My enterprise contacts’ comments show there are two basic models for an AI business case. The first is the business analytics model and the second is the worker process model. The first model is data-driven, closely coupled to company core databases. The second is directed at assisting workers in specific tasks, and is coupled more to the task and the required results than it is to data. There’s obviously a bit of slop in the separation of these models, which we’ll get to.

Business analytics business cases depend on the results of data analysis of data that is normally subject to governance, which means that the same constraints that are applied to cloud-hosting application components that use the data would be applied to cloud-hosted AI using the data. Nearly all the AI self-hosting interest evolves from this model, but it doesn’t get a lot of publicity because most vendors have found it time-consuming to develop this type of AI application. IBM has been the only vendor to really leverage this model, in fact.

The worker process model directs AI at the production of something, the completion of a task. This is often referred to as a “copilot” approach, because in it AI acts as a kind of partner to a worker, helping with things that are “routine” (things the worker is too high-valued to be doing), presentation-related (writing, image production, slides, etc.), or specialized (review, analysis). You can see from these points that “data” is often used in the completion of the tasks, which is the largest source of overlap between the two model definitions. However, most of the AI missions enterprises assign to this model do not expose governance requirements, and so do not favor self-hosting of AI.

You can also divide AI by the delivery method, which enterprises say either capitalized infrastructure or as-a-service. The latter means “expensed”, which means that formal AI project control is normally not applied to the project at all; many line managers, even at the lowest level, can approve as-a-service AI. However, as noted above, the business analytics model of AI is constrained in as-a-service delivery by the governance limits set for any data it depends on. In some cases, enterprises say they have “interdicted” departmental AI applications that have shared governed data inappropriately in as-a-service implementations, but most enterprises say that it’s likely that some violations of data security policy have managed to sneak past enforcement, just as that’s happened in SaaS-cloud services.

You may wonder how this relates to the who-is-at-risk question, so let’s hone in on that now. The question for AI is the extent to which the combined business cases that can be made for the two models can create spending sufficient to fuel continued AI advance, both overall and for each of the relevant market segments/players. The range of potential business cases, their credibility, their relationship to the players in the AI space, and the extent to which AI influences company profits all combine to create the risk profile we’re looking for.

Enterprise information I’ve collected suggest that the as-a-service elements of AI opportunity cannot, by themselves, sustain the current levels of AI investment. Data on this sort of thing is very rough, but what I hear suggests to me that current AI spending does not quite offer a reasonable ROI on investment, so the as-a-service providers (the Big Three in particular; Amazon, Google, and Microsoft) are investing as much on hope as on logic. However, there is an indication that the economic uncertainty associated with the Iran war may be delaying capital projects, which has benefited cloud spending and covered these players with Wall Street.

That doesn’t seem as likely in 2027. Enterprises are divided on whether there’s any chance that as-a-service AI will actually prove out, but almost 60% suggest that they’ll control the cost of AI next year than expand it. That’s highly speculative, of course, but it’s an indication that risk is rising for the Big Three. Of that group, Microsoft is most trusted by enterprises and its office suite provides a natural on-ramp for advanced AI features. Google is next in trust, and its specialized research and presentation tools are also valued. Amazon has the greatest risk of this group, but Oracle and most of all, Meta, face a much greater risk in 2027. So do both OpenAI and Anthropic. There is little they can really do to offset any Wall Street skepticism, and so they are vulnerable to any bad news in the AI sector.

Of the chip vendors, it’s pretty clear that Nvidia faces the greatest risk, since their financials rely on massive infrastructure spending that isn’t likely to be sustained if the Street loses enthusiasm for AI, as it had in the spring of this year, and doesn’t regain it. The volatility of the sector is a good indication that the Street is balancing its love for bubbles with its fear of a tragic shift in market sentiment. Broadcom and AMD have some exposure here, but not as much.

What would save the whole AI space is a set of real business cases. I’ve blogged regularly on my view that only self-hosted applications in general, and real-time process control in particular, can really change the game for AI, and in fact for the tech space overall. I won’t repeat the arguments here, but smaller models and chips are the most likely beneficiaries of this new and as-yet-unvalidated wave, so the AI giants already named can’t expect much from it.

It’s also theoretically possible that enterprises would accept cloud-hosting of AI for applications involving their business-critical data, but right now enterprises tell me that they are even more wary of AI governance issues than cloud governance issues. “Could my AI provider be training his models, and my competitors, on my practices? No thanks!” one said.

This adds up to AI being a high-risk space, all the more so because it’s not replete with accurate financial and technical stories online. Educated buyers aren’t usually what sellers want, but in the case of AI we may need education to get things on their optimum track.

Why would everyone be hyping up telco AI opportunity? OK, yes, part at least could be that everything AI gets hyped up, and everything is looked at a candidate for AI hype. Why is the telco vertical such a candidate, though? The only possible answer is that telcos need to find something to make money on besides routine connection services. That, in itself, is at least making the AI telco wave a validation of a real need. One question it raises is whether AI is more than that, and perhaps another question is whether AI is the only “more than that” out there. If it is, then might there be steps to consider that would open more than one potential new service opportunity. Is there an infrastructure of the future that we don’t have to assign to a specific future?

To try to get at these questions, we need to look at just what a telco role in AI might be. Well, one thing we keep hearing about is that telco use of AI for internal missions would then build an infrastructure base telcos could leverage in other AI service missions. AI-RAN, we’re told, is a stepping-stone, but stepping stones have to lead your steps to somewhere you want to be. The problem with AI is that it offers only three destination options, all of which have major problems, and so does that first stepping stone.

The stepping-stone problem is the familiar confusion of what AI can do with what justifies AI. Operators have told nearly everyone they don’t want another 3GPP-driven fork-lift upgrade to infrastructure. We don’t have AI-RAN now, so clearly it would require an upgrade, and a fork-lift? Maybe. The point is that there’s resistance to spending on the next generation of mobile infrastructure when the last one didn’t pay back. So it’s more likely that vendors would push AI hype to improve the prospective business case than because AI could actually do the improving. Then there’s those three AI destination options and their issues.

First, the GPUaaS story. This comes up all the time, and in theory there’s some financial sanity behind it since telcos have a very low IRR, and thus can afford a low-margin play. The problem is that it’s far from clear that GPUaaS is a viable opportunity. Not only is it not clear whether enterprises would see it as a low-cost-high-data-sovereignty solution to cloud AI problems, it’s not clear whether the current AI giants would address that opportunity if it did emerge, leaving telcos in a race to the bottom.

