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.

I remember when computers didn’t use chips; a 16 Kbyte machine that was slower and less powerful than a smartwatch today was three feet wide, five feet high, and seven feet long. The cost would have been more than almost any home at the time. Chip-based systems are why we have personal computers, smartphones, and even AI. They shrunk computing down radically, not only in size but in power consumption, cooling requirements, and cost. Chips were behind the computer revolution. What’s next?

I think that we can expect to see continued improvements in the sort of chips that make up personal computers, smartphones, and wearable technology, but I think the focus of the advances is going to shift, away from the general-purpose model and more to the system-on-a-chip (SoC) and to becoming specialized to the mission. This means that the chip wars will focus increasingly on AI and quantum computing.

You can rightfully wonder why that is, and the answer is simple. General-purpose computing is not infinitely distributable; once you’ve let people carry a smartphone you’ve hit a point where the value of shrinking the device hits the utility. I have a smartwatch, and while it can be (and sometimes is) a useful adjunct to my phone, most of its utility comes from non-traditional compute missions. Chips are important, in a financial impact sense, because they facilitated compute distributability, which means that underneath it all, it was the ability to distribute computing that mattered. Further distribution of computing will be about embedding it in special-mission devices, like wearables and IoT elements. For that, they’ll need to support the mission of the thing they’re embedded in, and that will mean stepping beyond general-purpose compute into things like AI/ML and quantum computing.

We are in the mainframe era of AI. The top-line Nvidia chips (the B200 or H200) runs more than I paid for my car and requires racking and cooling, making them minimally distributable. We are going to see the same trend in AI that we saw in computing; chips will shrink the size of an AI agent host to the point where we can deploy it closer to the processes and people it’s supporting. At some point, we’ll be able to embed it in things, first large expensive things like machinery and vehicles, and eventually in cameras, glasses, and sensors. If you want to see an AI revolution, this is the way it will develop, not through more and more B/H200 chip installations. Sorry, Nvidia.

The same thing is true of quantum computing, except that it is arguably in the pre-mainframe stage of evolution. The earliest computers were so large and expensive (they were based on vacuum tubes) that enterprises really couldn’t afford them; they were research tools. When I went to the University of Pennsylvania, the floors in the Moore School building where the first general-purpose programmable computer (ENIAC) was housed were permanently warped by the weight (30 tons) and heat that 18,000 vacuum tubes created. Twenty years later, we had IBM System 360 mainframes a hundred times as powerful for five percent of the cost, and twenty years after that the IBM PC that could match the bottom-end 360 in power for less than two thousand dollars. I don’t think that the realization of quantum computing will take that long, but there’s no doubt that it won’t come overnight. When it does come, it will be quantum chips that bring it.

Enterprises who follow the leading edge of these sorts of thing say that the driving force behind “real” AI and quantum computing is the need to distribute intelligence to the things we do and use, at work or otherwise. They’ve always seen AI agents, for example, as pieces of AI technology that can interact with business operations and workers’ activity directly. This, to them, leads things toward real-world, real-time AI missions. One enterprise AI expert told me “If you put AI in a hyperscaler data center, you deploy maybe a hundred thousand units. If you put it in a sensor, you could deploy a hundred billion units.” The idea behind that, the justification, is that once you believe in the AI agent, you’ll want to embed its intelligence in stuff to make the stuff self-smart. An autonomous vehicle that’s run by a data center will never be truly safe; one that’s smart in itself can be as good or better than a human-operated vehicle. And, of course there are almost two billion vehicles on the road today worldwide, and another ten million industrial/construction vehicles in factories, warehouses, and job sites. That’s a big market, with a big economic impact.

People believed in “time-sharing” computers in the late 1960s and early 1970s, but they were quickly devalued by minicomputer and personal computer advances. Distributable always wins. What can’t be distributed is almost surely doomed not to be revolutionary; it takes massive deployment to make a revolution, so this is what we need to be looking for in both AI and in quantum computing. Everything else is simply a side-show at best, and pure hype and nonsense at worst. Today’s AI is yesterday’s time-sharing, doomed to be overtaken by chips. Same with quantum computing, but in that area, we’ve not yet launched the presumption of a revolution that might actually be delaying an actual one.

