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title: Tester 2.0: becoming indispensable in the age of AI
description: Testers won't automatically get job security just because AI produces more code, Filip Hric argues. He identifies three comforting lies: that a 'golden age' of...
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og:description: Testers won't automatically get job security just because AI produces more code, Filip Hric argues. He identifies three comforting lies: that a 'golden age' of...
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# Tester 2.0: becoming indispensable in the age of AI

**[Filip Hric](https://daily.dev/sources/filiphric)** · 14 min read · 0 upvotes · 0 comments

## Summary

Testers won't automatically get job security just because AI produces more code, Filip Hric argues. He identifies three comforting lies: that a 'golden age' of testing hiring is coming, that quality inevitably worsens without testers, and that things will stay the same. He urges testers to abandon avoiding technical expertise, treating critical thinking as their only important tool, and treating test automation as their only tool. Instead he describes a 'generation and verification' workflow he uses himself, with a separate review agent, and points to roles like eval engineer and forward-deployed engineer as places where testing skills transfer. A skill-file experiment burned extra tokens without really improving results.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://filiphric.com/tester-2-becoming-indispensable-in-the-age-of-ai>

## Questions this post answers

### Should QA testers learn to code and understand technical systems in the age of AI?

Yes, testers should become technically proficient rather than avoiding it. Technical understanding is treated as a prerequisite for assessing quality in other engineering fields like motorsports and aviation, and LLMs reward users with more expertise, since a senior developer gets noticeably better results from AI than someone casually vibe coding without understanding the problem.

_Developers weighing how deep to go on AI tooling can track these skill shifts on daily.dev._

### How can I use AI agents to catch bugs before running end-to-end tests?

Split work into generation and verification: one coding agent implements a ticket, while a separate review agent (often a different model) checks whether the ticket was fully implemented, whether rules were followed, and whether there are bugs, security issues, or wider blast radius effects. This review can run on uncommitted changes, implementation plans, or even the ticket itself, giving feedback before end-to-end tests run.

_Teams redesigning their test feedback loops with AI agents can follow workflow write-ups like this on daily.dev._

### Does adding a skill file to an AI coding agent actually improve its output?

Not necessarily. In one comparison of an agent working with a skill file versus without it, token usage and response time were measured alongside output quality, and the team mostly burned a lot more tokens without meaningfully improving the response, showing that adding a skill file doesn't guarantee better results.

_Anyone evaluating AI agent configurations can compare real-world experiments like this on daily.dev._

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- [AI Isn’t Replacing Testers — It’s Replacing Outdated Testing](https://daily.dev/posts/ai-isn-t-replacing-testers-it-s-replacing-outdated-testing-gj6chv50o) · Medium · 0 upvotes · 0 comments
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---

Tags: [#career](https://daily.dev/tags/career), [#testing](https://daily.dev/tags/testing), [#ai-coding](https://daily.dev/tags/ai-coding)

[View this post on daily.dev](https://daily.dev/posts/tester-2-0-becoming-indispensable-in-the-age-of-ai-liomahwgt)

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