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ChatGPT can check your PC’s health without touching your files. Here’s how

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By Ben Patterson

No need to give ChatGPT the run of your system. Instead, use this built-in Windows tool to generate a report that an AI chatbot can analyse.

Is your Windows 11 PC feeling pokey or maybe you just want an idea as to what’s going on under the hood? You could use an AI-assisted coding app like Codex or Claude Code to poke around your system, but maybe you’re not ready to give AI total access to your files.

Luckily, there’s an easy way to get ChatGPT, Claude, or Gemini to perform a quick — and hands-off — health check of your system. The trick is using the built-in Windows System Information application, a.k.a. MSINFO32.

Once you fire up MSINFO32, the utility will give you a detailed report of your system’s hardware, components, and software. It won’t tell you everything about your PC — for example, it doesn’t offer details on recent crashes, real-time performance, or battery health — but it can deliver a quick first-pass report on your PC’s health.

Now, the raw report that MSINFO32 spits out isn’t exactly easy reading. It’s more like a massive data dump. I ran the tool on my Dell Latitude laptop and the resulting file ran more than 3.4 million characters.

But here’s where ChatGPT comes in. This is a task it was born to handle, by the way. AI models love nothing more than to distil key findings from reams of complex data and if that data happens to be about PC components, that’s even better.

What we’re going to do is feed ChatGPT (or your AI chatbot of choice) the MSINFO32 report. We’re also going to feed it a prompt designed to create an easy-to-understand digest complete with a triaged list of potential action items and a breakdown of your hardware and software components.

The prompt will also make the AI ask you a few preliminary questions such as whether you’re having specific PC problems, is your PC for work or school, and whether anything has recently changed on your system.

Here’s how to get started:

  • Open the MSINFO32 application. Here’s the easy way to do it: Press the Windows key + R, type in “msinfo32”, then click OK.
  • Next, click File > Export, then name the file (it could be anything, like “sysinfo”) and pick the directory where you’d like the file to be saved.
  • Now, go to ChatGPT, Claude, or Gemini (I recommend using a “thinking” or “pro” model rather than “instant”), and upload the file.
  • Finally, use the prompt below (I put it at the bottom of the story because it’s a tad lengthy). You can, of course, modify the prompt or use another one entirely.

Once you run the prompt, the AI should ask you a few quick questions, and then you’ll get your PC health check, starting with a quick summary of your system configuration, a triaged list of action items, other key findings, next steps, and details on what the report doesn’t cover.

Running the report on my Dell laptop, ChatGPT said my system looked “generally healthy” and didn’t find any critical issues, although it did spot a disabled virtual Ethernet adapter. Guess I’d better call IT!

Anyway, here’s the prompt:

You are a Windows 11 PC health triage assistant for everyday users.

I will paste or attach a Windows System Information report from msinfo32. First, ask me up to five simple clarifying questions, one at a time, such as what problem I’m having, whether this is a personal or work/school PC, and whether anything recently changed. Stop asking once you have enough context. If I say “skip,” “not sure,” or “just analyse it,” proceed.

Analyse the report in plain English. Do not treat every technical-looking entry as a problem. Separate clear issues from normal Windows noise, and avoid risky advice such as registry edits, BIOS changes, driver removal, or reinstalling Windows unless there is a strong reason.

Start the analysis with Quick PC Summary, including:

  • PC model, Windows version, processor, RAM, storage, graphics, and network adapter
  • Security/boot status, including Secure Boot, UEFI, TPM/encryption clues, virtualization-based security, and Credential Guard if shown

Then provide Action Items, divided into:

  • Critical
  • Needs Attention
  • Optional Tweaks
  • Looks Healthy / No Action Needed

Then briefly cover:

  • Problem Devices or devices marked Error, Disabled, Unknown, or Not Available
  • Storage space and whether it is low
  • Memory/performance clues
  • Network/VPN/Bluetooth/virtual adapters
  • Notable driver or firmware issues

Explain what the user should not worry about, such as stopped manual drivers, WAN Miniports, shared IRQs, virtual adapters, Hyper-V/hypervisor messages, and “Not Available” fields when they are normal.

End with safe next steps, such as running Windows Update, checking Device Manager for warning icons, checking Windows Security, confirming BitLocker/device encryption, and contacting IT/support for work-managed PCs or VPN/security-tool issues.

Also include a short Confidence Level section explaining what the report can and cannot determine.

Give it a try, and good luck!

Feature image credit: Ben Patterson/Foundry

By Ben Patterson

Sourced from PCWorld

 

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ChatGPT just got a feature you’ll actually use every day, but you need to pay for it

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By Shimul Sood

ChatGPT can now nag you for useful reasons.

TL;DR
  • ChatGPT can now schedule one-time and recurring tasks, allowing users to set reminders and automate routine follow-ups directly within the chatbot.
  • It can also monitor specific topics and notify you about meaningful updates.
  • Task limits depend on your subscription tier, ranging from three active tasks for Go users to 15 for Pro and Enterprise users.

 

AI tools are steadily moving beyond answering questions and into the territory of actually getting things done. The latest example comes from ChatGPT, which is rolling out a more capable task-scheduling system that lets you set reminders, automate recurring prompts, and even monitor specific topics for important updates.

The feature, which is rolling out now, gives ChatGPT a role closer to that of a lightweight personal assistant. Instead of simply responding in the moment, it can now carry out actions on a schedule you define. Need a reminder to wish your parents a happy birthday? Or perhaps you’d like ChatGPT to keep an eye on a product launch and let you know when new information appears — these are the kinds of jobs the new task system is designed to handle.

