Today, I’m thrilled to share that we’ve successfully raised $96 million in a Series C funding round, reaching a remarkable $1 billion valuation. This monumental achievement was spearheaded by Lightspeed Venture Partners, with the esteemed participation of Sequoia Capital, Kleiner Perkins, Evantic, Saga, and South Park Commons.
Inspired by this post on Try Profound Blog.
I’ve come to realize that AI has dramatically simplified the publishing process, but it also means standing out amidst the noise is increasingly challenging. The good news is, by focusing on clarity, intent alignment, and a few strategic SEO adjustments, we can make significant progress.
As AI breaks down the barriers to production, the web is getting flooded with content that is polished, optimized, but often lacks distinctiveness. When everything seems competent, you and I must strive harder to differentiate our voices.
Though AI has transformed how content is churned out, the core of what users seek—intent—remains unchanged. They sift through headlines and descriptions, rewarding clarity and effectiveness. This is why foundational elements matter even more now.
I find that keeping content fresh isn’t about being novel for novelty’s sake. It’s about diving back into what makes content truly unique: distinct messaging, structured delivery, and a deep grasp of our audience’s needs.
The crux of the issue with AI-generated content isn’t its factualness—it’s its sameness. AI draws from vast pools of existing content, often reproducing unremarkable tropes and conclusions. Individually, they seem fine; collectively, they’re indistinguishable.
This homogeneity is why so much content today feels the same. Even when relevant, it seldom provides a unique reading experience.
Both users and search engines are responding in kind. In a sea of similar content, differentiation becomes key. At this juncture, originality, specificity, and intent alignment have taken on heightened importance.
Ironically enough, AI has increased the value of originality. As automated content inundates the web, signals like clarity, usefulness, and intent alignment become beacons of high-quality content.
Many teams falter here, competing with AI by focusing on quantity over quality. Freshness isn’t about novelty; it’s about crafting content that feels distinctly human and undeniably helpful.
Ever since content creation tools evolved, what’s been constant is how people interact with search engines. Users still show up with an issue to solve, skimming through results to pick what seems most relevant.
Despite the rise of AI, this behavior endures.
Page titles, headings, and meta descriptions serve as that crucial first contact with the user. They function almost like ad copy, contrary to assumptions that these elements are becoming obsolete.
Classic SEO principles—clear search intent alignment, descriptive language, organized structure—continue to underpin fresh content.
Although these aren’t groundbreaking ideas, their importance has surged. A tweak in clarity doesn’t just help search engines index a page; it helps users find answers to their questions.
A recent experiment on my website examined whether more descriptive titles could boost clicks without altering the underlying content. We tested the hypothesis by aligning page titles more closely with search intent and user needs.
The result? A greater alignment led to a substantial increase in click-through rates, proving that small changes can powerfully impact visibility and engagement.
Remaining fresh in the AI era isn’t about jumping on every new tool but requires intentionality in creating, positioning, and maintaining content.
The essence of SEO has always been search intent, not keyword stuffing. Before crafting content, ask what problem the searcher is trying to address and what a good answer would look like in their context.
In a crowded SERP, an effective title is crucial to catch a user’s attention and make them click.
Oft-overlooked is the power of improving existing content. You don’t need to produce new content incessantly when updates can achieve better results.
While AI excels at general advice, human-guided content shines through specificity and context, offering expert insights and breaking down misconceptions.
AI should accelerate tasks that don’t require judgment. Editorial responsibilities still lie with us, ensuring content aligns with our goals.
It’s not the volume of content but engagement metrics like time on page and scroll depth that define freshness.
Mainstays like clarity, structure, and relevance have only gained importance in our AI-driven landscape.
While AI has revolutionized content speed and accessibility, truly effective content remains appealing and relevant, aligning with users’ search intent and preferences.
Inspired by this post on Search Engine Land.
