IT crisis: history, major blackouts and current effects

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IT crises have accompanied digital transformation for decades , although we sometimes only remember them when WhatsApp crashes, an airport is paralyzed, or the dreaded Windows blue screen appears on millions of computers simultaneously. From the first commercial computers to the explosion of artificial intelligence, recent history is punctuated by bugs, global blackouts, tech bubbles, and financial scares that demonstrate just how fragile the entire system can be.

Understanding the history and current effects of these cyber crises is key to quantifying our dependence on technology, assessing the role of cybersecurity , and anticipating what may come after the AI ​​boom, stock market bubbles, and massive software failures that cripple airlines, banks, hospitals, and governments worldwide.

From the Y2000K bug to the fear of global digital collapse

A few years ago, the entire planet braced itself for a supposed digital apocalypse : the infamous Y2K bug, also known as the millennium bug. The theory was simple yet unsettling: since many systems stored dates using only two digits for the year ("dd/mm/yy"), when transitioning from 1999 to 2000, 01/01/00 could be interpreted as 1900. This meant that programs of all kinds could "believe" they had gone back a century and begin to malfunction in unpredictable ways.

The origin of this problem dates back to the 50s and 60s , when memory and storage were extremely expensive and limited. To save space, programmers cut corners wherever possible, and one of the most practical ways was to abbreviate dates by omitting the century. Thus, January 1900 was stored as 01/00 and December 1999 as 12/99, a scheme we still see today, for example, on many credit cards.

For decades, no one paid much attention to the two-digit trick , because everything happened within the same century and there seemed to be no conflict. However, little by little, strange symptoms began to appear: records of centenarians listed in the database as four-year-old girls, batches of products with expiration dates "eighty years" before their actual date, and billing systems calculating impossible periods. These were clues that, when the millennium turned, the mess could be monumental.

In the early 90s, the warnings began to be taken seriously . Computer scientists and systems administrators warned that almost every sector was affected: banks, insurance companies, public administrations, construction companies, telecommunications operators, energy companies, transportation, hospitals, and defense systems. Any software that handled two-digit dates was a prime candidate to crash as the year 2000 approached.

Governments and large corporations reacted with a multi-million dollar investment . Programs, databases, files, and procedures had to be inventoried; all points where dates were handled had to be located; and enormous amounts of code had to be rewritten. Specific tools were developed to scan applications, extensive test plans were defined, and on-call teams were assembled to spend New Year's Eve 1999 in front of consoles and servers, ready to react to critical incidents.

The case of Spain illustrates the scale of the effort : the state alone allocated some €420 million to adapting systems and equipment for the millennium change, while globally it is estimated that around €214.000 billion was spent. Many organizations took advantage of this mandatory work to also introduce other strategic improvements, such as preparing their systems for the arrival of the euro.

The actual arrival of the year 2000 was a moment of contained tension . Technical teams were closely monitoring developments in countries like New Zealand, Australia, and Japan, which crossed the time threshold before Europe or America. The news arriving from the east was reassuring: the lights were still on, planes weren't crashing, and power plants were still operating.

In the end, the feared global computer meltdown did not occur . There were incidents, yes, but they were mostly minor: invoices generated with incorrect dates, offline service terminals, some devices that stopped working, or isolated errors at nuclear power plants or other critical systems that were resolved without serious consequences. In Spain, for example, minor glitches were detected at a couple of nuclear power plants, some gas stations, and certain automated traffic data collection systems.

The fact that the disaster didn't materialize led some to call it a myth or an exaggeration , but experts agree that the danger was very real and that the reason nothing serious happened was precisely the preventative effort. If those systems hadn't been reviewed and corrected in time, the jump from '99 to '00 would have caused operational chaos in banks, businesses, and public services, with a direct impact on the economy and public safety.

The Y2000K bug taught us a lesson that remains relevant today : we are inextricably linked to technology, and the more we depend on it, the greater the potential impact of a massive failure. Furthermore, it demonstrated that even when faced with a problem predicted well in advance, coordinating global responses, engaging all stakeholders, and mobilizing sufficient resources in time is extremely difficult.

From bugs to massive blackouts: global failures that bring the world to a standstill

Two decades after that millennium scare, the threat of a global technological shutdown has become far more tangible . It's no longer a prediction based on how dates are stored, but actual computer blackouts that have grounded planes, blocked ATMs, and overwhelmed emergency services in many countries simultaneously.

