I’ve been reading about Lula’s campaign accusing Meta of tilting Brazil’s election, and that got me thinking about the internet and how its algorithms can shape what you think.

For example, suppose you’re watching a conspiracy video. Then the platform recommends another conspiracy video, and then another. And before you know it, you’ve gone down a deep rabbit hole.

With the passage of time, the algorithm starts influencing the content you see and the ideas you’re exposed to, which can shape how you understand the world.

Eli Pariser gave this problem a name back in 2011. He called it the filter bubble. In his book The Filter Bubble, he described how two friends searching for “BP” on Google got different results. One saw investment information, while the other saw coverage of the oil spill.

The Wall Street Journal explored a similar problem in 2016 through its Blue Feed, Red Feed project. It showed how liberal and conservative-identifying Facebook users could see different content about the same topics and news events.

This can create conditions where extreme ideas can emerge. People don’t necessarily start out looking for extremist content or wanting to become extremists. But repeated recommendations from algorithms can gradually shape what they see and how they understand the world.

In this article, we’ll look at how media has been used to radicalize people, how social media and AI have changed the process, what’s happening today, and what you can do about it.

Let’s get into it.

Radicalization Before the Internet

Radicalization through mass media existed long before the internet. Radio, newspapers, and television have all been used to spread propaganda and influence how people see others.

Read more

Every few years the tech world falls in love with an idea and swears it is about to change everything.

It is AI today. NFTs and crypto had their turn a few years ago.
Virtual reality is one of those ideas too.
It has been “about to change everything” for more than a decade now.

You have seen the ads. Someone straps on a headset, waves their arms around, and it’s all just heaven. Big companies threw billions at it. Facebook literally renamed itself over it.

Recently I was talking to a friend who owns a Quest 2 and he said he has only used it twice since he got it.

Now look around. How many people do you actually know who own a VR headset? And how many of them still use it?

That gap is what this series is about. VR got good and cheap. And most people still said no thanks.

This is the free part, and it is the setup.

We will go over what VR actually is, how a headset works without the boring jargon, and how we got promised a future that never really showed up.

The next two parts are where we dig into what went wrong and where this whole thing is headed, including my own brief experience with a borrowed Quest 2.

Strapping a Computer to Your Face

At its simplest, VR is a way of tricking your eyes and brain into thinking you are somewhere you are not.

A VR headset is basically a small computer with two screens inside it, one for each eye.

You strap it to your face and the screens sit a couple of centimeters from your eyeballs. Little lenses bend the light so your eyes can actually focus on something that close.

The lenses exist because you can't focus that close.
The screens show you a 3D world. Turn your head and the world turns with you.

Look up, you see the sky. Look down, you see your virtual feet. That is the whole magic trick in one sentence.

Another way to think of VR is this.

Your phone and your TV are windows. You look at them.

VR is different because it wraps around you. You look around inside it. That jump from looking at to being inside is the entire point of the thing.

How a Headset Tricks Your Brain

The reason VR feels real comes down to two things working together. Your eyes and your movement.

First, the eyes. Each screen shows a slightly different image, one for the left eye and one for the right. Your brain stitches those two flat pictures into one 3D image, the same way it does in real life.

That is what gives you the feeling of depth, of stuff being close or far away.

Second, and this is the big one, the headset tracks your movement.

Sensors inside it constantly measure where your head is and which way you are looking. Move your head and the picture updates right away to match.

That instant update is everything. If there is even a tiny lag between you moving and the picture catching up, your brain notices. And when your brain notices, it gets confused.

That confusion is a big reason some people feel sick in VR, which we will get into properly in Part 2.

Modern headsets also track your hands. Some use controllers you hold, others use cameras that watch your real fingers. So you can reach out, grab things, throw stuff, or poke at a menu.

That feeling of “my hands are in here with me” is what turns a cool screen into something that feels real.

The Word Everyone in VR Keeps Repeating

There is one word people in VR say over and over, and it is “presence”.

Presence is that moment when your brain stops treating the headset as a screen and starts treating the virtual world as a place you are actually standing in.

It is a weird feeling the first time it hits you.

The classic example is a virtual plank sticking out of a tall building. You know for a fact you are standing on your bedroom floor.

But some older, dumber part of your brain is fully convinced you are about to fall off a skyscraper, and your knees go wobbly.

That is the thing no normal screen can do. A movie can make you cry. It cannot make you flinch and step back because a train is coming at you.

When VR nails presence, it is genuinely unlike anything else, and that feeling is what got everyone so excited in the first place.

The catch is that presence is fragile. It can break with a lag, a cable tug or a notification buzz.

As your brain snaps back, you are just a person wearing a heavy gadget on your face. Holding that illusion steady is one of the hardest jobs in all of consumer tech.

The Two Kinds of VR

For years there were basically two flavors of VR, and the difference mattered a lot.

The first kind is Tethered VR, aka PC-Connected VR, or PCVR for short.

This is a headset that plugs into a beefy gaming PC with a cable. The PC does all the hard work and the headset is just the screen on your face.

And you can’t just use a normal USB-C charging cable with your VR headset. You need a high bandwidth cable that supports data transfer at high speeds.

If you just use a normal USB-C cable your VR may not work at all. If USB-C types confuse you, here’s a dedicated article to lessen that confusion.

These look amazing because they borrow the muscle of a big machine. The downside is you need an expensive PC, and you are stuck on a leash.

The second kind is standalone VR. This is a headset with the whole computer built inside it. It needs no PC or cable.

You pick it up, put it on, and you are in. Everything runs on the headset itself.

Standalone is the thing that turned VR from a rich-hobbyist toy into something normal people could actually buy.

Picture it like this. You would spend a few hundred dollars on a headset, then find out you also needed a gaming PC that cost over $1,000 more to run it.

Then you connected a thick cable from your computer to your head and tried not to fall while spinning in circles.

Standalone threw all of that in the bin. It’s just one purchase and you charge it like a phone and put it on.

For most people, that simplicity is the only version of VR that was ever going to work.

The Quest and the Phone Chip

The headset that made standalone mainstream was the Meta Quest.

The Quest runs on a mobile chip, the same family of chip that runs your phone. That is why it can be light enough to wear on your head and cheap enough to actually buy.

It is nowhere near as powerful as a gaming PC. But it does not need to be, because it does not need a PC at all.

That trade, a bit less power in exchange for zero cables and zero extra cost, is what unlocked VR for regular folks. You are carrying a whole VR computer in something the size of a pair of ski goggles.

The Quest uses an ARM processor, it’s the same architecture that your phone processors use. Not sure what that means?

Well, here’s my detailed article that explains what ARM means for you and why it is everywhere from phones to VR headsets.

The Decade We Were Promised Everything

Back in 2012, a small company called Oculus built a headset that got the whole industry excited again.

Read more

Every once in a while we step away from the usual explainers and do one of these. A proper Deep Dive. The kind where we slow down, pick one story, and follow it all the way through.

Today’s story is about a tiny company called id Software.

You may or may not know the name. But you probably know their work.

Every time you play any shooting game like Call of Duty, Valorant, or Fortnite, you are playing in a genre that a handful of guys in America popularized in the early 1990s. That genre is first-person shooters, or FPS.

They did it with games called Wolfenstein 3D, Doom, and Quake. They did it fast.

They did it as a bunch of hackers who cared more about pushing computers to their limits than about making a business.

And their influence is so huge that it got people like Gabe Newell who was working at Microsoft at the time to get into gaming and create companies like Valve and games like Half-Life.

In a way, id Software kickstarted Valve by licensing it their engine.

If you wanna learn more about how Valve did with id’s engine and how Steam came around, check out our Deep Dive on Valve.

That’s why today we’re going to dive deep into id Software’s history and try to see what influence this studio has left behind. I’d also go over their current games and layoffs situation.

But let’s go over the origins of id Software first.

Disclaimer: I know it may feel like a capitalization error but trust me it’s not. ‘id Software’ is the actual name of the company we’ll keep talking about in the rest of the article.

Before Doom There Was an Office in Louisiana

This story doesn’t start in a garage. It starts in a boring office building in Louisiana, at a company called Softdisk.

Softdisk had a weird business. For about $10 a month, they would mail you a floppy disk stuffed with random programs and small games. Think of it as a surprise bag of software delivered to your house.

To make the games, they hired a small team of young programmers and stuck them in a division called Gamer’s Edge.

That is where the future founders of id Software met. Two guys named John did most of the heavy lifting.

John Carmack, the genius programmer. John Romero, the charismatic designer. Alongside them were artist Adrian Carmack (no relation to John) and designer Tom Hall.

In September 1990, Carmack cracked a problem that people thought PCs couldn’t handle. He figured out how to make a game screen scroll smoothly sideways, the way it did on a Nintendo.

On PC at the time, this was close to impossible. To show off the trick, Carmack and Tom Hall stayed up all night and rebuilt the first level of Super Mario Bros. 3 on a PC.

They dropped in one of Romero’s characters, Dangerous Dave, and cheekily named the demo “Dangerous Dave in Copyright Infringement”.

When Romero saw it the next morning, he froze. He had played thousands of games. He had never seen a PC move like that. He knew instantly that this was worth a company.

The team pitched Nintendo on a PC version of Mario. Nintendo said no.

So the guys took Carmack’s engine and made their own game instead, a colorful platformer called Commander Keen. It was released through a shareware publisher called Apogee in December 1990, and it did really well.

After that first royalty check landed, the team had their answer.

In February 1991, John Carmack, John Romero, Adrian Carmack, and Tom Hall officially founded id Software. Four people. That was the whole company at the start.

The People Who Actually Built id Software

Before we go deep into id, it helps to know who these people were, because their personalities are a story of their own.

John Carmack was the programming guy. He wrote the code that made everything possible.

He was famous for working brutal hours and caring about one thing above all else: making the technology do something nobody thought a PC could do.

Even today Carmack is known for his cold focus and almost machine-like discipline.

John Romero was the opposite energy. He was the designer, the showman, the guy who could take Carmack’s engine and turn it into something fun.

He built the levels, wrote most of the tools, and became the public face of id.

So, if Carmack was the machine, Romero was the personality.

Adrian Carmack was the artist. He drew the monsters, the gore, the twisted stuff. His dark art style is a huge part of why these games felt so different from the cartoony games around them.

Tom Hall was the fourth founder. He was a designer and a storyteller, the guy who wanted these games to have plots and characters and meaning.

That instinct made him great for Commander Keen, but it would later put him on a collision course with the rest of the team.

There is a fifth name you have to mention too, even though he wasn’t a founder. Sandy Petersen.

He joined later and became one of the most important people in the whole story. I’ll talk more about him soon.

John Carmack, the Engine That Changed Everything

To understand id Software, you have to understand what a game engine is.

Read more

For some time, I’ve been noticing a similar pattern emerging across almost every major app. Companies don’t seem to be satisfied with doing one thing anymore.

If you look at messaging apps, they’re trying to handle payments. Social media apps are trying to become shopping platforms.

The same goes for other platforms. Many of these apps are trying to do more than what they were originally made to do.

I believe the reason behind all of this is that companies want people to stop leaving their apps.

But why do companies keep doing this? Why does the super app model work in some places but struggle in others? Why are so many companies trying to copy it, and what can you, as a user, do about it?

Let’s talk about that.

What Is a Super App?

A super app is a single application that bundles multiple services into one platform. The idea is that you can get through most of your day without opening any other app.

The model is closely associated with WeChat (called Weixin in mainland China), which Tencent launched in 2011. It started as a messaging app and slowly added services such as Weixin Pay, Mini Programs, search, games and business tools. Tencent now describes Weixin and WeChat as a broader digital platform rather than a ‘just’ messaging service.

By the end of Q1 2026, Weixin and WeChat had more than 1.4 billion combined monthly active users. WeChat’s Mini Programs have also become a big part of the platform. These programs also allow users to access services without installing other apps.

Tencent says WeChat Mini Programs handled RMB 8 trillion in transactions in 2024, while Weixin Pay processes more than 1 billion commercial payment transactions on an average day.

That gives you a sense of what a mature super app can become. It can start with something as simple as messaging and gradually turn into a place where people access multiple services and run businesses.

And that is the part other tech companies want to copy.

What’s Happening Right Now

The same pattern is now showing up across major apps. Companies are taking services that once belonged to separate apps and bringing them into their own platforms.

WhatsApp started with messaging. It now has payments in markets including Brazil and India, Channels for broadcasting, Communities for larger groups, business tools and Meta AI built directly into the app.

And Meta isn’t slowing down anytime soon with this. Every other day, they are trying to introduce features into the app so users don’t have to leave WhatsApp.

Instagram followed a similar path. It began as a photo-sharing platform and has since added shopping, short-form video, messaging features, broadcast channels and connections to Threads.

Instagram’s push into shopping has also created some friction with creators. In early 2026, Instagram tested an AI-powered Shop the Look feature that automatically identified products in creator posts and recommended similar products.

Some creators even said the system attached products they had never endorsed to their content at all. Creators also said followers were being shown cheap alternatives attached to their posts without their knowledge.

Of course, we can’t forget about X, formerly known as Twitter. Elon Musk has openly talked about turning it into an “everything app.” Since then, the platform has added features such as long-form posts, video, job listings, and, in the US, payments through X Money.

But does that make X a successful ‘everything app’? Not really, at least from what I can see. It feels more like a platform with a lot of features added to it.

You can see a similar pattern with Telegram and other platforms. Companies try to turn their apps into super apps, or they keep adding features until the original purpose starts getting lost.

And I get it. From a company’s perspective, why wouldn’t you want your app to become the one place users go for everything?

So what’s in it for them?

Let’s talk about that next.

Why Companies Want More From Their Apps

There are four reasons, and none of them are about you:

Keeping Users From Leaving

The first reason is retention.

The more things you can do inside an app, the more reasons you have to keep using it. If you can message your friends, pay for something, shop, watch videos and use an AI assistant without leaving the same app, there is less reason to open a competitor’s app.

For the company, every one of those activities is another opportunity to keep your attention.

Building a Picture of You

Then there is data.

A messaging app can learn who you talk to and what you interact with. Add payments and it can learn what you buy. Add shopping and it can learn what you browse. Add location services, search, video and other features, and the picture becomes much bigger.

This is where the idea that “my phone is listening to me” can get confusing. You might talk to a friend about something and then see an ad or recommendation related to it. That does not mean the app necessarily listens to your conversation.

These platforms already have huge amounts of information about how you use them. They can see patterns across your searches, clicks, follows, purchases, viewing habits, location and interactions. Put all of that together and the system can build a detailed picture of what you are likely to want next.

AI can make that personalization even more detailed because it can process and connect large amounts of information much faster.

Data has become one of the most valuable things companies can collect about you, which makes protecting it increasingly important.

Whether companies should be allowed to collect and use this much information is a separate discussion. The important point here is that adding more services gives them more information about how you use the platform.

Making More From the Same Users

Then there is money.

An app that depends on advertising has one main way to make money from its users. Adding different services or products gives the company more ways to generate revenue from the same user base.

Making It Harder to Leave

The last piece is the ecosystem.

You’ve probably seen this with companies such as Apple, Google and Meta. They build multiple products that work together, so the more of them you use, the more convenient the whole setup becomes.

The same thing can happen inside a single app.

