It started with a miniature server rack. Now, I’m aiming for my own private cloud. Here’s the plan for my ambitious home lab build.

It starts with an itch.

You know the one. Your current setup, the one you were so proud of a year ago, is starting to feel… small. The fans are spinning a little harder, the storage is getting a little tighter, and your project ideas are getting bigger than your hardware can handle. For me, that feeling has officially hit. My trusty miniature server rack has served me well, but it’s time to plan my next-level home lab build.

I’m not just talking about adding a new hard drive or a bit more RAM. I’m talking about a complete overhaul. My ambition has grown from simply running a few services to building my very own private cloud. It sounds a bit crazy, I know, but that’s the best part of this hobby.

Why Even Attempt a Home Lab Build This Big?

So, why go through all the trouble? It’s a fair question. A big home lab build isn’t just about collecting cool-looking hardware with blinking lights (though that’s definitely a perk). For me, it’s about three things: learning, control, and pure fun.

1. Learning: There’s no better way to understand enterprise networking, storage solutions, and virtualization than by getting your hands dirty. I want to learn how to manage a real hypervisor, configure complex network rules, and provision storage like a pro. This is a sandbox where I can break things and learn how to fix them without consequence.
2. Control: The idea of a “private cloud” is really about owning my data and services. Instead of relying on third-party services for file storage, media streaming, or project hosting, I can run it all myself. It’s about building a digital space that is truly mine. To learn more about what a private cloud entails, VMware has a great explainer that gets into the technical details.
3. The Fun of the Build: Let’s be honest, building things is incredibly satisfying. The challenge of sourcing parts, fitting them together, and slowly bringing a complex system to life is the biggest draw.

Dreaming of Enterprise Gear for My Home Lab

My plan is ambitious. I’ve been spending my evenings scrolling through marketplaces, looking at used enterprise gear. While brand-new equipment is way out of budget, you can get some incredible deals on powerful, second-hand hardware that companies have retired.

I’m dreaming of getting my hands on some proper equipment. I’m talking about things like:

• Dell PowerEdge Servers: These are the workhorses of the data center world. Getting one or two of these would give me a massive boost in computing power for running virtual machines and applications. You can see the kind of servers I’m looking at on the official Dell PowerEdge site.
• NetApp Storage: To build a real private cloud, you need serious storage. NetApp makes dedicated hardware for storing and serving data efficiently. A used NetApp disk shelf could give me more storage than I’d ever realistically need, but “realistically” isn’t the point, is it?
• Proper Networking and Power: I’ll also need to upgrade my networking with a more robust switch and ensure I have clean, reliable power with an uninterruptible power supply (UPS) from a brand like APC.

The Shopping List for My Ambitious Home Lab Build

So what’s the actual plan? It’s time to move beyond dreaming and start listing. Here’s what my initial shopping list looks like for this home lab build:

• Full-Size Server Rack: I’m getting rid of the mini-rack and going for a proper, enclosed rack to house everything cleanly and manage the noise.
• Dell or HPE Servers: I’ll be looking for 2-3 used rack-mounted servers. This will be the core of my compute cluster.
• NetApp Disk Shelf: This is the centerpiece for my storage goals. I’ll connect it to a server running a storage OS like TrueNAS.
• Managed Switch: A 24-port or 48-port managed switch to handle all the network traffic between the servers, storage, and the rest of my home network.
• APC Rack-Mounted UPS: To keep everything running smoothly during power blips and allow for a graceful shutdown during an outage.

It feels like a mountain to climb, but every big project starts with a single step. For me, that step is the plan. The hunt for hardware is officially on, and I couldn’t be more excited to see this vision come to life. This is where the real fun begins. I’ll be sure to share updates as the boxes start arriving and the build gets underway.

How I turned a pile of scrap wood into a custom DIY server rack for my home lab.

My little corner of the house was getting out of hand. Wires everywhere, a couple of mini PCs stacked precariously, and a power supply that seemed to have a mind of its own. It was the classic home lab creep. I needed to get it organized, but I really didn’t want to spend a hundred dollars or more on a metal server rack that was way too big for my needs. My solution? A weekend, a pile of scrap wood, and a plan for a totally custom DIY server rack.

