Google has revealed plans to expand the capabilities of its AI assistant Gemini, introducing a new feature called “Deep Research.” This announcement came via social media following the company’s recent ‘Made By Google’ event.
According to Google’s post, Gemini will soon be able to “do in-depth research for you and synthesize the info to give you a simple, comprehensive plan.”
Google says this feature will be launched “in the next few weeks,” along with other functionalities showcased at the event.
Everything you’ve seen today will launch in the next few weeks, but the vision for our assistant isn’t over. We’re building so Gemini can do in-depth research for you and synthesize the info to give you a simple, comprehensive plan. Stay tuned 👀 #MadeByGooglepic.twitter.com/D10j2yUlJH
The Deep Research tool is designed to assist users with complex tasks, such as gathering information from multiple sources and compiling comprehensive reports.
Google provided an example showing how the feature might help a restaurant owner research the process of adding a sidewalk café in Seattle.
In the example, Gemini outlines its approach to creating a guide that includes permit requirements, application steps, shelter specifications, timelines, costs, and case studies.
Gemini informs the user it will research web pages, analyze results, and create a full report.
Potential Applications & Limitations
Google describes the tool as being “designed to help you with making big decisions, navigating multiple sources, or getting started on a project you might not know about.”
However, Google hasn’t provided any sample reports or details about its data-sourcing methods.
This development could impact how users interact with search engines and process information.
Instead of manually sifting through multiple web pages, people might rely on Gemini to compile and synthesize information from various sources.
Unanswered Questions
Gemini’s full research capabilities and potential impact on search habits are unclear. Google’s keeping quiet on specifics, functionality, and rollout plans.
We’ll be watching to see how this research tool performs and monitoring potential ripple effects on the search ecosystem.
A study by the Data Provenance Initiative, a collective of independent and academic researchers dedicated to data transparency, reveals a mismatch between ChatGPT’s training data and its typical use cases.
The study, which analyzed 14,000 web domains, found that ChatGPT’s training data primarily consists of news articles, encyclopedias, and social media content.
However, the most common real-world applications of the tool involve creative writing, brainstorming, and seeking explanations.
As the study states,
“Whereas news websites comprise nearly 40% of all tokens… fewer than 1% of ChatGPT queries appear to be related to news or current affairs.”
Diving deeper into usage patterns, the researchers analyzed a dataset called WildChat, containing 1 million user conversations with ChatGPT. They found that over 30% of these conversations involve creative compositions such as fictional story writing or role-playing.
This mismatch suggests that ChatGPT’s performance may vary depending on the specific task and its alignment with the tool’s training data.
Marketers should know that ChatGPT might struggle to generate content based on current events, industry-specific knowledge, or niche topics.
Adapting To ChatGPT’s Strengths & Limitations
Knowing what ChatGPT is trained on can help you align prompts with the tool’s strengths and limitations.
This means you may need to add more context, specify the desired tone and style, and break down complex tasks into smaller steps.
For AI-assisted content creation, leverage ChatGPT for tasks like ideating social posts or email subject lines. Reserve human expertise for complex, industry-specific content.
Use effective prompt engineering to optimize output. Always fact-check and edit AI-generated content to ensure quality.
AI tools can accelerate ideation and content creation but don’t expect perfection. Human review is essential for accuracy, brand consistency, and channel-specific copy.
Looking Ahead
This research highlights the need for marketers to be careful with AI tools like ChatGPT.
Understand what AI can and can’t do and combine it with human expertise. This combo can boost content strategies and help hit KPIs.
As the field evolves, we might see AI tools better tailored to real-world usage patterns.
Until then, remember that it assists but doesn’t replace expert judgment.
OpenAI has decided against implementing text watermarking for ChatGPT-generated content despite having the technology ready for nearly a year.
This decision, reported by The Wall Street Journal and confirmed in a recent OpenAI blog post update, stems from user concerns and technical challenges.
The Watermark That Wasn’t
OpenAI’s text watermarking system, designed to subtly alter word prediction patterns in AI-generated text, promised near-perfect accuracy.
Internal documents cited by the Wall Street Journal claim it was “99.9% effective” and resistant to simple paraphrasing.
However, OpenAI has revealed that more sophisticated tampering methods, like using another AI model for rewording, can easily circumvent this protection.
User Resistance: A Key Factor
Perhaps more pertinent to OpenAI’s decision was the potential user backlash.