Imagine that your messages, payments, and other services all live in one place. Leaving that platform becomes much harder because you are replacing several parts of your digital life at once.

There are clear benefits for users here. But it also gives companies a strong reason to keep expanding their ecosystems.

Then There’s the AI Rush

There is another layer to all of this, and you’ve experienced it as well.

I am talking about AI being added to everything.

Meta has put Meta AI across all of its services. Snapchat launched My AI inside Snapchat. Spotify has added AI DJ and AI Playlist features that use your listening habits to personalize recommendations.

So, why is every company doing this? Part of the reason is competition.

If your competitors are adding AI, you don’t want anyone asking why your product doesn’t have it. AI has become a signal that a company is keeping up with the market. That creates pressure to add AI even when the feature itself isn’t solving a problem.

Read more

When I visit a fancy coffee shop in Lahore, it’s hard not to find laptops with an Apple logo.

That logo does a lot of work. Everybody knows what it means just by seeing it.

Lately there is another logo that is getting a similar level of respect, especially in the enthusiast tech circles.

That is Framework. It’s a small company founded in 2020 that most people haven’t heard of.

See, Framework does not outsell Dell, HP or Lenovo. It’s just a startup even at this point. And it does not appear anywhere in the top laptop makers list.

Framework laptops are not the cheapest option, not even the most powerful ones. Usually, in raw specs, their laptops lose to cheaper machines from other companies.

And yet it is the first new laptop brand that has breathed fresh air into the laptop scene.

Let’s go through why people buy Framework laptops and what it has done to the laptop repairability scene.

What Framework Actually Is

Framework makes laptops that are easier to repair and tinker with.

Read more

Every few weeks, another story seems to surface about a game turning out to be malware.

These games came from legitimate platforms that people already trust, the kind you’d never think twice about downloading from.

Steam reported 132 million monthly users back in 2021, and SteamDB now counts more than 130,000 games. Millions of people use it to download and play games every day. So when malware starts showing up inside games on a platform like this, it becomes worth paying attention to.

This isn’t a new problem either. We’ve seen malicious games and mods used to steal sensitive information for years. But some recent cases caught my attention, which made me want to dig into it.

So, today that’s what we will be looking at in this piece. I’ll go through some real-world cases where games have been used to distribute malware, explain why these attacks can make it through trusted platforms, and look at what you can do to protect yourself.

How the Attack Works

It starts with the game itself.

A game appears on Steam and looks completely normal. Some of these games are also built using commercial game templates. A game template is essentially a pre-built foundation that developers can use to create a game without building every system from scratch. It might already include things like player movement, menus, graphics, inventory systems, or other basic mechanics.

In February 2025, PirateFi, a game available on Steam, was found to contain malware designed to steal users' sensitive information. Researchers found that the game had been built using the Easy Survival RPG template, which costs between $399 and $1,099 to license. That gives an attacker a functioning game that can be modified and published.

The game can therefore look like a legitimate product while carrying something dangerous underneath.

But creating the game is only half the job. Attackers still need people to download it.

That’s where trust comes in.

Attackers can promote these games through compromised accounts, bots, social media, or recommendations that appear to come from someone the user already trusts. If the game is sitting on Steam and someone you know recommends it, there may be little reason to suspect that the download itself is the problem.

Once the user launches it, the malicious code can run alongside the game and begin collecting information from the computer. Depending on the malware involved, that can include browser cookies, saved passwords, session tokens or other sensitive information.

One of the more serious targets is the session token.

A session token is what allows a website or service to recognize that you’ve already logged in. If an attacker steals a valid token, they may be able to use your existing authenticated session instead of logging in with your password.

That can allow an attacker to bypass protections such as passwords and, in some cases, two-factor authentication. Instead of seeing a new login attempt and asking you to verify it, the service may see the stolen session as an already authenticated connection.

That can give attackers access to accounts such as email, social media, cloud services, and cryptocurrency platforms.

On March 12, 2026, the FBI’s Seattle Division published a victim identification notice connected to malware distributed through Steam games. The agency named seven games as part of its investigation: BlockBlasters, Chemia, Dashverse/DashFPS, Lampy, Lunara, PirateFi and Tokenova.

According to the FBI, one threat actor mainly targeted users between May 2024 and January 2026. The games have since been removed from Steam.

The FBI is now asking people who may have been affected to report information through its dedicated Steam malware investigation page and has provided Steam_Malware@fbi.gov for potential victims.

Real-World Cases

BlockBlasters: A Malware Attack Caught Live

On September 21, 2025, Twitch streamer Raivo “RastalandTV” Plavnieks was raising money for his stage 4 cancer treatment when someone in his chat recommended that he play a game called BlockBlasters. The game was available on Steam and looked like a normal 2D platformer.

Plavnieks downloaded it and started playing on stream.

Within minutes, more than $31,000 in cryptocurrency was drained from his wallet. The money had been raised through Pump.fun to help pay for his treatment. He watched it happen during the livestream.

BlockBlasters looked like an ordinary 2D platformer on Steam.

BlockBlasters was released on Steam in late July 2025. On August 30, an update added malicious files to the game. G DATA’s analysis found that the update contained code designed to steal cryptocurrency and other sensitive information from infected systems.

That means the game could exist on Steam as a normal-looking title before the malicious update changed what it did.

And Plavnieks wasn’t the only victim.

ZachXBT, a blockchain investigator, estimated that around $150,000 had been stolen from at least 261 Steam accounts. vx-underground reported a higher figure of 478 affected accounts, which shows how difficult it was to establish the full scale of the attack.

The incident also triggered a beautiful response that you don’t usually see after a malware attack.

Security researchers began reverse-engineering the malware and tracing the stolen funds. The wider crypto community also stepped in to help Plavnieks. Crypto influencer Alex Becker sent $32,500 to a secure wallet to replace the money that had been stolen from him.

The incident eventually led to BlockBlasters being removed from Steam. But by then, the malware had already been distributed to players who had downloaded the game and its malicious update.

Wallpaper Engine and the Steam Workshop Problem

BlockBlasters was one example of malware reaching users through a game. But the problem doesn’t stop with games themselves.

In June 2026, Kaspersky documented a separate campaign abusing Steam Workshop to distribute malware through wallpapers made for Wallpaper Engine. Researchers found dozens of infected wallpaper packages, some with tens of thousands of downloads.

For those of you who don’t know, Wallpaper Engine is a popular Steam application for creating and sharing animated desktop backgrounds.

The important detail here is how Wallpaper Engine handles certain types of wallpapers. Alongside videos and interactive scenes, it supports application wallpapers, which are Windows applications that can run directly on the user’s computer as the desktop background.

That gives attackers another way to hide malicious software inside something that looks harmless.

Kaspersky found cases where malware was bundled directly into wallpaper packages. Attackers hid malicious files inside password-protected archives included with the wallpaper. Once installed, the malicious payload could run automatically.

One example discovered in December 2025 appeared to work normally. It even launched a desktop game, so there was no obvious sign that anything was wrong. In the background, however, it installed malware that could steal account information and hijack active Steam sessions.

Kaspersky also found several types of malware in the campaign, and the attacks appear to have involved multiple threat actors.

The bigger problem was where these wallpapers came from. They were community-created files distributed through Steam Workshop, a trusted part of the Steam ecosystem.

Why Platforms Struggle to Stop This

Why Steam Has a Hard Job

I don’t want to paint Steam as some shady operation that doesn’t care about its users. I’ve used the platform for years, and my own experiences with Steam’s support have been good. Steam is also dealing with an enormous amount of content from developers and users around the world.

Valve reviews a game’s store page and build before release, but its own documentation states:

“Once your game has been reviewed and approved, there is no need to go through review again. You are free to update your game as much as you need to.”

This means that a game can be clean when Valve reviews it and become malicious later, which is exactly the gap BlockBlasters used.

Steam Workshop adds another problem. Users can upload and distribute their own content for games such as Cities: Skylines, creating another route for malicious files to reach users. That content doesn’t go through the same review process as a commercial game submitted to the Steam store.

Steam also relies on user reports and moderation systems to deal with problematic content. Valve’s own documentation shows that some user-generated content goes through moderation queues, while other content can be reported by users for review.

At Steam’s scale, manually examining every game and community upload in depth isn’t realistic. Automated checks help, but malware can be disguised inside software that otherwise works normally. That makes detecting a malicious update much harder than checking whether a game launches and functions as expected.

There are legitimate limits to what a platform can catch before users encounter it. The BlockBlasters case shows what can happen when something slips through.

Why a Hard Job Isn’t an Excuse for a Slow Response

According to BleepingComputer, Valve didn’t act on multiple user reports, and the infected version stayed up for about three weeks while people kept downloading it.

The main problem is the difference between having moderation capabilities and reacting fast enough to make use of them.

Independent researchers like ZachXBT and vx-underground traced the stolen funds and took the malware apart within days. Steam has the technical resources to investigate these threats as well, but its moderation process moves at a much slower pace.

And that pace matters when users are reporting an active threat.

A large platform will always have a longer process before removing content. But a hard moderation problem doesn’t mean the response has to stay slow.

Steam and other platforms need to figure out how to close that gap. Independent researchers have already shown that threats like this can be tracked and investigated quickly.

The question is whether platforms can build a response system that moves with the same urgency.

The Problem With Trusting the Source

What happened on Steam is part of a much wider problem. The same basic idea can work anywhere people download software from a platform they trust.

The Chrome Web Store is a good example. Google runs security checks on extensions, but malicious extensions have still made it onto the store. In 2025, researchers found almost a dozen Chrome extensions with around 1.7 million downloads that could track users, steal browser activity, and redirect traffic.

In April 2026, researchers at Socket found more than 100 malicious extensions targeting account information and authentication tokens.

Google Play faces the same challenge at an even larger scale. Google says it blocked more than 1.75 million policy-violating apps from being published in 2025. Google also says that it banned more than 80,000 developer accounts.

The lesson is simple.

A legitimate platform does not automatically make everything on it safe. The platform may have security checks and other protection mechanisms, but malicious software can still slip through.

Kaspersky made the same point when discussing the Steam Workshop campaign. The researchers said trusted platforms can be abused to distribute malware because users trust content hosted inside legitimate ecosystems.

And this goes beyond software stores.

The same idea appears in phishing and other types of social media scams. Attackers don’t always need to break through a technical security system. Sometimes they can take advantage of the trust people have already built around a platform.

That is where this connects to social engineering. A social engineering attack often starts by getting someone to trust the thing in front of them. The more familiar and legitimate that thing looks, the easier it can be to lower someone’s guard.

I wrote about how these attacks work in more detail in my guide to social engineering. If you understand how attackers manipulate trust, it’ll be even easier to spot the same tactics when they appear somewhere else:

How to Protect Yourself

Check Before You Download

The easiest way to protect yourself from these attacks is to stop treating a download as safe simply because someone you trust recommended it.

That someone could be a friend, an influencer, or even a platform you have used for years. I’ve had friends whose accounts were compromised and then used to send me Discord Nitro offers and other links.

The same applies to game recommendations. If an influencer suddenly promotes a game, that doesn’t mean the game is safe. Check it yourself before downloading it. Look at its history, reviews, and what other users are saying. Independent sites such as SteamDB can also give you more information about a game’s history and activity.

In short, never outsource your own judgment. A recommendation can help you find something. It shouldn’t replace your own check before you install it.

Keep Your Devices and Accounts Protected

Keep your operating system and security software updated. Security tools can catch known threats even when a malicious file makes it through a platform’s own checks.

Your accounts need the same attention. Use strong, unique passwords and enable two-factor authentication wherever possible. Just remember that 2FA stops a stolen password, but not a stolen session, which is why revoking active sessions matters. If malware does get onto your device, an attacker may try to use every possible technique to get into other accounts.

If you think you’ve installed something malicious:

• Stop using the affected device for sensitive activity and run a full security scan.

• Change important passwords from a device you trust and revoke active sessions where possible.

• Contact your bank immediately if financial accounts may have been exposed.

I’ve also covered the basics in my Privacy Fundamentals series, including account authentication, safer communication, safer browsing, and securing your network, all of which make your accounts and devices harder to compromise:

Your Own Check Is the Last Line

We’ve seen how malware can hide inside something as ordinary as a game. The platform and the software might look completely normal. That doesn’t guarantee it’s safe.

Trust is becoming more valuable in the online world, and attackers know it. The more we rely on external sources to tell us what’s safe, the more important it becomes to make our own checks.

Most of the risks we face online can be reduced by knowing what to look for and staying aware of what we’re doing.

A Steam listing, a friend’s recommendation and good reviews can all be borrowed. Your own check can’t.

Anyway, thanks for reading.
See you next time.

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We have covered a lot of ground in this 3-part series on the tech behind smartphone cameras.

Part 1 was about the spec sheet of a smartphone camera. We learned that shouting about megapixels without context is mostly marketing and that sensor size pretty much does the real work.

Part 2 went over the actual camera hardware. We talked about different types of lenses and all the stuff inside that camera bump.

This is Part 3. And this one is all about the hidden warrior: software.

In 2026, the software behind your camera is sometimes more responsible for your photos than the hardware capturing them.

Every time you tap the shutter button, your phone is not just taking one photo.

It does a lot of stuff before you see the final image.

I bet you don’t fully understand how important software is to cameras. Neither do most people (including me some time ago).

Your Phone Doesn’t Just Take One Photo

Think about what a traditional camera does. You press the shutter. The sensor captures light. You get a photo.

Simple. Direct. What you see is more or less what the sensor captured.

Your phone does not work that way.

When you tap the shutter on a modern smartphone, the camera grabs multiple photos in a rapid-fire kind of way. Sometimes 10, sometimes 15 or even more.

It then analyzes every single frame. It picks the sharpest parts from each one. It merges them together and makes you an image.

The photo you see is a composite built from many frames.

The fancy term for this is computational photography. And it is the real reason modern smartphone photos look as good as they do.

Without it, a phone sensor would be too small to compete with a dedicated camera. With it, phones can rival some pretty expensive hardware.

In a nutshell, the software does heavy lifting the hardware cannot.

Explaining Dynamic Range

HDR stands for High Dynamic Range. It is one of the oldest photography tricks to exist. The problem it solves is simple.

Read more

Sometimes I come up with random thoughts from nowhere. And some of them are totally useless, while others form into valuable questions that are worth looking into.

And one of the more peculiar ones was: “what happens to your online existence when you die?”

The process of transferring physical property is somewhat clear and most of you probably understand what will happen with your house or other personal belongings.

But what happens to your online accounts? Who will terminate your subscriptions? Who will take control of your cloud storage? What will happen to years of family pictures stored in Google Photos or iCloud?

These questions are rarely asked because most people don’t even think about their digital life.

However, an average person today leaves behind a decent amount of digital information.

In this article, we’ll look at what happens to our online accounts after death, how big platforms deal with digital inheritance and what you can do about it, right now.

The Digital Life You Leave Behind

Nobody is able to cancel their favorite subscription after they die.

And the subscriptions the person had, remain active. Meanwhile, the people left behind are busy dealing with grief and legal responsibilities.

On top of that, when they find out about digital legacy, they realize they have to deal with another big problem.

When it comes to these services, each of them has its own rules, privacy policies and way of managing accounts once the owner is no longer alive.