I recently came across fascinating research revealing how diverse AI platforms like ChatGPT, Google AI, and Perplexity cite their sources. It’s intriguing to see how each platform approaches sourcing information and the implications for their visibility.
The study highlights substantial differences in citation patterns among these major AI players. This variation in sourcing methods significantly affects how each platform is perceived in terms of reliability and authority.
Understanding these citation patterns can offer valuable insights into the competitive landscape of AI visibility. As we explore this further, it becomes clear why recognizing these differences is crucial for anyone interested in AI optimization.
Inspired by this post on Try Profound Blog.
In today’s ever-evolving digital landscape, I’ve witnessed the transformation of PPC from its traditional search roots to a more dynamic form. By leveraging new ad formats, creative strategies, and sophisticated AI, I’ve realized that we can truly gain a competitive edge.
I had the opportunity to chat with Ginny Marvin from Google and Navah Hopkins from Microsoft about the direction PPC is heading. This discussion was a highlight for me during the SMX Next keynote. Here’s a recap of our conversation.
When we explored emerging ad formats and channels beyond search, Ginny and Navah shared their excitement for AI-driven ad innovations. Navah pointed out Microsoft’s strides in AI-first formats, highlighting showroom ads as a standout feature:
“Showroom ads allow users to interact directly with content provided by advertisers, and with tools like Copilot for brand security, it’s a game-changer.”
As a gamer myself, Navah’s insights into gaming as an evolving ad channel resonated with me. We’re all familiar with the frustration of intrusive ads, but more engaging, intelligent formats are on the horizon.
Ginny agreed, emphasizing how conversational AI and visual discovery tools are reshaping user intent. These elements make conversion journeys far more dynamic than our standard keyword-to-click pathways.
For me, it was clear that embracing this new landscape means recognizing that traditional search is just one of many opportunities for advertising.
I was particularly struck by the discussion on the ever-growing importance of visual content. Navah summarized it well for me with:
“Most people are visual learners, and visual content belongs in every stage of the funnel.”
This really encouraged me to rethink how I view visual content within marketing strategies—not just at the top of the funnel or in remarketing, but throughout the entire process.
Ginny also touched on how brand-forward visuals are becoming indispensable. She mentioned that successful marketers will need to consistently reflect their brand’s essence through curated creative libraries across various channels.
We also delved into some common myths regarding AI and creative processes. I related to Navah’s caution against overly depending on AI for creativity:
“AI is not a substitute for our creativity. Don’t delegate your entire creative process to AI.”
In my experience, the real power lies in using AI to enhance our creative strengths. Even solitary elements like a headline or image need to resonate individually.
Ginny’s reinforcement of the need for diverse visual assets was enlightening. Campaigns that span multiple channels benefit from a broad range of creative assets, crucial for optimal performance and storytelling.
The conversation naturally progressed to the strategic use of assets. Ginny’s point on AI systems evaluating individual performance was eye-opening for me:
“Swap out underperforming assets, and let niche high-performers reveal audience insights.”
This approach helps me maintain relevance and reduce AI chaos moments, as Navah aptly called them, where asset overlap hampers clarity. Streamlining through distinct creative assets is crucial.
Finally, as we wrapped up, Ginny and Navah shared insights on partnering with AI for measurement. Navah outlined the foundational inputs AI depends on:
“First-party data, creative assets, ad copy, website content, goals, budgets – these guide AI toward achieving our desired outcomes.”
She emphasized incrementality, urging us to grasp the additional value our campaigns generate, now more crucial than ever.
Ginny acknowledged the transition from granular metrics to broader, privacy-focused analytics. She encouraged us to focus on understanding audience themes rather than individual queries.
Inspired by this post on Search Engine Land.
I’ve noticed how search is evolving far beyond the typical blue-links framework. Now, discovery often happens within AI-generated answers—whether it’s Google AI Overviews, ChatGPT, or other LLM-driven platforms. It’s clear to me that visibility is no longer just about rankings, and influence doesn’t always lead to a click.
