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.