When facing a "brick wall" where user perception contradicts data (e.g., feeling ad load is high when it's low), incremental changes fail. The solution is to re-architect the experience from first principles. This can unlock growth in key metrics like ad load while simultaneously improving user satisfaction.

As frontier AI models reach a plateau of perceived intelligence, the key differentiator is shifting to user experience. Low-latency, reliable performance is becoming more critical than marginal gains on benchmarks, making speed the next major competitive vector for AI products like ChatGPT.

Reframing a call center problem from reducing actual wait time to reducing *perceived* wait time opens up non-obvious solutions, like playing comedy instead of repetitive hold music. Adding a single word to a problem statement can radically transform the potential solutions.

In a competitive market, prioritizing speed forces a team to be resourceful and figure out how to maintain quality under pressure. This mindset prevents the design team from becoming a bottleneck and keeps the company's momentum high.

When fintech bank N26 made its login process incredibly fast, users felt it was unsafe. To build trust, the product team had to artificially slow the login down and add visual cues, like a lock animation, demonstrating that sometimes perceived security is more valuable than raw speed.

Companies like OpenAI and Anthropic are intentionally shrinking their flagship models (e.g., GPT-4.0 is smaller than GPT-4). The biggest constraint isn't creating more powerful models, but serving them at a speed users will tolerate. Slow models kill adoption, regardless of their intelligence.

AI co-pilots have accelerated engineering velocity to the point where traditional design-led workflows are now the slowest part of product development. In response, some agile teams are flipping the process, having engineers build a functional prototype first and then creating formal Figma designs and UI polish later.

By handling repetitive production work, AI gives designers bandwidth to focus on high-impact, creative problems. This includes innovating on previously overlooked details like loading states, which have new importance in AI-driven products for building user trust.

The era of winning with merely functional software is over. As technology, especially AI, makes baseline functionality easier to build, the key differentiator becomes design excellence and superior craft. Mediocre, 'good enough' products will lose to those that are exceptionally well-designed.