AI CHIPMAKER CEREBRAS SAID TO PLAN IPO PRICE INCREASE MONDAY

At $125 a share, that makes Cerebras approximately a $26.5 billion company. Obviously, as it goes higher, that extends the valuation. (Reuters reported the original $115–$125 range; Bloomberg subsequently reported May 8 that the range is being raised to $125–$135 amid order books reportedly 20x oversubscribed.)

In today’s article, I wanted to take a look at Cerebras. I will discuss what this company is, what this company does, talk about their differentiator in the market, and cover some of their S-1.

What is Cerebras?

Cerebras is, in as few words as possible, a competitor to Nvidia. The company manufactures GPUs, but they do it in a novel way.

When you look at a GPU today the way that Nvidia does it, that GPU is made from a silicon wafer that was cut up into usually somewhere around 70 GPU dies. Cerebras does not cut that wafer up into individual dies. Instead of getting 70 GPUs out of one wafer, Cerebras gets one GPU out of one wafer.

Cerebras’ GPU is called the WSE-3, which is 46,225mm squared. Depending on how familiar you are with measurements, this either sounds like a big or a small number. Consider it in the scale of an Nvidia B200, which is a standard looking GPU. This one is about 58 times the size of that B200 in terms of the area it provides.

The WSE-3 has 900,000 cores, 44GB of on-chip SRAM, and 21PB/s of memory bandwidth. The whole idea here is that by keeping compute and memory on the same piece of silicon, you eliminate the off-chip data movement that can bottleneck GPU clusters. So you can get inference up to 15x faster on open-source models.

Why “No Cut” Matters

The concept of not cutting the wafer sounds simple and perhaps even gimmicky at first glance, but there are actually some real benefits to it.

Modern AI workloads are, more than anything else, communication bound. The bottleneck is not always how fast the chip can compute; it is how fast the chip can talk to memory and to other chips. So, every time you have to move data between chips in a GPU cluster, you pay a latency tax and you pay a power tax.

Whenever you are trying to run a frontier model on GPUs, you have to stitch thousands of them together. With expensive and power hungry switches and networking equipment, a meaningful fraction of your total power budget goes to moving bits around rather than computing on them.

Cerebras’ wafer-scale approach makes most of that problem go away. The model lives on one chip. Compute and memory are inches apart, not feet apart, and the networking layer between the chips is collapsed into the on-chip wiring. This makes it a lot faster than the off-chip equivalent. When Cerebras claims 15x faster inference, it is not software optimization or a marketing benchmark. It is just part of the structural makeup of what they build.

It doesn’t come without its risks, because the trade-off that they had to solve to make this commercially viable was yield. Silicon wafers have flaws, and the bigger the chip, the more likely it is that it has one. Historically, that is why nobody made wafer scale processors. Cerebras has contributed to the field with fault-tolerant architecture that routes around defects on a wafer the way a hyperscale datacenter routes around dead servers. That, plus their proprietary process for connecting dies across the wafer at the fab, is what makes the whole thing shippable.

We should talk about the moat and where that lives. The actual fabrication for Cerebras’ chips happens at TSMC on TSMC’s process technology. Cerebras owns the chip architecture, the fault tolerant routing, the packaging and the cooling work needed to operate the chip, and the software stack co-designed with all of it. That is the real moat for them. It has been built over a decade, but it is design and systems, not the foundry itself. A well-funded competitor with TSMC access could theoretically pursue the same approach, but no one has in roughly 20 years of this being something that was doable. (The Futurum Group’s S-1 teardown has a good walkthrough of the foundry-dependency angle if you want to go deeper.)

OpenAI Anchor

The OpenAI contract is the cornerstone of the whole IPO. In December 2025, Cerebras and OpenAI signed a master relationship agreement that is worth more than $20 billion. This includes 750 megawatts of contracted compute through 2028 with an option to expand to roughly 2 gigawatts for an additional $34 billion. To accelerate the build-out, OpenAI loaned Cerebras $1 billion in working capital that is secured by warrants for over 33 million Cerebras shares.

If this deal works out, there will be dilution, but it is worthy dilution.

