AI: The Apex Tech Predator
Way back in 2011, renowned Silicon Valley investor Marc Andreesen declared that “software is eating the world.” History has proven him right. Software didn’t just nibble at the edges of industries; it consumed them wholesale. Taxis gave way to Uber, Blockbuster fell to Netflix and Amazon redefined how the world shops. And there are countless more examples.
The common thread is that software allowed these companies to do more with less. Take Kodak, for example, which at its peak was valued at $30 billion USD with 145,000 employees. Today, Instagram is estimated to be worth more than $100 billion, and it achieved this valuation with only about 20,000 employees. Software, and software companies, became the predator at the top of the business food chain.
However, every predator eventually meets its match, and nearly 15 years later, software itself is being eaten. The new apex predator is artificial intelligence. In this blog, I will chart AI’s rise to the top of the tech chain and what implications it has for quality assurance and software testing.
Vibe coding, AI developers and software built on intent
Today, software developers no longer type out every line of code; they simply describe what they want, and AI copilots deliver it. Google’s 2025 DORA report revealed that 90% of developers are using AI at work. We are entering the age of vibe coding, where intent is enough to generate working software. Of course, there will be some issues to iron out and it might need a few attempts to get the code right, but developers have always had an appetite to move fast. Generative AI is enabling them to move faster than ever.
Agentic AI is the next step in this evolution. As these systems mature, the balance will shift from humans working with copilots to humans supervising agents. Eventually, this will progress to autonomous agents with decision-making and self-healing capabilities coding independently.
This will also change the way we think about roles in development. Instead of Java developers or Python developers, everyone will be an AI developer, using natural language to instruct machines that handle the syntax and frameworks. We will see entirely new roles, too: AI agent orchestrators or QA specialists dedicated to agentic systems, for example. Coding will be less about learning the quirks of a language and more about expressing intent clearly and testing the outputs rigorously.
Under the waterfall model, time to market was measured in weeks. Agile and DevOps reduced it to days. AI will shrink time to market once again to just a matter of hours or even minutes. While this is great news for businesses, it has major implications for QA. Speed thrills, but as testers know all too well, speed also kills. If testing cannot keep up, quality will be the first casualty. The traditional QA pipeline simply cannot handle the sheer volume of changes created by a fully autonomous development stream.
Webinar
Developing Reliable Agentic AI: Planning, Testing and Real-World Lessons
QA needs AI to keep pace
However, this is not the end of QA; it is its reinvention. While the speed of AI-powered development creates a challenge for testers, it also presents an opportunity. Our 2025 State of Digital Quality in Functional Testing survey found that 60% of respondents are using AI in their testing process. Testers must embrace AI not as a threat but as a tool.
Testers must use AI to combat the quality bottleneck. Some key use cases include:
- Test case generation: AI analyzes requirements and existing code patterns to automatically create new, relevant and comprehensive test cases, reducing manual effort.
- Defect prediction: Machine learning algorithms analyze historical test data to predict potential defects in codebases, allowing testers to focus their efforts proactively.
- Visual testing: Using computer vision, AI automatically detects visual changes, bugs and regressions in the user interface, helping ensure quality across devices and browsers.
- Test data generation: AI generates vast amounts of realistic, privacy-compliant synthetic data on demand, avoiding the bottlenecks and security risks associated with using production data.
- Autonomous automation: Agentic AI tools can self-heal broken scripts and independently adapt test logic to changes in the UI or underlying code, drastically reducing test maintenance time.
At the same time, the AI itself needs to be tested, which requires human-in-the-loop oversight. To do this, new methodologies must come to the forefront: red teaming to probe vulnerabilities, benchmarking to measure consistency, bias and ethics checks to guard against discrimination, and drift monitoring to ensure models do not degrade over time.
Crucially, this new reality means that QA is no longer just about ensuring code works; it’s also about ensuring the AI is working correctly, fairly and reliably.
Report
State of Digital Quality in AI 2025
Human oversight remains key
Amid the rise of AI, there is a quieter danger: cognitive offloading. If developers and testers outsource too much judgment to machines, we risk losing the very skills that make humans valuable in the first place. Critical thinking, risk analysis and root cause investigation are vital: they cannot be delegated away. AI may generate code at breathtaking speed, but it is still prone to hallucinations, blind spots and a remarkable lack of common sense.
That’s where human oversight is key. Getting the best results means guiding AI rather than instantly accepting its responses, experimenting with it while using human judgment and creativity, and always validating AI systems and their output.
Is your QA team struggling to keep up with the pace of AI-assisted development? Contact us today to see how Applause can help.
5 min