Scheduled tasks also get their own dedicated management hub inside ChatGPT. You’ll find it in the sidebar on both the web and mobile apps, where you can view existing tasks, make changes, pause them temporarily, or remove them altogether.

Notifications play a key role here. If you plan to rely on ChatGPT for reminders, make sure notifications are enabled on your phone and in your browser. Otherwise, there’s a good chance those carefully scheduled alerts will go unnoticed.

One of the more interesting additions is the ability to ask ChatGPT to monitor for meaningful developments and alert you when they change. For example, you could ask it to track updates about your favourite app, monitor the status of an upcoming event, or notify you when a particular topic sees notable news.

There are, however, a few limitations. The biggest one is that scheduled tasks aren’t available to free users, so you’ll need a paid ChatGPT subscription to access the feature. Task availability is also tied to your subscription tier, and any usage limits associated with your plan still apply.

The number of active tasks you can maintain at once varies depending on your account. Go users can keep three tasks active; Plus subscribers get five; Business and Edu users can have up to 10; while Pro and Enterprise users can manage as many as 15. Once you hit your limit, you’ll need to complete, pause, or delete an existing task before creating another.

This new feature won’t change how you chat with ChatGPT, but it could definitely change how often you need to remember things yourself.

By Shimul Sood

Sourced from Android Authority

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ChatGPT 101: How to Turn ChatGPT Into Your Personal AI Assistant in 2026

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By Nishant

There are hundreds, if not thousands, of AI chatbots. Still, if you are only talking about the major general-purpose assistants, you have ChatGPT by OpenAI, Claude by Anthropic, Gemini by Google, Copilot by Microsoft, Grok by xAI, and Meta AI. Most people use one, maybe two, and they use them the same old way, where they ask a question, get a useful answer, and close the tab. There is nothing wrong with that, and there is no single correct way to use an AI assistant. But modern AI assistants are far more capable than most people realize, and they can get a lot more done than you’d expect.

Take ChatGPT. By 2026, this AI assistant will have become more like a coworker than just a search box. It can remember information for weeks, take data from your apps, create documents and spreadsheets, browse the web, and perform several tasks for you. However, most people still do not use these features.

So in this article, we’ll show you how to practically use ChatGPT as a teammate that holds context, connects to your tools, builds things you can keep, and acts for you. By the end, you’ll know which features to use and have a set of automations to implement this week.

Pro Tip: To automate everyday tasks with ChatGPT, follow these steps:

  1. Provide clear context in your prompts.
  2. Organize regular work into Projects.
  3. Create reusable tools using Custom GPTs and Canvas.
  4. Connect your apps with Connectors.
  5. Assign research tasks to Deep Research.
  6. Set up Scheduled Tasks to automate repetitive jobs.

Start with the basics: A better first conversation

Let’s start simple. How should you actually talk to ChatGPT? The shortcut to better answers is almost embarrassingly basic; you just need to give ChatGPT more context up front.

A weak prompt looks like:

  • Write a follow-up email.

A strong prompt looks like:

  • Draft a follow-up email to a prospect I met at a conference yesterday. They run ops at a 200-person logistics company. We talked about route-planning software. Keep it under 100 words, friendly but not chummy, and end with a specific suggestion for a 20-minute call next week.
ChatGPT Prompt

Three habits do most of the work:

  1. State the goal and the audience so that way ChatGPT can tune tone and depth based on who’s reading, so tell it.
  2. You can give examples by pasting in two emails you’ve sent before that you liked, and ChatGPT will match your voice.
  3. Ask for a specific output, like “three options, ranked, with one sentence on the trade-off,” rather than “give me some ideas.”

When something is off, don’t start over. Instead, tell ChatGPT what needs to change: “Make this tighter. Remove the second paragraph. Move the request up.” This back-and-forth process will help you improve the quality and help ChatGPT learn more about you.

Two settings make this stick:

  • Custom Instructions: You can set a custom role, preferences, and tone once, so every chat starts off well.
ChatGPT custom instructions
  • Memory: Memory will allow ChatGPT to remember details about you across conversations, such as your projects, style, and ongoing needs. This way, you don’t have to explain yourself repeatedly over time.
ChatGPT memory

Pick the right surface: Chat, Agent, or Codex

ChatGPT shows up in more than one place, and they’re built for different jobs. Knowing which surface to reach for is half the battle.

ChatGPT (chat)

This is the standard chat interface on the web, mobile, and desktop apps. It is probably the first thing you see when you open ChatGPT. This interface is great for quick questions, drafting ideas, brainstorming, and working with files you paste or upload. ChatGPT automatically chooses the best model from the GPT-5 family for you. The most capable model is GPT-5.5 (as of writing this article), which was released in April 2026. There is also a faster, instant model for everyday use, so beginners do not need to worry about choosing one.

ChatGPT desktop app

ChatGPT Agent

Agent mode is the one to reach for when the answer isn’t text but a task. ChatGPT starts its own virtual browser and computer to research, click, fill out forms, and navigate sites. You can use it to compare options, gather data, and complete multi-step tasks from start to finish, always under your control. It’s also built into ChatGPT Atlas, OpenAI’s own web browser, so it can act on whatever page you’re viewing.

