The Next Frontier: What the Early Web Teaches Us About AI

If you were building on the web in the late 1990s, the current AI moment feels familiar. The same electricity. The same sense that everything is about to change. The same gold rush mentality, the same hype, the same breathless predictions, and — underneath it all — the same genuine technological shift that actually is significant, if you can separate the signal from the noise.

We've seen this pattern before. The early web and today's AI moment aren't identical, but the parallels are instructive. The early web's trajectory — from frontier to bubble to crash to quiet maturation to transformative impact — is a roadmap for what's likely ahead with AI.

The Pattern

Phase 1: The Frontier

The early web (1993-1996). A new technology that clearly matters but nobody's sure exactly how. Enthusiasts build things because they can. The tools are rough. The possibilities feel infinite. The people building are driven by curiosity more than profit.

AI today (2022-2025). ChatGPT launched and suddenly everyone could feel what this technology might become. Developers started building AI-powered everything. The tools are rough but improving rapidly. The possibilities feel infinite. The people building range from genuine enthusiasts to gold rush prospectors.

The lesson: The frontier phase produces the most creativity and the most garbage simultaneously. Most of what's built during this phase won't survive. A few things built during this phase will define the next decade.

Phase 2: The Gold Rush

The web (1997-2000). The dot-com boom. Every company needed a website. Every startup claimed the internet would disrupt their industry. Venture capital flooded in. Valuations detached from reality. Pets.com. Webvan. Kozmo.com. Companies with no revenue and billion-dollar valuations.

AI (2024-present). Every company needs an AI strategy. Every startup claims AI will disrupt their industry. Venture capital is flooding in. Valuations are detaching from reality. AI wrappers around existing products. Solutions looking for problems. Companies whose entire technology is "we call OpenAI's API."

The lesson: The gold rush phase creates real value and spectacular waste simultaneously. The waste gets the headlines. The value compounds quietly. Most gold rush companies will fail. The infrastructure and patterns they develop will survive and power the next wave.

Phase 3: The Correction

The web (2000-2003). The dot-com crash. Trillions in market value evaporated. "The internet was overhyped." Companies that were worth billions became worthless. The narrative flipped from "the internet changes everything" to "the internet was a scam."

Both narratives were wrong. The internet did change everything. Most of the companies built during the boom were also terrible businesses. Both things were true at the same time.

AI (upcoming). The correction hasn't happened yet as of this writing, but the pattern suggests it will. Not because AI isn't real — it is, profoundly — but because the valuations and expectations have outpaced the near-term reality. When the correction comes, the narrative will flip to "AI was overhyped." That narrative will be as wrong as "the internet was a scam" was in 2001.

The lesson: The correction kills the weak companies but doesn't kill the technology. The builders who survive the correction — because they're building on genuine value rather than speculation — emerge stronger. Amazon survived the dot-com crash. Google went public after it. The correction is a filter, not an ending.

Phase 4: The Quiet Maturation

The web (2003-2010). After the crash, the serious building happened. Google Search. Amazon's marketplace and cloud. Social media. SaaS. Mobile web. The technologies and business models that actually define how we live and work today were built during this phase — not during the hype, not during the crash, but during the quiet years of disciplined building that followed.

AI (future). The most transformative AI applications probably haven't been built yet. They'll be built by people who understand the technology's real capabilities and limitations, who build for genuine needs rather than hype, and who persist through the correction because their work creates real value.

The lesson: The transformative impact of a new technology comes after the hype cycle, not during it. Patient, competent builders who start during the hype and persist through the correction are the ones who build what lasts.

What the Early Web Got Right (That AI Should Learn)

Standards Matter

The web succeeded because it had open standards — HTML, HTTP, URLs. Anyone could build a browser. Anyone could build a server. Anyone could publish a website. The standards created an ecosystem rather than a walled garden.

AI's current state is more fragmented. Each model has its own API. Each company has its own approach to fine-tuning, deployment, and evaluation. Interoperability is limited. OpenAI's API isn't compatible with Anthropic's. Models can't easily be swapped.

Open standards for AI — standardized APIs, model interoperability, evaluation benchmarks — would create the same ecosystem effects that web standards created. Some movement toward this exists (OpenAI's API format becoming a de facto standard, open-source models). More is needed.

Open Access Wins

The web won because it was open. Anyone could create a website. Anyone could access any website. The network effects of universal access produced more value than any walled garden could.

AI's most impactful applications will likely be the ones that are widely accessible, not the ones locked behind enterprise pricing. Open-source models (Llama, Mistral, others) democratize access the way the open web did. The "AI for everyone" narrative is the equivalent of "a website for every business" — and it's the version that creates the broadest value.

The Boring Applications Are the Valuable Ones

The early web's most transformative applications weren't the flashy ones. They were email, search, e-commerce, online banking — boring, fundamental improvements to how people communicate, find information, buy things, and manage money.

AI's most transformative applications probably won't be the flashy demos — they'll be the boring integrations. Search that actually understands your question. Customer service that resolves issues without hold queues. Documentation that answers your specific question. Code review that catches the bugs humans miss. These aren't exciting. They're valuable.

Building for Yourself Beats Building for Hype

The web companies that survived the dot-com crash were the ones solving real problems: Amazon (buying things online), Google (finding information), eBay (selling things to strangers). The ones that died were solving theoretical problems or riding pure hype.

The AI applications that will survive the correction will be the ones where the builder — or the builder's customer — has a real problem that AI genuinely solves better than the alternative. Not "we added AI to our product" but "this specific workflow was painful and AI made it not painful."

The Builder's Advantage

If you were building on the web in 1998, you had an advantage: you understood the medium before it matured. You saw the possibilities before they were obvious. You built skills and intuitions during the frontier phase that served you for decades.

The same advantage exists now for people building with AI. Not the hype-riders or the gold-rushers — the genuine builders who are learning the technology's real capabilities and limitations, building for real needs, and developing the judgment that will matter when the correction clears the field.

The early web rewarded curiosity, persistence, and the willingness to build before the path was clear. AI rewards the same traits. The specific technology is different. The pattern is identical.

Key Takeaway

The early web and today's AI moment follow the same pattern: frontier, gold rush, correction, quiet maturation, transformative impact. The most valuable applications are built after the hype, by patient builders who understand the technology's real capabilities. Open standards and open access create more value than walled gardens. Boring applications that solve real problems outlast flashy demos. The builders who start now, build for genuine needs, and persist through the inevitable correction will be the ones who define how AI actually changes the world — just as early web builders defined how the internet actually changed the world.