从社区「硬件宅」到AI圈「浑水」：年入近亿美元的SemiAnalysis如何搅动半导体市场？
In the past month, the hottest niche sector in the US stock AI market has been optical modules.
An AI data center isn't just about stacking rows of GPUs and calling it a day. Massive amounts of data must also be exchanged between GPUs and between servers. The larger the model, the bigger the cluster, and the more easily that 'data transfer' between machines becomes a bottleneck. As a result, the market has turned its attention to the optical communication chain, with the hottest concept being CPO. CPO can be roughly understood as placing optical communication components physically closer to the core chip. This shorter distance allows for faster data transmission and lower power consumption. In increasingly massive AI data centers, this narrative sounds almost perfect.
This narrative was truly ignited thanks to Jensen Huang. As Nvidia continues to push the AI infrastructure story forward, optical communication chain companies like Marvell, Coherent, Lumentum, Corning, and AAOI have either been rumored to have secured large orders or have already seen their stock prices run up significantly.
However, a couple of days ago, a highly controversial research report suddenly threw cold water on this red-hot sector. Stocks across the optical communication chain experienced a collective pullback, with many falling by high single digits or even double digits.
The questions naturally follow: What exactly did this report say? Who is SemiAnalysis, the firm that published it? Why could one of their reports cause the market to re-price the entire AI optical module chain?
This article by BlockBeats will delve into this institution.
Why SemiAnalysis is Treated as 'Industry Gospel'
In the eyes of many institutions in the AI and investment circles, SemiAnalysis is hardly an unfamiliar name. However, to the average retail investor, it remains somewhat mysterious.
SemiAnalysis is one of the fastest-rising star institutions in semiconductor and AI infrastructure research in recent years. Although still an industry newcomer, it has quickly gained a reputation in AI and investment circles for its deep analysis and sharp insights. It currently has around 85 employees, focusing on providing in-depth reports and data models for the AI ecosystem, covering multiple aspects including data center construction, supply chain economics, chip deployment, networking, power, packaging, and equipment.
SemiAnalysis's official website introduction
SemiAnalysis's classic battle that earned the industry's respect was likely its recalculation of DeepSeek's costs.
In early 2025, DeepSeek exploded onto the global scene with a highly viral narrative: "It trained a model comparable to OpenAI's o1 for just $6 million." This figure directly shattered the investment logic for AI computing power. The market began to doubt whether the hundreds of billions of dollars in GPU capital expenditures were all wasted if models could be built so cheaply.
In the ensuing panic, Nvidia's market cap evaporated by approximately $600 billion in a single day, setting a record for the largest single-day market cap loss in US stock history.
While the world debated the truth behind the $6 million figure, SemiAnalysis recalculated DeepSeek's hardware costs in a research report. It didn't simply deny DeepSeek's technological progress; instead, it deconstructed the 'low-cost myth': What exactly did the $6 million cover? And what did it not cover?
SemiAnalysis concluded that the $6 million only covered the narrow cost of GPU pre-training, failing to account for R&D, infrastructure, cluster construction, and long-term operations. It estimated DeepSeek's actual server capital expenditure to be around $1.6 billion, with cluster operating costs close to $944 million.
SemiAnalysis's cost calculation data for DeepSeek
More importantly, it broke down DeepSeek's existing computing power stock. SemiAnalysis assessed that DeepSeek likely owned around 50,000 Hopper GPUs, but this wasn't 50,000 H100s; it was a mix of H800s, H100s, and the China-specific H20s. These cards were also shared with the affiliated quantitative fund, High-Flyer, distributed across multiple locations for tasks like trading, inference, training, and research.
Besides DeepSeek, another frequently cited example is SemiAnalysis's 'shorting' report on AMD.
At the time, a hot topic in the market was the possibility of AMD catching up to Nvidia. Most people were comparing the raw computing power of AMD and Nvidia GPUs. What SemiAnalysis repeatedly emphasized was that Nvidia's true moat was never just the chip, but its CUDA software ecosystem, networking, system design, supply chain capabilities, and the deployment experience accumulated by customers over many years. These elements are Nvidia's moat.
In December 2024, SemiAnalysis published a report based on five months of hands-on testing of the AMD MI300X. The report bluntly stated: "We had hoped AMD would become a strong competitor to Nvidia on the training side, but that day has not yet arrived." Its core conclusion 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, its actual performance didn't fully deliver, with the problem lying squarely on the software side.
Just one day after the report's release, AMD CEO Lisa Su proactively contacted SemiAnalysis founder Dylan Patel. A scheduled 30-minute call ended up lasting a full 90 minutes.
