Talent wars in AI: the impact of AI programming languages
MAY. 20, 2024
A powerful coding environment combines Python engineers for prototyping, experimenting and building models, and another team to adapt and deploy the models in another language for production.
Most AI talent acquisition starts with defining your organization’s AI programming language.
Python is a popular choice for AI engineers and data scientists. This is likely due to Python’s long-standing relationship with machine language and natural language processing — coupled with its extensive AI-focused libraries and ecosystem.
To be fair, there are equally powerful languages suitable for AI — including Java, Scala, and C++.
But, AI development involves a team of specialists. These include data engineers, data scientists, and machine learning engineers, all with different skills.
You must understand these specialists’ roles before defining the programming languages they use. Such clarity guarantees good choices for the organization by selecting the right people to join their teams. Clarity also helps engineers choose projects that will engage them and bring value to their careers.
Ultimately, engineers want to do what they love and contribute to AI’s promising future. Choosing the right programming languages for AI projects is crucial in helping them achieve these career goals.
We’ll look at how to attract and keep talented engineers from two angles:
- Technically: What AI programming language(s) are best for your AI company?
- Organizationally: What kind of teams and knowledge transfer are needed?
Let’s dive in.
What AI programming language(s) should you choose?
Advantages of Python programming language
Python’s easy, almost spoken-language syntax lets developers focus on the problem they are called to solve. Its open, linear structure enables developers to write and read a program’s logic flow in a way that mirrors each step in an algorithm.
This close relationship between code and execution is at the heart of Python. It’s key to Python’s fast prototyping and experimentation, which are crucial for building AI apps.
Python has an unparalleled library and framework ecosystem that simplifies AI’s challenging tasks. AI engineers don’t code alone. An active developer community builds, maintains, and shares these libraries.
It’s important to keep in mind that simple is relative.
Many modern developers do not have object-oriented backgrounds. Therefore, they will gravitate towards straightforward script languages like Python for AI and JavaScript for web development.
Python’s simplicity makes it easier to kick-start an AI project, enabling developers to get a model up and running. But their code doesn’t always make it to production, which is no fault of their own.
Typically, it’s just company policy to rewrite the code in a different language before going live. Production needs speed and error handling. It also needs the ability to handle many things simultaneously and the easier scalability of Java, Rust, and C++.
Programming in C++
C++ programmers often need to use C++’s “complex” features like objects, typing, and templating. They also require pre-execution error-checking. However, these programmers have their own ideas about complexity.
C++ developers find Java’s language and frameworks wordy and overthought. And both C++ and Java developers balk at Rust and Scala. Programmers working with C++ and Java view these languages as hieroglyphics and overly constraining.
Later impressions may change as engineers understand each language’s strong points. For example, C++ may be fast and low-level, Rust offers easy multi-threading, and Java has a robust, system-level framework that provides reliable memory and error management, among other things.
Combined programming languages and teams
A powerful and attractive coding environment combines the two: Python engineers for prototyping, experimenting and building models — the bulk of the AI work — and another team ready to take the baton, adapting and deploying the models in another language for production.
Consider ChatGPT. The programmers used a mix of programming languages and frameworks, including Python, TensorFlow, and PyTorch. The GPT-3.5 model was trained using Python, C++, CUDA, and OpenCL. CUDA offers more parallel processing for modern GPUs, which are better at running many tasks simultaneously. This ability is essential for neural network training and inference.
Language choices change to suit evolving requirements and preferences. For example, Julia’s strengths in computation, particularly in scientific simulations and models, meet the mathematical maturity and high-performance requirements of engineers, scientists, and analysts.
This suitability has led to its growing adoption for data science prototyping. Julia works well with Python and R code, which lets developers incorporate Python and R libraries into their Julia programs.
While Python’s exceptional machine-learning capabilities and more straightforward syntax explain its dominance in the AI domain, other languages are arguably better at addressing key considerations like performance, data handling, and maintainable structures.
There may not be a need to choose only one language. Let’s examine how engineers’ roles influence AI programming language choices.
How does defining clear AI roles help you choose your AI programming languages?
Clarifying AI roles helps select programming languages that best meet each user role’s specific needs, tasks, and objectives. These informed choices lead to more efficient development, collaboration, and project outcomes.
Here’s an overview of key AI roles and their programming languages.
