thinking-lindy-effect

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

The Lindy Effect

Lindy效应

Overview

概述

The Lindy Effect, named after a New York deli where comedians discussed career longevity, states that for non-perishable things (ideas, technologies, books, practices), future life expectancy is proportional to current age. If a technology has survived 20 years, it's likely to survive another 20. If it's survived 2 years, expect another 2.

Core Principle: Time is the ultimate test. Old things that still exist have proven their value; new things are still being tested.

Lindy效应得名于纽约一家熟食店，喜剧演员曾在那里讨论职业生涯的存续时长。该效应指出，对于非易逝性事物（理念、技术、书籍、实践方法），其未来的存续时长与当前已存在的时长成正比。如果一项技术已经存在了20年，那么它很可能还会再存续20年；如果它只存在了2年，那么预期还能再存续2年。

核心原则： 时间是终极测试标准。仍在沿用的老旧事物已经证明了自身价值；而新事物仍在接受考验。

When to Use

适用场景

Technology selection (languages, frameworks, databases)
Evaluating libraries and dependencies
Predicting tool longevity
Career skill investment
Methodology and practice adoption
Architectural patterns
Vendor/product selection

Decision flow:

Choosing between options?
  → Are some options significantly older? → yes → APPLY LINDY HEURISTIC
  → Is longevity important for this choice? → yes → FAVOR OLDER, PROVEN OPTIONS
  → Is the new thing solving a new problem? → yes → NEW MIGHT BE APPROPRIATE

技术选型（编程语言、框架、数据库）
评估库与依赖项
预测工具的使用寿命
职业技能投资决策
方法论与实践方法的采纳
架构模式选择
供应商/产品选型

决策流程：

Choosing between options?
  → Are some options significantly older? → yes → APPLY LINDY HEURISTIC
  → Is longevity important for this choice? → yes → FAVOR OLDER, PROVEN OPTIONS
  → Is the new thing solving a new problem? → yes → NEW MIGHT BE APPROPRIATE

Understanding Lindy

理解Lindy效应

What Lindy Applies To (Non-Perishable)

Lindy效应的适用对象（非易逝性事物）

Technologies: Languages, databases, protocols
Ideas: Mathematical concepts, design patterns, algorithms
Practices: Testing, version control, code review
Books: Technical references, foundational texts
Institutions: Standards bodies, open source foundations

技术类： 编程语言、数据库、协议
理念类： 数学概念、设计模式、算法
实践方法类： 测试、版本控制、代码评审
书籍类： 技术参考手册、基础理论著作
机构类： 标准制定组织、开源基金会

What Lindy Doesn't Apply To (Perishable)

Lindy效应的不适用对象（易逝性事物）

Hardware: Physical degradation limits life
Individual careers: Humans have biological limits
Specific products: Companies can fail, be acquired
Fashion-driven choices: Popularity cycles aren't Lindy

硬件： 物理损耗会限制其使用寿命
个人职业生涯： 人类有生理寿命限制
特定产品： 公司可能倒闭、被收购
受时尚驱动的选择： 流行周期不属于Lindy效应范畴

The Math

数学原理

Expected remaining life ≈ Current age

If survived 10 years → Expected to survive another ~10
If survived 50 years → Expected to survive another ~50
If survived 2 years → Expected to survive another ~2

Expected remaining life ≈ Current age

If survived 10 years → Expected to survive another ~10
If survived 50 years → Expected to survive another ~50
If survived 2 years → Expected to survive another ~2

Applying Lindy to Technology

Lindy效应在技术领域的应用

Programming Languages

编程语言

Language	Age	Lindy Expectation	Evidence
C	50+ years	50+ more years	Powers OS, embedded, will outlive us
Java	30 years	30+ more years	Enterprise backbone, not going away
Python	30 years	30+ more years	Scientific computing, ML, scripting
Go	15 years	15+ more years	Proven for infra, backed by Google
Rust	10 years	10+ more years	Growing, solving real problems
New hotness	2 years	2-5 years	Unproven, might disappear

