Resolver-driven radar and ticker monitoring with null-safe catalyst hooks for the later 4B merge.
WATCHING: 1 active source(s), confirmation 0.62, catalyst support 0.16.
Alphabet (GOOGL)’s Google Bets on Finland’s Cold Weather and Nuclear Power for its Next AI Buildout Yahoo Finance
Alphabet (GOOGL)’s Google Bets on Finland’s Cold Weather and Nuclear Power for its Next AI Buildout Yahoo Finance
Alphabet Inc. (GOOGL) Is a Trending Stock: Facts to Know Before Betting on It Yahoo Finance
Alphabet Inc. (GOOGL) Is a Trending Stock: Facts to Know Before Betting on It Yahoo Finance
I’m letting a squirrel trade options because I clearly shouldn’t be trusted I have a new strategy. Put nuts on the ground labeled SPY, QQQ, AAPL, GOOGL, NVDA. Whatever nut the squirrel grabs is the stock. Then more nuts for: call / put 0dte / 1dte / 7dte ATM / 1 strike OTM / 2 strikes OTM Then I just buy whatever the little bastard picks. I’m gonna track it against my own trades and probably a coin flip too because I genuinely want to know if a squirrel can outperform me. Same position size every time so he can’t revenge trade. Probably run like 50-100 trades and track win rate, P/L, drawdown, all that crap. If he beats me I’m retiring from technical analysis and making him CIO. If he somehow turns $50 into $5k I’m buying him unlimited walnuts and starting a hedge fund. submitted by /u/DaSquirrelly [link] [comments]
I’m not much of a WSB guy, late to the party sadly but enjoy reading the banter on here so thought I’d share my plan to hear opinions, both positive and negative! I’m very pro tech despite bubble talks and my overall investment plan is to focus more on monopolistic type stocks that are medium risk - so Apple is out as it’s in my view low risk / low growth for example. I’m trying to turn 300K into 1M in the next 5 years so it needs to be aggressive but not all or nothing. I know crap all about options and puts and the like so I’m purely long term buy hold type investor. With that in mind here is what I’m doing, which is dollar cost averaging for the next 4 months or so, first of the month buying allotments. The stocks I’m buying are: TSM / NVDA / MP / GOOGL / ASML. Very boring my WSB standards but I’m hoping it will achieve my goal with some safety net in place. This is all savings, no margin and I’ve been investing since about 16 (50+ now) and made just 13% last 12 months which is below the NASDAQ and I’ve been sitting in cash too long being too paranoid about crashes which is costing me. submitted by /u/Russta69 [link] [comments]
Anthropic’s S1 will be publicly disclosed after Labor Day. Pre-IPO investment choices include: AMZN, GOOGL, ZM, SKM and DXYZ. My preference is DXYZ, especially this week. Here is why: It currently trades for less than its NAV when it usually trades at a premium well above the NAV. It has about 15% of its NAV invested in Anthropic. Its value should 2X (maybe 3X, but not likely) when a valuation is specified. It has only a 30M share float. Anthropic interest should push it back to a premium. Significant interest could push it up more. The fund just invested $169M in OpenAi. That also now represents 17% of the fund. SPCX had huge demand, including this fund. However, many brokers like Fidelity offered shares directly to retail. So, there is no offering to retail for Anthropic. That demand needs to go to one of the proxy investments. I also own SKM, Amazon and Zoom, though Amazon for reasons unrelated to Anthropic. Seems like this the week to place your bets. What is your preferred Anthropic proxy? submitted by /u/BoatDrinks73 [link] [comments]
GOOGL Stock Delivers Like The Leader, Priced Like The Laggard Trefis
GOOGL Stock Delivers Like The Leader, Priced Like The Laggard Trefis
So i've been keeping an eye on AAOI for a long time, and i think theres a GOOGL partnership/deal to be announced. Probably already this coming week There has been going rumors around that AAOI and GOOGL will be partnering for a while now and i think that the time has come Under the last earnings call the CEO of AAOI mentioned that they would finish the test phase of their transciervers with a hyperscaler within 2-3 weeks. He also mentioned that they would be the 4th verified optical providder for this company. On thursday the 3 weeks have passed and i expect an announcement to be made there or before. Whether they'll announce that they are now qualifed, announce a deal or a partnership i don't know I'm not 100% sure that it will be google but it is a possibility since they already supply MSFT and AMZN They also just announced a $600m ATM program friday after close. I suspect that this ATM dilution is a signal that they have passed the qualification phase with the hyperscaler and that the ATM program is a proof of funds to show that they will be able to supply this hyperscaler Godspeed submitted by /u/The-Firearm [link] [comments]
Alphabet Stock (GOOGL) Opinions on Recent Earnings and AI Outlook Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent Earnings and AI Outlook Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent AI Developments and Earnings Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent AI Developments and Earnings Quiver Quantitative
I'm only down around $40k submitted by /u/desiopressballs [link] [comments]
I'm only down around $40k submitted by /u/desiopressballs [link] [comments]
I should probably just go back to betting on amateur tennis. submitted by /u/Evening_Result4070 [link] [comments]
I should probably just go back to betting on amateur tennis. submitted by /u/Evening_Result4070 [link] [comments]
The entire AI capital stack is one giant leveraged bet that intelligence stays expensive. Token deflation is already here. There's exactly one large-cap on Earth built to profit from it, and you clowned it because Siri can't set two timers. Positions or ban: Long AAPL shares, short patience. Eyeing puts on the Tier 2 baggies but IV is richer than a Nvidia intern's RSUs. NOT shorting NVDA — read before you @ me. TA;DR at the bottom. The risk section is not optional. I am a regard with a spreadsheet, not your advisor. We like the box. Open your portfolio. If you own anything with "AI" in the pitch deck, you own the same trade five times: a leveraged long on revenue-per-token. NVDA? Long expensive tokens. Neoclouds? Longer, with debt. OKLO? Longest — a power company for token factories that don't exist yet. Revenue-per-token is in freefall. Sir, this is a casino, and you're about to find out which chips were marked. 📏 1. The gap is four points. Yes. Four. Artificial Analysis Intelligence Index, right now: Model Score Weights Claude Opus 5 61 closed Kimi K3 (2.8T MoE) 57 open, downloadable DeepSeek V4-Flash-0731 50 open, MIT The distance between the best model on Earth and a file you can torrent is four points . I've seen wider bid-asks on a bankrupt pink-sheet. And look how Flash got there. DeepSeek changed nothing — same 284B params, same 13B active — they just sent the model back to grad school for a semester (re-post-training only) and it came back dunking on its own 1.6T-parameter older sibling on all nine agent benchmarks. DeepSWE 7.3 → 54.4. Terminal Bench 61.8 → 82.7. The scaling era is ending. The post-training era is starting. Post-training is a game open labs can play for free , which is the worst kind of opponent — the kind that doesn't need your business model to survive. 💸 2. The token price war is already here and everyone's pretending it's fine OpenAI cut GPT-5.6 Luna 80% Sonnet 5 ships Opus-class coding at a third the price Gemini 3.6 Flash scores 50 at $1.50/$7.50 DeepSeek Flash blends to ~$0.06 per million tokens with cache discounts Six cents per million. That's not a price, that's a rounding error with an API key. And it matches what cost ~50x more twelve months ago. The premium isn't dying. Its half-life is. Hold the open-vs-closed lag at 12–18 months and a frontier lab gets about a year and a half to monetize any capability before somebody torrents it. That's not a business model, that's a limited-time offer. Capability is depreciating faster than the capital financing it. Screenshot that. Frame it. Tattoo it next to your 0DTE losses. Datacenters, turbines, and reactors are being underwritten against 2026 revenue-per-token assumptions that do not survive a 50x price decay per tier — and demand never has to fall for that to hurt. 🧾 3. Nobody needs a 61 to summarize their email, you absolute regard Look at what tokens actually get burned on. Summarize this thread. Classify this ticket. Extract fields from this invoice. RAG. Autocomplete. Route this request. That's 30-index work being served by 61-index models because that's what the API sold you. You're paying Opus prices for Clippy tasks. The entire buildout is capitalized as if every token needs a PhD when most tokens need a GED. The frontier premium is real for maybe 10% of workloads and priced as if it's 100%. Every enterprise on Earth is running the same triage right now — "does this need frontier, or am I just setting money on fire?" — and the frontier keeps only what it genuinely wins. Spoiler: it wins less every quarter. 