Robotics = AI.

Just 4 years earlier in development and physical rather than digital.

Every concept in robotics has a parallel in AI, and these parallels are accurate, structural, and one-to-one to the point where it’s truly uncanny. Both are general purpose automation technologies, and in the universe, these are the only two in existence because there are only two types of activities to automate: digital and physical. This entire article will be mapping every concept in robotics to its AI counterpart, and you will see how much easier it makes intuitively understanding this technology and its future market potential.

Introduction

Why should two such different technologies rhyme this precisely? Because they both fully generalize to a human capability set, and in doing so replaces human labor rather than merely augmenting it.

In 1965 the mathematician I.J. Good wrote that “the first ultraintelligent machine is the last invention that man need ever make,” because a machine that can design machines will design its successors and leave human intelligence behind. Almost everyone who quotes this assumes there is one last invention, and that it is AI.

There are, in fact, two last inventions, because thinking and doing are separable, and AI only does the thinking. It doesn’t matter how many geniuses you can cram into a data center. They literally cannot move a paperclip. You could say, “Oh, they must be smart enough to design a humanoid robot to do it for them so humanoids are just a byproduct of AGI.”

However, let me ask you this: How would an AGI manufacture that humanoid robot? Does it have supply chain relationships? Can it build factories? Can it pour concrete? Yeah, that’s what I thought.

The second last invention is the general-purpose humanoid robot. Artificial General Robotics. Instead of an AGI, it is AGR. Together, and only together, the two cover the whole of human labor.

Most general-purpose technologies (electricity, the smartphone) augment. These two replace, and replacement is what creates a singularity. Technologies of that kind (of which there are only two) obey the same patterns: the same stages of autonomy, S- or L-curve, economic singularity, RSI, Moravec’s Paradox, and even very suspiciously similar big three labs.

The Second Singularity is coming — the physical singularity.

Welcome to A General Theory on Humanoid Robotics.

Contents

1. The Four Stages of Robotics

2. The Four Stages of AI

3. The Big Three Labs

4. The Elbow of The L-Curve

5. Moravec’s Paradox

6. VLAs vs. World Models

7. The Economic Singularity

8. Robotic Self-Improvement (RSI)

9. Conclusion: Predicting ChatGPT and Claude Code Moments

Beep beep boop boop beep boop beep boop terms and conditions. Beep boop not financial advice.

The Four Stages of Robotics

Robotics comes in four stages, each one more general than the last.

1. Caged Arms

2. Cobots (Collaborative Robots)

3. Autonomous Mobile Robots

4. Humanoid Robots

Each stage is its own market. And the difference between the markets is as follows: The more general the robot, the less of the world you have to redesign to use it, and the more exponentially it spreads.

Why do more autonomous and general robots grow exponentially? It is simple. Either the environment is rebuilt around the robot, or the robot adapts to the environment. The environment is expensive and glacial to change. Thus, the robot, as it generalizes, gets cheap and fast to deploy into environments it’s never seen before, unlocking use cases with orders of magnitude higher addressable markets, until it saturates all of physical labor.

Yes I know that is a funny looking curve. I’m being serious. When I say L-curve I really mean it.

Now let’s discuss each stage of robotics one by one.

Level 1 - Caged Industrial Arms

The arms that weld cars. They are blind, dumb, and pre-programmed, fenced off from people because they cannot perceive a human walking into the cell. The entire factory is designed around them, so every incremental robot requires designing and building another bespoke environment. That is the most expensive, least flexible form of adoption possible, which is exactly why the curve is linear and the terminal value low. The installed base is tiny despite the technology being decades old.

If you look in the market, it is clear that caged arms are not a frontier technology at all. Instead, it is an industrial appliance and grows perfectly in line with industrials. The International Federation of Robotics counts on the order of half a million new installations a year, and forecasts global installations to grow only about 7% a year through 2028, with the market value (roughly $45 billion in 2024) compounding at low double digits at best. Growth tracks manufacturing and auto capex so tightly that 2024 installations actually fell in Japan and Korea.

Conclusion: These robots are equipment and not technology.

Level 2 - Collaborative Robots (Cobots)

Cobots can sense enough to share a floor with people. The environment still bends around it, just less. Because each deployment now costs less environment, the curve steepens into a mild power law and the terminal value rises, even though the hardware is weaker than a caged arm. The inflection is arriving now.

Cobots serve the same end market but grow slightly faster due to its higher generality, resulting in a larger addressable pool of use cases within industrials. It is less mature, so it starts off a smaller base (on the order of $1 to 2 billion in 2024) and compounds at high-teens to low-twenties percent a year, several times faster than caged arms despite weaker hardware.

Level 3 - Autonomous Mobile Robots

Warehouse movers, autonomous mobile robots, self-driving vehicles, drones, quadrupeds, and even food delivery robots! Here the machine adapts to circumstances it was never built for: a self-driving car works on a road it has never seen, a quadruped walks an oil field. The environment barely changes, so this is where real robotics begins and the curve becomes a true exponential. But there is a ceiling, and naming it is the key to the whole ladder: these machines move through the world without reshaping it. They cannot manipulate their environment. That missing increment of generality is what caps Level 3.

The data takes another step up. The autonomous mobile robot market (around $4 billion in 2024) is compounding at roughly 20 to 27% a year, off an ever smaller and more immature base, and that is before you fold in robotaxis and drones, which are the same generality class. This is the first genuinely exponential robotics market as these machines adapt to existing warehouses, roads, and sites instead of demanding new ones.

Level 4 - Humanoids

This is the endgame. The robotic form factor that can replicate all of the human’s actions and fully replace physical labor.

The humanoid removes the last constraint, because it is built in the shape the entire built world was already designed for: the human body. Anywhere a person can go and act, it can go and act, so the environment needs no redesign at all. The redesign already happened, for us, over centuries.

Add manipulation, the ability to change the world and not just traverse it, and generality is complete. That is why Level 4 is the only rung shaped like an L-curve: a long flat approach, then a vertical wall.

Why is it vertical? Because for one, there is no market for humanoids today. It starts from a base of zero. And two, when you add manipulation, which is the final unit of generalization, the only difference between a robot and a human laborer is cost. For the world’s unglamorous manual labor (that humans don’t intrinsically derive enjoyment or purpose from and would much prefer to work another job) there is no reason to hire, not to hire a robot once the cost becomes lower than that of a human. Therefore, there is an economic crossing point after which, you can have a zero-to-one adoption curve.

“Analyst forecasts” are hilariously far apart: Goldman Sachs says $38 billion by 2035 (a figure it revised up sixfold in a single year), Morgan Stanley says $5 trillion by 2050. Basically, no one knows. Revenue is the wrong metric for a market that does not exist yet, and later I will argue the right metric is a capability number borrowed straight from how people forecast AI.

In conclusion, the more general the robot, the larger the addressable market, the smaller the current base, the more exponential the growth, and the more frontier-technology-like it is.

The Four Stages of AI

Now AI.

Level 1 - Pre-Programmed Expert Systems

If you’re deep enough in the AI rabbit hole, you might have heard of ELIZA.

Prior to neural networks, AI relied on expert systems, which is literally pre-written code.

Maybe some more relevant applications of expert networks are things like credit scores or insurance premiums or college admissions rubrics. This really isn’t even software. It’s literally just the concept of a decision tree. The equivalent of caged industrial arms. No one talks about them. No one cares about them. Yet they are ubiquitous and run a lot of our most important systems.

But they are not growing, they are not generalizable, and they are NOT technology.

Level 2 - Neural Networks

Have you heard of a CNN?

Convolutional neural networks were the major breakthrough in computer vision that allowed neural networks to tell the difference between cats and dogs, popularized in the 2010s.

This category also includes Google Translate and the chess engine Stockfish. They are all really good AIs! The only thing is they can only do one thing. When AI beat humans at chess, people freaked out for maybe two seconds and then didn’t gaf (give a fuck). The market didn’t gaf either. These things probably grew at the speed of the general software market and, despite being AIs, did not cause any technological acceleration or boom.

Why? Because you need to redesign you need to design the environment around the AI for the AI to work, just like you need to design the factory for industrial robots for those robots to work. For Stockfish to be the smartest human on the planet, you must give it a chessboard. For CNNs to properly classify images, you must collect labeled data.

Again, without proper generality, it is not a frontier technology with a true exponential.

Level 3 - ChatGPT

This is the first application of AI where people referred to it as a “moment.” Specifically, it was the ChatGPT moment. That’s how you know it’s a real technology with an S-curve.

This is the mobile robot equivalent because it moves. It moves not through physical space, but through domains of knowledge, from one field to another. It can do your high school close reading homework questions on The Great Gatsby then in the very same chat write you the code for a shitty SVG of a meme that you wanted to post online.

Yet it is still not able to act on the world. Exactly the reason why mobile robots are still separate from humanoids. And exactly the reason why there was still another moment ahead where the growth turned from an S-curve to an L-curve.

Level 4 - Agentic AI

Ah yes, Claude Code. The reason why SMH is up 76% YTD. It’s so useful that literally everything is in shortage.

Not an AI that answers, but an AI that does work, entering any computer and operating it. This is the humanoid of software, and the operative word is the same one that capped Level 3: manipulation. The agent manipulates the digital environment the way a humanoid manipulates the physical one, and that last increment of generality turns the S into an L.

Conclusion

We have had AI since the 1960s and genuinely useful AI since the 2010s, yet the world did not care until 2023. Why did decades of AI grow at the pace of the ordinary economy rather than like “AI”? Because Levels 1 and 2 were domain-specific, and a domain-specific technology spreads only as fast as the environments built around it, exactly like Level 1 and 2 robots. The curve bent only when generality arrived. The lesson transfers cleanly: robotics has already had its long 1960-to-2010 era, and it is about to have its 2023.

The Big Three Labs

Robotics is so AI-coded that even the big three labs are the same.

You know the AI labs, OpenAI, Anthropic, and Google. But what about the robotics labs?

…FigureAI, Apptronik, and Tesla!

They match one for one, do you see the resemblance?

…no?

Figure is (early) OpenAI. The “posterchild” pure-play private that has the highest valuation ($39b) and is “in the lead.” Both their names even end with AI!

They have a backer list (Microsoft, Nvidia, OpenAI, Jeff Bezos) that reads like OpenAI’s own cap table. If you have watched Brett Adcock say at least three words, you will understand that their entire ethos is we are the leader and no one is close. They are fully vertically integrated and have the Apple approach: building both the hardware and its own AI models (its Helix system, no reliance on an outside model vendor), and it was first to ship to a paying customer, BMW, back in 2024. It talks in maximalist terms, 100,000 robots over four years, a path to being one of the most valuable companies on earth. Figure is the maxi poster child, and like OpenAI it draws both the most hype and the most “they are promising way too much” skepticism. But if you think back to the OpenAI spending commitments (and ORCL chart manipulation), when you’re the maxi, people think you’re crazy at first until you get proven right, so this company might just be a fantastic bet.

Apptronik is Anthropic. The names literally rhyme. They are the pragmatic and heads-down-execution focused counterpart to Figure. They are valued way lower at $5b.

Its founders are openly disciplined and anti-hype, and they will more or less tell you the other guys are taking advantage of irrational exuberance and “not doing the work.” Strategy-wise Apptronik is the Android of the space: more modular, more model-agnostic, a platform play rather than a single sealed product. It partners with Google DeepMind for the AI model (DeepMind’s largest-ever investment) while owning the hardware, the platform, and the data flywheel.

Tesla is Google. This is self-explanatory as they are both Mag7 companies with a frontier lab division (Optimus for Tesla, Deepmind for Google).

Just like Google, they are vertically integrated in a way that the labs cannot be. The labs can be vertically integrated, but as a mega cap, you can be hyper vertically integrated: the robot, the factories, its own TPU edge chip (AI5), the batteries, the AI training stack, and, critically, its own first customer, since the first place Optimus has to earn its keep is inside Tesla’s own factories. This probably means a lower risk for initial deployments and a big data advantage. To push this Google parallel thing further, it’s like deploying Gemini across the entire Google ecosystem. So yeah the Google of Hardware.

Then there is of course Unitree (China) which is DeepSeek with the exception that they are already a behemoth. They have by far the lowest cost structure with a BOM 1/3 of that of western leaders and offering their flagship H1 at $16k. This is simply due to them being within ten miles of all their suppliers, unlike the West, which has to import parts from Germany and Japan. This works great for semiconductors, where niching down is an asset and talent is spread across the world (because quality and pushing the frontier is paramount), but in robotics it’s actually iteration time and cost structures that are most important.

But unlike with AI, there is no open sourcing. Western markets will ban Chinese robots. So the Western market will be divvied up by the leading Western labs. I do not believe Unitree will participate.

The Elbow of The L-Curve

This is an L-curve. The elbow is what the person is standing on (and I’ve circled it too).

Definitionally, when I say something has an L-curve, it must mean that I expect there to be an “elbow moment” when it turns from flat to vertical. In agentic AI the elbow was Claude Code and we reached it earlier this year.

So what is the elbow for humanoid robotics? And when do we reach it? I believe there is one answer.

When it is cheaper to hire robots than humans to perform repetitive, fixed-station, load-bearing manual labor.

There are several things that I am saying with this one sentence.

1. Cheaper. This means that we must analyze the cost structure of humanoid robots.

2. I only define one category of tasks. This means I implicitly believe that repetitive fixation load-bearing manual labor is THE use case sufficient to reach the elbow.

Let’s focus on the price first.

The price dynamic is less prominent for AI and knowledge work just because knowledge work is much more quality-focused than cost-focused. The takeoff moment for AI has always been caused by capability and not price. Think about it. The METR time horizon chart measures the longest task an AI can perform, NOT how cheaply it can perform a certain task.

This is structural. Think about it. Token generation is orders of magnitude faster than human writing. When working with bits, it’s only natural that the virtual-native entity wins in speed and efficiency. The only question is whether it can match a human’s original thought quality.

However, in the physical world, nothing innately makes a robot faster than a human, as they are bound to the same laws of physics that we are. Therefore, you could probably get a robot to perform any task today with enough point training, task-specific RL, completion time, patience, and teleoperation. The only difference would be cost. Therefore, the robotic equivalent of the METR time horizon chart is an hourly labor cost index, like this excellent one provided by SemiAnalysis.

In order for robots to cross the cost threshold, the all-in price of a robot (purchase price per hour of useful life + expected cost of maintenance + expected human teleoperation time + expected cost of quality/reliability) must be lower than the all-in price of a human (hourly wage + taxes, medicare, social security). The all-in cost of a robot is usually not itemized like this from the perspective of the end consumer, as it is usually wrapped into a robot-as-a-service (RaaS) contract. Also, by using hours, I am ignoring the idea that robots don’t get injured, sick, or tired and can work double shifts, because that is just calculated into the purchase price per hour of useful life by extending the useful-life-hours.

Let’s make this nice and mathematical.

for a specific task, r = (robot $/hour) / (human $/hour)

r > 1, hire some dude. r < 1, use a RaaS. When r falls below 1 on a task that is a large share of the economy, the elbow has arrived.

Now actually run the numbers. How far are we?

Figure leases its robot at around $100,000 a year, and experts describe a real deployment today as something like a $50,000 robot plus six figures of onsite integration plus a human minder to babysit it. Amortize the hardware, add a teleoperator who can supervise only a few robots, and account for low uptime and rework, and a robot’s all-in cost today lands somewhere around $85 per productive hour, against a human line of $15 to $30. So today’s r is roughly 3 to 6x.

Robots are not close, which is why current deployments are really subsidized pilots and data-collection exercises, not real business. But it’s not an order of magnitude off!

But look where the cost is. The sticker price is already near parity: a $100,000-a-year robot that runs two or three shifts is competing with two or three human workers, so the hardware alone is only slightly above the line. The thing keeping r at 3 to 6x is the teleoperation and reliability. That is the entire game. The crossing does not require a cheaper robot so much as a robot that needs far less babysitting and runs far more of the day. When autonomy rises enough that one supervisor covers fifteen robots instead of three, and uptime climbs toward multi-shift, the all-in number collapses toward $10 per productive hour and r drops below 1.

Moravec’s Paradox

In my definition of the elbow, I only defined one category of tasks. Now we will discuss why.

I believe for AI, there was one use case and one use case only which helped us achieve economic takeoff: coding. It was a task that is large enough in the economy and the first for automation to reliably outperform humans. I call a task this set of attributes a critical task.

To grok the nature of a critical task on a much deeper level, let’s visit a concept known as Moravec’s Paradox.

In the 1980s Hans Moravec, Rodney Brooks, and Marvin Minsky noticed something strange about AI: it is comparatively easy to give a computer adult-level performance on intelligence tests or chess, and almost impossible to give it the perception and mobility of a one-year-old. The explanation is evolution. The longer natural selection has worked on a skill, the more deeply optimized and the harder to reverse-engineer it is. As Moravec put it, encoded in the sensory and motor parts of the human brain is “a billion years of experience about the nature of the world,” while “abstract thought is a new trick, perhaps less than 100 thousand years old.” This yields the master ordering rule for the whole automation wave: machines conquer human tasks in roughly the reverse order of how long evolution spent building them. Evolutionarily recent skills, symbolic, abstract, structured, fall first; evolutionarily ancient skills, perception, dexterity, locomotion, social reading, fall last.

It is these evolutionarily recent skills, specifically the ones that matter for the economy, that are candidates for being a critical task.

Now let’s look coding’s fundamental attributes.

1. Data rich. The corpus already existed. BigCode’s “The Stack” assembled over 6TB of permissively licensed source code across 358 programming languages, roughly 900 billion tokens in its second version, scraped from public GitHub. Decades of human code were sitting there, labeled and ready, the way no physical-action corpus is.

2. Verifiable. Code compiles and runs, or it does not. That objective, automatic signal is what let reinforcement learning with verifiable rewards work on code and math when it stalls elsewhere: the reward comes from a compiler or unit test, it is binary and bias-free, and the model cannot easily hack it. A clean reward is a cheat code for machine learning, and code hands you one for free.

3. Evolutionarily novel, therefore scarce and highly paid. Programming is so recent that the brain has no dedicated machinery for it. An MIT study found that reading code does not recruit the brain’s language network at all, it lights up the general-purpose “multiple demand” network, and the authors note that “computer programming is such a new invention that there couldn’t be any hardwired mechanisms that make us good programmers.” So competent programmers are rare and expensive.

   1. Instead, human brains are optimized for social intelligence. Social interaction is inherently far more complex than writing a few lines of code. Yet our brains handle every micro input subconsciously, without us even having to notice it.

TLDR: Hard for us, useful for the economy, easy for a machine? Automate it.

So now let’s translate this to our physical bodies. What is the physical equivalent of coding, and what is the physical equivalent of social intelligence?

The body’s equivalent of social intelligence is endurance running. Humans are the best endurance runners in the animal kingdom, a capability that emerged about two million years ago in the genus Homo, with spring-like tendons, an upright gait, and, above all, a cooling system without peer: two to four million eccrine sweat glands spread over nearly hairless skin, giving humans the best evaporative thermoregulation of any primate and the ability to run prey to heat exhaustion in the midday sun, a tactic called persistence hunting. We can also add precision dexterity to the mix, as our hands have dozens of degrees of freedom and our senses are hyper-tuned to its positioning.

(As they always say, it is sweating and opposable thumbs that let humans take over the world.)

The body’s equivalent of coding is factory work. Evolution optimized the body for varied, intermittent movement, not for repetition or sustained static load. The assembly line is barely a century old, Ford put it together in 1913, and the body has no adaptation for performing the same motion thousands of times a shift or holding a loaded posture for eight hours. So it breaks, measurably and expensively. Overexertion and musculoskeletal disorders are roughly 30% or more of all US workplace injuries, transportation and warehousing carry the highest serious-injury rate of any major sector, and over a million Americans suffer serious back injuries each year (US BLS). Overexertion from lifting, carrying, pushing, and pulling is the single most expensive category of serious workplace injury, about $13.7 billion a year in direct US costs, the bulk of it manual material handling (Liberty Mutual Workplace Safety Index, 2024). That injury bill is the body “hating” the task, the precise physical analogue of the brain hating symbolic logic.

We have our answer. The critical task, the “coding” of robotics is repetitive, fixed-station, load-bearing manual labor. Machine tending, parts and package handling, palletizing, sorting, kitting, repetitive insertion and assembly. It clears all three bars at once, for the same reasons coding did.

In addition, factory work is pretty big and important to the economy if you ask me. This is why the first customers of these humanoid startups are all car factories.

Finally, let’s look at the other side. The last physical tasks to fall are the body’s social-intelligence equivalents: dexterous, varied, unstructured, socially embedded work, the nurse’s touch, the chef in a chaotic kitchen, and childcare. High evolutionary investment, high Moravec tax, low data, low verifiability, deeply social. That bundle is the robotics version of humor, and it is the part of physical labor that stays human longest. The same evolutionary moat that protects the social parts of white-collar work protects the dexterous, caring, and improvisational parts of physical work.

The Economic Singularity

What happens once the elbow’s reached and the critical task is automated? Well, my friends, I introduce you to the concept of economic singularity.

Economic singularity: the state in which a technology is so cheap and so useful that no actor retains the agency to stop its development, because demand alone now forces it forward.

It is not necessary for a technology to be in the economic singularity state for a huge build out to occur. Take AI in 2023 and 2024. Hundreds of billions were being poured into capex while the bears argued that there is no way chatbot subscriptions can pay for all of it, and I agree! But that doesn’t mean the build-out has to stop. It just meant that its continued development was contingent on the decisions of the parties funding the development. You could have stopped it by convincing Satya Nadella, Andy Jassy, and Sundar Pichai to halt AI capex.

I believe the economic singularity has occurred (or more accurately, an economic event horizon, a point of no return) when demand alone is sufficient to drive continued development. The reason why this is a “point of no return” and not simply a temporary supply-demand imbalance is that, theoretically speaking, the marginal utility of a general-purpose automation technology like AI or robotics never flattens out, similar to the marginal utility of money itself, as it can be traded for any other good or service. Smartphones saturate when everyone owns one, but here, the TAM is GDP itself.

This is what happened with Claude code. Now you can convince those same three CEOs to stop tomorrow and nothing changes, because demand is so overwhelming that someone else simply spends to meet it. Capability is now sufficient to ensure AI’s own continuation. Development left the agency of any company and passed to the market. That is the singularity: capitalism, not a boardroom, drives it.

This is what will happen to robotics once r < 1.

Today humanoid R&D is a discretionary bet placed by a few well-capitalized companies. Development contingent on their agency. Once the critical task crosses r below 1, Level 4 hits the elbow: buyers demand robots faster than anyone chooses to build them, demand becomes the driver, and production becomes self-pulling.

Robotic Self-Improvement (RSI)

It’s recursive self-improvement… but robots. Robotic Self-Improvement!

Recursive self-improvement is simple. An AI good enough to do AI research designs a better AI, which designs a better one, and capability compounds with no human in the loop. That is the digital singularity.

Robotic self-improvement is the same loop in the physical domain. A humanoid general enough to build robots and to build and run the factories that build robots closes a physical cycle: robots staff the lines that produce robots, which build the factories that produce more robots. They multiply like bacteria.

This is the end game. I believe we are likely to reach RSI for AI before RSI for robots, just because AI development is approximately 4 years ahead. However, once you have both, you have reached the ability to automate every single part of the economy.

They feed back on each other too. Better AGIs design better robots to speed up robot production even more and better robot neural nets to make them smarter, while robots build better fabs and datacenters to feed the AGIs. Double infinite recursion.

This is infinite GDP.

Conclusion: Predicting ChatGPT and Claude Code Moments

There are two robotics inflection points to try to predict:

1. The ChatGPT moment, which is when level 3 reaches the inflection point of its S-curve

2. The Claude Code moment, which is when level 4 reaches the elbow of its L-curve

I believe we’re in the robotics equivalent of AI’s 2022. The ChatGPT moment is likely just around the corner while the Claude Code moment is ~4 years away. This puts robotics at a development stage roughly 4 years behind AI.

The robotics ChatGPT moment: 2026 to 2028, just beginning now. This is Level 3 taking off, the first real exponential, mobile and perceptive machines crossing into broad use. We have not quite had it yet, but 2026 is where the inflection starts to show. Here we want to track the KPIs, like miles driven, revenues, number of deployments, and whatnot. There are many tasks and forms of robots (unlike level 4 and humanoids) so the correct measure is an aggregate of many measures. For example, autonomous mobile robots are compounding north of 20% a year, and robotaxis are growing pretty quickly: Waymo went from about 250,000 paid rides a week in early 2025 to roughly 450,000 to 500,000 by early 2026, doubling in under a year, with a stated path past one million a week by the end of 2026. Tesla is not doing bad either. We are honestly almost there; all that has to happen is for the public to start becoming enamored with one of these products, and I think there’s a solid chance it happens in 2027.

The robotics Claude Code moment: 2029 to 2031. This is Level 4 humanoids hitting the elbow, the critical task crossing r below 1, and the economic singularity engaging. Here revenue is useless, because there is none, exactly as there was no “coding revenue” to extrapolate before agents arrived. So borrow the trick that actually worked for AI. METR measured AI capability as a task time horizon, the length of task (in human time) a model can finish at 50% reliability, and showed it doubling roughly every seven months, an extrapolation that has tracked reality really damn well and let people call the agent inflection before it happened.

The robotics analog is the cost horizon on factory labor.

This is illustrative, but I believe a chart like this is going to become the robotics equivalent of the METR time horizon chart for AI, religiously tracked on X to answer the question of whether scaling laws are intact. If we assume that robotics costs decline in line with Moore’s Law (i.e., a halving every two years), then we will reach the elbow in 2030. If we assume that robotics development is four years behind AI and the robotics Claude Code moment happens 4 years after the AI Claude Code moment, which was 2026, then we also land at 2030. Therefore, 2029-2031 is my current timeline.

