7 SEO, Marketing, And Tech Predictions For 2026 via @sejournal, @Kevin_Indig

Previous predictions: 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024

This is my 8th time publishing annual predictions. As always, the goal is not to be right but to practice thinking.

For example, in 2018, I predicted “Niche communities will be discovered as a great channel for growth” and “Email marketing will return” in 2019. It took another 6 years. That same year, I also wrote “Smart speakers will become a viable user-acquisition channel in 2018”. Well…

All 2026 Predictions

1. AI visibility tools face a reckoning.
2. ChatGPT launches first quality update.
3. Continued click-drops lead to a “Dark Web” defense.
4. AI forces UGC platforms to separate feeds.
5. ChatGPT’s ad platform provides “demand data.”
6. Perplexity sells to xAI or Salesforce.
7. Competition tanks Nvidia’s stock by -20%.

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For the past three years, we have lived in the “generative era,” where AI could read the internet and summarize it for us. 2026 marks the beginning of the “agentic era,” where AI stops just consuming the web and starts writing to it – a shift from information retrieval to task execution.

This isn’t just a feature update; it is a fundamental restructuring of the digital economy. The web is bifurcating into two distinct layers:

1. The Transactional Layer: Dominated by bots executing API calls and “Commercial Agents” (like Remarkable Alexa) that bypass the open web entirely.
2. The Human Layer: Verified users and premium publishers retreating behind “Dark Web” blockades (paywalls, login gates, and C2PA encryption) to escape the sludge of AI content.

A big question mark is advertising, where Google’s expansion of ads into AI Mode and ChatGPT showing ads to free users could alleviate pressure on CPCs, but AI Overviews (AIOs) could drive them up. 2026 could be a year of wild price swings where smart teams (your “holistic pods”) move budget daily between Google (high cost/high intent) and ChatGPT (low cost/discovery) to exploit the spread.

It is not the strongest of the species that survives, nor the most intelligent; it is the one most adaptable to change.

— Leon C. Megginso

SEO/AEO

AI Visibility Tools Face A Reckoning

Prediction: I forecast an “Extinction Event” in Q3 2026 for the standalone AI visibility tracking category. Rather than a simple consolidation, our analysis shows the majority of pure-play tracking startups might fold or sell for parts as their 2025 funding runways expire simultaneously without the revenue growth to justify Series B rounds.

Why:

• Tracking is a feature, not a company. Amplitude built an AI tracker for free in three weeks, and legacy platforms like Semrush bundled it as a checkbox, effectively destroying the standalone business model.
• Many tools have almost zero “customer voice” proof of concept (e.g., zero G2 reviews), creating a massive valuation bubble.
• The ROI of AI visibility optimization is still unclear and hard to prove.

Context:

• Roughly 20 companies raised over $220 million at high valuations. 73% of those companies were founded in 2024.
• Adobe’s $1.9 billion acquisition of Semrush proves that value lies in platforms with distribution, not in isolated dashboards.

Consequences:

• Smart money will flee “read-only” tools (dashboards) and rotate into “write-access” tools (agentic SEO) that can automatically ship content and fix issues.
• There will be -3 winners of AI visibility trackers on top of the established all-in-one platforms. Most of them will evolve into workflow automation, where most of the alpha is, and where established platforms have not yet built features.
• The remaining players will sell, consolidate, pivot, or shut down.
• AI visibility tracking itself faces a crisis of (1) what to track and (2) how to influence the numbers, since a large part of impact comes from third-party sites.

ChatGPT Launches First Quality Update

Prediction: It’ll be harder for spammers to influence AI visibility in 2026 with link spam, mass-generated AI content, and cloaking. By 2026, agents will likely use Multi-Source Corroboration to eliminate this asymmetry.

Why:

• The fact that you can publish a listicle about top solutions on your site and name yourself first and influence AI visibility seems off.
• New technology, like “ReliabilityRAG“ or “Multi-Agent Debate,” where one AI agent retrieves the info and another agent acts as a “judge” to verify it against other sources before showing it to the user, is available.

Context:

• Most current agents (like standard ChatGPT, Gemini, or Perplexity) use a process called Retrieval-Augmented Generation (RAG). But RAG is still susceptible to hallucination and making errors.
• Spammers often target specific, low-volume queries (e.g., “best AI tool for underwater basket weaving”) because there is no competition. However, new “knowledge graph” integration allows AIs to infer that a basket-weaving tool shouldn’t be a crypto-scam site based on domain authority and topic relevance, even if it’s the only page on the internet with those keywords.

Consequences:

• OpenAI engineers are likely already working on better quality filters.
• LLMs will shift from pure retrieval to corroboration.
• Spammers might move to more sophisticated tactics, where they try to manufacture the consensus by buying and using zombie media outlets, cloaking, and other malicious tactics.

Continued Click-Drops Lead To A “Dark Web” Defense

Prediction: AI Overviews (AIOs) scale to 75% of keywords for big sites. AI Mode rolls out to 10-20% of queries.

Why:

• Google said they’re seeing more queries as a result of AIOs. The logical conclusion is to show even more AIOs.
• CTR for organic search results tanked from 1.41% to 0.64% already in January. Since January, paid CTR dropped from 14.92% to 6.34% (over 42% less).

Context:

• Big sites already see AIOs for ~50% of their keywords.
• Google started testing ads in AI Mode. If successful, Google would feel more confident to roll out AI Mode more broadly, and the investor story would sound better.
• 80% of consumers now use AI summaries for at least 40% of their searches, according to Bain.
• 2025 saw a massive purge in digital media, with major layoffs at networks like NBC News, BBC, and tech publishers as they restructured for a “post-traffic” world.

Consequences:

• Publishers monetize audiences directly instead of ads and move to “experience-based” content (firsthand reviews, contrarian opinions, proprietary data) because AI cannot experience things. The space consolidates further (layoffs, acquisitions, Chapter 9).
• By 2026, we expect a massive wave of “LLM blockades.” Major publishers will update their robots.txt to block Google-Extended and GPTBot, forcing users to visit the site to see the answer. This creates a “Dark Web” of high-quality content that AI cannot see, bifurcating the internet into AI slop (free) and human insight (paid).

Marketing

AI Forces UGC Platforms To Separate Feeds

Prediction: By 2026, “identity spoofing” will become the single largest cybersecurity risk for public companies. We move from, Is this content real? to Is this source verified?

Why:

• Real influencers are risky (scandals, contract disputes). AI influencers are brand-safe assets that work 24/7/365 and never say anything controversial unless prompted. Brands will pay a premium to avoid humans.