🔒 4. Some tokens can't leave the building (compliance says hi 👋) Here's the wedge no price war touches. Source code. Medical records. Legal discovery. M&A docs. PII under GDPR. HIPAA, SOC2, privilege, sovereignty. "Legal said no" beats any price cut. Zero dollars per million doesn't help if the data can't go. And it compounds: the data you most want in a 1M-token context — your whole codebase, your document archive — is exactly the data most banned from APIs. A 50 that's read your codebase beats a 61 that hasn't, on the only benchmark that pays your salary. Bonus wrinkle for later: long context is where cloud economics invert. Datacenter advantage = batching many users across one weight sweep. KV cache is per-user and scales with context — at 1M tokens the batching collapses. Local marginal cost stays zero and you prefill once. Remember this when we get to the machine. 🧠 5. The models went memory-pilled while you were buying FLOPS Every Chinese lab converged on the same move: stop doing math, start looking things up. DeepSeek Engram — hash the last few tokens, index into an embedding table. An address lookup, not a matmul. Meituan LongCat — same optimum, found independently. Both moved ~20–25% of sparse budget from experts into lookup tables. Moonshot linear attention — kills KV cache growth. Every one trades compute for memory. The models are literally telling you what the winning machine looks like, and it's not a FLOPS monster — it's a RAM goblin. Buy the goblin. 🔌 6. The Cable: a tragedy in three acts This is the section everyone hand-waves, and the hand-waving is where the thesis lives. Strap in. Act I: Every other computer is two buckets and a straw Pool Tech Size Bandwidth System RAM DDR5 DIMMs 32–256GB ~100 GB/s VRAM GDDR, soldered to the card 24–32GB ~1,000–1,800 GB/s Between them: PCIe , ~32–63 GB/s. The pipe connecting the pools is 30–50x slower than the GPU's own memory. Your GPU is a Ferrari; PCIe is the dirt road between the Ferrari and the grocery store. This creates three hard rules: The GPU only touches what's in VRAM. Everything else gets copied over the straw first. VRAM is a hard wall set at card design time. Model doesn't fit? Buy more GPUs, or... Offload layers to system RAM — which is death. Decoding reads every active weight once per token: 405B dense (~230GB @ 4-bit) offloaded over PCIe: approx 0.3$ tok/s. That's not a product, that's a screensaver. GUH. Act II: The datacenter cheats with infinite money Same problem, solved with capex instead of architecture. HBM — artisanal, hand-stacked, small-batch memory priced accordingly — sits on an interposer next to the die: 3.4 TB/s on an H100, ~8 on a B200. Gorgeous. Also physics-capped: you can only fit so many stacks around a die, which is why the biggest single-GPU memory on Earth is 192GB. So you network GPUs. NVLink, InfiniBand, tensor parallelism. A $3M NVL72 rack is 72 small pools in a trench coat pretending to be one big pool. The entire multi-trillion-dollar scale-up industry is an apology for putting the memory in the wrong place. And Nvidia knows the answer — Grace Hopper does coherent shared CPU/GPU memory — they just sell it at $50K+, because cheap answers eat racks. (Also: KV cache lives in that artisanal HBM. Section 4's long-context inversion? Same wall, other side.) Act III: Apple deleted the cable Apple asked the one question nobody with a datacenter business could afford to ask: what if there's one pool? M-series mounts LPDDR — literally phone memory — on the package , wired to a comically wide controller (512-bit on Max, ~1,000-bit on Ultra, two dies fused at 2.5 TB/s): Chip Bandwidth Max memory M4 Pro 273 GB/s 64GB M4 Max 546 GB/s 128GB M3 Ultra 819 GB/s 512GB M5 Ultra (est., not spec) ~1.2 TB/s 768GB? One pool, one address space, and every engine on the die is a peer : CPU, GPU, Neural Engine, media engines — all reading the same bytes at the same addresses. No transfer step, because there's nothing to transfer across. macOS lets the GPU use ~75% of total RAM by default (tunable higher), and MLX treats "device transfer" as a no-op because there is no device . Why this matters for "CPU + GPU + AI sharing memory," concretely: a real agentic stack is a tokenizer and tool logic on CPU, the LLM on GPU, speech/vision/embeddings on ANE/GPU, and a draft model for speculative decoding. On a PC, all of that cage-fights for 24GB of VRAM — load Whisper and it evicts your KV cache like a landlord in a housing crisis. On a 768GB Mac, the 550GB reasoner, the draft model, the embedder, the speech model, and 100GB of warm KV all sit resident simultaneously, forever, with zero copies between stages. Agents are CPU↔GPU ping-pong machines, and on a Mac the ping-pong is free. And the punchline: on a PC, memory capacity is a GPU spec decided by Nvidia's segmentation team. On a Mac, it's a dropdown menu . The math, now with the cable deleted Flash-0731 (13B active @ 4-bit ≈ 7GB/token) on M3 Ultra: approx 117theoretical, ~90–100 real. Frontier-adjacent at reading speed. 405B dense: Mac ~3 tok/s — slow, but 10x the 4090's screensaver, and the PC literally cannot hold a trillion-param MoE. The Mac does ~50. Notice the pattern: Macs win exactly when models are big-total, small-active, KV-heavy — which is where open architectures are sprinting (Section 5). The models are evolving toward the machine. Honest weakness — prefill: M-series GPU is ~28 TFLOPS, not 500+. A 1M-token prefill is napkin-math 10–60 minutes depending on attention tricks vs ~a minute on an H100. You pay it once , keep the KV warm forever, and agents are decode-heavy anyway. Concede it instantly if a reply guy brings it up. It's priced in. Why nobody copies it (the moat is structural, not technical) Who Can they? Why they don't Nvidia Obviously — GB10 exists Capped at 128GB. Every big cheap box is a rack that doesn't get rented. Grace Hopper proves they can — at $50K+. AMD Tried — Strix Halo Capped at 128GB (96 usable) because MI300X margins. ROCm on it is a war crime. Nothing till 2027. Intel lol — Qualcomm SoC, yes Laptop-only, ~135 GB/s, no framework story Boutique PC vendor Can't source it Pays spot for LPDDR during the worst shortage in history. Dies. Apple Did it No metered-compute margin to protect. The only player for whom this box is margin-accretive instead of cannibalistic. Also buys more LPDDR than anyone alive at iPhone scale. Everyone with the physics to copy the trick has a P&L that forbids selling it cheap. That's the moat. 📦 7. 768GB: the band nobody will sell you 128GB at 4-bit caps you at ~200B params — realistically ~150B after KV and OS. Now look where the models sit: Model Params @ 4-bit Index Fits in 128GB? Llama-class 70B 70B ~40GB ~35–40 ✅ Qwen 235B 235B ~130GB ~45 ⚠️ barely, no context DeepSeek Flash-0731 284B ~156GB 50 ❌ DeepSeek V4-Pro 1.6T ~800GB ~55 ❌ lol Kimi K3 2.8T ~1.4TB 57 ❌ Every non-Apple product's 128GB cap sits exactly one model below "threat." You can run toys. You cannot run a replacement. Coincidence? I'm a regard, not a conspiracy theorist — the effect is identical either way. 768GB puts the whole 500B–1.2T band, where the open frontier actually lives, on your desk (K3 still needs sub-4-bit or expert streaming; not hiding it). Box economics: $15K over 3 years at 300W ≈ $443/month for unlimited tokens ≈ ~130M tokens/month flat out. Same volume at Opus 5 output pricing: $3,250/month . ⚠️ Honest: on DeepSeek Flash's API it's ~$36/month. The box doesn't beat cheap API on cost — it beats frontier API on cost and everything on data you can't upload. Now the market map: Product Max memory Price RTX 5090 32GB ~$2K RTX PRO 6000 96GB ~$8–10K AMD Strix Halo 128GB (96 usable) ~$2K Nvidia DGX Spark 128GB ~4K ⬛ HERE BE DRAGONS ⬛ 128GB → 784GB ⬛ Nvidia DGX Station 784GB $100–123K Apple Mac Studio the whole band ~$5–20K A 656GB hole with a 25x price gap across it, and exactly one seller. DGX Station at $123K isn't a price, it's a restraining order. That's not a market segment — that's an absence shaped exactly like a moat. 