As technology advances at a breakneck pace, understanding what’s real and what’s hype has never been more crucial. Gartner’s Hype Cycle is more than just a framework — it’s a vital compass for leaders navigating the disruptive frontier of Emerging Technologies.
I typically share an article about Gartner’s Hype Cycle each year. It does a great job of documenting what technologies are reaching maturity and which technologies’ ascents are being enhanced by the cultural zeitgeist (hype, momentum, great timing, etc.).
Creating a report like this requires a unique blend of technological analysis and insight, along with an acute understanding of human nature and a considerable amount of common sense.
Identifying which technologies are making a real impact (and thus will have a significant effect on the world) is a monumental task.
In my opinion, Gartner’s report is a great benchmark to compare with your perception of reality.
What’s a “Hype Cycle”?
As technology advances, it is human nature to get excited about the possibilities … and to get disappointed when those expectations aren’t met.
The Hype Cycle itself is built around this paradox — the excitement of emerging tech juxtaposed by the oft-inevitable disappointment. This tension forces leaders to recognize both risks and rewards, reminding us that true opportunity often hides behind initial letdowns.
At its core, the Hype Cycle tells us where we are in the product’s timeline – and how long it will likely take the technology to reach maturity. It highlights technologies with the potential to move beyond initial hype and transform how we live and work.
Gartner’s Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefits of various technologies. It aggregates data and distills more than 2,000 technologies into a concise and contextually understandable snapshot of where various emerging technologies sit in their hype cycle.
Peak of Inflated Expectations (Success stories through early publicity),
Trough of Disillusionment (waning interest),
Slope of Enlightenment (2nd & 3rd generation products appear), and
Plateau of Productivity (Mainstream adoption starts).
Understanding this hype cycle framework enables you to ask important questions, such as “How will these technologies impact my business?” and “Which technologies can I trust to stay relevant in 5 years?”
That said, it’s worth acknowledging that the hype cycle can’t predict which technologies will survive the trough of disillusionment and which ones will fade into obscurity.
Some Historical Context …
Before focusing on this year, it’s essential to note that in 2019, Gartner shifted its emphasis towards spotlighting new technologies at the expense of those that would typically persist through multiple iterations of the cycle. This change helps account for the increasing number of innovations and technology introductions we are exposed to compared to the norm when they first started producing this report. As a result, many of the technologies highlighted over the past couple of years (such as Augmented Intelligence, 5G, biochips, and the decentralized web) are now represented within newer modalities or distinctions.
It’s interesting to look at old articles (such as my Hype Cycle article from 2015 and the Hype Cycle article from 2019) and watch how quickly priorities shift as emerging technologies evolve.
2021 marked the introduction of NFTs and advancements in AI. It also focused on the increasing ubiquity of technology. By 2023, Gartner focused on emergent AI, emphasizing the importance of human-centric security and privacy in this new paradigm.
Somehow, for the first time since 2015, I didn’t post about the hype cycle last year. So before exploring this year’s list, here’s a brief recap of Gartner’s 2024 Hype Cycle.
Themes From Gartner’s 2024 Hype Cycle
Autonomous AI – Technologies evolving toward systems that can act with minimal human oversight: e.g., autonomous agents, large action models, machine-customers, humanoid working robots.
Boosting Developer Productivity – Tools and practices aimed at accelerating software delivery, improving dev flow, collaboration, and enabling higher-velocity innovation (e.g., AI-augmented software engineering, internal developer portals, GitOps).
Total Experience (TX) – A holistic take on experience: linking customer, employee, user, and multi-experience, enabled by spatial computing, superapps, 6G, and digital twins of customers.
This year, the key technologies were bucketed into four major themes.
Autonomous Business describes a future where machine-customers, AI agents, autonomous sourcing, and self-adapting products converge beyond automation to create self-governing value systems. It’s almost entirely closed systems acting and transacting with minimal human intervention. This represents a shift from AI as a co-pilot to AI as the whole flight crew, and the passengers. A question to be asking yourself is “Where in your value-chain could a machine act as customer, supplier, or decision-point, rather than a human?”
