Remember when GPT-3 became GPT-4, and everyone wondered what the fuss was about? Turns out, we might be living through the AI equivalent of the Pentium processor era. One mental model is completely changing how we should think about artificial intelligence and where we’re headed.
The Reframe
GPT-3 to GPT-4 to GPT-5 is basically Pentium to Pentium II to Pentium III. Version numbers go up, but most people don’t notice the difference.
This insight comes from Chris Paik, who shared a mental model that reframes our current moment. While many people (myself included) were quick to compare AI to the internet age, Chris pointed out something crucial: the internet was explosive because it was a communication and distribution advance. AI, from his perspective, is more like the CPU era, which had a slower, less noticeable capability advance.
The Pattern
1970s: The Dawn
Most people had no idea what a “microprocessor” was or why they’d want one.
1980s: Early Adoption Phase
Computers moved from hobbyist curiosity to business tool.
1990s: The Slow Burn
Performance doubled every 18 months (shout out to Moore’s Law), but for average users, the difference between generations wasn’t dramatic.
Today
Most people using ChatGPT are using maybe 10% of what it can actually do, just like office workers in 1995 using Pentiums for basic Word documents. The gap between what is possible and what’s being done is enormous.
Companies are still figuring out how to integrate AI into workflows. There is no simple solution that is guaranteed to work reliably, like dropping in a website was. It requires rethinking entire processes and sometimes the company itself.
Critical Differences
But here’s what makes AI different from the CPU era:
The interface is language. CPUs required learning software. AI just requires talking. That’s fundamentally more accessible.
The AI can teach and direct users. It can actively demonstrate its value. A Pentium couldn’t tell you why you needed it. GPT can.
The improvement loop is incredibly fast. With CPUs, you waited 18 months for meaningful improvements. With AI, models are improving quarterly.
Compressed Timeline
We’re potentially following that gradual capability development pattern, but at 2-3x speed. If the CPU era was a 30-year journey, AI might be an 8-12 year one.
- 2022-2023: Foundations land (GPT-3/ChatGPT). We meet the shape of the thing.
- 2024-2025: Businesses adopt; consumers still ask, “So what?”
- 2026-2028: Capability becomes undeniable; mass adoption kicks in.
- 2029-2030: AI fades into the background as invisible infrastructure.
Spreadsheets Again
In every era, people love spreadsheets. Just in the past few weeks, we’ve had a wave of AI spreadsheet companies launch: Shortcut, Paradigm, and others.
But just like PCs found their killer app in games rather than spreadsheets, I think AI’s mass adoption will come through playable, explorable experiences rather than workplace productivity tools.
Gaming Revolution
There is a lot of discussion about AI taking over jobs in work contexts, but I think video games will drive the real AI revolution. Personal computers took off in work settings first, but the explosion of personal computing happened when video games came to market. This feels like the same pattern echoing again.
When Dynamics Lab launched Mirage 2, a real-time generative world engine where you can upload any image and walk around inside it as a navigable environment, it became clear that the majority of humans will probably explore the latent space this way and not via a chat box. It’s still early, but it just feels more fun.
Runway’s CEO, Cristóbal Valenzuela, recently pointed out that generative game worlds will enable people to make a playable experience about anything they want. Sometimes the audience is n=1, sometimes it’s a million. They have a product called Runway Game Worlds in this space, so he is talking his book, but the concept is still interesting.
This comes just weeks after Google DeepMind announced Genie 3, followed by an open-sourced version called Matrix Game by Skywork AI. The cherry-picked demos look amazing, though in reality, you can’t really interact with these systems for very long at this time.
But we are definitely getting closer to the world Jensen Huang predicted, where all pixels will be generated, whenever it may happen.
Conclusion
If this CPU era analogy holds, we are in for a slower burn than many expect. The explosive growth won’t come from workplace productivity tools or incremental model improvements that most users can’t perceive. Instead, watch the gaming and entertainment space. That’s where the real adoption curve begins.
The pattern has repeated before: hobbyist curiosity becomes a business tool, which becomes a mass consumer product. We are somewhere between steps two and three right now. The question is not whether AI will transform everything, but whether we have the patience to wait for the transformation to become obvious.
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