An AI crash would resemble a hybrid of the 1990s dot-com bust and the 2008 financial crisis—but centered around artificial intelligence infrastructure, data centers, and corporate overinvestment. It would likely begin as a sudden market correction in overvalued AI firms and GPU suppliers, then spread through the financial system and tech economy as debt and demand collapse.

Market and Investment Collapse

In early stages, overleveraged companies like OpenAI, Anthropic, or firms heavily reliant on GPU compute (e.g., Nvidia, Oracle, Microsoft) would face sharp valuation drops as AI-generated revenues fail to justify trillion-dollar capital expenditures. Investor panic could trigger a chain reaction, collapsing the leveraged network of data‑center finance. Bloomberg and the Bank of England have both warned of a “sudden correction” and circular investing between chip firms and hyperscalers that artificially props up earnings.transformernews+1

The Data Center Bust

According to historian Margaret O’Mara and business analyst Shane Greenstein, AI data centers—many purpose‑built for model training using GPUs—are highly specialized and often remote from urban demand. These centers might last only 3–5 years and have little reuse value outside AI or crypto mining. If capital inflows freeze, thousands of megawatts of compute could become stranded assets, comparable to the empty fiber networks after the dot‑com collapse.transformernews

Economic Impact

The International Monetary Fund estimates roughly a third of current US GDP growth depends on AI-related investment. If the bubble bursts, consumption could fall from loss of “AI wealth effects,” dragging global markets into recession. Analysts at Transformer News liken it to Britain’s 1840s railway mania: vast sums invested in technology that ultimately enriched the future economy—at the cost of investors’ ruin.globalcapital+2

Consequences for Jobs and Technology

For the workforce, the crash would begin with mass layoffs across the tech sector and data‑center construction, followed by second‑order layoffs in software, marketing, and education technology. However, as with the post‑dot‑com era, redundant talent and abandoned infrastructure could later fuel a new, leaner AI industry based on sustainable business models.reddit+2

Systemic and Political Risks

While the contagion risk is smaller than subprime mortgages in 2008, debt-financed AI expansion—Oracle’s $100 billion borrowing plan with OpenAI being one example—creates vulnerability for lenders and investors. Should a major firm default, cascading insolvencies could ripple through the supply chain, forcing governments to intervene. Some analysts expect this crash would prompt stricter AI regulation and financing guardrails reminiscent of those enacted after the Great Depression.transformernews

Long-Term View

If artificial general intelligence (AGI) does eventually deliver major productivity gains, early investments may appear prescient. But if not, a 2020s AI crash would leave disused GPU campuses and massive debt—an exuberant experiment that accelerated technological progress at ruinous human cost.unherd+2

Which industries would collapse first in an AI crash

In the event of an AI crash, several sectors would be hit first and hardest — especially those that have overexpanded based on speculative expectations of AI-driven profits or infrastructure demand. The collapse would cascade through high-capex industries, ripple across financial services, and disrupt employment-dependent consumer sectors.

Semiconductor and GPU Manufacturing

The semiconductor industry would be the first to collapse due to its heavy dependence on AI demand. Data center GPUs currently drive over 90% of Nvidia’s server revenue, and the entire sector’s value nearly doubled between 2024 and 2025 based on AI compute growth forecasts. If hyperscaler demand dries up, the oversupply of GPUs, high-bandwidth memory (HBM), and AI ASICs could cause a price crash similar to the telecom equipment bust in 2002. Chip makers and startups like Groq, Cerebras, and Tenstorrent—heavily leveraged to AI workloads—would struggle to survive the sudden capital freeze.digitalisationworld

Cloud and Data Center Infrastructure

AI-heavy cloud providers such as Microsoft Azure, AWS, Google Cloud, and Oracle Cloud would see massive write-downs in data center assets. Overbuilt hyperscale and sovereign AI campuses could become stranded investments worth billions as training workloads decline and electricity costs remain high. This dynamic mirrors the way dark fiber networks from the 1990s dot-com era lay idle for years after overinvestment.digitalisationworld

