Why the Future of AI Code Generation is Personalization

According to McKinsey, the economic impact of GenAI is the largest in the field of Product development and coding automation, resulting in a $900B impact.

Let’s dive deeper into the state of code automation, code personalization, and its potential.

State of GenAI & Code Automation in 2024

In 2023, ChatGPT and Github’s coding assistant, CoPilot, exploded into becoming mainstream amongst coders. GPT and similar models have shown that LLMs (large language models) can generate, complete, refactor, and transform code very well.

Today, there are a variety of coding assistants. While CoPilot is considered the category leader, there are GenAI coding assistants with different specialties. To name a few:

  • Anima specializes in front-end, turning designs into code (I.e., Figma to React)

  • Codium expertise is composing tests and managing pull requests

  • Replit offers an online, collaborative IDE with a dedicated AI assistant

  • Tab9 offers an on-prem, highly secured solution for the Enterprise

Rising rivals to CoPilot are announced frequently, for example, magic.dev and Poolside, promising better performance and a better experience. Models continue to evolve – GPT5 is expected to be announced soon, and LlamaCode offers a high-end open-source model, with fine-tuned versions popping up on HuggingFace [code models leaderboard]. It is only the beginning of code automation with LLMs.

According to Github, CoPilot speeds development by 55% [research]. Anima users report saving up to 50% of front-end coding time [case study], making them 2x faster while ending up with better product quality in terms of UX—and less ping-pong between designers and developers.

AI Code Personalization

JavaScript is the #1 most popular code language (Github 2023), and React is the most popular JavaScript web framework, used by over 40% of developers (Stackoverflow 2023).

Now, if you take 100 different engineering teams that build on top of React, you’ll find 100 different coding styles. Different teams have different ways to write code.

Each team has its tech stack (the set of technologies used on the software architecture). Some teams use open-source libraries such as Next.js, allowing them to optimize performance. Some use UI frameworks such as Radix, MUI, or Ant. Teams using React must add state-management packages, like React query, Redux, Mobx, etc. And there are thousands of other popular open-source JavaScript libraries.

In addition, the same functionality can be achieved in different ways. Some teams prefer a CSS grid layout, while others prefer a Flex layout and get the same results. There are syntactic preferences. Some use classic JavaScript functions, while others use arrow functions. There are naming conventions such as camelCase, kebab-case, and different ways to name components and functions. There are endless ways to organize your code, like how to wrap open-source components in a way that makes the code interface look the same for open-source or proprietary code.

When coding on a specific project, each developer follows the rules and conventions of that code base.

In order for AI to play a key role in coding for an engineering team, it should code like the team. This means that AI should have lots of context to customize and personalize its code.

Epilogue: The Potential in AI Code Generation

We are still scratching the surface of GenAI capabilities.

When discussing GenAI models, consider personalization as giving a model the best context for its task. Giving it a great context regarding the existing code, the UX, and the users’ job to be done will result in better results. In order to utilize GenAI models to their full potential, we package them as products with supporting systems working with “old-fashioned” algorithms and heuristics. This is how we maximize AI to its full potential.

Software will keep eating the world faster and faster, increasing productivity, margins, and GDP.

CEOs, IT leaders, and PM leaders who adopt automation will allow their teams to deliver 2x and maybe even 5x faster, getting an edge over the competition. Bringing products faster to market and at a lower cost will increase companies’ margins and eventually increase the GDP coming from tech.

Cheaper software development means software could come and solve more problems. What used to be ROI negative will become ROI positive. Software that solves niche problems could be worth it if the cost of development is down by 80%.

More people will code, and they will code faster. GenAI agents will produce, test & deploy code, and humans will do the creative parts, developing more architecture and UX than what’s considered today as coding. I see more developer positions in the future. That said, development will evolve into a higher level of abstraction.

The post Why the Future of AI Code Generation is Personalization appeared first on Unite.AI.

Unlock the power of our talent network. Partner with QAT Global for your staffing needs and experience the difference of having a dedicated team of experts supporting your enterprise’s growth.

Explore Articles from QAT Global