The AI Era: When the 'Human-Dog Gap' Shrinks to the 'Human-Human Gap'

marsbitPubblicato 2025-12-23Pubblicato ultima volta 2025-12-23

Introduzione

In the AI era, the gap between humans is narrowing from what was once likened to the difference between "humans and dogs" to a more comparable "human-to-human" difference. The author uses a hypothetical scoring system: a child scores 10 points, a PhD 60, a professor 75, and Einstein 100. With AI estimated at 40 points in cognitive value (effectively 80 when considering its generalist nature), a child with AI reaches 90 points, while Einstein with AI reaches 180—reducing their relative gap from 10x to 2x. Some argue that AI proficiency varies—novices may only leverage 20% of AI’s potential, while experts extract 100%, widening absolute gaps (e.g., child+AI novice=30 vs. Einstein+AI expert=200). However, the author contends this is temporary. As AI evolves, it will become smarter and easier to use, reducing the skill threshold. Future AI might score 240+ points, with humans consistently utilizing 80-120% of its potential. Eventually, a child with advanced AI could reach 1010 points, and Einstein 1100—a negligible 1.1x difference. The core idea: while current AI proficiency disparities may temporarily widen gaps, AI’s inevitable progress in intelligence and usability will democratize access, diminish human cognitive inequalities, and make individual expertise less impactful—like everyone having access to the same powerful tool.

I didn’t expect my last post to spark so much discussion. In essence, we’re all talking about the same thing—it’s just that our descriptions of the numbers differ slightly.

You’ve all heard the saying: sometimes the gap between people is bigger than the gap between humans and dogs. But this phrase was born before the current wave of AI.

Today, I’ll try to quantify this idea. The numbers are all rough estimates, just for fun—don’t take them too seriously.

Assume an elementary school student’s cognitive ability is 10 points, a PhD is 60 points, a university professor is 75 points, and Einstein is 100 points.

The gap between 10 and 100 points is indeed huge—a full 10x difference. It’s not wrong to call it the difference between humans and dogs.

By 2025, AI’s cognitive ability will be worth at least 40 points. Considering AI is a generalist, while PhDs and professors are usually specialists, AI’s value effectively doubles to at least 80 points.

So we have:

- Elementary student + AI = 90 points

- PhD + AI = 140 points

- Professor + AI = 155 points

- Einstein + AI = 180 points

With AI, the absolute gap between the elementary student and Einstein remains 90 points, but the relative gap shrinks from 10x to just 2x.

This is my point: AI is narrowing the gap between humans.

Some might object: “But an elementary student can’t develop AI like a professor can!”

It’s like in One Piece, where characters develop their Devil Fruit abilities differently. The same Gomu Gomu Fruit: Luffy at Gear 1 can’t beat Luffy years later at Gear 4 (a newbie vs. a seasoned expert).

True. If AI is worth 80 points:

- A casual user (e.g., asking occasional questions) might only harness 20 points.

- Someone highly skilled at using AI (e.g., intense vibe coding) might overclock it to 100 points.

So:

- Elementary student + AI newbie = 30 points

- Einstein + AI expert = 200 points

The gap widens from 90 to 170 points! So with AI, the gap between people actually increases!

This is the view of teachers Lao Bai and Alvin—and they’re not wrong.

But—and this is a big but—while our views seem conflicting, their core is similar. Why?

Because I assume AI will keep evolving:

First, it will get smarter.

Second, it will become easier to use.

2025 is just a transitional year. The further we go, the simpler it will be to become a prompt engineer. The barrier will lower until it’s “as easy as speaking.” Learning to use AI will get easier, not harder.

Assume AI gets smarter, reaching maybe 240 points. Utilization levels could range from low to high: 200, 240, 280 points.

Then:

- Elementary student: 10 + 200 = 210 points

- Einstein: 100 + 280 = 380 points

The gap is 170 points, but it’s not even 2x anymore—it’s just 1.8x. The absolute gap grew, but the relative gap shrank.

What about in 10 years? Super optimistically, assume AI’s cognition evolves to around 1000 points.

