从社區「硬體宅」到AI圈「渾水」：年收入近億美元的SemiAnalysis如何攪動半導體市場？

In the past month, the hottest niche track in the US AI stock market has been optical modules.

An AI data center is not just about stacking rows of GPUs. Massive amounts of data also need to be exchanged between GPUs and between servers. The larger the model and the bigger the cluster, the more likely data transfer between machines becomes a bottleneck. Consequently, 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 closer to the core chip. A shorter distance means faster data transfer and lower power consumption. In increasingly massive AI data centers, this story sounds almost perfect.

The credit for truly igniting this narrative goes to Jensen Huang. As Nvidia continues to push the AI infrastructure story forward, companies in the optical communication chain like Marvell, Coherent, Lumentum, Corning, and AAOI have either been rumored to have landed large orders or have already seen their stock prices surge significantly.

However, a few days ago, a highly controversial research report suddenly poured cold water on this red-hot track. Stocks across the optical communication chain corrected collectively, with many experiencing high single-digit or even double-digit percentage declines.

The question then arises: What exactly did this report say? Who is SemiAnalysis, the firm that published it? And why could a single report from them cause the market to re-price the entire AI optical module chain?

In this article, BlockBeats delves into this institution.

Why is SemiAnalysis Regarded as an "Industry Bible"?

In the eyes of many institutions in the AI and investment circles, SemiAnalysis is no longer an unfamiliar name. But to ordinary retail investors, it still seems somewhat mysterious.

SemiAnalysis is one of the fastest-rising star institutions in semiconductor and AI infrastructure research in recent years. Although still a newcomer to the industry, it has rapidly gained prominence in AI and investment circles for its in-depth analysis and sharp insights. It currently has about 85 employees, focusing on providing in-depth reports and data models for the AI ecosystem, covering areas such as data center construction, supply chain economics, chip deployment, networking, power, packaging, and equipment.

Introduction on the SemiAnalysis website

SemiAnalysis's classic battle that won the industry's respect was likely its recalculation of DeepSeek's costs.

In early 2025, DeepSeek took the world by storm with a highly viral narrative: "trained a model comparable to OpenAI's o1 for only $6 million." This number directly challenged the investment logic for AI computing power. The market began to doubt, if models could be this cheap, were those hundreds of billions of dollars in GPU capital expenditure all for nothing?

In the ensuing panic, Nvidia's market cap evaporated by about $600 billion in a single day, setting a record for the largest single-day market value loss in U.S. stock history.

As the world debated whether the $6 million figure was real or fake, SemiAnalysis re-examined DeepSeek's hardware expenses with a detailed report. It didn't simply deny DeepSeek's technological progress but dissected this "low-cost myth": What exactly did the $6 million cover? And what did it not cover?

SemiAnalysis's conclusion was that the $6 million only covered the extremely narrow cost of GPU pre-training and did not include R&D, infrastructure, cluster construction, and long-term operations. It estimated DeepSeek's real 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. SemiAnalysis estimated that DeepSeek possessed approximately 50,000 Hopper GPUs, but this was not 50,000 H100s. Instead, it was a mix of H800, H100, and the China-specific H20. These cards were also shared with the behind-the-scenes quantitative fund, High-Flyer, distributed across multiple locations for different tasks like trading, inference, training, and research.

Besides DeepSeek, another frequently cited case is SemiAnalysis's "bearish" 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 paper specifications of AMD and Nvidia GPUs. What SemiAnalysis repeatedly emphasized was that Nvidia's true moat was never just the chip, but the CUDA software ecosystem, networking, system design, supply chain capabilities, and the years of deployment experience accumulated by clients. These were Nvidia's real moats.

In December 2024, SemiAnalysis released a report based on five months of practical testing of AMD's MI300X. The report bluntly stated: "We had hoped AMD could be a strong competitor to Nvidia on the training side, but that day hasn't arrived yet." Its core conclusion was that while the MI300X should have been noticeably ahead of Nvidia's H100 and H200 in terms of paper specifications and total cost of ownership, its actual performance didn't fully deliver, and the problem lay squarely on the software side.

Just one day after the report was released, AMD CEO Lisa Su proactively contacted SemiAnalysis founder Dylan Patel. What was meant to be a 30-minute call ended up lasting a full 90 minutes.

Of course, this also led to speculation within the community that SemiAnalysis is an institution funded and supported by Nvidia.

