Những "thần cổ phiếu" Mỹ thế hệ này đã không còn xem báo cáo tài chính nữa

In the 2026 US stock AI wave, the most profitable move wasn't holding household names like NVIDIA, Microsoft, Amazon, or Google. These trillion-dollar market cap giants certainly rose, but it's hard for elephants to dance.

A new generation of "stock gurus" specializing in "supply chain sniping" is emerging en masse from Reddit, X, and Substack, leaving the returns of old-school Buffett-style value investors far in the dust. Their holdings are a basket of micro-cap stocks, worth between a few hundred million and a few billion dollars, which Wall Street analysts disdain to cover and ordinary investors can't even pronounce.

The person 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 new generation of "stock gurus," specializing in "supply chain sniping," is popping up in batches from Reddit, X, and Substack. They basically ignore financial reports; their focus is on the micro-cap stocks that are the "bottlenecks" in the upstream supply chain. Following this logic, the editorial team at Odaily has found some new stock gurus for your analysis.

Do All "New Stock Gurus" Come from Reddit?

Among this new wave of stock gurus, the most popular and viral recently is Serenity, emerging 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 involved with 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 solidified Serenity's "new stock guru" narrative wasn't these self-proclaimed credentials, but a stock he pumped on WSB: AXTI. His core argument was direct: The entire AI industry's construction depends on this monopoly company, valued at $700 million. Everyone, including Google, NVIDIA, and Microsoft, relies 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 entire "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 straightforward title.

Related Reading: "Rejecting an Offer from NVIDIA at $6, He Said He Could Make More Trading Stocks."

The stock price provided the best endorsement for Serenity. When he discussed AXTI, the stock was around $12. After that, AXTI rallied continuously, first to $70. Serenity himself called it a trade where the paper profit on a single position once reached 1000%. As of writing, public market data shows AXTI closing at $140.83, just shy of the $150 target price he initially set.

This makes Serenity's image more complex and multi-dimensional; he's not just a lucky gambler on WSB, but a deep researcher of the new 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 for short, is the most famous retail investor community for US stocks on Reddit. Its strength isn't because the people there are rational or always find the right answers.

Quite the opposite; WSB first gained fame for showcasing the two most extreme sides of US retail investors: on one hand, short-term options going to zero, going all-in and going bankrupt, mutual ridicule; on the other hand, the occasional post that can change market narratives.

The 2021 "Retail vs. Wall Street" saga started from WSB. A large number of retail investors clashed head-on with short-sellers around GameStop, turning a game retail stock, considered a relic of a bygone era by the market, into global financial news. After that, WSB became more than just a forum. It evolved into a trading culture: rough, exaggerated, risky, and out of control, but occasionally capable of uncovering real gems amidst the noise.

WSB is inherently a perfect breeding ground for "non-consensus trades." Serenity represents 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 "pumping stocks" remains, but the subjects have changed.

This Generation of Stock Gurus Never Reads Financial Reports

This culture has also spread from Reddit to X.

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

KawzInvests typically looks at high-beta areas like AI infrastructure, optical communications, defense robotics, biotech, in-vehicle software, and small-cap growth stocks. He finds his logic from supply chain positioning, order cues, partnerships, management changes, M&A possibilities, and valuation re-rating potential.

A call from KawzInvests

PhotonCap is another typical example.

There are rumors in the market that PhotonCap might be an institutional account behind Serenity, or another shell for him. This rumor has a certain underground charm and fits people's imagination of anonymous masters. However, currently available public information shows no such connection. PhotonCap wrote on their Substack that it's a research account run by an optics and photonics engineer, who comes into contact with lasers, optical fibers, and transceivers daily and wants to study how these things are priced in the stock market. They also thanked Serenity for the inspiration in a portfolio disclosure post.

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 is DOCN (DigitalOcean). This isn't the most familiar AI leader, but it fits into the middle-layer narrative of AI trades in 2026: Not every developer or SME will directly use AWS, Azure, or GCP, and not all AI/ML deployments require the complex systems of mega cloud providers.

DigitalOcean's story lies in its potential as a lighter, cheaper, and easier-to-use AI cloud infrastructure entry point. imacompnerd bet on this position. He once publicly disclosed owning 50,000 shares of DOCN in a position worth about $1.6 million, with an average cost of around $31.4; later, he posted a follow-up stating the trade yielded around $2 million in profit. At current prices, this isn't just a typical "bullish" call; it's a large concentrated investment with clear wealth effects.