Traditional SEO metrics like rankings, impressions, and CTR seem to fall short as search becomes more recommendation-driven and attribution becomes increasingly opaque. Clearly, a new measurement layer for SEO is needed.
This is where LLM consistency and recommendation share (LCRS) steps in. It helps measure how reliably and competitively my brand appears in AI-generated responses. It’s a modern equivalent to keyword tracking, tailored for the LLM era.
Traditional SEO metrics worked well when visibility was tied directly to ranking positions and user interaction pivoted on clicks. This relationship weakens in LLM-mediated searches. Even if my page ranks at the top, it may never appear in an AI-generated answer.
LLMs might favor another source with lower traditional visibility, exposing a flaw in conventional traffic attribution. Here, brand influence might occur without a measurably corresponding website visit. The impact exists but isn’t reflected in the traditional analytics landscape.
At the heart of this change is something that traditional SEO KPIs were not developed to handle:
Traditional SEO analytics often stop at indexing and ranking. However, in a world dominated by LLM-driven search, the true competitive edge lies in recommendation—a dimension current KPIs struggle to quantify. This is where the gap between influence and measurement creates a space for new performance metrics.
With LLM consistency and recommendation share, I can gauge how reliably my brand surfaces and is recommended by LLMs during search and discovery processes.
LCRS answers a crucial question that traditional SEO metrics can’t: When users look to LLMs for guidance, how often and consistently is my brand part of the conversation?
It evaluates my visibility across three dimensions:
LCRS is less about isolated citations and more about establishing a repeatable, comparable presence, enabling me to benchmark against competitors and track changes.
Although it’s not a replacement for established SEO KPIs, LCRS enhances them by addressing zero-click search scenarios where recommendations determine visibility.
LCRS comprises two primary elements: LLM consistency and recommendation share.
In LCRS, consistency measures how reliably my brand appears across similar LLM responses. High consistency means my brand surfaces across numerous, semantically similar prompts rather than relying on a single high-performing query.
Considerations like prompt variability, temporal variability, and platform variability come into play. Consistency reflects durable relevance beyond transitory exposure.
While consistency focuses on repeatability, recommendation share assesses competitive presence. It examines how frequently LLMs recommend my brand relative to others in the same category.
Not all appearances count as recommendations; it’s about how often my brand is positioned as a primary choice against competitors, reflecting the portion of recommendation space occupied.
To effectively measure LCRS, a structured approach is necessary, one that replaces anecdotal observations with repeatable sampling reflective of actual user interactions.
I start with choosing prompts representing my category, ensuring they include variations in phrasing to capture natural language nuances.
The choice between brand-level and category-level tracking hinges on focus. Most insightful at the category level, LCRS shows which brands LLMs choose to highlight.
Since managing data volumes is a challenge, I rely on programmatically executing prompts and parsing responses to identify which brands are recommended.
Automated data capturing is key, though human review is crucial for interpreting nuanced information. Tracking analysis over time is essential for stable directional signals.
LCRS is particularly valuable in environments where synthesized answers shape decisions. In marketplaces, SaaS, YMYL industries, and comparison searches, LLMs significantly influence visibility.
LCRS offers directional insight rather than definitive certainty, given LLMs’ non-deterministic nature. Short-term volatility is expected, so evaluating trends over time is vital.
This metric isn’t a replacement for traditional analytics but complements them by addressing influence areas without direct attribution.
More than a ranking tool, LCRS signals a shift toward brand presence engineering in the LLM-driven discovery space. Brand authority is becoming crucial, with successful SEOs adapting to optimize for retrievability, clarity, and trust.
As LLM-driven search reshapes discovery, expanding from ranking positions to presence and recommendation is crucial. LCRS allows me to explore this gap and complement existing performance metrics for a comprehensive visibility strategy.
My journey with LCRS shows that adapting SEO strategies for evolving landscapes boosts both visibility and influence within LLM-driven search experiences.