🍎 8. Apple isn't riding the wave. Apple IS the wave. Wrong frame: "Apple is well-positioned for token deflation." Right frame: Apple is the only large-cap on Earth whose profit motive requires tokens to be worthless. Player Monetizes Wants token prices to OpenAI / Anthropic the token 📈 stay high NVDA / AMD machines rented by the hour 📈 stay high CRWV / NBIS GPU-hours priced off token revenue 📈 stay high OKLO / GEV power for token factories 📈 stay high AAPL the box 📉 GO TO ZERO Every dollar of token price Apple destroys makes their hardware more valuable. And they're already shipping the weapons : They open-sourced the framework. MLX is Apple's, free, ~4,800 community models. Apple is subsidizing the commoditization of its competitors' product. Tim Cook is playing 4D chess while everyone else plays GPU Tetris. WWDC 2026 wired open weights into the OS. Any mlx-community model can back the Foundation Models API — every app gets local inference, first-party, zero marginal cost, across 2B+ devices. That's not a product launch. That's a price floor set at zero across the largest premium install base on Earth. That's a mugging. They shipped memory pooling while Nvidia removed it. JACCL (a direct NCCL pun — Apple is trolling) chains four Studios over Thunderbolt 5: trillion-param Kimi at 28+ tok/s, ~250W total. Nvidia deliberately stripped NVLink from consumer cards to stop exactly this. One company built the off-ramp. The other welded it shut. They named the use case themselves. March 2025 press release: 512GB Studio runs "LLMs with over 600 billion parameters entirely in memory." That's a pitch deck aimed at the API business. ☠️ Why Apple's deflation is worse than the API price war Luna's 80% cut and DeepSeek's six cents are brutal — but those tokens still run in a datacenter. Volume stays, somebody still rents the GPU. Apple's version removes the token from the metered economy entirely. Not less revenue — no revenue. No GPU-hour, no kilowatt on anyone's PPA. And the tokens that leave first are the easy, high-margin ones — the 30-index cream billed at 61-index prices. Apple doesn't take volume. Apple skims the cream and leaves the cloud with the hard, expensive, low-margin agentic sludge. Every infra name in this post is priced on the cream. GUH. 💰 What Apple actually books (hurting others isn't a thesis) Memory upgrades are the highest-margin SKUs they sell. 96→256GB costs $2,000 for maybe $600–800 of DRAM. Local AI pushing attach rates up is a pure-margin mix shift on hardware they already build. First real reason to upgrade a Mac in a decade. "Your machine physically cannot run this" is the best upgrade pitch since Retina. AI distribution for ~$14B/yr while hyperscalers spend ~$700B. No fleet to amortize, no PPA against a 2032 forecast. One company brought a RAM upgrade to a capex fight — and might win, because when the demand curve disappoints, Apple has nothing to write down. The demand signal is in the tape: Studio delivery blew from 6 days to 6 weeks and Apple pulled every high-memory config because it sold out. That's not a thesis, that's a shipping estimate. ⚖️ The asymmetry Apple doesn't have to win. Apple has to make "adequate" free. Once every enterprise negotiation has a zero-marginal-cost mac studio sitting on a desk, pricing power dies whether anyone deploys it or not. Linux never took desktop share and permanently capped what Microsoft could charge for a server OS. And nobody can respond without self-harm: Nvidia lifting the 128GB cap shoots its own racks, AMD can't till 2027, neoclouds can't sell boxes (the box is the threat). Apple is the only player with nothing to cannibalize. 🩸 9. The bag-holder tier list: who's short token deflation Organizing principle: how many derivatives you sit from the token price. Every arrow is a place the error compounds — and the first two links are already snapping in public like a leveraged regard on margin-call day. token price → what labs can charge → GPU rental rates → neocloud collateral value → datacenter capex → power demand forecast → OKLO's valuation Tier 1 — sells tokens: MSFT/GOOGL are genuinely ambiguous (Copilot is fixed-price with inference as COGS — fixed price, falling cost; they may be accidentally long deflation , the genius idiots). The truly exposed entity is OpenAI — unshortable, with Stargate commitments underwritten against future token revenue during a 50x-per-tier collapse. The biggest story here is private. Tier 2 — rents the machines 🔴 MAXIMUM BAGGAGE. Transmission already visible: H100 spot toward $1.99/hr, rental rates down 50–70%. Token prices fall → rents follow → collateral shrinks → the debt doesn't. 🔴🔴 CRWV — the biggest bag in the market: Metric Value Total debt $21B+ (was Debt/equity 4.8–8.9x Interest as % of revenue ~25% Microsoft as % of revenue 62–67% 2025 GAAP net loss -$1.17B Debt due 2026 $4.2B ≈ cash + one quarter of revenue Planned capex $30–35B, needs more debt The debt is investment-grade off the customer's credit, not CoreWeave's — a synthetic Microsoft bond wearing a GPU costume, collateralized by hardware whose rents fell 50–70% before amortization started. Kerrisdale models GB200 EBIT near zero at realistic 4–5-year lives; Burry flags ~$176B of understated industry depreciation; Vera Rubin ships H2 2026 to pressure B200 values on schedule. The fair counter, and it's the crux of the whole tier: 96% take-or-pay revenue, $99.4B backlog, H100s rebooked at 95% of original pricing. If contracts hold and GPU life is really 5–6 years, this entire bear case dies and we pour one out. Everything else is downstream of that one fact. 🔴 NBIS — same model, smaller, priced for 206% growth and flawless execution, which historically always works out. 🔴 Miner pivots (IREN, APLD, CIFR, WULF) — your cousin rebranding from "crypto day trader" to "digital asset manager": same bags, new LinkedIn. Estimates say 5–7 GPU clouds survive consolidation; these are not the survivors. Honorable mention ORCL — levered AI landlord with a Stargate side quest. Tier 3 — sells the machines: NVDA 🟡 — do not short the king on this thesis. Training anchors demand, they own CUDA/HBM/NVLink, and the 128GB cap is a choice. Multiple compression maybe, earnings collapse no. Weakest leg; you'll get run over and I'll post your loss porn. AMD 🟠 — entire AI pitch is the commoditizing tier, as #2 where #1 owns the software, and they capped their own box until 2027: a company playing defense against itself. AVGO — the arms dealer that also sells to the other army (Baltra partner through 2031). The non-obvious long. Respect. Tier 4 — supplies the buildout: optical/networking (COHR, LITE, ALAB, CRDO, ANET), electrical/thermal (VRT, ETN, PWR, FIX) — the plumbing of the plumbing, two derivatives out. MU note: memory needs the buildout and the Apple thesis needs cheap memory — if you're long both you're accidentally flat, the most WSB outcome possible: winning so hard you're flat. Tier 5 — power ⚡: 🔴 OKLO/SMR/NNE — zero revenue, ~$50M/quarter burn, first commercial op late 2027 at a site not authorized to sell power to the grid , Meta's campus at first power ~2030, and a 14–18GW pipeline that's almost entirely non-binding — the legal force of a pinky promise written in crayon. Four derivatives deep on a falling price. The 🌈🐻's Mona Lisa. 🟠 GEV/BE (backlog built on the forecast), 🟡 CEG/VST/TLN (real revenue today), 🟢 regulated utilities — your dad's boomer dividend stocks are the most insulated thing in the stack. Dad was right. Tell no one. 🤔 The app layer: cheap tokens collapse costs and pricing power — for thin wrappers, the moat was access to expensive capability. If anyone can run a 57 for free, what is the $20/month AI writing tool selling? Wrappers get squeezed like a short at a gamma ramp. Cheap tokens are only good for you if tokens weren't the product. 🟢 Actually long token deflation: AAPL · AVGO · app-layer with real moats · every enterprise on Earth · anyone with fixed-price revenue and variable inference cost. ⚡ 10. Power sidebar: it's the plumbing, not the juice Local inference is not greener — Mac ~6 J/token vs ~1–2 for a batched GPU node. Don't argue it; you'll lose and I'll laugh. The argument is where the capital goes. A Mac plugs into a wall that already exists, is already paid for, and sits idle overnight — exactly when you'd run long agentic jobs. A datacenter plugs into a fantasy: greenfield generation, transmission, substations, multi-year interconnect queues, 20-year PPAs signed against projections. And these assets don't need demand to fall — they need growth to come in under forecast. A 20% miss in 2032 impairs capital committed in 2026, because a substation has no plan B. A GPU gets written down and repurposed. A reactor site doesn't. Nobody builds a reactor to power a desktop. 