Now for closing thoughts. As investors, we are blessed. We have watched the first General Purpose Automation Technology take off, and through empirical observations, we have understood every stage of its inflection. Today, we sit at the precipice of the takeoff for the second. Robotics’ attributes are fundamentally analogous to what we observed and understood about AI, allowing investors to make predictions with far higher confidence than betting the farm on AI infra back in 2022.

Yet the world (and the market) doesn’t see it because it’s not yet “in your face.” That is our opportunity.

Luckily the free section still exists on X. Press F to pay respects to the paid section.

If you’ve been struggling to value NeoClouds or even think about them clearly and have figured out that they’re different from other businesses but don’t know why, do not worry (I figured it out)

Why NeoClouds Break Normal Valuation

Most companies are valued on what they already are. A memory company is a bet on book value through a cycle. Nvidia or TSMC is a bet on earnings: a durable moat that earns and grows profit as the market grows. Crucially, these businesses do not need to raise capital. Even the capital-intensive ones fund growth out of operating cash flow; the occasional debt or equity raise is a one-off, not the engine. Nvidia never has to raise capital. Micron never has to.

NeoClouds are the opposite. Raising capital is not incidental to the model; it IS the model. Annual capex runs at a large fraction of, and sometimes the entirety of, the company’s market cap, every year, while revenue grows just as fast. That breaks both standard lenses at once. Forward multiples stop meaning anything because the denominator is exploding. Price-to-book stops working because book value is unstable because it’s being rebuilt every quarter by fresh capital, and the price itself is unstable because enterprise value is constantly inflated by new debt and dilution. The equity is a moving target sitting on top of a balance sheet that is a moving target.

What you are actually holding is a corporate wrapper for raising capital and deploying it into compute. Once you see it that way, the right valuation lens falls out.

Spread

A NeoCloud takes money from debt and equity investors and invests it in GPUs and data centers, exactly as a bank takes money from depositors and invests it in assets. The value it creates is the spread between its cost of capital and its return on that capital, and the equity is worth the present value of that spread, compounded over everything it will ever deploy.

This is how the operators themselves underwrite it: a mid-to-high-teens unlevered IRR on compute investments over roughly the ~5-year life a GPU is depreciated to. A high-teens IRR simply means the contract price on that GPU is high enough above the all-in cost of the GPU to clear that return. So the entire bull-vs.-bear question collapses to one thing: is there a durable spread between return on capital and cost of capital, and is it widening or compressing?

Why Would a Spread Exist?

The bear case is clean: NeoClouds are middlemen who buy compute and resell it. If that is all they do, customers hold the pricing power and compete the contract price down until return on capital equals cost of capital, and the spread goes to zero. The bull case is that the spread is real and persistent. There are four sources of it: two genuine value-adds (durable), and two commodity-cycle dynamics (positioning):

1. Product / operating excellence (value-add). Being genuinely good at running a cloud, with software, orchestration, and API services on top of the raw metal. A better product commands premium pricing, which widens the spread directly.

2. The people (value-add). What a customer is really buying is the operator’s team. Its relationships with Nvidia, the OEMs, and the rest of the supply chain, plus a track record of delivering, are far stronger than any relationship the customer could build on its own. You are buying that team’s relationship with the suppliers, and the certainty that they will turn scarce silicon into delivered, on-time compute. That certainty commands a premium.

3. Allocation scarcity (commodity). In a shortage, access to Nvidia allocation is itself the scarce good. You pay the NeoCloud to get in line, economically identical to paying fees into an SPV to access an allocation in a hot private company.

4. Performance surplus (commodity / market inefficiency). Each Nvidia generation improves performance by roughly an order of magnitude, but Nvidia cannot raise price by an order of magnitude. That would invite antitrust scrutiny and customer revolt, so it passes much of that surplus downstream. NeoClouds are first in line to receive it. Everyone in the chain captures a slice, but the first recipient can take the most disproportionate share. The present value of that inefficiency lands straight in the IRR.

The first two are moats you build. The last two are a function of being in the right place at the right time in the cycle. Today all four are live, which is why the IRR is high-teens while the cost of capital is nowhere near it.

Growth IS Intrinsic Value

Here is the part that makes NeoClouds strange. Because the IRR sits well above the cost of capital, every dollar deployed is a positive-NPV project. And demand is not the constraint; it is effectively infinite. Operators will tell you that a fresh 500 MW would sell instantly. So the business is not gated by how much it can sell. It is gated by how much capital it can deploy and how fast it can build.

That inverts the usual logic. For a normal company, growth is good only if it earns above its cost of capital on incremental investment, and it is usually demand-limited. For a NeoCloud in a shortage, incremental deployment is reliably positive-NPV and supply-limited, so more deployment is simply more value. The equity is worth the present value of the spread on all the capital it will ever put to work. This is why a NeoCloud can trade at a “ridiculous” price-to-book or price-to-earnings and still be cheap: if the market is not pricing in that the company will grow its compute base by an order of magnitude, the present value of the spread on that growth can dwarf the current, supposedly-overvalued, equity value.

Enterprise value is the present value of future positive NPV projects, not just future cash flows!

What Makes a Good NeoCloud

It follows directly. There are four ways to be a good NeoCloud: two that widen the spread, and two that let you deploy more positive-NPV capital.

Widen The Spread

1. Build a great product. A real cloud and software/API layer earns premium pricing.

2. The people. A NeoCloud’s supplier relationships, credibility, and track record live in its team. A customer is buying that team and its relationship with the suppliers, stronger than its own, and pays a premium for the certainty it provides. This is what separates it from lever 4. Here, it is the relationship and credibility that earn pricing, not raw build throughput.

Deploy More Capital

3. Capital access and cost of capital. Raising quickly, at good prices, and lowering the cost of capital both widens the spread and removes capital as a growth constraint.

4. Construction and execution speed. Power access, building data centers faster than anyone else, and hiring and operating at scale. This is the most important lever, precisely because demand is infinite; the binding constraint is how fast you can physically build.

A bad NeoCloud is a pure commodity middleman: thin or no product, ordinary execution, and expensive or constrained capital. Its spread compresses toward its cost of capital and its growth stalls, so it has neither a wide spread nor a large base to apply it to.

The best operators combine all four levers: a genuine product and the right people to widen the spread, plus cheap, abundant capital and construction speed to deploy it. There is also more than one way to win: an operator can lean on product and software differentiation, or on raw scale, allocation, and a relentless capital-markets engine. What they share is the ability to compound a wide spread across a rapidly growing compute base faster than the market underwrites.

The Investment Translation

Don’t value a NeoCloud on a forward multiple or on book. Both are unstable by construction. Value it as the present value of the spread between return on capital and cost of capital, applied to all the capital it can deploy. Two questions decide everything: Can it sustain the spread? (product + people/relationship moats, against the bear case that customers compete it away) and Can it keep deploying? (capital access + construction speed, against a world where demand is never the limit). The best and most underpriced NeoClouds are the ones that compound a wide spread across an order-of-magnitude-larger compute base, faster than the market is willing to underwrite.

To be clear, believing in an Indium Phosphide shortage is different from just being bullish on optics broadly.

Optics is an end market. Indium Phosphide is a material input.

The intensity of the material usage in the end market can either go up or down. In addition, there are also supply dynamics. Demand could go to the moon but there is no shortage if supply goes to Mars.

There are also many layers to the Indium Phosphide supply chain, as I cover in this piece:

• Indium Phosphide substrate

• MOCVD/Epitaxy machines

• Laser device fabrication

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Some of which will be in much greater shortage than others.

The key to… bottleneck investing (sigh) is to figure out which layer controls the gating input to the final unit produced. It could be the upstream most layer, downstream most layer, or anything in between. There is a common perception that the more upstream something is, the more of a bottleneck it is, but that relationship is not necessary.

Our agenda today is simple. We start by highlighting why Indium Phosphide demand is going hyper-exponential beyond EMLs and regular commodity CWs powered by two main products:

• Ultra-high power CW lasers for CPO

• Indium Phosphide PICs

Then, we’ll build supply and demand models for all three layers of the stack: indium phosphide substrates, MOCVD machines, and laser devices.

Finally, we’ll finish with how this translates into the financials for the most important company in each layer.

Contents

1. Ultra-High-Power CW Lasers for CPO

2. Indium Phosphide PICs

3. InP Substrate Demand Build

4. InP Substrate Supply Build

5. MOCVD Tools Supply/Demand Model

6. InP Device Supply/Demand Model

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Ultra-High-Power CW Lasers for CPO

UHP CPO lasers are a topic that I’ve covered extensively. So much so that you are probably tired of me talking about it. Welcome to my nth laser lecture.

There are two types of lasers:

1. Vertical-Cavity Surface-Emitting Lasers (VCSELs, pronounced vik-sel) emit light from vertically… from a cavity… on the surface. They use GaAs, aren’t great for long reach because they can’t emit O band light, and fail at higher data rates

2. Distributed Feedback (DFB) lasers emits light horizontally through a bunch of tiny mirrors that bounce it in a feedback loop to get a really concentrated and clean beam. They use indium phosphide! These are the primary CPO lasers.

For CPO, the DFB light source must be remote for the purpose of avoiding heat and maintaining serviceability.

The only way to ensure that the light survives the coupling loss from this journey is to make it high power.

Why not multiple lasers instead? To maintain signal integrity, the light traveling from the laser to the optical engine has to stay in a consistent orientation — consistent polarization. This means every laser requires polarization-maintaining fiber, which is expensive, and the process of attaching it is painstaking and yield-sensitive. We must minimize the number of PM fiber connections per system at all cost, which means using only one laser.

These lasers are also continuous wave (CW). DFBs can either be CW or EML.

CW lasers are lasers that just stay on. It produces a continuous stream of light, like a light bulb or flashlight. A CW laser can provide a steady beam that gets modulated (gets the signal put into it) somewhere else.

EMLs (electro-absorption modulated laser) are lasers plus a built-in light switch. EML stands for electro-absorption modulated laser. The laser creates the light, and an attached modulator rapidly changes that light on/off or high/low to encode data.

Die Sizes

Now comes the fun part: die size estimation. We know that the three main types of lasers used for optical data center optical communication are

1. Low power CWs (30-70mW, used in Sipho 1.6T transceivers)

2. EML (used in all sorts of transceivers)

3. Ultra high power CW (400mW+, for CPO)

So to gauge demand for InP, it’s time to ask: how much Indium Phosphide does each type of laser actually require?

Our base unit will be the 100G EML because it ships in the most volume today. This is the optical equivalent of Toyota Corollas or Big Macs. Or football fields.

EMLs are very low power, even lower power than low power CWs. However, they have a built-in modulator. Therefore, low power CWs are smaller than 100G EMLs.

200G EMLs are obviously larger than 100G EMLs.

Ultra high power CW lasers on the other hand, are MASSIVE. You need a physically larger gain region to generate all of that light. This makes it 5-10x larger than a 100G EML.

This massive die area consumption is what makes UHP CWs an order of magnitude more InP heavy than existing products on the market. They are the first of our two InP demand drivers.

Indium Phosphide PICs

The second InP demand driver is Indium Phosphide PICs. These are not as widely discussed as ultra-high power CWs, so let’s take a moment to understand what they are and why they are needed.

Optical communication is simply transmitting data through light. There are two ways to encode data in light (known as modulation): IMDD and coherent (no not the company).

IMDD is basically flashing a flashlight on and off. Only encoding signals in brightness.

Coherent modulation is like having one of these:

It encodes signals in brightness, phase, and amplitude.

The benefit of coherent modulation is that it makes it much easier for the receiver to detect the signal, therefore allowing the it to survive a greater insertion loss (higher link budget). This is why coherent is used for long-reach telecom and scale across.

On the other hand, it is much more expensive and complicated, as you can imagine.

Scale-out in the data center is now running into the limits of IMDD’s link budget. This is because:

1. The links are too long

2. Optical circuit switching (OCS) is being adopted. Each time light passes through an OCS, it loses 1.5 to 3 dB of signal.

Therefore, scale-out is moving to adopt a light version of Coherent known as Coherent-Lite (Yes i know it sounds like a Coherent and Lumentum merger. Who named these modulation schemes?).

Coherent light modulation requires a far more complex optical device (InP PIC) than IMDD. It makes a 100g EML look like a 2x4 Lego brick.

All of this extra stuff is to modulate phase and amplitude.

Note that not all of this is Indium Phosphide. You can have some parts of it be silicon photonics, which makes it a lot cheaper. The standard version, which has the transmit on InP and receive on SiPho, has a substrate consumption intensity of approximately 100x that of 100G EMLs. This is what we’ll be using for our models.

InP Substrate Demand Build

Next, I will share my supply and demand models for:

• indium phosphide substrates (AXTI)

• MOCVD tools (Aixtron)

• indium phosphide devices (Lumentum, Coherent, Nokia, etc.)

We start with an overall demand build for 4-inch indium phosphide wafers that informs every model.

We’ll look at some pretty charts that show the equilibrium balance/imbalance intuitively. The numbers from every use case (demand) and every supplier (supply) shall be color coded. My research is really lit.

The metric unit of measure for indium phosphide demand today will be 4-inch wafers. This can be used to measure everything from substrate demand to epitaxy demand to final device demand. But this chart below is substrate-specific. You’ll see why this is important later.

If you didn’t grok the implications of the wafer area intensity numbers from the previous two sections, hopefully this graph shows you just how insane it is. All of the transceiver lasers are minuscule compared to ultra-high power CWs and Indium Phosphide PICs.

From 2026 to 2030, the total demand CAGR is 56%, increasing by nearly 6x, driven entirely by the two new use cases, UHP CW and InP PIC, which together contribute 6x more content than traditional transceivers by 2030.

But also observe that non-AI uses for InP still take up a big chunk of the demand. This means that even with gangbusters growth, AI is starting from a smaller base than many think. This is important for the substrate market, not so much for epitaxy or device fabricators, as they are much more AI-focused and don’t touch the telecom end market as much.

InP Substrate Supply Build

Substrates are our first supply build.

For more context on the three layers of the indium phosphide supply chain that we’ll be analyzing the supply of today, please read this article. It is my most popular article and is required reading.

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The results for our first look at supply might actually surprise you and not in the way you expect.

Interesting. Demand actually never fully exceeds supply? And it doesn’t even get close to announced nameplate, which is simply a measure for the aggregate supply ambitions of all substrate vendors, not how much they are actually likely to bring online.

The answer is there is a China problem. Particularly in substrate capacity, as once you are actually competent, it is quick to bring online.

One Chinese firm, Yunnan Germanium, will make up nearly half of all Indium Phosphide production by 2030. However, this is even a conservative estimate of their capacity, as they have publicly committed to 450k of 4-inch equivalent wafer supply, and our 2030 full ramp is only 250-300k, to add a buffer for export controls and qualifications.

As a result, despite explosive demand, supply never really becomes tight. This is obviously bad for AXTI. In a way, it is kind of the opposite of memory, which is a market with much more modest demand growth but extremely inelastic supply, as it takes years to bring on a new fab and is much more capital intensive. The market structure of memory, despite lower demand, is perfectly ripe for a massive shortage. Indium Phosphide substrates, despite having much faster demand growth, is not.

This teaches us an important lesson: the bottleneck is not always the upstream-most component. A shortage could, in fact, be somewhere in the middle of the supply chain if the upstream inputs are abundant and the scarce “magic” is added after the fact. Now let’s look at this scarce middle.

MOCVD Tools Supply/Demand Model

The MOCVD tool market is literally only Aixtron.

If you listen to enough earnings calls, you will know their capacity forecasts are approximately 1 billion in total revenue per year or 240 tools per year. However, this capacity is expandable. They are currently working a one-shift model, but they can instead work two shifts or weekends if demand necessitates, leading to higher effective capacity. In addition, they’re building a brownfield plant in Italy and a greenfield plant in Southeast Asia, which expands their capacity to 300 (after the brownfield plant) and even higher (after greenfield).

So the supply curve ends up looking something like this. However, not all those tools will be available for Indium Phosphide. Aixtron has a big power semi business, which will likely inflect with the advent of 800 VDC.

But notice demand is actually overwhelming enough that my model shows we will shift into a shortage by 2028! How come we will be short MOCVD machines but not substrates?

The simplest explanation is to blame it on China, but there’s actually a clever structural logic here that I figured out because I am a genius. Currently, MOCVD tools being shipped are all new shipments specifically for Datacom. Every incremental tool shipped is needed because of data center lasers — there is an existing installed base that produces legacy telecom devices which aren’t compatible with these new datacom lasers.

However, because substrates are a flow rather than a stock, the non-data-center shipments recur every year. This is why the “other InP uses” bar in our very first demand chart is so much bigger than the EMLs and CWs for data centers.

This means that demand quintupling matters way more for total MOCVD tool demand than total substrate demand as the base that you are quintupling is pretty much the entire pie rather than just a tiny slice of that pie.

InP Device Supply/Demand Model

Finally, let’s talk about InP devices. This is your Lumentum, Coherent, Nokia, etc.

This is a bit different because not all devices have fungible capacity. A fab qualified for ordinary EMLs cannot immediately pivot to making ultra-high-power CW lasers or InP PICs and vice versa.

Which results in the obvious outcome: some products can be in massive shortage while others are in crazy over-supply.

The Indium Phosphide PICs capacity is dominated by Nokia’s San Jose Fab. Nokia has by far the highest amount of capacity for this component (8 / 55 / 110 / 175 k wafers for 2027–2030). Coherent is a fast follower, ramping to 55k wafers with their 6 inch process. If demand for coherent light transceivers surprises to the upside, it is Nokia that wins disproportionately (alongside Marvell who supplies DSPs.

And yes, ultra-high-power CWs are, by far, the component that will be the most short. The mechanism is pretty simple: these are probably the hardest type of lasers to make, so the qualification process is brutal. Currently, only Lumentum has lasers that can meet the very demanding bar.

In addition, others may be able to qualify, but if their process quality isn’t nearly perfect, they can only achieve the right specs by enlarging the die size and thus reducing output. It is a brutal (very attractive) market.

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A lot of people enjoyed it so I will be copying the format one for one because I am very uncreative.

Introduction

An OCS is just a mirror box. Light goes in, bounces off mirrors (or other stuff), then goes out in a separate direction.

The most important concept to understand when analyzing the OCS market is the power of a t t a c h r a t e.

The switch itself has a bunch of ports. Higher port count is referred to as higher radix.

Now the unit of measure for OCS shipments isn’t the number of boxes because boxes can vary in size. Big boxes cost more, small boxes cost less. Instead, it is the number of ports as the cost of a box generally scales with its port count.

Attach rate can therefore be defined as the rate at which ports are attached to XPUs. 2:1 means 2 OCS ports for every accelerator chip.

And for every OCS use case, the attach rate is rising.

On top of that, OCS itself is a new technology that is in the early-mid stage of its S-curve.

Therefore, this is an excellent market because its growth rate scales with three vectors: AI growth, adoption rate, and attach rate.

OCS growth rate = AI growth * adoption rate * attach rate

Contents

1. Switch Primer

2. OCS vs Electronic Packet Switch (EPS)

3. Spine Switch Replacement

4. Google TPU Superpod OCS

5. Scale-Up OCS

6. Types of OCS

7. OCS Market Model

8. Lumentum

9. Coherent

10. Hidden Small-Cap OCS Vendor

11. Final Thoughts: Better Than CPO?

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Switch Primer

A switch is very simple.

Data centers must be organized in a hierarchical pattern otherwise every GPU has to talk to every other GPU which means links scale at O(n^2) which would fill up the entire data center with wires pretty quickly which is generally considered to be not good.

In order to form this network, traffic must be routed, and it is switches that route the traffic.

OCS vs Electronic Packet Switch (EPS)

An Earnings Per Share (EPS) (lol) is the standard electrical switch that is currently used almost everywhere.

It receives an optical signal as an input via transceiver, converts it to an electrical signal, reads and routes that signal via switch ASIC (logic chip), converts it back to an optical signal via transceiver, and sends it to its destination.

An OCS, on the other hand, just bounces the light off of a mirror.

So think about intuitively what the trade-offs are.

If you have to convert a signal from optical to electrical and back to optical (OEO), that certainly takes time. It also takes energy. The switch itself is also active, which takes even more energy. So generally the big problems with an EPS are latency and power.

It’s not like this is a small difference btw. OCS is practically free compared to an EPS. Lumentum on their R300 switch:

The R300 has demonstrated 98% reduction in switch latency vs. an OEO packet switch solution.

The power requirements of OCS is also less than 10% of an EPS.

But obviously an EPS is better in certain ways. Otherwise, why is it the most widely used solution?

Think about what happens when the signal exits the switch system via transceiver. It gets received, then re-sent, meaning that the signal coming out the other side is a fresh beam of light. On the other hand, in OCS, the light bounces off the mirror and loses a bit of signal and isn’t re-amplified again.

In addition, intuitively speaking, the light also has to travel twice the distance because it never gets to stop at the switch. Think of it like taking a direct flight instead of a layover.

As a result the signal suffers more insertion loss which means that when the signal finally arrives at its end destination it is a lot more mangled. Specifically, if the insertion loss from distance (in EPS setup) is x and the switching loss is y, the total OCS insertion loss is 2x + y.

The other tradeoff is that moving mirrors to redirect connections takes time. Not that much time, only milliseconds. But EPS can make fresh routing decisions for every single packet. This is the problem behind the core reason OCS was not a thing before AI, which I will lay out for you now:

Traditional cloud traffic is mouse flows.

These are tiny, short-lived, and unpredictable connections (a search query and API call, a checkout). You need a switch to actually make decisions for every packet. The connections change too fast for those micro mirrors to handle.

AI workloads are elephant flows.

Training points the same exact clusters of accelerators to each other and leaves the connections alone for hours.

OCS bears would say that as we move to inference, we move back from elephant flows to mouse flows, as each individual user query is distinct. This may be true at the leaf level. However, at the spine level (where OCS sits), the network traffic is still classified as elephant flows, as the only data that passes is prefill to decode KV transfer and not the individual tokens.

Spine Switch Replacement

Now, let’s talk mirror box use cases. There are three of note that we will discuss today:

1. Spine switch replacement

2. Google TPU Superpod OCS

3. Scale-up OCS

Spine switch replacement refers to the replacement of the spine switch.

This is the perfect (and generally the canonical) use case for OCS. At the spine switch level, almost all AI workloads are elephant flows. Spine OCS is nearly free compared to a spine EPS, so free in fact that it lowers the spine capex by 30% and power consumption by 40%.

The thesis is simple: current spine OCS penetration outside of Google is under 5%. That number is gonna increase. Each EPS that gets replaced with an OCS adds to the yearly CAGR. This is an adoption rate story.

However, we still have to get around this insertion loss issue. The current link budget of 4 dB can barely take one OCS hop.

So what’s actually going to spark the adoption?

Coherent-Lite Transceivers!

Optical communication is simply transmitting data through light. There are two ways to encode data in light (known as modulation): IMDD and coherent (no not the company).

IMDD is basically flashing a flashlight on and off. Only encoding signals in brightness.

Coherent modulation is like having one of these:

It encodes signals in brightness, phase, and amplitude.

The benefit of coherent modulation is that it makes it much easier for the receiver to detect the signal, therefore allowing the it to survive a greater insertion loss (higher link budget). This is why coherent is used for long-reach telecom and scale across.

On the other hand, it is much more expensive and complicated, as you can imagine.

Therefore, scale-out is moving to adopt a light version of Coherent known as Coherent-Lite (Yes i know it sounds like a Coherent and Lumentum merger. Who named these modulation schemes?).

As these transceivers get adopted, the link budget doubles or triples, allowing for plenty of room for OCS hops. In fact, we will have so much link room that the light can handle two hops at once. Meaning it’s possible to build networks with a spine and super spine architecture, both of which are OCS.

Google TPU Superpod OCS

Google’s architecture is weird.

Nvidia (NVL72) is very simple and intuitive and easy to understand. Each rack of 72 GPUs is its own scale-up domain (one compute and memory “unit”). The scale-out fabric connects these racks together via transceivers, leaf switches, and spine switches.

Google’s interchip interconnect (ICI) is its scale-up network, but unlike NVL72, it isn’t one rack. Instead, it is a hodgepodge of 9,216 TPUs which share one gigantic memory domain but are connected with “racks” that are each a 4x4x4 cube of 64 TPUs (copper inside the cube, transceivers connecting cubes to each other). That’s why it’s called a superpod.

The pod is shaped like a 3D torus, so basically a giant chip donut.

Each one of these super pods has 48 OCS. Therefore, there is a deterministic port-to-TPU ratio of 1.5:1. So in essence, OCS scales with TPU shipments.

But it gets better. There’s an attach rate story too.

This is based on the excellent work by FundaAI.

FUNDA

Deep|AI Infra 2026: Shifting from "Brain Power" Competition to "Whole-Body" Evolution

In 2026, the focus of AI development has pivoted from chasing high benchmark scores to pursuing AI Agents capable of multi-step reasoning and autonomous action. This infrastructure arms race is undergoing a transformation akin to biological evolution. If an AI system is viewed as an evolving organism: the GPU/TPU represents the calculating brain; Memory and Storage serve as the memory carriers for experience and context; the CPU acts as the hands coordinating tasks; while Optics and Networking function as the limbs supporting systemic data flow and response sensitivity. Under the framework of the Agent Scaling Law, the core bottleneck is no longer just the FLOPS of a single chip (brain power), but rather the communication efficiency (limbs), the memory wall (memory), and the Total Cost of Ownership (TCO…

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4 months ago · 74 likes · FUNDA

A 3D torus is built for an all-reduced communication pattern, which is common in training. Each chip mostly talks to its nearest neighbors, and results ripple through the network like a class of middle schoolers passing notes to each other until everyone reads it.

However, in inference for mixture-of-experts (MoE) models, the communication pattern changes to all-to-all. Every token gets dispatched to separate experts living on chips scattered anywhere in the pod.