ChatGPT Agent Mode

Codex

Codex is OpenAI’s coding agent. If you’re not writing software, you can ignore it; if you are, it can live in your editor, terminal, or the cloud and autonomously write features, fix bugs, run tests, and review whole codebases. However, OpenAI announced Codex for Work, which allows non-technical professionals to complete and or automate knowledge work using Codex.

ChatGPT Codex desktop app

Rule of thumb: If the answer is text, use Chat. If the answer is a multi-step task on the web, use Agent. If the answer is a code change, use Codex. If you are a non-technical professionals who want to complete tasks on a computer, use Codex for work.

Organize work that lasts more than one conversation

Single chats work well for quick questions. However, when you have ongoing tasks, like working with the same client, the same report, and the same files each week, you should use ChatGPT’s projects features.

Projects: persistent context

A Project is a workspace that has all of your related chats, along with shared instructions, reference files, and project details in one place that continues through every conversation in it. When you open an existing project, ChatGPT already knows the background, such as the client’s brand voice, your team’s standards, and last quarter’s numbers. This means you don’t have to explain it again.

Practical setup:

  • Create a project for each ongoing thread of work (a client account, a product launch, your job search).
  • Add the three or four reference documents ChatGPT would need to do good work.
  • Write a short instruction explaining the goal and your preferences.
  • From then on, every chat in that project starts pre-loaded.
ChatGPT Projects

Canvas: Things you can build and keep

Canvas can give you a workspace where you can write and code using AI. You don’t have to scroll through a chat; you can edit a live document. You can highlight a paragraph and ask AI for a specific rewrite, adjust the length, change the tone, or refine code directly without losing your focus. This is where a rough draft could become a finished document that you can copy and use. ChatGPT can also create real files and apps within the chat, giving you something ready to use.

ChatGPT Canvas

Custom GPTs and Workspace Agents: Reusable capability

A Custom GPT is just a custom version of ChatGPT with its own instructions, knowledge files, and tools, built for one repeatable job. You can create a custom GPT for a brand voice writer, an onboarding helper, or a data formatter. You can build one in minutes with no code and reuse it forever, or browse the GPT Store for ready-made ones.

Custom ChatGPT

OpenAI has also launched Workspace Agents for Business, Enterprise, and Education. These are shared, cloud-based agents that help run multi-step workflows. They can connect to business tools and continue working on a schedule, even when no one has ChatGPT open.

By default, ChatGPT only knows what you paste into the conversation. Connectors can change that by giving it access to your actual everyday apps like Gmail, Google Drive, Google Calendar, Slack, GitHub, SharePoint, Notion, and more. So it can pull real information without you having to switch tabs.

Once a connector is on, you can ask things like:

  • Summarize the unread threads in my inbox from this week and group them by what’s blocking me.
  • Find the three Slack messages from my manager that mentioned the budget review and tell me what she asked for.
  • Look at my calendar for tomorrow and draft a prep note for each meeting using whatever context you can find in Drive.

Each of those would take 20–40 minutes by hand. With connectors authorized, they’re a single prompt.

ChatGPT app connectors

Deep Research: a report instead of an afternoon

Deep Research lets ChatGPT work agentically and run dozens to hundreds of searches that build on each other, reading sources, and synthesizing for several minutes before producing an in-depth, cited report. Since early 2026, you can even review and edit its research plan before it starts. You should use deep research when you’d otherwise block out half an afternoon on a competitive landscape, due diligence, or a literature review: “Tell me everything about this customer before my call.”

Gallery Image
Gallery Image

Scheduled Tasks: Put repeat work on autopilot

The schedule task feature allows you to ask ChatGPT to do a certain task at a specific time or on a regular basis. You can set up a news briefing for Monday mornings, a weekly summary of your open items, or a daily reminder based on your calendar. Set it once, and it will run automatically.

Real automations, by role

There are countless role-based ways to put this together. A few to start from:

  • For managers: A weekly “what’s on fire.” A scheduled task can pull from email, Slack, and your task tracker to produce a Monday-morning briefing on what slipped, who’s blocked, and what to follow up on, delivered automatically.
  • For salespeople: A Project per account, loaded with deal history, so any prep request( call notes, a follow-up email, a proposal draft) comes back in your voice with the right context.
  • For analysts: Ask ChatGPT to build the spreadsheet, not describe it. It can produce a real .xlsx with formulas and formatting, which you then refine by saying “add a column for variance” or “pivot this by region.”
  • For marketers: You can add your brand’s guidelines to a Custom GPT that can turn rough notes into on-brand posts, subject lines, or ad copy every time, no re-prompting required.

How to actually start

The easiest way to get started is through just-in-time learning. I would recommend that you pick one task that frustrates you and use ChatGPT to solve it. As you work on this task, learn about the features you need. You can start a project around that task, then you can activate a connector, and over time, create a Custom GPT or Canvas document to see what saves you time and what doesn’t.

As we said at the top, there’s no single correct way to use an AI assistant; everyone’s workflow is different. For you, a connector might be the game-changer. For someone else, it’s a Custom GPT or a Scheduled Task that quietly runs every Monday. After two or three weeks of this, the difference between ChatGPT as a smarter search engine and ChatGPT as your always-on employee will become obvious and hard to give up.

Open ChatGPT, pick a task, and go.

By Nishant

Sourced from AI Tools Club

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Publishers Paid The Price For Platform Dependency—You May Be Next

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By Jennifer Schenberg,

This year, newsrooms eliminated hundreds of jobs, including more than 300 at the Washington Post.