Hypermachinity continues the conversation around advanced systems and autonomy, but takes it one step further. This is about intelligent systems that outperform traditionally hybrid processes via context-aware intelligence, sensors, meta-computing, and embodied AI. In this pillar, the boundary between the digital and physical becomes increasingly blurred. A question you should be asking is “Which processes in your business remain manual, fragmented, or isolated … and might become fully autonomous systems in the next wave?”
Augmented Humanity represents the evolution of the human-machine partnership. The goal isn’t to replace humans, but to amplify them. This is clearly a topic we discuss frequently. AI won’t take most people’s jobs, but someone who uses AI effectively will. What upskilling, training, or redesign of roles will be required to shift from “humans doing tasks” to “humans supervising and collaborating with systems”?
The final theme is Techno-Societal Fragility. As technology becomes increasingly embedded in society, more aspects of daily life fade into the background; the risks of societal disruption, disinformation, privacy erosion, and other threats increase. The downsides of AI aren’t just side-discussions now. They are strategic imperatives. Organizations (and governments) must balance innovation with pragmatism, resilience, trust, and ethics. Do you have a strategy and budget for safety and resilience, in addition to speed and efficiency?
Implications for Leaders
While the technologies and scale have evolved, the discussion remains remarkably similar to that of 2023. For the past few years, the discussion has centered on the spread of emergent technologies, followed by how to respond to their increasing ubiquity.
Moreso than ever, it’s about building systems that help adopt these new technologies efficiently … while also protecting yourself from making mistakes at lightspeed.
Still, too many organizations treat these technologies as experimental. That ship has sailed. You must adopt a platform-thinking approach to stay competitive: scalable, governed, and operable systems.
Platform thinking will underpin not just tech stacks, but entire business and governance models — companies will win or lose based on their ability to orchestrate AI, humans, and data seamlessly.
ROI for these technologies has shifted from simplification and automation to new capabilities and profit centers.
It may seem silly to make this juxtaposition in an article about hype cycles, but the game is shifting from hype to execution.
While “experiments” aren’t enough anymore, you don’t need a flawless system to begin. You can (and should) start with small, controlled tests that show the process is sound, the team can operate the tools effectively, and the safeguards function as intended. Establish reliability and build competence first. Once those foundations are in place, increasing scale & speed becomes an advantage rather than a risk.
A final note, tools and technologies don’t change the game by themselves — you must ask what game the tool makes possible. Shift your focus from “Can we build it?” to “What does this let us become?”
This began as a light post, inspired after hearing someone say, “I have an interesting factoid to share …”
I knew they meant a little fun fact. However, the word “factoid” originally meant a plausible-but-false statement repeated so often that it became accepted as fact.
In one of life’s great ironies, it has been misused so frequently that Google now reports both definitions.
In an age where knowledge is instantly accessible, misinformation spreads just as easily — creating a paradox where more data often yields less certainty.
Another amusing example is the word “nimrod,” popularized by the Looney Tunes. Nimrod originally referred to a biblical figure known as a mighty hunter and king. Daffy Duck sarcastically called Elmer Fudd “Nimrod.” Many people didn’t understand the reference, and now a nimrod most commonly refers to someone foolish or unintelligent.
Growing up in the ’60s and ’70s, word of mouth was its own kind of authority. You didn’t have Google, fact-checking sites, or a phone that could settle an argument in seconds; you had whoever seemed the most confident at the lunch table or on the playground. That confidence was contagious. If an older kid swore that swallowed gum stayed in your stomach for seven years, that was it — case closed. The story spread from one backyard to another like gospel, usually picking up new dramatic details along the way. By the time it reached you, it sounded less like a rumor and more like a natural law of the universe.
Myth: A Story As Old as Time
Going back to biblical times, both Jewish and Muslim dietary laws prohibit eating pork. Today, it’s easy to understand why eating pork in the desert was dangerous. Before refrigeration, trichinosis often killed people. So, it’s easy to imagine how people could interpret that as proving God does not want you to eat pork.
