Is AI Advancing Too Fast?

In the rush to capitalize on the AI boom, there’s a hard question lurking in the background: Is the technology advancing so fast that by the time your product launches, it’s already obsolete?

This is likely how things are playing out in startups — teams spend 6 to 12 months building a polished product around GPT-4 or Claude 2, only to find that GPT-5, new multimodal models, or plug-and-play agent frameworks drop right as they’re ready to launch. Suddenly their “cutting-edge” app looks dated. In a field where capability leaps happen quarterly, product timelines just can't keep up with model timelines.

Even Apple seems to be feeling this. For over a year, pundits and fans asked: Where is Apple’s AI response? While OpenAI, Google, and Microsoft raced ahead, Apple seemed unusually quiet. Now we know they delayed major Siri revamps and other generative features, possibly in part because they weren’t confident the tech was ready — or stable — enough. They finally revealed their “Apple Intelligence” initiative this month, but it’s clear they’ve had to thread the needle between integrating fast-moving LLMs and protecting the core polish Apple is known for.

If this all sounds familiar, it’s because we’ve seen something like it before. In the late 1980s and early 1990s, personal computing hardware was advancing at breakneck speed — faster CPUs, new graphics cards, evolving OS platforms. It was an era when entire product lines were rendered obsolete in 12 to 18 months. Hardware companies and software developers would spend months and years building something, only to ship into a market that had already moved on. Today’s AI landscape feels eerily similar: a fast-forward version of the PC boom, but now the pace is measured in months, not years.

There’s a deeper lesson here: when the underlying technology is evolving this fast, product strategy becomes less about execution and more about timing. Release too soon, and your product ages like milk. Wait too long, and someone else eats your lunch.

For builders, this means rethinking not just what you ship — but when you ship, how modular your stack is, and whether your roadmap can absorb paradigm shifts without blowing everything up.