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2 posts tagged with "Design"

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· 6 min read
Carl Liu
Kazem Jahanbakhsh

Goal

In this analysis, we explore how Large Language Models (LLMs) and AI technologies have fundamentally transformed the internet landscape since ChatGPT's debut. By examining market dynamics and how major players are adapting, we present four key predictions for the AI industry through 2025. Our forecast draws from current trends, market data, and emerging patterns in how businesses and users interact with AI technologies:

  1. New Frontiers in Product Design
  2. The Search Will Shift
  3. LLMs as a Market Disruptor
  4. Future of LLM Development

New Frontiers in Product Design

Shifting Metrics at Airbnb

A conversation with a senior design manager at Airbnb revealed a paradigm shift: instead of focusing on Page Performance Scores, the company now measures “Resolution Time”—how quickly an AI agent can meet user needs, whether it’s booking a stay or finding relevant info.

  • Implication: Traditional UI metrics, such as load times and animations, may soon take a back seat to AI responsiveness and contextual understanding.

· 8 min read
Carl Liu

Definition of Liberal Arts: Liberal Arts is intended to provide chiefly general knowledge and to develop general intellectual capacities (such as reason and judgment) as opposed to professional or vocational skills.

Growing up in two different countries, China and Canada, I encountered a common trend: faculties were often divided into the Faculty of Arts and Faculty of Science. However, my experience working as an engineer at Presence, a pioneering AR tech startup, has taught me that what is often underestimated in the tech industry is the value of liberal arts education.

In some extreme cases, engineers believe that hard skills like coding are the only skills that matter, while liberal arts education is dismissed as irrelevant or impractical. However, I argue that this is a flawed perspective. In fact, liberal arts education can be just as valuable as hard skills for engineers working in the tech industry.

Limitation of Engineering Education

Throughout my academic and professional experience in the technology industry, I have come to recognize three major issues that are rarely discussed.

1. Fixed Reward Mechanism

In academia, technical interviews, and in the industry, the standards for evaluating and rewarding engineers are often fixed. Engineers tend to obsess over code cleanliness, optimization of memory and computation usage, and test coverage. While these standards may contribute to the development of better engineers, they may result in less creative problem-solvers overall. In fact, some experts in the field, like Dan Abramov, have highlighted how an obsession with clean code can be problematic. Although there is value in these standards, they prioritize certain skills over others, and consequently, limit engineers' capacity to be well-rounded creators.

Examples of these reward mechanisms include getting an A in a course because your exam answers were elegant, or landing a job offer because you wrote a perfect algorithm that solved a Hackerrank problem faster than anyone else. Additionally, building a better API product than Stripe does not necessarily mean that people will abandon Stripe and use your product.