I believe the launch of TurboQuant will revolutionize AI and SEO as we know it. This cutting-edge algorithm from Google drastically reduces the computing power and energy needs by allowing the massive compression of LLMs and vector search engines.
Imagine using six times less memory and achieving eight times the speed without compromising accuracy. That’s how TurboQuant dramatically lowers the cost of running AI tasks.
As search engines evolve from simply listing links on a SERP to providing immediate AI-generated overviews, it’s crucial for us in the SEO industry to adapt. We need to focus on creating meaningful, trustworthy content and understand its impact on searches.
Before AI became prevalent, SEO was grounded in basic keywords and topics, which inefficiently represented user intent. High costs and energy consumption hindered mapping true meaning across the web, but now TurboQuant uses an advanced compression method, PolarQuant, to transform data into manageable coordinates. This breakthrough allows Google to process complex ideas far more efficiently.
TurboQuant can match exact search meanings in real time, thanks to its ability to understand user intent using past searches and real-world contexts.
The near-zero indexing lead time of TurboQuant eradicates delays between publication and ranking. Trusted publishers will gain instant recognition for their expertise, while the system also blocks manipulation and spam from appearing.
We must prepare for the fast-approaching era where AI summaries become the norm in responding to most queries. Thin content, which adds no original value, will vanish because AI can now summarize the web almost instantly, making unique viewpoints and genuine data irreplaceable.
Developing trust and authority with original thoughts, data, and experiences will prove essential, as AI-generated summaries merely consolidate existing information.
The focus of our SEO strategies should be to become a source AI recommends reliably, not just rankings based on keywords. TurboQuant maintains a more reliable index of facts by validating them against its real-time knowledge base.
This new system tracks a brand’s strength across various platforms, reinforcing the necessity of improving our knowledge graph as a trusted source.
With TurboQuant handling vast information without delays, hyper-personalization is set to explode in ways we’ve previously not imagined. AI agents could remember extensive user interactions to provide extensive personalization.
TurboQuant’s capability to integrate various signals into a cohesive perception of a brand’s value demands a strategic shift toward consistent, omnichannel representation.
We’ve prioritized quantity over quality for far too long in this industry. TurboQuant signals the end of this era, as it necessitates creating high-quality, meaningful content that establishes us as trusted entities.
Delivering a reliable message with a clear voice will guide how our messages are distributed and our brand credibility.
As I look ahead, I’m thrilled to share what we have in store with our latest product, Profound. Over the coming weeks and months, we are embarking on a journey that represents a much bolder move than anything we’ve previously attempted.
Internally, our team is buzzing with excitement, and we believe it’s time to extend that excitement to you, our valued customers. We’re eager to unveil our vision for the future and how it aligns with your needs.
SEO isn’t dead—far from it. But let’s face it, AI is definitely changing the game in ways we never imagined. This got me thinking about how things are looking different for us, especially with the rise of zero-click searches and AI Overviews. In 2026, these are becoming more like the hand guiding our SEO strategies.
With AI advancements, I’m seeing how crucial it is for all of us to adapt and build our SEO approaches around these innovations. Answer Engine Optimization (AEO) is making waves, and it’s fascinating to watch how it reshapes our tactics.
If we want to stay ahead, integrating AI into our SEO strategies isn’t just optional—it’s essential. The landscape is evolving, and so should we.
AI search is reshaping how SEO visibility is understood. It can often overlook high-ranking brands in buyer answers, urging us to refocus our strategies. Our mission as link builders is to optimize the sources AI systems use to retrieve and cite information.
Link building has evolved significantly over the years. Traditionally, visibility was measured by keywords, rankings, links, and click-through traffic. Although these metrics are still crucial, their influence, especially at the top of the funnel, has diminished.
There’s a seismic shift in how prospective customers resolve their issues. Today, buyers no longer compress their queries into keywords. Instead, they interact with AI systems using natural language, providing context to make informed decisions tailored to their needs.