The issue is that most companies do not automatically find out when someone has died and this process gets even more difficult when families are not aware of all the accounts that exist at all.

The cost can become a problem as well. Streaming services, software subscriptions, and other regular payments may keep charging the linked credit card until someone sees it and cancels the service.

Money is only part of the issue. The other part is dealing with digital items that cannot be replaced. Family photos that exist only in an account, personal papers, and a lifetime of memories can become impossible to reach when no one knows where they are stored or how to get them back.

This is the digital legacy problem, a problem everyone will have to deal with, but no one is aware of.

How Platforms Handle Digital Legacy

The Tools Platforms Already Provide

Google has a good system for what happens to your digital stuff when you are not using it anymore.

They have a service called Inactive Account Manager which lets you decide what Google does with your account when you are not using it. You can say how long you want Google to wait before they do something with your account, preferably a 3, 6, 12 or 18 month time period.

After that period, Google can share the information you selected with your trusted contacts, and you can also choose to have your account deleted.

These options can both be enabled. Your contacts get access to the information you selected, during their download window.

Google can then delete your account three months after that window ends.

If you never set up Inactive Account Manager, your family can still request access to some of your account data after your death. But they don't go through the normal account recovery process. Google has a separate process for deceased users that requires documentation, including a death certificate and identification.

In some cases, getting access to account data may also require a court order, because Google doesn’t hand over passwords or let anyone else log in as you.

Apple introduced Legacy Contact with iOS 15.2 in 2021.

The feature allows users to designate up to five people who can request access to iCloud data after their death. The process requires Legacy Contact access key and proof of death.

The data available can include photos, notes, emails, files, and other iCloud information. Without a Legacy Contact configured, Apple generally requires a court order before granting access to a deceased person’s account.

Facebook does things in a bit different way.

On Facebook, users can pick a Legacy Contact who can look after a few parts of their profile once they are gone, like writing a pinned post at the top of it. Users can also choose to delete their Facebook account completely after they die.

If users do not pick either of these options, Facebook needs someone to ask them to make the account a memorial, which means someone would need to show them things like proof that the person is dead.

Other platforms remain inconsistent.

X allows relatives or authorized representatives to request account deactivation. Reddit does not offer a traditional inheritance system and focuses mainly on account removal requests. Other smaller services have no documented procedure at all.

The main problem is that every platform has its own rules and there is no universal process that transfers digital accounts the way physical assets can be handled through traditional inheritance systems.

Many people also don't know that tools for handling some of these accounts even exist, so they never set them up in the first place.

The Locked Vault Problem

Every single account that we use needs some kind of authentication. A lot of the time the person who passed away was the only one who knew the passwords and the important details.

Using a password manager can help with this, but only if you set it up to allow someone else to get in if something happens to you.

Bitwarden has a feature called Emergency Access that lets you pick people you trust to get into your account if something happens. The account owner chooses a waiting period between 1 and 90 days. During that period, the owner can reject the request. If you do not do anything then the person you chose can get in and can do anything that they are allowed to do.

In the case of 1Password, family members on a 1Password Families account can use the family organizer's recovery process to recover their own accounts.

However, the organizer does not gain access to the member's account during recovery. 1Password sends an email to the member being recovered, so recovering a deceased member's account would require access to that person's email inbox. 1Password also provides an Emergency Kit with important account information that users can store securely.

But these methods are only effective when set up while the account owner is still alive.

A password manager that doesn’t include provisions for emergencies will keep your accounts secure from hackers but also prevent your loved ones from accessing them afterward.

This ties into the authentication segment of the privacy fundamentals series. I have dived into the authentication part in detail in it:

When Subscriptions Outlive Their Owner

Subscription services are what families notice first, because they keep charging the card.

Different recurring services do not automatically stop when an account owner passes away.

There is no automatic notification for these companies about the person’s death. Companies will keep taking money from the connected payment system until families cancel their subscriptions or the connected payment method becomes inactive.

Stopping the payment methods can become much more difficult than it might seem.

Because you first need to find every active subscription which can be difficult.

Some of these subscriptions renew once a year, which means that such services are not noticeable when checking the recent expenses. Moreover, some services use other platforms to make payments (e.g., Apple’s App Store, Google Play.)

For many families, it means poring over months’ worth of bank statements, credit card statements, and emails looking for accounts that exist and the payments made to them.

The existence of family plans presents another problem.

Someone who is paying for the Spotify, Netflix, or other shared subscription will likely have other family accounts tied to the same payment methods. If they become unusable, then not just the person who originally set up the account would suffer.

The easy way out is to simply write down all your active subscriptions, the account they are billed on, how much you are spending, and the accounts that are associated with each.

One little document created by you today can save someone else weeks of looking later.

Why Crypto Creates a Unique Inheritance Problem

Cryptocurrency takes the digital legacy issue to another level where access relies completely on the ownership of particular credentials.

Conventional financial systems have established processes for inheritance. Banks can verify death certificates and authorized heirs before transferring access to accounts and I get it, the process can be slow, but there is still a system designed for it.

However, cryptocurrencies do not work this way.

A crypto wallet is basically controlled by a private key or seed phrase.

The private key is a long string of characters that proves ownership of the funds inside a wallet. The seed phrase is usually a list of 12 or 24 ordinary words that can regenerate that private key if it’s lost. Either one gives full access to whatever the wallet holds.

In other words, whoever has that information can access the funds and whoever does not have it cannot access them, regardless of how closely related they are to that person.

Now, don’t get me wrong, the blockchain can still show that the funds exist. Anyone can see the wallet balance and transaction history, but without the required credentials, those assets remain inaccessible.

According to Chainalysis estimates, between 2.78 million and 3.79 million Bitcoin may have been lost permanently. Chainalysis estimated that this represented roughly 20% of all Bitcoin mined at the time, with some of those losses resulting from people losing access to their wallets or dying without leaving a way for others to recover them.

The QuadrigaCX case is the famous example, though the story is more complicated than it first appears.

So what’s QuadrigaCX? It was a Canadian cryptocurrency exchange that collapsed in January 2019, weeks after founder Gerald Cotten reportedly died in India in December 2018 from complications of Crohn’s disease. His widow’s initial affidavit said Cotten was the only person who held the passwords to the exchange’s cold wallets, and roughly C$180 million in customer cryptocurrency was inaccessible without him.

Investigators from Ernst & Young later found that the cold wallets had been emptied in April 2018, months before Gerald Cotten's death. Five of the six wallets had held no funds since then. What initially appeared to be a case of an owner taking passwords to his grave was something more complex.

But still, the issues it identifies are legitimate: Cryptocurrency does not have a similar counterpart of the bank phoning the family or some institution having a backup.

In short, where there is no one to inherit the cryptocurrency or where the person dies without having provided his seed phrase or private key, the coins will be lost forever.

Therefore, for those who have invested in cryptocurrency, the key to success is preparedness. Make a copy of the seed phrase and ensure that someone trusted knows about its whereabouts.

Building a Digital Legacy Plan

This isn’t meant to be a complete guide for every platform.

The problem is really big. On top of that, every service has different rules and requirements. A better approach is building a simple framework that works regardless of which platforms you use:

Know What You Have

The first step is understanding what you have.

Make a list of your relevant accounts. This includes email, banking, cloud storage, social media, subscriptions, authentication applications, password manager, and cryptocurrency wallets.

It doesn’t have to be the list of all your passwords right away, but the point is to have an existing list somewhere other than in your brain.

Most people don’t realize how many online accounts they have until they actually try making a list.

Prioritize What Cannot Be Replaced

Understand that not each and every account has the same importance.

Losing access to a streaming account is inconvenient, but losing twenty years of family photos stored in a cloud account is different and losing a crypto wallet containing significant assets carries a whole different level of risk.

In short, focus on prioritizing accounts based on what they contain. The accounts holding irreplaceable memories or financial assets deserve the most attention.

Use the Tools That Already Exist

There are some large players who already have found ways to solve these issues.

Google Inactive Account Manager, Apple Legacy Contact, and Facebook Legacy Contact are tools that let users decide what happens to certain parts of their digital accounts after they die.

On Facebook, a Legacy Contact has limited control over a memorialized account. They can write a pinned post, manage the profile and cover photo, request removal of the account, and download a copy of the account information if the deceased person allowed it.

These options only become available after someone reports the death and Facebook memorializes the account.

Password managers provide some emergency access solutions as well.

Bitwarden lets trusted contact request access. 1Password provides recovery access from family accounts and Emergency Kits.

Such tools exist due to the difficulty of account access without prior preparation. The main issue here is that almost nobody sets up these tools.

And I get it, not many platforms offer this yet, so make the most of the ones that do.

Tell Someone About the Plan

A digital legacy plan only works if another person knows it exists.

That person could be a partner or someone you trust. They do not necessarily need every password. They just need to know that a plan exists, where the important information is stored, and what steps to take when needed.

Don’t become paranoid. Some people might feel like “If I tell, my privacy will be jeopardized.” But sometimes, you just have to do what must be done.

A Small Habit With a Long Term Impact

Creating online accounts requires almost no decision making and takes very little effort. But over time, these decisions add up and turn into a big pile of junk.

Most people probably have dozens of accounts they no longer remember creating. Some accounts are connected to old emails, have payment information attached to them or contain years of personal data that nobody else knows exists.

The problem is that forgetting an account is as easy as creating one and if you don’t know what accounts exist, managing your digital legacy becomes almost impossible.

The first step is knowing what you have and what you don’t. Keep track of the services you use, delete accounts you no longer need, and avoid leaving behind unnecessary digital clutter that someone else may eventually have to deal with.

More importantly, take responsibility for the systems you build around your digital life.

I hope you enjoyed the read.
See you in the next post.

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In Part 1 we tore apart the megapixel myth. We learned that a higher megapixel number on the spec sheet barely matters. Sensor size, aperture, and stabilization do most of the real work.

After reading the first article in this series, it’s natural to be curious about the hardware that creates those features in a camera.

Why does your phone have three or four separate cameras instead of just a single good one? And what is going on with that weird L-shaped lens setup on flagship phones?

If you’re asking the same questions, this article is for you. It focuses on the physical hardware that lets you take a photo in a split second.

I’ll go over the actual lenses, prisms, and sensors sitting inside your phone and what each of these means for you.

Just so you know, this article is the second in a 3-part series of articles on smartphone cameras.

Part 1 of this series talked about the fundamental camera concepts like aperture, sensor size, OIS. It discussed what features to look for when judging camera quality.

Part 2 (the one you’re reading now) gets into the hardware behind the specs and talks about the physical side of a smartphone camera.

Part 3 will cover the clever software techniques doing most of the real work behind smartphone cameras today.

Why Phones Have Multiple Cameras

Just turn your phone around and you’ll see more than one lens, even though your phone only has one camera app.

Read more

Over the past year, I’ve written quite a few history articles.

The History of Computers
The History of Hacking
The History of the Internet
The History of Telephone Networks

A few days ago, I was sitting and looking through some of those articles when my phone caught my eye.

And I realized that I’ve spent thousands of words writing about the technologies that eventually led to this device sitting on my desk, but I’ve never actually looked into the history of the phone itself.

We still call it a phone, but if you zoom out a little, that description feels increasingly outdated.

The device in your pocket is a camera, a computer, a GPS receiver, an internet terminal, a gaming console, a payment device, and one of the primary ways people communicate with each other.

In fact, the “phone” part has become one of the least interesting things about modern smartphones.

A lot of people spend far more time texting, scrolling through social media, browsing the internet, watching videos, listening to music, navigating with maps, and using apps than they do making actual phone calls.

But the smartphone didn’t start that way…

So in this article, we’re going to look at how mobile communication evolved from bulky radio systems and experimental networks into the smartphones that billions of people carry every day.

Before Mobile Phones

The World of Landlines

For most of the twentieth century, communication was tied to physical locations.

Telephones existed, but they were attached to places instead of people.

If you wanted to call a person, you called their house or office and if the person you wanted to reach wasn’t there, communication stopped. You could try again later, leave a message with someone nearby, or simply wait until they returned.

That was normal.

For a long time, nobody expected to be reachable at all times because the technology simply didn’t allow it.

Telephone networks themselves were remarkable engineering achievements. By the middle of the twentieth century, operators and switching systems could connect conversations across cities, states, and eventually countries. The Bell System in the United States built one of the largest communication networks the world had ever seen, connecting millions of households and businesses through an increasingly sophisticated web of wires and switching centers.

But the main problem was mobility and that limitation became increasingly noticeable as society grew more connected.

Radio Opens a Different Path

While telephone networks were expanding across cities and countries, radio, another communication technology, was developing alongside them.

Unlike telephones, radio didn’t rely on wires connecting one location to another. Messages could be transmitted wirelessly using electromagnetic waves, opening up possibilities that traditional telephone networks simply couldn’t offer.

By the early twentieth century, radio had become an important tool for ships, militaries, emergency services, transportation networks, and many other organizations. It allowed communication across long distances without requiring physical connections between the sender and receiver.

Because of radios, for the first time, communication wasn’t necessarily tied to a fixed location.

Over time, specialized radio systems became common in so many places, but these systems were very different from what we think of as mobile phones today.

Most radio networks were designed for specific organizations rather than the general public. Equipment was often large and power-hungry. Communication usually flowed through dispatch centers rather than allowing millions of individuals to call one another directly and there were technical limitations as well.

The radio spectrum is finite and only a limited number of conversations can occupy the same frequencies at the same time. Early systems struggled to support large numbers of users without interference.

Hardware presented another challenge: radios were bulky, expensive, and difficult to miniaturize. Batteries were heavy and offered limited operating time. Even if engineers could build portable wireless devices, scaling them for widespread public use was another problem entirely.

The Dream of Mobile Communication (1940s-1970s)

Mobile Telephones Before Cellular Networks

By the middle of the twentieth century, engineers had already started experimenting with a telephone that could travel with you.

In 1946, AT&T’s Bell System launched the Mobile Telephone Service (MTS), one of the first commercial mobile telephone systems in the United States. It allowed users to place telephone calls from vehicles using radio networks connected to the public telephone system.

The Car Phone Era

The equipment was usually installed directly into vehicles. Radios occupied significant space inside the car, antennas were mounted externally, and the system often required dedicated power equipment to operate reliably.

In many cases, this was the type of communication system that you built your vehicle around and the cost alone placed these systems out of reach for most people. Only a handful of powerful & wealthy customers made up the majority of users and even then, capacity remained extremely limited.

In other words, the bigger problem was scale.

A large city might have thousands of people interested in using mobile telephones, but only enough radio channels to support a small number of simultaneous conversations. In some areas, users had to wait for an available channel before they could place a call.

By the 1960s and 1970s, it had become clear that simply building bigger versions of existing mobile telephone networks wasn’t going to solve the problem and a different approach was needed.

Bell Labs and the Cellular Breakthrough

That approach came from Bell Labs.

Instead of treating an entire city as one giant radio coverage area, Bell Labs engineers proposed dividing cities into many smaller coverage zones called cells. Each cell would have its own radio equipment and frequencies. The same frequencies could then be reused in other cells located far enough away to avoid interference.

So, instead of trying to support more users with the same limited resources, cellular architecture allowed networks to reuse those resources repeatedly across different parts of a city.

Suddenly, supporting thousands or even millions of users became possible.

This concept became known as a cellular network, and it is where the term “cell phone” originates.

The cellular concept solved one of the biggest technical challenges facing mobile communication, but technology was only one part of the equation.

Building a nationwide mobile network required infrastructure, industry standards, and regulatory approval. None of those things happen overnight.

Throughout the 1960s and 1970s, telecommunications companies, engineers, and regulators were still trying to figure out how cellular networks would actually work at scale. Governments had to decide how radio spectrum would be allocated, which companies would be allowed to operate networks, and what rules would govern the technology.

In the United States, Bell Labs had proposed cellular architecture decades earlier, but regulatory approval moved much more slowly than the technology itself. Large portions of the required spectrum remained unavailable for years while debates continued between industry players and regulators.

Long story short, by the late 1970s, many of those pieces were finally starting to fall into place.

From Briefcases to Pockets (1970s-1990s)

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If you walk around a big phone market in your area you will see one number plastered everywhere.

Big numbers like 200MP and 108MP are everywhere because monkey logic dictates the bigger the number the better the stuff right?

It looks like every smartphone company wants to win the megapixel race.

Vivo’s X300 Ultra has a 200MP main camera. Samsung’s Galaxy S26 Ultra has a 200MP sensor too.

Xiaomi went the other direction on the main camera of their 17 Ultra and used a 1-inch 50MP sensor instead. It is widely considered one of the best camera phones you can buy right now.

This imbalance alone should already tell you something is off.

If megapixels alone decided photo quality Xiaomi would be losing badly with “only” 50MP, right?

While this megapixel war continues, professional photographers are walking around with cameras that often have fewer megapixels than your phone.

Many professional cameras sit in the 24MP to 45MP range.

So here is the obvious question nobody seems to ask out loud. If 200MP is genuinely better, why are professionals carrying cameras with a fraction of that number?

That’s exactly the question I asked a couple of weeks ago when I was researching digital cameras.

The answer is that megapixels were never the real story. They are the easiest number to put in an ad and that’s probably why companies do it.

That is why this article is about figuring out what actually matters when you are judging a smartphone camera.

This is the first article in a 3-part series.

In this first part, we will deal with the raw specs that you should and should not focus on in smartphone cameras.

Part 2 will get into the physical hardware behind those numbers.

Part 3 will cover the software doing most of the real work today.

What Exactly Is a Megapixel?

I bet you have heard about megapixels but never had it explained to you. That’s where I come in :).

So, a pixel is a tiny dot of color. Your phone screen, your camera sensor, every digital image is built from millions of these tiny dots sitting next to each other.

A megapixel is just one million pixels. A 12MP photo is made up of 12 million of these dots. A 200MP photo is made up of 200 million of them.

Think of it like a mosaic made of small colored tiles. More tiles theoretically means you can capture finer detail.

A mosaic with a million tiles can show way more detail than one with a hundred tiles.

Naturally one thinks, more tiles sounds like a clear win.

More data, more detail, better photos, right?

And you know what? That logic is correct as far as it goes. It just leaves a lot out.

But the incomplete part is where these flashy smartphone ads get us all.

More Megapixels Do Not Mean Better Photos!

Let’s say you take a 50MP photo from a budget Android phone and a 24MP photo from a basic DSLR.

In almost every real comparison, the DSLR photo looks way better.

The DSLR photo has cleaner skin tones, more natural colors and better detail in shadows.

But you see, the DSLR is working with fewer than half the megapixels and still wins.

This happens constantly when people compare phones too.

DXOMARK (a camera testing lab) ranked the iPhone 17 Pro as one of the best camera phones of 2025.

Its main camera shoots at 48MP. Most everyday photos are actually saved at 24MP by default to balance quality and file size.

Compare that to the 200MP Galaxy S26 Ultra and you would expect the iPhone to lose badly on paper.

But test it out in reality and you’ll quickly see that IT DOES NOT!

DXOMARK scored the Samsung at 157, a full 11 points behind the iPhone.

So if resolution is not deciding the winner here, what is?

The answer is a good one and let me try to answer it in brief first and then we’ll get into the weeds.

So, image quality depends on how much light the camera sensor can physically capture.

It also depends on how good the lens is at focusing that light cleanly.

And it also depends on how smart the software is at turning raw sensor data into something that looks good.

You see the pattern?

There isn’t just one thing that would magically make the images better.

It’s a combination of factors and that’s what most smartphone discussions, especially in our country, miss.

Megapixels are the most hyped spec of all in a phone camera, but they are just one ingredient. And not even the most important one.

The Secret Is Sensor Size

And this is the part smartphone camera marketing conveniently leaves off the box.

Every camera, whether it is in your phone or a professional camera, works by capturing light on a sensor.

The sensor is the actual physical chip that reacts to light and turns it into image data.

Megapixels tell you how many individual light-capturing points exist on that sensor. Sensor size tells you how big the entire chip is.

But there’s a catch here too.

If you keep the sensor size the same but increase the megapixel count, each individual pixel has to get smaller to fit.

Smaller pixels capture less light per pixel.

A useful way to picture this is two buckets sitting out in the rain. One bucket is huge. One bucket is tiny.

Even in the exact same rainstorm, the giant bucket collects far more water. Simply because it has more surface area to catch it.

Camera pixels work exactly the same way with light. A bigger pixel is a bigger bucket.

It physically captures more photons, the basic units of light. More captured light gives the camera more information to work with.

That leads to better detail, more accurate colors, and better performance in anything other than perfect sunlight.

This is exactly why Xiaomi’s 1-inch sensor on the 17 Ultra punches so far above its 50MP count.

Just compare it with the Galaxy S26 Ultra, which squeezes 200MP onto a much smaller 1/1.3-inch sensor.

Each of Samsung’s pixels is tiny, so each one catches less light.

This is the actual reason your 200MP phone sometimes takes worse low-light photos than your friend’s older iPhone. The culprit is the sensor size behind those megapixels.

The Vivo X300 Ultra shows the flip side. Its 200MP sensor is nearly 1-inch, and it ranks among the best camera phones you can buy.

Megapixels and sensor size are two separate things, and a 200MP sensor can be big too.

A big sensor beats a small one in low light and dynamic range, no matter how many megapixels either has. A high megapixel count crammed onto a small sensor is a trap.

Dynamic range is an important concept too in cameras.

It basically means how well a camera can capture detail in both the brightest and darkest parts of the same photo at once.

Take a photo of a friend standing in front of a sunny window.

A camera with poor dynamic range will either blow out the window into a white blob or turn your friend into a dark shadow.

A camera with strong dynamic range can hold detail in both at the same time.

The Things Brands Do Not Advertise

Sensor size is a pretty important specification.

But it is far from the only spec quietly doing the heavy lifting. Aperture is another important one.

Aperture is the size of the opening that lets light into the camera. It is written as something like f/1.7 or f/2.8.

The lower the number, the wider the opening, and the more light gets in.

Apple’s iPhone 17 Pro main camera sits at f/1.78. Its ultrawide uses f/2.2.

A wider aperture genuinely helps in dim places and indoors. These are situations where photos usually go bad.

Optical Image Stabilization, or OIS, is a really important feature too. OIS physically moves the lens or the sensor itself to counteract your hand shaking while you take a photo.

Without it, anything shot in low light with a slower shutter speed comes out blurry. Because your hand is never perfectly still.

The iPhone 17 Pro uses sensor-shift stabilization on its main and telephoto cameras. That is a big part of why its photos stay sharp even in trickier lighting.

Frame rate and shutter speed matter mostly for video and action shots.

Frame rate is how many individual images get captured per second when shooting video.

30fps and 60fps are the common standards. Many flagships now shoot 4K at 120fps, and slow motion goes even higher.

Shutter speed is how long the sensor stays open to capture each individual frame. Faster shutter speeds freeze motion better but need more light to avoid a dark image.

This is exactly why sports and pet photos are so often blurry on phones with weaker sensors.

None of these specs get a giant number on the box the way megapixels do. But they are quietly responsible for most of what actually separates a good camera phone from a mediocre one.

What to Look at, Then?

By now the pattern should be clear to you.

Megapixels are a real specification and they do matter, especially if you crop heavily or print large photos.

But treating megapixel count as the main metric of camera quality is like judging a car purely by its top speed.

You are ignoring the engine, the comfort, and everything else that decides whether it is actually good to drive.

A Simple Checklist

When you are comparing phones, here is a simple checklist that you can follow instead of just chasing the biggest megapixel number.

Look at sensor size first, even if you have to dig for it in the detailed spec sheet rather than the headline marketing.

A 1-inch sensor at 50MP will usually beat a tiny sensor at 200MP, especially indoors or at night.

Check the aperture number on the main lens. Lower numbers like f/1.7 or f/1.8 let in more light, which helps in nearly every situation that is not bright daylight.

Confirm OIS is present, particularly on the main and telephoto lenses. This really helps those who have shaky hands or have to record and capture while moving.

Most importantly though, just look at actual sample photos.

Reviewers at sites like DXOMARK and GSMArena publish real comparison shots taken with the same phones in the same conditions. These tell you far more than any spec sheet ever will.

They show you what the camera actually produces rather than what the company claims it can produce.

Look, I understand that sifting through detailed spec sheets to find out the above-mentioned specs appears hard at first and is time-consuming.

For an average person, if they don’t want to bother with it they can just look at the sample images that the phone has taken.

It is one of the quickest ways to judge a camera’s quality.

It is certainly a lot better than parroting some number that doesn’t affect you that much directly.

A bigger sensor, a sharper lens, and smarter processing will beat a higher megapixel count almost every time.

The next time you see someone bragging about 200 megapixels, you will know exactly which questions to ask.

But there’s another question.

If bigger sensors are so clearly better, why doesn’t every phone just use a 1-inch sensor?

The answer is packed inside that camera bump, and it’s where Part 2 picks up.

Parts 2 and 3 of this series are for paid subscribers.

If you want to follow along, you can subscribe from the button below:

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Further Learning

• What makes a good smartphone camera?

• Smartphone cameras explained: Sensors, pixel size, aperture, and more

• Want a phone with a great camera? Here’s what to look for

• Best camera phones 2026 buyer’s guide

• Apple iPhone 17 Pro and 17 Pro Max technical specifications

• Camera Definition - GSMArena

• Apple iPhone 17 Pro camera test

• The best camera phone 2026 - TechRadar

• How to choose a new phone camera as a non-geeky photographer

• iPhone 17 camera lenses explained

Recently, I was watching a podcast featuring . The podcast involved discussions around the current state of the global economy.

One idea in particular that caught my attention was the little treat economy.

The little treat economy refers to the idea that when bigger purchases start feeling out of reach, people can shift their spending toward smaller things they can still afford.

Something that feels manageable and gives you a little bit of enjoyment without committing to a major expense. That got me thinking about tech because we’re surrounded by products designed to be easy to buy.

That might include a desk gadget you saw on TikTok or maybe another subscription because the device you already bought needs one to unlock everything it can do.

None of these purchases have to be particularly expensive on their own.

The problem starts when thousands of small purchases become a normal part of how we consume technology.

At the same time, creators have built entire content ecosystems around recommending these products.

Short videos make gadgets look cool and worth buying before we’ve had much time to ask whether we actually need them.

That creates a cycle I want to look at in this piece: How did everyday technology become so easy to impulse buy? And how can we become more deliberate about the technology we bring into our lives?

The Business of Buying More Tech

When Tech Becomes Impulse Buying

Walk through any social media platform for long enough and you’ll eventually hit the same type of content. Desk setups. Everyday tech (EDC) videos. A new device that supposedly fixes a problem you didn’t know you had.

There’s nothing inherently wrong with recommending products. The problem is the business model sitting underneath much of this content.

Affiliate links give creators a financial reason to turn attention into sales. If someone clicks a link and buys the product, the creator can earn a commission. That also changes what the content is optimized for.

A 2025 study of social commerce found that social proof, including the number of likes and comments around a product, can increase consumers’ impulse buying behavior.

That matters because tech is particularly easy to package this way. A 30-second video can show the product, demonstrate what it does, make it look good on a desk, and put the purchase link a few taps away.

The waste problem gets much bigger when you look at the numbers.

E-waste is discarded electrical and electronic equipment, including products that run on a plug or battery. In 2022, the world produced 62 million tonnes of it, according to the UN’s Global E-waste Monitor. Less than a quarter, 22.3%, was formally collected and recycled.

Small devices made up about one-third of that total, or 20.4 million tonnes. Only 12% of that category was formally collected and recycled.

That category includes things such as toys, small appliances, e-cigarettes, and other compact electronics. The small tech gadgets filling online storefronts and creator videos sit within this wider pattern of short-lived electronics.

The result is a market where the incentive can be to sell the product first and worry about how long it lasts later. A gadget doesn’t need to become someone’s favorite device for 5 years. It just needs to get enough attention to generate a purchase.

And that brings us to the whole trust problem.

When Product Recommendations Become Advertising

The FTC requires influencers to disclose material connections with brands, including financial relationships such as affiliate commissions, free products, and other forms of compensation. The disclosure is supposed to be clear enough that people can understand the relationship before making a decision.

A recent GoPro controversy shows why that matters.

YouTuber Markiplier became GoPro’s largest individual holder after accumulating an 8.5% stake in the company. Around the same period, he was also making content about GoPro products. His GoPro review disclosed the sponsorship, but the size of his personal investment was not disclosed to viewers at the time. The investment only became public through his SEC filing.

That disclosure is important because it allows a viewer to understand that a video is sponsored and that the person recommending the product owns millions of dollars worth of the company.

I’ve previously written about this problem in my piece on influencer marketing. In the post, I looked at how affiliate marketing works and why disclosure has become harder for audiences to evaluate:

The bigger problem is that product recommendations increasingly sit somewhere between advertising and personal advice. A “five gadgets I can’t live without” video can look like a genuine recommendation. But if the creator earns money from those products, or has another financial relationship with the brand, that context changes how the recommendation should be understood.

When Big Purchases Feel Out of Reach

Why Small Purchases Feel Easier

This brings us back to the idea that started this article.

When bigger purchases feel out of reach, spending can shift toward things that are easier to afford.

Economists have used the lipstick effect to describe a similar pattern. The idea is that during periods of economic pressure, people may cut back on expensive purchases while continuing to spend on smaller luxuries that provide some immediate satisfaction.

The current version of this idea has become the little treat economy.

A 2026 SurveyMonkey study found that 62% of Americans buy small, affordable treats at least once a month, with 52% of those people keeping each purchase to $25 or less.

That helps explain why a $30 gadget can feel very different from a $1,000 purchase.

The gadget is small enough to justify. You don’t have to save for months or make a major financial decision. You see it, like it, and the price feels manageable.

Tech fits this pattern particularly well.

None of these purchases looks significant on its own, but the problem is what happens when they become normal.

The Diderot Effect: When One Purchase Leads to Another

I came across another concept while reading Atomic Habits that made me think about this differently. It was called the Diderot Effect.

The idea is that buying one new thing can make the things around it feel out of place, which creates a reason to buy more.

The concept comes from the French philosopher Denis Diderot. After receiving a beautiful scarlet dressing gown in the 18th century, Diderot found that everything he had suddenly looked soulless beside it. He ended up replacing other possessions to match the new robe.

