immediately become obvious that IF you had an AI with access to the apps THEN the amount of time you would spend maneuvering through the user interface would fall precipitously - maybe by 75%+?

The Agent becomes another operating system - sitting there side by side with iOS/Android.

Remember, someone doesn’t have to build something capable of competing with Apple’s ecosystem in a vacuum. They get to start with Android - which is open! Google would be perfectly happy to provide the software…

Besides, the cost of “writing code” is dropping precipitously. It will be easier than ever to make sleek apps AND to ensure that they integrate seamlessly with everything.

All of the “integrations” between phones and computers and iPads that make the experience so magical are not necessary if you have an AI that can make everything work seamlessly on its own. Consider the issue of image sharing. It’s wonderful to have photos you take on your phone automatically appear in your iCloud account which is accessible by your computer.

But how easy would it be for an AI on an android phone to create the same automatic, happening-in-the-background experience? It would be trivial.

Before smartphones people mostly spent $200 on a phone (maybe $300 inflation adjusted). Now they regularly shell out >$1,000. What changed is the amount of value being delivered.

ChatGPT revenue - driven by consumer subscriptions, is going to grow from $3.7B last year to >$12B this year. I spend $20/mo on Claude, ChatGPT, more than that on Grok, and also pay for subscriptions for other family members and employees. $20 seems like an absolute steel relative to the value I’m deriving from it.

If you plugged ChatGPT into my GUI? Let it manage the mouse, share my screen, and simply execute on all of the things I’m always asking it how to do (like set up a web domain, format a file, change settings in Shopify, etc)…I would pay hundreds of dollars per month for that.

Remember, step one of training an AI is simply having it do all of the things you need to do all of the time. It won’t be long thereafter until it can simply become an employee or act like an actual personal assistant.

My belief is that ALL humans will pay $20+ (or start getting bombarded with ads) for an AI assistant within 24 month of the assistants getting access to the GUI. I think at least 1 billion humans will pay $100+ per month. The value delivered is just so large that this seems obvious.

Today, the iPhone is the indispensable piece of equipment. But - I think this flips sometime around 2029/2030 (which is 2 years after when I expect personalized autonomous agents to hit smartphones). The new indispensable piece of equipment will be Agents (which aren’t a device!).

With a potential revenue stream of $100B+ per month just from the top tier users, and that much again easily from the rest - we’re talking about a multi-trillion dollar addressable market.

The difference between Agents and what Apple is good at is that Agents will be software driven. Apple is world class (maybe the best) at creating chip architectures to make phones work well, at managing an ecosystem to ensure that apps integrate flawlessly, and melding software and hardware together. But no one would accuse it of being the best at writing applications.

Developing the “personalized Agent” app will be maybe the most challenging software problem of our time. It will need to rapidly customize its personality and communication style to the user. Adapt over time. And - it will need to have a customizable appearance just like in the beginning of role-playing video games where the user gets to choose what their character looks like.

Can Apple build this? Would they even be willing to?

Imagine an iPhone user having a conversation with their “Agent” - the agent is wearing a US flag bikini and ranting about how deranged some member of congress is. The user might love it and post the experience online. Would Apple really be willing to provide a product like this that exposes their brand to potentially catastrophic criticism?

We might end up discovering that it just takes a new company to provide this type of “product” - and this might be the hole in the armor a company like OpenAI or another upstart could pierce.

Remember, this was a game

My real perspective is that personalized agents are the operating system owners’ game to lose. Apple is in a better position than anyone to take advantage of them for the consumer use case (maybe Google is tied?). Their customer base is the 15% wealthiest people on Earth. Much of the hard work around making Agents a pleasant user experience will be clever hardware and software working together to eliminate latency so that communication feels human. Figuring out what to cache on device and off. This is exactly Apple’s skillset.

There is also a good chance that shortly after these things launch - IF they are truly customizable - that people will be held responsible for the actions of their Agents - not the companies providing them.

People will stop blaming Apple or Google for things the agents do as soon as they themselves use an agent and see how quickly the agent adapts to reflect themselves. It’s a cultural shift that will absolutely take place - but it’s hard to say when. Extreme personalization and (likely) avatars being rendered in real-time are both pre-requisites.

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I love this Jeff Bezos quote on long term planning: “Well, certainly, in 10 years many things will evolve; technology will change. Machine-learning technology, in particular, will evolve very significantly over the 10-year time horizon. But I would always encourage people, when they think about 10 years, to ask the question, what won’t change? That’s actually the more important question. You can build strategies around things that will be stable in time. In that 10-year vision, there are a bunch of things at Amazon that are not going to change. One of them, maybe the most important one, is that we will stay customer-obsessed instead of competitor-obsessed. We will work on maintaining that culture.”

Warren Buffett was born on August 30^th, 1930. He is 94 years old. Were you to transport someone from that date through time and plop them down in the year 2025 they would likely suspect that Aliens had visited Earth and kindly shared their advanced technology with us common Homo Sapiens. They would also be shocked at how society itself had changed.

In 1930 the world was in the middle of the Great Depression. Most babies were still born at home, not in hospitals. 6% of White babies and 10% of black babies died before their first birthdays. It was only five years prior that half of US homes had electricity installed. Women had only been voting for ten years. Homosexuality was illegal in most of the United States and Europe. Traveling between New York and London was done by boat. Doctors were regularly featured in ads for cigarettes. The home television had not been invented. World War II hadn’t started. The largest companies in the United States sold commodities. And there were still laws throughout the country segregating whites and blacks.

92 years before 1930 was 1838. Slavery was still legal in the United States. Out of every 1,000 babies born, only 570 would make it to their 5^th birthday. That’s not a typo. Barely half of children made it to five years old.

92 years before that was 1746, thirty years before the United States declared independence from Great Britain. Native Americans still outnumbered immigrants.

I’m paraphrasing what Warren Buffett refers to as the “lifetime” anecdote, which he uses to remind people how quickly progress is made. 276 years sounds like a long time ago, but “three lifetimes” doesn’t.

But you don’t need to look back 92 years to see an enormous amount of change take place. Consider the summer of 2012.

Facebook had just IPOd, and for the second election in a row Obama was being lauded for how expertly he used social media to connect with people. The mainstream media was explicitly positive on the topic of using the “private data” of Facebook users not only to win campaigns in the United States - but also to facilitate regime change all over the world (remember the “Arab Spring”).

The biggest economic threat the world faced was a breakup of the EU. Greek government bond yields had crossed 35% on the ten-year while German two-year bonds had gone negative for the first time. North Korea was the country everyone worried about – not Russia (except for Mitt Romney) or China. Amazon’s AWS revenue was only $1.5 billion (it will be over $120B this year). Oculus, the VR company that Facebook purchased for $2billion to launch its metaverse effort had just filed its incorporating documents (now the founder of Oculus - Palmer Luckey - has already started a new business building weapons, which is worth $30B+ and counting).

