These stock gods in the US market have stopped looking at earnings reports.

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

A batch of new "stock gurus" specializing in "supply chain sniping" are popping up en masse from Reddit, X, and Substack, leaving the returns of old-school Buffett-style value investors far in the dust. They hold a collection of micro-cap stocks with market caps ranging from a few hundred million to a few billion dollars — stocks Wall Street analysts disdain and ordinary investors can't even pronounce.

The one turning these micro-cap stocks into a trading consensus and trend is Leopold Aschenbrenner, a 22-year-old German who turned a starting capital of $200 million into $14 billion through stock trading, becoming synonymous with the "new stock guru."

After Leopold, the disillusionment with the Buffett school accelerated. A group of new stock gurus, specializing in "supply chain sniping," are emerging en masse from Reddit, X, and Substack. They generally don't look at financial reports; instead, they focus on those "bottleneck" micro-cap stocks upstream in the supply chain. Following this logic, the BlockBeats editor has found some new stock gurus for everyone to analyze.

Do All the "New Stock Gurus" Come from Reddit?

Among this new batch of stock gurus, the hottest and most viral recently is Serenity, who originated from the WallStreetBets channel on Reddit.

Readers who trade US stocks are likely familiar with Serenity's story. Simply put, he was once an AI research scientist, participated in the RISC-V Foundation, published papers in Nature, and even joked about turning down an offer from Nvidia's AI team when its stock was at $6.

What truly cemented Serenity's "new stock guru" narrative wasn't his self-proclaimed resume, but a stock he touted on WSB called AXTI. His core argument was straightforward: the entire AI industry's construction depends on this monopoly company with a $700 million market cap. All players, including Google, Nvidia, and Microsoft, rely on its indium phosphide substrates and materials. He argued that the entire AI industry is shifting from Google TPUs to photonics, adopting optical interconnect technology. Without indium phosphide substrates, the AI "growth" story would end in 2026.

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

Related reading: "He Turned Down Nvidia's Offer at $6 Stock, 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 rose steadily, first to $70. Serenity himself described it as a single-stock trade where unrealized profits once reached 1000%. As of writing, public market data shows AXTI closed at $140.83, just a hair's breadth away from the $150 target price he had set.

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 would such a person emerge from the WallStreetBets channel on Reddit?

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

WallStreetBets, or WSB for short, is the most famous retail investor community for US stocks on Reddit. It's formidable not because its members are rational, nor because it always finds the right answers.

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

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

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

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

The WSB culture of touting stocks is still alive, but the objects of their promotion have changed.

This Generation of Stock Gurus Never Look at Financial Reports

And this culture has also spread from Reddit to X (formerly Twitter).

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

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

KawzInvests' Touting

PhotonCap is another typical example.

There are rumors in the market that PhotonCap might be the institutional account behind Serenity, or another shell for him. This rumor has a certain folklore charm and fits people's imagination of anonymous masters. However, currently available public information does not show such a connection. In its own Substack, PhotonCap wrote that it is a research account run by an optics and photonics engineer who regularly deals with lasers, optical fibers, and transceivers, and wanted to study how these things are priced in the stock market. It also thanked Serenity for the inspiration in its portfolio disclosure article.

Returning to where Serenity started, 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 in the market, but it fits into the mid-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 require the complex systems of giant cloud providers.

DigitalOcean's story is that it could become a lighter, cheaper, and easier-to-use entry point for AI cloud infrastructure. imacompnerd was betting on this positioning. He once publicly disclosed a position of 50,000 shares of DOCN, worth approximately $1.6 million, with a cost basis around $31.4. He later posted a follow-up, claiming the trade yielded about $2 million in profit. At current prices, this is no longer just a simple "bullish" call; it's a large concentrated investment with a clear wealth effect.

More interestingly, he didn't just become a legend based on one DOCN trade. Public records also show his heavy positions and reviews of RDDT, GOOG, and MNDY. RDDT corresponds to the imagination surrounding Reddit's platform traffic, community, and AI data licensing; GOOG is a more traditional large AI platform company; MNDY represents another re-valuation attempt within enterprise software. The MNDY trade is particularly worth mentioning because it wasn't a screenshot of a clear victory: he disclosed a position of about $1.9 million, but his cost basis was higher than the stock price at the time of the post, making it look bad temporarily. Precisely because of this, this person appears more genuine than the average "screenshot of gains" account. His portfolio contains big wins and unrealized losses; AI cloud infrastructure alongside platform stocks and enterprise software; concentrated bets alongside position management.

