TECHnalysis Research Blogs
TECHnalysis Research president Bob O'Donnell publishes commentary on current tech industry trends every week at LinkedIn.com in the TECHnalysis Research Insights Newsletter and those blog entries are reposted here as well. In addition, those columns are also reprinted on Techspot and SeekingAlpha.

He also writes a regular column in the Tech section of USAToday.com and those columns are posted here. Some of the USAToday columns are also published on partner sites, such as MSN.

He also writes occasional columns for Forbes that can be found here and that are archived here.

In addition, he has written guest columns in various other publications, including RCR Wireless, Fast Company and engadget. Those columns are reprinted here.

February 5, 2026
The AI Business Model Dilemma

By Bob O'Donnell

As we move further into 2026 it’s becoming increasingly clear that AI momentum hasn’t stalled in the least. In fact, its impact is accelerating, particularly with some of the dramatic developments in AI-powered agents—or agentic AI—that we’ve seen recently.

Capital spending for AI projects is surging, not only at the big hyperscalers, but at businesses of all types and sizes as well. Generally speaking, that’s been great news for companies supplying the key hardware and software elements necessary to create AI-powered solutions. Yes, we’ve seen (and will undoubtedly continue to see) stock market bumps along the way, but there’s no denying that the bigger picture trajectory continues to be incredibly positive.

On the deployment and usage side of the equation, both businesses and consumers are still figuring out what they want to do with the technology. But, they’ve seen enough impressive outcomes to encourage more experimentation and they’re mostly excited—though often a bit scared too—to discover what else they can potentially do with it.

Even with all these positive trends in place, however, there’s still one key piece that’s missing: business models that make economic sense and help ensure the companies creating these innovations can stick around long enough to make a serious impact. The dilemma is straightforward, but not simple: customers want predictable spending and clear business value, while AI suppliers face variable—and in many cases rising—costs to deliver increasingly sophisticated capabilities. “Tokens” may be a clean measurement unit, but they’re a messy proxy for customer value.

To be clear, there’s an enormous variety of companies driving major AI developments and several of them are doing just fine financially. There are many others, however, that have been on the seemingly endless track of enormous investment with not much revenue to show for their efforts. Even worse, their future economic situations appear to be based on little more than a whole lot of crossed fingers.

The net result of all this, of course, has been the growing fear of an AI bubble or crash because of the uncertainty around how these future plans translate into durable revenue. While some of that impact has been obvious, increasingly these concerns seem to be spreading to all corners of the tech industry and even the overall global economy in ways that aren’t always logical. Now, whether these fears are unfounded or not, the negative impact they’re starting to have is unquestionably real. And, I’m guessing we’re not done seeing other types of reactions to these concerns that could prove to be even more harmful.

Because of all this, I remain convinced that 2026 is the year we need to start seeing more realistic types of business models coming to the AI side of tech industry. While the modern oracle of AI—Nvidia CEO Jensen Huang—loves to talk about how generating more tokens leads directly to generating more dollars, this argument feels less convincing than it used to. The complications of building complete AI solutions that are easy to deploy for businesses, and figuring out how to monetize consumers that will happily consume as many tokens as possible (for free) are pushing initial ideas about AI business models to the breaking point.

Thankfully, some steps are being made, though it’s not entirely clear how effective they’ll be. OpenAI’s plans to bring ads to ChatGPT, for example, have already hit some serious doubts (as well as inspiring some pretty hilarious ads from Anthropic). Creating products for businesses seems much more straightforward and yet, even here, the concept of paying for outcomes instead of paying for access to models is gaining momentum and it’s not certain how well that’s going to work economically. Outcomes-based pricing sounds great until you get into the messy details of measurement, accountability, and risk.

Plus, while there’s been a lot of recognition around the largest, multi-purpose frontier models, some of the most dramatic success has been with smaller companies creating more targeted models that are optimized for specific industries and applications. In many cases, the value is clearer because the workflow is clearer—and that matters a lot when budgets get scrutinized.

The per-seat business models for things like Microsoft’s Copilot initially seemed promising, but as time has passed there’s been concerns both about dramatic over usage by some employees and little to no usage by others. Metered usage models can help overcome some of these issues, but exactly how it gets successfully deployed remains to be seen. Plus, there’s the ongoing problem of little to no training on how to use these advanced AI tools within many organizations and that has a big influence on usage as well. On top of that, it’s hard to ignore the irony that some organizations are using AI to reduce headcount which, of course, will reduce the number of seats for which organizations will need to pay.

Finally, one other issue that needs to be addressed is trust, but not necessarily in the way you may first think. While it’s unquestionably true that building trust in the quality of the output—as well as the governance, safety and security of AI tools—is absolutely critical, so too is trust in the organization providing those capabilities. Given how potentially impactful and far-reaching the influence of AI tools can be, if a company who wants to deploy a cool new AI technology can’t be certain that the supplier of that technology is going to be around for a while, they simply won’t do it. Business success in this realm is going to be a key driver for trust moving forward.

There’s little doubt that 2026 will provide the backdrop for what’s going to be a fascinating journey as organizations and individuals start to discover more of the “art of the possible” when it comes to AI and agents. But for these developments to move beyond trendy hype and tech industry navel gazing and reach mainstream businesses and consumers, there’s got to be more validity to the business propositions being put forward. There are already enough technical, social, and even political battles to overcome when it comes to AI adoption. Building a solid base of business viability is going to be essential if the next step in AI industry evolution is going to occur.

Here's a link to the original column: https://www.linkedin.com/pulse/ai-business-model-dilemma-bob-o-donnell-uzhbc

Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on LinkedIn at Bob O’Donnell or on Twitter @bobodtech.