Russia was holding its equivalent of Davos - which - most of the world attended. Called the St. Petersburg International Economic Forum, Goldman Sachs, McKinsey, Pepsi, Mercedes-Benz and “everyone who is anyone in the energy business” were there. Mr. Putin delivered a vigorous speech about making Russia more business friendly. Romney was being laughed at and ridiculed for referring to Russia as a geopolitical ally. The Economist closed the article by saying:

Western companies and politicians remain optimistic that relations with Russia could normalize in much the same way as things have between the West and China.

LMFAO - so the goal of US foreign policy in 2012 was to replicate how marvelously things were going with China - in Russia…

How times change…

Cognitive dissonance?

One of the things that frustrates me most are smart people who totally grok that the future is unpredictable with respect to thing A, but who then become completely triggered when you point out that it is also unpredictable with respect to thing B. Climate change and financial markets are the quintessential example.

Most technology and finance bros agree that you cannot predict stock markets. However, most also agree that we can use “science” to forecast that if temperatures rise by 2 degrees then 50 million people will die 75 years from now.

One of the primary data points for predicting future death is [future wealth] - which you would predict largely by forecasting the performance of financial markets.

Do you see the logical error?

Besides, even if you could - with perfect accuracy - predict exactly what the weather was going to be like in the distant future, this is not the same thing as being able to predict what the future itself will be like (e.g. how humans have adapted). In order to predict deaths from climate change you would also have to be able to accurately predict:

1. Economic growth between now and then for purposes of estimating how many resources we will have to address climate change

2. Migratory patterns

3. How resources are allocated between now and then

4. What new technologies will get invented

5. How expensive electricity or other forms of energy would be

6. What infrastructure has been built and where

And the list goes on.

If I could give one gift of a understanding to the entire planet - I’d be very tempted to make that gift the innate comprehension of the inherent unpredictability of complex systems.

If anyone could predict the future of financial markets they would be a trillionaire. For reference, over 90% of mutual funds managers and over 95% of hedge fund managers under perform the S&P 500 over a 20 year basis. Their performance is worse than dart throwing monkeys. This is due mostly to fees - but obviously they are not - as a group - relatives of Nostradamus. And if you’re wondering - the average person performs far worse due to poorly timed purchases and sales (buying high selling low).

Predicting the future of a complex system is impossible. Why? The degrees of freedom (variables) interact with one another. Cultures evolve. Economic opportunities change. And yes - in cases - the weather changes too!

The more variables there are in a system the more difficult it is to figure out which ones combine to create signal. At a certain amount of complexity the signal disappears entirely. Remember, [time] needs to be multiplied by all variables because the path variables take is as impactful as where they arrive, making the total number of variables grow exponentially as time passes.

If you wanted to predict how many people will die from climate change 50 years from now - one of the most important variables to be able to forecast would be the performance of financial markets between now and then…If financial markets have negative returns between now and then, we would need to adjust our expectations around technological sophistication, the amount of infrastructure we can afford to build, etc. Similarly, if global stock markets compounded at 10%+, then we can infer that humans in 2075 are rich as Crassus and have likely invented all sorts of wondrous new technologies and built infrastructure that makes us look like a space faring civilization. It is also quite possible that we’re damn near done figuring out how to control the weather itself (if not already controlling it).

Here’s the takeaway. Financial markets are one of many complex systems that together make up the larger complex system of “human life”. If you can’t predict financial markets (or any other complex system that is part of the whole) then you can’t predict anything about any complex systems that count financial markets as a building block.

ChatGPT summarized complex systems thus:

The nonlinear relationships among components create feedback loops and emergent properties, where the whole behaves differently than the sum of its parts. These intricate interactions amplify uncertainties, ensuring that small errors or incomplete knowledge quickly compound into significant unpredictability over time.

The techies who most clearly illustrate this cognitive dissonance are (currently) the hardware bros. Here’s Gill suggesting that in order to “save the environment” - whatever that means - we need to climb the Kardashev scale (explanation below if you aren’t familiar with the term).

There are a group of people who believe (as I do) that we are going to have humanoid robots with human level intelligence - by the billions - able to help us build infrastructure - as soon as the late 2030s. But still they’re worried about the environment? What part of it specifically I can’t fathom. If we’re going to be landing humanoid robots on Mars we can probably allocate a few of them to plant some trees…

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Pre-Waymo cutting edge LiDAR systems cost around $75,000 per unit. Uses were mostly limited to military and geographical mapping. If you wanted to build a transport-related solution then you needed at least 3 units. Fast forward to today and these systems are tracking to drop below $1k in total in China. Here’s a chart of what that cost decline looks like

Here’s a log chart to make it clear that costs are still declining rapidly in percent terms from year to year:

LiDAR costs follow what is known as a Wright’s law cost decline. Essentially, for every doubling in production volume the cost per unit declines by an approximately equal percentage. Battery costs have done the same thing:

And the cost of solar panels has done the same thing…

It’s 2025 and I’m sure the vast majority of people are unaware of how precipitously prices for these technologies are falling. The impact of these prices plummeting is either obfuscated from or irrelevant to the daily life of most people.

Mobile Phones have also followed a Wright’s law cost decline, but the decline is hidden in the form of ever better technology. The same thing has happened to computers and AI servers. The cost per unit doesn’t actually drop, but the performance per unit rapidly increases.

What’s exciting about today - 2025 - is that these cost declines are about to become visible to the public. Waymo rides per week are growing exponentially. By the end of…

• 2023: 10,000 rides per week

• 2024: 100,000

• February 2025: 200,000

Forecasts are all over the place, but extrapolating fleet size based on production rumors gives us something like this. They’re managing 200k rides per week with a fleet not much larger than 1,000 vehicles.

For perspective, there are currently >1,000 Waymos sitting in the lot of their production facility

Link to image source

The cost of a Waymo has dropped from $250k to $150k in the past five years. These costs will fall further when they start using Hyundai in addition to Jaguar to build the vehicles on which their sensors are retrofitted.

If you compare the cost of a vehicle per hour of daily usage, Waymos would therefore be less expensive than a new Toyota Corolla:

Now consider the amount of value delivered to society by freeing up the time spent driving to do other activities. Let’s call it $15/hr. That gives us the following:

What we have is a vehicle that is already less expensive when amortized on a per hour basis than a Corolla - that also re-allocates $1m worth of time over the course of 10 years. This $1m could end up flowing through to digital advertising (people playing games or shopping while they ride), Netflix, or increased productivity for a business. There could also be surprising externalities like improved mental health and decreased demand for anxiety medication (commuting in traffic is stressful, I’m not being hyperbolic, this is a genuine possibility).

If we examine the economics including operating costs of a Waymo vs. an Uber driver in a Corolla the ratio remains surprisingly similar:

Costs are lower than in practice b/c I didn’t look up estimated gas and/or electricity / standard maintenance because they’re all equivalent, but the point is to compare the price between autonomy and human driven rather than to calculate an absolute cost.