SemiAnalysis's influence has also begun to spill over from report pages to the industry floor.

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 nearly bumped into Dylan's next visitor in the lobby: Sequoia Capital partner Shaun Maguire was sitting there waiting to meet him.

The most shining moment occurred at GTC in March 2026.

During Jensen Huang's keynote speech lasting over two hours, he only mentioned two people by name, one of whom was Dylan Patel. Not only did he cite SemiAnalysis's newly released chip performance ranking, InferenceX, but he also displayed SemiAnalysis's logo directly on the big screen, spending a full 5 minutes explaining it. During the speech, Huang even publicly "acknowledged": Dylan Patel said I was hiding my strength, saying the real performance is 50 times better. He wasn't wrong.

Nvidia CEO Jensen Huang raises his hands in celebration at the latest GTC developer conference, mentioning SemiAnalysis and its recent evaluation report on Nvidia's chips.

This status is directly reflected in its commercialization revenue.

SemiAnalysis's revenue is projected to hit $100 million this year, up from just about $20 million a year ago. Its clientele spans tech giants and top-tier investment institutions. It doesn't publicly display client logos, but the disclosed client types are telling enough: hyperscale cloud providers, major chip companies, and large institutional and private equity investors.

In other words, SemiAnalysis's primary revenue doesn't come from ordinary newsletter subscribers but from selling these reports to the startups, investors, institutions, and traders who can approve multi-billion dollar AI infrastructure expenditures.

From Anonymous Hardware Enthusiast to a Top-Tier AI Institution

Similar to the recent "White-Haired Stock God," the background of SemiAnalysis founder Dylan Patel also carries a strong internet flavor.

Dylan Patel

BlockBeats found that 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 average tech Twitter users, but it gathered many professionals in hardware, chips, and supply chain.

Some Reddit community users also mentioned that Dylan Patel was initially just a "nobody" on Reddit. Public Reddit archives we found show that u/dylan522p and u/SemiAnalysis appeared in moderation discussions on r/hardware.

These clues piece together a consistent picture: Dylan was active in Reddit and WordPress communities early on as a hardware enthusiast. At that 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 was itself related to the blog's content and industry connections.

Besides Dylan, his partner Doug O'Laughlin is also a key figure at SemiAnalysis and was the turning point in commercializing the blog.

After Doug also started posting on forums, Dylan found him "quite interesting," and they began interacting more. Later, Doug repeatedly urged him: you should use your real name, move to Substack, and start charging. A few years later, Doug ended up joining the company.

Today, SemiAnalysis is the largest tech newsletter on Substack by subscribers, with over 285,000 subscribers. In addition to Substack posts, it also has a podcast called Transistor Radio.

According to Dylan, the podcast serves to host industry perspectives that don't fit into formal articles. The articles cover complete, in-depth stories, while the podcast handles fragmented news commentary, casual judgments on the broader market, and weekly real-time industry discussions. It airs roughly every two weeks, featuring casual chats about semiconductor news from the past two weeks.

As it has developed, the podcast has become a regular operation, no longer relying solely on the two founders but featuring a rotating cast of team members. For example, an episode in March 2026 featured Sravan Kundojjala, Ivan Chiam, and Jordan Nanos dissecting the AI chip shortage, covering everything from TSMC and Nvidia CPO to how the memory crisis impacts GPU pricing and even next-generation smartphones.

Apart from his own channel, Dylan is a frequent guest on major tech and investment podcasts, almost becoming the go-to guest for AI hardware topics. He has appeared on No Priors, Invest Like the Best, Unsupervised Learning, and Dwarkesh Patel's show. He has also had in-depth conversations with Jon Y from Asianometry, which many viewers consider one of the best YouTube channels for discussing semiconductors and business history.

More Like an Intelligence Agency, Or a Muddy Waters Fund

A detail in The Information's report illustrates Dylan Patel's approach well.

In the early days of the startup, to fill gaps in his semiconductor knowledge, Dylan attended almost every industry conference he could. Once there, he would grab people to ask questions, not just exchanging pleasantries but probing relentlessly, turning engineers, supply chain professionals, and company executives into his sources of information.

This method didn't change after SemiAnalysis grew; 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 specific link in the AI economy, compressing all the news, leads, anomalies, and reasoning from the previous week into a single report.

