Martin Taylor is the Cofounder and Deputy CEO of Content Guru.getty​Consumers have already seen the effects of inflation reshape the products and services they use every day. In grocery stores, “shrinkflation” has become a familiar tactic, where products become smaller while prices stay the same, in an attempt to minimize rising costs. More than one in three grocery products has been reduced in size over the past four years without a corresponding reduction in price.​A similar trend is now emerging across the technology market. Subscribers of many well-known streaming platforms will have noticed that the experience they’d come to expect has been watered down, unless they are willing to upgrade to a higher-tier package. Users who remained on their standard-tier packages experienced a version of shrinkflation where the price stayed the same, but the quality deteriorated and features, such as multiple user logins or ad-free listening, remained limited.​The same economic pressures are now beginning to shape the AI market: The transition from aggressive adoption to aggressive monetization.​The "Launch, Scale, Monetize" Cycle ​When a new service launches, initial mass adoption and a growing customer base are critical for sustainable growth. As a result, providers will often offer introductory prices that cause the company to operate on a short-term loss. However, once the focus shifts from establishing and growing a core base to generating profit, prices can rise, and services can be scaled back for the "standard" packages.​Brands such as Uber built their dominance over multiple years, offering lucrative deals to both passengers and drivers. The aggressive approach allowed Uber to compete against both legacy taxi providers and other ride-sharing apps, and by the end of 2025, Uber had over 200 million monthly users. However, prices have increased with premium features such as priority journeys, shorter wait times and more luxurious cars offered at a more expensive rate. Uber’s average fare per trip rose 83% over 45 months from 2018 to 2022, highlighting the shift from a cheaper alternative to a service that must start to turn a profit.​The "launch, scale, monetize" cycle is now standard across the technology sector. Will AI take a similar path as market leaders look to see a return on their investments? If so, what could be the result for companies that incorporate AI into their solutions?​The Rising Costs Of AI​The economics underpinning the rapid scale of AI development are impossible to ignore. By 2030, global data center investment is projected to reach $6.7 trillion as providers race to keep pace with the growing demand for AI compute power.​At the same time, organizations are becoming increasingly dependent on AI. As AI becomes embedded into day-to-day operations, switching away becomes harder, giving providers greater pricing power. This has contributed to the emergence of what many organizations are beginning to describe as an “AI tax” on software: by the end of 2025, software subscription costs had risen between 20% and 37%, driven largely by AI-related pricing adjustments, according to an analysis by procurement platform Tropic.​The industry’s largest technology providers collectively spent $155 billion on AI in 2025 alone, and that figure will no doubt continue to grow. At some point, investors will inevitably expect returns on that level of expenditure, so there will be a significant amount of pressure to shift from user acquisition toward sustainable monetization models.​Unlocking ROI For AI​As costs rise, so will scrutiny from those signing off on AI-driven projects. However, if organizations can highlight the efficiencies AI has unlocked, then this could be the difference between future projects being approved or cancelled.​Certain industries will find it easier than others to highlight success. For example, within customer Experience (CX), there is a high volume of repetitive administration during customer interactions and a growing appetite from customers for more efficient self-service—all areas where AI can deliver immediate operational gains.​The key to demonstrating the return on investment (ROI) is benchmarking performance before and after implementation. For example, how long are teams spending on admin tasks versus the time they are spending speaking with customers?​In addition to demonstrating ROI, another key aspect of navigating changing pricing models will be having the ability to flexibly switch suppliers. The AI market is evolving at extraordinary speed, so a model considered best-in-class today may be overtaken within weeks by a faster, cheaper or more accurate alternative. Organizations tied too closely to a single AI provider may find themselves exposed to rising costs and stagnated innovation.​Businesses that can flexibly switch between models and providers will be better positioned to optimize both performance and cost. Rather than committing entirely to one ecosystem, organizations should prioritize AI orchestration that allows them to integrate multiple AI technologies simultaneously.​The Next Phase Of AI​AI has become embedded across many enterprise operations. The early era of low-cost experimentation is beginning to give way to an environment defined by monetization pressure and growing scrutiny around ROI. ​For businesses, success will depend on measurable ROI as well as avoiding dependency on single providers to maintain the flexibility to adapt as pricing models and technologies evolve.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?