News Analysis
In Brief:
- Microsoft’s Microsoft 365 price increases mark the end of subsidized AI, not a temporary pricing adjustment.
- AI no longer behaves like SaaS; its economics look more like a utility, with unavoidable, recurring infrastructure costs.
- Enterprises will pay for AI only where it delivers measurable ROI, forcing a reset in how software is priced and used.
Microsoft's latest Microsoft 365 price increases — the second round in four years after a decade-long freeze — are not corporate penny-pinching. They are the moment that AI's free lunch finally gets a bill.
Business Basic rises from $6 to $7 monthly, Business Standard from $12.50 to $14, while Business Premium holds at $22. The sharpest increases hit frontline worker subscriptions: F1 jumps 33% from $2.25 to $3, while F3 climbs from $8 to $10. Microsoft justifies these "updates" by citing enhanced security, management and AI capabilities, including more than 1,100 new features released in the past year.
The real story is not about features. It is economics catching up with reality.
Table of Contents
- The Math Was Never Going to Work
- Why AI Is Moving From Freemium to Paid Tiers
- AI Becomes the New Electric Bill
- Enterprises Will Demand Measurable AI ROI
- Next Stop, Tiered AI-Market
- Shadow AI Could Trigger Spiraling Costs
- What Could Bring Generative AI Prices Down?
The Math Was Never Going to Work
"AI prices had to rise, but it was just maths catching up," Datos CEO and co-founder Eli Goodman told Reworked. "A lot of money has gone into AI because people want to use it, not because it makes money."
Companies rushed to acquire users even when the numbers did not add up, creating a disconnect between customer expectations and infrastructure reality.
"We still haven't seen many AI companies show clear, consistent ROI, so the cost of compute was always going to come up sooner or later,” Goodman added. “It is impossible to keep subsidizing something forever once it is widely used.”
Geoff Webb, vice president at Conga and a pricing expert, framed it more bluntly: "This AI land-grab is on a colossal scale, and the price tag for dominating this new world is equally colossal,” he said. “Monetizing the services and recouping some of that investment is going to force some pretty significant changes in business models and service pricing, and those changes are likely to happen fast."
Why AI Is Moving From Freemium to Paid Tiers
The free-access model was inevitable initially — a necessary strategy to familiarize markets with poorly understood technology.
"Simply getting potential markets comfortable with both what the tech can do today, and how to plan to use it in the future, requires an open-door, try-before-you-buy model," Webb said. "Long-term, AI is simply too expensive to deliver for this to be sustainable."
AI does not work like traditional software, a reality many businesses have failed to grasp.
"The most common myth is that AI works like regular software," Goodman warned. "That's not true; every query has a real cost. The provider's bill goes up when you use more." Unlike conventional software as a service, where marginal costs approach zero, AI's economics resemble utility billing. Training gets the headlines, but inference, the ongoing cost of running AI models for every query, is the bill that keeps coming.
"Microsoft's increases aren't a temporary spike — they're the beginning of a new price baseline for the AI era,” said Nik Kale, principal engineer and product architect at Cisco Systems. “GPU capacity, inference scaling and the rising energy demands of large-model workloads have become structural, recurring costs. Vendors can't absorb them anymore."
Webb identified three "gravitational centers of cost": hardware, skills and energy. "In all three, there is a degree of scarcity, and when there's this much pressure to move fast, and a scarcity of the fuel needed to drive that movement, then the market responds as it always has — cost goes up and will keep going up." These escalating costs become anchors slowing innovation, forcing pricing to reflect economic reality.
AI Becomes the New Electric Bill
"This is less a bubble and more the start of a long-term repricing of software in an AI-centric world," Kale said. "AI is becoming the new electricity bill of the enterprise, being essential, expensive and impossible to ignore."
As AI moves from optional add-on to core feature, pricing models are recalibrating industry-wide. "Once Microsoft resets pricing, the market tends to normalize around it," said Kale. "Google and Amazon won't be far behind."
Normalization creates opportunities. Microsoft's changes "may unintentionally strengthen competitors who offer leaner, more cost-effective AI models," Kale noted, opening space for mid-tier vendors to differentiate on efficiency rather than scale.