The good news for Cerebras is that this is a take or pay structure. OpenAI has to pay for the contract capacity whether they use all of it or not. That converts what otherwise would be discretionary cloud spend into a fixed contractual obligation.

But OpenAI is successful. They have Codex, and Codex will be where they use this compute. Specifically, it is Codex Spark that runs on Cerebras. Codex Spark grew from 600,000 weekly active users in January to 4 million users in May, and there is still a long way to go until you open this up to the 50 million plus paid ChatGPT subscriber base.

On the Cerebras side of things, it is not all rosy. If they do miss milestones, that does allow OpenAI to terminate the contract. The contract also has exclusivity provisions that bar Cerebras from selling to “certain named competitors” of OpenAI, which, most plausibly, are companies like Anthropic, Google, and Meta.

Customer Concentration

One thing that you will see across all the posts about Cerebras is the callout of customer concentration.

It is a very worthwhile call out. In 2024, a company called G42, which is part of the UAE, was 85% of Cerebras’s revenue. In 2025, MBZUAI was 62% and G42 was 24%. That sounds good, right? It sounds like two different companies are now providing revenue. However, both of these companies are UAE affiliated entities and they are related parties, so effectively this money was coming from the same place.

OpenAI does step in and provide at least some breakdown of that concentration for the business. But at the volumes we are talking about from OpenAI, this now becomes a single customer concentration risk.

Cerebras’ chips being available on AWS would add a second whale into the mix that really does start to break down the customer concentration concern, but we still have to wait for those agreements to close. Beyond that, the diversification really depends on Cerebras converting its distribution partnerships and sovereign AI pipeline into named and contracted revenue, which today is forecast and not factual.

Valuation

Right now, the trailing multiple is about 52-56x sales, which is alarming, but it is also basically useless. Cerebras’ revenue is contractually obligated to triple by 2027, which puts us at a forward price-to-sales ratio of about 16 or 17x. This is broadly in line with peers in semiconductors.

Cerebras has that 750 MW contracted with OpenAI. If we look at a competitor like CoreWeave that usually trades around $20M per megawatt, that becomes a $15 billion enterprise value based on the OpenAI relationship alone. That is before you attribute anything to AWS or any other partner cloud that they may be able to work with.

If OpenAI gets up to that full 2 GW, then you are looking at a $40 billion valuation based on that metric.

You could absolutely argue that Cerebras deserves more per megawatt than CoreWeave does. CoreWeave leases infrastructure. Cerebras gets the same data center economics, plus the chip IP, plus the software stack. It makes it three layers instead of just one.

Against an IPO market cap that looks to be going somewhere around 28-30 billion dollars, the math somewhat supports the price, but it does not support a meaningfully higher one.

We do have a bit of a dilemma though because the real price tag is likely much higher. Between the 33M OpenAI warrants, the RSUs, and the options, the fully diluted share count for Cerebras comes out closer to 296 million. At $125 per share, that makes the fully diluted market cap about $37 billion.

The EV/MW math at full dilution effectively requires the full 2GW expansion from OpenAI to justify the day 1 price.

The Final Question

Cerebras, at the IPO range it is posting at, is a bet on a specific thesis: that wafer-scale architecture wins the inference layer of AI infrastructure. There is a subsequent bet that owning the chip, the system, and the software stack, plus operating the cloud business on top of it, captures more value per megawatt of contracted capacity than any pure-play competitor can match.

The OpenAI contract means that this is not just pure speculation; it is speculation on top of a contracted floor, which is a somewhat decent place to be.

It is a near given that the IPO will pop on top of an already juiced number and at fully diluted valuations that would give us a market cap meaningfully above $40 billion. That math would require not just the contracted 750 MW, but the full 2 GW expansion option to be exercised. This means the bet is no longer just that “wafer-scale wins inference,” but that “wafer scale wins inference and OpenAI’s compute demand grows fast enough to justify doubling the contract.”

The other open question is whether the OpenAI exclusivity provisions become binding. If named competitors include Anthropic, Google, and Meta, then Cerebras’ serviceable market for the next several years is narrower than the partner list suggests. They can still build a large business. OpenAI plus AWS plus Enterprise plus sovereign AI is real. But the diversification ceiling is lower than the bull case assumes.

