These stock gods playing U.S. stocks no longer read financial reports

In the 2026 US stock AI wave, the biggest profits weren't made by holding household names like Nvidia, Microsoft, Amazon, or Google. These trillion-dollar market cap giants are certainly rising, but it's hard for elephants to dance.

A new breed of "stock gurus," specializing in "supply chain sniping," is emerging en masse from Reddit, X, and Substack, leaving the old-school Buffett-style value investors far behind in terms of returns. They hold a portfolio of micro-cap stocks, valued at a few hundred million to a few billion dollars, which Wall Street analysts dismiss and average investors can barely pronounce.

The person turning these micro-cap stocks into a trading consensus and trend is Leopold Aschenbrenner, a 22-year-old German. Starting with $200 million, he reportedly turned it into $14 billion through stock trading, becoming synonymous with the "new stock guru."

After Leopold, the disenchantment with the Buffett school accelerated. A batch of "new stock gurus," specializing in "supply chain sniping," are emerging from Reddit, X, and Substack. They largely ignore financial reports, focusing instead on the "bottleneck" micro-cap stocks in the upstream supply chain. Following this logic, the Odaily editors have identified some new stock gurus for your analysis.

Do All "New Stock Gurus" Come from Reddit?

Among this new wave of gurus, the most popular and widely known recently is Serenity, who emerged from the WallStreetBets channel on Reddit.

Many readers who trade US stocks are likely familiar with Serenity's story. Briefly, he was an AI research scientist, participated in the RISC-V Foundation, published papers in Nature, and even joked about rejecting an offer from Nvidia's AI team when the stock was at $6.

What truly cemented Serenity's "new stock guru" narrative wasn't these self-proclaimed credentials, but his call on a stock called AXTI on WSB. His core argument was direct: the entire AI industry's construction depends on this $700 million market cap monopoly. Players including Google, Nvidia, and Microsoft all rely on its indium phosphide substrates and materials. He argues the entire AI industry is shifting from Google TPU to photonics, using optical interconnect technology. Without indium phosphide substrates, the "growth" story for AI would end in 2026.

In that viral AXTI post, he directly called for a price target from $15 to $150, with a very direct title.

Related reading: "Rejected Nvidia Offer at $6 Stock Price, Says He Can Make More Trading Stocks".

The stock price provided the best endorsement for Serenity. When Serenity discussed AXTI, the stock was around $12. After that, AXTI rallied continuously, first reaching $70. Serenity himself called it a trade where paper profits once reached 1000%. At the time of writing, public market data shows AXTI closed at $140.83, just a hair's breadth away from his $150 target price written earlier.

This makes Serenity's image more complex and multi-dimensional; he is not just a lucky gambler on WSB but a deep researcher of the new tech AI industry chain.

Why did this type of person emerge first from the WallStreetBets channel on Reddit?

We need to spend some time discussing the history of WallStreetBets.

WallStreetBets, or WSB, is the most famous retail investor community for US stocks on Reddit. It's powerful not because everyone there is rational or because you can always find the right answer.

Quite the opposite. WSB first became famous for showcasing the two most extreme sides of US retail investors: on one hand, short-term options expiring worthless, going all-in and going bankrupt, mutual mockery; on the other hand, occasional posts capable of changing market narratives.

The 2021 "retail vs. Wall Street" battle originated from WSB. A large number of retail investors clashed head-on with short-sellers around GameStop, turning a video game retailer considered a relic of a bygone era into a global financial news story. After that, WSB was no longer just a forum. It became a trading culture: rough, exaggerated, risky, out of control, but occasionally capable of digging up real gems amidst the noise.

WSB was inherently an extreme breeding ground for "non-consensus trades." And Serenity is a new variant of WSB in the AI bull market.

It used to be GameStop, AMC, short-term options, and meme stocks; now more and more posts discuss cloud infrastructure, enterprise automation, AI agents, HBM, optical modules, data center power, photonics, and supply chain bottlenecks.

WSB's culture of "pumping" stocks still exists, but what they pump has changed.

This Generation of Stock Gurus Never Reads Financial Reports

And this culture has spread from Reddit to X.