The most striking example is the recent computer blackout caused by a faulty update from CrowdStrike , a cybersecurity company that protects, among others, systems running Microsoft Windows. A simple content update to its security agent for Windows 10 was enough to trigger a cascade of critical errors on up to 8,5 million affected devices, displaying the iconic "blue screen of death" on computers worldwide.

The scale of the incident was such that many experts have already labeled it the biggest computer blackout in history , precisely what was feared with the Y2000K bug but which ultimately failed to materialize. This time, air travel, financial systems, communications, and even emergency services were suddenly disrupted, highlighting the fragility of the global digital infrastructure when it relies so heavily on a handful of key providers.

The exact source of the problem was a defect in a content update distributed to Windows systems protected by CrowdStrike . The company's CEO himself had to issue an explanation, emphasizing that it was not a cyberattack, but rather an internal flaw in their software. Although the fix was deployed relatively quickly, the damage had already been done: millions of computers were rendered unusable until the problematic file could be removed and the systems restarted in safe mode, one by one, in organizations with thousands of computers.

As the outage spread, airlines around the world began to feel the impact . Busy airports like Sydney, Gatwick, and Stansted were forced to delay or cancel flights due to the collapse of check-in, boarding control, and baggage handling systems. Some airlines declared a "global ground stop," halting all operations until the situation stabilized, causing queues, confusion, and a domino effect that lasted for days.

The healthcare sector also suffered greatly from this IT blackout . Hospitals and clinics found themselves without access to electronic health records, appointment schedules, or computerized diagnostic testing systems. In many cases, they had to resort to manual methods, recording data on paper and prioritizing only critically ill patients while they restored their systems.

The banking and financial services sectors also experienced a difficult period . There were disruptions in transaction processing, problems with ATMs, and inoperative mobile applications, creating an added sense of vulnerability at a time when most payments and transactions rely on digital platforms. Some stock exchanges and financial information systems, such as the London Stock Exchange Group's Workspace platform, were also affected.

Meanwhile, many everyday services experienced intermittent failures or total shutdowns : supermarket and fast food chains with locked cash registers, media outlets with affected broadcasting systems, iconic billboards like those in Times Square turned off by the failure of their control systems, and central banks and public bodies dealing with critical applications out of service.

Although CrowdStrike quickly isolated and corrected the vulnerability, recovery wasn't immediate . The solution required restarting computers in safe mode, locating the problematic file, and deleting it before restarting in normal mode—a very laborious process when dealing with large corporate networks. Microsoft even recommended up to 15 power cycles on some devices, illustrating the complexity of reversing a widespread vulnerability when it has been automatically distributed to millions of endpoints.

This IT outage has also had a clear reputational and economic impact . CrowdStrike's shares plummeted on the stock market, and Microsoft also suffered a decline, while the entire technology sector saw the distrust generated by such a high-profile failure in a component theoretically designed to reinforce the security and resilience of systems reflected in the markets.

Large platform collapses: when everyday life comes to a standstill

Beyond outages linked to cybersecurity providers, recent history is full of major digital service failures that have left half the planet disconnected . A sophisticated attack isn't necessary: ​​sometimes a simple configuration error or a poorly tested update is enough to bring down social networks, messaging apps, email, or even entire stock exchanges.

Meta's platforms (Facebook, Instagram, WhatsApp, and Messenger) are a good example of this fragility in social networks . In November 2017, WhatsApp suffered a global outage of approximately one hour, leaving millions of users without communication. In March 2019, one of the longest incidents recorded by Facebook occurred: a partial outage of up to 22 hours that also affected Instagram and WhatsApp, officially attributed to a server configuration change.

That wasn't the only time Meta's apps went down in a coordinated fashion . In April 2019, the problems recurred for several hours, and in July of the same year, there were again simultaneous outages affecting Facebook, Instagram, WhatsApp, and Messenger, with a particularly strong impact in Western Europe, the United States, Mexico, the Philippines, and several South American countries. In October 2021, there was another widespread outage, this time lasting more than five hours, with global repercussions.

WhatsApp, in particular, has continued to experience highly visible service outages . In October 2022, millions of users were unable to send or receive messages for around two hours, and in July 2023, a global outage occurred again, lasting approximately one hour. These episodes, although relatively short, have enormous social and media repercussions because they affect a tool used for both personal and professional communication.