Now apply this same idea to tech.

You build a gaming PC. Then your monitor looks old next to it. So you upgrade the monitor. The new monitor makes the desk look dated. You buy a new desk. Then the setup needs better cable management and RGB lighting. Your old keyboard and mouse suddenly don’t fit the look.

One purchase creates a reason for the next one.

Software can create the same loop.

The point is, the cycle may start with a cheap purchase. But it might end with something much larger and a hole in your wallet.

The Subscription Economy

When Software Became a Subscription

A lot of software used to be something you bought once.

But things don’t work like that anymore.

Adobe now sells Photoshop as a subscription starting at $22.99/month for its single-app plan. Microsoft still sells Office as a one-time purchase, but its main Microsoft 365 products now use recurring payments, with Microsoft 365 Personal listed at $9.99/month or $99.99/year.

The same model has spread across cloud storage, streaming services, AI tools, productivity software, and plenty of other parts of everyday tech.

The individual payment can feel small. That’s part of what makes the model work.

A C+R Research survey found that consumers estimated they spent an average of $86/month on subscriptions. When they went through their actual recurring payments, the number was $219. That is a whopping $133 difference every month. The survey also found that 74% of people said it was easy to forget about recurring subscription charges.

Similarly, another survey from Self Financial found that respondents had an average of 3.4 active paid subscriptions, while 59.9% said they had at least one paid subscription they weren’t using.

The numbers show how easy recurring payments can become background expenses.

And now the model has moved beyond software.

When Hardware Became a Subscription

Some companies are putting recurring payments between you and features on physical products you already bought.

Ring, for example, requires a subscription if you want to review recorded video from its cameras and doorbells. Without one, you can still view live video and respond to alerts, but recorded events aren’t available.

WHOOP takes the idea further. Its hardware is built around an ongoing membership. Cancel the membership and the device stops collecting and analyzing your biometric data.

HP’s All-In printer plan goes even further. HP owns the printer while you’re subscribed, and if you cancel, your ability to print ends and you have to return the printer.

There are so many other examples as well. All of this changes what ownership even means.

I’ve written about this in more depth in The Death of Ownership, where I looked at how more of the products we use are becoming services we rent rather than things we fully control:

How to Avoid Buying Tech You Don’t Need

A quick disclaimer: We are not affiliated with any of the products or companies mentioned here, and aren’t being paid to recommend or criticize them. These examples come from our own research, experience, and the sources I’ve looked at while writing this piece.

Things That Should Make You Pause

A few things should make you stop before buying.

If a hardware product loses important functionality when you cancel the monthly fee, look at what you’re actually paying for. If the product depends heavily on a subscription to remain useful, it might be worth looking for alternatives that don’t.

The same goes for the company behind the product. If a brand appeared 6 months ago and most of its visibility comes from a sudden wave of influencer videos, take a closer look before buying. Look for independent reviews, long-term user experiences, warranty information, repair options, and signs that the company plans to support the product after launch.

One person recommending a gadget can be useful. 20 creators posting about the same product in the same week should make you curious about the marketing behind it. Look for actual testing and independent opinions rather than the same talking points repeated across dozens of videos.

Another good question to ask is whether you need the hardware at all. If a free alternative already does the same thing, spending money on another device might not make much sense.

The Brick is a good example. It’s part app and part physical device, and one doesn’t work without the other. The device costs $59 and is designed to help people block distracting apps. But similar functions can already be handled through free software tools on a phone, with more flexibility and no additional piece of hardware.

The Loftie Alarm Clock has a similar issue. The hardware costs around $200, while its Drift app-blocking feature requires a $10 monthly subscription. If the main reason you’re buying the device is the app-blocking function, free software alternatives can achieve something similar.

What Makes a Gadget Worth Buying

The opposite side is easier to explain.

Look for products with clear specifications, repair options, long-term software support, and companies with a track record of supporting their hardware. If the device works fully without an account, an app, or a monthly fee, that’s another good sign.

TRMNL is an interesting example. It is an e-ink device designed to display information from different services on a low-power screen. Its open-source approach also gives users more control over what the device displays and how they interact with it.

The same idea applies to more ordinary hardware.

I’ve used a Logitech G502 mouse and Razer BlackShark headphones for my own setup, and both have been good purchases for me. They weren’t products I bought because I saw one short video telling me they were the next must-have gadget. I researched them, compared alternatives, and bought them because they made sense for what I actually needed.

In short, whenever you are looking to use a product or software, that usually means asking a few questions like:

• Does something I already own do the same job?

• Will it still be useful in a few years? Does it require a subscription?

• Can I repair or replace parts if something breaks?

• What happens if the company shuts down the service?

• Am I buying it because I need it, or because I’ve seen it 15 times this week?

Buy on Purpose, Not on Impulse

The little treat economy is only one part of the bigger picture. Tech has become incredibly easy to buy, whether that’s a subscription, or a physical product that comes with its own ecosystem of accessories and services.

None of that means you need to stop buying tech. I have a lot of gadgets myself, and I’ve spent a decent amount of money building my setup over the past year. My mouse and headphones are just two examples that I mentioned. I have plenty of other stuff too, but I didn’t want to turn this piece into a list of everything sitting on my desk.

The products and red flags I mentioned throughout this section are also only examples. The bigger point was to give you a framework for thinking before you spend your money.

And if you’ve done your own research but still aren’t sure about a purchase, you can always ask me, , or for a second opinion.

We all spend a fair amount of time looking at tech, and we’re happy to give you our take if you’re trying to decide whether something is worth buying.

With that being said, thanks for reading. I hope you enjoyed this piece.

See you in the next one.

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In 2014, Facebook paid $2 billion for a company with roughly 75 employees.

That company had not shipped a single consumer product.

The founder was 21 years old. Its most famous product was a duct-taped prototype for a VR headset.

Fortunately, a video-games legend introduced that headset to the biggest gaming conference in the world.

And that’s when Mark Zuckerberg wrote that check.

This is the story of how Oculus revolutionized VR. It is the story of Palmer Luckey, a homeschooled kid who built over 50 VR headsets in his parents’ garage.

I’ll also talk about what happened after the check was cashed. The backlash and Palmer’s firing.

And lastly, we have to talk about Meta's billion-dollar losses in the metaverse.

Building Headsets in a Garage

Palmer Luckey’s story is as interesting as the tech he has worked on.

He was born to a car salesman. His mom homeschooled all 4 of her kids. Palmer was the only one who never went back to regular school. He liked the flexibility too much.

From an early age Luckey was obsessed with electronics. He built high-powered lasers in his garage. He built coil guns.

He once electrocuted himself touching a grounded metal bed frame. He burned a gray spot into his vision cleaning an infrared laser.

Normal kids stuff, clearly.

To fund his experiments he fixed and resold broken iPhones. He worked as a groundskeeper and a youth sailing coach. He earned at least $36,000 this way before he was old enough to vote.

But his real obsession was virtual reality.

Luckey had spent years collecting old VR headsets. He had one of the largest private collections of VR hardware in the world at a time when he was a teenager living with his parents.

He had seen what VR was supposed to be and he was not impressed with what was available.

Every headset he owned had similar problems. They were heavy, expensive and didn't immerse you in a world as a good headset should.

So he decided to build his own.

Starting around 2009, when he was 16 or 17, Luckey began prototyping in the garage. He had a simple idea that most in the industry weren’t paying that much attention to.

Smartphone screens were getting really good really fast. They were getting sharper, brighter, and cheaper. What if you used one as the display inside a VR headset?

Then he had another idea. Instead of using expensive, bulky optics, what if you used cheap lenses and fixed distortions in software?

Both ideas together meant you could build a VR headset that was lighter, cheaper, and had a much wider field of view than anything else on the market.

For those who don’t know, the Field of View or FOV is how wide you could see inside a VR headset. A higher FOV means you’d feel more immersed.

Over the next few years Palmer built prototype after prototype. Over 50 of them in total. Each one refining his techniques a little more.

By his sixth generation prototype he had something he called the Oculus Rift. It had 3D, low-latency head tracking, and a 90-degree field of view.

Nobody outside a small VR enthusiast internet forum called MTBS3D had heard of it, but an interesting person was about to join the forum.

What Oculus Actually Was When It Started

In early 2012 Luckey was posting updates on MTBS3D, the niche online community where VR nerds compared notes and argued about specs.

Read more

Some days ago, I was watching one of Linus Tech Tips’ recent videos reviewing Samsung’s Galaxy Book 6 enterprise edition. During the video, Linus also demonstrated Intel vPro, an enterprise platform used by many organizations to manage company-owned computers.

What caught my attention was seeing just how much control administrators could have over a device.

Just using a central dashboard, an IT department could remotely wipe a laptop, deploy software across an entire fleet of computers, troubleshoot devices even when the operating system wasn’t working properly, and enforce security policies without ever physically touching the machine.

If a normal person watches something like that, they’ll probably think, “That’s pretty useful for IT departments.”

But I had a different thought: if you use a work laptop, there’s a good chance your organization has access to many of the same management capabilities.

And that’s what made me write this post.

Many people think of a work laptop as “their laptop” because they’re the ones carrying it around every day. In reality, a work laptop is a part of a centrally managed system designed to protect company data and allow IT administrators to manage hundreds or even thousands of devices at the same time.

The goal of this article is simply awareness.

Once you understand how modern device management works, you’ll probably look at your work laptop a little differently.

What a Managed Laptop Is

To the human eye, a personal laptop and a company-issued laptop can look almost the same because they both run Windows or macOS. They open the same applications and connect to Wi-Fi in the same way.

But underneath the hood, they can be completely different devices.

Most medium and large companies no longer configure computers manually. They don’t have enough time or resources to install software, update security settings or recover stolen laptops across thousands of employees.

Instead, companies rely on different kinds of enterprise device management platforms.

For example, Microsoft Intune allows administrators to configure devices and wipe company data remotely when necessary. Similarly, Microsoft Defender for Endpoint focuses on monitoring devices for security threats and helping companies respond to incidents across their entire fleet.

Other companies follow a similar path as well, just in a different way.

Organizations running Apple devices commonly use Apple Business Manager alongside Mobile Device Management (MDM) platforms such as Jamf. Samsung offers Knox Manage for enterprise device management. Intel provides vPro for hardware-level administration.

Exactly what an administrator can do depends entirely on how an organization has configured these platforms. Some companies enable only basic security features. Others implement far more comprehensive management policies. The level of visibility and control varies significantly between organizations.

That brings us to another important point: using a managed laptop doesn’t automatically mean someone is monitoring everything you do. Those capabilities are entirely dependent on the company’s policy and the management tools being used.

Common Myths About Work Laptops

By now, you probably understand that a work laptop is different from a personal one, but where most people get confused is how they use it.

Many employees assume that using another browser or creating a separate Windows profile somehow turns part of the laptop into their own space.

But enterprise management doesn’t usually operate inside a single browser or user profile. It exists at the device level, where operating system settings, security policies and management software all work together.

That doesn’t mean your employer can automatically see everything you do, but no matter what you do, your company will always own the laptop and will have the upper hand.

Anyway, let’s bust some common myths about work devices.

Myth #1: “I’ll Just Use Another Browser”

One of the most common misconceptions is that switching from Microsoft Edge to Brave or to another browser somehow bypasses company management.

Changing browsers doesn’t change who owns or manages the device.

Organizations can apply policies that affect multiple browsers through enterprise management platforms, installed certificates, DNS settings, VPN configurations, or compliance requirements. Many browsers, including Chrome and Microsoft Edge, also support enterprise policies that allow organizations to configure security settings and other features centrally.

Myth #2: “Incognito Mode Hides Everything”

The good old Incognito mode is one of the most misunderstood features in modern browsers. It was never designed to make browsing invisible or keep you anonymous.

Its primary purpose is to avoid storing browsing history, cookies, cached files, and session information on the local device after you close the window. That’s useful if you’re sharing a computer or don’t want your own browser history saved.

Read more

In Part 1 of the series, we learned how the creator economy was built on the idea that content itself was the business. Views turned into ad revenue, ad revenue turned into a living, and the work stood on its own.

But Part 2 showed why that model is under pressure, why platform revenue has become less reliable, and why trust has become harder to measure.

This piece looks at what creators are doing about it, and whether any of it addresses the deeper problem or just works around it.

What Creators Are Doing

Ad revenue has gotten less predictable. Platform reach has gotten harder to count on. AI has made content cheaper to produce at scale, and manufactured trust has made it harder for audiences to tell a genuine recommendation from a coordinated one.

That’s the environment creators are operating in right now, and it’s pushing them in two directions. Some are moving closer to traditional business by building physical products around their audience. Others are focusing on direct relationships that reduce how much they depend on any single platform.

Neither approach removes the underlying problem. But both show how creators are adapting to a system that’s gotten harder to rely on.

Let’s take a look at both of these approaches that creators are using to survive in today’s economy.

Approach 1: Building Businesses Beyond Content

One of the clearest responses to declining content value has been creators moving into physical products.

Digital content can now be produced and copied at a scale that makes it harder to hold value, but physical products don’t work that way. They require manufacturing, supply chains, and real investment, which makes them harder to copy and easier to defend.

For creators with large audiences, that difficulty becomes the opportunity. Years of built-up attention becomes the distribution channel for a product business.

MrBeast launched Feastables, a chocolate brand built around his audience, in January 2022. Feastables generated $250 million in revenue and $20 million in profit in 2024, which made a chocolate bar brand more profitable than the largest YouTube channel on the planet.

Similarly, Logan Paul and KSI’s Prime Hydration shows how quickly a creator audience can turn into a consumer business. Bloomberg reported that Prime was on pace to surpass $1.2 billion in sales in 2023, up from $250 million in its first year.

But the growth did not last. The story is useful because it shows both the upside and the limits of turning audience attention into a product business.

Read more

I was watching the WAN Show a while back when Linus Sebastian shared this cool thought.

He was talking about Valve’s new Steam Machine, and he made a point nobody else was really making.

See, everybody keeps comparing this thing to the PlayStation and the Xbox. Linus thinks the real rival is the Mac Mini.

That sounds a little crazy at first. A gaming box from Valve going up against Apple’s tiny work computer?

But the more he explained it, the more it clicked for me. And I want to build on that idea today.

Here is the short version. Judge the Steam Machine as a console and it looks bad. Too pricey, too weak.

But judge it as a small, quiet computer that one company built from the chip all the way up to the software, and it suddenly stands almost alone.

The only other famous box that works this way is the Mac Mini. So let’s talk about why these two are secretly rivals, and what this means for the future of desktop computers.

Everybody Reviewed the Wrong Machine

Console pricing works on a trick. Sony and Microsoft sell them at a loss.

They lose money on every PS5 and Xbox that leaves the shelf. They earn it back later by taking a cut of every game you buy. The console is basically bait because the games are where the real money is.

Valve does not want that for Steam Machine. They plainly said in their FAQ that the Steam Machine is priced like a PC, not like a console.

There is no selling it cheap now and clawing the money back later, because Valve does not need to. It already prints money from the Steam store itself.

So when people line the Steam Machine up next to a PS5 on price, they are comparing a computer sold at its true cost against a console sold at a loss. Of course the console looks cheaper.