Of course, this also led to community suspicions that SemiAnalysis was an institution funded and supported by Nvidia.
SemiAnalysis's influence also began to spill over from report pages into the industry landscape.
Dylan (left) with Supermicro's 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, he almost bumped into Dylan's next visitor in the lobby: Sequoia Capital partner Shaun Maguire was sitting there waiting to see him.
The highlight moment came at GTC in March 2026.
During Jensen Huang's over-two-hour keynote speech, he only mentioned two people by name, one of whom was Dylan Patel. Not only did he cite SemiAnalysis's recently released chip performance ranking, InferenceX, but he also put the SemiAnalysis logo directly on the big screen, spending a full 5 minutes explaining it. During the speech, Huang even publicly 'acknowledged': Dylan Patel (SemiAnalysis founder) said I was hiding our strength, that the real performance is 50x, and he wasn't wrong.
Nvidia CEO Jensen Huang raises both hands in celebration at the latest GTC developer conference, mentioning SemiAnalysis and its recent assessment report on Nvidia chips.
This status is directly reflected in its commercial revenue.
SemiAnalysis's revenue this year is projected to approach $100 million, up from just about $20 million a year ago. Its clients span tech giants and top-tier investment institutions. It doesn't publicly display client logos, but the publicly disclosed client types are telling enough: hyperscale cloud providers, major chip manufacturers, and large public and private investors.
In other words, SemiAnalysis's main revenue doesn't come from ordinary newsletter subscribers, but from selling these reports to the startups, investors, institutions, and traders who have the authority to approve tens of billions or hundreds of billions of dollars in AI infrastructure spending.
From Anonymous Hardware Enthusiast to Top-Tier AI Institution
Like the recently famous 'white-haired stock guru,' SemiAnalysis founder Dylan Patel's background also has a strong internet flavor.
Dylan Patel
According to BlockBeats' findings, 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 on a podcast that before starting the company, he ran an anonymous blog for many years within the 'Silicon Valley Twitter circle.' It was a niche community not necessarily familiar to the average tech Twitter user, but it gathered a large number of hardware, chip, and supply chain professionals.
Some Reddit community users also mentioned that Dylan Patel was initially just a 'nobody' on Reddit. Public Reddit archives we found show usernames like u/dylan522p and u/SemiAnalysis appearing in r/hardware moderation discussions.
Putting these clues together paints a picture: Dylan was active in Reddit and WordPress communities in his early days as a hardware enthusiast. At the time, he hadn't turned writing into a serious business. He did consulting on the side while maintaining an independent blog called 'A thousand million,' and this consulting work itself was related to his blog content and industry connections.
Besides Dylan, his partner Doug O'Laughlin is also a key figure at SemiAnalysis and was the turning point that commercialized the blog.
After Doug also started posting on forums, Dylan found him 'quite interesting,' and their interactions increased. Later, Doug repeatedly urged him: "You should use your real name, move to Substack, and start charging." A few years later, Doug simply joined the company.
Today, SemiAnalysis is the most subscribed tech newsletter on Substack, with over 285,000 subscribers. In addition to Substack posts, it also runs a podcast called Transistor Radio.
According to Dylan, the podcast is used to host industry perspectives that don't fit into formal articles. The articles handle complete, in-depth stories, while the podcast covers fragmented news commentary, off-the-cuff judgments on the macro market, and weekly, real-time industry discussions. It runs roughly bi-weekly, with casual chat about the semiconductor news from the past two weeks.
Now, the podcast operates routinely, no longer relying solely on the two founders but featuring rotating team members. For example, an episode in March 2026 featured Sravan Kundojjala, Ivan Chiam, and Jordan Nanos deconstructing the AI chip shortage, covering everything from TSMC and Nvidia CPO to how the memory crisis impacts GPU pricing and even next-generation smartphones.
Besides his own channel, Dylan is a frequent guest on major tech and investment podcasts, becoming almost a default 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 Jon Y from Asianometry, a channel many viewers consider one of the best on YouTube for semiconductor and business history.
More Like an 'Intelligence' Agency, More Like Muddy Waters Capital
An article in The Information contains a detail that perfectly illustrates Dylan Patel's modus operandi.
In his early entrepreneurial days, to supplement his semiconductor knowledge, Dylan Patel attended almost every industry conference he could. On-site, he would grab people and ask questions. Not just polite chit-chat, but persistent questioning, turning engineers, supply chain professionals, and company executives one by one into his sources.
When SemiAnalysis grew larger, this method didn't change; it just became 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 link in the AI economy, compressing all the news, leads, anomalies, and inferences from the previous week into a single document.