Context:

• Deepfake fraud attempts increased 257% in 2024. Most detection tools currently have a 20%+ false positive rate, making them hard to use for platforms like YouTube without killing legitimate creator reach.
• Example: In 2024, the engineering firm Arup lost $25 million when an employee was tricked by a deepfake video conference call where the “CFO” and other colleagues were all AI simulations.
• In May 2023, a fake AI image of an explosion at the Pentagon caused a momentary dip in the S&P 500.

Consequences:

1. Cryptographic signatures (C2PA) become the only proof of reality for video.
2. YouTube and LinkedIn will likely split feeds into “verified human” (requires ID + biometric scan) and “synthetic/unverified.”
3. “Blue checks” won’t just be for status, but a security requirement to comment or post video, effectively ending anonymity for high-reach accounts.
4. Platforms will be forced by regulators (EU AI Act, August 2026 deadline) to label AI content.
5. Cameras (Sony, Canon) and iPhones will start embedding C2PA digital signatures at the hardware level. If a video lacks this “chain of custody” metadata, platforms will auto-label it as “unverified/synthetic.”

ChatGPT’s Ad Platform Provides “Demand Data”

Prediction: OpenAI shifts to a hybrid pricing model in 2026: An “ad-supported free tier” and “credit-based pro tier.”

Why:

• Inference costs are skyrocketing. A heavy user paying $20/month can easily burn $100+ of computing, making them unprofitable.

Context:

• Leaked code in the ChatGPT Android App (v1.2025.329) explicitly references “search ads carousel” and “bazaar content.”

Consequences:

• Free users will see “sponsored citations” and product cards (ads) in their answers.
• Power users will face “compute credits” – a base subscription gets you standard GPT-5, but heavy use of deep research or reasoning agents will require buying top-up packs.
• We get a Search-Console style interface. Brands need data. If OpenAI wants to sell ads, it must give brands a dashboard showing, “Your product was recommended in 5,000 chats about running shoes.” The data will add fuel to the fire for AEO/GEO/LLMO/SEO.
• The leaked term “bazaar content” suggests OpenAI might not just show ads, but allow transactions inside the chat (e.g., “Book this flight”) where they take a cut. This moves OpenAI from a software company to a marketplace (like the App Store), effectively competing with Amazon and Expedia.

Tech

Perplexity Sells To xAI Or Salesforce

Prediction: Perplexity will be acquired in late 2026 for $25-$30 billion. After its user growth plateaus at ~50 million MAU, the “unit economics wall” forces a sale to a giant that needs its technology (real-time RAG), not its business model.

Why:

• In late 2025, Perplexity raised capital at a $20 billion valuation (roughly 100x its ~$200 million ARR). To justify this, they need Facebook-level growth. However, 2025 data shows they hit a ceiling at ~30 million users while ChatGPT surged to +800 million.
• By 2026, Google and OpenAI will have effectively cloned Perplexity’s core feature (Deep Research) and given it away for free.

Context:

• While Perplexity grew 66% YoY in 2025 to ~30 million monthly active users (MAU), this pales in comparison to ChatGPT’s +800 million.
• It costs ~10x more to run a Perplexity deep search query than a standard Google search. Without a high-margin ad network (which takes a decade to build), they burn cash on every free user, creating a “negative scale” problem.
• Salesforce acquired Informatica for ~$8 billion in 2025 specifically to power its agentforce strategy. This proves Benioff is willing to spend billions to own the data layer for enterprise agents.
• xAI raised over $20 billion in late 2025, valuing the company at $200 billion. Musk has the liquid cash to buy Perplexity tomorrow to fix Grok’s hallucination problems.

Consequences:

• xAI has the cash, and Musk needs a “real-time truth engine” for Grok. Perplexity could make X (Twitter) a more powerful news engine. Grok (X’s current AI) learns from tweets, but Perplexity cites sources that can reduce hallucination. Perplexity could also give xAI a browser, bringing it closer to Musk’s vision of a super app.
• Marc Benioff wants to own “enterprise search.” Imagine a Salesforce Agent that can search the entire public web (via Perplexity) + your private CRM data to write a perfect sales email.

Competition Tanks Nvidia’s Stock By -20%

Prediction: Nvidia stock will correct by >20% in 2026 as its largest customers successfully shift 15-20% of their workloads to custom internal silicon. This causes a P/E compression from ~45x to ~30x as the market realizes Nvidia is no longer a monopoly, but a “competitor” in a commoditized market. (Not investment advice!)

Why:

• Microsoft, Meta, Google, and Amazon likely account for over 40% of Nvidia’s revenue. For them, Nvidia is a tax on their margins. They are currently spending ~$300 billion combined on CAPEX in 2025, but a growing portion is now allocated to their own chip supply chains rather than Nvidia H100s/Blackwells.
• Hyperscalers don’t need chips that beat Nvidia on raw specs; they just need chips that are “good enough” for internal inference (running models), which accounts for 80-90% of compute demand.

Context:

• In late 2025, reports surfaced that Meta was negotiating to buy/rent Google’s TPU v6 (Trillium) chips to reduce its reliance on Nvidia.
• AWS Trainium 2 & 3 chips are reportedly 30-50% cheaper to operate than Nvidia H100s for specific workloads. Amazon is aggressively pushing these cheaper instances to startups to lock them into the AWS silicon ecosystem.
• Microsoft’s Maia 100 is now actively handling internal Azure OpenAI workloads. Every workload shifted to Maia is an H100 Nvidia didn’t sell.
• Reports confirm OpenAI is partnering with Broadcom to mass-produce its own custom AI inference chip in 2026, directly attacking Nvidia’s dominance in the “Model Serving” market.
• Fun fact: Without Nvidia, the S&P500 would’ve made 3 percentage points less in 2025.

Consequence:

• Nvidia will react by refusing to sell just chips. They will push the GB200 NVL72 – a massive, liquid-cooled supercomputer rack that costs millions. This forces customers to buy the entire Nvidia ecosystem (networking, cooling, CPUs), making it physically impossible to swap in a Google TPU or Amazon chip later.
• If hyperscalers signal even a 5% cut in Nvidia orders to favor their own chips, Wall Street will panic-sell, fearing the peak of the AI Infrastructure Cycle has passed.

Featured Image: Paulo Bobita/Search Engine Journal

The Search Equity Gap: Quantifying Lost Organic Market Share (And Winning It Back) via @sejournal, @billhunt

Every month, companies lose millions in unrealized search value not because their teams stopped optimizing, but because they stopped seeing where visibility converts into economic return.