🚩 11. How I'm wrong (read this, paper hands) How the short dies: Jevons. Cheaper tokens → more tokens. Infra prints anyway and you spent six months being right about price and wrong about volume. The oldest death in this trade. Volume never leaves the datacenter. The price war is fought by cheap open weights on rented GPUs — tokens stay, only the revenue leaves. This is the honest ceiling of the whole argument, placed here before some smug reply guy finds it. Training never goes local. Frontier runs are the anchor tenant for the power deals. This thesis is inference-only. Capex is sunk. This impairs 2030+ returns on 2026–2028 vintage capital, not whether the money gets spent. The CoreWeave crux. $99.4B take-or-pay backlog and 95% rebooking are real. If GPU economic life is 5–6 years, Tier 2 is wrong. Everything else is downstream of this single fact. How the AAPL long dies: You can't deflate what you can't ship. Studio cut to one 96GB config, prices raised, memory relief not forecast before late 2027–2028. The weapon is out of stock. Long AAPL = short DRAM. Memory is a far bigger share of a Mac's BOM than HBM is of an H100's price. You are pairs-trading the memory cycle whether you like it or not. Tight supply past 2030 kills it. The install base is small-memory. Two billion devices running a 3B model is a price floor at the kiddie table. The 768GB machine that binds at the adult table doesn't exist yet. The trade is Apple owning an uncontested band it currently cannot supply — a bet on the DRAM cycle turning. Baltra. Apple's building its own server chip with Broadcom through 2031. The "no conflict of interest" argument expires in 2–3 years. Clock's ticking. Apple's AI execution is genuinely bad. Siri. Next question. (The thesis only needs great hardware + adequate software — their historical pattern — but it's a real blemish.) Nvidia can respond. The 128GB cap costs margin to lift, but it's available. 📋 TA;DR for regards Open weights are 4 points off the frontier and free 🏴☠️ The price war already started — the premium isn't dying, its half-life is (~12–18 months per tier) Most tokens are Clippy work billed at PhD prices Some data can NEVER leave the building — and it's exactly the data you want in a 1M context PCs = two buckets and a straw. Datacenters = 72 buckets in a trench coat. Apple = one pool, no cable, capacity is a dropdown menu The math: $\text{tok/s} \approx \frac{\text{bandwidth}}{\text{active bytes}}$. 768GB @ 4-bit = trillion-param frontier-tier on a desk at 50–100 tok/s 128GB→784GB is an empty band with a 25x price gap and ONE seller. Nvidia's version is $123K 💀 Apple isn't positioned for deflation — Apple IS the deflation , and it skims the cream first CRWV/NBIS/miners: levered long token prices with debt due first. OKLO: four derivatives deep on a falling price with a crayon order book NVDA: don't short it on this. MSFT: ambiguous. AVGO: hedged. MU: your accidental hedge. Dad's utilities: fine Power risk is interconnect, not consumption — assets die on a growth miss, not a demand fall This is a DRAM cycle trade during the worst memory shortage on record. Size accordingly, regard Not financial advice. I'm a guy on the internet with a spreadsheet and a concerning amount of free time. Long AAPL, long popcorn, short my own free time. This is the way. 🍿🤝 submitted by /u/asiammyself [link] [comments]
Alphabet Stock (GOOGL) Opinions on Q2 Earnings and Capex Concerns Quiver Quantitative
Alphabet (GOOGL)’s Google Bets on Finland’s Cold Weather and Nuclear Power for its Next AI Buildout Yahoo Finance
Alphabet (GOOGL)’s Google Bets on Finland’s Cold Weather and Nuclear Power for its Next AI Buildout Yahoo Finance
Alphabet Inc. (GOOGL) Is a Trending Stock: Facts to Know Before Betting on It Yahoo Finance
Alphabet Inc. (GOOGL) Is a Trending Stock: Facts to Know Before Betting on It Yahoo Finance
I’m letting a squirrel trade options because I clearly shouldn’t be trusted I have a new strategy. Put nuts on the ground labeled SPY, QQQ, AAPL, GOOGL, NVDA. Whatever nut the squirrel grabs is the stock. Then more nuts for: call / put 0dte / 1dte / 7dte ATM / 1 strike OTM / 2 strikes OTM Then I just buy whatever the little bastard picks. I’m gonna track it against my own trades and probably a coin flip too because I genuinely want to know if a squirrel can outperform me. Same position size every time so he can’t revenge trade. Probably run like 50-100 trades and track win rate, P/L, drawdown, all that crap. If he beats me I’m retiring from technical analysis and making him CIO. If he somehow turns $50 into $5k I’m buying him unlimited walnuts and starting a hedge fund. submitted by /u/DaSquirrelly [link] [comments]
I’m not much of a WSB guy, late to the party sadly but enjoy reading the banter on here so thought I’d share my plan to hear opinions, both positive and negative! I’m very pro tech despite bubble talks and my overall investment plan is to focus more on monopolistic type stocks that are medium risk - so Apple is out as it’s in my view low risk / low growth for example. I’m trying to turn 300K into 1M in the next 5 years so it needs to be aggressive but not all or nothing. I know crap all about options and puts and the like so I’m purely long term buy hold type investor. With that in mind here is what I’m doing, which is dollar cost averaging for the next 4 months or so, first of the month buying allotments. The stocks I’m buying are: TSM / NVDA / MP / GOOGL / ASML. Very boring my WSB standards but I’m hoping it will achieve my goal with some safety net in place. This is all savings, no margin and I’ve been investing since about 16 (50+ now) and made just 13% last 12 months which is below the NASDAQ and I’ve been sitting in cash too long being too paranoid about crashes which is costing me. submitted by /u/Russta69 [link] [comments]
Anthropic’s S1 will be publicly disclosed after Labor Day. Pre-IPO investment choices include: AMZN, GOOGL, ZM, SKM and DXYZ. My preference is DXYZ, especially this week. Here is why: It currently trades for less than its NAV when it usually trades at a premium well above the NAV. It has about 15% of its NAV invested in Anthropic. Its value should 2X (maybe 3X, but not likely) when a valuation is specified. It has only a 30M share float. Anthropic interest should push it back to a premium. Significant interest could push it up more. The fund just invested $169M in OpenAi. That also now represents 17% of the fund. SPCX had huge demand, including this fund. However, many brokers like Fidelity offered shares directly to retail. So, there is no offering to retail for Anthropic. That demand needs to go to one of the proxy investments. I also own SKM, Amazon and Zoom, though Amazon for reasons unrelated to Anthropic. Seems like this the week to place your bets. What is your preferred Anthropic proxy? submitted by /u/BoatDrinks73 [link] [comments]
GOOGL Stock Delivers Like The Leader, Priced Like The Laggard Trefis
GOOGL Stock Delivers Like The Leader, Priced Like The Laggard Trefis
So i've been keeping an eye on AAOI for a long time, and i think theres a GOOGL partnership/deal to be announced. Probably already this coming week There has been going rumors around that AAOI and GOOGL will be partnering for a while now and i think that the time has come Under the last earnings call the CEO of AAOI mentioned that they would finish the test phase of their transciervers with a hyperscaler within 2-3 weeks. He also mentioned that they would be the 4th verified optical providder for this company. On thursday the 3 weeks have passed and i expect an announcement to be made there or before. Whether they'll announce that they are now qualifed, announce a deal or a partnership i don't know I'm not 100% sure that it will be google but it is a possibility since they already supply MSFT and AMZN They also just announced a $600m ATM program friday after close. I suspect that this ATM dilution is a signal that they have passed the qualification phase with the hyperscaler and that the ATM program is a proof of funds to show that they will be able to supply this hyperscaler Godspeed submitted by /u/The-Firearm [link] [comments]
Alphabet Stock (GOOGL) Opinions on Recent Earnings and AI Outlook Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent Earnings and AI Outlook Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent AI Developments and Earnings Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Recent AI Developments and Earnings Quiver Quantitative
I'm only down around $40k submitted by /u/desiopressballs [link] [comments]
I'm only down around $40k submitted by /u/desiopressballs [link] [comments]
I should probably just go back to betting on amateur tennis. submitted by /u/Evening_Result4070 [link] [comments]
I should probably just go back to betting on amateur tennis. submitted by /u/Evening_Result4070 [link] [comments]
