Americans Point to the Tasks They Want AI to Handle Most

AI tools have slipped into daily habits in quiet but steady ways. Many people no longer wander through long search pages and instead lean on systems that gather and summarize information for them. This shift sets the stage for a simple question: where do Americans actually want AI to step in?

Fresh numbers from Statista Consumer Insights outline the priorities. The strongest interest centers on personal assistance, and about 32 percent say they want help with organizing life details. Phones already carry enough data from calendars, messages, and apps to make that kind of support feel natural, so expectations stay grounded.

Daily chores follow closely. Twenty eight percent want AI to take routine tasks off their plate. Work related help attracts 27 percent, which shows how many people see room for support with planning, drafting, or sorting information. Teaching or tutoring lands at 26 percent, and that interest reflects how common quick on demand learning has become.

Health and wellness guidance captures 25 percent. The same share look for help refining communication or language skills. Content creation sits at 23 percent, since plenty of people now weave AI into videos, posts, or documents without treating it as a full creative engine. At the same time, 22 percent prefer to avoid AI entirely, keeping a clear boundary between their tools and their routines.

Across all categories, the requests line up with abilities that current systems already provide. The real work for many users comes from choosing the right tool and shaping a workflow that fits their habits.

Notes: This post was edited/created using GenAI tools.

Read next: Weak Password Culture Starts With the Websites and New Research Maps the Scale
by Irfan Ahmad via Digital Information World

Weak Password Culture Starts With the Websites and New Research Maps the Scale

Most people blame themselves when they fall back on simple passwords, but fresh research shows the habits often form long before a user types anything.

The rules set by the websites shape these choices, and most of the world’s most visited platforms make weak passwords far too easy. NordPass reviewed one thousand high traffic sites, and the findings point toward a system that pushes convenience ahead of basic safety.

The study covered twenty four industries and captured how the top destinations on the internet handle the basics of account protection. The team relied on traffic estimates gathered between late February and early March this year, then checked each site to see what it demands from users when they create a password. The criteria followed the same structure used in the NordPass generator, which looks for length, character variety, and case sensitivity. These checks reveal the minimum the websites expect from their users, and the picture that emerges shows widespread gaps.

A large share of popular platforms still accepts short or predictable credentials. The data shows that fifty eight percent of the tested websites do not ask for any special characters. This leaves passwords built from letters and numbers alone, the kind of combinations that can fall to brute force tools in very little time. Another forty two percent do not set any minimum length, so they leave room for short strings that attackers can test quickly. Eleven percent of sites do not require anything at all. Only one percent meets all the best practice criteria by asking for longer passwords that mix characters and respect case sensitivity.

The weaknesses stretch across sectors. Sites tied to government services, health records, and food related services show some of the lowest scores for policy strength even though they often handle sensitive information. Many of these platforms smooth out sign ups to speed up onboarding, and some rely on simplified website building systems that do not enforce strong checks by default. When the foundational rules start at a low bar, users fall back on easy combinations just to move through the form, and the pattern sticks.

The research also looked at the broader authentication landscape. Support for single sign on appears on thirty nine percent of the websites, mostly through major providers like Google. Passkeys appear on only a small share, around two percent. Five websites meet the strictest standards mirrored from NordPass and NIST. These results show how slowly stronger models move across the web even when the tools already exist.

Weak rules matter because they train people to expect low effort login habits. A site that accepts a simple string teaches users that simple works everywhere. Attackers count on that predictability and use automated tools to sweep across accounts at scale. Newer AI driven systems can test vast numbers of combinations faster than older methods, which makes the gap between strong and weak policies even more significant. Once a password leaks or gets guessed, the damage can spread through any platform where the same combination exists.

The ripple continues inside organizations. Employees carry personal habits into the workplace. If they create weak passwords for common services, they often recycle similar patterns for business accounts. Industries that handle financial data or confidential records feel the strain when attackers exploit these shared weaknesses. Government portals face the same risk. Oversights in one area can spill into many others.

Websites have ways to fix this pattern. Clear rules at the start help shape stronger habits. Asking for length and character variety increases the time it takes to break a password by automated means. Strength indicators help users adjust quickly without confusion. A simple set of visual cues can steer someone away from common strings without pulling them out of the flow of sign up. Passkeys offer another route by removing passwords from the equation and replacing them with cryptographic checks that block guessing attempts.

