Vibium: A Vision for Testing’s AI-Powered Future

Jason Huggins’ Vision for AI-Powered, Globally Distributed Testing

Disclaimer: As of August 31, 2025, “Vibium hasn’t been officially released yet”, it’s in the Proof of Concept stage. vibium.ai is “a demo site with demo data”, currently not “meant for wide distribution”.

𝚃𝚊𝚋𝚕𝚎 𝚘𝚏 𝙲𝚘𝚗𝚝𝚎𝚗𝚝𝚜:

• From “Click This Button” to “Test Everything” — How AI is Democratizing Quality Assurance
• The Man Who Built the Testing Industry
• The Problem with Testing Today
• Enter Vibium: Testing That Speaks Human
• Two Interfaces, One Vision
• The Infrastructure Revolution
• Real Devices, Real Networks, Real Results
• The Trust Challenge
• AI’s Current Reality Check
• The Open Source Foundation
• From Vibe Coding to Vibium
• The Democratization Promise
• Challenges Ahead
• Beyond Self-Healing: Adaptive Navigation for Tests
• The Bigger Picture
• What This Means for You
• The Road Ahead
• Testing’s Next Chapter

𝙵𝚛𝚘𝚖 “𝙲𝚕𝚒𝚌𝚔 𝚃𝚑𝚒𝚜 𝙱𝚞𝚝𝚝𝚘𝚗” 𝚝𝚘 “𝚃𝚎𝚜𝚝 𝙴𝚟𝚎𝚛𝚢𝚝𝚑𝚒𝚗𝚐” — 𝙷𝚘𝚠 𝙰𝙸 𝚒𝚜 𝙳𝚎𝚖𝚘𝚌𝚛𝚊𝚝𝚒𝚣𝚒𝚗𝚐 𝚀𝚞𝚊𝚕𝚒𝚝𝚢 𝙰𝚜𝚜𝚞𝚛𝚊𝚗𝚌𝚎

Imagine walking into a boardroom and saying, “Test our healthcare portal on everything we’ve got!”, then watching as comprehensive testing unfolds across dozens of devices worldwide. No scripts to write, no frameworks to configure, no technical team to coordinate. Just plain English instructions that somehow become reality.

This isn’t science fiction. It’s Vibium, the latest innovation from Jason Huggins, the creator of Selenium and Appium — the tools that power virtually every automated test running on the internet today.

𝚃𝚑𝚎 𝙼𝚊𝚗 𝚆𝚑𝚘 𝙱𝚞𝚒𝚕𝚝 𝚝𝚑𝚎 𝚃𝚎𝚜𝚝𝚒𝚗𝚐 𝙸𝚗𝚍𝚞𝚜𝚝𝚛𝚢

Before diving into Vibium, it’s worth understanding who Jason Huggins is and why his latest project matters. In 2004, Huggins created Selenium when web testing meant manually clicking through browsers for hours. In 2012, he built Appium when mobile testing was still a manual nightmare. Both tools became the invisible infrastructure powering billions of automated tests.

But Huggins isn’t content with past achievements. After a detour into robotics (yes, actual robots that could tap phone screens), he’s now tackling what might be testing’s final frontier: making it accessible to anyone who can describe what they want to test.

𝚃𝚑𝚎 𝙿𝚛𝚘𝚋𝚕𝚎𝚖 𝚠𝚒𝚝𝚑 𝚃𝚎𝚜𝚝𝚒𝚗𝚐 𝚃𝚘𝚍𝚊𝚢

Current testing approaches suffer from a fundamental disconnect. On one side, you have technical teams writing complex automation scripts that take weeks to develop and constantly break when applications change. On the other side, you have business stakeholders who just want to know if their software works but can’t bridge the technical gap to get answers.

The result? Critical bugs slip through because testing is either too slow, too expensive, or too technically complex to catch everything that matters.

