The equity market has rarely been this confident and divided. Major indices continue to hit record highs, mostly driven by a small group of AI-exposed megacaps.

Capital is pouring into artificial intelligence at a historic pace, valuations are expanding ahead of cash flows, and infrastructure spending is reaching levels previously reserved for national-scale projects.

To some, this looks like the early innings of a productivity revolution. To others, it has all the hallmarks of a classic bubble.

Even experts have polarized views. On one end of the spectrum, skeptics like Gary Marcus warn that expectations have raced far ahead of the technology’s actual capabilities, while valuation-focused voices such as Aswath Damodaran caution that parts of the current AI boom look a lot like the dot-com bubble. 

On the other end, market strategists like Josh Brown and Tom Lee argue that investors are still underestimating the scale of the opportunity, comparing AI to past platform shifts whose true economic impact only became clear years later.

Who’s right?

Why the Bears See an AI Bubble

To skeptics, the current AI boom looks less like a breakthrough and more like a familiar speculative cycle, where expectations are outpacing execution and fundamentals.

Echoes of the Dot-Com Bubble

For the bears, the capital behaviour surrounding the AI hype feels like a rerun of the dot-com era. Then, as now, a revolutionary technology collided with speculative enthusiasm, massive capital inflows, and expectations that didn’t match the practical timelines.

During the dot-com boom, the market was hyped by what the Internet era was promising. A small group of dominant companies captured most of the market gains, while hundreds of other smaller companies tried to ride the same wave. After brutal attrition, roughly 50–60% of venture-backed internet companies ultimately failed or disappeared within a few years of the crash.

Today’s AI landscape looks similarly crowded to the dot-com boom. An approximate number of 300 AI unicorns have emerged at unprecedented speed, supported by automation, open-source models, and cheap access to cloud infrastructure. 

Bears argue that many AI companies are structurally redundant because they all rely on the same generative AI models (e.g., ChatGPT, Claude, DALL-E) and on the same infrastructure expansion promised by hyperscalers. The market becomes oversaturated, with many startups offering products that essentially do the same thing with a layer of better user experience on top.

Moreover, the solutions they build are not backed by strong research. Over 40% of AI startups are expected to fail by 2027 because they target pain points that are not real needs, or they fail to capture a critical mass of customers in a way that can sustain healthy growth.

And that’s not all. There are additional concerns about their monetization models. Many AI companies fail to attract the right audience, and consequently, they are attracting non-paying or non-returning customers. In these cases, current investments are keeping them alive, but when capital becomes more selective, a culling may be inevitable.

AI Adoption Increases with Seemingly Little Impact on ROI 

While AI has been a hot topic on a global scale for some time now, some corporations are not impressed. A recent study conducted by ISG found that while AI adoption doubled in 2025 compared to 2024, two-thirds of the projects are not yet in production, and only about one in four initiatives meets revenue impact expectations. 

Similarly, a 2025 MIT report shows that 95% of enterprises investing in gen AI have produced zero returns.

Another concern centers around Baumol’s cost disease. This economic principle states that productivity in the service sector tends to fall behind, keeping costs actively high. 

A growing number of experts argue that AI has the potential to boost productivity a lot in some sectors (like software, finance, or data-heavy work), but it can do very little for others (like personal services or manual, hands-on jobs). If that happens, overall productivity gains could be smaller than expected on a global scale because of the parts of the economy that don’t benefit much from AI expansion.

Even in the sectors where potential exists, people need proof fast that the AI productivity narrative is not exaggerated, and that we can see a return on investment in this area.

The urgency helps explain why OpenAI (ChatGPT), Google (Gemini), Anthropic (Claude), and others are releasing new models and features at an increasingly rapid pace.

Never-Seen-Before Spendings In CapEx and R&D

The scale at which AI-exposed companies are spending their capital has bears on edge. Hyperscalers have committed over 35% more revenue to CapEx and R&D in 2025 compared to 2024, with Meta targeting around 70%. This is huge considering that the average S&P company usually raises these expenditures by 10% YoY.

And if you think Meta was already a big spender, a few days ago, it announced that it plans to spend between $115 billion and $135 billion on CapEx in 2026. That’s an increase of up to 86% compared to 2025. Meta’s stock was up 10% after that.

While investors generally support the AI expansion, substantial upfront investments in long-term assets can also increase financial and operational risks. This leads to increased uncertainty and can change how the market perceives these companies as they commit more of their firepower to areas that are not yet provably profitable, or, rather, are currently profitable only on paper.

An eloquent example is the time Oracle revealed on its Q2 earnings call that its 2026 CapEx would be $15 billion higher than previously forecast, bringing the total to roughly $50 billion, mostly financed with debt. The result? Shares fell sharply in after-hours trading. 

In fact, since September 2025, Oracle has been on a downward path, a sign that the investors are starting to waver in the face of the increased financial risk Oracle is exposing itself to, especially in today’s economy.

We recently had a similar case with Microsoft. At the time of writing this article, the company made public its revenue for the second fiscal quarter of 2025.

Although it exceeded all expectations in this regard ($81.3 billion revenue), Microsoft’s shares fell about 5%, as they also announced that the AI infrastructure spending would be higher than anticipated. 

Of course, it could just be an unfortunate coincidence with these two, considering that Meta is not following the same trend.

Valuations-Reality Mismatch

Another red flag is valuation. Companies in the S&P 500 that are most exposed to AI, like NVIDIA and Alphabet, are trading on expectations of future dominance rather than realized cash flows. Revenue growth is uneven, and margins remain uncertain.

From the bears’ perspective, markets are already pricing outcomes, not execution risk. That distinction matters when timelines stretch and costs compound.

At the same time, it’s becoming increasingly clear that no all AI companies will not be able to deliver everything they promised. In fact, the pace at which they are reaching their milestones is far from predictions. 

Let’s take, as an example, the OpenAI–Oracle cloud agreement, one of the largest ever signed and valued at roughly $300 billion. This refers to the Stargate project, which just started last year by building new data centers in Abilene, Texas.

Rumors have it that parts of the infrastructure build-out may face delays for various reasons, including difficulty in finding and attracting the qualified workforce needed for such a large-scale project, or the inability to produce, test, and deliver hardware components at the same pace as the construction of the facilities.

If this project fails to deliver, we think OpenAI and Oracle’s stock prices will take hits, and NVIDIA, AMD, and Broadcom – other important partners of this project, delivering GPU racks and chips – will most likely follow the trend.

Power availability is another issue that affects big data center projects. Microsoft CEO Satya Nadella has acknowledged that GPUs are already sitting idle, not due to a lack of demand, but because data centers cannot draw enough electricity from the grid. Add cooling challenges, water usage, permitting delays, and the prospect of future AI regulation, and the execution risk becomes harder to dismiss.

At the same time, OpenAI’s revenue trajectory raises eyebrows. Based on one of their recent reports, OpenAI generated $20B+ in annual revenue in 2025, while long-term ambitions reportedly exceed $100B by 2028 – officially less than two years right now. The huge gap between these numbers makes bears reluctant that operational reality will keep up with the company’s ambitions.

In short, the bear’s viewpoint is not that AI will fail, but that expectations have moved far faster than the infrastructure, economics, and physics required to support them. The problem is that it’s hard to know the exact state of the AI economy and infrastructure, especially since the big companies involved are putting effort into controlling the narrative, mass layoffs, and merging multiple roles into one by leveraging automation, at least on paper. 

Even so, if you look closely enough, there are things that slip through the cracks. Things that don’t really add up in a real-world economy. 

Interestingly, some bulls partially agree with bears on AI overconfidence, but think the benefits of AI will outweigh the constraints. Others just dismiss the bears’ arguments by trusting the ability of large companies to deliver what they promise, and the usefulness of AI in increasing work productivity and service quality. Let’s dive into their side of the story.

Why the Bulls Are Confident

From the bulls’ perspective, the current wave of AI investment is not speculative exuberance but a rational response to growing demand. Sure, the scale of spending may look extreme by traditional standards, but bulls argue that traditional standards no longer apply.

AI Is Becoming Infrastructure

A core bullish argument is that AI should be understood less as software and more as infrastructure. Data centers are being built, servers, chips, and cooling equipment are being produced, and power grids are expanded. Once built, this infrastructure underpins entire ecosystems of applications and services for decades.

Amid rising concerns that data center power consumption will increase Americans’ electricity bills, the Trump administration is in the process of closing deals with Big Tech companies to prevent this from happening. Microsoft was the first to sign an agreement to not only pay for the energy their data centers use but also to produce the energy needed by local residents.

This view is reinforced by the expansion of cloud-based computing beyond AI applications. NVIDIA’s GeForce NOW and Microsoft’s Xbox Cloud Gaming are early examples of high-performance workloads being offloaded from increasingly expensive personal hardware to centralized infrastructure. 

Although no absolute numbers were given, Microsoft reported that Cloud Gaming usage has gone up 45% in 2025 compared to 2024 in terms of subscriber counts.

As PCs, consoles, and specialized devices become increasingly expensive, cloud-based alternatives are far cheaper. Therefore, the justification of current CapEx spending is no longer exclusively linked to the success of AI projects, but also finds applications in other sectors.

Demand Is Not Theoretical

AI has been around longer than many might think. However, we can consider that mass adoption started with the official release of ChatGPT in November 2022. Since then, demand for AI services has grown exponentially. 

Companies are now embedding artificial intelligence into customer support, software development, marketing, and operations. Regular people use it for entertainment or as a search engine for various queries.

Bulls argue that once users integrate AI into daily workflows, usage becomes sticky and demand becomes less discretionary. From their perspective, the question is not if AI will be used, but how much compute will be required as usage deepens.

Early Signs of Enterprise Productivity Gains

We previously discussed the poor corporate adoption of AI, as well as the unsatisfactory impact on revenue. However, bulls point to mounting evidence that AI has all the prerequisites to improve productivity. 

A recent Anthropic report suggests that their AI-assisted engineers and researchers delivered productivity gains of up to 50% in 2025. 

Meanwhile, developer tools such as Google’s Antigravity (an agentic IDE platform released in mid-November 2025) are already receiving validation from senior engineers. Anecdotally, programmers report faster planning, execution, and reviewing of code to generate higher-quality output in the case of Antigravity.

These early signals matter because they address the most serious economic challenge facing AI: whether it can meaningfully improve service-sector productivity. Bulls argue that AI has the ability to let employees operate beyond their traditional expertise, even if the full impact takes years to materialize.

Optimism Is Part of Every Infrastructure Cycle

AI bulls also acknowledge the criticism that timelines set by the hyperscalers are overly optimistic, but refuse to see it as a flaw. Railroads, electricity grids, and the early internet were all financed on assumptions that proved optimistic in the short term, yet transformative in the long term.

From their perspective, delays and big CapEx investments are just evidence that a solid infrastructure system is being constructed. 

Even if execution falls short of current expectations, bulls believe that at this point, the underlying demand for a growing number of AI use cases is too fundamental to reverse.

Three Scenarios for 2026 and Beyond

The debate over whether AI is a bubble often assumes a single outcome. In reality, the future is likely to fall somewhere along a spectrum shaped by execution, infrastructure constraints, regulation, and demand elasticity. 

The results will also vary a lot by company type. Large, diversified platforms such as Microsoft and Alphabet can absorb missteps, delay timelines, and fund AI development through existing cash flows. 

By contrast, many AI unicorns, particularly those built around narrow applications (e.g., Morning Consult) or dependent on third-party models and cloud access (e.g., Harvey), face far less margin for error. 

For these companies, slower adoption, higher compute costs, or delayed infrastructure could quickly prove fatal, making consolidation and failure far more likely at the startup layer than among the hyperscalers.

Therefore, we outlined three plausible scenarios for how the AI cycle could evolve over the next year or more.

The Bull Case: Productive Scarcity

Welcome to our most optimistic scenario. AI demand continues to grow rapidly without overwhelming the limits of available infrastructure. 

Data center capacity expands just fast enough to remain scarce, allowing providers to maintain pricing power.

OpenAI does not miss its ambition of surpassing $100 billion in annual revenue by the end of the decade, driven by deeply embedded enterprise usage rather than novelty-driven consumer demand. 

At the same time, tools like Claude and Gemini steadily improve work efficiency: senior engineers report shipping in weeks what previously took months, while smaller teams increasingly deliver outcomes that once required entire departments.

Automation and AI-assisted tools help lower even service costs, finally curing Baumol’s cost disease across industries.

Tasks that once required large teams and specialized expertise are increasingly handled by smaller, AI-augmented groups, driving productivity gains.

As services begin to follow the cost trajectory of manufactured goods over the past two decades, the quality of life inherently improves globally. 

In such an environment, it is not unreasonable to imagine equity markets reflecting this structural shift, potentially pushing the S&P 500 into the low 8,000s by the end of 2026.

The Middle Path: Overbuild, Then Consolidation

A more balanced outcome lies between euphoria and collapse. In this scenario, overconfidence leads to some data centers never being used to their full potential, not immediately anyways. 

At the same time, many AI startups fail to differentiate or monetize effectively.

However, for the hyperscalers, the infrastructure investments pay off. As weaker players exit the market, companies such as Google and Microsoft remain central, with Microsoft’s deep integration and access to OpenAI’s technology providing an additional buffer. 

Capital and talent consolidate around a smaller group of platforms with proven demand and distribution.

The valuations of companies tied too closely to speculative AI narratives suffer drawdowns, potentially on the order of 10% or more, but the broader system remains intact. Over time, the surviving infrastructure underpins steady, long-term growth. 

In this case, an S&P 500 index of around 7,600 by the end of 2026 is a reasonable outcome.

The Bear Case: Capital Misallocation

In the most pessimistic scenario, the gap between AI demand and infrastructure supply widens too far. 

Power shortages, increased AI regulations, and rising capital costs slow expansion just as expectations peak. At the same time, higher AI pricing hinders adoption, particularly among cost-sensitive enterprises.

As investment slows, markets reassess, and AI-exposed equities fall sharply. However, even in this case, AI does not disappear. Instead of being used in every use case possible and at every company level, it is reframed as another layer of automation and efficiency. 

In the long run, only companies that integrate AI into workflows with clear, measurable returns on investment survive, as well as those whose balance sheet is already strong and profitable. The sector stabilizes, but at valuations below today’s highs.

Under such conditions, equity markets could effectively tread water: the S&P 500 may briefly spike into the low 7,300s in the first half of 2026 before a second-half reassessment drives a drawdown, potentially toward the high 6,000s as investors recalibrate expectations.

Conclusion: Can Both Sides Be Right?

People debating over the AI bubble either think that artificial intelligence is overhyped or that it’s the defining technology of the next economic era, but the reality will probably lie somewhere in the middle.

The bears are right to focus on excess. Capital is being deployed at unprecedented speed, valuations are racing ahead of cash flows, and infrastructure timelines are being treated as assumptions rather than constraints. When they say companies are overvalued, it’s because that’s actually the case – such valuations have never been justified before and are yet to be confirmed by demand and data.

History suggests that not all of today’s companies, projects, or revenue forecasts will survive contact with reality.

The bulls, however, are right about the direction of growing demand. A demand that is already embedded in how individuals work, how developers build, and how companies operate. 

Even if the full potential is not reached yet, automation and AI are drivers of work efficiency and balance sheet consolidation, even for some of the biggest companies in terms of revenue. 

Organizations that could previously afford to invest billions in random projects, say something related to VR and AR, and get away with it, are now under scrutiny, just because everyone has their eyes on AI right now.

However, the infrastructure being built will not vanish even if expectations reset.

In that sense, AI may indeed be a bubble in how capital is allocated on a macro level, but not in what is ultimately being built. The defining question is not whether AI matters, but who survives the transition, how long it takes, and how much capital is lost along the way.

The scenarios and market levels discussed in this article are illustrative and intended to frame possible outcomes, not to provide investment recommendations. Readers should conduct their own research before making investment decisions.