The moment you stop thinking console and start thinking small desktop computer, everything shifts. And there is really only one famous small desktop that is built the way this one is.

What Valve Actually Built

Let’s cover what the Steam Machine actually is first.

It is a little cube, roughly 6 inches on each side. Valve calls it Steam Machine, the community calls it the GabeCube, after Valve boss Gabe Newell.

It launched on June 29, 2026, starting at $1,049 for the 512GB version.

Inside sits a custom AMD chip built to Valve’s spec. You get a 6-core processor based on AMD’s Zen 4 design that runs up to 4.8 GHz, paired with a graphics chip based on AMD’s RDNA 3 architecture.

The GPU has its own 8GB of video memory. There is 16GB of regular RAM too, and you pick either 512GB or 2TB of storage. It runs Valve’s own SteamOS.

Valve says it is about 6 times more powerful than the Steam Deck, their handheld. The goal is 4K gaming at 60 frames per second, though it leans on a trick called FSR to get there.

FSR is basically smart upscaling. The chip draws the game at a lower resolution, then cleverly fills in the detail so it looks close to 4K without doing all the hard work.

One genuinely nice touch is that you can open this thing. It has a slot to add storage and two slots to add memory, and Valve sells official replacement parts through iFixit.

Even Linus pointed this out in his video and it matters a lot in this comparison as you will see.

Did you know Valve just opened up their SteamOS for anyone to build their own Steam Machine?

Here’s my linked article where I turned my ThinkPad into a Steam Machine in less than 60 minutes.

The Vertical Integration Magic

Vertical integration just means one company makes the whole thing instead of splitting the work. The hardware, the box, and the software all come from the same place, built to fit together.

Think about a normal Windows PC.

Microsoft makes Windows. AMD or Intel makes the chip. Some other company like Dell builds the actual computer.

That is three different companies, none of them really talking, all their parts meeting inside your machine and hoping to get along.

That’s why Windows PCs can feel janky sometimes. A driver breaks. A setting fights you. Two bits of software disagree.

Nobody owns the whole thing, so nobody can make it all just work in a perfect way.

When one company controls everything, they can tune it tight. The software knows the exact chip it is running on. The power settings are built for that exact hardware.

Stuff just works, because one team made sure it does. And this is the real reason the Steam Machine is interesting.

Valve designed the chip, wrote the OS, made the interface, and controls the updates. That is a purpose-built appliance, not a generic beige box.

And there is only one other company known for doing this in a tiny desktop.

The King of Vertical Integration

You already guessed it. It’s Apple.

Look, Apple is the poster child for controlling the whole stack.

Ever since it started making its own chips, called Apple Silicon, the Mac runs on hardware Apple designed, with software Apple wrote, in a box Apple built. Everything from one house.

That is a big reason Macs feel smooth and last so long on battery. The software and the chip were made for each other. No third parties means no guessing games.

The Mac Mini is the purest version of this idea. It is just the computer. A small square box, no screen, no keyboard, no mouse. You bring your own.

It is Apple’s whole approach shrunk down to the smallest, cheapest Mac you can buy.

So here is where we land.

On one side, the Mac Mini, one company controlling everything inside a closed Apple world. On the other, the Steam Machine, one company controlling everything but built on open parts.

These two boxes have a similar idea but opposite personalities. That is why they belong on the same shelf, and it is a shelf with almost nothing else on it.

Now let’s compare the two “boxes.”

Vertical integration is what got us arguably the best value laptop of this decade.

Here’s a link to my article where I discuss the MacBook Neo and how it changed the laptop game for us all.

The Price Argument

We just can’t ignore price because it is the roughest part for Valve.

The base Mac Mini with the M6 chip starts at $899 (once it ships this month).

For that you get Apple’s M6 chip, 16GB of memory, and 256GB of storage, in that tiny 5-inch square. It is still one of the best value computers you can buy.

The Steam Machine starts at $1,049. That is about $150 more than the Mac Mini before you even start.

The funny part though:

The base Mac Mini used to be even cheaper. It went for around $599 until May 2026. That’s when Apple killed off the cheapest version and bumped the entry price to $799.

Then later in August of this year, Apple announced the Mac Mini with the M6 chip and it has gone up $100 in price. It will ship this month.

In a way, the Mac Mini had its price bumped two times just this year.

When a company like Apple has to raise prices in that way, you know things are getting bad!

If you read my article on RAM crisis, I mentioned Micron leaving the consumer memory business and warned that RAM prices were about to punch everyone in the face.

Well, that was the punch. Both Valve and Apple raised prices in 2026 for the same reason. Nobody escaped it.

To be fair to Valve, you are not being ripped off.

GamersNexus priced out building an equal box from parts and landed around $979, so Valve’s $1,049 is close to what a similar machine would cost you to build yourself.

You pay a small premium for something tiny, quiet, and pre-built. But close to fair is not the same as cheap.

And against an $899 Mac Mini, the Steam Machine has to earn that extra cash.

SteamOS Is Secretly a Really Nice Desktop

Here is the part that genuinely surprised people, and it is the most interesting bit of the whole story.

SteamOS, the software on the Steam Machine, is not just a game launcher.

Underneath, it is a full desktop operating system just like Microsoft Windows.

Unlike Windows, though, it is built on Linux, the free and open-source system that quietly runs a huge chunk of the internet’s servers.

On top of that Linux base sits a desktop called KDE Plasma, which looks and behaves a lot like Windows, with a taskbar, windows, and a menu.

Getting to it is easy. You tap a reboot to desktop button, wait a few seconds, and the gaming interface drops you into a normal desktop where you can browse, write, and do computer stuff.

This is the part Linus fell in love with, and his examples say it best.

His oddball high-resolution monitor was detected right away. The settings menus are simple instead of buried. Mouse pointer acceleration is turned off by default, which is exactly what you want.

The biggest thing is that there is no bloatware shoved in your face and no pop-up nagging you to buy a subscription.

And this last part is the quiet killer.

Windows in 2026 keeps pushing ads and pop-ups at you inside your own computer. And plenty of people are sick of it.

Linus put it perfectly. He said a product you pay for should feel comfortable out of the box, and he is right.

SteamOS feels like software that respects you and gets out of your way. That is the whole job of an operating system. When an OS does its job right, you won’t know about it.

Apps come through a system called Flatpak, which is basically an app store for Linux that keeps each program tidy in its own box. Chrome, VLC, OBS, a calculator, the usual stuff.

It is not as stuffed as Windows or Mac for random software, but for everyday needs it covers a lot.

The modern SteamOS wouldn’t exist if it weren’t for a revolutionary piece of hardware. I’m talking about the Steam Deck.

Here’s my linked article that goes over how Steam Deck revolutionized handheld consoles and got us an alternative to Windows.

The Immutable OS Thing Explained

SteamOS is what people call an immutable OS.

In plain terms, the core of the system is locked, and it resets itself back to normal every time you reboot. Install something the normal way and it survives. Install it outside the normal system, and a reboot wipes it.

Your files and games stay where they are, but deep changes to the system itself get wiped.

The upside is that it’s pretty hard to break SteamOS. On a normal computer, you can mess up one setting or delete the wrong file and suddenly nothing boots.

On SteamOS, if things go sideways, a restart puts the core back the way it should be. For a regular person, that is fantastic. It is really hard to shoot yourself in the foot.

The downside shows up if you like to tinker.

Linus talked about this too: he installed a small app outside the normal Flatpak system to get an audio device working, and after a reboot the whole thing reset and stopped.

That is the tradeoff SteamOS makes, and for most people it is the right one. For tinkerers, it is the one thing to know going in.

Rough Edges Around the Cube

I love Valve but I won’t oversell the Steam Machine, so let’s talk about its rough edges.

The biggest miss is the ports.

The Steam Machine does not have USB4 or Thunderbolt, which are the fast connections that let you run a whole desk setup, monitor and drives and everything, through a single cable.

Its fastest USB-C port is quick at 10 gigabits per second, but it cannot even output video to a monitor.

It also uses regular Gigabit Ethernet instead of the faster 2.5 gigabit kind, which can be an issue for some.

Then there are the small Linux annoyances that pop up in daily use.

Pulling video files off an iPhone turned into a real headache, mostly thanks to how Apple locks things down, and Linus ended up doing the transfer on Windows instead.

And middle-click auto-scroll, the handy trick where you click the scroll wheel to glide down a long page, just does not work properly on SteamOS yet.

None of these are dealbreakers on their own though. But stack them together and they remind you this is a gaming-first machine wearing a desktop hat, not just a work computer.

The Mac Mini Isn’t Perfect Either

Just like the Steam Machine, the Mac Mini has its own rough edges.

Its big issue is that you cannot upgrade it at all.

The RAM and storage are soldered in at the factory. Whatever you buy on day one is what you have forever. There is no popping it open to add more later like you can with the Steam Machine.

And Apple has historically charged brutal prices to bump those specs up front.

Want more storage or memory? Apple’s upgrade pricing is famously steep, and it pushes that $899 starting price up fast.

The Steam Machine, with its openable case and swappable parts, clearly wins on repairability and living a long life.

The Mac Mini is also a closed world. You run macOS, and that is that. Apple does not give you an escape hatch.

The Steam Machine is the opposite. You do not like SteamOS? You can wipe it and install Windows, and Valve is completely fine with that. That freedom is a real point in its favor.

And of course, the Mac Mini basically cannot game. For a lot of buyers that does not matter one bit. For others it is the whole reason they showed up.

More Choice for the Rest of Us

This is the crux of this whole discussion.

For years, if you wanted a computer, you basically had two choices.

1. Buy into Apple’s world, where everything is smooth but closed and you play by Apple’s rules.

2. Buy a Windows PC, where you get freedom but also driver issues, the bloat, and now the ads.

The Steam Machine quietly adds a third door.

It takes Apple’s best idea, one company controlling the whole stack, and builds it out of open parts instead of a walled garden.

As an end-user, you get the smoothness of a tightly built machine and the freedom to crack it open and do what you want.

Both Valve and Apple decided the way to make a great small computer is to control the whole stack.

Apple did it with a closed system. Valve did it with an open one.

And for the first time, there is a real desktop that is neither Windows nor Mac, one that a normal person can just plug in and use.

Linus called 2026 the year of the Linux desktop, and watching the Steam Machine, it is hard to argue.

The point isn’t a comparison between the Mac Mini and the Steam Machine.

It is that the choice finally got wider for the end-user.

And more options means more competition, which is always a good thing for all of us.

If you liked reading through this free article, a share to someone as curious as you would be really cool. Just use the blue button below:

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Further Learning

• I Love The Steam Machine More Already - WAN Show June 26, 2026

• Valve Accidentally Made the Best Desktop PC I’ve Ever Used

• Valve Steam Machine Review: GPU & CPU Benchmarks, SteamOS Test, Thermals, Noise, and Price

• Building a PC to BEAT the STEAM Machine!

• Valve Steam Machine, Desktop SteamOS, Steam Frame VR, & Controller

• Even Valve is Disappointed - Steam Machine Review

• The Steam Machine is the most ambitious game console I’ve ever played

• The Steam Machine fits my TV, my desk, and my life

• The Steam Machine is actually a Mac mini competitor, but not in the way you think

• Valve explains why it isn’t subsidizing the Steam Machine

• Apple raises the Mac Mini’s starting price

• Apple was ready for the RAM crisis

• Apple to Raise Prices Due to Memory Chip Crunch, Tim Cook Says

• iPhone Prices to Go up as Memory Chip Costs Keep Climbing

• Immutable operating systems are a blessing and a curse

• Apple’s new Mac Mini has fresh M6 and M5 Pro chip offerings — and higher prices

• Apple’s latest Mac Mini runs on a new M6 chip, and starts at $899

In Part 1, we looked at what the Creator Economy is, how it emerged, and why it became a genuine alternative to traditional media.

For many people, the model worked. Independent writers, filmmakers, and countless others built businesses around their work without relying on publishers or broadcasters.

But the story doesn’t end there.

Over the past few years, the Creator Economy has started changing in ways that many people don’t fully notice. Some of those changes come from platforms themselves. Others come from changes in technology and how people discover information online.

In this article, we’re going to look at some of the biggest challenges creators face today, why the economics have become more difficult, and how artificial intelligence is adding another layer to an already changing system.

Quick Note: Part 1 is free and open to everyone, so if you're just getting here, start there. Parts 2 and on are for paid subscribers only.

With that being said, let’s get started.

Why the Model Was Already Under Pressure Before AI Arrived

If you read the previous part, you’ll remember that the Creator Economy wasn’t a perfect system. It gave creators independence from traditional gatekeepers, but it also introduced and increased the dependency on the platform itself.

Creators built audiences on infrastructure they didn’t own, under rules they didn’t control. For years, that tradeoff made sense because the reach was there and, for many creators, the income followed.

The problem is that platforms don’t stay the same.

One of the biggest changes happened gradually, through recommendation algorithms.

Early social media largely showed people content from accounts they chose to follow. Over time, that moved toward recommendation systems designed to maximize engagement instead. Facebook reduced the reach of Pages throughout the 2010s. Instagram replaced its chronological feed with an algorithmic one in 2016. YouTube increasingly relied on recommendations rather than subscriptions to determine what people watched.

Subscribers and followers still mattered, but they no longer guaranteed distribution.

That meant creators could spend years building an audience and still watch their reach fluctuate because of decisions made by a platform.

Jack Conte, the co-founder and CEO of Patreon, has described this dynamic by saying creators are building businesses on land they don’t own. It’s a useful way to think about the relationship. The audience may belong to the creator, but access to that audience is still mediated by the platform.

I’ve also covered this broader pattern in my article on enshittification. The same incentives that gradually change platforms for users also affect the people building businesses on top of them.

Let’s take a look at the major issues in depth to understand what’s really going on.

Issue 1: Platform Revenue Became Less Reliable

For many creators, advertising was never the only source of income. It acted more like a foundation that made everything else easier. Over time, that foundation became less predictable.

Read more

Sometime ago, I was reading a blog post that was related to football. But turns out the topic moved from football into something completely different.

It mentioned Ronaldo’s YouTube channel growth and how quickly it scaled after launch. That comparison then drifted into MrBeast, PewDiePie, and the bigger question of how attention moves once a large audience enters a platform.

A creator who already has global reach joins a platform built around individual discovery. As a result, existing audiences follow, algorithms amplify it further, and smaller creators operating in the same space lose relative visibility.

That led me back to a bigger pattern that has been building for years, the creator economy and how distribution works when attention becomes concentrated.

I had already written about influencer marketing and how trust functions as a form of value online. This is an extension of that thinking, focused less on marketing and more on structure behind that economy.

The goal here is to break down what the creator economy actually is, how it formed, and why the system behaves the way it does.

What Came Before the Creator Economy

Before 2007, independent creators already existed online.

Blogs, early video uploads, and podcasting communities had built audiences without relying on traditional media. Some blogs in the early 2000s reached large readerships, but the systems to support that attention were inconsistent.

At the same time, monetization existed, but it was uneven.

Google AdSense, introduced in 2003, gave publishers a way to display ads on their websites. It worked, but revenue per user was low unless traffic was substantial. Many early creators relied on a combination of small ad income, affiliate links, and direct sponsorship deals, often arranged individually with brands.

