The introduction of generative AI products in late 2022 caused a runup in the share prices for the biggest suppliers of AI, most notably the so-called magnificent seven: Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, and Tesla. Their combined market capitalizations rose more than $10 trillion between January 2023 and their peaks in late 2023. They have since fallen leading some to claim that the bubble has begun to burst (see table).

The Rise and Fall(?) of the Magnificent Seven’s Market Capitalization ($trillions)

Ten trillion is nothing to scoff at. Only the U.S. and China have gross national products that are bigger than $10 trillion. Applied across American citizens, it implies a per capita wealth of roughly $30,000, and that was just for the increases since January 2023.

Everyone’s Aboard the AI Train

The biggest winner has been Nvidia, which supplies the chips for processing data in the AI cloud services offered by other members of the magnificent seven, most notably Microsoft, Meta, Amazon, and Alphabet. These companies also offer AI software such as Microsoft’s Copilot, Meta’s Llama, Amazon’s Bedrock, Alphabet’s Gemini, and Tesla’s Grok.

Other companies have also benefited from the generative AI bubble, most notably OpenAI, the supplier of ChatGPT services. Other beneficiaries include data centers, electricity providers, and other semiconductor suppliers. In other words, the impact of generative AI on the market capitalization of companies is much bigger than the $10 trillion figure. The largesse of the AI bubble truly implies a new dawn in America for productivity.

But is it? One sign that it isn’t a new dawn is that neither of America’s political parties talk about this new dawn, and ordinary Americans don’t want them to do this. Instead, supporters of both parties are angry and want big changes to be made, even in tech where it is agreed that the magnificent seven have too much power.

Investors are also starting to question the $10 trillion figure. David Cahn, a partner at venture firm Sequoia Capital, wrote in a blog post last month, “The tech industry would need to generate around $600 billion in revenue a year to make up for all the money being invested in AI right now, yet it is far from close to that number,” OpenAI reportedly has a few billion in revenues but that is far from the $600 billion that David Cahn says is needed.

And those revenues aren’t profits. Profits are even further away. We need to get the revenues before we can get the profits. By the way, Amazon’s Alexa, an AI-based virtual assistant, reportedly has $25 billion in cumulative losses.

CNN asks:

“When will anyone start making actual money from artificial intelligence? In the 18 months since ChatGPT kicked off an AI arms race, tech giants have promised that the technology is poised to revolutionize every industry and used it as justification for spending tens of billions of dollars on data centers and semiconductors needed to run large AI models. Compared to that vision, the products they’ve rolled out so far feel somewhat trivial — chatbots with no clear path to monetization, cost saving measures like AI coding and customer service, and AI-enabled search that sometimes makes things up…Some investors question whether AI investments will ever pay off. Goldman Sachs analyst Jim Covello argued that “the technology isn’t designed to solve the complex problems that would justify the costs.”

–Has the AI bubble burst? Wall Street wonders if artificial intelligence will ever make money | CNN Business

There are many optimists, however, probably enough to maintain the high share prices. Many of the most optimistic supporters are board members who seem to think they and their consultants know much more about AI and a company’s processes than do low-level engineering managers. Although they are also concerned with the risks from AI, most notably lawsuits, they don’t seem to think that the possibility of a bubble is one of those risks.

Vinod Khosla, co-founder of computer network systems company Sun Microsystems and one of Silicon Valley’s most influential venture-capital investors, is one of the most optimistic. Although he doesn’t address the revenue issue that was raised above, he “compares AI to personal computers, the internet and mobile phones in terms of how much it would affect society. As AI changes the way people work, do business and interact with one another, many start-ups will fail, he said. But overall the industry will make money on AI. He predicts there will eventually be multiple trillion-dollar businesses in AI, such as humanoid robots, AI assistants and programs that can completely replicate the work of highly paid software engineers.”

Other recently turned pessimists ask more detailed questions, going beyond the issue of software revenues. Gartner, the inventor of the hype cycle, claims: “Gen AI projects can cost millions of dollars to implement and incur huge ongoing costs. For example, a gen AI virtual assistant can cost $5 million to $6.5 million to roll out, with a recurring annual budget hit of $8,000 to $11,000 per user.”

Do the benefits justify these costs? Gartner continues: “A virtual assistant may save employees time when searching for old documents or composing emails, but most organizations have no idea how much time those tasks have taken historically, having never tracked such metrics before.” 

Is AI Really Worth This Much?

Try comparing this to the software we use on our personal computer. Many of us pay $100 to $200 a year for a Microsoft Office annual subscription. Are the benefits from generative AI really hundreds of times higher than those from Microsoft Office?

The CIO, a website that provides advice on these issues to chief technology officers and chief information officers, quotes not only Gartner, but other pessimistic consultants: “In the enterprise, huge expectations have been partly driven by the major consumer reaction following the release of ChatGPT in late 2022” while meanwhile, “traditional AI technologies in use at the time, including machine learning, deep learning, and predictive analysis, continue to prove their value to many organizations.”

One question that I like to ask is what if companies decide to put most of their emphasis on “traditional AI technologies,” how would the stock market likely react? The share prices for AI suppliers didn’t begin their meteoric rise until after generative AI was released. So, if companies largely abandon generative AI, where does that suggest the share prices will go?

One software engineer with 40 years of experience said this privately to me last week in a message about Microsoft Copilot:

“It is an amazing tool, but I must say it falls way short of earth-shattering AI that can reason in any way shape or form. I’m constantly retyping questions that it should remember from a few minutes ago. And its ability to solve logic errors in code is much over hyped. It’s good for making rapid MVPs (minimum viable products) but when it comes to a being a strong enterprise tool it falls short in my opinion for what we pay.”

Gary Marcus says the “collapse of the Generative AI bubble may be imminent” and Paris Max asks “What comes after the AI crash?” On the other hand, one optimistic Forbes article claims that “Small Wins Prove AI Isn’t A Bubble” and the Wall Street Journal said on August 16th that “Stocks Rebound to Notch Best Week of 2024,” as the recent fall in interest rates and increasing economic output are expected to continue.

For me, the key is to get your arms around the notion of a $10 trillion increase in the market capitalization of the magnificent seven. Is AI truly worth that much? When even optimists talk of “small wins,” $10 trillion should be something we equate with Santa Claus, the Tooth Fairy, and a communist utopia. AI is here to stay, just not in the form of stratospheric share prices.