Key Takeaways

  • AppLovin uses AI for 80-90% of its code, but measures success by value creation and business KPIs, not just code volume.
  • Engineers at AppLovin function as product managers, directly understanding business needs and metrics.
  • Adam Foroughi predicts the traditional product organization will dissolve as engineers take on product ownership.
  • Adopting AI tools without clear measurement leads to unproductive "slop" and bad behavior.

The Method

Adam Foroughi's approach at AppLovin reshapes the conventional engineering structure, especially in an AI-native era.

1. No Separate Product Organization: AppLovin consciously operates without a distinct product team. This isn't a cost-cutting measure; it's a structural belief.

2. Engineers as Product Owners: Foroughi demands his engineers think like product managers. They are expected to grasp business needs, understand market dynamics, and focus on delivering measurable value. As Foroughi states, “Our engineers are meant to be product managers.”

3. Value-Driven AI Adoption: While AppLovin uses AI to generate "80-90%" of its code, the focus isn't on the percentage itself. It’s on the quality and impact of that AI-generated output. Foroughi warns against creating "slop" by simply chasing code quantity.

4. Measurable Business Impact: Every engineering initiative, particularly those involving AI, must tie directly to business KPIs. “What's important is are your engineers good enough to use these technologies to accelerate what creates value for the company? And can you measure that?” Foroughi asks. Unmeasured AI spending leads to wasted effort.

5. Role Convergence: Foroughi sees a future where the lines between engineering and product blur entirely. “Either your product people become engineers or your engineers become product people but you don't need both.” This creates leaner teams optimized for growth.

Where This Breaks Down

Foroughi's model works powerfully for a specific kind of company and context. It isn't universally applicable, especially for startups still finding their footing.

1. Early Product-Market Fit: If you are pre-product-market fit, where the core problem and solution are still undefined, engineers might lack the broad market, user research, and strategic vision needed for effective product discovery. Dedicated product managers excel at iterating on customer problems.

2. Deep UX/User Research Needs: Products requiring extensive user research, complex information architecture, or specialized UI/UX design might struggle. Expecting engineers to also be expert UX researchers can dilute focus and quality.

3. Highly Regulated or Complex Domains: In industries like healthcare, finance, or aerospace, where regulatory compliance, safety, and domain expertise are paramount, a specialized product role might be essential for translating complex requirements into actionable engineering tasks.

4. Talent Acquisition: Finding engineers who possess both top-tier coding skills and strong product instincts is a significant hiring challenge. Foroughi's approach requires a high bar for engineering talent.

What to Do With This

This week, audit your top three engineering projects. For each project, identify the specific business KPI it is meant to impact and how that impact is currently measured. If an engineer cannot articulate this immediately, schedule a meeting to define it together. Next, present a key, unresolved user problem to a small team of engineers. Ask them to scope a solution, including potential business outcomes, before any code is written. Observe how they approach the problem from a user and business perspective.