Inspired by this post on Search Engine Land.
As I delve into the world of AI searches for wearable technology, I’ve noticed a fascinating trend. It turns out that trusted third-party sites are more frequently favored over brand websites. This piqued my curiosity, and I wanted to dig deeper into these patterns and uncover how one can achieve AI visibility.
One of the key aspects that stood out is the consistency in how certain domains are cited across AI searches. These sites have established a level of trust and authority that AI algorithms consistently recognize. As I’m navigating through this data, I’m exploring the most frequently cited domains in this realm and the trust patterns they demonstrate.
Gaining AI visibility isn’t just about being present; it’s about earning trust and authority. By understanding these patterns, I feel more equipped to help others and myself in enhancing the visibility of our wearable tech offerings.
Inspired by this post on HiGoodie Blog.
I recently stumbled upon a fascinating study that shows how ChatGPT pulls most of its references from the beginning sections of content. It’s clear from this research that the AI favors straightforward definitions, a balanced tone, and densely packed entities.
According to Kevin Indig, a Growth Advisor who analyzed 1.2 million AI responses and 18,012 citations, ChatGPT has a strong preference for using citations from the top of the content. This was a revelation for me and definitely something to keep in mind when writing.
Why we care. The traditional search landscape often rewards depth and gradual payoffs. However, AI is changing that game by favoring clear entities and direct answers right at the start. If I don’t make sure my key information is front and center, it’s less likely to be cited by AI.
By the numbers. In examining various datasets, Indig’s team found a “ski ramp” pattern—44.2% of citations originate from the first 30% of content, 31.1% from the middle, and only 24.7% come from the final third, with a noticeable drop towards the end.
Breaking it down even further, I learned that at a paragraph level, AI citations largely come from the middle sentences (53%), with 24.5% from the first sentence and 22.5% from the last.
The big takeaway. This really drives home the importance of front-loading critical insights at the article level. Within paragraphs, focusing on clarity and meaningful content rather than trying to hook readers with a dramatic first sentence seems to be more effective.
Why this happens. Large language models like ChatGPT are trained on various styles of writing that prioritize a “bottom line up front” approach. It seems these models use the early sections as a framework for interpreting the rest of the data.
Efficiency and context establishment remain key priorities for these models, even though they can process large sets of data.
What gets cited. Indig noted five key traits of content frequently cited by ChatGPT: definitive language, a Q&A structure, entity richness, balanced sentiment, and business-grade clarity. Learning this has been incredibly insightful for how I craft my content.
Indig’s team looked at a massive volume of data, identifying the traits of highly cited content by analyzing 18,012 verified citations from ChatGPT responses. The study focused on where and why the AI pulls content, using advanced techniques to match responses to source sentences.
Bottom line. It seems the narrative approach of crafting an “ultimate guide” might not be the best for AI retrieval. Instead, a more structured, briefing-style format appears to be more successful.
This study convinced me that writers now face what Indig calls a “clarity tax.” We need to present definitions, entities, and conclusions upfront rather than saving them for the conclusion.
The report. For those interested, you can delve deeper into these findings in The science of how AI pays attention.
Inspired by this post on Search Engine Land.
Recently, I learned that Perplexity has decided to halt its advertising initiatives. The company started experimenting with sponsored placements back in 2024, but now they’re stepping back, believing these ads might jeopardize the trust that users place in their AI answer engine.
I read the Financial Times report stating that Perplexity phased out the ads and currently has no intention of reintroducing them. It’s an intriguing approach considering the rapid evolution of AI search companies.
As someone who utilizes AI-driven platforms, I find it important to monitor these changes. If Perplexity stays ad-free, brands miss out on direct paid access to a growing audience. Imagine how brands must navigate a landscape with 780 million monthly queries without the option for sponsored placements.
Perplexity was pioneering in testing ads, placing sponsored answers beneath chatbot responses. They claimed these ads were clearly labeled, ensuring they didn’t affect the quality of information. Yet, it’s evident that perception is as crucial as policy for them.
