Welcome back to the AI Geekly, by Brodie Woods. This week we’re trying a shorter format. Let us know what you think via the poll (it supports comments). Unfamiliar terms written in green are defined in the Glossary at the end of the note.
TL;DR BBERG AI Earnings Tool; Visionary Pro’s Hot Launch
This week, we’re taking the Hemingway approach. Short. Sweet. Lots of daquiris… Actually, kidding aside, we’re crafting a shelf piece that we believe readers will enjoy: An Introduction to AI / AI 101 Report. The report will serve as a kind of Guide for the Perplexed, helping readers build a foundational understanding of AI technology as it stands in early 2024 (and expanding it for those already well-versed). Among other things, this will provide a brief history, and then dive into explanations of key concepts including neural nets, transformer architecture, machine learning, parallel processing, key players, landscape and limitations.
Bloomberg Will Now Summarize Earnings Calls
No, not the mayor, the company, using AI
What it is: This week, dominant financial services data and terminal provider Bloomberg (BBG) announced the launch of AI-powered earnings call summaries. Bloomberg has been a leader and first-mover on AI in its peer group, introducing its Bloomberg-GPT model last March.
What it means: While earnings call transcripts have been somewhat ubiquitous over the past decade (FactSet, Refinitiv, and BBG all provide them), they are traditionally human generated, delayed considerably, error prone, and limited —it’s literally just a transcript.
Why it matters: Bloomberg’s new tool is laying the foundation for its AI as a key differentiator amongst market players. For now, those with access to a BBG terminal can more quickly analyze earnings calls and extract insights with lower latency than peers. If select players can consistently process tradeable insights either more rapidly or predictively (por que no los dos?), they could outpace the rest of the market. The result? Considerable advantage to them, but higher costs for others. As we well know from the success of the so-called Flash Boys, latency in trading and investment decisions is the difference between being a price setter and a price taker.
The Apple Doesn’t Fall Far from the Tree
AAPL’s foray into spatial computing meets early success
What it is: Apple opened pre-orders for its new spatial computing hardware, the Vision Pro (VP) on Jan 19th, with availability starting Feb 2. Priced at an obscene $3,499 (just for the 128GB model), the headset is a pricy introduction to AR/VR (XR). It’s also the most advanced piece of consumer hardware ever made, so there’s that too. Did we mention they sold an estimated 160k-180k VPs in the first three days? That’s 45% of analyst estimates for the year.
What it means: We’ve discussed our convergence thesis in prior Geeklies. We contend that spatial computing, robotics and modern AI are set to become more deeply intertwined this year. We expect to see Vision Pro used to control and train robots using artificial objects and even environments generated in XR, perhaps via VisionOS partner Unity’s rich asset library and advanced PolySpatial technology. Picture Stanford’s ALOHA and NYU’s Dobb·E robots being trained on objects and environments that are fully synthetic using VP. One could easily spin-up environments and objects to simulate a host of situations: search and rescue, medical, corporate, transportation, and more.
Final Thoughts: While Netflix, Google, and Spotify have so far elected not to produce apps for the VP, going so far as to release formal PRs, we’ve seen this movie before. Over the next 2-3 years, we wouldn’t be surprised to see AAPL break the backs of these laggards as its spacial computing offerings become more ubiquitous. There were smartphones before the iPhone, but Apple has a history of usurping established players in a fledgling market. That’s precisely what we see here.
Shelf piece: A report or document meant to provide deep foundational knowledge, as an introduction to a given topic.
The Guide for the Perplexed: a 12th century book by Moses Maimonides reconciling Aristotelean logic with Rabbinic Jewish Theology.
Neural nets: Brain-inspired AI that learns by adjusting connections between "neurons."
Transformer architecture: A top AI architecture for natural language processing tasks (and recently expanded to other domains).
Machine learning: AI that learns patterns in data to make predictions and decisions.
Parallel processing: Using multiple processors simultaneously for faster computing. Applying parallel computing to AI workloads using Nvidia’s GPU technology was a watershed moment in the development of modern AI.
Flash Boys: Eponymous book about high-frequency trading, a type of trading that takes advantage of very small differences in the price of securities.
Price setters: Players with an advantage wielding market power to influence and potentially control prices.
Price takers: Smaller players accepting the market's price due to lack of power/leverage or due to some other relative disadvantage.
Spatial Computing; XR: Overlaying and interacting with digital information onto the physical world through AR/VR technology.
AR (Augmented Reality): Superimposes digital elements onto the real world, enhancing your perception with virtual information. Think Pokemon Go but for everything!
VR (Virtual Reality): Creates a fully immersive computer-generated environment, transporting you to another world or experience. Escape reality with a VR headset!
VisionOS: Mixed reality operating system designed for intuitive interaction and seamless switching between real and virtual experiences, all orchestrated through eye, hand, and voice commands.
Before you go… We have one quick question for you:
With over 18 years of capital markets experience as a publishing equities analyst, an investment banker, a CTO, and an AI Strategist leading North American banks and boutiques, I bring a unique perspective to the AI Geekly. This viewpoint is informed by participation in two decades of capital market cycles from the front lines; publication of in-depth research for institutional audiences based on proprietary financial models; execution of hundreds of M&A and financing transactions; leadership roles in planning, implementing, and maintaining of the tech stack for a broker dealer; and, most recently, heading the AI strategy for the Capital Markets division of the eighth-largest commercial bank in North America.