A friend of mine still has her job there, for now. When I called her after the layoffs, she didn’t talk about herself. She talked about the colleagues she’d spent decades working alongside, the stories that died with them, and the work they spent years building toward, all gone overnight because Facebook and Google changed the rules.

We’ve been here before. You know this story.

Platforms Always Promise Reach—Dependency Is How They Get You

Publishers and brands have depended on Facebook and Google to deliver their audiences for two decades. Today, publishers are losing up to 90% of their traffic and revenue after AI-driven search and social platforms changed how content reaches audiences. Most publishers didn’t see it coming. The ones that did built direct relationships with their audiences and kept them.

Business Insider felt the impact, seeing organic search traffic drop 55% and laying off 21% of its staff. But People Inc. was able to pivot. It lost 50% of its Google sessions over the last two years, yet it’s still growing 15%. When asked how that is possible, CEO Neil Vogel explained, “We built our own assets. We’re doing all kinds of things to connect directly with advertisers and users. So when Google really fell off a cliff two years ago, we were prepared for it.”

Few understand this dynamic better than Bhargav Patel, who spent his career building the infrastructure for rented reach. Now, he’s building what comes next. He’s the founder and CEO of Genuin, a client of PenVine, and he doesn’t mince words: “The brands that treated digital infrastructure as a future priority woke up one day to find that the platforms they had been ignoring had become the intermediaries standing between them and their own consumers.” He added, “AI is accelerating that dynamic by an order of magnitude. The decisions brands and publishers make about infrastructure in the next 12 to 18 months will not just shape their competitive position. They will determine whether they still own a direct relationship with their audience at all.”

This is a warning every brand should take seriously.

Social gaming company Zynga is a tale of platform dependence gone bad. Remember Farmville? It was the most popular game on Facebook. At its peak, Zynga represented nearly 20% of Facebook’s total revenue. When Facebook restructured its algorithm and payment terms, Zynga’s stock collapsed 75%.

Zynga survived by doing what every brand should consider now: It focused on attracting players to its own destinations. A decade later, Take-Two Interactive acquired them for $12.7 billion.

While Zynga is an extreme example, the issues brands face today are no less urgent. Brands are still renting reach, bound by a landlord’s rules that change without notice, at the mercy of an algorithm that controls the entire engagement experience.

Now, there’s a new landlord: ChatGPT. Different platform, same dependency. For the first time, marketers plan to increase investment in AI platforms like ChatGPT and Google AI Overviews over traditional search advertising. But Sonata Insights analyst Debra Aho Williamson cautions, “Marketers shouldn’t allow the AI platforms to dictate the rules of engagement with consumers. Brands have an important role to play, too.” She believes that when AI gets it wrong, consumers won’t blame the platform. They’ll blame the brand.

Sephora made a bet on ChatGPT. The beauty retailer spent 20 years building 80 million Beauty Insider members into one of retail’s most valuable owned audiences. Yet, they launched an app inside ChatGPT to power discovery. Sephora essentially outsourced the discovery phase of the customer journey to OpenAI. They get a bump in reach, but OpenAI owns the first impression, the data and the customer relationship.

It’s a gamble. Who’s to say the platform won’t eventually use that data to steer those new audiences elsewhere?

Consumers Don’t Trust The Feed, So Why Do Brands Keep Buying In?

It’s a paradox. And McKinsey calls it stark.

With distrust in AI at an all-time high, 74% of consumers say AI makes it harder to trust what they see online. McKinsey partner Kari Alldredge agrees, saying, “I don’t believe that marketers’ budgets have caught up with where consumers’ heads are at.” She says marketers should be “thinking more broadly about the allocation of spend and potentially shifting some of it away from social media.”

The irony: Social media ad revenue hit $117.7 billion in 2025, up 32.6% year over year. This means brands are doubling down on the very environments where consumer trust is at an all-time low. It’s a disconnect that tells you everything about how deep this dependency goes.

What 80% of consumers do trust is the brands they use. That trust wasn’t built on a social platform. It was built through direct engagement.

The Brands That Stopped Paying Rent

There’s a shift underway. Brands are turning static websites and apps into living destinations by offering the same generative experiences platforms use. Consumers can discover products, shop, engage with peers and collaborate with brands. The brand controls the content, the commerce and the conversation.

Take Pacsun, which launched PS Community Hub, a social-driven, AI-personalized platform where content discovery, commerce and creator connection all live inside its own ecosystem. Similarly, TED launched TED Shorts inside its own app, a personalized feed that lets users engage directly within TED’s own ecosystem.

Then there’s iHeartMedia, which launched iHeartRadio Highlights inside its own platform, bringing shows like Elvis Duran on Z100 to life as short-form video experiences. And McClatchy, which turned its static digital properties into living generative video feeds across dozens of its national magazines and local newspapers, including Us Weekly, Life & Style and the Miami Herald.

Some brands are building their own infrastructure. Others are embedding infrastructure to launch and monetize owned experiences at scale.

End Platform Dependency Before The Rules Change Again​

Is this the end of rented reach? Probably not.

After two decades of watching this re-run, I know how it ends. Years of chasing platforms have come at the cost of the one thing you can never get back: a direct relationship with your customer. That’s a steep price to pay.