Huggins witnessed this firsthand during the Healthcare.gov crisis in 2013. While technical teams worked on sophisticated solutions, a congressman’s inability to click a simple “Create Account” button revealed the real problem — a corrupted cookie that broke the entire user registration flow. As he recalls:

“Nobody could even tell us if the system was up as we were sitting there in the Oval Office except by taking out our laptops and trying to go on it.”

This experience crystallized a key insight for Huggins:

“Someone — if it’s not you — it’s going to be Representative Senator Lankford who’s going to be going on your site clicking the button and calling you in the oversight committee, ‘Just nail that, and then we can get all the other stuff right. And if we aren’t nailing this, then what are we doing?’.”

Sometimes the most complex systems fail on the simplest interactions. After decades in the field, Huggins reflects:

“Sometimes I think like … my career … I can’t believe I’ve spent more than 20 years now. You would think buttons would be a solved problem by now. And literally my entire career you could put on my tombstone like ‘he clicks buttons’.”

𝙴𝚗𝚝𝚎𝚛 𝚅𝚒𝚋𝚒𝚞𝚖: 𝚃𝚎𝚜𝚝𝚒𝚗𝚐 𝚃𝚑𝚊𝚝 𝚂𝚙𝚎𝚊𝚔𝚜 𝙷𝚞𝚖𝚊𝚗

Vibium represents a fundamental shift in how we think about testing. Instead of requiring technical expertise to create tests, it accepts natural language instructions and translates them into comprehensive testing scenarios.

Want to test your e-commerce checkout flow? Instead of writing dozens of lines of code, you simply say: “Make sure customers can buy products on mobile devices in major cities.” Vibium’s AI interprets this request, identifies the critical user paths, and executes tests across real devices in those locations.

This simplicity is deliberate. As Huggins notes:

“If you’re going to make a startup or a website or whatever, you are going to succeed or fail… success is if you can boil it down into a field and a button. Twitter, Facebook, Google → literally a text field and then a button.”

𝚃𝚠𝚘 𝙸𝚗𝚝𝚎𝚛𝚏𝚊𝚌𝚎𝚜, 𝙾𝚗𝚎 𝚅𝚒𝚜𝚒𝚘𝚗

Vibium offers dual interfaces designed for different needs:

The Technical Interface serves developers and QA engineers who need granular control. They can still write detailed test specifications, configure specific parameters, and access comprehensive debugging information.

The Natural Language Interface serves everyone else.

• Product managers can request “test our new feature on popular Android phones”.
• Executives can ask “verify our site works during peak traffic hours”.
• Support teams can check “does our help page load correctly for international users”.

Both interfaces tap into the same underlying infrastructure, but the experience is tailored to the user’s expertise and needs.

𝚃𝚑𝚎 𝙸𝚗𝚏𝚛𝚊𝚜𝚝𝚛𝚞𝚌𝚝𝚞𝚛𝚎 𝚁𝚎𝚟𝚘𝚕𝚞𝚝𝚒𝚘𝚗

While competitors focus on improving AI interfaces for test generation, Huggins is building something more fundamental: a distributed network of real devices performing authentic testing. His journey to this realization wasn’t straightforward:

“I started a robotics company and now I’m not making robots. I am at a decision point, where if I want to stay with robotics I have to maybe pick a different thing… Or do I stay in the testing world, and maybe my next move is I just I’m not making robots anymore, but I’m doing something else?”

Current cloud testing services operate from centralized data centers, creating artificial conditions that don’t reflect real user experiences. A user in Australia testing through a San Francisco data center faces latency issues that don’t exist for actual Australian users. Simulated network conditions can’t replicate the complexity of real mobile carrier networks during rush hour.

Huggins saw this limitation firsthand:

“Why not any device anywhere, right? It would be nice if there was a data center in Australia, right? And but you can kind of roll this out to everywhere.”

Vibium’s vision is radically different: a global network of actual devices, owned and operated by individuals and organizations worldwide, available for testing on demand. Think “Uber for Testing” — connecting test requests with available devices anywhere in the world.

