5 signs of an AI bubble to watch for

Are we in an AI bubble, or a lasting boom?

The question on many investors minds in early 2026 is whether the surge in artificial intelligence is a bubble in the making or a multiyear growth story with staying power. The honest answer is that AI is doing both at once. It is driving genuine productivity gains and new profit pools, while also pushing some valuations above long-term averages.

We are not at late 1990s dot-com extremes. That said, investors should stay vigilant. History shows that bubbles tend to form when enthusiasm outruns cash flows, and when cheap money encourages risk taking. The smartest way to navigate the AI wave is to track a few clear signals that tend to separate real, compounding value from speculative excess.

Below are five signs to watch, along with what they look like today and what would suggest the market is overheating.

How today differs from the dot-com era

There are echoes of past booms. Valuations across the largest AI beneficiaries are higher than their 10-year averages, capital expenditures are climbing quickly, and a meaningful share of AI demand flows among a small circle of large companies. That circular financing, where capital, products, and revenues cycle through the same leaders, can make it harder to gauge end demand outside that group.

Even so, several differences stand out. Many companies are funding AI spending with current earnings rather than debt. The most valuable AI names trade at far lower multiples than the market leaders did in 1999. And across the economy, AI adoption is broadening beyond pilots, with banks, insurers, manufacturers, and utilities moving from experimentation to deployment.

This mix suggests AI is a real economic driver, but it does not mean the market cannot overshoot. That is where the five indicators below provide useful context.

5 signs of an AI bubble to watch

1) Earnings growth stays broad and durable

What to watch: In a healthy cycle, earnings growth is supported by revenue growth and shows up across more than a handful of companies. As of early 2026, aggregate earnings for large-cap stocks have posted a long run of quarterly gains, and analysts still expect above-average revenue and profit growth over the next year. That breadth matters because it suggests AI is lifting more boats than just the megacaps.

Why it matters for bubbles: When growth becomes narrowly concentrated or decouples from sales, markets become vulnerable. Expensive markets, like today, have punished negative earnings surprises more than usual in past cycles. A turn from widespread beats to frequent misses is often an early sign of excess expectations.

Red flags to monitor:

• Revenue growth lagging far behind earnings growth, which can indicate cost cuts rather than demand.
• A shrinking percentage of companies beating estimates, especially outside the largest AI names.
• Heavy reliance on aggressive guidance or one-time items to meet targets.

On top of that, keep an eye on the largest leaders. The so-called mega-cap cohort has an outsized weight in indexes. Slower growth among these firms can weigh on the broader market, even if fundamentals elsewhere remain solid.

2) Earnings quality holds up

What to watch: Strong profits should show up in cash. A good rule of thumb is that free cash flow should keep pace with, or exceed, reported net income over time. As of late 2025, aggregate measures of earnings quality across large caps looked healthy. Net margins remained above recent averages, cash generation was solid, and GAAP earnings trends were firm.

Why it matters for bubbles: Late in past cycles, companies sometimes stretched accounting to sustain the appearance of growth. That showed up in larger gaps between adjusted and GAAP earnings, rising receivables, or a spike in restatements. Genuine AI-driven productivity should translate into cleaner, cash-backed profits, not just adjusted figures.

Red flags to monitor:

• Cash flow falling behind reported earnings for many AI-linked companies.
• Net margins slipping while sales still look strong, which can hint at price pressure or rising input costs.
• A sharp increase in non-GAAP adjustments, stock-based compensation, or capitalized costs that flatter profits.
• An uptick in earnings restatements, especially among fast-growing firms with complex accounting.

If you see several of these cropping up at once, it suggests expectations are outpacing economic reality, a common feature of bubbles.

3) Valuations vs. history

What to watch: The market is paying a premium for AI exposure. Large-cap indexes trade above their 10-year average price-to-earnings ratios, and the information technology sector commands the highest multiples of the major sectors. Even so, current valuations remain below the most extreme levels reached during the dot-com peak.

Why it matters for bubbles: Elevated valuations are not a bubble by themselves. They can be justified if growth and returns are robust and persistent. The risk emerges when multiples expand faster than earnings, or when speculative activity spills into unprofitable or early-stage companies with limited visibility on monetization.

Red flags to monitor:

• Rapid multiple expansion without a matching increase in earnings or cash flow.
• A wave of newly public or thinly profitable AI-adjacent companies trading at steep sales multiples.
• Speculative rallies driven by headlines rather than fundamentals, especially in smaller or illiquid names.

Here is where it gets interesting. The largest AI leaders today trade at far lower forward earnings multiples than the market giants did in 1999. That offers a buffer, but it does not immunize investors from downside if expectations reset.

4) Capex is aggressive, but affordable

What to watch: AI infrastructure is expensive. Data centers, power upgrades, networking gear, next-generation chips, and model development all require heavy capital outlays. The biggest cloud and internet platforms have already spent hundreds of billions of dollars on capacity, and plans point to higher outlays through 2026. Many enterprises are also investing in AI software and workflow automation beyond pilot projects.