Instead of middle schoolers passing notes to each other, it’s like every kid has to talk to every other kid at the same time. But for our purposes, let’s now use a better analogy. Think of the data center doing an all-to-all communication like a city. Every household drives to the far side of the city at once.

Now let’s try to make the problem clear. Imagine in this city there is a central intersection. When there are thousands of cars trying to cross to the other side, there may or may not be mild traffic.

In the data center, this is known as the bisection bandwidth, which becomes a critical spec when all-to-all communication is necessary.

Eventually, this problem needs to be fixed. You’ve thought of every possible solution, but nothing seems to get over the fact that in a two-dimensional plane, there’s only so much space for roads, and you just can’t fit all the cars.

So you hire a genius city planner. By virtue of having infinite tax revenue, this person decides to give everyone a helicopter. Now, there’s not a central intersection. Instead, there’s a central airspace, but because you’ve added a dimension, the bisection bandwidth is no longer a constraint at all.

Bisection bandwidth for N chips given n dimensions scales with the following equation:

bisection bandwidth ∝ N^(n−1)/n

so

N^2/3 in 3D → N^5/6 in 6D → N¹ as n→∞

This is essentially what Google is thinking of doing: moving from a 3D torus to 4D or higher. And each time you increase the dimensionality, the OCS ports per TPU mathematically increase.

This will take a while. It’s not like it’s going to happen in 2027. The patents for a 4D torus are filed, but in TPU v8, Google is sticking with a 3D torus. This is likely something on the same timeline as scale-up TPU, like 2029 to 2030 or later. But it has the potential to double the attach rate in the medium term (3:1 in a 4D torus) and in the very long term, a ceiling of 5x or more (to 10:1 ratio).

Scale-Up OCS

Optical scale-up, the act of connecting the GPUs inside of a rack with fiber instead of copper, is the crown jewel of the CPO market, but for OCS, it is only an interesting call option.

This is because, unlike CPO, scale-up OCS is completely optional!

The idea is to place OCS directly into a rack to replace NVlink switches, but the thing is there’s no law of physics forcing the regular switches to fail in the optical scale-up world. It’s merely a choice by the vendor for what kind of switch they want to use. This is why I won’t be focusing on this market or modeling it very intently.

Types of OCS

As we discussed, the two main drawbacks of optical mirror boxes are the speed of redirecting connections and the insertion loss. These two drawbacks also serve as the main trade-offs between the different types of OCS. We will discuss liquid-crystal and MEMS in this section because they are the two relevant types of OCS for today’s data center deployments. Piezoelectric and Sipho will be saved for later — you will see why.

The simplest way to think about the difference between MEMS and liquid-crystal is that for MEMS you reflect light off of stuff (mirror), and for liquid-crystal you steer light through stuff (medium). All of the spec differences stem from this.

Here is a tier list of the relevant mirror box specs.

Let’s start with insertion loss and cost.

Liquid-crystal and MEMS are very similar cost-wise, but MEMS decisively wins in insertion loss. This is probably the biggest and most important difference between the two types of OCS. MEMS has a 1.5 dB insertion loss versus 3 dB for LC. Decibels are measured on a logarithmic scale too, with 10 dB being one order of magnitude, so 3 dB means that you lose half your signal in one hop. It’s pretty bad.

From Lumentum’s OCS webinar in 2025:

This capability is embodied in the R300, which currently supports up to 300x300 ports with a typical insertion loss of 1.5 dB or less. And a note about the importance of low-loss performance –this is a critical feature, as it is one of the key technical hurdles to the introduction of OCS technology into hyperscale environments. This is seen in the requirement to support additional OCS loss within transceiver link budgets – around 4dB for FR4/DR4 and 6dB for LR4/ER4.

The core mechanism behind this difference is that, for a liquid crystal, you must first split the light, then have it pass through an active medium where some light is absorbed, and then recombine it. Both the splitting and the passing-through-a-medium create insertion loss.

Meanwhile, MEMS is literally a mirror. A literal mirror.

Liquid crystal makes big claims about their reliability versus MEMS, some of which I think are a bit overstated. For example, there is one claim that MEMS wears out after three years because of its moving parts. This fundamentally overlooks the fact that even though the parts move, they don’t slide against each other, and there is, in fact, no friction. Moving parts doesn’t mean sliding parts that wear out.

Google’s MEMS fleet has been in production for a decade with tens of thousands of units. If MEMS switches failed after three years, they would probably notice and say something about it.

However, the reliability of liquid crystal is still much better.

This is due to the system having no moving parts (mirrors) and operating with much lower voltage components.

Now let’s discuss redirection time. This is what dictates how long it takes for the switch to change your port connections.

Currently, MEMS and liquid crystal are about tied at a range of 10-100 ms (liquid crystal slightly slower actually).

However, a new type of liquid crystal in development, ferroelectric LC, switches in 10-100 microseconds instead. This is a a next-generation material for Coherent. As a result, switching time is decisively won by liquid crystal.

But there’s a reason why I put redirection time in C tier.

If you even decide to use OCS, you only have to change port connections when you reconfigure your network. This happens on an order of minutes to days. Microseconds or milliseconds simply do not matter.

Finally, let’s discuss install base.

MEMS wins here for a very simple reason: Google.

Google is by far the largest employer of OCS in the world and has had a fleet of them operating for a decade. They are actually the pioneers of their technology, producing most of their switches in-house (under Palomar brand) before the massive wave of AI demand forced them to externalize. And it’s all MEMS.

Read their Mission Apollo paper if you’re interested in learning about their deployments. But notice how MEMS-centric it is.

This is S tier. This matters a lot. Proven reliability at scale is a defining requirement for data center procurement officers.

Overall, MEMS is way better.

But this doesn’t mean that liquid crystal won’t get any adoption. It is still viable technology and the specs here don’t decide adoption as a binary. If MEMS vendors’ capacity is sold out, hyperscalers will happily buy liquid crystal. Having an OCS at all is what matters more.

Below, I will share my detailed model for the OCS market with accelerator shipments, TPU shipments, external merchant share, ASP, and port ratios informed by industry checks. I will discuss Lumentum and Coherent as players in this market and how I expect it to be split between the two of them, with models for each. Finally, I will share a hidden small-cap OCS vendor.

OCS Market Model

Here is what our discussion about each of the OCS end markets looks like when turned into a model:

Bucket 2 and bucket 3 in reality are the same because they are both spine switches. I just separated out Google from non-Google.

Google has an internal OCS unit called Palomar. It makes up the majority of Google’s OCS consumption today. However, the demand for OCS within Google is growing so fast that its internal supply cannot keep up, and the difference must be filled by merchant capacity.

Based on industry checks, I expect external share to be roughly 50% of Google’s Superpod OCS demand in equilibrium, increasing from 35% today. Google’s spine switch OCS are much more internally supplied, so I model the external merchant share to be much lower.

Unlike Google, the other hyperscalers generally do not have their own internal OCS unit so merchant share is forecasted to be 90%.

This is an elegant market. It’s an almost 50/50 split between Google superpod and spine replacement.

Lumentum

I expect Lumentum to capture a higher share of the overall OCS market due to the superior architecture of MEMS, their incumbency at Google, and their higher revenue run rate today.

Lumentum makes modeling easy for us.

OCS ramps quite a bit faster than lasers because it is mostly assembly and doesn’t need the clean rooms and fab tools that create the crazy lead times.

As I discussed above, I expect MEMS to retain a higher share, and Lumentum, as currently the only supplier of MEMS, is bound to capture most of it.

I forecast their revenue well surpassing their $1 billion 2027 target and reaching over $2 billion by 2028 and $5 billion by 2030, culminating in a high double-digit CAGR.

Coherent

I expect Coherent to be the most viable OCS second source. Both Lumentum and Coherent’s market share, however, will decline as new entrants such as Chinese competitors and ODMs like Molex enter the market due to the relatively lower moat of OCS manufacturing. New entrants will qualify eventually, as OCS doesn’t have the same moat that laser manufacturing does, but I expect this to be limited as there is a lack of scaled players besides Lumentum and Coherent.

Coherent OCS revenue generally hovers around 50-60% of Lumentum’s but is still a substantial contributor to Coherent’s total revenue base.

Hidden Small-Cap OCS Vendor

In Switzerland, there is a tiny company that makes OCS called Huber+Suhner. The ticker is HUBN.

They make the piezoelectric OCS.

Unlike MEMS OCS, which uses micromirrors to redirect light by physically bouncing beams from one fiber to another, piezoelectric OCS uses piezoelectric actuators (tiny structures that move or bend when voltage is applied) to slightly shift optical paths or coupling points (the places where light transfers from one path into another), steering the connection without relying on tilting mirror arrays.

Their revenue is approximately one order of magnitude smaller than Lumentum’s. (CHF is approximately 1 USD, so basically you can treat them like the same.)

Their current run rate is $50 million vs Lumentum’s $400 million, and in 2028 they are projected to make $200 million vs $1.5-2 billion for Lumentum.

But so is their market cap. Even more so, as it is 20x smaller. So technically they have 2x the OCS exposure per $ of market cap that Lumentum does.

So the bet is really on which technology has the better chance to bring forth upside surprise. That means we have to think about the engineering tradeoffs.

Piezoelectric OCS has <1.0 dB insertion loss but is 3x more expensive.

!!!

Is this worth it? I don’t think so.

Insertion loss most likely has diminishing marginal utility. 1.5 dB is enough for one hop using IMDD transceivers and two or more (basically freed up) using coherent-lite. Reducing insertion loss further to <1.0 dB is good, but not the same level of goodness as going from 3.0 dB to 1.5 dB. There are probably very few scenarios in which you would need three or more OCS hops (super-super-spine layer?).

However, I still think this company can be a compelling bet because of what Lumentum said on their last earnings call (which caused a ~15% surge in HUBN).

"In OCS, the multiyear, multibillion-dollar purchase agreement we recently announced ensures sustained long-term growth. Our OCS ramp is largely on track, although our pace and slope are gated by the supply chain. We are experiencing considerable tightness in this product area due largely to the significant step-up in requested output. On the other hand, the number of new opportunities we are seeing for optical switches is putting tension on our road map, and we are having to make choices across the company in order to service them."

If you need OCS and Lumentum can’t provide it, where do you go?

Huber+Suhner is a bet on both OCS overflow demand and piezoelectric cost curve scaling.

By the way, their multiples aren’t all that bad. (this is on SELL-SIDE estimates so real #s prob higher)

Final Thoughts: Better Than CPO?

OCS is a fantastic market, but definitely not better than CPO.

CPO is, in my opinion, the best market in optical and could potentially be the best market in all of AI infra. The attach rate has nearly two orders of magnitude of runway (phase 0 → phase 2) while stacking with the fact that ASPs for UHP lasers are an order of magnitude higher.

With OCS, the two revenue buckets each has only one scaling vector. Spine replacement scales with adoption rate, and Google OCS superpod scales with attach rate. Neither gets the benefit of stacking scaling vectors like CPO does, and the attach rate runway is less than CPO too, at under one order of magnitude.

But the title is a little bit misleading. “Better Than CPO” is a very, very high bar. In terms of being attractive as an overall market, OCS absolutely clears it, and it shall create sustained high double-digit to triple-digit growth until the end of this decade.

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In terms of the YTD the reason why my monthly and weekly portfolio reviews have sort of different numbers is only because of the measurement technique since I’ve been adding capital throughout the year.

(imagine you are adding capital every day to an index fund, and the index is going up, your returns on your brokerage, AKA a MoM or MOIC, would be lower than the returns of the index, AKA capital agnostic CAGR.)

Measuring it the other way would be 195%.

I honestly don’t know which is the better measure, as both are gameable, so whatever. I am up anywhere from 150-200% in less than 1/2 a year without leverage which is not bad in terms of stock picking abilities.

There is a lot of change this time. Specifically, there are 4 new positions a bunch of exits and I’ve made one company ~25% of my portfolio. Also featuring CPO discussion, a cool podcast, sell-side research, macroeconomic theory (not microeconomics this time), and the interesting stuff that happens when you try to DCF a co-location provider.

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Lumentum (LITE)

Portfolio Allocation: 24.3%

May Performance: -5.25%

Change: Massively added

Yeah, I have a fat retarded LITE position.

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Optical Illusion: CPO is Dead, Long Live NPO

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2 months ago · 23 likes · 2 comments · Jason's Chips

CPO is already doing $200-400 million of revenue run rate at this very instance. This is while we are at the very bottom of the CPO S curve. The very first low-yield shipments of scale-out CPO switches (that people seem to be hating on) will S-curve, creating a ton of laser demand. Then phase one scale-up CPO will S-curve starting 2028. Then phase two scale-up CPO will S-curve starting 2030. So it’s just S curve after S curve after S curve.

We also have NPO and Lumentum CEO saying that it could be a larger opportunity than CPO. Obviously, it wouldn’t be the case long-term, but I think he’s referring to the medium-term, especially 2027. NPO seems to be replacing Kyber Racks in Rubin Ultra, which is exciting. They can sell lasers into this market. To anybody who thinks that NPO cannibalizes CPO and is bad for LITE, you can sell me your shares.

I’m also doing more work on OCS, which now seems to be much more compelling than I originally thought.

Bloom Energy (BE)

Portfolio Allocation: 16.4%

May Performance: +0.58%

Change: Massively added

Bloomentum now makes up 40% of my portfolio.

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3 months ago · 95 likes · 21 comments · Jason's Chips

I don’t cover this company in as much depth as the Optics names, so we’ll probably have some more articles out on this one. The next two will be on their native 800 VDC advantage and their unit economics, respectively.

There’s this great podcast by K.R. Sridhar (Bloom CEO) that I really like.

This dude is based. You need to listen to him talk on earnings calls and podcasts and whatnot. He will hype you up.

They’ve been doing this stuff since before 2000 and have accumulated hundreds of patents over the years, making tiny little tweaks in the process technology of making fuel cells. Each patent solves a small issue which becomes an integral part without which the whole system cannot function. You think pre-volume upstarts like Ceres can replicate all of that?

SK Hynix (HY9H.F)

Portfolio Allocation: 15.3%

May Performance: +74.34%

Change: Trimmed a tiny bit?

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2 months ago · 36 likes · 5 comments · Jason's Chips

Not my biggest position anymore.

Memory is still the single biggest binding constraint in AI. Intel (and maybe Samsung) might just solve leading edge logic. InP/CPO won’t be a problem until much later. Everything else only affects a certain architecture (like M9 CCL for Kyber) so in any case, the industry can probably route around it.

Memory is literally a tax on everything and everyone. I think it’s foolish not to have a memory allocation in an AI infrastructure portfolio, and honestly it is more of a hedge.

SK Hynix is also a backdoor Kioxia play. Did you know they own 33% of Kioxia? That is now 9% of their entire market cap because Kioxia has 7x’ed year to date.

Aixtron (AIXA.DE)

Portfolio Allocation: 13.8%

May Performance: +23.97%

My whole portfolio is literally just four companies.

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3 months ago · 28 likes · 3 comments · Jason's Chips

I pretty much haven’t touched a single share of this company since I bought it in October of last year. LMAO. And I will not ever touch it.

I will be working on finding out what is the purpose of those 80-100 tools, and when that capacity is coming online.

This is important because if they are for CPO (or coming online in 2028 or later which means they are for CPO) then Aixtron’s revenue will inflect as CPO adoption S-curves into 2030.

If they are not for CPO…

Well, remember that the die sizes for CPO are way larger, which requires more MOCVD machine time?

Then those tools are currently for EML/CW, and we would actually need an order of magnitude more tools when CPO and Aixtron become the single biggest binding constraint in all of photonics!

Also, Coherent-Lite could be interesting. Very big surface area.

Also, power semi (GaN and SiC) could be interesting.

Intel (INTC)

Portfolio Allocation: 5.2%

May Performance: +21.38%

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4 months ago · 31 likes · 2 comments · Jason's Chips

I trimmed this one. Nothing really changed except for the valuation. I still think it can go to over $1T, possibly even 2 or 3 trillion, but 1 trillion is less than a double from here.

Unlike with Soitec, I am never gonna sell this thing. We’ll just let this run no matter how expensive it gets.

Degen SPY Calls

Portfolio Allocation: 5.0%

May Performance: +5.26%

Change: Trimmed

I am a little afraid of the macroeconomic theory of higher productivity increasing equilibrium real interest rate (r*) and want to get into robotics, so low key this position will not last.

Basically, here’s the intuition. If AI/Semis/Robotics becomes the industry producing the highest, most unimaginable productivity gains in the economy, it becomes a capital black hole, sucking out investment from other industries. All of a sudden, owning Walmart and Costco at 50x PE or Coca-Cola at 25x starts to make no sense.

But more importantly, nobody would want to put their savings into treasuries and other passive instruments and make a 5% yield when the AI economy is going bananas. Therefore, yields rise, the risk-free rate rises, and macro valuations should compress.

There’s also another lens, which is more academic, which is that demand and supply for savings and investment gets impacted as the demand (investment) has a positive shock (companies building data centers) and the supply (savings) has a negative shock (people taking out their savings to either spend or invest in AI companies).

CoreWeave (CRWV)

Portfolio Allocation: 4.7%

May Performance: -1.85%

I’ll have another article out about CoreWeave shortly. Still think it is a shitco that may eventually un-shit itself.

Think about it this way: currently, CoreWeave signs deals at $9 million per megawatt, while SpaceX just signed $50m/MW with Anthropic and Google. CoreWeave’s margins are absolute dogshit, so they trade at very low multiples. What’s the core difference between CoreWeave’s data centers and Elon’s? What happens to CoreWeave’s dogshit financials and multiples if the revenue per megawatt closes the gap with SpaceX even slightly?

There can be a few mechanisms for this. Here’s one as a teaser: every year, Nvidia passes more value to its customers in performance gains than it is able to capture through higher ASP, and that is structural, because otherwise Nvidia would literally hike its prices by 100x every year. However, the economic value of this performance doesn’t just disappear. If you are the first entity in the chain to have access to this free producer surplus, why wouldn’t you capture it?

SoftBank Group (SFTBY)

Portfolio Allocation: 4.5%

May Performance: +39.12%

Wolfspeed (WOLF) *NEW

Portfolio Allocation: 3.6%

Wolf speed is a book value + long-term technology S-curve dumbbell hybrid and funny shitco memeco play.

The book value is that they have sunk $6 to $7 billion into SiC capacity pre-bankruptcy and now trade at less than half of that.

The long-term technology S-curve is solid-state transformers, which are necessary for the final stage of native 800 VDC, sort of like the scale-up CPO of the power world, which will inflect in 29/30.

Samsung Electronics (005930.KS) *NEW

Portfolio Allocation: 2.8%

Moar memory exposure.

With a bonus of a heavily discounted logic fab, which is implied to be worth 200 billion but probably should be worth way more. This probably doesn’t matter, because even if the value of the logic fab goes ballistic, most of the enterprise value is still memory. It’s really still a memory play. Oh well.

Degen Nvidia Calls *NEW

Portfolio Allocation: 2.4%

Rigaku Holdings (268A.T) *NEW

Portfolio Allocation: 2.0%

Opposite of a shitco.

These guys are a Japanese company that makes X-ray inspection equipment. Onto and Camtek competitor (but it’s better because Onto and Camtek use optical/infrared not X-ray).

Basic thesis is that x-ray machines are great for 3D chip structures and are needed in GAA/CFET, hybrid bonding, GaN/SiC and more. Consensus doesn’t have this thing growing fast at all. I haven’t done too much work into it yet, but it seems like a very safe bet.

More intuitively, you can think about it like: the installed base of X-ray equipment we need for advanced silicon with weird 3D structures is probably a lot higher than what is being implied in Rigaku’s earnings which are projected to grow at low double digits.

Core Scientific (CORZ) *EXIT

I exited to finance my LITE.

I spent more time modeling this one and just concluded it is too hard to have a view on the valuation of these things, because pretty simply, colo providers have to spend double-digit millions per megawatt to make single-digit millions per megawatt per year in revenue. Almost all of it must be equity financed.

So essentially, if you buy the stock today, you really don’t own any assets. It’s just a wrapper on a financing vehicle. Therefore…

Small tweaks in assumptions in CapEx costs, or contractual revenue, or growth rates, or interest rates can move the DCF NPV from negative to like $100b+.

I am still positively biased towards these companies because of their inherent time to power and permitting advantages, but again, it is too hard for me to have a view on the valuation/financials.

But usually whenever I sell something it’s a massive buy signal so this is your chance to buy these guys before a massive deal is announced!

SK Telecom (SKM) *EXIT

Just buy them at IPO honestly. The signal-to-noise ratio here is terrible, and the hedge is just nowhere near good enough lmao.

The takeaway from all of this news aggregated together is actually massively bullish and incremental positive to optics. NOT negative!! I’ll explain why.

All it takes is some very simple logical deduction.

Contents

1. SemiAnalysis Note

2. FundaAI Note

3. What Is NPO and Why NPO

4. Does NPO Use InP & ELS

5. Lumentum at Mizuho

6. Conclusion

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

SemiAnalysis Note

The (very sad) title of their report was “Powered Down, Lights Off.”

I will skip all of the 800 VDC stuff.

For optics, the gist is that they revised down both scale out and scale up CPO in the near term, pushing out adoption.

For scale out, the downward revision is for Spectrum X specifically, and the reasoning is that the optical engine attach yields are bad. Apparently, optical engine attach yields are 95% today (because there are 32 OEs per Spectrum 6 switch). The entire system has a 19% yield. Therefore, because COUPE is incompetent, these switches are being pushed out two quarters. These are the main scale-out CPO deployments, since Ethernet is shipping in higher volume than InfiniBand, which is why the market got so scared.

For scale-up, SemiAnalysis didn’t provide too much new information, but just reiterated that the deployments won’t come until Feynman and won’t ship in high volume until late 2028 or 2029.

But I’ve been saying this too!

I don’t know who thought scale-up CPO would be ramping in 2027. Just because Jensen announced NVL576 for Rubin Ultra doesn’t mean it would actually ship in volume, and it’s not.

Feynman brings the light.

FundaAI Note

At the very same time, FundaAI published a note claiming that NPO deployments are going to double.

I think the reason has to do with Kyber being scrapped.

Kyber racks require high-grade (M9) CCL (copper-clad laminate) because the per-lane rates are increasing, the layer counts per tray are increasing, and the cableless design pushes these signals through the midplanes and orthogonal backplanes, putting even more pressure on the CCL for M9 CCL. Traditional glass fiber won’t cut it. You need quartz fiber, which is much harder to manufacture and whose supply chain is much less mature.

This supply chain probably isn’t ready.

Therefore, whatever shipments were relegated to Kyber for Rubin Ultra probably switched over to NPO.

Now notice: Mass adoption of NPO plus rumors of CPO delay sounds like NPO is replacing CPO, but actually that’s not what happened at all. CPO was always going to be 28/29, and NPO is actually replacing Kyber, meaning that we have more incremental optical content than before.

The complete opposite of the consensus perception of this “optical illusion”. (get it)

What Is NPO and Why NPO

NPO is Near Packaged Optics. This is like co-packaged optics, except the optics aren’t co-packaged with the XPU. It’s only packaged near the XPU. Easy enough.

As a result, you would avoid this compounding yield problem that SemiAnalysis claims killed CPO. NPO is socketable. It is much, much easier.

Does NPO Use InP & ELS

Probably.

And this is what makes the market reaction to all of this so silly.

Look at the diagram for NPO.

According to this design (SemiAnalysis), the only difference is in the packaging. The laser, the fiber, and the optical engine have the exact same content. This means that every single NPO lane is just as much InP revenue for Lumentum and others as CPO.

Now I must caveat this with the fact that this design (specifically using an external laser source) is not forced for NPO the same way it is for CPO. This is because external lasers are the solution to heat (lasers hate being near the hot XPU (serviceability) and laser failing can just be swapped if it’s external instead of blowing up the entire tray; but for NPO, the module itself is placed away from the XPU and is also socketed, which reduces blast radius.

It seems like if NPO uses InP, the design is still external lasers. If NPO uses VCSEL, the lasers are likely internalized. However, because we’re talking about 1.6T and 3.2T, if a slow and wide approach is not adopted, lane rates are 200G or higher, which is beyond what VCSELs can support. Therefore, it is most likely that NPO uses the same ELS architecture that CPO does. It is okay if this architecture is not permanent, because in a few years CPO will be here and force external lasers to stay anyways.

Lumentum at Mizuho

There are two important takeaways from their comments at this conference (which occurred at the same time as the SemiAnalysis note).

First is their stance towards NPO.

Yeah, that’s right. Lumentum thinks NPO could be an even bigger opportunity than CPO (in the medium term).

This is only possible if NPO uses the same ELS architecture as CPO. As selling commodity lasers into the NPO market would simply never have numbers that can eclipse anything involving UHP CPO lasers (which are ~10x the price) no matter how many optical engines there are.

The second is that Michael Hurlston confirmed scale-out CPO could contribute $50 to $100 million of incremental revenue THIS QUARTER.

That is a $200-$400m run rate in the middle of 2026, which likely implies $400m full year revenue, which is 50-100k switches according to my flawless model.

SemiAnalysis says, “The street currently models 60 to 100k scale-out CPO switches shipped by 2027... We think today’s pace of scale-out production is too low to reach this bogey.”

According to Lumentum’s actual revenue, it seems like today’s pace of scale-out production is not too low to reach this bogey.

Conclusion

Based on the evidence I see in the past few days, the optical story has gotten better, not worse.

Scale-out CPO seem to be on track and pushing Lumentum’s revenue this quarter to the high end of their guidance.

Scale up CPO was never delayed, as it was always shipping with Feynman, not Rubin Ultra, in the first place.

And NPO does not cannibalize CPO. Instead, it cannibalizes the Kyber racks, making it purely incremental content for optics.

If NPO uses ELS, NPO would contribute the exact same revenue to optical players as CPO, which, based on Lumentum’s comments about the NPO market, seems by far the most likely scenario.