Until websites catch up, users still hold some control over their own safety. A password generator can help them build stronger combinations even when a site does not demand them. The complex password generator available on Digital Information World offers a straightforward way to craft long and varied credentials. It lets people create passphrases or random strings that resist automated attacks and store them through any manager they trust.

The issue sits at the intersection of user behavior and website design. People respond to the rules that sit in front of them, and for years many sites lowered expectations for the sake of quick onboarding. This shaped a culture where weak combinations feel normal. The new research shows that password carelessness did not emerge by chance. It grew from years of lax enforcement across the biggest platforms online.

Improving digital hygiene will require more than guidance aimed at users. Platforms need to raise their standards and adopt stronger criteria. When the system expects more, people adapt. Until then, the habits will remain uneven, and attackers will continue to exploit the gaps that weaker policies leave behind.

Notes: This post was edited/created using GenAI tools.

Read next: Finally, OpenAI Says ChatGPT Will Listen When People Tell It to Avoid the Long Dash
by Irfan Ahmad via Digital Information World

Finally, OpenAI Says ChatGPT Will Listen When People Tell It to Avoid the Long Dash

Sam Altman says ChatGPT finally listens when people tell it to drop the long dash (yeh this beast —) that has followed the chatbot everywhere. Users treated that punctuation as a flashing sign that a machine wrote their text. Some called it distracting. Others felt embarrassed when it slipped into work emails or reports even after they tried to rein it in.

OpenAI pushed out a small fix that changes how the model reacts when a user writes a clear instruction inside the personalization panel. Altman framed it as a simple win, and the move arrived shortly after the company rolled out the new GPT 5.1 model. It sounds minor on the surface. Yet people who rely on the tool know how often the bot stubbornly added that long mark even when asked to avoid it.

Writers said the habit broke their tone and made their work stand out for the wrong reasons. Many stopped using the dash in their own writing because they did not want readers to assume a chatbot drafted their text. Complaints piled up across forums where people kept posting examples of the model promising to avoid it, then slipping it back into the very next sentence.

The new behavior only kicks in when the user plants the instruction in the custom settings area. Altman did not promise success every time in regular chats. That fits with the broader reality of LLM behavior. These models shift output by leaning on probability patterns rather than fixed rules. If a user places the instruction in the right slot, the odds of a clean output increase, though nothing becomes absolute.

Some critics pushed the conversation in another direction. They pointed out that if OpenAI struggled for years to control one simple punctuation mark, talk of near term general intelligence feels a bit premature. The model may look sharp on the surface. Yet it still works like a giant pattern engine that tries to anticipate what should come next rather than follow strict commands with mechanical precision.

Older training data also played a role. People have used the long dash for centuries. It showed up across novels, editorials and essays that filled older datasets. Because the model tries to echo the shape of the writing it has seen, the dash became a default move. Once reinforcement learning kicked in and evaluators rewarded responses that felt polished, the preference grew stronger. That gave the model a habit that stuck around even as users pushed back.

OpenAI now says the fix is part of its work to hand people more control. The company already introduced tools that remember user preferences and let people fine tune how the bot behaves across sessions. The long dash update shows that simple choices matter to users just as much as headline features. For many, this is less about punctuation and more about trying to make the output feel like their own voice.

Every change will still depend on how the model handles probabilities in the background. That leaves room for odd behavior to creep back after future updates. Some users already say the fix works inside the settings panel but still fails if you only mention it inside the chat. With a system that keeps learning from new interactions, small shifts can break old tuning in unpredictable ways. Anyone expecting a crisp on off switch will need patience.

Still, for now, people who truly want to avoid the long dash have a practical way to do it.

How To Add a No Em Dash (—) Rule in Custom Instructions

Below is a clear set of steps based on OpenAI’s official customization guide. You only need to do it once. After that, ChatGPT will try to follow the rule in every conversation.

Step 1: Open the Custom Instructions Panel

1. Open ChatGPT in your browser or app.
2. Look for your profile picture in the bottom corner, then go to "Settings" option and then "Personalization" tab (you can also directly access it through this link).
3. Now in the Personalization tab you will be able to see Custom Instructions option.

