The generative AI bubble
Marco van Hurne
Partnering with the most innovative AI and RPA platforms to optimize back office processes, automate manual tasks, improve customer service, save money, and grow profits.
A common theme in the tech world in the second quarter of 2024 is debating when/if the AI bubble will burst (or not). Given the frantic speed at which the AI arms race has been developing, some forecasters say that the inflated expectations and over-investment in AI will likely lead to market saturation and disappointment. As market research firm Gartner puts it in its 2023 AI Hype Cycle published last year, generative AI was already reaching the “peak of inflated expectations” and close to tumbling into the infamous “trough of disillusionment.”
Before we start!
If you like this topic and you want to support me:
The Gartner Hype Cycle is like a roadmap that shows how new technologies are adopted and grow over time. It has five main phases: first, there's the "Innovation Trigger" where a new technology is introduced and starts to get attention. Then comes the "Peak of Inflated Expectations," when everyone gets really excited and expects big things from the technology, sometimes more than it can deliver. After that, there's the "Trough of Disillusionment," where people realize the technology isn't as amazing as they thought, and there's a bit of disappointment. But then, as people figure out how to use the technology better, it enters the "Slope of Enlightenment," where it starts to become more practical and useful. Finally, it reaches the "Plateau of Productivity," where the technology is widely accepted and used in a lot of different ways. So, the Hype Cycle shows us the ups and downs that new technologies go through before they become a normal part of our lives.
Anyone who had the misfortune of being alive during 2008 is painfully aware of the concept of an economic bubble. For those lucky enough not to be, or for those who have dissociative amnesia with 2008, an economic bubble is when shares or assets soar in value way past what they are really worth, thanks to unfounded speculation. This inflated “bubble” will then “burst” when a market correction unveils the genuine value of these assets, spooking investors and buyers, leading to mass sell-offs, and, therefore, crashing the valuation of assets, which leaves many high and dry. While 2008 was a spectacularly big bubble-bursting event, even smaller ones, like the dot com bubble, can decimate entire industries. With this in mind, is the AI industry, with its grand promises, insane stock valuations and investor hype, in a bubble?
Microsoft, Apple, Meta, Alphabet, NVIDIA alone are collectively valued at almost $11T. That’s $11,000,000,000,000! A year ago, their collective valuation was about $6T. Their market capitalizations have gone up by about $5T within the last 12 months. They have gained about 80% in value practically “overnight” since the mass debut of transformer generative AI models like ChatGPT and Google Bard.
Out of the five companies mentioned above, the poster children of the AI bubble are NVIDIA, Microsoft, and Meta. NVIDIA is the most bubblelicious of all with a market capitalizations of $2T. Microsoft is valued at $3T. Meta is valued at $1.25T. NVIDIA makes parallel processing microchips deemed essential for AI neural networks. Microsoft acquired 49% of OpenAI, the company behind ChatGPT. Meta, which just released LLAMA-3 will use AI to keep making social media ever more addictive and ever more powerful in targeting human brains for customized advertising. Alphabet and Apple just complete the panorama of the picture. Apple is valued at $2.82T, and holds the valuation least tied to AI for now. Alphabet is valued at $1.8T, and has benefited from the AI fever in Wall Street. All the bubbly signs are there.
On the contrary, there is the intriguing argument that AI is a foundational tech that does not conform to the regular innovation hype cycle. Tech entrepreneur and AI advocate Steve Pettit boldly claimed that “Gartner’s hype cycle is dead; it was killed by AI.” Essentially, Pettit argues that the relentless pace of sub-category AI developments, coupled with a “growing cultural acceptance of technological imperfection,” is causing a flattening effect in the market’s perception of AI. Instead of just one inflated peak of expectations, there will be an endless series of peaks that masks the valleys and drops. Therefore, some particular applications of generative AI may flame out and leave some investors holding the bag, but the train of AI development will keep chugging along.
While it’s tempting to think of AI as an exception to the rules, a closer read of the market sentiment strongly suggests otherwise. Call it AI fatigue, or simply the novelty factor wearing off, there is a certain blasé attitude about AI as a whole starting to set in among certain market sectors. This sentiment is not unfounded, as the rapid pace of AI development has desensitized some consumers and businesses, who have begun to take the advancements for granted, no longer seeing them as revolutionary but rather as incremental improvements.
The AI stakeholders also seem to be quite aware of this sense of growing fatigue and, to their credit, are starting to do some active expectation management. A few weeks ago, The Information published a story about Amazon and Google “tamping down generative AI expectations,” reporting that both companies are instructing their sales teams to tone down their enthusiasm about the AI capabilities that they’re hawking.
Taking a deeper look at the AI market as it stands reveals at least two major issues that could trigger a reevaluation of the AI gold rush: the profitability issue and the regulatory issue. Without solving both issues, the AI bubble would be bound to burst sooner or later.
AI’s Profitability Dilemma
It’s no secret that the ongoing AI arms race is a costly business. All the cutting-edge GPU chips and cloud services required to run a consumer-facing AI service at scale are costing the tech leaders hundreds of millions of dollars every month.
To top this of, an extensive MIT review found that AI is currently way to expensive to replace most jobs. Yet, if you listen to any leading AI company or startup, they are positioning their products as being able to disrupt and replace human workers. This positioning is not only unfounded and separated from the reality of AI as it is today, but it falsely inflates their share price, and investors speculate they could acquire vast amounts of revenue by directly replacing workers.