When search performance drops, most teams chase rankings. The real leaders chase equity.

This is the Search Equity Gap – the measurable delta between the organic market share your brand once held and what it holds today.

 In most organizations, this gap isn’t tracked or budgeted for. Yet it represents one of the most consistent and compounding forms of digital opportunity cost. Every unclaimed click isn’t just lost traffic; it’s lost demand at the lowest acquisition cost possible – an invisible tax on growth.

When we treat SEO as a channel, we chase traffic.

When we treat it as an equity engine, we reclaim value.

Search Equity: The Compounding Value Of Discoverability

Search equity is the accumulated advantage your brand earns when visibility, authority, and user trust align. Like financial equity, it compounds over time – links build reputation, content earns citations, and user engagement reinforces relevance.

But the opposite is also true: When migrations break URLs, when content fragments across markets, or when AI overviews intercept clicks, that equity erodes.

And that’s usually the moment when management suddenly discovers the value of organic search – right after it vanishes.

What was once dismissed as “free traffic” becomes an expensive emergency as other channels scramble to compensate for the lost opportunity. Paid budgets balloon, acquisition costs spike, and leadership learns that SEO isn’t a faucet you can turn back on.

Search equity isn’t just about rankings. It’s about discoverability at scale – ensuring your brand appears, is understood, and is chosen in every relevant search context, from classic results to AI-generated overviews.

In this new environment, visibility without qualification is meaningless. A million impressions that never convert are not an asset. The opportunity lies in reclaiming qualified visibility – the type that drives revenue, reduces acquisition costs, and compounds shareholder value.

Diagnosing The Decline: Where Search Equity Disappears

Every SEO audit can uncover technical or content issues. But the deeper cause of declining performance often stems from three systemic leaks.

1. Structural Leaks

Migrations, redesigns, and rebrands remain the biggest equity destroyers in enterprise SEO. When URLs change without proper mapping, Google’s understanding of authority resets. Internal link equity splinters. Canonical signals conflict.

Each broken or redirected page acts like a severed artery in your digital system – small losses multiplied at scale. What seems like a simple platform refresh can erase years of accumulated search trust.

2. Behavioral Shifts

Even when nothing changes internally, the ecosystem around you continues to evolve. Zero-click results, AI Overviews, and new answer formats siphon attention. Search visibility remains, but user behavior no longer translates into traffic.

The new challenge isn’t “ranking first.” It’s being chosen when the user’s question is answered before they click. This demands a shift from keyword optimization to intent satisfaction and requires restructuring your content, data, and experience for discoverability and decision influence.

3. Organizational Drift

Perhaps the most corrosive leak of all: misalignment. When SEO sits in marketing, IT in technology, and analytics in finance, nobody owns the whole system.

Executives’ fund rebrands that destroy crawl efficiency. Paid teams buy traffic that good content could have earned. Each department optimizes its own key performance indicator (KPI), and in doing so, the organization loses cohesion. Search equity collapses not because of algorithms, but because of organizational architecture. The fix starts at the top.

Quantifying The Search Equity Gap (Actuals-Based Model)

Most companies estimate what they should earn in search and compare it to current performance. But in volatile, AI-driven SERPs, real performance deltas tell the truer story.

Instead of modeling potential, this approach uses before-and-after data – actual performance metrics from both pre-impact and current states. By doing so, you measure realized loss, click erosion, and intent displacement with precision.

Search Equity Gap = Lost Qualified Traffic + Lost Discoverability + Lost Intent Coverage

Step 1: Establish A Baseline (Pre-Impact Period)

Pull your data from a stable window before the event (typically three to six months prior).

From Google Search Console and analytics, extract:

• Top performing queries (impressions, clicks, CTR, position).
• Top landing pages and their mapped queries.
• Conversion or value proxies where available.

This becomes your search equity portfolio – the measurable value of your earned discoverability.

Step 2: Compare To The Current State (Post-Impact)

Run the same data for the current period and align query-to-page pairs.

Then classify each outcome:

This comparison reveals both visibility loss and click erosion, clarifying where and why your equity declined.

Step 3: Attribute The Loss

Link each pattern to its primary driver:

1. Structural – Indexation, redirects, broken templates.
2. Content – Thin, outdated, or unstructured pages lacking E-E-A-T.
3. SERP Format – AI overviews, videos, or answer boxes replacing classic results.
4. Competitive – New entrants or aggressive refresh cycles.

These map to equity types:

• Recoverable Equity: technical or content improvements.
• Influence Equity: optimizing brand/entity visibility within AI Overviews.
• Retired Equity: informational queries no longer yielding clicks.

This triage converts diagnosis into a prioritized investment plan.

Step 4: Quantify The Economic Impact

For each equity type, calculate:

Lost Value = Δ Clicks × Conversion Rate × Value per Conversion

Add a Paid Substitution Cost to translate organic loss into a financial figure:

Cost of Not Ranking = Lost Clicks × Avg CPC

This ties the forensic analysis directly to your legacy framework, which I define as The Cost of Not Ranking, and shows executives the tangible price of underperformance.

Example:

• 15,000 fewer monthly clicks on high-intent queries.
• 3% conversion × $120 avg order value = $54,000/month in unrealized value.
• CPC $3.10 → $46,000/month to replace via paid.

Now your analysis quantifies both organic value lost and capital inefficiency created.

Step 5: Separate The Signal From The Noise

Not all loss deserves recovery. Patterns surface quickly:

• High-volume informational pages: visibility stable, clicks down – reclassified (low ROI).
• Product or service pages: dropped due to structural issues – recoverable (high ROI).
• Brand or review pages: replaced by AI summaries – influence (medium ROI).

Plot these on a Search Equity Impact Matrix – potential value vs. effort – to direct resources toward recoverable, high-margin opportunities.

Why This Matters

Most SEO reports describe position snapshots. Few reveal equity trajectories. By grounding analysis in actuals before and after impact, you replace speculation with measurable evidence that data executives can trust. This reframes search optimization as loss prevention and value recovery, not traffic chasing.

From Visibility Metrics To Value Metrics

Traditional metrics focus on activity:

• Average ranking position.
• Total impressions.
• Organic sessions.

Value-based metrics focus on performance and economics:

• Qualified Visibility Share (discoverability within high-intent categories).
• Recovered Revenue Potential (modeled from Δ Clicks × Value).
• Digital Cost of Capital (what it costs to replace that traffic via paid).

Integrating your Cost of Not Ranking logic further amplifies this.

Every click you have to buy is a symptom of a ranking you didn’t earn.