What I will be watching:

• Does OpenAI exercise the expansion option

• Does AWS convert from a term sheet to fully contracted revenue

• Is there further diversification a quarter or two after IPO (or is it just OpenAI replacing UAE and we keep single-customer concentration with a different flavor)

Amazon is a fun company. If you asked anyone what Amazon does, they would likely say it is an online store. If you asked anyone in tech what Amazon does, you might get that they run AWS. The true story is that they do literally everything.

Today, on May 4, Amazon announced that they are allowing others to use their logistics business. They are opening their entire logistics network for freight, distribution, fulfillment, and parcel shipping services to every business of all types and sizes. It does not matter if you are 3M and you are shipping Post-it notes, or if you are Procter & Gamble and you are trying to ship diapers. You can do it around the world now on Amazon’s Supply Chain Services.

Amazon does this a lot, and they have been doing it for a long while. In 2015, Social Capital put out a slide deck, and in that slide deck it said that Amazon turns every major cost into a source of revenue.

It got me thinking. What else is left for Amazon to do? Where else are they missing?

First, The Wedge

Amazon did not just jump straight into supply chain services. They have been slowly building these capabilities for years.

You have no doubt seen Amazon’s delivery vehicles on the streets you live on. That was one phase of this game plan. They needed to figure out last mile delivery, and they have been running last mile delivery for some other services for quite a while now.

More recently, Amazon needed to figure out how to move their goods around at the middle of the network, not just from a warehouse to a doorstep but from one distribution center to another, or from a partner’s distribution center to theirs. The answer there was a product called Relay. Relay is a truck board where Amazon posts loads that need to be transferred between distribution centers, and truck drivers or box truck owners pick those loads up and get paid as contractors.

That was another part of the wedge. Amazon does this persistently. They find their own needs, they iterate, and they push each piece a little further until it becomes a full-blown product like Supply Chain Services.

What is great about the way Amazon does this is that they are doing it with things they actually use. They needed last mile delivery. They needed truck drivers to move stuff between distribution centers. They needed planes to move stuff across the country. They build these things for themselves first, run them at Amazon scale, and then turn them into a product everyone else can buy.

A Quick History of Amazon Eating Its Own Costs

Just in case you are not familiar, this is a thing that Amazon does quite frequently. It is a predictable pattern, not just a one off.

It started with AWS in 2006, and Amazon Web Services now dominates the internet. Google Cloud Platform and Azure are still trying to catch up.

In the 2010s, Amazon entered the advertising game, allowing people to buy advertisements.

There was then Fulfillment by Amazon, where if you were a small time retailer, you could send all your products to Amazon. Every time somebody hit purchase on Amazon.com, Amazon would box that thing up and ship it off for you.

There is Amazon Lending, there is Buy with Prime, and there was Just Walk Out, the cashierless checkout technology that started in Amazon Go and has since been licensed out to over 180 third party stores including stadiums, airports, and college campuses.

AWS Ground Station launched in 2018, letting any satellite operator rent ground station time by the minute. Amazon built this for their own satellite ambitions, including Project Kuiper, their internet service provider that competes with Starlink. Companies like Capella and BlackSky now use it too.

This is not just a one off. This is Amazon in a nutshell.

So, What Got Hit?

The obvious names are UPS and FedEx. Both of these companies sold off massively on the news of Amazon launching its Supply Chain Services. Both stocks closed down roughly 10 percent on Monday.

This is a tech publication, so I am not here to comment on the merits of UPS and FedEx, but I imagine that those companies do have some contracts that Amazon will probably steer clear from, at least in the near term. These include things like being embedded in healthcare, defense, B2B contract logistics, and probably some other moats around areas like residential parcel delivery that Amazon cannot easily get to.

But that was the headline trade, right? Let’s look a little bit deeper. What are some other companies that might have some difficulties?

A more interesting exposure is in the middle of the supply chain, where the substitution starts to become a little bit more direct. You have freight brokers like C.H. Robinson, XPO, and Hub Group that exist to match shippers with truck capacity. Amazon has been quietly building that with Relay, and ASCS now formalizes that offering.