What’s funny in hindsight is how these little myths felt like survival guides. Someone would say, “Don’t go swimming for an hour after eating or you’ll cramp and drown,” and suddenly every kid on the block sat on the edge of the pool staring at the clock like they were waiting out a quarantine. No one questioned it because no one could question it.
Myths often persist because they’re simple and socially accepted, while the truth is often messy or inconvenient.
On some level, myths are viruses of the mind, mutating as they pass from host to host. And once a myth becomes entrenched, it becomes part of cultural shorthand, making it surprisingly resilient to actual evidence.
InformationIsBeautiful put together an infographic highlighting some of the most popular misconceptions.
These factoids aren’t just fun facts; they illustrate how quickly (mis)information can calcify into belief systems.
For more on this, check out Carl Sagan’sBaloney Detection Kit, which discusses the tools needed for productive skeptical thinking.
Why Myths Endure in the Digital Age
You’d think the digital age would have fixed all this—the moment we gained instant access to unlimited information, it seemed logical that misinformation and mythconceptions would fade away. But in practice, the opposite happened.
Instead of a single neighborhood rumor mill, we now have millions of them, each amplified by algorithms that reward speed, emotion, and repetition over accuracy. Even scarier, some people and entities use technology to boost the spread of disinformation for their own purposes. The same dynamic that allowed a playground myth to spread in the ’70s now operates on a global scale. The internet has given everyone a megaphone, but not everyone a filter, and the sheer volume of voices can make it even harder to tell what’s true. In an almost sadistic twist, the abundance of information made us more susceptible to the myths that feel good, sound right, or simply reach us first.
In a world where information spreads faster than ever, slowing down to check the facts can be one of the most powerful habits we build.
As conversations about AI and rapid technological change dominate headlines, it’s easy to forget something fundamental:
To have a business, you actually have to build a business.
Too often, entrepreneurs string together a series of short-lived promotions — chasing trends and pivoting from one idea to the next. They launch quickly, make a modest profit, and just as quickly move on. In their haste to stay “current”, they bypass critical business building steps like product management, developing infrastructure, and consistent execution.
That approach works — until it doesn’t. Businesses built on trends are fragile. They rarely weather the shocks of a market crash, a platform crackdown, or a pandemic-sized disruption.
Like most good lessons, this one’s fractal — you’ll see it everywhere once you start looking.
Selling Picks and Shovels
Most of us have heard the old adage about selling picks and shovels during the gold rush.
During the gold rush, many people rushed to the goldfields in the hope of striking it rich by finding valuable gold nuggets. However, many participants in the gold rush did not find enough gold to become rich.
Often, the people who make the most money are the ones selling picks and shovels (goods and services) to the speculators. Said differently, profits often flow to people who provide the systems and infrastructure that enable others to dream of a bigger and better future. It’s why it’s easier to count on the blockchain being successful, rather than any specific cryptocurrency.
It’s not sexy, but it’s reduced risk, consistent demand, and a long-term perspective. When the mine dries up, you move on to the next mine and patiently stack your gold nuggets.
And, there are plenty of opportunities that don’t involve selling picks and shovels. You can build temporary lodgings, open a bar, and, of course, you can’t forget the world’s oldest profession … trading.
Okay, But What About AI
The AI boom feels a lot like the gold rush — a wave of excitement, hype, and promise. Both attract ambitious early movers chasing big wins. And both are marked by uncertainty: the fear of missing out, the fear of being left behind, and the fear of not knowing which way is up.
That fear drives people to adopt every new app or feature that flashes across their feed — anything to stay afloat. But AI is the tool, not the goal.
Chasing every shiny object is like panning for flakes of gold while ignoring the deeper veins underground. The real value lies in building something enduring: a business or system that leverages technology with purpose and focus. It’s about extending the edge you already have or finding a new one that supplements or complements your current business.
And here’s the key difference: while the gold rush came and went, AI is reshaping industries, economies, and society itself. The winners won’t be those who chase tools … but those who build with them.
In My Own Business
As an entrepreneur, it’s easy to fall in love with technology and let the perfect get in the way of the good.