To understand why, we have to address two questions, “What chips?” and “From whom?” Both are difficult at this point. Right now, what enterprises are saying is that embedded, distributed, AI is likely to look a lot like machine learning or a pre-trained small-language model; edge AI goals are specialized to the mission that justified the distribution in the first place, the thing that created a need for a local response to an event. However, there is a value to creating a more generalized chip; manufacturing and distribution economies would be better, and it would be more capable of being repurposed, perhaps lending to building a durable business case.

On the “who” side, enterprises are mixed with regard to their views on Nvidia’s role. Some think they’d be the obvious ones to bring out distributed chips, given that they’ve been active in promoting “world model” deployments for AI. Others think that they’re too fixated on selling to hyperscalers, which dictate big expensive chips of the type that makes up their primary revenue source today. Certainly the Street would be concerned about any visible shift in direction.

There’s a chance nobody does this, of course. The problem I see is that we’ve not been talking about what enterprises consider “realistic AI business cases”, most of which would lead to real-time missions and distributed AI. Enterprises are not progressing with their self-hosted AI nearly as fast as they thought they would; almost two-thirds who expected to deploy “significant” AI internally this year now say they won’t meet that goal. The reason most offered is that senior management tends to believe the popular cloud-hosted-expensed-AI approach, making it hard to drive self-hosted projects. This creates a kind of negative feedback; lacking movement toward extensive self-hosting, cloud AI gets all the attention, which then makes it harder to do self-hosting projects. You get the picture.

This may be why the Street is so antsy about massive AI contributions to profits. SK Hynix stock was hammered even though it had a major profit increase, because the increase depended on AI. We might see meaningful progress toward self-hosting delayed until 2028, and I don’t think that the current boom can be sustained that long. Maybe the Street is starting to agree, and what that will mean for AI overall is something we’ll have to wait to determine.

For decades, network operators and enterprises have both clung to a notion that the best network and the best data center were built around playing vendors against each other. Ideally, it was said, you needed to have three competitors to beat on in order to get the best price/performance. However, as I’ve noted in past blogs (notably here), enterprises first and then even operators started to see things differently. The reason was the combination of exploding integration costs and fault isolation and correction problems related to vendor finger-pointing, inevitable when something breaks in a multi-vendor environment.

Like any buyer trend, this one got the attention of vendors, who had a problem of their own. In the same couple-decade period, the percentage of both data center and network spending attributable to new projects, and thus justifying incremental spending, fell by over 70%. That meant that almost all the budgets for network and IT gear was focused on just sustaining what was already there, and that makes it harder to gain revenue if you’re a vendor. If you can’t rely on new benefits, then you have to steal someone else’s market share while making sure nobody steals yours. The result was what the article calls the “platform” strategy.

New projects rarely justify the full range of network gear, from LAN to WAN, data center to desktop. Same with IT gear. Combine this with the old open-best-of-breed mindset and you had vendors pushing segments of equipment—workgroup LAN, data center LAN, servers, management tools, software and middleware. In the new platform world, IT and network vendors started to see value in bundling all their gear into a platform. You win the platform and you win it all, because nobody is going to forklift an entire infrastructure to change vendors.

But now, could the separation of network and IT itself be at risk to platformization? Every network and every computer in an enterprise is linked in some way. Many network vendors sell network interface cards and of course almost all server vendors do. We’re already bleeding between the spaces, and now we have an indication that both buyers and sellers want even tighter coupling.

In 2020, when I was still operating under CIMI Corporation, only 19% of enterprises told me that they’d like to have, or even be interested in having, the same vendor offer them IT and network products. In 2026, 54% of enterprises Andover Intel heard from said that would be something they’d be interested in, 25% said they’d be very interested in hearing that story, and 9% said they had started to actively converge on one vendor for both areas.

One reason for this could fairly be called “nostalgia”. Remember that there was a time (the 1980s and early 1990s) when IBM was both the network vendor and the computer provider, and some who learned their jobs in that period are now senior managers or executives. IBM lost dominance when cheap IP routers replaced expensive SNA controllers, and the shift led IBM to sell Cisco its network business in 1999. Many IBM types remember, with a happy smile, when all of IT was one, and they’re happy to explore reasons to return to that state.

