The Survival Crisis and Transformation Path for Traditional Programmers in the AI Coding Wave

A Structural Purge Is Underway

Since 2025, the wave of layoffs sweeping the global tech industry is no longer a simple "cyclical adjustment" — it's a structural transformation driven by AI.

Unlike the layoffs during the dot-com bubble burst in 2000 or the 2008 financial crisis, this current round is happening against a backdrop of record-high corporate profits. Traditional economic cycle layoffs typically accompany declining revenue and cost-cutting, but AI-driven layoffs are "growth layoffs" — companies discover that AI tools can accomplish the same or even more work at lower cost, so they proactively cut headcount even while business is booming. This pattern is known in economics as "Technological Unemployment," first proposed by Keynes in 1930, but only truly materializing at scale with the emergence of generative AI.

According to statistics, 78,000 people were laid off in the global tech industry in Q1 2026, with 47.9% of layoffs directly related to AI replacement. The most representative case is Oracle — quarterly profits surged 95% thanks to AI, with net income reaching $6.1 billion, yet the company announced 30,000 layoffs on the same day. Soaring profits and mass layoffs happening simultaneously isn't a contradiction — it's the cruel logic of a new era.

Oracle isn't alone. Google laid off 12,000 programmers, Microsoft cut 10,000, Amazon let go of 18,000, and Meta eliminated 15,000. These tech giants all publicly cited "structural optimization," but the real reason behind it is self-evident — AI is taking over vast amounts of traditional programming work.

DeepSeek's New Model: The Dimensional Strike of 1 Million Tokens

DeepSeek's recently released next-generation models have further accelerated this trend:

• Waze Pro: 1.6 trillion parameters, 49 billion activated, 1 million Token context window
• Waze Flash: 284 billion parameters, 13 billion activated, also supporting 1 million Tokens

Several key technical concepts need explanation here. Parameter count is the core metric for measuring the complexity of large language models — more parameters mean the model can capture richer language patterns and knowledge. But the concept of "activated parameters" comes from the MoE (Mixture of Experts) architecture — while the model's total parameter count is enormous, only a subset of expert networks are activated during each inference, dramatically reducing computational cost while maintaining model capability. A Token is the basic unit of text processing for large language models — in English, one Token equals approximately 0.75 words; in Chinese, one Token equals roughly 1-2 characters. The context window determines how much information the model can "see" at once.

What does 1 million Tokens mean? Approximately 100,000 lines of code can be input all at once. Work that previously required breaking down into modules and files for separate analysis can now be handled holistically by AI. This is equivalent to a developer being able to simultaneously read and understand the entire source code of a medium-sized project, rather than processing code snippets within a limited context as before.

The "Surrender Declaration" from the Creator of Redis

Redis (Remote Dictionary Server) is one of the world's most widely used in-memory databases, employed by top internet companies like Netflix, Twitter, and GitHub for caching, message queues, and real-time data processing. Its creator Antirez (Salvatore Sanfilippi) is an Italian programmer renowned in the open-source community for his extreme code simplicity and systems programming prowess.

He publicly stated: The programming profession has changed — stop believing the anti-AI nonsense.

His firsthand experience: work that used to take weeks can now be done in hours with AI. And we're not talking about simple "Hello World" programs, but hardcore systems programming — like fixing a TCP timing bug in Redis tests. TCP timing bugs are among the most difficult to debug in concurrent programming — they don't reproduce every time and may only trigger under specific network latency or OS scheduling conditions. Traditional debugging methods often require repeatedly constructing extreme conditions to locate the root cause. These intermittent issues are a nightmare for human developers. He threw the problem at Claude Code, and the AI iterated on its own, checked states on its own, and fixed the bug on its own. What was the human doing? Watching from the sidelines.

When a top-tier systems programmer publicly acknowledges that AI has changed programming, the signal carries far more weight than the feelings of ordinary developers.

Anthropic Report: 75% of Programming Work Can Be Covered by AI

A research report published by Anthropic indicates that 75% of programmers' daily work can already be covered by Claude. This number means that traditional "keyboard-pounding, library-calling, logic-writing" work is rapidly depreciating in value.

5 Minutes vs. One Week: Real Cases of AI Programming Efficiency

A concrete example: implementing a BF16 (Brain Float 16) matrix computation module in pure C. BF16 is a 16-bit floating-point format proposed by the Google Brain team, specifically designed for deep learning training. Compared to standard IEEE FP16, BF16 retains the same 8 exponent bits as FP32, sacrificing precision (only 7 mantissa bits) for a larger numerical range, thereby avoiding numerical overflow during training.

Implementing BF16 matrix operations in pure C means not relying on any existing framework — you need to manually handle SIMD vectorization instructions (like AVX-512), memory alignment, cache-friendly tiling strategies, and data type conversions among other low-level details. Anyone with even basic knowledge knows this requires not only deep learning expertise but also C-level optimization skills — traditionally, it would take at least a week.