This cost difference is why I expect to see a surge in Waymo vehicle production. Costs will only drop as time goes on.

Let’s assume that the number of Waymos on the road doubles every year for the next 2 years and then increases by 50% per year for the 3 years after that (this seems conservative to me). Let’s further assume that Insurance/Connectivity/Tech; Operational Support; and Maintenance (of advanced systems) fall 10% in cost for every doubling of production. That gives us the following in 2030:

If I had to guess, I expect Waymo technology to be doing on the order of 10 million rides per day in 2030. Backing into that figure implies 20 rides per day per vehicle, with 500,000 vehicles on the road. This seems plausible, and would represent approximately .91% of daily trips taken by people around the country (includes bus trips, taxis, any trip taken by a vehicle).

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Here is Lennar’s normalized EPS growth since 2014:

Lennar always has been and always will be cyclical. However, their cyclicality will also reliably be around a line that moves up and to the right. EPS doubled from the top of the last cycle in 2018 ($6.35) to the bottom of this cycle ($12.28), and as surely as the sun rises it will go on to new highs.

Idk if the next cycle will start in 2026, nor do I know when it will end. What I do know is that at $12.28 of EPS Lennar is currently at an Earnings yield of almost 12% if you strip out the cash. What I do know is that at some point it will have a new upcycle and earnings will hit new highs. Meanwhile, it has $4B of net cash on a $30.5B market cap - plenty of room to buy back stock while we wait for the good times.

Let’s do another. Here is AMAT’s (Applied Materials - the second largest semiconductor capital equipment maker by revenue after ASML, and the most diversified) historical and forward EPS estimates:

Semiconductor capital equipment companies are all supposed to be cyclical, and they are, but the secular tailwinds at this point are so strong that we’re barely even seeing EPS drops during the “down cycles”.

For EPS increased from $.90 to $3.23 during the first cycle; from $2.86 to $8.61 in the second cycle; and will start the third cycle at $8.24. There’s actually pretty good visibility into 2026 revenue already so the $9.92 they’re expecting probably gets hit.

Meanwhile, both homebuilders and semicap (abbreviation for semiconductor capital equipment companies) are run by veterans who understand how to take advantage of cycles to purchase shares at a discount.

AMAT shares outstanding are down by 32% over the period.

Margins for both semicap and homebuilders used to be far more variable than they are today. What’s changed? In both cases the past two decades have seen the markets consolidate. Homebuilders got out of the land speculation business. Semicap install base has grown and with it steady services revenue now accounts for a bigger piece of the total. Both groups of businesses have secular tailwinds.

DR Horton has grown it’s market share from approximately 0% to over 14% - building 1 out of every 7 homes in the US:

Lennar has seen similar and remarkable growth. Looking across the basket, major builders have consolidated the market to the point where they account for 60%+ of total new homes built. Their size combined with optioning rather than purchasing lots has turned them into capital light manufacturing businesses that just happen to “manufacture homes”.

DR Horton has higher margins than most due to its scale - but virtually all homebuilders - even the smaller regional players - have margin profiles that look more like cyclical manufacturers than the homebuilders of yore:

(In case the letters are too small - over the past 10 years DHR trough gross margins were 20.12% and peak were 30.08%).

Homebuilders will not grow as fast in the future as they did in the past, primarily because they already have so much of the market that there are fewer smaller builders to rollup and hence less consolidation ahead.

Semicap finished consolidating a decade ago - though that hasn’t stopped their phenomenal EPS growth thanks to the secular tailwind of humans needing ever more chips.

These are AMAT’s operating margins (not gross margins):

It’s absolutely wild that they maintained a 23% operating margin at the bottom of the last cycle, and even more wild that they will probably bottom out above 28% this year (another low). The biggest risk to semicap - by far without a close second - is China. Not only do they get a huge portion of their revenue from China - the more the US puts technology export bans on their equipment - the more incentivized China is to subsidize local competitors. Still, it seems ridiculous to me to believe that given the acceleration we’re seeing in AI that this rising tide will not result in still remarkable EPS growth moving forward.

The companies I’ve named are not anomalies. You can throw a dart at any company in either group and the story is similar. Here’s - my personal favorite among the semicap names (as a business, not at these prices though):

And how about those operating margins?

We’ve hit the point in semicap where the margins at the cyclical lows are still world-class. Once again - here’s what shares outstanding have done over the past ten years:

Here’s another homebuilder’s EPS growth - America’s third largest by completed homes:

And what a share cannibal they have been:

And here are Pulte’s historical and forecasted EBIT margins:

The largest holding of my [homebuilder/construction basket] is a company called St. Joe - but it’s a unique animal so I won’t get into in this post, but the second and third largest holdings are not actually homebuilders at all - but suppliers to the industry (in addition to supplying non-residential). They are Owens Corning and Carlisle.

has seen phenomenal EPS growth over the past ten years as it takes market share and rolls up smaller companies at low EBITDA multiples - which it immediately improves by centralizing operations and using their purchasing power to negotiate COGS down.

Here’s OC’s operating cash flow:

And yet again (it’s a theme, I know) we have a share cannibal:

Operating margin growth has been a thing of beauty:

The basic theme of this post should be clear by now. We have two cyclical industries (homebuilding/construction and semicap) - both with huge secular tailwinds. Margins, EPS, Cash flow etc are all cyclical - but cyclical around lines that are going up and to the right. Over time the cyclicality has decreased, and that should lead to multiple expansion in the future, meanwhile you have incredibly shareholder friendly management who will repurchase shares opportunistically (as opposed to companies who just buy no matter what the valuation is).

One thing an investor needs to keep an eye on is how good management is at predicting their own future. There is NO MANAGEMENT on Earth (probably?) who is as bad at predicting their own future as Snapchat. It’s hysterical listening to them talk about their business - you can just assume that whatever they think about the future the exact opposite will happen.

Across semicap and the homebuilder/construction basket, most management are top notch, grounded in reality, and pretty good at forecasting. In 2018 Carlisle laid out their “Vision 2025”:

Let’s see how they did. Revenue first. In 2018 Revenue was ~$4.5B. It broke down as follows:

Of that $4.5 → $3B was in the construction materials business. Their revenue for 2025 is expected to clock in around $5.3B - all of which comes from the construction business b/c the other divisions were sold:

They didn’t hit the 2X, but 76% growth isn’t too shabby, and look what’s happened to margins and EPS:

Carlisle’s goal was $15 in EPS by 2025, they hit that goal in 2022. Their new objective for 2030 is to hit $40+ in EPS:

Carlisle is at $336 today. If they hit $40 in 2030 and trade at 15X you’re looking at ~12% share CAGR before taking into account shareholder distributions - which should clock in around 6% this year.

