What I'm trying to explain is not that the artificial intelligence is a bubble, but that the money entering it is a bubble. Most people are investing in this field just to multiply their money without knowing what it is, and eventually this bubble bursts. Yes, Bitcoin and Ethereum have survived, but many coins have disappeared. The same investments were made in search engines back in the day, and many of them also vanished. This time, the numbers are very large. If exits from the money in the artificial intelligence field begin to be seen, the crisis could deepen.

Yes sir, I have been researching this topic for a long time and I have seen that the trend has shifted a little here, but artificial intelligence seems to be nothing more than a helpful tool.

The AI investment boom of 2025 is staggering: global spending, corporate and startup funding, hardware infrastructure – everything is accelerating. But with so much money flowing in, some analysts are warning of an AI bubble risk: when expectations outpace actual productivity or returns, a sharp correction could cause serious economic stress. This article examines how much money is in AI, technical and on-chain signs, scenarios for a burst, and whether it could lead to a broader economic crisis.
How Much Money is Flowing into AI?! (Global Scale & Recent Data)

Private & Corporate AI Spending

In 2024, corporate AI investment globally was about US$252.3 billion, up significantly from prior years.

Generative AI in particular raised US$33.9 billion in private investment in 2024, an ~18.7% increase vs. 2023.

Big tech giants (Microsoft, Alphabet/Google, Amazon, Meta, etc.) plan to spend about US$320 billion in 2025 on AI technologies & infrastructure—up from ~US$230B in AI capital expenditures in 2024.

Market Size, Forecasts & Projections

The global AI market (software, services, hardware) is currently valued around US$391 billion (2025) and is projected to reach US$1.81 trillion by 2030. Compound annual growth rate (CAGR) ~35.9%.

In terms of startups: generative AI start-ups and others are receiving large funding rounds; for example Figure was valued at US$39B in its recent Series C after raising over US$1B.

Also, hardware & infrastructure: data centres, inference chips are seeing massive investment. For instance, Groq recently raised US$750 million, pushing its valuation to US$6.9B.

Technical & On-Chain / Empirical Indicators of Overvaluation

These are signals that experts are watching to see if the hype is getting ahead of fundamentals.

 Productivity vs Hype Gap

Many companies adopting AI report usage, but measurable profit / productivity gains are often modest. According to the Stanford HAI 2025 report, many organizations using AI see cost savings or efficiency improvements, but rarely above ~10%.

The Atlantic, in its analysis, notes that despite billions invested, “companies trying to incorporate AI have seen virtually no impact on their bottom line.”

Valuation Multiples & Investor Behavior

Valuations of AI companies are often far ahead of actual revenue, or even business models. Many start-ups are still in early phases, without steady cash flows.

There is competition in hardware (GPUs, inference chips), infrastructure, data centers—areas which require huge capital expenditure and long-term returns. If demand or supply disrupts, costs could decline, but investors may suffer.

 Market Sentiment, Risk & Regulatory Pressure

Several analysts & media are asking: Is AI overhyped? Could be similar to past tech bubbles.

Rising interest rates, macroeconomic pressure, supply chain constraints, regulatory oversight (privacy, safety, data use) could all cut into-margin or slow deployment.

 What Happens If the AI Bubble Bursts? Scenarios & Possible Economic Fallout

 What I Mean by “Bubble Burst”

A “bubble burst” here means: valuations sharply correcting, venture capital drying up, many AI startups failing; overbuilt infrastructure (data centres, chips) underutilized; investor losses; reduced optimism.

 Short-Term Effects

DomainPossible EffectsTech sector valuationsPlunge in market caps, failed IPOs, write-downs of startups.EmploymentLayoffs in AI R&D, startups; hiring freezes especially for speculative projects.Investor lossesVCs, angels suffer; limited partners (pension funds, funds of funds) exposed.Consumer trustOverpromising and underdelivering may reduce adoption, slow further innovation.

 Mid- to Long-Term Effects & Economic Crisis Risk

GDP growth slowdown: If AI investments contributed significantly to growth (via corporate capex), a reversal could shave off growth percentage points. Some sources suggest AI investment is propping up certain economies.

Stock market correction: Big tech stocks could fall, which may cascade across indices, pensions, retirement funds.

Credit and debt strain: Companies heavily leveraged to build infrastructure might struggle; losses could affect lenders.

Labour market disruptions: If many workers were expecting AI to create jobs, but instead layoffs hit, could reduce consumer spending, increase unemployment claims.

 Could It Become a Broader Crisis?

Yes — under certain conditions:

If AI overinvestment is sharply reversed, it might lead to a credit crunch, especially for those companies whose debt is large.