If we ignore this change, we’re in for visibility nightmares that outdated metrics can’t explain. As link builders, our role has always been about more than just accumulating links. We must earn visibility on pages that convert.
Modern link building requires us to focus more closely on decision-making, understanding what buyers need, ensuring the information’s existence, and discerning which sources AI can trust and utilize.
That’s why our focus should shift towards citation optimization.
AI search changes the landscape of SEO visibility. Top-of-the-funnel strategies are still relevant, but they don’t yield the same impact as before. Ranking for key topics remains beneficial, as does maintaining visibility in searches and sources AI systems refer to for decision-stage prompts.
Core SEO principles such as creating useful content, fostering trusted references, establishing authority, maintaining source consistency, ensuring clarity, and building strong links still matter. However, the traditional process has weakened.
We’ve built an entire SEO model around keywords, but they were always simplified representations of real problems. People had to translate their questions, constraints, fears, or decisions into keywords to use search.
AI changes this behavior. People ask questions naturally, add context, and describe their problems, what they know, and their obstacles. Although simple, this represents a significant mental shift for SEO teams—from focusing on keyword rankings to assisting people in solving problems.
Citation optimization involves guiding AI systems to useful source material for decisions rather than simply adding another link.
AI makes visible the questions buyers once asked sales directly. We’ve observed enterprises with vast search visibility still missing in critical AI-driven buyer queries.
Massive keyword searches and site traffic don’t guarantee presence in these AI-centric answers, as more focused questions tie closely to buyer pain points and services. Competitors often appear instead.
Google’s AI Mode may not recognize some brands due to a lack of context necessary to confidently recommend them for specific buyer questions.
These aren’t traditional keyword questions. They’re deeper buyer-side queries typically surfacing during sales interactions, aiming for clarification on fit, use cases, proof points, and implementation, traditionally held in sales reps’ knowledge.
Nowadays, buyers conduct this research independently when narrowing down options, confirmed by our recent behavioral study.
As link builders, it’s our responsibility to extract this valuable information from within our organizations, posting it where AI tools are likely to source answers, not just focusing on backlinks.
This necessitates access to essential sales and implementation diagnostics insights.
When these questions arise, simply covering keywords isn’t enough. It showcases demand but doesn’t highlight necessary buyer trust elements nor uncover unasked questions (known as FLUQs) essential for decision-level information AI systems require.
AI systems need materials to answer buyer questions. Tracking BOFU prompts lets us examine these surfaces.
Direct prompt data remains inaccessible, but synthetic prompts can reflect real buyer intent, guiding insight without treating single rundowns as conclusive.
We must begin by considering what sources AI systems access when responding to buyer problems.
This changes link-building strategy. We assess cited pages in AI responses asking if they provide detailed, accurate answers:
Do they explain the offer?
Do they compare options?
Do they outline use cases?
Do they provide proof?
The source mix varies by prompt, industry, and intent. At the funnel’s bottom, AI tools often cite LinkedIn, YouTube, third-party comparison pages, microsites, and competitive or vendor content.
AI systems work with what they can swiftly access, requiring page content prepared for easy consumption, like tables or comparisons.
Our job is to earn not just links, but to enhance material AI systems reference, aiding their brand decisions.
Don’t over-analyze a single prompt. Track multiple prompts for recurring gaps. If a brand is visibly missing from valuable prompt categories, that gap signals an area to investigate.
Citation optimization involves identifying influential pages and websites and ensuring they properly mention your offering to boost brand visibility and accuracy within AI context.
Remember PARSE: Source-led research starting points for SEOs and link builders. Track relevant unbranded prompts, identify repeatedly cited pages and domains, and review them closely.
Questions to consider:
What sources shape the answer?
Which pages compare options?
Which provide a table, list, or framework AI systems can utilize?
Which omit your brand while mentioning competitors?
