Christina Wodtke, veteran product designer and Stanford lecturer, captured something profound recently: "The old timers who built the early web are coding with AI like it's 1995. They gave blockchain the sniff test and walked away. Ignored crypto. NFTs got a collective eye roll. But AI? Different story. The same folks who hand-coded HTML while listening to dial-up modems sing are now vibe-coding with the kids."
This isn't nostalgia talking. These are the pattern-recognition experts who've survived every tech cycle since Gopher. When they start acting like excited newbies again, it signals something fundamental is shifting.
They've Seen This Movie Before—But This One's Different
The dot-com comparison is inevitable, but it misses crucial differences. Yes, we're seeing familiar bubble symptoms: inflated valuations, breathless media coverage, and VCs throwing money at anything with "AI" in the pitch deck. But the underlying dynamics have changed dramatically.
During the dot-com crash, companies like Pets.com and Webvan went bust spectacularly, taking billions in venture funding with them. But what survived was more valuable than the failed startups: battle-tested engineers, proven software frameworks, and hard-won knowledge about building scalable web applications. The infrastructure left behind wasn't physical—it was human and technological expertise.
Today's AI bubble has similar characteristics but different economics. Yes, we're seeing the same pattern of overfunded startups chasing questionable business models. But the underlying infrastructure being built—open-source models, training frameworks, inference engines and physical infrastructure like data centers — is immediately useful and constantly improving. Every new data center comes online at full capacity. Companies report persistent capacity constraints rather than oversupply. McKinsey's research shows 92% of organizations plan to increase AI investments over the next three years, while current deployment remains nascent—only 1% of leaders call their companies "mature" in AI adoption.
The government recognizes this strategic importance. The Trump Administration's AI Action Plan identifies "Building American AI Infrastructure" as a core national priority, with over 90 policy actions focused on expediting data center permits and modernizing electrical grid capacity. This isn't hype-driven policy—it's recognition that computational infrastructure has become as critical as highways and power grids.
What the Veterans Recognize
Wodtke describes Gen X coders "vibe-coding with the kids," but their excitement stems from hard-earned wisdom. They understand the difference between platform shifts and feature releases. They've watched enough gold rushes to distinguish between fool's gold and the real thing.
What they see isn't AGI around the corner—it's a permanent expansion of computational capability. The stack they're experimenting with today—large language models, vector databases, agent frameworks, and increasingly powerful local hardware—represents a new computational substrate. Natural language becomes a programming interface. Probabilistic reasoning joins deterministic logic as a standard toolkit.
This isn't another app ecosystem. It's infrastructure-level change, comparable to the shift from mainframes to networked computing or from desktop to mobile-first architectures.
The Risks Are Real, But Different
The AI bubble will likely burst—economist Torsten Sløk warns that today's tech giants are more overvalued than their dot-com predecessors. When reality fails to match expectations, markets will correct violently. But unlike 2000, this correction won't eliminate the underlying infrastructure.
The computational capacity being built today will survive market downturns because it's immediately useful and constantly utilized. The models will continue improving. The hardware will keep getting more efficient. The integration patterns will become standard practice.
The bigger risk isn't bubble collapse—it's premature abandonment. Gartner predicts 30% of enterprise AI projects will be abandoned after proof-of-concept due to poor execution, not fundamental limitations. Organizations may retreat from AI initiatives precisely when the infrastructure is becoming robust enough to deliver consistent value.
Ignore the Noise, Watch the Builders
The AGI timeline debates are mostly irrelevant. Whether artificial general intelligence arrives in 2027 or 2037 doesn't change the immediate reality: a new computational infrastructure is being built at unprecedented scale and speed.
The veterans building on this infrastructure understand something the breathless headlines miss. They're not betting on AI consciousness or technological singularity. They're recognizing that the tools for natural language processing, pattern recognition, and automated reasoning are becoming as fundamental as databases and web servers.
When the hype cycle peaks and crashes, when valuations correct and startup graveyards fill up, the infrastructure will remain. The veterans will still be building on it, shipping products that seemed impossible just years before.
Just like last time, when the dust settles, those who kept building will be the ones who shape what comes next. The difference is this time, they're building on a computational foundation that's immediately productive, strategically critical, and economically sustainable.
The future isn't AGI. It's intelligence so deeply embedded in our computational infrastructure that we stop thinking about it as artificial intelligence and start thinking about it as simply computation.
Unlock the Future of Business with AI
Dive into our immersive workshops and equip your team with the tools and knowledge to lead in the AI era.