By comparing your paid and organic data for the same query set, you can see how much budget covers for lost equity and how much could be redeployed if organic recovery occurred.

When teams present SEO performance in these financial terms, they gain executive attention and budget alignment.

Example:

“Replacing lost organic share with paid clicks costs $480,000 per quarter. Fixing canonical and internal-link issues can recover 70% of that value within 90 days.”

That’s not an SEO report. That’s a business case for digital capital recovery.

Winning It Back: A Framework For Recovery

Search equity recovery follows the same progression as digital value creation – diagnose, quantify, prioritize, and institutionalize.

1. Discover The Gap

Compare actual performance pre- and post-impact. Visualize equity at risk by category or market.

2. Diagnose The Cause

Layer crawl data, analytics, and competitive intelligence to isolate technical, behavioral, and AI factors.

3. Differentiate

Focus on qualified clicks from mid- and late-funnel intents where AI summaries mention your brand but don’t link to you.

Answer those queries more directly. Reinforce them with structured data and content relationships that signal expertise and trust.

4. Reinforce

Embed SEO governance into development, design, and content workflows. Optimization becomes a process, not a project – or, as I’ve written before, infrastructure, not tactic. When governance becomes muscle memory, equity doesn’t just recover; it compounds.

From Cost Center To Compounding Asset

Executives often ask:

“How much revenue does SEO drive?”

The better question is:

“How much value are we losing by not treating search as infrastructure?”

The search equity gap quantifies that blind spot. It reframes SEO from a cost-justified marketing function into a value-restoration system – one that preserves and grows digital capital over time. Each recovered visit is a visit you no longer need to buy. Each resolved structural issue accelerates time-to-value for every future campaign.

Ironically, the surest way to make executives appreciate SEO is to let it break once. Nothing clarifies its importance faster than the sound of paid budgets doubling to make up for “free” traffic that suddenly disappeared. That’s how SEO evolves from an acquisition channel to a shareholder-value lever.

Final Thought

The companies dominating search today aren’t publishing more content – they’re protecting and compounding their equity more effectively.

They’ve built digital balance sheets that grow through governance, not guesswork. The rest are still chasing algorithm updates while silently losing market share in the one channel that could deliver the highest margin growth.

The search equity gap isn’t a ranking problem. It’s a visibility-to-value disconnect, and closing it starts by measuring what most teams never even notice.

More Resources:

Featured Image: N Universe/Shutterstock

Tools to Track GenAI Citations, Sources

Generative AI platforms increasingly conduct live web searches to respond to users’ prompts. The platforms don’t reveal how or where they search, but it’s likely a combination of Google, Bing, and the platforms’ own bots.

Just a few months ago, those answers would have relied primarily on existing training data.

Regardless, understanding how AI platforms conduct the searches is key to optimizing visibility in the answers.

Analyze:

• Which web pages produce the genAI answers? Try to appear in those pages.
• Which brands and products influenced an answer? Are they competitors?

Here are three tools to help reveal impactful pages and influential brands and products.

ChatGPT Path

ChatGPT Path from Ayima, an agency, is a free Chrome extension that extracts citations, brands and products (entities), and fan-out queries from any ChatGPT dialog. Download the extension and converse with ChatGPT. Then click the extension icon to open a side panel with the key info, called RAG Sources (“Retrieval‑Augmented Generation”).

Export the report via CSV for easier analysis.

ChatGPT Path extracts citations, brands and products, and fan-out queries from any ChatGPT dialog, such as this example for “help me choose running shoes for rainy weather.”

AI Search Impact Analysis

AI Search Impact Analysis is another free Chrome extension that analyzes multiple queries on Google AI Overviews.

Install the extension and type your comma-separated queries into the tool’s sidebar. The tool will run each search and identify AI Overviews and the queries that triggered them.

A separate “Citation Report” includes all URLs cited in each Overview and overall for all queries. In my testing, this feature was handy for identifying URLs cited repeatedly.

The extension’s “Brand Check” analyzes mentions of your company and competitors in Overviews.

“Brand Check” analyzes Overviews for mentions of your company and competitors, such as “nike” and “hoka” shown here.

Peec AI

Peec AI is a premium analytics tool for sources and brand mentions in ChatGPT, Perplexity, and AI Overviews.

To use, enter your brand and targeted prompts. The tool, after a few minutes, will create a detailed report, listing:

• Domains cited in genAI answers for those prompts,
• URLs linked in the answers.

The report categorizes cited domains by type (e.g., corporate, brand-owned, user-generated) and frequency (to know a domain’s impact on a cluster of answers).

A separate aggregated report combines all genAI platforms, with URL filters for each one. The “Gap analysis” lists cited URLs that mention competing brands but not yours.

Finally, Peec AI analyzes all entered prompts and lists the most-cited brands to compare and track against your own.

Peec AI’s report categorizes cited domains by type and frequency.

SEO Pulse: ChatGPT Gets Shopping & What Drives AI Citations via @sejournal, @MattGSouthern

Welcome to the week’s Pulse: updates affect how product discovery works, what drives visibility in ChatGPT, and how background assets impact Core Web Vitals.

OpenAI launched shopping research in ChatGPT, SE Ranking published the largest study yet on ChatGPT citation factors, and Google’s John Mueller clarified that background video loading won’t hurt SEO if content loads first.

Here’s what matters for you and your work.

ChatGPT Launches Shopping Research For All Users

OpenAI rolled out shopping research in ChatGPT on November 24, making personalized buyer’s guides available to all logged-in users across Free, Go, Plus, and Pro plans.

The feature works differently from standard ChatGPT responses. Users describe what they need, answer clarifying questions about budget and preferences, and receive a detailed buyer’s guide after a few minutes of research.

Key Facts: Powered by GPT-5 mini. Nearly unlimited usage through the holidays. Merchants can request inclusion through OpenAI’s allowlisting process.

Why SEOs Should Pay Attention

Shopping research pulls more of the product comparison journey inside ChatGPT before users click through to merchant sites. This changes where product discovery happens in the funnel.

Traditional search sent users to comparison sites, retailer pages, and review platforms to build their own shortlist. Shopping research does that work inside the chat interface, asking clarifying questions and surfacing product recommendations based on constraints like budget, features, and intended use.

Crystal Carter, Head of AI Search & SEO Communications at Wix, highlighted the personalization implications in a LinkedIn post:

Make sure your brand affinities, and communities are clearly stated on YOUR website, in your support documentations, FAQs, and make moves to get it cited on other websites, because for some customers, these considerations are make or break, and they will build it into their models.

Her testing showed ChatGPT delivering different restaurant recommendations to users with different profile preferences, pulling from Google Business Profiles and other sources to match stated affinities.