This is the view from the top of the Taipei 101. It’s really a beautiful city.

Anyways Computex basically takes place inside of this building and there’s a whole bunch of booths and it is an absolute meat fest (very packed).

There was a ton of alpha to be found by looking at the racks and talking to the various interesting people at the booths.

We will first highlight various interesting exhibits from investors POV. We will then talk about CPO. The main takeaway I’ve had is the end-state of scale-up CPO (and it is incredibly important!)

Contents

1. Immersion Cooling

2. Fuel Cells

3. Hiwin (Robotics Supplier)

4. DRAM

5. Kyber Racks

6. Astera Labs

7. Nvidia RTX Spark (Laptops)

8. MediaTek MicroLED

9. Scale-Out CPO

10. Scale-Up CPO, Takeaways for Stonks, and Lumentum EPS

SemiAnalysis Giveaway: The first person to subscribe using the paywall on this post will be selected to receive one free month of SemiAnalysis (sent to your email).

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Immersion Cooling

There is this company called QuettaFlow that literally submerges GPUs in liquid to cool them.

Isn’t that so sick?

Unfortunately, you can’t put an NVL72 rack in this liquid, as you may imagine. This is only for small chunks of compute, so basically enterprise on-prem.

The liquid isn’t water (duh). It is a non-conductive fluid that is a by-product of oil. You can plug stuff into the rack while it’s submerged. I actually touched it. It is very oily and greasy.

The benefits are that it is much cheaper (40-50%), much more compact (no IT room or raised floor), and saves a ton of electricity.

They’re private, of course, but fascinated to see where this tech can go.

Fuel Cells

Funny story.

We were at the Vertiv booth and asked the guy there about this 800VDC power module and what it does.

It is a combined transformer and rectifier that turns AC medium voltage from the grid into 800 VDC.

This is very big and expensive. I think they said like $3M ASP.

So we asked the guy, “What if you just had 800 VDC natively? Then you wouldn’t need this box, right?”

He said “Well, where would you get that?”

LOL

Jason's Chips

Bloom Energy Thesis (Free)

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3 months ago · 92 likes · 20 comments · Jason's Chips

Then we visited a booth for a company called Delta Electronics. They are competitor to Vertiv and Schneider. Basically, liquid cooling and stuff.

But did you know that they also have an SOFC solution?

Unlike Bloom, which uses ceramic plates, Delta licenses Ceres’ IP, which actually uses steel. Obviously specs are inferior to Bloom currently, so it’s more of an overflow capacity play.

They are currently shipping small volumes (30 to 50 MW) and installing them in Taiwan. They’re not fully independently scaled commercially, also, as the purpose of these fuel cells is more to provide a full portfolio of power solutions complementing their Vertiv-like products.

Ticker is 2308 on the Taiwan exchange.

Hiwin (Robotics Supplier)

Hiwin is a Taiwanese screw maker that supplies into robotics. They make actuators (components that help robots move). Did you know that actuators actually make up most of a robot’s BOM?

Interestingly, most of their sales are actually to North America, to companies like Tesla. This is important because a lot of this stuff comes from China, so American companies would find suppliers like this very attractive as a geopolitically safe option.

In the middle is their best component, which is a linear actuator. They are better (higher ASP, less commoditized) than the rotary ones on the sides.

I looked into them a bit further. Turns out that 50% of their sales are actually to the semis market. So yeah, here’s a company that has exposure to both robotics and semis. If you are interested, the ticker is 2049 on the Taiwan exchange. However, as you might imagine, screws are quite commoditized. They only have about 10% market share. LOL. And they trade at 40x multiple on sell-side estimates. I honestly would have expected a way lower multiple for a business like this.

DRAM

How to invest in semis 101:

1. Look at the PCB.

2. Identify what has the most surface area.

3. Buy.

One recurring theme that I noticed is how literally every single tray that you see at Computex has a shit ton of DRAM. It sounds obvious, but you really need to see it for yourself to intuitively grasp how much DRAM is in everything.

For example, this is the Vera CPU compute tray.

Those SOCAMM modules are stacked too, so it’s actually even more surface area than it appears to be.

Being bullish on CPU proliferation for agents is almost just as much bullish on DRAM. As it would drastically increase the total base of compute (accelerated or not) to which memory is attached.

Kyber Racks

Kyber racks are so cool.

GPUs go in vertically from the front. NVSwitch modules go in horizontally from the back. They meet in the middle via orthogonal midplane. Super compact! Save space!

This means you can fit 144 GPUs in one rack. It’s very beautiful and elegant. There are no cables, either.

However, the volumes at which they can actually be shipped for Rubin Ultra are constrained by the supply chain. Kyber racks require high-grade (M9) CCL (copper-clad laminate), because the per-lane rates are increasing, the layer counts per tray are increasing, and the cableless design pushes these signals through the midplanes and orthogonal backplanes, putting even more pressure on the CCL for M9 CCL. Traditional glass fiber won’t cut it. You need quartz fiber, which is much harder to manufacture and whose supply chain is much less mature.

If it does ramp, this has significant implications for future data center design. If data centers become much more compact, 800 VDC might be able to span the whole shell without needing to go through lossy transformer and rectifier steps (800 VDC cannot travel long distances). This is incredibly bullish for Bloom, as they output 800 VDC natively. Meaning that the TCO of a fuel cell becomes materially more competitive if smaller form factor racks and data centers get wider adoption. This deserves an entire article of its own.

Astera Labs

Asteria Labs makes connectivity chips, like retimers (which are chips that clean up signals and retimes it to the clock) for copper communication (CXL and PCIe).

Astera Labs is a huge Trainium supplier. The 2025 10K says that one customer was 70% of revenues. I wonder who that is!

(This is also why Astera is Gavin Baker’s biggest position. Gavin is a believer that Trainium is an even better ASIC than TPUs.)

Think of Trainium as the Amazon basics of XPUs.

SemiAnalysis

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They are literally air-cooled and also use PCIe for scale-up like regular computers. Sometimes I am flabbergasted that compute which doesn’t use NVLink… works.

But did you know that Astera Labs actually has an LPO solution?

(An LPO is just a transceiver without a DSP. Think of it like the transceiver is to LPOs as an AEC is to passive copper.)

They don’t actually make the LPO module itself. They sell the LPO switch ASIC and the software that modulates the SerDes signal.

This can be used for PCIe scale-up!

An LPO has the same latency as passive copper DACs because there is no retiming. Compare that with an AEC, which has active data processing components. It is pretty much strictly better, as the LPO can reach up to 50m while the DAC fails within a few.

Here is an eye diagram (PAM4, you can tell because there are three eyes).

I have no opinion on this stock besides that it is a good tracker for Trainium capex.

Nvidia RTX Spark (Laptops)

Nvidia’s new agentic laptops. The only thing I have to say here is that isn’t it so weird that they are turned off and facing away from the crowd? I wanted to try them, but they don’t even work. This is just fueling more criticism of their ARM + Windows design.

MediaTek MicroLED

For some reason, MediaTek is obsessed with microLED for scale-up CPO. When I was at their booth, a ton of space was dedicated to this (very widely hated) solution while their custom ASICs weren't present at all.

Interesting.

Micro-LEDs are an alternative to lasers. A lot of photonics engineers don’t like them because laser light is basically very concentrated (coherent, highly directional) and it is also a single wavelength (linewidth anyone?) while LED light is very diffuse.

Like literally think about the LEDs in your house versus a laser that you can shine.

As a result, it is much harder for the receiver to pick up the signal, and thus you have much higher insertion loss and bit error rate. This is a pretty fundamental difference between the two technologies that is sort of glossed over in these booths.

But they do have a lot of claimed advantages. They are much more power efficient, have superior thermal resilience and reliability, and can be integrated directly onto the CMOS transceiver chip.

Micro-LEDs are very short-reach. I was at the Molex booth, which is an ODM for micro-LEDs, and they said that it only reaches up to several meters, meaning that this is a potential scale-up CPO candidate.

Scale-Out CPO

There are two types of scale-out CPO switches: InfiniBand (Quantum-X) and Ethernet (Spectrum-X).

Let’s start with the InfiniBand one.

There are 18 ELS (external laser) modules (down on the bottom of top pic) and 4 switch ASICs with 18 optical engines surrounding each (see below, optical engines circled in red).

Now for Spectrum-X. There are 16 ELS… but only one ASIC!

That one ASIC has 32 optical engines around it. So less than 1/2 optical engines total, and 2:1 OE to ELS ratio rather than 4:1.

The ELS are also liquid-cooled, unlike quantum X where the ELS are air-cooled.

Here is the higher radix one. Basically 4 switches put together.

And the Spectrum-6 rack, which is four high radix switches put together (16 normal switches). This supports eight NVL 72 racks.

Scale-Up CPO, Takeaways for Stonks, Lumentum EPS

Below the paywall, we will discuss how my models (CPO ELS/OE builds) have changed after looking at Nvidia’s reference designs, the crazy mock scale-up CPO rack I saw (and the face-slapping implications for the scale-up CPO endgame I realized), and takeaways for optical stonks.

I will be providing the answer to the question I posed in this tweet.

We will be talking a lot about ratios and numbers and assumptions because small changes to these can either blow out or tank out-year revenues for optical companies.

And I will update my Lumentum model & EPS estimates.

This is my main CPO build. It’s from my Lumentum model, but I borrow this for all of my other CPO companies.

First, let’s update our scale-out CPO assumptions.

If each Spectrum 6 SPX rack has 16 Spectrum-X switches and supports eight NVL72 racks, then the XPUs per leaf switch are 36, which would end up almost doubling the scale-out OCS shipments adopting this. (Note that this is for Nvidia Spectrum-X only. Non-Nvidia and Nvidia Quantum-X are likely to be higher.)

Plus, talking to the guy at the booth made me realize that my adoption percentage assumptions are probably too conservative. He pretty much reinforced that customers liked the CPO solution way more because it has tons of advantages with almost no drawbacks. It’s got higher performance, higher reliability, and a lower price. The power consumption is an entire order of magnitude lower because you are cutting out the lossy copper signal that would require the DSP. My adoption rate was lower just for conservatism and uncertainty purposes, but now it seems like that is kind of unnecessary.

Also, I am changing the ELS per switch in the early years to 16 to reflect majority Ethernet adoption.

With these new assumptions, 2028 scale-out ELS shipments go up almost 3x. This is why the optical world has such high torque. Even minor changes to estimates on adoption scale can completely blow out your numbers.

Now let’s discuss scale-up CPO. This is the crazy rack I was talking about. I want you to examine it for at least 30 seconds. Really let it sink in.

LOOK AT HOW MANY ELS MODULES THERE ARE!!! (The blue pluggable-looking things)

(Take this all with a grain of salt because it is not a reference design.)

This is the final phase of scale of CPO. The phase two that Lumentum discussed at OFC.

There are literally 16 ELS in each and every compute tray. That is as many ELS in a compute tray as there are in a Spectrum X scale-out switch. If each compute tray has two XPUs, the ELS to XPU ratio blows out from approximately 0.5:1 (in scale-out only) to 8:1 (in full scale-up).

You can get to this number theoretically through research, but seeing it in person is a completely different experience. You can literally feel the laser content. Now do optical stocks look overvalued?

In addition, in this concept design there are also 16 optical engines per tray, which means that each ELS drives one optical engine compared to two or more in scale out. I did model the OE to ELS ratio decreasing over time, but this shows us that the industry wants to move to one-to-one much faster than I initially thought (likely due to higher lane speeds). This is very bullish for indium phosphide/lasers and slightly bearish for SiPho and SOI.

Phase 1 scale up CPO, which is to connect adjacent racks with the light source on the switch trays, goes from approximately 2.75 ELS per GPU to 4.0. At 50% adoption and with only Nvidia’s shipments modeled, this is already 1.5x more volume than scale-out CPO at full throttle.

Based on our rack, phase 2 scale-up CPO starts at 8 ELS per GPU and is fully incremental!

The market really never internalized this slide from Lumentum. Lumentum wasn’t lying. Each phase is actually 3-4x more lasers, and the numbers based on this simple truth are terrifying.

Here’s the final model (phase 2 still excluded because too hard to model; too far away). I also increased my laser ASP assumptions to $50 again as a midpoint to the conservative $35 I heard back in February and a more bullish $80 assumption from some conversations that I had at Computex and increased my gross margin assumptions to 75% to be a conservative reflection of industry chatter about 80%+ gross margins for Lumentum’s CPO lasers.

According to this model, they will now do about $5 billion of revenue in 2028, right in line with their $5b Greensboro fab revenue capacity guidance.

They now also trade at under 16x on 2028 EPS, which are projected to reach $57. And on out years, like 2029-2030... let’s not talk about it. (and then after that there’s another order of magnitude step-up with phase two scale-up. LMAO)

BTW, this is also unimaginably bullish for Aixtron and AXTI.

Finally, I want to talk about copper.

There are many racks at Computex. Everyone is usually standing in front of these racks and gawking at the compute trays, but actually, if you walk behind them, you can see something really interesting.

Look at how many copper cables there are! The sheer weight of this copper must be enormous.

Now imagine how much worse it gets when we try to push higher bandwidths, necessitating even wider cables (due to the skin effect). Do you see why we need CPO?

All of this copper is going to disappear in 2030 when we get full optical scale up. Now, this probably isn’t actionable at all anytime soon, but long term you don’t want to be the supplier selling this stuff. This is part of the reason why Credo is pivoting to optics so aggressively with both DSPs and micro LEDs.

Thus, the implications here are that the share price and numbers of copper companies (not soon, but eventually) are going to tank, and that you can predict that everyone will try to squeeze into the optics market in any way they can. The more commoditized parts of optics are about to become REALLY commoditized.

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I am a wizard and absolutely nailed their earnings with my tweet.

They did, in fact, announce that their "opportunity pipeline" expanded again, while being simultaneously missing on revenues.

Revenue Misses

I will bash them in other areas, but I am actually not going to do so here. I completely buy this excuse, because if this was any other company that I cover, I would have given them the benefit of the doubt as well. This honestly doesn’t matter as much at all for whether or not their CPO shenanigans will take off.

Opportunity Pipeline

Bashing time! If I were sell-side, I would be absolutely dishing out questions to management over this. Every single mechanism for defining the opportunity pipeline is up to the discretion of management. Not a single one is objective. Not a single one relies on contracts.

What does meaningful discussions mean? What does good conviction mean? What does determining that Sivers’s technology is a good fit mean? No one knows. The management can define those however they want. This doesn’t necessarily lead to the opportunity pipeline being an inflated metric per se. Management could be even more conservative if they chose to. It just means that this is a metric that is manipulatable and should mostly be treated as noise and not signal. I’m sure if you had the right incentives, you can easily manipulate this pipeline to be an entire order of magnitude larger or smaller than it currently is.

The biggest red flag is that Vickram Vathulya does not address any forward revenue guidance.

This question is a clear invitation to share numbers, and it was dismissed with a one-sentence answer on their themes.

Their end markets are growing way faster than this BTW. CPO should be triple-digit growth.

No qualifications, no production volume orders, nothing changed.

Competition

Here they just completely conceded that they are worse than Lumentum in every way. All they needed to mention, like most companies when they’re asked this kind of question, was at least one area in which they are competitive and to talk that up as the more important trade-off. You can see Coherent doing this all the time with their 6-inch wafers, even though they are uncompetitive everywhere else, but they still have something to talk about.

The only response Sivers could give was “…but the pie is growing!” TAM doesn’t pay the bills if you can capture none of it.

Margins

This is very good. COHR GMs are barely above 40% for reference. Achieving this would actually be remarkable. Credit where credit is due.

Conclusion

This last question in the Q&A captures perfectly the essence of Sivers today.

Maybe because they’re not actually a volume supplier to the market since they haven’t qualified anywhere?

Everything else is noise until they give purchase order announcements with clear numerical amounts, and provide year-by-year revenue guidance. Until then, they’re a startup, and nothing has changed.

Both on the good side and bad, for example, the revenue miss today doesn’t matter at all, and neither does the “opportunity pipeline.” I guess that just means I’m saying that earnings don’t matter at all. LMAO.

See you next quarter!

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Contents

1. Headline Numbers and Press Release

2. Memory Downturn

3. Price Action

4. Capex, Working Capital, and Cash Flow

5. Capacity and Contribution Margin

6. Physical AI

7. Photonics

8. Overall Thoughts (and My Positioning)

Headline Numbers and Press Release

First, the press release.

Revenue massively beat, however gross margin massively missed, and so did EBITDA margin, EBIT, and net income. Oh god. However, cash flow generation beat massively. Overall, just very confusing, but it makes a lot more sense once you actually listen to the call.

Big beat in Auto and Industrial??

Price Action

The stock reacted positively, but I am extremely, extremely certain that the price action today is actually quite low signal because a whole bunch of hedgies were short this thing, expecting management to kitchen sink it and absolutely rug pull the guidance due to the whole mobile memory situation.

And then they ended up not rug-pulling it as much as they think, so shorts covering it like crazy. I honestly think it had nothing to do with photonics. Believe it or not. Funny enough. And the stuff they said about photonics is mostly confirmation, clarification, and realization of things that we already know.

This article was actually vindicated down to every last word. Now I can say I told you so.

Memory Downturn

Overall, their starting tone was “patient guys, we are still in recovery mode,” near the beginning. Throughout the length of this two-hour long-ahh call, it slowly turned more positive and more forward-looking.

But surprisingly, I heard very little of memory itself, surprisingly little, which is… good? I mean, I guess people thought the BOM pressure would be a lot worse than it actually was.

Now turning to the outlook. This doesn’t seem too bad. Low key, I don’t really care about mobile, like, at all. But sequential improvements? Proven improvement being less pronounced than previous years isn’t “the market is literally crashing because of memory and we’re going bankrupt.” So I guess the short sellers have nothing to short anymore!

Capex, Working Capital, and Cash Flow

Apparently, the French are very good at working capital management.

If you hated working capital and whatnot from learning accounting, I am with you. I generally don’t really give a shit, since stocks are forward-looking, but in this case they did so much working capital management that they flipped free cash flow insanely positive so we actually need to talk about it.

Working capital is basically all of the capital tied up in temporary, nonpermanent assets like inventory, accounts receivables, etc.

Anyways, the gist of it is that they unloaded their inventories like crazy to clear working capital and generate cash so that they can, you know, invest in stuff.

And then on CapEx, they did a lot of good stuff here too, reducing CapEx overall and then redirecting it towards photonics, which is exactly the right thing to do. Very good job.

Capacity and Contribution Margin

One of the main claims in my original thesis was that their capacity is fungible. That means that their stupid RF overbuild, which they got bitch-slapped by memory for, is now going to be a tailwind, Citrini supply-chain-inheritance style.

And today, we get a whole slide about it, with plans for each fab! That’s pretty neat.

They also clarified their contribution margin economics. Incremental revenue contribution margin is currently 60% because of how underutilized it is, but gets closer to 50% in equilibrium. They specifically said that EUR 100m of revenue contributes EUR 50m of gross margin, which is actually exactly as I modeled. Nothing new there.

Physical AI

One of the most interesting things that I noticed from this call is actually their focus on physical AI. I was expecting photonics to get 100% air time today, but instead photonics got like 60% air time while physical AI got a surprising 40% (not real numbers, of course).

FD-SOI for robotics is actually very interesting because always-on devices or robots need very low power consumption.

And they pitched physical AI as the end state of AI after agentic.

Photonics

Now, on to the meat of this call, photonics. First is that they passed $100M of photonics revenue in FY26, perfectly matching the guidance that they gave in the past.

They did say that demand has reflected sharply since March. Now they have a lot more visibility, and they changed their guide from growing 20 to 30% year over year (which is laughably low) to “something above 30%.” But okay, come on, we obviously know that it’s way above that. It instead should be 50 to 100%, but it will take these conservative French folk a while to update their priors on AI growth.

They talked a lot about their photonics moat today, and, like I said in this piece, I would not fade them on this at all.

Jason's Chips

The Soitec Series | Part 2: Wafer Manufacturing and the Smart Cut Moat

I wish this was a real product…

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They have a practical monopoly (~95% share) on photonics SOI, almost as strong as Aixtron’s monopoly on InP MOCVD.

Actively renewing 4,800 patterns with decades of process learning across crystal engineering, interface engineering, refresh and repolish, and advanced processing. Photonics is much harder than regular SOI and requires precision in defects, human formity, and roughness, again just like I said in my article.

They were asked about competition and potential replacements, and their answer was pretty good. They said that there will be competition, but reinforced all of Soitec’s advantages. I am not at all concerned about competition for them.

They were also asked about TFLN and replacement potential for silicon photonics, as silicon photonics does not have the modulation speed required for higher data rates. However, they clarified that InP and TFLN are the same thing, and they have already highlighted InP as one of their key future investment areas. Also, TFLN and silicon photonics are complementary. You can add this material on top of an SOI wafer, so in reality it’s not a concern at all.

In terms of visibility, they said that there is visibility even in the long term, but what they want is for customers to have “skin in the game,” AKA prepayments. I’m not sure why they want prepayments so badly, given that they already have the capacity, but I think that, just like with Aixtron, these Europeans who went through a cyclical downturn are really conservative. I think that’s the reason why their guidance has been so low in the past.

They were also asked a few questions on CPO, which I had high hopes for but really didn’t get much out of it. Mostly clarifying that no, they are separate, that CPO is used for scale up, and they won’t really cannibalize each other, which is stuff that we already know. I was hoping for some clarity on potential mix of CPO once the technology matures so I can get a sense of how strong CPO SOI content is compared to transceivers, but we didn’t get that, unfortunately.

Finally, they had a comment on pricing dynamics, where the profitability of photonics is much better, and it’s very clear that when they shift the mix to photonics, they improve the bottom line. They have a firmer hand in pricing than they did in their other end markets, which is again something we already know, but it’s good that they are reinforcing it.

Overall Thoughts

I have a few thoughts here, but my main conclusion is that this is pretty positive. Not enough to convince me to get back in (yet), but still positive.

I can yap all I want about thesis confirmation, yada yada yada, but that would just be some AI-like slop. I’m just going to say that, to me, what is new here is really hearing management talk in this post-CFO inflection era and getting to know them better. I really like the team. They are very conservative. They’re very prudent, and they seem to be very good at what they do. That, to me, is the biggest incremental new information.

On top of that, robotics may surprise us in the future, but it’s not something that will move their valuation anytime soon. The story is still photonics.

The reason I’m not getting back in is I still don’t have clarity on their unit economics for CPO. This is too expensive to just trade off of transceivers (which is the same for basically every optics name today. You absolutely need CPO).

I am very confident that InP demand will explode with CPO, but less confident about SiPho demand. That’s why I prefer LITE and AIXA (and you could even argue AXTI) because it’s very clear that InP demand is way higher per UHP laser than per EML or low power CW. And this means aggregate InP demand for CPO will be much higher than for pluggables.

But will CPO as a paradigm use SiPho more or less intensely than 1.6T (SiPho-loving generation) transceivers? I don’t know yet.

It was only a few months ago when photonics was a niche, technically complex subsector of a subsector of a subsector of the AI infrastructure trade, covered by small, undiscovered, and specialized analysts. Today, it shares an investor base with 2021 NFTs and 2024 Solana meme coins.

But unlike crypto, the technology and fundamentals are still there!

So it’s truly become a fascinating contradiction where on one side there are companies with real technology and an almost perfect market position. There is some hype around them, but that hype is not nearly enough, and they still present a great opportunity (see ). On the other side there is Sivers.

Sivers is special. Before all the speculation promoted by unethical large social media accounts, there was a legit investment thesis! Credit to the several analysts that found it pre-cult.

:

Moody

Sivers Semiconductors: A Swedish Technology Dwarf Punching Far Above Its Weight in Three of Today’s Hottest Tech-Trends

When searching for the next semiconductor highflyer, most investors look to California, Taiwan, or South Korea. The fact that a Swedish company with just 125 employees, headquartered in the Stockholm suburb of Kista, is simultaneously fighting on three of the most exciting technology frontlines of our time – AI data centers, satellite internet, and auto…

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5 months ago · 44 likes · 3 comments · Moody

:

Silicon Matter

Sivers Semiconductors: a $130m mcap co-packaged optics, LiDAR and SATCOM play

Intro I’ve been sitting on this piece about Sivers Semiconductors for a while, but ahead of this years’ OFC conference I believe it’s prudent to push the publish button. For those unfamiliar with Sivers, it’s a relatively asset-light and Sweden-based technology company led by CEO Vickram Vathulya, with annual revenues of $33 million and approximately 130…

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5 months ago · 70 likes · Silicon Matter

:

Gaetano

$SIVE Deep Dive

Sivers Semiconductors ($SIVE) is one of the more interesting smaller names in this part of the market right now. I have received more requests on $SIVE than any other company in this sector…

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4 months ago · 46 likes · 9 comments · Gaetano

Now I will pretend that Sivers is $130 million market cap again, and pitch the original thesis. It is by doing this that we can fully understand why it has broken after a 20x, unlike names like and .

We will be analyzing Sivers (and finding out why it is insanely overvalued) through a purely fundamental lens. I will not comment on the market structure or price action or ethical/legal concerns, or criticize any of the individuals that have promoted this name. I will instead save that for my tweets (lol).

Oh, by the way, for anybody asking me to short, I wish I could, but I can’t because IBKR won’t let me. My account is restricted to cash only.

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Contents

1. The Original Thesis

2. The Valuation is Absurd

3. Sivers 101: Opportunity Pipeline & NRE

4. The (Fabricated) Bull Case

5. Why Is Sivers Fabless?

6. There is No Moat

7. The Wrong Architecture

8. Conclusion

The Original Thesis

Sivers is a company with two segments: wireless and photonics.

Wireless makes up 70% of 2025 full-year revenues. Photonics (which includes both data center photonics and LIDAR) makes up the other 30%.