Second, the telcos could get into the AI cloud business. This would have the advantage of higher margins, and enterprises trust telcos more with core data than cloud providers, by almost a 3:1 margin. But could telcos frame a set of AI services that would be competitive? There are only three of the Big Three to concentrate development in, and over a hundred telcos who largely can’t cooperate for regulatory reasons.

Finally, there’s the elusive edge. What makes it elusive is that the talk about “edge computing” takes on the classic “Field of Dreams” look, where you propose a technology advance with the theory that if you can deploy it, someone will figure out what to do with it, and then pay to do it.

Fact: You can’t justify edge computing without validating real-time applications. We already have “edge computing” to support real-time process control missions in factories, warehouses, etc. To presume that we can make a telco service opportunity from edge computing demands we find real-time missions where this self-hosted “local edge” doesn’t provide what we need. That would mean one of two things. First, you have a real-time process that spreads geographically, so a local edge is impossible. Second, you have a real-time application that involves a combination of compute complexity and frequency of use that makes owning your own edge financially unreasonable.

We have some limited and ineffective market validation of the first point, with self-driving vehicles. Here, the problem is that the vehicle itself can host much of the process control mission itself, and in fact the demands on latency and availability are too high to cede this edge mission to some shared and remote resource pool. Still, I believe there are applications in the production and transportation areas, as well as in utilities, where it would be possible to eke out a value for hosted edge services.

The second of our two hosted-edge opportunity areas is likely to be something involving people, consumers, rather than companies and their workers. I’ve often blogged about a future point where a consumer and phone moved through a series of “information fields” that represented available products and services, interacted with these fields in some way, and then took some action. The action might be simply telling the consumer that something they were looking for was available at the best target price just ahead on the right, and it might be as complex as ordering the “something” and directing the consumer to pick it up. The problem with this is that it would likely take a pretty broad community of investors to assemble something valuable enough to attract users, and of course this means more risk that a single non-player would derail the whole thing.

There is an interesting take-away here, though. All the credible opportunities for telcos involve “edge computing” of some sort. While AI hosting is almost certainly something telcos can’t exploit to improve profits, it is an example of an edge application, and it’s at least credible to assume that some of the future edge needs would include AI. All that means that we could in theory validate at least some of an AI investment with something real build on one of these edge opportunities.

I have always believed that the extreme edge is not a good target. Yes, you could stick AI-RAN in a cell site; you’d almost have to, in fact. However, you don’t get much economy of scale with that, and you don’t really have a great location to converge traffic from a wider area. Metro placement, meaning placement in a centralized location where access network technology would naturally concentrate traffic, makes more sense. There, you could deploy hosting, and so there you’d need a combination of a data center and a WAN aggregation point.

How unified this needs to be is, so far as operators are now concerned, an open question. Certainly there would be a value to having operational unity, the ability to manage the equipment on a common platform, but could even deeper integration be helpful? A half-dozen operators have, at times, commented to me on having a “metro platform” built on a single standard technology base. AT&T has, perhaps, talked most in public about the value of this sort of thing, and DriveNets is a vendor who actually offers it, though their current marketing is (no surprise) more focused on AI missions than on generalized metro.

I think that if there’s a new future service set to boost telco profits, it’s one that’s hosted in metro locations. I think that a metro-centric infrastructure plan is what telcos need to be looking at, and AI is simply a potential technology to further that plan. AI that preps for something real is better than AI that depends on the realization of the hype, and even on that realization creating a very broad opportunity. Might that happen? Sure, but is it a good strategy to hype up an AI story and hope something comes along that redeems it?

I think you can see some of the signals I’ve noted here in Meta’s “manifesto” on AI. The simple truth is that for nearly all vendors, the revenue booms of the past are almost ancient history. They’d love to have another, and this yearning feeds their participation in virtually any hype wave that presents itself. So what if AI is hype? Believing it buys us some time, and maybe during that time something real will come up. If not, well, we’ve never had a shortage of hype.

Of course, the other half of the message of this post is that while there’s always hype, there’s often some reality available to exploit, if hype starts to look a bit thin. In telco-land, it looks pretty clear that hype-thinning is happening now. In other areas of tech, there are signs as well, and the “elusive edge” is likely the converging point for any real opportunity, for any player in tech.

One Wall Street report seems to me to capture the company/Street thinking. It talks about the “mega-cloud” and says that capex monetization (meaning ROI) is stepping up. What’s really interesting here, particularly to me, is that the chart that’s presented talks about cloud growth not AI growth. The report also favors Amazon and Microsoft as the leaders in mega-cloud, with Google trailing, despite the fact that there’s been pretty continuous and convincing evidence that Google has done better with AI ROI than the others.

The reason this is particularly interesting to me is that I noted in the past that the cloud giants were seeing AI as another way of easing everyone toward the “everything moves to the cloud” position. That was a popular view at one time, but it was overtaken by reality, repatriation, and slowing cloud revenues. Remember, the Street loves a bubble, and so it’s been concerned that AI was running its course, bubble-wise, and that quantum computing was developing too slowly (and was too geeky) to take up the bubble banner. So why not attribute a reversal of slowing cloud growth trends to AI, thus boosting the latter bubble? In the chart I cited, all three of the mega-cloud players showed better cloud growth in the second quarter than in the first. Since we don’t get firm AI revenue numbers from the Big Three, who’s to know?

We are, perhaps, or at least maybe we can.

Enterprises have been telling me about three trends they’ve seen this year, and both could be important in decoding whether AI investments are really monetizing.

The first trend is the acceleration of line organization interest in the personal-productivity form of AI. For all the years I’ve been in IT, there’s been tension between the line departments and the IT organization. In one of my prior employers, this tension resulted in a periodic shift between having all IT independent under a CIO who reported to the CEO, and having an IT CIO and organization for each line department, reporting to its VP. In all organizations, it tends to manifest itself by line attempts to break projects free of IT. Think low/no-code, citizen developers, forth-generation languages. Even SaaS is an example. This is easiest to do when the technology you need can be acquired as a service, meaning expensed.

Publicity, particularly publicity related to bubbles, thrives most where populist interest can be leveraged. Thus, stories about how wonderful AI is and how much it can do for you are targeted at average people, not IT professionals. The latter make up only a bit over 7% of the US workforce, and less than half of knowledge workers. This AI publicity focus, then, empowers the average at the expense of supporting the needs of the specialists, which actually makes it harder to develop an in-house AI capability. That’s our second trend.

The third trend is the increased competition for sales in a troubled global economy. Sellers know that financial concerns are impacting buyer confidence, and in many cases actual buyer budgets. What do you do to respond? Cutting prices cuts your revenue unless you can make up the discount with volume, which is hard to do in a challenged economy. So you look prettier. You try to make your stuff, product or service, look more attractive, make it easier to decide on, make it more accessible to consider. All of these things tend to focus seller attention on the front-end piece of applications, which happen to be what runs in the cloud in most cases.

This focus on prettiness is also smart if you consider that one reported impact of economic uncertainty is an increase in seller reliance on affluent buyers. These buyers are more likely to be tech-literate, more likely to comparison shop, and more susceptible to clever messaging delivered online.

In the early days of AI, meaning largely last year, enterprises were focused on in-house AI deployments just as they currently focus their core applications on self-hosting the core elements. There is still progress being made here despite the second of our trends (Palantir’s quarter shows that), but the first trend has driven what Palantir calls “token slop” and the third has emphasized the cloud-resident piece of applications. As a result, nearly all the enterprises who talked about self-hosted AI project plans in 2025 say they are delayed now. About one in 8 says they’ll not likely make much progress this year at all.

Many of those same enterprises do say that they’ve increased cloud spending, and nearly all of them say that cloud AI usage in their company has grown significantly, though most of my contacts are on the IT side and don’t have a direct way of measuring what line departments are expensing. Thus, there is anecdotal evidence that the mega-cloud boost may be more than a couple of quarters long, which would be good news for the cloud giants.

The challenge is the other things there’s evidence for. Every single enterprise who told me that AI usage was growing also said that it was out of control, that budgets were likely to be blown, and that constraints were likely to be applied in 2027. The IT organizations also say that there is little chance that the current AI missions, the “token slop”, would simply be brought in-house down the line to control costs. They think, as one put it, “we hit pause on rational AI planning this year.”

That’s not good news, people. Absent new business cases to drive new spending, the mega-cloud story is really a story of transference of hype. Everything didn’t move to the cloud, so now everything will move to AI, which happens to be in the cloud. The cost and data governance problems of the cloud, the problems that led to “repatriation” stories, have not been resolved.

Unfortunately, the AI hype and the “token slop” have covered up attempts to bring rationality, business cases, to AI. We may be overlooking a fundamental point, too. AI doesn’t have to “move” to the cloud, it has to move to the data center, and it’s far from clear that we know how to do that, or even want to know.

Why do students love AI? One told me it was simple, “It lets me work less, and someone else is paying for it.” That’s the same view that enterprises think workers have. Maybe AI is like a teen romance in more ways than one.

The vendors are a good place to start. All my friends, representing 27 different vendors or service companies say that they love an opex-reduction story. They’re sorry to see the number of projects driven by business benefits has declined, for the simple reason that this shrinks the pot they can draw on for increasing their own sales.

On the buyer side, it’s a lot more complicated. Senior management (VP-level) tends to like opex reduction as much as the vendors, because there’s pressure on them to cut costs if their companies can’t achieve target growth rates on sales improvement. But, and it’s a big “but’, there’s the ongoing concern that something targeted at opex reduction will end up breaking a critical tech service, feature, or application.

Part of the reason for that risk adversity is feedback from subordinate managers. Concerns about the risk of opex reduction, particularly involving new technology like AI, rises with every management level you descend through. I think the obvious reason for that is fear of personal job impact, because the perception of risk is highest among operations personnel below the management layer, but the specific issues still need to be looked at.

The top problem cited by both operators and enterprises is unproven benefit claims. Only 18% of enterprise and only 11% of operator ops teams said that they were offered “convincing” proof of benefits. Of the rough quarter pf both groups that said they’d tried to get contractual steps to prove out promises, the great majority said that vendors had backed off claims when told there would have to be validation strategies included in the deal. This “vendors won’t back claims with benchmarks” group were the most vocal in disputing vendor promises, not surprisingly.

Vendors hedge a bit in their comments on this point. I had nine cases where I got both seller and buyer comments on the same deal, and in those the vendor said that the problem was that there were too many variables introduced by the buyers’ own operations practices and team skills to be able to make firm promises. Buyers, to a degree, understood that, but their point was that where specific practices and skill levels were needed, fulfilling the need had to be incorporated in the project to deploy the new opex tools, and so did the costs.

The second-most-sited problem was fear of losing key personnel for job-security reasons. “As soon as I launch a project that’s justified by cutting jobs or hiring, the most mobile of my team start looking for another job,” one senior manager told me. No amount of promises, they say, will prevent this from happening because everyone seems to know of a case where those kinds of promises were broken.

Specific risks of a major technology-induced failure is a very close third, and when you focus specifically on an AI tool, this risk rises to second place. A similar shift happens where the operations focus is a business-critical process. “I might reduce my operations costs by twenty percent, but if I take down all my stores I’ve killed my job and maybe the company,” one CIO said. The problem here is that, like benefit claims, stability claims are hard to prove as far as buyers are concerned. Because right now there’s a very polarized view of AI stability/suitability, it’s a greater risk as far as the technical decision-makers are concerned.

With AI, though, there’s considerable variation in buyer attitude depending on the source. Right now, Juniper customers are more willing to accept that AIops tools won’t blow their network out of the water than users of tools from other vendors, and AI agents supplied by someone other than the provider of the infrastructure whose operation is targeted get the least love of all. Self-hosting of AI tools doesn’t do much better, because senior management doesn’t see/read much about the future of self-hosted AI.

Next on our list is an issue that everyone cites, but not at the top of their list; there’s a difference between improving operations group productivity and reducing opex by cutting labor costs. As one manager put it, “You can’t cut fractional employees”. Another said “If people get a job done faster, it doesn’t matter if there’s no other task they can take on.” How many humans are needed in an ops organization? Unless you’re willing to trust AIops unsupervised, the number is greater than zero. You probably have three shifts you need to cover. You have to allow for sickness and vacations and likely training. So many businesses can’t really reduce opex without eliminating that human oversight.

A related problem here is simply who to target. Both enterprises and operators say you can’t target senior people because you can’t lose them; they’re the ones who know systems and “how to keep the lights on”. But targeting junior staff or even limiting hiring reduces the unit value of labor and the available savings, and “can you expect AI to grow up to be a senior operations specialist?”

