Algorithms and The Crisis of Human Brain Devolution

Siddhartha Swarup
6 min readNov 25, 2020


Part 1: The insufficiency of our brain

The Netflix docudrama, The Social Dilemma, has created such a stir! While it was a provocative execution, the documentary failed to produce a single insight that was shocking news. The fact that Google, Amazon, Youtube, Twitter and Facebook collect our data; that advertisements and posts are individually tailored based on our online activity; that the social media revenue models are largely advertisement based — are all well-known facts. The exclusion of Netflix, which so efficiently tailors the ‘recommendations’ of what we should watch was, perhaps, the only surprise (or was it, given that it was a Netflix production?). In fact, using big data, Netflix saves $1 billion per year in customer retention. In short, there is no surprise to anyone that social media is like a sweet cookie to a chronic diabetic — a delectable but harmful addiction.

The questions it raised, for me, is why algorithms are being used so rampantly. To answer this question we must look at the internet content more closely. According to an IDC study — The Digital Universe in 2020–40 trillion gigabytes (40 Zettabytes or 40,000,000,000,000,000,000,000 bytes) of data on the internet by the end of this year. If that doesn’t boggle your mind, take this — 90% of all data that exists today was created in just the last two years.

While there is 40 zettabytes of data readily available online to be inputted into our brains, the total processing power of an average human brain is limited to just 40 bits per second. In comparison, an average desktop can process 64 billion bits per second. Basic AI chips can process trillion of bits per second. The huge influx of information and low human brain processing power has obviously led to a disabling imbalance. While the volume of data and internet bandwidth has kept increasing the human bandwidth has not kept pace. Our brain simply cannot process so much information unless it is presented in a simple easy to digest, ‘byte-sized’ chunks.

That is where algorithms come in.

With terabytes of data and information available on the internet, searching for anything on the internet would be like finding a needle in a haystack of galactic proportions (in fact there are far fewer stars in the known universe as there bytes of data on the internet). It would be humanly impossible — and would take millions of lifetimes — if you were to let your mind search for what you are looking for. Critically, it would be impossible to consume, process and use the data if left to the human brain. And that is why algorithms are used. To simplify the search and to provide you with the closest relevant answers to the your search questions, in the shortest timeframe. That is why the only thing that really matters is ‘What is on Page №1’ (of Google search, obviously). And hence the monetisation of ‘Page 1 model’. Because really, we just trust that the algorithms would throw up the most relevant responses on page one.

Powerful algorithms have played the role of a hard working assistant doing the grunt work of finding the right information, data, photos, posts, videos, article and more for us. Now we depend on algorithms to give us directions on the road, predict the names of the person we want to dial, set our alarm, even choose our friends and life partners for us. In the process, this assistant became more observant of each of our ticks, likes and preferences. And started tailoring and pre-empting our requests better than we could do. The problem is that the algorithms also have another master. That makes us see advertisements, read blurbs that we don’t enjoy and buy things we never thought we liked. And while we may know of this dual reporting structure of the assistant, we simply cannot help but seek help. As I said the internet is an expanding ocean where we might be finding a specific oyster. Without powerful algorithms we would be lost.

Part 2: The devolution of our brain

As it happens with anything that makes our lives easier, we tend to use it more and depend on it more. And as we use algorithms that predict our behaviour, our likes, our desires correctly (most of the time), we start to use less and less of our own analytical skills to reflect and think. We stop questioning what we really want. We stop asking these critical thinking questions: is there something else I should search for? is there another perspective to explore? Are these the only choices available? Is there more data to look at? Is the data accurate or is this fake news? Similarly, when we start associating ‘likes’ with approval, we assume that our opinion is the right one and by default others must be wrong. Paradoxically Binary — just like the machine code. Analysis and logic is controlled by the cortex. It is the ‘slow’ brain — the part of brain that weighs and balances choices, uses reason and rationality to make choices. But because algorithms are designed to throw up choices that we instinctively like or prefer, the cortex does not have the opportunity to engage. Remember it is the slow part of the brain — 40 bits per second).

Unlike the cortex, our limbic brain, the 10000 year old part of our brain that still works on instinct and reflexes, jumps to react to the influx of information. Limbic brain is the fast reacting, instinctive part of us which would take action but not necessarily the most prudent one. When we are surrounded with terabytes of data — most of which is being custom made for our likes and dislikes, it becomes easier to believe than to question, to react than to analyse, to feel validated than to search for alternate views.

As use of cortex decreases and the that of limbic brain increases, we see a fast evolving devolution of our thinking process. Logic and rationality are giving way to belief and faith. Rise of populist leaders such as Modi in India and Trump in US are examples of faith based fanatical fan following. No amount of data about economy or pandemic or any other issue will convince a Trump or Modi fan that they are not great leaders or that any of their policies might be flawed. The same is true in business. Cult following of certain business leaders has led to astronomical rise in prices of the shares of their companies. A company called Nikola (so called Tesla rival) which has zero revenue and has not produced a single vehicle since its existence reached a market valuation of $30 billion (more than that of Ford) due to its charismatic leader’s claims that they had technology that was twice as good as Tesla. Investors poured billions of dollars on blind faith….until recently, when a report claimed that Nikola is a fraud with no proprietary technology.

If you are a Modi fan, you are fed more and more pro-Modi videos by youtube If you are a Modi hater, you are fed anti-Modi videos. That is the nature of algorithms. They pander to your limbic brain. They aim to please. As you get exposed to more of the same kind of information, it solidifies your position on that issue. As you see 10 anti-Modi (or pro-Modi) videos from different sources on youtube, you start believing that is the general view of the country. It turns you into a believer of your own initial bias. What you do not realize is that those 10 videos were curated by an algorithm that read your initial reactions to the first video. By the 10th video, your cortex is no longer in play. In fact it was not in play by the time you were watching the second video. The limbic brain takes over and it likes what it sees. It wants more of the same…and the algorithm obliges.

These mini-beliefs and the over efficient algorithms are leading to a dull cortex and more… stupidity. It is true, humans are slowly and surely becoming stupider — more reactive than analytical, more self-congratulatory than critical, more inert than secular. At 40 bits per second we are no match to the AI of the algorithms. Could Neuralink be our savior?

Written in collaboration with Rachna Mathur



Siddhartha Swarup

A communication specialist, an entrepreneur, writer and a serial innovator. An MBA by education, he has worked across 17 countries in 5 continents.