Yes, AI is in a bubble. No, it won't pop just yet.
With Nvidia surpassing $2T in market cap, Wall Street is divided. Valuations are extended, & capex is booming. But, what happens when user hype fades & risks come to the forefront?
«The 2-minute version»
It’s been a couple of weeks since one of the most celebrated public companies in technology, Nvidia, crossed $2T in market capitalization. At the same time, such a landmark moment is drawing intense battle lines with speculation that all things AI are in a bubble.
But first, what is a bubble? Put simply, an asset bubble is defined as an upward price movement over an extended range, that eventually implodes. The 1990s dot-com bubble is one such example.
So, are we in one now? Hard to tell, although Torsten Sløk from Apollo Global would point to the current valuations of the top 10 companies in the S&P 500, which now exceed the valuations of the top 10 companies during the tech bubble in the 1990’s.
Aside from valuations, the capex boom is no joke: According to McKinsey, the business investments that the US saw between 1995 and 1999, after the Telecommunications Act was passed, doubled from $250B to $500B . Similarly, the Chips and Science Act passed in 2022 has spearheaded the capex boom in AI as companies increase their capacity so that their machine learning models can run with vast amounts of data and faster.
Are we ready for the trough of disillusionment? According to Gartner’s hype cycle, the expectations of GenAI are in a heightened range. Take a look at the initial adoption of GenAI which has overtaken the initial adoption curves for the smartphone. Not to mention the rage about Sora. But as usage of AI deepens and broadens among the masses, important issues such as plagiarism, deepfakes, cybercrime, racial bias, & privacy are gradually starting to encroach on the AI party. Simultaneously, studies show that Americans are increasingly cautious about the growing role of AI in their lives.
Plus, Apple is awfully quiet in all of this: The iPhone maker is doing the most Apple-esque thing at the moment by staying mum, perhaps buying time to understand how exactly its devices can bridge the usability gap most devices have today between the end user and AI.
It’s been a couple of weeks since one of the most celebrated public companies in technology, Nvidia NVDA 0.00%↑ , crossed $2T in market capitalization. This is a watershed moment for the company which has almost become a beacon of hope for powering the endless possibilities of what AI can do.
For Nvidia, to say that business is booming is an understatement. Such was the demand through last year that many companies & startups (and some countries) began hoarding Nvidia’s semiconductor products. For those living under a rock, Nvidia produces semiconductor chips that accelerate the computing output of computers and servers used to power applications like media, games, cryptocurrency mining, data centers and now, AI.
At the same time such a landmark moment is drawing intense battle lines with speculations that all things AI are in a bubble.
In case you missed, and dove deep into Nvidia in the podcast below to unearth the fundamentals driving the optimism in AI and might the next phase in the innovation cycle look like. 👇🏼👇🏼👇🏼
💭What is a bubble anyway, and why is AI said to be in one?
Right after Nvidia crossed the historic $2T milestone, a corner of the market often identified as bulls cheered the company’s milestone. While Dan Ives, an analyst at Wedbush Securities, sported his Friday best 🥳 and predicted the party was just starting for artificial intelligence stocks, Wall Street’s loudest bull, Fundstrat’s Tom Lee, went further to say that over time the world will replace “salaried workers with silicon.”
On the other hand, Torsten Sløk, chief economist at Apollo Global, published his research note last week, in which he argued that “the top 10 companies in the S&P 500 today are more overvalued than the top 10 companies were during the tech bubble in the mid-1990s.”
All of this debate brings up a fundamental question that is key to understanding whether AI is in a bubble.
➡️But first, what is a bubble really?
In 2015, the US Federal Reserve published a paper that defined an asset bubble as "an upward price movement over an extended range that then implodes." Investopedia defines economic bubbles as “economic cycles that are characterized by the rapid escalation of market value, particularly in the price of assets,” a notion that is similar to the Federal Reserve’s explanation of a bubble. Taking this definition, if we were to simply chart the value of market capitalization of a few top stocks deemed to be beneficiaries of the AI wave, we would quickly observe the rapid escalation in the price of certain stocks, especially Nvidia.