The final issue cited is perhaps the most telling; general fear that impacted personnel throughout the company will blame those responsible for any changes they don’t like. Decades ago, a wise CIO told me “the worst project you can propose is a conversion; it’s all cost and no benefit because the best you can hope for is that nobody will ever know you did anything.” There have been many stories about how disappointment needs a focus of a responsible party to punish. AI is no exception.

This issue plays in concert with all the rest, because of something a senior ops manager said “AI is the employee you can’t read a resume for, can’t qualify for a job, and probably can’t get rid of.” Ops management will surely be held accountable for anything that anyone feels has gone wrong because of ops automation. One operations executive at a joint banking network consortium put it this way: “If I mess this up, there’s thirty banks I can never work for.”

Right now, AI usage (says the Economist) is growing faster than AI revenue, suggesting that people are fleeing one free model for another when they run out of free tokens. That’s not the behavior of someone who’s found value, it’s the behavior of someone despairingly seeking it. It argues that demonstrating an AI business case is a major issue. That’s true overall, and it’s surely true for operations missions. Both enterprises and operators believe AI can be helpful, but they’re not sure whether the help can justify the cost.

My view here is simple. Nokia wants to sell wireless infrastructure to operators, and a major 3GPP standard update is certainly a historical driver of major investment. The problem is that 5G didn’t deliver on the promises made for it, that there would be compensatory revenue generated to offset costs. When 6G work started, operators said they didn’t want “fork-lift” upgrades because they feared that the business case hype that surrounded 5G would likewise infect 6G. Fork-lift upgrades are what vendors want, of course, and if you look at the proposed 6G targets, they sure sound like the same stuff 5G was supposed to deliver and didn’t. So, what’s Nokia to do? Jump on the AI hype bandwagon.

Of the 64 operators who commented, all agree that their company uses AI. In fact, all said they used cloud-hosted AI, and 53 said they were totally blowing their budgets on the services. Those of my contacts who are truly AI literate say that their cloud AI costs far exceed what it would cost to deploy their own resources.

However, only 17 said they actually hosted any AI on the premises, and only 11 said their deployment was targeting generalized applications; the rest said that they had AI “embedded” in some application. For Nokia, the good news was that all 64 believed that AI-RAN could “enhance mobile network capacity and efficiency”, to quote the article I cited above. The bad news was that only 6 thought that AI-RAN gains would generate enough to actually make a business case for massive 6G spending.

The reason I picked the article I cited, which is a Wall-Street-focused analysis of Nokia, is that it demonstrates the issue that network vendors, including Nokia, face. The company is over-valued, says the article, and I agree. The reason is that you can’t keep increasing spending on tech when your own revenues can’t keep pace with the increase, or you look worse on the Street, which no public company can afford to do for very long. Network spending, across both enterprises and network operators, faces the problem of having no new revenue to justify major new spending plans.

I always told my consulting clients that a problem of public perception was a PR problem, one that had to be solved through marketing messaging because “Bulls**t has no inertia”. So, like many other companies, Nokia is jumping on the AI hype wave because it will provide them some air cover for what Street analysis says is an excessive valuation.

Getting cover, of course, isn’t solving the problem. Of my 64 operators, all agree that the only long-term solution to their current challenges is new revenue. Of the 64, 49 think that has to come, eventually, from higher-layer services. The remaining 15 still believe that new connectivity revenue is possible, but all 64 agree that it is likely of not certain that their initial revenue gains would have to come from connectivity. Where is this new connectivity coming from? Again, all 64 think it has to come from something related to IoT.

Pretty much all the telco infrastructure companies have touted IoT in the past, but the problem has been a combination of pathetically naive positioning (sell to machines because you aren’t limited by birth rate and maturation) and insipid offerings (device registration services without any notion of why the devices would be needed). Nokia actually did a bit more to expose things like the dependence of 6G on private 5G and the applications of private 5/6G than most vendors. However, they didn’t address the fundamental problem of new services, new benefits.

Which is? Well, if you look at networking today, you see applications that existed and were then connected. Connectivity only required that you prove marginal utility gains by extending application scope and capabilities via connectivity. Today, though, we’ve largely run out of applications to exploit. Transaction processing is the core of business networking, and entertainment the core of consumer networking. Been there, done those, got the tee-shirts. To open new connectivity opportunities today, we need new applications, which means that the infrastructure and willingness to pay for those has to be worked through. Opportunity in network services, in 2026 and beyond, needs an ecosystem of players to develop, and telcos aren’t much good at even thinking about ecosystemic issues.

Nor, perhaps, are their vendors. However, some of my friends in Nokia (who agree that AI-RAN isn’t going to make a 6G business case) think that the missing step is that the edge deployment of AI-RAN results in an edge deployment of AI, which in turn gives the telco first an opportunity to sell edge hosting, and then to rise up from an IaaS model toward an SaaS mode, meaning to host AI-based tools to facilitate IoT missions. Most of these people, though, are not confident that Nokia is going to exploit that transition of mission, or even that senior management recognizes what would be needed. The great majority agree that they’re not doing the necessary stuff today.

What’s needed today is an ecosystem-builder, and I think that the initial focus has to be on the use of private cellular technology and the “facility connectivity” mission. The basic problem with building an IoT ecosystem for 6G to support is that if 6G is needed for that ecosystem, you can’t start it without having telcos bear that dreaded “first cost”, the period when investment is huge and returns are yet to develop. But spread IoT out from an assembly line to a whole multi-structure facility doesn’t require 6G service, only facility-wide connectivity. You could do it with private 6G, even 5G. In verticals where a “company” is made up of multiple “facilities” that are metro-concentrated, you can bridge your facility starting point with targeted WAN connectivity.

To make this work, Nokia and other vendors would need to collect the players who would contribute value and gain returns, a broad enough group to assemble solutions for the target verticals. And they’d have to do this in a very public way, to stimulate coverage and create some air cover, even tie in with AI, which is itself in great need of some practicality injection. Could Nokia do this? I think they could. Will they? That’s a harder question, one even my Nokia friends can’t answer confidently.

Nor can the telcos, which is a shame given that they have the most to gain from an answer.