Digital Advertising and Marketing

The advertising and media sector—already experiencing erosion due to AI‑generated content—would decline abruptly. Companies like WPP have already lost 50% of their stock value in 2025 due to automated ad-generation technologies cannibalizing human creative work. As AI content generation saturates the market, profit margins in marketing, online publishing, and synthetic media platforms like Shutterstock and Wix could collapse.ainvest

Financial and Staffing Services

Financial services and staffing firms are another early casualty. AI has already automated large portions of transaction processing, compliance, and manual recruitment. Firms such as ManpowerGroup and Robert Half have reportedly seen 30–50% market value declines due to these pressures. In an AI crash, their exposure to risk-laden corporate clients and shrinking demand for human labor matching would deepen losses, while regulators tighten AI governance in compliance-heavy finance.ainvest

Transportation and Logistics

The transportation and logistics sector, closely tied to AI investment through autonomous systems, faces structural weakness. Millions of driving and delivery jobs could disappear due to automation, but the firms funding autonomous fleets—such as Tesla Freight and Aurora Innovations—would hemorrhage cash if capital dries up before widespread profitability. AI‑powered routing and warehouse systems could be written down as expensive overcapacity.ainvest

Secondary Collapse: Retail and Customer Support

Finally, customer‑facing retail and support sectors would be heavily affected. With AI chatbots now handling about 80% of common queries, these labor markets are already contracting. A market shock would worsen layoffs while eroding spending power, compounding the downturn.ainvest

Summary Table

Industry	Collapse Trigger	First-Order Impact	Example Companies Affected
Semiconductors	GPU oversupply, hyperscaler pullback	Revenue crashes, fab overcapacity	Nvidia, AMD, TSMC, Cerebras digitalisationworld
Cloud & Data Centers	Halt in AI training spend	Idle assets, megacenter write-downs	Microsoft, AWS, Oracle Cloud digitalisationworld
Advertising & Media	Automated ad content cannibalization	Loss of billable creative work	WPP, Shutterstock, Wix ainvest
Financial & Staffing	Job automation, credit exposure	Rapid fall in client demand, defaults	ManpowerGroup, Robert Half ainvest
Transportation	Delay in autonomous projects	Job losses, asset devaluation	Tesla Freight, Aurora, Waymo ainvest
Retail & Support	Customer service automation	Widespread workforce reduction	Shopify, Zendesk ainvest

In short, the first phase of an AI crash would decimate GPU suppliers and infrastructure providers, followed by cascading losses in services and labor markets that relied on sustained AI adoption and speculative investor optimism.

The Hyperscalers who would be most affected in an AI crash

The hyperscalers most severely affected by an AI crash would be those that have sunk the largest capital into AI‑specific data center expansion without commensurate returns—primarily Microsoft, Amazon (AWS), Alphabet (Google Cloud), Meta, Oracle, and to a lesser extent GPU‑specialist partners like CoreWeave and Crusoe Energy Systems. These companies are deep in an investment cycle driven by trillion‑dollar valuations and multi‑gigawatt data center commitments, meaning a downturn would cripple balance sheets, strand assets, and force major write‑downs.

Microsoft

Microsoft is the hyperscaler most exposed to an AI collapse. It has committed $80 billion for fiscal 2025 to AI‑optimized data centers, largely to support OpenAI’s model training workloads on Azure. Over half this investment is in the U.S., focusing on high‑power, GPU‑dense facilities that may become stranded if demand for model training plunges. The company also co‑leads multi‑partner mega‑projects like Stargate, a $500 billion AI campus venture involving SoftBank and Oracle.ft+1

Amazon Web Services (AWS)

AWS is next in risk magnitude, with $86 billion in active AI infrastructure commitments spanning Indiana, Virginia, and Frankfurt. Many of its new campuses are dedicated to AI‑as‑a‑Service workloads and custom silicon (Trainium, Inferentia). If model‑training customers scale back, AWS faces overcapacity in power‑hungry clusters designed for sustained maximum utilization. Analysts warn that such facilities are difficult to repurpose for general cloud usage due to 10× higher rack power and cooling loads.thenetworkinstallers+1

