I’m watching OpenAI discontinue ChatGPT Atlas, its standalone desktop browser, and move its browser-based AI features into the new ChatGPT desktop app. That app brings together ChatGPT Work, OpenAI’s work-focused agent, and ChatGPT Codex.
The end of Atlas. I’m taking note of an Aug. 9 retirement date after OpenAI’s James Sun confirmed the plan on X.
I’m also noting Sun’s exact wording: “The current targeted date for deprecation is 8/9, and we’ll share more information in the upcoming days both in-app and via email.”
One desktop app. I see the new ChatGPT desktop app becoming OpenAI’s primary desktop product, complete with built-in browser capabilities. Instead of maintaining a separate AI browser, OpenAI is combining browsing, work-agent features, and Codex in one place.
Chrome users can keep Chrome. If I prefer using Chrome, I can access ChatGPT and Codex through OpenAI’s Chrome extension without switching to a dedicated OpenAI browser.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
Why I care. I see this as an important shift because OpenAI is moving AI browsing into the main ChatGPT experience, where more people can ask questions, research brands, and complete tasks. In my view, that gives ChatGPT another opportunity to influence discovery beyond traditional search results.
I first saw ChatGPT Atlas launch on Mac in October. OpenAI later released a dedicated Codex app and added an in-app browser in April. Now, I’m watching those capabilities move into the new unified ChatGPT desktop app.
I’m introducing support for GPT-5.6 in Profound, bringing OpenAI’s newest flagship model family directly into the workflows I rely on for advanced AI performance.
With GPT-5.6, I can work across the new Sol, Terra, and Luna tiers, giving me the flexibility to support everything from frontier reasoning to high-throughput production workloads.
I’m especially focused on what this means for agentic workflows, coding, research, and enterprise knowledge work. GPT-5.6 delivers meaningful improvements in capability, reliability, and efficiency, making it easier for me to apply AI across a wide range of business use cases with more confidence.
I’m seeing OpenAI continue to build out ChatGPT Ads with a new round of updates for advertisers. In an email, ChatGPT Ads announced changes across ChatGPT Ads Manager and the broader ad experience, including custom audiences, a new overview tab, suggested ad drafts, a refreshed static ad card format, and expanded availability in Japan and South Korea.
Here is what stands out to me from the latest update.
Custom audiences: I can now upload audience lists with 25,000 or more users to include or suppress audiences from campaigns. OpenAI is also allowing bid multipliers for audiences at the ad group level, which gives advertisers more control over how aggressively they want to reach specific segments.
Overview tab: The new overview tab gives me a more centralized place to monitor account health, review recommended tasks that may improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.
A before-and-after look at ChatGPT's refreshed static ad card, turning a small sponsored grocery prompt into a cleaner, more readable format with larger visuals and a clear Ad badge.
Suggested ad drafts: If a campaign needs broader content coverage to improve delivery, I may see an option to select “Add new ad” from the campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description, which I can then review, edit, and assign to a campaign and ad group. Importantly, OpenAI says this does not generate new copy or imagery with AI.
Japan and South Korea expansion: ChatGPT Ads are now live in Japan and South Korea. That means campaigns can target users in both markets, giving advertisers more reach if they do business there.
Refreshed static ad card format: OpenAI is also rolling out a refreshed static ad card across web and mobile. I see this as a cleaner, more compact format designed to be easier to read while giving visuals more prominence. This format had already started appearing in late June.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Why I care: ChatGPT Ads are still new, and OpenAI is clearly moving quickly. New targeting tools, reporting views, draft workflows, market expansion, and format tests all point to a platform that is still taking shape.
My takeaway is simple: I need to keep watching these changes closely, test them as they become available, and continue refining ad creative, audience strategy, and campaign structure as ChatGPT Ads matures.
I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.
Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.
Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.
The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.
I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”
My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.
A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.
More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.
A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.
What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.
For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.
