Grow Google. Diversify away from Google.

Posted on by Brendan

That’s the whole strategy in six words. Both for Google and for any business that depends on it.

A month after Google I/O and Google Marketing Live, the instinct inside most companies is to react to every announcement. A ton gets announced at I/O; it’s a conference, and not everything sticks. The part of this job I love is the opposite of reacting: studying, thinking critically, and sifting the noise down to what’s actually going on and what businesses should do about it.

Where I landed is Google’s strategy unfolding now and over the next few years, runs on three moves: Protecting/Defending, Diversifying, and Exploring. I believe enterprises should mirror this in ways unique to them.

Most leaders stop at the obvious points: Google has to protect its cash cow, and it’s caught in an innovator’s dilemma. Both are true. But the I/O story is evidence of more than defense; it’s evidence of mobilizing. Protecting has short-term implications. Exploring is the 1-2 year horizon and beyond. Diversifying spans both.

Protecting / defending. Google is holding onto ad monetization for two search intents as long as it can: navigational and transactional. If and when does that break? I don’t know but it only breaks when another platform offers something good enough to make people switch. On transactional, I’m with Paul Graham’s critique, but an irrelevant ad in image search isn’t enough to move people. On navigational, Kevin Indig’s piece on the brand tax nails it and this certainly can’t keep climbing and going on forever.

Diversifying. Expect Google to push advertisers toward YouTube as a Meta alternative, maybe Discover too. Wherever it can open ad inventory across its surfaces. In my opinion, these are always worth a look, some of these may be well-priced, effective placements.

Exploring. The bets that matter most for anyone with products: Universal Cart, the Universal Commerce Protocol (UCP), AI Mode, the new Conversational Attributes schema in Merchant Center, WebMCP, and information agents. (There’s plenty more but those six are the ones reshaping the path from discovery to checkout.) I call it exploring because nobody knows yet what gains traction or how Google monetizes it (i.e. traditional ads, enterprise contracts, subscriptions). What’s obvious: Google is chasing (1) one thing social platforms have more of – time spent and (2) Share or ecommerce search from Amazon. 

So what does this mean for organic search strategy and where you deploy resources? Four principles:

  1. Differentiation: what you invest in is genuinely different from the field of competition.
  2. Experimentation: or it’s a new bet with first-mover or arbitrage upside, on Google or somewhere else. You want a mix of the two.
  3. Prioritization: you can’t chase everything. Pick the right number of bets, and remember they’re bets. AI lets you take on more at higher quality; apply the learnings, then move to the next one.
  4. Cost-effectiveness: for the channel overall and for each strategic investment. What will it cost? How complex is it? How much upside toward the channel’s total upside? How much time should the team spend?

One more thing: organic search can’t run in a silo. On the strategic bets leaders have to get teams talking, finding shared goals and common ground, to drive 1+1=3 outcomes.

There’s AI-driven revenue to capture, and I haven’t been this excited to chase something since I found SEO on Twitter in 2012. Cheers to that.

Workplace Agents vs. Personal Apps

Posted on by Brendan

After two years away from the blog, I’m trying to build the writing habit back into my weekly routine. I am starting with two different product marketing announcements that caught my eye this week:

Taking these posts in isolation, one wants to be your office manager; the other wants to be your personal lifestyle assistant.

I’m particularly interested in Claude’s “quiet” social post. In a world of loud announcements, a low-stakes social post is a genius way to gather real-time signals. They aren’t just announcing a feature; they are testing a hypothesis: Do people actually want AI in their personal lives, or just their work?

The Takeaway: Don’t get distracted by the pace of launches. The real work is following customer behavior and having the guts to ship “quiet” experiments mapped to big bets to see what sticks, like these companies are doing themselves.

Weekly Listen #4

Posted on by Brendan

I stumbled on Kevin Rose’s podcast and decided to watch this one first. I was excited to find this because I watched his first interviews about 12 years ago with tech entrepreneurs and got a lot out of them. I think he called the show The Foundation back then.

One piece they talk about is encouraging failure and learning from failure for kids which I believe is important too. I’ve been enjoying watching my oldest who is 3 during soccer on Saturday mornings. He’s young so he’s failing a lot whether he picks up the soccer ball instead of kicking it or doesn’t understand the drill. Rather than correct him, I just watch him. He’s having fun anyway so that’s another reason I sit back. He doesn’t need my help.

I was thinking the other day at some point that his consciousness will develop and we’ll see how he reacts to knowing about these small failures.

Learnings & Thoughts on RAG

Posted on by Brendan

I enjoyed this essay on X about RAG and learned a lot. 

Part of LLMs advancing is a technology called RAG (retrieval augmentation generation). RAG is an evolving technology in the world of information retrieval for LLMs but it has problems. 

  1. For large models, freshness of source data for time sensitive queries is a limiter right now. A LLM needs to update it’s data source and put it back through the AI model and know to site the new source when a user writes a prompt or query. 
  2. RAGs has a similar problem for smaller models too. Amongst a corpus of internal documents with perhaps conflicting information because there are drafts how does the RAG know which once to use? 

I think one important quote from Aaron that ties to a post from Google is this: “The AI’s answer is only as good as the underlying information that you serve it in the prompt.” Google’s Liz Reid wrote: “With our custom Gemini model’s multi-step reasoning capabilities, AI Overviews will help with increasingly complex questions. Rather than breaking your question into multiple searches, you can ask your most complex questions, with all the nuances and caveats you have in mind, all in one go.” 

This is something we started talking about 12+ months ago at this point where the better the prompt the better the answer. It’s clear search engines and LLMs are trying to nudge humans to change search behavior in this way to improve answers and reduce hallucinations. 

The question on my mind is, will consumers change their search behavior to more complex searches or will tech companies need to find other ways to improve source identification during RAG to improve output? 

Mortgage Interest Rates: Existing vs New

Posted on by Brendan

It feels like the talk of interest rates and how high they are is everywhere. I don’t think I go a day without reading or hearing someone mention how high rates are. I do it myself as well. As a homeowner, I talk and think about the rate I got versus the rate I would get today. I found this chart put together by Axios with info from NYT and Federal Housing Finance Agency puts into perspective why it is such a prominent topic. Dating back to 1998, the gap has never been as wide as it is now.  

When I shared this chart with someone who has bought homes since the 80s, I learned more about this delta further back in time. He told me about an assumable mortgage he used to finance a house in 1981. The market mortgage rate he said was 18% and he picked up the seller’s existing mortgage at 9% which is a wider delta than we have today. This gives me hope that America can get through this. We’ve done it before.

existing and new mortgage rates since '98