Data scientists
The data scientist, often aided by data analysts, collects, cleans, and transforms data, explores data patterns and insights, and prototypes the models used to train the AI solution.
Data scientists design their models using both R and Python. R is the top language for statistics and is widely used in data science because statistics are needed for AI, including probabilistic modeling, simulations, and data analysis. R’s packages allow data manipulation and display, which is critical for AI.
Data engineer
The data engineer builds and maintains the data infrastructure and pipelines used for the AI project. They design, implement, and optimize the data architecture, storage, and integration systems, ensuring data quality, security, and scalability. Data engineers also support data scientists by providing data access, ingestion, and transformation tools and services.
Data scientists and engineers use powerful SQL tools to handle large amounts of data. Of course, many unmentioned languages and frameworks exist for DevOps and web and mobile apps.
AI engineer
The AI engineer integrates the AI project’s data science and data engineering using software engineering and DevOps practices. They develop, test, and deploy the AI solution using the appropriate frameworks, libraries, and platforms and ensure its performance, reliability, and maintainability.
Front-end team
The front-end team comprises user experience (UX) designers and front-end engineers who design and create the user interface of the AI solution. DevOps is deeply involved in setting up, maintaining, and scaling a fast, robust, secure AI infrastructure.
Ultimately, hiring and keeping talented developers depends on the infrastructure, which needs to support an intelligent mix of many languages that run side by side. Python is certainly the top AI programming language. Putting it at the center of your system will attract the most devoted and talented AI engineers.
What teams and processes does your AI company need?
The balance of clearly defining your team’s technical roles must be placed in a well-thought-out organizational structure. There must be no compromises. Otherwise, the in-demand engineers will go elsewhere.
Developers need to be given quality teams with colleagues they can admire and learn from. Typically, they want to see ways to advance and extend their specialties, such as from data scientist to engineer.
AI engineers want to work on innovative projects with room for failure and experimentation. But they also want to see their teams win, such as with chatbots that match ChatGPT’s success. An organization that rewards grit, commitment, and best practices will keep your talent.
Let’s look at two of the most important organizational aspects that create a productive, attractive programming experience: agility and the onboarding and knowledge transfer processes.
Related: How to navigate custom GPTs
Agile and not agile
Agile is an iterative methodology. It groups differently skilled professionals to deliver small versions of software over time. Agile works well for most software projects and is an important selling point for any engineer.
Machine learning is different. Iterating on live ML models requires agility to parse through analytics and feedback as customers use the model in real time.
Despite this, releasing an ML model still initially takes time. The complex data treatment, tricky stats and testing make it less amenable to iterative releases. This is especially true given the need to experiment and fully test a model’s accuracy.
A half-complete algorithm is not ready for customers. AI success hinges on the quality of its results. If you get it wrong, your business may never recover.
Agility is important for developers. This must be nuanced in interviews. Developers want to know your organization is streamlined and without siloes. But they don’t want to see that undermine the patience for teaching machines or building AI models.
Onboarding and knowledge transfer
A well-documented system has a clear onboarding process. It is a selling point during recruitment and will make a good first impression on new hires. If they feel they’ve learned and even produced something cool during their first 3-6 months, they will invest themselves more in your AI projects and future.
Pair programming has become a norm. Engineers have gotten used to working in pairs. Pairs have two developers: a senior and a junior or two equally capable engineers who can teach each other. They work on the same part of the program, where one designs and describes the algorithm, and the other codes.
A balanced mix of seniors and juniors is also crucial for budgeting. For example, a company that wants to create a team of four data scientists will find hiring just one top data scientist supported by three lower-cost data analysts easier and less costly.
Build an AI-committed workforce
Companies with a clear and unique value proposition will find it easier to build an AI-committed workforce. They understand and offer an intelligent mix of AI skills and specialties, and invest in reskilling and advancement. These companies know what AI workers want and how to attract them.
Companies that boldly go into cutting-edge AI and reward experimentation truly understand AI talent engagement.
Competition for quality AI programmers is fierce, and your choice of technology and project organization will determine your success. As the title suggests, there is a talent war, not a language war — but language can be a significant factor in finding and retaining talent.
Engineers want to know they will code in a language they consider the best fit for the job and their careers. If you offer Python, you will find top-quality engineers. Since it is easy to learn and master, you can prioritize talent over experience.
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