I am seeing OpenAI roll out a new feature that lets ChatGPT Ads generate ads for advertisers, and I suspect AI is doing the heavy lifting behind it. The option appears under “Add new ad” and includes a prompt to “generate ads for you.”
From there, I can choose to let ChatGPT create the ad, then review it, edit it, and approve it before it goes live on the ChatGPT Ads platform.
ChatGPT Ads Manager preview highlights OpenAI's generated ad workflow, where marketers can review an AI-created variation before activating it for a campaign.
What it looks like. Anthony Higman posted a screenshot of the feature on X, showing how the ad creation flow appears inside the platform.
A ChatGPT Ads dropdown highlights the quick Duplicate Ad option, pointing marketers to a faster way to copy an existing ad for review, edits, and reuse.
In the screenshot, the interface says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” I can then move forward by selecting “Review and create.”
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
I also noticed that Higman spotted a quick duplicate ad option, which could make it easier to create variations faster.
Why I care. It makes sense to me that OpenAI would use AI to help advertisers create ads more quickly. If the tool reduces friction, it could lead to more ads being created, submitted, and activated on ChatGPT Ads, which would also help OpenAI generate more revenue from ChatGPT.
As a marketer, I would still be careful with AI-generated ads. I would review every version closely to make sure the messaging fits the brand, supports the campaign strategy, and aligns with performance goals, including ROI.
I am seeing OpenAI point to early momentum in its advertising business, with executives saying ChatGPT users are dismissing ads less often and engaging with them more. For me, that makes ad dismissal a key signal to watch as OpenAI looks for revenue beyond subscriptions and enterprise AI.
What is happening. OpenAI says ChatGPT ad dismissals have dropped by 50% since the company launched its advertising business in February. I read that decline as OpenAI’s way of showing that its ads are becoming more relevant, because the company treats dismissals as a proxy for whether users find an ad useful or intrusive.
The update came from OpenAI Chief Revenue Officer Denise Dresser, who framed relevance as a central focus for the company as it builds advertising into ChatGPT.
Why I care. If users are becoming more open to ads inside ChatGPT, I see conversational AI becoming a more serious advertising channel. A 50% drop in dismissals suggests better relevance and stronger engagement, which could give brands a way to reach people during high-intent, task-focused moments instead of relying only on interruptive ad formats.
Why relevance matters. I think ads inside AI experiences face a much higher bar than traditional display ads. People usually come to ChatGPT to complete a task, answer a question, compare options or solve a problem, so an ad that feels disconnected can quickly create friction and damage trust.
According to Dresser, OpenAI has been focused on making the format useful. “This form factor is about usefulness,” she said. “That’s great for the consumer, great for the user.”
The bigger picture. I see these results as an early look at how advertising may evolve inside generative AI platforms. Instead of interrupting content consumption, AI-powered advertising is moving toward recommendations that fit the user’s intent and the conversation already underway.
That shift means success may depend less on grabbing attention and more on being genuinely helpful. The lower dismissal rate suggests OpenAI is making progress toward that goal, even if the ad model is still early.
Competition extends beyond advertising. I also see this update in the context of OpenAI expanding its business on multiple fronts. While it builds an ads business, the company is also competing for enterprise AI spending against rivals such as Anthropic.
I see vibe coding as one of the most accessible ways to create small pieces of software with AI tools like ChatGPT, Cursor, Replit, and Gemini. Instead of writing code line by line, I describe what I want in plain language, receive working code in return, paste it into an environment like Google Colab, run it, and test the result.
In this guide, I’ll explain how I approach vibe coding, where I think it works well, where it breaks down, and which SEO examples can inspire practical projects of your own.
Vibe coding variations
I use “vibe coding” as a broad term, but it helps to separate it from nearby approaches:
Type
Description
Tools
AI-assisted coding
AI helps write, refactor, explain, or debug code. I usually associate this with developers or engineers who already understand the systems they are building.
GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding
AI handles most of the work after I provide the idea or prompt.
ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms
Platforms handle what I ask for through visual interfaces, drag-and-drop workflows, or background automation. Many now use AI, but they existed before AI became mainstream.
Notion, Zapier, Wix
For this guide, I’m focusing only on vibe coding.
The barrier to entry is low. In most cases, I only need a ChatGPT account, free or paid, and access to a Google account. Depending on the project, I may also need API access or subscriptions to SEO tools such as Semrush or Screaming Frog.
I also like to set expectations early: by the end of this kind of workflow, I’m usually aiming to run a small program in the cloud. If I want to build a SaaS product or software I plan to sell, I treat AI-assisted coding as the more realistic path because it usually requires more technical knowledge, more testing, and more budget.
Vibe coding use cases
I find vibe coding most useful when I’m working with clear buckets of data and need a helpful outcome, not a perfect one. That might mean finding related internal links, adding pre-selected tags to articles, comparing groups of URLs, or building something playful where the output does not need to be exact.
For example, I built an app that creates a daily drawing for my daughter. I type a short phrase about something she told me, such as “I had carrot cake at daycare.” The app uses examples of drawing styles I like and a few pictures of her, then generates a drawing as the final output.
When I ask for precise changes, the tool often gets worse. I once asked it to remove a mustache, and instead it recolored the image. That is exactly the kind of limitation I expect with this approach.
If my daughter were a client reviewing every detail, I would need someone with Photoshop or similar skills to make exact edits. For this use case, though, the result is good enough, and that is where vibe coding shines.
I would be cautious about building commercial applications solely through vibe coding. Some companies may even need vibe coding cleaners to clean up AI-generated work. But for demos, MVPs, internal tools, and quick experiments, I see vibe coding as a useful shortcut.
How I create SEO tools with vibe coding
When I create an SEO tool with vibe coding, I usually follow three steps:
I write a prompt describing the code I need.
I paste the code into a tool such as Google Colab.
I run the code and check whether the results match what I expected.
Here’s a real prompt example from a tool I built to map related links at scale. After crawling a website with Screaming Frog and extracting vector embeddings through the crawler’s OpenAI integration, I vibe coded a tool to compare topical distance between the vectors for each URL.
This is exactly what I wrote in ChatGPT:
I need a Google Colab code that will use OpenAI to:
Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).
The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.
I’ll upload a CSV with these columns and expect a CSV in return with the answers.
After ChatGPT generated the code, I pasted it into Google Colab, which is a free Jupyter Notebook environment for running Python in a browser. I then used “Run all” to test whether the program produced the output I wanted.
That is the clean version of the process. In practice, AI can make the workflow look perfect while still producing code that does not behave the way I need.
I expect issues along the way, and most of them are simple to troubleshoot if I keep the prompt and testing process clear.
First, I always state the platform I’m using. If I want code for Google Colab, I say that directly in the prompt.
Sometimes I still get code that depends on packages that are not installed. When that happens, I paste the error back into ChatGPT and ask it to fix the code or suggest an alternative. I do not need to fully understand the missing package to move forward. I can also ask Gemini inside Google Colab to identify the problem and update the code directly.
I also check outputs carefully because AI can sound confident while inventing data. One time, I forgot to specify that the source data would come from a CSV file, so the tool created fake URLs, traffic, and graphs. “It looks good” is not the same as “it is correct.”
If I connect to an API, especially a paid API from a provider such as Semrush, OpenAI, Google Cloud, or another platform, I need to request my own API key and keep usage costs in mind.
A Semrush subscription screen highlights 2 million Standard API units and a masked API key, underscoring the setup step needed for SEO automation and vibe-coded tools.
If I want an even lower execution barrier than Google Colab, I can use Replit.
With Replit, I can prompt what I want, and the platform can generate the code, design the interface, and let me test everything in one place. That reduces copy-and-paste work and gives me a shareable URL quickly. I still need to review poor outputs and keep iterating until the app behaves properly.
