If the AI Bubble Bursts in the Next 5 Years What It Means for India and a Contingency Plan

By Parikshit Khanna AI Trainer India Age 34

I train people on AI, and I still feel the market is moving faster than reality. So I wrote this for Indians in simple language. If the AI bubble bursts in the next five years, what happens to jobs, tools, students, and middle class families. And what we can do without panic.

I am writing this like a human. Some lines may not be perfect, but the plan is practical.

What an AI bubble burst means in real life

It does not mean AI disappears. It means hype reduces and money becomes strict.

This usually looks like slower funding, fewer experiments, and more pressure to show real results.

What you may see: Companies stop buying too many tools and start asking for ROI. Free tools become paid or limited. Startups shut down or merge. Hiring slows for hype roles. Practical roles remain strong, like operations, analytics, compliance support, and sales ops.

India in numbers so we stay grounded

India is not just talking about AI. India is already using it. But we must remember the economy works in cycles.

Table 1 India tech and startup facts

Area	Figure	What it means
IT industry revenue around FY2024	about 268.8 billion dollars	Huge industry but budgets are watched
IT industry employment FY2024	about 5.67 million people	Many Indian families depend on tech jobs
FY2025 revenue forecast	about 282.6 billion dollars	Growth continues but companies become selective
FY2026 expectation	crossing 300 billion dollars	Industry continues but not all roles grow equally
Startup funding 2025	around 11 billion dollars	Funding exists but checks are stricter
Startup shutdowns 2025	around 730	Still a warning sign
Startup shutdowns 2024	around 3,900	Shows how quickly the market can tighten
India tech deal value 2025	around 26 to 29 billion dollars	Consolidation increases during tight cycles

If the bubble bursts, India will not collapse. But hiring mood, startup funding, and tool spending becomes conservative.

AI adoption is high but production success is hard

Many people are using GenAI already, but scaling it safely inside companies is still difficult.

Table 2 Adoption numbers that matter

Indicator	Figure	Meaning
Students in India using GenAI	about 93 percent	Students are already dependent on these tools
Employees in India using GenAI	about 83 percent	Workplace usage is mainstream now
Employees regularly using GenAI at work	about 62 percent	This creates big governance need
Enterprises with multiple GenAI use cases live	about 47 percent	Many moved beyond pilots
Enterprises still in pilot stage	about 23 percent	A large chunk is still experimenting

So adoption is high. But safe scale is hard.

The big reason AI slows down inside companies

High error rate and reliability problems

AI can be confident and still wrong. This is the biggest issue I see during training.

Wrong outputs can create wrong customer replies, wrong finance calculations, wrong compliance interpretation, wrong HR communication, and wrong legal summaries.

Even one serious mistake can cost more than weeks of time savings.

Table 3 Reality check on AI initiatives

Data point	Figure	Meaning for India
Many pilots fall short of expectations	up to 95 percent in some observations	Pilots do not easily become production
Companies abandoning initiatives	around 42 percent in one analysis	Integration failure is common
Deployment efforts failing	70 to 85 percent in some estimates	Process, data, and governance are blockers

This is why companies will shift from fast adoption to controlled adoption.

AI is expensive and many players rely on large credit and financing

AI growth is not only subscriptions. It is also supported by large credit facilities and heavy infrastructure spending.

Table 4 Big AI financing figures

Player or ecosystem	Type	Publicly reported size	Why it matters
OpenAI	credit facility revolving	about 4 billion dollars	liquidity for heavy compute spending
Anthropic	credit facility revolving	about 2.5 billion dollars	multi year support for growth
CoreWeave	credit line expanded	up to about 1.5 billion dollars	GPU cloud scaling depends on demand
CoreWeave	term loan facility	about 2.6 billion dollars	long term hardware build out
Broader data center financing trend	SPV and off balance sheet financing	120 billion dollars plus	infrastructure risk rises if demand slows

Important note on payback years: Most companies do not publish a clear payback number like 3 years or 7 years. Payback depends on utilization and pricing.

Table 5 Simple way to judge payback reality

Item	What to check	Why it matters
Compute costs	cloud bills and model running cost	tells you if expenses are sustainable
Revenue quality	renewals and contract stability	tells you if demand is real
Utilization	whether capacity is booked	tells you if infra earns or stays idle
Contract length	multi year vs month to month	stability of cash flow
Margin trend	cost per output over time	tells you if scale helps

If utilization drops, payback becomes slow. This is where bubbles burst.

Real examples where AI created problems

Some companies tried AI and faced issues like bias, wrong answers, or content errors. Many projects got paused or reworked. The lesson is simple. AI without verification becomes reputation risk.

Contingency plan for Indians

This is the most important part. I am breaking it into three groups. Indian tech professionals, Indian students, and Indian middle class families.

Contingency plan for Indian tech professionals

Table 6 Plan for Indian tech and corporate employees

Time period	What you do	Target outcome
Next 30 days	track expenses, start emergency fund habit, update resume once	reduce panic risk
Next 60 days	create 5 work samples, SOP, dashboards, reports, templates	proof beats certificates
Next 90 days	pick one domain skill and build AI workflows with verification	AI enabled not AI dependent
6 months	network monthly, start one small side income option	job safety improves
12 months	move toward stable roles plus AI, ops, analytics, compliance support	employability rises in downturn

My personal rule. Become the person who reduces errors, not only saves time.

Contingency plan for Indian students

Table 7 Plan for Indian college students

Area	What to do	What it builds
Core skill	choose one base skill, coding, analytics, marketing, design, finance	stable foundation
Portfolio	3 to 5 real projects, case study, dashboard, report, internship style doc	proof for hiring
Verification habit	use two sources for facts, keep original PDFs and links	trust and accuracy
Placement plan	prepare for stable roles too, analyst, ops, support, inside sales	resilience in slow hiring

One truth. In a downturn, students with portfolio win faster than students with only certificates.

Contingency plan for Indian middle class families

Table 8 Plan for Indian households

Area	What to do	Why it matters
Emergency fund	aim for 6 to 9 months expenses	layoffs and delays happen in cycles
Debt	reduce high interest loans first	EMIs create stress during volatility
Insurance	keep health insurance active	medical shock should not destroy savings
Income mix	keep at least one stable income line if possible	stability anchors the family
Skill upgrade	one AI enabled workflow per working member	employability improves
Side income	tuition, freelance, consulting, local services	protects cash flow

This is not fear. This is smart planning. Indian middle class survives by stability, not by chasing hype.

Final note from me

If the AI bubble bursts, AI will still exist. The hype will reduce. Budgets will tighten. Companies will demand proof and reliability.

For Indians, the winners will be people with one strong domain skill, people who can show real work samples, people who verify outputs, and people who keep savings and avoid heavy EMIs based on unstable income.

If you tell me which group you are in—tech professional, student, or family—and your monthly expense range, I can convert this into a one-page plan you can follow week by week.