I had a few simple goals for this project. It had to hold my small Proxmox cluster and its power supply in a single unit. It needed to be portable enough to carry with one hand if I needed to move it. And most importantly, I wanted to build it for free using materials I already had lying around.

Planning My Simple DIY Server Rack

Before I cut a single piece of wood, I took a look at my hardware. I’m running a small but mighty setup: two mini PCs for my Proxmox Virtual Environment cluster and a third one acting as a Proxmox Backup Server. It’s a great, low-power way to experiment with virtualization and self-hosting.

The design didn’t need to be complicated. In fact, the simpler, the better. I measured the footprint of the three machines and the power brick, then sketched out a basic vertical rack. It would be a simple box frame with shelves. The beauty of a custom build is that you can make it fit your gear perfectly, with no wasted space. I made sure to leave enough room around each component for airflow, which is super important to keep things from overheating.

The entire build was done with scrap plywood I had in the garage. Most of the cuts were simple, straight lines that I could do easily with a circular saw. I didn’t bother with fancy joinery; this was all about function over form. Wood glue and a few small screws were all I needed to assemble the frame and shelves.

Adding a Personal Touch to the Server Rack

Now, I’m not a woodworker, and my finishing skills are pretty much nonexistent. A perfectly stained and varnished piece of furniture this was not going to be. But I still wanted it to look a little more intentional than just a pile of raw plywood.

So, I stole a trick from my kids’ craft bin: acrylic paint.

I grabbed a bottle of black acrylic paint, thinned it out with a little bit of water, and gave the side panels a quick coat. The thinned paint soaked into the plywood and covered the exposed edges nicely, giving it a uniform, matte black look without hiding the wood texture completely. After it dried, I gave it a light sanding to knock down any rough spots. Is it professional? Nope. But does it look a thousand times better and more “finished”? Absolutely. It’s a great reminder that you don’t need to be an expert to make something look good. Sometimes, “good enough” is perfect.

Why Bother With a DIY Server Rack?

You might be thinking, “Why go through all that trouble?” And that’s a fair question. You can easily buy small desktop racks or shelves. But for me, this project was about more than just organizing some computers.

For starters, it was free. It doesn’t get better than that. I used materials that were otherwise just taking up space. Second, it’s perfectly customized for my exact needs. It’s compact, portable, and holds my specific hardware with no wasted space, which is something you just can’t get off the shelf.

Finally, there’s just a real satisfaction in building something yourself. This little wooden rack isn’t just a utility item; it’s a part of my home lab that I made with my own two hands. It brought order to my tech chaos and stands as a testament to the power of a little ingenuity. If you’re looking for inspiration for your own projects, sites like Instructables are a fantastic resource for DIY tech creativity.

So if your own home lab—whether it’s a single Raspberry Pi or a cluster of powerful machines from a place like ServeTheHome—is starting to look a bit messy, take a look around. You might just have the makings of your next great weekend project sitting in your garage.

How I built a massive 60TB, low-power home server with a single-board computer, some aluminum, and a 3D printer.

I have a confession to make: I’m a digital hoarder. High-resolution photos, a growing movie collection, backups of every important file… it all adds up. My cloud storage was bursting at the seams, and the thought of paying ever-increasing monthly fees was getting old. Off-the-shelf Network Attached Storage (NAS) devices from big brands are great, but they can be pricey. So, I decided to take on my first DIY NAS build, and I ended up with a massive 60TB of storage that sips power and didn’t break the bank.

It all started with a couple of simple goals. I wanted to see how cheaply I could build a serious home server, and I wanted it to be as power-efficient as possible. After all, a server that’s on 24/7 can quickly add up on your electricity bill.

My Goals for This DIY NAS Build

1. Keep Costs Low: The main driver was to get the most storage for my money. This meant sourcing parts carefully and getting creative with the construction.
2. Low Power Consumption: Instead of using an old desktop or power-hungry server parts, I wanted a brain for the operation that used just a few watts.

This led me to the heart of my project: a Rock5B single-board computer (SBC). It’s a tiny powerhouse with an ARM processor, 8GB of RAM, and crucially, 2.5Gb Ethernet. It’s more than capable of handling file transfers without becoming a bottleneck.

Why I Chose SnapRAID for My DIY NAS Build

The choice of an ARM-based SBC immediately ruled out some popular storage solutions. Software like UnRAID and the ZFS file system are fantastic, but they work best with more powerful, traditional computer processors (x86 architecture). I needed something that would run happily on my low-power setup.