The tradeoff is cost. Google Colab is free unless I use paid API keys, while Replit charges a monthly subscription and usage-based API fees. The more the app runs, the more expensive it can become.
SEO vibe-coded tools that inspire me
Google Colab is the easiest place for me to start, but SEOs are taking vibe coding much further. I’ve seen people create Chrome extensions, Google Sheets automations, and even browser games.
I’m sharing these examples because they show what is possible when useful SEO ideas meet practical AI tooling. If I see a tool and wish it had a different feature, that is often a sign that I could try building a version for myself.
A Replit vibe-coding session turns a simple prompt for toddler-friendly daily jokes into a published web app, illustrating how AI tools can quickly prototype playful ideas.
GBP Reviews Sentiment Analyzer by Celeste Gonzalez
After vibe coding SEO tools in Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, pushed the idea further by creating a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.
Her extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis from reviews over the last 30 days and shows review velocity. It also exports the information to CSV and works on Google Maps and Google Business Profile pages.
Instead of relying only on ChatGPT, Celeste used Claude to create stronger prompts and Cursor to turn those prompts into code.
AI tools used: Claude (Sunner 4.5 model) and Cursor
APIs used: Google Business Profile API (free)
A vibe-coded GBP Sentiment Analyzer turns review data into a quick snapshot, showing negative sentiment trends, key topics, and an export option for SEO workflows.
Platform hosting: Chrome Extension
Knowledge Panel Tracker by Gus Pelogia
I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Later, I discovered that Google’s Knowledge Graph Search API lets me check the confidence score for any entity.
That led me to build a vibe-coded tracker that checks entity scores daily, or at any frequency I choose, and returns the results in a Google Sheet. I can track multiple entities at once and add new ones whenever I need to.
The Knowledge Panel Tracker runs entirely in Google Sheets, and the Knowledge Graph Search API is free to use. This guide explains how to create and run it in your own Google account, or you can see the spreadsheet here and update the API key under Extensions > App Scripts.
AI models used: ChatGPT 5.1
A spreadsheet-based Knowledge Panel Tracker turns entity searches into structured SEO data, comparing names, entity types, descriptions, and confidence scores at a glance.
APIs used: Google Knowledge Graph API (free)
Platform hosting: Google Sheets
Inbox Hero Game by Vince Nero
I also like the idea of vibe coding a link building asset. That is what Vince Nero from BuzzStream did with the Inbox Hero Game. The game asks players to use the keyboard to accept or reject a pitch within seconds, and it ends if they accept too many bad pitches.
Inbox Hero Game is more complex than running a small script in Google Colab, and it took Vince about 20 hours to build from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.
The game was built with HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.
A retro arcade-style Inbox Hero screen turns PR pitch triage into a fast keyboard game, challenging players to accept or reject emails before time runs out.
He also found that longer prompt threads became less useful over time, “to the point where [he’d] have to restart in a new chat.”
That became one of the hardest parts of the project. Vince would add a feature, such as a score, and ChatGPT would “guarantee” it had found the error, update the file, and still return the same problem.
In the end, Inbox Hero Game shows that it is possible to create a simple game without coding knowledge. It also shows where a developer becomes valuable when the goal shifts from “working prototype” to polished product.
AI models used: ChatGPT
APIs used: None
Platform hosting: Webpage
How I think about vibe coding with intent
I do not expect vibe coding to replace developers, and I do not think it should. What I do see is a practical way for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift.
The key is realism. I use vibe coding where precision is not mission-critical, I validate outputs carefully, and I stay alert for the moment when a project grows beyond “good enough” and needs stronger technical support.
When I approach vibe coding thoughtfully, it becomes less about shipping perfect software and more about expanding what I can test. For internal tools, proofs of concept, and SEO side projects, the best results come from pairing curiosity with restraint.
I’ve discovered an exciting development for UK advertisers as OpenAI launches the ChatGPT Ads Manager in beta. This new tool offers businesses an innovative way to engage with a potentially transformative advertising channel.
