The Applied Scientist’s Identity Crisis: Why We’re More Than Model Builders

Published on Medium: 2026-04-11 — https://medium.com/@danithaca/the-applied-scientists-identity-crisis-why-we-re-more-than-model-builders-1472f1cf3516

As someone working in Agentic AI at AWS, I’ve been asked countless times: “If you’re not building models, what makes you an applied scientist rather than a software engineer?” It’s a fair question that reflects a common misconception about what applied scientists actually do — especially in an era where large language models (LLMs) have fundamentally transformed our field.

The Three Eras of AI

Peter Norvig, co-author of the seminal textbook “Artificial Intelligence: A Modern Approach,” has articulated a compelling framework for understanding AI’s evolution through three distinct eras (reference):

1. Algorithm-Centered Era

I still remember 20 years ago when AI was in its algorithm-centric golden age. AI and machine learning courses required mastering a diverse toolkit: Support Vector Machines, Decision Trees, Random Forests, and countless other techniques. Each problem demanded careful consideration of which algorithm to apply.

2. Data-Centered Era

With the rise of Deep Neural Networks and GPU computing, the algorithmic landscape consolidated dramatically. Instead of choosing between fundamentally different approaches, practitioners focused on adjusting network architectures, tuning hyperparameters, and — most importantly — curating better datasets. Algorithms converged toward variations of deep learning, and data became king.

3. Human-Centered Era

Today, with the proliferation of LLMs, even the data-centric paradigm is shifting. Pre-trained models handle much of the heavy lifting, and AI can even write code. As Andrew Ng has noted in his discussion of the “Product Management Bottleneck”, the critical question has become: “What needs to be built?”

The Identity Question

In my current role working on human-centered AI, I spend significant time understanding customer pain points, developing evaluation frameworks, designing metrics, and building dashboards. I haven’t been directly involved in model development for quite some time. This naturally raises the question: if these activities — customer engagement, evaluation, dashboard development — are things software engineers also do, and if I’m not building models, what distinguishes an applied scientist from a software engineer?

The Flawed Narrative

The assumption that “applied scientist = model builder” is as reductive as saying “software engineer = code writer.” Just as software engineers are fundamentally concerned with building highly available, secure, scalable, and efficient systems — not merely writing code — applied scientists have a deeper purpose that transcends model development.

Redefining the Applied Scientist

The core mission of an applied scientist is to research the unknown and answer questions that no one has answered before.

This involves:

Defining meaningful research questions that address real-world problems
Forming hypotheses based on experience, intuition, and domain knowledge
Rigorously testing these hypotheses through systematic experimentation
Building frameworks and methodologies that provide answers to previously unanswerable questions

Training or building models using established methods, while certainly involving modeling work, is more accurately characterized as ML engineering. Applied science, by contrast, is about venturing into uncharted territory — creating new knowledge, not just applying existing techniques.

The Path Forward

In the LLM era, applied scientists have an opportunity to focus on what we do best: asking the right questions, designing rigorous experiments, and generating insights that push the boundaries of what’s possible. The tools may have changed — from hand-crafted algorithms to pre-trained models — but the fundamental mission remains the same.

We are not defined by the models we build, but by the unknowns we illuminate.

What are your thoughts on the evolving role of applied scientists? How has your work changed in the age of LLMs? I’d love to hear your perspectives in the comments.