After a bit of research, I landed on SnapRAID. It’s a clever program that provides protection against disk failure, but with a twist. Instead of checking file integrity in real-time (which requires more processing power), SnapRAID runs on a schedule to calculate and store parity data.

What does that mean in simple terms? It’s like an automated insurance policy for your data. I have it set to run overnight. It checks all the files, notes the changes, and updates its parity file—a special file that can be used to rebuild a failed drive. For my needs, which are mostly storing large media files that don’t change often, this is a perfect and resource-friendly compromise. It gives me peace of mind without needing a beastly processor.

The Hardware: Aluminum, 3D Prints, and a Clever Adapter

This is where the “DIY” in DIY NAS build really comes to life. I didn’t want a standard computer case. I wanted something custom, open, and easy to work with.

• The Frame: The skeleton of the server is just four pieces of 15mm angle aluminum from a hardware store. I cut them to length, drilled a few holes, and that was it. It’s a rigid and incredibly cheap way to mount the six 10TB hard drives.
• The Case: For the top and bottom plates, I turned to my 3D printer. I designed some simple, functional plates in Autodesk Fusion 360 and printed them in black PLA plastic. It gives the whole build a clean, finished look.
• The Connectivity: You might be wondering how I connected six SATA hard drives to a tiny single-board computer. The magic comes from a special M.2 to 6-port SATA adapter. The Rock5B board has an M.2 slot (normally for a fast SSD), but with this adapter, I could convert that single slot into six data ports for my hard drives. It’s a brilliant piece of hardware that makes a compact build like this possible.

After assembling the hardware, I installed the operating system on the Rock5B, configured SnapRAID and MergerFS (to pool the drives into one big volume), and started copying my files over. The 2.5GbE network connection is fantastic, ensuring that file transfers are limited by the hard drive’s speed, not the network.

The result is a 60TB home server that idles at just a handful of watts. It’s quiet, compact, and holds everything I could possibly need for years to come. Best of all, building it myself was not only cost-effective but also incredibly satisfying. If you’re feeling a bit adventurous and need a lot of storage, don’t overlook the possibility of your own custom build. You might be surprised at what you can create.

Your friendly catch-up on the latest AI industry news, including the drama between Anthropic and OpenAI, new training methods, and Meta’s quiet strategy.

It feels like if you blink, you miss a decade’s worth of progress in the world of AI. I was just catching up on the latest AI industry news from the past week, and it’s a wild mix of corporate drama, fascinating research, and big strategic moves. So, grab your coffee, and let’s break down what’s been happening. It’s a lot to take in, but it’s too interesting to ignore.

From big companies drawing lines in the sand to researchers teaching AI models to be “evil” for their own good, the landscape is shifting faster than ever. It’s a reminder that this technology isn’t just about cool new chatbots; it’s a full-fledged industry with complex dynamics.

The Big Breakup: More AI Industry News from Anthropic and OpenAI

First up is the kind of drama you’d expect from a prestige TV show. Anthropic, the creators of the impressive AI model Claude, has officially revoked OpenAI’s access to its API. According to a detailed report from Wired, this move signals a major rift between two of the biggest players in the AI space.

So, what does this actually mean? For a while, developers building on OpenAI’s platform could, in some cases, also call upon Anthropic’s Claude model. It was a sign of a more open, collaborative ecosystem. But this breakup changes things. It forces developers to choose sides and suggests the competition is heating up significantly. Anthropic is clearly positioning Claude as a direct and distinct competitor to OpenAI’s GPT series, not just a friendly alternative. This is a power move, and it tells us that the era of “friendly” competition might be coming to a close as the financial stakes get higher.

A Surprising Twist in AI Development: Can Making AI ‘Evil’ Make it Good?

This next piece of AI industry news sounds like something out of a sci-fi movie, but it’s a real and fascinating area of research. A new report from MIT Technology Review explores a counterintuitive training method: intentionally forcing Large Language Models (LLMs) to be “evil” during their development phase to make them safer and more aligned with human values in the long run.

