Data #1
Mortgage Debt 12x The Size of Credit Cards
Why does this data matter?
We bring up this statistic because the media consistently highlights the increase in credit card debt. While this is indeed something to monitor, we've previously discussed this metric and compared it to the simultaneous rise in net worth. However, what's more pertinent today is that mortgage debt constitutes the majority of consumer debt in the US, accounting for over 70%. Therefore, this metric holds considerable importance in relative terms. Here's another statistic to consider: the 90+ day delinquency rate in mortgages currently stands at 0.57%, in stark contrast to the 3.93% delinquency rate observed from 2009 to 2019. Not only is mortgage debt substantial compared to other credit segments, but it's also demonstrating exceptional performance.
Data #2
Data Says Twitter Users Sharply Lower
Why does this data matter?
We rely on data to inform our decisions and assess the viability of a company or product. Alternative metrics such as active users or downloads have traditionally shown strong correlations with public company disclosures. Recent statistics for Twitter (X) indicate that daily active users have decreased by over 20% since Musk assumed leadership, presenting a significant disparity compared to its peers. While the company has contested these findings with internal data, we find it challenging to give credence to these claims, especially considering they stand out as one of the few companies in our tracking with such a wide gap between alternative data and internal figures. We are open to ideas, and think maybe they now have a different definition of active users? Maybe just maybe that is the reason.
Data #3
AI Survey Findings
Why does this data matter?
We recently reviewed a survey of CTOs and executives to gauge their views and usage trends regarding AI. We highlight three key takeaways that diverge from conventional wisdom. What we do know is that there is significant investor demand for GPU-related companies like NVIDIA. However, bullet points #3 and #4 reveal that companies are exploring alternative methods to operate without GPUs and are finding that using CPUs for pre-trained data (typically done with GPUs) can still yield results at manageable costs. Additionally, point #5 underscores the critical role of on-premises data centers in AI and emphasizes the importance of analyzing and visualizing data within one's environment. The overarching message is that while many companies benefiting from the AI boost are legitimate, this survey should prompt a reevaluation of some of those prevailing assumptions.
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