In the 2026 AI wave of the US stock market, the real money wasn't made by holding familiar names like NVIDIA, Microsoft, Amazon, and Google. While these trillion-dollar giants are rising, it's hard for elephants to dance gracefully.

A new breed of 'supply chain snipers' is emerging en masse from Reddit, X, and Substack, leaving the far-behind returns of old-school Buffett-style value investors in the dust. Their holdings? A collection of micro-cap stocks with market caps ranging from a few hundred million to several billion dollars—names Wall Street analysts can't be bothered with and that average investors can't even pronounce.

The person who turned these micro-cap stocks into a trading consensus and trend is Leopold Aschenbrenner, a 22-year-old German who turned a starting fund of $200 million into $14 billion through stock trading, becoming the new synonym for a 'stock trading god'.

Following Leopold, the demystification of the Buffett school is accelerating. A wave of new trading gods focusing on 'supply chain sniping' is proliferating on Reddit, X, and Substack. They barely look at financial reports; instead, they focus on the 'chokepoint' micro-cap stocks in the upstream supply chain. Following this logic, our editorial team has found some of these new gods for analysis.

Are the 'New Trading Gods' All From Reddit?

Among this new generation of trading gods, the hottest and most prominent lately is Serenity, hailing from the WallStreetBets channel on Reddit.

Many US stock traders are likely familiar with Serenity's personal story. In short, he was once an AI research scientist, participated in the RISC-V Foundation, published a Nature paper, and even joked about rejecting an offer from NVIDIA's AI team when the stock was at $6.

What solidified Serenity's 'new trading god' narrative wasn't these self-described credentials, but his call on a stock called AXTI on WSB. His core argument was straightforward: the entire AI industry's construction relies on this $700 million market cap monopoly company, including players like Google, NVIDIA, and Microsoft—all dependent on its indium phosphide substrates and materials. He argues the entire AI industry is shifting from Google TPUs to photonics, adopting optical interconnect technology. Without indium phosphide substrates, the entire AI 'growth' story will end in 2026.

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

Related reading: "Rejecting NVIDIA's Offer at $6, He Says He Can Make More Trading Stocks."

The stock price gave Serenity the best endorsement. When Serenity discussed AXTI, the price was around $12. After that, AXTI rose continuously, first to $70, which Serenity himself claimed was a single-trade floating profit of 1000%. At the time of writing, public market data shows AXTI closing at $140.83, just a step away from his initial $150 target price.

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

Why do such people first emerge from the WallStreetBets Reddit channel?

We need to spend some time on WallStreetBets' history.

WallStreetBets, abbreviated as WSB, is Reddit's most famous community for US stock retail investors. Its notoriety isn't because everyone here is rational or because it always finds the correct answers.

On the contrary, WSB first became famous by putting the two most extreme sides of US retail investors on full display: one side is short-term options expiring worthless, all-in bets going bankrupt, and mutual mockery; the other side is the occasional post that can change market narratives.

The 2021 "retail vs. Wall Street" battle originated from WSB. Mass retail investors directly confronted short-selling institutions around GameStop, turning a game retail stock considered a relic of the old era into global financial news. Since then, WSB is no longer just a forum. It became a trading culture: rough, exaggerated, risky, chaotic, but occasionally unearthing something real amidst the noise.

WSB has always been an extremely fertile ground for "non-consensus trades" to sprout. And Serenity is a new variant of WSB in the AI bull market.

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

WSB's stock-pumping culture remains, but what's being pumped has changed.

This Generation of Trading Gods Never Reads Financial Reports

And this culture has spread from Reddit to X.

KawzInvests is also representative of the new generation of trading gods, an account focused on US stock trading views and thematic research. Similar to Serenity, his content is more "theme-driven" than traditional financial report analysis.

KawzInvests typically focuses on high-volatility areas like AI infrastructure, optical communication, defense robotics, biotech, in-vehicle software, and small-cap growth stocks, then finds logic in supply chain positioning, order clues, partnerships, management changes, M&A potential, and valuation re-rating space.

KawzInvests' stock call

PhotonCap is another typical example.

There are market rumors that PhotonCap might be the institutional account behind Serenity, or another shell for Serenity. This theory has a certain mystique, fitting the imagination of anonymous gurus. However, public information currently doesn't show this relationship. PhotonCap wrote in its Substack that it's a research account run by an optical and photonics engineer, daily working with lasers, optical fibers, transceivers, hence wanting to research how these are priced in the stock market. It also thanked Serenity for inspiration in a portfolio disclosure article.

Returning to where Serenity first gained fame, there are several similar "trading gods" on Reddit.

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

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

DigitalOcean's story lies in potentially becoming a lighter, cheaper, more user-friendly entry point for AI cloud infrastructure. imacompnerd bet on this position. He publicly disclosed holding 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 claiming this trade brought about $2 million in profit. At the current price, this is no longer ordinary "bullishness" but a significant concentrated investment with clear wealth effect.

