In the past month, the hottest niche in the AI market on the US stock market has been optical modules.
An AI data center is not simply about stacking GPUs in rows. There is also a need to exchange massive amounts of data between GPUs and between servers. As models grow larger and clusters become bigger, the act of "transmitting data" between machines can easily become a bottleneck. Therefore, the market has started to chase the optical communication chain, with the hottest concept being CPO. CPO can be roughly understood as: placing optical communication components closer to the core chip. The closer the distance, the faster the data transmission and the lower the power consumption. In the increasingly large AI data centers, this story sounds almost perfect.
This narrative was truly ignited thanks to Jensen Huang. As Nvidia continues to push the AI infrastructure story forward, companies in the optical communication chain such as Marvell, Coherent, Lumentum, Corning, and AAOI have either been rumored to receive large orders or their stock prices have already surged significantly.
However, a couple of days ago, a highly controversial research report suddenly poured cold water on this hot track. The stocks in the optical communication chain collectively adjusted, with many experiencing declines in the high single digits or even double digits.
The issues began to arise: what did this research report actually say? Who is SemiAnalysis, the author of the report? Why could a single report cause the AI optical module chain to be re-priced by the market?
This article, Rhythm BlockBeats, will delve into this organization.
Why is SemiAnalysis regarded as the "industry bible"
To many institutions in AI and investment circles, SemiAnalysis is no longer a strange name. However, it remains somewhat mysterious to ordinary retail investors.
SemiAnalysis is one of the fastest-rising star institutions in semiconductor and AI infrastructure research in the past two years. Although still a rookie in the industry, its deep analysis and sharp viewpoints have quickly brought it fame in AI and investment circles. The organization currently has about 85 employees, focusing on providing in-depth reports and data models for the AI ecosystem, covering multiple aspects such as data center construction, supply chain economics, chip deployment, network, power, packaging, and equipment.
SemiAnalysis's official website introduction
The classic battle that gained SemiAnalysis significant recognition in the industry may be the recalculation of DeepSeek's costs.
In early 2025, DeepSeek ignited global interest with a highly shareable narrative: "With only $6 million, we trained a model that benchmarks OpenAI's o1." This number directly shattered the investment logic for AI computing power. The market began to question whether the capital expenditures for GPUs, often amounting to over $10 billion, had been wasted since models could be so inexpensive.
In a panic, Nvidia’s market value evaporated by around $600 billion in a single day, setting a record for the largest single-day loss in market capitalization in US stock market history.
As the whole world debated whether this $6 million was real or not, SemiAnalysis published a report that recalculated DeepSeek’s hardware expenses. It did not simply deny DeepSeek’s technological advances but instead dissected this "low-cost myth": what exactly did the $6 million cover? What did it not cover?
SemiAnalysis concluded that this $6 million only covers the extremely narrow cost of GPU pre-training, not accounting for research and development, infrastructure, cluster construction, and long-term operations. They estimated that DeepSeek's true server capital expenditures were about $1.6 billion, with the operating costs of the cluster nearing $944 million.
SemiAnalysis's cost calculation data for DeepSeek
More critically, it broke down the computational power inventory of DeepSeek. SemiAnalysis assessed that DeepSeek has about 50,000 Hopper GPUs, but these are not equivalent to 50,000 H100s; rather, they are a mixture of H800, H100, and a China-special version H20. This batch of cards is also shared with the quantitative fund High-Flyer, distributed across various locations for different tasks including trading, inference, training, and research.
Besides DeepSeek, another widely discussed case is SemiAnalysis's "short" report on ADM.
At that time, a hot topic in the market was the possibility of AMD catching up with Nvidia. Most people were comparing the theoretical computing power of AMD and Nvidia GPUs. However, SemiAnalysis repeatedly emphasized that Nvidia's real moat is not just the chips, but the CUDA software ecosystem, network, system design, supply chain capabilities, and the deployment experience accumulated over many years by clients. These things are Nvidia's true moat.