KawzInvests is also a representative of the new generation of stock gurus, with an account focused on US stock trading views and thematic research. Similar to Serenity, his content leans more towards "theme-driven" rather than traditional earnings report analysis.

KawzInvests typically looks at high-elasticity areas like AI infrastructure, optical communications, defense robotics, biotech, in-vehicle software, and small-cap growth stocks. He then finds logic based on supply chain position, order clues, partners, management changes, M&A possibilities, and potential for valuation re-rating.

KawzInvests' stock call

PhotonCap is another typical example.

There are rumors in the market that PhotonCap might be the institutional account behind Serenity, or another shell for Serenity. This claim has a certain folklore quality and fits people's imagination of anonymous masters. However, currently available public information doesn't show such a connection. PhotonCap wrote on its Substack that it's a research account run by an optics and photonics engineer who deals with lasers, optical fibers, and transceivers daily, hence the interest in studying how these things are priced in the stock market. It also thanked Serenity for inspiration in a portfolio disclosure post.

Going back to Serenity's original starting point, Reddit still has many similar "stock gurus."

For example, the user with the ID u/imacompnerd.

u/imacompnerd's most famous trade was also DOCN (DigitalOcean). This company isn't the most familiar AI leader, but it fits into the middle layer narrative of AI trading in 2026: not every developer and SME will directly use AWS, Azure, or GCP, and not all AI/ML deployments need the complex systems of giant cloud providers.

DigitalOcean's story is that it could become a lighter, cheaper, easier AI cloud infrastructure entry point. imacompnerd bet on this position. He once publicly disclosed 50,000 shares of DOCN, a position worth about $1.6 million, with a cost basis around $31.4; later, he posted a follow-up, stating the trade generated about $2 million in profit. At current prices, this is no ordinary "bullish" call; it's a large concentrated investment with clear wealth effects.

More interestingly, he didn't become famous solely on the DOCN trade. Public records also show his heavy positions and reviews of RDDT, GOOG, and MNDY. RDDT corresponds to the imagination around Reddit's platform traffic, community, and AI data licensing; GOOG is a more traditional large-scale AI platform company; MNDY is another revaluation attempt within enterprise software. The MNDY trade is particularly noteworthy because it wasn't a screenshot of a beautiful victory: He disclosed a position of about $1.9 million, but his cost was higher than the post price at the time, making it look bad temporarily. For this very reason, this person seems more real than the average "screenshot of returns" account. His portfolio includes big wins and paper losses; AI cloud infrastructure, platform stocks, and enterprise software; concentrated bets and position management.

In 2026, the AI sector is fiercely contested in the market.

If the US stock AI sector pulls back intraday for half an hour, money quickly rushes in to buy the dip. When memory stocks like Micron and SK Hynix move, the Korean market follows, and then A-shares in semiconductors, memory, communications, CPO, and optical modules also move. The market trend spreads like fire from one AI market to another.

On the other hand, traditional assets are becoming increasingly awkward. Baijiu, real estate, insurance, pharmaceuticals, high dividends – these were once assets where you could present a logical investment thesis. Now they often become another form of psychological torture: they don't rise when AI rallies, and they fall along with everything else when the market declines. In the past, if you bought the wrong sector, you could comfort yourself by waiting for a style rotation. Now, the more the AI main line rises, the more it seems to be draining capital from other sectors.

At times like this, what people fear most isn't losing money, but being on the wrong side of an era. Watching others continuously make money from memory, optical modules, CPO, AI cloud, and small-cap semiconductors, holders of traditional assets can't help but question their life choices. Once anxiety sets in, it pushes capital further into the AI main line.

And when the most prominent AI leaders become too expensive, the most aggressive capital moves further into more niche tracks, further upstream, and even more obscure parts of the supply chain.

This is also the biggest characteristic of this generation of "stock gurus," and the biggest difference between them and the previous generation.

Buffett's work method involves reading 500 pages of materials daily, feasting on financial reports, 10-Ks, and 10-Qs. He once held up a thick stack of papers and told a reporter that knowledge compounds like interest. He looks at ROE, free cash flow, debt-to-equity ratios, and whether management honestly admits mistakes in shareholder letters. His targets are companies that have been operating for decades, have complete financial statements, and stable cash flow. After buying, he is willing to hold for ten or twenty years.

