State of AI Code Generation in 2023

Let’s dive into the current landscape of AI code generation and trends to look out for in 2024 and beyond.

AI-based code generators are increasingly becoming pivotal in transforming the way software is being written and optimized. Thanks to recent breakthroughs in Large Language Models (LLMs) that empower machines to analyze and comprehend code patterns, programming languages, and programming paradigms.

In most cases, developers can interact with AI using natural language to generate code, and in some cases, the input can be visual, such as a design file, mockup, wireframe, or even an image. For example, utilizes its Large Design Models (LDMs), trained on millions of designs and apps, to transform design files into high-quality, production-grade frontend code efficiently. This process leverages AI to interpret visual inputs, streamlining the transition from static designs to functional code and significantly enhancing developer productivity.

These tools can generate code snippets, auto-complete code, propose entire functions/classes, and develop deployable, production-quality code. Secondary use cases involve analyzing code, debugging, suggesting test cases, translating code from one programming language to another, streamlining code conversion, or modernization projects.

Background and Evolution

AI-assisted software development is not an entirely new concept. Previous tools used complex algorithms instead of relying on AI to assist developers by providing basic spell-check features, code completion, and static code analysis.

Initially, Microsoft's IntelliCode, introduced in Visual Studio in 2018, primarily enhanced C# code completion, focusing on context-aware recommendations. Despite its innovativeness, it had limitations in scope and language support.

Subsequently, the market witnessed the introduction of more advanced AI-assisted tools. GitHub Copilot, launched for the general public in 2022, demonstrated impressive coding capabilities, although it faced accuracy challenges in complex tasks and depended on detailed user input.

More recently, fully-fledged tools like Amazon CodeWhisperer by AWS and v0 by Vercel are providing comprehensive aid to developers, generating high-quality code from their inputs. Similarly, Microsoft's Sketch2Code offers the unique capability to convert images into functional frontend code. These developments signify the ongoing evolution and potential of AI code generators.

Source: MarketResearch.Biz

As per a study by MarketResearch.Biz, the AI code generation space is currently valued at USD 30.9 billion and is expected to surge to USD 169.2 billion by 2032. This represents a CAGR of over 21.4%, indicating the rapidly expanding role of AI in software development.

In a survey done by Zero to Mastery, 84.4% of the programmers have some level of experience with AI code-gen tools. The adoption is highest among full-stack developers (32.5%), followed by frontend developers (22.5%), and backend developers (8.9%). The U.S. and Europe lead this, with Asia also showing a strong uptake. 

Interestingly, 77.8% of programmers feel that these tools will positively impact code quality, and the majority (86.6%) are not worried about AI taking over their jobs, contrary to general beliefs.


Leading AI Code Generators in 2023

As the demand for AI code generation continues to rise, a multitude of AI code generators are emerging in diverse shapes and forms within the market: 

  1. Code Assistants 

    These generators assist in code completion, integrating Large Language Models (LLMs) and machine learning for context-aware coding assistance and pattern analysis. Notable examples include GitHub Copilot, JetBrains ReSharper, and IBM WatsonX.

  2. Design to Code Tools

    These tools transform visual inputs like Figma & Adobe XD designs into frontend code. Historically relying on heuristic methods, advancement in deep learning now enables the usage of Vision Transformers to process images or designs for tasks like object detection and classification to go from design to code. Some of the tools in this category are, Animaapp, Firejet,, and more. Although their specific technologies may differ, it's clear that more sophisticated AI-driven methods are becoming a trend in the industry for giving higher-quality code output.

  3. Automated Testing and Debugging

    Tools in this category assist engineers in writing test cases for their applications & improve the coding standards. Often, these tools integrate seamlessly into existing CI/CD pipelines, provide detailed logs, and test various cases for robust and reliable software. Some prominent tools in this space include, Appium, and Selenium.

  4. Code Translation

    These tools involve translating source code from one programming language to another. By leveraging large language models (LLMs) these tools aid engineers in translating legacy code like COBOL into a modern codebase like Python. Such tools are imperative for converting legacy code into modern languages and frameworks. Notable examples include Google’s Transcoder, Roslyn .NET Compiler Platform, and Tangible Software Solutions.

Technology and Functionality

The majority of AI code-gen tools use deep learning algorithms and large neural networks, trained on existing source code, often from open-source projects, to generate new code. LLMs process and understand language, enabling them to generate code, answer questions, and learn language patterns to suggest code. 

For example, when developers begin typing code, the AI attempts to autocomplete it or generates a suggestion based on the developer's intended goal. Sometimes code generators can have a simple chat interface where the developer chats directly with AI, asking it to write something specific (like functions or classes) or fix a bug.

On the other hand, tools like rely on your designs as inputs and directly generate frontend code from them using an in-house Large Design Model (LDM). LDMs are's innovative approach to transforming design models into responsive frontend code seamlessly. LDMs incorporate cutting-edge technologies across various AI domains to achieve their functionality. It is built on an ensemble of computer vision and transformer models to identify and rectify design discrepancies and apply the best practices gleaned from millions of designs for cleaner code output. 

Additionally, the multi-model object detection approach present in the LDMs automatically recognizes elements and extracts features from the designs. It can also intelligently assign appropriate CSS media queries for responsive code based on data from self-sourced large databases. LDMs also leverage transformer-based models for tagging and layer naming to generate interactive and human-readable code. Our model predicts interactive HTML tags, component names, class assignments, and the names of properties and assets, streamlining the process of translating intricate designs into functional and responsive frontend code.

