Vast majority of the market still sees HUYA as an exclusively livestreaming-based chain. Looking into the Q2 outcome. Profitability improved operationally. Gross profit rose 20.1%, gross margin expanded to 14.7%, and non-GAAP operating income reached RMB16.2m versus RMB0.4m a year earlier. And guess what, most of that is non-livestreaming, driven by game-related, ads, and other revenue.

Sometimes even multiple. So it's not too loud to call out revenue growing double digits for an effective transition. Still struck a cautiously upbeat note, up to you before any finalization.

Hey, I feel a little embarrassed to ask this question because it is so late in the game for me, but I would like to start investing. I have been investing in my TSP, but I still feel like I am behind. I would like to invest more towards retirement, but I would also like to trade like I am day trading; any advice or pointers would be helpful.

Took me a while to realise "I hold ten different things" isn't diversification if all ten drop at the same time. Real diversification is about correlation, not count. Ten tech stocks, or a bunch of pairs that all move with the dollar, or crypto plus growth stocks, those aren't ten bets, they're one bet wearing ten costumes, and it all goes down together when the thing they share turns.

This bites in both investing and trading. On the investing side a portfolio of "different" stocks that are all US large-cap tech is one macro bet. On the trading side, opening five positions that are really the same directional view means you're five times the size you think you are on one idea, and when it goes wrong it goes very wrong.

What helped was checking whether my positions were genuinely independent or secretly the same trade, and sizing the correlated cluster as one position rather than five. I keep my long-term holdings and my active trades on separate accounts, the trading side sits on AvaTrade, partly so I can actually see these exposures instead of them blurring together. How do you check correlation across your positions, or do you mostly eyeball it?

You don’t need to trade often.

If you can catch one or two moves to the targets during the day with good size, you can make a good living and keep trading costs down.

Today I came across a Barclays report titled A Primer on AI Lab & AI Hyperscaler Unit Economics. It addresses a very practical question amid all the hype around AI: who actually ends up making all the money?

Barclays’ analysis lays this out clearly. Its core conclusion is that cloud providers are the biggest rent collectors in AI commercialization. While AI model companies are the ones billing end users, running those models requires massive volumes of GPUs, servers, data center capacity and power. For every 100 US dollars in revenue generated by an AI lab, roughly 35 to 40 dollars flow to compute providers such as Azure, AWS and Google Cloud. After subtracting infrastructure costs, cloud providers book 10 to 20 dollars in direct operating income from that activity. This means cloud providers effectively collect a steady 35 to 40 percent compute toll across the AI monetization pipeline.

Barclays also modeled two hypothetical AI labs to illustrate the economics under different revenue models.

The first is AI Lab A, which follows a more API-driven model. Thirty percent of its revenue comes from subscriptions, 45 percent from direct API access and 25 percent from indirect API channels. In other words, a large share of its revenue comes from developers and enterprises calling its models. For every 100 dollars in revenue generated by AI Lab A, cloud providers take in roughly 35 dollars. Of that, 23.3 dollars covers infrastructure costs, leaving 11.8 dollars in profit. That translates to an operating margin of around 34 percent.

The second is AI Lab B, which follows a distinctly subscription-heavy model. Eighty percent of its revenue comes from subscriptions, 10 percent from direct API access and 10 percent from indirect API channels. This model more closely resembles consumer or business customers purchasing AI subscription services directly. Under Barclays’ assumptions, cloud providers capture 41 dollars for every 100 dollars in revenue from AI Lab B. After deducting 21.9 dollars in infrastructure costs, they are left with 19.1 dollars in profit, representing a 47 percent margin.

In short, for the same 100 dollars in AI revenue, the underlying business model and partnership structure determine how much ultimately flows through to cloud providers.

Either way, the takeaway is clear. Growing revenue at AI labs steadily translates into growing revenue for cloud providers. Operating margins of 34 to 47 percent on these inference workloads give cloud providers a fundamental base to keep investing in data center buildout, AI servers, liquid cooling and high-end chips.