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

If the US AI sector pulls back for even half an hour during the trading day, 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 move in turn. The market trend is like a fire, spreading from one AI market to another.

On the other hand, traditional assets are becoming increasingly awkward. Baijiu (liquor), real estate, insurance, pharmaceuticals, high dividends—these were all asset classes that once had a logical narrative. Now, they often become another form of psychological torture: they don't rise when AI rises, but they fall when the market drops. In the past, if you bought the wrong sector, you could comfort yourself by waiting for a rotation. Now, the more the AI main theme 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 the era. Watching others constantly make money from memory, optical modules, CPO, AI cloud, and small-cap semiconductor stocks, holders of traditional assets can hardly avoid questioning their life choices. Once this anxiety takes hold, it pushes capital further into the AI main theme.

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

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

Buffett's work style involves reading 500 pages of materials daily, consuming 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 generate 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 "financial reports are the soul of a company."

But the "new gods" of this generation, like Leopold Aschenbrenner and Serenity, basically don't read them. This generation of "stock gurus" focuses on: every detail of earnings calls, customer qualification cycles, industry chain and production line rhythm, upstream material monopolies, whether a certain technology path is moving from papers to mass production, or whether a certain company is being treated by the market as a relic of the old economic cycle.

They also differ from traditional sell-side analysts. Sell-side analysts look at DCF, EPS, guidance, and target prices. This generation of stock gurus bypasses financial reports entirely, jumping upstream in the industrial chain to find the "bottleneck" node: a small company with a market cap of a few hundred million dollars but a client list including Nvidia and Google; a substrate material monopolized by a single company; a certification cycle not yet covered by sell-side analysts.

Not looking at financial reports, but at the logic of the industrial and supply chains—this is the signature move of the stock touters of this generation from WallStreetBets.

These individuals emerged from the same era's climate and collectively form the new school within the 2026 AI bull market.

An Attention Economy Bull Market

Low-liquidity assets, early-stage narratives, strong symbols for propagation, community diffusion, and the feeling of "getting in before mainstream capital discovers 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 market today. The difference is that meme coins have always admitted they are attention games, 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 positioned within a sufficiently large industrial story. A company with a $700 million market cap is presented as the bottleneck link of the AI era; a cloud provider with a $3 billion market cap is framed as the AI entry point for SMEs; an obscure substrate manufacturer is presented as a shared upstream supplier for Nvidia, Google, and Microsoft. Once the narrative is established, the price will run ahead. Whether the fundamentals actually materialize will only be known after a few quarters.

The most interesting thing about micro-cap stocks is that they are not inherently a better battlefield for institutions. Quite the opposite — the further you go into small-cap, low-liquidity areas, the more Wall Street's advantages can become constraints.

An asset management institution managing hundreds of billions or even trillions of dollars, when looking at a small company with a market cap of $300-400 million, first thinks not "is this the best opportunity," but "can I buy in and get out?" They have 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 seem large enough; for an institution like BlackRock, it might be a negligible position. Buying too little is pointless, buying too much could directly push up the price or even trigger position disclosure. When it's time to sell, thin liquidity could result in significant slippage.

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

But the attention economy also has its own physical laws.

Therefore, whether this cross-market alpha can be sustained depends on three things.

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

Second, whether the fundamentals can catch up with the attention. AI optical communication is not an empty narrative, but the biggest problems with small-cap stocks are order uncertainty, customer concentration, financing dilution, and long production capacity verification cycles. A company could be in the right track but fail to capture the real economic value.

Third, the speed of propagation itself creates crowded exits. Rallies in low-liquidity assets are easily interpreted as "the market is validating the narrative," but they could just be a short-term influx of attention. The more it looks like a meme coin, the more one must be wary of a meme coin-style liquidity ebb — the story is still there, but the buying pressure is gone.

This also suggests a market migration: crypto traders are applying their on-chain narrative intuition to US micro-cap stocks, AI hardware, energy, power, and supply chain assets. This might be the most noteworthy trading culture change within the crypto sphere this year.

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

The times create heroes, and the times never lack new gods.