Alphabet (Google Cloud)

Google’s parent company, Alphabet, has pledged around $75 billion in AI infrastructure spending in 2025 alone—heavily concentrated in server farms for Gemini model operations. The company’s shift to AI‑dense GPU clusters has already required ripping and rebuilding sites mid‑construction. In a crash, Alphabet’s reliance on advertising to subsidize capex would expose it to compounding financial stress.ft+1

Oracle

Oracle, a late entrant to the hyperscaler race, ranks as the fourth largest hyperscaler and has become deeply tied to OpenAI’s infrastructure build. It is reportedly providing 400,000 Nvidia GPUs—worth about $40 billion—for OpenAI’s Texas and UAE campuses under the Stargate project. Oracle’s dependency on a few high‑risk customers makes it vulnerable to disproportionate collapse if those clients cut capital expenditures.ft

GPU Cloud Specialists (CoreWeave, Crusoe, Lambda)

Although smaller in scale, CoreWeave, Crusoe Energy Systems, and Lambda Labs face acute financial danger. Each is highly leveraged to GPU leasing economics that assume near‑continuous utilization. A pause in large‑model training would break their cash flow structure, causing defaults among the so‑called “neo‑cloud” providers.hanwhadatacenters

Comparative Exposure Overview

Hyperscaler	Estimated 2025 AI Capex	Primary Risk Channel	Vulnerability in a Crash
Microsoft	$80 billion	Overexposure to OpenAI workloads	Extremely high hanwhadatacenters
Amazon (AWS)	$86 billion	Idle compute, train‑specific sites	Very high thenetworkinstallers
Alphabet	$75 billion	Advertising decline + AI site overbuild	High thenetworkinstallers
Meta	$60–65 billion	Pure AI data center utilization risk	High hanwhadatacenters
Oracle	$40 billion (via Stargate)	Concentrated tenant risk (OpenAI)	Very high ft
CoreWeave / Crusoe / Lambda	$10–15 billion range	Debt leverage and GPU lease dependence	Extreme hanwhadatacenters

Summary

A sustained AI market collapse would first hit these hyperscalers through GPU underutilization, stranded data‑center capacity, and debt‑heavy infrastructure financing. Microsoft, Oracle, and Meta would face the most immediate write‑downs given their recent megaproject commitments. Amazon and Google, while financially stronger, would absorb heavy revenue compression. Specialized GPU‑cloud providers—CoreWeave, Crusoe, and Lambda—could fail outright due to funding constraints and dependence on short‑term AI demand surges.thenetworkinstallers+2

If you work for any IT company and see Slack users all of a sudden disappearing – then your company is performing a RIF. Out of the blue or with very short notice – a colleague or two’s Slack account is closed and you are left wondering why.

This trend has been around for a while now and sprung sites such as https://layoffs.fyi/ documenting the unprecedented amount of layoffs in the IT industry. Other sites document layoffs in other industries (e.g. UK education and civil service) too, and paints a gloomy picture of the state of unemployment and an extreme tough jobs market.

My current employer is make a round of RIF this moment in time! Hence this article about RIFs. I was affected by a RIF a year ago with a different corporation, so I am putting in motion things to do from the lessons I learnt from last time. I hope this will help anyone affected this time…

Trust No One

When you are being told that there is a round of RIF and “we are not affected” or “we are safe” by your manager or director – do not trust them. When this announcement is made interpret this as “you need to make plans and execute them ASAP in preparation that you will be affected”. Until the RIF round is “official” over, then consider this “unknown” period is your “at-risk” period.

By UK employment rules (the minimum that corporations will follow) your employer will have to give you an “at-risk” period (different from above!) When they give you this notice, you are able to stop work and look for other roles – internally OR externally).

My previous employer when they gave me this “at-risk” period, had already frozen hiring and no new “req”s were granted, making it impossible to get an internal role if you wanted to stay with your employer. In this situation you are effectively certain of being made redundant and will have to leave their employment…

You need to put things into place if you are going to survive the redundancy.

