nb2sy

BREAKING 🚨: Walmart $WMT Timberrrrrrrrrrrrrr 📉📉📉

The FOMO in semis is palpable, and investor positioning is now heavily crowded in that space. At the same time, the sentiment is negative on hyperscalers, because of concerns regarding their CapEx spend on semis and questions on the ROI of that spend. But the catch is that if hyperscalers don't see good returns on their AI CapEx (semi spend), there won't be sustainable demand for semis, like the valuations and margins of many of these stocks are now pricing. So both can't be true. Either semis valuations have gone too far, or hyperscalers are too low.

Hearing Northland is downgrading $INTC: Summary Downgrading $INTC on valuation. INTC is making measurable progress in its turnaround, and we expect estimates to rise as demand for server CPUs picks up. However, we are modeling overall datacenter spending to decline in CY27 as hyperscalers become increasingly cash-strapped. Assuming INTC’s DC business grows by 40% in CY27, we get to an estimate of $3.20 and a P/E multiple of 38x the out-year. Even under this optimistic scenario, shares are expensive. We suspend our price target.

UBS is raising their Micron forecast by 200% at the same time that DeepSeek has proven techniques that dramatically drop KV Cache needs. Exhibit 1. After years of “20% higher forecasts” they are racing to catch up to price action. Exhibit 2. Higher competition, higher input costs, algorithmic breakthroughs and long dated liability-style receivables are late-stage shortage indicators leading to early-stage gluts.

AI “trainers” for finance are getting up to $25k / day to teach AI applications Useful application of AI for pod shops and other analysts = instant model updates based on live earnings call transcripts: “Sinisterra, 30, then walked the class through how to scan transcripts from earnings calls with OpenAI's ChatGPT and Anthropic's Claude to find the most market-moving statements. The machine ran sentiment analysis and translated management’s spoken remarks into numerical spreadsheet inputs to forecast future financials. Participants could see how AI could help streamline some of the most labor-intensive parts of their jobs.” Wild how fast AI is changing the landscape

Finally, a big name has the courage to tell it: we are nowhere near AGI. Demis Hassabis, CEO of Google DeepMind and Nobel laureate for AlphaFold, put it neat and clear: "Today's systems are nowhere near [AGI]. Doesn't matter how many Erdős problems you solve… I think it's far, far from what a true invention, or someone like Ramanujan, would have been able to do." This is the elephant in the room that many AI enthusiasts prefer not to see, or are actively trying to hide. Erdős problems are well defined, often combinatorial, on finite spaces. They are exactly the kind of problems on which current AI can achieve spectacular performance with a lot of compute and knowledge. A neural network can search a huge graph of possibilities. It can recombine existing knowledge at unprecedented scale. It can discover surprising solutions inside an already defined conceptual space. But true invention is something else. True invention is not only solving a problem. It is inventing new objects, new dimensions, new connections. It is inventing new problems. From resolving to inventing there is a discontinuity that we don't know how to bridge. We are making extraordinary tools. But we are nowhere close to AGI.