For nearly two years, artificial intelligence has been treated as the one trade nobody could afford to miss. Investors piled in. Governments lined up support. Big Tech opened the spending taps. Startups added “AI” to their pitch decks and watched valuations climb. And yet, for all the noise, the key question remains unresolved: is this a revolution with durable economics, or a bubble still waiting for its breaking point? Right now, the most accurate answer is somewhere in the middle.
The bullish case is obvious. Gartner forecasts worldwide AI spending will reach $2.52 trillion in 2026, up 44% year over year, with infrastructure alone adding $401 billion as technology providers keep building the foundations for future demand. Reuters has also reported that Big Tech is expected to spend more than $600 billion on AI in 2026, up sharply from $410 billion in 2025. That is not what a market in immediate retreat looks like.
There is also still real demand. Amazon’s Andy Jassy said AWS could reach $600 billion in annual sales by 2036, roughly double his earlier projection, because of AI-driven demand. In India, Anthropic said its revenue run-rate there doubled in four months, while Reuters reported that major commitments announced at India’s AI summit included $100 billion from Adani for renewable-powered AI data centres and previously unveiled Microsoft plans for $17.5 billion of AI investment in India. Again, that is not a market that has frozen.
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But bubbles do not usually collapse because the story disappears overnight. They crack when the economics fail to catch up with the narrative. And that is where the AI story starts to look less comfortable.
The first major warning sign is the scale of spending versus the clarity of returns. Gartner says 2026 is the year AI sits in the “Trough of Disillusionment,” and argues that improved predictability of ROI still has to happen before AI can truly scale across the enterprise. McKinsey’s 2025 global survey lands in roughly the same place: less than one-third of respondents say their organizations are following most of the key adoption and scaling practices, and less than one in five say their organizations are tracking KPIs for generative AI solutions. In plain English, companies are spending aggressively, but many still do not know how to measure whether those bets are actually working.
The second warning sign is market stress inside software itself. Reuters reported in February that U.S. software and services stocks had underperformed the S&P 500 by nearly 24 percentage points over three months, one of the worst such gaps in decades, as investors started worrying that fast-moving AI tools could disrupt traditional software business models. That matters because software was supposed to be one of the cleanest and most profitable ways to monetize AI. If investors are already questioning the durability of those earnings, that is not background noise — it is a signal.
The third warning sign is debt replacing cash. Reuters reported in March that major tech companies are increasingly tapping debt markets to fund AI and cloud expansion, a notable shift for firms that historically relied on internal cash generation. That same report cited expectations for more than $600 billion in AI spending this year and noted Bridgewater’s warning that the boom has entered a “more dangerous phase,” defined by surging physical-infrastructure investment and greater reliance on outside capital. When the narrative is strong, debt looks strategic. When growth slows, the same debt can look like overreach.
The fourth warning sign is energy. AI is not just software hype; it is a physical buildout story. The International Energy Agency says global data-centre electricity consumption is projected to double to around 945 TWh by 2030, growing about 15% per year from 2024 to 2030. Reuters has separately reported that severe U.S. power bottlenecks — including turbine shortages, slow grid expansion and regulatory delays — could hobble AI expansion, with the largest AI sites consuming over 1 gigawatt of continuous load, enough to power up to 850,000 homes. That means the AI boom is now tied not only to chips and talent, but to power plants, transmission capacity and local politics.
That energy angle matters even more if macro conditions worsen. Reuters Breakingviews warned this week that an energy shock could derail the AI boom by raising operating costs, feeding inflation and making data-centre projects harder to justify economically. A bubble does not need one cause to burst. Sometimes all it takes is a market that is already stretched meeting an external shock at the wrong time.
There is also a labor-market signal worth watching. Reuters reported that Goldman Sachs economists warned in February that AI was responsible for 5,000 to 10,000 monthly net job losses last year in the most exposed U.S. industries, while recruiters at SThree said AI fears were helping slow hiring for software roles in an already weak market. Separately, Reuters reported that Meta is considering workforce cuts of 20% or more partly to balance heavy AI investment. Companies will describe this as efficiency. Investors may cheer it. But it is also a sign that firms are under pressure to prove the spending will pay for itself.
For India, the picture is especially complicated. On one hand, the country is clearly attracting capital, talent and strategic attention. On the other, Reuters reported that concerns over rapid generative AI adoption have already hit India’s IT services complex, with software exporter stocks shedding more than $47 billion in market value in February. That makes India both a beneficiary of the AI wave and one of the places where disruption could be felt most sharply if the economics disappoint.
Canada offers a different lens. Reuters reported in March that investors were rotating toward Canada’s resource-heavy stock market as a hedge against AI disruption, with sectors such as energy, metals, industrials and utilities seen as less vulnerable than software. Inference: if money is seeking shelter in “heavy asset, low obsolescence” businesses, that suggests at least some investors are already positioning for a world in which AI hype cools and tangible cash-flow businesses regain favor.
So, are there signals pointing to a collapse? Yes. They are real. ROI remains uncertain. Scaling is uneven. Energy and infrastructure are hard constraints. Debt is rising. Software winners are being questioned. Labor markets are wobbling. Some investors are already rotating toward safer, more physical businesses.
But the other side of the case is just as important: the market has not capitulated. Spending is still accelerating. Investor demand for hyperscaler debt remains strong. Governments and national champions are still making giant infrastructure commitments. User adoption is still broadening. Stanford HAI summed up the current phase well: the era of AI evangelism is giving way to an era of evaluation. That does not sound like the end of AI. It sounds like the end of easy hype.
That may be the real takeaway. The AI bubble may not burst in one cinematic moment. It may deflate more slowly, project by project, earnings report by earnings report, as markets separate genuine productivity gains from expensive theater. The companies and countries that survive that phase will not be the ones that shouted “AI” the loudest. They will be the ones that can prove where the value is, how the infrastructure gets built, and who ultimately pays for it.
For TechScoop readers in India and Canada, that is the real story. AI is still a historic buildout. But historic buildouts are not automatically good investments. Railways were transformative. So was the internet. Both also produced excess, overbuilding and painful shakeouts before the long-term winners emerged. AI may follow the same path: not a fraud, not a fad, but a genuine technological shift wrapped inside a market cycle that still looks overheated. That is why the question is no longer whether AI matters. It is whether today’s valuations, spending levels and national bets can survive first contact with economic reality.
