AI-First: The Bold Move That 2x'd Duolingo's Output

It’s safe to say we’re starting to witness the next great workplace transformation.

In a bold move, Duolingo CEO Luis von Ahn recently declared the company is going "AI-First", a strategic shift with far-reaching implications for how work gets done (a link to the full post is below).

Here's the big idea: rather than treating AI as just another productivity tool, companies like Duolingo are completely reinventing their operations with artificial intelligence at the core.

This approach mirrors what I wrote about several weeks back from the Harvard research about AI transforming teams.

#1. Not Just a Productivity Boost: AI as Core Infrastructure

The most compelling finding?

AI isn't just speeding up existing processes, it's enabling entirely new capabilities.

Von Ahn articulated this clearly:

"AI isn't just a productivity boost. It helps us get closer to our mission. To teach well, we need to create a massive amount of content, and doing that manually doesn't scale."

This mindset reflects a fundamental shift in how organizations view AI: not as optional technology, but as essential infrastructure that enables core business functions.

Duolingo demonstrated this with its recent launch of 148 new language courses, more than doubling its previous offerings.

What's remarkable?

Creating their first 100 courses originally took 12 years.

These 148 new courses were developed in under a year using AI.

This parallels what the Harvard study revealed about junior employees using AI performing at levels similar to senior colleagues.

The technology isn't just making work faster; it's transforming what's possible.

Quick Win: Identify core processes in your organization that are currently bottlenecked by manual content creation or knowledge work.

These are prime targets for AI transformation rather than mere optimization.

#2. Rethinking Team Structure: Smaller Teams, Bigger Impact

The Harvard research showed AI-enabled teams with just 2 people often outperforming traditional teams of 4.

Duolingo's approach follows this pattern.

Their "shared content" system allows small teams to create high-quality base courses that can be quickly customized for dozens of languages, work that previously required much larger teams.

Jessie Becker, Senior Director of Learning Design at Duolingo, explained the transformation:

"It used to take a small team years to build a single new course from scratch. Now, by using generative AI to create and validate content, we're able to focus our expertise where it's most impactful."

This allows organizations to move toward what the Harvard team called a "cybernetic teammate" model, where AI effectively serves as an additional team member, not replacing humans but amplifying their capabilities.

Try This Now: Run parallel projects with your current team structure versus a smaller "pod" model that integrates AI tools as core collaborators.

Compare both output quality and team satisfaction.

#3. Breaking Down Functional Silos

Before AI integration: Teams worked in functional silos.

After AI adoption: Cross-functional collaboration emerged naturally.

Duolingo's approach to creating 148 language courses demonstrates how AI can break down traditional organizational barriers.

Their model enables consistent knowledge and assets to flow across language teams that previously would have worked separately.

This mirrors the Harvard study finding that AI-assisted teams produced more balanced proposals that integrated multiple functional areas without forced team-building exercises.

The consistent knowledge layer that AI provides helps standardize approaches while still allowing for customization where needed.

At Duolingo, this manifested as their "shared content" approach, which maintains quality standards while dramatically accelerating production.

Smart Strategy: Identify where knowledge silos exist in your organization and create AI tools that can serve as cross-functional resources to standardize processes while preserving necessary team autonomy.

This could take a form of a GPT or a chatbot that acts as an “independent AI arbiter”.

#4. Moving with Urgency: Embracing the "Good Enough" Revolution

One of the most counterintuitive aspects of Duolingo's strategy is their willingness to accept occasional quality tradeoffs to maintain momentum.

Von Ahn was explicit about this approach:

"We'd rather move with urgency and take occasional small hits on quality than move slowly and miss the moment."

This philosophy challenges traditional perfectionist mindsets.

The company recognizes that waiting for flawless AI implementation would mean missing the critical adoption window.

The Harvard study showed similar patterns, revealing that teams that moved quickly with AI integration, learning and improving as they went, ultimately achieved better outcomes than those who delayed adoption until systems seemed "ready."

Leadership Opportunity: Create "minimum viable" AI implementations in low-risk areas first, with clear metrics for acceptable quality levels.

Build feedback loops that allow for rapid improvement while maintaining forward momentum.

#5. Empowering Creative Work by Automating the Routine

A crucial message in Duolingo's AI-first announcement was the emphasis on freeing human talent for higher-value work.

Von Ahn emphasized:

"This isn't about replacing [employees] with AI. It's about removing bottlenecks so we can do more with the outstanding [team members] we already have. We want you to focus on creative work and real problems, not repetitive tasks."

The evidence of this approach can be seen in how Duolingo's learning design team now focuses their expertise "where it's most impactful" rather than on routine content creation.

This aligns with the Harvard findings about the emotional benefits of AI assistance, which showed junior professionals experienced more confidence and less fear of failure when supported by AI tools.

Where to Start Tomorrow: Conduct an audit of your team's current workloads, identifying which tasks are routine/repetitive versus creative/strategic.

Create a transition plan that uses AI to handle the former while upskilling team members to excel at the latter.

The Bottom Line

Duolingo's AI-first declaration represents more than just another tech company adopting new tools, it signals a fundamental reimagining of how organizations operate.

By committing to AI as core infrastructure rather than optional enhancement, they've been able to achieve remarkable results:

• 148 new courses developed in less than a year,

• dramatically expanded access for over a billion potential learners worldwide,

• and an organizational structure optimized for innovation rather than routine production.

This mirrors what forward-thinking leaders across industries are discovering:

AI-first isn't just about technology adoption.
It's about organizational reinvention.

The companies that will thrive in this new era won't be those that simply add AI tools to existing processes, but those that fundamentally redesign their operations with AI at the center, embracing what the Harvard researchers called the "cybernetic teammate" model.

Start small.
Test often.
Be willing to accept some imperfection in service of forward momentum.

The organizations that learn to truly collaborate with AI (not just deploy it) will be the ones that win.

Never Stop Innovating,

Ben S. Cooper

P.S. Duolingo reported 38% year-over-year revenue growth in Q1 2025, with their AI investments clearly paying dividends.

If you're hesitant about taking similar steps, remember that the competitive landscape won't wait for perfection.