The AI Flywheel Paradox: OpenAI's Bet on More Compute Amid Overcapacity Fears

While the market raises alarms about GPU overcapacity, OpenAI just declared through its official channels: “We need more compute.”

OpenAI’s Recent Statement

“Compute is what made our first image generation launch possible, and in the three weeks since, weekly active users have grown 32%. There’s more coming… and we need more compute.”

A bold claim at a time when analysts are questioning whether the industry has already overbuilt.

The AI Industry Is Starting to Look Like Amazon’s Flywheel

More compute leads to better models. Better models drive more users. More users generate more revenue. More revenue funds more compute.

The virtuous cycle is clear. The question is timing.

Amazon proved this logic with e-commerce infrastructure decades ago. Now the same structural dynamic is playing out in AI - but at a pace and capital intensity the world has never seen.

The Gap Between Market Fears and Reality

Warnings about infrastructure overcapacity and excessive capex are flooding the market. But the fundamental question is this: “How much of the future can you pull into the present?”

Human greed has always outpaced technological progress. And that gap is exactly where bubbles form.

Every technology cycle in history has had a moment where investment exceeded near-term demand. The ones that survived were the ones where the underlying utility was real. The question for AI isn’t whether there’s overinvestment - it’s whether the use cases justify the infrastructure being built.

The Real Bottleneck Is Somewhere Else Entirely

Models are improving at a staggering pace. Training cycles are getting shorter. Benchmark scores keep climbing.

And yet, paradoxically, prompting has become more important than ever.

• AI performance benchmarks are measured using expert-level queries
• Real user questions fall far short of that level
• The result: capability soars while actual utilization flatlines

This is the paradox at the heart of the AI flywheel. You can pour billions into compute and push model performance to extraordinary heights - but if users can’t effectively communicate what they need, all that power goes underutilized.

The Winner Will Be Whoever Closes This Gap

Vibe coding showed us what it looks like when the gap narrows. When the interface between human intent and AI capability becomes seamless, adoption explodes.

The same principle applies to presentations, content creation, data analysis - every domain where AI can theoretically help but practically frustrates.

This isn’t a compute power battle. It’s a user experience battle. And only by winning the UX war can the compute arms race sustain itself.

The companies that invest in closing the gap between what AI can do and what users actually get from it - those are the ones that will justify the flywheel’s next turn.