In our previous article in this series, we went through the whole process of mining and purifying silicon to 99.9999999 percent, and all that it takes to create silicon wafers.

Those wafers are perfectly pure, flat, and fully blank. They don’t have any circuits or transistors yet.

Fresh Silicon wafers are somewhat like blank papers. They need someone to write on them to be useful for us in the form of chips.

This article is about exactly that. In this piece we’ll discuss how those blank wafers get turned into chips with billions of working transistors.

The process involves some of the most expensive and precise machines ever built. Light with wavelengths smaller than viruses. Chemistry happening at atomic scales.

And all of it has to be repeated 50 to 100 times to build a single chip.

I’ll try my best to give a full picture of the main processes without drowning you in technical details.

However, you shouldn’t expect this article to be like the regular ones we do here at SK NEXUS.

Saqib here, with one quick side track before you read on.

This week SK NEXUS got its first ever paid subscriber. We went paid only a few weeks ago, and it felt like a gamble. So having someone read our work and decide it’s worth paying for means a lot. To everyone who considered it, and to the one who took the leap, thank you. Really!

Silicon Wafers Arrive at Fab

The blank silicon wafers we covered in our previous article in this series get shipped to a fab.

If you’re unsure about a fab, you may want to check out the previous article in this series.

The fab could be Taiwan’s TSMC, Korea’s Samsung or America’s Intel. These are three of the largest fabs in the world.

These fabs are some of the most expensive buildings ever constructed. A single modern fab costs 20 billion dollars or more to build.

Inside these fabs the air is cleaner than an operating room. A single speck of dust can ruin a chip so the air gets filtered all the time.

Workers wear full body suits called bunny suits. These suits prevent skin cells, hair, or clothing fibers from contaminating the wafers. Just imagine how clean these rooms would be!

The silicon wafers arrive at the fabs in special containers that keep them clean. They get unpacked in cleanrooms where the air is filtered thousands of times per hour.

The real work begins once the wafers land at the fab because fabs are responsible for building billions of transistors on each wafer which are what make a chip.

But before I start nerding about transistors let’s spend a little time trying to understand what a transistor actually is.

What Is a Transistor?

A transistor is actually a switch. It can be on or off. Its purpose is to conduct electricity or block it at any given time.

By turning these switches on and off in specific patterns you get computers to do the work they do.

A modern chip that is in your phone or computer is made of billions of transistors.

And even if you somehow open up your chip, you can’t see transistors with the naked eye. That’s how small they are!

It’s beyond this article to fully cover how transistors allow us to make computers. Maybe, we’ll go through it in a separate article in the future.

On average, a modern phone chip has around 20 billion transistors while your high-end laptop CPU has around 50 billion transistors. All working together to run your apps and games.

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Are you a gamer who just can’t keep up with Microsoft?
Are you someone who hates Windows 11 with a passion and wants to game on something different?

If you are one of those people then cheer up because Substack gods have taken you to the perfect article.

You’d know that Valve finally launched the Steam Machine for the price of $1,049 for the base 512GB model. Yes, I get that it’s expensive and I know a lot of people who are not happy about that price.

Early predictions had this thing landing somewhere around $600 to $800. Instead we got a price that is closer to a mid-range gaming laptop.

If you go over online coverage of the Steam Machine, it looks like the whole world is burning over it.

But here’s what nobody is talking about.

With the launch of Steam Machine, Valve opened up SteamOS so anyone can install it on PC with compatible hardware.

And that too for free. There is no waiting list or price tag for that.

So, I followed Valve’s instructions and installed SteamOS on my own laptop a few days ago.

And this article is me detailing that process so you guys could follow it too.

This article will walk you through the exact steps that I went through. I have shared screenshots too, so you can try SteamOS by following this piece.

The Steam Machine Is Expensive

Quick context before we move on, because I know some of you are coming in hot.

Valve officially released the Steam Machine on June 22, 2026. The entry price of $1,049 for the 512GB model was notably higher.

Most people were guessing it would land around the $600 to $800 range.

The 2TB model runs $1,349.

Add the new Steam Controller and you are looking at $1,130 for the base bundle or $1,428 for the loaded one.

The reason is not some greedy cash grab but the reality of making hardware products in 2026.

Valve engineer Pierre-Loup Griffais confirmed the team had been tracking a lower price target back when they first started working on the Steam Machine in 2023.

A global RAM shortage wrecked those plans. Globally RAM prices went over 400%. That is 4 times!!

AI data centers are eating up the same memory chips that go into your gaming PC, your phone, and your laptop.

Three companies (Samsung, SK Hynix, Micron) produce roughly 95 percent of the world’s RAM.

All three companies shifted production toward high-margin AI chips, leaving consumer hardware fighting over scraps.

Valve even considered dropping the Steam Machine from dual-channel to single-channel RAM just to stretch the supply they could actually get.

So yes, $1,049 stings. But it is not really a Valve problem. It is everyone’s problem.

PlayStation, Nintendo, Xbox, even Apple, all of them have raised prices for their hardware.

Steam Machine is expensive largely because of the global RAM shortage. I covered this shortage in our dedicated article linked here.

We discussed how RAM quadrupled in price in a span of a year and how every computer is more expensive because of that.

It Is Not Really Valve’s Fault Though

Look, unlike Sony and Microsoft, Valve does not subsidize the Steam Machine.

Consoles are usually sold at a loss or near break-even because money comes back in the form of game purchases.

Valve cannot do that here. The Steam Machine is fundamentally a PC.

Someone could buy it, wipe SteamOS, install Windows, and never give Valve another dollar through game purchases.

So Valve has to price the hardware to actually cover its costs.

For context, you can build an equivalent or better desktop PC for the price of the base Steam Machine.

But you cannot have that PC remotely turn on your TV or fit silently inside that TV shelf.

This comfort is something that quite a few people are after and it’s their choice if they want to buy the Steam Machine for that price.

Look, I am not here to tell you to stop being annoyed. I am annoyed too.

But there is a silver-lining to all of this that most people are not paying much attention to.

SteamOS Opens Up

Around the Steam Machine launch Valve quietly shipped SteamOS 3.8.10.

Valve engineer Pierre-Loup Griffais told The Verge that starting with this release, you can put together your own Steam Machine using whatever PC parts you want.

Until now SteamOS was basically locked to AMD hardware, because it powers the Steam Deck and the official Steam Machine.

Installing it on anything else required janky workarounds using the Steam Deck recovery image, and the results were inconsistent at best.

SteamOS 3.8 changes that. The update adds proper support for Intel GPUs including the newer Arc B series, alongside fixing bugs that didn’t allow other PCs to boot into SteamOS.

Valve has also changed the graphics stack of the Steam Deck to a more modern package called Wayland. This will provide better switching between Desktop and Gaming Mode.

And one of the biggest surprises is that Nvidia support is actively being worked on too.

Griffais said Valve has “a growing team” collaborating with Nvidia “very closely” on proprietary driver support for GeForce cards.

He was careful not to promise a date, saying support “might not come this year,” with most outlets pegging a realistic target somewhere in 2027.

Nvidia has had a rocky relationship with consumer Linux drivers and Valve is working on changing that.

This means that a large chunk of gamers who boast their GeForce graphics cards can soon get SteamOS working on their hardware too.

Right now if you want the best experience with SteamOS, you should aim for AMD CPUs and GPUs that were released based on the RDNA 2 and RDNA 3 architectures because that is what the Steam Deck and Steam Machine use.

RDNA 4 support is also in there through the updated Linux kernel and Mesa drivers SteamOS 3.8.10 ships with.

If you’re not sure what SteamOS is, here’s a dedicated article we did covering what it is and how it’s causing a revolution in the gaming space.

The article is linked here for you to check out.

Before You Install SteamOS

Look, I get that it’s really exciting to get rid of Windows and try out SteamOS but let me tell you a couple of things to keep in mind before you begin.

1. Installing SteamOS WILL ERASE ALL DATA ON YOUR COMPUTER!!

   It’s best that you don’t use your primary computer and instead use a secondary computer for trying out SteamOS because SteamOS wipes the drives connected to the computer.

2. Be ready to debug for bugs and issues like any other software, SteamOS will have bugs. And it’s up to you to fix them and debug them which can sometimes take a few minutes to many hours. You have to be willing to spend hours browsing through internet forums, Reddit threads or working alongside ChatGPT figuring out why SteamOS won’t install for you.

3. No one is responsible for SteamOS. You can install SteamOS on hardware you own even if it’s not officially supported by Valve. But it also means you are totally responsible for your install. Valve support won’t be there to help you if you’re facing any issue with SteamOS.

There are some other limitations too. Once you install SteamOS, it will replace operating systems like Windows that are already installed on your system. You have to reinstall them again if you want to remove SteamOS from your computer.

Dual-booting alongside Windows is also not officially supported yet, so you should plan to dedicate an entire drive or a computer to SteamOS.

Secure Boot has to be disabled to install SteamOS. You’d find this setting in your motherboard’s BIOS.

I have an older laptop lying around that I do not use for much anymore, so none of that mattered to me. I figured it was the perfect guinea pig.

Look, I spend my days playing around with operating systems and stuff and I understand most people won’t be doing that. Only go ahead with SteamOS if you actually have read through the disclaimers above.

It’s totally fine if you don’t want to spend hours debugging issues on a command line.

What You Need Before We Start

If you’ve decided to go ahead with SteamOS anyway, here’s what you need.

1. You’d need a USB drive that is at least 8GB in size. Though I would recommend 16GB or larger just to be safe. This drive will get completely wiped, so back up anything important on a separate computer first.

2. A PC you are okay with completely wiping. SteamOS does not currently support dual-booting. So, whatever drive you install this on, expect SteamOS to wipe all data that exists on that computer

3. A stable internet connection. The SteamOS updates itself after install and you’d need that to finish initial setup.

4. A separate computer or laptop to create a bootable USB.

Step 1: Download The SteamOS Image

First step is grabbing the actual SteamOS image from Valve from the link below: https://help.steampowered.com/en/faqs/view/65B4-2AA3-5F37-4227#install

On the page above click the link next to the ‘Download the SteamOS image’ button. You will need to agree to the terms before the download button becomes available.

Click through and the download will start. Depending on your connection this can take a few minutes since the image itself sits at a few gigabytes.

Once it finishes downloading, do not touch the file yet. We need balenaEtcher ready first.

Step 2: Flash It With balenaEtcher

If you have not already, install balenaEtcher through the link below:

https://etcher.balena.io/

balenaEtcher is the software that will convert our normal USB drive into a bootable SteamOS USB. Installation depends upon your operating system and is genuinely just clicking through a few prompts.

Once Etcher is open, plug in your USB drive.

Click the “Flash from file” button and navigate to wherever your SteamOS image is downloaded to.

Etcher should automatically detect your USB drive if it is the only one connected.

If you have multiple drives plugged in, double check you are selecting the right one before continuing, since Etcher will completely erase whatever is on it.

Hit the Flash button and wait. This part took me roughly 10 to 15 minutes, though your mileage will vary depending on how fast your USB drive actually is.

One tip: if you are on a slower drive and the estimated time looks insane, a few people in Steam Deck communities have reported that decompressing the image file first before flashing speeds things up dramatically on certain setups.

Worth knowing if Etcher is telling you the flash will take several hours. Once Etcher confirms the flash completed, you are ready to actually boot into this thing.

Only change: the period after “insane” became a comma and “A few” dropped to “a few,” so the tip is one complete thought instead of a dangling “if.”

Step 3: Boot Into The Installer

Plug the freshly flashed USB drive into the PC you are installing SteamOS on.

Restart the machine and get into your boot menu.

This is usually done by spamming a specific key during startup, commonly F12, F2, Esc, or Delete depending on your hardware manufacturer.

If you are not sure, a Google search of your specific laptop or motherboard model plus “boot menu key” will tell you.

Select your USB drive from the boot menu instead of letting it boot normally into Windows or whatever is already installed.

Give it a minute. You should see SteamOS boot into what looks like a stripped down live environment.

This is essentially the same recovery and install environment that Steam Deck owners use, just running on your own hardware now.

From here you will see the install options. This is where things get real.

Step 4: The Actual Install

Once you are in the SteamOS Live Environment just click the button on the desktop that says ‘Wipe Device & Install SteamOS’.

Once you confirm, the installer takes over. There is not much for you to do at this stage besides wait. SteamOS copies itself onto your selected drive and sets up the base system.

This part took me somewhere around 15 minutes on my setup, though again this depends heavily on your drive speed.

When the install finishes, you will be prompted to remove your USB drive and reboot the machine.

Go ahead and do that now. Pull the USB stick out before the reboot actually happens so your machine does not just boot back into the installer again.

Step 5: The Reboot And Welcome Wizard

This is the part that actually feels like magic the first time you see it.

Your machine reboots and instead of the usual Windows logo or whatever you are used to seeing, you get the SteamOS boot animation.

This is the same screen Steam Deck owners see every time they turn their handheld on.

From here SteamOS launches its welcome wizard. This walks you through the basics.

You have to set your language, your timezone and your WiFi network.

I have censored the networks around my area but you’d see the actual names of the networks around you.

The whole wizard felt genuinely smooth on my end, very close to the experience you get on actual Steam Deck.

In the end you need to sign in with your Steam account.

As you sign in you are dropped straight into Gaming Mode.

Your Steam library shows up out of the box, complete with controller support and pre-optimized settings.

There is also a Desktop Mode tucked away if you want to poke around under the hood, install other software, or just see what is going on beneath the gaming interface.

And that is genuinely it. Start to finish, including download time, the whole process should take you less than an hour.

My install took a bit more time but that was more because of my specific hardware that is a bit old so things can get different depending on the hardware you’re installing SteamOS on.

What This Means Going Forward

If you have gone through the steps above you should have a working SteamOS install on your PC.

It is the same OS that the Steam Deck and the Steam Machine use.

Again, the Steam Machine price is annoying and I am not going to pretend otherwise.

But the existence of an open, free, supported SteamOS that runs on hardware you already own is genuinely a bigger deal long term than the Steam Machine itself.

And that’s what I feel is Valve’s goal in all of this. It’s exactly what they’ve said in the Steam Machine launch blog post:

SteamOS is quite new and as people report bugs and Valve fixes them there is an exciting path that one can go to if they’re fed up with what Microsoft has been doing with Windows all the while.

Windows still dominates the desktop market share and it will continue to do so for quite some time.

But it’s really cool that we have an alternative that is slowly and gradually becoming better than ever and all of us can be a part of that.

On Steam, Linux is now more popular than macOS driven largely by SteamOS adoption through the Steam Deck.

And this will only improve through the Steam Machine.

In my opinion, As Valve opens up SteamOS for more platforms and works with partners like Nvidia, it is building something bigger than even the Steam Machine.

If you already have a decent AMD GPU sitting around, or even an Intel Arc card, you do not need to wait or pay a thousand dollars for a Steam Machine

You just need a spare drive, a USB stick, and about an hour of your time (ideally).

So yeah. The Steam Machine price is rough. But Valve also just handed you the actual loophole, and barely anyone is talking about it.

I hope this guide helped you build your own setup if you decided to give it a shot.

Please don’t hesitate to share your experience with SteamOS if you’ve gone through it. Or just share any thoughts related to SteamOS in the comments down below.

Further Learning

• Valve Steam Machine hits $1,049 as the RAM crisis forces a major price rethink

• AI crisis turned Valve’s $750 Steam Machine into a $1,049 one - Memeburn

• Steam Machine price, specs and release date guide 2026

• Valve will finally let you build your own Steam Machine with SteamOS for desktop - The Verge

• Valve now allows you to run SteamOS on your own DIY Steam Machine, with Nvidia GPU support in the works

• SteamOS Nvidia support confirmed: Valve has a growing team but AMD builds work now

• You don’t need a Steam Machine to run SteamOS - Valve opens it up to any AMD PC

• Valve confirms SteamOS is coming to any PC, with Nvidia support in the works

• Steam Machine release date, specs, and price - PCGamesN

• How to use the Steam Deck recovery image - Pi My Life Up

• BalenaEtcher Official site

You might know that countries, especially the US and China, are trying their best to bring chip manufacturing back to them.

TSMC’s Arizona fab is already printing out advanced chips for the likes of Apple and NVIDIA, with 3nm production planned there for 2027-28.

All of that is interesting and exciting but none of that makes sense unless you understand how chips actually get made.

The weird fact is that the processor in your phone, the GPU in your gaming PC and the chip in your car’s computer, they all started as sand on a beach somewhere.

Literal sand. The stuff you walk on. The stuff kids build castles with.

The question is how do you go from literal sand to a complex chip with billions of transistors, where a single transistor is about tens of thousands of times smaller than a human hair.

The answer is a process so complex and expensive that only a handful of companies in the world can actually do it at scale. You can count these companies on one hand.

This is the first article in a Series where we will cover chip manufacturing from sand to the device in your hand.

As you may have figured out, chip manufacturing is one of the most complex processes in human history. There is simply a lot of maths and physics involved.

However, in this Series, I’ll keep most of the stuff relatively simple for you guys while talking about the important stuff.

One quick side track -

Just so you know, SK NEXUS recently went paid. Linked here is the detailed post by that goes into the specifics of it all.

To keep it short, here’s what to expect moving forward:

Our 3-part article Series is going paid. Readers will have to pay a small amount to access our Series.

However, first part of a Series (like the one you’re reading) will be free for all.

Money wasn’t the motivator for going paid. We want quality tech writing to be accessible for most.

That’s why the first part of a Series will remain free, while other parts will be exclusive to those who pay.

If you have any thoughts, please share your feedback down in the comments.

Silicon Is Everywhere But Pure Silicon Is Rare

Silicon is the second most abundant element in Earth’s crust after oxygen. It is everywhere.

Rocks, sand, dirt, glass. All of it contains some form of silicon in them.

But here is the thing. Silicon almost never exists alone in nature. It is always bonded to other elements, usually oxygen.

When silicon bonds with oxygen you get silicon dioxide. That is what sand is. That is what quartz is. That is what most rocks are made of.

The silicon we need for chips is not silicon dioxide. It is pure silicon. Just silicon atoms and nothing else.

Getting pure silicon from sand is like trying to extract gold from seawater. Technically possible but incredibly difficult and expensive.

The purity required for chips is absurd. We are talking 99.9999999 percent pure silicon. Nine nines!

One impurity atom for every billion silicon atoms. That is the standard.

Anything less and the chip does not work properly. Transistors fail and there’s a short circuit.

So the entire chip industry is built on a process that takes one of the most common materials on Earth and refines it into one of the purest substances humans have ever created.

What Makes Silicon Special for Electronics

You might be wondering why we use silicon at all. There are other materials. Why not use something easier to work with?

The answer is that silicon has a unique set of properties that make it perfect for electronics.

First, silicon is a semiconductor (Now you know why we call them semiconductor chips).

Being a semiconductor means it can conduct electricity sometimes and block it other times depending on how you treat it.

Metals like copper conduct electricity all the time. Insulators like rubber block electricity all the time. Silicon sits in the middle.

You can control whether silicon conducts or blocks by adding tiny amounts of other elements to it. This process is called doping and we will cover it in a later article.

Silicon’s ability to switch between conducting and insulating states is what makes transistors work. Transistors are the building blocks of every chip and we’ll go over them in detail too in a later article in this Series.

Second, silicon forms a stable oxide layer when exposed to air. That oxide layer is silicon dioxide which is basically glass.

This matters because you can grow extremely thin, extremely uniform layers of silicon dioxide on top of silicon. These layers act as insulators between different parts of the chip.

Third, silicon is abundant and relatively cheap to source. You can find silicon dioxide anywhere on Earth.

Fourth, we have spent 70 years learning how to work with silicon. The entire manufacturing infrastructure is built around it.

There are other semiconductor materials like gallium arsenide or silicon carbide. Some are faster and even more efficient than silicon.

But none of them have the combination of properties, manufacturing knowledge, and cost-effectiveness that silicon has.

That is why every major chip in your life uses silicon. Your phone, your laptop, your car, your router, all silicon.

Making Pure Silicon from Quartz

We know that we need pure silicon to make chips and in its natural form silicon is bonded with oxygen and we need to remove that bond and extract pure silicon.

That journey starts with quartzite (aka quartz for short). It is a type of rock that is almost pure silicon dioxide.

Mining companies dig up quartz from a mine. They crush it into smaller chunks. Then they load it into massive electric furnaces to melt it.

Inside the furnace the quartz gets heated to over 2000 degrees Celsius (Yup, furnaces are that hot!). Carbon in the form of coal also gets added to the mix.

At those temperatures a chemical reaction happens. The carbon pulls oxygen away from the silicon dioxide leaving behind molten silicon and carbon dioxide gas.

What comes out is called metallurgical-grade silicon or MG-silicon. It is about 98 to 99 percent pure.

That sounds pretty pure. But for electronics it is nowhere near good enough. MG-silicon is fine for making aluminum alloys or solar panels.

But semiconductor chips are much more demanding. They need something far more pure.

The impurities in MG-silicon include things like iron, aluminum, calcium, and other metals. Even at 1 or 2 percent contamination that is billions of unwanted atoms.

Those impurities will ruin a chip. So the next step is to purify the silicon even further.

Getting to 99.9999999% Purity

The industry standard for purifying silicon is called the Siemens process. It was developed in the 1950s and we still use it today because nothing better has been invented.

This is also where things get extra expensive. Here is how we purify silicon further:

You take metallurgical-grade silicon and grind it into powder. Then you react that powder with a special acid to produce a chemical compound that is liquid at room temperature.

If you’re curious, that compound is called Trichlorosilane.

Trichlorosilane can be distilled. Distillation is a purification process where you heat a liquid until it evaporates, then cool the vapor back into liquid.

Impurities have different boiling points than trichlorosilane. So when you distill it multiple times the impurities get left behind.

After enough distillation cycles you end up with ultra-pure trichlorosilane and that’s where things get more and more expensive.

You take thin rods of already-pure silicon and place them inside a reactor chamber. Then you pump ultra-pure trichlorosilane gas into the chamber.

The rods get heated to over 1000 degrees Celsius. At that temperature the trichlorosilane gas decomposes and deposits pure silicon onto the rods.

Layer by layer, atom by atom, the rods grow thicker as silicon builds up on their surface.

This process runs for days. The rods slowly grow from pencil-thin to telephone-pole-thick.

When the process finishes you have polycrystalline silicon rods that are 99.9999999 percent pure. Nine nines.

This is called polysilicon or poly-Si. This is what the chip industry uses.

The Siemens process is slow and energy-intensive. It takes enormous amounts of electricity to run those reactors for days on end.

That is why polysilicon is expensive. And that is why companies that make polysilicon are critical to the entire chip supply chain.

Most of the world’s polysilicon comes from a handful of companies in China, Germany, and the United States.

Making the Silicon Ingot

Polysilicon is pure but it is not in the right form still. Chips need single-crystal silicon not polycrystalline silicon.

The difference is in how the atoms are arranged. In polycrystalline silicon the atoms are arranged in small random crystals all jumbled together.

In single-crystal silicon all the atoms line up in one perfect crystal structure.

Single-crystal silicon has better electrical properties. Transistors work more reliably and the overall chip performance is more predictable.

So the next step is to turn polysilicon into a single crystal. The process used is called the Czochralski method or CZ method.

Here is how it works.

You take chunks of polysilicon and melt them in a crucible at over 1400 degrees Celsius. The silicon turns into a liquid pool.

Then you take a small seed crystal of silicon, a tiny piece that already has the perfect crystal structure, and dip it into the molten silicon.

The seed crystal acts as a template. As you slowly pull it up out of the liquid, silicon atoms from the melt attach to the seed and copy its crystal structure.

You keep pulling slowly while rotating the seed crystal. The silicon keeps freezing onto the seed in perfect alignment with the crystal structure.

Over several hours you pull a massive cylinder of single-crystal silicon out of the melt. This cylinder is called an ingot.

A typical ingot is about 2 meters long and 300 millimeters in diameter. Some newer fabs are moving to 450mm diameter ingots but those are not widespread yet.

The entire ingot is one perfect crystal. Billions of atoms all lined up in the same direction.

This is the raw material that chips are made from. But we are not done yet.

Slicing Silicon Into Wafers

You cannot build chips on a 2-meter-long cylinder of silicon. You need thin flat discs almost like CDs (I hope you’re old enough to remember those).

So the ingot gets sliced into wafers.

First the ingot gets trimmed. The top and bottom get cut off. The sides get ground down to a precise diameter.

