Why Developers Should Care About Generative AI (Even They Aren’t AI Expert)

Software development is about to undergo a generative change. What this means is that AI (Artificial Intelligence) has the potential to make developers more productive, as three systems on the market already provide this: GitHub Copilot, Anthropic’s Claude and OpenAI’s ChatGPT.

Hence, every developer, no matter if he or she specializes in AI or not, needs to understand and realize that as this technology is advancing so rapidly, any of us needs to know what it is, why it is relevant, and how to use it.

In this article, we will explain what generative AI exactly is, what functionality current systems bring, why it is going to be ubiquitous for developers, and tips on how to start working with it. Additionally, we’ll bust some of the generative AI replacement of developers altogether. Acquiring this new tool and doing so while not becoming too attached to it will give technology an advantage over other forms of technology.

Machine learning systems that generate digital content, novel and of high-quality on-demand are generative AI. It includes anything from images, audio, and video to text as well as computer code and companies are increasingly turning to generative AI development services to harness their full potential.

In contrast to the analysis most AI has been focused on to date (categorizing existing data), generative models create brand-new artefacts. Advances in deep learning have enabled them to take in enormous datasets and to build an understanding to generate outputs that have never been seen before.

Prominent examples include DALL-E 3 for images, Jasper for audio, and GitHub Copilot for code. These models can take in a text prompt and return a relevant, realistic output in seconds without manual programming.

For programmers specifically, generative AI promises to boost their productivity. Modern systems can suggest entire code functions or applications based on English descriptions to save developers time and reduce bugs.

GitHub Copilot, for example, is a plugin for code editors like VS Code. As a developer writes a function, Copilot can suggest full implementations by analyzing existing code and understanding the English context. It can complete boilerplate code, debug issues, integrate APIs, and more.

Claude 3.7, Anthropic’s writing assistant, offers interactions that fall into the same category as writing assistants. Conversing with an AI in plain English to translate ideas into code in code. You can give an application what to do, walk through examples, ask questions to clarify, and Claude gives you runnable programs.

Such early examples show how generative AI accepts natural language rather than rigid, fixed rules as older technologies were; it is based on accepting natural language as input. With this, it is more intuitive and even accessible.

Even if you don’t specialize in AI, there are compelling reasons all technologists should closely monitor advancements in generative models:

It will become ubiquitous whether you like it or not

There is too much momentum and progress for generative AI not to infiltrate software workflows. OpenAI alone recently received nearly $750 million from Microsoft, plus more from other tech giants using its systems. With so much investment, expect rapid improvements.

It will make you more productive

Studies on Copilot show it can boost developer velocity by at least 30% once accustomed to the workflow. You don’t want competitors leveraging generative AI to build faster while you code manually.

It reduces simple but time-consuming tasks

No developer enjoys manually writing boilerplate code, documentation, tests, etc. But these tasks still take up a lot of time. Generative AI can autogenerate these routine but necessary functions to focus human efforts on complex problem-solving.

It handles legacy systems, so you don’t have to

Generative models are great for working with legacy systems. They integrate easily with outdated codebases, protocols and architectures that human engineers don’t want to work with. This allows you to build new solutions as opposed to keeping old ones.

It makes you a 2x engineer

Conceptual breakthroughs are created by outstanding developers and used to have an impact across the board. Generative AI’s power is that any engineer can create any logic in reality at least 2x faster (PDF). This technology enables world-changing applications to be brought to market today instead of months or years from now.

Generative AI will soon be a basic skill for professional engineers, like using IDEs or version control. Here are key ways to get ahead of the curve:

• Experiment with GitHub Copilot, Claude, and related playgrounds. Get firsthand experience prompts that can spark whole applications.

• Don’t fear; these tools will “take your job.” Enhance your human creativity with AI as a force multiplier. Find gaps in existing models where humans still shine over pure automation.

• Advocate for adopting these tools at your company early, before the competition does. Pitch how it benefits users and customers, alongside developers.

• Develop feedback loops, sharing why certain generated outputs are valuable or inadequate. The more humans train models, the better they become for everyone.

• Get to understand the latest research from the company and its efforts. To keep up to date with anthropic frontiers, sign up for newsletters and updates from pioneers: Anthropic, Cohere, Google and others.

AI will never have the capacity to master all tasks, whether those are too unique, dynamic or complex for AI to do on its own. However, use models for what they do well and augment your human skills. This will be the future path of development: combining minds with machines.

With any disruptive technology, fears and misconceptions often arise around impacts on jobs. Rest assured, human developers will remain essential even as generative AI becomes widespread.

Here are common myths about generative models replacing engineers rather than augmenting them:

• Myth. AI can build full-stack consumer web apps today with no human involvement.

• Reality. Modern systems only generate code, not deploy full-stack apps. You still need developers to integrate outputs, train models, and handle infrastructure.

• Myth. These models understand user needs and product requirements without human input.

• Reality. AI has no sense of end-user behaviour or product thinking. Humans must provide a creative vision and real-world grounding.

• Myth. Generated code is highly reliable and secure.

• Reality. Raw model outputs still contain bugs and vulnerabilities. Humans provide QA, testing, auditing and oversight where AI falls short.

• Myth. Anyone can prompt an AI to build an app without programming skills.

• Reality. You still need engineers to validate code quality, connect components, identify limitations, and guide iterative improvement.

The key is viewing AI as a supportive tool rather than a wholesale replacement. Developers themselves utilize generative models just as they do compilers, debuggers, and clouds. But human creativity, judgment, and oversight remain indispensable.

Hopefully, this breakdown demystifies generative AI and why it matters. These technologies aim to free engineers from drudgery so they can focus on more fulfilling, impactful work only humans can perform.

However, development will transform in the next five to ten years. Those who embrace this shift will have the most dynamic, stimulating, and rewarding careers. Others who deny how software creation is changing risk frustrating obsolescence.

Increasing general coding and automated task democratization means more people can make more ambitious projects than ever before possible. The future of AI-assisted inventions is something developers should cheer about rather than fear, with things remaining the same.

An open-minded attitude towards human and AI coexistence allows engineers to dream bigger, and faster and to make those dreams real. Complex problems in the world are desperate for innovative applications. Shortcuts are provided to that long timeline imposed by traditional code: generative models.

So while modern AI does have clear limits, its rapid pace of progress makes anticipating its potential more prudent than downplaying its capabilities. Get hands-on experience today rather than dismissing models as toys. Help drive breakthroughs by training them ethically. Then code the future you wish to see.