More interestingly, he didn't become a legend based on just one DOCN trade. Public records also show his heavy positions in and reviews of RDDT, GOOG, and MNDY. RDDT corresponds to Reddit's platform traffic, community, and AI data licensing potential; GOOG is a more traditional large AI platform company; MNDY is another re-rating attempt in enterprise software. The MNDY trade is particularly noteworthy because it wasn't a beautiful victory screenshot: he disclosed a position of about $1.9 million, but his cost basis was higher than the price when he posted, showing a temporary paper loss. Precisely because of this, this person feels more authentic than ordinary "profit-showing accounts." His account has big wins and floating losses; AI cloud infrastructure, platform stocks, and enterprise software; concentrated bets and position management.

The 2026 AI sector is fiercely contested in the market.

When the US stock AI sector intraday corrects 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 China's A-share semiconductors, memory, communications, CPO, and optical modules move in turn. The rally spreads like fire from one AI market to another.

On the other side, traditional assets are becoming increasingly awkward. Baijiu, real estate, insurance, pharmaceuticals, high dividends—these were all assets that could be argued logically in the past. Now, they often represent another kind of psychological torment: they don't rise when AI rises, but they fall together when the market falls. In the past, buying the wrong sector, you could comfort yourself waiting for style rotation; now, the stronger the AI theme rises, the more it seems to be sucking blood from other sectors.

In such times, what people fear most isn't losing money, but being on the wrong side of history. Watching others continuously make money from memory, optical modules, CPO, AI cloud, and semiconductor small-caps, holders of traditional assets can't help but doubt their life choices. Once anxiety forms, it pushes more capital into the AI theme.

And when the most prominent AI leaders become too expensive, the most aggressive money will continue flowing into more niche sectors, further upstream, and more obscure parts of the supply chain.

This is also the biggest characteristic of this generation of 'trading gods,' and the greatest distinction from the previous generation.

Buffett's working method was reading 500 pages of material daily, feeding on financial reports, 10-Ks, and 10-Qs. He once held up a thick stack of papers to a reporter, saying knowledge accumulates like compound interest. He looked at ROE, free cash flow, debt-to-equity ratios, whether management honestly admitted mistakes in shareholder letters. His targets were companies operating for decades, with complete financial statements and stable cash flows. After buying, he was willing to hold for ten or twenty years.

The entire art of value investing is built on the premise that "financial reports are the soul of a company."

But Leopold Aschenbrenner, Serenity, and this generation of 'new gods' basically don't read that. This generation of 'trading gods' looks at: all details of earnings calls, customer certification cycles, industry chain and production line rhythms, whether upstream materials are monopolized, whether a certain tech path is moving from paper to mass production, whether a company is being treated 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, target price. This generation of trading gods directly bypasses financial reports, jumping to the upstream industry chain to find that 'chokepoint' node—like a small company with a market cap of a few hundred million dollars whose client list includes NVIDIA and Google, a substrate material monopolized by a certain company, a certification cycle not yet covered by sell-side analysts.

Not reading financial reports but analyzing industry chain and supply chain logic—this is the signature move of this generation of stock-pumpers from WallStreetBets.

This group emerged from the same era's climate, collectively forming 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 sense of "not yet discovered by mainstream capital."

Listing these terms together, you'll find they can describe both meme coins and the hottest batch of micro-cap stocks in today's US stock market. The difference is that meme coins always admit 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, little institutional coverage, yet often positioned within an industry story that sounds sufficiently grand. A $700 million market cap company is portrayed as a chokepoint in the AI era; a $3 billion market cap cloud provider is portrayed as the AI entry point for SMEs; an unknown substrate manufacturer is portrayed as the common upstream for NVIDIA, Google, and Microsoft. Once the narrative is established, the price moves first; whether fundamentals truly materialize will be known only several quarters later.

The most interesting aspect of micro-cap stocks is that they aren't inherently a field where institutions excel. On the contrary, the smaller the market cap and lower the liquidity, the more Wall Street's advantages can become constraints.

For an asset management firm managing hundreds of billions or even trillions of dollars, looking at a small company worth $300-400 million, the first thought isn't "Is this the best opportunity?" but "Can I get in, and can I get out?" They have position limit rules, 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 daily trading volume of tens of millions might be sufficiently large; for an institution like BlackRock, this might be a negligible position. Buying too little is meaningless; buying too much might directly push up the price, even trigger position disclosures. When it's time to sell, shallow liquidity might cause significant slippage.

So, they aren't blind to it; often, they simply can't play. The larger the institutional money, the more powerful it is in large-cap assets; but in micro-cap stocks, scale becomes a cage. The micro-cap pond is too shallow for big ships.

But the attention economy has its own physical laws.

So, whether this cross-market alpha can persist depends on three things.

First, whether the information gap still exists. If only a few FinTwit accounts can clearly explain the photonics supply chain, CT (presumably Crypto Twitter) following them might indeed provide early access to a batch of under-covered assets. But once mainstream sell-side analysts, ETFs, and quant funds start covering, the narrative premium will quickly be compressed.

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

Third, the speed of dissemination itself creates exit congestion. Rising prices in low-liquidity assets can easily be interpreted as "the market validating the narrative," but it might just be short-term attention influx. The more it resembles a meme coin, the more one must be wary of meme coin-style liquidity withdrawal—the story remains, but the buying pressure is gone.

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

The attention economy nature of US stock micro-caps existed long before meme coins emerged.

An era creates heroes, and an era is never short of new gods.