In the technology sector, if you aren’t innovating, you’re falling behind. This is no different for Apple, who is not used to being behind on features. Generally, Apple waits until they’ve perfected a technology before introducing it to the public. Recently, this isn’t the case when we consider the cutbacks of the Apple Vision Pro, and this past week, AI features.
Famous Apple watcher, Mark Gurman, who is usually correct on Apple predictions published a scathing blog post about Apple falling behind with respect to LLMs and AI as a whole in his Daring Fireball post entitled, “Something is Rotten in the State of Cupertino”.
I don’t normally share clips from podcasts I listen to, but in this case, it’s well worth it. On Intelligent Machines, Episode 808, the creator of Mathematica and founder of Wolfram Alpha, Stephen Wolfram, shares his views on how he sees AI progressing from here.
He brilliantly discusses how AI will augment humans, not completely replace them in the workforce (something I’ve been advocating for a while); including why AGI is not what we think of it today.
We’re well in the middle of earnings season, but something stood out to me regarding a firm in particular. UPS revealed a significant shift in its strategy: a reduction in its delivery volume for Amazon by 50% by the end of next year. This move, while surprising to some, is a calculated step aimed at improving UPS’s profitability and streamlining its operations. Let’s delve into the reasons behind this decision and what it means for the future of last-mile delivery.
One of my goals going into 2025 was to talk less and listen more. In this goal, I’m attempting to become more knowledgeable and a more well-rounded person. Self-improvement is important to me for my personal and professional lives. In this endeavor, I’ve been reading and listening to more eBooks and audiobooks, respectfully. You’ll see me post more about what I read and less about what I believe.
The next titles I plan on consuming (in no particular order) are as follows:
DeepSeek, a Chinese AI startup, has rapidly become a major disruptor in the AI landscape with its new AI model, R1. This model has gained global attention for its ability to compete with models like OpenAI’s ChatGPT, but at a significantly lower cost. The emergence of DeepSeek has caused ripples across the tech industry, impacting stock markets and sparking debates about data privacy and the future of AI development.
DeepSeek was founded in mid-2023 by Liang Wenfeng, a Chinese hedge fund manager.
Yes! You heard it here first. Like all of you, I was excited for Instagram when it first hit the scene back in late 2010 and still had my original account from that time. Meta (formerly Facebook) famously purchased the business for $1 billion and successfully integrated it into its ad network and social graph, but I'm not here to relive or debate history -- we can save the positives and negatives for another post.
LLMs, such as GPT-3.5 & 4 developed by OpenAI, possess impressive language processing capabilities. However, despite their remarkable abilities, LLMs are not poised to replace human workers. In this blog post, we will explore how LLMs will augment employment rather than supplant it, providing evidence to support this claim.
Contrary to the doomsday predictions of job losses due to automation, LLMs are not designed to replace human workers entirely. These machines excel at processing and generating human-like text, but they lack the cognitive abilities, creativity, and emotional intelligence that make human workers invaluable.
Earlier this morning before the bell, Kellogg announced that it would be splitting itself into three-separate tax-free spinoffs: the slow growing cereal business, a snacking business, and an unnamed plant-based food business mainly consisting of Morningstar Farms. In the press release, the company said splitting these businesses will unlock shareholder value. During the past year, we've seen a number of larger, slower-growing business attempt to divest to grow.
Kellogg itself was unlikely to be sold due to its slowly growing cereal business, such as what Post and General Mills have previously announced.
Yesterday, I was reminded on LinkedIn that yesterday in 2001, the State Department notified the Russian government that four of its diplomats were considered persona non-grata and immediately jetted from the country (National Counterintelligence and Security Center, 2022). This is an older piece I wrote on Robert Hanssen, in particular.
During the transition period from the Soviet Union into the Russian Federation, an FBI agent, Robert Hanssen was handing over top secret, and code word clearance intelligence to the Russian authorities at the KGB and its successor, the SVR (FBI, 2017).
Much like your own investment portfolio, companies continually must diversify to negate the ebbs and flows of their business segments. Up until 2001, Apple, then still called Apple Computer, was primarily a hardware company. The iPod was released and forever changed how the music industry conducted business. In 2007, Apple then released the iPhone, knowing it would cannibalize it's own iPod dominance in the market Followed by the inclusion of the App Store, then eventually iPads would see the light of day, all while still selling their signature computers to "
Ever since the release of the iPhone in 2007, Apple has designed and fabricated its own chips for its own devices. At the time, "owning the supply chain" or the vertical, was the way of controlling the full stack of hardware down to software of the manufacturing and distribution process. Since then, we have seen the practice known as economies of scale, for Apple to make more revenue on each phone sold.
Back in college we all had access to those often bulky, hard to use research databases, that sometimes worked, but often steered us in the wrong direction. We had to teach ourselves Boolean operators to properly navigate them. There was a reason other than torture for utilizing those; to help us all find primary source materials to write our research papers.
As is often the case, that's about the only time we used academia, raw data and studies to conduct our analysis on any given topic.