Kevin Van Gundy, CEO of Hypermode, shares insights on how curiosity, empathy, and AI integration are key to building resilient engineering careers in today’s tech landscape.
As part of Gun.io Saloon Seminars, a weekly presentation around engineering leadership topics, we sat down with Kevin Van Gundy, CEO of Hypermode, an AI cloud for developers, to do a deep dive into what having a resilient career in the Age of AI looks like. These ideas come from recent conversations he’s had with other engineers in AI, observations he has made and explored about how these qualities are shaping the future of AI and its integration into our daily workflows. He has spent years working alongside some of the brightest minds in the engineering world, and his insights into what makes great engineers tick reveal a profound understanding of the traits and skills that drive innovation in technology.
The Hallmarks of Great Engineers
Curiosity: The Catalyst for Innovation
Something Kevin repeatedly notices: great engineers are insatiably curious. They constantly ask questions about how things work, how different systems resemble one another, and, more importantly, how they work together. Kevin recommends that engineers “pick apart the whys and understand how systems map together. [It] gives lateral agility to be like, okay, this looks like this system and this system. Helping solve problems more quickly.” By understanding how concepts stack together, engineers can map out different problem sets, allowing them to pitch in and provide valuable insights across various domains.
Masters of Analogy
Van Gundy highlights that the best engineers excel at using analogies to communicate complex topics. This skill is crucial in bridging the gap between technical and non-technical stakeholders. For example, at Neo4j, Van Gundy had a mentor spend considerable time teaching how consensus protocols work, explaining the intricacies of different algorithms in a way that was accessible to everyone involved. By spending the time to understand and articulate these systems, engineers ensure that their audience can grapple with the concepts effectively.
Wildly Empathetic
Empathy is another standout trait. Kevin shared an example of his time at Vercel: When deciding to name the company Vercel, world-class engineer and founder of Vercel, Guillermo Rauch, spent hours typing out the new name to understand how it felt to the end user. This level of empathy ensures that the solutions you develop are not only technically sound but also user-friendly and intuitive.
Partnering with Non-Technical Teams
Effective engineers are adept at partnering with business organizations and non-technical individuals. They aim to transmit understanding rather than just technical accuracy. “Partner well with business organizations or folks that are non-technical. Seek to transmit understanding, not technical accuracy. While we may care deeply about precision, it’s more important to explain the broader picture.” Van Gundy refers to a framework borrowed from the Rolfe model, “What, Why, So What,” with an added emphasis on a confidence interval. This approach helps assess and communicate the implications of technical decisions with appropriate context and clarity.
The Impact of AI on Engineering Skills
What changes now that AI is here? As Kevin puts it, this is a common sentiment within the engineering community, “AI won’t take your job, but someone who knows AI might. There is an AI reskilling happening. Folks who know how to use AI will be in high demand.” Understanding and integrating AI into workflows is becoming an essential skill set.
Realistic Expectations for AI
While large language models (LLMs) represent significant advancements, Van Gundy notes that AI is complex and not all-encompassing. “AI is still really tough to use, and we’re seeing all of these step changes in usability, but large language models aren’t going to solve all the problems in the universe.” He argues that “developing acuity and understanding for where these [AI] pieces fit into problem sets” is the most important aspect of utilizing AI. Miscommunication in requirements can lead to flawed AI implementations, underscoring the importance of clear and precise inputs.
Incremental Deployment of AI
Van Gundy predicts that the AI revolution will begin by being increasingly deployed across existing applications, acting as an interstitial fabric between different services and “dumb” features. Traditional search methods will evolve into more sophisticated natural-language searches, and tools will sort and prioritize information intelligently. For instance, an AI-driven email inbox could score sentiment and prioritize messages based on business rules.
The Magic Skill: Knowing Where AI Fits
Van Gundy encourages engineers to embrace tools that enhance their workflow and focus on conceptual understanding. To Kevin, “conceptual understanding now trumps working memory.” Engineers should focus on how different systems work together, using tools to augment their working memory. AI, while powerful, doesn’t have all the answers. Engineers must navigate “idiosyncratic issues as you stack together systems that are rarely well-documented” and become custodians of these complex integrations.
The key to successfully integrating AI is understanding where and how it fits into your application. Not all products require complex machine learning models; sometimes, a rules engine or simple if/then loop will suffice. “AI is a great superpower, but not a panacea. It’s incremental to the needs of your business, where it appropriately fits.” Developing this intuition for your organization is crucial, Van Gundy argues. He advises engineers and companies to consider AI a feature to help an organization: “It’s a feature, not a business; companies will get smoked mistaking those two.”
Integrating AI into the workplace is inevitable in the grand scheme of the future of work, but it doesn’t spell the end for human workers. Instead, it signifies a shift towards more meaningful, complex roles that leverage human creativity and problem-solving abilities. Continuous learning and adaptability are key. Engineers should embrace AI tools, focus on conceptual understanding, and develop the skills needed to stay competitive.
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