The entire AI capital stack is one giant leveraged bet that intelligence stays expensive. Token deflation is already here. There's exactly one large-cap on Earth built to profit from it, and you clowned it because Siri can't set two timers. Positions or ban: Long AAPL shares, short patience. Eyeing puts on the Tier 2 baggies but IV is richer than a Nvidia intern's RSUs. NOT shorting NVDA — read before you @ me. TA;DR at the bottom. The risk section is not optional. I am a regard with a spreadsheet, not your advisor. We like the box. Open your portfolio. If you own anything with "AI" in the pitch deck, you own the same trade five times: a leveraged long on revenue-per-token. NVDA? Long expensive tokens. Neoclouds? Longer, with debt. OKLO? Longest — a power company for token factories that don't exist yet. Revenue-per-token is in freefall. Sir, this is a casino, and you're about to find out which chips were marked. 📏 1. The gap is four points. Yes. Four. Artificial Analysis Intelligence Index, right now: Model Score Weights Claude Opus 5 61 closed Kimi K3 (2.8T MoE) 57 open, downloadable DeepSeek V4-Flash-0731 50 open, MIT The distance between the best model on Earth and a file you can torrent is four points . I've seen wider bid-asks on a bankrupt pink-sheet. And look how Flash got there. DeepSeek changed nothing — same 284B params, same 13B active — they just sent the model back to grad school for a semester (re-post-training only) and it came back dunking on its own 1.6T-parameter older sibling on all nine agent benchmarks. DeepSWE 7.3 → 54.4. Terminal Bench 61.8 → 82.7. The scaling era is ending. The post-training era is starting. Post-training is a game open labs can play for free , which is the worst kind of opponent — the kind that doesn't need your business model to survive. 💸 2. The token price war is already here and everyone's pretending it's fine OpenAI cut GPT-5.6 Luna 80% Sonnet 5 ships Opus-class coding at a third the price Gemini 3.6 Flash scores 50 at $1.50/$7.50 DeepSeek Flash blends to ~$0.06 per million tokens with cache discounts Six cents per million. That's not a price, that's a rounding error with an API key. And it matches what cost ~50x more twelve months ago. The premium isn't dying. Its half-life is. Hold the open-vs-closed lag at 12–18 months and a frontier lab gets about a year and a half to monetize any capability before somebody torrents it. That's not a business model, that's a limited-time offer. Capability is depreciating faster than the capital financing it. Screenshot that. Frame it. Tattoo it next to your 0DTE losses. Datacenters, turbines, and reactors are being underwritten against 2026 revenue-per-token assumptions that do not survive a 50x price decay per tier — and demand never has to fall for that to hurt. 🧾 3. Nobody needs a 61 to summarize their email, you absolute regard Look at what tokens actually get burned on. Summarize this thread. Classify this ticket. Extract fields from this invoice. RAG. Autocomplete. Route this request. That's 30-index work being served by 61-index models because that's what the API sold you. You're paying Opus prices for Clippy tasks. The entire buildout is capitalized as if every token needs a PhD when most tokens need a GED. The frontier premium is real for maybe 10% of workloads and priced as if it's 100%. Every enterprise on Earth is running the same triage right now — "does this need frontier, or am I just setting money on fire?" — and the frontier keeps only what it genuinely wins. Spoiler: it wins less every quarter. 🔒 4. Some tokens can't leave the building (compliance says hi 👋) Here's the wedge no price war touches. Source code. Medical records. Legal discovery. M&A docs. PII under GDPR. HIPAA, SOC2, privilege, sovereignty. "Legal said no" beats any price cut. Zero dollars per million doesn't help if the data can't go. And it compounds: the data you most want in a 1M-token context — your whole codebase, your document archive — is exactly the data most banned from APIs. A 50 that's read your codebase beats a 61 that hasn't, on the only benchmark that pays your salary. Bonus wrinkle for later: long context is where cloud economics invert. Datacenter advantage = batching many users across one weight sweep. KV cache is per-user and scales with context — at 1M tokens the batching collapses. Local marginal cost stays zero and you prefill once. Remember this when we get to the machine. 🧠 5. The models went memory-pilled while you were buying FLOPS Every Chinese lab converged on the same move: stop doing math, start looking things up. DeepSeek Engram — hash the last few tokens, index into an embedding table. An address lookup, not a matmul. Meituan LongCat — same optimum, found independently. Both moved ~20–25% of sparse budget from experts into lookup tables. Moonshot linear attention — kills KV cache growth. Every one trades compute for memory. The models are literally telling you what the winning machine looks like, and it's not a FLOPS monster — it's a RAM goblin. Buy the goblin. 🔌 6. The Cable: a tragedy in three acts This is the section everyone hand-waves, and the hand-waving is where the thesis lives. Strap in. Act I: Every other computer is two buckets and a straw Pool Tech Size Bandwidth System RAM DDR5 DIMMs 32–256GB ~100 GB/s VRAM GDDR, soldered to the card 24–32GB ~1,000–1,800 GB/s Between them: PCIe , ~32–63 GB/s. The pipe connecting the pools is 30–50x slower than the GPU's own memory. Your GPU is a Ferrari; PCIe is the dirt road between the Ferrari and the grocery store. This creates three hard rules: The GPU only touches what's in VRAM. Everything else gets copied over the straw first. VRAM is a hard wall set at card design time. Model doesn't fit? Buy more GPUs, or... Offload layers to system RAM — which is death. Decoding reads every active weight once per token: 405B dense (~230GB @ 4-bit) offloaded over PCIe: approx 0.3$ tok/s. That's not a product, that's a screensaver. GUH. Act II: The datacenter cheats with infinite money Same problem, solved with capex instead of architecture. HBM — artisanal, hand-stacked, small-batch memory priced accordingly — sits on an interposer next to the die: 3.4 TB/s on an H100, ~8 on a B200. Gorgeous. Also physics-capped: you can only fit so many stacks around a die, which is why the biggest single-GPU memory on Earth is 192GB. So you network GPUs. NVLink, InfiniBand, tensor parallelism. A $3M NVL72 rack is 72 small pools in a trench coat pretending to be one big pool. The entire multi-trillion-dollar scale-up industry is an apology for putting the memory in the wrong place. And Nvidia knows the answer — Grace Hopper does coherent shared CPU/GPU memory — they just sell it at $50K+, because cheap answers eat racks. (Also: KV cache lives in that artisanal HBM. Section 4's long-context inversion? Same wall, other side.) Act III: Apple deleted the cable Apple asked the one question nobody with a datacenter business could afford to ask: what if there's one pool? M-series mounts LPDDR — literally phone memory — on the package , wired to a comically wide controller (512-bit on Max, ~1,000-bit on Ultra, two dies fused at 2.5 TB/s): Chip Bandwidth Max memory M4 Pro 273 GB/s 64GB M4 Max 546 GB/s 128GB M3 Ultra 819 GB/s 512GB M5 Ultra (est., not spec) ~1.2 TB/s 768GB? One pool, one address space, and every engine on the die is a peer : CPU, GPU, Neural Engine, media engines — all reading the same bytes at the same addresses. No transfer step, because there's nothing to transfer across. macOS lets the GPU use ~75% of total RAM by default (tunable higher), and MLX treats "device transfer" as a no-op because there is no device . Why this matters for "CPU + GPU + AI sharing memory," concretely: a real agentic stack is a tokenizer and tool logic on CPU, the LLM on GPU, speech/vision/embeddings on ANE/GPU, and a draft model for speculative decoding. On a PC, all of that cage-fights for 24GB of VRAM — load Whisper and it evicts your KV cache like a landlord in a housing crisis. On a 768GB Mac, the 550GB reasoner, the draft model, the embedder, the speech model, and 100GB of warm KV all sit resident simultaneously, forever, with zero copies between stages. Agents are CPU↔GPU ping-pong machines, and on a Mac the ping-pong is free. And the punchline: on a PC, memory capacity is a GPU spec decided by Nvidia's segmentation team. On a Mac, it's a dropdown menu . The math, now with the cable deleted Flash-0731 (13B active @ 4-bit ≈ 7GB/token) on M3 Ultra: approx 117theoretical, ~90–100 real. Frontier-adjacent at reading speed. 