OpenAI is expanding its advertising tools, providing UK businesses with early access to the self-serve Ads Manager for ChatGPT. This is a clear indication of OpenAI’s commitment to scaling its advertising capabilities on their fast-evolving AI platform.
What’s happening. According to a recent email from OpenAI, the Ads Manager Beta is now available for UK businesses, allowing advertisers to explore the platform’s potential.
The self-serve interface is user-friendly, built to help businesses quickly set up accounts and dive into campaign management with ease.
How it works. The dashboard is organized into four key areas: campaigns, tools, billing, and settings, ensuring digital marketers find the navigation intuitive and straightforward.
The platform’s interface feels familiar, with campaign controls and user management features easily accessible through streamlined navigation.
For agencies. OpenAI suggests that agencies and freelancers should avoid creating accounts on behalf of their clients.
Clients should independently:
Create their own Ads Manager account.
Go to Settings → Users → Invites.
Invite agency partners with suitable permission levels.
Once invited, users receive an email to accept access and can then switch between client accounts on the platform.
The catch. Unlike Google Ads’ MCC structure, current limitations mean users can’t manage multiple accounts simultaneously in a centralized way. Account switching is required for individual access.
Why we care. The UK launch of Ads Manager represents a significant opportunity for brands and agencies to familiarize themselves with the interface and workflows before it gains wider acceptance.
By eliminating upfront billing requirements and simplifying account creation, OpenAI reduces barriers for marketers eager to explore ChatGPT’s burgeoning advertising environment.
What to watch. The rollout in the UK suggests OpenAI is transitioning from experimental phases to establishing a scalable advertising platform.
Advertisers will soon need to consider inventory, targeting options, measurement tools, and how ads integrate into ChatGPT conversations.
For now, marketers are getting a firsthand look at this promising new ad infrastructure that could shape OpenAI’s future advertising success.
First spotted. This update was first shared by Chris Ridley, Head of Paid Media at Evoluted, on LinkedIn.
As part of OpenAI’s exciting expansion, I’ve learned they’re extending ChatGPT ads into five fresh markets, including the UK. Excitingly, new campaign management features are on the horizon!
I can see OpenAI ramping up its ad strategies within ChatGPT through an early test that presents the possibility for multiple advertisers to showcase their ads in a single space.
What’s happening. From what I’ve gathered, OpenAI is trialing a new multi-advertiser format over a limited number of ChatGPT ads, which was confirmed in a recent update to their advertisers.
This new approach consolidates several relevant ads into one space instead of just one sponsored result. I understand these ads will be sold using a second-price auction model, commonly employed in digital advertising.
I’m excited to share that OpenAI aims to enhance user product discovery and provide ample avenues for advertisers to connect with users during high-intent interactions.
Meanwhile, in Ads Manager Beta. There’s more good news, as OpenAI rolled out some updates to campaign management features, and here’s what caught my attention:
It’s now possible to shift existing campaigns from lifetime budgets to daily budgets, which makes budgeting more flexible.
CPM campaigns can seamlessly transition to CPC bidding with just a click.
I’ve noticed that impression-based campaigns now support customized CPM max bids.
Bulk editing right in the Ads Manager interface—how convenient is that?
Daily budgets will start working under an average daily budget system, touting weekly pacing flexibility.
There’s fantastic geographic targeting expansion, beyond the U.S., Canada, Australia, and New Zealand, now including the U.K., Japan, South Korea, Brazil, and Mexico.
Why we care. The updates are instrumental in aligning OpenAI’s ad structure with what we as marketers expect from established ad systems, easing campaign management while widening international targeting.
What to watch. This multi-advertiser test might just be the indicator of how OpenAI plans to monetize ChatGPT. If it’s successful, the strategy could be key to expanding advertisers’ reach during users’ purchasing and research phases.
The bottom line. I see OpenAI carefully crafting its advertising framework, with the introduction of multiple advertisers in a single placement potentially redefining sponsored content’s role within AI-driven conversations.