Step 2: Add Your Style Requirement

You will see two large text boxes. One controls how ChatGPT should respond. This is where you add the rule.

Write something like:

"Do not use em dashes in any response. Replace them with normal punctuation."

or

"Avoid using em dashes unless necessary for clarity or emphasis; otherwise, use standard punctuation."

Keep it short and clear so the model can pull the instruction into every session.

Step 3: Save the Setting

Scroll down and hit Save.

The instruction becomes active across all chats unless you turn the feature off or erase it later.

Step 4: Test the Behavior

Start a new conversation. Ask the model to write a few lines of text.

Scan the output to confirm it avoids the long dash. (or even ask Chatgpt to analyzes and assess the — use)

If it does not, double check the instruction or rewrite it with simpler language.

Step 5: Adjust Anytime

You can change, refine or remove the rule by visiting the same panel.

This helps if you want different styles for different tasks.

Note: This post was edited/created using GenAI tools and proofread/fact-check by human editors.

Read next: 

• ChatGPT Experiments With Real Group Conversations in a Limited Rollout

• 3 Out of 4 Americans Willingly Trade Personal Data For Discounts Despite Privacy Fears

by Asim BN via Digital Information World

Search Atlas Review: I Tested the AI SEO Platform Powering the Future of Search [Sponsored]

Search Atlas is an AI-powered SEO platform that covers keyword research, content optimization, site audits, and backlink tracking in one place, but its most distinctive features are AI tools OTTO SEO, OTTO PPC, and the new Vibe SEO tool, OTTO Agent.

I decided to test the Search Atlas SEO platform using its 7-day free trial to see whether it lives up to the buzz around its AI automation features. I looked at the company’s history, its awards, and user reviews, tested all of its tools, and compared its pricing to competitors. Here’s what I found.

What is Search Atlas?

Search Atlas is an AI-powered SEO platform that combines keyword research, content optimization, site audits, and backlink tracking in one place. It focuses on automation and workflow simplification, using its proprietary AI engine, OTTO SEO, to handle technical, on-page, off-page, local SEO, press release distribution, cloud stacking, content, and many more tasks automatically. The Search Atlas platform aims to replace multiple SEO tools while offering a more affordable alternative to competitors like Semrush and Ahrefs.

It was created in 2022 by the entrepreneur Manick Bhan, a 3x INC 5000 founder and the company’s CTO. It has received several industry awards, the latest of which is Best AI Search Software Solution at the Global Search Awards 2025 for OTTO SEO. A significant part of the team is remote and global. Search Atlas keeps a strong focus on SEO testing and research, and offers a scholarship.

Who Should Use Search Atlas

From what I saw, Search Atlas suits anyone who needs to manage SEO at scale without juggling multiple tools. It’s built for freelancers, agencies, and enterprises that want a single, automated platform for everything—keyword research, content optimization, link building, site audits, and even PPC campaign creation.

Freelancers and small teams will appreciate how easy it is to set up and how much time it saves, while enterprise clients can take advantage of its scalable infrastructure and detailed reporting. The pricing also makes it accessible, which lowers the barrier for smaller operations.

Key Takeaways (TL;DR)

• OTTO SEO is great for people who want to automate their processes, especially technical SEO, there’s no manual work.
• Search Atlas works as a single platform that handles most SEO and some PPC tasks.
• The Local SEO toolkit is super useful.
• The platform can be buggy.
• It’s more affordable than competitors, and it integrates tools that only come as add-ons on competitor platforms.
• The reporting is completely white-label and automated.

Pros and Cons

Search Atlas is great for complete automation, innovative tools, and features based on the team’s research of thousands of websites. The platform offers a 7-day free trial with complete onboarding and excellent customer support.

Pros:

• The platform handles a lot of automation on its own, reducing manual work.
• Pricing is more affordable compared with industry giants like Ahrefs.
• Strong support and lots of tutorial videos.

Cons:

• The interface isn’t always intuitive; some tools are tucked in the upper right corner, which took me a while to locate.
• There’s a bit of a learning curve to get fully comfortable with all the features.
• Occasional bugs occur, which is common for newer platforms.

