From community "Hardware Enthusiasts" to AI's "Muddy Waters": How SemiAnalysis, Nearing $100 Million in Revenue, is Disrupting the Semiconductor Market?

In the past month, the hottest sub-sector in the US stock AI market has been optical modules.

An AI data center isn't just about stacking up rows of GPUs. Between GPUs and servers, massive amounts of data need to be exchanged. 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 CPO being the hottest concept. CPO can be roughly understood as placing optical communication components closer to the core chip. Shorter distances mean faster data transfer and lower power consumption. In the increasingly massive AI data centers, this narrative sounds almost perfect.

This narrative was truly ignited by Jensen Huang. As Nvidia continues to push forward the AI infrastructure story, companies in the optical communication chain like Marvell, Coherent, Lumentum, Corning, and AAOI have either been rumored to have secured large orders or seen their stock prices surge ahead significantly.

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

This raises the question: 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?

This article by BlockBeats will dig into this institution.

Why is SemiAnalysis Treated as 'Industry Scripture'?

In the eyes of many institutions within the AI and investment circles, SemiAnalysis is hardly an unfamiliar name. But for ordinary retail investors, it remains somewhat mysterious.

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

SemiAnalysis 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 globally with a highly contagious narrative: "Training a model comparable to OpenAI's o1 for only $6 million." This figure directly shattered the investment logic for AI computing power. The market began to doubt whether the massive capital expenditures on GPUs, often reaching tens of billions of dollars, had all been wasted if models could be this cheap.

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 value loss in US stock history.

While the world debated the truth of that $6 million figure, SemiAnalysis used a research report to recalculate DeepSeek's hardware costs. It didn't simply deny DeepSeek's technological progress but 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 extremely narrow cost of GPU pre-training, excluding 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 cost calculation data for DeepSeek

More critically, it broke down DeepSeek's computing power inventory. SemiAnalysis estimated that DeepSeek possesses approximately 50,000 Hopper GPUs, but these are not all H100s; rather, they are a mix of H800, H100, and the China-specific H20. This batch of cards is also shared with the quantitative fund High-Flyer, distributed across multiple locations for different tasks such as trading, inference, training, and research.

Beyond DeepSeek, another widely cited case is SemiAnalysis's report on AMD, which was perceived as a "bearish" piece.

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. What SemiAnalysis repeatedly emphasized was that Nvidia's true moat has never been just the chip, but the CUDA software ecosystem, networking capabilities, system design, supply chain prowess, and the deployment experience accumulated by its customers over many years. These are Nvidia's real moats.

In December 2024, SemiAnalysis released a report based on five months of practical testing of the AMD MI300X. The report candidly stated: "We hoped AMD would be a formidable competitor to Nvidia on the training side, but that day hasn't arrived yet." Its core conclusion was that while the MI300X's paper specifications and total cost of ownership (TCO) should have given it a clear lead over Nvidia's H100 and H200, its actual performance didn't fully deliver, with the problem lying squarely on the software side.

Just one day after the report was published, 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 suspicions within the community that SemiAnalysis is an institution funded and supported by Nvidia.

SemiAnalysis's influence also began to overflow from the pages of its reports into the industrial 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, he almost bumped into Dylan's next visitor in the lobby: Sequoia Capital partner Shaun Maguire was sitting there waiting to see him.

The most spotlight moment occurred 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. He not only cited SemiAnalysis's just-released chip performance ranking, InferenceX, but also displayed SemiAnalysis's logo prominently 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 holding back power, saying the real performance was 50x. He wasn't wrong."

Nvidia CEO Jensen Huang celebrates at the latest GTC developer conference, mentioning SemiAnalysis and its recent evaluation report on Nvidia chips.

This status is directly reflected in its commercial revenue.

SemiAnalysis's revenue is projected to approach $100 million this year, compared to just about $20 million a year ago. Its client base spans tech giants and top-tier investment institutions. It doesn't publicly display client logos, but the types of clients it openly discloses are telling enough: hyperscale cloud providers, major chip manufacturers, large public mutual funds, and private equity investors.

In other words, SemiAnalysis's primary revenue doesn't come from regular newsletter subscribers but from selling these reports to the startups, investors, institutions, and traders who can make billion-dollar decisions on AI infrastructure spending.

From Anonymous Hardware Enthusiast to Top-Tier AI Institution

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

Dylan Patel

BlockBeats found that Dylan Patel's friend, Dr. Ian Cutress, once 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 ran an anonymous blog for many years within the "Silicon Valley Twitter circle." It was a niche community not well-known to the average tech Twitter user, but it gathered many professionals in hardware, chips, and supply chains.

Some Reddit 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 the moderation discussions of r/hardware.

These clues roughly paint the same picture: Dylan was active in Reddit and WordPress communities early on as a hardware research 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 that commercialized the blog.