If you are Procter & Gamble and you can move freight on Amazon’s network with Amazon’s pricing and Amazon’s tracking, and then you can also very quickly put most of that stuff on Fulfillment by Amazon, your reason for using a broker shrinks. Brokers run on the margins and they have very little structural protection from something like this if it were to scale significantly.

The worst off will be third party logistics providers, the 3PLs of the world. These are companies like GXO. Worth noting that GXO actually fell more than UPS or FedEx on the announcement, dropping more than 13 percent on Monday. These companies operate warehouses and run fulfillment for major brands. We have named Procter & Gamble a few times. That company was named in Amazon’s launch letter. But also names like 3M, Lands’ End, and American Eagle. They are not just going to start shipping with Amazon; they are warehousing with Amazon. That starts to really eat into some of these 3PL relationships.

Shopify is another name worth looking at. Shopify did try to build its own logistics business but ultimately ended up selling it to Flexport in 2023 for a 13 percent equity stake in Flexport. Flexport has been integrated with Shopify ever since, helping it out in this domain.

Shopify merchants now have a cleaner path to using Amazon for their entire backend fulfillment solutions while keeping their storefront on Shopify. Maybe the next logical step for Amazon might be to allow for better branded storefronts, thus eating Shopify’s lunch.

That one is a bit of a stretch, but you have got to throw it out there.

A thing worth pointing out to close this section off is that the market tends to overprice the immediate damage to companies like C.H. Robinson or FedEx immediately after megacorps like Amazon announce something. However, that damage is not immediate. It tends to happen over 6 to 18 months.

What’s Left?

I started the article by asking: what is left? Amazon seems to have turned everything it does into a product. Is there anything left for Amazon to still sell?

I dug in, and the answer is absolutely yes.

Energy procurement is one of the big things. Amazon is the largest corporate buyer of renewable energy on the planet, and has been for years. They have built sophisticated PPA structuring and grid procurement capabilities. Could they do this as a service for others? Could this be the AWS for energy? It definitely could be. It tends to be along the lines of the capability that they would externalize if they could.

The more logical path right out the gate is tax, trade, and customs compliance. Amazon handles international VAT, customs classification, and cross border trade at a scale that nobody else operates at. Most of this is currently embedded inside Seller Central, but the capability could easily be unbundled into a service that could be used by any importer. The market for this is fragmented and full of legacy software vendors, and it is a classic Amazon target that is along the lines of what they announced today.

Back to the wild side, we have robotics fleet orchestration. Amazon Robotics runs the largest mobile robot fleet in the world inside their fulfillment centers. The orchestration software, fleet management, and simulation environments are all mature. This would compete with businesses like Symbotic if they were able to bring it to market. Symbotic has one big customer in Walmart, so it is probably not going to take away that customer, but it could affect future sales for a company like Symbotic.

The Trade

Every time Amazon externalizes a capability, they are not just creating a new revenue line; they are restructuring an entire industry around themselves.

AWS did not just compete with enterprise IT vendors; it changed what enterprise IT even means. Most companies no longer run their own infrastructure because Amazon made it irrational to.

Amazon FBA changed what it means to be a small retailer. Now you can get started really cheap and easily. You no longer need to go to a 3PL for quite some time unless you are doing something very specific that requires a little more handholding than you can get from Amazon.

Supply Chain Services is the same kind of shift in a different vertical. Procter & Gamble is not going to keep its existing logistics relationships and add Amazon on top. They are going to gradually consolidate spend towards whoever offers the best price, the best tracking, and the most reliable network.

Guess who has all 3 of those? It is Amazon, because they built it for themselves first. The whole logistics industry is about to get reorganized around Amazon as the default backbone, the same way that enterprise IT got reorganized around AWS.

Amazon used to be just a retailer. Many still know it as a retailer, but it is so much more than that now. It is the infrastructure that other companies build their livelihoods on.

AWS is the compute layer of the internet. FBA is the fulfillment layer of e-commerce. Ads is the discovery layer of commerce. Supply Chain Services is going to become the logistics layer of physical goods. Each of these is a layer that sits underneath whatever other businesses are doing, and each of them was originally built to serve Amazon itself.