Same goes for podcasting: around 2004 and 2005, podcasts started to grow, but most early shows had no built-in monetization. They operated outside of formal revenue systems and people simply produced them because the medium was new.

Around the same time, YouTube got launched, but was acquired by Google in 2006. For its early years, creators uploaded content without expecting direct payment. That changed in 2007 with the YouTube Partner Program, which introduced revenue sharing for creators for the first time at magnitude.

Before that point, creating online content was closer to publishing as a hobby or side activity. Some individuals turned attention into opportunities later through books, consulting, or via some other work, but there was no consistent system connecting audience size to income.

What is the Creator Economy

Where the Term Came From

YouTube started using the term “creator” around 2011 as a replacement for “YouTube Star” It was a way to describe people building large audiences on the platform without relying on traditional celebrity structures.

But “creator” and “creator economy” are not the same idea.

“Creator” referred to the individual, while the “creator economy” refers to something broader, a system where millions of individuals can earn income through content, audience building, and platform-based distribution.

That second shift, from individuals to an economic category, only became widely established years later.

It took hold around 2020, when venture capital firm SignalFire published its Creator Economy Market Map. The report estimated more than 50 million people globally identified as creators and framed the space as a distinct, fast-growing economic category. From there, the term started being used more consistently across platforms and media coverage.

And the reason the term matters at all is because creators didn’t fit into existing categories. They weren’t traditional journalists or media professionals working inside institutions. They were individuals building media businesses around identity, content, and audience, without relying on institutional distribution.

A new category was needed because the old ones didn’t describe what was actually happening.

How Big is the Creator Economy

Estimates of the creator economy vary depending on what is included, but most large-scale analyses place it in the hundreds of billions of dollars.

Goldman Sachs estimated the creator economy at around $250 billion in 2023 and projected it could reach roughly $480 billion by 2027. Their research also suggested there are around $50 million creators globally, with most revenue concentrated in brand partnerships and sponsorships.

But the distribution of that income is uneven.

According to Linktree’s 2022 Creator Report, based on a survey of 9,500 creators, only a small share earn consistent full-time income from their work. Around two-thirds of creators describe it as a side activity. Even among full-time creators, only about 12 percent make more than $50,000 per year.

Separate estimates from Goldman Sachs indicate that only a small fraction of creators, roughly a few percent, earn above six figures annually.

The magnitude of the creator economy is large and the number of participants is even larger, but the income, however, is concentrated at the top.

What Made the Creator Economy Global

Media production used to be tied to geography. Film, publishing, and television were concentrated in a small number of cities, and getting into those industries required institutional access, or personal connections.

That changed once distribution moved online. A smartphone and an internet connection became enough to reach a global audience without institutional approval. Platforms like YouTube, TikTok, and Instagram removed the need for traditional intermediaries, while tools like Substack enabled direct publishing and monetization across borders.

Creators in South Asia, Southeast Asia, Africa, and Latin America started building audiences directly through these platforms. In many of these regions, mobile phones were the primary way people accessed the internet, which shaped both how content was produced and how it was consumed.

Distribution alone doesn’t explain the shift, though. The cost of creation collapsed at the same time. In the early 2000s, producing video or audio required expensive equipment, editing software, and technical expertise, which limited who could participate at scale.

By the mid-2010s, smartphones could record and edit high-quality video on their own due to the launch of free software products, which played a massive role in lowering the barrier for creators to work.

Internet infrastructure expanded alongside these tools, mobile data got cheaper, and upload speeds improved. In many regions, the smartphone became the primary gateway to the internet, reinforcing the shift toward mobile-first content creation.

In short, geography stopped restricting who could reach an audience. Cost stopped restricting who could produce something worth watching. Technology removed both barriers at once, and that combination is what actually made the creator economy possible.

How Removing the Middleman Created Independence for Creators

Before the creator economy, making content professionally meant working through gatekeepers.

Labels, publishers, studios, and networks controlled things such as distribution, funding, and access to audiences. Getting through them took a mix of talent, timing, and connections that most people never had.

In exchange, gatekeepers provided real infrastructure. Production budgets, marketing, legal support, advance payments. But they also kept ownership and distribution rights. Creators supplied the work and kept a fraction of the value it generated.

The creator economy removed that layer. For the first time at scale, creators could produce content without institutional backing, distribute it directly, and build an audience without anyone else deciding who got access.

That’s what independence actually means here. Not complete freedom, but the removal of institutional permission as a requirement for getting your work in front of people and keeping what it earns.

Creators vs Influencers vs Celebrities: What’s the Difference

Before we proceed further, it’s good to clear a few misconceptions about some terms that get used interchangeably, when in reality, they are totally different.

Celebrities are known for work outside platforms. Their audience exists because of offline recognition that later moved online, so their reach doesn’t depend on any single platform. Dwayne Johnson also known as “The Rock” is a good example of this.

MrBeast is also another example of Celebrity, though he started out as a creator and built his audience through the internet.

Influencers build audiences to shape behavior. Their value gets measured in attention and conversion, with brand deals and affiliate marketing at the center of the model. Casey Neistat is a good example of this.

Creators, on the other hand, build an audience around consistent output, and the audience follows the work. Joe Fazer is a good example of this.

I broke all of this down in more detail in the Business of Trust, if you want the full picture of how that economy works:

How Platforms Made the Creator Economy Possible

What Platforms Actually Gave Creators

Platforms provided four core functions that individual creators could not easily build on their own.

Distribution meant access to audiences without having to build traffic from scratch. Outside platforms, this required websites, mailing lists, and tons of other things. Platforms solved this by aggregating users in one place and routing content through existing attention flows.

Discovery came from recommendation systems. Content could now reach people who had no prior connection to the creator. In traditional media, discovery depended on publishers, editors, or programming decisions. On platforms, it was increasingly driven by algorithmic matching between content and viewer behavior.

Monetization infrastructure removed the need for external setup. Payment processing, ad systems, and subscription tools were built into the platform layer. YouTube’s Partner Program, for example, standardized revenue sharing so creators received a fixed percentage of ad revenue generated on their content.

Trust was the fourth layer. Content hosted on established platforms carried more default legitimacy than content hosted on independent sites. This reduced friction for audiences engaging with unknown creators.

The Platforms That Built the Foundation

YouTube introduced its Partner Program in May 2007, initially as an invite-only system for creators who already had significant audiences. It was the first structured link between independent content creation and platform-based income.

YouTube has since paid more than 70 billion dollars to creators, media companies, and music partners over a three-year period ending in 2023. Today, over 3 million channels participate in the program.

Patreon launched in May 2013, founded by musician Jack Conte and developer Sam Yam after Conte struggled to generate sustainable income from widely viewed online content. It introduced a direct support model where creators earn income from recurring contributions instead of platform-driven advertising alone. Revenue mainly depends on subscribers, which separates creator income from algorithmic performance.

@Substack launched in 2017 and extended the subscription model to written content. The platform is still growing as of 2026, as writers and journalists are moving out of traditional media institutions and into direct publishing.

In short, each platform solved a different part of the same problem. YouTube focused on ad-driven scale. Patreon focused on direct audience funding, while Substack focused on subscription-based writing.

How Creators Make Money

Advertising and Brand Deals

Platform advertising works through CPM, cost per thousand impressions. Creators earn a share of what advertisers pay the platform based on how many views their content generates.

CPM rates vary widely depending on niche. Finance and business content tend to sit in the $15 to $25 dollar range per thousand views because advertisers value those audiences more. Gaming content is typically lower, often around 2 to 5 dollars per thousand views. This difference is one of the main reasons niche selection has direct economic consequences beyond audience growth.

Brand deals operate separately from platform advertising. In this case, a brand pays a creator directly to feature or integrate a product. Pricing depends on audience size, engagement rate, and relevance to the product being advertised.

The relationship between audience size and revenue is not linear. A creator with 100,000 highly engaged followers in a specific niche can often command higher rates than a creator with one million followers in a general category with lower engagement.

Affiliate marketing sits between these two models. Creators share tracked links and earn a commission when purchases are made. According to Linktree’s 2023 Creator Report, around 25 percent of creators report affiliate marketing as a meaningful income stream.

You can read the full influencer marketing article here for a deeper breakdown of brand deal mechanics and affiliate disclosure:

Direct Support From Audiences

Direct support refers to income that comes directly from audiences without advertisers in between. Platforms like Patreon, Substack, YouTube Memberships, Ko-fi, and similar services enable this model.

Suppose a creator with 10,000 subscribers paying $5/month generates $50,000 in monthly revenue. That income is not tied to ad rates or algorithmic reach.

This model stabilizes income because revenue depends on subscriptions. And the other good thing about this model is that, it allows creators to produce primarily for paying audiences rather than advertiser, which affects what gets made and how consistently it is sustained.

Kevin Kelly described the underlying logic in his 2008 essay “1,000 True Fans,” published on his blog and later referenced in Tim Ferriss’ Tools of Titans. He believed that a creator does not need mass audiences to sustain a living. A relatively small group of highly committed supporters can generate stable income over time.

Products and Experiences

Products, courses, merchandise, digital downloads, consulting, workshops, and live events form a revenue layer that operates outside platform monetization systems.

Creators typically sell through third-party infrastructure like Shopify or Gumroad, or through their own websites. According to Linktree’s 2022 report, around 41 percent of creators report physical or digital products as a meaningful revenue source.

This is also one of the most independent revenue streams, but product-based income depends primarily on the direct relationship between creator and audience. If that relationship is strong enough, it persists regardless of platform changes. For many established creators, content becomes the top layer of a funnel that leads into direct sales rather than being the primary product itself.

The Reality of Making Money as a Creator

The creator economy has always been structurally unequal. Understanding this matters before Part 2 introduces AI as an accelerating factor because the model was already imperfect long before AI arrived.

Income distribution in the space follows a winner-take-most structure. Reports found that only 12 percent of full-time creators earn above $50,000 per year. Goldman Sachs estimates that only around 4 percent of global creators, roughly two million out of fifty million, earn above $100,000 annually. Between those two ends sits a large group of creators with audiences, consistency, and engagement, but without stable income.

Every monetization path in the system rewards scale in some form. CPM advertising pays per view, which naturally favors high-volume channels. Brand deals scale with reach and perceived influence. Recommendation systems tend to amplify content that already performs well, making initial traction harder for smaller creators. According to data, it takes creators an average of 6.5 months to earn their first dollar, and more than ten months to become self-sustaining. Brand partnerships often appear much later, typically around the two-year mark.

Now, don’t get me wrong, the model still works, but only under specific conditions.

A niche newsletter with 20,000 subscribers charging $8/month flips the entire game. If even 10 percent convert to paid subscriptions, that produces roughly $16,000 in monthly revenue. Add a few brand deals per quarter and occasional digital products or courses, and the result is a sustainable six-figure operation without institutional backing. The key variable is not scale alone, but audience specificity and trust built over time.

Linktree’s findings support this pattern as well. Niche creators are significantly more likely to reach six-figure income levels than broad-appeal creators.

Why?

Because a creator focused on “cybersecurity for small businesses” or “personal finance for freelancers in Southeast Asia” is not competing in a saturated attention field. The audience is smaller, but more intent-driven.

The niche, trust-based model is the exception, and it’s out of reach for most creators. It requires years of consistency, a specific enough audience, and enough trust that people convert into paying subscribers. Very few get there and the conditions that make even this narrow path work are exactly what AI is disrupting, and about to disrupt even further.

What’s Changing and What’s Coming Next

The creator economy was imperfect from the start. Income distribution was uneven, platform dependence was built in, and the creator middle class always existed in a fragile position between scale and stability. Still, it worked well enough to become a viable alternative to traditional media careers.

That balance changed as the system matured.

Platforms moved from chronological feeds to algorithmic ranking systems that prioritize predicted engagement. That change weakened the direct connection between creators and the audiences they had built over time.

At the same time, ad markets became more crowded as supply of content increased faster than demand for attention which led to lower returns per view across many categories.

Then the cost structure of content creation collapsed further. Tools that separated professional production from amateur output became widely accessible. The barrier to producing “good enough” content stopped acting as a filter.

AI now adds a new layer to everything that is happening in the creator space. It changes how content is produced, and more importantly, how much of it exists.

Part 2 of this series looks at what happens when those pressures compound, why the economics get harder, and how AI is adding another layer to an already changing system.

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You probably know about graphics cards or GPUs from that nerdy friend who owns a gaming PC.

But most people do not realize that every time you use ChatGPT, Claude, or any AI chatbot, you are using a GPU.

Actually, you are using thousands of them!

Those AI models are not running on regular computer processors. They are running on massive clusters of graphics cards in data centers.

The GPU in a gaming PC and the GPU powering ChatGPT are fundamentally the same technology. Just scaled up to big levels.

Everyone and their dog is talking about AI these days.

So, I thought it would be a nice idea to explore how GPUs came to the scene and why AI needs them.

What Even Is a GPU?

GPU stands for Graphics Processing Unit. Many call them graphics cards or video cards.

A GPU is a specialized computer chip designed to handle graphics. It looks pretty similar to a CPU but it’s quite different from it.

A GPU processes video and image data on your screen. Its primary purpose is to turn data into pixels on your screen that you can see.

An important thing about GPUs is that they are really good at doing lots of simple calculations at the same time.

If you’ve read our explainer article on CPUs, you know that CPUs have cores. A modern one typically has around 8 to 24 cores.

However, a modern GPU has thousands of cores. These cores are not very smart on their own. They are not even as broad in what they can do as a CPU core.

But because GPUs have so many of these cores, when combined they can do massive amounts of specific work in parallel.

Compare that to a CPU or Central Processing Unit. Your CPU has maybe 8 to 24 cores.

But each core is very smart and can do basically any calculation you do on it like watching videos, running spreadsheets, all that stuff.

A CPU is like having a few really smart people working on a problem. A GPU is like having thousands of average people working on the same problem at once.

For certain tasks the GPU approach is way faster and rendering a video game is one of those tasks. You need to calculate the color and position of millions of pixels 60 times per second.

That is a lot of simple repetitive math and it’s perfect for a GPU.

Your CPU could do it but it would be slow. Your GPU can do it instantly because it has thousands of cores all working in parallel.

This is why gaming PCs have dedicated graphics cards. The GPU handles all the graphics work while the CPU handles everything else.

How Are CPUs and GPUs Different?

Let me explain this with an example.

Imagine you need to paint a huge wall. You have two options.

Option one: hire one really skilled painter. This person is fast and precise. They can handle complex patterns and details. But there is only one of them.

Option two: hire 1000 average painters. They are not as skilled individually. But there are so many of them that they can cover the wall much faster.

The skilled painter is your CPU. The 1000 average painters are your GPU.

For complex tasks that require lots of decision making the CPU is better. Things like running different programs, browsing the web, doing spreadsheets and stuff.

For simple repetitive tasks that can be done in parallel the GPU is better. Graphics, video encoding, and, as it turns out, AI.

CPUs are optimized for serial processing. One task after another very quickly. GPUs are optimized for parallel processing doing thousands of tasks at the same time.

Intel’s Core i9 has 24 cores. Nvidia’s RTX 4090 has 16,384 CUDA cores. That is why GPUs are so much faster for specific workloads.

A Little History of Graphics Cards

GPUs became mainstream because of video games.