语言	已存在时长	Lindy预期存续时长	依据
C	50+年	50+年	支撑操作系统、嵌入式系统，存续时长将超过我们的寿命
Java	30年	30+年	企业级系统的核心支柱，不会被淘汰
Python	30年	30+年	用于科学计算、机器学习、脚本编写
Go	15年	15+年	在基础设施领域已得到验证，由Google背书
Rust	10年	10+年	持续发展，解决实际问题
新晋热门语言	2年	2-5年	未经验证，可能会消失

Databases

数据库

Database	Age	Lindy Expectation	Notes
PostgreSQL	35+ years	35+ more years	SQL is 50+ years old
MySQL	30 years	30+ more years	LAMP stack foundation
MongoDB	15 years	15+ more years	Survived NoSQL hype cycle
CockroachDB	10 years	10+ more years	NewSQL, still proving itself
Latest DB	2 years	Unknown	High risk for production use

数据库	已存在时长	Lindy预期存续时长	说明
PostgreSQL	35+年	35+年	SQL语言已有50+年历史
MySQL	30年	30+年	LAMP栈的基础
MongoDB	15年	15+年	挺过了NoSQL的炒作周期
CockroachDB	10年	10+年	NewSQL数据库，仍在证明自身价值
最新数据库	2年	未知	用于生产环境风险高

Frameworks

框架

Framework	Age	Lindy Expectation	Notes
React	10+ years	10+ more years	Dominant, ecosystem mature
Rails	20 years	20+ more years	Productive, battle-tested
Django	18 years	18+ more years	Python's Rails, stable
Express	14 years	14+ more years	Node.js standard
Newest framework	1 year	1-3 years	Likely to be replaced

框架	已存在时长	Lindy预期存续时长	说明
React	10+年	10+年	占据主导地位，生态系统成熟
Rails	20年	20+年	开发高效，经受过实战考验
Django	18年	18+年	Python版Rails，稳定可靠
Express	14年	14+年	Node.js的标准框架
最新框架	1年	1-3年	很可能被替代

Patterns and Practices

模式与实践方法

Practice	Age	Lindy Expectation
Version control	50+ years	Permanent
Automated testing	40+ years	Permanent
Code review	40+ years	Permanent
Agile (core ideas)	30+ years	Very long
CI/CD	20+ years	Very long
Microservices	10 years	Moderate
Latest methodology	2 years	Unknown

实践方法	已存在时长	Lindy预期存续时长
版本控制	50+年	永久存续
自动化测试	40+年	永久存续
代码评审	40+年	永久存续
Agile（核心理念）	30+年	存续极长时间
CI/CD	20+年	存续极长时间
微服务	10年	中等时长
最新方法论	2年	未知

The Lindy Decision Process

Lindy决策流程

Step 1: Assess Age of Options

步骤1：评估各选项的已存在时长

For each option, determine how long it's been in significant use:

markdown

Options for message queue:
- RabbitMQ: 17 years (2007)
- Kafka: 13 years (2011)
- NATS: 11 years (2013)
- NewQueue: 2 years (2022)

针对每个选项，确定其被广泛使用的时长：

markdown

Options for message queue:
- RabbitMQ: 17 years (2007)
- Kafka: 13 years (2011)
- NATS: 11 years (2013)
- NewQueue: 2 years (2022)

Step 2: Apply Lindy Heuristic

步骤2：应用Lindy启发法

markdown

Lindy expectation:
- RabbitMQ: 17+ more years
- Kafka: 13+ more years
- NATS: 11+ more years
- NewQueue: 2-5 more years (high uncertainty)

markdown

Lindy expectation:
- RabbitMQ: 17+ more years
- Kafka: 13+ more years
- NATS: 11+ more years
- NewQueue: 2-5 more years (high uncertainty)

Step 3: Consider Context

步骤3：考量上下文

Lindy is a heuristic, not a law. Consider:

markdown

When older is better:
- Long-term production systems
- Core infrastructure
- Skills investment
- Dependencies with many consumers

When newer might be appropriate:
- Solving genuinely new problems
- Performance-critical new workloads
- Specific capability older tools lack
- Temporary/experimental projects

Lindy是一种启发法，而非定律。需考量以下情况：

markdown

When older is better:
- Long-term production systems
- Core infrastructure
- Skills investment
- Dependencies with many consumers