A company survey found that while global support for AI detection tools was strong, almost 30% of ChatGPT users said they would use the service less if watermarking was implemented.
This presents a significant risk for a company rapidly expanding its user base and commercial offerings.
OpenAI also expressed concerns about unintended consequences, particularly the potential stigmatization of AI tools for non-native English speakers.
The Search For Alternatives
Rather than abandoning the concept entirely, OpenAI is now exploring potentially “less controversial” methods.
Its blog post mentions early-stage research into metadata embedding, which could offer cryptographic certainty without false positives. However, the effectiveness of this approach remains to be seen.
Implications For Marketers and Content Creators
This news may be a relief to the many marketers and content creators who have integrated ChatGPT into their workflows.
The absence of watermarking means greater flexibility in how AI-generated content can be used and modified.
However, it also means that ethical considerations around AI-assisted content creation remain largely in users’ hands.
Looking Ahead
OpenAI’s move shows how tough it is to balance transparency and user growth in AI.
The industry needs new ways to tackle authenticity issues as AI content booms. For now, ethical AI use is the responsibility of users and companies.
Expect more innovation here, from OpenAI or others. Finding a sweet spot between ethics and usability remains a key challenge in the AI content game.
Research shows that name-dropping AI in marketing copy might backfire, lowering consumer trust and purchase intent.
A WSU-led study published in the Journal of Hospitality Marketing & Management found that explicitly mentioning AI in product descriptions could turn off potential buyers despite AI’s growing presence in consumer goods.
Key Findings
The study, polling 1,000+ U.S. adults, found AI-labeled products consistently underperformed.
Lead author Mesut Cicek of WSU noted: “AI mentions decrease emotional trust, hurting purchase intent.”
The tests spanned diverse categories—smart TVs, high-end electronics, medical devices, and fintech. Participants saw identical product descriptions, differing only in the presence or absence of “artificial intelligence.”
Impact on High-Risk Products
AI aversion spiked for “high-risk” offerings, which are products with steep financial or safety stakes if they fail. These items naturally trigger more consumer anxiety and uncertainty.
Cicek stated:
“We tested the effect across eight different product and service categories, and the results were all the same: it’s a disadvantage to include those kinds of terms in the product descriptions.”
Implications For Marketers
The key takeaway for marketers is to rethink AI messaging. Cicek advises weighing AI mentions carefully or developing tactics to boost emotional trust.
Spotlight product features and benefits, not AI tech. “Skip the AI buzzwords,” Cicek warns, especially for high-risk offerings.
The research underscores emotional trust as a key driver in AI product perception.
This creates a dual challenge for AI-focused firms: innovate products while simultaneously building consumer confidence in the tech.
Looking Ahead
AI’s growing presence in everyday life highlights the need for careful messaging about its capabilities in consumer-facing content.
Marketers and product teams should reassess how they present AI features, balancing transparency and user comfort.
The study, co-authored by WSU professor Dogan Gursoy and Temple University associate professor Lu Lu lays the groundwork for further research on consumer AI perceptions across different contexts.
As AI advances, businesses must track changing consumer sentiments and adjust marketing accordingly. This work shows that while AI can boost product features, mentioning it in marketing may unexpectedly impact consumer behavior.
AI-powered answer engine Perplexity AI announced a revenue-sharing plan with publishers when their content is referenced, but there are few details on how smaller publishers will benefit. Some in the digital marketing community expressed skepticism that only the biggest and most powerful publishers will be paid.
Perplexity AI Revenue Share
Perplexity recently announced the establishment of a new enterprise called Perplexity Publishers Program that promises revenue share. Perplexity swung the doors open wide for six big brand publishers who will receive cash payments in advance representing double digit revenue percentage shares. But there were literally no details about what ordinary publishers who lack the clout to get invited will earn or how to even join.
Short on details but long on promises, according to Perplexity:
“Revenue sharing: In the coming months, we’ll introduce advertising through our related questions feature. Brands can pay to ask specific related follow-up questions in our answer engine interface and on Pages. When Perplexity earns revenue from an interaction where a publisher’s content is referenced, that publisher will also earn a share.
We’re also excited to work with ScalePost.ai, a platform that streamlines collaborations between content publishers and AI companies and provides AI analytics for publishers. Our collaboration with them will enable our partners to gain deeper insights into how Perplexity cites their content.”