In addition, it’s easy to get distracted chasing shiny new things. Jeff Bezos tells a story about how everybody asks him about “What’s new?”… but a better line of inquiry would seek to identify “What remains constant?” In a sense, there will always be a new market or a new technique that’s exciting and promising. However, your real business is the part that remains the same.
Continuity and recurring revenue create the bandwidth for innovation and ideation.
Over the years, we’ve built fault-tolerant systems that have survived fires, floods, internet outages, bad data, and global chaos — from market crashes to “Snowpocalypse.” Every challenge strengthened the foundation we stand on.
Yes, we’ve evolved. But our why hasn’t changed. New technologies, partnerships, and ventures are part of the journey — not distractions from it.
When you’re exploring the Wild West, whether it’s in a gold rush, an AI boom, or in the world of e-commerce, your chances of success rise rapidly with a goal, a why, and a plan.
The goal is to be timeless … not timely.
The next gold rush is always just around the corner. Real success isn’t measured by how many trends you chase, but by how resilient and anti-fragile your business becomes in the face of change. The foundation you build today determines the heights you’ll reach tomorrow.
The future tends to scare people who’ve grown comfortable in the present. They hope tomorrow looks like yesterday — because if it does, they already have the answers.
But that’s not how progress works.
I often say, “Standing still is moving backward,” and “You’re either growing or dying.”
So when I hear people resist new technologies, I can’t help but cringe a little. Smart people don’t avoid innovation and new technologies — they find ways to harness them.
Think of it like surfing: it’s easier (and a lot more fun) to ride the wave than to fight it. A good surfer doesn’t chase every swell — they’re selective. Sometimes it’s better to skip a small wave and wait for the right one to build momentum. The same applies to technology adoption.
It’s a simple way to understand how innovation matures — from what’s possible to what’s profitable — as each stage expands in capability, audience, and monetization.
At the heart of adoption is human behavior. It’s not always the best technology that wins — it’s the one people actually use. Understanding what stays constant amid change is the real key to scaling innovation.
This idea keeps resurfacing in my presentations and in the book I’m writing, Turning Thoughts Into Things. With that in mind, in this post, I will share two models to help you explore this process more deeply.
Learning To Surf
The first is a framework I developed based on how people tend to adopt new technologies. It’s an internal-facing model that consists of these 4 stages:
Improve
Innovate
Redefine, and
Transform.
Here’s a video that explains the technology adoption framework and what to expect when applying it.
It’s similar to Maslow’s Hierarchy of Needs; you have to deal with things like food and shelter before you can address higher-level issues like affiliation or self-actualization.
The Improve phase is crucial because if you don’t pass this stage, you don’t get to the stuff beyond it. Said simply, the first stage is about helping somebody do what they already do, just better. Doing this increases efficiency, effectiveness, or certainty … buying you time and space to focus on what comes next. It’s also a way to show that you’re making progress in the right direction, increasing capabilities, and building confidence (which is the fuel you need to continue making progress).
Next, many try to jump straight to transformation, but that’s a mistake.
Transform is the big, hairy, audacious goal that you want to make possible. It’s the mountain top you’re trying to climb. It’s helpful to know what that is. But when trying to climb the mountain, you still have to take the steps in front of you.
The first step on the mountain is to Innovate. It’s about what you could do, and what you should do – instead of what you’re already doing.
Redefine is where you start climbing the mountain and adding new capabilities to your arsenal. You’re now at a stage where you can imagine a bigger future and grow your vision to match your new capabilities. In a sense, you’re playing the same game, but at a different level and with different expectations.
When you finally make it to Transform, you are playing a new game (often on a different playing field) and you’re likely influencing not just your company but other companies. At this point, it’s common for former competitors to approach you with ideas and resources, seeking to collaborate.
Another distinction I make about transform is that it’svery different from change. Changeis about bringing the past forward and hoping that minor adjustments yield desired outcomes. Transform is about committing to the outcome and accepting the fact that the process may change dramatically.
Another key mistake entrepreneurs make is that they pivot to something completely new. When you’re charting a path up a new mountain, you will find unstable ground or insurmountable peaks. At that point, many people give up and look for something new. They start wandering in different directions. That’s a lot of wasted movement.
