We’Ve all seen the charts. The capital flowing into AI is nothing short of historic. But as the hype reaches a fever pitch, the big question remains: Are we in a 1999-style dot-com bubble, or is this the 1920s electrification of industry?
The truth is likely somewhere in the middle. To understand where we’re headed, we need to look at the fundamentals.
🔍 The Core Fundamentals
1. The "CapEx" vs. "Revenue" Gap Big Tech is spending billions on GPUs and data centers. However, the enterprise revenue—the actual "value added" to the bottom line—is still catching up. For a bubble to stay inflated, the ROI needs to show up soon.
2. The Moat Problem If everyone has access to the same LLMs, where is the competitive advantage? Companies that win won't just "use AI"; they will own the proprietary data that makes the AI useful.
3. Compute as the New Oil We are seeing a shift where compute power is treated like a commodity. But unlike oil, the efficiency of AI models is doubling every few months. Are we overbuilding infrastructure for models that will soon be 10x smaller and cheaper?
⚖️ The Verdict
Is there a bubble? Probably. History shows that we almost always overestimate the short-term impact of new tech and underestimate the long-term transformation. Some "AI-first" startups will vanish, but the underlying shift in how we process information is permanent.
The "bubble" bursting isn't the end—it’s usually the beginning of the Utility Phase. (Think Amazon and Google emerging from the 2000 crash).
💬 Let’s Debate:
• Are we paying for "productivity" or just "automated plagiarism"?
• Which companies are actually solving problems, and which are just "GPT-wrappers"?
• Is your team seeing a real ROI yet, or are you still in the "experimentation" phase?
👇 Drop your thoughts below. Is it time to double down or hedge?
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