Weekly Readings #1: Chaos, problem solving and AEO

I'm playing a risky game. Most newsletter subscribers expect consistency—they want content on the topic they signed up for. Search engines reward focus, pushing you to go deep on one thing and one thing only. Stray too far, and you risk losing readers and rankings.

But is going broad really dangerous? If writing about the same thing over and over kills my enthusiasm, readers will leave anyway. And who honestly believes Google will keep rewarding those who "follow the rules" when the rules are shifting in real time?

I’m still figuring out which topics to explore in depth. But as I reflect, I stumble upon insights worth sharing—both for you and for me. That’s why I’m launching Weekly Readings, a collection of the most interesting ideas I’ve come across each week.

This week, I dive into optimizing in the age of AI, problem-solving strategies, and why chaos isn’t the best economic policy.

I hope you find it valuable! If you have any must-reads, send them my way—I’d love to check them out.

Until next week,

Norbert

1. Playbook for Answer Engine Optimization

by Josh Blyskal from Profound

The way we are searching is changing. Recently I had to introduce myself to a group of new people. I shared with them that I do SEO and they asked me if that still makes sense when everyone is using ChatGPT.

They were a group of early adopters. Not everyone is using ChatGPT, Perplexity or Deepseek, but it is a trend one can not ignore. I am more and more reliant on these tools as well. The answers of LLMs reached a point when I feel they are significantly better than just searching in any search engine if you have a defined question in mind. Google noticed this as well.

So I try to prepare for the future of informational search and I read everything that includes "AI + search". One of these weekly reads was an article from a tool called Profound. Their promise is to help you to "Get your brand mentioned by ChatGPT, Perplexity...". Sounds fun, so I read an article about their approach to Answer Engine Optimization.

Their main points and actionable takeaways are:

• Analyze: Begin by identifying the key topics and queries relevant to your industry. Utilize AI visibility tools to determine which sources and competitors frequently appear in AI-generated answers. Assess the content formats that are most commonly cited, such as listicles, expert interviews, or how-to guides.
  • I don't see a difference between SEO and AEO here. You still use even an SEO tool like Ahrefs or an AEO tool and you are constrained by queries.
• Create: Develop high-quality content tailored to the preferences identified in the analysis phase. Ensure your content directly addresses common user queries and is formatted in a way that aligns with AI citation patterns.
  • In the article they also suggest trying to get into listicles that are mentioned in AI answers. I doubt that guest blogging and digital PR trough link insertion will be the future of search.
• Distribute: Share your content across appropriate channels to maximize reach. This includes publishing on your website, contributing to industry blogs, and engaging on social media platforms where your target audience is active.
• Measure: Monitor the performance of your content by tracking metrics such as engagement rates, AI citation frequency, and audience reach. Use these insights to evaluate the effectiveness of your AEO strategy.
  • This is the part which I find interesting. AI answers are not a big enough market that big SEO tools would have invested significant effort into them. If small players can provide a visibility metrics that are not only showing AI Overview in Google, then it makes sense.
• Iterate: Based on the data collected, refine your content and distribution strategies. Continuously update your approach to align with evolving AI algorithms and changing user behaviours.

I am not impressed so far. Based on what I read about AEO so far, it is SEO branded under a different name without promising results (you don't get clicks you get visibility). But its still early days and I am sure with the increased usage of LLMs there will be better methods to optimize for them.

2. How to solve problems

by Torsten Walbaum from Operator's Handbook

I like reading about mental models of problem solvers. You can read books on the theory of decision making but they list so many potential questions that at the end you don't ask any.

This article of Torsten can be easily translate into the type of problems I encounter working with online businesses.

Some sneak peak:

• Most people can only solve problems they have encountered before. For example, you hire someone who built Marketing attribution at another company to do the same at yours (and they’ll pretty much copy/paste what they did the last time).
• Very few people, however, are good at reliably solving problems they’re encountering for the first time. After all, you can’t just do what you did last time, and there might be no guide for this exact problem.
• The number one mistake people make then it comes to problem solving is to start discussing solutions before they’ve fully understood the problem.
• Questions to ask
  • Is it a one-way door or two-way door decision?
  • Is there an initial small test you can do to gather data and decide on next steps?
  • What are the opportunity costs of the decision?

3. 63% of Websites Receive AI Traffic

by Louise Linehan from Ahrefs

I was comparing laughably small number of Visits from LLMs with friends and checking at Linkedin on what others are having. Usually when someone shared a monthly visits bigger than 1000 from ChatGPT it turned out that they are working with a big US publishers with hundred of millions of organic sessions.

But finally Ahrefs did a study on this on a sample of 3,000 websites.

What did not surprise me:

• Three chatbots—ChatGPT, Perplexity, and Gemini—account for 98% of all AI-driven traffic. Notably, ChatGPT alone contributes to 50% of this traffic.
• On average, AI chatbots account for 0.17% of a website's total visitors. While this percentage may seem modest, it underscores the growing influence of AI in web traffic generation.
• Smaller websites, particularly those with fewer than 999 monthly visitors, receive a higher proportion of their total traffic from AI sources compared to larger sites.

What I was not thinking about:

• The study also highlights that some AI-driven traffic may be underreported, as certain AI tools might not pass referrer information, causing such visits to appear as "direct" traffic in analytics. This suggests that the actual influence of AI on web traffic could be more significant than current data indicates.

4. How Chaos Became America's Economic Strategy

by Kyla Scanlon

There is too much noise around Donald Trump to say anything for sure, but I like to read what can be the impact of the ever changing news cycle. Kyla does a great job on crafting stories around the economy given current events that I could never come up with.

• The real threat isn't Chinese efficiency, it’s American complacency. Our obsession with digital optimization is blinding us to the importance of physical infrastructure (by the way, there is no AI moat and we need high speed rail).
• Perhaps most concerning is the administration's stated plan to use tariff revenue to offset tax cuts. It's a circular logic that ignores basic economics: tariffs are ultimately paid by American consumers (this bears repeating, again) not foreign countries.
• China, Europe, and even Canada are watching America burn diplomatic capital in real time. And while we’re busy taxing maple syrup and tequila, they’re cutting deals with each other on energy, tech, and global security.
• America's real strength has never been in our ability to bully our trading partners. It's been in our ability to create a system where other nations want to trade with us, want to invest in us, want to partner with us.