The company is cheap. With a $130 million market cap and around $33 million of revenue, Sivers trades at almost exactly 4x EV/revenue.

Profitability is constrained as EBITDA is currently negative, but that’s the whole point. A company that is cheap on EV/revenue with negative EBITDA that has a hidden gem segment (photonics) can show massive operating leverage if that hidden gem segment explodes.

This is precisely what sell side projects. While the legacy wireless segment is forecasted with pretty unexciting growth (5G and radar and satellite stuff), photonics, which includes both LIDAR needed for physical AI and, most importantly, CPO for data centers, carries the company to double-digit sales CAGR and their 20% EBITDA margin goal.

With revenues doubling by 2028 and EBITDA margin inflecting positive, suddenly you go from 4x EV/revenue to 2x EV/revenue and 10x EV/EBITDA. By 2028, photonics would make up half of the entire company’s sales, making this an attractive bet.

Not much has to go right here. At a market cap of $130 million, the thesis can simply rely on management delivering on their own stated goals.

The Valuation Is Absurd

The sell-side price target is less than 10% of the current market price, which I find absurdly funny. (It is really funny.)

I think the ridiculousness of this valuation is best explained by my tweets during my retard crusade last weekend.

And the way it got to this valuation is also extremely unhealthy.

As you can imagine, if the stock is nearly 20x more expensive at a $2.3 billion valuation, the old thesis doesn’t work anymore.

This is not true for all companies. Some, like or , sell a raw material or commodity whose supply/demand scarcity dynamics have no natural ceiling. This means that one thesis (memory ASP go brr) can justify nearly any valuation.

But for other companies competing in the 10-bagger arena, the thesis must evolve as the valuation gets more stretched.

Sivers could rely on a simple CAGR back at $130 million. At $2.3 billion, they now trade at more than 230x their disclosed 2025 photonics revenue. Keep in mind that the photonic segment isn’t purely data center revenue, either. It also includes LIDAR, and they do not break down sub-segment granularity, so in reality, the actual multiple is higher. I am also only able to use trailing multiples, as the company has not disclosed a single DC photonics volume purchase order or given any DC photonics revenue guidance. They are basically a pre-qualification startup.

All management disclosed is their goal of compounding their revenue at 25 to 30% CAGR, expanding to 65% gross margin, reducing R&D to 20% of revenue, and having an EBITDA margin around 30%.

“Based on the cost structure and margin profile of the business, we would expect to reach cash flow breakeven at annual revenues of approximately $50 million-$55 million, and 65% of revenue stemming from products. This we are targeting in roughly two years’ time. Our long-term ambition is to sustain 25%-30% revenue CAGR, with fabless financial KPIs at around a 65% gross margin, R&D costs of around 20% of revenues, and an EBITDA margin around 30%. We are striving for world-class performance because we believe we have the technology to achieve it.”

This is no longer aggressive enough to save the stock.

Now, we have to evaluate whether there can be an order one or two order of magnitude step function growth from a sudden qualification breakthrough along with massive purchase orders (that can actually be manufactured with their current owned capacity and WIN allocation) that can even remotely justify the current valuation levels. This also means that Lidar and Wireless are pretty much irrelevant because the only segment that could be explosive enough to drive a fundamental breakthrough that justifies the current valuation is the massive CPO market. Other factors that people usually consider “bullish” like Chips Act funding (which is actually for their wireless segment for defense) and a Nasdaq listing (with like $16 million of inflows or something) also, don’t really matter at a $2.5 billion market cap.

If photonics breaks, the entire story breaks too.

Sivers 101: Opportunity Pipeline & NRE

Every earnings call, Sivers presents a KPI known as their Opportunity Pipeline. Their Opportunity Pipeline right now is $453 million, which is up 64% in 2025.

It sounds really big and cool because it takes the place of a number of other companies’ reports, which is very important: the backlog. However, the opportunity pipeline is not a backlog at all, not even close, and treating it as a backlog is extremely misleading.

The Opportunity Pipeline is literally everyone that they’re talking to. If they’re having a conversation, it’s part of their Opportunity Pipeline. For it to actually convert into contracted backlog, they need to move into evaluation and sampling, which is still very far away. Then get a design win and finally make it to production orders. I’ve never seen any other company in the world report the numbers this way. It makes it extremely inflated.

In my estimation, only about 10 to 20% of the Opportunity Pipeline actually converts to backlog. Which is around $68 million of their $453 million opportunity pipeline.

I started from the idea that the $453m opportunity pipeline is a five-year, non-binding funnel, not a one-year order book. If all of that were close to backlog, Sivers would already have several years of contracted revenue sitting behind a company that only did about SEK 306m / ~$30m of revenue in 2025. That does not fit the disclosures: contract liabilities were only SEK 11.6m, management says the pipeline is non-binding, and much of Photonics is still sampling/qualification. So the true committed layer has to be much smaller than the headline pipeline.

Then I sanity-checked it from the other direction: Sivers still needs enough near-term committed or near-committed work to support 2026 revenue around SEK 360-376m, plus named programs like CHIPS, Tachyon, Doosan, ALL.SPACE, LiDAR, and FWA. That gets you to a few hundred million SEK of credible revenue-converting opportunity, but not billions. Converting that into pipeline percentage: 10% of $453m is ~$45m, 15% is ~$68m, and 20% is ~$91m. That range maps to roughly 1.5-3.0x FY2025 revenue, which feels plausible for a mix of signed work, near-term NRE, product orders, and late-stage customer forecasts. Above 20-30%, the implied committed revenue starts looking too large versus what they actually disclose. Hence my practical estimate: 12-20%, with ~15% as the center.

The next Sivers term we need to discuss is NRE.

NRE means Non-Recurring Engineering Revenue. This is customer-funded R&D. The customer pays you to do R&D, and you book revenue. That’s basically what they do to get ciphers to customize their IP to the customer’s needs.

Now, most of their revenue is NRE, both wireless and photonics. That means they’re pretty much a startup at this point. This isn’t product revenue, and it’s not sustainable. Honestly, this would be a bear point at a much lower valuation, but at a valuation this high, this doesn’t even matter. They’re essentially a very frothy VC startup at this point, so NRE or not, what actually matters is if the product revenue can 100x with some massive qualification. Who cares if the tiny revenue base isn’t sustainable? That’s not what is making it a multi-billion dollar company anyways.

The (Fabricated) Bull Case

The few individuals who pump this stock focus on two main things.

This picture expresses the first one.

Basically, “omg $SIVE is in conversation with Ayar, Poet, LightMatter, and Optical Startup #463 and the ex-director-of-shits-and-giggles of one of these startups once had dinner with Matt Garman’s guinea pig, which means that they are supplying lasers to every single hyperscaler. $SIVE to the fucking moon”

This is completely and utterly wrong because, as I talked about before in the backlog section, conversations don’t mean shit without qualification and production orders. It’s not the hard part. It’s not what’s valuable. I don’t give a shit who supplies what to whom. If the product is uncompetitive, the buyer will find another supplier. If you have no customers, build a great product and the customers will come. What’s valuable is having a good product that passes qualification and performs well and then having the manufacturing expertise to ramp it at high yields and high gross margins. I can write a cold email and say I’m in conversations with Ayar Labs tomorrow if I wanted to. And not only that, the architecture of that startup has to actually be viable too (looking at you POET why do you even think you can beat hybrid bonding).

The second is possibly worse, which is that Sivers is a “chokepoint” or “bottleneck.”

(And no, a chokepoint is not different from a bottleneck. They’re the same thing.)

Sivers is literally the opposite of a bottleneck. They’re a fabless startup that people are calling a bottleneck.

Sivers hasn’t qualified anywhere. They are not choking off anyone. It’s like saying that the bottleneck for AI inference is Cerebras (lmfao). To find the real bottleneck for inference, you must look at who has majority share (Nvidia), then who owns the hard assets behind that majority share (memory and TSMC N3). The equivalent for optics here is Lumentum and AXTI.

A bottleneck must have both hard physical assets and substantial majority market share. Sivers has neither.

Why Is Sivers Fabless?

Today, Sivers is a fabless company. They have a facility in Glasgow which produces pilot volumes for R&D purposes, but in order to scale up to production volumes, they have WIN Semi as their contract manufacturer.

However, it wasn’t always this way. Turns out that in the past, Glasgow was pitched as a fab that could do up to $150 million in revenue. Yes, that is something around ten times their current photonics revenue base. It was only around the end of 2024 to the beginning of 2025 where this changed.

What happened? My best guess is yield.

Making lasers is really fucking hard. InP is brittle. Aixtron’s management said that Lumentum is their best customer in terms of their ability to use MOCVD machines to make lasers and even the best player out there only achieves 50% yields (on EML). That means one in every two dies is binned!

Manufacturing is brutal. Manufacturing is the moat, and that moat is what kept Sivers out of making their own lasers. That means that what they have left, which is just design IP, is a lot less valuable.

There is No Moat

Technology analysis will always be superior to supply chain analysis.

Here’s where I want to introduce you to a surprise. Sivers has no moat. Turns out that Lumentum also makes these laser arrays, and in fact Lumentum’s specs are even better, offering up to 100 mW per channel at 50°C, way higher than Sivers’ 65 mW per channel (with unspecified temperature).

As in classic Lumentum style, they also disclose way more other specs like spacing uniformity, channel power deviation, and reliability results. They literally do it better in every way.

This leads us to two main conclusions:

1. Laser arrays are not proprietary to Sivers. They don’t have a moat here that makes them better at making this specific technology.

2. Regular ultra-high power 400 mW lasers that send one powerful beam of light and split it, instead of using WDM and an array of lasers, are the superior default architecture.

The Wrong Architecture

Let’s zero in on the second one a bit further.

Lasers for CPO must also be placed far away from the optical engine.

Because this laser is so far away, in order for the light to reach the optical engine, it needs to survive a lot of coupling loss meaning that the light gets much less powerful every time it has to go through fiber and whatnot.

There are two ways to feed light into a CPO system.

The first is the ultra-high-power approach. You start with one very powerful continuous-wave laser outside the optical engine. That light enters the CPO package, survives the coupling loss, and then gets split into multiple lanes. This is the Lumentum-style architecture. The whole point is simple: start with so much clean light that even after losses and splitting, each lane still has enough photons to carry data.

The second approach is WDM, or wavelength division multiplexing. WDM just means using different colors of light. Instead of one very powerful laser, you use a laser array: many small lasers on one chip, each at a different wavelength. One color carries one data stream, another color carries another data stream, and so on. This is what Sivers is pitching.

WDM is a real and useful optical technique. But it solves the wrong problem for our beloved CPO application.

WDM solves a bandwidth density problem. It lets you push more data through fewer paths by stacking multiple colors together (and each data stream is one color).

UHP solves a more important problem: giving you way more starting photons.

CPO does not care about multiple colors. It fails if the light is too weak by the time it reaches the optical engine. Every connector, fiber attach, splitter, coupler, and waveguide eats optical power. If the source starts weak, adding more colors does not fix that. You just have many weak colors instead of one usable color.

In addition, Lumentum’s regular UHP architecture does not need WDM to push enough data. It can scale bandwidth through spatial parallelism: more lanes, more fibers, more waveguides, more parallel optical paths. That may be less elegant than WDM on paper, but it focuses on the one thing customers actually need first: actually having photons per lane after all that coupling loss.

WDM adds its own complexity. You need wavelength locking, thermal control, filters, tighter testing, more channel matching, and more ways for one bad channel to ruin the package. In CPO, the binding constraint appears to be optical power, reliability, and qualification.

So the conclusion is very clear!

Use many smaller lasers with different colors < < < Use much stronger lasers and split the light.

And this isn’t a Lumentum vs. Sivers thing where Lumentum wins the market with their architecture. It is that Lumentum has both architectures. They’re the leader in ultra-high power CW, and they’re also the leader in WDM laser arrays, but look at what they chose to ramp for production volume. What is their Greensboro fab going to churn out? UHP CW lasers!

Sivers is in the right market, but it is targeting the wrong architecture. Laser arrays may be useful in R&D programs, lower-loss systems, or niche WDM architectures, but the default CPO production ramp is being built around ultra-high-power external light. That is where the volume is.

Conclusion

Let’s recap. Sivers used to be an undiscovered small cap with a respectable thesis, but after a 20x return facilitated by large social media accounts (unethically and illegally) promoting the ticker, the valuation has gotten absurd. The only way to save the stock is through its optical division gaining a foothold in the CPO market, which is much less likely than what the cult is hoping for.

The cult believes Sivers will gain a foothold because they are in conversations or have remote connections to hyperscalers through various optical startups. This is wrong because what actually matters is its technology, and it is precisely technology where Sivers falls apart.

Sivers is fabless because they lack the manufacturing expertise in the very difficult process of indium phosphide fabrication. Which means they lack a moat, as they are skipping the hard part to do the easy part.

Sivers owns no exclusive technology, as Lumentum makes the same exact laser arrays, but better.

And finally, but most importantly, Sivers is targeting the wrong architecture entirely. Lumentum has a choice between UHP and WDM, and they made a choice to ramp the exact thing that is the opposite of what Sivers is doing, for volume production.

Sivers Semiconductor is the most overvalued stock in optics.

The last of my “big 5” (LITE, AIXA, INTC, BE) I haven’t started to super-intensely cover. But it was one of the first ever chip companies that I discovered and invested in!

It’s also up 10% today and 200%+ YTD and I’ve only ever added. I am proud of this one.

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The thesis here is 90% memory go brrr so it’s kind of not an idiosyncratic thing at all. SK hynix is only my favorite memory maker by a margin, but own Samsung and Micron, and really you should have the same result. Honestly, I don’t think it matters, because if once you’ve picked your memory maker and it starts going up, the cost of switching to another memory maker and paying taxes is probably going to be greater than whatever marginal benefit the other one has. This is basically to say that my largest position isn’t exactly a company, but a memory.

Although, if you look at my portfolio, memory isn’t my largest thematic exposure. Lumentum and Aixtron both contribute to optics, and that would then make up nearly 30% of my portfolio, so my favorite theme is still optics. But memory go brrr.

Contents

1. Supply vs. Demand

2. Book vs. Earnings

3. DRAM vs. NAND

4. Samsung vs. Micron vs. SK hynix

Supply vs. Demand

The point I’m about to make requires you to notice a visual quirk in any memory supply-demand graph from the sell-side or wherever.

Notice anything? The gap is small!

We are in perhaps the greatest memory shortage of all time ever in the universe, and DRAM is only about 5% behind demand, while NAND supply is about 4% behind demand. Those are not large absolute numbers.

If you’ve been in the weeds of memory, maybe take a step back and think about some other subsectors of AI infra, like optics, where Lumentum regularly says that they are 30% behind demand for their EML lasers and even more for scale across components.

However, very unintuitively, the words I just typed above are the most bullish sentences that you have ever read about memory.

The supply-demand gap is small. Yet, ASPs look like this.

Even Lumentum, being 30% behind demand, is barely able to raise prices. This is because of two reasons.

First, this is the power of a commodity market: the difference between monopolistic and perfect competition, between price makers and price takers.

When we’re not in the world of custom-designed stuff, moats, differentiation, and long-term contracts (a.k.a. perfect competition), all that is needed for earnings to go vertical is for demand to slightly inch above supply.

Second, both supply and demand in the short to medium term are extremely inelastic. Inelastic curves mean that a small change in the quantity results in a big change in the price.

What this means is that memory can be a very levered bet on AI. All you need to do is have a variant view that demand isn’t 5% above supply, but is instead 10%, 15%, or even 20%. Then the equilibrium price that you arrive at for bits is way higher. That’s not a tall ask.

Book vs. Earnings

Ever since I bot my first memory stonk, I have struggled with the valuation question.

Is it P/B or P/E?

Yeah, memory has always been cyclical in the past, and P/B has always been the right way to value such cyclical commodity businesses. It obviously should be P/B, right?

But valuing memory on P/B would make you look like an idiot, because not only are they trading at such low P/E that they can earn back their entire market cap in just a few years, but also those earnings keep getting revised up as memory prices keep increasing!

The answer is that the idea of P/B being too conservative and P/E being too aggressive is not the correct way to frame the problem. Fundamentally, nothing says that valuing a company by its assets is more conservative than valuing it by their earnings!

They are different tools for different purposes, but at the end of the day, there’s only one intrinsic value for a company: the present value of all future cash flows. Both ratios are just a way to approximate it.

P/E is just a shorthand way to do a DCF. P/B assumes that the cash flow generating assets have a value that itself can be estimated, essentially making the corporation only a wrapper around those assets. P/E is much more popular because, in reality, for most companies, the operations of the company cannot be explained by one asset base that’s just easily valued. There is IP, proprietary processes, talent, and a whole bunch of other shenanigans that happen within a company, which allows it to generate value beyond what its assets can do by themselves (ROIC > WACC). For most businesses, they are not commodities and thus are not cyclical, so earnings aren’t very volatile.

Memory, on the other hand, is the complete opposite. There’s not much you can add to a memory fab to differentiate it from other memory fabs. At the end of the day, a bit is a bit. Memory is a commodity. A Micron fab is pretty much the same as a Samsung fab. Because of that, there isn’t much proprietary stuff that earnings capture over the book value, so P/B becomes a far less bad way to approximate intrinsic value. On the other hand, earnings is now complete and utter garbage because commodities have a cycle and a market price, and therefore estimating the intrinsic value of all future cash flows using the cash flows for only one year is a terrible idea. That one year could be very unrepresentative of a through-cycle earnings number.

So yes I’m completely arguing that memory should be valued on price-to-book.

But then why is a memory company my biggest position? Well I believe the book number is far too low (the “real book” or the actual intrinsic value of the fabs if they were properly priced and traded on a market would be much higher). The worse the supply-demand imbalance, the higher the value of the fab. This will simply play out through the price of memory continuing to increase.

However, I actually don’t think the market agrees with me. I think because the earnings of memory companies are so massive and the P/E is still so low, if the earnings keep going up, the stock has to go up with it, because you cannot compress the P/E to like 2x as the laws of cash gushing onto the balance sheet prevent the company from being valued that low. Which makes memory companies quite safe. As long as the demand, supply, and balance keep worsening, the earnings will keep going up and the stock will keep going up.

DRAM vs. NAND

DRAM wins supply and NAND wins demand.

Jason's Chips

DRAM vs. NAND | The Supply Side

Memory is the next great theme I have yet to cover…

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4 months ago · 58 likes · 8 comments · Jason's Chips

DRAM wins supply because it is much more EUV intensive, and EUV machines are more likely to be a semi-cap constraint than etch-and-deposition. It has a much tighter oligopoly with three players (basically just the big three memory makers) versus NAND, which also has SanDisk, Kioxia, and YMTC. It also has a China problem in that YMTC is as competitive or even ahead in some aspects in terms of NAND, while CXMT is way behind in DRAM, especially HBM. HBM cannibalizes DRAM like crazy, which NAND doesn’t have.

NAND, on the other hand, wins demand and therefore is much more degen and can have more upside over DRAM.

1. Think about AI data as a stock and flow model. The KV cache of all inference happening at any time is the flow, while the amount of data created by AIs in inference is the stock. NAND therefore captures the stock, while DRAM captures the flow. This is a vast oversimplification, but you can imagine that investing in the stock is much more attractive than investing in the flow.

2. NAND KV cache offload. This is a bet on long context overflowing our current ability to store it in DRAM. NAND therefore acts like a call option on all of the data demanded but not able to be fulfilled in the KV cache. Obviously, this would be beneficial if you are trying to play the possibility of bit demand vastly outstripping supply.

3. AI workloads cause SSDs to be much more write-heavy than before, so the more intense AI demand is the faster SSDs get worn out from all of the writes and the more of a consumable that SSDs become.

But the point that I’m trying to make here is that I think it’s much more prudent to bet on both than to have a bias towards one side. The reason is because the beta is far more important than the alpha.

Are you really sure that DRAM is going to vastly outperform NAND, or NAND is really going to vastly outperform DRAM if one wins in supply and one wins in demand? You would have to know how much better DRAM supply is or how much better NAND demand is. The magnitudes are almost impossible to estimate and know in advance.

What we are more certain about is that bits will win, and betting on both ensures that you don’t miss that critical thesis.

Samsung vs. Micron vs. SK hynix

This is the simplest section because the answer is that IT DOESN’T MATTER.

A fab is a fab. A bit is a bit. HBM has some value add, but at the end its biggest input is DRAM, and the value add that each player puts on top of the HBM for performance won’t ever get close to the value of the memory fabs themselves.

(Think about it this way: If a memory maker didn’t have HBM lines at all, they could just sell their DRAM to a hypothetical firm that only turned DRAM into HBM through backend packaging lines. The DRAM maker will still be much more valuable than this hypothetical HBM OSAT even though making HBM has a lot more differentiation. Of course, this would change in a down cycle, but if you think memory is gonna be in a down cycle, I don’t want to talk to you. Actually, I do want to talk to you because there are literally no memory bears right now, and that concerns me just a little bit. You probably have some really interesting insights. If you are a memory bear, please DM me.)

So why did I pick SK Hynix? Well, at the time, Samsung was too capital and inefficient, as it also had a big phone business and a bunch of other junk. Micron has a much smaller NAND business than SK Hynix (12% vs 20% market share and also no Kioxia stake), despite being almost the exact same market cap, so it is much more DRAM oriented, and I prefer balance.

However, if our end goal is to invest in AI and semiconductors are only a means to that end, I believe it’s ignorant not to look further downstream. This is exactly why I wrote my $100 trillion dollar Anthropic article.

Jason's Chips

Why Anthropic Is a $100T Company

ANTHROPIC IS THE MOST VALUABLE COMPANY IN THE WORLD, AND IT’S NOT EVEN CLOSE…

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3 months ago · 42 likes · 18 comments · Jason's Chips

Model labs are an even sharper expression of one’s belief in AI, because they generate the tokens themselves. Plus they capture both the upside from token value creation and from Moore’s law-driven cost reductions. I think they are the greatest business models (both in and out of AI) of all time.

But this is already well known. People are selling their kidneys for Anthropic stock. I am only saying that model going to be even more valuable than people predicted. But people are definitely already VERY excited. And when there’s that much excitement, alpha is harder to find.

But funny enough, by going just one step further downstream, to the thousands of regular businesses consuming the tokens Anthropic sells, all of that excitement ends. Here’s where you can be a true contrarian. Meaning possibly much more asymmetry.

This is the Shiller Cyclically Adjusted P/E Ratio, or CAPE.

Today CAPE is at 42x. We are in unprecedented territory that has only ever been reached at the height of the dot-com bubble. We’re even higher than in 2021, which involved a speculative frenzy and preceded a brutal year-long bear market.

Every single time the Shiller CAPE was elevated, a crash followed. It is the world’s most reliable bubble indicator. Even if we don’t crash, these high valuations can be back-solved into a terrible forward CAGR.

It creates the perfect excuse to ignore stocks on the index level, and therefore the perfect mechanism for markets to underprice the singularity.

Tokenmaxxing is about to agentmog the Shiller CAPE-cels.

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Contents

1. Microtokenomics

2. Why The “Intelligence Crisis” Won’t Happen

3. Timing and Revisions

4. The Trade

Microtokenomics

In every business or market, there is value creation and value capture. It’s a fancy way of saying consumer surplus and producer surplus, where value creation is the sum of all surplus and value capture is simply producers’ portion of it.

In AI, we can think of value creation as the value of the work that AI agents do for end consumers (how much benefit I get from tokenmaxxing) and value capture as the price charged by the model labs for inference (which also flows down to Nvidia, TSMC, the memory makers, ASML, etc.).

The higher the value capture compared to value creation, the more valuable that AI labs and semiconductors should be relative to the rest of the economy, and vice versa.

To analyze how big the value capture is compared to the value creation, we can look at microtokenomics. There’s not actually a thing called microtokenomics lmao this is just what I call looking at the value per token generated on a per-task basis.

SemiAnalysis did some amazing work on this. This is possibly one of the most impactful SemiAnalysis articles I’ve ever read.

SemiAnalysis

AI Value Capture - The Shift To Model Labs

A day in AI now feels like a year in any other industry. Model releases, software breakthroughs, and hardware improvements are compressing multi-year cycles for any other industry into weeks. Over just the past few months, agentic AI has crossed a real inflection point, driving a step-change in the value of tokens while software and hardware improvements have sharply reduced the cost of generating them…

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3 months ago · 273 likes · Daniel Nishball, Dylan Patel, Cheang Kang Wen, Crystal Huang, Max Kan, Ray Wang, Myron Xie, Zane Fong, and Clara Ee

Here is the most important quote from the article. This states very plainly an idea that I’ve believed for a while but struggled to articulate.

“End users are enjoying a productivity bonanza - tasks that used to take tens of person-hours costing thousands of dollars can now be accomplished in minutes with a just a few dollars’ worth of tokens. This huge surge in revenue and margins is because the value of tokens being created is dramatically improving businesses. For example, SemiAnalysis has reached as high as $10.95 million dollar annual spend rate on Anthropic Claude tokens, but the value we derive allows us to outcompete all our competitors and gain market share.”

Later in the article, SemiAnalysis goes on to demonstrate traditional knowledge work tasks that would have taken several hours of traditional analyst labor, costing from $150 to $500, being able to be automated with a token cost of $2 to $8. They included tasks like:

• pulling 5 years of financials and emailing the results with Excel tables attached

• searching past conferences for optical networking and OCS trends

The ROI on tokens spent in these examples is over 50x.

So yeah, the ROI question is solved.

This has allowed Model Labs to expand their gross margins from the 30s and 40s all the way up to 70s and soon moving into the 80s as they take advantage of their pricing power. At the current point in time, if the ROI on tokens spent is over 50x the cost of the tokens, producer surplus is about 2% of the pie, while consumer surplus is 98%.

