Something funny kept happening at my AI workshops.
People would come to the first one, get the fundamentals, and then… come back again a few months later. I was confused at first. I figured once they had the basics, they’d go figure out the rest on their own.
But then I realized: nobody has time to keep up with AI on their own. The tools change every few weeks. A model that was best-in-class in January might be middle of the pack by March. It’s a lot.
And here’s the thing — these weren’t people who lacked curiosity. They were smart, busy professionals who genuinely wanted to stay current. They just couldn’t find 2-3 hours a week to read all the articles and newsletters and watch all the YouTube videos.
So they came back to workshops as a way to stay updated. Which is kind of a compliment, I guess. But it also told me there’s a real gap here.
Staying current on AI shouldn’t require a workshop every few months.
Here’s how I actually do it.
The Walk Trick
Last fall during my AI research masterclass, I showed something that got a bigger reaction than I expected.
I had collected about 15 AI news sources for the week — articles, newsletters, a few YouTube video summaries — and dropped them all into Notebook LM. Then I used the audio overview feature to generate a 20-minute audio summary. Two AI voices, back and forth, covering the key points from all 15 sources.
Put it on my phone. Went for a morning walk. Came back caught up.
That was it. No extra time blocked in my calendar. No reading piled up from the week before. Just a walk I would have taken anyway.
The audio isn’t perfect. It misses things sometimes, or summarizes a point too quickly. But 85% caught up on 15 sources beats 0% caught up after skipping another week of reading.
If you haven’t tried the audio overview feature in Notebook LM, this is the use case for it. Collect whatever you’ve been meaning to read — 5 articles, 10 articles, doesn’t matter — drop them in, generate the audio, and take it for a walk. The sources can be PDFs, links, YouTube transcripts, text files, anything.
(Side note: I also imported 300 Lex Fridman podcast episodes into Notebook LM once, just to see what would happen. In a few minutes I had access to 600 hours of content I could query by topic. Not something you need to do every week, but it shows how far this thing goes.)
The Daily Briefing I Set and Forgot
The walk trick handles my weekly catch-up. The other thing I set up is a daily briefing.
ChatGPT has a feature called Tasks. Most people don’t know it exists. It lets you schedule a prompt to run automatically — daily, weekly, whatever cadence you want.
My prompt runs every day at 11am:
Summarize in bullets the latest AI news. For each item, suggest: (1) how I could create content or courses around it, (2) whether it’s interesting from an investing angle, (3) what I should look into deeper.
And then it just… runs. Every day. Without me doing anything.
The briefing shows up in my ChatGPT chat at 11am. I skim it over lunch. Some days there’s nothing new. Some days there’s something worth digging into. Either way it takes about 3 minutes.
The key is the framing. I didn’t ask for “summarize AI news” — I asked for news plus three specific lenses: content creation, investing, and deep dives. That turns a generic news summary into something actually relevant to what I’m building.
You can customize it however you want. Your lenses might be different. The point is making the briefing work for your context, not just a generic download of what happened.
Why I Stopped Using Google for AI Research
This is a little embarrassing to admit, but I honestly can’t remember the last time I used Google Search for research.
I asked a room full of people at that October masterclass: if you use ChatGPT or Perplexity, do you still use Google? Almost nobody raised their hand.
That tells you something.
For research — real research, not “what time does this restaurant close” — ChatGPT and Perplexity are just better. They synthesize. They pull from multiple sources. They give you a summary with citations instead of a list of links you have to click through individually.
I use Perplexity most for real-time stuff because it’s pulling current information. ChatGPT for synthesis and analysis. Claude for longer reasoning. Gemini for anything involving Google’s ecosystem or images.
This is what I call being Multi-Tool Native — not picking one AI and treating it like a hammer, but routing the work to whatever actually fits the job. Different tools have different strengths and you get better results when you use them accordingly.
But the main point is this: for staying current on AI specifically, dedicated AI research tools are faster and more useful than a search engine. The research you do with Perplexity or ChatGPT in 10 minutes used to take 45 minutes on Google.
Putting It Together
Here’s the whole system, simplified:
Daily: ChatGPT Tasks briefing at 11am (set it up once, runs forever). Skim it over lunch. Takes 3 minutes.
Weekly: Collect whatever AI content has accumulated (articles, newsletters, YouTube transcripts). Drop into Notebook LM, generate audio, take a walk.
That’s it. Two habits. One requires almost no active time. The other turns a walk you’d take anyway into a research session.
You don’t need to read everything. You don’t need to be expert-level on every new model release. You just need enough signal to know what’s worth paying attention to and what you can ignore.
The goal isn’t omniscience. It’s “good enough to keep moving.”
Want to see how I use these tools in practice? My AI workshops are small-group, half-day sessions where we actually build workflows together — not just theory. Details at asianefficiency.com.
