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

marsbit发布于2025-12-23更新于2025-12-23

文章摘要

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?

热门币种推荐

相关问答

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.

你可能也喜欢

如何让自己变得让人工智能永远也无法取代

面对人工智能的冲击,许多人担心工作被取代。然而,真正的威胁在于个人对他人和系统的依赖,以及由此产生的“薪资奴役”——即为生存而从事无意义、枯燥的工作。摆脱这种困境的关键,不是抵制技术,而是成为拥有高自主性的“不可受雇”个体。 文章提出了成功抵御AI替代的五个核心要素:自主性(主动行动的能力)、品味(判断事物价值的经验)、说服力(让他人关注你工作的能力)、毅力(坚持并从错误中学习)和迭代(根据反馈持续改进)。这些能力无法仅通过理论学习获得,必须通过实践来培养。 要启动转变,首先要彻底改变环境,重塑身份认同。其次,应选择一个能获得真实、快速反馈的实践领域,例如创业。在众多技能中,内容创作(媒体)比编写代码更具优势,因为其价值是主观的,需要独特的审美和判断力,这正是AI目前难以完全复制的。 具体行动上,可以从三个步骤开始: 1. **挖掘原始素材**:反思自己长期痴迷的知识领域、轻松解决的难题或童年被压抑的兴趣,找到独特的个人经验。 2. **确立反向思考主轴**:找出你坚信但主流观点错误的地方,或行业内普遍忽视的“皇帝新衣”,形成独特的批判性视角。 3. **立即发布**:将前两步的思考融合,撰写并发布第一个核心内容(如帖子、视频),勇敢接受真实世界的反馈,并在此基础上持续学习和迭代。 最终,抵御AI的关键在于构建一份与自身身份深度契合的毕生事业,通过持续的内容创作和真实互动,建立无法被自动化取代的独特价值和影响力。行动,从今天发布第一个想法开始。

marsbit4小时前

如何让自己变得让人工智能永远也无法取代

marsbit4小时前

通过掷骰子离线保管比特币密钥:并非人人愿意为之

文章探讨了通过投掷骰子生成比特币钱包种子短语的安全方法及其现实挑战。核心观点如下: **1. 骰子提供物理熵源** 骰子结果由众多微小变量决定,理论上虽可预测,但实践中无法被攻击者复制或计算,从而提供高质量的随机性。每个六面骰子投掷约产生2.585比特熵,50次投掷即可满足典型12词助记词(128比特熵)的安全需求。 **2. Coldcard漏洞事件凸显手工熵源的价值** 近期Coldcard硬件钱包因固件漏洞导致其内部随机数生成器存在缺陷,致使约1128枚比特币被盗。但那些**完全**通过足量骰子投掷生成种子短语的用户未受此漏洞影响,因为他们的主密钥未使用有缺陷的生成器。 **3. 重要警示:手工种子并非万能保护** 安全研究员指出,即使用户使用骰子生成了安全的种子,若他们使用了Coldcard的其他功能(如生成纸钱包、克隆密钥、共享签名密钥、密码等),这些**衍生密钥**仍可能调用有漏洞的随机数生成器,从而存在风险。安全种子不保证设备生成的所有秘密都安全。 **4. 手工生成熵源的现实局限性** 尽管数学上可靠,但该方法对大多数用户并不友好: * **过程繁琐易错**:需投掷50-99次,精确记录,任何输入错误都会导致钱包完全不同。 * **引入新风险**:用户可能在记录、转换过程中泄露信息,或使用有偏的骰子/投掷方式。 * **用户体验差**:难以想象大规模推广需要用户手动投掷近百次骰子。安全措施需适应现实生活场景和普通用户的知识水平。 **5. 给用户的建议** 受影响的Coldcard用户应: * 更新固件至最新版。 * 检查是否使用过有漏洞的功能生成了次级密钥或密码,如有则需立即更换。 * 考虑采用多签方案,使用不同厂商的设备分散风险。 **结论**:手工投掷骰子生成熵源是技术娴熟用户的一个有效安全选项,但其过程复杂、容易出错,不适合作为主流用户的默认方法。长远目标是依赖安全、透明且无需专业知识的硬件/软件随机数生成方案。

cryptonews.ru7小时前

通过掷骰子离线保管比特币密钥:并非人人愿意为之

cryptonews.ru7小时前

交易

现货

热门文章

如何购买ERA

欢迎来到HTX.com!我们已经让购买Caldera(ERA)变得简单而便捷。跟随我们的逐步指南,放心开始您的加密货币之旅。第一步:创建您的HTX账户使用您的电子邮件、手机号码注册一个免费账户在HTX上。体验无忧的注册过程并解锁所有平台功能。立即注册第二步:前往买币页面,选择您的支付方式信用卡/借记卡购买:使用您的Visa或Mastercard即时购买Caldera(ERA)。余额购买:使用您HTX账户余额中的资金进行无缝交易。第三方购买:探索诸如Google Pay或Apple Pay等流行支付方法以增加便利性。C2C购买:在HTX平台上直接与其他用户交易。HTX场外交易台(OTC)购买:为大量交易者提供个性化服务和竞争性汇率。第三步:存储您的Caldera(ERA)购买完您的Caldera(ERA)后,将其存储在您的HTX账户钱包中。您也可以通过区块链转账将其发送到其他地方或者用于交易其他加密货币。第四步:交易Caldera(ERA)在HTX的现货市场轻松交易Caldera(ERA)。访问您的账户,选择您的交易对,执行您的交易,并实时监控。HTX为初学者和经验丰富的交易者提供了友好的用户体验。

1.6k人学过发布于 2025.07.17更新于 2026.06.02

如何购买ERA

相关讨论

欢迎来到HTX社区。在这里,您可以了解最新的平台发展动态并获得专业的市场意见。以下是用户对ERA(ERA)币价的意见。

活动图片