FAANG: What’s Up with Big Tech?

NOTE: I’m not a journalist, and my citations are neither exhaustive nor comprehensive. The infractions go back farther than I’m writing here, and they will go farther into the future than I’ll ever write. This is just to show the scope within a window of time as someone in the “biz” about what I’ve run across.

Hackers aren’t the only groups that individual users should be concerned about. Large organizations have tremendous information power.

Since the beginning, the complexity of computers has meant there have always been huge, monolithic organizations that design most of the software/hardware, with a horde of various smaller public and private organizations that design smaller things around it or create open-source alternatives:

  • In the 1960’s, everyone called the big players “Big Blue and the Seven Dwarves”, with the computer industry run by 8 companies:
    • IBM (“Big Blue”)
    • Burroughs (which eventually renamed to Unisys)
    • Sperry-Rand (which merged its computer-based operations with Unisys in the 1980’s)
    • Control Data (which split up and part of it becoming Ceridian)
    • Honeywell (which suffered many strange changes of ownership and is no longer in the general-purpose computer business)
    • General Electric (which sold its computer-based business to Honeywell)
    • RCA (who got out of computers altogether and focused strictly on radio technology)
    • NCR (which was bought out by AT&T and then spun off again)
  • In the 1970’s, the government broke up AT&T in an anti-trust lawsuit. A few years later, Microsoft signed a non-exclusive contract with IBM and happened to sit in that power vacuum. As the company gained control, he continued to use fierce monopolistic tactics to drive out or absorb competitors, and Microsoft practically owned the market through the 1990’s and 2000’s. They practically destroyed the shareware industry in the process, and most Microsoft Windows features that most users take for granted were blatantly copied from third-party utilities. Microsoft famously had pay Apple $150 million for stealing code for Apple’s Quicktime.
  • During the dot-com boom in the 1990’s, computers shifted to becoming internet-centric, and a few tech companies rose to the top in their respective specializations once mobile devices became ubiquitous. in the mid-1990’s, Microsoft almost entirely used Internet Explorer 6 to turn the internet into a Microsoft-themed walled garden.
  • After the dot-com crash, FAANG emerged after a decade: Facebook/Meta, Apple, Amazon, Netflix, and Google/Alphabet, but other companies like Tencent (owned by China) and Microsoft still fit into it as well.
  • Given the tremendous power that comes from all the data extraction, governments can often fit into this as well, so this issue isn’t strictly a matter of corporate power.
  • Lately, the acronym is more accurately Facebook/Apple/Microsoft/Amazon/Government, or FAMAG.
  • Most recently, in the 2020’s, there has been significant public attention toward privacy, and new AI technology in the 2020’s has been released on an open-source license, so corporations have been scrambling to find ways to profit while also not losing customers.

The largest difference between the computers of today and of decades ago (besides processing speed) comes in how networked they all are. Up until the internet became popular, computers were stand-alone, so the information couldn’t be used as quickly, and often required physically inserting a disk into the computer. Now, the computers are all linked together, communicating exponentially more information, and constantly.

The story with Microsoft isn’t entirely over, either. There was irrefutable evidence in 1998 that Microsoft was suppressing competition with a Court’s Findings of Fact in 1999 and a full-on antitrust case in 2001, but the company was never formally broken up. To this day, they’re still trying to adversely affect the open-source industry with things like walling off the Hot Reload feature in non-proprietary versions of .NET as of 2021.


Most of the power of huge private organizations comes through a few harmonizing realities:

  1. The invention of the corporation over a century ago, which is a legally living thing that never technically dies and can hold assets forever for tax reasons.
  2. These corporations have accrued tons of power, often before anyone presently alive was born.
  3. The business entities gain ever-more power through many, many mergers and acquisitions (e.g., 2021-08 AT&T presently owns many, many entities, Facebook/Meta presently owns Instagram and WhatsApp).

This is ongoing while antitrust litigation interprets favorably to allow large corporations to continuously merge and acquire without regulation. Many of them are now as powerful as small countries.

Generally, gigantic all-powerful industry players persist in the USA because the definition has loosely been defined as acting against smaller competitors.

As long as the organizations have competition with other organizations, there aren’t any issues. Any government oversight needs contrasting oversight of those oversight-holders (i.e., “who will watch the watchers?”).

In practice, the only thing a government can do is stop or break up monopolies. Further litigation can create crony capitalism by preventing other new startups to enter that domain.

In technology industries specifically, antitrust litigation is more difficult than normal because the definition of “monopoly” is very hard to pin down, even if you simply say “over X% control of an industry”:

  • Google, for example, does not consider themselves a monopoly because they define their industry as the information market, and they certainly don’t own more than 5% of all human information.
  • With the right angle, entire industries are hard to clarify. Even if Amazon controls 90% of all web servers, is it a monopoly when someone else can theoretically spin up their own?
  • More specialized technology means more capacity to gain complete dominance. For example, in a classical sense most social media is a feed-based database, with granted permissions being defined as following/subscribing/friends.

A few examples of the results of this power consolidation:


Very often, apps/programs have built-in features that collect information the user isn’t aware of:

  • “Trackers” designed to collect specific targeted data (e.g., geographical location, phone call length).
  • Elevated “permissions” about pretty much anything (recording video, writing to external storage, etc.).

While much of the collected data is necessary for debugging, the organization collecting it can abuse it, especially if the user doesn’t conform to the same social/political views as that app’s organization.

Generally, the larger and more powerful the organization and the more features the software has, the more trackers and permissions it’ll ask for. Unfortunately, many operating systems design software permissions as opt-in with no timeout, so that one time you use a voice recording feature may mean the organization will track everything you say for the functional life of the device.

