Over the past month, the hottest segment in the US stock market AI field has been optical modules.
AI data centers are not just about stacking GPUs in rows. Between GPUs and GPUs, between servers and servers, there is also a massive amount of data exchange. The larger the model, the larger the cluster, the more likely data transmission between machines becomes a bottleneck. Therefore, the market has begun to focus on 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 increasingly large AI data centers, this story sounds almost perfect.
This narrative was truly ignited thanks to Jensen Huang. As NVIDIA continues to advance the AI infrastructure story, companies in the optical communication chain such as Marvell, Coherent, Lumentum, Corning, AAOI, either received major orders or have seen their stock prices surge.
However, a highly controversial research report a couple of days ago suddenly poured cold water on this hot trend. The optical communication chain targets experienced a collective pullback, with many seeing high single-digit or even double-digit declines.
Questions arose as well: What did this report actually say? Who is SemiAnalysis, the entity that published the report? Why did a single report from them lead to a market reevaluation of the AI optical module chain?
In this article, BlockBeats will dig into this organization.
Why is SemiAnalysis Considered an "Industry Bible"
In the eyes of many institutions in the AI and investment circles, SemiAnalysis is already a well-known name. However, for the average retail investor, it still holds some mystery.
SemiAnalysis is one of the fastest-rising star institutions in semiconductor and AI infrastructure research in the past two years. Although still a newcomer to the industry, it has rapidly gained prominence in the AI and investment circles with its in-depth analysis and sharp insights. With approximately 85 employees, it focuses on providing in-depth reports and data models for the AI ecosystem, covering various aspects such as data center construction, supply chain economics, chip deployment, networking, power, packaging, and equipment.
SemiAnalysis's classic showdown, which made the industry take notice, may have been a recalibration of DeepSeek's costs.
In early 2025, DeepSeek ignited a global frenzy with a highly viral narrative: "Trained a model comparable to OpenAI o1 for just $6 million." This figure directly challenged AI compute investment logic. The market began to question whether the massive GPU capital expenditures, often in the tens of billions of dollars, were all in vain if a model could be trained so inexpensively.
In a panic, NVIDIA saw its market value evaporate by around $600 billion in a single day, setting a record for the largest single-day market value destruction in U.S. stock market history.
While the world debated whether the $6 million was real or fake, SemiAnalysis took a fresh look at DeepSeek's hardware costs in a research report. Instead of simply denying DeepSeek's technological advancement, it dismantled the "low-cost myth": What did the $6 million actually cover? What did it not cover?
SemiAnalysis's conclusion was that the $6 million only covered the narrow cost of GPU pretraining and did not account for research and development, infrastructure, cluster construction, and long-term operation. It estimated DeepSeek's actual server capital expenditure to be around $1.6 billion, with cluster operation costs close to $944 million.
More importantly, it dissected DeepSeek's compute inventory. SemiAnalysis determined that DeepSeek had approximately 50,000 Hopper GPUs, but these were not all H100s; they were a mix of H800, H100, and a China-specific H20. These cards were also shared with the quant fund Golden Cayman, spread across various locations for different tasks such as trading, inference, training, and research.
Aside from DeepSeek, another widely discussed case was SemiAnalysis's "short" report on ADM.
At the time, a hot topic in the market was AMD's potential to catch up with NVIDIA. Most people compared AMD and NVIDIA based on GPU theoretical compute power. However, SemiAnalysis repeatedly emphasized that NVIDIA's true moat was never just the chip but the CUDA software ecosystem, networking, system design, supply chain capabilities, and the deployment experience accumulated with customers over many years. These elements were NVIDIA's real moat.
In December 2024, SemiAnalysis released a report after spending five months evaluating the AMD MI300X. The report bluntly stated, "We had hoped that AMD would become a strong competitor to NVIDIA in the training space, but that day has not yet come." Its key finding was that while the MI300X should have been significantly ahead of NVIDIA's H100 and H200 in terms of paper specifications and total cost of ownership, the actual performance did not fully materialize, with the issue lying precisely on the software side.
Just a day after the report was published, AMD CEO Lisa Su proactively reached out to SemiAnalysis founder Dylan Patel. What was initially planned as a 30-minute call ended up lasting a full 90 minutes.
Naturally, this also raised suspicions in the community that SemiAnalysis was an institution supported by NVIDIA.
SemiAnalysis's influence also began to spill over from the report page to the industry scene.
Last year, Dylan was invited to visit the Supermicro factory, with CEO Charles Liang personally showing him around. According to The Information's description, when they visited Dylan's San Francisco office, 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 at GTC in March 2026.