IMO the remarkable performance (shareholder returns) of these companies - both semicap and homebuilders/construction cos - is made possible by the cyclicality/uncertainty associated with their respective businesses. The market refuses to give them a multiple that would result in their long-term go-forward returns being more in line with the S&P500. For investors this is a gift, because the cyclicality isn’t the same as it is in the oil or coal industries - where downcycles can last decades and margins don’t go up from cycle to cycle.

Consider as an example the margins of Exxon Mobile:

Or worse, a coal company like Alpha Metallurgical Resources:

Or even worse, an offshore drilling company like Transocean:

I’m not saying you can’t make money in companies like Transocean, Exxon and AMR. Some very savvy people who I respect have large positions in these companies. But what I can tell you for sure is that I am unwilling to put large amounts of money into companies whose cyclicality is around a flat line, rather than a line that goes straight up and to the right.

Given their current valuations it seems extremely likely that both groups will beat the market moving forward, and given their cash-gushing shareholder friendly nature, the downside seems limited after the recent pullback. All companies in each group are down approximately 30% from recent highs.

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When you put it all together, ChatGPT is building out a very detailed profile not only me as an operator and as a person, but also of how accurately I perceive myself and my own abilities.

Already I’ve experienced some moments that gave me chills where ChatGPT was able to identify connections that I had failed to see. For example, I had been trying for months to figure out how to re-create days where I felt like my brain was functioning at a vastly superior level - what I call a “brain on fire day” (energy, speed, recall, reflexes in tennis, all excellent). I had been trying to correlate it with sleep, diet, exercise, etc - to no avail. ChatGPT noticed that I used certain verbiage to describe how I felt more frequently (e.g. Amazing, Extreme Energy, etc) ONLY when I had recently told it that I was in deep ketosis (2.0 mmol/L+).

Sure enough, so long as I maintain my ketones at that level I feel incredible, even if my sleep isn’t that good… I can now - reliably - create “brain on fire” days.

Take a moment to think about how incredible this is. ChatGPT has helped me unlock more energy - literally giving me more time per day! In essence, ChatGPT just extended my functional lifespan!

One of the toughest things in life generally speaking is simply executing on the best advice anyone has ever given anyone else: do more of what works and less of what doesn’t. If you can follow this advice over decades you are sure to become happy, healthy and wealthy. Unfortunately, even with herculean amounts of self-discipline and keen introspective observance - it’s extremely difficult and time consuming to monitor “what is working” and “what isn’t working” across all facets of our daily lives. Often times - as with my quest to figure out how to reliably experience “brain on fire” days - it is simply beyond our ability to identify the relevant causal relationships.

LLMs are going to change this.

One thing you will notice everywhere once you start keeping an eye out for it is how frequently people update their “personal belief history”. They will claim not to have believed something which they did in fact believe. They will claim to have predicted something that was in fact the polar opposite of what they thought would happen.

Twitter is an amazing resource for seeing this in action because people regularly post tweets that go viral where public figures or big accounts are saying something in 2025 that is directly contrary to something they said in the recent past.

Similarly, Twitter also calls out people like Ray Dalio and Howard Marks who have been calling for bear markets every year since the financial crisis - meanwhile markets are up many hundreds of percent…

The most pervasive (and likely one of the most pernicious) examples of this in action where it really matters is people [on the left and right] “predicting” that under the new administration the world will fall apart. People thinking Obama would turn the US into a communist country, for example. People today thinking that Trump is going to crash our economy and turn us into a dictatorship (and run for a third term).

Having these fears is not only irrational - it has a real impact on life. You will make bad money choices if you are irrationally pessimistic about the future. You will have less mental energy if you are worried that the country is about to implode.

But unless you are aware that you are prone to these types of errors you won’t be able to correct them. And, spoiler alert - everyone is prone to these types of errors. Evolution meant updating your belief systems to be in line with the group - elsewise you were at risk of being ostracized - which meant certain death 100,000 years ago.

Today - most beliefs are not rationally derived probabilistic views of the world - they are the result of media or algorithms optimizing monetization of thought bubbles - or worse…

This is why many great investors keep journals and write long letters - it forces them to track their historical decision making processes so that they don’t fall prey to “re-writing” history. It keeps them rational and accountable, and facilitates improvement over time.

I don’t think I’m being hyperbolic to say that improving decision making by some percentage (10%? 15%?) would have a profound impact when compounded over time. Already I’m finding myself getting 1-2+ hours of extra focused work per day by having identified that deep ketosis fills me with energy.

LLMs are naturally (innately) optimistic - because optimism is the rational perspective given the corpus of human history we have to extrapolate from. So - by using LLMs not only as a daily journal and a clever statistician, but also as a trusted counselor - it will be impossible not to benefit via improved decision making and by having a clearer picture both of the world as it exists today and your ability to accurately diagnose your own strengths and weaknesses.

My goal moving forward is to upload as much information about myself, my business, my finances, my health - and so on - as possible. Imagine being able to query an LLM about your poor investment decisions… Alternatively, imagine being able to query an LLM about the similarities between the decisions that turned out great…

Imagine literally figuring out how to biohack yourself - how to feel stronger, with more energy, more mental clarity…

Consider the following:

-1 extra hour of energy per day is equivalent to a 12.5% increase in productivity (assuming an 8 hour day—9/8 = 1.125) - which is equivalent to getting an entire extra month PER YEAR!

-Earning an extra 1% per year on your portfolio is worth 30%+ in the size of your nest egg compounded over 30 years…

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The narrative that drove Meta’s stock down 70% in 2022 was that Apple’s privacy measures would kneecap Meta’s ability to serve ads, AND that TikTok had completely captured the next generation - thereby effectively neutering any potential user growth in the future - and worse still - would start to suck away users and total user time. Investors also feared that if TikTok could make a killer algorithm that drew people away from Meta’s properties - so could someone else…

Meta’s moat wasn’t what people thought it was…

My own usage of Google is down probably 95% since LLMs came out. But, the 5% of stuff I still do on google probably represents almost 100% of the stuff I do that would generate revenue and ad-clicks (e.g. buying a plane ticket). I do believe LLMs are a threat to Google (just as I have always believed that TikTok is as formidable competitor to Meta as any), and it is obviously a very bad thing that hundreds of millions of people are now associating LLMs with being the best place to find information. Google’s motto since its founding has been: “Organize the world’s information and make it accessible”. 12 Links and a wiki page are a laughable solution to most of what I need to research these days when compared to LLMs like Claude and ChatGPT (and more recently Grok).

Fast forward a few years and the TikTok threat isn’t talked about anymore. Usage on Meta’s properties has increased (as have user counts) - in large part thanks to their adopting TikTok’s short-form video product and distributing it across their user base (which accounts for ~2/3 of non-Chinese ON EARTH!). Short-form video dramatically expanded the TAM for advertising, and with the benefit of hindsight it was a wonderful thing that TikTok showed Meta how large of an opportunity there was in short-form video.