Then the ingot goes to a wire saw. This is a machine with a thin wire coated in diamond particles. The wire moves at high speed and slices through the silicon like a slicer cutting meat.

Each slice is incredibly thin. Modern wafers are about 0.75 millimeters thick. Thinner than a credit card. One ingot produces hundreds of wafers.

After slicing the wafers are rough and covered in microscopic damage from the cutting process. So they get polished.

Polishing involves grinding the wafer surface with finer abrasives until it is perfectly smooth and flat.

The final wafer surface is so smooth that if you scaled it up to the size of a big city the tallest bump would be less than a centimeter high.

That level of flatness is required because the circuits built on the wafer are nanometers tall. Any bump or scratch ruins the chip.

Blank silicon wafers ready to be turned into chips. The finished wafers are inspected, cleaned, and packaged for shipment to chip fabs.

Fab stands for Fabrication Facility. These are companies which take the wafers and build the transistors onto each wafer, layer by layer.

TSMC, Samsung, and Intel are some of the most sophisticated fabs out there.

Bigger Wafers = Cheaper Chips

You might have noticed that the industry keeps pushing for bigger wafers. 200mm, 300mm, 450mm.

The reason is pretty simple. Each wafer can hold a certain number of chips depending on the chip size. A bigger wafer holds more chips.

The cost of processing a wafer does not scale with wafer size. Processing a 300mm wafer does not cost twice as much as processing a 200mm wafer even though it has more than twice the area.

So bigger wafers mean more chips per wafer with only a modest increase in processing cost. That drives down the cost per chip.

A 300mm wafer has 2.25 times the area of a 200mm wafer. That means 2.25 times more chips from roughly 1.3 times the processing cost.

The industry has been on 300mm wafers since the early 2000s. The transition to 450mm has been talked about for over a decade but it keeps getting delayed just like GTA 6 :p

The problem is that moving to 450mm requires redesigning every tool in the current fab supply chain. The furnaces, the lithography machines, the deposition tools, everything.

And that is way too expensive and time-consuming.

Just know that the most advanced lithography machines cost around $350 million per unit.

The cost of going to a bigger wafer is in the billions of dollars. And right now the companies can not justify it simply.

So for now 300mm (aka 12 inches) is the standard and will be for the foreseeable future.

What’s Next After Silicon Wafers?

We started with sand on a beach. We ended with a perfectly flat, perfectly pure, single-crystal silicon wafer.

This wafer is the foundation for everything that comes next in the Series.

In the next article we will head into the fab, where those blank wafers get turned into chips with billions of transistors. We will cover lithography (drawing circuits with light), then deposition, etching, and doping, the actual construction of the transistors.

Then in the final article we will cover how chips get tested, cut apart, and packaged, and why some chips cost $50 while others cost $2,000.

But all of that starts here. With sand. The fact that your phone started as sand is not just some analogy I used. It is literally true.

Someone literally mined quartz out of the ground. Someone refined it into polysilicon. Someone grew it into an ingot. And someone sliced it into wafers.

Those wafers traveled to a fab in Taiwan or South Korea where billions of transistors will be built on them.

Then those chips got packaged, tested, and shipped to your phone manufacturer who assembled them into the device you are holding right now.

The entire process from sand to smartphone takes months and involves dozens of companies across multiple countries.

But it all starts with the humble silicon which most people don’t think about when it comes to advanced processors.

I hope this article helped you understand where chips actually come from. The next articles will dive deep into the processes that happen on a wafer.

While writing this article, the exciting thing for me was how simple the chip-making process sounds until you try to dive a bit deep.

Most of us never imagine how complex the process of taking literal sand into polished wafers is.

In the next article, things will get more complex as I go over the transistor side of things.

I would love to know how this newer Series format feels to you guys. Please share any feedback you have down in the comments.

Similar Articles From SK NEXUS

Further Learning

• The humble mineral that transformed the world

• 6 crucial steps in semiconductor manufacturing - ASML

• How Silicon Chips Are Made (Explained in 7 Mins)

• Semiconductors and the Power of Silicon

• A Background to Silicon and its Applications

• From Sand to Silicon “Making of a Chip” - Intel

• Silicon Ingots: The Building Blocks of Modern Electronics

• Silicon Wafer Manufacturing: From Sand to Silicon

• Why 450mm wafers?

• TSMC Expecting to Start 3 nm Production in Arizona

I’ve been wanting to write this piece for a long time.

Every few weeks, another major development would happen somewhere in Europe and I’d end up delaying the article again just to gather more material. But at some point, the pattern became too large to ignore and I got tired of waiting.

None of what you are about to read in this piece happened overnight. The transition has been gradual, but over the last few years, a very noticeable change has started taking shape inside European public infrastructure.

Denmark is moving government staff toward Linux and LibreOffice. Schleswig-Holstein has been replacing large parts of its Microsoft infrastructure across regional government systems. Similarly, France has also accelerated sovereign open-source adoption at a scale very few people were paying attention to until recently.

For years, products from Microsoft were treated almost like default infrastructure inside governments and became deeply embedded into public administration across Europe.

But that premise is now being questioned because governments are increasingly starting to view software dependence as a strategic problem, due to which the question ”Should critical government infrastructure depend almost entirely on foreign corporations?” has become unavoidable.

This article breaks down what’s actually happening across Europe, why it’s happening now, and what it could mean for the future of digital infrastructure.

Before we begin, a quick note. This is the first deep-dive article we are publishing after moving SK NEXUS to a paid model. If you haven’t already, I recommend reading the transition post where explained why we made that decision and what it means for the future of this publication.

Your support allows us to spend more time researching pieces like this, especially topics that require weeks of following developments.

Anyway, let’s get started.

The Broader European Strategy

Before we move on, it’s important to keep in mind that what’s happening across Europe is easy to reduce to “Europe versus Microsoft,” but that misses the bigger picture entirely.

Microsoft is simply the most visible symbol of a much broader transition already happening across Europe.

The movement isn’t limited to replacing Windows with Linux or Office with LibreOffice. Governments are also looking at de-googling initiatives, sovereign cloud infrastructure, local data hosting, open-source software adoption, and reducing dependence on foreign technology vendors more generally.

What’s changing is the way software infrastructure itself is being viewed.

For decades, governments treated software mostly as a procurement problem. You bought products, signed contracts, outsourced maintenance, and moved on.

But it’s not like that anymore - European policymakers are increasingly beginning to treat digital infrastructure the same way they treat energy infrastructure, or defense systems. And quite frankly, the concern isn’t difficult to understand.

If a geopolitical conflict, or legal disagreement suddenly affects access to critical software infrastructure, what happens next? More importantly, what happens to - Government communication systems? Citizen records? Public administration? Healthcare infrastructure?

Those questions become very serious when governments realize how much of their infrastructure depends on external vendors operating under foreign jurisdictions and that is the whole reason this transition matters.

Europe’s Sovereignty Push

Digital sovereignty has gradually become one of the central ideas shaping Europe’s technology strategy.

Put it this way, governments want control over the systems they depend on. That includes software infrastructure, cloud platforms, citizen data, communication systems, and the long-term direction of public-sector technology.

A large portion of global cloud infrastructure is currently dominated by American companies like Amazon Web Services, Microsoft Azure, and Google Cloud. European governments rely heavily on these systems for public services, data storage, communication, and so much more.

You can see how dependent not just Europe, but the entire internet has become on a relatively small number of infrastructure providers. In 2025 alone, multiple major outages involving Cloudflare disrupted huge portions of the web, affecting thousands of services simultaneously and leaving large parts of the internet barely functional for hours.

And once governments realize how much critical infrastructure depends on external providers, concerns around control start becoming much more serious, because at the end of the day dependence is also legal and geopolitical.

One major example is the U.S. CLOUD Act, which allows American authorities to request data from U.S.-based companies even when that data is stored outside the United States. For European governments already focused on privacy and regulatory independence, that creates an uncomfortable reality.

Europe’s GDPR framework addresses privacy protections, but regulation alone does not remove the fundamental dependence if the infrastructure itself remains controlled elsewhere.

And that’s really the main point of the sovereignty discussion. European governments are not trying to isolate themselves from American technology entirely for the sake of nationalism. They just want to ensure that critical public infrastructure is not entirely dependent on decisions made in Silicon Valley, or corporate headquarters outside European jurisdiction.

How Governments Became Dependent

Understanding why this transition is so difficult requires understanding how governments became this dependent in the first place.

Back in the 1990s and early 2000s, Microsoft became the obvious choice for government IT infrastructure across large parts of Europe, because if everyone used the same software stack - it would be easier to cooperate, support would be easy to standardize and on top of that training costs would stay manageable.

At the time, all this made perfect sense, but in reality this was the biggest mistake that the governments made.

Microsoft’s ecosystem is designed to work best within itself. Office formats like .docx, .xlsx, and .pptx became the default format for documents across governments and institutions. Exchange became the standard for enterprise email infrastructure and entire administrative workflows were built around these products.

So, basically governments were replacing decades of workflows, retraining employees, migrating legacy systems, and making sure everything remains compatible with external organizations that still depend on Microsoft products themselves.

Read more

This one’s a little different.
No tech breakdown today.

After more than a year of explaining technology here, and the better part of a decade doing it everywhere else, I want to use this one post to talk about SK NEXUS itself.

Where we are, what’s changing, and why.

If you’re new here, the short version: almost none of this affects you.
Most of what we publish stays free, and you can keep reading exactly as you have been.

If you’ve been with us for a while, this is the one I’d really like you to read to the end.

Some context first; SK NEXUS is old, by internet standards. It started around eight years ago as a blog about tech, and it has lived on Facebook Groups, Medium, Wix, a custom-built site, and now Substack.

The platforms kept changing.
The job never did: explain what tech actually means, not what marketing says it means.

Today it’s three of us. I edit and write, and two contributors research and write alongside everything else going on in their lives.

Between us, this publication takes 70 to 80 hours a month. More than a hundred articles on this site, readers in 65 countries, and every word of it free since day one.

I’m laying that out so the rest of this post has its shape.
This is a small, stubborn thing, older than half the platforms it has lived on, run by people who do it because they love it.

The Work That Gets Scraped

Some of you may be familiar with our Deep Dives.

When we publish one, the kind where we trace a company’s whole arc or take a technology apart piece by piece, that single article can take a month, sometimes more.

Researching it, sourcing it, checking it, rewriting it until it actually makes sense to a normal person.

These are the pieces we’re proudest of: the story of the one Dutch company every advanced chip on earth depends on, the month-long trace of how NVIDIA went from a gaming-chip company to the backbone of AI, the full arc of how computers became the thing in your pocket.

Go read any of them and you’ll find something you won’t get easily on an internet drowning in listicles and hundred-word pages dressed up as articles.

Here’s what happens to that month of work.

An AI scrapes it. The summary gets served to someone who never visits the page. The impressions climb, and almost nobody reads the real thing.

That chip-monopoly piece has been shown in search results more than forty-seven thousand times.

Forty of those turned into actual visits.
Forty.

And I can see the same story in our own logs: the AI engines crawl us constantly, and on a good day they send back one or two visitors. The scraping only goes one way.

And it’s not us having a bad quarter. Independent tracking found that scraping of tech and consumer-electronics content more than doubled over the past year, making it the single most scraped category on the internet.

Our exact lane.

The State of the Feed

There’s a second half to this, and it’s the platform itself.

That month of work goes out into a feed where newsletters publish AI-written filler every single day. Generic, churned out without anyone really thinking it through. And promoted anyway, because the system mostly rewards whoever switched on a paid button and kept posting.

For a while I wondered if I was imagining it.

Then someone measured it. An independent analysis ran AI detection across the top bestselling publications on Substack, and technology came out as the most AI-flooded category on the platform. Roughly one in four top tech posts showed heavy machine generation. The category we write in is the one drowning fastest.

And then there are badges.
The bestseller badges here are earned with paid subscribers, a hundred of them for the first one. The leaderboards rank publications by paid revenue and paid subscriber growth.

Go look for a free publication on any of them; we haven’t found one. As far as we can tell, most of the discovery machinery runs on paid subscriptions, which tracks, because a cut of paid subscriptions is how the platform earns.

I’m not blaming Substack, it’s a business making rational choices. But it means a free publication is close to invisible to that machinery, no matter how careful the work is.

The Problem With Free

There’s a third thing I’ve had to admit to myself, and this one’s about me.

When everything you make is free, people treat all of it the same. And the assumption attached to free is that it can’t be worth much. I caught myself doing exactly this not long ago. I paid for a workshop, real money, and I sat through it taking notes, valuing every minute. The same material offered to me for free, I’d probably have half watched and forgotten. Paying changed how I treated it.

That’s just how people work.

Before a price tag is ever about money, it’s a signal that says this is worth your attention. Right now our deepest work goes out free into a feed where free reads as disposable, and gets mistaken for the same filler we’re trying so hard not to be.

Lost Patience

And one day, it just ticked me off.

I haven’t lost interest in tech, or in writing about it.
We’ve got something like thirty pieces in the pipeline right now, and every one of them gets reviewed and edited until it’s the best we can do at the time. I myself write across more than four surfaces on a regular basis.

What I’ve lost patience with is the growth game.
Chasing numbers on a platform that buries careful work under a daily flood of the careless kind.

I wrote recently on my personal blog about how every metric eventually rots into a vanity metric. Followers can be bought. Engagement can be botted. Somewhere in the last year, my math changed.

I would rather write for one person who genuinely cares what we think than a hundred who tap a like and scroll on without really reading.

A smaller room of people who value the work beats a big number that doesn’t.

The Decision

You’ve probably read a dozen going-paid posts by now.
They follow a script: a wall of reasons, a pricing table, a paragraph of feelings.

So I’ll give you the part that script usually skips.
This took most of this year to decide.

And what finally settled it was noticing what the current setup was doing to us: the work we care most about getting treated as disposable, and the three of us slowly learning to care a little less about making it.

That’s the thing being protected here, more than any revenue line: the will to keep doing this properly.

So here’s the change, and we’re rolling it out as we go.
The switch flips on with this post. The rest gets handled over the coming weeks: sorting the mechanics, working back through the archive, catching the edge cases. I’d rather start straight with you and adjust than wait for everything to be perfect.

Deep Dives go paid, the ones already up as well as the new ones. That’s the certain part. They cost the most to make, and they’re the work I least want to see treated as disposable, so that’s where the paid tier begins.

For series, we’re trying a split. Part 1 stays free, so you can read the opener and decide if it’s for you. The later parts go to paid subscribers. You always get a real piece of every series for free, and the deeper installments help fund the work behind them. This one’s more of a judgment call than a rule, and we’ll see how it feels in practice.

Paid subscribers also get Substack Chat, where we share rougher thoughts and what we’re digging into before it becomes an article.

Where the support goes is simple: paying the team fairly, covering the tools, and buying the room to go deeper. This is still a passion project. It just needs to become one that can sustain itself.

What’s Not Changing

Every standalone article in the categories you already read us for stays free: consumer education, emerging tech, industry highlights, gaming, everyday security, and before you buy.

Those are the core of SK NEXUS, and they stay open. So does everything we have already published in them, going all the way back. Deep Dives are the exception.

And the free side is going to get better, not thinner. We’re going to be more active on Notes, post more often, put out more of the short, useful stuff.

Nobody here is trying to starve the free reader into paying. The deep work goes on a pedestal where it stops getting mistaken for filler, and the everyday work, which is growing, stays open to everyone.

To Wrap Up

We write for the everyday curious person, whether they’re in Karachi or Dubai. And I know that in a lot of the world, the price of a subscription is not a rounding error. It’s a real decision, weighed against real things.

If that’s you, hear me clearly: the free side of SK NEXUS was built for you, on purpose, and it is not a consolation prize. Most of our articles, every Note, the comments, and the whole back catalogue outside the Deep Dives, all of it stays open, and there will be more of it than before.

Stay, read, push back in the comments, send a piece to a friend. That counts as support too. It always has.

In the end, depending on who you are, this goes one of two ways.

If you just found us, none of this should worry you. Most of what we do is free, you’ve got more than enough articles to dig through without paying a cent, and the conversation’s open. Read, comment, stick around.

If you’ve been here for a while, reading, replying, sending our stuff to a friend, then this one’s basically for you, and I’ll just say this - Thank You!

Genuinely.
You’re the reason there’s anything worth making sustainable in the first place.

This wasn’t an easy call. It’s the kind of decision you only make because the ground shifted under you, the scraping, the flood of machine made filler, the way good work gets buried now.

We’d rather meet that honestly, with you, than let the thing wind down without ever telling you why. And if going paid doesn’t work, you’ll hear that from me too.

A while back we ended our Year in Review by saying independent media survives on reader trust.

I still believe that.

But now I’ve just learned the other half: trust is what you build on.
It doesn’t pay the writers or fund the next big step by itself.
Sustainability is what turns the trust into something that lasts.

We’ve spent a year earning yours. If you’re in, you can support the work.
If you’re not there yet, keep reading. The door stays open either way.

See you in the next one.

Saqib Tahir
Founder and Editor
SK NEXUS

If you play PC games, something a little strange is happening on your screen right now.

Your graphics card is drawing frames as fast as it can. Your monitor is refreshing at its own fixed speed. Two different machines, two different clocks, and nobody told them to agree.

Most of the time you don’t notice. But every so often they fall out of step, and you get screen tearing: that ugly horizontal split where the top of the screen shows one frame and the bottom shows the next.

You’ve probably seen it a hundred times, mid-turn in a shooter or during a fast camera pan. You just never had a name for it.

Let’s get into it.

So, this article is the last in a series of 3 articles where we talked about the hidden tech behind our games that most of us don’t see.

The first article in this series covered upscaling. It explained how your GPU is kind-of lying to you when it comes to rendering your games. It is linked below:

The second article covered frame generation. In this one I talked about how AI has been inside frames of your video games for quite some time now. It’s linked below too

The article you’re going through now is the last article in that series where we’ll go through frame-sync and latency improving technologies like Reflex, Anti-lag and so on.

The Monitor GPU Sync Issue

Your GPU sends frames to your monitor. Your monitor refreshes on its own clock. Those two clocks do not naturally sync. That speed is measured by Hz for your monitor and fps for your GPU.

When the Hz of your monitor doesn’t sync with the FPS your GPU is generating screen tearing in your video games.

That is when a horizontal split appears on screen where two different frames show up at the same time. Very noticeable in fast camera pans or quick turns in shooting games.

Most people have seen this happen but never knew what it was called.

The reason it happens is simple. Your monitor refreshes from top to bottom at a fixed rate, usually 60 times per second on a standard display.

If your GPU sends a new frame mid-refresh the monitor draws part of the old frame and part of the new one. The result is that visible horizontal line across the screen.

It gets worse the more your frame rate fluctuates. And in real games your frame rate fluctuates constantly.

One moment you are staring at a wall getting 120 frames per second. Next moment you turn around and see an explosion with ten enemies on screen and your frame rate drops to 45.

Your monitor does not care. It keeps refreshing at 60Hz no matter what your GPU is doing.

That mismatch is the core problem every sync technology has tried to solve.

What Screen Tearing Actually Is

Let’s get specific about what is happening when you see that split.

Your monitor refreshes from top to bottom at a fixed rate. If your GPU sends a new frame mid-refresh the monitor draws part of the old frame at the top and part of the new frame at the bottom.

The result is a visible horizontal line across the screen.

In a racing game when you pan the camera quickly the tear line is obvious. Half the image shows your car in one position, the other half shows it slightly ahead.

Your brain notices this immediately even if you do not know what to call it.

The faster your frame rate moves relative to your monitor refresh rate the more tearing you see. A game running at 100fps on a 60Hz monitor will tear constantly.

A game locked at exactly 60fps on a 60Hz monitor will not tear at all because the refresh rate matches perfectly. But real games do not hold perfect frame rates.

You hit a particle effect, frame rate dips to 50. You look at the sky, frame rate jumps to 80. The monitor refresh stays at 60 the whole time.

That is why tearing has been a fact of life in PC gaming for decades.

The Old Fix That Made New Problems

The first widely used fix for screen tearing was VSync. Short for Vertical Synchronization.

VSync forces your GPU to wait until your monitor finishes refreshing before sending the next frame. This eliminates tearing completely.

To explain it simply, VSync forces your GPU to slow down to the speed of your monitor so both can remain in sync and there is no screen tearing.

But as with most technologies, there is a big tradeoff with VSync. If your GPU misses a refresh cycle you drop to the next sync point.

On a 60Hz monitor that means if you dip below 60fps you suddenly drop to 30fps because the GPU has to wait for the next available refresh window.

That causes stutter. Very noticeable stutter but still a stutter.

The wait time also adds input lag. The delay between when you click your mouse and when you see the result on screen gets longer.

Competitive players have hated VSync for years because of exactly this. Turning it off and accepting tearing was often considered the lesser evil.

For a long time that was the choice. Tearing with VSync off, or stutter and input lag with VSync on.

Neither option was great but VSync gave us the option to solve the sync issue even though it added some latency.

That latency is normally fine with story-mode games but it was never a solution for games that move fast.

G-Sync and FreeSync Enter The Chat

G-Sync and FreeSync arrived in 2013 and 2015 respectively. They solved the problem by flipping it around.

Instead of making the GPU wait for the monitor, these technologies make the monitor wait for the GPU.

The monitor dynamically adjusts its refresh rate to match whatever the GPU is outputting at that moment.

If your GPU sends a frame at 47fps the monitor refreshes at 47Hz. If your GPU jumps to 92fps the monitor refreshes at 92Hz.

No fixed clock conflict. No tearing. No stutter.

G-Sync is NVIDIA’s proprietary version. It requires a hardware module inside the monitor which is why G-Sync monitors used to cost significantly more than regular displays.

Basically, if you wanted to use G-Sync you had to buy a G-Sync capable monitor and when this technology started out, they weren’t that cheap.

FreeSync is AMD’s open standard version built on VESA Adaptive-Sync. It does not require special hardware in the monitor so FreeSync displays are cheaper.

Since 2019 NVIDIA GPUs also work with FreeSync monitors under the G-Sync Compatible label. NVIDIA tests certain FreeSync monitors and certifies them as working properly with their cards.

The practical difference today is minimal for most users. Both technologies eliminate tearing and stutter by letting the monitor follow the GPU instead of the other way around.

If you play on a PC built in the last five years and your monitor supports either technology you are probably already using it without thinking about it.

G-Sync monitors still tend to be more expensive but the performance gap has closed significantly. However, this situation is changing fast because NVIDIA just released sort-of version 2 of G-Sync.

This is called G-Sync Pulsar and not only is this technology much better but also cheaper to implement for monitor makers. G-Sync Pulsar mainly deals with ghosting issues inside gaming.

The technology is so cool that it’s hard to go through it in this article but I’ll be covering it in a separate article in full detail here on SK NEXUS.

What Is NVIDIA Reflex?

Screen sync fixes tearing but it does not fix input lag caused by frames sitting in a render queue waiting to be sent to the display.

That is where NVIDIA Reflex comes in.

Reflex works by synchronizing CPU and GPU work so frames are produced right when they are needed rather than queued up ahead of time.

Normally your CPU submits work to the GPU in advance. The GPU builds up a backlog of frames to render. By the time a frame reaches your screen it is showing input from several frames ago.

Reflex reduces that queue. Frames are produced closer to the moment they are displayed which dramatically cuts the gap between your mouse click and what you see on screen.

NVIDIA says Reflex reduces system latency by up to 50 percent across supported games.

At the moment, 150+ games support NVIDIA Reflex. Most competitive shooters like Valorant, Apex Legends, Call of Duty, and Fortnite, all support it.

The effect is most noticeable in fast-paced games where reaction time matters. Casual single-player games do not benefit as much.

For competitive players Reflex is considered essential. The difference between 60ms latency and 30ms latency is the difference between hitting a shot and missing it.

Although for our average Bashir playing story-mode games, it isn’t that necessary for him. He can choose to use Reflex or not.

You do not need special hardware to use Reflex. Any NVIDIA GPU from the GTX 900 series onward supports it as long as the game has implemented it.

Innovation in tech like Reflex has made NVIDIA that 5 trillion dollar giant it is today. But technologies like these aren’t what make NVIDIA its most money these days.

To find out why, check out my NVIDIA deep-dive article linked here.

Reflex 2 and Frame Warp

NVIDIA announced Reflex 2 at CES 2025 alongside the RTX 50 series cards. Note it was announced and hasn’t released yet.

Reflex 2 adds a new feature called Frame Warp. It warps the already-rendered frame to reflect the very latest mouse input right before it is sent to your display.