The six big brand entities who are receiving VIP invitations are:
Der Spiegel
Entrepreneur
Fortune
The Texas Tribune
TIME
WordPress.com
Is ScalePost.ai Legit?
There is an ad-hoc feeling to Perplexity’s announcement, not just because it’s short on details, but because it’s made in partnership with a boutique advertising network whose website only has two pages on it, the home page and the “contact us” page. There isn’t even an About Us page or office address listed.
Screenshot Of ScalePost.AI Home Page
The Internet Archive only discovered the site a few months ago, which makes the website younger than the condiments rolling around in most people’s refrigerators.
Screenshot Of ScalePost AI At Internet Archive
Despite all the typical signals that ScalePost is not a legit company, it actually is a legit company.
The founders and senior advisors are are associated with high profile people like the ex-engineering director for Google Peter Norvig and executives from top big brand publishers like Hearst, Conde Nast, Wired and Fast Company. Those aren’t who are people who are associated with the elite upper tier of publishers and technologies, not known championing the earnings of smaller publishers.
Agreement With WordPress
WordPress.com is a web publishing platform and web host owned by Automattic and is not the same as the non-profit WordPress.org, which produces the free content management system (CMS) that powers the majority of the world’s websites.
Their announcement shared details about how the revenue sharing is triggered:
“Being part of Perplexity’s Publishing Partners Program means that knowledge from WordPress.com can now be included in the variety of answers that are served on Perplexity’s “Keep Exploring” section on their Discover pages. That means your articles will be included in their search index and your articles can be surfaced as an answer on their answer engine and Discover feed. your website is referenced in a Perplexity search result where the company earns advertising revenue, you’ll be eligible for revenue share. “
WordPress.com announced that participation in the revenue share program is on by default for publishers but that there is a way to opt out should publishers who utilize the free-tier of their publishing platform desire to not participate.
A spokesperson for WordPress.com clarified to Nieman Lab that VIP level publishers who pay to host on their premium tier will not be a part of the deal.
“Megan Fox, a spokesperson for Automattic, clarified the deal excludes publishers hosted on the premium WordPress VIP, including customers like NewsCorp. The deal also carves out an exception for smaller news outlets that use Newspack, a service for local news publishers hosted on WordPress.com, including CalMatters, Capital B, Reveal and Houston Landing.”
Matt Mullenweg, the founder of Automattic, had no specific details for publishers:
“We’ll share more details of how it works as this partnership evolves, including how we’ll be distributing revenue-share payments to those whose content qualifies.”
…If you want to opt out, we already offer the ability to opt out of content sharing.”
Skepticism About Receiving Perplexity Revenue Share
Influential digital marketer Ryan Jones expressed doubt on X (formerly Twitter):
“Unpopular opinion: Unless you’re one of the top few thousand websites on the internet, LLMs or search engines are never going to pay you for your content.”
Ryan expressed the opinion that only big sites with large amounts of traffic will ever see payments.
Terry Van Horne agreed (and he wasn’t the only one):
“I’d say more like top 100…”
Is There Reason To Be Skeptical?
At this point in time, the arrangement between Perplexity AI and a brand new advertising network is long on promise and doesn’t show any evidence of expertise or experience. Of course some people are skeptical, it might be abnormal to not be skeptical of the arrangement.
Despite initial concerns, Chat GPT has not replaced search. Q2 record earnings show Google Search does better than ever. That’s why OpenAI’s new search engine, Search GPT, makes only sense after a second look.
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$5b USD
Why would OpenAI launch a search engine if its main product poses one of the biggest threats to Google?
Image Credit: Kevin Indig
Searches for “LLM Search” are growing, but it’s not consumer demand that pulls OpenAI in that direction. There are six good reasons (in order of importance):
1/ OpenAI’s problem is that Chat GPT is not perceived as a search engine despite similar capabilities, so the company positions Search GPT as a direct Google alternative to gain more Search market share.
Rumors about launching a search engine just before Google I/O in 2024 and the impact of the actual announcement on Alphabet’s stock show the ambition to compete directly.
The Information reports that OpenAI loses $5b a year in expenses.1Just capturing 3% of Google’s $175b Search business would allow OpenAI to recoup expenses.