From my perspective, the notion that users might doubt the integrity of responses if ads appear is understandable. One of Perplexity’s executives mentioned that maintaining users’ belief in receiving the best possible answer is paramount.
It’s worth mentioning that while Perplexity opts out of ads, other platforms are diving in. For instance, OpenAI is testing ads in ChatGPT for free users, and Google is running ads in AI Mode within Search, although not in Gemini. Meanwhile, Anthropic is committed to keeping Claude ad-free, which reflects different strategic approaches in the industry.
Sustainability in business is key, and Perplexity sees subscriptions as its core model. They offer both free and paid plans ranging from $20 to $200 monthly, boasting over 100 million users and approximately $200 million in annual revenue. This model reflects their focus on accuracy and providing the truth, minimizing conflict of interest.
Despite launching shopping features, Perplexity doesn’t take a transaction cut, aligning with their cautious stance on revenue models that might undermine trust.
For more detailed insights, one could explore the full report from the Financial Times, though it’s a subscription-based service.
Inspired by this post on Search Engine Land.
During Airbnb’s Q4 2025 earnings call, CEO Brian Chesky shared an intriguing insight that has captured my attention: bookings from AI chatbots surpass those driven by Google in terms of conversion rates.
Chesky revealed, “And what we see is that traffic that comes from chatbots convert at a higher rate than traffic that comes from Google.” However, he was less forthcoming about the exact conversion rates or the volume of traffic these AI chatbots generate for Airbnb.
I find it fascinating that despite lacking specific conversion data, it seems clear that guests reaching Airbnb via AI chatbots are further along in the booking journey compared to those originating from Google searches.
The chatbots contributing to this traffic boom weren’t explicitly identified, but Chesky did mention well-known models like OpenAI’s ChatGPT and Google’s Gemini, among others.
This evolution is significant because AI assistants are starting to prove themselves as powerful tools in the early stages of customer engagement, potentially surpassing traditional search methods in terms of quality lead generation.
Chesky portrays these chatbots as not only similar to traditional search platforms but as vital components in the journey of customer acquisition.
He believes that, “These chatbot platforms are gonna be very similar to search. Gonna be really good top-of-funnel discoveries,” highlighting their potential in broadening Airbnb’s reach.
Airbnb is excited about what lies ahead as they envision an AI-native experience where their app evolves from merely assisting in searches to genuinely understanding user preferences.
“So AI search is live to a very small percent of traffic right now,” Chesky mentioned, emphasizing that Airbnb’s strategy involves a lot of quick iterations and experimentation rather than launching big, bold changes.
Currently, within Airbnb, AI tools are not only external but also internal assets. Their AI-powered customer service agent significantly reduces the workload by resolving nearly one-third of North American support tickets.
The company aims to expand this AI tool globally with multilingual capabilities, including voice support, with hopes of handling more than 30% of tickets within the year.
An AI-powered conversational search feature is live for a limited user base, showcasing Airbnb’s commitment to embracing AI as part of their development cycle rather than waiting for a massive roll-out.
While the idea of sponsored listings remains in the background, Chesky notes that traditional ad formats might require tweaking to align with the conversational nature of AI environments.
Previously, before generative AI and AI-powered searches became trends, Airbnb shifted its budget focus to brand marketing, reducing expenditures on search marketing, a move that now aligns with their evolving AI approach.
Inspired by this post on Search Engine Land.
I embarked on a journey to uncover whether AI crawlers favored Markdown over HTML. By conducting a controlled experiment, I aimed to see if serving content in Markdown format would result in increased bot traffic. After analyzing data from 381 pages over the span of three weeks, I’m eager to share what I discovered.
The results of this experiment could provide valuable insights for those interested in enhancing the visibility of their content through strategic formatting. Stay tuned as I reveal the intriguing findings.
Inspired by this post on Try Profound Blog.