That orange sliver is the entire AI token supply chain that we are all invested in, everything from the Model Labs to the fabless chip designers to the foundries to the memory makers to the equipment vendors to the optical networking providers and to the power generators. All of that is 2% of the pie. The 98% gets spread out across every other firm in the economy. For every trillion dollars added to Anthropic’s market cap, $50 trillion or more should be added to the market cap of the S&P 500.

People see the ROI question mostly as a question for the AI infra value chain, but but it’s actually far more important for the economy. Negative ROI on AI means that every dollar spent reduces aggregate earnings for the S&P 500, while positive ROI means that every dollar spent adds to Aggregate Earnings.

In our case ROI is extremely positive. Every dollar of revenue for a model lab is an order of magnitude more value created for the economy (in the form of aggregate earnings). In addition, this aggregate earnings boost definitionally grows at the same exponential clip as the model lab revenues as AI penetrates deeper and deeper, meaning that earnings growth for the S&P 500 should accelerate beyond what historical norms suggest.

This is why the Shiller CAPE is flawed. Of course, stocks, which are supposed to price in future earnings, look funny based on past earnings if the future earnings are all of a sudden growing way faster than before!

Why The “Intelligence Crisis” Won’t Happen

This famous and very well-written article by Citrini single-handedly made consensus bearish on AI-impact-on-stocks.

Citrini Research

THE 2028 GLOBAL INTELLIGENCE CRISIS

Preface…

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6 months ago · 9205 likes · 90 comments · Citrini and Alap Shah

Today, after seeing more empirical data, I am vastly more bullish, and the reason is a sort of a Jevons paradox but for labor.

Nathan put it well in his podcast, the AI Daily Brief.

“And what was interesting in all of this is that both the promise and the fear of AI, the promise of AI that would reduce how long it took to do your work so you could go enjoy more leisure time, and the fear of AI that would negate your value as a worker.

We’re both very far away from the lived reality of the most advanced users.

In fact, instead of finishing your work day at 3:00 PM, the more common challenge was people having to force themselves to go to bed at 3:00 AM, tearing themselves away from the next thing they could accomplish, which was always just sitting there waiting for them.”

What we see empirically is that when people use AI, they work way more rather than way less.

More quantitative evidence for this can be found in proxy metrics for the formation of small businesses. In the post-agent era, the number of domain names being registered and small business applications filed has sharply inflected.

The economic theory behind this effect is perhaps more important than the data itself.

Citrini’s original scenario went something like this: AI can do the same things that humans can do. It starts in coding, which is why software engineers get replaced first. These displaced workers stop spending, which weakens the economy and forces more businesses to cut workers and replace them with AI, further reinforcing the cycle. This continues until the drop in spending destroys the credit markets and causes a financial crisis.

The core premise of this scenario is that there is a fixed amount of work to do. The first software engineers get replaced and stay unemployed as AI has taken a portion of that fixed pie of work. This is why the empirical evidence that AI has created more work to be done than ever before is important because it shows us that the pie is not fixed. AI reduces the cost of providing many services in the economy. For example, coding, writing, and creating infographics. Many goods and services that were previously too expensive and uneconomical to produce (such as starting a software business which used to take millions of dollars of engineering talent and multiple rounds of VC financing, now only requiring a $200/month subscription to Claude) now become viable. These new goods and services present themselves in the economy as business ideas that are only possible in the age of AI. These businesses then hire more people and that then increases employment.

The end result is essentially a Jevons paradox but for labor. By making work cheaper we vastly expand the universe of economically useful work to be performed and cause the need for human labor to go up and not down.

Those first software engineers don’t get replaced because they can now do far more within the organization, or they find a new job because new businesses are created that are hiring, or they start their own businesses. The vicious cycle never begins.

There’s also another argument that humans are partially a complement to agents, rather than agents fully being a human substitute, which is argued by Dan Shipper in his essay “After Automation.”

You can read it here.

I am much less confident in this theory as we have yet to see the empirical evidence for this with much more powerful models. If this is indeed the case, AI can be treated much more like past automation technologies like spreadsheets, factories, or farming equipment which are a complement to labor (and are the examples used for the “the Luddites are wrong again” argument) rather than the theoretical substitute that is more consensus.

Timing and Revisions

The biggest risk to this trade is probably timing.

The AI build out happens before AI creates value. That is why it’s been so much easier to invest in AI infrastructure and make money than it has been to try to predict anything at the application layer or the end economic effects.

Dylan Patel has said in a podcast before that the hardest part of his job isn’t analyzing the semiconductor supply chain (supply-side) but is instead analyzing the demand side.

“I think the hardest area for us and for everyone is understanding tokenomics, economics of tokens. I think we have a really tremendously like good insight into how much it cost to run infrastructure, what the cost of tokens are, what the cost of models are, what the margins of these labs are. But the usage and adoption is what’s really difficult to model, you know, continuously, right?

We, we have these like we had like crazy in January, we had crazy estimates for February, Anthropic smashdown. How do we calibrate this model? What are the data sources for this February? We had crazy assumptions for March and then they smashed them and everyone sees the number of 10 billion and they’re like, what the fuck? How do they add 10 billion of revenue? Who is using all these tokens? Why are they using them? What are they building with them?

And then more importantly, with what they’re building with these tokens, how is that actually diffusing into the economy? And what value is that generating? Because it’s not really something that you can capture in any any GDP statistic, right?

All of the value of the tokens that I use get transformed into better information, which I then sell at a discount to what people used to sell information for relatively because and therefore that information is now making its way throughout the economy and and people are making better investment decisions or better competitive decisions.

But if they’re semi data company or data center company or hyperscaler. And now how, how much what, what is the value of this? And what is that? What is that done to the economy? It’s clearly by every subjective metric, amazing.

But where is the phantom GDP? What is the phantom GDP? How do we track the real economic because because the GDP metrics are not, you know, accurate.

If you were to say what is the GDP that Dylan Patel is making? It’s tiny compared to what the value that I think is being created. And so ultimately, what is the value being created by these tokens, not on a basis of, you know, just simple, you know, what is the knock on effect, right? What is the knock on effect of all the things that these things are doing? And I think that’s the real question and challenge that that’s hard to measure.

I think we’ve got a tremendous, you know, reading on the supply side of things. I think we’ve got a tremendous reading on even a lot of the demand side signals. But it’s it’s what is the value these tokens are generating that’s hard to quantify and measure.”

Thanks, Dylan, for the very long quote that says literally exactly what I wanted to say.

We know how much hyperscaler capex is going to be spent this year, but we can’t predict when that capex is going to turn into token revenues, let alone when those token revenues are going to contribute to productivity in the economy at large, let alone how much productivity it’s going to contribute and if that is big enough to actually move aggregate S&P 500 earnings or aggregate GDP. It’s really hard.

However, even though we can’t draw perfect causation, our job as investors is to take imperfect information and make an interpretation of it that allows us to make asymmetric bets. We can analyze the empirical data from today and come to our own conclusion on if we are too early to see the economic diffusion of AI.

And even though we are still super early in the S-curve adoption of agents, we are already seeing some anomalous data that suggests we are not too early for their macro effects.

First, 2026 EPS revisions are completely outpacing the historical norm. Funny enough the kink in the chart is quite literally right when Claude Code got widely adopted in January and February of 2026.

Second, S&P 500 margins are also increasing much faster than history would suggest, showing that the same revenues can be achieved on a much smaller cost structure, a structurally healthy sign for economy-wide ROIC.

While it is impossible to link these effects to agentic AI and it is easy for it to become another example of the classical correlation and not causation logical error, this is enough evidence for me to conclude that there is likely an effect of AI here and “likely” is good enough for a trade.

The Trade

I called out a trade in my subscriber chat not too long ago (May 8th). You know which one this is lol.

It’s performed quite well since.

It is…

This trade has the additional benefit of being long an option, which is also another amazing way to take advantage of AI because AI causes the rate of change in the entire economy to accelerate. While implied volatility is priced based on what happens in a year, what happens in a year can now happen in mere months, and therefore volatility should be systematically underpriced too as more stuff can happen before your option expires. However, the IV still sits around historical norms, which ensures the return hurdle for options to double or triple stays low and allows for higher effective leverage.

This call gives you 7x effective leverage to SPY. Unlike calls on other indices like QQQ or SMH, which are often used to play AI, SPY has much lower IV. For QQQ and SMH, you can only get 5x and 3x effective leverage, respectively. Which happens because AI infra IV has already repriced. QQQ and SMH have a higher concentration of AI infra names than SPY; however, the IV for your standard corporation hasn’t moved one bit. This means we can take advantage of the low IV to get a super high effective leverage on the “AI Boosts the Broad Economy” trade.

The option expiry is around 20 months out. I think this is close enough to retain sufficient torque, yet far enough out to ensure enough of the agentic AI’s curve is captured.

We can see the effects of the low IV here. A simple 9.6% annualized return over the life of the option already allows us to break even, and a 14.6% return, which would be slightly above historical norms, would cause the option to return 100%. This is not a high bar. The S&P has returned far higher than that in non-singularity times.

If you truly believe in the broad repricing of all productive assets. You can look further down the table to a 30% or even 40% S&P annualized return, which gets you to 400% and 600% returns, respectively. That’s pretty asymmetric.

Please don’t tell us that we’re going to overbuild because we double the capacity of optical widgets.

There are two layers to this exponential.

First, I think networking will grow as a share of the BOM over compute over the long run. You can see this specific value-add transition in NVIDIA’s earnings, as they missed on compute but massively beat in networking.

It’s a pretty simple concept, and I’m unsure why this isn’t talked about more in general. If we are hitting the limits of Moore’s Law and traditional dies can only be so big (sorry Cerebras), the only way that we get more powerful compute units is by linking them together. This means that the networking BOM is about proportional links:chips (inside a rack) or links:rack (inside a DC). And according to graph theory, the number of edges will always grow faster than the number of nodes.

The second layer to the exponential is the CPO copper replacement story itself. Within this layer, there are three more sub-layers. That is, we can split CPO adoption into three phases, each with half an order of magnitude more CPO content than the last.

1. Phase 0 is scale-out CPO. This actually doesn’t add any new optical content for the total market, but it does add CPO content for the CPO vendors. In Phase 0, transceivers that connect switches in the scale-out network are replaced with CPO. Though it’s a little bit confusing because the ramp happens first but happens the slowest. This does not mean that transceivers will all of a sudden disappear.

2. Phase one is inter-rack scale-up CPO. This is the art of taking multiple racks, which are usually their own scale-up domain (one coherent piece of compute to the software), and linking them together with optics to make an even larger scale-up domain. This is represented by NVIDIA’s NVL576 Oberon solution for their Vera Rubin platform.

3. Phase two is intra-rack scale-up CPO. In this final phase, we are ripping out all the copper SerDes and interconnects of a single rack and replacing it all with CPO. As you may imagine, this has by far the most CPO content out of any of the phases, but is also very far away.

Lumentum has this slide at OFC which describes it. They are very excited, as you may imagine.

The timing is also important. CPO is not going to happen as soon as the most optimistic bulls think.

Volume shipments of phase one scale-up CPO will not occur until the Feynman generation. This is despite Rubin Ultra having the NVL576 Oberon 8-rack system, as this will ship in far less volume than regular Kyber NVL144 and Oberon NVL72.

But I’ve, of course, been a very vocal CPO bull. So where do I stand on this?

I think what is underrated about CPO is, instead, its inevitability and sheer VOLUME. People simply do not understand how much CPO will be needed once it actually does ramp.

Anyways, let’s talk about all of the widgets needed for CPO and the different widget makers in the CPO supply chain.

Contents

1. InP Substrate

2. Epitaxy

3. Laser

4. Optical Engine

5. Conclusion

6. AXTI

7. Aixtron

8. Lumentum

9. Coherent

10. Soitec

11. Tower Semi

12. My Ranking (and Positions)

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By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

InP Substrate

InP is the pure laser raw material. Indium + Phosphide is a III-V (named after periodic table columns) compound which has a direct band gap, meaning that it can emit light, unlike silicon.

This chemical is very hard to make. You must refine high-purity indium (99.99999%) and phosphorus and then grow this compound into an ingot. It’s really hard to grow this stuff. This isn’t memory, where it’s a commodity. You gotta get qualified and develop proprietary processes (like AXT’s VGF).

InP substrates are the bottleneck to end all bottlenecks.

Jason's Chips

What is an AXTI (and why is it up 80x in 12 months)

AXTI is a funny company…

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3 months ago · 30 likes · 3 comments · Jason's Chips

AXTI, the poster child, actually doesn’t have majority market share. They are only the second-place player with roughly 36% share, while Sumitomo Electric holds 42% share. However, I believe AXTI will soon become the largest player here, as they have the most expandable capacity due to their ability to convert gallium arsenide capacity into indium phosphide via brownfield switcheroo.

The relevance for CPO is simple: CPO lasers are high-power CW lasers that require a much larger cavity in order to be so powerful. This means the die area is much larger, and each laser a b s o r b s much more InP substrate. And then you get these lasers to grow exponentially across three phases, each with half an order of magnitude of content increase. Today, despite having no CPO ramping at all, indium phosphide is so constrained that Lumentum is 30% behind demand in just traditional EMLs. See the problem here?

The question for this market is how big the price hikes are going to be. AXT trades at something like 30x exit 2027 ARR, which is ridiculous by any traditional measures. The management team has obviously not announced any crazy memory price hikes, but we all know that eventually they are going to raise the prices. The market is obviously discounting for some level of price hiking, but we just don’t know how much it’s going to be. Bulls will say the ASPs are going to 10x. Bears say it’s only going to 2x.

Epitaxy

Epitaxy is the next layer. In order for indium phosphide substrates to be turned into real lasers, you have to perform a process of crystal growth called epitaxy to turn them into epiwafers first. It’s an intermediate stage.

To do this, you use Aixtron’s MOCVD (metal organic chemical vapor deposition) tools. Aixtron has a proprietary process called the planetary reactor, which allows you to batch this activity. Because of this, Aixtron has a literal monopoly on this whole market. They claim “well north of 90% market share,” which is just a kind way of saying this. If you want Veeco tools, you have to throttle your throughput because you won’t have planetary reactor. Aixtron is a monopoly. You have no other choice.

Then there are the buyers of these tools and those who actually do the epitaxy-ing.

Lumentum and Coherent have this process vertically integrated, but for anyone who doesn’t, you can buy from IQE.

They are a pure-play epiwafer maker.

Laser

Once you grow the epiwafer, it goes through a more traditional compound semiconductor fab process with lithography and etch to get turned into the final laser. This is purely the domain of laser manufacturers like Lumentum and Coherent.

Since we’re only looking at the indium phosphide supply chain today, which is the vast, vast majority of CPO anyways, we are only going to consider DFB lasers and not VCSELs.

CPO lasers are high-power continuous wave lasers. This means that they have two important characteristics:

1. Being continuous wave (rather than EML)

2. Being high-power (rather than low-power)

CW lasers are lasers that just stay on. It produces a continuous stream of light, like a light bulb or flashlight. A CW laser can provide a steady beam that gets modulated (gets the signal put into it) somewhere else.

EMLs are lasers plus a built-in light switch. EML stands for electro-absorption modulated laser. The laser creates the light, and an attached modulator rapidly changes that light on/off or high/low to encode data.

Lasers for CPO must also be placed far away from the optical engine because CPO literally stands for co-packaged optics, which means the optics are co-packaged with the chip and the chip is really hot, so if the laser is right next to the chip, it overheats and breaks and stuff.

Because this laser is so far away, in order for the light to reach the optical engine, it needs to survive a lot of coupling loss. Thus, the starting power of the light must be very high. For CPO, this means generally in the range of 400 mW rather than 30 to 70 mW of traditional CW lasers for SiPho transceivers.

The higher the laser power, the harder it is to make. These lasers must meet critical thresholds on two noise specs: linewidth and RIN. This is essentially the purity of the light. I won’t go into much more detail on noise specs, but if you’re interested, I talk more about them in this article.

High-power CW lasers are currently a market almost completely dominated by Lumentum.

Optical Engine

CPO lasers are CW, which means that in order to actually use it to communicate signal and not just be a flashlight, you need external silicon photonics components to do something with the light.

The optical engine is what you can see in the above diagram of the CPO. It is made up of a photonic integrated circuit, or a PIC, and an electrical integrated circuit, or an EIC, hybrid-bonded together (using something like TSMC’s COUPE). The EIC is your standard logic chip. The PIC is a SiPho chip. It is the SiPho PIC which actually modifies and modulates the light, but both of them come in a package whenever we are thinking of CPO. Here is an article if you want to go deeper into SiPho.

Jason's Chips

Short n' Casual Intro to SiPho

SiPho is pretty cool ngl…

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5 months ago · 75 likes · 2 comments · Jason's Chips

There are three layers to making an optical engine:

1. Making the SOI wafer

2. Fabbing the PIC in a foundry

3. Hybrid bonding the PIC to an EIC

SOI stands for silicon on insulator. It’s basically a silicon sandwich and needed to contain the light waves as they travel through the waveguides.

Soitec is a monopoly here. They control the majority of the IP needed to make these wafers, which they call Smart Cut. Just like with indium phosphide, this is a very difficult process.

I cover this topic a lot more deeply in this article.

Jason's Chips

The Soitec Series | Part 2: Wafer Manufacturing and the Smart Cut Moat

I wish this was a real product…

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4 months ago · 55 likes · 3 comments · Jason's Chips

Tower Semi is the leading silicon photonics foundry.

Soitec provides the starting wafers that go through Tower Semi’s specialty foundry manufacturing process to become a fully fledged PIC.

They are the best at it. Globalfoundries and TSMC also play in this market, but Tower CEO has specifically flagged and quoted that they have by far the highest market share.

When Krish Sankar of TD Cowen asked Russell about market share, Russell said:

“There’s other people that claim very high market shares. I don’t see how it’s possible at all.”

LOL

Finally, the PIC and the EIC need to be hybrid-bonded to form an optical engine, and the leading platform here is TSMC’s COUPE. TSMC did something really smart here, which is that they require that the PIC is fabricated at TSMC in order to use their COUPE packaging. Now their customers have to choose between the superior PIC manufacturing of Tower Semi and the superior EIC and packaging capabilities of TSMC.

Currently, it is COUPE that has won NVIDIA CPO ramp.

Conclusion

The big takeaway you should have here is that every step in the CPO supply chain (especially the raw materials) is physical and linear. Nothing here is built to handle exponential demand. Everyone has capacity limitations.

However, refining indium phosphide substrates, building MOCVD machines, growing epiwafers, manufacturing lasers, and fabbing PICs all have different capacity dynamics: different speeds of adding shipment volume, different capital intensity, and different substitutability. It is by understanding these dynamics through a lens of perpetual supply constraints and exponential demand, you can identify which companies stand to benefit.

Below I will give my opinions on AXTI, Aixtron, Lumentum, Coherent, Soitec, and Tower Semi, give you revenue builds/EPS estimates for all of them (making the same assumptions about the total industry-wide unit shipments), and share my personal ranking based on my opinion and the positions I have.

Yes, you heard that right. That’s six models!

I also call out why AXTI could still have the most upside, how I modeled the bombshell Aixtron dropped on their last earnings call, why Coherent management speak makes me believe they are uncompetitive, and a very interesting (!!!) component ratio dynamic between Soitec/optical engines and Lumentum/ELS.

AXTI

This is NOT my model. It was simply shared on X. If you are the original creator of this, please let me know and I will credit you.

Anyways, as we can see, the market obviously assumes significant ASP uplift, but we all know they’re going to hike prices. The only question is how much. It’s always been correct to be bullish on everything AI infra related, so we probably use the bull case here, and in this case, with a 90% ASP uplift, AXTI trades at 29x 2028 EPS.

This is a bit rich for me, just because if this scenario can fully play out while the stock stays completely flat for the next two years, there’s a very, very probable chance that we get a much higher ASP uplift as well. AXTI could really work as a stock. I’m probably more bullish than bearish, however, I have no idea how to estimate the ASP hikes that are included that will occur, and doing so would take me dozens of hours of research, which I’m not going to do now.

I’m a big fan of parabolic charts. However, I like to stay around the 10x parabolic chart range rather than aping into an 80x like AXTI is. I could easily see this changing if I see different evidence. CPO is really going to make the indium phosphide shortage go apeshit, as it’s literally an exponential market coming into an already backed-up, supply-constrained industry.

Aixtron

Aixtron dropped a bombshell on their last earnings call.

“80-100 tools is a good approximation of Opto tools needed every year.”

Based on the call comments, they trade out a cool 17x 2028 EPS multiple.

I haven’t released my opto tool intensity model yet, but this upward revision is hyper bullish, as it basically communicates that epi intensity for non-cpo lasers is a lot higher than we thought. When CPO lasers start ramping, which have a much larger die area, the intensity will be even crazier. As you can see, that is why my out-year tool shipments grow so fast.

Lumentum

Lumentum is the star of the CPO show and the center of the CPO universe as they make the highest ASP component of the CPO, the UHP CW lasers that take up nearly 50% of the ELS module BOM.

Besides CPO, their earnings are also driven by four other optical business lines:

• transceivers

• EML lasers

• OCS

• scale across

All of which are growing like gangbusters, especially OCS and scale across. I have my full Lumentum model in this earnings review, which you have access to.

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Lumentum Earnings Review | Being Sold Out is Now A Problem

It’s LITE day (again)! Let’s go…

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3 months ago · 48 likes · 5 comments · Jason's Chips

Also I am low-balling Lumentum pretty hard here. My estimates are pretty much in line with the sell-side, and every business line I think will be revised upwards soon. Here’s just one example:

Lumentum could be making 80% gross margins on their CPO lasers. Irrational Analysis thinks this can be highest 87%. This is a nearly 8x markup! In my model, I’ve assumed 60%. This also means that the ASP probably pushes $50-100, while I’ve only assumed $35. This is around what consensus thinks, so yeah, revisions. But until the guide comes, I won’t move my numbers up just yet.

Even with this much low-balling, they trade at a very reasonable 25x multiple on 2028 numbers.

Coherent

Coherent is the sheer transceiver and laser capacity play. If 6-inch works (and doesn’t only yield photodiodes lol), then everything that they say about capacity math becomes true.

What did they say about capacity math? Well, 6-inch wafers have four times the number of dies at half the cost.

Problem is, people have a lot of critiques about 6-inch wafer uniformity, basically meaning that these larger wafers are a lot harder to make uniform across the entire circle. They tend to be a lot more brittle and harder to handle.

There is probably a reason why Lumentum and AXTI generally focus on 3- and 4-inch wafers instead, and Coherent isn’t putting much counter evidence to this. Their gross margins are still not above 40%, while Lumentum’s have exceeded that long ago. In their earnings calls, they talk a lot about gross margin uplift, but it’s funny because they talk as if their gross margins have gone up 20 percentage points, but in reality it’s something like 170 basis points.

Yes, I really don’t think coherent will get much volume from CPO at all. They aren’t much of a CPO play. Most of their revenue comes from vertically integrated transceivers.

On earnings calls, management loves to talk about TAM, telling investors that they have a massive $15 billion addressable market rather than giving revenue forecasts and purchase order sizes. It is quite clear that they are uncompetitive and unable to capture much of the market.

Again, high-power lasers must have very competitive noise specs, which are really hard to achieve. That is why Lumentum has majority market share.

Soitec

My original thesis has played out almost perfectly.

We already know why we invest in optics. Transceiver demand is off the charts, CPO begins replacing copper in just a few years, and we are headed to an endgame where every chip communicates through light.

But what is the best name to play this theme? I believe it is Soitec, a French manufacturer of Silicon-on-Insulator (SOI) substrates required for silicon photonics applications.

Soitec benefits from the three largest demand inflections in optics with ramps perfectly staggered sequentially to create one decade-long unrelenting growth runway, a characteristic shared by no other public company.

1. 2026-2027: The near term ramp of SiPho 1.6T transceivers.

2. 2027-2029: The medium term ramp of scale-out and switch tray level scale-up CPO.

3. 2029+: The long term adoption of GPU-to-GPU optical I/O.

It is also a near monopoly with no other competitor manufacturing SOI substrates specifically for photonics at scale.

Meanwhile, its valuation is compressed because of underperformance in its large legacy segment. Millions of wafers are stuffing the mobile market’s inventory channels in a classic cyclical trough. We are at the rare inflection point where photonics revenue is becoming meaningful enough to impact the financials while the market still sees Soitec as a legacy RF mobile company.

Management confirmed photonics revenues will be €100m in the fiscal year ending March 2026. In just 2 years as 1.6T and CPO ramps, I project this number surpassing €400m, with high contribution margins as no extra fab builds are needed thanks to fungible capacity.

With a current enterprise value of only €1.6b, Soitec represents the cheapest yet highest quality exposure to the optical interconnect supercycle.

Something that is underrated here is that the capacity between legacy RF-SOI and photonics SOI is mostly fungible, creating a similar capacity dynamic AXTI enjoys. There doesn’t need to be much incremental capex to expand capacity.

Soitec has incredible business characteristics but is the most expensive of the bunch (although I think it should probably be valued on EV/EBITDA, unlike the others on P/E).

There is an interesting dynamic in the revenue build that I’d like to call out. Soitec’s CPO revenue is based on optical engines. Over time, optical engines per ELS shrink as data rates increase because the light requirements grow. This is offset by the size of the PIC growing, which enables more wafers to be sold as you can fit more PICs per wafer.

But it is less attractive than Lumentum, which turns that tailwind into a headwind as the ELS to optical engine ratio rises.

I do want to get a small position back into this one, but the problem is, unlike with Lumentum, I’m much less certain about the unit economics of their participation in CPO. Based on the wafer ASP that they’ve told me and publicly, scrapable PIC areas and stuff, it doesn’t seem to be as strong in terms of CPO content per dollar market cap. However, this can easily surprise me as well, just like with AXTI, just need to wait for their earnings results.

Tower Semi

Tower has unfortunately gotten quite expensive, but they’ve also been a revision machine.

