I’ve discovered that the most successful GEO and AEO strategies are deeply rooted in traditional SEO. It’s fascinating how these foundational principles seamlessly translate to AI visibility. Let me share why it’s crucial not to overlook these basics.
In our quest to harness the power of AI, many of us might feel tempted to skip straight to advanced strategies. However, without a solid SEO foundation, even the best AI-driven tactics can fall short. The rules that govern traditional SEO are critical to unlocking AI’s full potential in search visibility.
Consider this: AI systems thrive on structured data and clear content hierarchies. It’s precisely these elements that traditional SEO prioritizes, ensuring that our websites are not only user-friendly but also AI-ready. This is why every AI optimization journey should begin with tried-and-true SEO practices.
As someone who loves diving into the nuances of AI and SEO, I’ve seen firsthand how these two fields complement each other. Embracing the basics doesn’t merely prepare us for AI; it catapults our strategy into an era of smarter, more efficient digital marketing.
I’ve been intrigued by Google’s latest test in the Google Business Profile: AI-generated responses to customer reviews. This innovative tool offers businesses the ability to create suggested replies to reviews, which I can then review, tweak, and manually submit.
Why It Matters to Me. Engaging with customer reviews significantly impacts conversions and trust. However, I’m aware of the risks associated with generic AI replies, especially for negative reviews where sincerity is crucial. Personalized, quality responses are more influential than merely replying for the sake of it.
What I Saw. Here’s a sneak peek of how the feature appears:
The Details I’ve Discovered. It seems Google is conducting a limited roll-out of this ‘Reply to reviews with AI’ feature within the Google Business Profile.
It generates proposed responses to customer reviews.
I can review and modify these suggestions before submitting.
The availability fluctuates across different accounts and reviews.
The feature is spotted in the U.S., Brazil, and India, but not yet widely in Europe.
Initial Impressions. Some users, like me, noticed prompts targeting older, unanswered negative reviews.
In one test I observed, it’s possible to generate AI responses in bulk.
I’ve read mixed reports on automation—some claim bulk responses still need a review, while others experienced fully automated replies that require no edits.
How I First Learned About It. This feature caught my attention first through LinkedIn, thanks to Chandan Mishra, a freelance local SEO specialist, and it was further amplified by Darren Shaw, founder of Whitespark.
As someone deeply invested in SEO, I’ve often pondered: Could AI eventually render SEO obsolete? This question has sparked considerable debate as AI capabilities continue to expand.
While AI can streamline technical tasks, there’s a consensus that it won’t entirely replace the need for human expertise in SEO. Early studies affirm that human input remains vital.
AI efficiently handles structured data tasks, yet it falls short without meticulous data oversight and expert human guidance.
The advent of AI signifies a shift in workflow dynamics, raising the bar on execution and focusing human expertise on more strategic areas.
AI’s potential to reduce reliance on semi-technical expertise is notable, especially in well-structured domains like coding. However, crafting AI-driven solutions without human refinement often proves inadequate.
The challenge for generative AI lies in its machine-like processing. Only those with technical know-how can truly harness its potential for tasks like generating functional product descriptions or scalable alt text.
AI’s effectiveness is directly linked to the quality of human instructions. Expertise in creating carefully structured prompts is indispensable.
Despite the aid AI offers, its reliance on structured data and human oversight underscores why SEO isn’t fading anytime soon.
A closer look at AI’s progression reveals the persisting need for human intervention, especially as the web’s uncurated nature challenges AI’s data processing capabilities.
While AI tools are growing more sophisticated, they still depend on human expertise to function seamlessly within comprehensive SEO strategies.
The complexity of implementing full SEO automation highlights the irreplaceable value of human judgment in managing intricate data environments.
As AI tools evolve, they serve as companions to SEO, boosting efficiency but not substituting the strategic insight SEO professionals bring to the table.
For SEO to truly become obsolete, AI must autonomously manage tasks reliably and efficiently, a feat still eluding current technology.
Society’s adoption of AI faces barriers; perceptions of AI as a threat slow its integration despite its potential to enhance SEO practices.
As AI becomes normalized, its role within SEO will likely evolve, but the human touch remains essential in delivering creative and impactful results.
As I explore the ever-evolving landscape of Google’s AI Mode, it’s fascinating to witness how ad formats, reporting, and control are taking shape. Google seems to have a master plan in place that competitors just can’t keep up with.
I find myself intrigued by Google’s entry into this next phase of conversational search. It’s not just about user numbers but who can effectively monetize them. Google’s mature ad systems and extensive advertiser base offer a significant edge.
The initial panic surrounding Google’s position is over. Google’s long-standing advantages and huge investments have leveled the playing field with ChatGPT in LLM search.
Back in December 2025, when Google declared code red, it became clear that they were serious. Apple’s decision to partner with Google for its AI needs is indeed telling.
Initially, it seemed plausible that Google would struggle against ChatGPT, but the market has since adjusted its views. The company’s valuation reflects renewed confidence, rivaling even Apple at a substantial $3.6 trillion.