Where are you mentioned without enough context?
This approach produces a richer target list beyond mere backlinks. It’s about refining material AI might use to identify brand presence in an answer.
Incorporate your brand into cited pages, enriching existing mentions, or improving thin comparisons with clearer ones, adding tables, graphics, or explanations to create more valuable content chunks.
Links remain important but aren’t standalone solutions. You need more than anchor text; contextual material surrounding it is critical for AI understanding, forming effective citations.
Whether you’re managing link-building internally or with partners, seek more than just a backlink. Ask for comprehensive anchor context, including insights into the offer, use cases, beneficiaries, and reasons for its place in the AI-driven answer.
This marks the first step from traditional link building to the realm of citation optimization, enhancing both search and AI visibility.
I’ve always found it exciting when Google Ads updates its features. Now, they’ve integrated Gemini into Ads dashboards, transforming data analysis into an engaging, interactive experience.
What’s happening. Google Ads is introducing a new Dashboards feature, designed to provide advertisers with performance data through visually appealing charts, graphs, and tables, all powered by Gemini.
What makes this even more fascinating is how users can effortlessly customize their views by typing prompts. The dashboard dynamically updates in real-time based on these input queries.
Why we care. Traditionally, data analysis in Google Ads required manual setups and navigating countless reports. This update shifts towards a more intuitive approach, letting advertisers ask questions and receive immediate visual feedback.
Zoom in. These new dashboards will showcase crucial metrics such as impressions, clicks, video views, and costs. You’ll also find them breaking down performance data across various dimensions like devices, audiences, and campaign types.
The main goal is to empower advertisers with a clearer and faster way to understand what’s happening within their accounts.
What to watch. I’m curious to see how broadly this prompt-driven reporting will be adopted and if it will lessen the need for custom reports and additional analytics tools.
I’m thrilled to introduce two groundbreaking Agent Analytics nodes from Profound: Bot Visits and Human Referrals. These innovative additions bring AI bot and Answer Engine referral data directly into Profound Agents, revolutionizing how we understand and optimize interactions.
Imagine the possibilities with Bot Visits, where you can track and analyze the influx of AI bot visits to your platform. This insight helps us optimize how we engage with automated traffic, ensuring that our systems are always at peak performance.
On the flip side, the Human Referrals node provides in-depth data about Answer Engine referrals. It’s like having a direct line to understanding how humans are being referred to our platforms, enabling us to craft more targeted strategies that resonate with real users.
With these nodes in place, we’re not just collecting data; we’re gaining a strategic advantage, allowing for more informed decisions and streamlined processes. Let’s embrace this new era of analytics that’s powered by AI intelligence.
AI assistants are revolutionizing how recommendations, purchases, and transactions are made, shifting the competitive landscape for brands. It’s not enough to chase clicks anymore; gaining algorithmic confidence is where the real battle lies.
The AI engine pipeline is complex, running through 10 gates from discovery to winning. The initial five gates—discovered, selected, crawled, rendered, and indexed—make your page legible to machines.
The critical competitive gates—annotated, recruited, grounded, and displayed—decide which brand the algorithm will showcase to potential buyers.
Reaching the ‘won’ milestone means your brand secures a click or a recommendation. This gate has evolved drastically in recent years. Previously, it meant securing a user’s attention through traditional search results. Now, it can also mean having your brand named by an assistive engine or an agent transacting on behalf of the user.
Delegation is at the heart of this evolution—deciding what to entrust to machines and when. Although the concept isn’t new, the boundaries of delegation have expanded, allowing more of the journey to be handled by technology. Brands must prepare for this spectrum of delegation.
What Hasn’t Changed: The Point of Search
The ultimate objective of search remains unchanged: offering users the most efficient solution to their problems. AI doesn’t alter this aim but enhances the speed and smoothness of arriving at that solution, reducing friction encountered in traditional searches.
