"Reflexive AI usage is now a baseline expectation."

This declaration from Shopify CEO Tobias Lütke in a recently publicized internal memo signals a profound shift that extends far beyond one company's walls. It’s a transition from AI as a "𝐧𝐢𝐜𝐞-𝐭𝐨-𝐡𝐚𝐯𝐞" to a "𝐦𝐮𝐬𝐭-𝐡𝐚𝐯𝐞.” Now, we’ve heard takes from both ends of the spectrum. Some people in tech believe this transition represents a challenge. Others, including specifically in insight-generating functions, see it as an opportunity.
I am camp opportunity, and here’s why.

Why this matters now

We're witnessing a fundamental transformation in how organizations view AI adoption. As Tobias bluntly states:

"I don't think it's feasible to opt out of learning the skill of applying AI in your craft... Stagnation is almost certain, and stagnation is slow-motion failure. If you're not climbing, you're sliding."

This isn't isolated to Shopify . Across tech, forward-thinking leaders are establishing similar expectations. The message is clear: AI proficiency isn't just about efficiency - it's about expanding what's possible. Another indicator is data from Carta over the last 12-months. Investors are paying a premium for AI-Native startups observed in valuations and $ amounts of rounds raised. In the most recent a16z speedrun demo day by Andreessen Horowitz the vast majority of startups were AI Native.

What "reflexive AI usage" means for researchers

For research industry, developing an AI reflex means:

• Automatically considering AI throughout your research process - not just for data analysis (or whatever the most obvious use case is to you)

• Instinctively turning to AI when facing research challenges

• Seeing AI as an insight “multiplier”, not just a productivity tool

The most compelling aspect of Tobias Lütke memo? His observation about the multiplier effect:

"We are all lucky to work with some amazing colleagues, the kind who contribute 10X of what was previously thought possible... And what's even more amazing is that, for the first time, we see the tools become 10X themselves."

Imagine what happens when exceptional researchers harness AI effectively - this is the 100X potential he is referencing.

Three ways to become an AI-native researcher

1. Make experimentation your priority

"What we have learned so far is that using AI well is a skill that needs to be carefully learned by... using it a lot. It's just too unlike everything else."

Dedicate time specifically for AI experimentation in your research practice. Try different approaches, document what works, and build your AI intuition.

2. Start with AI in mind

"The prototype phase of any... project should be dominated by AI exploration."

Consider AI approaches during research design, not just analysis. How might AI help you plan better research? Generate more diverse recruitment criteria? Identify themes in your data?

3. Learn collectively

"We'll learn and adapt together as a team. We'll be sharing wins (and losses!) as we experiment with new AI capabilities."

Create opportunities to share effective AI approaches with fellow researchers. The learning curve accelerates dramatically when we share discoveries rather than struggle individually.

The opportunity ahead

This shift represents one of the most significant transformations in the research market and the methodologies used in decades.

Most exciting is how AI can help us focus more on what makes us uniquely valuable as researchers - empathy, contextual understanding, and insight activation - while automating the more mechanical aspects of our work.

And on that note, this is your reminder that with Genway AI , you can start by crafting with AI and finish by generating meaningful insights and driving action. We’re building a platform that supercharges your research with AI, and we invite you to dive into the AI-native research space with us.

The future belongs to researchers who can seamlessly integrate artificial and human intelligence. So get started at genway.ai/try-it-free

Feel free to DM me if you have any questions or would like to chat,

This article was inspired by a recently leaked internal memo from Shopify CEO Tobias Lütke on AI expectations within the company.