Hello and welcome to Eye on AI. In this edition: Why AI isn’t a bubble quite yet…ChatGPT gets chattier…Microsoft connects U.S. datacenters into the first “AI superfactory”…and “shadow” AI systems are causing problems for organizations.Hello, Beatrice Nolan here, filling in for Sharon Goldman while she’s on vacation this week. Lately, there’s one question investors can’t seem to stop asking: Has the AI boom crossed into bubble territory?One analyst thinks he has an answer to, and a way to keep track of whether the AI industry is in a boom or bust phase through a special mechanism that measure key industry stressors on a scale of safe, cautious, or dangerous.The framework was created by Azeem Azhar, a renowned analyst and author, who says the data shows that the AI industry is not in a bubble—at least not yet.What’s the difference between a healthy boom and a dangerous bubble? According to Azhar, the two are very similar, but a bubble is “a phase marked by a rapid escalation in prices and investment, where valuations drift materially away from the underlying prospects and realistic earnings power of the assets involved.” In a boom, by contrast, the fundamentals eventually catch up. “Booms can still overshoot, but they consolidate into durable industries and lasting economic value,” Azhar writes.Azhar’s framework for determining which situation we’re in relies on five indicators—economic strain, industry strain, revenue momentum, valuation heat, and funding quality—which have been tested against past boom-and-bust cycles and converted into a live dashboard.According to this dashboard, if none or one gauge is in the dangerous or “red” zone, it indicates the AI industry is still in a boom; two reds mean caution; and three or more mean imminent trouble and definite bubble territory. Since Azhar launched this in September, just one of the gauges has slipped into the red zone. Perhaps unsurprisingly, that gauge is “industry strain,” which tracks whether AI industry revenues are keeping pace with the massive capital investment flowing into infrastructure and model development. Capital expenditure from Big Tech and hyperscalers is being funneled into data centers, GPUs, and chips at a much faster rate than the revenues generated from AI products and services. While AI revenue is rising, it still only covers about one-sixth of total industry investment.(It’s worth noting that the gauge’s flip to red was also partly attributed to a methodological update. Earlier estimates included forward projections for 2025 revenue. The new model now measures both revenue and investment based on trailing 12-month actual data, rather than forecasts.)Funding conditions and valuation heat have also veered into cautious and worsening territory. This is largely due to questions about the stability of financing, such as riskier deals like Oracle’s $38 billion debt raise for new data centers and Nvidia’s backing of xAI’s $20 billion round. Getting financing for big data center buildouts is starting to become more complicated and slightly riskier, even as the companies continue to deliver solid finances and steady cash flow.The gap between investor optimism and “earnings reality” is also widening, with industry price-earnings multiples increasing though still well below dot-com era peaks. Revenue momentum, as well as economic strain, are still in the “safe” green zone, but are both worsening.At a glance, all this means we are in an AI boom, at least for now. And other analysts agree, including Goldman Sachs, which said in a note earlier this week that although AI-related equities are highly valued, the U.S. market isn’t yet displaying the broad macroeconomic distortions typical of past asset bubbles like the late-1990s tech boom.While there’s reason to stay cautious—and no shortage of froth—it still might be too early to call this a bubble. And with that, here’s the rest of the AI news.Beatrice Nolanbea.nolan@fortune.com@beafreyanolanThis story was originally featured on Fortune.com