AI, JavaScript, and the End of the Website

▶️ Video Guide: AI, JavaScript, and the End of the Website

Note: This video and podcast was generated using AI, adapting the original content and technical insights created by the author of the iJS blog post.

▶️ Podcast Guide: AI, JavaScript, and the End of the Website

Post-AI Shift: What Is Changing?

The foundations of web development remain remarkably stable. Modern web applications are still built with HTML, JavaScript, and CSS. What is changing is not the technology stack itself, but how software is created and who can create it.

Framework expertise remains valuable, but AI increasingly lowers the barrier to entry. Knowledge that once required years of hands-on experience is now embedded in the models developers use every day. With a solid understanding of software engineering principles and effective use of AI tools, developers can work productively across frameworks, languages, and ecosystems that may not be their primary area of expertise.

At the same time, AI is increasing the importance of genuine expertise. Models can generate code, explain APIs, and suggest solutions, but they cannot reliably judge whether a design decision is appropriate, a security risk is acceptable, or an architectural trade-off makes sense in a particular context. The more capable AI becomes, the more valuable expert judgment becomes as the mechanism that validates, challenges, and guides its output.

How AI Is Reshaping Fullstack JavaScript

Fullstack as a discipline won’t go away. It’s gaining an additional dimension. Frontend, backend, and AI each have their own focus, but borders between expertise areas are getting blurrier. Frontend engineers can now deploy applications more easily, implement backend APIs, and cross into areas they previously couldn’t.

If an app’s only server dependency is AI inference, that inference could now move to the frontend, potentially eliminating the server entirely in some cases. Small, focused apps that previously needed a backend just to proxy an API call can now run the whole thing client-side. That’s a genuine architectural change, not just a blurring of roles. But the backend model overall will remain. It may mediate AI inference or connect to a cloud provider.

The question is no longer “are you a frontend or backend developer?” but “where does each piece of logic actually belong, and what’s the most appropriate place to run it?” AI adds a third answer to that question that didn’t exist before.

Learn more about how AI changes software development at iJS New York (September 28 – October 2, 2026):

• Signals, Standalone, and Super-Brains — Hrushikesh Pokala
  How AI agents can modernize Angular applications at scale.
• Unlocking Agentic Engineering with Spec-Driven Development — John Riviello
  A practical approach to structuring AI-assisted software development.
• Full-Stack AI Engineering in TypeScript Bootcamp — Nir Kaufman
  Build production-grade AI systems with TypeScript and LangChain.

Does AI Introduce a New Layer of Architectural Complexity in JavaScript?

AI doesn’t alter JavaScript itself, as basic principles stay the same. It changes how systems are extended and orchestrated. Christian Liebel, frontend specialist and frequent iJS speaker, argues that we should think of “AI as an addition to JavaScript, not a transformation of it.”

In the monolithic era, you added code blocks. In the microservices and micro-fronted era, you added services. Now, you add tools and agents. The core idea is similar, but the connectors, dispatching, and routing logic are different.

Sebastian Springer, a React and Node.js expert and regular iJS speaker, argues that AI introduces a new type of architectural component into modern applications. “Usually, you program the decision into your code: is it true or false, is it A, B, or C? Now, you have a smart node in your application, and this makes the decision. So your software becomes much more flexible, much more dynamic.”

Learn more about how AI changes software architecture at iJS New York (September 28 – October 2, 2026):

• Contract-First APIs in the Age of AI Agents — Chad Green
  Why AI agents require a new generation of APIs.
• The Good, the Bad, and the Agentic: Node.js Backends Are the New Wild West — Laurie Lay
  How backend architectures evolve when agents become active consumers.
• Engineering Interactive AI with WebMCP and MCP Apps — Gil Fink
  Building systems that can act, render, and interact.

Where Does AI Inference Fit in Server-Side JavaScript?

Fundamentally, calling an AI is just another HTTP call to an external service. It’s another tool and another abstraction layer. What’s different now is that instead of structured data, you exchange naturally formulated prompts and then have to force the output back into structure to make it machine-processable. However, the challenge consists of enforcing reliable structured output from inherently probabilistic systems.