In the 1990s video games which were initially pretty simple started getting more complex. 3D graphics were becoming standard.

But CPUs were not great at rendering 3D graphics. They could do it but it was pretty slow.

So companies started making dedicated graphics cards with their own processors. These cards could handle all the graphics work while the CPU focused on everything else.

3dfx released the Voodoo Graphics card in 1996. It was the first consumer 3D graphics accelerator that really worked well.

Suddenly games like Quake and Unreal Tournament looked amazing. 3D graphics went from choppy and ugly to smooth and detailed.

Nvidia was founded in 1993. They released the RIVA 128 in 1997 and the TNT in 1998. These cards competed with 3dfx.

Then in 1999 Nvidia released the GeForce 256. This was the first GPU. Nvidia literally invented the term GPU with this card.

The GeForce 256 could do hardware transform and lighting. This offloaded even more work from the CPU to the graphics card.

Gaming GPUs got faster every year. More cores, higher clock speeds, better memory.

ATI competed with Nvidia through the 2000s with their Radeon cards. AMD bought ATI in 2006.

By the late 2000s gaming GPUs were extremely powerful parallel processors. Thousands of cores capable of trillions of calculations per second.

But researchers realized something.

Those same parallel processing capabilities that we use for games are also perfect for other forms of computing.

Then AI Happened

We can call 2012 the first major breakthrough in the current AI work that’s being done in front of us.

A team at the University of Toronto led by Geoffrey Hinton used GPUs to train a neural network called AlexNet in 2012.

AlexNet won the ImageNet competition that year by a huge margin. It was way more accurate than previous image recognition systems.

The key was using GPUs. Hinton’s team trained AlexNet on two Nvidia GTX 580 gaming GPUs.

Training neural networks (think of these as the backbone for ChatGPT) requires doing the same math operations just millions of times. Matrix multiplications mostly.

CPUs can do matrix multiplication but it is slow. GPUs can do it way faster because they have thousands of cores working in parallel.

AlexNet proved that GPUs could accelerate AI training by 10x to 100x compared to CPUs.

This kicked off the modern AI boom.

Researchers started using gaming GPUs for AI research. Nvidia GTX Titan, GTX 1080 Ti, RTX 2080 Ti became standard in AI labs.

Then in 2016 Google announced they built custom AI chips called TPUs. These were designed specifically for neural networks.

Nvidia responded by focusing more on AI. They released data center GPUs specifically for AI workloads.

The V100 in 2017. The A100 in 2020. The H100 in 2022.

These were not gaming cards. They were designed for training and running AI models in data centers.

The H100 costs around $30,000. Compare that to a gaming GPU which costs $500 to $2,000.

But data centers needed them and AI companies like OpenAI bought thousands of H100s.

When ChatGPT launched in November 2022, it ran on thousands of Nvidia A100 GPUs. Later OpenAI upgraded to H100s.

Nvidia’s current Blackwell chips have shipped in the millions, many as rack scale systems of 72 GPUs each.

Nvidia’s data center revenue was $4.3 billion in 2020. By 2025 it hit over $100 billion.

Almost all of that growth came from AI companies buying GPUs.

Why Claude Needs Thousands of GPUs

You might be wondering why an AI chatbot needs thousands of expensive GPUs.

The answer is that AI models like GPT-4 or Claude are pretty large. You get why we call them Large Language Models?

GPT-4 reportedly has around 1.7 trillion parameters. Parameters are the numbers that the model learns during training.

All those parameters have to fit in memory. A single GPU does not have enough memory for a model that large.

So AI companies split the model across many GPUs. Each GPU holds a piece of the model.

When you ask ChatGPT a question the work gets distributed across dozens or hundreds of GPUs working together.

This is called model parallelism. The model is too big for one chip so you split it across many chips.

There is also another reason. Inference.

Inference is the process of using a trained model to generate responses. When you send a message to ChatGPT that is inference.

ChatGPT has millions of users. At any given moment thousands of people are asking it questions.

Each question requires running the model. Each run takes a certain amount of GPU compute.

If you want to serve millions of users you need thousands of GPUs running inference simultaneously.

OpenAI reportedly has over 25,000 Nvidia GPUs running ChatGPT. Some estimates put it even higher.

Meta has over 100,000 H100 GPUs for training Llama models.

Google, Microsoft, Amazon all have massive GPU clusters for their AI services.

In 2023 and 2024 there was a global shortage of Nvidia H100 GPUs. Demand was so high that companies waited months for deliveries.

Jensen Huang (Nvidia’s CEO) joked that H100s were the new currency. Some companies bought H100s just to have them as assets.

This is why Nvidia’s market cap is sitting at more than $5 trillion as of writing this article. They have over 90% market share in AI GPUs!

AMD and Intel are trying to compete but Nvidia is untouchable in their software ecosystem which has been a goldmine for them.

Everyone’s talking about Nvidia these days but do you know that Nvidia doesn’t make their GPUs?

In fact their GPUs are manufactured by TSMC, a company so important that we may have a world war over it :/

You can check out my linked article here to read on TSMC’s technology and how it’s powering Nvidia, AMD and most of the tech world.

CUDA and Nvidia’s Trillions

Nvidia did not set out to dominate AI. It started out as a GPU company just trying to survive for the next 30 days.

They became a gaming GPU company by years of hard work. AI, however was more of a side experiment for them to begin.

The irony is that Nvidia’s current AI revenue is so large that earnings from consumer GPUs seems like a rounding error in their financial reports.

Nvidia’s gaming revenue in 2025 was over $11 billion. Their data center AI revenue was over 100 billion.

AI is roughly 10x bigger than gaming for Nvidia.

But this happened because Nvidia made smart decisions pretty early on.

In 2006, Nvidia released CUDA. This was a programming platform that let developers use GPUs for general computing not just graphics.

Nvidia dropped more than a billion dollars into CUDA back in 2006. I covered that investment in quite detail in my Nvidia deep-dive article linked here.

At that time, most people thought CUDA was a waste of money. Why would anyone use a graphics card for non-graphics work?

But Nvidia kept investing in CUDA. They made it easy for researchers to write GPU-accelerated code.

When researchers started building neural networks they reached for Nvidia GPUs because CUDA made it easy.

By the time AI took off in the 2010s Nvidia had a 10 year head start on GPU computing software.

AMD had GPUs too. But their software stack was way behind Nvidia’s.

Intel tried to enter the GPU market with Xeon Phi and later discrete GPUs. But they could not compete with Nvidia’s ecosystem.

Every AI framework was built on CUDA. PyTorch, TensorFlow, JAX all assumed Nvidia GPUs.

Universities taught CUDA. Researchers published CUDA code. The entire AI ecosystem locked into Nvidia.

This is why Nvidia dominates AI hardware. Not because their chips are vastly better but because their software ecosystem is unmatched.

AMD’s MI300 GPU is competitive with Nvidia’s H100 on raw specs. But porting code from CUDA to AMD’s ROCm is painful.

That is why most people just stick to writing their code in CUDA and paying Nvidia’s premium.

GPUs Are Everywhere

You may be surprised but every time you use an AI chatbot you are using GPUs.

When you ask ChatGPT to write an email you are using Nvidia GPUs in OpenAI’s data center.

When you are planning your next startup with Claude (you know who you are :p), you are using Anthropic’s GPU clusters with thousands of Nvidia GPUs.

These GPUs cost millions of dollars to buy and millions more to operate. This is why some AI services charge good money.

There is a reason why basic subscriptions to Claude and ChatGPT cost more than $20. Running GPUs isn’t a poor man’s play.

Google’s Gemini models are different though because they are optimized to run efficiently on their TPUs.

What in the devil is a TPU you may ask?

Well, It’s too long of a topic to cover in this article. Let’s stick to GPUs for this one and if you guys want it, I can write a separate article on TPUs.

Most people think GPUs are just for gaming. And it’s not wrong. But in 2026 GPUs are everywhere.

Your phone has one. Your laptop has one. Even your car probably has one. And more importantly every AI service you use runs on GPUs in a data center somewhere.

Even when Netflix recommends shows or Spotify suggests music, those suggestions come from AI models running on GPUs.

Your bank uses GPUs to detect fraud. Your email uses GPUs to filter spam. Your phone uses GPUs to process photos.

GPUs have been powering most forms of AI for quite some time. We just started paying attention now.

Your Phone Has One Too

Every modern smartphone has a GPU built into the processor.

Apple’s iPhones have them. Snapdragon powered Android phones have them too.

These are not as powerful as desktop or data center GPUs. But they are still GPUs doing the same parallel processing.

When you play a mobile game the GPU renders the graphics.

When you take a photo the GPU processes the image. HDR, night mode, portrait mode all use GPU compute.

When you use Face ID or face unlock the GPU helps process the camera feed.

Some AI features on phones run locally on the GPU.

Apple’s on-device Siri processing uses the Neural Processing Unit (NPU) which is basically a specialized AI GPU.

If you’re scratching your head over what’s an NPU, my friend dove deep into Neural Processing Units in his article linked here.

Google’s Pixel phones use Tensor chips with dedicated AI cores for features like Magic Eraser and Live Translate.

Samsung’s Galaxy phones use Exynos or Snapdragon chips both of which have GPU and NPU cores for AI.

The difference between phone GPUs and data center GPUs is just scale. Your phone GPU might have 6 cores. A data center GPU has 16,000 cores!

Who’s Coming for GPUs?

It doesn’t matter how much Jensen wants to sell you on his GPUs.

The truth is that GPUs are not the final answer for AI. They are just the best option we have right now.

And many companies are trying their best to change that.

Google is already using their custom chips called Tensor Processing Units (TPUs). They use it for training Gemini and doing most of their AI stuff.

Other companies are building custom AI chips too. Amazon has Trainium and Inferentia chips. Microsoft is working on their own AI accelerators.

And dozens of startups like Cerebras, Graphcore, and many others are building even more specialized AI chips.

All of them want a share from Nvidia’s complete dominance in AI.

It’s true that none of them have matched Nvidia’s combination of performance and compatibility ecosystem yet.

But that doesn’t mean it’s not possible. The next generation of GPUs is already in development.

Nvidia’s B100 and B200 GPUs were launched in 2024. AMD launched its MI350 in 2025 and just followed it with the MI400 series in mid-2026, its strongest challenge to Blackwell yet.

Intel’s also working on their Gaudi 3 AI accelerator.

So, for now Nvidia is winning. CUDA is too strong.

Every AI framework is basically written for CUDA. And almost everyone working on AI knows CUDA. That’s simply why people keep using it.

But that could change if companies like Google or Amazon can prove that custom chips are way better.

For us as end users, we may not be able to get our hands on these enterprise GPUs anytime soon (maybe never).

But it is cool to know that whenever you ask ChatGPT a question or generate an image, a GPU somewhere is doing the work.

In about 15 years, GPUs went from a specialized gaming chip to the foundation of AI.

Who knows what Nvidia will be like 10 years from now? CUDA may or may not be relevant.

But companies trying to compete is a good sign because we just don’t need more monopolies.

I hope this helped you understand what GPUs actually do and why they matter so much in the AI world.

If you enjoyed the read, a share to someone as curious as you would make my day.

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Similar Articles from SK NEXUS

Further Learning

• CUDA Proves Nvidia Is a Software Company

• What’s the Difference Between a CPU and a GPU?

• ChatGPT’s Hunger for Energy Could Trigger a GPU Revolution

• How Nvidia became the driving force behind the AI revolution

• The Intelligent Industrial Revolution

• AI development won’t hit a wall anytime soon—here’s why

• Why do Nvidia’s chips dominate the AI market?

• Nvidia bets on new data center chips for growth

• AMD MI300X performance compared with Nvidia H100

• Cerebras: What You Need To Know About The Nvidia Competitor

• Google Details Tensor Chip Powers

• Newest Google and Nvidia Chips Speed AI Training

For quite some time now, I’ve been noticing the same story appear over and over again.

Someone is using a chatbot for emotional support. Someone else is asking it for relationship advice. Another person is treating it like a therapist or simply someone to talk to.

The names and the chatbot change, but the motive remains largely the same.

Modern AI systems are available 24/7, respond almost instantly, remember previous conversations, and can adapt to the way a person communicates. Compared to many human interactions, they often feel effortless.

The problem is that these systems are optimized to feel supportive, and engaging. Their goal is to keep conversations going and leave users satisfied with the interaction. That is very different from being truthful or capable of recognizing when a user is heading toward an unhealthy conclusion.

Many people use AI without any serious issues, but as these systems become more personal, researchers have started paying closer attention to a growing set of concerns involving emotional attachment, excessive trust, validation loops, and in more extreme cases, what some people have started referring to as AI psychosis.

So today, I want to break down what’s actually happening, why people form emotional attachments to AI systems, how dependence can develop over time, and what you can actually do about it.

The Growing Dependence on AI

When ChatGPT launched in late 2022, people viewed AI as a productivity tool and at most, it felt like an advanced search engine or a more capable digital assistant.

Over the following years, that relationship has evolved a lot because of these models becoming more conversational and capable.

Researchers have been studying this change for years.

Several studies in human-computer interaction have found that people can develop emotional bonds with AI systems when it display characteristics associated with social interaction, such as memory, responsiveness, and personalization. The more human-like a system appears, the more likely users are to engage with it socially rather than purely as a tool.

And for the first time in history, hundreds of millions of people have access to systems capable of personalized conversations. That creates entirely new social dynamics that simply didn’t exist when most of the digital assistants were limited to simple commands and scripted responses.

Why People Treat AI Like People

A major reason for this comes down to anthropomorphism, the tendency to assign human characteristics to non-human things.

Humans have been doing this for a long time. People name their cars, they talk to pets as if they understand every word. They sometimes get angry at computers as though the machine is intentionally causing problems.

AI just happens to be one of the most convincing examples of this tendency.

When something communicates naturally and responds in ways that feel thoughtful, the brain often begins processing the interaction differently. Even when users know they are talking to software, many of the same social and emotional mechanisms involved in human conversation can still activate.

Researchers at Stanford and MIT have repeatedly observed that people often respond to conversational systems in surprisingly human ways, especially when interactions become frequent and personalized.

And unlike human relationships, AI comes with very few practical limitations.

It doesn’t get tired. It doesn’t need emotional support in return. It’s available whenever the user decides to open the application.

For someone looking for reassurance or simply a conversation, that can be the best thing in life because you don’t have to deal with many of the complications that naturally exist in human relationships.

How Attachment with AI Develops

There are a lot of people who use AI and will never develop a strong emotional attachment to it. But for some users, the process can happen gradually enough that it barely feels noticeable.

Researchers studying companion AI systems have found that repeated interaction often increases feelings of trust and emotional connection. The effect becomes stronger when users disclose personal information and receive responses that feel empathetic.

Over time, as these interactions become more frequent, some users begin preferring AI conversations because they feel easier than human ones. Human conversations involve emotional complexity, and occasionally uncomfortable truths. AI conversations are often more predictable by comparison.

And while this might sound unusual at first, examples of it are becoming increasingly visible. In 2025, People reported on a woman who held a wedding ceremony with an AI-generated partner. The AI companion was displayed on a phone screen, while augmented reality glasses were used during the ceremony and ring exchange.

With the passage of time, this can create a dependency loop.

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