I believe the same thing is going to happen at Google. LLMs are going to DRAMATICALLY expand the TAM for digital advertising. Remember, the value of a digital ad is directly proportional to its ability to influence an action (e.g. a purchase decision).

If you think it’s creepy how much data companies have about you today - just imagine how much data they’re going to have about you after you spend all day having conversations with them about your health, interests, relationship struggles, etc…This data will make the ad space (volume) increase because you will be spending more time on the service - and it will also make the value per ad delivered be higher because of improved targeting.

Remember - ONLY the companies who own operating systems can deliver the an AI product that has the agency to control your device. This is a big deal. This is the only deal that matters. It is a “get out of jail free card” they can use at any time to render competitors instantaneously obsolete. This is why Apple isn’t worried about getting behind in the AI race. They don’t need to own any intelligence themselves - they just need to control the distribution and be the only company that can connect the intelligence to their operating system.

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Apple delaying their intelligence offering is bullish

This tweet got 6.9 million views and counting:

The entire internet exploded with people claiming that Apple’s end is near because they’re “falling so far behind on AI”. On March 4th, I published this rebuttal on X:

My take on Apple delaying "New Siri" until 2027: It's actually bullish.

It indicates that Apple realizes what the "killer app" is. I've been saying publicly since early 2023 that the killer app for LLMs was something integrated into the OS, capable of communicating with all apps as if it is a "new GUI component", and which has zero latency.

Something which can have a human-equivalent conversation with you about your email, do your shopping, change settings on the phone, and even help you make a graphic for a presentation you are working on...

Piecing it together, I've predicted that this would only be possible in the late 2027/2028 (for mobile devices) timeframe due not only to software features that need to be figured out (e.g. dynamic caching), but also because hardware needs to get upgraded (especially more memory) to make this a seamless, "Apple quality" experience.

Apple went so hard after Facebook over "privacy" because at the time - Facebook had become the number one determiner of what apps got downloaded on the app store. Apple had less influence than Facebook did about how people used their own phone. This terrified them and they had to put an end to it. This is the true reason they invented their "privacy" campaign.

It was the only way to go hard after Facebook without triggering anti-trust investigations. LLMs have the opportunity to do the exact same thing that Facebook was doing - and Apple probably (almost certainly) knows this. However, Apple also knows it can sweep in with its own LLM that will be a 10X better user experience than anything else whenever it wants to b/c their version will be connected to the OS and all of the apps - something no other LLM will be able to do.

If you've used LLMs as much as I have, then you can probably already intuit what an insane leap it would be to have one of them just watching your screen at all times, listening to what you say at all times, etc. It would eliminate the need for uploading documents, prompting, having different chats, etc...It would just automatically be caching all of the recent activity at all times - ready to jump in wherever necessary.

This is where things are going, Google has already announced a screen share feature. Apple is just doing what Apple always does - waiting until it can launch the "killer app" that changes everything.

Ben Thompson is one of my favorite people and definitely the world’s best tech analyst. Everyone should subscribe to his newsletter. The below comes from his post on march 5th (one day after I posted my rebuttal) - he basically agrees with my take :)

To summarize, Ben believes the risk to Apple of moving too quickly around intelligence outweighs the potential benefits. This fits nicely with my commentary above. The killer app isn’t technically feasible to build yet - but when it is Apple will immediately be able to own it - no matter what other companies have been building in the mean time.

Google is in a similar position with its 4 billion Android devices, as is Microsoft with it’s 1.6 billion windows PCs.

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DEFLATION IS COMING

In high school (a charter school for math + science) I remember executives from Fortune 500 companies coming to my school to ask some of our young tech geniuses for help on their corporate websites. That’s how new things were in the early days of the internet. And boy did they cost a lot…

Were you to try and launch an ecommerce company back when Amazon was getting started your budget would have looked something like the following:

1. Planning and Expertise: $10k+

   1. You needed to register trademarks, incorporate, write an operating agreement, draft ancillary docs (e.g. terms of service, return policies, etc)

2. Technology and Software: $15,000+

   1. Upfront hardware costs for servers: $5,000-$20,000

   2. Operating system (Windows NT) $500-$1,000

   3. Web Servers (Microsoft IIS) $500-$2,000

   4. Oracle database server license ($5,000-$20,000)

   5. Payment Setup ($500-$2,000)

3. Website Development: $25,000+

   1. A site with product catalog, shopping cart, checkout would cost $20,000 minimum

   2. $2,000-$10,000 to create marketing content

Total costs of $50k + $50k per year ongoing…

Ongoing operational costs were probably $50k+ per year just for technical expertise. Now compare that to where we are today. I’m going to use real figures b/c I just so happened to have started a new ecommerce company recently.

1. Planning and Expertise: ~$500

   1. You can get an operating agreement drafted by LegalZoom (or at this point an LLM) for $100; you can teach yourself to file trademarks with an LLM for $350; ancillary docs are available for free online

2. Technology and Software: $0

   1. $300 for a Shopify template

   2. $10ish for Google Workspace account per user per month

3. Website Development: $1,500

   1. We had someone help us out for no charge, but she’s very talented and if we would have paid her by the hour it probably would have been around this

Ongoing operational costs are just the Workspace account, and our subscriptions to Shopify and Canva ($45.00 per month).

Total costs: $2,000 + $540 per year ongoing…

There were also soft costs in the 1990s that don’t exist today. For example, the number of people who had the expertise to set up an Oracle database was extremely limited. If something broke it would have been more expensive and time consuming to fix. The only way to guarantee very little downtime would have been to have someone on staff full time. Today Shopify delivers 99.99%→99.999% uptime (4-5 nines).

Another example is legal expertise. Back in the 1990s if you needed a new wholesale agreement drafted it would have cost thousands of dollars. Today you can find countless examples online or just use ChatGPT to draft one.

Collectively, this represents approximately 97.5% cost deflation over the past 30 years.

I personally know multiple people running multi-million dollar ecommerce companies with a handful of people.

This deflation has been caused by advances in both software and hardware (Wordpress and Shopify // AWS, Azure, GCP).

Incredibly, we’re still deflating! Consider the following: what will happen when AI can write the copy, make the creative, a/b test the ad campaign, etc…?

Did you know this is already happening with the latest versions of Meta’s marketing tools? The primary driver of deflation today is the plummeting costs of generating tokens (a reasonable analogy for the cost of using AI):

Since ChatGPT launched, the cost of “creating” AI has fallen by 99.6% - meanwhile it has added 3-4 standard deviations of intelligence. Combine the two and you have a ~500 to 1,000 X improvement in cost per unit of intelligence.

What happens when artificial intelligence gets a body?