Image Credit: Kevin Indig
Searches for ChatGPT on Google are growing so much, they get close to searches for “Google”. They’ve already surpassed searches for other search engines by a lot.
To be fair, people search less for “Google” on Google (maybe in their browser bar to get to the Google homepage), and traffic numbers between Google (465b, according to Similarweb) and Chat GPT (660M) are still magnitudes apart.
Image Credit: Kevin Indig
OpenAI has a strategic advantage over Google: Search GPT can provide a very different, maybe less noisy, user experience than Google because it’s not reliant on ad revenue. In any decision regarding Search, Google needs to take ads into account.
2/ OpenAI crawls the web for training data and already has half the ingredients for a search engine on the table. Consumers are already familiar with the concept of a search engine, making adoption more likely.
I have no doubt that OpenAI will see a lot of curious sign-ups for Search GPT but the bigger challenge will be retaining users.
It’s also important to point out that the market hasn’t found the final form of LLMs yet. Chatbots made sense because of their prompting nature, but voice devices will likely become much better devices for LLMs.
3/ Search can deliver better user signals than prompting because it’s a more specific use case.
The beauty of prompting is that it’s an open field. You can do whatever you want. But that’s also a disadvantage because most people have no idea what they want to do and where to start.
As a result, success and failure are harder to measure at scale for chatbots than search engines.
A search engine, despite being versatile, has clearer use cases, which could drive more adoption and deliver better signals for LLMs to learn. In return, those learnings could transfer to chatbot answers, which are a big part of Search GPT.
4/ OpenAI wants to throw publishers a lifeline to secure a content pipeline. LLM developers need fresh content to train models and serve timely answers.
Search is the biggest source of publisher traffic2, but publishers are growing more frustrated with Google due to Algorithm updates, site reputation abuse penalties and AI Overviews.
It’s good timing for OpenAI to offer another source of revenue and get publishers “on their side”, especially after OpenAI itself has received a lot of criticism from publishers and a lawsuit from the NY Times.
The launch of SearchGPT follows a long list of publisher licensing deals:
News Corp (+$250 million over five years): WSJ, New York Post, The Times, The Sun
Associated Press (AP)
Axel Springer: Bild, Politico, Business Insider
Financial Times
Le Monde
Reuters
Prisa Media
Dotdash Meredith
Time magazine
Vox media
Wiley (one-time fee of $23 million for previously published academic articles and books)
But even the best deals don’t help if publishers cannot sustain the creation of fresh content. If Search GPT can become a new traffic and revenue source for publishers, it would be a way to keep the critical ecosystem alive and get on the good side of publishers.
5/Perplexity is a small challenger to OpenAI, but even a small challenger can take away mind share, and you never want to underestimate the competition. A search engine would conveniently fence in their growth. Why use Perplexity when Search GPT, which looks very similar, can do the same thing?
6/ OpenAI might bet on regulators breaking up Google’s exclusive search engine deal with Apple and hope to become part of a search engine choice seton Apple devices.
Granted, we’re talking about a very small chance, and certainly not the decisive factor for building a search engine, but it could be a small factor nonetheless.
Publisher GPT
Search GPT is clearly the sibling of Chat GPT. Besides SERP Features like weather charts and table stakes features like auto-suggest, the experience feels like Chat GPT.
The differences are hard to spot at first but meaningful in their potential to drive revenue, compete with Google and strengthen OpenAI’s data mining.
But one change stands out: Search GPT has more pronounced links to web results, a clear hat tip to publishers.
The Search GPT landing page mentions the word publisher 14 times and underlines how important publishers are for the open web and how dedicated OpenAI is to working with them.
OpenAI uses a different user agent to crawl websites for its search engine than for LLM training and strongly separates the two.
Importantly, SearchGPT is about search and is separate from training OpenAI’s generative AI foundation models. Sites can be surfaced in search results even if they opt out of generative AI training.
It’s not an accident that OpenAI tries to regain its grip on the web. A recently published study3 found that 25% of words (tokens) in Common Crawl stem from domains that have now excluded AI crawlers, with OpenAI at the top of the list, in their robots.txt or ToS.
SEO Implications
The two questions every SEO is asking themselves is whether they should care about Search GPT and how it might work.
Search GPT has a chance to become relevant for SEO quickly, given Chat GPT’s adoption. The Apple Intelligence integration and a potential phone would spur adoption even more.