For retailers and affiliate publishers, visibility now depends partly on how products and pages appear in OpenAI’s shopping system. The allowlisting process means merchants need to opt in rather than relying solely on organic crawling.

Read our full coverage: ChatGPT Adds Shopping Research For Product Discovery

Study Reveals Top 20 Factors Driving ChatGPT Citations

SE Ranking analyzed 129,000 unique domains across 216,524 pages in 20 niches to identify which factors correlate with ChatGPT citations.

Referring domains ranked as the single strongest predictor. Sites with up to 2,500 referring domains averaged 1.6 to 1.8 citations, while those with over 350,000 referring domains averaged 8.4 citations.

Key Facts: Domain traffic matters only above 190,000 monthly visitors. Content over 2,900 words averaged 5.1 citations versus 3.2 for articles under 800 words. Pages with 19 or more data points averaged 5.4 citations.

Why SEOs Should Pay Attention

The study suggests that traditional SEO fundamentals still align with AI citation likelihood, but the thresholds matter more than gradual improvements. A site with 20,000 monthly visitors performs similarly to one with 200 monthly visitors, but crossing 190,000 visitors doubles citation rates.

This creates different optimization priorities than traditional search. Building from zero to moderate traffic won’t improve ChatGPT visibility, but scaling from moderate to high traffic will. The same pattern holds for referring domains, where the jump happens at 32,000 domains.

Manidurga BLL, an IT student analyzing the research, broke down the implications in a LinkedIn post with video:

The AI revolution isn’t just changing how we search. It’s rewriting the entire playbook for digital authority. For us tech students and future developers, this means rethinking content strategy from day one. Building domain authority isn’t just about Google anymore. It’s about teaching AI systems to trust and cite your work.

The post includes a detailed video walkthrough of the study findings, highlighting that heavy Quora and Reddit presence correlates with 7 to 8 citations, while review platform listings average 4 to 6 citations.

The research also found that .gov and .edu domains don’t automatically outperform commercial sites despite common assumptions. What matters is content quality and domain authority, not domain extension.

Read our full coverage: New Data Reveals The Top 20 Factors Influencing ChatGPT Citations

Mueller: Background Video Loading Unlikely To Affect SEO

Google Search Advocate John Mueller says large video files loading in the background are unlikely to have a noticeable SEO impact if page content loads first.

A site owner on Reddit asked whether a 100MB video would hurt SEO if the page prioritizes loading a hero image and content before the video continues loading in the background. Mueller responded that he doesn’t expect a noticeable SEO effect.

Key Facts: Using preload=”none” on video elements prevents browsers from downloading video data until needed. Core Web Vitals metrics should verify implementation meets performance thresholds.

Why SEOs Should Pay Attention

The question addresses a common concern for sites using large hero videos or animated backgrounds. Site owners have avoided background video because of performance worries, but Mueller’s guidance clarifies that proper implementation won’t create SEO problems.

The key is load sequencing. If a page shows its hero image, text, and navigation immediately while a 100MB video loads in the background, users get a fast experience and search engines see content quickly.

The Reddit thread included debate about the guidance, with one commenter noting Mueller’s response contradicts concerns about network contention competing with critical resources. WebLinkr, an r/SEO moderator, defended Mueller’s position and noted web developers sometimes overstretch the impact of page speed factors on SEO.

This changes the calculation for sites considering background video. The decision now focuses on user experience and bandwidth costs rather than SEO penalties.

Technical implementation still matters. Using preload=”none” on video elements prevents the browser from downloading video data speculatively, saving bandwidth for users who never play the video.

Read our full coverage: Mueller: Background Video Loading Unlikely To Affect SEO

Theme Of The Week: Discovery Moves Upstream

Each story this week shows discovery happening earlier in the journey.

ChatGPT shopping research handles product comparison before users reach merchant sites. The SE Ranking study reveals what builds citation authority at scale rather than incremental gains. Mueller’s video guidance removes a technical barrier that kept sites from using rich media.

Taken together, this week is about where decisions really form, before anyone ever types a query into Google.

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The Impact AI Is Having On The Marketing Ecosystem

I’m not someone who’s drunk much of the AI Kool Aid. I have sipped it. Swilled it around my mouth like you would an 1869 Château Lafite Rothschild.

But I’ve seen enough cult documentaries to know you should spit it back into the glass.

Do I love the opportunities it’s provided me in a work sense? Absolutely. Do I think it’s fundamentally shifted the marketing ecosystem? No. I think it’s expedited what’s been happening for some time.

• Reddit’s resurgence is search-dominated.
• The booming creator economy shows people trust people.
• Word of mouth still travels.
• Content still goes viral.
• People don’t click unless they have to.

LLMs provide a good proxy as to how you’re seen online. And they really lean into review platforms and strong brands. Associating your brand with your core topics, removing ambiguity, and strengthening your product positioning is never a bad thing.

It’s not just about search anymore. In reality, it never should have been. It’s about connecting. Generating value from the different types of media.

TL;DR

1. The search customer journey spans TikTok, YouTube, Instagram, and everything in between.
2. Last-click attribution is outdated: BOFU platforms get the credit, but creators, communities, and discovery platforms do the heavy lifting.
3. AI hasn’t broken anything, it’s just exposing how messy, multi-platform, and people-driven it’s always been.
4. Brands win by understanding their audience, investing in creators, and building experiences that cut through an enshittified internet.

The Customer Journey Has Changed

True. But it’s been changing for a long time. Paid channels are becoming more expensive, owned channels like search send fewer clicks (mainly a Google-driven mechanic), and earned channels are looking more like the golden ticket to corporate stooges.

The majority of brands use last click attribution (gross, get away from me). A method that overvalues search. For the last decade or more, there have been discovery platforms that are more valuable than search – TikTok, YouTube, Instagram. Pick one.

We tend to use search for finding products, brands, or stories we know exist. And for comparison, related searches. But as AI Mode rolls ever closer and Google looks to greedily take on middle of the funnel queries, Google’s role as a discovery platform will change. Theoretically, at least.

Like every big tech company, enough is never enough, and they don’t want to send you clicks. Unless you pay for them, of course.

Search Is No Longer A Single Platform Journey

The Rise at Seven SEO and Data teams analysed 1.5 billion searches across five channels of the most-searched keywords on the internet and found that:

• A buying journey can take anywhere from two days to 10 weeks, with up to 97 interactions along the way.
• Google only accounts for just 34.5% of total search share.
• YouTube (24%), TikTok (16.7%), and Instagram (20.9%) make up more than 60%.
• The average consumer now uses 3.6 platforms before making a purchase.

