A few thoughts on the $META AI model's progress, because I think it is significant. 1. It does seem that $META has now leapfrogged $GOOGL in model quality when it comes to Muse 1.2 for many use cases, which is very surprising given the timeframe. 2. This is still the "Muse Spark" family of models; $META already told us that bigger and more capable models (Watermelon) are coming. Given Muse Spark 1.2's performance already, the Watermelon family should be in the Fable category, which is impressive. 3. It does seem like $META is finally on a product scaling velocity curve (so the scaling foundations of their lab are set after 1 year of overhaul). And the shipping velocity is very good (3 releases in 4 months). 4. Given the recent rumored price hike of DeepSeek (if you use DeepSeek directly), it is clear that having enough compute to serve customers is critical. It doesn't help you if you have a great model, but most can't use it. $META is one of the few companies with compute capabilities comparable to, if not larger than, those of Anthropic or OpenAI.

Interesting with $META, based on the already guided CapEx for this year they are probably going to end 2027 with 7-10GWs of AI data center capacity. They could rent out 2-3GWs of that capacity, that would boost their net income by around $15-$30B (+20-40% of net income).

A new inference optimized hardware. They won’t have enough $TSM allocation to make serious market share gains in the near term, but this could shake the semis complex a lot. Strong backers. Looks like a complex build. Can help with the HBM bottleneck.

I just published my article on how hyperscalers like $AMZN, $MSFT, and $GOOGL will benefit in the token optimization era and why I believe the companies will re-rate much higher.

Hyperscalers $AMZN $GOOGL and $MSFT are about to be the main beneficiaries of the token optimization trend. They get to squeez more revenue (tokens) from their existing infrastructure + they become the default orchestration layer for managing models, fine-tuning etc.

$META is announcing a new set of smartglasses at $299. It won't have a screen, but will include a camera and personal speakers with Meta AI. A great launch moment and a very accessible price point for people to try. If the AI is good enough, people will get hooked on it.

Didn't think I would see $MSFT trading at a 20x forward P/E again in the $MSFT "Azure era" now coming at the April war lows. The interesting part is that growth for Azure looks as good as it has ever been (+40%) with a massive backlog and severe global compute shortage. On the software part, there are no real signs of companies canceling Office licenses (despite the fears around it).

A MUST-read interview with a Siemens employee explaining just how high demand is for energy equipment right now because of AI: 1. The whole situation is shocking even for people who have been in the business for 40 years. They are getting orders that are double the size of what their entire factory can produce in a year. 2. Demand is so high in the last 5-8 months that they don't need to convince or send any analysis (such as CO2 emissions, etc.) to clients because they just want the equipment, because there's so much backlog that they just want to catch the order. 3. Decisions are being made very quickly by clients; the backlog for some of the energy equipment companies is 5-6 years. For transformers, the situation is even more difficult. 4. He mentions that right now, data center builders do not care about sustainability; they just want power at any expense, reliable power. They say they will think about sustainability later. 5. The orders have gone from previous 20-30 MW orders to now 200-500 MW units. Customers have previously wanted to get equipment from different OEMs, but now they prefer an integrated standardized solution. 6. An interesting dynamic is that even though the data center requires 100 MW, the builders are buying N+1 units of gas turbines (so more than just for 100 MW) as backups, as well as having more energy capacity, as they believe they will continue to grow that data center. 7. He does believe there is some double booking going on on transformers and switchgears because of extra-long lead times. 8. Everyone is trying to reduce PUE, and water use effectiveness, but even after improving, they just use the same power to run more compute. 9. The problem is also liquid cooling, as it is expensive, and water availability in many regions is a problem. 10. Margins on equipment in the sector have gone from 4-6%, where they were 2-3 years ago, to 20-23% and in some cases even 40%. The data center builders know the margins are high, but they are fine with it because they just want to get it. found on @AlphaSenseInc

The market reaction of punishing $META because of the increase in CapEx will turn out to be very wrong IMO. In the $META Q325 earnings call, Zuck already explained well that if $META overbuilds their compute infrastructure for internal needs, they can sell it to external parties. We are in one of the biggest compute demand/supply imbalances that seems to be getting worse, not better. $GOOGL GCP CEO Kurian expects demand to be bigger than supply for 10 years. $META's enormous AI compute capabilities will either return ROI in the form of their products (which they are already showing) or/and give great ROI by selling compute to external companies. $META investing in AI data center compute is a big asset, not a cash burn, similar to what metaverse investments were in large. The market is viewing it as a negative, instead of an enormous strategic advantage in a compute-constrained world.