How AI Summarization Changed My News Reading

I used to feel guilty about my RSS feeds. Not because I was reading too much, but because I wasn’t reading enough. At some point I had accumulated well over a hundred subscriptions—tech blogs, niche newsletters, industry news sites, personal blogs from people whose writing I genuinely enjoy. The math was simple and depressing. If each feed published even one article a week, that’s over a hundred articles I’d need to process weekly. Nobody has that kind of time.

The old approach was to skim headlines religiously, clicking on anything that looked interesting and hoping I’d land on something worth reading. Most of the time I’d read a paragraph or two before realizing the article wasn’t going to deliver what the headline promised. It felt like a slot machine, except instead of money I was spending attention.

AI summarization changed the equation entirely. Instead of reading the headline and guessing whether an article deserves my time, I can read a two-sentence summary that captures the core argument or key information. This sounds like a small difference, but it’s genuinely transformative when you’re dealing with volume.

Here’s a concrete example. I follow several cybersecurity blogs that publish detailed technical analyses of recent vulnerabilities. The headlines tend toward the dramatic—“Critical Zero-Day Found in Popular Framework”—which could mean anything from a theoretical edge case to something actively being exploited in the wild. The summary cuts through this immediately. If the summary says “affects version 2.x only, patch released, no active exploits observed,” I can decide to skim later or skip entirely. If it says “actively exploited, all versions affected, no patch available,” that becomes priority reading.

This isn’t about outsourcing my thinking to a machine. The summaries are starting points, not replacements. They help me decide where to invest my attention, which remains entirely my own resource to manage. The articles I do read in full, I read properly—no skimming, no distractions, just the writer’s full argument laid out in front of me.

There’s a secondary benefit that surprised me. AI summaries tend to strip away the rhetorical flourishes and get to the substance. This has made me a better reader of long-form content in general. When I know the core claim upfront, I can follow the author’s reasoning more carefully. I’m not trying to figure out what the article is about while also trying to process the argument. The summary handles the former, freeing me to engage with the latter.

Of course, this approach only works if you trust the summaries to be accurate. A bad summary is worse than no summary—it gives you false confidence that you’ve understood something when you haven’t. This is where the quality of the underlying AI model matters enormously. I’ve experimented with a few different approaches, and the difference between a model that genuinely understands context and one that just extracts sentences at random is stark. The good ones capture nuance. They preserve the author’s tone when it’s relevant to the meaning. They distinguish between claims and evidence.

In Stratum, the summarization feature works on every article in your feeds automatically. You see the summary alongside the headline in your feed view. This means you never have to click through just to figure out whether something is worth your time. The decision happens at the feed level, where you can quickly triage dozens or hundreds of items without leaving your reading flow.

The other thing I’ve noticed is that AI summarization has changed which feeds I keep. I used to hold onto feeds that published infrequently but with high density—those long investigative pieces that come out once a month. I justified keeping them because I didn’t want to miss the occasional masterpiece. But the reality was I’d forget about them entirely between publications, and when something did come out, I’d find it through other channels anyway. Now I’m more ruthless. If a feed doesn’t publish regularly enough to build a reading habit, I let it go. The summaries help me stay disciplined here too, because when I do see an article from a low-frequency feed, the summary tells me immediately whether it matters.

This whole approach rests on the assumption that you actually have too many feeds to read manually. If you’re subscribed to five blogs and three news sites, you don’t need AI help. You can just read everything. But the promise of RSS has always been about building a custom information ecosystem tailored to your interests. That ecosystem tends to grow over time. You discover new writers, new sources, new perspectives. The friction of adding a new feed is nearly zero. The friction of deciding you’ve hit capacity and need to prune is enormous. AI summarization doesn’t solve the pruning problem, but it does raise the ceiling on how much you can reasonably process.

I still read long-form content the old way—slowly, carefully, with highlights and marginalia. The AI doesn’t touch that process. What it does is handle the gatekeeping. It helps me decide what deserves the slow reading treatment and what can be acknowledged and set aside. That decision, made consistently and efficiently, has given me back hours every week.

The broader argument here isn’t really about AI or RSS or any particular tool. It’s about designing your information diet to serve your attention rather than exhaust it. We’re all drowning in content. The question isn’t whether you can keep up—it’s whether you’re choosing what to pay attention to, or whether something else is choosing for you. AI summarization is one tool for taking that choice back. It’s not the only tool, and it’s not right for everyone. But for people who live in their RSS readers, who have built sprawling feed collections over years of careful curation, it’s been worth exploring.

• Ai
• Rss
• Productivity
• News