When newer might be appropriate:
- Solving genuinely new problems
- Performance-critical new workloads
- Specific capability older tools lack
- Temporary/experimental projects

Step 4: Calibrate by Ecosystem Age

步骤4：结合生态系统成熟度校准

A 5-year-old tool in a 5-year-old ecosystem is "old" for that ecosystem:

markdown

Kubernetes ecosystem: ~10 years old
- Helm: 8 years → "Lindy" for K8s
- ArgoCD: 7 years → "Lindy" for K8s
- New tool: 1 year → Not Lindy yet

Node.js ecosystem: 14 years old
- Express: 14 years → Maximally Lindy for Node
- Fastify: 8 years → Moderately Lindy
- New framework: 1 year → Unproven

在一个仅存在5年的生态系统中，已存在5年的工具对该生态系统而言就算是“老旧”的：

markdown

Kubernetes ecosystem: ~10 years old
- Helm: 8 years → "Lindy" for K8s
- ArgoCD: 7 years → "Lindy" for K8s
- New tool: 1 year → Not Lindy yet

Node.js ecosystem: 14 years old
- Express: 14 years → Maximally Lindy for Node
- Fastify: 8 years → Moderately Lindy
- New framework: 1 year → Unproven

Lindy Failure Modes

Lindy失效模式

Survivor Bias Confusion

幸存者偏差混淆

Lindy predicts future survival given current survival. It doesn't say all old things are good:

Correct: "COBOL has survived 60 years, will survive 60 more"
Incorrect: "COBOL is the best choice for new projects"
(Survival ≠ Optimal for new use cases)

Lindy效应是基于当前存续情况预测未来存续时长，并非所有老旧事物都是优质选择：

Correct: "COBOL has survived 60 years, will survive 60 more"
Incorrect: "COBOL is the best choice for new projects"
(Survival ≠ Optimal for new use cases)

Ignoring Paradigm Shifts

忽视范式转变

Lindy works within stable paradigms. Paradigm shifts create discontinuities:

- Pre-cloud: On-premise databases were Lindy
- Post-cloud: Managed databases emerged
- But: Core database concepts (SQL, ACID) remained Lindy

Lindy效应在稳定的范式内有效，范式转变会造成不连续性：

- Pre-cloud: On-premise databases were Lindy
- Post-cloud: Managed databases emerged
- But: Core database concepts (SQL, ACID) remained Lindy

Confusing Perishable and Non-Perishable

混淆易逝性与非易逝性事物

Perishable: Specific SaaS vendor → Can be acquired, pivoted, shut down
Non-perishable: The practice the vendor enables → Likely Lindy

E.g., Heroku might change, but "platform-as-a-service" concept is Lindy

Perishable: Specific SaaS vendor → Can be acquired, pivoted, shut down
Non-perishable: The practice the vendor enables → Likely Lindy

E.g., Heroku might change, but "platform-as-a-service" concept is Lindy

Lindy in Practice

Lindy效应的实践应用

Technology Selection

技术选型

markdown

undefined

markdown

undefined

Lindy Analysis: Database for New Product

Requirements: ACID transactions, relational data, long-term stability

Options:

Option	Age	Lindy Score	Fit for Requirements
PostgreSQL	35 years	Excellent	Excellent
MySQL	30 years	Excellent	Good
CockroachDB	10 years	Good	Excellent
PlanetScale	5 years	Moderate	Good

Decision: PostgreSQL (Lindy + excellent fit) Consider CockroachDB for scale needs (worth the Lindy tax)

undefined

Requirements: ACID transactions, relational data, long-term stability

Options:

Option	Age	Lindy Score	Fit for Requirements
PostgreSQL	35 years	Excellent	Excellent
MySQL	30 years	Excellent	Good
CockroachDB	10 years	Good	Excellent
PlanetScale	5 years	Moderate	Good

Decision: PostgreSQL (Lindy + excellent fit) Consider CockroachDB for scale needs (worth the Lindy tax)

undefined

Skill Investment

技能投资

markdown

undefined

markdown

undefined

Lindy Career Analysis

Which skills to invest in?