Does that mean we are in a bubble? Well, we wouldn’t know until it implodes. But it would echo Apollo Global economist Torsten Sløk’s concern of high valuation levels, especially when compared to the 1990’s tech bubble as a baseline.
The 90’s dot-com bubble may give us some clues
In order to understand what Sløk was referring to when he pointed to the tech bubble in the mid-90’s, it made sense to look at a few milestones achieved in the 1990’s leading up to the dot-com bubble that eventually burst at the end of that decade.
Netscape started the dot-com party as one of the first dot-com era companies to go public in 1995. This was really the ‘Big Bang’ of the internet era, ushering in a boom of IPO’s that would eventually become overinflated in terms of market valuation. Along the way, there were some successful companies during those years that still exist in some shape or form today: Amazon AMZN 0.00%↑, eBay EBAY 0.00%↑, whatever is left of Yahoo, and others.
But looking at the chart above, a key driver of this all could be traced to the Telecommunications Act that was passed by the U.S. Congress in 1995.
According to a testimony by a senior McKinsey executive to the U.S. FCC during a 2001 Senate hearing, the passing of the Telecommunications Act led to a huge surge in investments by corporations. Companies were purchasing all kinds of “communications stuff” as a result of the Act—equipment, software, devices, services, etc. Based on the McKinsey executive’s testimony, the business investments that the US saw between 1995 and 1999 doubled from $250B to $500B in just four years. All of this at a time when interest rates were rising and had reached almost 7% just before the dot-com bubble burst.
The story is quite similar to today’s AI, too. All the excitement in the stock market around AI can primarily be traced to businesses spending large sums of money to invest in increasing the capacity needed to scale their AI ambitions. For example, McKinsey estimates that the CHIPS and Science Act passed in August 2022 is projected to infuse at least $280B in the semiconductor and related technologies space, the same industry of which Nvidia is a part of. So far, this money is being spent on a wide range of capital investments, such as semiconductor chips, GPUs, data center infrastructure, and other services and software that are key to training and deploying machine learning models and AI applications.
All the technology companies in the chart above are customers of Nvidia’s GPU products and have been significantly investing in GPUs and other hardware to increase capacity so that their machine learning models can run with vast amounts of data and faster. Both Meta Platforms META 0.00%↑ and Tesla TSLA 0.00%↑ have already advised its investors that they will be spending at least 19% more this year on buying things like GPUs and data center infrastructure for their AI models.
So far, these large companies are locked in a race to expand their computing capacity, similar to the business investments made in the early to mid-90's. This is creating almost inelastic demand dynamics, leading to skyrocketing valuation levels for some beneficiaries, such as Nvidia. But there are risks to such significant ramp-ups in investments.
After all, history may not repeat, but it often rhymes.
AI has a bright future. But there are risks along the way.
The folks at Gartner have created the Hype Cycle framework, which plots different technologies along the course of a 5-wave lifecycle. It’s not the best of predictions, but the essence of the hype cycle is to really illustrate the phases that every major emerging technology goes through. All major technologies, including AI, have started with an innovation trigger, leading to heightened expectations that get overinflated into bubbles. But in the end, only those technologies that are able to suffer through periods of disillusionment are eventually able to deliver long term productivity goals. So far, Gartner’s hype cycle shows that the expectations of GenAI are in a heightened range.
There is no doubt that AI has the potential to change the course of human civilization. The excitement in AI’s potential is reflected in research that points to how the initial adoption of GenAI has overtaken the initial adoption curves for the smartphone.
And startups like OpenAI only seem to be raising the stakes - first launching DALL·E, then ChatGPT and now Sora. Nowadays, every weekend feels like this 🥸 when OpenAI drops their new Sora-generated AI videos.
But as usage of AI deepens among the masses, important issues such as plagiarism, deepfakes, cybercrime, racial bias, & privacy are gradually starting to encroach.
Here’s an example of how LLM’s are often prone to racial bias, based on African American English and Standardized American English texts in prompts. This can lead to serious consequences when we apply these models to make decisions about who qualifies for a loan, who gets the job, or who is guilty of committing the crime.