In NVIDIA’s own testing it reduced latency to ~14ms in The Finals at 4K on an RTX 5070 running at max settings. Down from 56ms without Reflex.

That is a massive improvement if it works as advertised.

Here is the catch though. NVIDIA has still not officially released Reflex 2 in a shipping game as of April 2026.

A modder named PureDark built a working demo from extracted binaries in late 2025 and confirmed it works on RTX 20 series cards too. But NVIDIA has not made it available to the public yet.

The company has not given a clear timeline on when Reflex 2 will actually ship in games.

So for now Reflex 2 exists in demos and modder builds but not in anything you can actually download and play officially.

AMD Anti-lag and the Other Side

AMD’s equivalent to Reflex is called Anti-Lag.

It reduces the render queue between CPU and GPU to cut input lag similarly to what Reflex does.

Anti-Lag 2 introduced per-game integration starting with Radeon Software Adrenalin. It works best paired with FreeSync just as Reflex works best paired with G-Sync.

AMD users do not get Reflex because it is NVIDIA-exclusive. But in supported games Anti-Lag 2 closes most of the gap.

FreeSync Premium monitors are widely available at prices significantly lower than full G-Sync hardware modules. That makes the AMD path more accessible if you are building on a budget.

The technology itself is comparable but the main difference is game support. Reflex has over 150 games as of 2026. Anti-Lag 2 supports around 30 or so games roughly speaking.

If you play competitive shooters on PC chances are the game supports Reflex but not Anti-Lag. That has been the pattern for years.

AMD is working to close that gap but developer adoption has favored NVIDIA’s solution so far.

Just so you know AMD has renamed Anti-Lag 2 to FSR Latency Reduction 2.0 but I went with the simple one because it sounds cleaner.

AMD’s story as a company is more interesting than their Anti-lag technology. The company has risen from the ashes multiple times.

If you’re interested in reading more about AMD’s story, check out my dedicated article linked here.

Neural Texture Compression Is Coming

Nvidia and AMD are both working on AI-powered texture compression for video games.

Instead of storing raw texture data the system stores textures in a compressed AI format. The GPU reconstructs that data into normal textures using AI.

This matters for game install sizes, texture streaming, and how much VRAM your graphics card needs to hold all the visual detail in a scene.

The promise is better quality at lower storage requirements. Instead of saving every pixel you save instructions on how to rebuild the texture.

Nothing has shipped to consumers as of now though. Both companies have demonstrated early versions but neither is available in a product you can actually use.

It fits the same pattern as upscaling and frame gen. Less real data, more AI filling in the gaps.

There will of course be some trade-offs with this compression though. It could be visual glitches or inconsistencies but the hope is that with time these issues will be ironed out.

For now this is mostly a research project. But it is worth knowing about because it shows where the industry is headed.

RTX 5090 And Your Kidney

If you’ve read through the three articles in this series you should have this stamped into your head:

Our GPUs render fewer real pixels and fewer real frames. And smarter delivery technologies (like G-Sync, FreeSync) make our games run better, especially when we’re constrained by our hardware.

As everything is claiming to be “AI-powered” these days, PC gaming has been using AI for quite some time now. It has been used in technologies like DLSS, FSR, XeSS.

These technologies may not make much sense for someone who owns an RTX 4090.

But as the Steam Hardware Survey shows, 99% of us gamers aren’t playing on that $2k RTX 5090 GPU. Maybe because we love our kidneys :p.

And if we turn our heads to developing countries like ours these technologies become even more relevant considering gaming hardware isn’t cheap here.

For people with aging hardware or tight budgets these technologies are the difference between playing new games or not being able to play them at all.

Look, I use an AMD R9 290 GPU from 2014 so I precisely know how gaming feels with these technologies turned off. FSR alone lets me play games that would otherwise be unplayable on this old card.

Things like FSR, DLSS didn’t exist ten years ago. Now these are assumed by default.

Game developers design and test their games with these technologies turned on because that is where most players are at.

And that’s the main thing, these technologies don’t just make your frames go up but they’re the difference between being able to play a game or not for many people.

Access to games you could not otherwise run. Access to smooth performance on hardware that should have been obsolete years ago.

The tech industry loves to sell you new hardware. These technologies let you squeeze more life out of what you already own.

That matters more now than ever with hardware prices where they are.

For now the short version is this. Your GPU and monitor are working together in ways they never used to.

The result is smoother, faster, and more responsive than what the raw hardware should be capable of producing.

Whether that is a good thing or a concerning one depends on how you feel about AI doing more of the work your hardware used to do.

And it’s not like anyone is forcing you. You’re free to disable DLSS, G-Sync but for those who can’t play their games without these technologies, there is an actual impact in their lives.

And many people don’t like the idea of these technologies. Some people genuinely hate “fake frames”.

But whether one likes these technologies or not that is the way how gaming works at the moment. And it is not going back it seems.

If you enjoyed going through the article this far, a subscribe to our publication would make my day, just hit the button below, it’s free:

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Similar Articles From Us

Further Learning

• Nvidia G-Sync puts processors inside your PC monitor

• Nvidia’s new G-Sync monitors automatically adjust brightness just like a laptop

• AMD FreeSync explained

• What is Nvidia Reflex

• Counter-Strike 2 is getting Nvidia’s latency-reducing Reflex tech

• NVIDIA Reflex 2 With New Frame Warp Technology

• NVIDIA Reflex reduces latency by up to 50%

• AMD Quietly Renames Anti-Lag 2 to “FSR Latency Reduction 2.0”

• Nvidia G-Sync vs AMD FreeSync: Which monitor tech is best?

• What Is Screen Tearing and How to Fix it

• What Is VSync, and Should You Enable It?

• Nvidia’s groundbreaking VRAM solution only works best on its fastest GPUs

A few months ago, I arrived at work expecting a normal day.

Instead, I spent a good chunk of it waiting.

Our website had become inaccessible, and since a large portion of my work revolves around publishing, editing, and managing content online, there wasn’t much I could do until things came back.

And it wasn’t just us.

When major cloud providers experience problems, the effects spread far beyond a single company. Over the past few years, outages involving Amazon Web Services (AWS), Microsoft Azure, and Cloudflare have repeatedly disrupted services used by millions of people.

Most of us rarely think about the infrastructure that makes modern technology work. We just assume everything will be there when we need it. And most of the time, it is. But every now and then, something breaks and we’re reminded that the digital world depends on infrastructure that can go down and disrupt the whole world.

The funny thing is, modern technology often feels more reliable than ever. Yet at the same time, many of the systems we rely on have become increasingly centralized, interconnected, and dependent on a surprisingly small number of companies.

And that's what this article is about: how the internet ended up here, why so much of modern life depends on a handful of critical services, and what we can personally do about it.

How the Internet Became More Centralized

When the web started becoming mainstream during the 1990s, the digital world was far more distributed than it is today.

Websites typically ran on their own servers. Companies managed much of their own infrastructure. Services were separated from one another and if one website experienced problems, the impact was usually limited to that particular website.

With the passage of time, that began to change.

As internet services became larger and more complex, companies started looking for ways to reduce costs and simplify operations. Running servers is expensive. Maintaining data centers is difficult. Scaling infrastructure for millions of users requires expertise that many organizations simply don’t have.

Cloud computing solved a lot of those problems.

Instead of purchasing hardware, building server rooms, and hiring teams to manage everything, companies could rent infrastructure from providers like Amazon Web Services, Microsoft Azure, and Google Cloud.

The advantages were enormous - startups could reach global audiences without building their own infrastructure. Businesses could scale faster than ever before. Developers could focus on building products instead of managing hardware.

The cloud, in a way, transformed the internet, but every solution comes with tradeoffs.

When more organizations moved into the same cloud platforms, dependence became inevitable. Thousands of companies that once operated independently now relied on the same providers, the same infrastructure, and in many cases, the same underlying services.

The same thing happened across other parts of the internet - companies adopted the same content delivery networks. The same authentication providers. The same software repositories. The same security certificate authorities.

Each decision made sense on its own.

Collectively, however, they created a very different internet than the one that existed decades ago.

The Hidden Fragility of Modern Technology

The Companies Holding Up the Internet

Some people might argue that the internet itself remains distributed. While that may be true, many of the services running on top of it have become heavily centralized.

Cloudflare is a good example - the company provides a lot of services for millions of websites. Depending on the measurement used, Cloudflare helps power or protect roughly one-fifth of the web, making it one of the most important pieces of infrastructure most people have never heard of. Recent outages have demonstrated just how much disruption a single failure can create when so many services depend on the same provider. In June 2026, another major disruption involving internet infrastructure providers affected many services, once again demonstrating how failures can ripple across large portions of the web.

Amazon Web Services represents another major concentration point - thousands of companies no longer operate their own infrastructure. Instead, they rent computing resources from AWS. That includes streaming platforms, collaboration tools, financial services, government systems, and countless other online services.

When AWS experienced a major outage in late 2025, the effects extended far beyond Amazon itself. Services including Slack, Zoom, Canva, Snapchat, Roblox, and many others experienced these disruptions as well.

Similarly, Let’s Encrypt issues a huge portion of the internet’s TLS certificates, helping websites establish secure connections. GitHub hosts an enormous percentage of the world’s software projects and development workflows.

More recently, AI providers have started becoming another concentration point. The internet itself does not depend on ChatGPT or Claude in the same way it depends on cloud providers or certificate authorities, but many organizations increasingly rely on these platforms for day-to-day activities.

When these services experience outages, productivity often slows down across thousands of organizations at the same time. Both OpenAI and Anthropic experienced multiple service disruptions throughout 2026, highlighting how quickly businesses have started building workflows around a relatively small number of AI providers.

The Growing Complexity of Modern Systems

For years, technology companies have been competing to add more features to their products. Your email application gets an AI assistant. Your phone receives new AI capabilities every few months. Every software update promises to do more than the one before it.

From a user perspective, some of these additions are genuinely useful, but the problem is that every new feature introduces additional moving parts behind the scenes.

A modern application is very different from the software people used twenty years ago. Back then, a program was often self-contained. Today, even relatively simple applications can rely on hundreds or thousands of software packages, external services, APIs, and cloud-based systems working together at the same time.

And with time, that complexity starts to accumulate.

You’ve probably noticed this yourself - computers are dramatically faster than they were a decade ago. Yet somehow, many applications feel heavier than the software they replaced.

Part of this comes from what developers often refer to as software bloat - companies keep adding functionality to their existing product because new features are easier to market than stability improvements. Nobody runs an advertising campaign around reducing technical debt or simplifying backend infrastructure. But AI-powered search, collaboration tools, assistants, and dashboards are much easier to sell.

The consequences become even more apparent when you look at how modern services are built.

Suppose you open your banking application. At first glance, it feels like you’re communicating directly with your bank. But the request might pass through content delivery networks, authentication providers, cloud infrastructure, and several other third-party systems before you ever see your account balance.

A lot of the time, none of this matters because everything works, but when one component fails, the effects don’t always remain isolated.

Researchers studying distributed systems have repeatedly documented what are known as cascading failures. A problem in one service can spread into another service, which then affects another, creating a chain reaction that becomes increasingly difficult to predict and diagnose.

And recently, Artificial Intelligence has been added on top of all this. Every company is in a race to shove more “AI powered” features into their products which creates even more dependencies that didn’t exist a few years ago.

The Volunteers Holding Up the Internet

Another weakness that often goes unnoticed is how much of the internet depends on software maintained by people many users have never heard of.

A huge portion of the internet also depends on open-source software projects that are freely available for anyone to use.

OpenSSL is one example. The software helps secure communications across millions of websites and online services. For years, despite being used throughout much of the internet, the project operated with limited funding and was maintained by a surprisingly small team.

In 2014, researchers discovered Heartbleed, one of the most significant internet security vulnerabilities in recent history. The flaw affected a huge number of systems around the world and exposed how much critical infrastructure depended on a project that lacked the resources many people assumed it had.

A similar story played out years later with Log4j. People didn’t even know about its existence until the Log4Shell vulnerability was discovered in 2021. Yet the software was deeply embedded inside enterprise systems, cloud platforms, government networks, and business applications around the world.

Neither OpenSSL nor Log4j is an unusual example, and the same pattern is everywhere.

Large portions of the internet depend on software maintained by small groups of developers, nonprofit organizations, and volunteers. In many cases, these projects are not generating billions of dollars in revenue despite being used by companies that are.

This has become such a well-known issue that a famous XKCD comic about software dependencies is regularly referenced throughout the open-source community. The comic jokingly depicts modern digital infrastructure resting on a tiny project maintained by a random individual that nobody has heard of.

The Infrastructure Dependence Trap

At this point, a reasonable question starts to emerge.

“If companies know they’re becoming dependent on a small number of providers, why don’t they simply move elsewhere?”

The answer is that leaving is much harder than joining.

You can see a smaller version of this in consumer technology as well.

Suppose you use an iPhone. Chances are you’re also using iCloud, AirPods, Apple Photos, Apple Notes, Apple Watch, and several other Apple services. None of these products are impossible to replace, but doing so requires time, effort, and often a willingness to change habits you’ve built over years.

The same thing happens with Google’s ecosystem. Gmail connects to Google Drive. Google Photos connects to your phone. Chrome syncs across devices. Everything becomes interconnected.

Now imagine that same problem on a much larger scale.

Companies spend years building systems around platforms like AWS, Azure, Google Cloud…etc. Applications are designed around specific services. Employees are trained to use particular platforms and workflows.

With the passage of time, entire businesses become tied to the ecosystems they started with.

Moving away isn’t impossible, but it can take months or years of planning, testing, and migration. In some cases, organizations discover that leaving creates more disruption than staying.

This is often referred to as vendor lock-in - the deeper an organization becomes embedded inside an ecosystem, the harder it becomes to leave. And because of that, many companies continue relying on the same providers even when they are uncomfortable with how dependent they’ve become.

The Risks Extend Beyond Consumer Technology

Up until now, most of the examples we’ve discussed have focused on websites, applications, and online services, but the consequences extend much further than that.

Modern cloud infrastructure now supports healthcare systems, financial institutions, transportation networks, educational platforms, and government services. Many of the systems that societies rely on every day are increasingly built on the same digital foundations.

Suppose some outage happens and a hospital loses access to critical systems, the consequences are very different.

Over the past several years, outages involving cloud providers have repeatedly affected organizations far beyond the technology sector. Hospitals have experienced disruptions to scheduling systems and patient records. Banks and payment providers have dealt with service interruptions. Government agencies have experienced outages affecting public services and internal operations.

The exact impact varies from incident to incident, but the bigger trend is becoming difficult to ignore - more and more critical services are becoming dependent on the same underlying infrastructure and as the concentration increases, failures have the potential to affect much larger portions of society than they did in the past.

Why These Risks Matter Beyond Technology

It’s hard to ignore the fact that more services are being consolidated onto the same cloud providers. More organizations are becoming dependent on the same platforms. More systems are becoming interconnected.

None of this guarantees a disaster, but it does increase the consequences when something eventually goes wrong.

We’ve already seen glimpses of what that can look like. Cloud outages have taken thousands of services offline simultaneously. Cyberattacks have disrupted supply chains and critical infrastructure. Undersea cables have been damaged by accidents, natural disasters, and geopolitical tensions, affecting connectivity across entire regions.

And that’s before we start considering scenarios involving multiple failures occurring at the same time.

Suppose a major cloud provider experiences a significant outage while a cyberattack is actively targeting connected systems. Or imagine a geopolitical conflict affecting data centers, internet exchanges, or critical communication infrastructure. Individually, each of these events is manageable. Together, they become much harder to predict and that’s where the real danger lies.

Reducing the Impact on Yourself

By now, you might be wondering what any individual can actually do about this.

The reality is that most people are not going to build their own infrastructure, run their own data centers, or completely disconnect from large technology platforms. Modern life simply doesn’t work that way anymore.

What people can do is reduce how dependent they become on any single service.

One simple example is keeping backups of important information. Many people assume that cloud services are permanent, but outages happen, accounts get locked, companies shut down products, and mistakes occur. If something is genuinely important, it’s usually worth keeping a copy somewhere under your own control as well.

It’s also worth paying attention to which services you rely on most. Some important questions which you should ask yourself are:

• If your email provider suddenly became unavailable tomorrow, what would happen?

• If your cloud storage stopped working for a day, could you still access important documents?

• If your password manager experienced an outage, would you still be able to access critical accounts?

Most folks never think about these questions until something breaks. A small amount of preparation can often take you a long way.

Where This Leaves Us

If there’s one thing I hope this article showed, it’s that modern technology is often far less independent than it appears.

The internet gives the impression of being a vast, distributed network made up of millions of different services. And on the surface, that’s true.

But underneath that surface, many of those services depend on the same infrastructure and the same organizations. That doesn't mean the internet is on the verge of collapse. But it does mean that stability isn't the same thing as resilience.

For years, technology has become so capable. At the same time, many of the systems we depend on have become increasingly interconnected, which we never notice because everything works just fine. It's usually only during an outage that we get a glimpse of how much depends on the same underlying foundations.

As individuals, there is only so much we can do. The larger questions around infrastructure concentration, competition, and regulation will ultimately be decided by governments, companies, and policymakers.

What we can do is understand the systems we rely on, reduce unnecessary dependence where possible, and prepare for the reality that outages are not exceptions. They’re part of how complex systems behave.

What You Might Like

• The Illusion of Choice in Tech

• Strong Passwords Still Get Hacked - Here’s Why

• The Illusion of Choice: How Tech Decides Before You Do

• The Death of Ownership: Why Your Tech Isn’t Really Yours Anymore

If you read our last piece on upscaling, you already know that your GPU has been tricking you for a while now.

Drawing your games at a lower resolution and then letting AI fill in the missing pixels. This was your GPU’s first trick but it doesn’t stop there.

In addition to upscaling, your GPU has another trick up its sleeve which it uses to smooth out your gameplay. That is called Frame Generation.

Where upscaling makes your games look sharper without actually drawing all the pixels, frame generation goes one step further.

It makes your games feel smoother without your GPU actually drawing all the frames.

In extreme cases, only one out of every six displayed frames may be traditionally rendered. The rest are software inventions.

That sounds weird to read but the frame generation technology is pretty cool.

And it’s more relevant now than ever, because of AI demand adding to graphics card prices.

Like any other technology, frame generation isn’t a silver bullet and it comes with trade-offs that marketing material often downplays.

That’s where this article comes in. I’ll go over what Frame Generation is and how it affects us gamers, especially those on a budget.

This article you’re reading is second in a 3-part series on gaming technologies that go unnoticed. Other articles in this series are linked below:

What Even Is a Frame?

Before frame generation makes any sense, let me explain how video games generally work.

When you play a video game, your graphics card is basically drawing lots of images in a matter of seconds. Each one of those images is called a frame.

Your monitor then displays those frames one after another, and your brain stitches them together into smooth motion.

The number of frames your GPU draws per second is your frame rate. If you are getting 60 FPS (frames per second), your GPU is drawing 60 full images every second.

The general rule is: the higher the FPS, the smoother the gameplay.

At 30 FPS, your GPU is drawing a new frame roughly every 33 milliseconds. At 60 FPS, it is every 17 milliseconds.

Now here is the catch.

More demanding games running at higher resolutions push even powerful GPUs to their limits. Hitting 60 FPS in a heavy game at 4K is genuinely hard work.

Hitting 120 FPS is much harder. And modern high refresh rate monitors, some running at 240Hz or higher, want a lot of frames to stay busy.

What Is Frame Generation?

Frame generation (aka Frame Gen) is a software technology that basically invents new frames and inserts them between the real ones in real-time while you play your games.

Your GPU still does the actual work. It draws a real frame, then draws another real frame.

Frame generation software then looks at those two real frames, analyzes what moved and where it moved to, and creates a new frame that sits between them in time.

Your monitor then displays the sequence: real frame, generated frame, real frame, generated frame.

In its basic form, frame-gen looks like twice the frame rate, even though your GPU only did half the drawing work. And that is basically frame generation.

The basic version of this is called 2x frame generation. One generated frame for every real frame. That is what AMD has offered for a while.

But the technology has moved fast and things have gotten more aggressive.

Frame generation is sometimes confused with upscaling because companies like NVIDIA bundle both into the same package like DLSS.

However, from a technology POV upscaling and frame generation are actually separate things doing separate jobs.

Upscaling makes each frame look sharper. Frame generation makes more frames appear.

You can use one without the other, though in practice most games bundle both together under a single option like DLSS or FSR.

How Frame Gen Actually Works

What frame generation does is not as simple as just copying a frame or blurring two together.

The software analyzes motion between two real frames. It builds what is called an optical flow map, which is basically a picture of how every part of the scene moved between those two moments in time.

Then it uses that map to predict where everything should be at the midpoint and renders that.

Think of it like watching two photos of a running person and being asked to draw the frame in between. If you know where their leg was in photo one and where it is in photo two, you can make a pretty good guess at where it was halfway through.

Modern frame generation, especially the AI-powered versions, is significantly more complicated than this example but you get the point, right?

In recent years, frame generation has gotten so good that it now looks quite real to the eyes of mere mortals :p.

NVIDIA’s Frame Gen

Just like upscaling technology in video games, NVIDIA was the company that pushed frame generation into the mainstream.

They introduced it with DLSS 3 back in 2022 with the RTX 40-series cards.

The basic idea was the same as above: draw one real frame, invent one in between, display both. This effectively doubled the visible framerate.

What made NVIDIA’s tech stand out was the hardware behind it. RTX cards have dedicated AI chips called Tensor Cores. These are chips designed specifically to run AI workloads quickly.

NVIDIA uses them to power its frame generation, which means the process is faster and adds less overhead than doing the same work purely in software.

Then at CES 2025, NVIDIA introduced DLSS 4 with Multi Frame Generation for their RTX 50-series cards.

Multi Frame Generation or MFG can generate up to 8x fps over traditional rendering when used with DLSS upscaling.

MFG is basically the big brother of frame generation :p

By early 2026, over 250 games had already added support for DLSS Multi Frame Generation, making it NVIDIA’s most rapidly adopted gaming technology.

Then at CES 2026, NVIDIA went further with DLSS 4.5.

The company increased the maximum frame generation multiplier from 4x to 6x for RTX 50-series users.

DLSS 4.5 also introduced Dynamic Frame Generation, which lets the software automatically adjust how many frames it is generating based on what your monitor can actually display.

By March 2026, the full DLSS 4.5 feature set became available including Dynamic Multi Frame Generation and the new 6x mode.

The important caveat here is that 6x Multi Frame Generation is exclusive to RTX 50-series cards. You’re kinda left out if you don’t have a 50-series NVIDIA card.

RTX 40-series cards are limited to traditional 2x Frame Generation, while RTX 20 and 30-series cards do not officially support DLSS Frame Generation.

GeForce graphics cards are what made NVIDIA’s name but today, revenue from graphics cards seems tiny compared to their AI-revenue.

To learn how NVIDIA got to be a $5 trillion empire and the heart of this AI revolution, you can read my deep-dive article on NVIDIA linked here.

AMD’s Take on Frame Gen

AMD introduced frame generation with FSR 3 in 2023. Their pitch, as always, was broad compatibility.

FSR 3 frame generation works on any GPU, not just AMD cards. You don’t need an AMD card to use it. That openness is genuinely useful for people (like me) with older or non-AMD hardware.

The trade-off was quality. The original FSR frame generation ran on regular shader cores rather than dedicated AI hardware. It produced some glitches on fast-moving objects and the results were not quite as clean as NVIDIA’s implementation.

AMD’s newer answer is FSR Redstone.

The AI-based FSR Frame Generation is designed specifically for AMD RDNA 4 architecture GPUs, the RX 9000 series, delivering higher quality results with fewer artifacts than the older version.

Here is where AMD is behind the competition right now. AMD’s frame generation is still limited to 2x. This means one generated frame for each real frame.

Intel and NVIDIA have both moved to multi-frame generation while AMD has not yet.

However, that appears to be changing soon because AMD recently hinted at upcoming multi-frame generation support.

AMD is probably having the best moment of its gaming history but for the most part they’ve been catching up with NVIDIA.

To know more about why AMD had to play catch-up with big boys like Intel and NVIDIA, check out my deep-dive article on AMD’s comeback here.

AMD has previewed FSR Diamond, the evolution of its frame-generation technology especially for consoles like next Xbox.

So if you are on an AMD card and frustrated that you are stuck at 2x while NVIDIA users brag about 6x, help may be on the way.

