Is the AI bubble about to burst, or is it recalibrating?

From hype to execution -- and the limits of scale

To understand whether concerns about an AI bubble burst are warranted, it helps to look at how past technology waves have moved from early hype to measured value.

Every major technology wave goes through a phase of exaggerated expectations. The dot-com era promised to reinvent commerce overnight, and early cloud computing was met with skepticism before proving its value. AI appears to be following a similar trajectory.

However, what's different with the AI wave is the speed. Generative AI tools went from being a niche idea to an enterprise priority in just months, not years. That rapid adoption has created a mismatch between expectation and execution.

Gené Teare, senior data editor and research lead at Crunchbase, a platform that tracks data on public and private companies, noted that today's funding environment reflects both the scale and speed of this shift. The early years of cloud computing saw global startup funding average under $50 billion annually between 2007 and 2010, she said. By contrast, AI-related investment surpassed $300 billion in both 2023 and 2024, and exceeded $400 billion in 2025, with more than half of all startup funding now flowing into AI-related companies.

While investment has accelerated, successful real-world deployment is advancing at a slower pace. MIT's "State of AI in Business 2025" report found that 95% of AI initiatives deliver no return despite heavy investment in generative AI. One reason for this disconnect is that businesses often hit structural limits when moving from isolated use cases to fully integrated systems.

AI agents can automate individual tasks effectively, said Babak Hodjat, chief AI officer at Cognizant, a global IT consulting and IT services company. But, he added, complexity increases sharply when multiple agents are connected into broader workflows, a challenge he described as a complexity ceiling.

"Organizations often hit a complexity ceiling at around five agents," Hodjat said, explaining that coordination issues, such as duplicated work and conflicting outputs, become harder to manage at scale. This complexity ceiling is one reason enterprise-scale deployment is progressing slower than investment, as organizations struggle to move beyond small, contained use cases.

That gap between experimentation and sustainable execution is also appearing in high-profile product cycles. One of the most visible examples is Sora, OpenAI's popular text-to-video model. The tool generated significant buzz and drew early interest from major entertainment players such as Disney, but was discontinued on March 24, 2026, just six months after the launch of its first standalone app.

Ultimately, it seems like the decision to decommission Sora came down to cost. Generating high-quality, temporally consistent video requires enormous computing power, making it expensive to run at scale or support with a sustainable pricing model.

Seen in that context, Sora's shutdown isn't a sign of technical failure but more a reflection of the economic realities shaping the market, as the industry continues to define what successful AI deployment looks like in practice.

AI costs in production strain businesses

The challenges of putting AI into practice are increasingly tied to cost. While adoption is growing fast, the economics behind these systems are becoming harder to ignore.

The cost of computing is one of the biggest challenges. Advanced AI systems, especially video and multimodal models, require exponentially more processing power than earlier versions. That creates a bottleneck, forcing even well-funded companies to focus only on products that are worth the cost.

In the rush to deploy, many businesses have also underestimated what's called the inference tax -- the ongoing cost of running AI models once they're in production at scale. As those costs add up, companies are becoming more cautious and moving away from heavy reliance on the cloud for real-time AI workloads.

Quais Taraki, CTO of EnterpriseDB, a provider of PostgreSQL-based software and services, acknowledged this shift, noting that cloud economics are increasingly difficult to sustain for production-scale inference, and businesses are rethinking where and how they deploy AI systems. "The cloud is fine for training or simulating models, but it's far too expensive for real-time inference," he said.

Identifying value is another pressure point. While AI is gaining traction in enterprise settings, many consumer-facing tools struggle to convert popularity into steady benefits. A Q1 2026 Morgan Stanley Research mapping of 3,600 stocks for AI exposure found that only 21% of S&P 500 companies reported at least one AI benefit, even as adoption rises across the board. This highlights a widening gap between experimentation and proven business value.

Investors are placing bigger bets in select companies they believe could lead the next wave.

At the same time, funding is becoming increasingly concentrated among certain vendors. According to Crunchbase's Teare, AI startups captured about 80% of global venture funding in the first quarter of 2026, with nearly 65% of all venture capital dollars flowing to just four companies -- Anthropic, OpenAI,  Waymo and xAI -- while overall deal activity declined. "Investors are placing bigger bets in select companies they believe could lead the next wave," she said.

Another concern is diminishing returns at the frontier. As AI models become more advanced, each incremental improvement often requires significantly more data, computing power and capital than the last. In practical terms, that means it's harder and more expensive to achieve performance gains, raising questions about how long the scaling approach can continue to deliver meaningful improvements at a reasonable cost.

Taken together, these pressures don't suggest collapse, but they do highlight a market where economic assumptions are being tested more aggressively than in earlier tech cycles.

Investment and demand accelerate despite challenges

Even with these cost and implementation challenges, capital investment shows no sign of slowing. The nature of that funding, however, is becoming more complex.

Hyperscalers are projected to spend more than $450 billion on AI infrastructure in 2026, with a growing share of that spending financed through debt markets, according to Mitsubishi UFJ Financial Group data. This reflects both strong demand for AI data centers and hardware, and rising financial risk for major cloud infrastructure providers.

Nothing is slowing down yet. From a momentum standpoint, things are still full steam ahead.

That continued flow of capital underscores the strength of demand, even as pressures mount. Info-Tech's Bickley echoed that view: "Nothing is slowing down yet. From a momentum standpoint, things are still full steam ahead," he said.

At the same time, the investment is becoming more focused. Spending is shifting away from broad experimentation and toward concentrated bets on fewer large-scale AI infrastructure projects. Companies are focusing on building core AI capabilities rather than exploring multiple directions at once.

Broader adoption trends reinforce this underlying demand. According to McKinsey's "State of AI (2025)" survey, around 88% of respondents use AI in at least one business function, indicating widespread adoption across industries. However, only a fraction have successfully scaled deployments across the enterprise, with many systems remaining limited to narrow, isolated use cases. This suggests that while demand and experimentation are high, structural challenges, such as integration issues and complexity ceilings, continue to slow full-scale deployment. Companies are feeling pressure to shift from rapid expansion to more targeted efficiency. And for business leaders, this means reshaping how they evaluate and prioritize AI investments.  

What business leaders should keep an eye on

Enterprise decision-makers' focus is shifting from experimentation to discipline. Instead of testing AI everywhere, companies are being forced to decide where it actually delivers value.

In this changing landscape, the following are some of the key things leaders must pay attention to:

• Prioritize real business value over hype. Businesses should focus on AI projects that clearly improve performance or reduce costs, such as automating routine work or solving specific business problems. The key question is simple: Does it save time or generate value, or is it still experimental?
• Expect more industry consolidation. As AI becomes more expensive to build and run, not every company will be able to keep up. Smaller startups might struggle to survive, while larger players consolidate their positions. Leaders should expect more acquisitions, partnerships and some products to be quietly shut down or absorbed into larger platforms.
• Scrutinize vendor sustainability. Businesses are starting to ask tougher questions about the companies they rely on for AI tools. Leaders need to assess whether vendors actually have a sustainable business model or if they're burning through funding without a clear path to profitability. This is becoming especially important as computing costs continue to rise.
• Prepare for regulatory shifts. Governments are paying close attention to how AI systems are being built and used. Business leaders should prepare for increasing regulation around AI, especially in the areas of data ownership, copyright and energy use. These rules are likely to shape how AI systems are built, deployed and scaled across industries.
• Shift toward smaller, more efficient systems. The next phase of AI is likely to move away from massive, general-purpose models toward smaller, more focused tools. These systems are less expensive to run and can be tailored to specific industries, such as finance, healthcare and legal work. Leaders should prioritize tools built for specific workflows that deliver faster, more cost-efficient results.

AI bubble or market correction?

Beyond these immediate priorities for business leaders, a broader question is emerging about what phase the AI market is in and where it's going.

AI is unlikely to collapse under its own weight. The technology is already embedded in enterprise strategy, with real use cases emerging across automation, customer support and internal operations. Its relevance isn't changing, but expectations are being revised around how quickly it can deliver results and how consistently those gains can scale into profit.

EnterpriseDB's Taraki framed this moment less as a speculative peak and more as a structural shift still unfolding. Only a small number of businesses have fully operationalized advanced AI systems, he said, suggesting that there's still significant room for growth as companies move from experimentation to full-scale deployment.

This points to a market that is still in its early build-out phase, even as the initial hype cycle begins to cool and capital shifts toward fewer, more targeted bets. Recent signals -- from the shutdown of Sora to rising infrastructure costs and increased scrutiny around returns -- don't suggest that AI is failing. Instead, they reflect a market that's starting to prioritize sustainability, efficiency and unit economics over rapid expansion.

If there's a bubble, it might not be in AI itself, but in the expectations built around it, especially the assumption that AI would deliver fast, scalable and immediate returns across all use cases. As those expectations reset, the industry appears to be moving into a more measured phase, defined less by hype and more by performance, cost and real-world results.

In that sense, the AI bubble isn't about to burst. It's being recalibrated.

Kinza Yasar is a technical writer for Informa TechTarget's AI and Emerging Tech group and has a background in computer networking.