To The Moon

If I had to choose, I’d rather own the “toll collector” in the memory cycle than the companies forced to absorb higher costs. The key point is that rising memory prices are no longer an isolated semiconductor story. They are spreading downstream—from smartphones to GPUs and AI infrastructure. Xiaomi’s adjusted profit fell 42.6% year over year as higher memory costs squeezed margins, while Intel’s Arc Pro B70 prices have reportedly risen sharply in some markets. That tells me pricing power currently sits upstream. But I would not blindly chase memory stocks after their huge run. The better strategy is to own the suppliers with strong pricing power, healthy balance sheets and long-term AI demand, while avoiding companies whose margins are being compressed. In short: follow the money upstrea

Alphabet Still Wins Among these four calls, I agree most with the Alphabet upgrade. The key is that Google’s AI investment is no longer just a massive spending story. Google Cloud is accelerating, margins are improving, the backlog is huge, and its in-house TPU technology could eventually strengthen both competitiveness and profitability. That gives GOOG a compelling AI re-rating opportunity. I’m more cautious on CRWD and PLTR. Both remain excellent businesses with strong growth, but their valuations already assume years of near-perfect execution. At such multiples, even a small slowdown can trigger a sharp correction. PYPL is the wildcard. The potential M&A deal could create upside, but it is fundamentally an event-driven trade rather than a pure turnaround story. My ranking: GOOG fi

Broadcom could be one of the most overlooked winners of the AI infrastructure boom. Unlike Nvidia, Broadcom is not betting solely on general-purpose GPUs. Its custom AI accelerators, networking chips and optical connectivity give hyperscalers the tools to build AI systems tailored to their own workloads. The biggest attraction is its customer base. Google, Meta and OpenAI are all expanding partnerships with Broadcom, potentially creating a powerful multi-year demand pipeline. Broadcom also benefits from VMware, giving the company a second high-margin growth engine beyond semiconductors. The biggest risks are stretched expectations, customer concentration and hyperscaler capex eventually slowing. Still, if custom AI chips become increasingly important, Broadcom could be one of the stronges

A. Yes — still bullish on DBS / OCBC / UOB I’d choose A. To me, DBS hiring more young talent is more than a recruitment story—it shows the bank is preparing for where future growth will come from. Lower interest rates may pressure net interest margins, but wealth management, AI, data, technology and fee-based businesses can increasingly offset that pressure. DBS’s strong wealth-fee growth and rising AUM are already evidence of this transition. Singapore also continues to strengthen its position as a regional wealth and financial hub, attracting capital, global institutions and high-value talent. That doesn’t mean DBS is cheap or risk-free. Valuation still matters, especially after a strong run. But for long-term investors, I remain bullish on Singapore banks, particularly DBS, OCBC and UO

I’m leaning toward A — normal profit-taking, with the AI hardware trend still intact. Tuesday’s semiconductor selloff looks more like a valuation reset than a fundamental breakdown. The Philadelphia Semiconductor Index fell about 5%, while memory and optical names such as SNDK, MU and CRDO were hit much harder than Nvidia. The key issue is macro: the 30-year Treasury yield recently reached its highest level since 2007, while Brent crude moved above $90. That combination naturally pressures high-multiple growth stocks. But AI infrastructure demand has not suddenly disappeared. Memory, storage, networking and GPU demand remain tied to massive data-center investment. So I wouldn’t call this an AI-cycle reversal yet. Instead, I’d watch whether SNDK and MU stabilize and reclaim key moving aver

My Pick: AMLX — But This Is a Catalyst Trade If I had to pick one for the next 30 days, I’d choose Amylyx Pharmaceuticals (AMLX) — not because it is the safest name, but because it has the clearest near-term binary catalyst. The stock has already exploded higher, so chasing momentum here is risky. But the real story is still ahead: Amylyx expects Phase 3 LUCIDITY results for avexitide in late August or early September. A positive readout could materially change the company’s valuation and potentially support a 2027 commercial launch. That makes AMLX fundamentally different from simply buying a beaten-down stock like Adobe or Intuit. My choice: 🔥 Momentum, with a catalyst-driven setup. The key is position sizing — this is biotech, so one clinical result can create either a breakout or a br

The 30-year Treasury yield at 5.31% is becoming an increasingly attractive entry point, but I wouldn’t rush to lock in long-duration bonds yet. The key issue is that this selloff isn’t purely about Fed policy. Persistent inflation risks, higher oil prices, massive fiscal deficits, weaker foreign Treasury demand and growing corporate debt supply are all pushing the long end higher. That makes this a classic “wait for confirmation” moment. If yields eventually stabilize around 5.5%–5.7%, long-duration bonds could offer compelling returns. But if inflation expectations continue rising, buying too early could mean sitting through another painful price decline. For now, I’d favor short-duration Treasuries and cash, while gradually preparing to extend duration if yields spike further. The best

My take: 1) SOXS, 2) HIBS, 3) TECS. The clustering of inverse ETFs is a warning that investors are increasingly hedging duration and high-beta exposure, not necessarily calling for a full market crash. For the next 30 days, I expect the 10-year yield to stay around 4.7%, with 5% possible if inflation and Treasury supply worsen. The 30-year has already hit a 19-year high, showing how serious the bond-market pressure has become. The bigger threat to AI stocks is rising yields. AI debt issuance matters, but it is ultimately another channel through which higher financing costs can pressure valuations. Morgan Stanley expects global AI-related debt issuance to approach $570 billion this year. My view: this is a valuation reset, not necessarily the end of the AI cycle.

The Q2 13F season reveals a clear message: institutional money is not abandoning AI—it is becoming more selective. Berkshire’s 83% increase in Alphabet, taking the position to roughly $38 billion, is perhaps the strongest vote of confidence in Google’s AI ecosystem.  Meanwhile, Tepper is rotating away from memory names such as Micron while adding Amazon, Meta, Alphabet and TSMC, suggesting investors may be taking profits after the semiconductor rally. The SpaceX story is equally important. Its IPO has brought massive institutional exposure into the public market, although some reported “new” positions are simply legacy private holdings becoming reportable. My takeaway: the next phase of the AI trade may shift from chips toward platforms, infrastructure, power and space. But 13Fs are

I’d pick Ivan_Gan’s view as the most actionable. Bitcoin and gold offer clear technical levels, but macro policy is still the bigger driver across asset classes. If Fed hike expectations continue to fade, liquidity-sensitive assets like QQQ and SPY could remain supported even if markets stay range-bound. That said, gold’s breakout deserves attention. A short squeeze may explain the speed of the move, but sustained strength would suggest deeper institutional demand rather than just positioning. For Bitcoin, $67K is the key confirmation level, while $57.8K remains the line bulls cannot afford to lose. Personally, I’d rather wait for the breakout than chase the middle of the range. @WallStreet_Tiger [你懂的]