Intel and the Frame Gen Race

Intel is the newest player in this space and they have been quietly doing cool stuff.

Intel introduced XeSS frame generation as part of their Arc GPU lineup. For a while it was basic, similar to early FSR. But in early 2026 they made a big move.

Intel released XeSS 3 with Multi Frame Generation in January 2026, offering 2x, 3x, and 4x modes. Any game already supporting XeSS 2 frame generation would be compatible with XeSS MFG with no developer update required.

Then things got even more interesting.

In April 2026 Intel expanded XeSS 3 Multi Frame Generation support to virtually all Arc GPUs, including their older generation graphics cards and integrated graphics too.

That last part is remarkable. Multi-frame generation on integrated graphics chips inside laptop processors. Nobody else is doing that.

Intel even enabled multi-frame generation support across three generations of Intel Arc integrated graphics solutions.

Few GPU vendors have extended frame-generation support this broadly.

The catch with Intel is game support. XeSS as a whole has fewer supported games than DLSS or FSR. That is the ongoing challenge for Team Blue.

Intel’s technology works but it takes time for developers to actually implement it which is the harder problem.

Intel’s first graphics card entry failed and now they’re probably the newest entrant to graphics.

Intel lost the crown of being the best CPU maker to AMD. We covered Intel’s fall from the top in our Intel deep-dive article linked here.

The Input Lag Problem

Here is the thing nobody in the marketing materials will say very clearly.

Frame generation makes your game look smoother. It does not make your game feel more responsive.

Those are two different things and the difference matters a lot depending on how you play.

When your GPU draws a real frame, that frame contains information about where your mouse was and what buttons you pressed at that exact moment.

It is a true snapshot of what the game world looked like when you were interacting with it.

A generated frame does not have that. It was invented based on two real frames that already existed. It does not know what your mouse did. It fills in the gap based on what was already moving in the scene.

In competitive settings, the extra frames from frame generation do not help reduce latency.

The input lag remains tied to the speed at which the base game loop runs, and frame generation adds overhead that can actually increase the total time between your input and the response appearing on screen.

Think of it like getting a text message summary of a conversation that already happened. The summary looks detailed and covers everything. But it was written after the fact. Your input was not part of it.

NVIDIA has Reflex and AMD has Anti-Lag to help reduce this penalty. These tools lower the overhead and try to get you back toward a normal latency baseline.

These technologies reduce latency but they only bring you back to a baseline level. They do not make frame generation faster than native frames. They reduce the penalty without eliminating it.

The practical rule that most experienced PC gamers have landed on is: if you are playing single player story games like GTA 5, Red Dead Redemption 2, frame generation is great.

But when you are playing multiplayer games (competing with humans online) like Valorant, Call of Duty, etc., frame generation is not a good idea.

When Frame Gen Helps and Hurts

Look, frame generation isn’t magic.

It works best when your base frame rate is already decent. If you are running a game at 50 to 60 FPS natively and frame generation bumps that to 90 or 100, that is a genuine improvement.

Your game will feel smoother and the experience is noticeably nicer without obvious downsides.

Playing games like Cyberpunk 2077 on max settings can quickly take your performance down to 40 or 50 FPS.

Frame generation in these scenarios can push your displayed frame rate past 100 without requiring you to drop visual settings.

Where it falls apart is when your base frame rate is already too low. Multi-frame generation generally works best with a base frame rate of at least 60 FPS to avoid latency issues.

If your GPU is only getting 25 or 30 FPS natively and you turn on frame generation to get 60, you’ll have a bad experience. Visual glitches also get more noticeable at very low base frame rates.

Some developers have also been using frame generation as a crutch, launching games with heavy optimization issues and relying on frame generation to mask the stutter.

Frame generation cannot fix CPU bottlenecks. It can hide GPU frame rate problems but not everything.

You may have seen games in the last year or two that ran at oddly low native frame rates but shipped with frame generation on by default. That is not always a sign of a well-made game.

The ideal profile for frame generation is a game that runs at a solid 50 to 70 FPS natively, where you want visual smoothness more than you need razor-sharp response times.

That is the sweet spot and usually most people don’t need razor-sharp response times all the time so that makes technologies like frame generation actually useful.

The Future Of Frame Gen

Frame generation has gone from an exclusive feature to something all three major GPU makers now offer. That happened in just about three years.

The multipliers keep going up. NVIDIA is at 6x on their latest hardware. Intel is at 4x. AMD is catching up with multi-frame support likely coming soon.

Valve’s recently released Steam Machine uses upscaling and frame generation to achieve their performance targets. Valve has partnered with AMD to bring FSR 4 to their Steam Machines as well.

So, I feel there are two angles to frame generation:

The first one is that frame generation has been a really cool technology that has allowed gamers on a budget to play games that once ran badly on their systems.

I’m a testament to that. It’s great to have FSR supported on my graphics card even though it’s more than a decade old at this point.

And the way gaming hardware has shot up in prices because of AI-related demand, not everyone can afford 40-series graphics cards.

That’s where Intel’s open approach helps people who may not even have an Intel card, that’s a really positive thing.

But there is another concern: many developers have become complacent because of these technologies.

Instead of doing their best to optimize their games so they use resources efficiently, some game developers have started to lean on frame generation to take care of that.

Frame generation is like a supplement rather than a replacement for native performance. But the line between what your GPU rendered and what software invented keeps getting harder to find.

For now though, frame generation is increasingly common on gaming systems that support it.

If you have a reasonably modern GPU and you are playing a supported game, there is a good chance software is inventing at least some of what you are seeing.

And in single player games (that most of us play) the majority won’t even notice it.

Now that you’ve made it so far let me share a nugget of information.

The next article in this series is going to be on technologies like VSync, G-Sync, NVIDIA Reflex which make our games and hardware in-sync.

I’ll go over how gaming companies fix some of the issues that frame-gen and upscaling add to gameplay.

See you there :)

As always your feedback is what counts for SK NEXUS.

I’d really appreciate if you could just take a moment out and share this article to someone who’d benefit from learning about frame-gen technologies. You can use the button below:

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Similar Articles From SK NEXUS

Further Learning

• What Is DLSS And Should You Use It?

• AMD’s Answer to DLSS: How FSR Works

• Do “Fake frames” suck?

• NVIDIA DLSS 4.5 Dynamic Multi Frame Generation Now Available

• NVIDIA introduces DLSS 4.5 and Multi Frame Generation 6X at CES 2026

• Why DLSS Dynamic Frame Generation Is a Game Changer

• AMD teases Multi Frame Generation with FSR update

• AMD SDK suggests 4x and 6x frame generation multipliers are in the works

• Intel enables XeSS 3 Multi-Frame Generation in latest drivers

• When a high frame rate can lose you the game

• Input latency is the all-too-frequently missing piece of framegen analysis

• Frame Gen Isn’t a Performance Boost; It’s a Masking Agent

• AMD May Finally Add Multi-Frame Generation Support for Its Latest GPUs

• AMD Prepares FSR Update with Multi-Frame Generation

• Valve is working with AMD to bring FSR 4 to the Steam Machine

Do you remember the time when making a call actually meant something very specific?

You had a phone sitting in one place. You picked up the receiver, dialed each number one by one, and waited for the connection. There was a clear start, a clear end, and in many cases, a cost attached to how long you stayed on.

But underneath that experience was a very rigid setup. Calls were built on dedicated lines and once a connection was established, that line stayed reserved for the entire duration of the call.

Now compare that to how things work today.

You open WhatsApp or Discord, tap once, and you’re instantly connected with ‘Undertaker69’ from Netherlands and you can talk with him for as long as you want, just with an internet connection.

This change wasn’t the result of some sort of magic, rather, it came from a complete change in how voice communication works underneath the surface and like every major change in technology, it brought both convenience and new risks.

That is what we will be exploring in today’s piece. Let’s start!

What Voice Over Internet (VoIP) Means

Voice over Internet Protocol, or VoIP, is a method of transmitting voice over the internet instead of traditional telephone lines.

In older systems, a call required a dedicated connection between two points. That connection stayed active for the entire duration of the call, even if no one was speaking.

But VoIP handles this differently - your voice is converted into digital data, broken into smaller packets, and sent across the internet. These packets travel through networks just like any other type of data, independent of its format.

Because of this change, the system focuses on delivering data efficiently. Packets can take different routes before reaching their destination, depending on network conditions, which allows the voice to be integrated into platforms, combined with other features, and scaled without relying on traditional telecom systems.

That’s why modern communication tools treat voice as a part of a larger environment.

How Voice Over Internet (VoIP) Works

At a basic level, VoIP follows a structured process.

When you speak into a device, your voice is captured as an analog signal. That signal is converted into digital form using codecs, which prepare the data for transmission. The data is then compressed and divided into smaller packets.

These packets are sent across the internet using packet switching. Unlike traditional systems, there is no single fixed path. Each packet can take a different route depending on network availability.

On the receiving side, those packets are collected, reassembled, decompressed, and converted back into audio.

The process happens quickly enough that it feels like a real-time conversation, but the underlying behavior is very different from traditional telephony because everything depends on network conditions, factors like latency, packet loss, and jitter can affect call quality. This is the trade-off. The system gains flexibility and scalability, but it becomes dependent on the quality of the internet connection.

There is also a coordination layer involved. Systems use protocols such as Session Inition Protocol (SIP) to establish, manage, and terminate calls. These protocols handle the signaling side of communication, ensuring both ends know when a session starts and ends.

How Voice Calling Worked in the Past

Before voice moved onto the internet, communication ran through traditional telephone networks built on circuit-switched systems.

These systems relied on in-band signaling, where the same line carried both voice and the control signals that told the network what to do. Routing a call, marking a line as busy, connecting long-distance, all of it depended on specific tones moving through the line.

The design worked well for its time, but the system trusted whatever signal it received and that assumption turned out to be a big problem.

As covered in the earlier phreaking piece, people eventually figured out that if the network responded to tones, those tones could be reproduced. Once that happened, the system could be influenced from the outside.

Over time, it became clear that this approach couldn’t be secured through small fixes. The entire design needed to change which led to a transition where control signals were separated from voice communication. Instead of relying on tones coming through the same line, signaling moved to dedicated systems like Signaling System No. 7 (SS7).

If you want to dive into more details regarding how earlier telephones worked and what phreaking is, you can check out the following piece:

Important Note

It’s easy to assume that VoIP replaced older telephone systems like Signaling System No. 7 (SS7) or was introduced to fix issues like phreaking, but keep in mind that this is NOT correct.

SS7 belongs to traditional telecom infrastructure. It was introduced to separate control signals from voice and reduce the kind of weaknesses exposed in earlier systems, while VoIP treats voice as data and sends it over packet-based networks instead of dedicated lines.

These technologies exist at different layers and solve different problems.

Early Foundations of Voice over Internet (VoIP)

Now that the fundamentals are clear, it makes sense to step back and look at how all of this began.

Voice over the internet came from decades of experimentation around how voice could be processed, transmitted, and eventually detached from physical communication systems.

To understand where we are today, we need to move through that timeline.

Pre-VoIP Era

1920s–1960s: Early Voice Research

The foundations of VoIP can be traced back to 1925, when AT&T and Western Electric established Bell Labs to advance communication technologies.

A major breakthrough came in 1939, when engineer Homer Dudley developed the Voder, the first electronic voice synthesizer. It used oscillators, filters, and manual controls to recreate human speech, demonstrating that voice could be generated and manipulated electronically.

Read more

There is a setting buried somewhere in almost every PC game released in the last few years called DLSS, or FSR, or XeSS.

Most people leave it on whatever the game sets by default and never think about it again. And that is honestly fine. It works. Your game looks good. Life moves on.

But here is the thing. If you have played any PC game, used a PS5 Pro, or owned a gaming handheld in the last couple of years, your screen has basically been lying to you.

It has almost certainly been showing you something that was never actually created by it. Your graphics card drew some pixels. Software invented the rest.

That is upscaling. And it quietly became one of the most important technologies in gaming.

The reason to talk about it now is because the current AI boom has already made most gaming unaffordable for many.

It’s honestly one of the worst times to be a gamer because RAM prices have shot to the moon, SSD have done the same and modern games become bloated day-by-day.

And upscaling helps in all of this by making your games run better on systems that are aging while being basically free for the most of us.

So it is a good time to start from the beginning and learn what is upscaling, how did we get here, and what is actually happening on your screen right now.

This article is special because it’s part of 3 article series we’ll call ‘Beyond The Frame’.

In this series we’ll focus on gaming technologies that work under-the-hood without us knowing. Technologies like NVIDIA’s DLSS, AMD’s FSR and low latency tech like G-SYNC and V-Sync will be our focus.

The main point is to discuss how they power gaming from consoles to PCs.

The next article will focus on Frame Generation, it’s benefits and limits. We’ll discuss Multi-Frame Gen and try to understand if it’s a good idea to use that or not.

What Even Is Upscaling?

Before any of this makes sense, you need to understand one basic thing about how games work.

When your PC or console renders a video game, it has to basically draw every single frame you see. At 60 frames per second or 60 Hertz, it is drawing 60 complete images every second.

Each one of those images has to have every object in the scene calculated, lit, and placed correctly. That is a lot of work.

The higher the resolution you play at, the more pixels need to be drawn per frame. Running a game at 4K means drawing roughly four times as many pixels as 1080p. For a GPU trying to hit smooth frame rates, that adds up fast.

If you’re not sure what the words “resolution” or “hertz” mean here, you might want to check our Monitor Buying article where we talked about those in more detail, that article is linked right here.

Upscaling is the answer the industry landed on. Instead of drawing the game at full 4K, your GPU draws it at a lower resolution, say 1080p or 1440p.

Upscaling software then takes that lower-resolution image and blows it up to 4K before it appears on your screen.

The result is that your GPU does far less work per frame. Games run faster, or can look much better, without needing a significantly more powerful graphics card.

This is the gist of upscaling in video games. You run your game at a lower resolution than your native resolution, this makes the game run smoother at first. Then AI takes over and upgrades those frames to a higher resolution.

That is basically what we need to know as gamers when it comes to upscaling. Of course there’s a lot more to it but I won’t bore you by converting this article into a research paper :p

How AI Upscaling Works?

You might be wondering what the catch is. How can the graphic card just take a normal resolution image and make it better?

Well, we all know that if you zoom into a digital photo, it gets blurry and pixelated the more you zoom into it. Upscaling in a way is doing that too.

That begs the question: Why does upscaling not give the blur effect?

The old way of upscaling, which games used for years, did look blurry. It was called bilinear or bicubic upscaling, and it basically averaged pixel values to fill in the missing ones.

The result was softer than native resolution and often quite noticeable. Modern upscaling however, is different because it uses AI.

Modern upscaling software is trained on thousands and thousands of high-resolution game images. Over time it learns how edges, textures, hair, grass, and other game elements look at full resolution.

When it sees a lower-resolution image, it does not just average pixels. It looks at the scene and intelligently guesses what the missing detail should look like, based on what it has learned.

Modern upscalers like DLSS and FSR all provide AI-based upscaling and they are genuinely hard to tell apart from native resolution, even to experienced eyes looking closely.

That is not just marketing. Blind tests have repeatedly confirmed it.

If you don’t have two computers running side-by-side with one using upscaling and the other without it, you probably won’t figure out if AI upscaling is being used in a video game or not.

How NVIDIA’s DLSS Became The Gold Standard

DLSS stands for Deep Learning Super Sampling. This is NVIDIA’s upscaling technology that was launched in 2018 alongside their RTX 20-series cards.

When it comes to upscaling technology inside PC gaming, DLSS is the industry standard. But that wasn’t the case at its launch.

The first version of DLSS was not impressive. Most reviewers said native resolution still looked clearly better and the performance gains were not worth the visual trade-off.

And this was the main thing, reviewers were able to tell the difference just by looking at the image.

DLSS 2 got to us in 2020. It was the real moment. NVIDIA rebuilt the underlying AI model completely, and reviewers who had dismissed the first version changed their minds quickly.

At quality settings, DLSS 2 output was genuinely difficult to distinguish from native resolution in most games.

What makes DLSS different from every other upscaler is that it requires dedicated hardware inside NVIDIA’s RTX graphics cards called tensor cores.

These are chips specifically designed to run AI workloads fast. That is why DLSS only works on NVIDIA RTX cards. There is no version for AMD or Intel GPUs.

The trade-off is that NVIDIA can train a much more sophisticated model because it knows exactly what hardware will be running it. The quality shows.

DLSS 3 and 4 built on DLSS 2 supporting an even bigger catalog of games and gradually fixing much of what was wrong with DLSS 2.

At CES 2026, NVIDIA announced DLSS 4.5, which introduced their second-generation AI model. The new model was trained on a significantly larger dataset and uses five times the compute of the previous version.

The result is better stability, cleaner edges, and improved motion clarity across all RTX cards.

DLSS 4.5 on RTX 50-series cards also brought a 6x Multi Frame Generation mode, which I will explain more in the frame generation section.

AMD’s Answer To DLSS

FSR stands for FidelityFX Super Resolution. AMD launched it in 2021 as an answer to NVIDIA’s DLSS.

The big pitch from day one was that FSR works on any GPU. FSR has been open-source which means that no matter if you have an NVIDIA, AMD, Intel card, you can just use FSR as long as the game supports it.

FSR does not require special AI hardware because it is built on algorithms that run on the regular shader cores every GPU has.

That openness was genuinely valuable. Anyone who had a graphics card could turn FSR on and get more performance out of it.

The trade-off was image quality. Early versions of FSR were noticeably softer and shakier than DLSS, especially in motion.

When a game had lots of small moving objects, or fine detail like hair or chain fences, FSR tended to struggle while DLSS held up well.

FSR 4, released in 2025, changed that significantly. AMD finally adopted an AI-powered approach similar to DLSS, and the jump in quality was substantial.

Blind tests confirmed that FSR 4 finally brought AMD’s upscaling into competitive territory with NVIDIA.

The catch is that FSR 4’s best quality requires AMD’s new RDNA 4 cards, like the RX 9070 series.

The older broad compatibility that made FSR famous does not fully apply to FSR 4’s highest-quality mode. Older AMD cards, and NVIDIA and Intel cards, still get the older FSR versions.

In March 2026, AMD also released FSR 4.1, which is based on the same neural network as Sony's updated PSSR. I'll talk about that in a moment.

FSR being competitive is one of the big reasons behind AMD’s comeback. If you want to know how, you can checkout the article linked here where we went deep into AMD’s failures and successes in graphics and computing.

Intel Joining The Upscaling Bandwagon

XeSS stands for Xe Super Sampling. It is Intel’s upscaling technology, released alongside their Arc graphics cards.

It does not get much attention because it is the new kid on the block when it comes to upscaling tech on PC graphics cards.

Like FSR, XeSS works on any GPU. But when running on Intel Arc cards, it uses dedicated AI acceleration hardware on those chips to produce better results than the standard version.

Think of it as a middle ground between DLSS and FSR in terms of how it scales with hardware. It is not totally exclusive like DLSS and not open-source like FSR. You can run it on other GPUs but it works best on Intel GPUs.

Image quality on XeSS sits between older FSR and DLSS in most comparisons. Not quite at DLSS levels, but better than FSR 3.

In 2025, Intel released XeSS 2.1, which quietly expanded frame generation support to NVIDIA and AMD cards as well, not just Intel hardware.

That makes Intel the most broadly compatible option in the room right now for both upscaling and frame generation across different GPU brands.

It does not get the headlines DLSS gets. But if you are on a non-NVIDIA, non-AMD card, or simply want the widest compatibility, XeSS is worth knowing about.

PlayStation Uses Upscaling Too

Consoles used to rely on fairly basic upscaling. The PS4 and base PS5 used AMD’s FSR in many games, which as we covered was not always flattering.

Sony decided to build their own.

PSSR stands for PlayStation Spectral Super Resolution. It launched with the PS5 Pro in late 2024.

Sony built PSSR as part of a joint project with AMD called Project Amethyst. The collaboration let Sony tune an AI upscaling model specifically for the PS5 Pro’s hardware and how it renders games.

The original PSSR was well-received. PS5 Pro games using it looked noticeably sharper and cleaner than the same games on the base PS5 using FSR.

Then in March 2026, an updated version of PSSR launched alongside Resident Evil Requiem. This version, often called PSSR 2.0, is based on the same neural network as AMD’s FSR 4.1.

PS5 architect Mark Cerny confirmed in an interview that the two share the same core technology, just trained differently to suit the different hardware in the console versus a PC GPU.

Reviewers who tested the updated PSSR said it is now very close to DLSS quality. For PS5 Pro owners, that is a meaningful upgrade.

In a way, PSSR and FSR 4.1 are like cousins. They have the same underlying neural network but are trained differently for different hardware.

Frame Generation

Upscaling makes your image look sharper. Frame generation does something different. It makes your frame rate appear higher.

Here is how it works. Your GPU renders one real frame. Frame generation software then invents additional frames between each real one and inserts them into the sequence your monitor displays.

With DLSS 4.5’s 6x Multi Frame Generation on RTX 50-series cards, your GPU renders one frame and the software generates up to five more around it before display.

The frame rate shown on screen can be dramatically higher than what your GPU is actually producing.

This is where the “23 out of 24 frames are AI” number you might have seen comes from. At 6x frame generation, only one in six frames is real.

The obvious question is whether those generated frames look and feel real. For the image quality side, they largely do in slow-paced or cinematic games. The bigger issue is input lag.

Generated frames are invented after the fact. They do not know what you did with your controller or mouse in the moment they represent. Real frames do.

So frame generation adds a small delay between your input and what you see on screen.

For a story-based game or an open-world exploration game, that delay is barely noticeable. For competitive multiplayer, especially at competitive skill levels, it matters.

Most professional or competitive players turn frame generation off.

I will cover frame generation in much more depth in a separate article, because it genuinely deserves its own piece.

For now, the short version is that Frame Generation works but it has real trade-offs, and you should understand it separately from upscaling even when companies sometimes mix them up.

DLSS 5 and the AI slop Controversy

A couple of months ago NVIDIA took the stage at their GTC keynote and announced DLSS 5.

This is not upscaling. This is something different even though they call it DLSS.

DLSS 5 uses generative AI (think of tech behind ChatGPT) to actively alter how games look in real time, not just sharpen a lower-resolution image.

It analyses the scene and applies what NVIDIA described as neural rendering, re-drawing things like skin texture, lighting, and facial detail to look more real.

The first game they showed was Resident Evil Requiem, one of the most visually praised games in recent memory.

The demo showed the main character Grace Ashcroft with smoother skin, removed eye bags, and softened facial features. The internet called it “yassified” immediately.

It looked like an Instagram beauty filter applied to a character that was already beautifully designed. The YouTube reveal was heavily disliked.

Then things got more complicated. It was reported that developers at Capcom and Ubisoft found out about the demo at the same time as the public.

One Ubisoft developer was quoted saying “We found out at the same time as the public.”

Jensen Huang, NVIDIA’s CEO, said in an interview the day after the reveal that critics were “completely wrong” because the technology gives developers full control over the result.

Then, a few days later, on the Lex Fridman podcast, his tone shifted. “I think their perspective makes sense and I can see where they’re coming from,” he said. “I don’t love AI slop myself.”

The important context is that DLSS 5 is not yet available. It launches in fall 2026, only on RTX 50-series cards.

When it comes to DLSS 5, everything shown up till now was a controlled demo. We can only know for sure when it gets released later this year.

How Upscaling Is Helping Gamers Like Me

Upscaling went from a niche setting you turned on if you wanted a few extra frames to something that is now assumed by default.

Chances are that if you have a compatible GPU and a supported game you’re already using upscaling technologies to play your games.

Game developers increasingly design and test their games with upscaling turned on because that’s where consumers are at. It is no longer an optional setting for modern gaming.

New gaming hardware that would come out (like Valve’s Steam Machine) would actively use the upscaling tech from AMD to provide a promised 4k 60Hz experience.

DLSS 4.5 and FSR 4 both produce results that are hard to distinguish from native resolution in most scenarios at least for the average Bashir.

And especially now when AI has jacked prices for most components that make up our gaming consoles and gaming PCs, it’s high time that we get better upscaling technologies.

I use an AMD R9 290, it’s an old graphics card from 2013 and guess what? I can use FSR on it. Isn’t it great that I can still suck some juice out of this old beast even when it has given a decade of service?

A couple of months ago I wanted to play Uncharted 4 on this same graphics card. And it was really struggling to launch the game.