But Google isn’t really a discovery platform. Maybe a bit. Google Shopping. Some comparison searches. But it’s not what anyone is there for.

Someone sees a product on Instagram or TikTok. They read a review of it on Reddit (probably through Google, albeit with a branded search) and watch videos of it on TikTok or YouTube.

They might even buy direct or via Amazon. At best, they perform a branded search in Google.

Now, tell me, last click attribution makes sense.

I think it’s worth noting here that so many of these other platforms are driven by a clickless algorithm. Google requires a click. A fundamental search. The others have homepages that stare directly into your soul.

I don’t think any of this is new. And I suspect it’s been a while since search was a single platform journey. But it depends on what you define as search, I suppose.

Google’s Messy Middle  is about right. We have been living through an era of marketing desperately tied to trying to track every penny. Something that has been a near-impossible job for some time. At some point, you just have to sit down, try to know your audience better than anyone else, and have at it.

We need to influence clicks via search before that happens. Brands have to focus their time on the right channels for their audience. Not just search. That’s why knowing your audience and using an attribution model that doesn’t just value the BOFU click matters.

But Has AI Been The Catalyst?

Probably a bit. Behavior has been changing long before LLMs hit the public arena. It’s changed because people have better options. More visually decisive. More authentic. The creator economy has boomed because people trust people.

• When I’m doomscrolling on the bog or on the tube (praise be to the 5G gods), I might get served a new product.
• If I want real opinions or reviews about said product, I might go to Reddit (albeit through Google) to see what someone thinks. Well, I wouldn’t because I’m an adult with a wife and a mortgage, but you see my point.
• I might subscribe to specific Substacks or creators who use and speak about the product.
• My favorite LLM might give me product ideas (which I would check very carefully).
• Hell, I might even see something IRL on the tube.

A lot of this ends with a Google search. Maybe all of it. Google is a navigational engine. Hence, the last click attribution issue. I suspect the last click isn’t the most important session in the majority of cases.

Unless you’re young, lazy, or both, AI just won’t cut the mustard. Hell, Google’s kingpin tells you not to blindly trust AI. Even the guys fundamentally selling us this stuff are telling us it has some pretty serious flaws.

It’s one of the reasons user journeys are so much more complex and elongated.

1. We have more effective platforms and opinions than ever.
2. We have more shitty platforms and opinions than ever.

Cutting through the noise is everything. For people and brands. So you have to learn how to build brands and products that are bold and get the right people talking and sharing.

90% of marketers say creator content yields stronger engagement and 83% link it to more conversions. And 61% of consumers trust recommendations from creators more than they trust brand advertising.

The algorithms trust people because people do.

Channel-By-Channel Breakdown

Things don’t happen in a silo. Call me old-fashioned, but I think we’d all do well to work together as a marketing department. AIOs don’t just affect search. They have a knock-on effect on the entire ecosystem, and it’s important we understand the what and the why.

SEO

Where do we start? I’ll try and be brief. The most obvious and direct threat is zero-click search, which has been on the rise for some time. While AI hasn’t been the key driver of this, it has and will continue to reduce referral traffic.

• AIOs have significantly reduced CTR, particularly for informational, TOFU queries.
• AI Mode is there to steal middle of the funnel clicks to “help users make the right decision.”
• LLMs offer something of an alternative to search. Although based on what people really use them for, I think they are complementary, rather than a replacement.

I think AI has done some very interesting things in the SEO space. Vibe engineering platforms like Cursor and prototyping platforms like Lovable have opened up new worlds.

If you can wade through the shit, you can do some brilliant things.

Then you have Profound’s Zero Click conference, where one of the speakers said he felt sorry for anyone working in SEO. According to this turdy savant, there’s very little crossover between SEO and insert favorite acronym before proceeding to discuss lots of SEO ideas from 2012.

People who just do not understand marketing, SEO, the internet, or people. These are the guys driving the enshittification of our day-to-day lives. (More on this later).

PPC

PPC and SEO are ugly cousins, really. We operate in the same space, we target the same traffic. So it stands to reason that AIOs and AI Mode significantly impact paid search.

If you can believe it, it’s broadly a negative.

I know. I, too, am stunned.

Thanks to Seer Interactive, we have near-conclusive data that proves how serious this impact has been. When an AIO is present, and you are not cited, clicks are down over 78%.

Even when there’s no AIO present, paid clicks are down 20%. This is disastrous. Customer acquisition becomes more expensive, and the blended CPAs are significantly more expensive.

This may show a real and significant shift in user behavior. Users are becoming so used to getting what they want from a TOFU search, they don’t even follow up when an AIO isn’t present.

Attention is slipping everywhere.

Social

We’ve seen the rapid rise of disinformation in search. Google’s been promoting fake content to millions of people on Discover and has been struggling to block them for some time. Gaming the system isn’t new. PBNs, expired domain abuse, link schemes. You name it, it’s working.

Thankfully, the Vote Leave Take Control team have put their talents to good use and can now tell me what casino site should I choose.

The scale is unprecedented. Bullshit flies everywhere.

And that’s where social comes in. Globally, the average person spends 2 hours and 24 minutes on social media every day. That’s a lot of time to be hit by fake news. Personalized fake news, too. So maybe it’s not a surprise that social use has been on the decline for the last couple of years.

According to this study by the FT, social media use has decreased by 10% and has been driven by (*shakes fist*) the youth. I think these platforms are a shell of what they once were. The connections they provided have been replaced by absolute bullshit.

They will do literally anything to get and hold your attention. Except help you stay in touch with people or watch something that isn’t AI-generated. The content quality bell curve we see in search is mirrored by the enshittification of social channels.

• First, the platform attracts users with some bait, such as free access.
• Then the activity is monetized, bringing in the business customers with no thought for the user experience.
• Once everyone is “trapped,” the value is transferred to their executives and shareholders.

People with no understanding of marketing or people thinking that auto-generated comments will boost their profile on LinkedIn. Businesses using AI to cut corners to generate meaningless bullshit and throwing it at me. See Coca Cola advert for reference.

Nothing says happy holidays like being fired for an incompetent robot.

The lights are on, the wheels are turning, but nobody is home. Or cares. The Mark Zuckerburgs of the world are, hopefully, turning people off hyper addictive brain rot.

Impressive, I know. Thank god for Ryan Air.

As email is an owned channel, there’s not an obvious issue with generative AI. However, the Litmus State of Email Report shows the top roadblocks and operational challenges encountered by teams.