As I dive deeper into how monetization will shape this race, I’m struck by how Google’s recent advances have significantly boosted its valuation.
It’s clear that the visibility of financial projections plays a massive role in how the company is perceived financially. Google’s approach to shifts in user behavior is crucial in maintaining its robust business model.
From my perspective, much of your digital advertising budget likely goes to Google. Its prominence demands attention, not just in search but also in emerging AI platforms like ChatGPT and Claude.
The competition in LLM conversations is intriguing. Google and ChatGPT are vying for different monetization models, a fascinating case study of differing strategies.
For those of us in advertising, it’s essential to monitor developments like ad formats, rollout pace, and public reception to ads within these platforms.
OpenAI’s current monetization model is intriguing but still nascent, reliant on a small group of major advertisers. We’ll see how they expand and fine-tune this model over time.
Outsourcing inventory to programmatic partners is a smart move for OpenAI but highlights their early stage in building an ads business.
For Google advertisers, the shift to AI Mode need not be alarming. I’m watching for the ways these LLM sessions are shaping user experiences and ad placements.
One thing is for sure; the enhancements in AI Mode continue, promising more seamless and user-friendly interactions. The potential for ads remains, though their form is still evolving.
Monitoring key areas like the extent of monetization, advertiser control, and campaign types becomes more important as we navigate this new landscape.
Ultimately, the future of advertising in AI-driven search is one of adaptability and strategic planning, aligning closely with user and advertiser behaviors in this exciting yet challenging era.
As I look back on 2025, it’s astonishing to see the AI search traffic growth leap by an impressive 180% year-over-year. I’m diving into the data to better understand how this impacts our visibility strategies. We’ll explore insights on ChatGPT, Gemini, Perplexity, and Claude usage trends in this review.
With AI technologies rapidly advancing, I’ve noticed how they continue to reshape how we think about search and brand visibility. The increased use of AI-powered tools signifies a pivotal shift in the way we approach digital marketing strategies.
In 2025, ChatGPT saw a remarkable surge in use, closely followed by interest in platforms like Gemini and Claude. This data is crucial as we plan for future visibility tactics, ensuring that our brand remains competitive in an ever-evolving digital landscape.
How does this data affect your brand’s approach? I believe understanding and leveraging these trends will be key to optimizing AI-driven search capabilities and visibility while crafting more personalized and effective content strategies.
I recently embarked on a fascinating journey to explore how ChatGPT’s Shopping feature is activated. It’s intriguing how product categories seem to play a more significant role compared to purchase intent language.
In my analysis of 1.18 million prompts, supported by a detailed review of 7,500 labeled examples, I discovered a notable pattern. Prompts that specifically mention shippable consumer goods are highly likely to trigger Shopping cards. However, prompts about software, services, travel, and financial products almost never have the same effect.
I noticed that adding specific constraints, like price, features, or intended use, boosted the chances of the Shopping trigger, though only within the confines of product categories.
The process boils down to a straightforward rule: if the primary noun in your prompt is something you could easily buy on Amazon, there’s a good chance the Shopping feature will appear. Using this logic, I developed a classifier that can replicate ChatGPT’s Shopping behavior with an impressive accuracy of around 95–97%.
I’ve got some exciting news to share about Google’s latest developments! They’re expanding their innovative Personal Intelligence feature across AI Mode in Search, the Gemini app, and in Chrome—specifically for U.S. users.
Google’s Personal Intelligence now moves beyond its beta phase, reaching more everyday users. It’s an exhilarating step toward a truly personalized search experience, thanks to clever use of first-party data like Gmail and Photos. This shift makes search outcomes more personalized and unique, especially in AI Mode, where results adapt to individual user behaviors, previous purchases, and search histories.
Why I care
Google’s push into personalized search fascinates me. It’s creating a landscape where search results become increasingly individualized, but consequently harder to predict or replicate.
The details
Personal Intelligence will now function across:
AI Mode in Google Search (available now in the U.S.)
Gemini app (currently rolling out to free users)
Gemini integrated in Chrome (ongoing rollout)
How it works
I can connect applications such as Gmail and Google Photos, allowing Google to give me personalized responses. Some of the cool examples I’ve come across include:
Shopping suggestions rooted in my buying habits and favorite brands.
Tech troubleshooting aided by receipt details for the exact devices.
Travel tips tailored to my flight schedules and past getaways.
Custom itineraries and local recommendations.
Hobby proposals based on my interests.
Availability
It’s worth noting that these features are reserved for personal Google accounts and won’t extend to Workspace users—for now, at least.
Want to know more?
You can check out the details on the ad-free promise Google made for AI Mode users here.
Catch-up quick
Originally, Google introduced Personal Intelligence for Gemini subscribers in January with limited access to AI Pro and Ultra users. At that point, it hadn’t been integrated with Search—something they’ve since rectified.