In Huang Renxun's two-hour keynote, he only mentioned two people throughout, one of whom was Dylan Patel. Not only did he reference SemiAnalysis's newly released chip performance ranking, InferenceX, but he also displayed SemiAnalysis's logo prominently on the big screen, spending a full 5 minutes explaining it. During the speech, Huang Renxun even publicly "acknowledged": Dylan Patel (SemiAnalysis founder) said I'm hiding my strength, saying the actual performance is 50 times, he is not wrong.
This status is also directly reflected in its commercial revenue.
SemiAnalysis is expected to reach $100 million in revenue this year, up from about $20 million just a year ago. Its clients include tech giants and top-tier investment firms. While it does not publicly disclose client logos, the disclosed client types are enough to make the point: hyperscale cloud providers, chip majors, large public and private equity investors.
In other words, SemiAnalysis's main revenue comes not from ordinary newsletter subscribers, but from selling these reports to startups, investors, institutions, traders, and others who can make decisions on tens to hundreds of billions of dollars in AI infrastructure spending.
From an Anonymous Hardware Enthusiast to a Top AI Circle Institution
Similar to the recent "White-Haired Stock God," SemiAnalysis founder Dylan Patel has an Internet-savvy background.
According to BlockBeats, Dylan Patel's friend Dr. Ian Cutress once recalled in an article that before founding SemiAnalysis, Dylan was a popular hardware forum moderator.
In a podcast interview, Dylan himself recalled that before starting the company, he had run an anonymous blog in the "Silicon Valley Twitter Circle" for many years. It was a small circle not necessarily familiar to the average tech Twitter user, but it attracted a large number of hardware, chip, and supply chain professionals.
Some Reddit community users mentioned that Dylan Patel was simply an early Reddit "nobody," an unknown. Public Reddit archives show discussions in r/hardware mentioning u/dylan522p and u/SemiAnalysis.
All these clues, when put together, roughly point to the same picture: Dylan was active in the early years on the Reddit and WordPress communities, being a hardware enthusiast. At that time, he didn't take writing as a serious business. While doing consulting work on the side, he maintained an independent blog called "A thousand million," with the consulting business itself being related to the blog content and the industry.
In addition to Dylan, his partner Doug O'Laughlin is also a key figure at SemiAnalysis, being a pivotal point in the blog's commercialization.
After Doug started posting on forums, Dylan found this person "quite interesting," and their interactions increased. Later on, Doug repeatedly advised him: you should use your real name, move to Substack, and start charging fees. A few years later, Doug outright joined the company.
Today, SemiAnalysis is already the largest tech newsletter on Substack, with over 285,000 subscribers. In addition to the Substack blog posts, it also has a podcast called Transistor Radio.
According to Dylan, the podcast is used to carry industry viewpoints that do not fit into formal articles. Articles are responsible for complete in-depth stories, while the podcast is responsible for fragmented news commentary, casual market evaluations, and weekly real-time industry discussions. It airs approximately every two weeks, focusing on the semiconductor news of the past two weeks and casual chat.
As it has developed, this podcast has now standardized its operations, no longer relying solely on the two founders but with team members taking turns. For example, in a March 2026 episode, Sravan Kundojjala, Ivan Chiam, and Jordan Nanos dissected the AI chip shortage, discussing everything from TSMC and NVIDIA's CPO to how the memory crisis affected GPU pricing, and even the next generation of smartphones.
In addition to their own channel, Dylan himself is also a frequent guest on major tech and investment podcasts, almost becoming a standard guest for AI hardware topics. He has appeared on No Priors, Invest Like the Best, Unsupervised Learning, and Dwarkesh Patel's show. He also had an in-depth conversation with Asianometry's Jon Y, which many viewers consider one of the best channels on YouTube to discuss semiconductor and business history.
More Like an “Intelligence” Agency, But More Like Muddy Waters
An anecdote from The Information perfectly illustrates Dylan Patel's approach.
Early in his entrepreneurial journey, Dylan Patel, eager to fill gaps in his semiconductor knowledge, attended nearly every industry conference he could. Once on-site, he would approach people and start asking questions. Not mere pleasantries, but a relentless barrage of inquiries, turning engineers, supply chain professionals, and company executives into his own sources of information.
As SemiAnalysis grew, this approach remained unchanged, just more industrialized.
The Information reports that the company now has 85 employees spread across 11 countries. Every Monday, Dylan reviews the weekly briefs submitted by each team manager. Each team focuses on a segment of the AI economy, condensing all the news, clues, anomalies, and insights from the previous week.