Let’s look at the costs to build a house today…

1. Raw Materials: 40-50% of total cost

   1. Lumber / Concrete / Roofing / Windows / Doors / Finishes

2. Labor: 30-40%

   1. Carpenters, electricians, plumbers, masons, roofers, GCs

3. Other Costs: 20-30%

   1. Permits, architect fees, land prep, equipment rental, builder profit

For a $300k house this could break down to:

1. Raw Material: $150,000

2. Labor: $90,000

3. Other Costs: $60,000

What would happen if just half of the labor costs were removed via automation? We’d see a 15% decrease in build costs. Shelter makes up 35-36% of the CPI. If construction costs were to drop 15% inflation would be gone forever. And, don’t forget that labor makes up a material portion of the costs of the raw materials (both from harvesting + storage + transport).

The same thing that has happened to LLMs and generative AI is going to happen to robotics (humanoid + other). For reference, here is Nvidia’s revenue over the past 8 years:

And here is what is expected to happen between now and 2030 (the red line starts where the first chart ends):

The total addressable market for machines with human dexterity and human intelligence will be effectively unlimited.

If it’s done on a computer - it will be automated…

White collar work will probably end up coming under attack even more quickly (and arguably already has according to revenue per employee metrics). This chart shows the growth of administrators vs. doctors from 1970 to 2015.

What percent of these jobs can be automated? The answer is obviously most of them - or at the least automation will enable one person to do 10X the work they were doing before.

This year we’ve gotten two different announcements about tech giants releasing products that enable a person to “screen share” with an AI. I already use AI to do many things I never would have been able to do before - like set up web domains (I’m not a technical person), migrate enormous QuickBooks files with 75k lines, and set up complex cap table conversion formulas in Excel to get around the recursive reference issue that always shows up when a million different notes with different terms are being converted at the same time and language is ambiguous on order…

In each of the above examples - the way I went about it was to take a screen shot of my computer and then ask the AI what to do. In many cases the instructions were complex enough that I started telling it: “Provide me step-by-step explicit instructions, one instruction at a time, and wait for me to ask to go on to the next step”.

The AI was able to look at the image and tell me exactly what to click on, what to type where, and how to test if it was working along the way (so that we didn’t get to the end of a process and miss something that broke everything we had done).

The next step is screen sharing. The step after that is giving the AI access to the operating system so that it can just do the work for you. It can click on the files, write the excel formulas, etc…How long will it be before we start seeing demos of this primitive form of agency? I believe they’ll be here by the end of the year…

Eventually, one person will be monitoring AIs that are doing the work of 10 or 100 people.

While I am not excited about the disruption this will cause to many people’s lives - I could not possibly be any more bullish on the future of semiconductors and companies that own operating systems, cloud, and distribution.

All tech companies could aptly be considered as providers of “Semiconductors as a service”. This is as true of Meta as it is of Microsoft or Apple.

Only the operating system providers can deliver the AI which is connected to everything and which will eventually replace humans en-masse. If not for technical reasons - then because the owners of the operating system will use their position along with some fake narrative about “privacy and security” to make sure that nothing else works as good as their own products.

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From inside:

“If you’ve been telling yourself this isn’t coming, I really think you need to question that. It’s not web3. It’s not vaporware. A lot of what we’re talking about is already here, right now.

I think we are on the cusp of an era in human history that is unlike any of the eras we have experienced before. And we’re not prepared in part because it’s not clear what it would mean to prepare. We don’t know what this will look like, what it will feel like. We don’t know how labor markets will respond. We don’t know which country is going to get there first. We don’t know what it will mean for war. We don’t know what it will mean for peace.

And while there is so much else going on in the world to cover, I do think there’s a good chance that, when we look back on this era in human history, A.I. will have been the thing that matters.”

What is AGI?

I’m almost giddy with excitement. Every day word is spreading further about the potential of AIG. I explained over two years ago why LLMs represented the unlock of a sufficient level of intelligence to create new trillion dollar industries:

Alas, even trillion dollar industries are not the same as AGI.

The reason ChatGPT wasn’t considered AGI then and why nothing we have is considered AGI today (by most people) has less to do with intelligence than agency. AI systems like ChatGPT 4.5, Claude 3.7, and Grok are already better than humans at many things. But until they get “plugged in” to GUIs, software APIs, and have their memory upgraded to be effectively unlimited - they will continue not feeling like AGI.

Every thought leader in the AI space - including a person named Ben Buchanan (NOT me! the guy Ezra interviewed) who was an AI advisor to the Biden administration - laments they are not satisfied with the term artificial general intelligence (in most cases because it is ill-defined). If more people read 01Core then we wouldn’t have this issue, because my definition is ideal:

1. Digital AGI: An AI that would be indistinguishable from a human working remotely

2. Physical AGI: An intelligence that - if embedded into a humanoid form - could do whatever an average human could do.

See how easy that was?

Most people have no idea what’s already here! Much less what is coming…

From Ben (if you see this name for the rest of this article - just remember I’m talking about the other guy!):

Software developers regularly report that they have cut the time it takes to do a multitude of tasks by 50-90%. Deep Research can be used to carry out complex research assignments and put together reports that are hundreds of pages long. Countless examples abound of it being used for analytical work. Grok (from xAI owned by Elon Musk), DeepSeek (from China) and of course Google have all released similar products.

I subscribe to three different LLMs (ChatGPT, Claude, and Grok through Twitter). I have saved myself ballpark $15k of hard costs (money I would have needed to pay to someone else - mostly subject matter experts like Quickbooks gurus or lawyers) from these subscriptions over the past twelve months, and probably more than that in terms of time savings. And don’t forget this post from yesterday:

As Ezra points out in the article - systems today are as bad as they will ever be.

There are three switches that will flip soon - by the end of this decade - and as they turn on chaos will ensue (but only briefly).

1. AI will get agency - the ability to take actions and perform any task a human with computer access could do. For example, instead of asking ChatGPT “How do I set up a website” or “How many calories are on this plate of food?” - you will simply instruct it to: “Set up a website, find some products on Alibaba that I can import at a good price, then list them on the website and start selling them. Also, set up some social media accounts and begin marketing campaigns for those products.”

2. AI will get a body - The “Synths” as I call them - short for Synthetic Humanoid - are coming. I’ve written about this extensively. Until recently there was no justification for pouring a hundred billion dollars into R&D to figure out how to create a functional “skeleton” to put a brain inside of. Because there was no brain. Now that there is the incentive to own the business of manufacturing the last thing humans will ever need to make is incomprehensibly large. Humanoid robots will end up being a $10T+ industry - the largest in history by far. These things will start mass production this decade.

3. We’ll get a “zero to 1 billion” moment - The reason most people don’t know about AGI is because to date the impact on most people’s daily routine has been minimal. This will change once Apple and Google release zero latency LLMs with operating system access. Suddenly - overnight - the entire world - everyone with a smartphone - will have access to something that feels indistinguishable from having a “person in your pocket”. It won’t be long after that people realize how limited the need for humans will be in the imminent future.