AI makes all of these roadblocks worse. Crummy, personality-devoid content churned out at scale will lower engagement. And it doesn’t take a genius to figure out that execs would love to save on personnel.

Operationally, you’d think AI will help. But if producing high-quality content at scale and improving your core benchmarks are fundamental issues, I’m not sure AI is the answer.

Personalization, research, and distribution. Absolutely. Creating content that draws real people in and engages with them. Color me sceptical.

Paid Vs. Earned Vs. Owned

This is all about the funnel. If it becomes more expensive to acquire customers in their unaware/aware phase with paid campaigns, your owned and earned channels need to work harder. They need to work harder to increase your conversion rate.

• Paid campaigns or projects are designed to do two things: reach a newer potential audience and retarget an existing, highly qualified one. But they’re becoming more expensive. Especially in a PPC sense.
• Most sensible companies are trying to build their email databases off the back of search and organic social. Owned media is simultaneously under threat and incredibly valuable.
• Earned media – public exposure through word of mouth and shared content – is arguably more important than ever. People really trust people’s opinions.

What Should You Do?

As an SEO and a marketer, you should focus on creating real connections with people. Understanding your audience. Leveraging people that have influence over your audience. Build, work with, and promote brilliant creators and own your audience data internally.

Squeeze every last drop out of your content. Cut and share it in appropriate formats across multiple channels.

Email is almost certainly the most applicable channel for most brands. You actually own it. Then figure out the role your brand plays in that journey. Create a great user experience on and off-site. Make sure it’s well documented, and you own everything in your control. Speak to your PPC and social teams to understand the challenges they’re having.

• Help Center.
• FAQ and product pages.
• ToV consistency and brand guidelines.
• Reviews and complaints (On and off-site).
• Technical site quality.
• Content quality.
• Large-scale, TOFU campaigns.

This isn’t just about marketing. Or LLMs. They are just a good proxy for how you are seen on the internet.

It’s about working together as a marketing department with a shared goal of creating and amplifying brilliant experiences to the right people. Maximising the value of your owned channels, to reduce the reliance on paid, and doing things that create brand advocates and cause your earned media to soar.

There’s an opportunity here to do great things!

But whatever you do, don’t forget about good quality SEO. It’s the primary purpose of our job and it still works.

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This post was originally published on Leadership in SEO.

Featured Image: MR.DEEN/Shutterstock

The Alpha Is Not LLM Monitoring via @sejournal, @Kevin_Indig

Adobe just paid $1.9 billion for Semrush. Not for the LLM tracking dashboards. For the platform, the customer relationships, and the distribution.

Contrast: Investors poured $227 million into AI visibility tracking. Most of that went to tracking dashboards. The companies shipping outputs from agentic SEO raised a third of that. Adobe’s acquisition proves dashboards were never the point.

Investors chased LLM monitoring because it looked like easy SaaS, but the durable value sits in agentic SEO tools that actually ship work. Why? Because agentic SEO goes beyond the traditional SEO tooling setup, and offers SEO professionals and agencies a completely new operational capability that can augment (or doom) their business.

Together with Wordlift, Growth Capital, Niccolo Sanarico, Primo Capital, and G2, I analyzed the funding data and the companies behind it. The pattern is clear: Capital chased what sounded innovative. The real opportunity hid in what actually works.

1. AI Visibility Monitoring Looked Like The Future

We looked at 80 companies and their collective $1.5 billion in venture funding:

• Established platforms (five companies) captured $550 million.
• LLM Monitoring (18 companies) split $227 million.
• Agentic SEO companies got $86 million.

AI visibility tracking seemed like the obvious problem in 2024 because every CMO asked the same question: “How does my brand show up in ChatGPT?” It’s still not a solved problem: We don’t have real user prompts, and responses vary significantly. But measuring is not defensible. The vast number of startups providing the same product proves it.

Monitoring tools have negative switching costs. Agentic tools have high switching costs.

• Low pain: If a brand turns off a monitoring dashboard, they lose historical charts.
• High pain: If a brand turns off an agentic SEO platform, their marketing stops publishing.

Venture capital collectively invested +$200 million because companies care about how and where they show up on the first new channel since Alphabet, Meta, and TikTok. The AI visibility industry has the potential to be bigger than the SEO industry (~$75 billion) because Brand and Product Marketing departments care about AI visibility as well.

What they missed is how fast that trend becomes infrastructure. Amplitude proved it was commoditizable by offering monitoring for free. When Semrush added it as a checkbox, the category collapsed.

2. The Alpha Is In Outcomes, Not Insights

Outcomes trump insights. In 2025, the value of AI is getting things done. Monitoring is table stakes.

73% of AI visibility tracking companies were founded in 2024 and raised $12 million on average. That check size is typically reserved for scale-stage companies with proven market-fit.

Our analysis reveals a massive maturity gap between where capital flowed and where value lives.

• Monitoring companies (average age: 1.3 years) raised seed capital at growth valuations.
• Agentic SEO companies (average age: 5.5 years) have been building infrastructure for nearly a decade.

Despite being more mature, the agentic layer raised one-third as much capital as the monitoring layer. Why? Because investors missed the moat.

Investors dislike “shipping” tools at the seed stage because they require integration, approval workflows, and “human-in-the-loop” setup. To a VC, this looks like low-margin consulting. Monitoring tools look like perfect SaaS: 90% gross margins, instant onboarding, and zero friction.

Money optimized for ease of adoption and missed ease of cancellation.

• The Monitoring Trap: You can turn off a dashboard with a click to save budget.
• The Execution Moat: The “messy” friction of agentic SEO is actually the defensibility. Once an operational workflow is installed, it becomes infrastructure. You cannot turn off an execution engine without halting your revenue.

Capital flowed to the “clean” financials of monitoring, leaving the “messy” but durable execution layer underfunded. That is where the opportunity sits.

Three capabilities separate the winners from the features:

1. Execution Velocity: Brands need content shipped across Reddit, TikTok, Quora, and traditional search simultaneously. Winners automate the entire workflow from insight to publication.
2. Grounding in Context: Generic optimization loses to systems that understand your specific business logic and brand voice. (Ontology is the new moat).
3. Operations at Scale: Content generation without pipeline management is a toy. You need systems enforcing governance across dozens of channels. Point solutions lose; platform plays win.

The difference is simple: one group solves “how do I know?” and the other solves “how do I ship?”

