AI-era hiring: why learning speed and curiosity can outweigh experience

This guide is for foreigners who hire, manage or work in Korean companies and want to know how AI is changing hiring. It looks at Shopify, a global e-commerce platform company, which in 2025 told teams to prove AI could not do a job before hiring, yet kept growing its intern program. The article's lesson: in the AI era, the ability to learn and to use new tools can matter more than years of experience.

What the company did

In April 2025, Shopify CEO Tobi Lütke published an internal memo. Before asking for more headcount and resources, teams must show why they cannot get the work done with AI (TechCrunch, April 7, 2025). The memo also said AI use would become part of performance and peer reviews, and that AI should be used from the prototype phase of each project (BNN Bloomberg, April 7, 2025; Hankyung, April 8, 2025).

At the same time, the company did not cut its intern program. In mid-2025, Head of Engineering Farhan Thawar explained on a podcast why Shopify was hiring 1,000 interns (The Pragmatic Engineer, July 2, 2025). In May 2026 he wrote that the program had grown about tenfold and that Shopify was hiring 1,000 interns again that year. Reports give different figures for the program's earlier size.

The article reads this as one consistent strategy, not a contradiction: automate the work AI can do, and bring in people who work well with AI. It also notes that the meaning of "work AI cannot do" keeps changing. At first it meant tasks that need creativity or emotional judgment. Now the ability to use AI to multiply your own output has itself become a core skill.

Why new hires can have an edge

Thawar compares this with the mobile era. About a decade ago, Shopify hired heavily from graduates who had grown up with mobile, because mobile was the only world they knew. He sees the same in graduates who have grown up with AI: they do not have old habits to unlearn.

He also calls a "beginner's mind" hugely valuable. New people ask "Why are we doing it this way?" without being tied to "That is how it has always been done." The article adds that this kind of questioning puts healthy pressure on other staff to look again at their own methods.

Thawar has described sharing his AI work as showing "how lazy I am", meaning working smarter rather than harder (Bessemer Venture Partners). The article sees the same instinct in young hires: they push back on repetitive, wasteful work and look for a better way.

AI use spread beyond engineers

When Shopify rolled out the AI coding tool Cursor, Thawar expected engineers to be the main users. It quickly caught on with sales, finance and HR teams as well. Sales teams began writing queries, building reports and decks and preparing monthly business reviews without waiting for engineers. Thawar calls these small tools built for one person's workflow "n-of-1 software". An internal tool called Quick lets any employee publish a simple web app. In one example, a colleague sent him a dashboard that gathered what he needed to know about a merchant before a meeting.

The article's point: with a new technology, everyone is a beginner, and that can speed up learning across the whole organization.

The qualities the article says now matter

1. Learning ability over existing expertise. Skills age quickly while AI keeps changing. The ability to learn fast and to redefine your own role keeps its value.
2. Curiosity over experience. A person who asks "Why do we do it this way?" creates more value than one who answers "That is how it is done." The article stresses that this is constructive questioning aimed at a better method.
3. Experimentation over polish. The article argues that ten imperfect attempts can lead to more innovation than one perfect result.

What HR teams can take from this

• Hiring: assess openness to new tools and willingness to learn, more than past AI experience. For interns and new graduates (신입), give more weight to potential and growth than to experience.
• Culture: accept failed experiments. When you introduce AI tools, do not stop at training. Give people room to try the tools and find their own uses.
• Performance reviews: credit process improvements, new methods and gains in team productivity, not only individual task output. Shopify itself added AI use to its review questions.

For foreigners working in or with Korean companies, these are management recommendations, not legal rules. How far they fit depends on each company's own policies.

Checklist

• Ask candidates how they learned a new tool in the past year.
• Give interns real work and regular feedback, not only access to tools.
• Share examples of AI use across teams, including non-engineering teams.
• Leave room for prototypes and treat failed experiments as learning.
• Check whether your evaluation criteria reward process improvement.