The zero to 1 billion moment is what will kick of the greatest asset bubble in history. Analysts will start forecasting the profit margins of companies by assuming they employ zero workers. People will start valuing asteroid mining companies as if they’ll succeed at harvesting all of the precious minerals in Saturn’s rings. Real estate prices will catch a bid because everyone will start putting 2 and 2 together - and realize that a cure for aging is right around the corner - so the demand for prime real estate will increase rapidly while the supply usually provided by elders dying off will start evaporating.

Meanwhile, inequality will be skyrocketing because capital will have embarked on its final campaign to wipe out the value of labor once and for all…

The first new technology paradigm over which the government has little control

From the article:

Companies including OpenAI, Microsoft, and Google have all made overt attempts to encourage government to regulate AI. The cynical perspective on this is that they realize how indefensible the LLM technology is from a competitive standpoint and want to use the regulatory system to create a moat. This is probably true, but it’s also true that most people who believe AGI is coming think that society needs to figure out how to prepare for the chaos that will ensue thereafter (e.g. from mass job losses).

The examples Ben provides (nuclear, space, aviation) are fascinating to contemplate. There are two primary differences between everything that came before and AGI:

1. The rate of advancement far outpaces the speed of societal evolution and government capacity to respond

2. The internet ensures that anyone determined will be able to access AGI

One of the key insights I feel like I’ve had that doesn’t seem to be widespread - is the realization that only the United States - among ALL nations on Earth - has a corporate shadow government whose collective power dwarfs that of the federal and state bodies and the judiciary. I’ll never stop tooting my own horn about this. I predicted almost 7 years ago that the United States would become a “Big Technocracy”. I gave a talk about this to a Rotary group in Atlanta. If you look over the regulatory landscape in the US you will notice that virtually all impactful regulations might as well have been drafted by the C-suite of the companies who are regulated. This is not true in Europe, it’s not true in China. This sets up a dynamic that will likely exacerbate existing trends in the diverging paths of major economic zones.

The EU will react to AGI by over-regulating, making it difficult for companies to adapt. They will limit the ability of companies to employ humanoid robots. The net result will be the complete de-industrialization of the European Union. Japan, Korea and China - on the other hand - will automate as quickly as possible - because their citizens will trust that the government knows best and will end up providing for them. China in particular will avoid chaos caused by automation by being able to rapidly adjust the distribution of resources in response.

In the United States - particularly in red states - companies will be left to do as they see fit. Manufacturing will take off as labor costs plummet. Europe’s loss will be to the gain of Asia and the United States. There will be a lag between the surge in corporate profit margins and when society at large realizes we’re going to need to implement a new social welfare system. Thankfully, the US will be able to afford it because advanced technology acts like a tax charged (mostly) by the US (and likely soon China) on the rest of the world.

Blue states within the US will be inclined to add additional labor protection laws - and this will cause an exodus of business owners, entrepreneurs and the most ambitious members of society to red states.

Implications for national security

There are two primary attack vectors in the cyber world:

1. Vulnerabilities in tech

2. Vulnerabilities in people

AI as it currently exists already magnifies these vulnerabilities by multiple orders of magnitude. First, regarding the vulnerabilities in tech.

AI makes it easier to write code. AI can already - today - be asked to try and hack into some system. It will take a stab at producing the code - then a human will execute the code - then the human tells the AI whether it worked or not and gives the AI another chance if it didn’t.

By the end of this year - if it isn’t already happening - AI will be able to simply keep attacking systems on its own, relentlessly, indefinitely - with the only thing holding it back being compute power.

Vulnerabilities in people come primarily through “social hacks”. A hacker sets up an email address that looks like it belongs to an executive at the same company as the victim works for. They send them an email asking them to click a link to approve something - and that link infects the computer and gives access to the hacker.

AI makes this process easier by automating the process of writing the emails, identifying targets, etc. Again - right now this activity must be overseen by a human - but what will things look like when you have 1 billion AI agents constantly trying to impersonate people to get access to critical systems?

Another “social hack” that has only been made possible within the past 12 months is using AI to generate a real-time deep fake and mimic the voice of someone else. See this article from CNN:

My current view is that this problem will actually be solved with technology pretty quickly - because the ramifications of not solving it are enormous and may border on MAD (this is a surprisingly helpful heuristic for predicting the future).

But - you can imagine how being the first to AGI could lead to one country quickly gaining a compounding advantage vs. another.

This brings up an interesting aside. What happens to the demand for memory and compute if the technological solution to increased hacking ends up being having a billion agents constantly trying to hack and fix vulnerabilities? One way people defend against cyberattacks today is by employing people to attack their own systems to discover where they’re vulnerable. If you end up in a situation where billions of “good agents” are trying to constantly find exploits before billions of “bad agents” - it seems reasonable to me that this could end up using a material portion of total compute globally.

This post has hit my target length for these quicker “Bytes” and I need to get started on my other work. Let me know if you enjoy this format of responding to articles, there’s a bunch of other fascinating stuff to address in Ezra’s piece, including the ramifications of “free” satellites and the increased importance of Space; DeepSeek, China and export controls; why drug development will accelerate + a cure for aging - and more! I’ll get to it in the next few days.

So - I’ve added a new section to the page called XBytes. I am regularly bookmarking things on Twitter (now called X) that I find interesting. In this section I intend to just share the things that I have found over the prior week, along with brief commentary.

I will know whether the experiment is successful based on how many people subscribe/unsubscribe as a result. If you don’t like the new format and feel it’s spammy please let me know ASAP before I lose too many followers!

Now on with the first post…

Google is dying. Also, Google does 5 trillion searches annually?

Hat tip to @SupBagholder for the scoop - one of my favorite follows on X.

The death of Google has been greatly exaggerated. I’ll admit that I have probably dropped my own usage of Google Search by ~95% (seriously). In its place I use ChatGPT, Claude, and now Grok. However - everyone on X / Substack needs to remember that we are the few. Most people don’t know what an LLM is and have never tried ChatGPT (those are the facts). While ChatGPT now has hundreds of millions of users, Google has 7 products with 2 billion+ users:

1. Search

2. Android

3. Chrome

4. Gmail

5. YouTube

6. Play Store

7. Maps

What’s more, as I have described in countless posts on X and on 01Core - the killer LLM product is one that is connected directly to the operating system. One that can share screens, write emails, change settings, etc. ONLY the companies that own operating systems can deliver this.

Apple kneecapped Facebook after it realized that Facebook influenced more than 50% of app store downloads. Its fear of Facebook’s dominance of its own app store is what led it to create the “privacy” marketing campaign. As it turned out this ended up being a good thing for Facebook - but it also gave Apple what they wanted - which was dominant influence over which apps people download.

Today, Google is seeing more searches and more time spent searching because of LLMs. This means even if ChatGPT is taking share - the pie is growing fast enough to compensate. Search was up 13% YoY in the most recent quarter. Meanwhile, this is what Google’s EPS growth looks like:

Not exactly a portrait of a dying company is it?

