Reflections on Building Products at Scale
Personal insights from years of product management, team leadership, and the lessons that shaped my approach.
Oliver Newth·December 10, 2023·6 min read
Loading...
Preparing your experience
Preparing your experience
Personal insights from years of product management, team leadership, and the lessons that shaped my approach.
After years of building products and leading teams across different companies and domains, I've collected some hard-won insights about what actually matters when building products at scale. These aren't the polished lessons from business school—they're the messy, practical truths I wish I'd known earlier.
Early in my career, I believed that thorough planning was the key to product success. I would spend weeks creating detailed roadmaps, user journey maps, and feature specifications. The reality is that products evolve through contact with users, not through planning in isolation.
The most successful products I've worked on followed a pattern:
1. Start with a strong hypothesis about user needs 2. Build the smallest possible version that tests that hypothesis 3. Get it in front of users immediately 4. Learn and iterate rapidly
This isn't groundbreaking advice, but the devil is in the execution. "Learning rapidly" means setting up systems to capture and analyze user feedback, not just launching and hoping for the best.
Products are built by people, and people problems are usually the biggest obstacle to success. Technical challenges are often easier to solve than alignment issues between team members with different priorities and perspectives.
The best product teams I've been part of shared several characteristics:
It's tempting to equate product progress with feature development. More features feel like more value. But I've seen too many products collapse under the weight of feature bloat.
Some of the most successful products I've worked on succeeded because of what they didn't do:
This requires discipline and clear vision from leadership. It's easier to add features than to maintain focus.
Early in my career, I was obsessed with vanity metrics—user counts, page views, time on site. These metrics felt important because they were easy to improve and impressive in presentations.
The metrics that actually predicted product success were usually more specific and harder to game:
As someone who came to product management from an engineering background, I initially overvalued technical elegance and undervalued user experience. Beautiful code doesn't matter if users can't figure out how to use your product.
The most successful products found ways to align technical excellence with user value:
Product management continues to evolve, especially with AI changing how we think about user interfaces and product capabilities. But the fundamental challenges remain the same: understanding users, building great teams, and maintaining focus while executing rapidly.
The best product managers I know are lifelong learners who adapt their approaches based on new information while holding onto proven principles. That's the kind of product manager I aspire to be.
A practical framework for product leaders to assess AI readiness, define gating criteria, and navigate the journey from prototype to production platform at scale.
How to design evaluation metrics that actually predict revenue, retention, and user satisfaction—not just model accuracy. A practical guide for product managers shipping AI products.
You can optimize for two, but not all three. A strategic framework for product leaders to navigate the fundamental tradeoffs in AI product development and make informed decisions.