Reason two is more practical; HPE, Dell, and Cisco all sold both LAN and computer products, and in particular Dell and HPE both encouraged data center expansion based on their servers and switches. Some enterprises deployed these converged platforms when they had projects involving expansion. Over time, those enterprises say they found it easier to manage these combined infrastructure pieces than those involving a mix of vendors, and now they’re thinking about making the one-vendor-fits-all a policy.

Another reason for the shift, and for the 9% in particular, was the HPE merger with Juniper. Juniper is a full-scope network equipment vendor, not just a provider of LAN switches, and so the deal provides enterprises and operators an opportunity to build units of infrastructure that include servers, switches, and WAN gear from a single source.

For the vendor, HPE in this case, there’s a tactical benefit because it’s rare for an enterprise to look at expanding a data center in server racks without expanding networking, and vice versa. Having more skin in the game means HPE sales can spend more time. In theory, it could also be a strategic advantage, which if true could be an instrument to spread the converged platform concept to other vendors.

Converged data center and network platforms are most valuable to companies who are deploying a new chunk of hosting resources and need the whole package. Given that new projects with new benefits to fund such a thing are increasingly rare, it would seem to me that to gain an offensive advantage through this sort of convergence, you’d need to promote the new projects. That’s why I’ve not been fully satisfied with HPE/Juniper integration; I think it’s been tactical rather than strategic.

The strategic benefit would be simple; a company with more skin in the game has a greater incentive to promote those new projects, even if it requires some of the vendor-dreaded “educational selling”. There is some technical symbiosis between networking and hosting, of course; you can’t build a resource pool without connecting it. The symbiosis is greatest where the hosting involves something unfamiliar to buyers, which of course is why there’s so much attention being paid to AI.

AI is not, at present, a major factor in enterprise data-center building and connecting, but it’s a concern to planners. That means that it’s possible to leverage the notion that AI is a driver of converged-platform procurement, a reason to buy IT and network equipment from the same vendor. HPE may be benefitting, to a degree, from this, but I’m not seeing it yet in enterprise comments to me, which suggests that the impact is so far highly contained.

Is it enough to change the competitive landscape. Judge an enemy by their capabilities, not their intentions, says military-think. Could HPE competitors, especially Dell or SMC, see HPE as more of a threat because they might, just might, start thinking more strategically? Might IBM, who has mastered strategic thinking but doesn’t sell either network equipment or traditional servers, decide to broaden out; after all, buyers see “the data center” as a unity of hosting and connecting, and major competitors already offer both. Or will they rely on their Red Hat software as the hosting side, and play the notion that hardware is simply an essential and unglamorous underpinning to software, whatever kind of hardware we’re talking about? We’ll surely find out.

Most people know quantum computing only as a threat. The potential for it to break all of today’s encryption algorithms in seconds, exposing everything they’re supposed to protect, has been talked about for several years. Does this remind you of how AI’s threat to destroy humanity has been a hot topic? It should. OK, this is something that should be addressed, but first and foremost any promising technology has to realize its promise or it’s unlikely to get far enough to pose a threat to anyone. Quantum chips are the critical element of the future of the technology, because if you can’t make the cost manageable, next to nothing will justify its use, and to make a quantum chip successful you have to sell it to more than the hacking community. In any case, what quantum computers can break, the experts tell me it can render unbreakable. So we need to move from threat to promise, which is what IBM’s report aims to do.

The document was produced by IBM’s Institute for Business Value, looking at what the report calls “the innovators and risk takers—those who seized frontier opportunities and are now advancing out front.” These, IBM says, are concentrated in “aerospace, genomics, material sciences and financial services.” The primary focus within each of these verticals are problems that conventional computing technology has not addressed effectively, but future-proofing computing strategy and accelerating innovation were also cited by more than half the companies involved. Note that I’ve gotten quantum computing implementation comments from only 25 enterprises, and of that group only five were actually involved in trial/test applications, none yet in production.

