Every major technological shift seems to come with a familiar pattern, excitement about what's now possible alongside fear about what it might replace. A recent piece draws a specific parallel: today's AI boom resembles the PC era's "bloatware spiral," where software got heavier and more feature-packed without necessarily getting more useful. The author's claim is pointed: benchmark scores keep climbing, but those improvements don't necessarily translate into proportional gains in real-world productivity.
That distinction is the part worth sitting with. A rising benchmark and a rising benchmark that translates into genuinely better output aren't the same thing, and it's easy to treat the first as proof of the second. Speed and impressive demos don't guarantee that what gets built is useful, reliable, or actually solves the problem in front of you.
At Vunoh, this feels directly relevant because we're not just learning about AI, we're building with it, which puts real weight behind how we answer that question. If a feature performs well on a demo or a benchmark but doesn't measurably reduce a client's actual workload or error rate, we haven't delivered value, we've delivered a good demo. Before reaching for AI on a given problem, it's worth asking what specific, measurable outcome it needs to improve, and checking that outcome after shipping, not just at launch. If this pattern from previous technology shifts is repeating itself, the lesson isn't to resist the new technology, it's to keep asking whether the benchmark and the actual benefit are still the same thing.
REFERENCES
Marco Sbragi, Is History Repeating Itself? From the PC Era to the AI Era: A Developer's Take on the Hype and Healthy Skepticism, DEV Community, 6 September 2026: