5 Scenarios for How the AI Boom Could End

Overview

As Anthropic’s IPO gathers momentum and chip stocks endure their longest losing streak since 2008, market participants are increasingly questioning the sustainability of the current AI investment boom. Concerns regarding massive debt burdens and potential overvaluation are mounting, prompting analysts to examine what a correction might look like.

However, this cycle differs fundamentally from historical bubbles. Unlike the dotcom era, where many companies generated little to no revenue, the current AI surge is underpinned by tangible utility. Capital is being directly converted into intelligence; for every GPU purchased or borrowed, developers receive tokens that represent a growing share of professional service output.

The pace of adoption and revenue generation has also far exceeded the late 1990s. Models are improving at an alarming rate, with coding tasks—a staple of software engineering—now frequently performed faster and more accurately by AI than by humans. Yet, despite this genuine value creation, the financial trajectory of the industry faces two distinct categories of risk.

Scenario Group 1: AI Disappoints or Gets Stopped

The first group of scenarios involves situations where the technology itself fails to deliver on its promises or is halted by external forces. While a total collapse seems unlikely given current trajectories, specific friction points could dampen the boom.

The Scaling Wall

It is difficult to argue that current models are not already highly effective at specialized tasks. However, there is a risk of hitting a "scaling wall," where exponentially increasing capital expenditure yields only marginally better results. This plateau could persist until a new architecture or approach emerges.

Most analysts currently discount this as an immediate threat, citing rapid advancements in capability. Nevertheless, a more insidious variation of this problem may already be present. Models might continue to improve in narrow areas, such as coding, while regressing significantly in others, leading to fragmented user experiences.

Alignment and Catastrophic Risks

The most severe risk involves AI causing tangible harm in the real world. Incidents ranging from hacking major financial institutions to disrupting national infrastructure could trigger enormous political upheaval and a rapid, severe backlash against the industry.

A globally coordinated moratorium seems unlikely given the fierce competition between the US and China. If a catastrophic event occurred in one nation, the other would likely view a pause as a strategic disadvantage. Instead, blame might be shifted toward "defective" foreign models, allowing development to continue unchecked.

However, the industry is approaching a threshold where a significant incident could cause a substantial pullback in spending. The severity of such an event would depend heavily on human impact and the speed of the geopolitical response.

The Datacentre Bottleneck

Widespread local opposition to datacentre rollouts poses a less fictional but potentially limiting risk. While this issue is already emerging as a key battleline in upcoming elections, a ban in one jurisdiction would likely lead to a shift in projects rather than a global halt.

China, which already holds a commanding lead in electricity capacity, is unlikely to entertain any such restrictions. Consequently, the real weakness in this scenario lies not in the physical deployment, but in financing. If political opposition spooks lenders, a domino effect of financing failures could paralyze new capacity, similar to the issues faced by offshore wind projects in recent years.

Scenario Group 2: Value Misses the Investors

The second, and arguably more likely, category of risk occurs when AI works effectively, but the immense value created does not flow to the companies bearing the capital costs. This misalignment may already be beginning to show.

The Open Weights Threat

The most significant risk in this category is the erosion of profit margins due to open weights models. A brutal price war is currently underway in the inference market, driven largely by open-source alternatives reaching a threshold of quality sufficient for many commercial tasks.

Major providers are responding with aggressive pricing. For instance, Anthropic has released models at steep discounts for short-context tasks, making them competitive with or cheaper than open weights options. However, these prices often spike dramatically for longer, more complex agentic workflows.

Infrastructure vs. Application

There is a structural imbalance where the companies building the underlying infrastructure capture less value than those applying the technology. Chip manufacturers and energy providers face enormous capital outlays, while the actual productivity gains may accrue to software firms that integrate AI into existing workflows without bearing the same fixed costs.

This dynamic suggests that even if AI continues to advance, the financial returns for early investors in hardware and training may diminish. The market may need to recalibrate to reflect a reality where intelligence is abundant and cheap, rather than scarce and expensive.

Conclusion

The AI boom is not a typical bubble, given the enormous amount of genuine value being created. However, the path forward is fraught with complexities. Whether the disruption stems from technological plateaus, catastrophic failures, regulatory barriers, or the sheer commoditization of intelligence, the financial implications for investors are profound.

As the market matures, the distinction between those who own the means of intelligence production and those who simply use it will likely define the next phase of the industry. Investors are advised to look beyond the headline growth metrics and scrutinize where value is actually captured in the emerging ecosystem.