Most countries around the world have their own systems to monitor everyone’s activities, and they can often use that information in their governance:


One of the best ways to stay private is using end-to-end encryption, which means only the sender and intended recipient can see the content. This means anyone who intercepts or holds it (e.g., the company that holds the cloud storage, a government official) won’t be able to see what it is. Many entities don’t like this:

There’s plenty of reason for governments to rally support for their cause, so they’ll distort the truth and shut down E2E as much as they can:

Tons of User Data

Large organizations can track tons of seemingly unimportant user information. Depending on the organization, this gives them a tremendous amount of power over individuals’ lives.

Even when an organization doesn’t have your name, they will often attach an ID to you. That ID can contain a “shadow profile”, which can include all sorts of seemingly private information such as your personal preferences, lifestyle patterns, and political views.

The easiest way to do this (though it’s becoming harder) is through small files called “cookies”. While most of them can be benign (e.g., you don’t have to re-enter your password every time you go there) some work across websites and can record anonymized user data of where you went.

Private organizations can often customize an algorithm to created personalized targeted advertising by cross-referencing your interests to find other interests that people like you have liked. Simply permitting an app to access your contacts is enough for them to create a social graph of you and everyone you know.

Those systems can be terrifyingly accurate across millions of people, and with a surprisingly small amount of information. They can essentially tailor behaviors to 3-5 people they know who are just like you, then provide a precisely-tailored marketing experience that works to their best interests.

There is a multi-billion dollar industry for this user data, so the allure to sell it is too much for most companies to turn down:

This data tracking often forms into a synergistic relationship:

However, they don’t want you observing them doing it:

Data Mining

Sometimes, an organization will abuse private individuals’ trust by using their computers’ resources without permission for their own use. The most profitable purpose for this is cryptocurrency mining by running the CPU at full speed even when the user isn’t using their computer, though AI can also function for that purpose:

AI Training

Microsoft has found another alternative use of user data through GitHub Copilot since 2021. By funneling all the code stored in its Git libraries, it regurgitates that code through an AI algorithm (often copyright-protected or GPL) then charges for the service. It violates copyright and intellectual property law proportionally to how much AI-generated content inspired by copyrighted works is lawful.

However, the code isn’t strictly pulling and reusing the information, and there is at least some curation involved:

GitHub isn’t finished, and will try to expand their services:

Other groups are keeping their options open for AI training as well:


Very powerful organizations don’t like a few things:

  • Threats to that organization’s power (or that organization’s partners’ power).
  • Differing political views that may work against the desires of that organization.

If you trust large organizations on the presumption that “larger is more secure”, don’t be surprised when you face tremendous banning/blocking/silencing. They’ll very frequently create horrifying bureaucratic automated systems to enforce those policies, where there’s little to no chance for individuals to question or appeal their unique situation without public defamation on social media:

Further, those organizations (both public and private), when large and powerful enough, will make major moves to suppress things that don’t serve in their interests or opinions:


They’ll also use systems like dark patterns and tailored data to promote things that people wouldn’t normally choose.

There are different versions of this:

  • Google bombing – making a website’s search engine results irrelevant to bury that information
  • Googlewashing – changing the perception of a term by manipulating search engine results
  • Privacy washing – claiming to protect individuals’ privacy while not doing it

Another mechanism to manipulate the situation is to prevent net neutrality:

  1. By its nature, a computer should be unaware of what it sends and receives on the lower networking layers.
  2. Software in the computer itself should monitor that information for risks (i.e., layers 5 and up).
  3. Internet service providers are giving the lowest network layers (i.e., levels up to 4), so they shouldn’t monitor that information.
  4. However, there is plenty of political incentive to allow prioritized web traffic for some websites over others.
  5. If implemented, the internet service provider both gets to see everything someone does, and also steer traffic as they wish.

There are many examples of this:

Large organizations will also often try to draw more money out of people and abuse intellectual property rights beyond any just or sensible measure:

Further, AI and machine learning give even more opportunities to distort the truth:

The Right to Repair

One closely connected aspect, beyond gathering data, is making more money through inferior products.

  1. Consumers obviously want high-quality products, so manufacturers build better products to stay ahead of the competition.
  2. However, if those products are too good, they can last a long time. Singer sewing machines, for example, became so robust that grandmothers were handing them down to their granddaughters.
  3. Computer parts are no exception to this reality, and manufacturers make them with “planned obsolescence”, which are engineered to break down after a predictable period of time, typically a few months outside the window of the warranty period presuming typical use.

This is a scientifically proven trend with electronics. For example, Western Digital budget hard drives are arbitrarily slowed-down by up to 50% as of 2021.

Computer owners can push back on this by learning how to replace parts, meaning an aftermarket battery or hard drive can often make devices last years beyond when the company wants them to buy another product.

Since the consumers aren’t making large companies any money by doing this, they often try to thwart the users’ “right to repair” by inserting arbitrary barriers to the product:

This makes general sense on why people accept this situation. As computers get cheaper and smaller, they become more commodities than tools. However, it means people can’t repair their own things, meaning that companies can ratchet up prices with consumers being stuck either paying for it or living without it. It also means it creates more waste.

Some companies are taking political action to prevent the Right to Repair:

A few companies have pivoted the other way to compensate for failings in the Right to Repair (mostly Apple), but their size makes their actions potentially image-based:


Of course, large organizations are made of many people, and humanity is prone to fallacy. Sometimes, things fall through the cracks in large, unwieldy systems with zero malicious intent:

Next: How to Fight Back