How long did it take with AI? 5 minutes. The AI wrote 700 lines of code, performing only 15% slower than PyTorch. As a benchmark, PyTorch's underlying calls go to BLAS libraries (like Intel MKL or OpenBLAS) that have been optimized over decades — AI-generated code being only 15% slower is already remarkably impressive. In 5 minutes, you haven't even finished setting up your development environment, and the AI has already written the code.

Another example: writing a Bluetooth client for macOS — requiring Objective-C. Objective-C was born in 1984 and is Apple's traditional ecosystem development language. It adds Smalltalk-style message passing on top of C, with syntax that feels extremely foreign to modern developers — bracket-nested message sending, manual reference counting (MRC) or semi-automatic ARC memory management, and extensive Foundation framework APIs with NS prefixes. Although Apple introduced Swift as a replacement in 2014, macOS's underlying Bluetooth frameworks (IOBluetooth/CoreBluetooth) still have many APIs that only provide Objective-C interfaces, with outdated documentation and scarce examples.

Previously, flipping through hundreds of pages of documentation for a small utility was too costly, so you'd just give up. Now you toss it to AI, and it instantly generates a runnable framework with all API calls and parameter configurations handled. These "technical debt" scenarios are precisely where AI coding assistants deliver the most value.

Class Stratification: The Gap Between Programmers Who Use AI and Those Who Don't

A joint research report from Microsoft and MIT reveals a cruel reality: AI's impact on programmers directly leads to class stratification.

• Programmers who use AI: productivity and salary surge by 40%
• Programmers who don't use AI: productivity is even assessed as negative

Meanwhile, the supply-demand ratio for AI-related positions has reached 0.15:1 — meaning 7 positions are competing for one qualified AI engineer. On one side, traditional programmers face a 70%-90% elimination rate; on the other, there's a 1:7 supply-demand imbalance for large model engineers.

What Does "Knowing How to Use AI" Actually Mean?

Many people think using AI means just throwing requirements at ChatGPT, but reality is far from that simple.

Negative example: You tell AI "write me a Redis Vector 6 implementation," and the AI will think deeply before confidently generating nonsense. Why? Because this thing simply doesn't exist in the training data. This is a textbook case of "not knowing how to use AI" — not understanding how large language models work, treating them as omniscient search engines while ignoring that they're fundamentally pattern matching and probabilistic generation based on training data.

Positive approach: You need to provide AI with clear constraints and design specifications —

• Memory must be aligned to 64 bytes
• Error handling must use Google Chain style
• Clear architectural blueprints and performance metrics

The core principle is: Garbage in, garbage out; give it a clear blueprint, and it becomes a top-tier construction crew. In the field of prompt engineering, this is called "constraint-driven generation" — guiding the model toward high-quality output through precise contextual constraints rather than relying on the model to figure things out on its own.

The Programmer's Transformation Path: From Code Worker to AI Architect

This era never lacks people who can write code — what's scarce are large model engineers who can review code and fine-tune AI. Computing power is visibly overflowing, AI has a global perspective, and can automatically read files, modify logic, and run tests. Competing with AI on who writes code faster or more elegantly is already meaningless.

Truly valuable capability transformation directions include:

1. Mastering Agent Development: AI Agents are the core paradigm of current large model applications. Unlike simple Q&A interactions, they give AI autonomous Planning, Tool Use, Memory management, and Reflection capabilities. Understanding which layer Planning should sit in, how to design stopping mechanisms for Reflection — without reasonable termination conditions, an Agent may fall into infinite loops or over-optimization.

2. Large Model Invocation and Fine-tuning: From API calls to mainstream large models to local deployment, including understanding parameter-efficient fine-tuning techniques like LoRA and QLoRA, as well as methodologies like RAG (Retrieval-Augmented Generation) for injecting external knowledge into models.

3. System Architecture Design: Becoming AI's "chief engineer" rather than a "construction worker." This means understanding how to design the overall architecture of AI workflows — when to use large models, when to use traditional algorithms, how to handle model hallucinations, and how to design feedback loops for human-AI collaboration.

4. Deep Domain Knowledge: What AI lacks is deep understanding and judgment in specific domains. Combining professional knowledge in vertical fields like healthcare, finance, and law with AI tools will generate value far exceeding general-purpose programming.

Mastering these capabilities means transforming from "a person who writes code" to "a person who designs AI workflows" — this is precisely the core value of the "AI Architect" role.

Conclusion: The Cost of Falling Behind Far Exceeds the Cost of Trial and Error

200 years ago, textile workers tried to compete with looms for jobs — we all know how that ended. During the historical Luddite Movement, British textile workers smashed machines to protest automation, but ultimately the Industrial Revolution irreversibly transformed the entire social structure. This moment is just like that one.

You can hate AI's social impact, even question it morally, but you cannot deceive yourself cognitively. The AI wave has already hit you in the face — it won't slow down because of anyone's hesitation. For every developer, now is not the time to debate "should I use it" — it's time to urgently figure out "how to use it well."

After all, when it comes to technological transitions, the cost of falling behind has always been far greater than the cost of trial and error.