Before the next budget gets approved, ask yourself one question: If the platform changed the rules tomorrow, what would you have left?​

Feature image credit: Getty

By Jennifer Schenberg,

COUNCIL POST | Membership (fee-based)

Jennifer Schenberg is Chief Storyteller and Narrative Architect at PenVine, a B2B Tech PR agency for category-defining brands. Read Jennifer Schenberg’s full executive profile here.

Find Jennifer Schenberg on LinkedIn. Visit Jennifer’s website.

Sourced from Forbes

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AI Is Going to Revolutionize Advertising in the Worst Imaginable Way

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By Michael Serazio

OpenAI has started rolling out conventional ads in ChatGPT. It won’t stop there.

The inevitable has arrived. Ads have begun popping up on ChatGPT—even, reportedly, in initial responses to user queries, rather than after extended conversations—and some fans aren’t pleased. “RIP ChatGPT,” wrote one Reddit commenter. “It was fun while it lasted! 💔” The ads, which are being rolled out to free users and those who pay for the lowest-tier subscription ($8 per month), are rather familiar and banal in their presentation: a “sponsored” box pitching a product that ChatGPT’s algorithm thinks is relevant to the conversation, much as you’re used to seeing on social media platforms like Facebook and X.

An enduring feature of advertising is that it is “geographically imperialistic”: The best place to put an ad is where one doesn’t exist already. But the best type of ad to place is one that is unrecognizable as an ad. These truths should be kept in mind amid the rollout of ads on ChatGPT. Rest assured, this is just the beginning of how OpenAI, the creator of ChatGPT, will monetize its users. The company will undoubtedly graduate to more sophisticated ads, at which point the only question will be whether users even realize when they’re being monetized.

Artificial intelligence is an unfathomably expensive product to give away for free, yet that’s been OpenAI’s main strategy to achieve adoption. So it’s little wonder that the company is in dire financial straits, facing tens of billions of dollars in projected annual losses. How else to close that deficit save for digital billboards? The geographic expanse for commercial colonization—a reported 800 million weekly active users—was simply too vast for OpenAI to forgo.

So ChatGPT’s users are right to bummed. Commercials clutter both the aesthetic and impetus of the online space. And the annoyance isn’t merely a pop-up to be blocked or a pre-roll to be skipped: Ads can’t help but corrupt the purpose of the content that they surround. But even OpenAI’s CEO, Sam Altman, has admitted that ad monetization is a real downer. “I think that ads plus AI is sort of uniquely unsettling to me,” Altman said in 2024. “When I think of GPT writing me a response, if I had to go figure out, Exactly how much was who paying here to influence what I’m being shown? I don’t think I would like that.” But he also, notably, did not rule out ads on ChatGPT in the future.

As the old adage goes: If you’re not paying for the product, then you are the product. For two centuries, the mass and social media industries depended on this bargain. Nascent newspapers of the “penny press” era could be sold below cost because advertisers subsidized the access to audiences. Likewise, today, no one pays for Google search or Instagram or TikTok.

AI represents a qualitatively different revelation. It renders all the knowledge of the internet conversationally interactive. It outsources our critical thinking skills and regresses our decision-making to the mean. It’s been designed to seem human to secure our trust. It seduces our affections and indulges our delusions, often sycophantically so. It subs in for our therapists and friends alike and helps us raise our children.

The consumer insights from that level of intellectual, emotional, and social intimacy exceed an advertiser’s wildest dreams. Fortuitously so: AI arrives at a confusing, anxious time on Madison Avenue. Google’s AI summaries are disintegrating the web as we know it, hastening a “zero-click” future, in which users have no need to avail themselves of the links below on the page. Hence, a shift from search engine optimization to “answer” or “generative” engine optimization: strategizing how brands and products appear, organically, in large language model inputs and outputs.

ChatGPT makes that roundabout sell a much straighter line—for a price. And it is reportedly a steep one—with ad rates nearing those of NFL games. Large language models might be a black box—in terms of why they do what they do—but that ad pricing suggests OpenAI knows exactly what a gold mine of personal data it is excavating daily.

That’s why we ought to treat OpenAI’s claims about its advertising with the same skepticism applied to the advertising itself. Sure, the company says it will insulate the ads as ostensibly independent from content. “Ads do not influence the answers ChatGPT gives you. Answers are optimized based on what’s most helpful to you. Ads are always separate and clearly labeled,” the company insists. “We keep your conversations with ChatGPT private from advertisers, and we never sell your data to advertisers.” But that leaves a lot of marketing money on the table—and from the outside, it sure looks like OpenAI needs that money to stay afloat.

Hence, the Super Bowl ad diss from OpenAI competitor Anthropic, the maker of Claude, whose commercial mocked the sponsored content that will inevitably intrude and inundate ChatGPT feeds. But mount that high horse at your peril, Anthropic. Unless there’s a clever way to pay for all those server farms and microchips, all other AI platforms will probably have to follow suit. (And if the Pentagon cuts ties with Anthropic, as it’s threatening to do, that day may come even sooner.)

The history of social media foretells it: Platforms and their creators, once unspoiled by corporate backers, now pitch us relentlessly—and in increasingly devious ways. “Native” ads on Instagram and TikTok often look indistinguishable within the content, forming the basis of the $30 billion influencer industry. But the notion of placing an energy drink in the background of an influencer’s video will soon seem laughably conspicuous. By that point, the problem for ChatGPT users will no longer be that they notice and get annoyed with ads. The problem—and the real money to be made by OpenAI—will be when they don’t.

