When it comes to software engineering, the rise of artificial intelligence will place greater—not lesser—emphasis upon the value of HUMAN intelligence. While tools like GitHub’s Copilot are awesome, they are only as good as the engineer using them.
And while many engineering leaders are discussing how AI is changing the nature of the work they do (AI-specific skills and tasks), teams often miss the two prerequisites for building excellent technical teams in the AI era: who those people are, and how they’re consistently hired and onboarded into a company.
In our work at Byteboard, supporting technical and hiring teams at world class companies like Lyft, Figma, and Webflow, we have seen three key aspects in an emerging playbook to hiring and building in the AI era that can benefit all technical teams:
- How engineers work: tasks
- How you hire: processes
- Who you hire: roles
1. How engineers work: adapting tasks to a new reality
The skills demanded of technical teams are undergoing a fundamental shift. As technical hires spend less time coding and more time scrutinizing AI-assisted code, engineering leaders need to recalibrate their criteria for hiring.
The acceleration in code generation provided by AI tools necessitates a heavier focus on code review. Engineers spend more time deciphering and refining AI-generated code, making attention-to-detail a critical skill. While coding skills remain crucial, the ability to analyze, review, and enhance code becomes equally vital.
In the AI era, organizations should prioritize hiring engineers adept at navigating the complexities of AI-assisted workflows. The emphasis shifts towards individuals who can seamlessly integrate AI tools into their processes, ensuring the produced code is not just expedited but also of the highest quality.
KEY ACTION: Dig deeper on how AI has shifted how engineers work, and what engineering leaders must do about it
2. How you hire: disrupting the legacy process for technical hiring
In the AI era, the process of finding, validating, and hiring technical talent has been disrupted at every stage. From top-of-funnel searching to the technical hiring interview loop, organizations are grappling with the challenges posed by AI-assistance tools and the risk of potential cheating.
Traditionally, coding challenges were the go-to method for assessing technical ability. However, these tasks align closely with the capabilities of AI tools, leading to a surge in anti-cheating measures. Organizations resort to in-person assessments, blocking internet access or IDE usage, and employing unfamiliar mediums like pen-and-paper or whiteboards. While intended to prevent cheating, these measures often hinder candidates from showcasing their true potential, introducing stress and unfamiliarity while increasing costs for organizations.
An alternative approach involves injecting complexity into assessments. Real-world problems, rich in contextual nuances, present a more accurate measure of a candidate's capabilities. Questions that challenge AI tools due to their inherent messiness in complexity become a litmus test for genuine engineering proficiency.
KEY ACTION: Lead a discussion between engineering and talent teams about your organization’s approach to applied AI skills. Arriving at consensus now will save you time and confusion in the long run.
3. Who you hire: the roles that will build the future
It’s not about just updating job descriptions. Roles within engineering teams are evolving beyond traditional boundaries and the process for hiring them must keep pace. It's no longer sufficient to focus solely on prompt engineering; technical talent must now possess the ability to seamlessly collaborate with AI tools throughout the entire product-building workflow.
AI-assistance tools exhibit both strengths and limitations. Their remarkable speed in content generation empowers engineers to accomplish tasks more efficiently than ever. However, the Achilles' heel lies in trustworthiness. Identifying correct answers from those that merely appear correct demands a higher level of expertise, especially in complex problem-solving scenarios.
In this changing landscape, the role of an engineer shifts. While AI tools expedite the 'how' of software engineering, engineers are liberated to delve into the 'what' and 'why' questions, focusing on product and systems design. The ability to review and rectify AI-generated code becomes a critical skill, emphasizing attention-to-detail over sheer productivity.
Organizations must adapt their hiring strategies to align with this paradigm shift. Hiring engineers with the right skills becomes paramount – individuals capable of distinguishing flawed code from correct code, analyzing product spaces, and understanding organizational goals. As the pace of engineering work accelerates, the responsibility to consider the broader impact on users and the world becomes an integral aspect of the hiring process.
KEY ACTION: Engineers using AI tools need to be strong at code review and attention to detail. Add assessments that focus on these important qualities in your 2024 hiring process.
HOW TO BUILD AND HIRE IN THE AI ERA
At Byteboard we are helping engineering leaders in this new era by incorporating complexity and ambiguity into our assessments that emulate real work—this includes collaborative use of AI tools. Rather than focusing solely on preventing cheating, the emphasis is on assessing candidates fairly in an environment where competency requirements are rapidly evolving, specifically in terms of producing code with generative AI and then reviewing it for quality and accuracy. That’s why our new applied AI assessment is designed to accurately measure candidates who leverage AI as a resource.
For a deeper dive on how to put this into practice, here’s an Elevate 2023 talk I gave alongside Akash Jain, Senior Engineering Manager at Byteboard client, Webflow.