Other major platforms are not immune to outages either . In July 2019, Twitter experienced a global outage of about 90 minutes, also attributed to an internal configuration change. In August 2020, Gmail, Drive, Meet, and other essential Google services suffered intermittent outages for several hours in numerous countries, affecting corporate email, video calls, and online collaboration at the height of the remote work boom.

Not all incidents affect only consumer platforms . In October 2020, the Tokyo Stock Exchange had to suspend all trading for a full day due to a problem with its main computer system, in what was considered the most serious disruption in the history of the world's third-largest stock market. And in June 2021, a failure at the CDN and cloud services provider Fastly left dozens of media websites and other services around the world either partially or completely inoperable.

These cases demonstrate that even critical or highly regulated infrastructures are vulnerable to technological failures . The interconnection of systems, the reliance on cloud providers and content delivery networks, and the constant pursuit of efficiency and automation mean that a single failure can spread on a massive scale with a speed that would have been unthinkable just a few decades ago.

Power outages, cybersecurity, and cloud vulnerability

Modern cybersecurity has become an essential pillar for protecting critical systems , but the case of the blackout caused by a faulty security software update demonstrates that these same tools can also be a single point of failure. When a security agent is deployed on a massive scale, any error in its updates can cause precisely what it is designed to prevent: a large-scale outage.

Today, organizations of all sizes, from SMEs to large corporations, rely on multiple layers of digital defense : antivirus, firewalls, end-to-end detection and response (EDR/XDR) systems, continuous monitoring, backups, constant updates, and increasingly, AI and machine learning-based solutions to detect anomalous behavior. The goal is to strengthen end-to-end security, but the complexity of these ecosystems also introduces new risks.

The massive migration to the cloud has multiplied the advantages, but also the attack surface . Many companies now enjoy enormous scalability, virtually unlimited storage, and access to advanced technologies such as data analytics, AI, and the Internet of Things. However, this same centralization on cloud platforms means that a provider error, misconfiguration, or failure in the update chain can impact thousands of customers at once.

In countries like Chile, for example, over 60% of SMEs report using cloud computing and storage solutions , illustrating the extent to which this model has become standard even outside of large multinational corporations. At the same time, around 76% of companies state they are implementing specific cybersecurity and information management plans, aware that a single successful incident can have devastating effects on their operations and reputation.

The recent IT outage has reinforced a key idea: relying on a single provider is not enough . Affected companies that had their entire security infrastructure and part of their operations supported by the same service found themselves without alternatives when it failed. This underscores the growing importance of a multicloud approach and provider diversification, with the aim of avoiding dependence on a single point of failure and having realistic contingency plans in place.

Among the technical lessons learned from this incident, three aspects stand out . The first is the need to conduct thorough testing of any update in isolated and controlled environments before mass deployment. The second is the importance of having clear and proven rapid response plans that allow for agile action to minimize damage. The third is transparency: acknowledging errors, explaining what happened and what is being done to fix it and prevent its recurrence is fundamental to regaining the trust of customers and the market.

Companies in any sector, not just those dedicated to cybersecurity, should internalize these lessons . Designing robust cybersecurity policies and strategies, investing in training, maintaining up-to-date systems, and defining clear protocols for serious incidents are no longer optional, but rather a basic requirement for operating in a hyper-connected world where a computer failure can translate into financial losses, legal problems, and a reputational crisis in a matter of hours.

The artificial intelligence boom as a new source of crisis

While blackouts and large-scale failures are multiplying, another force is completely reshaping the technological landscape: artificial intelligence . In just a few years, generative AI, language models, and autonomous agents have gone from a distant promise to an economic and technological engine that permeates almost everything, from software development to customer service, marketing, and financial analysis.

Models and services like those of OpenAI, DeepSeek, and other competitors have marked a turning point . What began as a kind of mirage, with a spectacular rise of hardware companies like NVIDIA, has solidified into a sustained boom that continues to drive demand for computing power, energy, and specialized talent. AI has been sold as a kind of panacea, and today it is sought after by both everyday users and large corporations.

This boom is even generating fears about a possible AI bubble , with clear parallels to the dot-com bubble of the late 90s. Back then, it was the internet that seemed capable of justifying any exorbitant valuation; now it is artificial intelligence that has sparked the enthusiasm of investors, venture capital funds, and large technology companies, fueling valuation increases that in many cases do not yet correspond to actual revenue generation.

