It feels like AI is competing against itself but in two very different forms.
On one side are standalone AI products like ChatGPT, Claude, Gemini, and Perplexity. On the other are AI-powered features quietly making the software we already use smarter.
The question isn’t whether AI is the future. It’s whether the future belongs to AI as a product or AI as a feature.
This debate was recently highlighted by popular tech creator MKBHD (Marques Brownlee), who compared AI’s evolution to the rise of social media Stories. Snapchat pioneered Stories as its core product, but the feature eventually spread across Instagram, Facebook, WhatsApp, LinkedIn, and countless other platforms. While Snapchat introduced the idea, billions of users ultimately experienced Stories through products they were already using.
AI appears to be following a remarkably similar path.
When ChatGPT launched in late 2022, it became one of the fastest-growing consumer applications in history. It proved that people were willing to use AI as a destination — for writing, coding, research, brainstorming, and problem-solving. Since then, products like Claude, Gemini, Perplexity, Mid journey, and others have shown that AI-native applications can attract millions of users on their own.
At the same time, some of the world’s largest technology companies have taken a different approach.
Rather than building separate AI applications, they’re embedding AI into products people already rely on every day.
Microsoft integrated Copilot across Microsoft 365, Windows, GitHub, and Dynamics. Google brought Gemini into Workspace, Android, Search, and Chrome. Adobe added Firefly to Creative Cloud. Salesforce introduced Einstein AI across its CRM platform. Notion, Canva, Atlassian, Slack, Zoom, and many SaaS companies have done the same.
Perhaps the most interesting example is Apple.
When Apple entered the AI race, it didn’t launch a ChatGPT competitor. Instead, it introduced Apple Intelligence a collection of AI capabilities deeply integrated into iPhone, iPad, and Mac. Apple made AI part of the operating system rather than a separate destination.
That decision says a lot.
If one of the world’s most valuable technology companies believes AI creates the most value inside existing products, it reinforces a growing industry trend: users don’t necessarily want another AI application — they want the software they already use to become more intelligent.
This approach also lowers the barrier for software companies.
Training foundation models requires enormous investments in infrastructure, GPUs, research teams, and ongoing optimization. For most businesses, building their own large language model simply isn’t realistic.
Instead, they’re leveraging models from providers like OpenAI, Anthropic, and Google through APIs while focusing on what actually differentiates their products: solving customer problems, streamlining workflows, and delivering better user experiences.
It’s similar to how cloud computing evolved.
Few companies build their own cloud infrastructure today. They build products on top of cloud platforms because infrastructure is no longer the competitive advantage.
AI models are rapidly becoming that new infrastructure.
The value increasingly comes from how intelligently AI is integrated into products not just from the model powering it.
Does that mean standalone AI products will become less important?
Not at all.
Products like ChatGPT, Claude, Gemini, and Perplexity continue to push the boundaries of reasoning, coding, creativity, research, and AI agents. They introduce capabilities that often make their way into productivity software, developer tools, and enterprise applications months later.
In many ways, AI products drive innovation, while AI features drive adoption.
Rather than replacing one another, the two approaches are creating a cycle where each makes the other stronger.
What Does This Mean for Software Testing?
The same shift is happening in software testing.
For years, automation transformed QA by reducing repetitive manual work. AI is now taking that evolution a step further — not just automating testing, but making it smarter.
Some companies are building AI-native testing platforms where AI sits at the center of the entire testing lifecycle. Others are embedding AI capabilities into established testing tools, helping teams improve their existing workflows without replacing them.
Today, AI is being used to support testing through capabilities such as:
- AI-generated test cases
- Codeless automation
- Dynamic test selection and prioritization
- Self-healing automation
- AI-powered visual testing
- Failure analysis and root cause detection
- Intelligent test maintenance
- The industry reflects both approaches.
Platforms like Mabl position themselves as AI-native testing solutions, while companies such as SmartBear, QAlity and Applitools have integrated AI into mature testing ecosystems. Both strategies address different customer needs, and both are contributing to the evolution of modern software quality.
Ultimately, the question isn’t whether AI products or AI features will win.
The future of AI isn’t about choosing one over the other.
Standalone AI products will continue expanding what’s possible. AI-powered features will make those breakthroughs accessible inside the applications people already use every day.
The real winners won’t be the companies building the biggest models or adding the most AI features.
They’ll be the ones that solve meaningful problems and create better experiences for their users — whether AI is the product itself or simply the feature that makes the product indispensable.
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