Artificial Intelligence, Present Tense: Modest Revenues and Serious Problems

There is a massive amount of AI material online.  Almost all of it, though, deals with capabilities in development, business deals, stock performance, organizational changes, and forecasts.

How is it doing now?  I don’t know, and you don’t either.  It moves too quickly, and most of the information is proprietary and unassembled.  But if we can go back four months, to “10 Companies With Largest AI Revenues:  Success Cases from OpenAI, Anthropic & More” (Tim Keary, Technopedia, January 17^th), we get some insight.  In the first paragraph it relates how “the global AI market was valued at $196.63 billion in 2023,” close to Statistica’s October 2023 $207.9 billion determination.  However, per an unsourced answer to a Google search, “the “market size” is made up of the total number of potential buyers of a product or service within a given market, and the total revenue that these sales may generate.”  As Keary told us, though, that’s not the same as what’s actually being sold.  In his section titled “10 Leading Companies in Terms of AI Revenue,” he named OpenAI, Anthropic, Microsoft, Nvidia, Hugging Face, Stability AI, Perplexity AI, IBM, Google, and Salesforce, giving annual sales figures for each.  After taking one-quarter of those from the last two, which were not broken down into AI and other offerings, I got a total of $29.7 billion, meaning that something over 15% of the market is producing revenue.  By comparison, a recent Wikipedia list of largest companies by annual sales – not entire industries – showed the 50th-largest one with over $157 billion.

Accordingly, “A.I. Start-Ups Face a Rough Financial Reality Check” (Cade Metz, Karen Weise, and Tripp Mickle, The New York Times, April 29^th).  The main point here was that “the A.I. revolution… is going to come with a very big price tag.  And the tech companies that have bet their futures on it are scrambling to figure out how to close the gap between those expenses and the profits they hope to make somewhere down the line.”  Although “investors have poured $330 billion into about 26,000 A.I. and machine-learning start-ups over the past three years,” they may not maintain that pace.  Indeed, “Wall Street’s Patience for a Costly A.I. Arms Race Is Waning” (Andrew Ross Sorkin et al., New York Times DealBook Newsletter, April 25^th). 

Also, “A.I. is running out of power” (Andrew Ross Sorkin et al., New York Times DealBook Newsletter, April 18^th).  As a result, “tech executives are increasingly warning that electricity supplies need a boost,” with a notice that “there’s “not enough energy right now” to power new generative A. I. services,” and because of that the technology “could also spur a geographic shift for tech” to areas with more of it to spare.  With that, it is fair to ask “How Bad is A.I. for the Climate?” (the same source on May 6^th), as it “risks throwing companies’ climate pledges off track” since “the A.I. revolution will largely run on fossil fuels.”

Another copyright infringement lawsuit materialized last week, as “8 Daily Newspapers Sue OpenAI and Microsoft Over A.I.” (Katie Robertson, The New York Times, April 30^th).  These papers, including the New York Post and the Chicago Tribune, all owned by one company, claimed that “chatbots regularly surfaced the entire text of articles behind subscription paywalls for users and often did not prominently link back to the source,” and erroneously named the papers as sources of things they did not produce.

Although many predictions show massive increases in profits as well as capabilities, “CEOs make the message clear on AI’s big payoff:  Be patient” (Lloyd Lee, Business Insider, April 25^th).  To prevent AI-company stock prices from reverting to whence they came, when “many (companies) have yet to see any significant returns on their investment,” “CEOs hope to re-assure shareholders that this is to be expected.”  Venture capitalists have not always been known for patience, so even if the titanic success so often forecast for AI materializes, without every firm running out of power, data, chips, perceived accuracy, or legal acceptance, many may fail due to running out of money. 

We don’t know where or how far artificial intelligence is going, but it hasn’t done much yet.  From here, there are no guarantees – and plenty of doubts.