And that is why it is a pattern that is so hard to fight. Incumbents in any of these industries are not competing with just another company. FedEx is not competing against another UPS. C.H. Robinson is not just competing against another XPO. They are instead competing with infrastructure that has been pressure tested at a scale that they themselves can never reach.

Our job as investors is to try and figure out what industry is next. I believe it will be robotics. Amazon already runs the largest mobile robot fleet in the world inside their own facilities, they have been deploying Agility Robotics’ Digit humanoid in their warehouses since 2023, and they acquired the Belgian robotics firm Cloostermans in 2022. The internal capability is being scaled up aggressively, and that is exactly the pattern we saw with AWS in the years leading up to 2006.

The most exposed name, as we already mentioned, is Symbotic (SYM) and a London listed company Ocado (OCDO). If Amazon were to announce robotics, you can bet that these companies would plummet overnight.

Symbotic was a name I was already looking into for a TechBreakdowns deep dive, but I ended up stopping because of the Walmart concentration. If I were invested in Symbotic now, I would be worried just knowing that Amazon is going to keep collecting these puzzle pieces. Because yes, this is Amazon’s world and we are just here to buy it.

The trillion dollar question is whether AI infrastructure is actually earning its cost of capital. Tonight, three different names gave three different answers.

What Was Reported

Alphabet gets the prize for best slide. Search is clearly not dead at 19% YoY, Cloud is killing it at 63% growth and a $460B backlog, 350M paid subscriptions, and Waymo (the side project that isn’t a side project anymore) is hitting 500K autonomous rides per week. I still haven’t tried it.

The table here tells the rest of the story for the three behemoths:

Amazon

Highlights for Amazon include operating income of $23.9B versus the $21.5B guide. AWS grew 28%, its fastest rate since Q2 2022.

Alphabet

Cloud up that whopping 63% we already mentioned, accelerating from 48% the prior quarter. It’s worth saying clearly that these are huge numbers. $20B for Google Cloud is the segment, and putting up double-digit growth at that scale would have been unthinkable a decade ago.

The backlog has also doubled and operating margin grew 200bps to 36.1%. All around a fantastic quarter and the cleanest possible answer to “does the spend earn its cost of capital.”

Microsoft

Total revenues were $82.9B (+18%). AI-related revenue grew 123% YoY to a $37B run rate. There’s also a solid $625B backlog.

Now the kicker: Q3 capex came in at $30.9B. That’s $3.4B below estimate. Microsoft was the only mega cap to announce spending less than expected, and that’s worth digging into.

Here’s a look at the AI-related items I was able to extract from the earnings releases.

Microsoft’s Capex Tell

We just mentioned it, but buried in Microsoft’s Q3 release was the most underrated number of the night. I haven’t really seen anyone discussing it.

Capex came in at $30.9B, $3.4B below Wall Street estimates.

Compare that to the rest of the field:

• Amazon reaffirmed $200B in spending for 2026. No moderation, no scaling back, no scaling up.

• Google held the $175B to $185B commitment. They didn’t raise it, didn’t cut it.

• Meta raised spending by $10B to $125B–$145B and suspended buybacks.

• Microsoft spent $3.4B less than expected and still re-accelerated Azure to 40%, while printing 123% YoY growth in AI revenue.

The demand side of the AI capex thesis is no longer a question. Three of the four companies confirmed the spending is monetizing as expected. The return side, whether this earns its cost of capital across the cycle, still needs another four to six quarters of margin data to settle.

What’s already settled is the differentiator. Going forward, it isn’t who spends the most. It’s who spends the most efficiently. Tonight, only one of these companies actually put up a strong quarter while spending less.

This is a post-earnings write-up, but I want to explore this idea in a future article. If you haven’t signed up for Tech Breakdowns yet, please do so and we’ll get back to it in a week or two.

TechBreakdown’s Octagon

This is a new publication, so we haven’t fully introduced the Tech Breakdowns Octagon yet. I promise a full write-up of the rationale soon. For now, just know that the Octagon is the eight-rule scoring framework I use on every Tech Breakdowns Format A piece.

Today, I want to touch on the three names in this article. All three score an 8 out of 8.

