LLMs are reshaping the future of programming languages

The success of AI-generated code relies heavily on the volume and quality of training data. And here’s the kicker: LLMs are trained predominantly on open-source code. That means languages like Python and JavaScript, which dominate GitHub, Stack Overflow, and countless open repositories, give LLMs an unfair advantage in mastering them.

These languages are the lingua franca of open-source innovation. Python powers data science notebooks, web backends, and automation scripts. JavaScript owns the frontend and runs across virtually every website. They’re well-documented, widely discussed, and heavily tested in public.

As a result, LLMs are becoming frighteningly good at generating code in these languages—often faster and more accurately than human developers.

The silent struggle of enterprise languages

On the flip side, enterprise-oriented languages like Java, C#, and frameworks like SwiftUI often live behind closed doors. The codebases are proprietary, the architecture complex, and the public examples few and far between. That means LLMs have less to learn from.

Sure, these languages are still supported—but not with the same richness or depth. The result? AI-generated code in these languages is less reliable, more error-prone, and slower to evolve.

In a world where AI-enhanced productivity is becoming the baseline, these slower-moving ecosystems are at risk of stagnation.

Studies show that LLMs have a strong bias toward Python, using it in 90%–97% of cases for language-agnostic tasks, and even when it’s not the best fit, Python is still chosen in 58% of project initialization tasks

The real threat to developers isn’t no-code—It’s AI-powered peers

Many developers worry that AI will replace them. But the immediate threat isn’t from project managers using drag-and-drop builders. It’s from fellow developers who embrace AI tools, master AI-native languages, and build faster than you ever could manually.

If you’re working in a niche language or framework that AI doesn’t handle well, your velocity drops. Meanwhile, your peer who codes in Python with a Copilot-style assistant is shipping 3x faster, refactoring in seconds, and writing tests in milliseconds.

This isn’t a future scenario—it’s already happening.

What can the developer community do?

If we want to preserve a healthy, diverse programming ecosystem, we can’t leave language survival to chance. Here’s how we can fight for the rest:

1. Open the gates: Encourage more open-source contributions in enterprise languages. Share code examples, libraries, and real-world apps publicly to enrich LLM training datasets.
2. Train smaller models on closed code: Within companies, consider training small, private LLMs on your internal Java/C#/Swift codebases to assist your teams effectively.
3. Invest in language-specific AI tools: Communities around less-represented languages need to build their own Copilots, linters, and smart IDE integrations powered by private or fine-tuned models.
4. Document aggressively: Rich, high-quality documentation helps LLMs learn. Treat it as a community service that extends your language’s longevity.
5. Educate and advocate: Spread awareness within your language community about the importance of open data and AI readiness. It’s not just about tooling—it’s about survival.

The rise of AI in development is accelerating certain languages while leaving others behind. It’s a silent selection process driven by data—not by quality, performance, or elegance.

If your favorite language isn’t thriving in the AI era, it’s time to act. Otherwise, we risk a monoculture where only a few AI-favored languages dominate the future of software.

In short: code smarter, share openly, and remember—AI learns what we teach it.

UX has always been about reducing friction

Every major leap in user experience came from removing steps between desire and outcome.

• Command-line interfaces let tech-savvy users operate computers with cryptic text commands.
• Graphical UIs made computers accessible to the masses, replacing syntax with icons.
• Touch interfaces (smartphones) collapsed input complexity to the simplest action: a finger tap.
• Voice assistants started to remove interface altogether—but only for basic tasks.

And now? We stand on the edge of the next evolution: autonomous agents acting on our behalf.

From interfaces to intentions

Today’s best UX still relies on you doing the work. You enter data. You compare options. You click through forms. But what if you could simply express your intent, and the system would handle the rest?

That’s what AI agents promise:

• Search → done in the background
• Booking → handled through delegated action
• Research → summarized and ready before you ask
• Negotiation → done by your agent, within your limits

This isn’t about convenience. It’s about freeing up cognitive load. And it changes the very idea of what a user experience even is.

But the web is not ready

Recently, we tried to let our AI agents (using Nanobrowser) book a restaurant. It searched fine—but the moment it reached Tripadvisor, the site slapped it with a CAPTCHA.

Why? Because the system flagged it as a bot.

And that’s the core issue: the internet treats AI agents as intruders, not extensions of ourselves.

CAPTCHAs, anti-bot scripts, login flows designed for human eyes—all are remnants of a web built before agentic action was possible.

The UX revolution requires an architecture shift

If we want to move from interfaces to intentions, we need a web that supports agentic interaction at its core.

Here’s what that might look like:

✅ Agent identity & trust
Let users vouch for their agents. Introduce verifiable agent credentials and permission scopes.

🔁 APIs designed for agents
Instead of scraping HTML designed for eyeballs, agents should access standardized, lightweight APIs to perform tasks securely and efficiently.

🤝 Consent-based delegation
Let me tell a website: “Yes, this agent can act on my behalf.” That consent should be cryptographically verifiable and revocable.

🧠 A browser layer made for agents
Just like mobile transformed the web once, agent-first browsers (like Nanobrowser, AutoGPT plugins, or custom headless stacks) will do it again.

From UX to DX (Delegated Experience)

We’re entering the era where UX is not just about how users experience software—but how they delegate action to it. The new metric won’t be “How many clicks to checkout?” but “How much can I offload to my agent without thinking twice?”

This is a big shift. It requires developers, designers, and platform architects to reimagine their stack, their assumptions, and their role in a world where the user is no longer the one doing the clicking.

Who’s building this future?

The agentic UX revolution is being driven by a diverse ecosystem:

• Agentic browsers (Nanobrowser, Opera Neon, OpenAI Operator, and open-source alternatives)
• Open-source LLM agents (AutoGPT, AgentGPT, OpenInterpreter, BabyAGI, Superagent)
  Agent-native application platforms (LangChain, CopilotKit, Orq.ai)
• Web3/identity projects (DIDs, VCs for agent trust and delegation)
• But it’s early days. The agent-native web won’t build itself. So let’s connect, collaborate, and contribute.

Because the future of UX isn’t what users touch.
It’s what they don’t have to touch at all.