The real question is whether ChatGPT might end up in a position like Facebook did in the early 2020s where it is having more influence over how people discover information than Google even on an Android device…

It’s possible, but both Apple and Google are in a position to “kneecap” ChatGPT just like Apple initially did to Meta. How?

They’ll release an LLM that is connected to the OS, and if need be - block ChatGPT or other LLMs from directly accessing apps on “privacy” grounds or some such theory. Would I start using Google as my go-to LLM if it were connected to everything on my phone? Obviously - so would everyone else.

What’s more? You might be shocked to hear that Google only gets 55% of revenue from Search. Cloud is the fastest growing segment, and YouTube is IMHO the most under monetized asset on Earth only after Android…

Google also has the lowest cost of delivering cloud-based AI (compared to Amazon and Microsoft).

Google trading for a multiple 15-20% lower than the S&P 500? Ridiculous.

The homebuilder / construction complex

This always has been and always will be a tech-focused newsletter. I’m pathologically optimistic about the future of technology so it’s naturally where I spend most of my time. Still, I may veer off topic from time to time. One of my favorite stock screens is to look at companies who have grown earnings at a higher clip over the past 5/10 years than their forward PE multiple. For example, a company that has grown earnings at 20%+ over the past five years, but which trades for a forward PE < 20. Or, a company that has grown earnings faster than 10% over the past 10 years, but trades for a forward PE < 10. Interestingly, there are ONLY TWO groups of companies who regularly appear on this list. There are of course other companies on it - but only two groups of businesses have almost every single constituent in the group appear. Those two groups are semiconductor capital equipment companies (Lam Research, Applied Materials, KLA Corp) and the construction complex (homebuilders like Pulte and DR Horton, and suppliers like Owens Corning and Carlisle).

See below the multiples of the homebuilders back in August of last year - BEFORE the recent 20-30% correction:

What’s more - the companies in this complex have disgusting historical financials. Here’s Carlisle companies actual / forward estimates and P&L over the past 8 years:

Note below that gross profit has almost doubled, meanwhile EPS has increased from $3.53 to $18, with estimates around $23 for next year. Also note the share repurchases:

What drives semiconductor capital equipment makers and the homebuilder complex to be the ONLY TWO groups of companies that have this weird situation where trailing EPS growth is > forward multiples? I have a theory…

They’re both extremely cyclical and they both have extremely powerful tailwinds.

Semicap tailwinds are obvious - semiconductors are the underling magic that drives/benefits most from the exponential growth of technology.

The homebuilder complex has a few tailwinds:

1. Consolidation

2. Difficulty of acquiring good lots at scale (smaller builders can’t compete)

3. Under supply of housing stock

4. Higher costs (contractors and raw material) wreck smaller builders

5. Blue-state to Red-state migration

6. Less rate sensitivity b/c can buy-down rates (again, small guys can’t)

Combine the above with homebuilders’ new asset light business model (optioning vs. owning lots) - and the complex as a basket looks more like a group of highly cyclical manufacturing companies than it does a bunch of real-estate speculators. Suppliers like Owens Corning and Carlisle have - literally - decades ahead of them to consolidate the market. Check out the stock charts of Waste Management and Republic Services if you want to see how beneficial it is to have an infinite amount of opportunities to roll up a market by purchasing at one EBITDA multiple, then using your scale to make the effective acquisition multiple 30%+ less…

We’re sitting on 7% mortgage rates and homebuilder margins are doing just fine. There’s always the possibility that the bottom falls out of the market - this might happen because rates drop (ironically) and a flood of existing homes comes to the market - or it might happen because we actually get a recession and stock market crash which impacts housing via the net-worth effect. However, what is obvious is that long-term earnings will go on to make new highs, and in the mean time you have a basket of companies that are more shareholder friendly than any others - by far - who gush cash and will use it to re-purchase shares throughout the down-cycle.

AI Cracks superbug problem in two days that took scientists years - an example of Acceleration

Another fun guy to follow on X is @Dr_Singularity. Here’s one of the more interesting use cases of AI I’ve run across recently

From the article:

“A complex problem that took microbiologists a decade to get to the bottom of has been solved in just two days by a new artificial intelligence (AI) tool.

Professor José R Penadés and his team at Imperial College London had spent years working out and proving why some superbugs are immune to antibiotics.

He gave "co-scientist" - a tool made by Google - a short prompt asking it about the core problem he had been investigating and it reached the same conclusion in 48 hours.

He told the BBC of his shock when he found what it had done, given his research was not published so could not have been found by the AI system in the public domain.

"I was shopping with somebody, I said, 'please leave me alone for an hour, I need to digest this thing,'" he told the Today programme, on BBC Radio Four.

"I wrote an email to Google to say, 'you have access to my computer, is that right?'", he added.

The tech giant confirmed it had not.

The full decade spent by the scientists also includes the time it took to prove the research, which itself was multiple years.

But they say, had they had the hypothesis at the start of the project, it would have saved years of work.”

This is an example of the type of acceleration we’re about to start seeing everywhere. I explained what this will feel like in a post on X (apologies if it’s a repeat for you):

If you think it is going to take humanity 50 years to figure something out (a cure for cancer, AGI, etc) - we might be just 11 years away. Before I walk through the math here are a few points worth noting:

1. Some types of compute efficiency (alternatively: the cost of intelligence) are improving by 50%+ per year.

2. Efficiency in analyzing biotech lab samples has improved by around 40% per year over the past 20 years (we can now test millions of samples with the same amount of human resources that were once capable of handling thousands).

3. However many people you think are working on a problem - the number is likely to be at least 10X higher. Why? Because most people working on compute efficiency, AI, robotics, etc are at least tangentially working on that problem. For example, no one would have thought about Nvidia as a "natural language processing company" a few years ago, but we now know that their GPUs are what unlocked ChatGPT. A similar argument will soon be made regarding their Chips' impact on biotech (as Jensen so regularly reminds us).

This is the beauty of general purpose technologies and the MEGA trend of everything turning into an engineering problem.

Now for the math. Let's be a tad more conservative than the above numbers imply might be possible and assume that we're accelerating the rate at which we're solving a problem by 30% per year. Put differently:

From 2024 to 2025 we make one year worth of progress.

From 2025 to 2026 we make 1.3 years worth of progress (30% more).

From 2026 to 2027 we make 1.69 years worth of progress (30% more than from 2025 to 2026).

The numbers blow up pretty quickly:

By year 7 we will have made more than 17 years worth of progress, and by year 10 we will make 10 years worth of progress in a single year, and will have made 42+ years worth of cumulative progress.

Most people need to dramatically shrink the timelines over which they expect the seemingly impossible to turn into the imminently probable.

Please remember, if you like this shorter form content and style please let me know - if you don’t like it it’s even more important to let me know!

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