I was hardly getting frames in the 30s I guess. But turning on FSR made the experience so great that I was able to play the game.

I didn’t finish the game but the point is that I had the option to play it on my old hardware just because companies like NVIDIA and AMD invested their time and effort in upscaling technologies.

With PC prices rising globally especially around this AI hype cycle that we’re going through, upscaling technologies are what gamers like me rely on.

And while generally upscaling technologies are great, NVIDIA just showed us in their DLSS 5 demo that things can really get out of hand too.

Whether NVIDIA ships DLSS 5 the way it is this fall is to be seen honestly. The people have shown their frustration, let’s see how NVIDIA takes that.

For now, the simplest version of all of this is that your GPU has likely been lying to you all this while. But the pixels it is inventing are getting very good.

And that’s breathing some more life to our aging gaming hardware.

This article is special because it’s the first in a series of three articles on background graphic technologies in video games.

The next article would go deep into Frame Generation technologies from NVIDIA, AMD and Intel.

Please leave any feedback for this article or the series, in the comments down below:

Further Learning

• What Is AI Upscaling?

• What Is DLSS? Demystifying Nvidia’s Deep Learning Supersampling Technology

• What Is AMD FSR? FidelityFX Super Resolution Explained

• NVIDIA DLSS 4.5 announcement

• Gamers Hate Nvidia’s DLSS 5

• “I Don’t Love AI Slop Myself” – Jensen Huang

• Nvidia CEO Jensen Huang backtracks

• Capcom and Ubisoft devs were out of the loop on Nvidia DLSS 5

• AMD locked FSR 4 behind new GPUs

• DLSS vs FSR vs XeSS Support Across 650+ Games

• Intel Opens Up XeSS 2 to NVIDIA and AMD GPUs

• PlayStation 5’s PSSR 2.0 uses AMD’s FSR 4

• Nvidia says DLSS 4 looks better than native resolution

• AMD FSR 4 vs. Nvidia DLSS 4: Shockingly close

In 2015 AMD’s stock was less than 2 dollars per share.

The company was bleeding money. Their CPUs were getting destroyed by Intel. Their GPUs were losing to Nvidia. There were talks of bankruptcy.

Fast forward to today. AMD’s stock is near 450 dollars. They have arguably the best consumer CPUs.

AMD controls 30 percent of the consumer CPU market. Their server chips are taking share from Intel in data centers too.

Their GPUs still trail Nvidia but the gap is closing. The latest version of AMD’s upscaling technology is pretty good for most of us out here and FSR5 is going to be even more exciting.

People used to say AMD GPUs are not stable enough but today every major gaming console from Playstation, XBOX to handhelds like the Steam Deck, all use AMD GPUs.

And the next generation of consoles are also going to use AMD. Valve has confirmed that for their upcoming Steam Machine.

Meanwhile PlayStation and XBOX have leaks that they’re working with AMD for their next consoles.

This is the story of how AMD refused to die despite being smaller, poorer, and less powerful than both of their competitors.

It is also the story of one of the biggest comebacks in tech history. And why that comeback still might not be enough.

AMD Before Lisa Su

AMD’s story of the last decade revolves around her current CEO who took charge back in 2014.

She has been so instrumental in the company’s success that I think it’s best to see AMD as the one before she took over and after she became CEO.

AMD was founded in 1969 by Jerry Sanders.

Sanders was a former Fairchild Semiconductor executive who got pushed out in a corporate shakeup.

The company started in Sanders’ living room in Santa Clara with eight employees. The plan was simple. Make copies of other companies’ chips under license.

Back in the 1970s big companies did not want to depend on a single chip supplier. So AMD became a second source manufacturer.

They made Intel 8080 processors. They made more unknown chips. They made whatever customers needed them to make.

This worked for a while. AMD went public in 1972. In 7 years, their revenue hit 100 million dollars.

But they were always a follower, never a leader and Sanders knew this.

In a famous quote Sanders said “Real men have fabs” meaning that serious chip companies needed to own their own manufacturing.

So AMD built fabs. They invested in R&D. And in the 1990s they started designing their own x86 processors.

The K5 launched in 1996. It was AMD’s first internally designed CPU. It was not very good. Intel’s Pentium was faster.

The K6 in 1997 was better. AMD partnered with NexGen whose technology formed the basis of the K6. It competed well with Intel’s Pentium II in the budget segment.

But the real breakthrough came in 1999 with the Athlon.

Athlon Joins The Chat

The Athlon was the first x86 processor to hit 1 GHz. AMD beat Intel to that milestone by a few months.

More importantly, the Athlon was genuinely fast. In many benchmarks it matched or beat Intel’s Pentium III. Suddenly AMD was not just the budget option. They were competitive.

AMD’s revenue grew to 2.8 billion dollars in 1999. Then 4.6 billion in 2000. The company was on a roll.

Then came the Athlon 64 in 2003.

The Athlon 64 was AMD’s most important chip ever. It introduced 64-bit computing to the x86 platform.

Intel was pushing Itanium as their 64-bit solution. Itanium was not x86 compatible. It required all new software.

Nobody wanted that. The industry wanted 64-bit extensions to x86 so old software would still work.

AMD delivered exactly that with AMD64. Intel eventually had to license AMD’s 64-bit extensions and rebrand them as Intel 64.

From 2003 to 2006 AMD had the better product. The Athlon 64 used less power than Intel’s Pentium 4. It ran cooler. And in many workloads it was faster.

Intel’s Pentium 4 architecture was deeply flawed. Intel had bet everything on clock speed. The Pentium 4 hit 3.8 GHz but it achieved that speed through a very long pipeline.

The Pentium 4 was fast on paper but slow in practice for many tasks. The Pentium 4 also consumed massive amounts of power.

Intel’s Pentium 4 could hit 115 watts at load. That is more than many GPUs.

AMD’s Athlon 64 consumed around 90 watts and was often faster. Enthusiasts and gamers loved it.

AMD’s market share hit 25.3 percent in Q4 2006 according to Mercury Research. That was AMD’s peak.

Then Intel struck back.

Intel Strikes Back With Core 2 Duo

In July 2006 Intel launched the Core 2 Duo. This was a completely new architecture. Intel had basically admitted the Pentium 4 was a mistake and started over.

The Core 2 Duo was everything the Pentium 4 was not. It was power efficient. It had excellent performance per clock. It scaled well with more cores.

And it destroyed AMD’s Athlon 64 in benchmarks.

By late 2006 Intel had regained the performance crown. AMD’s market share started dropping. Revenue peaked at 5.6 billion in 2006 then declined.

Intel also had way more money to spend. In 2006 Intel’s R&D budget was 5.9 billion dollars. AMD’s entire revenue was 5.6 billion.

AMD could not compete with Intel’s spending power. They needed something to change the game.

The ATI Acquisition

In July 2006 AMD announced they were buying ATI Technologies for 5.4 billion dollars.

ATI was Nvidia’s main competitor in graphics cards. They made the Radeon GPU series. They were roughly equal with Nvidia in market share at the time.

AMD’s vision was to combine CPUs and GPUs on the same chip. They called this an APU or Accelerated Processing Unit.

The idea made sense. Graphics were becoming more important for computers. Games needed GPUs. Video playback benefited from GPU acceleration.

If AMD could integrate a decent GPU with their CPU they could offer better value than Intel whose integrated graphics were terrible.

But the acquisition was a disaster from a financial perspective.

AMD paid 5.4 billion dollars. They paid 2.5 billion in cash and took on 2.5 billion in debt. The remaining 400 million was in stock.

This was AMD’s entire cash reserve plus massive new debt. And the timing was terrible.

2008 Financial Crisis

The 2008 financial crisis hit. PC sales collapsed. AMD’s revenue dropped from 6.0 billion in 2007 to 5.8 billion in 2008 to 5.4 billion in 2009.

AMD was burning cash. Their debt was ballooning. They had to pay interest on billions of dollars while losing money too.

In 2009 AMD lost 1.2 billion dollars. In 2011 they lost 1.8 billion. In 2012 they lost 1.2 billion again.

The company was slowly dying.

To survive AMD had to sell assets. The biggest sale was their chip manufacturing business.

In 2009 AMD spun off their fabs into a separate company called GlobalFoundries. The deal was structured as a joint venture with a company from Abu Dhabi.

AMD got 700 million dollars immediately and another 500 million later. This gave AMD cash to survive. But it also meant AMD no longer controlled their own manufacturing.

AMD went from being an integrated device manufacturer like Intel to being fabless like Nvidia and Qualcomm.

They had to rely on GlobalFoundries to make their chips. From the looks of it, they lost the ability to optimize manufacturing for their designs.

However, this is something that is going to flip the table as you read on.

Meanwhile on the product side things were getting worse. AMD’s CPU designs were falling further behind Intel. And their GPU designs were competitive but not winning against Nvidia.

AMD needed a new CPU architecture. Something to leapfrog Intel. They bet everything on a new CPU architecture from the ground up.

The CPU Architecture That Nearly Killed AMD

Bulldozer was supposed to save AMD. Instead it nearly killed them. The irony lol.

Read more

Some time ago, I covered prediction markets, what they are, how they work, and why they’ve suddenly become part of mainstream conversation.

At the time, I thought that was enough. Another weird internet-native financial product sitting somewhere between gambling and politics. But then I ended up watching one of Nilay Patel’s podcast discussions around prediction markets, and it pushed me back into the rabbit hole again.

And to me, this whole thing feels strangely familiar.

Think about where we were around ten years ago. Sports betting ads were controversial. Gambling companies weren’t deeply embedded into mainstream entertainment yet. Today, betting platforms sponsor broadcasts, creators openly promote gambling apps, and odds are treated like part of the viewing experience itself.

Now look at prediction markets.

We’ve reached a point where market odds are being cited as news before the event itself has even happened. A politician makes a statement, odds move instantly, and suddenly the movement becomes part of the story.

And that’s really the purpose of this piece, to look at what happens when speculation starts becoming the lens through which people process reality itself.

The Pseudo-Event Problem

Traditionally, news had a fairly simple foundation - something happened, reporters verified it, evidence emerged, and coverage followed. The sequence mattered because journalism was tied to documenting events that had already occurred.

But modern media ecosystems introduced another category entirely - Pseudo-events.

These are events designed primarily to generate attention and coverage rather than communicate something meaningful. Press conferences teasing announcements, staged debates, “major updates coming soon,” carefully timed leaks…etc.

So what’s the real purpose of these pseudo-events? It’s to create anticipation & engagement around the possibility of something happening.

Prediction markets fit perfectly into this environment because they thrive on uncertainty and the markets just need people to guess on whether it might happen or not.

You can already see how the cycle forms.

A political campaign announces that a “major policy change” is coming next week. Prediction market odds react immediately. News outlets begin reporting that movement as if it reflects meaningful new information. Social media discussions explode around the odds change itself.

Then the announcement gets delayed or watered down, but by that point, it doesn’t really matter because what’s done is done.

And that changes the role of journalism in subtle ways.

Instead of reporting on verified developments, coverage begins orbiting around speculative momentum. The narrative becomes the event. Odds movement becomes the update and overall, the market sentiment becomes something reporters feel pressured to include because everyone else is watching it too.

How Prediction Markets are Reframing Themselves as Truth Infrastructure

The language around prediction markets has changed a lot over the last few years.

Instead of “gambling,” the industry talks about “forecasting,” “market signals,” and “information aggregation.”

Polymarket itself describes itself as a system that uses markets to make sense of real-world events. Kalshi uses the language of “event contracts” and regulated forecasting markets, emphasizing legality and data rather than speculation.

And quite frankly, the reframing works because if you’re a news outlet trying to support a story with numbers, prediction markets will do the job, just fine because the odds look pretty similar to polling data. On top of that, both move over time and both appear to reflect public expectations.

But they measure completely different things.

Polls measure what people say they believe. Prediction markets measure where money is flowing based on what participants think might happen and this distinction matters a lot more than people realize.

During the 2024 U.S. presidential election cycle, prediction market odds started appearing in mainstream political coverage alongside traditional polling. Headlines discussing changes in Polymarket odds were presented almost like electoral indicators, which these markets are not.

And this creates a loop very pretty quickly - A market moves. News outlets cover the movement. Readers see that coverage and assume something important changed. More people enter the market, the odds update again, and another round of coverage follows.

Before you notice, the conversation revolves around market activity itself.

This is also where pseudo-events fit naturally into the system. A campaign hinting at a “major announcement,” can move odds within minutes, even if nothing concrete actually exists behind it.

The coverage still happens and it becomes clear that the whole world is reacting to narratives moving through the media cycle. And when those reactions start getting treated as cues for something important, it becomes really hard to separate actual reporting from speculations.

Digital Déjà Vu

A lot of this feels familiar because we’ve already seen a very similar transition happen with sports betting.

The language around sports betting slowly evolved over time.

Betting platforms stopped presenting themselves purely as gambling products and started positioning themselves as part of the viewing experience. Odds became live analytics. Gambling became fan engagement and real-time betting became interactive sports entertainment.

Today it’s difficult to watch major sports broadcasts without betting odds appearing directly on-screen. These companies sponsor teams, creators, podcasts, and even entire segments during live coverage. The integration became so normalized that for younger audiences, it barely even registers as gambling anymore.

Now look at how prediction markets are being framed.

The same vocabulary transition is happening again and the underlying behavior hasn’t changed much. People are still putting money behind uncertain outcomes.

Prediction markets operate much closer to real-world events. Elections, regulations, wars, economic policy, company decisions - all of it becomes something users can financially bet on.

That changes how these systems interact with public perception. These markets are in real-time influencing how people process information itself due to which a market movement is soon going to look like some kind of signal about reality.

And that creates a very different kind of environment compared to traditional gambling platforms.

The Gacha Parallel

This pattern also resembles what happened with Gacha systems and loot boxes in gaming.

For years, games introduced mechanics built around randomized rewards, especially in mobile titles. Players spent money for a chance at obtaining specific characters or items, often through systems designed around probability and repetition.

A large part of the audience exposed to these systems was underage.

Regulatory responses came slowly, and by the time serious discussions started happening around loot boxes and gambling mechanics, millions of players had already grown up with these systems as a normal part of digital entertainment.

I covered this in much more detail in my gambling article, so I won’t go too deep into it again here.

But prediction markets follow a surprisingly similar structure.

Betting on things has been gamified. Platforms encourage users to track outcomes, compare positions, share predictions, and continuously engage with unfolding events. And unlike Gacha systems, prediction markets aren’t confined to gaming communities because these markets target anyone consuming information.

That’s the scale difference - sports betting monetizes attention around sports. Gacha systems monetize progression and reward loops inside games. Prediction markets monetize uncertainty surrounding real-world events themselves.

The Future of Market-Driven Information

The bigger concern here is that - what happens when prediction markets become embedded into everything else.

We’re already seeing the early stages of it. Prediction market widgets appear inside social feeds. Creators reference market odds during discussions. News outlets cite betting probabilities alongside reporting.

Furthermore, political campaigns are beginning to optimize for market reactions. Headlines are written around odds movement because it generates engagement. Financial incentives are starting to shape public discourse at an individual scale.

For younger audiences growing up inside these systems, checking the odds is going to become more of a common reflex than verifying the source.

And this ties directly into the ecosystem discussion we had earlier.

Prediction markets are following the same pattern most modern platforms follow. First comes integration, after that normalization & dependency follow. And the more deeply odds become embedded into feeds and conversations, the harder it will become to separate speculation from the way people interpret events altogether.

Why This is Worse Than You Think

Fake News Becomes Profitable at Individual Scale

Right now, misinformation mostly runs on attention. People spread false or misleading content because it generates clicks & engagement.

Prediction markets introduce another layer on top of that which adds a direct financial incentive attached to shaping public sentiment itself.

In November 2025, a staffer at the Institute for the Study of War made an unauthorized edit to one of the organization’s maps showing Russian forces capturing a key intersection in the Ukrainian town of Myrnohrad. That was a big move because Polymarket relied on those maps to settle war-related contracts.

Because of that, users who had bet on Russia taking the city reportedly made returns as high as 33,000%, with the total market reaching around $1.3 million. Later, the edit disappeared. The Institute for the Study of War (ISW) stated that the change had not been approved and removed it from the next day’s workflow. Shortly after, one of the organization’s geospatial researchers disappeared from the website and reports indicated the staffer had been fired.

The important part here is the realization that if somebody can manipulate information connected to a market, it becomes possible to financially benefit from influencing real-world situations tied to those bets.

Markets Can Be Gamed by Coordinated Actors

Another issue is how easily markets can create artificial signals.

A small but coordinated group placing concentrated bets can move odds significantly, especially in thinner markets. Once those odds move, social media notices. News coverage follows. People outside the market start interpreting the movement as meaningful information.

And the cycle feeds itself from there.

We already saw a version of this during the Maduro case covered in Part 1.

In early 2026, a Polymarket account placed large bets predicting Venezuelan leader Nicolás Maduro would lose power before the end of the month. The timing immediately raised eyebrows because the trades were placed only hours before a surprise U.S. military operation ultimately removed him from office.

When the outcome was confirmed, the account reportedly walked away with close to $400,000 in profit.

What made the situation stranger was the account itself.

It had very little trading history and appeared to exist almost entirely around those specific wagers. Naturally, people started asking whether the trader simply got extremely lucky or whether the bets reflected access to information the public didn’t have.

And even if no direct wrongdoing occurred, the bigger issue remains the same.

Prediction markets create environments where market activity itself starts behaving like a signal. A sudden odds movement can influence public perception long before anyone knows whether the underlying information is reliable which can create a system where coordinated actors just need enough momentum to make people believe something important is happening.

What You Should Keep In Mind

Understanding What You’re Looking At

The first thing to understand is that prediction market odds are not truth indicators. They just reflect where money is moving. So when you see headlines citing market odds as evidence of something becoming “more likely,” it’s worth asking a few questions:

Who benefits if public perception changes?
Who is placing the bets?
And what incentives exist behind the movement itself?

Markets can aggregate sentiment, but sentiment and reality are not the same thing.

In short, a percentage attached to an outcome does not automatically make the underlying claim more credible which is why verification still matters

Don’t Let Odds Replace Sources

Prediction markets become much more influential when they get embedded directly into the platforms people already use.

Odds inside social feeds, prediction widgets attached to news coverage, creators casually referencing betting percentages during discussions - all of this gradually conditions audiences to treat markets like informational shortcuts which plays a key role in forming a new habit that involves people depending on odds instead of credible sources.

So what’s the solution?

If platforms give you the option to disable prediction widgets or betting integrations, turn them off. If creators constantly frame discussions around market activity, recognize with your whole heart that you’re being pushed toward interpreting speculation as meaningful information.

Closing Thoughts

Prediction markets are moving through a very similar process to what we have seen in the past with the whole gambling matter.

If these systems become normalized, reversing it would be much more difficult.

And all of this is happening in an internet environment that is already messy enough.

We’re surrounded by AI-generated content, bots, misinformation, deepfakes, algorithmically amplified narratives - distinguishing between reality and manipulation is already getting harder by the day.

Prediction markets add another layer to that environment and the substructure around it is still being built.

That’s why awareness matters now, before these systems become deeply embedded into the way people consume information every day.

Anyway, thanks for reading.
See you next time.

Most people use technology every day without ever stopping to ask how it works, who controls it, or what happens when things go wrong.

This publication exists for the opposite reason.

So, if you enjoy deep dives into emerging technology, privacy, AI, cybersecurity, and the systems shaping the modern world, consider subscribing.

That way, the next article will show up directly in your inbox.

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• Ensh*ttification - How Platforms Turn Against You

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• The Business of Trust - Influencer Marketing

• The Death of Ownership: Why Your Tech Isn’t Really Yours Anymore

It’s good to have you back.

While working on my last piece on digitization of gambling , I kept running into something strange during research - Prediction markets.

At first, I didn’t think much of it. Then I started seeing odds for elections, wars, policy decisions, even oddly specific future events. And later I realized that these were entire markets.

The deeper I looked, the more unavoidable this became. Instagram reels, YouTube shorts. Clips of people bragging about how much they made by betting on real-world outcomes. Someone won because they predicted this election. Someone else cashed out because they bet on that crisis.

A lot of questions started to pile up in my head.

Why are people trusting markets to predict reality? Why do odds feel more convincing than experts or institutions? And when did forecasting quietly turn into a form of gambling wrapped in data and probability?

And once again I guess, I decided to find the answers to those questions.

You see…gambling doesn’t always look like a casino. Sometimes it looks like insight, sometimes it looks like intelligence. And sometimes, it slips into daily life without you noticing.

In this article, I’ll break down what prediction markets actually are, where they came from, what their growing popularity says about how we understand the future and much more…

Every second counts, let’s continue.

What Prediction Market Actually is

Polymarket or Kalshi are prediction markets. In other words - they are gambling platforms dressed up as forecasting.

On the markets - users are able to bet real money using the USDC stablecoin on who would win. You can wager on everything from the next James Bond actor to whether the U.S. government will confirm the existence of aliens.

What sets Polymarket apart is perception. It’s now regularly cited by mainstream media and amplified by high-profile figures like Elon Musk, who has argued that polymarkets beat polls because real money forces honesty.

At its core, it works by letting people buy and sell shares tied to future outcomes. If the event happens, the share pays out. If it doesn’t, it goes to zero. The price reflects what participants collectively believe, not what’s guaranteed to happen.

Where Prediction Markets Came From

Humans have always tried to price the future. Long before spreadsheets, people were informally betting on wars, royal successions, political outcomes...etc In the 1500s and 1600s, political betting markets were already operating in coffeehouses across Europe.

But it wasn’t as smooth as it is right now.

Prediction markets as a serious concept didn’t emerge until the late 20th century.

In 1988, economists began formalizing the idea that markets could aggregate scattered information better than polls or expert panels. This thinking led to the launch of the Iowa Electronic Markets in 1989, a small academic experiment that allowed participants to trade contracts tied to election outcomes.

Throughout the 1990s and early 2000s, prediction markets gained academic validation. Economists studied them as forecasting tools, not gambling systems. Corporations experimented with internal markets to predict product launches, sales targets, project delays…etc

The idea behind the prediction market was clear - when people have skin in the game, they reveal information more honestly which most of the times lead to more accurate results.

So yes…prediction markets were initially built for forecasting and over time, everything got flipped.

The real public boom came decades later. During the 2024 U.S. presidential election, prediction markets were suddenly everywhere. Odds were screenshotted, shared on social media, cited by journalists, and compared directly with polls. The election acted like a spotlight for prediction markets and that moment changed everything…

How Prediction Markets Gained Momentum

The main reason behind why prediction markets exploded is - the change of environment.

Back in the ol’ days, there was no internet.
But that isn’t the case now.

The internet removed the old gatekeepers. You no longer needed academic credentials, or any permission to participate. At the same time, regulation is slower than ever. Prediction markets didn’t fit neatly into existing gambling, which gave platforms room to operate in legal gray zones while regulators played catch-up.

Crypto accelerated everything. Stablecoins made global participation, bypassing banks, borders, and payment processors frictionless. Suddenly, anyone with an internet connection could participate in markets tied to real-world events.

Once a market starts gaining traction, social media does the rest. Screenshots spread. Wins go viral. Algorithms amplify what performs well, nudging more people towards what it wants them to see.

On top of that - Journalists notice what’s already trending, they along with influencers also start to hop in the shiny train ultimately giving birth to a never ending loop.

That’s exactly what happened with prediction markets.

And if you want to understand how platforms quietly shape what you see, what you believe, and what you pay attention to, I broke it down in detail here:

How Prediction Markets Work

At a high level, prediction markets turn beliefs into prices.

Each possible outcome is represented by a share. When more people believe an outcome will happen, demand for that share increases and its price rises. When confidence drops, the price falls.

As new information appears, let’s say a court ruling or breaking headline or a sudden injury, prices adjust almost instantly. People who react early and correctly are rewarded. Those who guess wrong lose money. That incentive structure is the entire system.

So basically, you’re buying into a probability shaped by the crowd. If the outcome happens, the share pays out. If it doesn’t, then you lose money.

What you’re really seeing is a live signal of collective belief, constantly updated as people put real stakes behind their assumptions. And the market only cares whether you are right or not, it doesn’t give two cents about why you believe something.

Polymarket is also called ‘wisdom of the crowds’ though, it’s partly true because the information can be manipulated via insider trading which is the biggest problem of this system about which we will be talking next.

What the Problem with Prediction Market is

The real problem with prediction markets is the asymmetry.