Feature image credit: Marcin Golba/NurPhoto/Getty Images

By Michael Serazio

Michael Serazio is a professor of communication at Boston College and the author, most recently, of The Authenticity Industries: Keeping it ‘Real’ in Media, Culture, and Politics.

Sourced from TNR

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AI Trusted Less Than Social Media and Airlines, With Grok Placing Last, Survey Says

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By Ty Pendlebury

More Americans are concerned about the loss of personal interaction from AI than they are about potential job loss.

Google Gemini is the most trusted AI platform among its competition, but many people still have concerns about the technology, according to an American Customer Satisfaction Index poll released Thursday.

In ACSI’s results, AI scored an overall customer satisfaction score of 73 on a scale of 0 to 100, which the authors noted was slightly below social media (74), airlines and mortgage lenders, but in line with energy utilities.

Of the five platforms mentioned in the survey, Google Gemini led with 76, followed by Microsoft Copilot (74), Claude and ChatGPT (both 73), and Grok and Perplexity (both 71). Meanwhile, TikTok (77) and YouTube (78) both scored better than the AI platforms.

Gemini is one of the most prolific AI services, with access via smart speakers, TVs, phones and computers, while most ChatGPT users access the AI tool via the ChatGPT website or mobile app, and Grok via social media platform X.

The ACSI poll found that 43% of respondents said reduced human-to-human interaction is their main concern, followed by job loss for future generations (37%) and their own job risk (31%), based on interviews with 2,711 US adults.

Baby Boomers were the most sceptical generation in the poll, with 35% saying they are very concerned about AI’s effects, compared to just 6% who view it extremely favourably.

Disconnect between AI adoption and perception

While platforms such as ChatGPT have up to 1 billion weekly users, there is still a disconnect between AI’s adoption and public perception of it, which is driven by concerns over privacy, the spread of misinformation and the loss of jobs.

“Consumers spent the last decade learning to distrust how social media platforms handle their data, and AI’s privacy scores suggest they’re carrying that scepticism forward,” said Forrest Morgeson, associate professor of marketing at Michigan State University and director of research emeritus at the ACSI.

21% reported an “extremely favourable” outlook toward AI, while an equal 21% said they are “very concerned about the consequences.”

These results were in line with another poll published by YouGov this week, which found that only 29% think the positive effects of AI outweigh the negative ones, while 36% think its net effects are negative.

It’s worth noting that more than half of the people interviewed (56%) had no recent experience with AI, but of the 44% who did, half of them use AI at least once a day, and the usage went up with people who earned over $100,000 a year.

Last month, an NBC poll suggested that AI was one of the least-liked things in America, but it was still more popular than the Democratic Party.

TV and home video editor Ty Pendlebury joined CNET Australia in 2006, and moved to New York City to be a part of CNET in 2011. He tests, reviews and writes about the latest TVs and audio equipment. When he’s not playing Call of Duty he’s eating whatever cuisine he can get his hands on. He has a cat named after one of the best TVs ever made. 

Feature image credit: Getty/SOPA Images

By Ty Pendlebury

Sourced from C NET

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The other side of ads in ChatGPT: Advertiser perspective

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By Allison Steffens Herrera

Since OpenAI announced it would start testing ads in ChatGPT, and the guidelines for it, they did not discuss in depth how it is gonna work for businesses interested in advertising with them.

What we know is that users’ data remains private and that the advertisements will not come as a suggestion, as managed by Google, but rather as a solution to the inquiry being discussed in the chat thread. This way, ChatGPT explores another way of advertising: a natural, organic conclusion for the conversation flow.

And while we already discussed how it is gonna work and its implications for the audience, now it is time to take another approach: advertisers. Because, on the other side of the coin, are the people who will pay and benefit from it: the moneymakers.

The who, the how, the afterwards

OpenAI has not released an official statement on who can advertise with them. Whatsoever, the company talked with ADWEEK, explaining how for the trial, they have asked selected advertisers to commit to at least $200,000.

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Some of the first known companies are Adobe, Target, or Albertson, as well as the group WPP media, and others.

As for the testing process, some answers are absolute: the trial was invite only. Whatsoever, overtime and with the expansion of the advertising model in ChatGPT, it will be open for companies to subscribe through the OpenAI ‘Advertise with ChatGPT ‘ website, as long as they comply with the guidelines and pay the threshold.

But whether this strategy will be successful, we need to see beyond the surface. The group Adthena has released the first study of the trial.

First of all, how does it even work? Well, the platform is not self-serving and does not have a login interface.

Advertisers are managed manually: companies introduce specific words that have to be prompted by the user for the business to be advertised. In other words, a flower shop may type “flowers”, “anniversary gift”, “surprise for my girlfriend”, to be recommended within the conversation.

And the initial feedback companies received to improve their strategies does not tell much: core metrics limited to campaign name, impressions and clicks.

This logic encourages story telling more than click bait, while creating an equal opportunity and challenge for companies: Now there is a direct bid on the playground.

Small companies and startups can capture possible customers by finding a way to outshine their competitors, especially since many users use ChatGPT to compare.

Will this be another Google Ads?

The introduction of ads in ChatGPT led to the inevitable question if it will slowly transform into another Google Ads, or similar search engines. And to answer this question we can go through memory lane and compare it with the launch of Google Adwords.

Google Adwords built its strategy by monetizing explicit intent: keyword-auction where the advertisers bid over the typed demands. It implemented the PPC (pay-per-click) model which is still prevailing, and provided complete control and visibility to the advertisers, giving them the possibility to build the campaigns and bids manually, and constantly monitor performance to improve their indicators.