You can think of it as an AI infrastructure intelligence weekly report. Someone monitors each line: GPU, HBM, packaging, data centers, power, cloud providers, optical modules, chip manufacturing equipment. This team even includes Jeffrey Koch, a former ASML engineer specializing in the semiconductor equipment chain. When analyzing AI supply chain bottlenecks, his focus has already moved beyond power to whether chip manufacturing equipment might become the first constraint.

SemiAnalysis is also particularly adept at gathering information from gray areas.

The article mentioned that Dylan once saw an internal Google memo circulating on Discord. He downloaded it and then verified its authenticity with a Google insider.

A Reddit community also pointed out: when SemiAnalysis was founded around 2020 or 2021, the content it published wasn't particularly special. But around the end of 2022, as the AI boom heated up, it began to expand rapidly. This user believes SemiAnalysis has collected a large amount of non-public or semi-public information primarily from Taiwanese companies, information that circulates among analysts and some Taiwanese journalists.

"To some extent, SemiAnalysis is like Kuo Ming-chi, famous simply because he built good relationships with the Apple supply chain."

A recent lawsuit between SemiAnalysis and a former employee has brought this "gray information-gathering ability" into the spotlight.

According to a filing in the Superior Court of San Francisco County, former SemiAnalysis employee Wei Zhou alleges that Dylan Patel operated SemiAnalysis while personally investing in Fluidstack and using non-public information obtained from this relationship for 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 within court documents, not yet finally determined by the court.)

Former SemiAnalysis employee accuses Dylan Patel of improper information gathering.

The complaint alleges 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 alleges that Patel invested in Fluidstack through a $50 million SPV (Special Purpose Vehicle). Patel also received a 2% management fee from this SPV, shared in the investment appreciation, and potentially earned additional income by introducing other investors.

More critically, the complaint alleges that Patel obtained a confidential Excel spreadsheet from Fluidstack through this personal investment relationship. The spreadsheet contained Fluidstack's revenue, sales data, and deployment projections for TPUs and other AI infrastructure, with end customers including Anthropic, OpenAI, Meta, and other potential clients.

Zhou's implication is that this customer demand and deployment information is not only Fluidstack's own trade secrets but could also impact the valuation of a group of public companies like Amazon, Nvidia, Google, Broadcom, and Microsoft, as these companies all sit on the AI cloud, GPU/TPU, networking, and data center infrastructure supply chain.

From these third-party accounts, we can broadly discern SemiAnalysis's research methodology: a complete intelligence gathering machine involving forums, Discord, industry conferences, networks, shipping records, government documents, supply chain data, on-site data center photos, benchmarks, and models, all culminating in a weekly internal brief.

According to Dr. Ian Cutress, when researching, institutions like SemiAnalysis use data sources far more complex than ordinary people imagine: submitting public information requests, digging through public shipping manifests, combing through supply chain documents and government records. In terms of data centers, they even apply for permits, fly drones over construction sites, and take high-resolution photos of the equipment being installed.

SemiAnalysis's own product page states this quite plainly. Its AI Data Center Model tracks over 5,000 data centers globally, with data sources including property records, building permits, electricity usage, FOIA requests, and satellite imagery. To process 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 extend tracking to every data center in every country.

This approach resembles an open-source intelligence company more than a traditional research firm.

Interestingly, it reminds us of the investigative methods of the renowned short-selling research firm "Muddy Waters." Muddy Waters' famous battles were also against specific Chinese companies.

For example, Muddy Waters' investigation of Oriental Paper involved on-site factory visits, observing the factory environment, machinery, and inventory, chatting with workers and nearby residents, even secretly squatting outside the factory to record the loading and unloading of trucks and gathering photographic evidence. They eventually found that the so-called inventory was basically a pile of waste paper.

Another case was Muddy Waters' investigation of China MediaExpress. They personally inspected in-vehicle advertising playback on over 50 buses, discovering drivers preferred playing their own DVDs, indicating weak control by the media company over its terminals. When investigating Multi-Global Water & Wastewater Management, they found one of its office locations essentially non-functional, with employees showing no sign of work, calling it an "adult daycare center."

One of the most recent sensational short-selling cases involved Luckin Coffee, which we drink daily. Muddy Waters mobilized 92 full-time and 1,418 part-time investigators to monitor over 620 stores in 38 cities across China. They recorded 11,260