It’s an article of faith that PCs and consumer electronics always get faster and cheaper at a mind-blowing rate. But that’s not really true, is it? Sure, they get a little faster and a little cheaper, but nothing like the performance and price gains the pundits and vendors promise. The Gold Standard for predicting performance […]
It’s an article of faith that PCs and consumer electronics always get faster and cheaper at a mind-blowing rate.
But that’s not really true, is it?
Sure, they get a little faster and a little cheaper, but nothing like the performance and price gains the pundits and vendors promise.
The Gold Standard for predicting performance improvements is the venerable Moore’s Law, which says transistor density per dollar doubles every 18 to 24 months. That means, theoretically, that computer chips that cost a given amount of money should double in speed every 1.5 to 2 years.
Intel can demonstrate that its processors have roughly kept up with Moore’s Law over the years. So can the many other chip vendors that contribute electronics to your PCs and gadgets.
According to Moore’s Law, your PC should be 32 times faster than the PC you used in 1998. And, assuming you buy a new cell phone every two years, each one should be twice as fast as the previous one.
Something is horribly wrong here.
My feeling is that my PC feels a little faster than the one I used in 1998 (as measured by the amount of time I spend waiting for it), but not, say, twice as fast — and definitely not 32 times as fast, as Mr. Moore might suggest. And my new BlackBerry Pearl feels about 10% — not 100% — faster than my old one.
You could argue that everything is better now, that Moore’s Law has been applied to a qualitative improvement, rather than directly applicable performance improvements. Software and hardware has evolved, and nobody wants to go back.
After all, what’s stopping us from dusting off those old Windows 98 disks and installing them on a 2008 Windows Vista PC? Would that give us 32 times the performance? The answer is: no, not even close. Old operating systems weren’t written to take advantage of new microprocessing architectures.
The ugly reality is that there is simply no way for users to realize anything close to the actual performance improvements both promised and achieved by Moore’s Law inside chips.
One major reason is that performance improvements at the chip level are squandered by everyone from OS makers to driver writers to hardware makers and component manufacturers. They see Moore’s Law as an opportunity to bulk up on new features that motivate buyers to dump old versions and upgrade to new ones.
Performance gains make up for sloppy software development. Software makers see Moore’s Law as a benefit for them, not you, so they speed products to market by banging out bloated spaghetti code. The art of concise software writing is dying. Everything is in beta, under construction and scheduled for optimization for a future ship date that never comes.
By the time finished products get to the consumer, the actual user experience is the same as it always was: too slow.
Ten years ago, Microsoft promised a new world of “instant on” PCs and laptops. Where are they?
Sure, Lenovo sells a smattering of “instant on” netbooks. But most of the chatter about “instant on” is by vendors like HP and Dell who are working on, about to ship and on the brink of unleashing a new age of “instant on” laptops and even desktops. But that’s exactly where we were in 1998. Back then, “instant on” was available but rare, and a year or two from becoming totally mainstream.
The industry needs to stop padding products with ever longer lists of features and “improvements,” and start focusing on raw performance.
If Moore’s Law holds, which it will, the speed of processors will double over the next two years. Which would you rather have: Bloated new features that gobble up all those clock cycles before they get to you? Or would you rather have all your stuff run twice as fast?
Industry: It’s time to put the feature bloat and spaghetti code on hold for two years, and get to work optimizing, minimizing and streamlining everything. We want speed. And we want it yesterday.
Huawei’s AI Update: Things Are Moving Faster Than We Think
FEATURE | By Rob Enderle,
December 04, 2020
Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 18, 2020
Key Trends in Chatbots and RPA
FEATURE | By Guest Author,
November 10, 2020
FEATURE | By Samuel Greengard,
November 05, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 02, 2020
How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 29, 2020
Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 23, 2020
The Super Moderator, or How IBM Project Debater Could Save Social Media
FEATURE | By Rob Enderle,
October 16, 2020
FEATURE | By Cynthia Harvey,
October 07, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
October 05, 2020
CIOs Discuss the Promise of AI and Data Science
FEATURE | By Guest Author,
September 25, 2020
Microsoft Is Building An AI Product That Could Predict The Future
FEATURE | By Rob Enderle,
September 25, 2020
Top 10 Machine Learning Companies 2020
FEATURE | By Cynthia Harvey,
September 22, 2020
NVIDIA and ARM: Massively Changing The AI Landscape
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
September 18, 2020
Continuous Intelligence: Expert Discussion [Video and Podcast]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 14, 2020
Artificial Intelligence: Governance and Ethics [Video]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 13, 2020
IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI
FEATURE | By Rob Enderle,
September 11, 2020
Artificial Intelligence: Perception vs. Reality
FEATURE | By James Maguire,
September 09, 2020
Anticipating The Coming Wave Of AI Enhanced PCs
FEATURE | By Rob Enderle,
September 05, 2020
The Critical Nature Of IBM’s NLP (Natural Language Processing) Effort
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
August 14, 2020
Mike Elgan is an American journalist, blogger, columnist, and podcaster. He is a columnist for publications including Computerworld, Cult of Android, Cult of Mac, Forbes, Datamation, eWeek, and Baseline.
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
Property of TechnologyAdvice. © 2025 TechnologyAdvice. All Rights Reserved
Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.