Initially, the feature was optional and off by default.
New updates deliver on Google’s plan by making it part of Search AI Mode.
They’re rapidly expanding access to more users, even for free accounts.
Plus, it’s now merging into Chrome.
Privacy and control
Google emphasizes user choice:
Opt-in is required to connect apps like Gmail.
Users can enable or disable connections whenever they choose.
Importantly, Gmail and Photos content isn’t directly used to train AI models.
However, Google may use limited data like prompts and responses to enhance their systems.
For further reading, check out Google’s blog post on this impressive expansion of Personal Intelligence here.
As I delve into the evolving landscape of web traffic, I find Yahoo CEO Jim Lanzone’s insights on AI-powered search engines, particularly Google’s AI Mode, incredibly fascinating. He believes this technological evolution poses a significant threat to the web’s traditional traffic model.
Jim highlights a major concern: “I think that the LLMs are one big reason they’re under threat, with AI Mode in Google being the biggest challenge.” This makes me ponder the impact on publishers who rely heavily on these traffic flows.
I resonate with Jim’s view that publishers truly deserve this traffic. He articulates a fundamental truth: “Those publishers deserve [traffic], and we’re not going to have the content to consume to give great answers if publishers aren’t healthy.” This reflects the delicate balance required in the digital content ecosystem.
Why I care. Many websites, mine included, are noticing a dip in traffic coming from answer engines such as Google and OpenAI. It feels like a looming concern that could worsen. Yahoo’s dedication to maintaining the “search sends traffic” model is reassuring, as Jim passionately explains: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”
Yahoo’s unique AI approach. Listening to Jim on the Decoder podcast, I learn that Yahoo is carving its own path with AI. Unlike the more conversational chatbot models, Yahoo isn’t pursuing to be an AI assistant: “Ours looks a lot more like traditional search and it is more paragraph-driven. It’s not a chatbot that’s trying to act like it’s a person and be your friend.” I see this as a move towards emphasizing informative search experiences.
Moreover, “We’re not a large language model. We’re not going to be the place you come to code. We’ve really launched Scout as an answer engine.” This strategy, I believe, could provide a clearer, more reliable information source online.
What’s next: Embracing personalization. In observing Yahoo’s strategy, I’m excited to see their efforts to evolve. They’re embedding AI across platforms: “You are very shortly going to see us get into very personalized results. You’re going to see us get into very agentic actions that you can take.” This indicates a future where user-specific solutions take precedence.
For instance, Jim notes, “There’s a button in Yahoo Finance that does analysis of a given stock on the fly… It is in Yahoo Mail to help summarize and process emails.” Such tools could transform how I interact with content on various platforms.
Yahoo vs. Google: A non-competition. Interestingly, Yahoo isn’t trying to directly outplay Google. Instead, as Jim points out, the focus is on existing users and enhancing their experience: “Nobody chooses, you will not be surprised, Yahoo over Google or somewhere else to search. The way that we get our search volume is because we have 250 million US users and 700 million global users in the Yahoo network at any given time. There’s a search box there. And infrequently, they use it.” It’s more about nurturing the loyalties of existing users.
A word of caution. The conversation also shines a light on the potential pitfalls of heavily relying on AI platforms. Jim references past experiences with Google: “You are tempting fate by opening up a way for consumers to access your product within a large language model.” This analogy resonates with me deeply, remembering the cautionary tales in tech history.
Yet, he warns: “The big bad wolf will come to your door and say everything’s cool.” It’s a timely reminder of the ever-competitive and unpredictable nature of tech alliances.
As a content strategist, I often wonder how my work feeds into the AI pipeline, especially the critical ‘rank and display’ stage.
Understanding the annotation, recruitment, grounding, display, and won gates is crucial to ensure that AI engines trust and recommend my content.
The DSCRI infrastructure phase kickstarts the journey by handling discovery through indexing, where content is either picked up or left out.
In the competitive phase, ARGDW tests not only require content to pass but to outperform alternatives, ensuring it doesn’t end up losing to better-annotated competitors.
The ARGDW phase is about survival of the fittest, determining if assistive engines will utilize the content I create.
Where ‘rank and display’ once muddied distinctions, understanding and optimizing each gate individually can significantly improve content visibility and ranking success.
The Competitive Turn: Transitioning from Absolute to Relative Tests
This transition is pivotal—the moment where content quality impacts competitive performance most critically.
When moving from DSCRI to ARGDW, the system stops merely verifying presence and starts comparing content quality against competitors.
Every piece from annotation forward requires content to excel over potential alternatives, making confidence scores relative to others on similar topics.
Here, efforts at preparing content fully come to fruition as the engine pits it against competitors.
I’ve found an incredible new way to streamline content creation, competitive analysis, reporting, and monitoring with the latest Profound Agents feature. We can now effortlessly integrate prompt volume data directly into any Profound Agent, bringing together all our workflows into a single platform. This innovation is perfect for marketers looking to enhance efficiency.