Plus, amidst all the AI hype through last year, a study published in November last year showed that Americans were increasingly cautious about the growing role of AI in their lives generally. Over half of the adults in the study mentioned they were more concerned than excited about AI in their daily lives, up from just under a fourth of survey participants a year ago.
Some may point to bias in the survey given that Americans might be perturbed due to the upcoming elections this year. But a separate global study published just this week echoed similar findings where trust in AI fell to 53%.
This type of evolution in user behavior as adoption deepens is not new.
An examination of an old survey from research archives published in 1998 showed how the number of people going on the internet jumped from 23% in 1996 to 41% in 1998. At the same time, over half of the users surveyed reported feeling anxious about their privacy. Simultaneously, 60% of the users surveyed also reported feeling frustrated with the speed of their internet connection as well as the accuracy of information being found online, thus echoing some of the similar sentiment around AI today.
These developments are increasingly pointing towards some kind of a top in the bubble, when it comes to users' expectations of AI, as more fundamental questions start getting asked. In our view, this could very well lead AI through Gartner’s trough of disillusionment phase next, but once key issues are solved, the long term productivity boost that AI promises could be widespread.
One of the largest tech companies in the world is stubbornly quiet about AI
So far, enterprises are falling over one another to get ahead in front of the consumer and showcase their AI value propositions. But, interestingly enough, Apple AAPL 0.00%↑ has stayed conspicuously quiet in all of the AI hype.
Every time Tim Cook was asked a question in the past about Apple’s AI projects, he has always given a heavily templated response by talking about “machine learning” while referring to anecdotes about how it is already in use in iOS or the Apple Watch. The iPhone maker is doing the most Apple-esque thing by staying mum, maybe buying time to understand how exactly its devices can bridge the usability gap most devices have today between the end user and AI.
Think about it: for all the productive intelligence AI has to offer, it is rather counterintuitive for users to open their phone or laptop, search for their AI app, or worse, open their browser, navigate to that AI service, and then interact with it when it has become easy to just converse with AI.
Parading AI out into the world paints a compelling vision for most tech companies today because the world has never experienced what AI could really do. Most of the machine learning models were confined to the erudite halls of academia and whitepapers until 2022, when users actually got to play with image generators and chatbots.
But the moment users’ experience with AI starts to wane and users become cognizant of their devices’ usability limitations with respect to AI or the fundamental risks that AI poses, general interest in this space will taper off, and suddenly all those colossal investments that tech companies made to rapidly scale their AI hardware will instantly be underutilized, leading to lower revenues.
That could mark the start of the bubble being popped. But until then, the bubble can continue to grow as the party in AI carries on.
🤔🤔Do you think we are in AI bubble?
🎥For those who want to tune in…
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who writes and on Tech Tonic AI Thought Show at 4 pm EST tomorrow. I am thrilled to be jamming with both of them and exploring some key macroeconomic cycles from the past and use that to help us understand and position for what lies ahead of us at a key juncture of the long-term debt cycle in the US economy, and the innovation cycle that is unleashed by AI.In case you haven’t checked out Kes’s work, here is his latest post, that I am sure you will enjoy.
This is all disconcerting to me. I’m never comfortable when big tech starts hyping their latest innovations. This bit from your article was very disturbing:
Wall Street’s loudest bull, Fundstrat’s Tom Lee, went further to say that over time the world will replace “salaried workers with silicon.”
This comes at a time when many are seeking longer, more productive lives. Let’s face it, not everyone will benefit from the AI boom. There will be far more who see nothing from it or will be negatively impacted by it. What becomes of those people when our lives are radically altered?
I’ve lived long enough to see that many technological breakthroughs have brought as much grief as convenience for individuals. The effects of crime, exposure of personal info and discrimination that were also mentioned in the article are real and destroy lives. So far, we’ve not handled it well and failed to protect many of our most vulnerable.
So I choose to look beyond the potential profits to money seekers in favor of humanity in general. In a bubble or not, AI has no real value in my eyes until it’s proven that the potential damage can be successfully mitigated. It’s past time we draw a line and begin to consider restraining some of this madness.
Brilliant article. This bubble is not at all like the dotcom bubble purely because valuations were WAY ahead of earnings in that period. The opposite is mostly true this time.
Completely agree with most of your points here Amrita!