Pay for Thinking, Not Typing, Viveta Gene, PhD on Building Quality with Humans and AI

Introduction

Viveta Gene, PhD is Head of Global Localization Solutions at Intertranslations and a researcher focused on human-in-the-loop, machine translation, and AI. In our conversation, she explains why quality in localization improves when teams measure and reward cognitive work, not just keystrokes, and how buyers can scope and pay for value instead of velocity. We also look at Greece’s role in the global market, the shape of its language industry, and what it takes to build resilient teams and outcomes.

Main Insights and Highlights

A career built across roles, with a 360 view of localization

Viveta’s path spans translation, project management, vendor management, and leadership, which gives her a practical view of how decisions upstream affect quality downstream. That broad experience fuels her people-first approach and informs how she designs processes that work in the real world.

From PhD to postdoc, research that values cognitive effort

Her research centers on compensation models that emphasize thinking over typing. Instead of paying only by word or edit distance, she proposes a structure with a base rate plus weighted factors tied to text difficulty and the effort required to reach the target quality. The aim is simple, align incentives with the cognitive work that prevents errors.

How the model works in practice

The method begins with accuracy and fluency measures, then layers training and keystroke-based metrics to estimate effort. Out of that comes a training plan and a compensation curve that pays more when the task’s complexity or risk rises. In Viveta’s view, fair models can increase balance and satisfaction across teams while protecting outcomes.

Human in the loop is not a slogan; it is a design choice

Technology keeps improving, yet quality still depends on professionals who guide memories, correct MT output, and shape model behavior. Tools are means; the outcome is what matters. Use AI, and design for human judgment where nuance and risk live.

Greece in the global market, partnerships, and potential

Greece mirrors much of Southern Europe with a handful of larger providers and many small firms and freelancers. Opportunity flows from globalization and partnership, and outcomes depend on leaders willing to invest in innovation and international standards. Talent is there; process plus partnership unlocks it.

Associations and a research lane

Greece has translator-focused associations along with a translation studies association that brings researchers together. That ecosystem supports both practitioners and academics, a foundation that matters when you test new models for training and compensation.

Buyer playbook, scope, and measure what actually moves outcomes

Buyers who want durable quality should scope for outcomes instead of word count, measure cognitive effort during post editing and review, and reserve space for expert judgment. The practical order is, prove your current motion works, then automate to scale. Tools amplify fundamentals; they do not replace them.

Why paying for thinking protects quality

A word rate pushes everyone to optimize speed. A model that pays for difficulty and risk invites analysis, better intent matching, and early error detection. Technology vendors have a chance to help operationalize the shift so that fair pay and quality reinforce each other.

The human outcome that matters

Underneath the metrics is a simple principle: teams do better when professionals feel their cognitive work is recognized. When people are paid fairly and work happily, balance returns and quality hold, even as tools change.

Conclusion

Localization teams succeed when compensation and measurement align with the work that prevents mistakes. Viveta Gene’s model pays for thinking, not just typing, and her broader message is clear: design humans in the loop on purpose, scope for outcomes, and use AI to accelerate what humans do best. As budgets tighten and tools get smarter, buyers and providers who adopt these practices will see fewer rework loops and more trust in the results.

Watch the full interview

https://youtu.be/hnqBVF4az2E

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