From 1946 to 2009, computing efficiency—performance per watt—doubled every 1.5 years. This trend, documented by Koomey and colleagues, transformed where computing could happen. Workloads migrated from mainframe rooms to desktops, then laptops, then pockets. The transition from centralized time-sharing to personal computing didn't occur because PCs surpassed mainframes in raw performance. It occurred when efficiency gains made computing capable enough within the power constraints of personal devices.
100% Utilization
Since the end of the Second World War, we have come to expect continual growth in computing power and the rapid development of digital technology. This dynam...
Computer power and human reason : from judgment to calculation
xii, 300 pages : 24 cm; Includes bibliographical references and index
What Even Is An OS Now?
Computers now work the way I thought they did when I was 8. What does computing look like when most applications have an audience of 1-2 people? I'm leaving Fly.io to build a phone for that world.
Andrej Karpathy on Twitter / X
Sharing an interesting recent conversation on AI's impact on the economy.AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are…— Andrej Karpathy (@karpathy) November 16, 2025
What’s Next in Computing?
The computing industry progresses in two mostly independent cycles: financial and product cycles. There has been a lot of handwringing…
CEOs Say AI Is Making Work More Efficient. Employees Tell a Different Story.
How much time workers say the technology saves them on the job is vastly different from what executives report.
Defining the Dimensions of the “Space” of Computing
The first computing machines were so large they filled entire rooms. Today they are ubiquitous, built invisibly into our environments. While it's tempting to view this change within a predetermined space of progress, we can still shape the future on our own terms.
AI Isn’t Lightening Workloads. It’s Making Them More Intense.
The technology is increasing the speed, density and complexity of work rather than reducing it, a new analysis of 164,000 people’s work activity shows.
Thousands of CEOs just admitted AI had no impact on employment or productivity—and it has economists resurrecting a paradox from 40 years ago | Fortune
In the 1980s, economist Robert Solow made an observation that reminded economists of today’s AI boom: “You can see the computer age everywhere but in the productivity statistics.”
Intelligence Per Watt: A Study of Local Intelligence Efficiency
Jon Saad-Falcon*, Avanika Narayan*, John Hennessy, Azalia Mirhoseini, Chris Ré
We studied 1,584 data centers – and found the buildings full of machines are tied to modest local job growth
This employment boost took three to five years to show up, two researchers found after studying more than two decades of data.
We studied 1,584 data centers – and found the buildings full of machines are tied to modest local job growth
This employment boost took three to five years to show up, two researchers found after studying more than two decades of data.
Thousands of CEOs admit AI had no impact on employment or productivity—and it has economists resurrecting a paradox from 40 years ago | Fortune
In the 1980s, economist Robert Solow made an observation that reminded economists of today’s AI boom: “You can see the computer age everywhere but in the productivity statistics.”
Be Prepared: We're Entering A Post-Device Era - Fast Company
The new frontier in personal computing may be cloud services that run on less powerful devices.
Retrocomputing as a Model for Sustainable and Cognitive Computing: Lessons from Low-Resource Systems
This paper argues that retrocomputing constitutes a conceptually productive model for rethinking sustainability in contemporary computing. Early computing systems operated under strict hardware constraints, necessitating efficiency, clarity, and minimalism in both software and interaction design. In contrast, contemporary systems are frequently characterised by computational excess, software bloat, and attention-fragmenting interfaces. Drawing on examples from early home computing and command-line environments, this paper analyses how low-resource systems make visible forms of computational minimalism with relevance to present-day design challenges. A central theoretical contribution is the distinction between interface-level and system-structure-level design analysis, which demonstrates that cognitive effects arise not only from visual and interactional surface features but from deeper computational organisation. The discussion integrates perspectives from human-computer interaction and cognitive science, particularly in relation to attention, cognitive load, and user experience. The paper adopts a conceptual and exploratory approach grounded in systematic literature review, aiming to identify design-relevant patterns rather than to provide empirical validation. It introduces the concepts of computational minimalism and cognitive sustainability as complementary analytical frameworks, and derives five principles for sustainable system design. Crucially, the paper also address