The entire skill set of the value investing school is built on the premise that "the financial report is the soul of the company."

But this generation of "new gods" like Leopold Aschenbrenner and Serenity basically don't read that. This generation of "stock gurus" looks at: all the details of earnings calls, customer qualification cycles, the rhythm of the industry chain and production lines, whether upstream materials are monopolized, whether a certain technological route is moving from paper to mass production, and whether a certain company is being treated by the market as a player in an old cycle.

They also differ from traditional sell-side analysts. Sell-side analysts look at DCF, EPS, guidance, and target prices. But this generation of stock gurus bypasses financial reports entirely, jumping to the upstream of the industry chain to find the "bottleneck" node: like a small company worth a few hundred million dollars with Nvidia and Google on its customer list; a substrate material monopolized by a single company; a qualification cycle not yet covered by sell-side analysts.

Ignoring financial reports, focusing on the logic of the industry and supply chain – this is the secret technique of the WSB generation of stock pumpers.

These people come from the same era climate and together form a new school in the 2026 AI bull market.

An Attention Economy Bull Market

Low liquidity assets, early-stage narratives, strong communication symbols, community diffusion, and the feeling of getting in "before the mainstream money finds it."

Listing these terms together, you'll find they can describe both meme coins and the hottest batch of micro-cap stocks in the US stock market today. The difference is that meme coins always admit they are an attention game, while micro-cap stocks wear the cloak of "hard tech supply chain research."

But the essence is the same. Small market cap, thin trading volume, low institutional coverage, yet often standing within a seemingly massive industry story. A $700 million market cap company is described as the bottleneck of the AI era. A $3 billion market cap cloud vendor is pitched as the AI entry point for SMEs. A little-known substrate manufacturer is presented as the common upstream for Nvidia, Google, and Microsoft. Once the narrative is established, the price runs first; whether the fundamentals truly materialize won't be known for several quarters.

The most interesting thing about micro-cap stocks is that they aren't inherently a better battlefield for institutions. Quite the opposite: the further you go towards small market caps and low liquidity, the more Wall Street's advantages can become constraints.

A asset management institution with hundreds of billions or even trillions of dollars, when looking at a small company with a market cap of three or four hundred million, first thinks not "is this the best opportunity?" but "can I buy into it, and can I sell out of it?" It has position size limits, liquidity rules, risk committees, disclosure requirements, and transaction impact costs. For a retail investor, a small-cap stock with a $300 million market cap and tens of millions in daily volume might be large enough. For an institution the size of BlackRock, this might be a position too small to matter. Buying too little is meaningless, buying too much could directly push up the price or even trigger position disclosure. When it's time to sell, the shallow liquidity could cause significant slippage.

So it's not that they can't see it; it's that they often can't play the game. The larger the institutional money, the more power it has in large-cap assets. But in the micro-cap world, size becomes a cage. The micro-cap pool is too shallow for big ships to enter.

But the attention economy also has its own physical laws.

So whether this cross-market alpha is sustainable depends on three things.

First, does the information asymmetry still exist? If only a few FinTwit accounts can clearly explain the photonics supply chain, following them might indeed provide early access to a batch of under-covered assets. But once mainstream sell-side, ETFs, and quantitative funds start covering, the narrative premium will quickly flatten.

Second, can the fundamentals keep up with the attention? AI optical communication isn't an empty narrative, but the biggest problems for small-cap stocks are order uncertainty, customer concentration, financing dilution, and long production capacity verification cycles. A company might be in the right track but fail to capture real economic value.

Third, the speed of dissemination itself creates exit congestion. Rallies in low-liquidity assets are easily interpreted as "the market is validating the narrative," but it might just be a short-term surge in attention. The more it resembles a meme coin, the more one must guard against a meme coin-style liquidity ebb – the story remains, but the buying power is gone.

This also hints at a market migration: crypto traders are applying the narrative intuition trained on-chain to US micro-caps, AI hardware, energy, power, and supply chain assets. This might be the most noteworthy change in trading culture within the crypto space this year.

The attention economy nature of US micro-cap stocks existed long before the advent of meme coins.

Times create heroes, and times never lack new gods.