It’s another earnings season on Wall Street, and we continue to see evidence that the Street is antsy about the ROI that AI can generate. The difference between this and the rosy stories about AI we keep reading is a bit alarming, an indication of a problem I’ll look at tomorrow. Today, we need to look at the AI story’s twists and turns through Wall Street, Media-Street, and main street.
Google, Amazon, Meta, and Microsoft have all reported, with Google coming out well on the Street, Amazon coming out OK, and Microsoft and Meta both facing issues relating to their AI spending. There are two views, as is often the case, coming out of Street research. The dominant one is that companies are doing OK, even OK on AI, but there is a fear that the players are at risk of overspending any hope of return. That, I’d point out, is my own view. The contrary view is that AI success is supply-constrained not demand-constrained. Microsoft’s call is cited by this group as the proof. Let’s see what enterprises say.
First, enterprises (by a 4:1 margin) tell me that they believe AI will have a “significant” value to their business, but they believe, by almost the same margin, that this value is likely to take two to three years to realize, and almost all say that they’d be less surprised if it took more time than they would if it required less than that. They still (by a 5:1 margin) think that AI agents are the most likely sources of value, and (by a similar margin) that these valuable agents will likely be primarily self-hosted for governance reasons. In fact, working through the technology issues of self-hosting is the main reason why they see a delay in realizing AI value.
Second, enterprises note that these views are coming largely from the IT organization. Line departments have a completely different view, one that embraces the “copilot” or “chatbot” models (3:1 acceptance of one or both among line organizations), who see value in AI without access to governed core data (4:1), and who believe AI should be consumed as a service, from some giant like our big AI four (almost 5:1).
The line organizations like the notion of agents (4:1) but they favor the definition that gets all the tech ink; an agent is an autonomous AI element. It’s not something that looks like a trusted work partner, but rather something you can give a task to.
Let’s take these two points and apply them to the situation of our big four.
Google has always been seen by enterprises as having AI tools rather than offering AI as a virtual partner, even before the agent concept gained traction in the media. They are still seen by enterprise IT as the player who offers AI solutions rather than AI conversations, and enterprise IT pros tell me that’s also the view of their line organizations. IT pros also say that Google has an approach to AI agents that’s more workflow-driven, and thus better aligned with their own views, and Google is more trusted to handle “lightly governed” data than any other AI service.
What makes this important is that Google’s quarterly numbers, and its AI return so far, seems to be drawing increasingly from symbiosis between Google Cloud and Gemini. IT pros also tell me that Google’s AI tools are encouraging “citizen AI” (more on that later), and to respond quickly, AI organizations are hurrying to respond, perhaps by doing a bit more in the cloud than usual. In any event, it sure looks like both enterprises and Wall Street are starting to see Google as the best of the AI players.
Microsoft’s situation seems, say enterprises, more linked to that “citizen AI” stuff. They are not highly connected to internal AI projects run by IT organizations, but the copilot model is very easy for line organizations to adopt, since it integrates with personal productivity tools they already rely on. There is also, say enterprises, increased interest in line organizations engaging with their own IT to bring AI features into Azure applications, either already partly in the cloud or new ones under consideration. This is responsible for much of the order backlog Microsoft has reported, I think, but Microsoft still seems to be struggling to find an agent story that looks like what enterprises and line departments alike want to hear.
Amazon’s position with AI, particularly with AI agents, is difficult for both the Street and enterprise AI prospects to decode. Of all the AI giants, or cloud giants, Amazon has the least trust among enterprises for data security. Amazon is also not widely considered for AI tools, in no small part because their AI position isn’t nearly as visible as those of Google and Microsoft. The recent deals it’s cut with OpenAI and Anthropic are largely aimed at getting Amazon cloud/AI hosting revenue from their partners, not opening a direct AI play. To the Street, though, a new AI position for a company is at least not actively disproved, where many established ones seem to be on that path. It’s too early to say if Amazon really has any risk or benefit in the wings.
Meta is by far the most problematic player. It’s not that they have no monetization path, but that their obvious path of ad targeting has a limited upside and limited runway given that the ad market is finite and clearly smaller than the theoretical market for AI tools. The fact that it’s cutting staff to offset AI costs is less a proof that their AI can replace workers than a decision made to protect them from Street skepticism on AI spending’s impact on their bottom line.
Alongside all of this is a big problem for everyone; the return on current and promised AI investment. My model says that the current level of cloud AI spending could be sustained only by increasing the monthly AI subscription price to 1.79 times current levels. The promised level would require increasing it by 2.29 times. The question then is how many AI dabblers would continue to dabble at those higher prices. Enterprises (line buyers using AI services) say that their own maximum tolerance for an increase would be less than 1.5 times. Usage pricing of AI, which all the giants are considering, would be generally unacceptable to line organizations because it would make AI costs uncontrollable. For AI agents, the IT organizations can only speculate at this point, but they say that roughly half the proposed agent applications would “likely” require significant trials to validate usage before they could be approved.
The overall story here is, or should be, clear. AI cannot go on as it has so far because ROIs are too low and investment levels (driven by hype and desperation) are too high. Behind the scenes, driven by players like Nvidia and groups like the Digital Twins Consortium, we are seeing actual progress in finding paths to a new set of business cases that AI could facilitate, but this sort of thing is a poster child for what I wrote about the problem with media coverage of tech. Most people, even many in enterprises charged with AI planning, have never heard of some of the important developments in this area, and without it, AI faces ROI problems down the line…and maybe not that far down it.