Why it matters for bubbles: In past booms, spending sprees financed by debt often set the stage for busts. The picture looks different today. Across broad equity indexes, the ratio of capital expenditures to free cash flow is below 1, which suggests companies are largely funding AI projects from operating cash rather than borrowing. In the late 1990s, that ratio spiked meaningfully higher, a hallmark of speculation.

Red flags to monitor:

• Capex outpacing free cash flow for long stretches across many AI-focused firms.
• Rising leverage and deteriorating interest coverage ratios tied to AI buildouts.
• Vendor financing and cross-holdings that obscure true demand or transfer risk around the ecosystem.
• Power and chip bottlenecks that delay deployments and compress returns on investment.

Capex may evolve, too. As platforms stabilize, companies might shift costs from capital expenditures to operating expenses, extending the spending cycle while improving flexibility. Track how that shift affects margins and cash generation.

5) The interest-rate cycle stays supportive

What to watch: Liquidity is the lifeblood of booms. Historically, major bubbles have formed in periods of easy money and have burst after inflation or instability forced central banks to raise rates. In the current cycle, policy rates have moved lower since late 2024, which has helped support risk assets and credit conditions.

Why it matters for bubbles: Lower borrowing costs can lift spending and asset prices. The tipping point comes when inflation reaccelerates or growth imbalances build, prompting a hawkish shift. A sustained rise in rates increases the cost of capital, pressures valuations, and can expose weak balance sheets.

Red flags to monitor:

• Signs of renewed inflation that could push policymakers to tighten policy sooner than expected.
• Widening credit spreads, especially for borrowers funding large AI projects.
• A turn higher in default rates or downgrades across leveraged tech ecosystems.
• Clear guidance from central banks that the easing phase is ending.

The rate backdrop does not flash warning signs today, but it is the lever history tells us to watch most closely if conditions change.

Why the AI trend still looks durable

Spending on AI has compounded for more than a decade, echoing how investment in microprocessors and personal computing ramped over multiple decades. Governments and enterprises are building capacity, not just proof-of-concept pilots. Demand for the underlying hardware and services remains strong, and use cases are moving from productivity experiments to revenue-driving applications.

Importantly, monetization tends to lag investment in the early innings of platform shifts. That does not invalidate the opportunity. It does suggest a period where spending grows faster than revenue, which can be uncomfortable for markets. Patience is required while returns catch up to outlays.

What we are not seeing yet

Despite stretched headlines, several classic bubble markers are not broadly present as of early 2026:

• Shrinking free cash flow amid aggressive AI infrastructure spending.
• Rising cross-holdings of common stock among corporates that inflate demand.
• Leverage ratios deteriorating due to debt-fueled AI expansion.
• Multiple compression driven by prolonged power or compute constraints that stall growth.
• Wider credit spreads that signal financing stress across the ecosystem.

These can change quickly. Build a habit of checking these signals when quarterly results and credit updates arrive. They provide early warnings if optimism starts to outrun cash flows.

Practical moves for investors

AI is a powerful theme, but thematic concentration cuts both ways. You want exposure to durable winners without taking on avoidable bubble risk. A few practical guidelines can help balance those goals.

• Favor cash generators with clear ROI. Companies that can show AI projects driving revenue uplift, cost savings, or higher margins have a better buffer against valuation resets.
• Look for breadth, not just the headliners. Opportunities exist in infrastructure, power, software, industrials, health care, and services. Concentrating solely in a handful of megacaps or speculative newcomers raises risk.
• Track capex discipline. Sustainable spending supported by free cash flow is healthier than debt-fueled buildouts.
• Respect the rate cycle. A hawkish pivot can deflate premium multiples faster than fundamentals change.
• Maintain diversification. Balance AI exposure with other sectors, market caps, and geographies, and use fixed income where it fits your goals and risk profile.

All investing involves risk, including the risk of loss. The goal is not to avoid AI entirely, but to own it thoughtfully, with a framework that keeps you on the right side of risk.

Bottom line

AI is powering real economic activity and profit growth, even if the long-term magnitude is hard to quantify. Valuations are elevated relative to recent history, but they are not at dot-com extremes. For now, earnings growth looks broad, earnings quality is healthy, capex is largely funded by cash flow, and the rate backdrop is supportive.

The picture can change. Watch the five signals above for early signs of overheating. If they start flashing red, tighten risk. If they stay green or mixed, the odds favor a durable, albeit uneven, AI-led expansion.

Key takeaways

• AI remains a multiyear theme. Productivity and efficiency gains are real, though monetization can lag investment.
• Five signals help spot bubble risk: earnings growth, earnings quality, valuations, capex sustainability, and the interest-rate cycle.
• Valuations are high, not extreme. Today looks expensive versus the 10-year average, but calmer than 1999.
• Capex is big but mostly cash funded. That reduces systemic risk relative to past booms.
• Stay diversified and disciplined. Favor cash-backed growth, monitor the rate backdrop, and avoid chasing speculative surges.