On their last earnings call, they announced $1.3 billion of contracted SiPho revenue for 2027.

There has been lots of speculation that this could be NVIDIA and Tower could have won a major CPO order, but we don’t know for sure. Tower also has a very competitive COUPE competitor to TSMC. They could win a lot of market share back if they execute well. However, they will never be a monopoly here. TSMC is too good at packaging and EIC, so this would always be a contested market.

My Ranking (and Positions)

My ranking is as follows:

1. Lumentum

2. Aixtron

3. Soitec

4. AXTI

5. Tower Semi

6. Coherent

Lumentum and Aixtron are super close, so this was difficult, but Lumentum is just slightly higher because they are not a semi-cap. Being more downstream allows them to have higher convexity if demand for CPO goes parabolic. Both are the highest because they have the combination of great valuations, high leverage to CPO, and potential for massive revisions every quarter. I have massive positions in both.

Soitec is slightly behind Lumentum and Aixtron just because their unit economic exposure to CPO is slightly weaker. This is because each wafer that they sell contains hundreds of PICs, and even though they are much smaller than Lumentum, the size-to-CPO-content ratio is worse. They also have tons of spare capacity and can convert their shrinking mobile manufacturing lines to photonics SOI lines, which is a massive advantage in a world of capacity constraints. I really like that aspect of them. If I get more certainty that their CPO content is higher than what I have modeled, I will get back in. For now, it’s just too difficult to underwrite the same level of conviction that I have in Lumentum and Aixtron as they haven’t addressed the CPO opportunity publicly and provided visibility in the same way.

AXTI is 4th just because of how much uncertainty there is and how highly the market has already valued them, but this could easily be a super degen winner with 10,000% hidden price hikes.

Tower and Coherent are last because they are not guaranteed to win from CPO. But Tower is ahead of Coherent because Tower has a higher chance of winning CPO market share. They are actually very competitive versus COUPE, while Coherent is farther behind Lumentum.

But now maybe it is time to trade on fundamentals, which will be thoroughly analyzed via this piece of writing.

Today I will be taking you on an all-inclusive journey. We start from the technical foundations of their wafer-scale engine before evaluating the business and market side of their products. Everything that you’ve ever wanted to know about Cerebras, all in one place.

Contents

1. Introduction to Dinner Plate Computing

2. The Tale of Two Cities & The Nightmare Compiler

3. Wafer-Scale Yield

4. PVT Calibration

5. Fast Inference (Tensor vs Pipeline Parallelism)

6. Memory Constraints

7. The I/O Bandwidth Problem

8. Economic Relevance

9. Financials

10. How to Think About Cerebras

11. Testing 5.3 Codex Spark

12. Bull Case

13. Conclusion

As a Substacker, I am obligated to say that this is the kind of research that comes with a paid subscription!!

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Introduction to Dinner Plate Computing

Cerebras is the well-known pioneer of dinner plate computing.

Their foundational premise is that, BIGGER IS BETTER.

Normally, when you order a wafer from TSMC, you slice up your wafer into dies. Cerebras decided to keep all of the dies intact and instead network them together into one really big chip.

These spaces between the dies are called keep-out zones because normally you keep everything out of them because they will be cut up by the diamond saws. In this case, these keep-out zones are the home for complex wiring and networking. This means that Cerebras needed to collaborate intimately with TSMC to develop proprietary IP in order to fill up these keep-out zones with networking.

Throughout their investor materials, they make a plethora of comparisons to traditional computing with a bunch of metrics of stuff that chips do (compute cores, transistors, memory bandwidth, FLOPs), and because their chip is bigger, they can do more chip things.

This can mislead retail investors, as in real life there is always a trade off. Cerebras is simply a computing architecture optimized very differently than traditional GPUs.

The Tale of Two Cities & The Nightmare Compiler

SRAM is the super-fast expensive memory at the very top of the memory hierarchy. Faster than DRAM and even HBM.

This memory is usually made with a logic process and sits very close to the compute.

In a traditional GPU, all of its cores share the same unified pool of SRAM.

However, at a wafer scale, a unified memory pool is physically impossible. Therefore, Cerebras designed each compute core to have its own distributed 48 KB of SRAM. This is very little memory (the entire WSE would only have 44 GB of memory total compared to 13.5 TB of HBM3E for the GB200 NVL72 rack).

I like to think of this as a tale of two cities.

One is a small village with a few hundred residents. In this village, there is a shared library in the middle where residents go to fetch the information that they need. It is very easy for this village to coordinate as any two residents can read the same books.

Another is a nightmarish-looking homogeneous suburbia with 900,000 identical tiny homes. Each house has its own small bookshelf. You can only read the books that are in your own house. As you might imagine, it becomes a nightmare for this civilization to coordinate and communicate any information.

A compiler is the machine that translates human (or agent ha) intent (code) into silicon action. It needs to tell the transistors what to do.

In the village with the shared library the compiler’s job is easy. In the homogeneous suburbia, it’s a nightmare as you would have to write a master schedule that guarantees person number 452,000 has the exact right piece of data placed on their tiny bookshelf at the exact right millisecond and then passes it on to person number 452,001 right as they finish using that piece of data.

Because this hardware fundamentally guarantees the compiler complexity, Cerebras can never achieve the software optimization of NVIDIA. However this does serve as a pretty intense barrier to entry for new wafer-scale competitors.

Wafer-Scale Yield

Manufacturing chips is very hard. Sometimes a speck of dust can ruin your chips. Wafer yield is simply the percentage of good dies divided by the total number of dies on the wafer.

No one can ever get close to 100% yield; it’s always somewhere between 50% to a hundred percent.

But wait a minute! Wait a minute. Cerebras is making its chip an entire wafer. If you can never get a hundred percent yield, doesn’t that mean that Cerebras can never get a functional wafer-scale engine? Every single wafer is guaranteed to have at least one defect!

Not at all. Cerebras did a very clever thing where they built in an extra redundant communication pathways between the cores. If they find that a portion of the wafer, a certain die, has a defect, the compiler simply routes around it. This is how Cerebras claims a 100% yield rate on their Wafer-Scale Engines.

PVT Calibration

PVT stands for process, voltage, and temperature variation.

Semiconductor fabrication is a highly chemical process. All the etching and depositing and stuff are all done through chemical reactions, even though in diagrams it may FEEL physical.

Because these processes are all chemical, they are never fully uniform across the entire dinner plate. Transistors near the edge may end up switching faster or running slightly hotter than the same transistors located closer to the middle.

Normally, this wouldn’t matter for a traditional GPU because the dies are cut up anyways. But for Cerebras, it becomes a problem because the entire Wafer-Scale Engine needs to be fully synchronized.

This is the exact problem that caused the industry to abandon wafer-scale computing decades ago. If you’re required to clock the speed of the entire Wafer-Scale Engine chip to the speed of the slowest transistors, the performance would be atrocious.

Cerebras, however, developed a system to dynamically recalibrate the entire wafer on the fly. They detect the temperature and voltage and frequency characteristics of every part of the wafer and then actively manage the power delivery accordingly. Basically, every single core gets exactly what they need.

This is by far one of their most important innovations. PVT calibration is a real moat for Cerebras and represents an enormous barrier to entry for Wafer-Scale competitors.

Fast Inference (Tensor vs Pipeline Parallelism)

This is our main course today. Tensor vs Pipeline Parallelism is the single trick up their sleeve which allows them to perform their main value proposition to the market: fast inference.

Tensor Parallelism

Let’s think about how a standard GPU runs inference, called tensor parallelism. Usually, the model weights are too much to fit inside of the GPU’s SRAM. It has to be chopped up and processed by multiple GPUs in parallel.

Once the separate GPUs are done, they have to share their answers via an all-reduce, where every GPU has to talk to all others. This generally happens within the same NVLink scale-up domain so we’re talking about microseconds of latency, which doesn’t sound that bad, but there are two things that actually make it pretty problematic for fast inference.

First is that this all reduce must happen for every single token the model generates as decode is sequential.

Secondly, not only does it have to happen for every token, it has to happen for every layer of the model. Transformers are made up of many attention and multi-layer perceptron layers.

So for a 100-layer model, every token generated requires 100 all-reduce operations across the tensor parallelism group. If you have a lot of tokens, that’s a lot of latency!

There is also a separate latency bottleneck caused by HBM. Each time you generate a token, you have to load the model weights from the HBM to the compute. This is completely separate from the all-reduce and the tensor parallelism approach we just discussed, but is another reason why Cerebras is able to absolutely speedmog Blackwell.

All in all, several microseconds of latency per token results in the general hundreds of tokens per second achieved by regular normal GPUs.

Pipeline Parallelism

Pipeline parallelism is like a pipeline.

Like I said the model is made up of a bunch of layers. Each time you do the math of one layer, you get an activation output, which you pass downstream of the pipeline to the next layer.

Now here’s where Cerebras comes in. Cerebras has a big ahh chip. Whereas GPUs cannot fit entire layers of a model on their SRAM, Cerebras can. Therefore, when Cerebras does matrix math, it does not need to run an all-reduce operation to combine the answers for different slices of the model layers. It also doesn’t need the HBM. All the communication happens on-chip, and on-chip communication is very speedy.

Notice I say model layers and not model. This is important. Cerebras doesn’t put the entire model on one chip. It’s still not big enough to do that. It simply strings a whole bunch of dinner plates together, with each one processing many layers, getting the activation outputs and passing that downstream to the next set of layers. Cerebras has major IO bandwidth bottlenecks (which we’ll talk about later), but that is not an issue here because the activation outputs are very small.

The result? Data literally flows down the wafers like water through a river. Smooth, buttery, and unbothered. 15 times faster inference by cutting out the all-reduce and HBM communication latency.

Memory Constraints

Remember when I told you they have 48 KB of SRAM per compute core, which is 44 GB per dinner plate? Well, turns out this is not nearly enough to do anything useful in the world of frontier models.

To serve a model, you must fit both the weights and the KV cache (context) into the SRAM. 44 GB isn’t even enough for most models’ weights; and the KV cache is way larger too. To fit a classic open source model, like one of the smaller, dumber Llama ones or the more important DeepSeek V4, you will need a single-digit number of dinner plates to hold the weights and a double-digit number of dinner plates to hold the KV cache.

Because each dinner plate has so little memory but yet is so expensive, this makes serving models using Cerebras very, very capital inefficient.

The I/O Bandwidth Problem

Now you might be asking, why don’t we just offload the KV cache to HBM outside of the wafer?

Funny enough, the picture of the chip itself should hand you an obvious clue.

BIG AREA SMALL PERIMETER

The off-chip bandwidth is 1.2 terabits per second, compared to 21 petabits per second for internal bandwidth. That means that data within the chip travels more than ten thousand times faster than data going off the chip.

Beyond that, it also does not compare favorably to GPUs. For the B200 as an example, it has 14.4 terabits per second, which is over 10 times that of the WSE-3. This is like having a massive Olympic-sized swimming pool, but the only physical way to fill it or drain it is by sucking the water through a single, thin plastic drinking straw.

This is also a major reason why their hardware failed in training. Training requires data to be constantly passed from on-WSE to external memory which it absolutely does not have the I/O bandwidth for. This is intuitive, as you can imagine the WSE’s job in training would be to mimic a massive, optically linked GPU cluster while in inference, it only needs to imitate a few servers or a rack.

But the important conclusion here is that there is no escape: both the weights and the KV cache must be held by the on-chip SRAM at all times.

Economic Relevance

What does this all mean?

Well, think about what we’ve covered.

1. We’ve established that the WSE is a fundamentally different architecture than the GPU. It requires an incredibly complex compiler, redundant communication pathways, and PVT calibration. It is very, very different.

2. We explored what makes this very different chip better via pipeline parallelism, a feature that allows Cerebras to run inference over 15 times faster than traditional GPUs by avoiding the all-reduce and HBM latency.

3. We looked at what makes this very different chip worse, namely the suffocating memory constraints and tiny I/O bandwidth.

Notice something here: the advantages are about speed and quality of the experience of the user being served by the chip, while the drawbacks are all measured in number of bits and are about the sheer cost and economic unattractiveness of such quality service.

This leads us to our conclusion. What makes this chip better is that it generates tokens faster, but what makes it worse is that it is far more expensive to generate each token. Cerebras and wafer-scale computing in general is a provider of fast, premium-priced tokens.

The business side of this company is all about exploring whether such speed is worth the extra cost. Are people actually willing to pay for such expensive but fast tokens? Will this type of token take share from the traditional NVIDIA-generated kind? Are there new use cases that require this type of token that we haven’t been able to unlock yet because the hardware didn’t exist, or is the current regime already good enough for all of our uses of AI?

Let’s find out together.

Financials

Cerebras IPO was 20 times oversubscribed at the initial $130 share price. People really want to buy this thing. Yesterday, they raised the IPO price to $160. Polymarket still has almost a 100% chance that they will close above a $50 billion market cap (that is for their basic equity value, which is only around 70% of their fully diluted, so it is way higher than what’s shown in the table).

Their 2025 revenue is miniscule and irrelevant at $510m. Currently, they have $24.6b RPO and $20b of contract value from OpenAI, which can be expanded over time, but this implies a $6.7 billion steady-state ARR.

At the assumed IPO price of $160 per share multiplied by the fully diluted share account, the implied revenue multiple to OpenAI’s ARR is 7.4x.

The First Noble Truth of Cerebras

The following is the first Noble Truth of Cerebras.

The financials are useless. Completely utterly useless. Forward estimates are useless. Growth rates are useless. Multiples are useless.

Now, why is that? Let’s think about this from first principles.

How To Think About Cerebras

Cerebras is a canonical example of an unproven technology early in its S-curve with potential for high double-digit to triple-digit growth. This high-level conceptual profile allows us to anchor to two companies that have plenty of coverage on this channel: Lumentum and Bloom Energy.

Lumentum

The 30,000ft intellectual framing of Lumentum is simple: one market (optics) is guaranteed (by the laws of physics) to replace another (copper) to achieve full dominance of a certain use case (AI networking).

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5 months ago · 56 likes · 4 comments · Jason's Chips

Because the transition is inevitable, the leading players in the new market have a massive guaranteed growth runway. This is why optics are so hot.

At the same time, the growth is measured and measurable. It is easy to model. We can confidently predict that Lumentum has 50% year-over-year growth because we can simply use the growth rate of optics as an anchor.

Bloom Energy

Bloom is slightly different. One company provides an objectively superior solution for one single market, which has the potential to fully displace the incumbent.

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3 months ago · 79 likes · 19 comments · Jason's Chips

This is different from Lumentum because the market doesn’t change. Bloom plays in behind-the-meter energy, just like GE Vernova. They are direct competitors. There is no copper equivalent. There is no transition from one market to another.

Because of that, Bloom’s growth can be a little bit more unpredictable and bursty. There is no big Kager number to go off of. It is just a matter of how quickly customers adopt the new solution and how much, or if, the new solution is superior to the old one. However, growth here is still predictable. If the new solution is better, it will still slowly displace the incumbent.

Cerebras

Cerebras is completely different.

From our first article, we ended with the ultimate conclusion that Cerebras is a provider of a fundamentally different product than Nvidia: premium tokens. Premium tokens are way faster but cost way more.

Is Cerebras like Lumentum? No, there is no world in which premium tokens will inevitably displace normal tokens. They are simply two different products each with a different trade-off.

Is Cerebras like Bloom Energy? No. WSE is not a superior way to generate the same end product (electricity for Bloom, regular tokens for NVIDIA).

In both comparisons, Cerebras is actually much worse. It’s not guaranteed to take share from anyone, really. However, there is one critical distinction, and it’s very critical. Very, very critical.

The inference market is the single biggest market in the world.

AI networking and behind-the-meter power are big markets, but they’re not the biggest. Inference is, and the current incumbent is the biggest company in the world. If these premium tokens carve out a large enough slice of this overall pie, Cerebras is a very attractive investment. The only question is, will it carve up a larger left slice? Again, unlike Lumentum, it is not guaranteed to take any share at all.

Now you probably understand why I say that the financials are useless. Single-digit billions of ARR do not matter at all. If you are unprofitable and your market is the biggest market in the world, what matters much more, and really what matters at all, is the slice of the fast inference pie.

No amount of revenue will save Cerebras from offering a fundamentally unwanted solution. No valuation is too high if they serve a sizable fraction of all inference.

Therefore, the point of this article is to simply assess how likely it is the fast inference market ends up being incredibly large.

5.3 Codex Spark

5.3 Codex Spark is the custom-developed model by OpenAI, specifically made for running fast inference on Cerebras hardware. It is currently the only custom design model for Cerebras hardware, so in reality this model is one of, if not THE most important element of any Cerebras investment analysis. But for some reason, it is very under discussed.

It trades a large chunk of its performance to be smol. It features substantially lower benchmark performance, a context window that is one-eighth of the leading frontier models today, but with inference speeds of over 1,000 tokens per second.

As we established in the last post, in order to run a model of a certain size, a certain number of dinner plates must be strung together. The larger the model weights, the more wafer-scale engines are needed, and the more uneconomical it becomes. Therefore, small models are much more cost-efficient for Cerebras to run.

Now, how good is this model exactly? Here is where I found something funny.

The Cerebras IPO is one of the most hyped events of all time, but the only custom-designed model for its hardware, 5.3 Codex Spark, is one of the most un-hyped and un-loved models out there.

X Sentiment

AI lives on X, so let’s start with the X sentiment. Funny enough, I couldn’t find big accounts that posted about 5.3 codex spark just because of how underdiscussed it was, but there were enough posts for me to come to the conclusion that people generally did not like this model at all.

The main complaint is just the performance. It is a stupid model, to put it simply. Frontier intelligence unsurprisingly requires a certain amount of brute force that small models cannot muster.

My Own Testing: Equity Research

I have a little bit of a bias against benchmarks and testing that people usually do for these models. I don’t care about your score on Humanity’s last exam or whether you can build a snake game in one shot. These are not tasks that I will be doing in real life. This is a point people say a lot, but I think it is underappreciated because they are categorically different from benchmark tasks.

So, to that end, we will be doing two tests that I literally came up with by just asking myself what the next thing I have to do is. Which are equity research and coding.

In a very meta turn of events, I asked both 5.5 and 5.3 Codex Spark to look for pricing and usage data for 5.3 Codex Spark. So using Cerebras to evaluate Cerebras.

This is mainly a speed test. It is very difficult to gauge quality on research tasks because it is very subjective and could require large sample sizes all evaluated qualitatively before you can get a real sense of the difference. That is why we’re doing coding too.

But for speed, you might notice something interesting. Although Cerebras is supposed to be over ten times faster, 5.3 codex spark took 2 minutes and 19 seconds, while 5.5 took 3 minutes and 9 seconds. This means that 5.5 only took 36% more time rather than 900% more time.

This is because of two reasons. Both of which I observed first hand in my testing and is something us semiconductor people talk about all the time.

1. A large fraction, or maybe even a majority, of the time spent on doing the task was on web searches, tool calls, and JavaScript executions. Sound familiar? That’s right! CPUs strike again. All those research reports on how a large fraction of the latency in inference is CPU-bound. Well, turns out it isn’t just theory. 5.3 Codex does this really funny thing where it generates tokens in a millisecond, and then you have to wait two or three seconds for the CPU to run the tool call. You all should try it. It feels really weird, but you will immediately understand why CPUs are needed and why having fast inference isn’t all that it’s cracked up to be if you’re only speed boosting half of the workload.

2. Because the context window is so small, compaction happens very often. Every time the model has to compact its context, more latency is introduced. Even in that short two-minute task, GPT-5.3 Codex Spark had to compact its context, which is why it didn’t finish that far ahead of 5.5, which has a context window of a million tokens. Having a larger context window actually reduces latency by allowing the model to run without compaction for longer.

My Own Testing: Coding

I’m currently developing a full stack terminal for semiconductor investors, which I hope to release by the end of this month. It has a bunch of cool features like:

• A Pokédex of every single AI infra company, split up by industry and niche

• A map of all HPC co-location sites from the Bitcoin miners

• A portfolio lab for backtesting and paper trading

• An earnings calendar where you can view upcoming earnings, add events to your calendar, and download transcripts for past events

• A Taiwan revenue tracker

• A memory price tracker

• An API endpoint for AI agents to access the app’s features autonomously

• 10 different calculators

Therefore, I assigned both models to build my next feature, which is a paper trading feature for the portfolio lab.

This was not a good test for speed because 5.5 ended up pulling up the in-app browser to test as if it were a real user, while 5.3 Spark just shipped it immediately. They made completely different workflow decisions, which means that it cannot be compared apple to apples, unlike for the equity research task. Even then, GPT 5.5 took 16 minutes, while 5.3 codex Spark took 3 minutes, which is only about a 5x difference. That’s still not 10x, even with one model doing way more!

How did they perform? Let’s look at 5.5 first. GPT 5.5 successfully implemented the paper trading feature, and I was able to create my new paper portfolio called Bloomentum.

5.3 Codex Spark, on the other hand, had this result.

To see the actual finished product that 5.3 Codex Spark can create, I pasted the error back in and allowed it to fix the bug.

The quality of the work done by 5.3 Codex Spark is far worse. First of all there is another bug: the pricing quotes do not load at all. But if you just look at the UI of this tool, you can already notice the difference. It is very unintuitive and unfriendly to use. It is black and white instead of matching the colors of the app. The buy button isn’t even legible!

Additionally there’s this weird jittery effect where whenever you click a button or type something in, the elements sort of flicker.

Conclusion

Is 5.3 Codex Spark faster? Yes, without a doubt. However, is it 10 times faster like Cerebras claims? Not at all. Real-world workloads are not just pure feed forward. There are tool calls and there are context limitations. Just like doing well on the SAT doesn’t guarantee that you will be successful in a real-life career, running fast inference on a benchmark test in a lab does not guarantee that that same speedup applies in real life.

The performance difference, however, is extremely significant. There is a reason that people gravitate towards frontier-level intelligence even when cheaper open source alternatives exist. OpenAI and Anthropic’s combined ARR is not $80 billion for no reason. More intelligent models do things better. Not only do they do things better, they usually find ways to do them more efficiently, using less tokens in the process, and do it quicker as well. Not because of the sheer inference speed, but because of their superior reasoning abilities. The time it takes to correct bugs and failures, like the one seen in our test, will most likely overwhelm any gains from faster inference as the magnitude of the speed up in real life work just isn’t big enough.

And finally, one overlooked signal is the existence of 5.3 codex spark itself. OpenAI does not have an ultra-fast Cerebras option for GPT 5.5. They purposefully created a smaller weaker model optimized to run on this hardware. That’s actually pretty telling because it was a deliberate choice. They definitely thought about serving the frontier model on Cerebras first and then through testing and deliberation decided against it as it was either impossible or uneconomical.

Bull Case

Let’s be honest, these 5.3 codex spark tests were pretty bearish, so what’s the actual bull case? Well, I think I have a few.

The Undiscovered TAM

The undiscovered TAM argument simply posits that nobody really knows what fast inference could be used for, so the addressable market is completely undiscovered. Because nobody knows about these use cases, there is no market recognition of it, and because there’s no market recognition of it, it must be underpriced.

Here are three potential use cases that I can think of.

Fast Fundamental Investing

I’ve tried once or twice to trade earnings calls and catalysts and have failed every time. I come in with sheer, utter confidence in my fundamental understanding of the company and then get immediately humbled as my thinking speed is way too slow to process the information that was just presented to me and be able to connect that to what happens to the fundamentals of the company.

As you might imagine, this could be something that Cerebras is very good at. Imagine you’re a hedge fund and you have a lot of contacts in a company and a model already built. All you have to do is just give all of that to an LLM and tell it to watch out for catalysts. Once the event hits or the earnings call starts, the model is able to immediately run that inference and tweak the model and come to a conclusion. Having this fundamental-based conclusion just seconds or minutes before everyone else means that you can catch a 10 or 20% move while the rest of the market is still racking their brain around what just happened.

Embodied/Humanlike AI

What if I want an AI in my Zoom call? What if I want to talk to a model and have it interrupt me like my friends do? There is a level of speed required in humanlike back-and-forth conversation to enable idea exchange at a cadence that humans are familiar with.

Think about the way you talk to an AI right now. It is essentially long monologue after long monologue. This is due to the inference speed limitation. You may be able to imagine the plethora of use cases that can be unlocked with this constraint gone.

Robotics is another interesting application here. Imagine if humanoid robots can talk exactly like humans do. This would be an insanely large addressable market that includes all service industries like dining, hospitality, and front desk work.

Real-Time Human Augmentation

Finally, you have real-time human augmentation, which is essentially giving humans an earpiece that can allow an LLM to help that person in real time. Think about high-stakes meetings, live negotiations, that sort of thing.

The Low Hanging Fruit

The second category is low-hanging fruit. Low-hanging fruit being architectural innovations that Cerebras can easily make, which would be a step function change in their unit economics. Because their architecture is so much less mature than traditional GPUs, it is very unoptimized and therefore can be optimized.

FP8 & FP4 Support

The first thing that they can do is to enable FP8 and FP4 support. It is quite puzzling to me why they don’t do this already. They only currently support FP16, which is like having very limited storage on your phone, but the software only permits you to store photos and videos in 4K HD.

I think it’s likely because if they enabled this support, they would have to redesign their cores. Since their original design was meant for the training market, it was never optimized in this way. Anyways, there are rumors that this should be coming to the WSE-4, and that would unlock an easy 2x improvement in memory capacity.

Hybrid Bonding

The second thing they can do is more speculative. What if they hybrid bond an entire SRAM wafer on top of their WSE? It would be quite difficult, as it involves solving a ton of engineering problems and foundry shenanigans.

If they can do this, they scale their memory by an order of magnitude. Honestly, it would make Cerebras a completely different story.

But again, it is very speculative, and there’s no indication that they’re pursuing this.