How I Tested Search Atlas

The platform offers most of the standard tools, such as rank tracking, keyword research, and link and competitor analysis. However, it also has plenty of unique tools so I focused on them a bit harder here.

Setup, Onboarding, and Training

First, when you sign up for the 7-day free trial, the platform asks you if you’re using it as an agency or a brand. I picked “brand” (also suitable for individuals) and the platform took me to the onboarding page to set up my project.

Also, you do need to give your credit card details, which I’m always wary of, but I didn’t have any issues cancelling later.

It guides you through the steps and lets you research the tools, connect to GSC, GBP, and GA4, and pick additional services such as additional link building packages and local data aggregation.

The final step takes you to the SEO Theory Facebook group link, tutorials, and personal onboarding sessions.

The company also sends you a step-by-step onboarding email sequence during the trial, and the support is highly responsive, so this part is a plus for me.

For more solo research, there’s a Knowledge Base available, too.

UI

The dashboard has a dark theme and a very modern look. While it isn’t the most important thing, it can be refreshing compared to tools that have a Windows XP-era aesthetic.

While I do like the overall look, I had issues finding some tools, until I figured out they are way up in the right corner. This could be organized much better.

Automation and Vibe SEO Tools

The next thing I tested was the flagship automation tools. What stood out first is how much automation it offers beyond a typical SEO dashboard. The OTTO ecosystem—including OTTO SEO, OTTO PPC, OTTO Agent, and OTTO Implementation Services—feels more like an AI operations team than a set of tools.

OTTO SEO won Best AI Search Software at the Global Search Awards for 2025, and I was pretty excited to test it. The platform guides you through the installation process, which is a relief since I got a bit confused. Namely, OTTO SEO recently switched from pixel-based tracking to DNS verification, so I expected a different process. Anyways, DNS is definitely cleaner and more accurate.

So what does OTTO SEO do?

OTTO SEO monitors different issue categories, including technical fixes, content optimization, schema markup, instant indexing, GBP optimization, link building, and digital PR. You get 24/7 tracking of issues, and not just recommendations on how to fix them. You see all of them in the dashboard, choose what to execute, and once you approve changes, OTTO SEO implements them instantly on your site, no matter the CMS.

Inside OTTO SEO, there’s a Link Building Exchange tool that leads to LinkLaboratory, which is the world's biggest publisher exchange. The AI finds the most relevant sites for you to outreach to, scans for spam, and speeds up the process with outreach tools. Serious timesaver.

The latest OTTO addition is OTTO Agent, an AI companion that lets you execute SEO tasks through a conversational UI, latching onto the trend of Vibe SEO. It can do almost anything, such as auditing sites or Google Business Profiles, distributing press releases, and mapping topical clusters.

However, it’s clearly a new tool and needs a few loose ends tied up, given that it got a bit buggy. Still, I’m curious to see where they go with it next.

On the paid side, OTTO PPC (OTTO Google Ads) builds full campaigns in a few clicks, generating ad groups, keywords, and copy automatically. I was skeptical at first, but the tool does have plenty of good reviews, although I didn’t create an actual campaign with a budget and all that. But given how much time setting up a Google Ads campaign takes, full automation with AI is worth a try. Plus, the platform regularly adds improvements, having recently enabled retargeting campaigns as well.

And finally, for teams that want a hands-off approach, OTTO Implementation Services lets the Search Atlas team oversee execution and ensure automation aligns with strategy. Not my cup of tea, as I like to test things out myself, but busy brands might enjoy the service.

Site Audit & Technical SEO

The combination of site auditing and automation is one of the platform’s main selling points, and I can tell why. Combined with OTTO SEO, you get to monitor and fix issues with more efficiency and less technical knowledge required.

The live monitoring part is a must these days, so good that it’s available. And you can see all issues at once, so this part is simplified for anyone who isn’t a fan of technical SEO.

The overview shows you how your site's health changes over time, and it’s not much different than standard technical SEO tools at first glance.

I’d like to highlight Crawl Monitoring in this section, as it lets you see which bots recently crawled your site, including LLM bots, which might be crucial info given the recent industry changes.