In December 2024, SemiAnalysis released a report that took five months to test the AMD MI300X. The report candidly stated: "We originally hoped AMD could be a strong competitor to Nvidia in training, but unfortunately that day has not yet arrived." Its core conclusion was that while the MI300X should have easily surpassed Nvidia's H100 and H200 in terms of specifications and total cost of ownership, its actual performance did not fully deliver, with the problem being primarily on the software side.
Just one day after the report was released, AMD CEO Lisa Su proactively contacted SemiAnalysis founder Dylan Patel. What was originally planned as a 30-minute call ended up lasting a full 90 minutes.
Of course, this also led the community to question whether SemiAnalysis is an organization funded and supported by Nvidia.
The influence of SemiAnalysis has also begun to extend from report pages to the industry scene.
Dylan (left) with SuperMicro founder and CEO Charles Liang (right)
Last year, Dylan was invited to tour the Supermicro factory, personally guided by CEO Charles Liang. According to a reporter from The Information, when visiting Dylan’s San Francisco office for an interview, the reporter almost bumped into Dylan's next visitor: Sequoia Capital partner Shaun Maguire was sitting there waiting to meet him.
The most highlight moment occurred during the GTC in March 2026.
In Jensen Huang's two-hour keynote speech, he mentioned only two names, one of which was Dylan Patel. He not only quoted the recently released chip performance ranking InferenceX from SemiAnalysis but also displayed the SemiAnalysis logo on the big screen and spent a full five minutes explaining it. During the speech, Jensen even publicly "acknowledged": Dylan Patel (founder of SemiAnalysis) said I was hiding my strength, saying the true performance is 50 times; he was not wrong.
Nvidia CEO Jensen Huang celebrating at the latest GTC Developer Conference, mentioning SemiAnalysis and its recent evaluation report on Nvidia chips
This status also directly reflects in commercialization revenue.
SemiAnalysis is expected to reach $100 million in revenue this year, while just a year ago it was about $20 million. Its clients range from tech giants to top investment institutions. It does not publicly display client logos, but the disclosed client types are already telling enough: large-scale cloud vendors, major chip manufacturers, and large public and private investors.
In other words, SemiAnalysis’s primary income does not rely on ordinary newsletter subscription users, but rather on selling these reports to the startup companies, investors, institutions, and traders who can greenlight tens of billions or hundreds of billions in AI infrastructure expenditures.
From anonymous hardware enthusiasts to top institutions in the AI circle
Like the recent "white-haired stock god," SemiAnalysis founder Dylan Patel also has a strongly internet flavor to his origin.
Dylan Patel
According to Rhythm BlockBeats, Dylan Patel's friend Dr. Ian Cutress recalled in an article that before founding SemiAnalysis, Dylan was a moderator on a popular hardware forum.
Dylan himself recalled in a podcast that before starting the company, he had run an anonymous blog for many years within the "Silicon Valley Twitter circle." It was a small circle that an ordinary tech Twitter user might not be familiar with, but it gathered a large number of hardware, chip, and supply chain practitioners.
There were also Reddit community users who mentioned that Dylan Patel was just a "nobody" on Reddit in the early days, a nameless individual. The public Reddit archives we found show that in discussions about r/hardware, u/dylan522p and u/SemiAnalysis had appeared.
Piecing these clues together points to the same picture: Dylan was active on Reddit and WordPress communities as an enthusiast studying hardware in his early years. At that time, he hadn't taken writing seriously as a business. He was consulting while maintaining an independent blog named "A thousand million," which was itself related to the content of the blog and industry connections.
Besides Dylan, his partner Doug O'Laughlin is also a key figure in SemiAnalysis, further pushing the commercialization of this blog.
Doug also began posting on forums, and Dylan found this person "quite interesting," leading to greater communication between them. Eventually, Doug repeatedly urged him: you should do this under your real name, move to Substack, and start charging. A few years later, Doug simply joined the company.