405B dense: Mac ~3 tok/s — slow, but 10x the 4090's screensaver, and the PC literally cannot hold a trillion-param MoE. The Mac does ~50. Notice the pattern: Macs win exactly when models are big-total, small-active, KV-heavy — which is where open architectures are sprinting (Section 5). The models are evolving toward the machine. Honest weakness — prefill: M-series GPU is ~28 TFLOPS, not 500+. A 1M-token prefill is napkin-math 10–60 minutes depending on attention tricks vs ~a minute on an H100. You pay it once , keep the KV warm forever, and agents are decode-heavy anyway. Concede it instantly if a reply guy brings it up. It's priced in. Why nobody copies it (the moat is structural, not technical) Who Can they? Why they don't Nvidia Obviously — GB10 exists Capped at 128GB. Every big cheap box is a rack that doesn't get rented. Grace Hopper proves they can — at $50K+. AMD Tried — Strix Halo Capped at 128GB (96 usable) because MI300X margins. ROCm on it is a war crime. Nothing till 2027. Intel lol — Qualcomm SoC, yes Laptop-only, ~135 GB/s, no framework story Boutique PC vendor Can't source it Pays spot for LPDDR during the worst shortage in history. Dies. Apple Did it No metered-compute margin to protect. The only player for whom this box is margin-accretive instead of cannibalistic. Also buys more LPDDR than anyone alive at iPhone scale. Everyone with the physics to copy the trick has a P&L that forbids selling it cheap. That's the moat. 📦 7. 768GB: the band nobody will sell you 128GB at 4-bit caps you at ~200B params — realistically ~150B after KV and OS. Now look where the models sit: Model Params @ 4-bit Index Fits in 128GB? Llama-class 70B 70B ~40GB ~35–40 ✅ Qwen 235B 235B ~130GB ~45 ⚠️ barely, no context DeepSeek Flash-0731 284B ~156GB 50 ❌ DeepSeek V4-Pro 1.6T ~800GB ~55 ❌ lol Kimi K3 2.8T ~1.4TB 57 ❌ Every non-Apple product's 128GB cap sits exactly one model below "threat." You can run toys. You cannot run a replacement. Coincidence? I'm a regard, not a conspiracy theorist — the effect is identical either way. 768GB puts the whole 500B–1.2T band, where the open frontier actually lives, on your desk (K3 still needs sub-4-bit or expert streaming; not hiding it). Box economics: $15K over 3 years at 300W ≈ $443/month for unlimited tokens ≈ ~130M tokens/month flat out. Same volume at Opus 5 output pricing: $3,250/month . ⚠️ Honest: on DeepSeek Flash's API it's ~$36/month. The box doesn't beat cheap API on cost — it beats frontier API on cost and everything on data you can't upload. Now the market map: Product Max memory Price RTX 5090 32GB ~$2K RTX PRO 6000 96GB ~$8–10K AMD Strix Halo 128GB (96 usable) ~$2K Nvidia DGX Spark 128GB ~4K ⬛ HERE BE DRAGONS ⬛ 128GB → 784GB ⬛ Nvidia DGX Station 784GB $100–123K Apple Mac Studio the whole band ~$5–20K A 656GB hole with a 25x price gap across it, and exactly one seller. DGX Station at $123K isn't a price, it's a restraining order. That's not a market segment — that's an absence shaped exactly like a moat. 🍎 8. Apple isn't riding the wave. Apple IS the wave. Wrong frame: "Apple is well-positioned for token deflation." Right frame: Apple is the only large-cap on Earth whose profit motive requires tokens to be worthless. Player Monetizes Wants token prices to OpenAI / Anthropic the token 📈 stay high NVDA / AMD machines rented by the hour 📈 stay high CRWV / NBIS GPU-hours priced off token revenue 📈 stay high OKLO / GEV power for token factories 📈 stay high AAPL the box 📉 GO TO ZERO Every dollar of token price Apple destroys makes their hardware more valuable. And they're already shipping the weapons : They open-sourced the framework. MLX is Apple's, free, ~4,800 community models. Apple is subsidizing the commoditization of its competitors' product. Tim Cook is playing 4D chess while everyone else plays GPU Tetris. WWDC 2026 wired open weights into the OS. Any mlx-community model can back the Foundation Models API — every app gets local inference, first-party, zero marginal cost, across 2B+ devices. That's not a product launch. That's a price floor set at zero across the largest premium install base on Earth. That's a mugging. They shipped memory pooling while Nvidia removed it. JACCL (a direct NCCL pun — Apple is trolling) chains four Studios over Thunderbolt 5: trillion-param Kimi at 28+ tok/s, ~250W total. Nvidia deliberately stripped NVLink from consumer cards to stop exactly this. One company built the off-ramp. The other welded it shut. They named the use case themselves. March 2025 press release: 512GB Studio runs "LLMs with over 600 billion parameters entirely in memory." That's a pitch deck aimed at the API business. ☠️ Why Apple's deflation is worse than the API price war Luna's 80% cut and DeepSeek's six cents are brutal — but those tokens still run in a datacenter. Volume stays, somebody still rents the GPU. Apple's version removes the token from the metered economy entirely. Not less revenue — no revenue. No GPU-hour, no kilowatt on anyone's PPA. And the tokens that leave first are the easy, high-margin ones — the 30-index cream billed at 61-index prices. Apple doesn't take volume. Apple skims the cream and leaves the cloud with the hard, expensive, low-margin agentic sludge. Every infra name in this post is priced on the cream. GUH. 💰 What Apple actually books (hurting others isn't a thesis) Memory upgrades are the highest-margin SKUs they sell. 96→256GB costs $2,000 for maybe $600–800 of DRAM. Local AI pushing attach rates up is a pure-margin mix shift on hardware they already build. First real reason to upgrade a Mac in a decade. "Your machine physically cannot run this" is the best upgrade pitch since Retina. AI distribution for ~$14B/yr while hyperscalers spend ~$700B. No fleet to amortize, no PPA against a 2032 forecast. One company brought a RAM upgrade to a capex fight — and might win, because when the demand curve disappoints, Apple has nothing to write down. The demand signal is in the tape: Studio delivery blew from 6 days to 6 weeks and Apple pulled every high-memory config because it sold out. That's not a thesis, that's a shipping estimate. ⚖️ The asymmetry Apple doesn't have to win. Apple has to make "adequate" free. Once every enterprise negotiation has a zero-marginal-cost mac studio sitting on a desk, pricing power dies whether anyone deploys it or not. Linux never took desktop share and permanently capped what Microsoft could charge for a server OS. And nobody can respond without self-harm: Nvidia lifting the 128GB cap shoots its own racks, AMD can't till 2027, neoclouds can't sell boxes (the box is the threat). Apple is the only player with nothing to cannibalize. 🩸 9. The bag-holder tier list: who's short token deflation Organizing principle: how many derivatives you sit from the token price. Every arrow is a place the error compounds — and the first two links are already snapping in public like a leveraged regard on margin-call day. token price → what labs can charge → GPU rental rates → neocloud collateral value → datacenter capex → power demand forecast → OKLO's valuation Tier 1 — sells tokens: MSFT/GOOGL are genuinely ambiguous (Copilot is fixed-price with inference as COGS — fixed price, falling cost; they may be accidentally long deflation , the genius idiots). The truly exposed entity is OpenAI — unshortable, with Stargate commitments underwritten against future token revenue during a 50x-per-tier collapse. The biggest story here is private. Tier 2 — rents the machines 🔴 MAXIMUM BAGGAGE. Transmission already visible: H100 spot toward $1.99/hr, rental rates down 50–70%. Token prices fall → rents follow → collateral shrinks → the debt doesn't. 🔴🔴 CRWV — the biggest bag in the market: Metric Value Total debt $21B+ (was Debt/equity 4.8–8.9x Interest as % of revenue ~25% Microsoft as % of revenue 62–67% 2025 GAAP net loss -$1.17B Debt due 2026 $4.2B ≈ cash + one quarter of revenue Planned capex $30–35B, needs more debt The debt is investment-grade off the customer's credit, not CoreWeave's — a synthetic Microsoft bond wearing a GPU costume, collateralized by hardware whose rents fell 50–70% before amortization started. Kerrisdale models GB200 EBIT near zero at realistic 4–5-year lives; Burry flags ~$176B of understated industry depreciation; Vera Rubin ships H2 2026 to pressure B200 values on schedule. The fair counter, and it's the crux of the whole tier: 96% take-or-pay revenue, $99.4B backlog, H100s rebooked at 95% of original pricing. If contracts hold and GPU life is really 5–6 years, this entire bear case dies and we pour one out. Everything else is downstream of that one fact. 🔴 NBIS — same model, smaller, priced for 206% growth and flawless execution, which historically always works out. 