The delegation boundary is a dynamic line marking the division between what users manage independently and what is handed over to the engine. Shifting this boundary towards the engine accelerates reaching ‘won,’ while holding back delays it.
From Problem to Purchase in 15 Minutes with ChatGPT
As a professional double bass player, picking up a guitar gig at the last minute threw me into an unexpected scenario. My trusty bass amp had to double up for my guitar since I was unprepared to buy new gear for a singular event.
This need led me to ChatGPT, quickly transforming a typical week-long search into a smooth 15-minute journey. Conversations with ChatGPT guided me from curiosity to purchase by expertly recommending pedals and vendors, even ensuring delivery timelines were met.
ChatGPT managed everything leading up to the purchase decision, understanding my requirements, and effortlessly condensing possibilities into an actionable recommendation. This seamless experience underscored how AI can streamline purchasing, tailoring pathways to fit personal preferences.
The real win for my chosen brand, Thomann, was AI’s confidence in their consistency and reliability. They earned my repeated business owing to structured and precise visibility in AI databases, allowing ChatGPT to confidently stake its recommendation.
The Single-Mode Assumption Is Dead: Three Modes Coexist Now
Gone are the days when ‘optimize for search’ sufficed. Now, brands juggle three pathways, integrating search with assistive and agentic modes, which can be interchanged throughout the user journey.
The assistive mode leverages AI to recommend and reduce decision friction, while agent mode eliminates friction altogether, completing transactions independently of the user. Each mode redefines what ‘won’ looks like.
The flexibility of delegation boundaries urges brands to adapt, strategizing for each unique user journey from the deliberate search of a professional to the convenience-seeking consumer.
Map your strategies to account for these dynamics, recognizing diverse customer pathways, and be prepared for all forms of AI interaction.
The strategies that drive success in this AI-driven landscape are centered on confidence—whether users search, rely on recommendations, or let AI transact. Mastering AI’s learning mechanisms and understanding user intent create pathways to success, allowing dynamic flexibility in engaging potential buyers.
As a Profound customer, I’m excited to share that I can now clearly see where my site and pages stand in terms of AI citations compared to other peers in the Profound Agent Analytics Network.
This feature empowers me with detailed insights, allowing for a competitive analysis that helps in enhancing my digital strategy and boosting my AI visibility effectively.
As I delve into the world of artificial intelligence, I’ve been stunned by the numerous legal risks that businesses face, including those related to copyright, privacy, misinformation, and compliance. While AI is still growing, the risks are growing rapidly with it.
The legal landscape is changing, especially with Europe leading the charge through the EU Artificial Intelligence Act. In the US, almost 20 states have enacted AI-related legislation. Yet, the federal government’s stance on keeping regulations light is evident in the AI policy wishlist from the White House.
Despite the pace at which new regulations appear, AI isn’t reshaping the legal landscape; it’s accelerating it. Risks often trace back to known legal domains such as intellectual property, privacy, consumer protection, and liability.
So rather than considering ‘AI law’ as something entirely novel, it’s more beneficial for me to identify where familiar legal risks stem from within business operations.
I learned that AI risks are prominently apparent in nine business areas. Addressing them doesn’t require legal expertise, just keen questioning to address each concern effectively.
Let me walk you through these areas:
1. Intellectual Property The key question here is: Who owns the work, and are we unknowingly using someone else’s intellectual property?
In AI, ownership is still being defined. However, the U.S. Copyright Office indicates that works purely generated by AI are not protected. Human creativity must play a significant role in shaping AI’s outputs for potential protection.
Using patented ideas conceived by humans but developed with AI remains in question as per the U.S. Patent and Trademark Office’s revised guidelines. These questions aren’t theoretical; they highlight real, current challenges organizations face.
Emerging case filings, such as The New York Times lawsuit against OpenAI, showcase the ever-growing concern over infringement risks.