If macroeconomic conditions are weak, combined with geopolitical tensions, supply chain shocks, inflation etc., it could amplify negative feedback loops.

Governments stepping in may face difficulties: stimulus may be required; regulation may restrict future funding flows; geopolitical competition (US-China) might exacerbate risk misallocation.

 Cause-Effect Analysis

Because huge sums are invested in AI infrastructure & startups → this creates high expectations for returns. If returns are slow, investor sentiment sours → rapid valuation declines.

Because many AI firms are in early stage, with unproven business models → high risk of failure. If a large number of them fail, losses accumulate.

Because the technology requires capital-intensive hardware (data centers, chips), which have long lead times and upfront cost → underutilization or oversupply can create wasted capacity and losses.

Because public policy and regulation are playing catch-up → unexpected regulatory burdens or restrictions could reduce profitability or delay rollout, worsening mismatch between expectations and reality.

Is It Likely / When Could It Happen?

Leading Signals to Watch

Slowdown in funding rounds / fewer late-stage raises.

Declining margins or evidence that many AI projects do not scale.

Overcapacity in hardware / data centers.

Rising interest rates or credit tightening.

Regulatory clampdowns (privacy laws, AI safety rules, export controls etc.).

Possible Timing

It might not be immediate. Maybe 1-3 years before serious correction if several factors align (economic slowdown, policy changes, market saturation).

Conclusion

AI investment in 2025 is massive: hundreds of billions of dollars from private and corporate sources, rising valuations, intense competition, and expanding infrastructure. But there is a real risk of an AI bubble: where hype outpaces proven returns. If that bubble bursts, we could see stock market corrections, startup failures, job losses, and broader impact on economic growth.

That said, AI also has fundamental potential: significant productivity gains, new business models, transformation in many sectors. Even if there is a correction, it won’t erase all progress — likely it will reshape the landscape, with winners and losers.

Not financial advice.

The crypto market has entered a critical stage as Bitcoin consolidates in the $115K–$125K zone. On-chain data shows whales unloading massive amounts of BTC, ETF inflows slowing, and retail traders stuck underwater. The big question: is this distribution or accumulation?

Whale Distribution in the $115K–$125K Zone

Recent on-chain data confirms wallets holding 1,000–10,000 BTC sold around 116,000 BTC in the last 30 days, worth nearly $13B. This marks the largest whale sell-off since July 2022.

Whales use sideways chop to distribute gradually without triggering panic.

Market makers unload in the $115K–$125K zone, hiding footprints on charts.

Without strong new inflows, demand cannot absorb this selling pressure.

This sideways action matches the Wyckoff distribution phase, where whales offload supply to retail.

Retail Traders Still Underwater

Most retail longs and spot entries are between $117K–$122K over the past 3 months. Many remain in mild loss but haven’t capitulated yet.

Small panic sells occurred near $107K in early September, but not a major shakeout.

This leaves a dangerous setup: retail holds, whales sell, and once support breaks, cascading liquidations are likely.

Are Altcoins a Real Rally or Exit Liquidity?

While Bitcoin chops sideways, altcoins have pumped aggressively. The so-called “altcoin season” has sparked excitement, but evidence suggests a trap:

Over 70% of alts are outperforming BTC temporarily.

The strongest gainers are newly listed, exchange-driven tokens with no fundamentals.

High leverage in alts makes them fragile.

History shows alt rallies during Bitcoin distribution often serve as exit liquidity for whales.

Technical & On-Chain Evidence
Accumulation/Distribution Indicator

The A/D line is falling despite stable price action → clear distribution pattern.

ETF Inflows and Market Liquidity

After strong July inflows, Bitcoin spot ETF demand has slowed to ~500 BTC/day. Without consistent inflows, whales dominate supply.

Whale Netflows to Exchanges

Exchange inflows spiked in late August → whales are actively sending coins to sell.

Key Strategic Takeaways
Zone   Implication   Strategy
$115K–$125K   Heavy whale distribution   Take profits or hedge shorts
$110K–$106K   Weak support, liquidity zone   Risk of cascading sell-offs
$90K–$100K   Major liquidity pool   Potential long-term buy zone
Final Market Outlook

The $115K–$125K range is not accumulation — it’s distribution. Whales are unloading into retail optimism, while altcoin pumps provide distraction.

Unless ETF inflows return strongly, BTC risks a correction below $110K, with liquidity extending down to $90K. Retail celebrating small bounces in this zone may be acting as exit liquidity.

⚠️ Not financial advice. Educational content only.