Today, SemiAnalysis has become the largest technology newsletter by subscriptions on Substack, with over 285,000 subscribers. Besides Substack blog posts, it also hosts a podcast called Transistor Radio.
According to Dylan, the podcast serves to carry industry insights that don’t make it into formal articles. Articles are responsible for complete in-depth stories, while the podcast handles fragmented news commentary, casual judgments on the market, and ongoing industry discussions that occur weekly. It releases approximately bi-weekly episodes where they chat about semiconductor news from the past two weeks.
As it has developed, this podcast has become regularly operated, supported not just by the two founders but by team members rotating in. For example, in a March 2026 episode, Sravan Kundojjala, Ivan Chiam, and Jordan Nanos together analyzed the shortage of AI chips, discussing everything from TSMC and Nvidia CPO to how the memory crisis affects GPU pricing and even the next generation of smartphones.
Besides their own channel, Dylan is also a frequent guest on major tech and investment podcasts, almost becoming a standard guest in AI hardware discussions. He has appeared on No Priors, Invest Like the Best, and Unsupervised Learning, as well as on Dwarkesh Patel's show. He has also had in-depth conversations with Jon Y of Asianometry, the latter being considered one of the best channels on YouTube for discussing semiconductors and business history.
Like an "intelligence" agency, more like Muddy Waters Capital
A detail from The Information's report effectively illustrates Dylan Patel's approach.
In the early days of starting the company, Dylan Patel attended almost every industry conference he could to supplement his semiconductor knowledge. Once on-site, he would probe people with questions, not just making small talk, but persistently interrogating engineers, supply chain personnel, and company executives until they became his sources.
As SemiAnalysis grew, this method did not change; it only became more industrialized.
The Information noted that the company now has 85 employees located in 11 countries. Every Monday, Dylan reviews the weekly briefs submitted by team managers. Each team focuses on a particular aspect of the AI economy, compressing news, leads, anomalies, and reasoning results from the previous week into concise reports.
You can think of it as an AI infrastructure intelligence weekly report. GPU, HBM, packaging, data centers, electricity, cloud vendors, optical modules, chip manufacturing equipment—each line has someone monitoring it. Among them is even a former ASML engineer, Jeffrey Koch, who specializes in studying the semiconductor equipment chain. When examining the bottlenecks in the AI supply chain, he looks not just at power but also whether chip manufacturing equipment might get stuck first.
SemiAnalysis is also quite adept at extracting information from gray areas.
The article mentioned that Dylan once saw an internal Google memo circulating on Discord. After downloading it, he sought validation for its authenticity from someone within Google.
Additionally, Reddit community users pointed out that when SemiAnalysis was founded in 2020 or 2021, its published content had nothing particularly special. But around the end of 2022, as the AI frenzy escalated, it began to expand rapidly. One user believed that SemiAnalysis collected a large amount of non-public or semi-public information primarily from Taiwanese companies, and this information circulated among analysts and some Taiwanese reporters.
"To some extent, SemiAnalysis is like Ming-Chi Kuo, famous only because it has established good relations with the Apple supply chain."
The recent lawsuit with a former employee has also brought this "gray information acquisition capability" to the forefront.
According to documents from the San Mateo County Superior Court, former SemiAnalysis employee Wei Zhou has accused Dylan Patel of running SemiAnalysis while personally investing in Fluidstack and using the non-public information he gained from it for research. When Zhou refused to include this information in SemiAnalysis's products, he faced retaliation and was fired. (It should be noted that these are currently accusations within the lawsuit documents and have not yet been legally determined by the court.)
Former SemiAnalysis employee accuses Dylan Patel of improper information acquisition
The lawsuit claims that SemiAnalysis's clients were unaware that Patel was personally investing in Fluidstack. Fluidstack is a private cloud services company, reportedly valued at billions of dollars. Zhou accused Patel of investing in Fluidstack through a $50 million SPV, or Special Purpose Vehicle. Patel is also said to collect a 2% management fee from this SPV and share in investment appreciation returns, potentially receiving additional rewards for introducing other investors.