However, OpenAI might integrate Search GPT into Chat GPT, which could change the relevance as a traffic source.
We cannot yet know how Search GPT works because it’s not live, but one big differentiator will be whether Search GPT includes results from the broad web or only from publishers OpenAI made a deal with.
If it’s the broad web, Search GPT has a high chance of being relevant. If it’s limited to partnering publishers, SEO won’t make sense for anyone not a partner because the answer set is limited.
If Search GPT uses RAG and ranks results similar to Google’s AI Overviews, we could use AIO performance as indicator and predictor for SearchGPT performance.
There is also chance that an answer from Chat GPT for queries that don’t require QDF (query deserves freshness) is the same on Search GPT, which would give us a way to understand what works before Search GPT launches publicly. Hard to validate without access Search GPT, though.
Search GPT could gain the web’s favor by sending relevant traffic, making it easy for sites to submit content, for example, through XML sitemaps, and providing some sort of webmaster console. As a result, Search GPT would position itself even stronger against Google.
A New Way To Search
If the main benefit or Search GPT for OpenAI is a revenue stream and access to more user data, the next logical step for OpenAI is to build a (AI-powered) browser.
Browser data is incredibly valuable for understanding user behavior, personalization and LLM training. Best of all, it’s app-agnostic, so OpenAI could learn from users even when they use Perplexity or Google.
We’ve seen the power of browser data in the Google lawsuit, where it turned out Google relied on Chrome data all along for ranking. The only layer that’s more powerful is the operating system and device layer.
Image Credit: Kevin Indig
There is already news that Sam Altman is working with Jon Ivy on building a phone. No wonder since Apple holds immense power over other ecosystems and platforms.
Remember when Apple blew a $10b hole into Meta’s annual revenue? Apple could develop its own models and surface them on the OS level—a critical threat to OpenAI. A browser could alleviate at least some of that threat.
Bing released its own update to Search, giving us an idea of what Search GPT could look like. The new Bing prominently features AI answers at the top and search results on the side. A fitting metaphor for classic blue links.
Getty Images announced an updated AI model for their image generator that generates images faster and with a higher quality. The changes benefit users of Generative AI by Getty Images and also Generative AI by iStock.
Fully Licensed High Quality Images
The Getty AI generated images are trained on their own content which means that all generated images can be fully licensed and commercial use is indemnified which means that users can license the images without ethical worries about how the AI models were trained.
High Quality Image Generation And Modification
A benefit of the updated Generative AI By Getty is that both generated images and existing stock images can be edited and modified by the AI. An image can easily be extended horizontally or vertically, individual elements can be added or removed, including the entire background of the image.
This solves so many problems for publishers who are looking for images with specific qualities in them because now they can more easily edit images to make them fit their exact needs – without having to use an expensive image editing software or SaaS.
These are some of the features users can take advantage of:
“Industry-leading generation speed: Image generation speeds set to reach around 6 seconds, doubling the performance of the previous model, putting it at the forefront of the industry.
Advanced 4K generation detail: Enhanced detail and fidelity in generated images, with advanced upscaling and increased 4K generation detail.
Expanded support and adherence for more detailed prompts: Higher level of detail prompts results in images that more closely match the descriptions provided in the text prompt.
Longer prompts: Supports more complex and longer prompts, up to 250 words.
Advanced camera controls: Greater control over output using shot type and depth of field.”
Create Your Own AI Model
Enterprise level customers have the ability to fine-tune their own AI image generator models by training with their own images. This means that customers can create AI generated images based on their products, models and other image assets that are exclusive and proprietary to the users.
Getty Images Democratizes High Quality Images
Getty’s announcement represents a milestone in the business of stock images, enabling both pro and enthusiast level users to create and modify images at a level that was unthinkable only a few years ago.
Google’s Gary Illyes cautioned about the use of Large Language Models (LLMs), affirming the importance of checking authoritative sources before accepting any answers from an LLM. His answer was given in the context of a question, but curiously, he didn’t publish what that question was.
LLM Answer Engines
Based on what Gary Illyes said, it’s clear that the context of his recommendation is the use of AI for answering queries. The statement comes in the wake of OpenAI’s announcement of SearchGPT that they are testing an AI Search Engine prototype. It may be that his statement is not related to that announcement and is just a coincidence.