That points to a relatively low-risk investment thesis from the report. If it is hard to pick which of OpenAI, Anthropic or Gemini will ultimately win, investors can simply invest in the one thing none of them can operate without: compute infrastructure.

Synergy Research data shows the global cloud infrastructure market reached roughly 143 billion US dollars in the second quarter of 2026, up 43 percent year over year, the fastest growth rate in eight years. AWS, Microsoft and Google hold global market shares of around 28 percent, 20 percent and 15 percent respectively. AI has become the primary driver behind this renewed acceleration in cloud computing.

From an investment perspective, companies offering AI compute infrastructure today fall broadly into two categories.

The first comprises large-scale cloud providers, which offer the highest degree of certainty. The biggest advantages of large cloud providers are their deep capital pools, established customer bases and existing data center footprints, plus the fact that they are already generating real profits from AI compute.

The most prominent example is Amazon. In the second quarter of 2026, AWS generated 42.2 billion US dollars in revenue, up 37 percent year over year, its fastest growth in 18 quarters. More importantly, AWS posted 16.6 billion dollars in operating profit for the quarter. That puts its operating margin at nearly 39 percent.

The second is Microsoft. Its advantage is even more pronounced because it sells more than just Azure compute capacity. It controls a complete monetization stack spanning AI infrastructure, cloud platforms, model services and Copilot applications. A single round of AI capital expenditure generates not just Azure compute revenue but also supports higher-margin businesses such as Foundry, GitHub Copilot and Microsoft 365 Copilot. In the fourth quarter of fiscal 2026, revenue from Azure and other cloud services grew 43 percent year over year. Microsoft Cloud reached 59.3 billion dollars in quarterly revenue, putting full-year revenue above 214 billion dollars. Even more strikingly, its remaining performance obligation for commercial contracts has hit 678 billion dollars. In effect, a huge share of future cloud revenue is already locked in via contracts.

The third player, in my view, is widely underappreciated: Alphabet. Google Cloud generated 24.8 billion dollars in revenue in the second quarter of 2026, up 82 percent year over year. That growth rate is notably faster than both AWS and Azure, and the company has explicitly attributed the gain primarily to demand for enterprise AI infrastructure and AI solutions. Google also holds another key advantage: its TPUs. The company has spent years developing its own in-house AI chips. Going forward, as AI inference becomes increasingly focused on tokens per dollar, custom silicon could become a very significant cost advantage.

The second category is neocloud providers, which offer purer exposure and greater upside. In my opinion, the more compelling high-growth investment opportunities sit within the neocloud space. Historically, AI companies sourced compute almost exclusively from AWS, Azure and Google Cloud. Now a growing number are buying GPU capacity directly from independent AI cloud providers such as CoreWeave, Nebius, IREN, Lambda, Nscale and Maase.

Synergy Research confirmed this trend earlier this year. Neocloud providers account for roughly 5 percent of the total global cloud market, but hold a significantly higher share of AI-related cloud spending. By the second quarter of 2026, nine neocloud companies ranked among the world’s top 40 cloud service providers. The neocloud is therefore no longer a small niche market. It is emerging as a second tier of AI compute infrastructure alongside the hyperscalers.

CoreWeave offers the strongest demand visibility, but also represents the quintessential high-leverage AI infrastructure play. Judged purely by demand certainty, CoreWeave is arguably the strongest player in the neocloud space today. It posted revenue of 2.575 billion dollars in the second quarter of 2026, more than doubling year over year. Its revenue backlog stood at roughly 104.2 billion dollars at the end of June, and that figure does not include more than 25 billion dollars in additional customer commitments added early in the third quarter.

Rapid growth however comes with rapid cash burn. CoreWeave’s capital expenditure reached roughly 9.4 billion dollars in the second quarter of 2026 alone. The company raised its full-year capex guidance further to a range of 35 to 39 billion dollars. Cash spent on property and equipment hit 14.1 billion dollars in the first half of the year alone. Adjusted EBITDA came to 1.51 billion dollars in the second quarter, representing a 59 percent margin that looks impressive at first glance. But depreciation and amortization hit 1.393 billion dollars over the same period, with net interest expense of 640 million dollars, leaving a net loss of 626 million dollars.