𝚁𝚎𝚊𝚕 𝙳𝚎𝚟𝚒𝚌𝚎𝚜, 𝚁𝚎𝚊𝚕 𝙽𝚎𝚝𝚠𝚘𝚛𝚔𝚜, 𝚁𝚎𝚊𝚕 𝚁𝚎𝚜𝚞𝚕𝚝𝚜

This distributed approach offers several advantages:

• Geographic Authenticity: Test from actual user locations to identify region-specific issues like CDN problems or regulatory compliance failures.
• Network Diversity: Experience real ISP connections, mobile carrier networks, and Wi-Fi configurations rather than simulated conditions.
• Device Reality: Access devices with genuine user configurations, installed apps, and hardware variations that pristine virtual machines can’t replicate.
• Global Coverage: Reach underserved regions where traditional cloud providers don’t maintain infrastructure.

𝚃𝚑𝚎 𝚃𝚛𝚞𝚜𝚝 𝙲𝚑𝚊𝚕𝚕𝚎𝚗𝚐𝚎

The distributed model introduces what Huggins calls the “stranger danger” problem. Just as early ride-sharing faced skepticism about getting into strangers’ cars, distributed testing must address concerns about remote device access.

“There’s a certain level of like ‘it’s not just technical’. There’s also a social… ‘stranger danger’ to this idea. It’s as crazy as getting in a stranger’s car, staying at a stranger’s house or having a stranger deliver your groceries.”

The risks flow both directions. Test requesters worry about device operators manipulating results or accessing sensitive data. Device operators worry about requesters misusing their devices or exposing them to legal liability.

Solutions likely mirror other trust-based platforms: identity verification, reputation systems, insurance models, and community moderation. The technical challenges are solvable — the social engineering required to build trust at scale represents the harder problem.

𝙰𝙸’𝚜 𝙲𝚞𝚛𝚛𝚎𝚗𝚝 𝚁𝚎𝚊𝚕𝚒𝚝𝚢 𝙲𝚑𝚎𝚌𝚔

Huggins demonstrated current AI capabilities with live testing during his presentation. The results were both impressive and humbling.

When shown a digital clock displaying “6:17”, ChatGPT accurately described it as “A 3D grid of red blocks arranged on a pink base, forming the number ‘6:17’ in a digital clock style.” and correctly identified both the time and that it was indeed a clock display.

But when asked to find the number “1” on a calculator interface, the same AI system consistently failed at this elementary task.

This illustrates a crucial insight: AI excels at pattern recognition and description but struggles with seemingly simple interactive tasks. Vibium’s architecture acknowledges these limitations, positioning AI as a powerful tool for interface interpretation while relying on real infrastructure for authentic functionality verification.

As Huggins puts it:

“AI can lie to you, but does that actually work in the real world? That’s the thing that I’m working on and making it all open source, and awesome, and everything! Everyone can build a company on top of it.”

His focus on infrastructure sets Vibium apart from the competition:

“While everyone is falling over themselves on the AI’s part, and really like it’s all just OpenAI, Microsoft, Google… I’m working on this open-source actual infrastructure thing, because the AI — at some point we’ll have to run it on a real phone somewhere real.”

𝚃𝚑𝚎 𝙾𝚙𝚎𝚗 𝚂𝚘𝚞𝚛𝚌𝚎 𝙵𝚘𝚞𝚗𝚍𝚊𝚝𝚒𝚘𝚗

Following the tradition of Selenium and Appium, Vibium will be open source. This isn’t just philosophical commitment — it’s strategic necessity. Testing infrastructure becomes valuable only through widespread adoption, and open source enables the community-driven development essential for global-scale platforms.

The open source approach also addresses a key concern about AI-generated code. When Huggins revealed that his entire demo platform was built by AI in one week (he wrote no code himself), it highlighted both the potential and risks of AI-assisted development. Open source transparency allows community audit of AI-generated systems, building trust through visibility.