Then:

- Elementary student: 1010 points

- Einstein: 1100 points

(If this day comes) even Einstein can’t pull ahead of the elementary student.

Those who think AI has widened the gap between humans are seeing a temporary state—because AI is new, and people’s ability to leverage it varies widely now.

But AI has replaced writers, artists, dancers... profession after profession falls. Are you really worried AI won’t replace the trainers who teach “how to unlock 100% of AI’s potential”?

Come on—that’s AI’s home turf.

In the future, humans routinely harnessing 80%–120% of AI’s potential will be the norm, not the exception.

The smarter AI gets, the smaller human roles become, and the narrower the gaps between people.

It’s like two martial arts masters suddenly allowed to use rocket launchers. What difference does it make if one trained 10 years in fists and feet, and the other 15 years with a blade?

Domande pertinenti

QWhat is the main argument of the article regarding AI's impact on human intelligence gaps?

AThe article argues that while AI may temporarily widen the absolute gap in cognitive capabilities between individuals (e.g., a novice vs. an expert AI user), it ultimately reduces the *relative* gap as AI becomes smarter and easier to use. In the long term, human differences become negligible when augmented by highly advanced AI.

QHow does the author use a scoring system to illustrate AI's effect on human capabilities?

AThe author assigns hypothetical scores: a小学生 (elementary student) scores 10, a博士 (PhD) 60, a大学教授 (professor) 75, and Einstein 100. With AI, their scores combine with AI's value (e.g., 80 points in 2025), shrinking the relative gap from 10x (e.g., 10 vs. 100) to 2x (e.g., 90 vs. 180).

QWhy do some believe AI increases human inequality, and how does the author counter this?

ASome argue that AI increases inequality because skilled users (e.g., AI experts) can leverage it better than novices, widening the gap. The author counters that this is temporary—as AI evolves, it will become easier to use (e.g., '有嘴就行' or 'just need a mouth'), and AI itself will replace the need for specialized training, reducing individual differences.

QWhat analogy does the author use to describe AI's long-term effect on human competition?

AThe author compares it to two martial artists using rocket launchers: their individual training (e.g., 10 vs. 15 years of skill) becomes irrelevant when both have access to the same overpowered tool, diminishing the significance of human effort.

QHow does the author envision the future of AI's role in human capabilities?

AThe author envisions AI becoming extremely intelligent (e.g., 1000 points) and easy to use, allowing even a小学生 (elementary student) to reach 1010 points, nearly matching Einstein at 1100. Human differences will shrink as AI does most of the cognitive work, making individual gaps trivial.

Letture associate

How Many Tokens Away Is Yang Zhilin from the 'Moon Chasing the Light'?

The article explores the intense competition between two leading Chinese AI companies, DeepSeek and Kimi (Moon Dark Side), and the mounting pressure on Yang Zhilin, the founder of Kimi. While DeepSeek re-emerged after 15 months of silence with its powerful V4 model—boasting 1.6 trillion parameters and low-cost, long-context capabilities—Kimi has been focusing on long-context processing and multi-agent systems with its K2.6 model. Yang faces a threefold challenge: technological rivalry, commercialization pressure, and investor expectations. Despite Kimi’s high valuation (reaching $18 billion), its revenue heavily relies on a single product with low paid conversion rates, while DeepSeek’s strategic silence and open-source influence have strengthened its market position and valuation prospects, now targeting over $20 billion. Both companies reflect broader trends in China’s AI ecosystem: Kimi aims for global influence through open-source contributions and agent-based advancements, while DeepSeek prioritizes foundational innovation and hardware independence, notably shifting to Huawei’s chips. Their competition is seen as vital for China’s AI progress, with the gap between top Chinese and U.S. models narrowing to just 2.7% on the Elo rating scale. Ultimately, the article argues that this rivalry, though anxiety-inducing for leaders like Zhilin, is essential for driving innovation and solidifying China’s role in the global AI landscape.

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How Many Tokens Away Is Yang Zhilin from the 'Moon Chasing the Light'?

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