The prevalent freemium model that the market-leading generative AI tools runs on today sets a consumer expectation that access to basic AI functionality should be free. It’s only when accessing the latest LLM or enterprise features that a subscription would be required. This expectation, of course, is not conducive to getting regular consumers to start paying for AI out of pocket, as long as the free-to-use models are “good enough.”
领英推荐
Subscribe to the TechTonic Shifts newsletter
This is where the bubble comes from. AI technology itself is incredibly powerful, but this miselling and overvaluation is creating an economic bubble. Don’t believe me? Let’s look at two examples.
Tesla is one of the most well-known AI companies, with its self-driving AI making it one of the highest-valued car companies of all time, thanks to investors speculating that their self-driving vehicles could replace taxi and truck drivers and push sales way up. That isn’t hyperbole, as Ark Invest has predicted that Tesla could be worth 11 times what it is today by 2027, thanks to its AI. However, Tesla’s self-driving systems are still miles away from being fully self-driving, and they are facing a mountain of federal investigations and lawsuits over their dangerous nature. Not only that, but as Tesla uses a computer vision-only approach, many in the industry believe Tesla can never reach full self-driving.
In 2023, Tesla made a gross profit of $13.4 billion, and its current valuation is $535.98 billion. But Toyota, which has technology that could eclipse Tesla, including its own gigacasting, AI self-driving systems (though marketed as a driver assist system), and affordable super-long-range solid-state batteries, and a gross profit of $61.699 billion, is only valued at $331.30 billion. These numbers are the smoking gun of an AI bubble.
OpenAI is another example of this value false narrative.
Their generative AIs like ChatGPT and Sora have been hailed as industry crushers, able to wipe out an entire section of our economy. In their current guise, they definitely can’t. ChatGPT, for example, continues to hallucinate false facts and fumbles basic grammar, and Sora still creates videos firmly in the uncanny valley. However, OpenAI and investors have stated that the subsequent iterations will solve these issues by using larger training datasets, making them way more capable. This, in turn, has pushed up the speculated value of OpenAI.
Yet, OpenAI CEO Sam Altman has warned that these larger models will use so much energy to develop, train and maintain that they will require an energy breakthrough like nuclear fusion to be viable. You see, even with the most affordable energy we currently have, the energy bill for these next-gen AIs will render companies like OpenAI unprofitable.
This is another example of a bubble. Investors, market analysts and alike are valuing OpenAI way above where it should be, even though its CEO has admitted they have a baked-in upper limit to what it can do. Funny enough, these same energy cost limits also apply to Tesla.
If that doesn’t convince you, then how about the recent poll by Bank of America that found 40% of fund managers think that AI-related stocks are in a bubble.
So, the question must be: what will the fallout be when this bubble eventually bursts? Well, I’m no financial analyst, and even financial analysts have difficulty answering questions like that. It depends on which funds or banks have bet the farm on AI and who has correctly mitigated this potential risk. But if/when this bubble bursts, it isn’t likely to take out the leading players. Tesla, for example, is incredibly cash-rich, and its business isn’t entirely based on AI.
They could survive even the worst AI market crash if they don’t waste all their money on dead-end AI development and keep their EV sales buoyant. It would be severely kneecapped, but it won’t die. The same applies to other AI giants like NVIDIA, Google and Meta.
Sadly, businesses solely based on AI, or AI startups heavily leveraged to grow, will almost certainly not survive such a burst.
I genuinely believe an AI bubble burst will happen, as it did with other bubbeling technologies, and judging by how badly shares like Tesla’s are doing right now, I also think the market correction is only around the corner. I really hope I’m wrong, as this could severely financially damage a vast number of people. I guess only time will tell.
Well, that's a wrap for today. Tomorrow, I'll have a fresh episode of TechTonic Shifts for you. If you enjoy my writing and want to support my work, feel free to buy me a coffee ??
Think a friend would enjoy this too? Share the newsletter and let them join the conversation. LinkedIn appreciates your likes by making my articles available to more readers.
Signing off - Marco
Other articles you might be interested in:
Impressive analysis! To amplify growth, consider leveraging the power of micro-segmentation in your marketing strategies, targeting ultra-specific user profiles for higher engagement and conversion rates.
Pharma | Compliance | Quality | Visual Inspection | AI | Passion
5 个月Totally agree. I just wrote something similar about AI in regulated industries. There is great potential and a wide range of possible applications. But in order to leverage this potential, a strategy is needed to translate the rather enormous investments into tangible added value in the medium term (standardisation, increased efficiency, shortening of approval procedures, etc.). Unfortunately, the many free riders on attractive slides promise that now that all the problems have been solved, all you have to do is grab them. But the shown concepts deliver neither new nor innovative added value..
Sharing about IoT AI EdgeComputing Cybersecurity, or help you discovering them (mix of personal thoughts) | Edge Intelligence advocate | Not ex-MIT, not ex-Google, not ex-Meta, not GPT-free-courses…
5 个月In my opinion, the bubble is on B2C side. And what I see, humbly without cristal ball ??, is that B2B markets may crack Gartner forecast, if new players invest in B2B instead of running (slower) behind tech giants.
Exited founder turned CEO-coach | Helping founders scale their companies without sacrificing themselves.
5 个月Wow, the tech giants are on fire. $11T is no joke.
Helping High-Ticket Coaches & Consultants Create a Consistent Lead Flow System that Generates Consistent Cash Flow | Turn Your LinkedIn Presence into an Authority Brand that Attracts Your Ideal Clients ??
5 个月Wow, those numbers are mind-blowing. The tech world moves fast. What's next?