🔴 Miner pivots (IREN, APLD, CIFR, WULF) — your cousin rebranding from "crypto day trader" to "digital asset manager": same bags, new LinkedIn. Estimates say 5–7 GPU clouds survive consolidation; these are not the survivors. Honorable mention ORCL — levered AI landlord with a Stargate side quest. Tier 3 — sells the machines: NVDA 🟡 — do not short the king on this thesis. Training anchors demand, they own CUDA/HBM/NVLink, and the 128GB cap is a choice. Multiple compression maybe, earnings collapse no. Weakest leg; you'll get run over and I'll post your loss porn. AMD 🟠 — entire AI pitch is the commoditizing tier, as #2 where #1 owns the software, and they capped their own box until 2027: a company playing defense against itself. AVGO — the arms dealer that also sells to the other army (Baltra partner through 2031). The non-obvious long. Respect. Tier 4 — supplies the buildout: optical/networking (COHR, LITE, ALAB, CRDO, ANET), electrical/thermal (VRT, ETN, PWR, FIX) — the plumbing of the plumbing, two derivatives out. MU note: memory needs the buildout and the Apple thesis needs cheap memory — if you're long both you're accidentally flat, the most WSB outcome possible: winning so hard you're flat. Tier 5 — power ⚡: 🔴 OKLO/SMR/NNE — zero revenue, ~$50M/quarter burn, first commercial op late 2027 at a site not authorized to sell power to the grid , Meta's campus at first power ~2030, and a 14–18GW pipeline that's almost entirely non-binding — the legal force of a pinky promise written in crayon. Four derivatives deep on a falling price. The 🌈🐻's Mona Lisa. 🟠 GEV/BE (backlog built on the forecast), 🟡 CEG/VST/TLN (real revenue today), 🟢 regulated utilities — your dad's boomer dividend stocks are the most insulated thing in the stack. Dad was right. Tell no one. 🤔 The app layer: cheap tokens collapse costs and pricing power — for thin wrappers, the moat was access to expensive capability. If anyone can run a 57 for free, what is the $20/month AI writing tool selling? Wrappers get squeezed like a short at a gamma ramp. Cheap tokens are only good for you if tokens weren't the product. 🟢 Actually long token deflation: AAPL · AVGO · app-layer with real moats · every enterprise on Earth · anyone with fixed-price revenue and variable inference cost. ⚡ 10. Power sidebar: it's the plumbing, not the juice Local inference is not greener — Mac ~6 J/token vs ~1–2 for a batched GPU node. Don't argue it; you'll lose and I'll laugh. The argument is where the capital goes. A Mac plugs into a wall that already exists, is already paid for, and sits idle overnight — exactly when you'd run long agentic jobs. A datacenter plugs into a fantasy: greenfield generation, transmission, substations, multi-year interconnect queues, 20-year PPAs signed against projections. And these assets don't need demand to fall — they need growth to come in under forecast. A 20% miss in 2032 impairs capital committed in 2026, because a substation has no plan B. A GPU gets written down and repurposed. A reactor site doesn't. Nobody builds a reactor to power a desktop. 🚩 11. How I'm wrong (read this, paper hands) How the short dies: Jevons. Cheaper tokens → more tokens. Infra prints anyway and you spent six months being right about price and wrong about volume. The oldest death in this trade. Volume never leaves the datacenter. The price war is fought by cheap open weights on rented GPUs — tokens stay, only the revenue leaves. This is the honest ceiling of the whole argument, placed here before some smug reply guy finds it. Training never goes local. Frontier runs are the anchor tenant for the power deals. This thesis is inference-only. Capex is sunk. This impairs 2030+ returns on 2026–2028 vintage capital, not whether the money gets spent. The CoreWeave crux. $99.4B take-or-pay backlog and 95% rebooking are real. If GPU economic life is 5–6 years, Tier 2 is wrong. Everything else is downstream of this single fact. How the AAPL long dies: You can't deflate what you can't ship. Studio cut to one 96GB config, prices raised, memory relief not forecast before late 2027–2028. The weapon is out of stock. Long AAPL = short DRAM. Memory is a far bigger share of a Mac's BOM than HBM is of an H100's price. You are pairs-trading the memory cycle whether you like it or not. Tight supply past 2030 kills it. The install base is small-memory. Two billion devices running a 3B model is a price floor at the kiddie table. The 768GB machine that binds at the adult table doesn't exist yet. The trade is Apple owning an uncontested band it currently cannot supply — a bet on the DRAM cycle turning. Baltra. Apple's building its own server chip with Broadcom through 2031. The "no conflict of interest" argument expires in 2–3 years. Clock's ticking. Apple's AI execution is genuinely bad. Siri. Next question. (The thesis only needs great hardware + adequate software — their historical pattern — but it's a real blemish.) Nvidia can respond. The 128GB cap costs margin to lift, but it's available. 📋 TA;DR for regards Open weights are 4 points off the frontier and free 🏴☠️ The price war already started — the premium isn't dying, its half-life is (~12–18 months per tier) Most tokens are Clippy work billed at PhD prices Some data can NEVER leave the building — and it's exactly the data you want in a 1M context PCs = two buckets and a straw. Datacenters = 72 buckets in a trench coat. Apple = one pool, no cable, capacity is a dropdown menu The math: $\text{tok/s} \approx \frac{\text{bandwidth}}{\text{active bytes}}$. 768GB @ 4-bit = trillion-param frontier-tier on a desk at 50–100 tok/s 128GB→784GB is an empty band with a 25x price gap and ONE seller. Nvidia's version is $123K 💀 Apple isn't positioned for deflation — Apple IS the deflation , and it skims the cream first CRWV/NBIS/miners: levered long token prices with debt due first. OKLO: four derivatives deep on a falling price with a crayon order book NVDA: don't short it on this. MSFT: ambiguous. AVGO: hedged. MU: your accidental hedge. Dad's utilities: fine Power risk is interconnect, not consumption — assets die on a growth miss, not a demand fall This is a DRAM cycle trade during the worst memory shortage on record. Size accordingly, regard Not financial advice. I'm a guy on the internet with a spreadsheet and a concerning amount of free time. Long AAPL, long popcorn, short my own free time. This is the way. 🍿🤝 submitted by /u/asiammyself [link] [comments]
Alphabet Stock (GOOGL) Opinions on Q2 Earnings and Capex Concerns Quiver Quantitative
Alphabet Stock (GOOGL) Opinions on Q2 Earnings and Capex Concerns Quiver Quantitative
Bought 1dte 30 contracts of 350$ strike calls expiring today for about 0.09 cents apiece yesterday morning. Sold over the course of the day for about 10k total, with an average gain of 3600%. Had I timed the top perfectly, I could have made over 20k. Not a lot of money in the grand scheme of things, but definitely my most successful trade ever from a purely gain % standpoint. submitted by /u/medievalsteel2112 [link] [comments]
Bought 1dte 30 contracts of 350$ strike calls expiring today for about 0.09 cents apiece yesterday morning. Sold over the course of the day for about 10k total, with an average gain of 3600%. Had I timed the top perfectly, I could have made over 20k. Not a lot of money in the grand scheme of things, but definitely my most successful trade ever from a purely gain % standpoint. submitted by /u/medievalsteel2112 [link] [comments]
Although i lost all the gains I made since April, I got the 2500 I started with back by not selling. Also avoided the classic warsh rugpull. Im happy. I learned to use less money to win more than I was in the beginning, so if I can make 20k in 2 months as a greenhorn, then I can definitely do it again now that ive been witnessing warshs casino. Im happy submitted by /u/AncientAd3846 [link] [comments]
Although i lost all the gains I made since April, I got the 2500 I started with back by not selling. Also avoided the classic warsh rugpull. Im happy. I learned to use less money to win more than I was in the beginning, so if I can make 20k in 2 months as a greenhorn, then I can definitely do it again now that ive been witnessing warshs casino. Im happy submitted by /u/AncientAd3846 [link] [comments]
Why Retail Traders Couldn’t Take Their Eyes Off These Stocks Last Week: NVDA, GOOGL, ORCL, TSLA, INTC Yahoo Finance
Why Retail Traders Couldn’t Take Their Eyes Off These Stocks Last Week: NVDA, GOOGL, ORCL, TSLA, INTC Yahoo Finance
New weapon of choice? https://www.cmegroup.com/media-room/press-releases/2026/6/30/cme\_group\_to\_launchsinglestockfuturesonjuly27.html Standard Single Stock Futures (55 contracts) Contract size: 100 shares of underlying stock Tick: 0.01 index points = $1.00 Settlement: Financially settled Listing: Quarterly (Mar, Jun, Sep, Dec), 2 consecutive quarters Tickers: AAPL, ABBV, ADBE, AMAT, AMD, AMGN, AMZN, AVGO, BA, BAC, BKNG, BRKB, CAT, CMCSA, COP, COST, CRM, CSCO, CVX, DIS, GOOGL, HD, IBM, INTC, JNJ, JPM, KO, LLY, LMT, MA, MCD, META, MRK, MSFT, MU, NEM, NFLX, NVDA, ORCL, PANW, PEP, PFE, PG, PLD, PLTR, QCOM, SBUX, SPCX, TSLA, TXN, UNH, V, VZ, WMT, XOM Micro Single Stock Futures (22 contracts) Contract size: 10 shares of underlying stock Tick: 0.01 index points = $0.10 Settlement: Financially settled Listing: Quarterly (Mar, Jun, Sep, Dec), 2 consecutive quarters Tickers: AAPL, AMD, AMZN, AVGO, BA, BAC, CSCO, GOOGL, INTC, JPM, META, MSFT, MU, NEM, NFLX, NVDA, PFE, PLTR, SPCX, TSLA, WMT, XOM Note: The Micro list is a subset of the Standard list. All contracts are cash-settled, no physical delivery. Minimum margin is 15% of notional value. Covers stocks across S&P 500, Nasdaq-100, and Russell 1000. submitted by /u/WhenGeniusFail [link] [comments]