Lindy skills (high confidence in future value):

SQL (50+ years)
Unix/Linux (50+ years)
Git/version control (40+ years)
Testing fundamentals (40+ years)

Moderate Lindy (good bet):

Python (30+ years)
JavaScript (28 years)
Docker/containers (12 years)
Kubernetes (10 years)

Low Lindy (speculative):

Latest framework (1-3 years)
Trending language (variable)

Investment strategy: Core in Lindy skills, experiments in new

undefined

Which skills to invest in?

Lindy skills (high confidence in future value):

SQL (50+ years)
Unix/Linux (50+ years)
Git/version control (40+ years)
Testing fundamentals (40+ years)

Moderate Lindy (good bet):

Python (30+ years)
JavaScript (28 years)
Docker/containers (12 years)
Kubernetes (10 years)

Low Lindy (speculative):

Latest framework (1-3 years)
Trending language (variable)

Investment strategy: Core in Lindy skills, experiments in new

undefined

Dependency Selection

依赖项选型

markdown

undefined

markdown

undefined

Lindy Dependency Audit

For each critical dependency:

Dependency	Age	Last Update	Contributors	Lindy Risk
lodash	12 years	Active	Many	Low
express	14 years	Active	Many	Low
new-lib	1 year	Active	3	High

Policy: Critical path requires 5+ year Lindy Experimental features can use newer dependencies

undefined

For each critical dependency:

Dependency	Age	Last Update	Contributors	Lindy Risk
lodash	12 years	Active	Many	Low
express	14 years	Active	Many	Low
new-lib	1 year	Active	3	High

Policy: Critical path requires 5+ year Lindy Experimental features can use newer dependencies

undefined

Lindy Template

Lindy分析模板

markdown

undefined

markdown

undefined

Lindy Analysis: [Decision]

Options with Age

Option	First Stable	Age	Category
			Proven/Moderate/New

Option	First Stable	Age	Category
			Proven/Moderate/New

Lindy Expectations

Option	Expected Longevity	Confidence
		High/Medium/Low

Option	Expected Longevity	Confidence
		High/Medium/Low

Context Adjustments

Is this a new problem domain? [Yes/No]
Is the ecosystem mature? [Yes/No]
Do newer options solve critical gaps? [Yes/No]

Is this a new problem domain? [Yes/No]
Is the ecosystem mature? [Yes/No]
Do newer options solve critical gaps? [Yes/No]

Lindy-Adjusted Decision

Primary choice: [Option with best Lindy + fit] Rationale: [Why this balances Lindy with requirements]

Risk if Lindy is Wrong

[What happens if the non-Lindy option outlasts expectations?]

undefined

[What happens if the non-Lindy option outlasts expectations?]

undefined

Verification Checklist

验证清单

Key Questions

关键问题

"How long has this technology/practice existed?"
"Is this Lindy (non-perishable) or perishable?"
"What's the Lindy expectation for each option?"
"Is the newer option solving a problem that didn't exist before?"
"Am I betting against Lindy? If so, why?"
"What's proven vs. what's hyped?"

“这项技术/实践方法已经存在多久了？”
“这属于Lindy类（非易逝性）还是易逝性事物？”
“每个选项的Lindy预期存续时长是多少？”
“新选项是否解决了之前不存在的问题？”
“我是否在违背Lindy效应做决策？如果是，原因是什么？”
“哪些是经过验证的，哪些是炒作的？”

Taleb's Wisdom

塔勒布的智慧

"If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years."

"Technology is at its best when it is invisible."

The technologies you don't think about—TCP/IP, Unix, SQL—are the most Lindy. The technologies that demand constant attention are still being tested.

"The old is to be respected; the new is to be examined."

Lindy doesn't mean reject the new. It means: the burden of proof is on the new. New must demonstrate value; old has already demonstrated survival.

“如果一本书已经出版了40年，我预计它还会再出版40年。但更重要的是，如果它又存续了10年，那么预计它还能再出版50年。”

“技术的最佳状态是让用户感觉不到它的存在。”

那些你不会特意关注的技术——TCP/IP、Unix、SQL——是最符合Lindy效应的。而那些需要你持续投入精力关注的技术，仍在接受考验。

“要尊重老旧事物；要审视新事物。”

Lindy效应并非意味着拒绝新事物，而是说：新事物需要证明自身的价值，而老旧事物已经证明了自己的存续能力。