The stock gods trading U.S. stocks these days have stopped reading financial reports

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

A new breed of "stock gurus" focused on "supply chain sniping" are emerging in droves from Reddit, X, and Substack, leaving the returns of old-school Buffett-style value investors far in the dust. They hold a basket of micro-cap stocks, valued from a few hundred million to a few billion dollars, that Wall Street analysts disdain 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 disenchantment with the Buffett school has accelerated. A new breed of "stock gurus" focused on "supply chain sniping" are emerging in droves from Reddit, X, and Substack. They basically ignore financial reports, focusing instead on those "bottleneck" micro-cap stocks in the upstream supply chain. Following this logic, the Odaily editor has found some new stock gurus for everyone to analyze.

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

Among this new wave of stock gurus, the most recent and popular breakout star is Serenity, from the WallStreetBets channel on Reddit.

Many 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 a paper in Nature, and even joked about turning down an offer from Nvidia's AI team when the stock was at $6.

What truly cemented Serenity's "new stock guru" narrative wasn't his self-described resume, but a stock he called out on WSB: AXTI. His core argument was direct: 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 believes the entire AI industry is shifting from Google TPUs to photonics, adopting optical interconnect technology. Without indium phosphide substrates, the entire 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: "Turning down Nvidia's offer at $6, he 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. Afterwards, AXTI rose steadily, first reaching $70. Serenity himself called it a trade where the paper profit on a single position once reached 1000%. As of the time of writing, public quote websites show AXTI closing at $140.83, just one step away from the $150 target price he wrote about.

This makes Serenity's image more complex and multifaceted. He's not just a lucky gambler in WSB, but a deep researcher of the new tech AI industry chain.

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

We need to take some time to talk about the history of WallStreetBets.

WallStreetBets, or WSB, is the most famous community for US retail stock investors on Reddit. Its power doesn't come from the rationality of its members, nor from always finding the right answers.

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

The 2021 "retail vs. Wall Street" battle was fought from WSB. A large number of retail investors directly confronted short-selling institutions around GameStop, turning a game retail stock considered a relic of a bygone era into global financial news. After that, WSB was no longer just a forum. It became a trading culture: rough, exaggerated, adventurous, out of control, but occasionally capable of unearthing real gems amidst the noise.

WSB was always an extremely suitable 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 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 still exists, but what they are pumping has changed.

This Generation of Stock Gurus Never Reads Financial Reports

And this culture has also 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 is more "theme-driven" than traditional earnings interpretation.

KawzInvests usually looks at high-elasticity directions like AI infrastructure, optical communications, defense robotics, biotech, in-vehicle software, and small-cap growth stocks. He then finds logic from supply chain positioning, order clues, partners, management changes, M&A potential, and valuation re-rating space.

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 alias for Serenity. This claim has a legendary feel and fits everyone's imagination of anonymous masters. However, current public information shows no such relationship. PhotonCap wrote on its Substack that it is a research account run by an optical and photonics engineer who deals daily with lasers, optical fibers, and transceivers, and wants to study how these things are priced in the stock market. It also thanked Serenity for inspiration in a portfolio disclosure article.

Going back to where Serenity started, Reddit still has many similar "stock gurus."

For example, the user with 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 2026 AI trading: 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 lies in its potential to become a lighter, cheaper, and easier-to-use AI cloud infrastructure entry point. imacompnerd bet on this position. He once publicly disclosed 50,000 shares of DOCN, a position of about $1.6 million, with a cost basis of around $31.4; he later posted a follow-up, stating the trade yielded a gain of about $2 million. At current prices, this is no ordinary "bullish" call, but a large concentrated investment with clear wealth effects.

What's more interesting is that he didn't just ride one DOCN trade to fame. Public records also show his heavy positions 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 represents another revaluation attempt in enterprise software. The MNDY trade is particularly worth noting because it's not a pretty victory screenshot: he disclosed a position of about $1.9 million, but his cost basis was higher than the price at the time of the post, making it look bad temporarily. This makes him more real than the average "returns screenshot" account. His portfolio has big wins and paper losses; it includes AI cloud infrastructure, platform stocks, and enterprise software; it features concentrated bets and position management.

