Elon Musk filed a lawsuit in San Francisco’s Superior Court accusing OpenAI and its CEO, Sam Altman, of betraying the startup’s initial commitment to openness, the betterment of society, and lack of profit as a motive. Among other things, Musk’s 35-page complaint argues that OpenAI has violated its original deal to share its GPT large language models with Microsoft, which stated that the software giant would lose access to new LLMs once OpenAI had achieved AGI. According to the complaint, OpenAI reached that epoch-shifting moment a year ago with GPT-4, its most powerful model to date.

Musk—who cofounded OpenAI but left in 2018—is at least as entitled as anyone to come up with his own definition of AGI. His complaint describes it as “a general purpose artificial intelligence system—a machine having intelligence for a wide variety of tasks like a human.” That does sound like GPT-4 as I, a mere layperson, experience it in ChatGPT Plus.

But Musk’s declaration that the AGI era is already upon us is hardly the consensus among AI scientists. Even those who think it’s not far off predict arrival dates that are least a few years away. And GPT-4 falls well short of meeting OpenAI’s own explanation of the term: “A highly autonomous system that outperforms humans at most economically valuable work.”

Consider the evidence:

GPT-4 isn’t remotely autonomous; indeed, it does its best work when humans provide plenty of hand-holding in the form of detailed prompts. The world is still in the process of figuring out what tasks GPT-4 can do, and we frequently overrate its competence. That’s not even getting into the fact that OpenAI’s reference to “most economically valuable work” suggests that true AGI may involve not just software but also sophisticated robotics that don’t exist yet. To guess when OpenAI—or a rival such as Google, Anthropic, Meta, Mistral, or Perplexity—might reach AGI, as OpenAI defines it, is to expect that it’ll be an obvious moment in time. But OpenAI’s definition, like all the others, is squishy and difficult to put to a conclusive test. To riff on Supreme Court Justice Potter Stewart’s famous comment about pornography, maybe we’ll know it when we see it. At the moment, however, I’m convinced that obsessing over AGI’s existence or nonexistence is counterproductive.

The whole notion of AGI is predicated on the assumption that AI started out dumber than a human but could someday match or exceed our level of thinking. Already, though, generative AI is different than human intelligence—far closer to omniscient than any individual flesh-and-blood thinker, yet also preternaturally gullible and prone to blurring fact and fiction in ways that don’t map to common human frailties. That’s because it’s a predictive engine, trained to string together words without truly understanding them. If its present trajectory of simulated brilliance mixed with boneheadedness continues, it might wander off in a direction far afield from most definitions of AGI.

Even if the world lands on a new, more inclusive definition of AGI, it may be hard to prove whether a particular LLM has attained it. Musk’s lawsuit cites proof points of GPT-4’s reasoning power, such as its scoring in the 90th percentile on the Uniform Bar Exam for lawyers and the 99th percentile on the GRE Verbal Assessment. That it can do so is astounding. But acing tests is not synonymous with performing useful work. And even if it were, who gets to decide how many tests an LLM must pass before it’s achieved AGI rather than just bobbled somewhere in its vicinity?

For decades, the Turing Test—which a computer would pass by fooling a human into thinking that it, too, was human—was computer science’s beloved thought experiment for determining when AI had gotten real. Strangely enough, it’s useless as a tool for assessing today’s LLM-based chatbots. But not because they know too little to fake humanity convincingly, or can’t express it glibly enough—but because they betray their artificiality by being so good at churning out endless wordage on more topics than any human knows. AGI could end up in a similar predicament: a benchmark, devised by humans, that’s rendered obsolete by the technology it was meant to measure.

DID YOU HEAR THE ONE ABOUT THE “MAC CAR?” Last week, Apple’s long, expensive quest to build an autonomous EV entered its rearview-mirror phase—a sad fate my colleague Jared Newman blamed on the company’s sometimes counterproductive pursuit of perfection. Wondering what an Apple car would be like has been an obsession for techies since 2012, when news broke that Steve Jobs had toyed with getting into the automobile business even before there was an iPhone. Or maybe it started in 2008, when reports of a meeting between Steve Jobs and Volkswagen’s CEO led to wild speculation about an “iCar.”

Or how about 1998? According to Snopes, that’s when a joke involving cars designed by software companies began spreading like crabgrass across the internet, eventually evolving into an urban legend involving a Bill Gates keynote and a General Motors press release. Along with a Microsoft car that crashed twice a day and occasionally needed its engine replaced for no apparent reason, it mentioned a “Mac car” that “was powered by the sun, was reliable, five times as fast, twice as easy to drive—but would only run on 5% of the roads.”