It wasn’t like that before earnings. Amazon was sitting at a 7 going in, with a slight reduction on Rule 7 (capital efficiency). Today that flipped from a 5 out of 10 on capital efficiency to a 6 out of 10. The capex thesis they’ve been running, all this spending, went from promise to actual proof. I’m still scoring it a 6 because of the extended capex spend, but it’s at least trending in the right direction.

The eight rules, briefly:

1. Long-term stewardship.

2. Category position. Specifically, is it a category leader in its segment.

3. Insider alignment.

4. Data, network, or process moats.

5. Switching costs. How likely or easy is it to switch away from this provider.

6. Pricing power. Can they raise prices without losing customers.

7. Capital efficiency. For these three companies in particular, what does capex look like. There’s some M&A read-through here too.

8. Innovation velocity.

This is a qualitative score, but I run it on every company we cover. It tells me whether this is a business I want to own and hold for the long term. You’ll notice price isn’t in here. That comes later. Each company gets a 1 to 10 score on each rule, passes a rule at 6+, and the aggregate is the count of rules passed out of 8.

For Google, Microsoft, and Amazon, the final score is a perfect 8 out of 8 as of earnings.

Here are the actual scores I give for each company.

Alphabet

Microsoft

One thing worth flagging on the Microsoft scorecard: it grades higher than Alphabet on both pricing power and switching costs. That isn’t a vote against Google, it’s a feature of how deep Microsoft is embedded in the enterprise stack. Active Directory, Office, Teams, GitHub, the ISV ecosystem on Azure. Multi-decade integration with sticky pricing on top. The framework is telling you something the valuation section will confirm.

Amazon

Valuation Outlook

The scores are great. They show these are high-quality businesses, which I don’t think anyone really disputed. The next question is whether right now is a good time to be buying.

I look at three things.

First, expected long-term annual return. FCF yield plus long-term growth.

Second, margin of safety on the implied growth, built using a reverse DCF.

Third, a multiple sanity check. Forward P/E or EV/forward FCF versus the historical 5-year range. For some companies I’ll adjust for cycle position and capex normalization.

Google. FCF yield is roughly 2.5%. Implied growth in the reverse DCF is sitting in the 9% to 10% range, which Alphabet has historically beaten, but the margin of safety is thin. Forward P/E is around 22 to 24x, which is closer to the upper half of the 5-year range than the lower. Middling on rule one, middling on rule two, middling on rule three. This one falls into the hold bucket.

Microsoft. FCF yield of 3.5%, which is a much nicer place to start. Decent margin of safety on the reverse DCF. Forward P/E is sitting at 22 to 24x, which is well below the 5-year average of 30x. The multiple has actually contracted through one of the strongest fundamental stretches in the company’s history.

Amazon. FCF yield of 3%, which is fairly decent for a company in Amazon’s position. Implied growth is 11% to 12%. Forward P/E is 31 to 33, sitting at the 5-year low. The multiple appears to have compressed precisely because the market is mispricing the temporary capex-driven free cash flow dip.

For the long-term holder

I’m a long-term stockholder, which means I’m looking for excellent companies at fair prices. The Warren Buffett line, a wonderful company at a fair price, is what I’m after. I lead with discipline, but I also don’t cut names like Google just because they’re slightly overvalued at a point in time. Google still has a lot of potential. I also don’t want to incur a tax bill by selling it in my taxable portfolio.

NVIDIA is a useful reference point here. NVIDIA in early 2023 was an 8 out of 8 shaped business, and it 5x’d in the following 18 months. The same NVIDIA in 2021 was also an 8 out of 8, and it lost 60% in the next 12 months. Same quality, different prices.

On that spectrum, Amazon and Microsoft today look closer to the 2023 setup. Google looks closer to the 2021 setup. That doesn’t mean Google goes down 60%. It means the entry price is doing more of the work.

A framework’s job is to keep us honest. If everything that beat earnings was a buy, you wouldn’t need a framework. Google isn’t a “don’t own.” It’s a “don’t add.”

Of the three, Google is still the one I most want to buy from a future-position point of view. Search grew 19% YoY. I have no idea how Google Search keeps growing at 19% on this base. It has to be one of the greatest businesses ever built, and Google keeps bolting new businesses onto the side of this rocket ship. I don’t know where that company ends. I’m holding for the long term.