Think of it as an AI infrastructure intelligence weekly report. GPU, HBM, packaging, data centers, power, cloud providers, optical modules, chip manufacturing equipment – every line is monitored by someone. This includes Jeffrey Koch, a former ASML engineer dedicated to studying the semiconductor equipment chain. When analyzing AI supply chain bottlenecks, his focus has already moved beyond power to whether chip manufacturing equipment will be the first constraint.
SemiAnalysis is also very adept at mining information from gray areas.
The article mentions that Dylan once saw an internal Google memo circulating on Discord. He downloaded it and then found someone inside Google to verify its authenticity.
Another Reddit community pointed out: Around its founding in 2020 or 2021, SemiAnalysis's published content wasn't particularly special. However, around the end of 2022, as the AI boom intensified, it began to expand rapidly. The user believes SemiAnalysis collected a large amount of non-public or semi-public information, primarily from Taiwanese companies, which circulated among analysts and some Taiwanese journalists.
"In a way, SemiAnalysis is like Ming-Chi Kuo, famous simply because he built a good relationship with the Apple supply chain."
Recently, a lawsuit between SemiAnalysis and a former employee brought this 'gray information gathering ability' to the forefront.
According to court documents from the Superior Court of San Francisco County, former SemiAnalysis employee Wei Zhou alleges that Dylan Patel simultaneously ran SemiAnalysis while personally investing in Fluidstack, using the non-public information obtained from this position for his research. When Zhou refused to incorporate this information into SemiAnalysis products, he was retaliated against and fired. (It's important to note that these are currently allegations from one party in the lawsuit and have not been finally determined by the court.)
SemiAnalysis former employee accuses Dylan Patel of improperly obtaining information
The complaint alleges that SemiAnalysis customers were unaware that Patel was personally invested in Fluidstack. Fluidstack is a private cloud services company reportedly valued in the billions of dollars. Zhou alleges that Patel invested in Fluidstack through a $50 million SPV (Special Purpose Vehicle). Patel could also collect a 2% management fee from this SPV, share in the investment's appreciation, and potentially earn additional fees for introducing other investors.
More critically, the complaint alleges that it was through this personal investment relationship that Patel obtained a confidential Excel spreadsheet from Fluidstack. The spreadsheet contained Fluidstack's revenue, sales data, and forecasts related to TPUs and other AI infrastructure deployments, with end customers including Anthropic, OpenAI, Meta, and other potential clients.
Zhou's implication is that these customer demands and deployment information are not just Fluidstack's own trade secrets but could also influence the valuation of a range of publicly listed companies like Amazon, Nvidia, Google, Broadcom, and Microsoft, as these companies are all part of the AI cloud, GPU/TPU, networking, and data center infrastructure supply chain.
From these third-party accounts, we can roughly discern SemiAnalysis's research methodology, which is backed by a complete intelligence-gathering machine: forums, Discord, industry conferences, network contacts, shipping records, government documents, supply chain data, data center site photos, benchmarks, models, plus the weekly internal briefs.
According to Dr. Ian Cutress, when institutions like SemiAnalysis conduct research, their data sources are far more complex than ordinary people imagine. They file public information requests, scour public shipping manifests, and dig through supply chain documents and government filings. On the data center front, they might even apply for permits to fly drones over construction sites to take high-resolution photos of the equipment being installed.
SemiAnalysis's own product page is also very straightforward. Its AI Data Center Model tracks over 5,000 data centers globally, with data sources including property records, construction permits, electricity usage, FOIA requests, and satellite imagery. To handle the massive volume of satellite photos, they have specifically trained computer vision models (CNNs) to automatically identify the scale, capacity, and construction progress of each data center. The goal is to cover every data center in every country.
This approach resembles less a traditional analysis firm and more an open-source intelligence company.
Interestingly, this reminds BlockBeats' editors of the famous research methods of the short-selling firm Muddy Waters Capital. Muddy Waters' own claim to fame also involved going after Chinese companies.
For example, Muddy Waters' investigation of Orient Paper included on-site factory visits to observe the workshop environment, machinery, and inventory; chatting with workers and nearby residents; and even secretly squatting outside the factory compound to record the load of incoming and outgoing vehicles and take photographic evidence. They concluded that the so-called inventory was essentially a pile of scrap paper.
In investigating China MediaExpress, Muddy Waters observed in-person the advertising playing on terminals in over 50 buses, finding that drivers preferred to play their own DVDs, indicating weak control over the terminals by China MediaExpress. When looking at Duoyuan Global Water, they found one office location to be virtually empty, with employees showing no signs of working, wryly calling it an 'adult daycare center.'
One of Muddy Waters' most sensational recent short-selling cases targeted Luck