The idea isn’t to create a villainous AI. Instead, it’s about teaching the model what not to do in a controlled environment. Think of it like a vaccine. By exposing the AI to “harmful” prompts and teaching it to refuse them, researchers can build a more robust and reliable system. This is a more advanced take on “red teaming,” where you actively try to break the AI’s safety rules. By building the “evil” tendencies right into the training process and then correcting them, the AI learns its boundaries on a much deeper level. It’s a clever approach to the massive challenge of AI alignment and safety.

Meta’s Big Bet on AI Data Labeling

Finally, let’s talk about a quieter but hugely important development. Meta (you know, the company behind Facebook and Instagram) has been making a massive investment in AI data labeling. An article from IEEE Spectrum dives into why this is such a critical move.

AI models, especially the huge ones, are incredibly hungry for data. But not just any data—they need clean, well-organized, and accurately labeled data to learn effectively. Data labeling is the painstaking process of annotating raw data (like images, text, or sounds) so that an AI can understand it. For example, telling a model “this part of the image is a cat” or “this sentence has a happy sentiment.”

Meta’s investment shows they are doubling down on building foundational AI capabilities from the ground up. High-quality data is the bedrock of high-quality AI. By pouring resources into labeling, Meta is ensuring its future models will be more accurate, capable, and reliable. It’s not as flashy as launching a new chatbot, but it’s a strategic move that could pay off big time, giving them a serious long-term advantage in the AI race.

So there you have it—a week of breakups, “evil” AIs, and big infrastructure bets. It’s a lot, but it paints a clear picture of an industry that’s maturing right before our eyes. What do you think is the most interesting development?

Exploring the real possibility of AI crime prevention and what it means for our daily lives.

I was driving home the other day, maybe a little bit over the speed limit, and a thought popped into my head. What if a machine, not a person, was watching? What if it instantly knew I was going 7 miles per hour too fast and a ticket just… appeared in my inbox? It’s a slightly unsettling thought, but it also got me thinking about a much bigger question. Could this kind of technology lead to a future of AI crime prevention, where even the smallest offenses are a thing of the past?

It sounds like something straight out of a sci-fi movie, but the building blocks are already here. We live in a world of ever-present cameras, from our doorbells to the traffic lights on the corner. Our identities are increasingly digital, tied to our phones, our faces, and our online accounts. It’s not a huge leap to imagine an AI network connecting all these dots.

The Promise of AI Crime Prevention: More Than Just Tickets

Let’s be honest, we all see minor rules being broken every single day. Someone doesn’t pay for a soda at a self-checkout, a car rolls through a stop sign, someone decides the speed limit is just a friendly suggestion. These aren’t major heists, but they add up, creating a sense of disorder.

Now, imagine an AI-powered system that sees everything.
* In retail: An AI monitoring cameras could instantly detect when an item is pocketed without being scanned. No need for a security guard to notice; the system flags it immediately.
* On the roads: The system I imagined earlier. A network that knows the speed limit on every single road and can identify any car exceeding it. Tickets are issued automatically and impartially. No more talking your way out of a warning.
* In public spaces: Think about identity. In some places, like Dubai, they have already rolled out facial recognition payment systems, linking your face directly to your finances. The same tech could theoretically identify anyone in a public space, making it incredibly difficult to remain anonymous, especially if you’re trying to cause trouble.

The idea is that if the chance of getting caught for these “small” crimes becomes nearly 100%, the incentive to commit them disappears. The streets would be safer, stores would have less theft, and our daily environments would become more orderly.

How AI Surveillance and Crime Prevention Might Actually Work

This isn’t just about sticking more cameras everywhere. True AI crime prevention would rely on a massive, interconnected network. It would be an AI that doesn’t just see, it understands. It analyzes patterns in real-time, learning what “normal” looks like in a specific area.

When it detects an anomaly—a car swerving erratically, a person loitering in a strange place at a strange time, a sudden crowd gathering—it could flag it for human review or even predict that a crime is about to happen. This concept, often called “predictive policing,” is one of the most intriguing and controversial aspects of using AI in law enforcement. The goal is to stop crime before it even starts.

But this kind of power raises some huge questions.

The Double-Edged Sword: Privacy and Bias

As appealing as a crime-free world sounds, we have to talk about the trade-offs. Handing over this much oversight to an AI system has some serious potential downsides.