Streaming APIs are critical, Liebel adds, and many developers are not yet comfortable building backends around them. Node.js now has the Web Streaming API implemented. It allows sharing streaming code between client and server, which is a significant practical improvement.

AI in the Browser

AI in the browser isn’t just the big hype of generative AI. It includes traditional machine learning. Background blurring in Google Meet, for instance, is a long-established example of an AI model running locally. The main question here is, “where is the AI model executed?” There are two approaches:

Approach 1: Bring your own AI

The website brings its own pre-trained model (e.g., from Hugging Face open-weights models) and runs it inside the JavaScript engine already present in the browser. Relevant APIs include WebGPU and WebNN. On the frameworks side of things, you can use Apache TVM, Transformers.js, or ONNX Runtime Web. 

Approach 2: Built-in AI

The browser itself holds the model and runs it. The developer picks the use case, not the model. This is currently implemented in Chrome and Edge. Chrome uses Gemini Nano, and it’s downloaded into the browser and executed on the device. The prompt API is the language model interface for this approach.

Tradeoffs between the two approaches

“Running AI locally is sort of edge AI, meaning on the user’s device itself. Nobody else can see it. It’s offline-capable and very good for privacy,” says Christian Liebel.

When Local AI Beats the Cloud

There are some cases where local/in-browser AI makes more sense than cloud inference. Liebel identifies the following four points:

• Cost: No cloud inference costs. This is relevant for companies and hobbyists who want AI features but can’t afford per-user inference fees.
• Latency: Example: background blurring. Sending video frames to a server and back would be too slow. Local processing is near zero latency.
• Privacy: Prompt and input data never leave the device.
• Offline capability: Works regardless of connection state.

Security and Privacy in AI-Powered Web Apps

We need to make a distinction between two security dimensions: AI-assisted attacks, when attackers use AI to find vulnerabilities, and AI-introduced vulnerabilities that emanate from integrating AI into applications.

AI models are good at pattern detection and can find vulnerabilities much more effectively than humans. The errors aren’t necessarily more numerous, they’re just more detectable. Anthropic’s Mythos project is a striking example of this. It uncovered a bug in BSD, a widely used operating system, that had gone undetected by humans for roughly 30 years. This is a reminder of how fragile the IT world actually is and how much AI can see what we can’t.

A practical countermeasure for AI-assisted attacks, suggested by Springer, is to make AI-based security analysis a standard part of your software development process. Use the tools your attackers would use before they can use them against you. AI-assisted vulnerability scanning should be part of your pipeline.

These security concerns exist because AI is increasingly becoming part of the application itself. That raises another question: if AI becomes a native building block of web applications, does it also change what an application looks like?

From Websites to AI-Native Applications

Applications have traditionally been organized around pages. Users navigate through menus, search results, forms, and workflows to reach the information or functionality they need. AI-powered interfaces challenge that model.

Nir Kaufman, fullstack developer, AI instructor and keynote speaker at iJS, describes a future in which users no longer navigate complete applications. Instead, they interact with an AI assistant that guides them through a conversation while dynamically assembling “the entire website chopped into tiny pieces and streamed to me.” A map appears when location matters, a booking component when a reservation is needed, and a payment widget when it’s time to complete a transaction.

This represents more than a new frontend pattern. It changes the role of the application itself. Rather than guiding users through pages and navigation structures, applications expose capabilities that can be invoked as part of an ongoing conversation. The website becomes less of a destination and more of a collection of services, data, and interaction components that an AI assistant can draw upon when needed.

For developers, Kaufman believes this may require a different way of constructing applications. Instead of organizing software around pages, routes, and navigation hierarchies, applications may increasingly be built as collections of reusable components that can be selected and assembled dynamically. In this model, developers design capabilities and interactions rather than predefined user journeys.