Two primary risks stand out: unintentional incorporation of protected material in AI outputs and proving ownership without sufficient human creativity involved. In content creation, human involvement isn’t a luxury; it’s an absolute necessity.
2. Advertising and Misinformation The pivotal question I consider is: What message are we crafting, and is it accurate?
AI tools empower us to create vast amounts of content, which is advantageous. However, the risk of distributing misleading or incorrect information exists. I witnessed Google Bard’s numerous errors during a product demo, which negatively impacted its market value by $100 billion.
The emergence of hallucinated data, fabricated citations, and flawed reasoning are challenges businesses face when publishing under their brand. I understand that a single error can severely damage reputation.
3. Privacy and Personal Data The question guiding me is: Are we handling people’s data lawfully, transparently, and respectfully?
Consumer expectations on data privacy have significantly shifted. Legal frameworks like the EU’s GDPR, Canada’s PIPEDA, and California’s CCPA set new standards for collecting, using, and disclosing personal data.
We’ve seen how regulators treat these matters seriously; Italy blocked ChatGPT over privacy issues. Clear policies on data handling are crucial for any organization, and swift communication is required when a customer inquires under prevailing laws.
As I continue exploring AI’s implications on business, these areas underscore the necessity of thoughtful and deliberate strategies to manage AI’s legal implications effectively.
I’ve noticed it’s not uncommon to come across articles proclaiming that AI agents are about to revolutionize Google Ads, SEO, or social media. Initially, these AI agents seem promising, at least in theory.
But when I dive deeper into what data these agents actually utilize, it’s almost always platform-native. For Google Ads, this translates to impressions, clicks, conversions, and ROAS.
This simplistic approach is why PPC AI agents often stumble right from the start. If they only have platform-specific data, managing true marketing strategies becomes impossible.
Why Many PPC Agents Are Just AI Assistants
Many tools labeled as PPC agents are mostly AI assistants, focusing on tasks such as:
Generating various headline options
Describing product images for Responsive Search Ads
Drafting CTAs for Performance Max asset groups
While these tasks are beneficial in freeing up time, they’re not quite the PPC agents they claim to be—they’re just dressed up generative AI tools.
A true PPC agent operates directly on an ad account by analyzing performance data and making strategic decisions, like adjusting budgets and optimizing campaign structures based on informed insights.
How AI Agents Create a Closed Loop
Google Ads has a limited view of your business data, causing AI agents to often optimize a closed loop focused solely on improving platform metrics, which may negatively affect business performance.
For instance, Google Ads doesn’t know specifics like average deal size or which products have high margins. This ignorance can lead to suboptimal decisions.
Performance Max: A Precursor to AI Challenges
This conundrum isn’t new. PMax campaigns already demonstrated the pitfalls without adequate data, as they often optimized towards the wrong goals without necessary business insights.
PPC Agents Risk Misalignment Without Business Data
AI agents exacerbate the speed at which misaligned strategies can cause harm. Even the best systems need backend business data to make informed decisions, just as your agent would.
3 Essential Types of Business Data for PPC AI Agents
To enhance PPC agent performance, integrating CRM, product, and operational data is crucial.
1. CRM Data
CRM data is vital for understanding lead values beyond mere conversion counts. You can bridge this gap with offline conversion tracking or direct CRM access for a deeper analysis.
2. Product Margin Data
Understanding product margins is essential for eCommerce success. This data should come from supplementary feeds or direct backend connections, allowing for more strategic budget allocations.
3. Operational Data
Operational signals, like fulfillment capacity, also impact decision-making. Effective coordination and data flow help prevent suboptimal choices that might appear beneficial only theoretically.
Questions to Ask Before Building a PPC AI Agent
Before developing a PPC AI agent, pinpoint the essential business data required to optimize campaign performance, starting with OCT and progressing to direct CRM links for comprehensive insights.
Ultimately, the challenge isn’t building the agent but integrating it seamlessly with business realities for genuine value extraction.
