Created on 2025-06-06 20:06
Published on 2025-06-07 04:00
Over the last thirty years, the software industry has become more powerful and pervasive—but also more complex. That complexity has largely stemmed from a persistent gap in user interface and user experience design. In response, a host of specialist roles emerged—systems integrators, support engineers, onboarding teams, and more—whose primary job was to help users cope with software’s friction.
Now, we’re at a watershed moment. Large Language Models (LLMs) and generative AI have introduced a long-missing component into the computing stack: natural language as a UI/UX primitive. This isn’t a minor improvement. It’s a tectonic shift.
Natural language radically reduces the barriers to software use. Complex interfaces, scripting, and even command-line knowledge can be replaced by simple conversation. In plain terms:
Installation? Simpler.
Usage? Smoother.
Support? Increasingly self-service.
We’re finally seeing a reversal in the historic pattern of humans learning machine syntax. Now, machines are learning ours.
Despite the ease-of-use revolution, LLMs are not to be blindly trusted. They are not deterministic systems and not reliable sources of truth. They are language prediction models—powerful, yes—but still prone to hallucination, bias, and inconsistency.
This introduces a non-negotiable operational principle for this new AI-powered stack:
Never trust. Always verify.
This is not optional. It’s structural. And ignoring it creates massive risk.
This is where things take a hopeful turn. Just as previous computing shifts created entire job categories—from spreadsheet auditors to database admins—the AI era is creating demand for Verifiers.
These are professionals focused on validating, guiding, and grounding LLM outputs within organizational and ethical boundaries:
Prompt designers and safety verifiers who shape input for clarity and reduce harmful or misleading outputs.
Knowledge graph curators and fact-checkers who ensure that model outputs are grounded in trusted data.
Human-in-the-loop reviewers who act as decision and ethics buffers for AI-influenced operations.
AI UX designers and workflow overseers who ensure that natural language interfaces are both useful and safe.
This isn’t about job loss—it’s about job evolution. Manual support and integration roles may fade, but in their place we’ll see a rise in oversight, context-building, and orchestration roles.
The history of computing is the history of abstraction:
From binary to assembly.
From terminals to graphical interfaces.
From scripting languages to automation platforms.
And now, from structured commands to natural language dialogue.
Each step has made computing more accessible—and each has come with new vulnerabilities, new dependencies, and new responsibilities.
AI is no different. In fact, it may be the most powerful—and most dangerous—abstraction yet.
If we fail to adapt, if we delegate blindly, or if we stagnate in legacy thinking, this shift could tip the balance of control in ways we’re unprepared to manage.
This is not just a technical evolution. It’s a societal one. And those who move early—who learn how to harness LLMs, verify outputs, and embed safety and trust into their AI systems—won’t just thrive. They’ll help safeguard the rest of us.
This is the work now:
To embrace the power of AI, without surrendering to it.
To build new tools, and new roles, that ensure trust is earned—not assumed.
To balance innovation with accountability.
To create software that’s not only easier to use, but also safer, more transparent, and more human-centric.
This article was triggered by this post by Reuven Cohen