The Non-Nvidia Ecosystem

Because Nvidia acquired Grok, they are the only major accelerated computing provider to have a fast inference solution. AMD and the hyperscalers are left without one. As you can see this argument is quite simple: the only way for them to compete effectively with Nvidia’s Grok LPU racks is to partner with Cerebras. All of Cerebras’s competitors are far behind and do not have comparable commercial traction, whether it’s MatX, SambaNova, or D-Matrix. Cerebras would be the only choice.

Conclusion

Because there are fixed, unchanging real-world constraints on total latency of agentic inference, there comes a phenomenon of diminishing returns on speeding up the feed forward itself, as there is no amount of pure transformer feed forward shenanigans that you can do that will make your CPUs do web searches, run JavaScript, and call tools faster. I believe Cerebras is well past that point. In my experience, GPT 5.5 low fast latency is already far more dependent on CPUs than the GPU.

The only model that is custom made for Cerebras is a subpar performer and has very little commercial traction. This makes me believe that the technical trade-off that they made and their “premium token” value proposition that we discussed in our last article does not currently have a market fit.

Therefore, I stayed out of the IPO.

However, I will still be watching this company very closely because of the bull cases. None of them are currently unfolding today, but if they do, this entire story can flip on its head. The thing is, I am not going to speculate on them. I want to see them happen before I get in. If I miss the first 100% to guarantee that the thesis actually exists, that’s okay. Just look at the Bloom Energy or Lumentum charts. Anybody who missed the first 100% probably aren’t complaining about it.

This is despite an increasing magnitude of their beats over time, and probably is frustrating Jensen right now.

We will see if today breaks the pattern!

Here’s today’s Buy Side Consensus and Street Consensus courtesy of Jukan on X.

Same pattern as always: they guide for $78 billion, which means that they will probably beat by $2-3 billion due to historical conservatism, Sell Side models just above their guide because of Sell Side conservatism, and the stock probably dumps and takes down the whole AI infra ecosystem along with it unless they meaningfully exceed even buy-side consensus.

In terms of their rev guide, buy-side expects $90 billion. That’s a lot.

Gross margin expectation is 75% as always, and I bet it’s going to come out at 75% as always.

The Print

My initial reaction was WOW. $1 billion above buy-side on the guide and almost $1 billion above buy-side on the current quarter revenue. But then I realized it was only a $1 billion beat. Which means… nothing really happened.

The price action is really funny. We went down 3%, then flat, then up 2%, and now we’re flat again.

Two other pieces of information that were reported in the print (and we don’t have to wait until the call for closing parentheses) are:

• Nvidia said that they have not generated any revenue under their H200 program and do not know if any imports will be allowed into China (by China) at all.

• They said that Rubin is not delayed to counter all the Twitter rumors lol.

IMO, the real signal can only come from the call today. There’s a reason that nothing really interesting happens with the numbers ever, because Nvidia’s numbers are mostly a lagging indicator of AI hardware procurement. They’re also easy to estimate because Nvidia is so big. The market would rather look forward.

There have been tons of rumors on Rubin being delayed by a quarter or two. There are a few speculations on why this is.

1. Competition especially, AMD has pressured Nvidia to start upping the specs of Rubin. For example:

   • Power from 1800 W to 2300 W

   • Memory bandwidth from 13 Tb/s to 22 Tb/s

   • Increasing the HBM4 requirements to over 11 Gbps per pin

2. The increased HBM4 requirements meant that the Big Three, the Big Memory Three, had to redesign their samples and resubmit.

3. There was also a claim that Nvidia had unresolved SerDes chip interface problems and was “dialing down specs” as a response.

4. And also heat-spreader redesign?

Anyways, Nvidia is probably going to get a lot of pressure from the sell-side analysts to explain what’s going on here, so we should learn a lot today.

I have never seen a post-earnings chart as perfectly flat as this one today. It’s so perfect and so flat, it’s funny. Probably won’t be the case after the call, though!

The Call

Contents

1. New Reporting

2. Compute vs. Networking

3. Neoclouds

4. Growth Rate vs Hyperscaler Capex

5. Frontier Labs share

6. CPUs

7. Other

   1. Supply Commitments

   2. Capital Returns

   3. Groq LPX

   4. VR Ramp

New Reporting

Nvidia changed how they now report revenues. Before they reported revenues in very standard categories that included data center compute, networking, gaming, auto, and other. Now they have this strange new taxonomy.

Data center and Edge are the higher-level categories, and then within data center, hyperscale and ACIE are the lower-level categories.

Hyperscale is Hyperscaler. ACIE is basically Neoclouds, Enterprises, and everyone else. Funny enough, they’re 50/50 even though by intuition, you’d think it’s mostly hyperscale.

Jensen provided a pretty good answer for why they segmented this way. The essence of it is that Hyperscale is serving the big players that can pick and choose different Nvidia “AI Factory” parts, and Neoclouds and ACIE want a turnkey solution.

Jensen also made the point that hyperscale grows first, and then the long tail grows second.

“And so for many of the other applications, industrial applications, enterprise applications, until the AI is very capable and thus really productive work and does it safely and it could do it in a way that can actually generate impact and income It does not really get used. And so you expect the second category to develop slower than hyperscale. And you could see that in the numbers. However, long term, if you look at look at industrial and enterprise, clearly, that is where future economics is going to be. Because it represents some you know, $50 trillion to $80 trillion of the world's economy. And so and it is gonna be larger than that because of AI.”

But that non-hyperscale will become a larger business over time.

Compute vs Networking

If my interpretation of this is correct, they might not be splitting their revenues into compute and networking anymore. But this quarter, they still split out compute and networking on the call.

Crazily enough, they actually missed in compute and then beat massively in networking. I think this is a very underrated piece of signal for networking gaining more market power and significance in the rack. The networking train is not going to stop, and overall this is quite bullish for photonics.

Neoclouds

On Neocloud specifically, Jensen also made a comment that taught me something new about the business model, which is that he emphasized that it is the most rentable and the easiest to finance. H/t to Austin Lyons.

Not only is it that the Neocloud ecosystem was created by Nvidia and only buys from Nvidia. The Neocloud ecosystem also requires cheap financing, and cheap financing only comes to the most reliable, most common solution. NVIDIA being dominant also means that NVIDIA is the easiest to finance. It’s a self-reinforcing compounding loop.

Growth Rate vs Hyperscaler Capex

Nvidia is generally seen as having revenue that literally grows in line with hyperscaler capex because, after all, they have majority share in AI compute. How could they possibly outgrow them?

Jensen had a very interesting answer to this, and actually it makes quite a lot of sense. He says that they are growing faster than hyperscaler capex for two reasons:

1. First is that they basically have 100% share in Neoclouds and enterprises since they need turnkey solutions, and that means they practically only buy from Nvidia. And since they will inflect in growth later than hyperscalers, the overall growth rate for Nvidia will shift to these segments in which they have 100% share over time. Not a huge driver, but definitely still relevant.

2. They are also gaining share in hyperscale itself because they are gaining share in Anthropic.

Frontier Labs Share

Today Jensen bragged a lot about gaining back share at Anthropic.

“So the amount of capacity that we are gonna bring online for Anthropic this year and next year is going to be quite significant. Very significant. And so we are growing and our coverage of anthropic has been largely zero until this until just recently. And so we are gaining share tremendously fast in inference.”

Which ties into a broader comment about broadly actually gaining share in inference rather than losing share to custom silicon as everyone predicted.

“Well, we are growing share in inference. And we are growing share in inference very, very quickly. And the reason for that is this year, the number of frontier model companies grew. And so there is Cursor and Perplexity and there is some new model companies, TML and Reflection and list goes on.”

Their share used to be literally 0. Anthropic ran everything off of TPUs and Traniums, but now Nvidia is in, and based on what they’ve said, they are in big.

In fact, when a sell-side analyst asked them about potential upside over their 1 trillion forecast from GTC and sort of hinted at maybe CPUs being the largest driver of that, Jensen actually said that it is growing share in the Frontier Labs (a.k.a. Anthropic) being the largest driver.

CPUs

‎ Jensen said that they have visibility to $20B in total CPU revenue this year. Later in the Q&A, he specified that this is pure play Vera CPU racks. If you assume that the total server CPU market is $40B, Nvidia has 50% of the server CPU market which is pretty crazy.

I heard, funny enough, both an analyst on the sell-side in the Q&A and people on Twitter say that this whole CPU thing is bearish for Nvidia GPUs, but I honestly think that is a dumb take. CPUs are completely an incremental market. It’s because agents need to interact with the world. It does not cannibalize GPU workloads, because it is not that the more an agent needs to interact with the world (CPU work), the less it needs to think itself (GPU work). In reality, they are complements and not substitutes.

Other

Supply Commitment

Their supply commitment is now up to $119 billion, up from $90-ish billion last quarter. Continuing their mission of supply-chain-maxxing and mogging AMD and Broadcom

Capital Returns

They’re returning 50% of free cash flow to shareholders, which resulted in increasing their dividend from one cent to 25 cents per share per quarter and an $80 billion share repurchase authorization.

This one I feel like gets more press than necessary. Whether it’s a dividend or share purchase doesn’t really matter. I honestly don’t think it matters too much for the stock, since it’s basically consensus that everyone knows that Nvidia really doesn’t have anywhere to put all their cash. Either they invested in random companies and become an AI index fund, or they just return it to shareholders.

Groq LPX

Jensen made a comment that fast inference can be 20% of the market. However, today he kind of walked it back.

He had this very interesting quote.

“The LPX is designed for low latency and high token rate. Its throughput is low, Size capacity is low. And it is context processing, its ability to absorb a lot of context, for example, for software coding. For agentic workloads, its ability to absorb a great deal of context is lower. And so and so the challenge the challenge is simply and I have explained before, that the use case for LPX is not broad. it is, you know, intended for somebody who has a fairly large portfolio of different types of token services, And for the high token rate, maybe these services are quite premium. And the number of customers is not significant.”

This was a lot of qualifiers for their very own product.

“And I think I think whether it is 20% or 10% just depends on where we are in the development of AI. I think today, it is a lot less than 20%. Someday, these premium tokens could be 20%. And I am know, we are we are ready to work with work with service providers to enable this capability.”

A lot less than 20%.

Jason's Chips

Cerebranalysis | Part 2: The Perils of Real World Inference

Welcome (back) to Cerebranalysis (part 2…

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3 months ago · 27 likes · 10 comments · Jason's Chips

This aligns with my existing views on Cerebras. Basically, it could be a huge market some time in the future, but until we find exactly what use case turns it into a huge market, I don’t think the space will inflect.

VR Ramp

Finally, they had a very brief comment on the Vera Rubin rack, and yes, they confirmed that it will still start shipping in Q3 and ramp in Q4. When asked to compare it to Blackwell, they said it is hard to say and declined to comment on which one they think would ramp faster.

His 13F showed a very scary amount of puts on literally every single large-cap semi company.

Yes, I am aware I played a part in the FUD. Citrus correctly called it out.

The lesson today is simple. OPTIONS ON 13-Fs ARE MISLEADING!!

How Options Are Reported on 13Fs

The market value row shown above is simple for shares, but not so simple for options.

For shares, it literally is just the market value at the end of the quarter.

For options, however, it is not the value of the options. Instead, it is the market value of the shares controlled by the options.

This means that the actual market value of the options is way, way smaller than the market value shown on 13F. Plus, it is completely unknowable because we do not know the expiry date and moneyness (if it is in or out of the money and by how much) of the option. These are important because the closer the expiry, the lower the market value of the option relative to the shares it controls. The more out of the money the option is, the lower the market value of the option relative to the shares it controls.

This visual really hits home. There are two main takeaways that we need to have here:

1. The option value is guaranteed to be very low compared to the reported value on the 13F. In many cases, it is near zero.

2. The uncertainty is massive. Near-term out-of-the-money options have pretty much zero market value compared to the shares they control (i.e. you can control 100 or 1,000 times your capital in shares) why long-term in-the-money options can reach 1/3 to 1/2 the value of the shares. It is an order of magnitude of uncertainty. The market value therefore is incredibly low signal.

There is a very large chance that these were one- or two-month out OTM puts to hedge Iran, in which case they are like 3% of what the 13F says they are.

Leopold’s Real 13F

So, how should we read 13Fs?

STRIP OUT ALL OF THE OPTIONS. SHARES ONLY. ONLY SHARES.

Here was Leopold’s 13F from last quarter. Meaning what he owned on December 31 2025.

As you can see, this is basically your classic super concentrated thematic equity portfolio. Bloom, Lumentum, and CoreWeave pretty much make up half of the portfolio. The rest make up the other half. This was that big Bloom bet that he took in the beginning of the year. Notice that we’re completely ignoring the Intel calls and the CoreWeave calls. They look optically very large in the December 13F, but again, we have no idea how large they are.

Here is how his holdings performed during Q1. As expected, Sandisk and Lumentum absolutely mog everyone else. The Neoclouds and miners are the laggards. Leopold has a pretty high affinity for the Neoclouds and miners and regularly adds to them, even through some rough performance over time. He gets his god-like status purely from his bets on the vertical chart crew (aka Sandisk, Lumentum, and Bloom). And honestly, that’s enough because I’ve never seen anybody successfully bet on all three. I am a Big Bloom Bull (BBB), but I came in late.

Here is his portfolio if he’d held the exact same equities through the entire quarter. His Q1 return would have been 27.3%. This is interesting because, despite a very large divergence in the performance, it looks relatively similar. At least everything outside of SanDisk. That’s really the only difference. SanDisk now takes up 14% of the portfolio, where before it was 7%. It is doubled in size. But of course, he partook in a common hedge fund activity known as “trading.”

This is his new portfolio. Notice anything different?

First of all, it is only slightly (5%) larger than the Q4 portfolio.

Second of all, both Lumentum and Coherent are gone. Tower Semi, which weren’t in the top 10, are gone as well. Every single optics play was liquidated, and because this isn’t options, this is actual shares, this is real signal.

Third of all, because everything optics is gone, even though he reduced Bloom slightly by around 20%, it still makes up around a quarter of the portfolio (bc shares appreciated). His relative conviction on Bloom hasn’t really changed. The similar thing can be said for Core Scientific: reduced very slightly, but now actually makes up a larger portion of the portfolio.

Continuing on this train of thought, anything kept or slightly added to actually now takes up a larger portion of his portfolio. This includes CoreWeave, IREN, and Applied Digital.

However, the standout here is SanDisk. Not only did it appreciate materially more than the others, but he also added to it through the quarter. It is now his second-largest position, right between CoreWeave and Bloom Energy.

Below the paywall, I will attempt to read the mind of Leopold and tell you what he is thinking. I think I have read the original Situational Awareness essay like five times, and I followed every single 13F since Q2 2025, soooo I think I’m pretty qualified.

Contents

1. Optics Exit

2. Bitcoin Miners

3. NAND

4. Energy

Optics Exit

I think there’s a simple explanation here that’s generally pretty underrated. Leopold is the cult leader of fast timelines. After all, he wrote Situational Awareness.

On a podcast with Dwarkesh, Dylan Patel said this:

I think obviously Leopold, Leopold jokes that, you know, he’s the only client of mine that tells me our numbers are too low.

Everyone else tells me our numbers are too high almost ad nauseam, you know, whether it’s a hyper scaler saying, hey, that other hyper scaler, their numbers are too high, you know, and we’re like, Nah, that’s it.

And they’re like, no, no, no, no, it’s impossible, blah, blah, blah.

And then you’re like finally have to convince them through all these facts and data when we’re working with hyper scalers or AI labs that in fact know that number isn’t too high.

But eventually and eventually, like sometimes it’s like 6 months later, it takes them to realize or a year later, I think, I think other clients like on the trading side also use our data, right.

And I will say Leopold is pretty much the only person who tells me my numbers are too low always.

If you have by far the most bullish view on AI out of all major investors, and your fund is literally a mandate to express that view, would you be biased towards or against a technology that only reaches mass adoption in 2028?

The logic is that if we know the beats across the infrastructure ecosystem will be gigantic, we will see massive beats in more near-term plays like memory, Neoclouds, and miners, causing those stocks to re-rate, while optics will have to wait until Feynman to see major beats. We might have to work through years of conservative inline guidance before we see any upside.

I am not Leopold, so I don’t know if this view is right. I am just speculating here, but it seems like a solid guess and would be a logical position to take.

Bitcoin Miners

Leopold aped 35% of his portfolio into miners in Q4 and 37% of his portfolio into miners in Q1. CORZ, IREN, APLD, CLSK, RIOT are all pretty much the same type of business.

Jason's Chips

The Bitcoin Miner Iceberg | The Owners of the Final Bottleneck

Some of you have wondered why I didn’t cover the Bitcoin miners/co-locators (Core Scientific, Iren, Applied Digital, Cipher, etc.). Well, I just never really found them interesting…

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3 months ago · 24 likes · 10 comments · Jason's Chips

I think this is a basket bet. These miners don’t really have any differentiation besides execution, and their moat is near infinite switching cost and cornered resources. This means that stock picking is a lot harder, and a basket approach actually can reduce risk.

His favorite is still CORZ, as is mine, due to their forward observer pre-build strategy, which can allow them to get higher contract pricing, as well as their densification of their campuses using Bloom.

A.K.A better execution.

But why so much of the portfolio? Bitcoin miners are a time to power bet. It fits very well with the fast timeline thesis. Self-build takes way longer. Much of it is the permitting of sites, but the labor and experience is underrated as well. The miners provide both the sites and the experience of building high-density compute clusters from their crypto days.

NAND

NAND is probably the most interesting one. Notice that he bet on SanDisk and not Micron or SK Hynix.

Why NAND and not DRAM? I believe it comes down to supply and demand. DRAM wins the supply side while NAND wins the demand side. I generally don’t have a view that one is absolutely better than the other, but I do think that DRAM is the safer bet while NAND is more degen but has more upside.

Jason's Chips

DRAM vs. NAND | The Supply Side

Memory is the next great theme I have yet to cover…

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4 months ago · 57 likes · 7 comments · Jason's Chips

Read this article if you want to know why DRAM wins in supply.

For why NAND wins in demand, there are a few simple reasons:

1. Think about AI data as a stock and flow model. The KV cache of all inference happening at any time is the flow, while the amount of data created by AIs in inference is the stock. NAND therefore captures the stock, while DRAM captures the flow. This is a vast oversimplification, but you can imagine that the stock can grow much faster than the flow.

2. NAND KV cache offload. This is a bet on long context overflowing our current ability to store it in DRAM. NAND therefore acts like a call option on all of the data demanded but not able to be fulfilled in the KV cache. Obviously, this would be beneficial if you are trying to play the possibility of bit demand vastly outstripping supply.

3. AI workloads cause SSDs to be much more write-heavy than before, so the more intense AI demand is the faster SSDs get worn out from all of the writes and the more of a consumable that SSDs become.

These factors are all demand-side drivers and kick into high gear in a fast-timelines world. In a slow-timelines world, supply matters much more, which is where DRAM provides more of a safety net. Given Leopold’s world view, NAND fits it much better.

Energy

Leopold’s energy portfolio is mostly Bloom with a tiny bit of mobile gas turbines (Solaris Energy) and solar (T1 Energy). We will focus on Bloom here because it is pretty much a dozen times larger than his two other energy bets combined.

Bloom Energy is, in my opinion, the best AI energy solution in the world. Leopold obviously agrees. However, this is debatable, and you could reasonably take the other side. The capex per megawatt is still noticeably higher today. But there are two unarguable benefits that Bloom provides, which fit perfectly into the fast timelines thesis.

First is time to power. Bloom is the only solution that is available in months rather than years. This is pretty much consensus, even among Bloom bears. They argue that time to power is not enough to drive investment due to the higher upfront cost. But if you are playing fast timelines, fast time to power is a must.

Second is flexible capacity.

Bloom essentially acts as an overflow valve for gas turbines. This is perfect for fast timelines because the more demand is above expectations, the more orders fall into the overflow bucket. Think of it like a call option that captures only the upside above the current available capacity.

I don’t understand why this topic isn’t talked about more. In public equities, we look at every company, calculate their 2-3 year out revenues and earnings using detailed supply chain analysis, evaluate their competitive positioning, analyze their engineering in data sheets, build multi-tab quarter-by-quarter financial models, and apply the fairest valuation that we can.

And yet even then, we can get price charts (which is essentially the change in valuation over time where investors say “we got it wrong”) that look like this.

Thousands of market participants and investors in mid-2025 were wrong about this one company’s valuation by a factor of almost 100 times. Think about that for a moment. These shares were changing hands for almost 1% of what they are worth today. They were wrong. So wrong. Dead wrong.

And they are wrong about Anthropic today.

The 1 trillion-ish valuation is not really questioned too much. But once you start asking questions, you realize that there is completely zero chance it is anywhere close to big enough.

Today, I will justify a $100 trillion valuation for Anthropic as a fun thought exercise to show just how undervalued the model labs are. In reality, it is completely silly to claim that a company already worth nearly a trillion dollars will increase in value over 100-fold and become 20x more valuable than the current most valuable company in the world. The point of this exercise is to show that it is logically plausible. It is within the realm of possibility. If the ceiling is this high, at least we must be thinking in the wrong order of magnitude today.

Abstract

Model Labs are the greatest businesses of all time. Tokens increase in value over time as frontier models become more capable and able to automate longer time-horizon tasks, unlocking new use cases and increasing willingness to pay. Meanwhile, they decline in cost over time due to both hardware efficiency gains and algorithmic improvements in the models themselves. The combined effect of higher value at lower cost increases profit non-linearly, creating a near infinite terminal value. They are able to defend this attractive business because their moat strengthens over time via recursive self-improvement.

Anthropic’s $45b ARR represents growth never before seen in capitalism’s history, and using a Gompertz-style power law curve, we estimate its exit CY26 ARR at $300 billion, its exit CY27 ARR at $1 trillion, and exit CY28 ARR at $1.5 trillion. Yet it is absurdly still valued on trailing and current-year financials. We believe that it is a fallacy to not apply a far out forward multiple to the model labs like we do with high growth high margin public software companies like Palantir and Cloudflare. Doing so, we arrive at our $100 trillion valuation. Despite being optically unreasonable, this valuation is theoretically supported by the idea of the technological singularity, a concept commonly cited by AI theorists but that not yet entered the investment discourse.

However, retail access to this company is heavily gated and the IPO dynamics are incredibly unfavorable to retail investors. This is why I share a trade I have entered providing near 1:1 economic exposure to Anthropic to hedge against the IPO.

Contents

1. Model Labs = Greatest Businesses of All Time

   1. Increasing Value Over Time

   2. Declining Cost Over Time

   3. Strengthening Moat Over Time

2. Revenue and Valuation

   1. Estimating CY26 ARR

   2. Public Markets and Forward Numbers

   3. Estimating CY27 and CY28 ARR

   4. Margins

   5. Valuation

3. The Singularity

4. The IPO

5. How Retail Investors Can Play This

This is a longer, higher quality article than usual. Because of the time it takes me, I will only be able to do one or two of these a week. Would you prefer two of these types of higher quality articles or five articles at my usual length and depth? Earnings coverage is not impacted.

By accessing this content, you acknowledge and agree to our terms and conditions. This research is not financial advice.

Model Labs = Greatest Businesses of All Time

The popular media has it backwards. Model Labs are the greatest businesses of all time.

Model labs share a unique combination of characteristics that provide them with a near infinite terminal value:

1. The value of their product rises over time.

2. The cost of their input declines over time.

3. The moat strengthens over time.

As a result, the leading model lab(s) are currently on a fast track towards a monopoly position on all future intellectual and knowledge-based labor, an addressable market comprising most of global GDP.

Increasing Value Over Time

As AI models become more powerful, the value delivered by Model Labs per token rises accordingly. This is intuitive, as smarter models can do more useful things.

As the task set that models can accomplish broadens, people start discovering new ways to use models that have never been discovered before. It is only because of improving model capabilities that the agentic AI phenomenon, widely considered to be as important as the ChatGPT moment, was able to materialize.

Two of the most important examples for how more powerful models have delivered value through agents are Claude Code and OpenClaw.

Claude Code

The term vibe coding was coined by Andrej Kaparthy approximately one year ago.

At that time, when the leading model was Sonnet 3.7, vibe coding was heavily looked down upon and discouraged. I tried to build apps with it too and failed miserably. It will run into bugs and then run around in loops, building up more technical debt and fixing nothing.

Today, with Opus 4.7, Claude code is completely conquering software engineering as it is quickly moving towards 10% of global GitHub commits.

And it hasn’t stopped at just conquering coding. The only difference between an LLM and an agent is its harness: what tools it is given, what software it can access via API.

Now, most knowledge tasks can be automated via these agentic harnesses (which seem to improve just as fast as the models themselves). Agents can perform knowledge work far cheaper than humans, as shown by SemiAnalysis in one of their most recent articles.

Which also led to Doug O’Laughlin’s famous Claude Code psychosis and Anthropic’s ARR compounding at 50% month over month in 2026.

OpenClaw

A similar phenomenon happened with OpenClaw. As the model capabilities enabled longer horizon tasks, an open-source harness that enabled such work inevitably gained immense popularity.

And while OpenClaw hype has died down a bit, usage has not. Funny enough, I am the best example of this. While I talk about OpenClaw way less nowadays, I use my claws more than ever before.

Cron jobs are especially valuable. For example, a common use case in consumer SaaS is to provision a customer service agent, running once per day to respond to requests and modify the account information of real users. Without the significant improvements and model capabilities that occurred over the past year, this would not be a realistic use case, and if it wasn’t realistic, the harness would have never been developed.

Declining Cost Over Time

As the value of a token has risen, the cost to produce it has declined.

NVIDIA’s hardware enables multiple OOMs of efficiency gains in inference every generation.

This is because, with each generation, we get more flops, more memory, better software, lower precision, algorithmic improvements, and a larger scale up domain. Each effect compounds on the other.

But it gets even better. While the hardware becomes more efficient, new models are also getting trained.