𝙵𝚛𝚘𝚖 𝚅𝚒𝚋𝚎 𝙲𝚘𝚍𝚒𝚗𝚐 𝚝𝚘 𝚅𝚒𝚋𝚒𝚞𝚖

The name “Vibium” plays on “vibe coding” — Huggins’ term for the intuitive, natural language approach to software development that AI enables. He sees this as potentially transformative:

“I have a hunch that vibe coding as a phrase is as genius of a marketing word now as cloud computing was in 2008.”

Just as “cloud computing” seemed like meaningless marketing speak in 2008 but became foundational to modern technology, “vibe coding” might represent the next paradigm shift in how we interact with software systems.

The rapid development of his demo platform exemplifies this shift:

“The scary thing is I didn’t write a line of code for this thing. This is all just been the conversation with me and the AI in the last week. So things have gotten scary good.”

Vibium embodies this philosophy: testing that understands intent rather than requiring precise technical specification. You describe the vibe of what you want to test, and the system figures out how to make it happen.

𝚃𝚑𝚎 𝙳𝚎𝚖𝚘𝚌𝚛𝚊𝚝𝚒𝚣𝚊𝚝𝚒𝚘𝚗 𝙿𝚛𝚘𝚖𝚒𝚜𝚎

If successful, Vibium could democratize testing in unprecedented ways. Small startups could access the same testing breadth as large enterprises. Non-technical stakeholders could verify functionality without depending on engineering teams. Global coverage could become accessible to organizations that can’t afford international infrastructure.

The platform could also identify patterns that might otherwise be missed. As Huggins observes about the timing of automation tools:

“iPhone and Android came out 2007-2008, but Appium was in 2011-2012, right? So there was a several-years-gap, where there were clearly amazing platforms. The automation tools… came on the scene years later. So, if you ever had dreams of creating an automation tool… you’ve got time because there’s literally years to wait.”

Understanding these patterns could help the industry better anticipate and prepare for testing challenges on emerging platforms.

𝙲𝚑𝚊𝚕𝚕𝚎𝚗𝚐𝚎𝚜 𝙰𝚑𝚎𝚊𝚍

Several significant hurdles remain:

• Technical Scalability: Coordinating testing across potentially millions of devices while maintaining performance and reliability.
• Quality Consistency: Ensuring reliable test execution across diverse hardware and software configurations.
• Regulatory Compliance: Operating across multiple jurisdictions with different laws regarding testing, privacy, and financial transactions.
• Market Adoption: Convincing organizations to trust distributed testing over established cloud providers.
• Economic Viability: Building sustainable business models around open source infrastructure.

𝙱𝚎𝚢𝚘𝚗𝚍 𝚂𝚎𝚕𝚏-𝙷𝚎𝚊𝚕𝚒𝚗𝚐: 𝙰𝚍𝚊𝚙𝚝𝚒𝚟𝚎 𝙽𝚊𝚟𝚒𝚐𝚊𝚝𝚒𝚘𝚗 𝚏𝚘𝚛 𝚃𝚎𝚜𝚝𝚜

One of the most controversial aspects of AI-powered testing is the concept of “self-healing tests” — automated systems that attempt to fix broken tests without human intervention. Huggins acknowledges the skepticism this generates among testing professionals who worry about losing control and visibility into what their tests are actually doing.

His solution reframes the problem entirely. Rather than “self-healing,” Huggins proposes what he calls “adaptive navigation” — essentially treating test execution like GPS navigation.

“When I’m driving and there’s an accident, traffic jam, or I miss an exit, Google Maps doesn’t just stop and fail. It reroutes me toward the destination. That reroute isn’t invisible, it’s marked as a detour, but the journey can still succeed.”

This metaphor captures something crucial about how testing should evolve. When a test encounters a changed interface or broken locator, instead of failing immediately, Vibium’s “MAP” (Model for Adaptive Paths) system would attempt to find alternative routes to the same goal.

The key insight is transparency. Just as GPS shows you when it’s taking a detour, adaptive tests would flag any deviations with a “yellow” status — neither pass nor fail, but requiring human review. This maintains tester oversight while preventing the brittleness that plagues current automated testing.