Emerging approaches such as Google’s Agent-to-UI (A2UI) protocol explore this idea. Agents describe interface elements as structured objects, while clients render the appropriate user interface on demand. Rather than sending complete screens, systems can stream focused interaction components into an ongoing conversation as they become relevant.

Viewed this way, AI is not simply another feature added to existing systems. It becomes a new access layer between users and applications. The architectural challenge shifts from designing pages and user flows to designing capabilities that can be discovered, combined, and orchestrated dynamically.

Learn more about how AI changes web applications at iJS New York (September 28 – October 2, 2026):

• Engineering Interactive AI with WebMCP and MCP Apps — Gil Fink
  Moving beyond text-only interactions toward AI-native interfaces.
• Full-Stack AI Engineering in TypeScript Bootcamp — Nir Kaufman
  Building applications designed around agents, tools, and conversations.
• Designing with Intent: A New Layer for AI-Generated UI — Selam Moges
  What happens when interfaces are assembled dynamically around user intent?

Designing AI-Native User Experiences

If AI becomes a new access layer to applications, the next challenge is no longer primarily technical. It is about designing interactions that people can understand, trust, and control.

Christian Kuhn, UX researcher and consultant, approaches this question through a set of six design principles. Rather than treating AI as a feature, he focuses on how people collaborate with intelligent systems. The principles emphasize human agency, empathy, transparency, controllability, and the ability to recover from mistakes.

This perspective starts with a fundamental shift in how interactions are conceived. As Kuhn puts it, “The central unit of interaction is no longer the page but the conversation itself.” Users increasingly express goals, preferences, and constraints, while the system determines how best to support them. The challenge for designers is therefore not only to create interfaces, but also to shape the interaction between human intent and machine capabilities.

Kuhn’s principles provide a practical framework for this challenge:

• Human First: AI should amplify human capabilities rather than replace human judgment.
• Empathy First: Systems should understand user context and needs instead of forcing users to adapt to the machine.
• Automation vs. Augmentation: Not every task should be fully automated. Users often benefit from remaining active participants in the process.
• Transparency and Confidence: Users need to understand where information comes from and how reliable it is.
• Control and Editability: AI-generated results should remain editable, reversible, and subject to user control.
• Mental Models and Graceful Failure: Systems must help users understand how they work and provide clear recovery paths when mistakes occur.

Several of these principles become increasingly important as AI systems move beyond the chat interface. In Kuhn’s view, “Text writing is de facto the most exhausting and worst input mode.” As voice, vision, and contextual awareness continue to mature, conversational experiences are likely to expand far beyond typing into a text box.

For UX designers, this creates a new design discipline. The goal is no longer limited to optimizing screens, navigation paths, or workflows. It increasingly involves designing how people and AI systems cooperate, communicate, and recover when things go wrong.

Learn more about how AI changes user experience at iJS New York (September 28 – October 2, 2026):

• Designing with Intent: A New Layer for AI-Generated UI — Selam Moges
  How AI-generated interfaces challenge traditional UX assumptions.
• Engineering Interactive AI with WebMCP and MCP Apps — Gil Fink
  Designing interactions that combine conversation with dynamic UI elements.

Where Is Web Development Heading?

The architecture of web applications is changing. So is the way users interact with them. As AI becomes part of the web stack, questions that once belonged primarily to developers increasingly overlap with concerns traditionally associated with architects, product teams, and UX designers. Decisions about inference, interfaces, workflows, and user guidance can no longer be treated as separate domains.

This makes collaboration more important, not less. Building AI-powered web applications requires technical, architectural, and experiential perspectives to work together much earlier in the process than many teams are accustomed to today.

At the same time, the emergence of AI does not invalidate the foundations of software engineering. If anything, it reinforces them. Clean architectures, clear documentation, reliable automation, comprehensive testing, strong observability, and security that is considered from the beginning rather than added later remain essential. As systems become more dynamic and more complex, these disciplines become increasingly important.

For web developers, architects, and UX designers alike, the challenge is therefore twofold: to remain open to new architectural and interaction models while continuing to apply the engineering principles that have always been necessary to build reliable software.