Skip to main content

Pacquiao - Mayweather Jr: Why it wont happen.

Now that Manny Pacquiao proved he was right. What's next? Facing Floyd, that is. A thrilling idea that just became a concept: a what if. But why it will never happen?

Because Mayweather Jr. want's to retire as undefeated. This is the only reason I can think of. If you say he fears Manny, I don't think so. I bet he can stand toe to toe with Pacman. He has the skills. He is undefeated. And he knows how to box.

Now the question is why he wants to retire undefeated? Two reasons I can think of - money and/or prestige. You wont get anything else out of being undefeated except for those two. If you're undefeated, you're value goes up. You become famous. You have control over your fights... Things like those. Can't blame the man for thinking about his future. Specially if you have a colleague like Mike Tyson. Being reminded of how it will be without money will definitely make you think twice.

I'm just not sure why would he choose a good retirement over proving that he is the best. If I know that I am the best then I know that I can defeat whoever is placed in front of me. Specially for a boxer, nothing is more precious than proving to the world that I am the best. And for Mayweather Jr., that should be a piece of cake. That's if he, really, is the best.

Maybe this is all just work for him. A job that he was to do even if he doesn't want to do it. In a way that's being professional and I think I can admire him for that. But,being a boxing fan, or an spectator at least, I would think low of how a boxer he is. He maybe undisputed or undefeated, but he will never be the best or the greatest as far as my opinion is concern. Even without considering Pacquio in the picture, Marquez will be better or greater that him.

Being undisputed or undefeated doesn't mean being the greatest or the best.

Popular

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles —starting with the most fundamental questions and building up from there. Step 1: What is Intelligence? Before we talk about AI, let’s define intelligence at the most basic level: Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns. Humans do this by processing language, recognizing patterns, and forming logical connections. Now, let’s apply this to machines. Step 2: Can Machines Imitate Intelligence? If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by: Storing and processing vast amounts of text. Finding statistical patterns in language. Predicting what comes next based on probability. This leads us to the core function of LLMs : They don’t think like humans, but they generate human-like text by learning from data. Step 3: How Do LLMs Wor...

Contextual Stratification - Chapter 8: Scales

  The Microscope Analogy Imagine looking at a painting. Stand close, inches from the canvas and you see individual brushstrokes, texture, the physical application of paint. Step back a few feet, and you see the image: a face, a landscape, a composition. Step back further, across the room, and you see how the painting relates to its frame, the wall, the space it occupies. Step back outside the building, and the painting disappears entirely into the larger context of the museum, the city, the culture. Same painting. Different scales of observation. And at each scale, different features become visible while others disappear. The brushstrokes that dominated up close are invisible from across the room. The composition that emerged at medium distance fragments into meaningless marks up close. Neither view is "wrong". They're both accurate descriptions of what's observable at that scale. This is what scale means in contextual stratification: the resolution of observation, th...

Contextual Stratification - Chapter 6: A Different Possibility

The Uncomfortable Question We've spent five chapters documenting a pattern: frameworks work brilliantly within their domains, then break down at boundaries. Physics, economics, psychology, medicine, mathematics; everywhere we look, the same story. We've examined why the standard explanations fail to account for this pattern. Now we must ask the question that makes most scientists uncomfortable: What if the boundaries are real? Not artifacts of incomplete knowledge. Not gaps waiting to be filled. Not temporary inconveniences on the road to unified understanding. What if reality itself is genuinely structured into domains, each operating under different rules, each requiring different frameworks to understand? This is not the answer we want. We want unity. We want simplicity. We want one elegant equation that explains everything from quarks to consciousness. The history of science seems to promise this; each generation unifying more, explaining more with less, moving toward that ...