One thing everyone actually involved in quantum computing agrees with is that it’s not the same as “traditional” computing. Quantum computing is about algorithms, about mathematics at a fundamental level. Linear algebra, a vector/matrix math, is arguably the underpinning of quantum computing, to the point where what doesn’t fit that model doesn’t optimally fit quantum as a use case, and arguably doesn’t even belong there. This is central to understanding how companies trying to identify quantum use cases, like those IBM cites, are working to accomplish their goals.

The key point to all the cited applications, IMHO, is that they’re “planning and analysis” more than “production and operations”. Right now, quantum computers aren’t practical purchases for most enterprises, so they buy time on systems for their applications. Material and chemical analysis and simulations used to assess techniques or systems seems to be the main missions. In these areas, quantum solutions are explored to enhance traditional computing models. Pangenomics, meaning population-scale analysis of genetic factors, is cited as an example of a field where there really are no models based on current technology, so they’re a proving ground for greenfield quantum solutions.

Vanguard, the financial/investment company, offers an example of quantum applications that are likely to be more readily adopted across verticals. Their work has focused on optimizing portfolio performance and modeling risk to assess downside potential. This is an approach that obviously many business processes could benefit from, by applying common methodologies to different statistical bases. Detecting illegal behavior like money laundering, one mission being explored, is a technique that could be applied to many different kinds of fraud/theft detection. Bond portfolio analysis techniques could be applied to optimizing inventory, shelf space, and even production relationship to sales patterns.

Simulation, though, seems to be the early-adopter brass ring for quantum computing. Almost any complex system can be optimized through simulation. In the design/deployment stage, this gets the “base” state working at its best, and this mission is suitable for quantum service users. As quantum chips improve, and the price per qbit comes down, more and more “operations” missions could evolve from the base design mission. Networks, utility grids, transportation systems, warehousing, and so forth could be optimized closer and closer to real-time with improved price/performance, and the techniques could all evolve from simulation applications used in that baseline planning mission.

Simulation can also guide R&D, which is where a lot of medical/health-care interest is focused. Screening for diseases and looking for a cure are both areas where real-world trials are essential, but simulation using quantum computing could identify the most likely fruitful approaches, reducing both time and cost to a final, proven, approach. The director of the quantum initiative for a university notes that getting a drug to market today is typically about 15 years, and so a ten or twenty percent reduction in that through quantum computing simulation and analysis could “change lives”.

Today, most quantum simulation is linked to traditional computing technology for real-world application. Simulation, then, essentially defines an approach that is implemented using familiar IT tools, including AI. One cited pioneer notes that quantum computing won’t replace AI but will “sit above it” in the sense of framing the application architecture in novel ways. Every quantum maven says that traditional computing in some form has to envelop the quantum-algorithm-and-linear-algebra applications, just to handle linkage with the real (and business) world.

There are obviously commonalities between quantum computing adoption and AI adoption. In both cases, my own enterprise contacts say that the primary barrier is internal skills. There is also a tendency for early applications to rely on a service rather than on self-hosting, and there’s an expectation that costs will have to decline considerably before large-scale deployment of actual infrastructure can be expected.

One interesting thing here, though, is that both enterprises I chat with and those cited by IBM in the report seem to believe in the notion of an “ecosystem” for quantum computing. Governments have been funding or heading initiatives on this; the IBM report cites one example, and IBM was awarded a billion dollars in USDoC funds to build a quantum computing chip foundry. IBM says it’s adding its own funds to this, and has signed more than $1.1 billion in contracts for quantum computing trials, tests, and even some actual production.

When will quantum computing see more actual applications? Right now, my enterprise contacts are even in trials at levels below statistical significance, and only three tell me that they have done any real production work. That doesn’t mean that no such work has been done; another ten indicated that they knew of companies who had actually leased time on quantum computer systems for real work. Healthcare and materials/chemicals seem to be the active areas today, but absent a determined broad-based survey effort, I don’t think we can get any reliable data on actual quantum applications. Enterprises think that there will be “some” production quantum-as-a-service adoption this year, and “significant” adoption by 2028. They think that self-hosting is likely to come along in the second half of 2028 or early 2029.

I think that while more people and companies will use AI than will use quantum computing, at least in the next ten or fifteen years, quantum computing may be more important to businesses, and indirectly to people, in the long run.