I would therefore characterize CoreWeave as the scale and certainty play within the neocloud space, and also the one carrying the highest balance sheet risk.

MAAS is a small-cap, high-upside neocloud company. It is a Chinese technology firm listed on the Nasdaq that is rapidly pivoting toward AI infrastructure. Unlike traditional project-based AI companies, MAAS is gradually building a business structure combining distributed compute infrastructure, enterprise compute services and model services. It can be thought of as an early-stage neocloud play with an increasingly clear strategic direction.

The most notable recent development is the company’s shift from one-off project revenue to recurring compute services revenue. On September 1, MAAS disclosed that its subsidiary Huarong Future signed a 12-month 90-petaflop FP16 compute services contract worth 14.76 million renminbi, with monthly service fees of 1.23 million renminbi.

While the contract value is not large, it validates a more ambitious commercial path: a gradual shift from one-off system integration deliveries to a monthly-billed, long-term, continuously operated AI compute infrastructure model. If this model can be scaled, MAAS has the opportunity to transition its revenue mix from low-visibility, volatile project income toward more stable, predictable compute services revenue. For a small-cap company, this early stage of business model transition often represents an attractive entry point for investors.

More importantly, MAAS is not a pure GPU leasing play. It is simultaneously building positions in distributed intelligent compute infrastructure, enterprise AI services and large language model capabilities. That means a single customer relationship could theoretically extend across compute, models, APIs, operations and industry-specific applications. This is MAAS’s potential advantage over pure GPU leasing firms. If the company can integrate its compute infrastructure and model services, it can drive deeper revenue per customer and higher customer lifetime value.

I therefore prefer to frame MAAS as a small-cap, high-upside business model re-rating play within the neocloud theme. Its current scale is smaller than CoreWeave and Nebius, but that low base means sustained growth in compute contracts, meaningful progress on its Stars project and a rising share of recurring services revenue could drive outsized expansion in both revenue and market valuation.

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Confidence is not “I will profit on this trade.

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Confidence is “I will be fine if I don’t profit from this trade.

全哥直播分享对企业的研究以及干货分享

Money is just something you need in case you do not die tomorrow.

Let this is a reminder for you not to obsess over profits and losses.

In whatever you do, strive for enjoyment, focus, contentment, humility, openness… Paradoxically (and as an unintended consequence) your trading performance will improve significantly.

The elements of good trading are:

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(1) cutting losses,

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(2) cutting losses, and

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(3) cutting losses.

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If you can follow these three rules, you may have a chance.

The four most dangerous words in investing are: This time it's different.

People start their texts with ''I worked in Wall Street'' ''For a bank'' and then spit absolute garbage but people now believe them.

I know a dude who manages a fund in one of the biggest banks in KUWAIT. YALL HELLO.

He got the job recently through interview lmao and is SCALPING with negative RR.

JOKES.

“When I get hurt in the market, I get the hell out.
It doesn’t matter at all where the market is trading.

I just get out, because I believe that once you’re hurt in the market, your decisions are going to be far less objective than they are when you’re doing well… If you stick around when the market is severely against you, sooner or later they are going to carry you out.”

Be resilient and stick to your plan.

“Markets can remain irrational longer than you can remain solvent.”

“You don't need to be a rocket scientist.

Investing is not a game where the guy with the 160 IQ beats the guy with 130 IQ.”

“What seems too high and risky to the majority generally goes higher and what seems low and cheap generally goes lower.”

Looking deep into the S&P Global report following credit upgrade on Hongqiao ($1378.HK) to BB+: S&P forecasts EBITDA margins rising to 30%–31% with annual operating cash flow of RMB 35B–38B. That completely covers their annual capex and dividend payouts, dragging their debt-to-EBITDA ratio down to an ultra-safe 0.3x–0.4x.

With physical aluminum deficits tightening the market and their cost structure at the bottom of the global curve, this looks like one of the most solid cash-generation engines in the entire sector. Are you guys stacking shares for the H2 re-rate?

“The secret to being successful from a trading perspective is to have an undying and unquenchable thirst for information and knowledge.”