Gary first explained how LLMs craft answers to questions and mentions how a technique called “grounding” can improve the accuracy of the AI generated answers but that it’s not 100% perfect, that mistakes still slip through. Grounding is a way to connect a database of facts, knowledge, and web pages to an LLM. The goal is to ground the AI generated answers to authoritative facts.
This is what Gary posted:
“Based on their training data LLMs find the most suitable words, phrases, and sentences that align with a prompt’s context and meaning.
This allows them to generate relevant and coherent responses. But not necessarily factually correct ones. YOU, the user of these LLMs, still need to validate the answers based on what you know about the topic you asked the LLM about or based on additional reading on resources that are authoritative for your query.
Grounding can help create more factually correct responses, sure, but it’s not perfect; it doesn’t replace your brain. The internet is full of intended and unintended misinformation, and you wouldn’t believe everything you read online, so why would you LLM responses?
Alas. This post is also online and I might be an LLM. Eh, you do you.”
AI Generated Content And Answers
Gary’s LinkedIn post is a reminder that LLMs generate answers that are contextually relevant to the questions that are asked but that contextual relevance isn’t necessarily factually accurate.
Authoritativeness and trustworthiness is an important quality of the kind of content Google tries to rank. Therefore it is in publishers best interest to consistently fact check content, especially AI generated content, in order to avoid inadvertently becoming less authoritative. The need to verify facts also holds true for those who use generative AI for answers.
Meta just released multiple updates to Meta AI which brings advanced image generation and editing capabilities directly to Facebook, Instagram and WhatsApp feeds, plus availability in more countries and languages.
New Meta AI Creative Tools
Meta AI is bringing AI generated and AI Edited photography that can be generated at the moment a user is making a post or sending a message with a new tool called Imagine Me.
Imagine Me is a prompt that can be used to transform an uploaded image that can be shared. This new feature is first rolling out as a beta in the United States.
Meta explains:
“Imagine yourself creates images based on a photo of you and a prompt like ‘Imagine me surfing’ or ‘Imagine me on a beach vacation’ using our new state-of-the-art personalization model. Simply type “Imagine me” in your Meta AI chat to get started, and then you can add a prompt like “Imagine me as royalty” or “Imagine me in a surrealist painting.” From there, you can share the images with friends and family, giving you the perfect response or funny sidebar to entertain your group chat.”
New Editing Features
Meta products like Facebook, Messenger, WhatsApp and Instagram now have advanced editing capabilities that allow users to add or remove objects from images, to change them in virtually any manner, such as their example of turning a cat in an image into a dog. A new Edit With AI button is forthcoming in a month that will unlock even more AI editing power.
Adding AI generated images to Facebook, Instagram, Messenger and WhatsApp within feed, posts, stories, comments and messages is rolling out this week in English and coming later to other languages.
Meta AI In More Countries And Languages
Meta AI is now available in seven additional countries, bringing the total countries to to 22. It is also available in seven more languages.
List of Seven Additional Countries:
Argentina
Cameroon
Chile
Colombia
Ecuador
Mexico
Peru
Meta AI is now also available in the following seven additional languages:
French
German
Hindi
Hindi-Romanized Script
Italian
Portuguese
Spanish
Advanced Math And Coding
Meta AI is making their most advanced model, Llama 405B, available for users to take advantage of its advanced reasoning abilities that can answer complex answers and excells at math and coding.
Meta AI writes:
“You can get help on your math homework with step-by-step explanations and feedback, write code faster with debugging support and optimization suggestions, and master complex technical and scientific concepts with expert instruction.”
AI is terraforming tech. The content and SEO ecosystem is undergoing a massive structural change.
Human-written content gains value faster for LLM training than for end consumers as the pure profit licensing deals between LLM developers and publishers show.
Publishers struggle to survive from digital subscriptions but get millions that go straight to their bottom line for providing training data.
Content platforms, social networks, SaaS companies and consumer apps coat their products with AI. A few examples:
Spotify DJ (AI-generated playlist).
AI Overview (AI answers in Google Search).
Instagram AI personas (celebrity AI chatbots).
Ebay’s magical listing (turn a photo into a listing).
Redfin Redesign (try interior designs on real house pictures).
Image Credit: Kevin Indig
The quality of machine-generated content (MGC) challenges human-generated content (HGC). I ran an experiment with my Twitter and LinkedIn followers: I asked them to choose which of two articles was written by a human and which by a machine – and they had to explain their answer.