The Nature of IT RIFs

Two is not a pattern to draw definite conclusions on but it seems to me that when a corporation announces a record profit-making quarter, they follow this up by a record spend which forces them to make a RIF. This is the way…

The nature of IT recruitment and redundancies seem to have established a boom and bust pattern. Corporations overspend and over-recruit to achieve a commercial objective or goal (usually adding as much value to the corps as possible) and when this funding-period is over, they then perform a RIF to be able to start the next project. This is an evil cycle for the all employees, not just those who are let go.

When a RIF occurs, there’s little rhyme or reason why specific individual is affected. The main directive or goal of a RIF is to reduce costs so the corps can make up the huge spend or fund the new project – nowadays it is certain to be AI. The lowest hanging fruits will be picked first and then maybe the projects that are costing most but have delivered little and then just randomly in areas that (to the bosses) are not important. Of course, to the individual, we are all important so we ask the question why me? Why my team? Why my organisation?

There is no reason – even if your manager or director gives you a reason – this will not be it!

Accept and Move On

Successful people turn disadvantages to advantages – they accept the situation, deal with it fast – learn from the situation – and move on! They do not “sulk” or “get down” or “get stuck” – they learn, try again, try something different until they succeed. This is what anyone affected by RIF must do. When I say “accept and move on” yes, I mean accept the severance package and start on your CV/Resume and start job hunting… or if you are due a good package, buy that Porsche you’ve always wanted and drive it… into a traffic jam…

One of the help that might be available to someone who is “at-risk” is free consultation with a career coach. I must admit, I was very skeptical about this free facility at first, but once I ventured out to look at the job market, I find myself turning around and was open to help, tips, advice and motivation of any kind to get a head start.

The job market has changed a lot and has also gotten tougher and tougher with each round of redundancies. You need al the advice and coaching you can get. The successful things you did to attain the job/role that you’ve juts been made redundant from will NOT work this time! You need new job hunting skills, tools and be adaptable to the current state of the market.

Those who have not hunted for new roles or moved jobs in the past 5 or 10 years will have to learn and act fast! I see that even talent advisors and experience recruiters struggle to find new roles for themselves let alone for others…

What To Do?

This is my list – it needs to be adapted for your personal needs/situation – it is just to give you something to start with:

Update/rewrite your CV/Resume
- Your CV/resume will be current so update
- Your CV/resume will not be in a modern format/layout
- Your CV/resume will need to be tailored to the role
- Your CV/resume will need to be in a format for auto-form-filling easier
- Your CV/resume will need to be in a format for AI to process and not reject you without passing it on to a human!
Create a generic cover letter
- Your roles will be very similar in requirements, so a generic letter will save time
- Leave areas for specifics, but don’t forget to change those specifics
Sign up to LinkedIn and other job boards
- These sites will have job hunting tips and advice so take advantage
- These sites allow you to network so take advantage
- These sites might have training courses or practise facilities
Reach-out to contact and ex-colleagues
- There maybe suitable vacancies with their employer
- Ask them to spread the message that you are looking for a new role
Create a spreadsheet of job applications
- You will soon lose track of what company, the recruiter, the role, etc that you’ve applied for an why – keep a spreadsheet of all relevant info
Create a routine of job searching/application and rest
- You will need to be disciplined so a route that works with rest breaks to relieve the stress will keep you going until you are successful
Practise interviews. conversations, and coding tests, etc.
- You will need to be sharp and effective in your interview, practise and deploy all the tricks and methods for effective interview e.g. using S.T.A.R. method and the like.
- Practise in a Zoom session and record yourself, playback to evaluate how you perform and what you should do and not do, say and not say

Good Luck!

Do not give up! And do not stop once you’ve achieved a new role! Work as though you are under threat of being made redundant – there is no such thing as a safe job any more – always actively develop and progress to the next role…

I am writing this in a situation when I am actually in my “at-risk” period… But as I’ve started this process well before the RIF news, I think I am ahead in the job queue (although not necessary near the very start!)

BLU // SAS

Category Archives: Employment

What would an AI crash look like?