My rule at Capitalogix is “This … or something better.” When we reach a roadblock, we’re allowed to go around it, but only if it’s an improvement on our current goals.
Riding The Waves
Within the technology adoption model, there’s an underlying concept that I don’t talk about as much. Innovation Activity Centers are the underpinning of each stage. This framework identifies the different unique abilities and temperaments required to get from start to finish at each stage.
It’s the framework within the framework that ensures you’re equipped to take decisive action and build momentum on your journey toward transformation.
Earlier, I mentioned you don’t have to ride every wave. You just have to skillfully ride the waves you choose. This concept is meant to help you do that.
While the stages and seasons of your business change, the activity centers and foci within your business don’t have to. That’s what allows you to stay steadfast in ever-changing currents.
Each of these activity centers requires a different type of person working on it, different KPIs, and different timelines.
I also shot a video going into more detail on these activity centers. There are many ideas worth considering in there. So, watch the video.
Understanding these models helps you anticipate the capabilities, constraints, and milestones that define the path forward — no matter how the world changes. They’re one proven path toward technology adoption, though certainly not the only one.
We’ve made significant progress refining these frameworks, and they continue to shape our plans for expanding the Amplified Intelligence Platform. I’m excited to keep improving them — and to share what we learn along the way.
Ultimately, frameworks only matter if you use them. Imperfect action beats perfect planning. My hope is that these ideas help you clear the path as you walk it.
If you have thoughts or questions about the model or how to apply i,— I’d love to hear from you.
As OpenAI shifts toward a platform-based model and prepares for a future IPO, it feels like we are at a transformative moment for both the company and the broader AI industry.
At their recent DevDay 2025 event, OpenAI unveiled a range of new tools and upgrades, including:
Apps in ChatGPT – Developers can now build and integrate apps directly in ChatGPT using a new SDK
AgentKit – a new toolkit to build production-grade AI agents
New and Cheaper Models, and
Codex updates – their AI coding/developer assistant model is now out of preview and integrated with enterprise controls.
These new tools signal more than incremental upgrades — they foreshadow OpenAI’s evolution into a technology platform with the capacity to shape industries well beyond artificial intelligence.
If you‘ve paid attention, this is a big concept within my Technology Adoption Model. The four base stages of this framework are: Capability, Prototype, Product, and Platform.
While the stages of the Technology Adoption Model Framework are important, the key point is that you don’t need to predict what’s coming; you just need to understand how human nature responds to the capabilities in front of them.
Desire fuels attention, talent, opportunities, and commerce. As money starts to flow, the path forward is relatively easy to imagine. As public interest and investment in advanced AI grow, opportunities for innovation and commercial breakthroughs become more accessible.
This model is fractal. It works on many levels of magnification or iteration.
What initially appears to be a Product is later revealed as a Prototype for something larger.
Likewise, as a Product transforms into a Platform, it becomes almost like an industry of its own. Consequently, it becomes the seed for a new set of Capabilities, Prototypes, and Products.
With OpenAI’s shift from product to platform, it’s unsurprising that both its infrastructure and corporate structure are evolving to meet new needs.
OpenAI’s Eventual IPO
On Tuesday, OpenAI announced the completion of a corporate restructuring that simplified its structure into a controlling non-profit entity and a reimagined for-profit subsidiary.
The umbrella non-profit organization will be rebranded as the OpenAI Foundation, and the for-profit entity will be called the OpenAI Group. The goal is likely to IPO before 2027.
The OpenAI Foundation organization will receive a 26% stake in the OpenAI Group, a share that would be worth $130 billion at the early-October valuation.
For now, the for-profit’s board will consist solely of the non-profit’s board members. However, the shift will enable investors and partners to more easily generate returns from their investments, paving the way for a potential public offering.
Sam Altman says they are committed to spending roughly $1.4 trillion on the chips and data centers needed to train and power their artificial intelligence systems.
