i am heavy Cyber stocks & do think in 2026 there will be doubts about Cyber- especially around disruption by AI (like other SaaS) If you know what you own, you will probably make a lot of money.
Invest in what you know. Give me sale!
$PANW $NET $CRWD
if you have the ability to identify if a stock price action is irrelevant to the business & avoid noise, it lets you place your bets early so that you don’t have to FOMO chase
& vice versa ie dont have to bag hold and be caught pants down!
I wish $UBER would give more details around their AI strategy- not internal AI usage but like AI as their product. Uber has a HUGE opportunity there (eg I heard $META is using a lot of employees for internal data labeling)
$UBER here is cheap but I like $META more. Uber has a great CEO, brand & cash flow but I don’t think they have a strong moat. I think Uber outperforms market in the next 5 years.
$GOOGL is the best business on earth. I can’t think of another business with the same impact, leadership, moat, optionality …
This isn’t an investment comment- but a general observation.
$GOOGL announcing a $80B capital raise is a big moment for the industry and quite a shocking one. It means the limits of spending on compute by the hyperscaler are no longer their cash flows + bond issuance capabilities (to still keep an IG status). The limit is now the market and the market sentiment.
There are a few possible options why $GOOGL went this path:
1. Option 1 : They see another big step up in accelerating demand for GCP and their Gemini models and need to increase CapEx substantially more than they already did.
2. Option 2: They want to front-run OpenAI, SpaceX, and Anthropic IPOs and drive some liquidity out of the market, and with the cash, lock up more of the semi supply chain for them to not be locked out by OAI or Anthropic.
3. Option 3: They have reached an internal breakout in their model development or some other front, and they see they are going to need a lot more compute to serve this and want to get ahead of it while the market is still sentiment positive on it.
$META is probably getting to that “load the boat” territory
Instagram as addictive as ever. I absolutely trust Zuckerberg to navigate the ship here. Maybe Meta Cloud will be the play for them… if they put out a cloud business the stock is up 20% that day
Nvidia's Much-Anticipated, Reportedly Upcoming N1X / Windows PC Processor: Supply Chain Checks and Key Takeaways
▌Supply chain checks point to around 10M shipments of N1X-based devices over the next two years.
➡ Still a niche market, aimed at power users who need on-device AI compute.
➡ Whether shipments get revised up will come down to price, but mainly to whether Windows can deliver apps and workflows that truly orchestrate on-device AI compute.
▌Today, the main ways people use AI on a PC (both Windows and Mac) are accessing cloud LLM services through a browser and calling LLMs via API to consume a cloud provider's compute / tokens:
➡ In both cases, the core AI compute happens in the cloud, not on the device.
▌So far in 2026, the two hottest stories in the PC market have had almost nothing to do with on-device AI compute:
➡ Strong MacBook Neo sales. My industry checks suggest 2026 shipments of Neo models were revised up by roughly 100% (5M → 10M). Buyers are paying for price, design, and ecosystem, not for on-device AI compute.
➡ Cheap mini PCs, still niche, are drawing a lot of attention because they can run AI agents (like OpenClaw) around the clock (e.g., Mac mini). These agents also run inference in the cloud.
➡ Bottom line: neither the sales nor the buzz has much to do with on-device AI compute.
▌The key to on-device AI driving an upgrade cycle is the operating system (OS):
➡ What really sets on-device AI apart from the cloud is its ability to deeply integrate a user's data and workflows across apps while keeping things private. But that needs OS support.
➡ AI in today's PC OS is still mostly about adding AI features to first-party apps and loosely connecting workflows across apps.
➡ Some apps already make good use of on-device AI compute, like speech-to-text, but not enough to drive meaningful upgrade demand.
▌The N1X devices could give AI power users another solid option:
➡ Thanks to the N1X, device makers can strike a better new balance across AI compute, memory, design, and portability.
➡ For power users running LLMs on-device, an N1X device is a solid alternative to the Mac when it comes to capable on-device AI compute and large memory.
➡ But if the goal is a real upgrade cycle, then beyond price, OS support (Windows) is still what matters.