More interestingly, he didn't become legendary solely on the DOCN trade. Public records show his heavy positions and reviews of RDDT, GOOG, and MNDY. RDDT relates to Reddit's platform traffic, community, and AI data licensing potential; GOOG is a more traditional large-cap AI platform; MNDY represents another re-evaluation attempt in enterprise software. The MNDY trade is particularly noteworthy because it wasn't a pretty screenshot of success: He disclosed a position of about $1.9 million, but his cost was higher than the stock price at the time of the post, looking unfavorable. This is precisely why this person seems more real than the usual "profit screenshot" accounts. His account shows big wins and unrealized losses; it includes AI cloud infrastructure, platform stocks, and enterprise software; it has concentrated bets and position management.

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

When US AI stocks pull back intraday for half an hour, capital quickly rushes in to buy the dip; when memory stocks like Micron and SK Hynix move, the Korean market follows, and then A-share semiconductors, memory, communications, CPO, and optical modules move in turn. The行情 (market momentum) spreads like fire from one AI market to another.

On the other hand, traditional assets are increasingly awkward. Baijiu, real estate, insurance, pharmaceuticals, and high-dividend stocks, which used to have strong investment narratives, now often cause a different kind of psychological torment: they don't rise when AI surges, but they fall along with everything else during market downturns. In the past, buying the wrong sector meant you could comfort yourself by waiting for a style rotation; now, the stronger the AI theme rises, the more it seems to be sucking 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 consistently profit from memory, optical modules, CPO, AI cloud, and small-cap semiconductor stocks, holders of traditional assets can't help but question their life choices. Once anxiety sets in, it pushes capital further into the AI theme.

And when the most prominent AI leaders become too expensive, the most aggressive capital will move further into more niche tracks, further upstream, and into 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 modus operandi was reading 500 pages of material daily, feasting on financial reports, 10-Ks, and 10-Qs. He once held up a thick stack of paper and told reporters 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 operated for decades, with complete financial statements and stable cash flows. After buying, he's 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 company's soul."

But the "new gods" of this generation, like Leopold Aschenbrenner and Serenity, basically don't read them. This generation of "stock gurus" looks at: every detail in earnings call transcripts, customer certification cycles, the rhythm of the industry chain and production lines, whether upstream materials have a monopoly, whether a certain technological path is moving from papers to mass production, and whether a certain company is still being valued by the market as an old-cycle business.

They are also different from traditional sell-side analysts. Sell-side analysts look at DCF, EPS, guidance, and target prices. But this generation of gurus bypasses the financial reports entirely, jumping upstream in the industry chain to find the "bottleneck" node. For example, a small company with a market cap of a few hundred million dollars whose customer list includes NVIDIA and Google, a substrate material monopolized by a single company, or a certification cycle that hasn't been covered by sell-side yet.

Ignore financial reports, focus on the logic of the industry and supply chain – this is the signature move of the WallStreetBets generation of stock pumpers.

These individuals come from the same era and collectively form a new school within the 2026 AI bull market.

An Attention Economy Bull Market

Low-liquidity assets, early-stage narratives, strong viral symbols, community propagation, and a sense of "entering before mainstream capital discovers it."

List these few terms together, and you'll find they can describe both a meme coin and the hottest batch of micro-cap stocks in today's US stock market. The difference is that meme coins always admit they're 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 company with a $700 million market cap is portrayed as the bottleneck of the AI era; a cloud provider with a $3 billion market cap is positioned as the AI entry point for SMEs; an obscure substrate manufacturer is framed as the common upstream for NVIDIA, Google, and Microsoft. Once the narrative is established, the price runs first; whether the fundamentals actually materialize is something we find out quarters later.

The most interesting thing about micro-cap stocks is that they aren't inherently a preferred battleground for institutions. On the contrary, the further you go into small-cap, low-liquidity territory, the more Wall Street's advantages can become constraints.

An asset management institution with hundreds of billions or even trillions of dollars under management, when looking at a small company worth $300-400 million, doesn't first think "is this the best opportunity," but rather "can I buy enough, and can I sell it?" There are position limits, liquidity rules, risk committees, disclosure requirements, and trading 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 like BlackRock, it might be too small a position to matter. Buying too little is pointless, but buying too much could directly push up the price or even trigger disclosure requirements. When it's time to sell, the shallow liquidity could cause significant slippage.

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

But the attention economy also has its own physical laws.

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

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

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

Third, the speed of propagation itself can create exit congestion. Price increases in low-liquidity assets can easily be interpreted as "the market is validating the narrative," but they might simply be a short-term influx of attention. The more it resembles a meme coin, the more one should be wary of a meme coin-style liquidity drain – the story remains, but the buying power is gone.

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

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

Times create heroes, and times never lack new gods.