Enterprises Will Demand Measurable AI ROI
The sticker shock is forcing enterprises to become more selective. "Enterprises will pay more for AI only when it produces measurable outcomes," Kale said. "CIOs are already renegotiating bundles, reducing usage of compute-heavy features and focusing their spend on the capabilities that actually move the needle. Everything else will face pressure or de-prioritization."
Businesses will not back down from AI adoption, Goodman predicted, but they will measure return on investment (ROI) more rigorously, reduce vendor proliferation and concentrate on use cases that add value. For smaller businesses, this may hasten a split: absorb the new costs or explore more efficient, AI-lite alternatives.
For individual users, the effects will show up through more paywalls, usage limits and premium tiers, though open-source models and mid-tier products will fill accessibility gaps.
The result: market segmentation. "We'll probably see a sorting effect rather than a blanket price increase," Goodman predicted. "Frontier models, such as the ones that require enormous compute and are used heavily, will remain expensive. But improvements in efficiency, distillation and hardware will steadily drive down the cost of 'good enough' AI."
Next Stop, Tiered AI-Market
The tiered market this creates will see cutting-edge AI priced according to its real cost, while everyday AI becomes progressively cheaper. The potential result is a wider gap between organizations that can afford frontier capabilities – the largest, most compute-intensive AI systems that deliver maximum capability — and those that cannot.
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How that tiered market develops depends on which adoption path businesses choose. "It's really important to understand that there are two fundamentally different models for AI adoption," Webb said. "The first is the 'generalized AI toolkit' approach, where LLMs and agentic frameworks are licensed to build solutions. The second is the embedded model, in which AI is threaded into and through enterprise tech."
In the first case, pricing fluctuation and costs are likely to grow as platforms recoup huge investments. The second model offers more stability. "The scope of functionality is more tightly defined, and therefore the costs scale more linearly," Webb explained. Predictable, consumption-based pricing for domain-specific services offers businesses a better path to cost controls.
For generalized platforms, providers will seek more revenue paths before pushing prices higher still. "Efficiency gains are out there, certainly, but that's not the focus today. The focus now is expanding capabilities and securing market share. Neither of those are particularly conducive to efficiency," said Webb.
The market is in "after-burner mode, accelerating as quickly as possible" — hardly the conditions for cost optimization.
Shadow AI Could Trigger Spiraling Costs
Webb draws a parallel to shadow IT. "For businesses, the problem with AI is very similar to the problem we saw with shadow IT 10 years ago — many departments spinning up projects without connection to broader business initiatives, or oversight from central IT leadership,” he said. “This led to spiraling costs, security vulnerabilities and management headaches."
With AI, this pattern emerges "writ large — potentially rapidly spiraling costs, uncertain compliance and security implications, and a proliferation of data and services that will take a decade to untangle,” Webb said. The solution requires balancing bottom-up innovation with top-down strategy, focusing on specific business problems that demonstrate generative AI ROI sooner rather than generic testbeds.
Price pressure will force more discipline. "They will cut down on random experimentation but increase planned experimentation, which usually leads to further refinements," Goodman said. "When access was almost free, a lot of teams tried things out just because they could. You need clearer hypotheses, better ways to measure things and a more thoughtful way to decide when to use a huge model as costs go up."
What Could Bring Generative AI Prices Down?
Technical breakthroughs that make AI genuinely cheaper to run are the only things that will reduce costs, not vendors undercutting each other. Enterprises should plan for AI to remain expensive unless fundamental efficiency improvements materialize.
"The only thing that will bring prices down is innovation in model efficiency, not competitive pressure," said Kale.
Microsoft's price increases may be the opening salvo, but they are unlikely to be the last. The industry spent years conditioning enterprises to expect AI bundled into their software subscriptions at minimal cost. Now the bill has arrived, and as Webb warned, "simply pouring money into the potential future opportunity, without a plan to drive revenue and profit, is not a sustainable business model for anyone, and at this scale, it's not sustainable for long."
The AI subsidy era is over. Enterprises that treated AI as a free experimentation budget are about to discover its infrastructure investment — with infrastructure costs to match.
Editor's Note: