A collection of bookmarks from the past week covering the physical reality of AI infrastructure, the evolving landscape of software development, and the timeless principles of leadership accountability.
In Defense of Data Centers#
In defense of data centers
Summary: Ng argues against limiting data center growth due to environmental concerns, suggesting the benefits of AI infrastructure outweigh the costs.
Why it Matters: Data centers are the factories of the 21st century. Restricting them constrains national and corporate competitiveness in the AI era. It’s also bad for the environment: in the Step Change podcast on Data Centers: The Hidden Backbone of Our Modern World, hosts Ben Eidelson and Anay Shah discuss the massive efficiency gains of hyperscale data centers (like those of AWS, Google, and Microsoft) compared to traditional on-premise servers. Moving workloads from on-premise facilities to hyperscale data centers can reduce the associated carbon footprint by up to 99% (with a median reduction of roughly 88%).
My Take: Infrastructure is Strategy. Just as railroads defined the 19th century, compute capacity defines this one. We need sustainable growth, not caps.
Leading with Accountability#
It took me 20+ years to figure out how to lead a team with real accountability. Here's my blueprint: pic.twitter.com/b577S6cXC7
— Dave Kline (@dklineii) January 16, 2026
Summary: A blueprint for team leadership focused on “real accountability” developed over 20+ years.
Why it Matters: As organizations scale, the “founder mentality” often dilutes. Re-instilling accountability is the primary challenge for growth-stage CEOs.
My Take: Culture is Code. Accountability isn’t a policy; it’s a protocol you run every day. This is a must-read for the “Leadership” category.
Lenny Skills Database#
Introducing the Lenny Skills Database!
— Sid Bharath (@Siddharth87) January 16, 2026
Ever wanted to tell stories like @aprildunford or lead teams like Brian Chesky?
Well, now you can. Earlier this week, @lennysan made all his podcast transcripts available for anyone to download.
And instead of just building another… pic.twitter.com/vqrquOWWMC
Summary: Lenny Rachitsky’s podcast transcripts are now a searchable agentic AI skills database, democratizing access to top-tier product and growth advice and ready for AI.
Why it Matters: The barrier to knowledge is zero. The differentiator is now synthesis and application.
My Take: Curation > Creation. This database is a goldmine for our “Product Guidelines” track. With GenAI automating much of product development, we must codify and index the best skillls.
Software’s YouTube Moment#
Software's YouTube Moment is Happening Now
Summary: Coding is becoming a form of content creation. Just as YouTube democratized video, AI is democratizing software creation.
Why it Matters: The supply of software will explode. The value shifts from “building” to “distributing” and “servicing.” I wrote about moats in the era of AI just this past week.
My Take: The Long Tail of Software. We can now build bespoke internal tools for single-use cases. Has the “Buy vs. Build” calculus has fundamentally changed?
Reed Hastings & Netflix Strategy#
.@reedhastings built Netflix around two ideas that everyone talks about, but are extremely hard to do in practice.
— Patrick OShaughnessy (@patrick_oshag) January 6, 2026
The first is finding a simple idea and taking it extraordinarily seriously. Even from Netflix's inception in 1997, Reed explains how the DVD business was always… pic.twitter.com/mQXYo9Fv9O
Summary: Highlights how Netflix focused on a simple idea (DVDs) with extreme seriousness, long before streaming.
Why it Matters: “Strategy is focus.” Most startups die from indigestion (too many ideas), not starvation.
My Take: Ruthless Prioritization. A classic case study for our “Strategy” section. Innovation isn’t just new things; it’s doing the one right thing with intensity.
SOA in Practice#
Summary: Old book, timeless observation. Acknowledging the permanence of “temporary” fixes in software architecture.
Why it Matters: “Nothing is more permanent than a temporary solution.” This is the source of most crushing technical debt. Does this get exacerbated in the era of AI coding?
My Take: Honest Engineering. If you build a hack, label it as a “Load Bearing Hack” and schedule its funeral. Otherwise, it becomes the foundation of your legacy system.