Those of my readers who know me likely know that I’m well beyond the traditional retirement age. I actually did my first network project in the 1960s, one that required me to modify IBM field-developed software to build a distributed computing application for IBM 360s. I also led the team that built the first IBM SNA financial network that included no IBM computers at all; we had to write SNA for a minicomputer. I ran another team that implemented the X.25 packet-switching standard on another minicomputer. I’ve been a successful industry analyst in networking since 1982, and a writer for industry publications from that same time. I’ve seen the dawn of the Internet, broadband, and the transformation in telecom, been a partner in a hedge fund and learned what Wall Street has done, and is doing. It’s been profitable, fun, and work, and sometimes disappointing, but through it all I’ve been dedicated to facing reality, facing the truth.

I’m a technologist, a programmer and software architect. I’m also a writer, a speaker, a communicator. When I became an analyst (around 1982) I started surveying users to find out what technology they needed, and my work was helping vendors align product plans to buyer needs, or helping buyers exploit technology successfully. That, to me, was interesting, honorable, and fun.

It’s less fun today, perhaps because truth and reality seem less valued, important. I think that the shift to ad sponsorship of publications, the explosion of online publications, and the “chase clicks” mindset that’s resulted, has changed both the media and the role of analysts…like me. There was a time when companies wanted to know what buyers wanted and needed, and asked me for that information. Now, they want others to buy what they sell, want me to promote that, and that’s a PR role not a technology role. Not a role I want to play.

Tech publications are under “click pressure” these days, to match stories to SEO, to maximize ad revenue, and to manage costs. More and more often, I find that I can’t write what a publication wants because I don’t believe it’s true. Or that, when I do write it, they can’t seem to get the payments to me on time. I’ve gradually stopped writing for publications because of this, and focused on blogs. But even with blogging, I find myself constantly saying that this or that technology is over-hyped, that there’s a bubble. How many times can that be said, and how many times are you willing to read it? It sure looks like this is a waste of my time, and yours.

I’m not blaming vendors or publications for the state of affairs. Every company has to think about its profits, and make a decision about what serves them best. If that’s chasing clicks, I get it, but I’m also convinced that the click-dominated mindset we see today is pushing us toward tech stories as entertainment rather than enlightenment, and it’s enlightened people who can make business cases. I think that short-term sales focus and riding hype waves is a bad profit strategy, but the decision has to be made at the individual level, by a business and by decision-makers.

And so, I’ve made my own decision. I’ve had a long and successful career as an analyst, keeping true to my own values, but the industry doesn’t seem to share them. Change is inevitable, but I’m not going to change my way of working to support something I don’t believe in, and that I think will be bad for everyone who loves tech as much as I do.

For the enterprises who have shared stories with me, I’ll keep your email link up until early 2027. Don’t worry about the privacy of the information you shared under my promise of complete confidentiality. I will destroy all the data next year, and under no circumstances will it be passed in any form to others. If you like the idea of having all that good enterprise data, don’t bother to contact me with an offer to get access to it; I refuse no matter what terms you offer. For those who contributed, I thank you all for your help in understanding tech reality.

I plan on keeping my hand, though a light touch and a weekly-only blog, through early 2027, and if some consulting or writing comes along that fits my profile, I’ll look at it. Beyond that, I leave tech, with a combination of sadness and gratitude, to others.

IBM’s CEO attributed the problem to a failure by IBM to recognize the shift of spending to open-architecture servers and storage that IBM didn’t make. Mainframe hardware, which had been strong, softened. The Financial Times attributed the problem to a shift in spending away from software to AI. Wall Street research generally dismissed the shift as a short-term reaction. Let’s start with what enterprises have said.

First, it’s important to note that IBM’s systemic strength has always been with its mainframe products. Mainframes go back to the System/360 of the mid-1960s, and they were expensive and powerful giants of the data center, typically purchased by large enterprises. Not only that, these enterprises regularly wrote their own software to support core missions, and often this software was difficult to redo. Arguably, IBM made it easy to stay with mainframes, and IBM’s decision to largely stay out of the open-model Linux server business surely didn’t facilitate any shift.

The mainframe was the basis for much of the enterprise transaction processing activity, and “transaction processing” is the handling of the routine business paper flow that gradually became computerized. Over time, IBM customers tell me that their work to “modernize” their core applications really focused on shifting the front-end portion of those applications off the mainstream, the GUI, and leaving the core data management and analysis portion on the mainframe.

Transaction processing load advances, generally, at the pace of commercial activity. Industry-wide, GDP growth is a reasonable measure. Shopping, the front-end and GUI processing stuff, has tended to grow faster as companies shifted to more online shopping support. Thus, one could expect mainframe growth to be linked to business results, and business aspirations could reasonably be seen as the driver of front-end activity, the things that have typically been handled by open servers, the Internet, and the cloud. Economic uncertainty, of the sort created by the Iran war and oil crisis, impacts buyer confidence, and as a result impacts the sellers’ IT plans.

AI enters into the picture here, but not as an alternative to mainframe software. No enterprise has ever told me they would be likely to even consider moving transaction processing to AI, even to the self-hosted form. Some (about a third) think AI might impact some of that front-end stuff, but the impact is as much the impact of a shift from capital hosting to expensed cloud/AI as it is a statement that AI could actually do a better job. Right now, enterprises sometimes find that AI pricing is artificially low, making an AI solution to what was originally seen as a cloud/SaaS application more financially attractive.

But what AI has done is drive up the cost of that open server technology, memory and CPUs and so forth. That is generally seen by enterprises (almost 80%) as a short-term impact, and Wall Street agrees, so it’s prudent to defer projects that could require more open-server investment to a later time when components are cheaper. The actual suppliers of this are somewhat insulated by the fact that AI is also hosted with the same sort of gear, but IBM doesn’t sell that stuff.

How about IBM’s position as the smart player in the AI game from enterprises’ perspective? IBM is the champion of self-hosting AI, but their role is mostly in consulting, in software, and in prepping core data repositories for access by AI. The problem with that isn’t just that it’s a limited upside—it was enough of one to be beneficial to IBM’s profits last year and up to this quarter—but also that it isn’t how the vast majority of AI influence is being directed. What do you hear, read, about AI? The stuff that favors the plans of those AI giants I blogged about earlier this week. This resulted in something I’ve noted in past blogs, a growing disconnect between how IT organizations saw AI and how line departments saw it. Even CEO/CFO types tended to think more about the personal productivity applications of AI than the applications of AI to data analytics, which is where the real business cases could be made. Part of the reason was that you could expense cloud AI, and usually adopt it without approvals. Not so with self-hosted AI. As a result, well over three-quarters of enterprises who talked about self-hosting plans early this year now say that their plans have been delayed. Almost 20% say the projects are no longer being actively pursued.

OK, where does this leave us with IBM? I do think that IBM has benefited a bit from the AI hype wave, and now that benefit, and the stock appreciation associated with it, could rightfully be withdrawn. However, I think that much of the impact of that has already been felt, and that the Street is right saying that the second-quarter is a blip that will correct later in the year. A drop of over 20% is short-selling, not a threat to IBM’s value. Less than half that would be appropriate.

But…there is a signal here, maybe even two. IBM’s power in the market is linked to a small field of giant customers who are perhaps a bit of a captive audience. Captive now, but forever? If IBM could have pushed self-hosted AI successfully in the first half of this year, they could have created a market wave of reality that would have served as a counter to all the AI hype, and they could have spread the influence of self-hosting to a broader market. Red Hat was the path this should have taken, but IBM didn’t take it effectively.

Which raises the second signal. Could this be a hint that IBM might acquire an open-server player? Might they have pre-announced a reason for an M&A deal that would/could become public before their earnings date? Tough call, since this might impact the willingness of other open server vendors to endorse a Red Hat strategy with their customers, doubly undesirable now given that Red Hat is trying to win over disgruntled VMware customers. Could it mean some new AI direction for Red Hat, a software direction? Or something new for IBM? Obviously, we’ll have to see what develops, but something could very well be in the wind.

IBM isn’t the only one at risk here, though; it isn’t even the major one. Who should really be sweating? The AI chip players. What AI has proved up to now is that if you give something away, people will take it. AI has also proved that people, voters, don’t like the giant AI data centers. What it’s already starting to prove is that if you invest without a return, you’re going to be punished by Wall Street. The real hope for AI to sustain itself is new, real, business cases, and IBM is proving that we’re not working hard enough to develop them, to distribute both the value and the impact of AI. If that doesn’t change before the end of this year, 2027 could be a very bad year for AI.

A digital twin is a replica of something, obviously. That something could either be a real physical system, which we could call a “world model”, it might be a totally imaginary system like a computer game, or if might be a mixture of the two, an artificial framework that is “inhabited” by some real things or people. This last concept, to me, relates to Meta’s “metaverse”. The point is that a digital twin is a tool that generates output as a system, based on events that may be external or internal.

As a software architect, I’m naturally interested in the way that something like this could be created. To me, the solution has to lie in the fundamental rules that would define the system the digital twin represents. If the twin creates a fully imaginary framework to represent, then it has to set those rules completely. If it represents a real-world system, it has to be based on the rules of that system, and be synchronized with its state or it’s no longer a twin. The latter is clearly a sensor/event problem, but the former isn’t so clear-cut. We need to think about how those rules could be inferred.

Arguably, a collection of people is the most complex real-world system. Every element of the collection is autonomous, so do you have to model every aspect of human behavior for each element, and then every possible interaction? I submit you do not. A collection of random people isn’t a “system” in the sense of IT targeting; for that, you need some common link. Work, process automation, provides this sort of focus, and not surprisingly most digital twin work has been done in this space. I’ll look at the rest below, but I’ll start with process automation missions.

The nice thing about process automation twinning is that the process frames the IT structure. There are steps in almost every process, and the steps proceed in a sequence determined by conditions. Each step in the process is itself a kind of black-box micro-process, and the complexity of the process as a whole is hidden by the fact that the step complexity isn’t visible outside the step.

If you think about it, this mirrors the way biological organisms seem to work. We “see”, “hear”, “walk” and so forth, without conscious awareness of the details. Some of these things seem to be hard-wired, or at least assisted by some built-in functionality, and it’s fair to call these “instincts”. A digital twin used for process automation is, in a sense, a set of instincts linked in a way that’s similar functionally to how they’d be linked in a brain.

When you introduce human workers into this, you don’t change the high-level process much; the worker has a greater breadth of adaptation than a device would, and so can respond to a broader range of conditions, many of which won’t require a specific definition/action to be preplanned. “Place a broken item in the bin” relies on human ability to detect an abnormality that’s out of range, without cataloging in advance every property that makes it so.

Machine learning could, in theory, be used to “train” a micro-process implementation, providing that the properties that made something “broken” can be detected at the compute/ML level and reported to the model. What enterprises tell me is that when you try to replace workers in roles like “inspector”, you quickly find that it’s probably smart to rely on the AI/ML equivalent of eyesight, because what the worker is using is that sense. It’s not only more logical to assume the machine analog of eyesight is essential in training AI/ML to perform human-like functions, it’s certainly easier to visualize how you’d train the model.

What I think this leads to might be broadly important. If you want to use AI in any form to perform or manage human activity, you have to consider how humans themselves make decisions and take actions. Whether artificial general intelligence (AGI) is a goal or not, whether AI could achieve human-like consciousness or not, humans and other critters have evolved to deal with complexity at least in part by dissecting “living” into tasks that can be executed autonomously, without conscious involvement. Some of this is accomplished by hard-wiring the brain, by “instincts”, and some can be trained in through repetition, which I’ll call a “habit”. This is why I favor the notion of “distributed” AI over the notion of a centralized all-knowing model.

Now let’s look at the non-process-automation applications, the best example of which might be the “metaverse” or a computer game. You have avatars that represent real beings, including real people, and you have spaces that the avatars inhabit. The people behind the “real” avatars see, on their device, the metaspace that visualizes the metaverse area their avatar occupies. Since the goal is to make the virtual-reality experience realistic, we would want the metaverse experience to mimic reality, though we could argue that a metaverse and a gamespace would differ depending on just how close to objective reality the experience was supposed to be. Meta proposed imposing rules to prevent metaverse avatars from doing things that their real human equivalents would not be able, or permitted, to do.

The rules that collectively govern the virtual-reality experience, that decide what can be “seen” and “done”, could be established by software/model constraints, or trained in perhaps by having a model analyze video. The latter approach is what seems to be emerging as a means of controlling robots that are supposed to perform at least a credible subset of human tasks.

Perhaps the most obvious, and most important, question all this raises is how it might impact LLMs and what we almost universally see as “AI” today. An LLM does not think, nor does it learn in the way people do. It builds word sequences based on your prompt as a “seed”, and on models of sentences learned from its training, often from the Internet. Could any of this be improved by introducing some of my points above?