“If the locator changes or a step deviates, the run shouldn’t fail outright as long as the user’s end goal is still reachable. In that case, we’d flag it as yellow (warning), neither green nor red, so it always triggers follow-up review. That way testers maintain oversight without being blocked by brittle failures.”

This approach moves beyond the common but ineffective strategy of simply increasing wait times for elements to appear. As Huggins puts it: “If the ‘road’ is truly blocked, simply waiting won’t help, it’s better to find another viable route.”

𝚃𝚑𝚎 𝙱𝚒𝚐𝚐𝚎𝚛 𝙿𝚒𝚌𝚝𝚞𝚛𝚎

Vibium represents more than a testing tool — it’s a vision of how AI and distributed computing can make sophisticated technology accessible to anyone. The principles underlying Vibium could apply to other domains where technical complexity creates barriers to access.

The platform also addresses a fundamental question about AI’s role in software development. Rather than replacing human expertise, Vibium positions AI as an interface layer that makes human knowledge more accessible. Technical experts still design the underlying systems, but anyone can benefit from their expertise through natural language interaction.

𝚆𝚑𝚊𝚝 𝚃𝚑𝚒𝚜 𝙼𝚎𝚊𝚗𝚜 𝚏𝚘𝚛 𝚈𝚘𝚞

Whether you’re a developer, product manager, or business leader, Vibium’s approach suggests several implications:

• For Developers: Focus on building robust, testable systems rather than maintaining complex testing scripts. AI can handle routine verification, freeing you for strategic testing challenges.
• For Product Teams: Direct access to testing capabilities could accelerate product validation and reduce dependency on technical resources for basic functionality verification.
• For Organizations: Global testing coverage could become economically accessible, enabling better user experiences across diverse markets and conditions.

𝚃𝚑𝚎 𝚁𝚘𝚊𝚍 𝙰𝚑𝚎𝚊𝚍

Despite building something nobody explicitly requested, Huggins remains committed to his vision:

“The weird thing about this is no one has asked me to build it… I’m at the point where I just want to freaking make it. And if even, if only three people use it, I’ll be happy.”

The timeline remains unclear — distributed trust systems and AI reliability take time to mature. But the direction seems clear: testing will become more accessible, more authentic, and more globally distributed.

𝚃𝚎𝚜𝚝𝚒𝚗𝚐’𝚜 𝙽𝚎𝚡𝚝 𝙲𝚑𝚊𝚙𝚝𝚎𝚛

Twenty years after creating Selenium, Jason Huggins continues pushing the boundaries of what’s possible in software testing. Vibium might succeed as envisioned, or it might evolve into something different. Either way, it represents important progress toward a future where ensuring software quality doesn’t require deep technical expertise.

The core insight remains constant across Huggins’ career: whether someone is a congressional representative clicking a button in an oversight hearing, a user in an underserved geographic region, or an AI conducting automated verification, testing must validate real-world functionality for real users in real conditions.

Vibium aims to make that validation accessible to anyone who can describe what they want to test. In a world where software touches every aspect of life, that democratization of quality assurance might be exactly what we need.

Jason Huggins’ work on Vibium is ongoing. While the platform is not yet publicly available, the concepts and demo interfaces showcased suggest significant potential for transforming how we approach software testing. As with any emerging technology, actual implementation may differ from current concepts as development progresses.

🔗 Sources: Testing on 7 Billion Mobile Phones: A Modest Proposal — Jason Huggins at InnovateQA Seattle 2025 + presentation slides.

🐞 𝓗𝓪𝓹𝓹𝔂 𝓣𝓮𝓼𝓽𝓲𝓷𝓰 & 𝓓𝓮𝓫𝓾𝓰𝓰𝓲𝓷𝓰!

P.S. If you’re finding value in my articles and want to support the book I’m currently writing — Appium Automation with Python 📚 — consider becoming a supporter on Patreon. Your encouragement helps fuel the late-night writing, test case tinkering, and coffee runs. ☕

Vibium: A Vision for Testing’s AI-Powered Future was originally published in Women in Technology on Medium, where people are continuing the conversation by highlighting and responding to this story.