Artificial Intelligence (AI) is an umbrella term that denotes a series of programs and algorithms designed to mimic human intelligence and perform cognitive tasks efficiently with little to no human intervention. Reinforcement through Machine Learning (ML) changes the game by enabling the models and algorithms to keep evolving based on outcomes.
Unlike other next-big things, such as nuclear fusion, quantum computing, and flying cars, which are practically (and literally) pies in the sky, AI has been around for quite some time, influencing how we shop, drive, date, entertain ourselves, manage our finances, take care of our health, and much more.
However, the technology came into the limelight late last year with the release of ChatGPT, which in its own description, is “an AI-powered chatbot developed by OpenAI, based on the GPT (Generative Pretrained Transformer) language model. It uses deep learning techniques to generate human-like responses to text inputs in a conversational manner.”
The easily accessible chatbot, believed to be capable of eventually disrupting how humans interact with computers and changing how information is retrieved, took the world by storm by signing up 1 million users in five days and amassing 100 million monthly active users only two months into its launch. To put this in context, TikTok, the erstwhile fastest-growing app, took nine months to reach 100 million users.
ChatGPT is one of the several use cases of generative AI, the subset of algorithms that creates and returns content, such as human-like text, images, and videos, on the basis of written instructions (prompts) provided by the user.
Including this subset, AI in its various forms and applications is capable of analyzing large volumes of data generated during the entire course of our increasingly digital existence and identifying trends and exceptions to help us develop better insights and make more effective decisions.
Given its massive importance, it’s hardly surprising that Zion Market Research forecasts the global AI industry to grow to $422.37 billion by 2028. Hence, this field has understandably garnered massive attention from investors who are reluctant to miss the bus on such a watershed development in the history of humankind.
Although OpenAI, the creator of ChatGPT, is not a publicly listed company, Microsoft Corporation (MSFT) has bet big on the company with a multiyear, multibillion-dollar investment deal. CEO Satya Nadella discussed, at the World Economic Forum held in Davos this year, how the underlying technology would eventually be ubiquitous across MSFT’s products. The process has already begun with updates to its Bing search engine.
MSFT’s rival, Alphabet Inc. (GOOGL), is in hot pursuit. With AI-enabled technology ubiquitous across its platforms, the company has unveiled its response to ChatGPT, called BardAI, with which the company is eager to reclaim its reputation as an early bird in the domain of conversational AI.
However, more recently, the company which made headlines when its stock got its moonshot due to the widespread public interest in AI is NVIDIA Corporation (NVDA). Post its earnings release on May 24, the Santa Clara-based graphics chip maker has stolen the thunder by becoming the first semiconductor company to hit, albeit briefly, a valuation of $1 trillion.
NVDA’s A100 chips, which are powering LLMs like ChatGPT, have become indispensable for Silicon Valley tech giants. To put things into context, the supercomputer behind OpenAI’s ChatGPT needed 10,000 of Nvidia’s famous chips. With each chip costing $10,000, a single algorithm that’s fast becoming ubiquitous is powered by semiconductors worth $100 million.
Notwithstanding all the transformative qualities of AI, investors, who poured a record $8.5 billion of cash into tech funds last week, would be wise to be aware of the limitations and loopholes of investing in technology before FOMO drives them to inflate a "baby bubble" growing in plain sight.
While the technology is powerful (and useful, unlike most cryptocurrencies), the adoption is fast becoming so widespread that it remains unclear how it could help a specific business differentiate itself by developing enduring competitive advantages (read moats) and generating consistent profitability.
Moreover, LLM-based generative AI chatbots such as ChatGPT and BardAI are simply auto-complete on steroids that have been trained on a vast amount of data. While they are really good (and continually getting better) at predicting what the next word is going to be and extrapolating it to generate extensive literature, it lacks contextual understanding.
Consequently, the algorithms struggle with nuances such as sarcasm, irony, satire, analogies, etc. This also leads to the propensity to “hallucinate” and generate responses even if those are factually and logically incorrect.
Additionally, with the widespread adoption of LLMs and other forms of generative AI, a massive amount of content will be ingested and regurgitated as canned responses echoed in infinite permutations and combinations. This oversupply could dilute the value and increase demand for qualitatively superior insight and discernment, which (still) requires human intervention.
(Relatively) Safe Havens
Just as we have learned during the dot-com, cryptocurrency, real estate, and numerous other bubbles through the ages, markets can stay irrational longer than investors can stay solvent.
Therefore, even if the next big thing comes along and changes the world (and electricity, automobiles, personal computers, and the Internet really did), it’s the fundamentals that determine whether a business can survive to capitalize on those windfalls.
Hence, it could be wise and safe for investors to stick to big tech mega caps (mentioned earlier in the article), which are involved in providing the infrastructure and computing horsepower required to make the data and power-hungry AI algorithms work.
Moreover, since AI is well-embedded into their business operations and market offerings and AI as a service is (still) a small portion of their revenue, concentration risks can be more easily managed.
Rather than getting too carried away and stretching a worthwhile and useful innovation to frothy excesses with unrealistic expectations, it could be useful to remember that legendary investor and polymath Charlie Munger doesn’t think that AI is the silver bullet that can solve mankind’s pressing problems all by itself.
Even AAPL co-founder Steve Wozniak, who knows more than a thing or two about technology, agrees with the ‘A’ and not the ‘I’ of Artificial Intelligence.
We hope this discourse will help investors cultivate discernment, discretion, and, if necessary, dissent while investing in this revolutionary technology since those are the ultimate indicators of intelligence.