OTTO SEO also lets you automate schema markup, helpful for large websites and teams, as you choose a type, enter the details, and you get schema markup you can just copy where needed.

So far, OTTO SEO has left the strongest impression. Instead of just giving recommendations, it lets you implement fixes directly from the dashboard, and it covers a really wide range of tasks. For agencies, this makes auditing multiple sites much more manageable, and the pricing scales so adding more sites actually gets cheaper per site.

Keyword research

The platform provides the Keyword Research tool, the Keyword Gap Tool, the Keyword Rank Tracker, and the Keyword Magic Tool for finding related terms. I first tested the Keyword Magic Tool by entering a seed keyword and selecting a target location. It returned related terms with volume, difficulty, and search intent. Then I tried the Keyword Gap Tool, which lets you compare your site against up to five competitors. It highlighted ranking gaps, shared terms, and unique opportunities, and it organized them into Gap, Opportunities, and Unique Keywords.

So far so good, but the research tools aren’t particularly groundbreaking. And while they worked fine during my testing, I’ve seen users mention occasional bugs in keyword research.

The platform is better known for its rank tracking, as it gives you a choice to really narrow the rank tracking location down, and it’s directly connected to GSC, so you have a reliable overview of where you stand.

Full Content Pipeline

Search Atlas puts a strong focus on content, with a full pipeline that covers everything from research to optimization. The Topical Map Generator is where you start: you enter a topic, choose clusters, and set how many long-tail keywords and blog titles to generate. It helps connect themes, guide internal linking, and keep topical consistency across a site.

I also like the Content Planner, which is especially useful for agencies managing multiple clients or freelancers trying to save time. You input a seed keyword, homepage URL, and region, and it generates keyword clusters with volume, competition, and search intent to guide writing priorities.

For drafting, Content Genius includes workflows for manual writing, AI-assisted writing, or bulk content generation. It applies brand context, adapts tone, and can even generate topic-related images. The writing itself definitely needs some polishing, but it does information-retrieval and competitor research really well. One-click publishing is convenient, though not unique.

The platform’s on-page audit works across large numbers of pages, checking meta data, keyword use, and other on-page signals in a single view—great for bigger sites. Scholar is an interesting addition: it scores content and competitors on ranking factors like entities, clarity, and factual language. Some of these metrics take time to understand, but it’s a unique angle for assessing content quality, and it’s been confirmed through Search Atlas research based on Google Leaks.

Backlink Analysis Tools

The platform has three main backlink tools: the Backlink Research Tool, the Backlink Gap Analysis Tool, and the Backlink Profile Comparison Tool.

They’re in the Site Metrics section, mostly, with some also in the upper right corner thing, so the navigation here is confusing. Still, the tools are doing what they’re supposed to and giving you a pretty good overview of your site and competitors.

The Backlink Research Tool analyzes backlinks by domain, subdomain, or specific URL, showing linking domains, anchor text patterns, link types, and page-level metrics. For profile comparison, you get to analyze up to six domains at once, side by side, with pretty nice visualizations.

It helps that the outreach tools are integrated into the platform, so you can just finish the process without switching to another tool.

Competitor Analysis

In the same Site Metrics section, there’s a solid set of competitor overview and research tools. Some are standard tools that look similar to Ahrefs Site Explorer, but with additional features. For example, Search Atlas has its own authority metric, Domain Power, and research so far shows it’s more accurate in predicting actual rankings. This is primarily useful for link building.

Also, you see other authority metrics, traffic, keywords, LLM visibility, and an analysis based on Holistic SEO, which the founder of Search Atlas is a great proponent of.

So unlike keyword research tools which serve the standard industry offer, the competitor research offer in Search Atlas is much more unique and innovative. Topical Dominance, for example, is one of a kind, and it shows you exactly how you stand against competitors for each topic, while also showing which keywords they rank for in each.

LLM-Visibility

LLM Visibility is a part of Site Metrics, but it gets a separate section given that it’s becoming a highly necessary feature, and not all platforms have it. The tool tracks your brand across AI-powered search tools like ChatGPT, Gemini, and Perplexity. It shows brand mentions, sentiment, share of voice, and ranking in AI answers.

