Amazon uses AI technology throughout its core e-commerce business. That includes customer product recommendations, search relevancy, supply chain management, fraud detection, image recognition and customer service. Amazon AI strategy appears to focus heavily on utilizing computer algorithms to drive more cloud computing usage in the coming 12 to 18 months. AI could unlock new potential cloud workflows and help re-accelerate cloud demand. AI could start to drive greater revenue to cloud as IT spending on innovation projects resumes and ramps. Cloud customers will look at generative AI to create new customer experiences that result in greater revenue opportunities.
A large part of Amazon’s AI strategy is focused on the use of natural language processing (NLP). NLP is a machine learning technology that gives computers the ability to interpret, manipulate and comprehend human language. Organizations today have large volumes of voice and text data from various communication channels like emails, text messages, social media newsfeeds, video, audio and more. They use NLP software to automatically process this data, analyze the intent or sentiment in the message, and respond in real time to human communication. Finally, beyond Alexa devices, it's not clear whether Amazon has an intuitive means of distributing a generative AI to get directly in front of users in a more visible way.
Apple’s strategic AI initiative appears to be centered around acquiring a slew of AI startups, including WaveOne, Drive.ai and Xnor.ai. Apple recently snapped up U.S.-based WaveOne, which was developing AI algorithms for compressing video. WaveOne's main innovation was a "content-aware" video compression and decompression algorithm that could run on the AI accelerators built into many phones and an increasing number of PCs. Analysts expect more direct processing on smartphones rather than remotely via a PC or laptop to access the Internet. This trend favors Apple with its 1.5 billion active iPhone users.
Phones have graphic processing units (GPU) built into them for powering things like Face ID and advanced web applications that all transfer well over to AI. Apple is working on an artificial intelligence-powered health coaching service and new technology for tracking emotions, its latest attempt to lock in users with health and wellness features. The new coaching service — code-named Quartz — is designed to keep users motivated to exercise, improve eating habits and help them sleep better. The idea is to use AI and data from an Apple Watch to make suggestions and create coaching programs tailored to specific users.
One area that Apple appears to be trailing the pack is in the development of AI-focused chatbots. As Microsoft Corp., Alphabet Inc., and — now — Amazon.com Inc. blaze ahead in the race to deploy advanced chatbots like Microsoft’s ChatGPT, one rival remains nowhere to be seen. Apple’s Siri voice assistant, using speech recognition and machine learning to understand a request and execute a solution, was released 12 years ago. At this point it seems that Apple’s strategy in AI is more focused on the iWatch and iPhone, opposed to expanding the AI capabilities of the Siri voice assistant.
Meta Platforms Inc., the Facebook parent, began its full-scale AI research in 2013 and is among various companies having the most studies published. Much of Meta’s strategic initiative in AI is focused on improving an ad’s effectiveness, partly by suggesting to the advertiser the tools to use to make it. Meta’s new AI technology will also let advertisers create multiple images that work for different audiences, helping to save on time and cost.
Despite the early start, Meta seems to still have fallen behind in adopting the hardware and software to support its AI ambitions. Meta, which also owns Instagram and WhatsApp, is now trying to grow its AI prowess amid ChatGPT’s meteoric rise. In February, Meta announced Large Language Model Meta AI (LLaMA) that it would use in its chatbots and other tools to compete with rivals Google and Microsoft. Part of the reason for Meta’s slow start is the reliance on custom propriety inference chips. Opposed to adopting GPUs — which can help speed up tasks, cutting down the time needed to process vast amounts of data and making it a fit for certain AI projects — Meta has been increasing its capital expenditures at a rate of $4 billion per quarter through 2022, in large part to beef up its AI capacity.
As the AI solutions “Arms Race” heats up between the likes of Microsoft, Google, Amazon, Apple and Meta, it has resulted in an increase in investor enthusiasm for AI chip makers that can produce microprocessors to handle tasks used in robotics, smart homes, and audio-visuals. The microprocessor acts as the brains of any system. GPU maker Nvidia is a top AI chip producer in an otherwise fragmented market. Other leading AI chip producers include: Alphabet’s ASIC “Tensor Processing Unit,” Apple’s “A11 and A12 Bionic” CPUs, Intel’s “Xeon Family” CPUs, AMD “EPYC” CPUs and “Radeon Instinct” GPUs, along with leading FPGA vendors, such as Xilinx’s “Versal.” Further, both QuickLogic and Lattice Semiconductor are creating compelling solutions for industrial AI applications. ABI Research, a global tech market advisory firm, estimates that the edge AI chipset market will grow from USD $2.6 billion in 2019 to $7.6 billion by 2024, with no vendor commanding more than 40% of the market.
Since the Internet’s 1990s explosion, is AI the next “big thing” in Technology? Based on all the buzz from companies with AI exposure, you would think so. Even Wall Street fund managers are smelling gold as AI exchange-traded funds (ETF) are popping up daily. These ETFs include: the Global X Robotics & Artificial Intelligence ETF, ROBO Global Robotics and Automation Index ETF, as well as the iShares Robotics and Artificial Intelligence ETF fund.
Despite all the hype, the biggest question that remains is: how can AI generate additional profits for the tech and chip companies? At this point, much of the foundational structure in AI is developmental. The types of applications that AI can be used for are still being constructed. No doubt AI can create more efficiencies about specific tasks increasing productivity, but directly monetizing a particular AI application may be tricky.
At this point, I would expect investors to zero in on the tech and chip players who they believe have a leg up on the competition. But, with little color on how these AI applications will directly impact each Tech company’s profits today, it remains difficult to clarify the exact tangible impact for tomorrow.
Daniel Morgan, Senior Portfolio Manager