Real-world Applications

These code generators have impacted software development across all sorts of industries. They are proving particularly advantageous for fast-moving startups, enterprises, and agencies, speeding up product development and empowering lean teams to deliver world-class products. Freelancers, too, are leveraging these tools to take on more clients and eventually earn more. A survey by the AI Accelerator Institute highlights this impact, with 26.7% of respondents noting improved development efficiency due to AI code generators.

These tools help not just in creating basic websites such as landing pages or portfolios, but can also assist in building complex web & mobile applications. For example: RED, a non-profit organisation based out of Peru, saved 1 month of development time while building their mobile app. Forga, a European product studio, was able to launch 3 projects back to back in record time. Likewise, Ditto, a video dating app, saved 240 hours of development time with is saving time for development without compromising the quality of the deliverable and that's what is the ongoing problem to find out ways in which we can save delivery time without impacting the quality of deliverables. is right on target to solve the exact pain point. Another thing I like is they are the only product that is consistent in generating high-quality code and product support was a great help to navigate through any blocker in our development.

Lalit Garg
Co-Founder, CTO @ Ditto

As a designer, I've always aimed for a smooth handoff between design and development, and Locofy has completely transformed this part of my workflow. It effectively converts design specifications into code, guaranteeing that the finished output is pixel-perfectly identical to the original design. This level of preciseness not only saves developers time but also reduces the amount of back-and-forth communication between designers and developers.

Nikola Marinkovic
Design Director @ Forga

These are just a few of the success stories from teams who have incorporated AI code generation tools into their workflow.

Challenges and ways to overcome

AI code generators are reliant on the principle that the output quality is heavily dependent on the input quality. These tools, while advanced, often face challenges in processing accurate inputs and generating responsive code. Addressing these issues requires a multifaceted approach: robust data validation, advanced language processing, high-quality inputs, and the continuous enhancement of AI algorithms to improve the quality of generated code. Furthermore, some of these tools can struggle with complex coding scenarios and specific programming languages.  

There are also ethical concerns surrounding this space. Microsoft highlights concerns like job displacement, bias, and data privacy. Moreover, these tools will need human intervention, at least for now. “No computer software is flawless. Databases get outdated, games have bugs, and websites crash. The same goes for an AI algorithm.” - Microsoft.

Having said that, early adopters can always follow some of the best practices to ensure the code is reliable and up to the mark. For those using chat-based tools such as GitHub Copilot, providing clear and specific prompts is crucial. This clarity helps the AI understand precisely what is needed, leading to more accurate code generation. Selecting a tool that integrates seamlessly with your existing workflows, tools & stacks is equally important to prevent any disruption to the development process.

It’s not just about the tool itself, but how it fits into the bigger picture of your team’s operations. Effective training for the team on using these tools is another key aspect. When the whole team is proficient at using AI, the integration of such technology becomes much smoother. After the AI has done its part, it's essential to rigorously test and debug the generated code, ensuring it meets the high standards required for your projects. That's precisely why human intervention is a good thing since it also means AI won't entirely replace developers; instead, it will support them.

By adopting these practices, teams can ensure a smoother transition to using AI code generators and fully utilise their potential, leading to more efficient and effective development processes. Moreover, it is the ethical obligation of these code generators to utilise diverse datasets for training and ensure compliance with data privacy regulations.

Future Outlook and Predictions

The AI code generation market is expected to reach $169.2 billion by 2032. This signifies a major shift in the software development landscape. TechRepublic reports that 31% of companies are already using AI for coding

Looking ahead, this trend implies that AI will lower the entry barrier to getting started in software development, leading to a growing number of individuals building products, irrespective of their background, which is a net positive impact. The U.S. Bureau of Labor Statistics predicts a 21% growth in software developer jobs by 2028, reflecting the impact of this paradigm shift.

The rapid growth in AI code generation is clear with tools like GitHub Copilot and ChatGPT. Copilot reached a million users in 26 weeks, while ChatGPT amassed one million users in a record 5 days time period. These successes show the strong appeal of AI in coding, indicating a market ready for more AI-driven software development and automated coding.

Source: Dialpad

As AI rapidly progresses, an increase in advanced code-generation technologies is anticipated. This advancement is marked by continuous enhancement and adaptation in AI, suggesting a fundamental change in the methods of code generation and management. The emergence of AI tools capable of fully creating applications is expected soon. It will soon be able to manage both the frontend and backend coding, set up databases, implement logic, and design interfaces, all according to a developer's instructions. In simple terms, users will be able to describe what they want in an app, and AI will build it for them, making the whole process faster and more accessible to everyone.

However, it's important to note that these technologies are still in their infancy. Current advancements are setting the stage for the future by automating coding tasks and providing guidance. While the complete realization of end-to-end application development by AI is on the horizon, we are in a phase of anticipation and gradual progress.

Closing Remarks

2023 has been a standout year for AI code generators, significantly transforming the tech industry. This trend is expected to gain momentum in the coming years, with these tools becoming increasingly sophisticated and mainstream in the coding process. These advancements are set to offer a competitive edge across the tech industry, benefiting not only large corporations but also smaller startups, and individuals. 

The journey ahead, while full of promises, will also bring challenges in refining code quality and addressing ethical concerns. Yet, the continuous advancements in technology are likely to overcome these hurdles over time. AI's ongoing evolution is set to enhance software development, paving the way for greater creativity and efficiency in coding practices.

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