More critically, the lawsuit alleged that Patel acquired a confidential Excel sheet from Fluidstack through this personal investment relationship. The sheet contains Fluidstack's revenue, sales data, and forecasts regarding TPU and other AI infrastructure deployments, with end clients including Anthropic, OpenAI, Meta, and other potential customers.
Zhou implied that these client demands and deployment information are not just Fluidstack's own trade secrets but may influence the judgments of several publicly traded companies like Amazon, Nvidia, Google, Broadcom, and Microsoft, all of which are part of the AI cloud, GPU/TPU, network, and data center infrastructure supply chain.
From these third-party information sources, we can roughly infer SemiAnalysis's research methods, supported by a comprehensive intelligence collection machine, including forums, Discord, industry conferences, personal connections, transport records, government documents, supply chain data, data center site photos, benchmarks, models, along with weekly internal briefs.
According to Dr. Ian Cutress, institutions like SemiAnalysis have far more complex data sources during research than the average person might imagine. For instance, submitting information requests for public records, analyzing public shipping receipts, and examining supply chain documents and government documents. In the data center area, they may even apply for permits to send drones over construction sites to capture high-resolution photos of what equipment is being installed on-site.
SemiAnalysis's own product page made it quite clear. Its AI data center model tracks over 5,000 data centers globally, with data sourced from property records, construction permits, electricity consumption, FOIA information requests, and satellite images. To process the vast amounts of satellite images, they have trained computer vision models, specifically CNNs, to automatically identify the scale, capacity, and construction progress of each data center. The goal is to expand their tracking coverage to every data center in every country.
This approach is closer to that of an open-source intelligence company than merely an analytical institution.
Interestingly, it reminds the author of the famous short-selling research institution "Muddy Waters" and its investigation methods. Muddy Waters also gained fame for targeting certain Chinese companies.
For instance, Muddy Waters' investigation of Oriental Paper Manufacturing included site visits to factories, observing the factory environment, machinery, and inventory, chatting with workers and nearby residents, and even secretly monitoring vehicle traffic coming in and out of the factory to document the situation, ultimately discovering that the so-called inventory was essentially just a pile of waste paper.
When investigating Chinese Express Channel, Muddy Waters directly observed the advertising played on over 50 buses, finding that drivers preferred to play their own DVD programs, revealing that the express channel had weak control over terminals. During the investigation of Global Water Resources, they found that one of the offices was virtually non-existent, with employees showing no signs of work, jokingly calling it an "adult daycare."
The most recent sensational short-selling incident targeted Luckin Coffee, which the author drinks daily. Muddy Waters mobilized 92 full-time investigators and 1,418 part-time investigators, monitoring over 620 stores across 38 cities, recording 11,260 hours of store surveillance, covering 981 business days and 100% of the store's operating hours, and collecting 25,843 customer receipt copies as well as a large amount of internal WeChat chat records.
With this firsthand data, Muddy Waters calculated that Luckin's daily sales per store were inflated by at least 69% and 88% in the third and fourth quarters of 2019, respectively, with actual customer spending far lower than disclosed amounts. After the report was released, Luckin soon self-reported a financial fraud of 2.2 billion yuan, and its stock price collapsed.
Of course, we currently have no evidence to prove that SemiAnalysis shorted optical module stocks before releasing the report. Based on existing information, its business model still primarily revolves around turning research results into products to sell to hedge funds, semiconductor companies, and tech giants' internal teams.
However, it is clear that SemiAnalysis's investigation methods share many similarities with Muddy Waters. It simply operates within the context of the AI era and the hardware niche, employing more refined information-gathering tools: upgrading from surveillance, interviews, receipts to satellite images, supply chain databases, engineering tests, and algorithmic models.