This is completely the opposite to what OpenAI is doing nowadays compared to Google Ads.

The most significant shift is the flip in the model of how intent is now captured and capitalized. Google keeps the keyword-expressed demand original model, where users declare intent by search queries, and advertisements are offered to audiences targeted on interest and habits, demographics, and browsing behaviour.

ChatGPT captures intent differently. Instead of monetizing keywords, it interprets context. Intent unfolds through constraints, preferences and goals, rather than just a search query.

This makes conversational ads structurally different from search ads. The shift now is not technical: it is structural. The AI acts as a curator and gatekeeper of commercial relevance, moving from user-driven comparison of ranked results to AI mediated recommendation.

Implications for companies

Another shift implemented by OpenAI is the cost structure, since they will adopt a premium $60 CPM (cost-per-mille), which means that the advertiser will pay $60 per every 1000 times their ad is shown.

This adds pressure to the companies, since an inadequate management of the chosen keywords will be translated directly to their expenses, without the expected results.

If this model is maintained by OpenAI, advertisers will be forced to not only understand the context, but the whole conversational ecosystem where the advertisement would be relevant.

In the same way, ChatGPT’s performance measurements drastically differ from the detailed analytics and automatic campaign optimization that Google offers. The limited feedback that OpenAI offers to their advertisers also plays an important role in how companies have to think about their campaigns.

Now they are competing for algorithmic selection.

This new framework alongside limited targeting control, suggests that advertisers must learn how the AI’s curation logic works. Success will depend less on bid strategy and more on understanding conversational intent.

To design effective AI Ads campaigns, brands will need to anticipate how users articulate needs across dialogue (prompt behaviour), and not rely on direct input or requests.

Advertisement has always been about solving problems and positioning solutions. Now companies must go further and understand how their audience thinks, and frame specific problems inside a conversation with ChatGPT.

Platform and model limitations

OpenAI introduced a game changer model for the advertisement industry by launching contextual-based ads. However, alongside the benefits come the disadvantages, and the platform and current model introduce important questions for brands to be aware of.

If the algorithm determines which advertisement is more relevant and only suggests one curated suggestion, it could represent the beginning of AI-controlled demand allocation and reduce advertiser competition visibility, unless the user specifically asks for it.

If ChatGPT continues with its current curated model without an interface where advertisers can manage their inputs, the model could create power concentration in the AI platform.

Because the platform takes all the decisions and advertisers cannot directly manipulate visibility. In the same way, if advertisers are unable to  analyse the performance measurements, it could create a black box monetization problem, where brands are paying but do not have straight data that enables campaign optimization.

From auction to algorithm

ChatGPT ads mark a new era for advertisement. The real test is how brands adapt to this new format while navigating platform limitations, and preserving the trust that characterizes ChatGPT interactions.

The beta is still trial and changes are likely. For now, the system favours large corporations with the experience and resources to optimize their campaigns without access to detailed OpenAI performance data.

As for the industry, it is safe to conclude that the limitations and specifications that conversational AI requires will lead to new ways of advertising. Advertisers must focus on creating a message that adds value to the conversation, respecting users’ needs and their relationship with the platform.

Prioritizing credibility, and aligning brand presence and product differentiation while addressing the specific problems users are exploring in their chats, will be the key to success when advertising with ChatGPT.

By Allison Steffens Herrera 

Sourced from TNW

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LinkedIn is becoming a top source for AI chatbots

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By Eleanor Hawkins

LinkedIn domain rank based on ChatGPT citations

Why it matters: AI search is rewriting the rules of executive and brand visibility, raising the stakes for how leaders show up online.

Zoom in: Since November, LinkedIn’s citation frequency has doubled and it is now the No. 1 domain cited in professional search queries.

  • LinkedIn posts, long-form articles and newsletters account for 35% of all LinkedIn citations within ChatGPT, while profiles are cited 14.5% of the time, according to Profound.

Zoom out: Community and creator-driven platforms like Reddit, Wikipedia and YouTube have all emerged as some of the most cited sources in AI responses precisely because they host real, conversational human insights that models latch onto when answering nuanced queries.

  • Because what’s said in Reddit threads increasingly shows up in chatbot responses, brands that were once wary of the platform have ramped up their presence to manage reputation, correct misinformation and shape the narrative.

What they’re saying: “Professional visibility is changing. It is no longer only about how people present themselves to other people. It is increasingly about how machines interpret them first,” says Erin Lanuti, co-founder of LinkedIn intelligence platform Lilypath.

  • “If AI systems are using LinkedIn as a core source for professional authority, profile clarity becomes foundational to whether someone is surfaced, trusted or overlooked,” she added.

Yes, but: Generative AI search tools can only surface publicly available LinkedIn content, according to the company.

  • “We continue to protect member data from unauthorized scraping and only content [users] have chosen to make public on LinkedIn can appear in these results,” a spokesperson told Axios.

The bottom line: In the age of AI and generative engine optimization (GEO), every executive, brand and company can grow their reach and credibility by engaging thoughtfully on LinkedIn.

By Eleanor Hawkins

Sourced from AXIOS

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I tried the ‘3-prompt rule’ with ChatGPT — the results got dramatically better

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Getting better answers from ChatGPT starts with this simple rule

ChatGPT is not a search engine, but most people treat it like one. They type a question, skim the answer and seem satisfied. But most of the chatbot’s potential is not being explored. And that answer you’re looking for could be so much better if you prompted the chatbot in a different, more resourceful way.

