How the AI Bubble Might Burst: What Frontend Devs Should Actually Worry About

There is a growing discussion about whether the current AI investment cycle is sustainable. The honest answer is: probably not at this pace. But for frontend developers, the more useful question is what this means for the tools you rely on and the skills worth building.

The Bear Case, Briefly

AI-native companies are burning enormous capital. The number of funds large enough to write the checks required to keep pace is shrinking. IPOs are being pushed as the remaining option to sustain funding flows. Meanwhile, the gap between "developer love" and "enterprise revenue" is collapsing — companies no longer have years to bridge it.

The structural dynamic is this: the Magnificent 7 (Google, Microsoft, Meta, etc.) are increasing capex not necessarily to win, but as a defensive move. If they commit $50B, OpenAI and Anthropic need $100B to stay competitive. At some point, the math stops working for someone.

What Survives a Correction

History suggests that infrastructure and distribution survive tech bubbles; pure hype tools do not. For AI dev tooling, that points toward:

• Models with real moats: Google (distribution + search data), Anthropic (safety research), OpenAI (developer mindshare)
• Embedded tools: GitHub Copilot, Cursor, Vercel AI SDK — things baked into existing workflows
• Open source models: Llama, Mistral — do not disappear when VC money dries up

Skills That Remain Valuable Either Way

Rather than betting on specific AI SaaS tools, focus on primitives:

// These patterns stay valuable regardless of which AI company "wins"

// 1. Provider-agnostic LLM integration (Vercel AI SDK)
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { anthropic } from "@ai-sdk/anthropic";

const provider = process.env.USE_ANTHROPIC ? anthropic("claude-opus-4-5") : openai("gpt-4o");
const { text } = await generateText({ model: provider, prompt: "..." });

// 2. Streaming UI patterns (works with any model)
import { useChat } from "ai/react";
export function Chat() {
  const { messages, input, handleSubmit } = useChat({ api: "/api/chat" });
  // Rendering logic stays the same regardless of backend model
}

The Practical Takeaway

A correction would be painful for AI startups but largely a non-event for frontend developers who build on stable abstractions. The risk is over-investing time in tools that disappear:

• ⚠️ High risk: Proprietary AI IDEs from Series A startups, niche AI SaaS with thin moats
• ✅ Low risk: Open source models, established SDKs (Vercel AI, LangChain), core providers

Build on foundations. Abstract over providers. The underlying capability — LLMs that can reason and generate code — is not going away. Just some of the companies selling access to it might.