Think of it as an AI infrastructure intelligence briefing. GPUs, HBMs, packaging, data centers, power, cloud providers, optical modules, chip manufacturing equipment—each thread is closely monitored. It even includes former ASML engineer Jeffrey Koch, specializing in semiconductor equipment. When he looks at AI supply chain bottlenecks, his focus is no longer just on power, but on whether chip manufacturing equipment might be the first to bottleneck.
SemiAnalysis also excels at extracting information from the gray areas.
The article mentions an incident where Dylan came across an internal Google memo circulating on Discord. After downloading it, he sought validation of its authenticity from internal Google sources.
Furthermore, a Reddit community pointed out that when SemiAnalysis was founded around 2020 or 2021, its content was not particularly noteworthy. However, by late 2022, with the AI hype on the rise, it began to expand rapidly. The user believes that SemiAnalysis has gathered a significant amount of non-public or semi-public information primarily from Taiwanese companies, circulating among analysts and some Taiwanese journalists.
“To some extent, SemiAnalysis is like Ming-Chi Kuo, famous simply because of their strong relationship with the Apple supply chain.”
Recently, a lawsuit between SemiAnalysis and a former employee has brought this "gray information acquisition ability" to the forefront.
According to San Francisco County Superior Court documents, SemiAnalysis's former employee Wei Zhou accused Dylan Patel of running SemiAnalysis while personally investing in Fluidstack and using the non-public information obtained to conduct research. When Zhou refused to incorporate this information into SemiAnalysis' products, he faced retaliation and dismissal. (It is important to note that these are currently allegations from one side in the lawsuit and have not been finally proven in court.)
Former SemiAnalysis Employee Accuses Dylan Patel of Improper Information Access
The complaint stated that SemiAnalysis's clients were unaware that Patel was personally investing in Fluidstack. Fluidstack is a private cloud services company reportedly valued at tens of billions of dollars. Zhou accused Patel of investing in Fluidstack through a $50 million Special Purpose Vehicle (SPV). Patel could also receive a 2% management fee from this SPV, share in investment appreciation gains, and potentially earn additional income for introducing other investors.
Most importantly, the complaint stated that through this personal investment relationship, Patel gained access to a confidential Excel spreadsheet from Fluidstack. The spreadsheet contained Fluidstack's revenue, sales data, predictions regarding TPU and other AI infrastructure deployments, and end customers including Anthropic, OpenAI, Meta, and other potential clients.
Zhou's implication is that this client demand and deployment information is not only Fluidstack's own trade secret but may also affect the judgment of a group of publicly traded companies such as Amazon, Nvidia, Google, Broadcom, Microsoft, and others. This is because these companies are all part of the AI cloud, GPU/TPU, network, and data center infrastructure chain.
By examining these third-party pieces of information, we can roughly discern SemiAnalysis's research methodology, which is backed by a comprehensive intelligence gathering system, including forums, Discord, industry conferences, connections, transportation records, government documents, supply chain data, data center site photos, benchmarks, models, and weekly internal briefings.
According to Dr. Ian Cutress, organizations like SemiAnalysis have a much more complex data collection process than the average person might imagine. This involves submitting information requests, scouring public shipping manifests, analyzing supply chain documents and government filings. In the data center realm, they even go as far as obtaining permits, deploying drones to fly over construction sites, and capturing high-resolution images to identify the equipment installed on-site.
SemiAnalysis's own product page is quite straightforward. Their AI data center model tracks over 5000 data centers worldwide, sourcing data from property records, construction permits, power usage, FOIA requests, and satellite imagery. To process a large volume of satellite photos, they have specially trained a computer vision model, namely CNN, to automatically identify the scale, capacity, and construction progress of each data center. The goal is to extend this tracking to every data center in every country.
This approach, rather than being just an analysis firm, is more akin to an open-source intelligence agency.
Interestingly, it reminds the author of that famous short-selling research firm, Muddy Waters, and its investigative methods. Muddy Waters also targeted some Chinese companies in its rise to fame.
For example, in its investigation of Orient Paper, Muddy Waters conducted on-site visits to factories, observed the factory environment, machinery, and inventory, talked to workers and local residents, and even secretly camped outside the factory, documenting the transportation of goods in and out of the facility and taking photographic evidence. The investigation ultimately revealed that the so-called inventory was essentially a pile of waste paper.
When investigating the China MediaExpress high-speed channel, Muddy Waters personally observed the playback of terminal advertisements on over 50 buses, finding that drivers preferred to play their own DVD programs as the high-speed channel had weak control over the terminals. During the examination of Sino-Forest, one of their office locations turned out to be a facade, with employees showing no signs of work activity, humorously referred to as an "adult daycare center."