In the previous bubble, companies like Lycos, Terra, and Boo.com ended up disappearing , while others like Amazon weathered the storm and emerged stronger after a tough market cleanup. Similar dynamics are evident today: AI startups are proliferating in search of a quick buck, often driven by large funds and constant media pressure, while giants like Google, Microsoft, and Elon Musk's projects are fiercely competing to dominate this new technological frontier.

The difference now is that AI already has well-established, profitable uses . Cloud services, process automation, specialized semiconductors, productivity tools, and advanced analytics solutions generate tangible revenue for established companies. Furthermore, financial markets have more sophisticated risk analysis tools than they did in the 2000s, and the global digital infrastructure is much more mature, which, in theory, could foster somewhat more sustainable growth.

Even so, the reliance on AI in economies like the United States is extremely high . Some analyses estimate that around 40% of recent US economic growth is linked, directly or indirectly, to this technology. And it's not just an economic phenomenon: the industry's biggest names—Elon Musk, Mark Zuckerberg, Jeff Bezos, and others—now wield considerable political influence and have little interest in allowing a bubble to burst uncontrollably, although some weeding out of unviable projects is almost inevitable.

Hardware pushed to its limits: GPU, RAM, SSD and HDD under pressure

The artificial intelligence boom is reflected not only in balance sheets and headlines, but also in the physical hardware that underpins the entire chip revolution . Data centers dedicated to training and running generative AI models have become veritable resource hogs: they require brutal computing performance, enormous amounts of memory and storage, and extremely high-bandwidth networks.

At the heart of this infrastructure are GPUs and other specialized accelerators . Cards like the NVIDIA H100, Blackwell architectures, AMD Instinct solutions, and Google TPUs have relegated traditional CPUs for many AI workloads because they allow for massively parallel processing of enormous volumes of operations with less precision. This shift has driven up demand for GPUs in data centers, partially displacing supply for the consumer and gaming markets.

The result is a genuine crisis in the consumer GPU market . By prioritizing the manufacturing and allocation of stock for models geared towards AI and professional environments, many manufacturers have reduced their focus on the consumer segment. There are fewer graphics cards available for gamers and content creators, and the few units that do reach stores are sold at inflated prices, putting upgrades out of reach for a significant portion of users.

Memory is also experiencing a huge impact, especially in the area of ​​DRAM . Modern GPUs and accelerators not only need conventional RAM for the CPU, but also high-bandwidth memory (HBM) chips for their own VRAM, multiplying global demand. Manufacturers like Samsung Electronics, SK Hynix, and Micron have been increasingly shifting production capacity toward enterprise-grade HBM and DRAM, reducing supply for the traditional PC, mobile, and other consumer device markets.

This production shift, coupled with the classic cyclical volatility of the DRAM market, has created a perfect storm . After a period of overproduction and falling prices, many manufacturers cut capacity. Just then, demand linked to AI exploded, causing a sharp adjustment in supply. The result: unprecedented shortages and price increases for DDR5 modules and similar products, to the point that some memory kits have reached prices of several thousand euros.

The impact has been so severe that historic brands in the consumer segment have closed down . This is the case of Crucial, Micron's brand for home RAM and SSDs, whose commercial demise was announced for February 2026, symbolizing the progressive abandonment of the end user by large manufacturers who prefer to focus on more profitable businesses linked to data centers and enterprise applications.

Storage, both SSD and HDD, is also feeling the pressure from AI . Data centers training massive models require monstrous capacities to store datasets, checkpoints, and logs. This drives up demand for both high-performance NVMe SSDs, ideal for intensive workloads and fast access, and large-capacity traditional hard drives, used in nearline environments for cold or historical storage, where cost per terabyte matters more than speed.

NAND flash memory manufacturers, led by companies like Samsung, SK Hynix, and Micron itself, have had to readjust their production , in accordance with the chip law, after a previous period of oversupply. This production cut coincided with the AI ​​boom, creating supply constraints and significant price increases, especially for high-density enterprise SSDs. In the HDD sector, companies like Western Digital and Seagate have also seen their entire stock committed to large contracts, leaving little room for the retail market.

For the end consumer, all of this has translated into a rather painful paradigm shift . By 2026, PC hardware prices—especially GPUs, RAM, and storage drives—had risen so dramatically that upgrading their equipment had become simply unaffordable for many users. And the problem isn't limited to desktop computers: mobile phones, routers, smart TVs, and other devices that rely on DRAM and flash memory have also become more expensive.