Imagine a streaming platform sitting on a yet-to-be-released finale. There’s a market betting on whether a major character dies. Writers, editors, interns - dozens of people already know the answer. Any one of them can quietly place a bet days or weeks in advance and walk away with easy money.

That’s literally information leakage with a payout.

Some markets are genuinely uncertain. Elections, championships…etc no one has the final answer beforehand. But many Polymarket questions aren’t like that. They revolve around private relationships, unreleased announcements, medical events, or corporate decisions. In those cases, someone always knows what’s going to happen next.

Markets asking whether a celebrity couple will split, or if some celebrity is going to get pregnant or whether a famous CEO will step down…etc These are invitations for insiders, friends, assistants, or employees to cash in on knowledge the public doesn’t have.

This turns many prediction markets into sucker’s games. You think you’re participating in a neutral forecasting tool. But you’re just trading against people who already have the answer.

The Grey Zone: Forecasting or Just Gambling?

Prediction markets are sold as sophisticated forecasting mechanisms. The language is polished to make it sound like Wall Street rather than DraftKings - “probability signals”, “information efficiency”, “decentralized knowledge”, “wisdom of crowd”...etc.

Yes, the mechanics are dressed in financial language. Yes, there are probability charts instead of point spreads. But under the hood, it’s simple - risk capital on uncertainty, win if correct, lose if not.

Regulation reflects this ambiguity. In some regions, prediction markets are treated like financial derivatives. In others, they’re classified as gambling. In many places, they operate in a gray zone where neither framework cleanly applies. This inconsistency is a clear reflection of what these markets actually are.

And if you’ve been paying attention to how gambling has been digitalized over the last decade, this should feel familiar.

Fun fact - I recently broke down how gambling is moving online, embedding itself inside video games and different consumer apps. If you want the deeper breakdown of how digital gambling is evolving and realize how prediction market is just another form of gambling, you can check this post out:

How Prediction Markets Are Blending Into Content

Prediction markets are beginning to intersect with the creator economy in subtle ways. These markets aren’t any niche financial tool anymore, discussions about them are increasingly appearing in livestreams and social media.

Streamers do not need to actively place bets to influence participation. Simply discussing probabilities or live market movements is more than enough for promoting these markets.

For example - Atrioc, a youtuber, whose audience already gravitates toward economics and probability-driven analysis, represents the kind of community where prediction markets naturally gain traction - even if markets are not the primary focus of the stream.

The overlap is expanding beyond commentary. Sports figures are also entering the space. Giannis Antetokounmpo recently joined prediction platform Kalshi. This is proof of how the market is slowly growing.

Meanwhile, platforms such as Polymarket routinely host markets tied directly to internet personalities.

At this point, it’s important to ask - whether viewers are consuming commentary or are they interacting with tradeable outcomes linked to personalities they follow? Makes sense right?

Comment down below and let me know what you think

Leave a comment

Just to be clear - none of this is inherently ‘wrong’. But the blending of entertainment, and monetized markets raises practical questions. If a streamer discusses a market, is it organic curiosity or sponsored promotion? In a digital ecosystem where affiliate relationships are not always obvious, audiences may struggle to distinguish between commentary and marketing.

This issue is a whole thing in itself - I explored this dynamic in more detail in a previous piece on how online trust itself has become monetizable and why not every recommendation is neutral:

Case Studies That Made People Question Things

Let’s take a look at real cases that grabbed attention and forced people to think about what goes on behind the scenes of this market.

The $400,000 Maduro Contract That Drew Attention

In early January 2026, one of the most striking stories to emerge from the prediction market world centered on a massive payout tied to Venezuelan leader Nicolás Maduro’s sudden removal from power.

A Polymarket account placed significant wagers that Maduro’s leadership would end by month’s end and those trades were made just hours before a surprise U.S. military operation ultimately removed him from office. When the outcome was confirmed, the bets paid off at near-full value, generating a profit of nearly $400,000.

What made the story so crazy was the timing and the mystery surrounding the bettor. The account had been created only weeks earlier and had little trading history outside of these specific wagers. That led many online observers and analysts to ask the same question:

“Was this luck, or did someone have access to privileged information that the broader public and markets didn’t?”

NFL Banning Prediction Market Ads During the Super Bowl

Prediction markets have officially been placed on the NFL’s list of prohibited advertising categories, according to Front Office Sports. A source familiar with the matter told the outlet that these ads were barred not just from the Super Bowl, but from NFL broadcasts throughout the 2025 season.

The Super Bowl is the most-watched television event in the United States. Last year’s broadcast drew an average of 127.7 million viewers across platforms - the largest audience ever recorded for a single U.S. television event, according to Front Office Sports. Thirty-second ad spots have climbed as high as $10 million this year.

So why block prediction markets?

The answer is pretty simple.

The NFL has spent years carefully managing its relationship with gambling. Sports betting is now integrated into broadcasts, but it’s tightly regulated and politically normalized. Prediction markets operate in a much shadier zone.

They don’t sit cleanly inside gambling law.
They don’t sit cleanly inside financial regulation either.

This ambiguity creates legal and reputational risk and the NFL does not gamble with reputational risk during its events.

Allowing prediction market ads during the Super Bowl would effectively place them alongside established sportsbooks. That’s a stamp of approval - A signal to the public that these platforms belong in the same category.

This decision is pretty subtle, but it reveals something bigger if you look closer - Major institutions are cautious about prediction markets because they understand them very well. This matter suggests that even powerful institutions see them as closer to gambling than forecasting and them taking this decision is a sign of that belief.

What This Means Going Forward

I believe prediction markets deserve a more balanced assessment.

Platforms like Polymarket have proven to be useful as information tools. When people put their own money behind an outcome, that creates a stronger signal than a traditional poll. As Elon Musk has pointed out, markets can sometimes be more informative than surveys because participants have financial exposure. Incentives matter.

However, a priced probability is still just a probability.

At the same time, their expansion is undeniable. As reported by Forbes, trading volume across major prediction platforms surpassed $3 billion in Q3 2025 alone - more than five times higher than the same quarter a year earlier. Industry estimates project the sector could reach nearly $95.5 billion by 2035, with annual growth rates approaching 47 percent.

And now, the very platform you’re reading this on - Substack - has partnered with Polymarket. Writers can embed live prediction widgets in Notes, showing odds for elections, policy moves, or even celebrity drama, all with real money on the line.

This level of growth suggests prediction markets are growing into something bigger.

In my view - prediction markets will continue to expand, and their influence will likely grow. But growth does not resolve their structural ambiguity between gambling and finance. And expansion does not eliminate uncertainty.

What remains unsettled is how regulators, institutions, and the public will ultimately choose to classify prediction markets. That decision may determine whether prediction markets become embedded infrastructure or not…

Because once prediction markets start influencing journalism and the way people interpret information itself, the conversation changes completely.

And that’s exactly what we’re going to explore in Part 2, stay tuned for that!

One Last Thing

Nearly all people interact with systems without questioning them. This space is about doing the opposite aka understanding how things work, where they fail, and what that means in practice.

If that’s the kind of content you want more of, subscribe. And if this piece added something new to how you see things, pass it on. Good information only matters if it travels.

And if you’re active elsewhere, follow along on Bluesky or X (Twitter) or LinkedIn - those are the two places I show up most consistently outside Substack. A follow, a like, a repost - it matters to me, more than you think.

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A while ago, I wrote about hacking as a lifestyle, breaking down what hacking actually is and how it’s more about mindset than just code. But there’s a bigger misconception that keeps showing up.

Nearly all people think hacking started when the internet came into being, but that’s not true.

Hacking started with a whistle and a phone line. It started long before modern cybersecurity, the first hackers were experimenting with telephone systems and were trying to figure out how a global communication network actually worked.

So how did that even happen?
How did a system built for communication become something people could manipulate? And why does it still matter today?

That’s what this piece is about.

But before we get into the history, you need to understand one term.

What Phreaking Actually Is

Phreaking is one of the earliest forms of hacking. It sits at the intersection of curiosity and exploitation, focused entirely on telephone networks.

The term itself comes from combining phone and freaking, which gives you a sense of what it represents - people experimenting with systems they weren’t supposed to touch.

At its core, phreaking was about understanding how telephone systems worked and then using that knowledge to manipulate them. Sometimes that meant making free long-distance calls. Sometimes it meant accessing parts of the network that were never meant to be public.

What made this possible was how early phone systems were designed. Those phones weren’t like the ones we have now, they relied on tones.

Every action in the network such as routing a call, switching lines, connecting across regions, was controlled by specific audio signals. Back then the system didn’t verify identity and blindly trusted the signal behind it.

Phreakers figured this out and reverse engineered those tones and started reproducing them. Once you could generate the right frequency, you could tell the network what to do.

The Origins of Telephone Hacking

How Phone Systems Worked

Long before computers were everywhere, the global communication system ran on sound.

Early telephone networks relied on what’s called in-band signaling. This meant the same line that carried your voice also carried the control signals that told the network what to do. Things like: routing a call, marking a line as busy, connecting long-distance, all of these, depended on specific tones moving through the line. These telephone networks had a major problem, they assumed all those tones being received by it were always legitimate.

Somewhere in the late 1950s and early 1960s, a few curious individuals started noticing the flaw in the system and wondered if the system responded to tones, what would happen if you reproduced them yourself?

That question was the starting point.

The First Phreaker: Joe Engressia (Joybubbles)

One of the earliest figures in this space was Joe Engressia, later known as Joybubbles.

He was born blind and early on he developed an unusually sharp sense of hearing. As a child in the late 1950s and early 1960s, he began experimenting with telephone tones because he wanted to understand them more and due to these experiments, he discovered something critical.

He was able to whistle at a frequency of around 2600 Hz, a tone used by the telephone system to signal that a line was idle. When he produced this tone into the phone, the network responded. Calls could be disrupted, redirected, or reset.

This proved that the system could be controlled using sound.

His work didn’t stay isolated. It spread through early communities and inspired others who were trying to understand the same system. According to early telecom histories and accounts documented in 2600 Magazine, Engressia’s discoveries became a foundation for later phreaking techniques.

John Draper (Captain Crunch) Taking It Public

Put it this way, if Engressia proved it was possible, John Draper made it widespread.

In the early 1970s, Draper discovered that a toy whistle found in Cap’n Crunch cereal boxes could produce the same 2600 Hz tone. The whistles were originally used by sailing officials to signal mealtimes or command, but somehow - one of them fell into the hands of John Draper, a former U.S. Air Force electronics technician.

The uniqueness of this whistle involved producing certain tones that allowed the whistler to bypass AT&T’s analog system and get free long-distance phone calls.

Draper’s work helped shape an early underground culture around telephone hacking. His influence extended beyond telecom systems. Figures like Steve Wozniak and Steve Jobs famously built and experimented with blue boxes before founding Apple, a story later referenced in multiple tech retrospectives.

The Core Vulnerability of the System

The reason all of this worked comes down to design.

Telephone networks used in-band signaling, where control signals traveled along the same path as voice communication. A 2600 Hz tone told the system a line was idle. Additional tones could then direct where the call should go.

This vulnerability existed for decades. It wasn’t fully addressed until the introduction of out-of-band signaling systems like Signaling System No.7 (SS7), which moved control signals off the voice line, but by that point, the damage had already been done.

The Tools That Broke the System

Once people understood that telephone networks responded to tones, the next step was obvious. Phreakers knew that if they wanted consistent control, they needed something more precise.

That’s when phreaking moved from curiosity-driven experiments to something more structured. Instead of trying to imitate tones, hobbyists started building devices that could generate them exactly. Over time the knowledge started spreading and tools began to standardize.

Blue Boxes: The Turning Point

The most important of these tools was the blue box. A blue box was a device designed to reproduce the exact tones used by telephone systems for long-distance routing. It allowed users to send precise signals into the network and direct calls however they wanted.

In practical terms, this meant you could take control of a call after it connected. By sending the right sequence of tones, the system would treat you like an internal operator. Calls could be rerouted, extended, or made across long distances without being billed correctly.

John Draper played a key role in popularizing these devices, but the idea didn’t stop there. In the mid-1970s, even Steve Wozniak and Steve Jobs experimented with building and selling blue boxes before starting Apple Inc. That alone tells you how accessible the concept had become.

Other Tools in the System

The blue box was the most well-known tool, but it was part of a larger set of devices.

Phreakers didn’t stick to one approach. As they learned more about how the network behaved, they built different tools to experiment with different parts of it. Black boxes and red boxes are some examples circulating within the same communities.

The Mechanism Behind It

At their core, all of these tools did the same thing - They generated sound.

Telephone networks used predefined audio frequencies to manage connections. A blue box could produce combinations of these tones, allowing users to simulate internal commands. By controlling the sequence, you could influence how a call moved through the network.

That’s what made these tools so powerful. All one had to do was understand how to speak to the system. And for a brief period, the network couldn’t tell the difference between a legitimate signal and a manufactured one.

Jobs and Wozniak’s Phreaking Phase

By now, you’ve probably noticed that whenever phreaking comes up, the names Steve Jobs and Steve Wozniak aren’t far behind. Their involvement shows up again and again, but it’s worth looking at it directly to understand why.

In the early to mid-1970s, Wozniak came across phreaking through the same circles influenced by John Draper and then built his own version of the blue box and improved it to make it more stable and reliable.

What Wozniak built as a technical challenge, Jobs saw as something that could be sold. Together, they started building and selling blue boxes to students. And don’t get it wrong, it wasn’t organized crime - it was two curious outsiders testing a system and realizing they could push it further.

Jobs later summed it up in a line that still gets quoted:

“If we hadn’t built blue boxes, there would be no Apple.”

That same mindset still carries forward. The mindset that forces people to learn how systems behave, where they fail, and what happens when you take control of them. If you want to go deeper into that way of thinking, I’ve already broken it down in more detail here: Hacking as a Lifestyle: Beyond Computers.

The Consequences of Phreaking

The concept of phreaking didn’t stay contained for long. It started spreading like a virus with the passage of time.

And surprisingly, this pattern shows up everywhere. A small group explores a system to understand how it works. They find weaknesses along the way. Then a second group arrives, interested, only in using those weaknesses.

That’s when things begin to change.

By the 1970s, phreaking had moved beyond isolated experimentation. As techniques spread, telecom companies began facing real consequences. Phone fraud was costing companies tens of millions of dollars annually and the bigger issue wasn’t just the money.

Phreaking exposed that the entire system relied on trust, and that trust could be bypassed. Media coverage also started picking up on it. Stories about “phone hackers” began appearing more frequently, bringing attention to a problem tons of users didn’t even know existed.

At that point, ignoring this problem wasn’t an option anymore and once that realization set in, the pressure to completely redesign the system became unavoidable.

The End of an Era

Legal Action Against Phreakers

By the mid-1970s, phreaking, which started as small-scale experimentation, had grown into something telecom companies could measure. AT&T and other carriers began reporting consistent revenue losses tied to toll fraud. According to some sources, from that period, losses were running into millions of dollars annually as long-distance networks were exploited.

After that, the big guys aka law enforcement got involved. Phreaking moved into the category of criminal activity, prosecuted under fraud and unauthorized access laws. Arrests became more common, and well-known figures like John Draper were arrested for wire fraud charges.

Telecom companies also adapted. They began working directly with law enforcement, setting up monitoring systems and sting operations to track down active phreakers.

The System Had to Change

As I have mentioned previously in the piece - In-band signaling had a built-in flaw. It trusted whatever came through the line, as long as it sounded correct. But the moment outsiders figured out how to reproduce those signals, things started going downhill because there was no patch for this.

So the industry moved toward a redesign. Through the late 1970s and into the 1980s, telecom networks began separating control from communication. Instead of sending instructions over the same line as voice, signaling was moved to a dedicated channel.

This change eventually led to systems like Signaling System No. 7 (SS7), where the network no longer relied on tones from the user side to make decisions. And while it fixed the original weakness…it wasn’t perfect and like most large systems, it came with its own trade-offs.

If you want to understand that side of it without going too deep here, this video does a solid job:

How Phone Hacking Evolved

If you look close enough, you’ll realize that phreaking only evolved. The old systems ran on tones. Today’s systems run on software and networks. But the core idea stayed the same. If you understand how a system works, you can find where it breaks.

Take Voice over Internet protocol (VoIP) systems. Instead of physical phone lines, calls now move over the internet. That makes them flexible, but also easier to manipulate. Attackers can spoof caller IDs, reroute calls, or inject traffic into poorly secured systems.

Then there’s SIM swapping. An attacker convinces a telecom provider to transfer your number to a new SIM card. Once that happens, they control your calls and messages. That means access to OTPs, account resets, and anything tied to your number.

Telecom fraud operates at a broader level. According to the Communications Fraud Control Association, global losses reached $38.95 billion in 2023, showing how large this problem has become.

Lessons from Phreaking

Long before cybersecurity became an industry, a small group of outsiders looked at a global system and refused to treat it as something untouchable. They didn’t have formal training or access. But what they did have was - curiosity and time.

Back then there was no internet to learn from. Knowledge only moved through conversations, small circles, and eventually through publications like 2600: The Hacker Quarterly, a culture formed around understanding systems instead of blindly using them. That mindset still shows up today.

Anyway, if you’ve made it this far, you’ve basically walked through that entire phase. From a system that trusted tones to a world where everything is connected and constantly targeted.

Thanks for reading.
See you next time.

One Last Thing

Most people use technology every day without ever stopping to ask how it works, who controls it, or what happens when things go wrong.

This publication exists for the opposite reason.

So, if you enjoy deep dives into technology, privacy, AI, cybersecurity, and the systems shaping the modern world, consider subscribing.

That way, the next article will show up directly in your inbox.

What You Might Like

• Hacking as a Lifestyle: Beyond Computers

• Social Engineering - When People Become the Exploit

• How We Got Online: The Hidden History of the Internet

In the previous part, we talked about ecosystems and why they exist. To be fair, most of that discussion focused on the good side of things. These systems make life easier because everything connects, everything syncs, and you don’t have to think too much about how things work together.

But like everything else, there’s another side to it.
You only start noticing it when you ask a simple question:

“What happens after you’re inside an ecosystem?”

And the strange part is, there’s no moment where the system suddenly turns against you. Everything continues to work, just like before, but the difference shows up slowly.

Small changes get introduced over time. Each one feels minor, almost insignificant on its own. But as they stack, the system starts behaving differently. And because the change is gradual, the majority of people don’t notice it happening.

It’s like that boiling frog analogy. The temperature doesn’t spike. It rises slowly enough that you don’t react to it.

Anyway, let’s get started.

Lock-In Phase

In Part 1, we broke down how ecosystems form. There’s a stage in that process where everything starts connecting, for example: your data, your habits, and the tools you rely on.

That’s where lock-in happens.

Take a simple example.

If you’ve been using Google products for years, chances are you’re not just using one service. You probably have Gmail for communication, Drive for files, Photos for backups, Chrome for browsing, and maybe Android as your operating system.

None of these decisions feel connected when you make them. Each one solves a small problem, but with the passage of time, they start overlapping.

Your files, emails, photos, documents…etc. everything is tied to the same account. Even the way you use your devices starts adapting to that system.

Now add another layer.

The people around you are often using the same tools. Your family shares photos through the same apps. Your work depends on the same platforms. That creates a network effect. Communication becomes easier inside the system, which makes leaving less appealing.

At that point, switching is still possible, but there’s a lot of friction involved in that process because you’re moving years of data, rebuilding workflows, and adjusting habits that took time to form. Most people won’t be willing to go through that due to which they stay because leaving feels like unnecessary effort.

Control Starts Shifting

As discussed earlier - when the users are settled into a platform, the priorities inside that platform begin to change.

In the early stages, everything is built around attracting users. Features are designed to feel open and easy to use. But once a large user base is established, the pressure to compete in the same way starts to reduce.

A clear example of this is how feeds changed over time.

When Instagram launched, posts were shown in chronological order. You saw content based on who you followed and when it was posted. Over time, that changed. The platform introduced an algorithmic feed that decides what you see and in what order. Even when chronological feeds were brought back later, they weren’t set as default. You still have to switch manually each time.

Alternatively, when YouTube introduced autoplay, it was turned on by default. The next video starts automatically, selected by the system which plays a key role in how people consume content without them actively choosing it.

In another case, TikTok was fined €345m in Europe for default settings that pushed younger users toward more public visibility instead of keeping accounts private.

Individually, these changes might seem small, but together, they show a pattern - platforms start by giving users control. Over time, they begin to guide behavior more actively. Not in a way that breaks the experience, but in a way that gradually reshapes it.

There’s a term for this - dark patterns and I have already covered in detail on what these patterns are, how the algorithm is manipulating you and how to protect yourself from it, check it out here:

Case Studies

Although this isn’t limited to one company and different platforms approach it in different ways, the underlying structure stays the same.

And just a side note - the goal here isn’t to label companies as good or bad. It’s to understand how the system works.

Google

If you want to understand how ecosystem lock-in works in practice, Google is one of the clearest examples.

At the center of it all is a single Google account. That account connects almost everything - Gmail for communication, Drive for storage, Photos for backups, Chrome for browsing, Play Store for apps, and a long list of other services. Each of these tools works fine on its own, but the real value comes from how they connect, and that’s where things start to compound.

As time passes, your entire digital footprint begins to settle inside that system and everything starts living in one place. It’s not something you consciously plan. It just builds gradually as you keep using the same tools.

And when you imagine trying to leave. Suddenly, you’re dealing with years of accumulated data. While tools like Google Takeout allow you to export your data, that’s only one part of the process. You still have to move everything, set up alternatives that don’t behave the same way. That friction is enough to stop most people.

Another layer that pulls users deeper into the system is single sign-on. A large number of third-party apps and services allow you to sign in using your Google account. It’s convenient, but this creates a dependency chain. Your access to multiple services becomes tied to that one account, which means if you leave Google, you’ll have to reconfigure access to everything connected through it.

Then there’s the infrastructure layer - even if you try to step outside the ecosystem, say by installing apps from alternative app stores, many of those apps still rely on Google Play Services in the background. Things like notifications, location tracking, authentication, and payments often depend on it. This means that even outside the visible layer, parts of the system still depend on Google’s infrastructure, which makes full separation harder than it seems.

There’s also a less obvious dependency that builds over time. Google updates Play Services independently of Android system updates. That means even older devices, which may not receive full operating system updates anymore, still rely on Google’s services to function properly.

Storage plays another role in this. Google offers around 15GB of free storage shared across Gmail, Drive, Photos, and other services. At first, that feels like more than enough. But as your usage grows, that limit gets reached. At that point, you’ll either clean up years of stored data or move to a paid plan like Google One. Most people choose the second option, ultimately making leaving even more difficult now the person has to deal with both data migration and subscription dependency.

Then there’s the data layer - Google’s services improve as they collect more user data. From a user perspective, this often feels like improvement. From a system perspective, it strengthens the ecosystem, because the more data that stays inside, the harder it becomes to replicate that experience somewhere else.

This extends beyond software. If you use Google Home, Nest devices, Android Auto, Chromebooks, or Wear OS devices, the ecosystem expands into hardware as well. These products are designed to work best within Google’s environment. Outside of it, functionality often becomes limited. Even services like YouTube Premium or Google Workspace add another layer. Each additional service increases reliance on the system.

At this point, I could keep going. There’s a lot more to cover when it comes to Google alone. But that would take us out of scope for this piece.

If you’re interested in going deeper, I’ve covered Google in much more detail in my privacy series. Worth checking out if you want a clearer picture of what’s happening behind the scenes and how to manage your exposure better.

Industry-Wide Pattern

Google is just one example, and probably the most visible one, but this pattern exists across the industry.

Companies like Proton, Ente, Apple…etc. All of them either have a strong ecosystem or are building one in their own way. Some focus on privacy, others focus on hardware integration or start with a single product and expand outward.

Some companies handle this more responsibly than others and genuinely prioritize user interests, but the model itself remains the same because at the end of the day, ecosystems do two things very well:

Increasing convenience
Increasing dependency

The Cost of Staying

By this point, every interaction already feeds into the platform. That data is used to refine how the system behaves. On the surface, this feels like improvement. But it creates a dependency that isn’t easy to replicate elsewhere.

The same data that improves your experience is also used to extract value from it. Advertising becomes more precise. Features and pricing can be adjusted based on usage. Visibility is influenced by systems you don’t control.

This ties in directly with enshittification.