The most recent high-profile short attack targeted the author's daily coffee choice, Luckin Coffee. Muddy Waters mobilized 92 full-time investigators and 1418 part-time investigators who staked out over 620 stores in 38 cities nationwide, recording 11260 hours of in-store surveillance footage covering 981 business days and 100% of store operating hours. They also collected 25843 customer receipts and a significant amount of internal WeChat messages under the guise of regular customers.
Relying on this firsthand data, Muddy Waters calculated that Luckin Coffee's per-store daily sales were inflated by at least 69% and 88% in the third and fourth quarters of 2019, respectively, while the actual average selling price was much lower than disclosed. Following the report's release, Luckin Coffee promptly admitted to a $2.2 billion financial fraud, leading to a stock price collapse.
Of course, we currently have no evidence to prove that SemiAnalysis shorted the solar module stock before releasing the report. Based on the available information, its business model still mainly involves turning research findings into products and selling them to hedge funds, semiconductor companies, and in-house teams of tech giants.
However, we can see that SemiAnalysis's investigative approach bears many similarities to Muddy Waters. The difference is that it operates in the AI era and the hardware track, with more sophisticated information-gathering tools: transitioning from fieldwork, interviews, and receipts to satellite imagery, supply chain databases, engineering tests, and algorithm models.
A Token Costing $7 Million Annually
In an interview, Dylan himself stated that SemiAnalysis signed an enterprise contract directly with Anthropic, amounting to $7 million, compared to their annual employee salary expenditure of $2.2 million.
SemiAnalysis leverages AI for information gathering and data production. Dylan's assessment is straightforward: they are in the information business, selling analysis, providing consultancy services, and constructing datasets. If they do not continue to raise the bar, AI will quickly commoditize these offerings. The first batch of data products they plan to sell in 2023 is already being replicated by an increasing number of others. If SemiAnalysis does not keep advancing, someone else will inevitably catch up using similar tools.
Illustrating this point most effectively is their foray into energy data services. Over the past year, SemiAnalysis has been trying to develop an energy model because AI data centers are increasingly constrained by electricity, with factors such as the power grid, substations, transmission lines, and regional power shortfalls determining where data centers should be built, their size, and when they should come online. Energy data services represent a market of nearly $9 billion, into which SemiAnalysis has been eager to enter. However, after a year of effort, progress has not been especially swift.
Subsequently, Jeremy, responsible for data center energy and industrial operations, began becoming "addicted" to using the Claude Code. Dylan mentioned that in just three weeks, he was spending approximately $6,000 per day utilizing AI tools, which was an exorbitant cost. However, the results were also remarkable: Jeremy captured every power plant in the U.S., every transmission line above a certain voltage level, and integrated a large amount of demand-side data sources, all obtained from public information. In the end, he built a complete map and dashboard of the U.S. power grid.
This system can visualize power shortages and surpluses in different micro-regions of the U.S.
When SemiAnalysis showed it to some clients who both buy their data center data and engage in energy trading, their initial reaction was one of surprise: How long did it take you to create this? It's better than some professional energy data companies. Further inquiry revealed that those companies may have hundreds of employees and have been in operation for a decade.
Dylan also admitted that SemiAnalysis' offering is not as mature or robust as products from traditional energy data companies. However, in some aspects, it is faster, more detailed, and even better. This is the new form of SemiAnalysis' investigative approach: not relying solely on an analyst attending meetings, conducting interviews, and reviewing documents, but rather integrating public data, engineering judgment, industry connections, and AI programming capabilities to quickly produce something that would take a traditional data company years to develop.
Ultimately, the most intriguing aspect of SemiAnalysis may lie in this hybrid nature.
On one hand, it operates like a nearly cold-blooded intelligence agency, using satellite imagery, construction permits, shipping manifests, supply chain interviews, AI programming, and engineering testing to piece together the true map of the AI infrastructure world. On the other hand, its founder, Dylan Patel, always carries a hint of internet-native mischief.
When a reporter from The Information visited Dylan's San Francisco office, Dylan 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 Dylan, with a mischievous tone, mentioned that there is not just him and Dwarkesh in the office, but a third person. When the reporter inquired about this individual, Dylan refused to disclose, saying, "Let's play a game, you figure it out yourself."
To uncover more information, The Information reporter had to engage in this detective game with Dylan, and the ultimate revelation did not disappoint.
Sharing the office space with Dylan is Leopold Aschenbrenner, the former OpenAI researcher who later founded his own AGI investment fund, Situational Awareness, turning $200 million into $5.5 billion in just a year, earning him the title of "AI Stock God."
One can only say the top-tier AI circle is still too small.
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