From Memo to Movement: How Shopify Scaled AI Adoption

From Memo to Movement: Lessons from Shopify’s AI Journey

I’ve been thinking a lot about how companies embrace change—and not just in small ways, but in ways that ripple across entire organizations. Shopify’s story with AI is one of those rare examples. When Tobi Lütke, Shopify’s CEO, published his internal memo declaring reflexive AI use a baseline expectation, it wasn’t just a note—it became a movement. Companies around the world, from Box to Fiverr, even the Prime Minister of Canada, shared their own versions externally. But what really fascinates me isn’t the memo itself—it’s what Shopify had already built beneath the surface for years.

Long before ChatGPT made headlines, Shopify had been experimenting with AI tools like GitHub Copilot. Farhan Thawar, VP & Head of Engineering, shared that a year before ChatGPT, they were already using Copilot—and adoption quickly rose to 80%. Thawar admits, “At the time, I thought it was terrible. 20% of engineers still aren’t using Copilot? But we got to 80% so quickly because people were finding value quickly.”

This early adoption wasn’t just luck. It came from a philosophy of unrestricted access, a willingness to invest in infrastructure, and a culture that made AI adoption a shared mission rather than a checkbox.

Tip for us: AI adoption is less about directives and more about culture. Start early, give people access, and let value emerge organically. The memo is the spark, but the groundwork is what fuels the fire.

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Letting Everyone Participate: Access Without Borders

One of the most radical moves Shopify made was giving every employee access to the best AI tools. Most companies restrict powerful models to technical teams, but Shopify believes that high-value AI use can emerge from anywhere.

Farhan Thawar shared an anecdote that stuck with me: he ordered 1,500 Cursor licenses, and they ran out so quickly he had to order another 1,500. The fastest-growing adopters weren’t engineers—they were support and revenue teams. A sales rep built a site-audit tool using AI that previously would have taken days of manual work. It automatically pulled data from a prospect’s website, benchmarked it against Shopify’s metrics, and generated talking points for sales conversations.

The underlying lesson? Innovation doesn’t always come from the “experts.” Sometimes the most transformative ideas come from people who experience the problem firsthand, even if they aren’t engineers.

To enable this, Shopify implemented three key tactics:

1. Default “yes” from legal – Instead of facing resistance, Thawar approached legal proactively: “How can we do this safely?” The answer was collaboration, not obstruction.

2. Unlimited AI spend – Friction kills adoption. If engineers find value, let them spend. Even $10,000 per month per engineer is worthwhile if productivity rises.

3. Centralized AI access – Shopify built an internal LLM proxy and MCP servers, letting employees access and switch between models seamlessly, and reuse workflows created by colleagues.

Tip for us: Give your team freedom to experiment with the right guardrails, and don’t underestimate the creativity that comes from unexpected corners of your organization.

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Workflows That Transform Work

Shopify didn’t stop at access—they built workflows that fundamentally changed how work gets done. Here are some examples that really resonated with me:

• Sales engineers’ “What should I do today?” dashboard – By integrating GSuite, Slack, Salesforce, and other tools into a single AI-powered interface, engineers receive prioritized tasks based on context. No more bouncing between apps. AI becomes their assistant, synthesizing real-time data into actionable next steps.

• RFP agent – Responding to large Request For Proposals is time-consuming. Shopify built an AI agent using LibreChat to answer multiple RFP questions at once, leveraging internal repositories and public documentation. It assigns a certainty score to each response and learns from past wins. Over time, responses improve automatically, saving hours of manual work.

• Code “Roast” – In one of the largest Ruby on Rails applications in the world, Shopify engineers use Roast, an open-source framework, to check, fix, and iterate on code. Roast provides constructive criticism, explains reasoning step by step, and pairs deterministic tools with AI. This makes large-scale contributions manageable and helps engineers learn from AI rather than blindly following it.

Thawar calls this “discovering process power.” By making workflows trivial, cheap, and AI-assisted, Shopify is uncovering opportunities to rethink entire processes, like moving site audits earlier in the sales funnel or changing how sales conversations are structured.

Tip for us: AI isn’t just a productivity hack—it’s a process accelerator. Look at the repetitive or time-consuming tasks in your work and ask: Could AI make this trivial, freeing us to rethink the process entirely?

The Human Side of AI

One fear I’ve often heard is that AI will make people passive—disengaged from the work. Shopify flips this. Tools like Roast and the weekly AI-written project updates force humans to engage critically. Project champions review AI summaries, challenge assumptions, and bring misalignments to light.

Pair programming also became a model for showing AI in action. Thawar pairs with engineers while actively using AI, demonstrating reflexive AI usage, not as a crutch but as a partner. Over time, Shopify even incorporated AI adoption metrics into performance reviews. Engineers who use AI effectively tend to have a positive correlation with impact and learning.

Shopify also fosters a beginner’s mindset. Instead of cutting entry-level positions, they hire more interns—people naturally curious, experimental, and eager to explore AI tools. Thawar calls them “AI centaurs”—half human, half machine—who push the organization forward in ways senior engineers might not.

Tip for us: Use AI to enhance human judgment, not replace it. Teach, engage, and experiment with beginners—they often see possibilities seasoned employees overlook.

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Prototyping, Experimentation, and Closing Thoughts

Finally, Shopify emphasizes prototyping. Instead of trying to build perfect solutions first, engineers iterate rapidly. The internal AI chat tool started as a rough prototype, exposing flaws, testing scalability, and evolving through early feedback. This approach allows one to explore thousands of potential solutions, rather than stopping at the first workable option.

Reflecting on this, I realize the real lesson isn’t just about AI—it’s about mindset. Shopify’s story teaches us to:

1. Build infrastructure that compounds experimentation.

2. Give people access to tools freely and widely.

3. Let AI show its reasoning; don’t hide the messy middle.

4. Engage humans critically, not passively.

5. Embrace beginners, prototyping, and iterative discovery.

When done right, AI doesn’t replace humans—it unlocks hidden potential, accelerates workflows, and allows us to rethink how we work entirely. Shopify’s memo was the spark, but their culture, infrastructure, and mindset were the fuel.

So as we reflect on our own teams and processes, maybe the question isn’t how do we use AI? but how can AI help us rediscover the best way to work?

Stay curious, stay experimental, and never underestimate the power of beginner’s eyes.

What’s your next spark? A new platform engineering skill? A bold pitch? A team ready to rise? Share your ideas or challenges at Tiny Big Spark. Let’s build your pyramid—together.

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