A $7 million token expense per year
Dylan himself mentioned in an interview that SemiAnalysis signed a corporate contract with Anthropic, with this expenditure amounting to $7 million, compared to their annual employee salary expenditure of $2.2 million.
SemiAnalysis views AI as a lever for information gathering and data production. Dylan's judgment is straightforward: they are in the information business, selling analyses, providing consulting, and building datasets. If they do not continuously raise their standards, AI will soon commoditize these things. The first batch of data products they sold in 2023 are now being produced by an increasing number of people.
The most telling example is their foray into energy data services. SemiAnalysis has been trying to build an energy model for the past year, as AI data centers are increasingly constrained by power lines, substations, transmission lines, and regional electricity shortages, which will, in turn, determine where data centers are built, their size, and when they go live. The energy data service itself is also a nearly billion-dollar market, and SemiAnalysis has long wished to penetrate it, but the team has been slow to make progress over the past year.
Later, Jeremy, who is responsible for data center energy and industrial business, began to "get into" using Claude Code. Dylan said that in just three weeks, he spent about $6,000 every day utilizing AI tools, an exorbitant cost. But the results were equally astonishing: Jeremy gathered data from every power plant in the US, every transmission line above a certain voltage level, and integrated a wealth of demand-side data, all sourced from publicly available materials—eventually creating a complete map and dashboard of the US power grid.
This system can reveal the power shortages and surpluses in different micro-regions of the US.
SemiAnalysis showcased it to clients who both purchase their data center data and engage in energy trading; the first response was surprise: how long did it take you to develop this? This is better than some professional energy data companies. Further inquiry revealed that those companies might have over a hundred employees and had been in operation for ten years.
Dylan admitted that this set of products from SemiAnalysis is not as mature or stable as traditional energy data company products. However, on certain dimensions, it has become faster, more detailed, and even better. This encapsulates the new form of SemiAnalysis's investigation methods: no longer merely relying on a single analyst to run meetings, ask questions, and sift through documents, but rather combining public data, engineering judgment, industry connections, and AI programming capabilities to create something in a fraction of the time that would have taken a traditional data company years to put together.
Ultimately, the most captivating aspect of SemiAnalysis might lie in this hybrid nature.
On one hand, it resembles a serious, almost cold intelligence agency, using satellite images, construction permits, shipping documents, supply chain interviews, AI programming, and engineering tests to piece together the true landscape of the AI infrastructure world; on the other hand, its founder Dylan Patel always retains a touch of mischievousness typical of internet natives.
When The Information reporter visited Dylan's San Francisco office, he mentioned that he shares the office with Dwarkesh Patel. Dwarkesh is the host of the popular podcast "Dwarkesh Podcast," and the two are friends, roommates, and office partners. They also live together with Anthropic researcher Sholto Douglas in Noe Valley, San Francisco.
But then Dylan shifted gears and mentioned that there is a third person in the office. When the reporter asked who it was, he refused to say, stating, "Let's play a game; you go investigate yourself."
The Information reporter, in an effort to extract information, could only indulge Dylan in this detective game, and the final answer did not disappoint.
The person working in the same office as Dylan is Leopold Aschenbrenner, a former OpenAI researcher who later founded his AGI investment fund Situational Awareness, turning $200 million into $5.5 billion as the "AI stock god" within a year.
One can only say that the top AI circle is still quite small.
References:
1. The Information, "Both an Analyst and an Investor: This 29-Year-Old Is Gaining Influence in AI";
2. Dr. Ian Cutress, "Dylan Patel's SemiAnalysis Is Being Sued," More Than Moore (Substack);
3. San Mateo County Superior Court public documents, case number CGC-26-635328;
4. SemiAnalysis, "DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts";
5. SemiAnalysis, "MI300X vs H100 vs H200";
6. EE Times, "GTC 2026 Keynote: Long Live the Inference King";
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