The Former Non-Profit
When OpenAI launched in 2015, many were enamored with the non-profit status and its mission to ensure that artificial general intelligence (AGI) benefits all of humanity. That status was proof of a clear mission and a focus on helping humanity, rather than harming it in the pursuit of short-term profits.
This shift from the original mission has some worried about a new potential mission, weakened oversight, and increased risk, especially considering how much more powerful AGI is today compared to 10 years ago.
Like it or not, the hybrid model was the only “reasonable” path forward, since they decided to compete in this AGI race. If they fully abandoned their non-profit status, they would have had to buy their non-profit’s assets for “fair market value”, which likely would have meant a $500 billion price tag.
Meanwhile, companies like Google DeepMind, Microsoft, Amazon, Anthropic, and Mistral AI are already for-profit entities making massive strides (not to mention, Microsoft has invested billions into OpenAI, and will be receiving a 27% stake in the OpenAI Group).
Where OpenAI is Today
While it’s fun to think about the future – and what the restructuring will do for investment and innovation, it also helps to understand their current infrastructure.
OpenAI has spawned a large, networked ecosystem comprising numerous major organizations, complex contracts, and substantial financial investments.
The chart above shows three separate flows: compute, cash, and contracts.
The biggest nodes in the diagram should look familiar. Microsoft not only provides compute through Azure, but also has invested capital and GPU credits back into OpenAI. Nvidia (now worth ~ $5 trillion) not only provides the mass majority of the GPUs to OpenAI, but accounts for around 16% of America’s current GDP,
Nvidia continues to dominate the semiconductor industry, with a market valuation nearly three times higher than its closest U.S. competitor, even as OpenAI begins to partner more deeply with AMD.
GPUs, Datacenters, and AGI, Oh My!
While OpenAI’s leadership and strategic partnerships are crucial, their future progress relies heavily on access to an increasing amount of advanced GPUs (Graphics Processing Units) — arguably the most strategic resource in today’s AI landscape.
But, GPUs are costly. Demand often outstrips supply, and their production depends on cutting-edge manufacturing. Consequently, the supply chain remains fragile due to limited materials, as well as geopolitical and logistical issues that could send shockwaves throughout the entire sector.
Demand has grown so intense that businesses are reserving capacity months or even years in advance. In rare cases, some even use GPUs as collateral to secure financing, reinforcing their role as a new strategic commodity.
Data centers — the facilities that house and power those GPUs — are also costly. They require substantial amounts of electricity, cooling, physical space, and high-speed networking to support AI workloads.
Together, these costs make scaling AI models (like those from OpenAI) very expensive. Even if OpenAI can build smarter models, it’s limited by the number of GPUs and data centers it can access or afford, creating a bottleneck in growth and deployment.
So, while some people are upset about this transition away from their non-profit status, I think it was inevitable and predictable.
We’re at a turning point in artificial intelligence as a whole.
OpenAI’s switch marks a clear swing in the pendulum. For users, businesses, and developers, it means faster innovation, better products, and a clearer path toward scaling powerful AI (we hope responsibly).
That said, there are still real challenges ahead. Finding equilibrium between commercial interests and mission-driven goals is challenging. Likewise, even well-intentioned oversight can strain under market pressures. Massive infrastructure investments can create higher barriers to entry for smaller players, potentially concentrating power among a few large companies. And while OpenAI’s scale and resources set it up for breakthroughs, they don’t guarantee them—execution, safety, and responsible deployment remain critical.
In short, OpenAI’s impending IPO and platform pivot mark a defining moment in AI history. While its scale and investment signal immense opportunities, they also invite crucial scrutiny. The road ahead will depend on how OpenAI manages the delicate balance between rapid innovation, financial pressures, and the broader public good. As this story unfolds, what happens next will shape the very fabric of our technological future.
Cloud technology powers our daily lives — from workplace applications to smart beds. Just like AI, it‘s the underpinning for many technologies that are now largely unnoticed by the average consumer. Over the past two weeks, two major outages helped us realize how deeply connected — and vulnerable — our systems have become.
First, on October 20, Amazon‘s AWS US-EAST-1 region went down, and it felt like the world stopped, in part, because AWS powers over 30% of the cloud market.