Only a handful of people figured out that AI wrote both pieces. I intentionally framed the question in a leading way to see if people would challenge the setting or believe that one piece was written by a human if told so.
Not an isolated experiment: A survey of 1,900 Americans found that 63.5% of people can’t distinguish between AI content and human content.1
People seek help: Google search demand for [ai checker] has reached 100,000 in May 2024 (Glimpse).
Dark side: scammers use MGC to make money, as 77% of AI scam victims lost money.2
Image Credit: Kevin Indig
The quality level of LLMs pushes SEO work towards automating workflows and learning with AI, while writers will take content from good to great instead of zero to one.
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Image Credit: Lyna ™
System Builders
Clients, podcasters and panel hosts often ask me what skills SEOs need to build for the AI future. For a long time, my answer was to learn, stay open-minded and gain as much practical experience with AI as possible.
Now, my answer is SEOs should learn how to build AI agents and workflows that automate tasks. AI changes the way search works but also the way SEOs work.
AI + No-code Allows SEOs To Automate Workflows
A few examples:
1/ Cannibalization
Old world: SEOs download search console data and create pivot tables to spot keyword cannibalization.
New world: SEOs build an AI workflow that sends alters, identifies true keyword cannibalization, makes content suggestions to fix the problem, and monitors the improvement.
2/ Site Crawling
Old world: SEOs crawl websites to find inefficiencies in internal linking, status code errors, duplicate content, etc.
New world: SEOs build an AI agent that regularly crawls the site and automatically suggests new internal links that are shipped after human approval, fixes broken canonical tags and excludes soft 404 errors in the robots.txt.
3/ Content Creation
Old world: SEOs do keyword research and write content briefs. Writers create the content.
New world: SEOs automate keyword research with AI and create hundreds of relevant articles as a foundation for writers to build on.
All of this is already possible today with AI workflow tools like AirOps or Apify, which chain agents and LLMs together to scrape, analyze, transform data or create content.
Moving forward, we’ll spend much more time building automated systems instead of wasting time on point analyses and catalogs of recommendations. The SEO work will be defining logic, setting rules, prompting and coding.
Building workflows with AirOps (Image Credit: Kevin Indig)
You Can Learn (Almost) Anything With AI
I never made the time to really learn Python or R, but with the help of Chat GPT and Gemini in Colab, I can write any script with natural language prompts.
When the script doesn’t work, I can paste a screenshot into Chat GPT and describe the issue to get a solution. AI helps with Regex, Google Sheets/Excel, R, Python, etc. Nothing is off-limits.
Being able to write scripts can solve problems like data analysis, a/b testing and using APIs. As an SEO, I’m no longer dependent on engineers, data scientists or writers to perform certain tasks. I can act faster and on my own account.
I’m not the only one to figure this out. People are learning to code, write and many other skills with AI. We can learn to build AI workflows by asking AI to teach us.
Image Credit: Kevin Indig
Image Credit: Kevin Indig
Image Credit: Kevin Indig
When you can learn almost anything, the only limit is time.
The Work Of Writers Changes
Against common belief, writers won’t be crossed out of this equation but will play the critical role of editing, directing and curating.
In any automated process, humans QA the output. Think of car assembling lines. Even though AI content leaps in quality, spot checks reduce the risk of errors. Caught issues, such as wrong facts, weird phrasing or off-brand wording, will be critical feedback to fine-tune models to improve their output.
Instead of leg work like writing drafts, writers will bring AI content from good to great. In the concept of information gain, writers will spend most of their time making a piece outstanding.
The rising quality work spans from blog content to programmatic content, where writers will add curated content when searches have a desire for human experience, such as in travel.
Tripadvisor’s attraction pages feature human-curated sections. (Image Credit: Kevin Indig)
Unfair Advantage
As often with new technology, a few first-mover people and companies get exponential value until the rest catch up. My worry is that a few fast-moving companies will grab massive land with AI.
And yet, this jump in progress will allow newcomers to challenge incumbents and get a fair chance to compete on the field.
AI might be a bigger game changer for SEOs than for Google. The raw power of AI might help us overcome challenges from AI Overviews and machine learning-driven algorithm updates.
But the biggest win might be that SEOs can finally make something instead of delivering recommendations. The whole value contribution of SEOs changes because my output can drive results faster.