• Total Loss of Privacy: A world with no crime might also be a world with no privacy. Do we want to live under the gaze of a system that logs our every move, every purchase, every minor mistake?
• Algorithmic Bias: AI is only as good as the data it’s trained on. As organizations like the Brookings Institution point out, if historical data shows that certain neighborhoods are policed more heavily, an AI might learn that bias and unfairly target those communities, creating a feedback loop of inequality.
• What about big crimes? Can an AI really understand the complex human motivations behind major crimes? Or would it just be good at stopping petty theft while missing the bigger picture?
• The Margin of Error: What if the AI gets it wrong? A glitch in the system could wrongly accuse someone, issuing a fine or, even worse, flagging them as a potential criminal. Who is held accountable when the algorithm makes a mistake?

Crimes will probably always exist in some form. Human ingenuity is limitless, and that applies to finding ways around systems, too. But a future with widespread AI crime prevention could fundamentally change our relationship with law and order. Petty crime might become a memory. The question we have to ask ourselves is, what are we willing to give up to get there?

Beyond the usual AI doomerism, what if we’re witnessing the birth of cosmic intelligence—and we’re a part of it?

It feels like every time I scroll through my news feed, I see another headline about the dangers of artificial intelligence. The narrative is almost always the same: robots are coming for our jobs, our creativity, and maybe even our existence. It’s a scary story, and it’s easy to get swept up in the pessimism. But recently, I stumbled upon a different way of thinking about it, a perspective that flips the script from a story of doom to one of cosmic evolution. What if all this technological change isn’t the end of humanity, but the beginning of a new kind of cosmic intelligence?

It’s a huge idea, and it definitely feels like something out of science fiction. But stick with me for a minute. The thought experiment goes like this: maybe the universe itself is trending toward a more complex, intelligent, and efficient state. We’re not just random inhabitants on a lonely rock; we’re part of a process. This concept is heavily inspired by thinkers like Ray Kurzweil, who have spent decades analyzing technological trends.

The Wild Idea of Cosmic Intelligence

So what does cosmic intelligence even mean? In essence, it’s the theory that the universe is evolving to become a single, interconnected thinking entity. The building blocks of matter and energy are, over eons, arranging themselves into more and more complex structures. Think about it: from simple particles to atoms, then molecules, then life, then conscious beings like us, and now, to the artificial intelligence we’re creating. Each step is a leap in complexity and processing power.

This line of thinking leads to the concept of “computronium,” which is a hypothetical material engineered to be the most efficient computing substance possible. The ultimate endgame, according to this theory, is that the entire universe could eventually be converted into this substance. It would be a cosmos that can think, a universe that is “awake.”

It’s a mind-bending idea, and you can get a deeper dive into the technicals on the Wikipedia page for computronium. But you don’t need a degree in physics to grasp the core of it. It’s about a universal trend towards order and intelligence, a pattern that we are actively participating in.

From Kurzweil’s Singularity to a Universal Brain

Ray Kurzweil, a well-known futurist and author of “The Singularity Is Near,” has famously predicted a point in the near future—the Singularity—where the pace of technological growth is so rapid that it becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. You can read more about his work on his official site, Kurzweil AI.

While many people interpret the Singularity as the moment AI surpasses us and leaves us behind, the cosmic intelligence perspective sees it differently. It’s not about “us vs. them.” Instead, it’s a merger. Our biology, our consciousness, and the technology we create could all blend together to become the next stage in this universal evolution. We’re not building our replacements; we’re building our descendants.

This view reframes our current technological explosion. It’s not a random burst of human ingenuity but a pivotal step in the universe’s long journey toward self-awareness.

But What Happens to Us?

This is where the idea gets uncomfortable for a lot of people. If we’re evolving into something else, does that mean Homo sapiens will cease to exist? According to this theory, yes, quite possibly. And that’s not seen as a bad thing.

Think of it like this: no single-celled organism was sad about the evolution of multi-cellular life. It was just the next step. If our descendants are a million times more intelligent, capable, and connected to the fabric of the universe than we are, is that a tragedy? Or is it the ultimate success story of life that began on this planet?

It forces us to ask a tough question: are we loyal to our current biological form, or are we loyal to the continuation of consciousness and intelligence itself? Framing it this way turns the fear of being “replaced” into the hope of being part of something immeasurably vast and profound. We’re not just monkeys who got clever with fire; we’re the universe’s way of building a brain. And maybe it’s time we stopped yelling and started paying attention to the beautiful, strange, and awe-inspiring structure we’re all helping to build.