I took a small nibble on Amazon when it dipped post-earnings. It was only a tiny one because it’s been a name I held for years. I sold earlier this year when I got bored of waiting for the company to actually do something. Of course it did something after I sold. I’m back in now for a little bit, mainly to keep tabs on it and to have it in the portfolio.

Microsoft is more of a personal take. The numbers are stellar and the business is firing on all cylinders. My bias: I prefer GCP and AWS as products, and I’m not sold on Copilot Chat as the consumer-facing front end. That’s a taste claim, not a thesis, and the numbers don’t care about my preferences. I’m flagging the bias rather than dressing it up.

Closing

It was a fantastic night of earnings for three of the largest companies on Earth. All three knocked it out of the park. All three remain stellar businesses.

The most intriguing from a “should I buy right now” point of view is Amazon. Google is the one that keeps catching my eye even at a stretched valuation. I’ll continue to hold it as a core position.

More on the capital efficiency angle in a future piece.

One year I wanted to break down exactly how technology worked, inspired by the likes of ByteByteGo. Other times I wanted to write about how to run technology companies, and other times I wanted to write about how to invest in technology. Each generation was a restart, but they all ultimately led back to the same path.

And that same path is what I’m relaunching with today. It’s investing and technology. It’s not chasing the high-growth, high-beta names. It’s not going to be “YOLOing” into the latest thing. Instead, we’ll be taking a well-thought-out path inspired by the likes of Worldly Partners.

The Inspiration

Worldly Partners might be familiar to anybody who has listened to the Acquired podcast. This investment firm seeks to buy and invest in companies for the long term, and they talk about 20 years plus. That’s the kind of thing I want TechBreakdowns to be.

I want it to be low stress. I want it to be: hey, we’ve looked at every part of this company and we believe we can sleep safe at night investing in it. But I also want to combine that with technology. I don’t want to sit back and just say “Coca-Cola is fine,” which, maybe, you know, Coca-Cola is fine. I’m sat here with a Diet Coke right now, and it’s pretty good.

So we’ll look at names like Meta, sure. We’ll consider investing in companies like Apple if it makes sense. But what I really want to do is turn over the stones of some of those smaller companies and find names we can invest in for the long haul.

Up First

The best way I can think of doing this is to create a content calendar right now. I have an idea of the companies I want to build a portfolio around, so here’s the list of names we’ll be looking into deeply over the coming months.

These companies fall into a few different buckets. It’s a near certainty that, given our requirements, we won’t invest in every single one of these, but we’ll take a look at them anyway and explain why.

First, let me give you the bullet-point requirements (they’re super short):

1. The company has to be one we could see ourselves holding forever.

2. Ideally, it’s strictly in tech.

3. Because we’re holding it forever, we’re a bit price insensitive.

4. Ideally, the target is not some behemoth that everybody already holds.

We’ll have to break rule #4 quite frequently, just to make sure we have some foundation to the portfolio that keeps things stable, especially if we’re planning on holding these things for 10 to 20 years.

Some of the large-cap names we’ll look at include Taiwan Semiconductor, Intuitive Surgical, Meta, Palantir, and maybe even Tesla. These are big companies that could form a foundation.

On the smaller side of things, involving companies that are not necessarily small, we’re looking at Rocket Lab, which I have a long history of covering, and AST SpaceMobile, which is launching satellites for cellular connectivity. We’re also watching Symbotic, a robotics and automation company, and CoreWeave, which is an AI infrastructure pure play. These are some of the names on the watchlist right now. Over the coming months, we’ll start putting together deep dives on these and similar names to build a portfolio that should last for a decade and beyond.

About the Author

My name is Ash Anderson, and I’m the author of TechBreakdowns. I’ve been writing and talking about finance for a very long time. I started on MotleyFool.com before it was Fool.com. I also wrote on Seeking Alpha, where I had high ratings that can be verified on TipRanks. Over the past few years, as you can see at the top of this post, I’ve tried to create TechBreakdowns a few times. I’ve gone back and forth on what I wanted it to be and what I wanted it to become. I’m a software engineer by day and an enthusiast of the financial markets by night. That’s why I’m building this. I love investing and tracking various companies, and this will essentially be my journal on that journey.