We recently saw GOOGL increased their CapEx coupled with negative FCF, triggering a massive selloff. It seems intuitive that MSFT and other hyperscalers will probably be increasing their CapEx too. But will they have negative free cash flow too? I am currently holding MSFT at $410 avg cost. If I could hold I would just hold long term but I could only hold for maybe about 5-6 months. Where do you see MSFT be in 5-6- months from now? submitted by /u/Several-Librarian-63 [link] [comments]
I need to thank everyone on this sub for showing all of their loss and gain porn here. I feel like I could really get fucked if I ever grew the balls to throw money away like some of you psychopaths, or maybe earn enough to make all my dreams come true, but seeing this stuff makes me really understand that and keep my portfolio in "safe spaces". I know that I just don't fucking get it and that's ok as long as I don't pretend to. Lol My first dip into stocks was in March 2020. Sure, I never made millions, but I put all my money into AAPL, GOOGL, and AMZN when they dropped before they all split and blew up. I earned enough to put a lot of money on an amazing home in a fantastic area. I didn't really gamble(I know it always is, but that was pretty much as surefire as you could get), so I didn't make the big bucks like I could have, but I also didn't lose everything and came away very much on top. I fucking love living vicariously through you guys. Wins, losses, or just ridiculous fucking memes. It satiates something in me I know would drive me to make stupid fucking decisions because I have no real clue what I'm doing. You guys are some of the most entertaining motherfuckers around. You troll, roast, and take Ls like no one I've ever seen. I fucking love you guys. submitted by /u/GruntledVeteran [link] [comments]
Wish me luck I bought 1000 shares of Google at $319.35, hopefully it goes to $399 in the next few months and I can take 80k profit, if not I will be holding for a while. submitted by /u/Past_One3442 [link] [comments]
Wish me luck I bought 1000 shares of Google at $319.35, hopefully it goes to $399 in the next few months and I can take 80k profit, if not I will be holding for a while. submitted by /u/Past_One3442 [link] [comments]
Listen folks, I'm not a tech bro and I know nothing about this AI stuff. I don't care how it works, it's just hype and buzzwords for me. However, I've never made so much easy money in my life and I wanted to share a few insights on how I did it. My strategy for the past 3 years has been to invest in a company as soon as they use the word AI in their brand. This has worked very well for me and a few of my friends across several stocks. For example, as soon as Intel has rebranded to "Built for AI", we knew that it was about to skyrocket, and sure enough, the green candles started to show up in the next weeks. It worked so well that I've created an index called the Frequency AI Ratio Tracker (FART). It's basically the amount of time the word "AI" is used on a company's homepage. For example, AMD currently has 60 occurrences of the word "AI" on their homepage for roughly 876 words in total, meaning a ratio of 6.85%. To put that in perspective: statistically, 1 out of every 15 words on AMD's homepage is "AI". That is a critical-level , enterprise-grade FART score and it was a no-brainer once I discovered that out. The higher the score is, the greater the potential was. However, for the first time in the past 3 years, I have a strange feeling that this strategy is no longer working and the momentum is starting to fade away. Looking at all the OG companies from the .com bubble, it sure looks like a peak to me, and I've actually started to use a reversed-FART strategy since July 4th. The higher the score is, the greater the risk of a downfall once the AI bubble pops. Some OG stocks The General Rule of Thumb for the reversed-FART stategy: 0%: Low risk 0.1% – 2%: Medium risk 2% – 5%: High risk > 5%: Extreme risk Here's the most recent compiled version of FART for some tech companies as of July 24th. ========================================================================== Name of company | Stock | Total Words | AI Mentions | FART Score ========================================================================== AMD | AMD | 876 | 60 | 6.85 % NVIDIA | NVDA | 2663 | 115 | 4.32 % Oracle | ORCL | 786 | 27 | 3.44 % ServiceNow | NOW | 651 | 22 | 3.38 % Qualcomm | QCOM | 765 | 24 | 3.14 % Arm Holdings | ARM | 862 | 26 | 3.02 % Cisco Systems | CSCO | 450 | 13 | 2.89 % SAP | SAP | 1004 | 25 | 2.49 % IBM | IBM | 560 | 11 | 1.96 % Micron Technology | MU | 635 | 12 | 1.89 % Microsoft | MSFT | 645 | 10 | 1.55 % Broadcom | AVGO | 462 | 7 | 1.52 % Salesforce | CRM | 936 | 14 | 1.50 % Samsung Electronics | 005930.KS| 375 | 5 | 1.33 % Meta Platforms | META | 303 | 4 | 1.32 % Alphabet (Google) | GOOGL | 422 | 4 | 0.95 % TSMC | TSM | 688 | 6 | 0.87 % Intel | INTC | 245 | 2 | 0.82 % Palantir | PLTR | 3151 | 23 | 0.73 % SK Hynix | 000660.KS| 167 | 1 | 0.60 % Amazon | AMZN | 355 | 2 | 0.56 % Applied Materials | AMAT | 209 | 1 | 0.48 % ASML | ASML | 596 | 1 | 0.17 % Corning | GLW | 576 | 1 | 0.17 % Apple | AAPL | 883 | 1 | 0.11 % Tesla | TSLA | 181 | 0 | 0.00 % Lam Research | LRCX | 275 | 0 | 0.00 % Legal notice: I am dumb and this is not financial advise. This was compiled by hand by copy-pasting in Microsoft Word like a fool and searching for the word AI. Consider compiling your own data and use FART at your own risk. I am not responsable for any loss based on this strategy. The level of risk estimated by FART was established out of thin air. Ask your financial adviser about FART. submitted by /u/SiropErableSucre [link] [comments]
The Discount On GOOGL Stock Looks Overdone Trefis
The Discount On GOOGL Stock Looks Overdone Trefis
Why Alphabet (GOOGL) Shares Are Getting Obliterated Today Yahoo Finance
Why Alphabet (GOOGL) Shares Are Getting Obliterated Today Yahoo Finance
I trusted GOOGL. Thank you for the attention to this matter! submitted by /u/Worth_Resolution1865 [link] [comments]
Stock Market Today: Dow Jones Index Falls 300 Points As Alphabet, Tesla Dive On Earnings News (Live Coverage) Investor's Business Daily
Google upped its capex forecast for 2026 yet again. These stocks are benefiting. (GOOGL:NASDAQ) Seeking Alpha
Google upped its capex forecast for 2026 yet again. These stocks are benefiting. (GOOGL:NASDAQ) Seeking Alpha
Stock Market Today: Dow Jones, S&P 500 Futures Slip As GOOGL, Elon Musk's TSLA Drag After Q2 Earnings—Int Benzinga
Stock Market Today: Dow Jones, S&P 500 Futures Slip As GOOGL, Elon Musk's TSLA Drag After Q2 Earnings—Int Benzinga
Biggest stock movers Thursday: TSLA, GOOG, NOW, and more (NASDAQ:GOOGL) Seeking Alpha
Biggest stock movers Thursday: TSLA, GOOG, NOW, and more (NASDAQ:GOOGL) Seeking Alpha
alphabet spent billions on AI just to make Google Finance objectively worse. As we can see, thats the only reason $GOOGL closed red yesterday and will open red today! ———- ps. but for real, UX so bad it makes you question whether anyone actually tested the app before forcing onto end user. submitted by /u/KARALISinc [link] [comments]
alphabet spent billions on AI just to make Google Finance objectively worse. As we can see, thats the only reason $GOOGL closed red yesterday and will open red today! ———- ps. but for real, UX so bad it makes you question whether anyone actually tested the app before forcing onto end user. submitted by /u/KARALISinc [link] [comments]
Big Afternoon for Q2 Earnings: GOOGL, TSLA & More Yahoo Finance
Big Afternoon for Q2 Earnings: GOOGL, TSLA & More Yahoo Finance
After-Hours Stock Movers: GOOGL, TSLA, NOW, IBM, TXN, MOH, URI Investing.com
GOOGL: GOOGL Stock Delivers Like The Leader, Priced Like The Laggard - Trefis (contagion_watch, score 0.64)
GOOGL: Alphabet Stock (GOOGL) Opinions on Recent Earnings and AI Outlook - Quiver Quantitative (early_signal, score 0.52)
GOOGL: Why i think AAOI will moon this coming week (early_signal, score 0.66)
GOOGL: Why i think AAOI will moon this coming week (contagion_watch, score 0.66)
GOOGL: MSFT capex should I hold? (early_signal, score 0.61)
GOOGL: Frequency AI Ratio Tracker (FART) stategy (early_signal, score 0.61)
GOOGL: Why Alphabet (GOOGL) Shares Are Getting Obliterated Today - Yahoo Finance (early_signal, score 0.50)
GOOGL: Y'all have a nice day! (early_signal, score 0.49)
GOOGL: Big Afternoon for Q2 Earnings: GOOGL, TSLA & More - Yahoo Finance (early_signal, score 0.60)
GOOGL: GOOGL earnings tonight. Good idea to go long on AVGO? (early_signal, score 0.52)