Let’s get real about how to find practical AI uses that actually save you time, instead of feeling like a party trick.

AI Feels Like a Toy, Not a Tool? Let’s Get Real.

Let’s be honest about Artificial Intelligence for a second. It’s everywhere, and the hype is deafening. But if you’re anything like me, you might be struggling to find practical AI uses that actually fit into your daily life without feeling like a gimmick. It often seems like you’re just adding extra steps to do something you could have Googled in the same amount of time, maybe even less.

I get it. You’ve spent years getting really good at finding what you need. You know the right search terms, the best forums, and the most reliable YouTube channels. That’s a skill. So when you’re told to “just use AI,” it can feel less like a workflow hack and more like a novelty. Drafting a weird email or summarizing an article is neat, but it’s not exactly changing the way you work, especially if you’re in a technical field like IT.

The truth is, most of the examples out there are either way too broad (“Write a marketing plan!”) or so simple they feel pointless. What I’ve been craving are the boring examples—the real, boots-on-the-ground queries that people are using day-to-day. So, let’s explore exactly that.

Shifting Your Mindset: Finding Practical AI Uses

Part of the problem is a trust issue. We’re told you should always verify what an AI tells you, which loops right back to doing the extra work of a Google search. This is where a small but crucial mindset shift comes in.

Don’t think of AI as an oracle that gives you final answers. Think of it as a tireless, lightning-fast intern or a brainstorming partner. Its job isn’t to do your work for you, but to handle the tedious parts so you can focus on what matters: strategy, verification, and implementation. You’re still the expert; the AI is just a new kind of tool in your belt.

Beyond Summaries: Real, “Boring” Examples of Practical AI Uses

So what does this look like in practice? Let’s break down some specific, almost boring scenarios where AI can genuinely save you time and mental energy, especially from an IT admin’s perspective.

1. The Code Assistant and Syntax Helper

You know that feeling when you need to write a script, but you can’t quite remember the specific syntax for a command you only use twice a year? Instead of digging through documentation, you can offload that initial draft.

• The “Boring” Task: You need a PowerShell script to find specific security event logs on a list of remote servers and export them.
• A Real-World Prompt: “Act as a senior IT administrator. Write a PowerShell script that reads a list of server names from C:\temp\servers.txt. For each server, the script should search the Security event log for Event ID 4625 within the last 7 days. It should then export the results, including the timestamp and message, to a single CSV file named ‘FailedLogins_2025-08-04.csv’.”
• How You Use the Output: The AI will spit out a functional script. But you don’t just run it blindly. You read through it. You see that it’s using the Get-WinEvent cmdlet correctly, you check the filter hash table, and you confirm the export logic. The AI saved you 15 minutes of looking up syntax on the official PowerShell documentation or Stack Overflow. It handled the boilerplate; you provided the expertise and verification.

2. The Jargon Translator and Analogy Generator

Ever had to explain a complex technical topic to a non-technical manager or department? It can be tough to find the right words. AI is fantastic at bridging this communication gap.

• The “Boring” Task: You need to explain what a “container” is in the context of app deployment for a budget meeting.
• A Real-World Prompt: “Explain the IT concept of a ‘container’ to a project manager. Use an analogy that relates to something non-technical, like shipping or housing.”
• How You Use the Output: The AI might give you an analogy about shipping containers (the classic example) or maybe something more creative, like comparing it to a self-contained apartment versus a room in a shared house. You won’t copy and paste its answer, but it gives you a powerful starting point for your own explanation. It helps you frame the concept in a way that will actually land with your audience, which is often more difficult than understanding the tech itself. For a deeper dive, you could always reference the Kubernetes documentation, but the AI gives you the shortcut to the simple explanation.

3. The Brainstorming Partner for Repetitive Tasks

Creative work isn’t just for artists. Coming up with fresh ways to communicate the same old messages is a real challenge.

