Why True Craftsmen of Code Will Always Be in Demand

In the 1970s, when C emerged as a high-level alternative to assembly language, many predicted the death of low-level programming. Why wrestle with registers and memory addresses when C offered clean, portable syntax? The industry rushed to adopt this new abstraction - and by the late 1980s, most new systems projects had shifted from assembly to higher-level languages.

Yet something unexpected happened: assembly programmers didn’t disappear. Instead, they found new life in specialized, high-value niches. Hardware companies continued hiring assembly experts for optimization work. Game studios paid premium rates for developers who could squeeze performance from hardware through carefully crafted low-level code. The emergence of embedded systems, IoT devices, and security research created entirely new domains where assembly expertise commanded respect and premium compensation.¹

History may be rhyming again. AI coding assistants are creating a similar divide, and we’re witnessing comparable economic forces at play - though the picture is more complex than many assume.

The Economic Reality: Specialization Creates Lasting Value

The C revolution didn’t eliminate assembly programming, but transformed it into a specialized skill. The transition revealed a fundamental pattern: when technology democratizes a field, deep expertise doesn’t vanish; it migrates to the domains that need it most. Good examples are cybersecurity and reverse engineering: “knowledge of assembly language is relatively rare” and this “scarcity increases the value of the skill” - particularly essential for malware analysis and vulnerability research.²

Consider how assembly programming evolved rather than disappeared:

• 1980s-90s: Game developers like John Carmack became legendary for assembly optimization skills.³ While id Software’s Doom was “programmed largely in ANSI C language,” it still relied on “a few elements in assembly language” for critical performance sections.⁴

• 2000s-Present: Embedded systems exploded with IoT growth. This year (2025), 75 billion IoT devices are expected globally, many requiring assembly-level optimization for resource-constrained environments.⁵

The lesson is nuanced but clear: abstraction doesn’t eliminate the need for foundational knowledge; it concentrates that need in specialized, high-value applications.

Today’s Shift: AI’s Mixed Reality

We’re witnessing a massive adoption of AI coding tools. GitHub reports that 92% of developers now use AI tools either at work or personally,⁶ with AI generating an estimated 41% of all code - 256 billion lines in 2024 alone.⁷ Just as C democratized programming by abstracting away assembly complexity, AI tools are now democratizing software development by abstracting away coding complexity itself.

This democratization creates the same fundamental dynamic we saw in the 1970s: when AI handles routine programming tasks, the critical bottlenecks shift to problems that require deep understanding of what lies beneath the abstraction. Consider the inevitable scenarios:

• AI-generated React apps that render slowly need someone who understands virtual DOM mechanics;
• Prompted APIs that leak memory at scale require experts who can profile heap allocation patterns;
• Auto-generated database queries creating performance bottlenecks demand specialists who can optimize query execution plans;
• AI-hallucinated security vulnerabilities necessitate professionals who can audit actual attack surfaces.

The pattern from the assembly era repeats: as the abstraction handles more routine work, specialized expertise becomes more valuable, not less. However, early evidence suggests this value transfer isn’t automatic - it flows specifically to those who can bridge the gap between AI capabilities and fundamental engineering knowledge.

The Inevitable Evolution

The assembly-to-C transition provides our roadmap, but not in the way many expect. Assembly programmers didn’t simply become scarce and therefore valuable - they evolved.⁸ The market didn’t pay premiums just for scarcity; it paid for specialized expertise that solved problems.

Today’s evolution follows a similar but more complex trajectory. As AI tools handle routine coding tasks, the real value lies in:

1. Understanding when and how to use AI effectively (avoiding the 19% productivity penalty);⁹
2. Optimizing and securing AI-generated code at scale;
3. Architecting systems that humans and AI can collaborate on efficiently;
4. Specializing in domains where deep technical knowledge remains irreplaceable.

The market rewards expertise that combines foundational knowledge with new tool mastery - not just resistance to change or blind adoption of new technologies.

The Nuanced Conclusion

History doesn’t repeat exactly, but it does rhyme with important variations. When C democratized programming in the 1970s, assembly programmers who adapted became highly paid specialists building compilers and optimizing kernels. Their evolution, not just their scarcity, transformed them from replaceable coders into irreplaceable craftsmen.

Today’s pattern is similar but more complex. The value won’t flow to those who simply reject AI tools or blindly adopt them - it will flow to adaptive craftsmen who master both deep fundamentals and effective AI collaboration.

These hybrid experts who combine irreplaceable technical knowledge with new tool mastery won’t just survive the AI revolution - they’ll define its next phase. In a world where most developers struggle with productivity-reducing AI tools, that adaptive expertise is exactly what the industry needs.

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Sources

1. Assembly language - Wikipedia - Overview of assembly language domains and continued usage ↩

2. Importance of Assembly Language in Cyber Security and Reverse Engineering - ResearchGate - Assembly scarcity and value in cybersecurity ↩

3. Fast inverse square root - Wikipedia - Fast inverse square root algorithm ↩

4. Development of Doom - Wikipedia - Programming languages used in id Software games ↩

5. Industry Insights: Embedded Software Engineering Job Market - MoldStud - IoT growth driving embedded demand ↩

6. Survey reveals AI’s impact on the developer experience - GitHub Blog - 92% AI tool adoption statistics ↩

7. AI-Generated Code Stats 2025 - Elite Brains - 41% of code AI-generated, 256 billion lines in 2024 ↩

8. What happened to the job market for assembly programmers - Retrocomputing Stack Exchange - Programming market expansion analysis ↩

9. Measuring the Impact of Early-2025 AI on Developer Productivity - METR - 19% productivity decrease with AI tools ↩