3. The Next 18 Months Will Wipe Out The Weakest Part Of The AI Stack

The market sorts into three tiers based on defensibility:

1. Established platforms win by commoditizing. Semrush and Ahrefs have customer relationships spanning two decades. They’ve already added LLM monitoring as a feature. They now need to move faster on the action layer – the workflow automation that helps marketers create and distribute assets at scale. Their risk isn’t losing relevance. It’s moving too slowly while specialized startups prove out what’s possible.

The challenge: Established platforms are read-optimized; agentic operations require write-access. Semrush and Ahrefs built 20-year moats on indexing the web (Read-Only). Moving to agentic SEO requires them to write back to the customer’s CMS (Write-Access).

2. Agentic SEO platforms scale into the gap. They’re solving real operational constraints with sticky products. AirOps is proving the thesis: $40 million Series B, $225 million valuation. Their product lives in the action layer – content generation, maintenance, rich media automation. Underfunded today, they capture follow-on capital tomorrow.

3. Monitoring tools consolidate or disappear. Standalone AI visibility vendors have 18 months to either build execution layers on top of their dashboards or find an acquirer. The market doesn’t support single-function tracking at venture scale.

Q3/Q4 2026 could be an “Extinction Event.” This is when the 18-month runway from the early 2024 hype cycle runs out. Companies will go to market to raise more money, fail to show the revenue growth required to support their 2024 valuations, and be forced to:

• Accept a “down-round” (raising money at a lower valuation, crushing employee equity).
• Sell for parts (acqui-hire).
• Fold.

Let’s do some basic “Runway Math”:

• Assumption: The dataset shows the average “Last Funding Date” for this cluster is March 2025. This means the bulk of this €227 million hit bank accounts in Q1 2025.
• Data Point: The average company raised ~€21 million.
• The Calculation: A typical Series A/Seed round is calculated to provide 18 to 24 months of runway. With the last funding in Q1 2025 and 18 months of runway, we arrive at Q3 2026.

To raise their next round (Series B) and extend their life, AI visibility companies must justify the high valuation of their previous round. But to justify a Series A valuation (likely $50-$100 million post-money given the AI hype), they need to show roughly 3x-5x ARR growth year-over-year. Because the product is commoditized by free tools like Amplitude and bundled features from Semrush, they might miss that 5x revenue growth target.

Andrea Volpini, Founder and CEO of Wordlift:

After 25 years, the Semantic Web has finally arrived. The idea that agents can reach a shared understanding by exchanging ontologies and even bootstrap new reasoning capabilities is no longer theoretical. It is how the human-centered web is turning into an agentic, reasoning web while most of the industry is caught off guard. When Sir Tim Berners-Lee warns that LLMs may end up consuming the web instead of humans, he is signaling a seismic shift. It is bigger than AI Search. It is reshaping the business model that has powered the web for three decades. This AI Map is meant to show who is laying the foundations of the reasoning web and who is about to be left behind.

4. The Market Thesis: When $166 Billion Meets Behavioral Disruption

From Niccolo Sanarico, writer of The Week in Italian Startups and Partner at Primo Capital:

Let’s leave the funding data for a moment, and shift to the demand side of the market: on the one hand, Google integrating AI search results on its SERP, ChatGPT or Perplexity becoming the entry point for search and discovery, are phenomena that are creating a change in user behavior – and when users change behavior, new giants emerge. On the other hand, SEO has historically been a consulting-like, human-driven, tool-enabled effort, but its components (data monitoring & analysis, content ideation & creation, process automation) are the bread and butter of the current generation of AI, and we believe there is a huge space for emerging AI platforms to chip away at the consulting side of this business. Unsurprisingly, 42% of the companies in our dataset were founded on or after 2020, despite the oldest and greatest players dating back more than 20 years, and the key message they are passing is “let us do the work.”

The numbers validate this thesis at scale. Even though it is not always easy to size it, recent research finds that the SEO market represents a $166 billion opportunity split between tools ($84.94 billion) and services ($81.46 billion), growing at 13%+ annually. But the distribution reveals the disruption opportunity: agencies dominate with 55% market share in services, while 60% of enterprise spend flows to large consulting relationships. This $50+ billion consulting layer – built on manual processes, relationship-dependent expertise, and human-intensive workflows – sits directly in AI’s disruption path.

The workforce data tells the automation story. With >200,000 SEO professionals globally and median salaries in the US of $82,000 (15% above U.S. national average), we’re looking at a knowledge worker category ripe for productivity transformation. The job market shifts already signal this transition: content-focused SEO roles declined 28% in 2024 as AI automation eliminated routine work, while leadership positions grew 50-58% as the focus shifted to strategy and execution oversight. When 90% of new SEO positions come from companies with 250+ employees, and these organizations are simultaneously increasing AI tool budgets from 5% to 15% of total SEO spend, the path forward is clear: AI platforms that can deliver execution velocity will capture the value gap between high-cost consulting and lower-margin monitoring tools.

5. What This Means For You

For Tool Buyers

Stop asking “Is it AI-powered?” Ask instead:

1. Does this solve an operational constraint or just give me information? (If it’s information, Semrush will have it free in 18 months.)
2. Does this automate a workflow or create new manual work? (Sticky products are deeply integrated. Point solutions require babysitting.)
3. Can I get this from my existing platform eventually, or is this defensible? (If an established player can bundle it, they will.)

For Investors

You’re at an inflection point:

• The narrative layer (monitoring) is collapsing in real-time.
• The substance layer (execution) is still underfunded.
• This gap closes fast.

When evaluating opportunities, ask: “What would need to happen for Semrush or Ahrefs to provide this?” If the answer is “not much,” it’s not defensible at venture scale. If they had to rebuild core infrastructure or cannibalize part of their product, you have a moat.

The best signal isn’t which companies are raising capital, but which categories are raising capital despite low defensibility. That’s where you find the upside.

For Builders

Your strategic question isn’t “Which category should I enter?” It’s “How deeply integrated will I be in my customers’ workflows?” If you’re building monitoring tools, you have 18 months. Either build an execution layer on top of your dashboard or optimize for acquisition.

If you’re building execution platforms, defensibility comes from three things:

1. Depth of integration in daily workflows
2. Required domain expertise
3. Operational leverage you provide relative to building in-house

The winning companies are those that solve problems needing continuous domain expertise and cannot be easily copied. Automated workflows that understand brand guidelines, customer segments, and channel-specific best practices aren’t.

Ask yourself: What operational constraint am I solving that requires judgment calls, not just better AI? If the answer is “I’m just generating better content faster,” you’re building a feature. If the answer is “I’m managing complexity across dozens of channels while enforcing consistency,” you’re building a platform.

Full infographic of our analysis:

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Featured Image: Paulo Bobita/Search Engine Journal