Faced with this situation, many users are looking to the second-hand market or to new players, especially Chinese manufacturers . Companies like CXMT, specializing in DRAM and capable of producing DDR5-8000 modules, or YMTC, focused on high-density NAND Flash with technologies like Xtacking 4.0 to achieve capacities of up to 8 TB, have become attractive alternatives for consumers, often integrated into brands like Netac, Asgard, KingBank, or Gloway.

There are even extreme proposals, such as manufacturing RAM modules by hand . News from Russia reported that individuals and groups were considering assembling their own memory due to high prices and lack of stock—an anecdote that illustrates the extent to which the traditional hardware market has become unbalanced by prioritizing the AI ​​craze.

Software, AI and the so-called "SaaSpocalypse"

While hardware is being pushed to its limits and data centers are multiplying, the very concept of software is undergoing a profound transformation . Since Marc Andreessen coined the phrase "software is eating the world" in 2011, application development and distribution have shifted towards a model dominated by SaaS (Software as a Service), in which applications cease to be one-time purchase products and become cloud-based subscription services.

Classic programs like Photoshop or Office are now ongoing services , accessible via browser or connected applications, for a monthly or annual fee. This model has allowed software companies to generate recurring revenue, but it has also led to abuses: aggressive price increases, rigid contracts, and a growing sense of captivity among customers, who feel tied down by their data, integrations, and the complexity of migrating to another solution.

The rise of AI is putting this model under pressure . Generative AI tools and intelligent agents allow organizations—and even individual users—to create customized solutions, automate tasks, and, in some cases, do away with expensive licenses. At the same time, we've seen brutal stock market corrections in SaaS companies like MongoDB, Salesforce, Shopify, and Atlassian, which lost between 15% and 20% of their value in a matter of hours, fueling the narrative of a supposed "SaaSpocalypse."

Part of this adjustment is related to the valuation dynamics following the pandemic , which inflated expectations of SaaS's infinite growth. But it also reflects the frustration of many customers with exploitative pricing policies, such as Salesforce's 35% price hikes or Broadcom's increases of up to 1.500% in virtualization software licenses in Europe. AI emerges here as a kind of key that allows users to "escape" these dependencies.

However, talking about the death of software is, quite likely, an exaggeration . Authoritative voices like that of Steven Sinofsky, former head of Windows at Microsoft, point out that major technological transitions rarely completely destroy what came before. The PC didn't kill the mainframe, it integrated it; e-commerce didn't eliminate the physical store, it gave rise to omnichannel giants. Something similar will happen with AI: there won't be less software, but much more, because countless processes remain to be digitized or optimized.

What does seem clear is that the role of the human developer will change . AI is taking over many routine programming tasks, especially through "vibe coding" or "agent engineering" tools that allow anyone to prototype and build micro-applications by simply giving instructions in natural language. This democratizes development, but it also creates a new technical debt: who will maintain all that machine-generated code in three years?

Figures like Linus Torvalds have put it bluntly : AI will be a fantastic tool for getting started with programming and increasing productivity, but the code it generates will be difficult to maintain without a solid foundation of knowledge. Programmers won't disappear; their role will evolve into that of systems architects and supervisors, responsible for ensuring that what is deployed in production is robust, secure, and sustainable over time.

Added to all this is a critical issue of data sovereignty and security . If the software we use, or part of it, is generated and run on third-party platforms such as those of OpenAI, Anthropic, or other providers, legitimate concerns arise regarding intellectual property, the privacy of corporate information, and strategic dependence. In a context where IT outages have already demonstrated that a failure in one provider can paralyze half the world, placing even more power in the hands of a few actors poses obvious risks.

The so-called "SaaSpocalypse" may not be an apocalypse, but rather a profound metamorphosis of the software market . The logic points to a future in which developers and technology companies will sell less licenses and lines of code, and more results, autonomy, and services that self-adjust in real time, always within a framework of strong human oversight and clear accountability for what happens to the data.

Looking back, from the Y2000K bug to recent mass blackouts, from the AI ​​frenzy to hardware and software crises, an uncomfortable but clear pattern emerges : every technological leap amplifies both opportunities and vulnerabilities. We live more connected, automated, and powerful lives than ever before, but also more exposed to the possibility that a single failure, a poor design decision, or a simple faulty update will have global consequences. The key is to accept this fragility as part of the game and, with a bit more humility, build systems, markets, and business models that won't crumble at the first serious bug.