I’d looked at “knowledge extraction” myself over the years, and one thing I found was that it seemed important to classify sentences in order to extract information. You had, for example, “identity” sentences that asserted what something is, was, isn’t, wasn’t. You had “descriptive” sentences that qualified something by stating properties like color, size, or shape, and you could apply both these to objects (“The car is red”) or actions, (“Running is fun”) You could also classify sources based on credibility and intent. A dictionary is an authoritative source of definitions, Wikipedia is a more credible source than a randomly selected site. Also, in general, sentences are structured in a specific way, which tells us what to expect for terms within them. How much do LLMs accommodate this? I don’t know; some of my contacts who do say that it varies.

What I really wonder is whether the training process mirrors how we learn, which would seem important if artificial intelligence is our goal. We learn through observation, through tutoring, through reading or listening. We are exposed to these stimuli not all-types-at-once, but successively over time, which suggests that we probably focus on sensory classifications first, then on parsing language in spoken or textual form. Would an LLM learn differently, better, if it tried to mimic that? Would it learn better if, instead of learning “a bird can fly” as a word sequence, if first had a solid view of what a bird was, and what flying was? Does learning order matter? We probably learn through the creation of what I’ve called “habits”, which are sequences of thought that have become almost automatic. When we see a bird fly, we’re not conscious of how we identify it, or how it’s flying, but we can learn that, and if we do, we’re not conscious of how we apply the lesson. Could AI mimic that?

I think there’s a need to look at AI more through the lens of digital twins and ML, to refine models and training to align with the way we learn and think. Otherwise, we don’t have artificial intelligence, we just have artificial.

What we’re going to do for each company below is to assess their AI exposure, what specific AI developments they depend on for long-term success, what they’re doing to firm these developments up in the market, and how likely that is to be successful. We’ll take the companies alphabetically.

You’ll note that the so-called “magnificent seven” are all on the list. They are Nvidia (NVDA), Microsoft (MSFT), Alphabet, Amazon (AMZN), Meta Platforms (META), Apple (AAPL), and Tesla (TSLA). The Seven are poster-children for the Street’s AI angst; the group has under-performed significantly, with a P/E valuation premium relative to the S&P 500 at its lowest level in over 10 years. Only Google outperformed the S&P 500 this year, and you’ll see why below.

Amazon is only indirectly exposed to AI, for the obvious reason that it has a wide range of businesses. Its AI push is through AWS, where it’s promised a $200 billion AI infrastructure investment. The company’s AI focus, then, is really the enterprise, and its cloud business is an obvious path to business AI services. Amazon also understands that enterprises have not been willing to surrender core business data to the cloud for governance reasons, but it’s not yet clear that they have an answer to those concerns. One possible strategy is to focus on government as a vertical; they already have a strong Federal systems approach, and they’re rumored to be looking more at state/local and international government customers. Wall Street is still concerned about their ability to monetize their AI investment fully.

Anthropic is one of two AI giants that is not yet publicly traded, though it has filed confidentially for an IPO later this year. It’s focus, with its Claude model, is enterprise agentic knowledge worker support. Its tools tend to act as autonomous assistants to key workers, which enterprises tell me would make up somewhere between 8% and 15% of their workforce, depending on the vertical and the nature of the business. The big challenge they face is governance, since this mission could easily involve using company-private data. Like OpenAI, they’ve been making announcements to increase the buzz around AI, including a recent one on “J-space” an internal mechanism they claim to have found in their model, which might explain how AI works.

Apple may be one of the two smartest players in the AI game. They recognize two things. First, we still can’t identify roles that users or workers would pay for, to generate a return on AI investment. Second, it’s always a risk to ignore a hype wave. Their response has been to respond to AI publicity with relationships (like the recent deal with Google) that minimize their direct investment but still satisfy the demand for them to demonstrate an AI strategy. Historically, Apple is more focused on the consumer/individual than on the enterprise, which could be a risk for them because consumer willingness to pay for AI is likely harder to develop.

Google is, IMHO, the smartest player in the AI game. Android and Pixel give them a major tool in promoting personal and “edge” AI. Their search business gives them a consumer position, and their cloud business an enterprise position. Google’s workspace tools, from documents to email, are an on-ramp to focused AI agents. Their Gemini models are among the best, by enterprise rating, and their agent focus on documents, videos, audios, and images mean that they can apply AI to the creation of personal and business material fairly easily. Their cloud business gives them a strong on-ramp to evolving IoT and world-model missions, too. So far, they have not focused on wearables, which means that could be a risk if those devices play a major role.

Meta has perhaps the biggest challenge of the giants. Their real strength lies in Facebook, a consumer offering, and in their “metaverse” direction. The problem is that consumer AI is the most problematic source of ROI, and while the metaverse is perhaps the first full articulation of a world model, the company failed to develop the initiative properly when it was announced, and so a difficult re-launch would be needed to make it more broadly useful, especially to enterprises. Meta has AR/VR glasses, though, and these are arguably a major piece of any real-world AI application, even for enterprises.

Microsoft is largely focused on business use of AI, and like others with this goal, is focusing more on agents designed to be embedded in workflows. That’s consistent with how enterprises see their own goals, but whether than can succeed is again highly dependent on governance/trust resolution. The company has historically linked to OpenAI models, but has been self-developing smaller models that could, in theory, be distributed to the user level. Microsoft Azure is perhaps the most credible enterprise cloud service, and Microsoft’s Office and Teams tools give it a direct link to workers, but is it at risk to data governance? Is it trying to use AI hype to boost its cloud business? Maybe, but they have a good enterprise shot.

Nvidia is in an interesting position. It’s current revenue is almost totally dependent on cloud AI giants, but that makes it dependent on the cloud model and almost an automatic enemy of self-hosted AI. At the same time, though, they’ve done more work on world models, digital twins, IoT applications, and even enterprise self-hosting than most of the other giants. I think they know that the current hype wave will crest and fall, but they also know that any attempt to promote a successor concept like self-hosting will hasten the dip and likely hit their bottom line, temporarily. Thus, they’re likely to continue to promote the hype while preparing for it to fail.