Anthropic’s recent trajectory demonstrates the pattern. The company signed a $200 million contract with the Pentagon in 2025, but made clear it did not want its technology used for mass surveillance of people in the United States or for fully autonomous weapons systems. According to BBC reporting, when Pentagon ordered Anthropic to allow unrestricted military use of its AI and Anthropic refused, the Pentagon retaliated by designating the company a “supply chain risk” and ending the contract. The ethical stance attracted users seeking an alternative to OpenAI that contributed towards a large user base boost for Anthropic.

The user base boost allowed anthropic to conduct different experiments. In April 2026, Anthropic updated its pricing page showing Claude Code as unavailable on the Pro plan. The company later stated this was only a test affecting 2% of new signups, though The Register noted the change appeared across all public-facing documentation and support materials. Anthropic reversed the change within hours after user backlash. The company maintains the test was limited in scope and not intended as a permanent policy shift.

The bigger picture is, once you’re locked in - the platform has the power to degrade service quality, change pricing structures, run A/B tests to determine willingness to pay, adjust data collection practices, or make the service shit*y because you are the one dependent on the platform.

The platform depends on you only as part of an aggregate user base. And individual complaints don’t carry weight when switching costs are high enough that most users won’t leave regardless of what changes.

What You Can Do

At this point, you should have a clear understanding of how these systems work. But the question is…what can you actually do about it?

First of all, it helps to check where you stand.

Signs of Dependency

Look at your current setup and answer honestly.

• Does losing access to one account affect your email, files, photos, and payments?

• Does the idea of exporting your data feel complicated or unfamiliar?

• Do most of your logins depend on “Sign in with” options from large platforms?

• Do you rely on default apps for search, maps, or storage without considering alternatives?

If the answer to even one of these is yes, then you’re likely more dependent on a system than you realize.

How to Reduce Lock-In

The goal here isn’t to completely avoid ecosystems. That’s neither realistic nor practical. The goal is to avoid depending entirely on one.

If you want a more unified setup without the same level of data collection, companies like Proton offer a reasonable alternative for core services like email, calendar, and storage. It is still an ecosystem, but the incentives are different compared to most large platforms. Even then, I still wouldn’t rely on it for everything.

A mix-and-match approach works better - use one service for communication, another for browsing, another for search. For example, you can keep Proton for email and storage, use browsers like Brave or Mullvad Browser, and rely on search engines like DuckDuckGo instead of defaults.

For platforms like YouTube, using a separate browser profile helps isolate activity instead of feeding everything into one account.

The same idea applies to hardware.

If you prefer a specific brand, that’s fine. Just avoid building your entire setup around it. The deeper the integration, the harder it becomes to step away later.

When choosing tools, a few things matter more than anything else.

Make sure your data can be exported easily. Prefer services that work across platforms instead of locking you into one environment. Where possible, choose tools built on open standards or open-source foundations.

Final Thoughts

An important thing to note - the article’s point wasn’t to paint every tech company as a problem. A lot of these products are genuinely useful. They solve real problems and make everyday tasks easier. That’s exactly why ecosystems work as well as they do. The convenience is real, and it’s understandable why people rely on it.

But it’s important to acknowledge that convenience comes with trade-offs.

The majority of people don’t really know how things behave once you’re inside the system. But if you understand how the system works, you’re able to make your own decision instead of defaulting into whatever is presented to you.

You can still use these tools, just remember to not rely on them for everything.

Thanks for reading.
See you next time.

One Last Thing

Nearly all people interact with systems without questioning them. This space is about doing the opposite aka understanding how things work, where they fail, and what that means in practice.

If that’s the kind of content you want more of, subscribe. And if this piece added something new to how you see things, pass it on. Good information only matters if it travels.

And if you’re active elsewhere, follow along on Bluesky or X (Twitter) or LinkedIn - those are the two places I show up most consistently outside Substack. A follow, a like, a repost - it matters to me, more than you think.

What You Might Like

• Ensh*ttification - How Platforms Turn Against You

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• Privacy Fundamentals - Part 2: Communication

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There is a good chance you have seen a drone in the sky at some point. Maybe at a wedding, a concert, or an expensive photo shoot.

And there is an even better chance that the drone was made by DJI.

You might not know the name.

But if you have ever watched a travel video on YouTube, seen aerial footage in a movie or documentary, you have almost certainly encountered DJI’s products.

The company single-handedly controls somewhere between 70 and 90 percent of the global consumer drone market depending on which segment you look at.

A 2024 estimate puts DJI accounting for over 90% of all consumer drones sold globally.

More than the numbers, the interesting part is DJI’s story.

It starts with a boy losing his remote-controlled helicopter and figuring out why it crashed the way it did.

And the funny side to this story is that he initially didn’t plan to build this giant drone company.

So, in this article I’ll start with that story of how DJI came to be, how it stands against its competition and what the future looks like for consumer drones and DJI.

The Drone We’re Talking About

Before we go further, let’s make sure we are all on the same page about what a drone actually is and how it stays in the air.

A drone, in the context we are talking about, is basically a radio-controlled helicopter with four rotors. The pilot controls it remotely, either with a handheld controller or from a phone or tablet.

The kind of drones DJI makes are called multirotor drones. Most of the popular models are quadcopters, meaning they have four propellers arranged around a central body.

To understand how a drone stays up, think about what keeps a regular helicopter in the air. A single big rotor spins and generates lift.

The problem is that spinning a single rotor creates a rotational force that would spin the whole body of the helicopter in the opposite direction. That is why helicopters have a small tail rotor, to counteract that spin.

Drones solve this better. Two of the four propellers spin clockwise, and two spin counterclockwise. The opposing spins cancel each other out, so there is no spinning body.

To move forward, the drone tilts its front motors slightly down by spinning them a bit slower. To turn, it adjusts the speed of individual motors.

All of this happens automatically, dozens of times per second, through the flight controller chip inside the drone.

The flight controller is the brain of a drone. It reads data from sensors, processes it, and adjusts motor speeds constantly to keep the drone stable.

Without a flight controller, a drone would be nearly impossible for a human to fly manually.

This is the initial problem Frank Wang set out to solve.

Crash Of An RC Helicopter

Frank Wang is a guy born in China, in 1980.

As a child, he read a comic book in which the protagonist flew around the world in a small red helicopter. That image stuck with him and he wanted one of his own.

Our guy Frank studied hard enough that his parents eventually bought him a toy model helicopter. He crashed it the moment it left his hands.

That crash, instead of putting him off, made him obsessed. The problem was not the helicopter. The problem was control. Why was it so hard to keep these things stable in the air?

That question followed Frank Wang all the way to university.

He went to the Hong Kong University of Science and Technology. In his senior year, he took on a class project to build a flight control system for a model helicopter.

He stayed up nights working on it and presented the result to his professor, Li Zexiang.

The presentation was messy. The system did not fully work. But Professor Li saw something in Wang and brought him into the graduate program to keep developing it.

By January 2006, Wang and his small team had cracked it. The helicopter flew. It hovered. It landed. Stable and controlled.

Out of curiosity, Wang posted his result on a model airplane forum and offered the system for 50,000 Chinese yuan, which was about 7,000 US dollars at the time.

It costed him 2,000 dollars to produce it. To his surprise, people were willing to pay. And that’s when he saw a business.

In March 2006, he co-founded DJI with two teammates. The name stands for Da-Jiang Innovations, which loosely translates to Great Frontier Innovations.

His goal at the time was small. “If I can feed a team of 10 to 20 people with this, that would be great,” he said in an interview years later.

From A Dorm Room To The Skies

The early days of DJI were not glamorous.

Wang moved with his co-founders to Shenzhen after graduation, renting a small apartment and funding the operation with leftover scholarship money and support from family.

A family friend named Lu Di provided 90,000 US dollars and helped manage finances.

DJI’s first product was the XP flight controller series. These were not drones. They were the flight controllers that we just talked about above.

Essentially, flight controllers are brains that went inside drones that hobbyists were building themselves at the time.

Before companies like DJI existed, flying a radio-controlled aircraft was serious business.

You had to buy components from different sources, assemble them yourself, calibrate everything, and then pray it did not fall out of the sky.

Most people who tried lost aircraft in spectacular fashion.

The XP flight controller changed that for a lot of hobbyists. It came pre-built and provided stable, reliable performance out of the box.

DJI sold around 20 units a month, mostly through word of mouth and online model airplane forums.

In 2009, a team used DJI’s flight control system to fly a drone to the peak of Mount Everest. It was a proof of concept that the technology worked in extreme conditions. Orders started coming in more consistently after that.

Professor Li joined DJI as chairman. A family friend sold his apartment to invest in the company. A high school friend of Wang’s joined in 2010 to run marketing and began pushing DJI into hobbyist markets outside China.

The early years were hard. Wang was a perfectionist and an exacting boss. Most of the founding team eventually left.

There was even an incident where an early employee tried to steal DJI’s intellectual property and sell it to a competitor.

Wang wanted to sue but could not afford the legal fees. Professor Li helped settle the dispute and raised additional capital to keep the company alive.

Through all of this, Wang kept iterating on the technology.

The Drone That Made DJI

By 2012, DJI had built enough components around drones. They had the technology for flight controllers, motors, and GPS systems.

Wang decided to put it all together into a single product. And on January 7, 2013, DJI launched the Phantom 1, its first ever drone.

It was priced at 629 US dollars. It was white, compact, and required no assembly. You pulled it out of the box, charged the battery, connected a controller, and flew it.

It was the first consumer drone you could just fly. Full stop.

Every other quadcopter on the market was either a DIY kit or an industrial product costing tens of thousands of dollars.

It used GPS to hover in place automatically. If it lost signal from the controller, it would fly back to where it launched from on its own.

The press release DJI sent out was two paragraphs. It said: “You can fly your Phantom the moment you receive it.”

That year, DJI’s revenue went from about 4 million dollars to 130 million dollars.

The drone community had never seen anything like it. Filmmakers realized they could now get aerial shots that used to require hiring a helicopter crew at thousands of dollars per hour.

Wedding photographers (esp in our part of the world) realized they could shoot from angles no one had ever captured before. YouTubers could make their travel videos look cinematic too.

By 2015, DJI was the largest consumer drone company in the world. Competitors who had existed before the Phantom began quietly shutting down or pivoting away from hardware entirely.

But the Phantom was just the beginning of what DJI was building.

How DJI Builds Almost Everything Itself

Read more

So, I was scrolling through YouTube the other day and came across an old video by Marques Brownlee on Apple’s ecosystem and it got me thinking…how do we actually end up using the products we use?

I bet none of you just sat down and decided - “I’m going all-in on this ecosystem.” It doesn’t work like that. You buy a phone, then maybe a laptop. Then something else that just works with it and before you know it, you’re inside a system.

But does all this happen by itself? Probably not.

Tech companies focus on building connected environments where everything is designed to work together and feel smooth. But behind that smooth experience, there’s a structure shaping how you interact with everything.

Let’s take a closer look at that.

What an Ecosystem Actually Means

At its core, an ecosystem is when a company designs its hardware and software to function as a single, connected experience. The value doesn’t just come from one device or one app. It comes from how everything works together.

Whether it’s your phone syncing with your laptop or the messages showing up across devices or even the files moving without you thinking about it - that’s the ecosystem doing its job.

Take the Apple Inc. ecosystem as an example. Phones, laptops, watches, services, subscriptions…etc. everything is built to connect without friction. It’s one of the most polished implementations of the ecosystem out there, and that’s exactly why it works so well.

How Ecosystems Are Built

Frankly, the ecosystem - like everything in life - doesn’t just pop out of nowhere. There is a whole strategy consisting of multiple steps behind it:

Step 1 - The Entry Point

It all begins with a product that solves a clear problem.

This could be a phone, an email service, or even a search tool.

The companies craft such a product which is easy to adapt aka The product works pretty well on its own & is easy to start using. And once it becomes part of daily use, it forms the base for everything that follows.

Step 2 - Expansion

Once the initial product gains traction, more services start appearing around it - Storage, messaging, backups, and other tools begin to extend the original experience. Each addition makes the system more useful and keeps more of your activity inside it.

Every moment you spend inside the app, starts to feel better because features that were once missing are now there and the overall experience is just better.

Step 3 - Integration

At this stage, products begin to work together.

For example - your devices start to sync automatically and certain features only work when multiple products are used together…etc. In other words, the experience becomes consistent across devices and it feels like you are operating inside a connected setup.

This also starts influencing decisions. Suppose if you already use one product, choosing another from the same system feels easier.

Step 4 - Dependency

Over time, the system becomes part of routine use because everything is perfectly connected together.

Leaving remains possible, but it requires effort because data needs to be moved and habits need to be adjusted. All of this requires a person to step out of their comfort zone and majority people don’t like to do that pretty often.

This is often referred to as lock-in.

Now, don’t get me wrong - tech companies don’t literally put some steel collar on you, but a digital steel collar which is enforced through convenience? Yes.

This connects with the enshittification pattern discussed in my other article as well. I talked about how once the users are settled inside a system, the focus moves from adoption to retention. Worth checking out that piece as well.

Case Studies

The companies listed here aren’t the only ones building ecosystems. Almost every major tech company follows a similar approach in some form.

The reason for focusing on a few names is because larger systems make the pattern easier to observe.

Apple Inc

Apple Inc. is one of the clearest examples of a tightly integrated ecosystem.

Its approach is based on controlling both hardware and software. This allows it to maintain consistency across devices. Products like the iPhone, Mac, Apple Watch, and AirPods are designed to work as parts of a connected system rather than standalone tools.

Features such as iMessage, AirDrop, FaceTime, and iCloud sit at the center of this setup. They connect devices and keep data moving across them.

This level of integration is what makes the experience feel seamless. As also discussed in content by Marques Brownlee, the system works well because everything is designed for that environment.

At the same time, this design introduces limits.

Devices often assume that other Apple products and services are already in use. For example, products like the Apple Watch depend heavily on the iPhone to function fully. Outside that setup, their usefulness drops.

Another aspect is scale.

Apple’s ecosystem is not just large in design, but also in reach. A single product line like AirPods generates tens of billions in revenue annually, with estimates placing it in the range of roughly $18–20 billion in recent years. That is comparable to the size of entire standalone companies.

This gives a sense of how deeply these products are embedded into everyday use.

Over time, moving away from the system becomes more complex because as discussed earlier - users are not only switching devices. They are moving away from stored data, app purchases, communication history, and features that connect across devices.

The friction comes from how many parts of daily use are tied together and this does not make the ecosystem inherently negative. It highlights the trade-off. A more connected experience brings more dependence on the same system.

Google

Google, on the other hand, takes a different route, but the outcome is similar.

Instead of starting with hardware, its ecosystem grew from software. Search, Gmail, Google Drive, YouTube, and Android form the base. All of these are tied together through a single Google account, which allows data and activity to move across devices.

Over time, hardware was introduced as well. Pixel phones, Chromebooks and wearables extended the system further.

Compared to Apple, this setup is more flexible. It works across platforms like iOS and Windows. But flexibility doesn’t mean independence. Once you’re logged into a Google account, a large part of your digital activity sits in one place and this kind of integration becomes stronger over time because services are designed to depend on each other.

A good example of this is the Play Store.

On Android, it’s the default source for apps. Alternatives exist, but they don’t offer the same range or reliability. Nearly all users stick with what’s already there.

There’s also another layer.

Many apps rely on Google Play services to function properly. Notifications, location, payments, and other features often depend on it. Even if you install apps from outside the Play Store, parts of them still connect back to Google’s infrastructure.

This makes moving away from the ecosystem harder than it seems.

So while Google’s system feels more open on the surface, it still builds the same kind of dependency over time, just through a different approach.

Why Ecosystems Work So Well

At this point, it’s clear that ecosystems are everywhere. Large companies have them and smaller ones are trying to build them.

The question is why they work so well.

Here’s a hint - convenience is part of it, but it’s not the only reason.

Let’s take a look at the other reasons.

1 - Connected Features

Ecosystems create features that only work when multiple parts are used together. - Services like cross-device messaging, instant file sharing, synced notifications, or shared clipboards depend on this connection.

Individually, these tools might not seem like a big deal but together, they offer something harder to replicate using separate services.

There’s also a behavioral side to it - if using one more device unlocks additional features, an average Joe won’t think twice. He’ll just add it and that’s exactly how the system expands.

2 - Less Choice, Less Effort

Take streaming platforms - you open Netflix or Hulu and get hit with endless options due to which you spend more time deciding than actually watching.

More choices don’t always help and ecosystems reduce that which is why instead of choosing between multiple software or hardware devices - people just end up going for a company that provides an ecosystem which has all the necessary hardware devices or services because that’s what an average Joe needs.

3 - Staying Becomes Easy

When everything is all set up - the go-to decision becomes staying where you are and that means continuing using what already works because switching requires effort and staying doesn’t.

And over time, that turns the ecosystem into the environment where everything happens aka work, communication, storage, daily tasks…etc.

Why? Because it became the easiest option.

The Tradeoffs

The same structure that makes ecosystems work well also creates trade-offs.

Flexibility decreases with the increase of tools. That means switching to another service or device means losing certain features or rebuilding how things work which takes a lot of effort.

And this isn’t about companies trying to make things worse for users, rather it’s more about how systems are designed in a way that makes leaving harder than staying.

There have also been cases where even basic actions were made difficult.

For example, the Federal Trade Commission filed a case against Amazon over its subscription practices, where canceling Prime was described as unnecessarily complicated.

Situations like this show how friction can be built into systems and once you look at it from that angle, you start to see how a more connected experience increases dependence on the same system.

Final Thoughts - Don’t Put Everything in One System

Ecosystems are not inherently good or bad.

They exist because they solve real problems. They make technology easier to use. For the majority of people, they provide real value and voiding them completely isn’t realistic.

The real question is how much you rely on a single system.

Keeping everything in one place makes things simple in the short term. Over time, it also increases dependence. Spreading tools across different systems adds some friction, but it also gives flexibility.

I believe that technology is moving toward more connected systems and understanding how they work is the only thing that will allow you to use them, instead of being used by them.

Make sure to stay tuned for the second part of the tech ecosystem series in which we will be diving much deeper into the system.

One Last Thing

Nearly all people interact with systems without questioning them. This space is about doing the opposite aka understanding how things work, where they fail, and what that means in practice.

If that’s the kind of content you want more of, subscribe. And if this piece added something new to how you see things, pass it on. Good information only matters if it travels.

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If you’re into PC gaming these days you might have gone over headlines calling for Intel’s comeback.

And honestly, I get why you might be skeptical. Intel has been saying it’s coming back for years now and most of the time it was just corpospeak.

But this time something actually feels different.

See, Intel just launched their latest desktop processors and once you go over their specs and the price, you have to give them a hand. It’s exciting news from Intel since quite some time.

So let me take you through the whole story. Not just the chips that launched last week, but how we got here.

In today’s article I’ll share how Intel went from owning the chip market to becoming a meme, how AMD quietly took over, and now what these new chips actually mean for anyone buying a PC in 2026.

I’ll also talk about something that could be even bigger. NVIDIA making CPUs (Yes, the GPU company). It could be the most interesting thing to happen to PC hardware in a long time.

But first, let me explain what a CPU actually is because not everyone knows and that is completely fine.

What Is A CPU Anyways?

Before I even start let’s just establish that the words ‘processor’ and ‘CPU’ stand for the same thing.

In any computer, CPU is the brains of the operation, a tiny chip that does billions of small mathematical calulations in a second to get you to browse the internet or play games or whatever you do on your computer

A CPU generally dictates how fast and powerful your computer would be. Your PCs, smartphones, laptops - basically every computer needs a processor to function properly.

If you want to understand CPUs at a more deeper level, you can checkout my full blogs where I dive deep into Cores, Threads and what you should know when selecting a CPU for your PC. That article is linked here.

The x86 Duopoly

For about two decades now only two companies have been making CPUs for regular consumers using PCs. Those are Intel and AMD.

And it’s not because other companies cannot make CPUs for PCs but rather both Intel and AMD keep a secret to them that they don’t share with others.

That secret is the foundation of the processors both companies make. That is the x86 instruction-set architecture.

Think of an instruction-set architecture (ISA) like an underlying blueprint or manual for making processors.

At this point, x86 is decades old but only Intel and AMD have a license to it. It’s age is the reason it is the dominant platform in desktop PCs even to this day.

Having access to x86 makes Intel and AMD sort of a monopoly (duopoly to be exact) because only these two companies have license to it.

Even if x86 is decades-old, it is being challenged by a new player: ARM. It is the one your MacBooks use.

If you’re curious about ARM, I’ve talked about it too here on SK NEXUS, you can read the full article here.

That is partly why historically there has been little to no competition in the CPU market. The usual trend is that the dominant player gets lazy with time and stops innovating.

However this is changing because a new player is entering the ring. I’ve talked more about it later in the NVIDIA section.

Intel Before Ryzen

For a long time, Intel has been the dominant CPU maker in the desktop and enterprise computer markets. The company used to own the PC world.

It was called ‘Chipzilla’ for its market dominance because it single-handedly controlled somewhere around 80 to 90 percent of the CPU market for decades.

However, Intel’s insane success made it lazy.

If you bought an Intel CPU in 2015 and then looked at what Intel was selling in 2017, the improvement was honestly pretty borderline.

Just to give you an idea how lazy Intel got, I wrote a deep-dive article discussing the major issues with Intel that made it lose the ‘Chipzilla’ title, the deep-dive article on Intel is linked here.

Intel was stagnating from the technology side in the mid 2015-era and around that time AMD showed up with a punch to Intel’s face.

AMD’s Ryzen Changed Everything

In early 2017, AMD released its Ryzen processors. Those were the first generation processors on their new architecture they called Zen.

Ryzen brought something Intel wasn’t offering at those prices: more cores, more threads, better multitasking, and a lower price tag.

The second generation Ryzen processors (Zen 2) in 2019 were a big leap forward. Third generation Ryzen processors (Zen 3) in 2020 finally took the gaming crown away from Intel.

By 2020, recommending Intel over AMD for most PC builds was genuinely hard to justify.

AMD had more than doubled its market share in the desktop segment.

Intel tried to respond by stuffing more cores into its chips and pushing clock speeds higher. But efficiency was still not there.

The chips ran hot, used a lot of power, and fell behind in too many comparisons.

And that’s when things started to get genuinely bad for Intel.

Intel’s Dark Years

From around 2019 through 2024, Intel was struggling real hard.

But the worst part of this whole era was what happened with Intel’s 13th and 14th generation processors.

People randomly started noticing that their latest-gen Intel CPUs were quietly dying. Not just slowing down or underperforming. Physically, permanently degrading over time.

The 13th and 14th generation i9 processors (Intel’s top consumer chips) were the main culprits. Systems would crash. Games would fail. Blue screens of Death became a daily thing for some users.

What was happening is that these chips were being pushed with too much voltage for too long. This was causing permanent physical damage to the intel inside (pun intended).

And the worst thing was that this damage could not be reversed by any software update. Intel first tried to shift blame to motherboard manufacturers. Then it released patches. Multiple rounds of them across 2024.

Eventually Intel admitted there was a fundamental firmware-level issue with how the chips handled voltage, extended the warranty on affected chips to five years, and told owners to update their BIOS.

The update patches could prevent future degradation on newer chips but they could not undo damage that was already done. If your chip had already degraded, it stayed degraded.

And that is when consumers started losing trust.

AMD had none of these issues during this period. Ryzen chips just worked. Stable, efficient, reliable.

AMD’s market share was also growing.

Intel went from being the leader in chip manufacturing to a company looking to just survive especially after Ryzen gave it a punch to the face.

We covered Intel’s decline in a lot more detail starting from the time they missed the smartphone wave and lost Apple as their customer.

If you’re interested in learning more about Intel’s issues, here’s our deep-dive article which you can read here.

Intel’s 2026 Comeback

This March, Intel launched two new desktop chips:

1. Core Ultra 7 270K Plus at $299

2. Core Ultra 5 250K Plus at $199

Keeping the weird naming scheme aside, the chips are actually impressive. LinusTechTips also titled their review video as ‘Intel is back’.

GamersNexus reviewed the 270K Plus and called it “Not a Waste of Sand.” And that’s coming from a reviewer who termed the previous generation Intel processors as ‘A Waste of Sand’ :p.

Tom’s Guide also said Team Blue is getting its desktop swagger back.

So you might be asking: What actually makes these chips different from what came before?

The 270K Plus is a 24 cores beast with 8 performance cores and 16 efficiency cores.

For $300. That is the same core configuration as Intel’s previous flagship chip, the 285K, which launched at over $600.

Intel basically figured out how to offer its best silicon at half the original price.