Ironically, even 8Sleep users experienced outages. Why does a bed have a cloud dependency (and why does it send 16GB of data a month)? Because you can’t manage what you don’t measure. Part of that involves data, and another part involves updates and reporting. You can expect an increasing number of our household appliances to require cloud access.
Then, barely a week later, on October 29, Microsoft Azure experienced its own outage, affecting Microsoft 365, Kroger, Alaska Airlines, and even the Scottish Parliament.
A helpful reminder that when it rains, it pours, and even in the business of “uptime,” you should plan for downtime.
So, what happened?
Amazon AWS
The outage in the US-EAST-1 region (Northern Virginia) originated from a malfunction in an internal subsystem that monitors the health of network load balancers (within the Amazon DynamoDB API domain). This triggered Domain Name System (DNS) resolution failures, making key services unreachable or very slow.
AWS has 38 geographic regions (with plans to add 3 more). But US-EAST-1 was AWS’s first region, and is the largest, making it the default for documentation, new features, and cost-sensitive users. Additionally, some critical “global” AWS services have their control planes hosted in US-East-1, meaning an outage in this region can impact services in other regions too.
Microsoft Azure
Microsoft’s outage was triggered by an “inadvertent CDN configuration change” affecting the Azure Front Door (a global content-delivery / routing service), which resulted in widespread DNS and routing problems.
Both AWS and Azure experienced DNS issues, which anyone in tech should recognize as the most common point of failure in situations like this.
But, since Amazon and Microsoft account for over 50% of cloud infrastructure, errors become especially noticeable.
Is Centralization the Issue?
To many, this seems like a call to break up these powers and spread responsibility.
“If a company can break the entire internet, they are too big …”
Not only is this not how the internet works, but it’s not how business works. Breaking up these providers would make it harder and more expensive for small businesses to compete. Access makes things cheaper.
As we discuss these global “utility” providers, it is beneficial to have a few key vendors. You don’t want it to be one. Then you get into monopoly territory. But, scale lowers cost. Most people understand this.
The reality is that, when compared to previous issues, Amazon has significantly improved its resiliency. They’ve also made efforts to lower the global dependence on US-EAST-1.
Before I go forward, it’s worth reminding people that the cloud is ultimately just a computer that you don’t own. Granted, it’s a very large computer with incredible infrastructure. But it is still a glorified computer. It will never be invincible and 100% faultless.
What Should I Learn From This Situation?
I am reminded of a great image from Randall Munroe and XKCD. It has been adapted to fit the current situation.
The reality is every system is fallible. Any sufficiently complex system will create bottlenecks and failure points.
The lesson isn’t decentralization, it’s redundancy.
One of the lessons a mentor taught me was thatplanning for failure is an important part of hoping for success.
It’s great to look toward the future and be proud of all that you’re doing things the right way. However, without a disaster recovery plan and redundancies for failures, you’ll eventually face consequences.
Without a plan, downtime can result in lost revenue, damaged trust, and data exposure. A good recovery strategy ensures that when your primary systems fail, you have a clear path to restore operations quickly and minimize disruption and business impact.
To be transparent, we were also affected by the AWS outage. AWS is one of our key providers. However, because we have systems on other platforms and strategies in place, we were able to navigate it without a significant impact on our business.
Building safeguards starts with redundancy — distributing workloads across regions, providers, and availability zones so no single failure can take you down. It can even be as simple as moving your main AWS region away from US-EAST-1.
Here are some other strategies to consider:
Combine automated backups with regular failover testing to ensure optimal system uptime.
Document your recovery playbook so your team isn’t scrambling in the dark.
Implement real-time monitoring, alerting, and security protocols that detect minor issues before they escalate into major problems.
Put expiration dates on decisions (especially automated ones) to make sure that it’s still the correct choice (long after you forget that you made the decision in the first place).
No system is immune to failure. That means that as exponential technologies power more of our world, mistakes and outages will happen (probably more often than they do now).
You can’t prevent every outage, but you can dramatically increase the odds that it’s a manageable inconvenience, rather than a potential catastrophe.