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

If the US AI sector pulls back intraday for half an hour, funds quickly rush 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 movement spreads like fire from one AI market to another.

On the other hand, traditional assets are becoming increasingly awkward. Baijiu, real estate, insurance, pharmaceuticals, and high dividends were once defensible investments. Now they often feel like another kind of psychological torment: they don't rise when AI rises, but they fall when the market falls. In the past, buying the wrong sector meant you could comfort yourself by waiting for a style rotation. Now, the more the AI main line rises, the more it seems to drain 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 continuously profit from memory, optical modules, CPO, AI cloud, and small-cap semiconductor stocks makes it hard for holders of traditional assets not to 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 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 work style involved 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 accumulates like compound interest. He looked at ROE, free cash flow, debt-to-equity ratio, and whether management honestly admitted mistakes in shareholder letters. His targets were companies that had been operating for decades, with complete financial statements and stable cash flow. After buying, he was 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 Leopold Aschenbrenner, Serenity, and this generation of "new gods" basically don't read that. This generation of "stock gurus" looks at: all the details from earnings calls, customer certification cycles, the rhythm of the industry chain and production lines, whether upstream materials are monopolized, whether a certain technology route is moving from papers to mass production, and whether a certain company is being treated by the market as a legacy 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 stock gurus bypasses financial reports entirely, jumping upstream in the industry chain to find that "bottleneck" node – like a small company with a market cap of a few hundred million dollars but NVIDIA and Google on its client list, a substrate material monopolized by a certain company, or a certification cycle not yet covered by sell-side analysts.

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

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

An Attention Economy Bull Market

Low-liquidity assets, early-stage narratives, strong viral symbols, community diffusion, and the "not yet discovered by mainstream capital" entry feeling.

Listing these terms together, you realize 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 industrial story that sounds big enough. A company with a $700 million market cap is touted as the bottleneck of the AI era. A cloud vendor valued at $3 billion is pitched as the AI entry point for SMEs. An obscure substrate manufacturer is described as the upstream supplier for NVIDIA, Google, and Microsoft collectively. Once the narrative is established, the price runs first; whether the fundamentals actually deliver won't be known for several quarters.

The most interesting thing about micro-cap stocks is that they are not naturally a better battlefield for institutions. On the contrary, the further you go towards small market caps and low liquidity, the more Wall Street's advantages can become constraints.

An asset management institution managing hundreds of billions or even trillions of dollars, looking at a small company with a market cap of three or four hundred million, doesn't first think "is this the best opportunity," but "can I buy into it and can I sell out of it." It has position limits, liquidity rules, risk committees, disclosure requirements, and transaction impact costs. For a retail investor, a small-cap stock with a three billion market cap and tens of millions in daily trading volume might be large enough; for an institution like BlackRock, it might be a position too small to matter. Buying too little is pointless, buying too much could directly push up the price or even trigger position disclosure. And when it's time to sell, the shallow liquidity could lead to significant slippage.

So, it's not that they don't see them; it's that they often can't play. The larger the institution's capital, the more powerful it is in large-cap assets; but in micro-cap stocks, size becomes a cage. The micro-cap stock pool 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 persist depends on three things.

First, whether the information asymmetry still exists. If only a few FinTwit accounts can explain the photonics supply chain clearly, following them might indeed lead 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 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 capacity verification cycles. A company might be in the right track but fail to capture real economic value.

Third, the speed of propagation itself creates exit congestion. Rallies in low-liquidity assets can easily be interpreted as "the market validating the narrative," but they could also just be a short-term influx of attention. The more it resembles a meme coin, the more one must be wary of a meme coin-style liquidity ebb—the story remains, but the buying power is gone.

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

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

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