In multi-threaded workloads, so things like video rendering, photo editing in batch, 3D work, compiling code, the 270K Plus beats AMD’s Ryzen 7 9700X by over 100 percent.

TechSpot showed the 250K Plus was 85 percent faster than AMD’s Ryzen 5 9600X in Cinebench multi-thread. Both Intel chips cost the same as their AMD competition.

To be clear, multi-threaded work is the stuff that uses many cores at once. It’s what content creators, editors, and developers care most about, not our average Bashir. But Intel is now dominating at this price.

Intel also launched a driver suite with these new processors. Think of it like a software driver that handles advanced optimizations for your processor.

It’s Intel’s equivalent of what AMD has had for its Ryzen platform for a while. A bit late to the party but it does the job.

Is Intel Better Than AMD Now?

Honestly, this is where the honest answer is not as straightforward as you’d want it to be.

In productivity workloads (general tasks like browsing, content consumption) at the $300 price point, yes, Intel is genuinely better right now. AMD has nothing at $300 that matches the 270K Plus even in rendering and similar tasks. Nothing close.

In gaming however, AMD still has the edge. Especially with their X3D chips.

The Ryzen 7 9800X3D costs around $480 and it is the best gaming CPU you can buy right now. It beats the 270K Plus in most gaming benchmarks. But it costs $180 more.

The 270K Plus is not that far behind in gaming though. It’s competitive. And for someone who uses their PC for a mix of gaming and productivity work, the Intel option at $300 suddenly makes a lot of sense.

A review described the 270K Plus as a “corker of a comeback for Intel, particularly in the realm of productivity.”

The value picture is genuinely different from what it was a year ago.

Intel is now the chip you would recommend to productivity-heavy users at $300. AMD is still the chip you recommend for pure gaming if budget allows.

Just look how the tables have turned and we’re seeing Intel be the value brand.

Not A Bed Of Roses

There is something important I need to tell you before anyone runs out to buy one of these chips.

The motherboard these Intel chips launched on, called LGA 1851, is a dead-end platform. Intel has already said it’s moving to a new socket with its next generation of processors.

What this means practically: if you buy a 270K Plus today and want to upgrade your CPU two or three years from now without buying a new motherboard, you cannot. The next Intel generation will need a new motherboard.

AMD’s current motherboard platform, AM5, still has more life left in it. AMD has promised at least one more CPU generation on AM5. So AMD users can upgrade the CPU later without replacing the whole motherboard.

This matters if you are someone who upgrades bit by bit. If you buy a chip and use it for five or six years without touching it, the platform situation barely affects you.

The 270K Plus is strong enough to last that long. But if you like swapping parts every couple of years, this is worth knowing upfront.

GamersNexus said it clearly in their review: “If Intel can repeat a launch like the 270K Plus but on a platform with some actual life to it, they’d be negating the number one complaint.”

Also worth noting: early reviews showed the chip running slightly below its advertised clock speeds in some configurations. Not dramatically but reviewers did flag the small differences.

And there is always the question of whether the listed prices hold once the chips actually land on shelves at scale.

And honestly that is the biggest concern. Because of AI we’re already in a crisis- like situation and then there are two major wars happening just before us.

Supply issues can push prices above MSRP quickly. And by the time this stuff gets to our local shopkeepers we’re likely to see a significant price increase which is just sad.

NVIDIA Joining The CPU Chat

Now here is something no-one saw coming. There have been rumors that NVIDIA is going to launch its laptop CPUs.

Yup, that graphics cards maker which just hit 5 trillion dollar market cap. The same people behind your RTX GPU.

Wondering how a GPU company just became the most valuable company in the world? Then, you may checkout my recent article on NVIDIA that answers this burning question that many people have.

They are making their own processor chips called the N1 and N1X and these are confirmed for Dell and Lenovo laptops that we may see somewhere around next year.

A Dell 16 Premium laptop with an N1X chip inside was spotted in a supply chain document from late 2025. Multiple OEMs are reportedly preparing products around this processor.

What makes this interesting is what the chip is reportedly capable of.

The N1X is expected to pack GPU performance in the ballpark of an RTX 5070/5060 TI, take a second and read that again, RTX 5070 performance by an integrated chip!!

If you read our MacBook Neo article, you already know what happens when a company puts CPU, GPU, and RAM all within one chip using a shared architecture.

Performance goes up. Power consumption goes down. Battery life improves dramatically. The machines become thin and quiet. That is what NVIDIA is attempting for Windows laptops.

The gaming implications alone are significant. Right now a gaming laptop needs a dedicated GPU which adds cost, weight, and heat. Just try and use a gaming laptop and an ultrabook side-by-side.

An N1X laptop with integrated RTX 5070 class performance could in theory give you serious gaming in a machine that looks and feels like a normal thin laptop.

Though I’ll be honest there are still real questions.

Windows on ARM is not perfect yet. App compatibility has improved a lot since the early days but gaming on ARM is largely unheard of.

The N1X has reportedly hit some engineering delays and may not land until late 2026 or early 2027. However there are reports that Dell and Lenovo are up for a reveal at Computex 2026 coming up in June.

However, it may be some time until we see products with these chips become available for purchase.

But the direction of travel is clear. NVIDIA entering this space forces Intel and AMD to think differently about what an integrated chip can do.

Competition Gave Us Better Processors

If you’re a regular reader of SK NEXUS you’d have noticed a trend of us shouting that competition in products is a good thing for the end-user. And that’s something we really believe in.

At the end of the day, it’s a bloody competition that delivers great products.

If Ryzen didn’t give a solid punch to Intel we’d not have seen processors like the 270K and 250K Plus from Intel certainly not at this price.

When Ryzen came to the scene Intel had to improve almost immediately. It added more cores to its chips for the first time in years. Prices dropped. Performance jumped faster than it had in a long time.

That is what competition does to a market.

The 270K Plus at $299 offering double the multi-threaded performance of a same-priced AMD chip is a direct result of Intel needing to fight again.

A year ago that chip would have cost $600. Competition brought the price down.

So, no matter how big of a ‘Chipzilla’ you are, the product is what matters in the end.

The CPU wars being back is good news. Not just for people who enjoy reading nerding out on specs but those who are on a budget and want a good value for their money.

More competition, better products, lower prices. That is the story here. And honestly, it is only going to get more interesting from here now that NVIDIA is joining the chat.

As always your comments are what I look forward to when writing these articles. Your feedback directly helps us become better writers so please be generous with that in the comments down below.

Further Learning

• Not a Waste of Sand: Ultra 7 270K Plus CPU Review & Benchmarks

• Intel is BACK. THIS IS NOT A DRILL

• NVIDIA N1X and N1 CPU: Everything we know so far

• NVIDIA N1/N1X SoC surface on laptop motherboard

• Intel finally confirms instability issues with 13th-gen

• A timeline of the rise and decline of Intel

• With an Intel recovery underway, all eyes turn to its foundry business

Artificial intelligence is now everywhere. The term appears in product launches, marketing campaigns and everyday conversations about technology. Yet much of what happens behind the scenes remains poorly understood.

In 2025, a small controversy on YouTube highlighted this confusion.

The platform was quietly testing a feature designed to enhance video quality on Shorts. The system used algorithms to reduce blur and improve clarity during processing techniques similar to those already used in modern smartphone cameras.

But, the creators didn’t like it and quickly pushed back.

Many worried the platform was introducing AI slop into their content without consent, assuming the feature relied on generative AI to alter or recreate their videos.

YouTube responded publicly:

“No GenAI, no upscaling. We’re running an experiment on select YouTube Shorts that uses traditional machine learning technology to unblur, denoise, and improve clarity in videos during processing.”

The company emphasized that the experiment relied on conventional machine learning methods rather than generative AI.

For many observers, the explanation raised an obvious question.

“Aren’t those the same thing?”

They are not.

And that confusion points to a larger problem in how artificial intelligence is discussed today. Terms like machine learning, deep learning, generative AI, and even broader ideas such as AGI and ANI rarely make it into public conversation. Instead, the label “AI” is slapped on top of them. The result is a blurred understanding of what these systems actually are and how they work.

This article focuses on clearing up that confusion.

The Foundation: What Machine Learning Actually is

Machine learning (ML) is a branch of artificial intelligence focused on building systems that learn from data rather than following fixed instructions. Instead of programming every rule manually, developers train algorithms on datasets so the system can recognize patterns and make decisions on its own.

In other words, machine learning allows computers to classify information, detect patterns, and make predictions based on what they have already seen.

Take phishing emails for example - it might occasionally slip into your inbox, but the system quickly learns from patterns shared across similar messages and as more data is processed, the filter improves its ability to recognize and block those messages automatically.

The concept itself is not new. Researchers have been exploring machine learning since the 1950s, long before the current wave of AI hype. Today, many everyday systems rely on it, including spam filters, recommendation engines, tools that upscale video quality and much more.

Most of these systems do not generate new content. Their role is to analyze existing data and make informed decisions based on patterns within it.

Self-driving vehicles are another great example. These systems process enormous amounts of sensor and camera data to recognize road signs and navigate traffic. The model improves by learning from previous driving data and continuously refining how it responds to real-world situations.

In many ways, machine learning forms the technical foundation beneath much of what we now describe as AI.

Machine learning itself comes in several forms. While the field is broad, most applications fall into three major categories.

Supervised Machine Learning

Supervised learning involves training a model using labeled data, where both the inputs and the correct outputs are already known. The algorithm learns how to map inputs to the correct results by studying these examples.

For instance, a model can be trained using labeled images of animals. Each image already includes the correct label. After training on enough examples, the system learns the visual features associated with each category. When it encounters a new image, it can predict which animal it contains.

Unsupervised Machine Learning

Unsupervised learning works differently. Instead of labeled data, the model receives raw information without predefined answers and must identify patterns on its own.

A common example appears in customer data analysis. If a company has large datasets about purchasing behavior but no predefined categories, an unsupervised model can group customers with similar habits. These clusters can then help businesses understand more about each customer and then develop marketing strategies accordingly.

Reinforcement Learning

Reinforcement learning trains systems through trial and error. Instead of labeled examples, the model interacts with an environment and receives feedback in the form of rewards or penalties.

Consider an AI system learning to play chess. When it makes strong moves, it receives positive feedback and vice versa. Over time, by experimenting with different strategies and observing the results, the system learns how to make increasingly effective decisions.

Where the Confusion Begins: Traditional ML vs Generative AI

This distinction is where the YouTube controversy started to get messy. People assumed the feature involved generative AI, when the technology behind it was much closer to the machine learning systems that have existed for decades.

Traditional Machine Learning

Earlier we discussed machine learning as the broader concept and within it, traditional machine learning handles the “analyzing data and making predictions part” which I previously explained in detail.

Just keep this in mind - traditional ML works with existing data and tries to determine what something is or what might happen next and it does NOT generate new material.

Generative AI

Generative AI follows a different approach.

Instead of focusing only on prediction or classification, generative models are trained to produce new outputs that resemble the data they were trained on. The model studies patterns across massive datasets and learns how to recreate similar structures.

This ability allows generative systems to produce things like written text, digital images, music, computer code, or even video.

Tools such as ChatGPT generate natural-language responses. Image generators like Midjourney produce artwork from simple prompts, while emerging video models such as Sora can create entire scenes from text descriptions.

Deep Learning and the Architecture Behind Modern AI

Beyond machine learning and generative AI, two technical ideas power much of the AI people interact with today - deep learning and transformer models.

These systems are rarely discussed outside technical circles, yet they sit at the core of many modern AI advancements. Together, these technologies power many of the systems that define modern AI.

Understanding Deep Learning

Deep learning is a branch of machine learning that uses artificial neural networks with many layers to analyze data.

The relationship between these fields is often easiest to picture as a hierarchy. Artificial intelligence sits at the top, machine learning forms a major subfield within it, and deep learning represents a more specialized area inside machine learning.

The key difference between traditional machine learning and deep learning lies in how systems learn patterns.

Traditional ML systems often rely on human engineers to define the features a model should examine. For example, if a model needs to identify stop signs, programmers may specify characteristics such as shape, color, or edge patterns.

Deep learning models reduce this manual work. By analyzing large datasets, neural networks can learn those characteristics automatically. Instead of being told what features matter, the system discovers them during training.

This ability to learn features automatically allows deep learning models to handle far more complex tasks. Today they power technologies ranging from computer vision systems and speech recognition to robotics and self-driving vehicles.

Understanding Transformer Models

In 2017, researchers introduced a new neural network architecture called the transformer. This design quickly became the foundation for many of today’s most powerful AI systems.

Transformers are built to process sequences of information, such as sentences, lines of code, audio signals, or even frames in a video. Their key innovation is a mechanism known as attention, which allows the model to evaluate how different parts of an input relate to each other.

Instead of reading data strictly in order, transformers can analyze relationships between all elements at once. This helps the model understand context far more effectively.

Although transformers are often associated with text generation, the architecture is not limited to language. Variants such as Vision Transformers (ViT) are used in computer vision, while similar approaches power multimodal models that combine text, images, and audio.

Many modern AI systems rely on this architecture. Models such as ChatGPT and Claude are built on transformer-based designs, typically using encoder–decoder or decoder-only variants.

The Three Levels of AI

When people talk about the future of AI, they usually refer to three stages: narrow intelligence, general intelligence, and superintelligence.

Artificial Narrow Intelligence (ANI)

Artificial Narrow Intelligence is the only form of AI currently in real-world use. The word narrow simply means specialization. These systems are built to perform specific tasks extremely well. They operate within defined boundaries and do not extend beyond them.

Everyday technologies fall into this category. Voice assistants respond to commands, recommendation systems analyze user behavior, and tools like ChatGPT generate text or code. Each system performs its function with somewhat impressive accuracy, but none of them truly understand the world beyond their training.

And of course, the limitations exist, for example - A system trained to recommend movies cannot suddenly start driving a car or managing finances.

Artificial General Intelligence (AGI)

Artificial General Intelligence represents a system that can think, learn, and adapt across multiple domains, much like a human.

Unlike ANI, an AGI system would not be limited to a single task. Theoretically, it could possibly learn new skills, apply knowledge across different fields, and solve unfamiliar problems without retraining.

Despite growing public discussion, AGI has not been achieved. Headlines suggesting systems becoming conscious or achieving human-level intelligence remain speculative and often driven by hype rather than technical reality.

Artificial Superintelligence (ASI)

Artificial Superintelligence goes a step further. It describes a system that surpasses human intelligence, reasoning, creativity, decision-making, and even social understanding.

In theory, an ASI system would be able to operate at a scale far beyond human thinking.

The idea often appears in science fiction because of its extreme implications. A system capable of compressing decades of scientific progress into seconds would fundamentally reshape every field it touches.

It’s important to note that - at present, ASI remains entirely theoretical. Even reaching AGI is still an unsolved challenge, which places superintelligence far beyond current capabilities.

Why Everything is Suddenly AI

“If most systems are just machine learning, why does everything get labeled AI?”

It’s because of moneyyy!

A useful comparison comes from the term greenwashing, where companies exaggerate how environmentally friendly a product is to appeal to consumers. A similar pattern is now visible in tech, often referred to as AI washing - the practice of overstating products as “AI-powered” to make them appear more advanced than they actually are.

The thing is - investors associate AI with “future growth”, consumers view AI features as '“cutting-edge”, and companies feel pressure to keep up with competitors adopting the label. Calling something “AI” signals “innovation”, even when the underlying system is little more than standard automation or basic machine learning.

As a result, the definition of AI has been modified a lot. Tools that would have once been described as algorithms or analytics systems are now rebranded under the AI umbrella.

There have already been public examples of this trend. A campaign by Coca-Cola drew criticism after claiming a new drink had been co-created with AI, without clearly explaining what role the technology actually played. Reporting by Forbes highlighted how the claim appeared to rely more on branding than substance.

Regulators are also beginning to respond. In 2024, the U.S. Securities and Exchange Commission charged two firms with making misleading statements about how extensively they used AI in their investment strategies.

The point is - AI has become a marketing label and like any label tied to hype, it gets stretched.

The Rise of Agentic AI

While much of the confusion around AI comes from marketing, the technology itself is still evolving. One of the latest developments is what many now call agentic AI.

Unlike traditional systems that wait for prompts, these models are designed to take action. They can coordinate and interact with different software tools to complete an objective:

• Generative AI models, such as tools like ChatGPT or image generators like Midjourney, focus on producing content.

• AI agents go a step further by using that intelligence to perform specific tasks, such as scheduling actions, retrieving data, or executing workflows across applications.

• Agentic AI extends this idea even further. It involves multiple agents working together, coordinating tasks and decisions with minimal human input.

Projects like OpenClaw highlight how multiple agents can coordinate to handle complex tasks. However, this space is still in its early stages. These systems often struggle with reliability and introduce serious security risks when given access to external environments.

Despite these limitations, progress is being made. Agentic systems are likely to improve and expand, making them an area worth watching closely.

Why Understanding This Matters

At a glance, the distinctions between different types of AI models and technologies can seem academic. In reality, they directly affect how AI systems behave.

A recommendation engine predicts preferences. A generative model produces content that may or may not be accurate. An agentic system can take action, which introduces a different level of risk and responsibility.

Knowing what kind of system you are dealing with, helps in setting expectations and answering questions like - can it make decisions on its own? Can it generate misleading outputs? Should its actions be verified?

It also helps filter out hype.

We also have terms like “singularity” - the idea that AI could surpass human intelligence and rapidly evolve beyond control - which are often used to create urgency and attention. In reality, current systems remain far from that scenario.

Understanding the layers of AI makes one thing clear - the technology is advancing, but it is still bounded. And recognizing those boundaries is what keeps expectations grounded.

What Comes Next

Much of today’s AI conversation revolves around how current systems are rapidly approaching Artificial General Intelligence (AGI).

Despite their capabilities, modern AI systems still struggle with core problems such as reasoning, long-term planning, and understanding cause & effect. They can generate convincing outputs, but that doesn’t mean they truly understand what they produce.

These limitations matter. They suggest that simply scaling current models may not automatically lead to human-level intelligence. And this doesn’t mean progress has stalled. It means expectations need to be calibrated.

In short, AI is not a single system or breakthrough. It is an umbrella term that covers a range of technologies built on top of each other - from machine learning to deep learning, from transformer architectures to generative models.

Each layer adds capability, but not all of them represent the same level of innovation.

Some, like generative models, have introduced visible and impactful changes in how people interact with technology. Others are slow improvements that refine existing systems rather than redefine them.

With that being said, thanks for reading.
Peace out.

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Let’s begin with a question. What comes to mind when you hear of the term “gamer”?

My guess is, it’s someone on a couch with a controller in hands, TV glowing in the center room.

Or maybe it’s a PC setup with RGB lighting which alone costs more than my laptop.

That’s the general image gaming culture has spent decades building. But here’s the thing. That image is wrong. Or at least, it no longer holds true thanks to the rise of mobile gaming.

The biggest gaming platform in the world right now is sitting inside your pocket without many of us knowing.

It’s been there this whole time, we just weren’t paying that much attention. And it’s not just us, big players in the gaming industry ignored it for quite some time too.

Yet now, all the big players which includes Sony, Microsoft, Nintendo, and even Valve are all scrambling to get a piece of it.

Mobile gaming isn’t a side category anymore. It’s the main event. And most people have absolutely no idea.

Even though I wasn’t sure that the mobile gaming market is that big, when I saw the numbers for my recent article on Carcinization in the Gaming Industry, I was somewhat shocked.

Mobile gamers who most hardcore gamers don’t even like to call gamers have given rise to an industry that is double the revenue and 5x the size of the console market.

This realization was the idea for this article. Initially I thought of mentioning it in a small section in the Consoles and Carcinization article.

But there was a lot more to this topic so I had to dive deep and write a proper article explaining where this whole thing started and where the mobile gaming space is headed in the years to come.

The Surprising Numbers

I’ll just say it at the start. Mobile gaming made roughly $103 billion out of the total of $188 billion the entire gaming industry made in 2025. Yes, that’s BILLION with a B.

Just let that sink in for a second.

Console gaming which includes your PS5s, Xboxes and Nintendo Switches of the world pulled in $45 billion. And PC gaming came in 3rd pulling in $39 billion in 2025.

The entire global gaming market was worth roughly $188 billion in 2025, and mobile alone is carrying nearly half of that number. By itself. From the phones in people’s hands.

There are 3.4 billion mobile gamers in the world right now. Compare that to roughly 630 million console players. Mobile gaming isn’t just bigger, it’s in a completely different league.

And yet, if you asked most people around you to name the biggest gaming platform, I’d bet they’d say PlayStation or Xbox before they even thought about their phone. That gap between what we think and what the numbers actually are, is exactly what this article is about.

Isn’t Mobile Gaming just Candy Crush?

I know what you’re thinking. Okay, a lot of people play phone games. But it’s all Candy Crush and Subway Surfers and ads every 30 seconds. That’s not real gaming.

I used to think the same thing, honestly. I would laugh at mobile guys even calling themselves “gamers”. But the numbers and the impact speak for themselves.

It’s hard to find students who in the COVID days haven’t tried PUBG Mobile at least once. Even if you didn’t play it you almost certainly have heard it make headlines.

I remember jumping ranks and grinding for new costumes like it was my full-time job during lockdown days :p

PUBG Mobile generated over $3 billion in revenue in a single year. Mobile Legends has tournaments with prize pools that rival traditional Esports.

These are not simple games, not even cheap to make actually. They’re built by massive studios with hundreds of developers, cinematic trailers, and dedicated competitive scenes even in countries like Pakistan.

Honor of Kings, a mobile MOBA (Multiplayer Online Battle Arena) also made by Tencent crossed $2 billion in annual revenue in 2024 with over 100 million daily players.

Just for context, that’s One Hundred Million people playing one game, every single day. That’s not casual. That’s a media empire that basically lives on our phones.

Genshin Impact crossed $10 billion in lifetime player spending faster than any mobile game in history.

And mind you, this game has console-quality graphics, a massive open world, regular content updates, and a player base that would make some AAA studios jealous.

The Candy Crush phenomenon kinda exists in the sense that casual games loaded with ads still exist and make a lot of money. But dismissing actual mobile gaming because of these games is just not a good strategy.

The Math Is Mathing

Let’s actually break down where mobile’s $92.6 billion in the year 2024 comes from because the breakdown seriously hits the eyeballs.

In-app purchases made up about $80.9 billion of that $92.6 billion number. Advertising brought in $18.9 billion. Subscriptions are a small but growing slice on top of that.

Here’s the interesting part though. Downloads are actually going down. There were 49 billion mobile game downloads in 2024, which is down 7% from the year before.

Fewer people are downloading games yet the spending is going up, sounds weird, right?.

But that just means that existing players are spending more money per person, not that new people are flooding in. And that’s what makes the total number go up.

Strategy games led all mobile genres at $17.5 billion in revenue. This includes games like Clash of Clans or the one I used to play, Clash Royale.

RPGs followed strategy games at $16.8 billion. These are not Candy Crush numbers. These are genres that require real time investment, learning, and often a decent amount of money spent inside the game.

And this isn’t slowing down anytime soon. Mobile gaming is projected to go over $130 billion in 2026 and the numbers only balloon up when you look at projections for the end of this decade.

If 2024 shows roughly $90 billion revenue, just imagine what the numbers would look like by 2030 if the mobile gaming industry keeps on growing as is.

So, Who Actually Plays Mobile Games?

Here’s where it gets really interesting from a global perspective.

Asia Pacific (which includes our Pakistan) generated 49% of all mobile gaming revenue in 2025.

Basically, nearly half the world’s mobile gaming money comes from one region. China alone generates over $40 billion annually, which is more than the entire console market in most other countries.

India is the next one to watch. 450 million gamers, getting by with their sub-$100 smartphones and some of the cheapest mobile data plans in the world.

Despite that massive player base though, India generates only 1.1% of global gaming revenue right now.

That gap between the number of players and the amount of money being spent tells you exactly where the next wave of growth is coming from. Game publishers are already paying very close attention.

Japan and South Korea punch way above their weight. Average revenue per user in those markets is above $80, which more than makes up for their smaller populations.

But here’s the stat I find most interesting whenever this topic comes up. The average mobile gamer is 36 years old. And 46% of gamers worldwide are women.

The image of a skinny teenager in a dark room with a headset was simply never accurate for mobile.

The Business Model that Prints Money

Okay so we know mobile gaming is massive. But how exactly is it making this much money? Because most of these games are free to download. I’ve played games like Clash Royale and I’ve never spent a single rupee.

Read more