OpenAI seems to have a dual strategy for AI. The “real” track is toward enterprise AI agents, cloud-hosted and providing the same sort of knowledge worker support that Anthropic targets, buttressed by the creating of custom models and integrating with partner companies to create an ecosystem. The “hype” track is aimed at keeping the company’s name in the media by talking consistently about things like artificial general intelligence (AGI). OpenAI is prepping for an IPO, but they recently developed a major problem that could impact their IPO and credibility overall, a lawsuit from Apple alleging theft of trade secrets.

xAI (including Tesla, SpaceX, and so forth) seems to be focusing more directly at the consumer, despite the fact that some of the elements of Musk’s AI empire are clearly non-consumer (SpaceX). All of the technologies in the empire support each other; Tesla and SpaceX gather or will gather data that populates the AI tools for training. He is also working to create “virtual employees”, which take personal applications into worker applications. Musk’s own mindset is the major driver of policy and innovation here, which is potentially an advantage and also a risk. His breadth of products relating to or using AI means he can likely take the ecosystem in many different directions depending on market requirements.

Which of our giants is truly gigantic? If that was a question with an easy, solid, answer, everyone would be hunkered down on it. The fact is that there are three ways AI success could be achieved. First, sell it to consumers. Second, sell it to workers (perhaps through their business) directed at personal productivity, and third, sell it to businesses to somehow improve operations at a higher level. Right now, the focus has been to pluck the low apples, which means go after the consumers and workers who have the greatest willingness to pay, the highest economic value. Are there enough of these to make AI a big long-term success? I’ve never believed there were enough, and I still don’t. The giant of the future has to leverage another path, one we can’t yet prove out.

My view is that Google, Apple, and Microsoft have the clearest path to AI success, simply because they seem best aligned to weather the hype-to-reality shift that’s surely coming, meaning they can dance in at least one credible direction. Meta and xAI have a big upside and almost equally big downside depending not only on market direction but on their own actions, which are difficult to predict. Nvidia’s success depends on the delicate balance between exploiting the current hype and dealing with its inevitable crash without actually hastening that event. For the rest, it’s simply too soon to tell how they’ll shake out.

Light Reading notes, in THIS piece, that the problem with 5G stemmed from the failure to realize the IoT connections that were claimed for it. We have, today, roughly 4.7 billion cellular IoT devices according to my modeling (LR quotes Ericsson as saying 7.5 billion, but I don’t see that as credible). That’s a tenth of what a Cisco 5G cheerleader told us to expect by 2020. So sure, under-realizing device connection potential destroyed a telco pay-for-connections revenue plan, but that doesn’t address the question of whether telcos could have done anything to connect more devices.

Vendors, of course, always want to portray an exploding need for their product. Get your capacity in place right now, dear telco, or be swamped by a zillion sensors who will be demanding connectivity. Your competitors are already plotting how to divide up the sensor customers you’ll lose. The threat of IoT explosion was enough to serve vendor purposes. But why was this all hype and nonsense? Don’t we have real applications out there? It depends on your definition of reality.

I had a nice exchange with a public utility who used cellular connectivity for meter reading. They pointed out that the big problem with the notion that every metered service would evolve to use cellular-connected meters is that the cost of this transformation to and execution of an IoT strategy has to be significantly less than the cost of reading the meters manually. That cost includes the cost of the new cellular-linked meters, the cost of installing them, and the cost of the service used to connect them. This utility said that their meter-readers read an average of 600 meters per day, and the meters had to be read only monthly. For this utility, the cost/benefit of cellular reading was unsatisfactory. The point is that most of the hypothetical applications of cellular IoT are really extensions of simple transactional missions, which means that you don’t need the connectivity until you’re ready to generate a transaction. The thing that transforms the mission is the introduction of some process-control requirement. Suppose you want to be able to identify a customer whose usage rate is suddenly abnormally high, to avoid having something like a leak or short consume more than you’ll likely be able to bill, risk damage to a facility, or whatever? Suppose you’re going to manage usage in peak periods? Now it’s a lot easier to justify real-time connectivity, but those missions are a fraction of the total IoT missions today.

Rural areas pose a much greater challenge to manual reading, of course. One rural utility told me they could read only 10 meters per day per reader, and extensive vehicle use was required. However, they also said that they used their meter-readers for other missions, including inspection and replacement of equipment. This utility was very interested in RFID or “proximity” reading, where instead of having a call-home capability built into meters, the meter simply responded to a query issued by a reader that might simply drive past. Proximity automatic meter reading lets readers do thousands of meters per day, and the cost of a cellular connection is eliminated.

The point here is that everything that you could do in a theoretical, technical, sense isn’t necessarily going to be a smart thing to actually do. Marginal business cases rarely realize the full market potential of a technology, and encourage a search for alternatives, even non-technical ones or simply one that stays the present course, that can build ROI better. The best solution would be to find non-marginal business cases, but an alternative would be to lower the cost points for the stuff that’s already being considered.

The problem with Door Number Two here is that telcos can’t control all of the cost elements. Many of the 5G and proposed 6G connectivity options target what’s essentially a message-based service, whose costs would be proportional to the number of events reported rather than the number of devices, However, anything like this will lower telco revenues and impact the business case for the service-facilitating infrastructure investments. It doesn’t mean that the new sensors or their installation will be less costly. This sort of IoT, then, doesn’t seem to offer a real mass-connection opportunity.

So what might? Telco experts seem to be collecting around the potential offered by smart glasses, meaning AR/VR, and utilities are also interested. The two groups have different visions, however. Telcos see the glasses as being cellular-connected, where utilities see them as an extension to phone/tablet connectivity. The difference, the comments I get suggest, comes from targeting presumptions. Telcos seem most interested in seeing smart/connected glasses as an evolution of smart glasses, a pathway to device connectivity. Utilities see the glasses as tools in worker empowerment, facilitating tasks that are likely to involve smartphones already. For example, most utilities see the glasses as a means of linking a worker to diagrams/photos of complex equipment to guide operations/maintenance, and in these missions many workers also rely on smartphones to access reference material.

Consumer missions for smart glasses may be the most essential element in a 6G future, but not likely in the form that telcos seem to expect. I don’t think that smart wearables need to have their own cellular connections; nobody who depends on one would be likely not to depend on a smartphone even more, and the wearable is indeed a logical satellite to a phone. But visual integration between technology and the real world, tight and optimally useful integration, has to involve video both to capture reality and to communicate between the tech world and the real one. If telcos hope to sell 6G service to glasses, they need to come up with some realistic reason why a direct connection would be needed, and that’s true for IoT applications across the board.