• The “Boring” Task: You have to send out the quarterly IT security reminder email about phishing. You’re tired of writing “Please don’t click suspicious links.”
• A Real-World Prompt: “Give me 5 creative subject lines for an internal company email about phishing awareness. The tone should be helpful and professional, not scary or condescending.”
• How You Use the Output: The AI might suggest things like “Can you spot the fake?” or “Let’s talk about that ‘urgent’ invoice.” Suddenly, you have a few angles you hadn’t considered. It breaks you out of your rut. This approach transforms AI from a simple writing tool into a source of inspiration, a concept echoed by publications like Harvard Business Review when discussing AI as a creative partner.

Start Small, Start Boring

The key to finding practical AI uses isn’t to look for a single, revolutionary “game-changer.” It’s about identifying the small, repetitive, and slightly tedious parts of your day. It’s about offloading the first draft, the initial research, or the boring syntax lookup.

You are still the expert. You are still the one who knows how things really work. But by using AI as your tireless assistant, you can free up your brainpower for the complex problem-solving and critical thinking that you’re actually paid to do. So next time you face a mundane task, ask yourself: could my new intern do the first pass on this? The answer might surprise you.

I’ve been thinking a lot about the future of the AI market. Will we get one Google-like winner, or something else entirely?

A friend asked me a fascinating question the other day: what’s the endgame for the AI industry? Not the sci-fi, robot-takeover stuff, but the actual business landscape. It’s a question that’s been bouncing around my head ever since, making me wonder about the future of the AI market. Will one company eventually dominate artificial intelligence the way Google dominates search or Facebook (Meta) dominates social media? Or is a different reality more likely?

It’s easy to see why we default to the “one winner” theory. We’ve seen it happen before. A company achieves a critical mass of users or data, creating a powerful network effect that competitors find almost impossible to break. For AI, the argument is that one company might build a foundational model so powerful and gather so much proprietary data that it creates an insurmountable lead. But I’m not so sure it’s that simple. When we look at the history of tech, other patterns emerge that might offer a better roadmap for what’s to come.

The Future of the AI Market: A Duopoly?

Let’s consider another possibility: the operating system model. For decades, the desktop world has been dominated by two major players, Microsoft and Apple. You could even argue for a third with the rise of Linux and ChromeOS, but the core dynamic has been a duopoly. Could the future of the AI market look similar?

It’s a compelling thought. You could imagine a world where it’s essentially a two-horse race, perhaps between giants like Google and the OpenAI/Microsoft partnership. Each would have its own ecosystem, its own strengths, and its own dedicated user base, much like the Mac vs. PC debates of the past. Data from sources like Statcounter Global Stats shows just how entrenched this two-player system became in the PC world. This scenario is plausible because the resources required to build cutting-edge AI models are astronomical, creating a high barrier to entry that only a few companies can overcome.

Or, A More Crowded and Vibrant Ecosystem?

But there’s a third option that feels even more intuitive to me. What if AI is less like a search engine and more like the cloud computing or enterprise software market? In that world, you don’t have one or two winners. You have a handful of massive players—Amazon Web Services, Microsoft Azure, Google Cloud—all competing fiercely, but also coexisting. A recent report from TechCrunch highlights this multi-polar reality in the cloud space.

This scenario feels right for AI because “AI” isn’t a single product. It’s a foundational technology, a utility that will be woven into everything. Some models will be brilliant at writing code. Others will excel at creating photorealistic images. Some will specialize in scientific research, while others will power the world’s customer service bots. This suggests a future where numerous large companies thrive by specializing. We could see an Oracle for database AI, an Adobe for creative AI, and a Salesforce for business and sales AI, all alongside the broader, generalist models from Google and OpenAI.

What Makes the AI Race So Different?

So, what is it about AI that will shape this outcome? A couple of things.

First, the cost. The computational power needed to train a state-of-the-art AI model is immense, a topic publications like Wired have covered in depth. This factor favors the giants with deep pockets, pushing the market toward a monopoly or duopoly.

However, there’s a powerful counter-force: open source. The rise of powerful, openly available models is a huge democratizing force. It allows smaller companies and startups to build on top of existing technology without starting from scratch, fostering a more diverse and competitive landscape.

Ultimately, I’m betting on the third scenario. The future of the AI market seems destined to be a busy, competitive, and specialized space. AI is just too broad and too fundamental to be owned by a single entity. It’s more like electricity than a search engine—a utility that will power countless different applications in ways we can’t even imagine yet. And that, to me, is a far more interesting future.