Predicting the Future of Coding from Military Automation

Shed Your Old Identity and Learn to Think at Higher Level

In modern warfare, automation now handles decisions once made by human instinct.

Radar-guided missiles. AI-powered drones. These systems are fast, accurate, and reliable—so much so they’re now reshaping how we think about software.

The same shift is coming to coding. As military systems evolved from manual inputs to real-time, autonomous decisions, developers are moving from line-by-line coding to high-level oversight. Think: system architects instead of syntax typists.

This piece walks through what military automation has to teach us—especially as AI tools like Copilot start to take over more of the code-writing. It’s about how coders must rethink their role, sharpen new skills, and start operating at a higher level of abstraction.

How Automation Changed Military Over the Years

Automation in the military started in the 1940s out of necessity.

During WWII, analog computers like the M9 gun director helped calculate anti-aircraft firing angles. They weren’t perfect, but they cut down human error and sped things up.

By the 1990s, the battlefield had gone digital. Systems like the U.S. Navy’s Aegis Combat System automated the entire process of detecting, tracking, and neutralizing threats. Missiles like the Tomahawk used GPS and onboard computers to hit precise targets without constant human control.

Then came the networked era. Satellites, early internet protocols from DARPA, and digital comms gave rise to globally coordinated operations.

In the 2010s, AI took over frontline decisions.

Drones like the MQ-9 Reaper could recommend targets based on live image analysis. Cyber defenses learned to auto-respond to breaches. And ground-based systems began firing counterattacks in seconds after triangulating enemy fire—humans now made approvals, not calculations.

By today, the systems are huge and fast. Satellite networks manage real-time data. AI helps rank threats, allocate resources, and route logistics. But it’s not perfect. Speed sometimes backfires—misreading radar data, or allowing a breach before anyone catches it.

Even so, military automation has produced one of the most hardened tech environments in the world. That journey gives us a preview of where coding is heading.

What It Can Teach Us About Coding’s Future

Military automation didn’t eliminate people—it changed their role.

Same with coding.

AI tools now handle more of the repetitive work. GitHub Copilot, Cursor IDE, and others can write scaffolding code from plain language prompts. Gartner said that by 2027, 30% of code could be AI-generated. That doesn’t mean coders disappear. It means they move upstream—setting constraints, reviewing outputs, and refining goals.

Think of how military operators set targeting parameters and rules of engagement. Coders will do the same for software behavior. But it means knowing how to evaluate AI-generated outputs for bugs, security flaws, or edge cases. And fixing them.

There’s also a growing shift toward abstraction.

Military systems hide complexity behind interfaces. A radar operator doesn’t code the radar—they manage outcomes. Coding is heading in that direction too. Frameworks like React and platforms like Bubble let you build without touching every low-level line.

Forrester said 65% of companies now use low/no-code tools. Coders are starting to design systems at a macro level—deciding how APIs, services, and data flows interact.

That means more system thinking. Less code writing.

But here’s the catch: too much abstraction, and you lose flexibility. Military tech had to remain adaptable. So does modern software. Coders still need to know what’s happening under the hood.

There’s also a major push into real-time and distributed systems.

Missile defense systems need global coordination with sub-second latency. Cloud apps, IoT, and autonomous vehicles have similar needs. O’Reilly’s 2023 tech trends flagged big demand for concurrency, latency tuning, and container orchestration.

Kubernetes is becoming table stakes.

Coders are building architectures that operate across the globe. It’s complex. And it pushes them out of their comfort zones—but it’s also where the high-value work is happening.

One final lesson: human oversight still matters.

Military systems require final sign-off from humans to avoid disaster. Coders working with AI need to do the same. Because AI can replicate bad behavior. Hiring models can get biased. Code generation tools can create security flaws—just like Log4j showed in 2023.

The best coders of the future won’t just write code. They’ll check it. Stress-test it. Understand its implications across privacy, security, and human impact.

Yes, automation will displace some roles. McKinsey projected 20-30% of coding jobs could disappear by 2030. But others are being created just as fast: AI trainers, MLOps engineers, system architects. LinkedIn reported 20% job growth in AI-related roles in 2023 alone.

That transition won’t be easy. Bootcamps cost five figures. Free resources exist—like Codecademy or MIT’s open courses—but access still isn’t equal.

And over-reliance on automation introduces risk. In software, just like in warfare, one unchecked algorithm can cause real harm.

Conclusion

Military automation didn’t end the need for skilled operators. It turned them into decision-makers.

The same shift is hitting coding.

Soon, AI will write more code. Frameworks will handle the heavy lifting. Coders will architect distributed systems and make sure outputs are safe, fair, and performant.

This future demands a new mindset.

Not: "How do I write this function?"

But: "What system am I building—and how do I ensure it works, safely and reliably, at scale?"

The coders who adapt won’t just survive.

They’ll drive what comes next.