The AI and Corporate Dilemma

-

 Mega corporations and companies are not your friends. They are not there to help you through tough times. They are emotionless, logical machinations designed for one thing: making money. Securing the bag. Hearing the Ka-Ching. Whether it's selling pizzas, computers, or automated cars, the goal is always to maximize profit, minimize cost, and hope that the consumer becomes the product. This is the reality of the world we live in, and AI is just the latest cog in the relentless race for efficiency.

And it should be no different. Darwin said it the best. People who adapt, survive, those who don’t… get laid off. We all wish we lived in a world like “Three Idiots”. All of us saw Virus as the villain. I think he was just being realistic. “Life is a race. If you’re not at the front of the pack, you’ll be left behind sooner or later.”

So, when companies lay off 9-5 workers in favor of those who can work 24/7 without a break, is it cruel? Yes, definitely. Is it unfortunate? Sure. But is it wrong? That depends on how you define "wrong." If a business’s aim is to survive in a competitive market, then the utmost efficiency is crucial.

Throughout history, new technology has always displaced workers. The Industrial Revolution put millions of craftsmen out of business. The internet eliminated entire industries. And now, AI is doing what AI was built to do—work faster, cheaper, and without so much as a breather between work. But blaming corporations for this shift is like blaming texts for putting carrier pigeons out of a job. Innovation doesn't wait for anyone.

If AI can handle customer service, write software, or design graphics better, faster, and cheaper than a human employee, why wouldn’t a company want to make the switch? They aren’t charity organizations. Not adapting to better technologies will result in them being left in the wake of their competitors.

AI has not resulted in the system being broken. It has made the system evolve.

Comments

Post a Comment

Popular posts from this blog

Destiny

-
Image
Destiny. Fate. Whatever you want to call it. The notion that everything that might happen to an entity is predetermined. People have been known to give their life to this concept, with the faith that whatever destiny has in store for them, will happen, no matter what. So why try? If the future is already written, why should I make an effort to build it? This kind of mentality is extremely, extremely dangerous, which is why it is necessary to clarify what destiny means for me. If we take the most common interpretation of it, destiny is what is behind an invisible and intangible curtain. It is a piece of paper that stretches all the way from our conception to our eventual demise. On the paper is written everything that has happened and everything that will.   Everything that has happened is already in your mind, as memories, and whatever will happen is constantly entering your mind, again, as memories. These remembrances-to-come are not known to you until they have already happened. ...
Read more

Hating on What's Popular

-
Image
Hating on music that is very popular in the world is really really common. Scorning artists like Drake, Taylor Swift, Sabrina Carpenter, Morgan Wallen, Adele, Billie Eilish, Maroon 5, One Direction, The Weeknd, is so quotidian now that it in itself has become mainstream.   Yet so many people listen to them. Drake is raking in 41 million+ streams daily, Taylor Swift is accumulating 64 million streams every day and is on track to overtake Drake as the most streamed artist of all time. So clearly people are listening to these terrible, art-destroying, world-ending, glass-shattering artists.   Numbers don’t lie. People do. The numbers show that the 200 decibel loud hate these mainstream artists get is just noise. People bash them from the outside, but secretly? They’re pressing play. The music’s good. That’s why so many people love it. So why do humans do this? Humans have a habit for rooting for the underdog. Always. No one ever roots for the king, they always root for the poor m...
Read more

BMI Estimation from an Image

-
This text details the technical workings of a BMI prediction application built using Streamlit.  It uses a dataset of 526 individuals, and a Random Forest Regression model. The application leverages several machine learning techniques alongside the dlib library for facial landmark detection, aiming to deliver BMI estimates based solely on facial features. Data Preparation The core of this application is a dataset obtained from a publicly available source. This dataset contains facial measurements and corresponding BMI values, which serve as the foundation for training a predictive model. The dataset was preprocessed to exclude missing data, and relevant features were selected for the model. The features considered in the final model exclude demographic factors such as age and height, focusing on physical facial measurements derived from the images. These features were then scaled using StandardScaler to normalize the values, ensuring the model can learn effectively from the data w...
Read more