After Doug also started posting on the forums, Dylan found this person "quite interesting," and they interacted more frequently. 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 largest tech-related newsletter by subscriber count on Substack, 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 handle complete, in-depth stories, while the podcast covers fragmented news commentary, casual judgments on the broader market, and real-time weekly industry discussions. It runs approximately every two weeks, chatting about the semiconductor news of the past fortnight.

As it has developed, the podcast has become a regular operation, no longer relying solely on the two founders but featuring different 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.

Beyond his own channel, Dylan is a frequent guest on major tech and investment podcasts, almost becoming 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, which many viewers consider one of the best YouTube channels for explaining semiconductor and business history.

More Like an 'Intelligence' Agency, Reminiscent of Muddy Waters

A detail in The Information's report effectively illustrates Dylan Patel's modus operandi.

In the early days of the startup, to supplement his semiconductor knowledge, Dylan Patel attended almost every industry conference he could. Once there, he would grab people and ask questions. Not just casual chit-chat, but relentless questioning, turning engineers, supply chain personnel, and company executives one by one into his sources.

Even after SemiAnalysis grew, this approach remained, only becoming more industrialized.

The Information reported 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 results from the previous week into a single document.

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. Among them is even former ASML engineer Jeffrey Koch, who specializes in the semiconductor equipment chain. When he looks at AI supply chain bottlenecks, his focus extends beyond power to whether chip manufacturing equipment will be the first constraint.

SemiAnalysis is also adept at digging up information from gray areas.

The article mentioned that Dylan once saw an internal Google memo circulating on Discord. After downloading it, he verified its authenticity with an internal Google contact.

Some Reddit communities also pointed out: SemiAnalysis's content wasn't particularly special when it was founded around 2020 or 2021. However, around the end of 2022, as the AI boom intensified, it began to expand rapidly. This user believes SemiAnalysis collected a large amount of non-public or semi-public information, primarily from Taiwanese companies, which circulates 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."

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

According to court documents from the San Francisco County Superior Court, former SemiAnalysis employee Wei Zhou alleges that Dylan Patel ran SemiAnalysis while personally investing in Fluidstack and using non-public information obtained through that relationship for research. Zhou claims he was retaliated against and fired after refusing to include this information in SemiAnalysis's products. (It should be noted that these are currently allegations from one party in the lawsuit and have not been finally determined by the court.)

Former SemiAnalysis employee accuses Dylan Patel of improperly obtaining information.

The complaint states that SemiAnalysis's clients were unaware that Patel was personally investing in Fluidstack. Fluidstack is a private cloud services company reportedly valued at several billion dollars. Zhou alleges 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 appreciation, and potentially earn additional returns by introducing other investors.

More critically, the complaint alleges 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 around TPU and other AI infrastructure deployments, with end customers including Anthropic, OpenAI, Meta, and other potential clients.

Zhou's implication is that this customer demand and deployment information is not just Fluidstack's own trade secrets but could also affect the judgments of a group of public companies like Amazon, Nvidia, Google, Broadcom, and Microsoft, as they are all players in the AI cloud, GPU/TPU, networking, and data center infrastructure supply chain.

Reading between the lines, we can roughly discern SemiAnalysis's research methodology. Behind it lies a complete intelligence-gathering machine: forums, Discord, industry conferences, personal networks, shipping records, government documents, supply chain data, data center site photos, benchmarks, and models, all combined with weekly internal briefings.

According to Dr. Ian Cutress, when institutions like SemiAnalysis conduct research, their data sources are far more complex than ordinary people imagine. This includes submitting FOIA (Freedom of Information Act) requests, sifting through public shipping manifests, and digging into supply chain documents and government filings. In the data center domain, they 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 states this quite plainly. Its AI Data Center Model tracks over 5,000 data centers globally, with data sources including property records, construction permits, electricity consumption, FOIA requests, and satellite imagery. To process the vast number of satellite photos, they have also trained a custom computer vision model (CNN) 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 an open-source intelligence (OSINT) company more than a traditional analysis firm.

Interestingly, it reminds this BlockBeats editor of the investigation methods of the famous short-selling research firm Muddy Waters. Muddy Waters also made its name targeting Chinese companies.

For example, Muddy Waters' investigation of Orient Paper included visiting the factory in person, observing the factory environment, machinery, and inventory, chatting with workers and nearby residents, and even secretly squatting outside the factory to record the loading and unloading of incoming and outgoing vehicles, taking photographic evidence. They ultimately concluded that the so-called inventory was basically a pile of waste paper.

Another example is when investigating China Media Express (CME), Muddy Waters physically observed the advertising terminals on over 50 buses. They found drivers preferred playing their own DVD programs, indicating CME had weak control over its terminals. When investigating Multi-Pure International, they saw that one of its office locations was essentially a shell with employees showing no signs of actual work, calling it an "adult daycare center."

A more recent high-profile short-selling case involved Luckin Coffee, which this editor drinks daily. Muddy Waters mobilized 92 full-time investigators and 1,418 part-time investigators, staking out