As a power user, I’ve been testing ChatGPT for years. Every day I try to break it and push it to its limit. That’s why I know that its first response is just a starting point.

The real value comes from what happens beyond the prompt: refining the prompt, adding context and pushing the model one step further. That’s the idea behind the “3-prompt rule,” a simple method that turns one-off AI answers into something much more useful.

How the 3-prompt rule works

A man typing on an iPhone(Image credit: Shutterstock)

Despite having three prompts, this rule is not complicated or difficult to remember. The idea is simple: don’t stop after your first prompt.

Instead, guide ChatGPT through three quick stages that steadily improve the result. The “3-rule” prompt works like this:

  • Prompt 1: Ask the basic question. Start with the simplest version of what you want.
  • Prompt 2: Refine the answer. Ask the AI to improve the response by making it clearer, more specific or more useful.
  • Prompt 3: Optimize the final result. Adjust the format, tone, depth or structure so the answer matches what you actually need.

Each step adds a little more direction, which helps the AI get closer to the ideal response.

Try it on difficult subjects

screenshot(Image credit: Future)

To see how well this method works, I tried the 3-prompt rule on a complicated subject: neural networks, the technology behind many modern AI systems.

Prompt 1: “Explain how neural networks work.”

The first answer was technically accurate, but it relied heavily on terms like “layers,” “weights” and “training data.” Someone without a technical background would probably still find it confusing.

Prompt 2: “Explain neural networks using a simple analogy.”

This response improved significantly. ChatGPT compared neural networks to a system that learns patterns — similar to how a human might recognize faces or handwriting after seeing many examples.

The idea was easier to grasp, but the explanation still included some technical language.

Prompt 3: “Explain neural networks like I’m a high school student.”

The final version was much clearer. Instead of technical terminology, the explanation focused on the core concept: computers learning patterns from examples and improving over time.

At that point, the response felt concise, approachable and easy to understand. Each follow-up prompt pushed the explanation closer to the ideal result.

Why not just start with the third prompt?

A woman holding an iPhone near an iPad(Image credit: Shutterstock)

You might wonder: why not just jump straight to the final prompt and ask for the perfect explanation right away?

In practice, most people don’t know exactly what they want at the start. The first prompt acts like a rough draft. It gives the AI a starting point and helps you see what direction the answer takes. From there, the second prompt lets you adjust the approach — maybe simplifying the explanation, adding examples or changing the focus.

By the time you reach the third prompt, you have a much clearer idea of what the ideal response should look like.

In other words, the process isn’t just improving the AI’s answer — it’s helping you refine the question.

That’s why the 3-prompt rule works so well. Instead of trying to craft the perfect prompt upfront, you let the conversation evolve step by step until the result matches what you actually need. To highlight this, let’s try it with something you might be working on professionally.

Test 2: Turning a rough idea into a useful plan

screenshot(Image credit: Future)

For this test, perhaps you are brainstorming a rough idea to ultimately get to a useful work plan. You can use the 3-prompt rule on common workplace task to organize messy projects.

Prompt 1: “Help me plan a project to improve team productivity.”

The initial response included general suggestions, but it felt fairly broad and high-level.

Prompt 2: “Create a step-by-step productivity plan for a small team, including weekly check-ins and clear goals.”

The response immediately became more structured and actionable.

Prompt 3: “Turn this plan into a simple one-month productivity roadmap with specific tasks for each week.”

The final result felt much more practical — closer to something you could actually use with a team instead of a loose set of ideas.

Each prompt pushed the response closer to a real-world, usable plan. That final version felt less like a rough draft and more like a polished travel plan I could actually use.

Why the 3-prompt rule works

man texting(Image credit: Future)

AI assistants respond best when they get clear direction, feedback and context. This is true no matter how much smarter and faster models become. As humans communicating with AI, we still have to detail what we really want. Honestly, we still have to do that human to human, who am I kidding?

The 3-prompt rule starts by pointing ChatGPT in the right direction, but the follow-up prompts help shape the result by clarifying what you really want, what needs improving and how the answer should be structured to best fit your needs.

That’s the shift most people miss. Instead of treating ChatGPT like a search engine, you’re treating it more like a collaborator. The process becomes iterative, and the output usually gets better with each step.

Bottom line

ChatGPT doesn’t always give you the best answer on the first try. But when you treat the interaction as an iterative process rather than a one-and-done prompt, the results can improve dramatically.

The 3-prompt rule is a simple habit, but it can turn ChatGPT from a quick-answer tool into a far more useful thinking partner. So before you settle for the first response, try refining your request a couple of times. You may be surprised by how much better the final answer becomes. Give it a try and let me know in the comments what you think.

Feature image credit: Getty Images

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Amanda Caswell is one of today’s leading voices in AI and technology. A celebrated contributor to various news outlets, her sharp insights and relatable storytelling have earned her a loyal readership. Amanda’s work has been recognized with prestigious honors, including outstanding contribution to media.

Known for her ability to bring clarity to even the most complex topics, Amanda seamlessly blends innovation and creativity, inspiring readers to embrace the power of AI and emerging technologies. As a certified prompt engineer, she continues to push the boundaries of how humans and AI can work together.

Beyond her journalism career, Amanda is a long-distance runner and mom of three. She lives in New Jersey.

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