It is usually an expensive add-on, but in Search Atlas it’s integrated. While that is a big plus, I can see it's still a new tool, so it will need more work.

GBP Galactic

What I liked about GBP Galactic is that it has a set of tasks that it tracks, so it’s much easier to organize your time, especially with a lot of clients. Automated review responses, Q&As, and GBP posts are another plus. You can also manage service descriptions, business addresses, and completely organize and automate your local SEO workflow.

Also, the company’s Local SEO Heatmaps let you track how you rank in any location with a lot of customization, from area size to map shape.

I heard a lot of users pick Search Atlas because of its affordable data aggregator, which gives you the 5 biggest data aggregators with a discount if you use all of them. This is cheaper than local SEO specialized tools.

Authority Building

The tools in the Authority Building section help you automate outreach, which is time-consuming, especially for freelancers. The Link Building Outreach and Digital PR Tool manages outreach campaigns, link prospecting, and HARO-style pitching, and it speeds up the whole process with automated filters, scheduled follow-ups, and centralized messaging.

You also get to create cloud stacks automatically, and easily distribute press releases with the help of AI, which is excellent for boosting your authority.

Another specialty of Search Atlas is LLM Quest. With this tool, you improve your visibility in LLMs as it lets you find their sources for a query and contact the site directly to build links with them, and hopefully, end up in the LLM's knowledge base.

IMO, this will come in handy in 2026 if AI browsers start really taking off, although it’s also a top feature now.

Report Builder and White-Label Options

I tested the Search Atlas Report Builder and found it useful for pulling all SEO data into one place. It connects to Google Search Console, GA4, Rank Tracker, Backlinks, and Local Heat Maps, so I could combine everything into a single client report. The drag-and-drop layout makes it easy to customize sections, add a logo, and adjust widgets. I liked that I could schedule automatic reports, and the AI summary really helps clients who don’t have time to get into the details.

Portfolio Summary also stood out. It gives a quick overview of all client accounts and assigns a health score to each, labeling them as Biggest Wins, Stable, or At Risk. It’s a good way to see which campaigns need attention without checking every dashboard.

User Reviews, Case Studies, Testimonials

Search Atlas has a solid ranking on G2 (4.7/5) and Capterra (4.8/5), and mixed but mostly positive reviews on Reddit.

This fits the solid impression I got from the Local SEO tools. Other reviews mention the usefulness of automation, as it lets them focus on strategy and leave the low-level tasks to AI.

However, some users mentioned OTTO SEO created issues for their sites. I also noticed the Search Atlas team responding quickly, and I expect the new DNS installation system will resolve these occasional problems.

Also, a common complaint was that Deep Freeze (keeping the OTTO SEO changes after cancelling) was paid. I checked, and it is now free as the company decided to pay attention to the complaints.

Pricing

Starter	Growth	Pro
$99/month	$199/month	$399/month
1 OTTO SEO Project, 10 OTTO Google Ads campaigns, 3 GBP Galactic projects, 2 user seats, 2000 tracked keywords, 5 GSC projects	2 OTTO SEO projects, 10 OTTO Google Ads campaigns, 10 GBP Galactic projects, 3 user seats, 3500 tracked keywords, 15 GSC projects	4 OTTO SEO projects, 10 OTTO Google Ads campaigns, 25 GBP Galactic projects, 5 user seats, 6000 tracked keywords, unlimited GSC projects

There is also an Enterprise Plan with custom pricing and quotas. Also, additional OTTO SEO activations scale in price: $99 per site initially, dropping per site as volume increases. This makes the platform highly affordable for enterprises.

Overall, the tool is cheaper than its biggest competitors and offers plenty of integrated tools that others sell as costly add-ons. For example, the report-building tool is $999 per year, while here, reporting is integrated and comes with the price.

How Does Search Atlas Compare to Competitors?

Let’s look at the two biggest ones, as the platform claims it can replace them.

Search Atlas vs Semrush

After testing both, I’d say Semrush feels like the safer, more established choice, while Search Atlas focuses on automation and speed.

Semrush impressed me with its massive keyword database, long historical data, and detailed competitive intelligence. It’s the go-to option for large companies that need deep market research and advanced PPC features. However, it’s expensive, takes time to learn, and offers little automation, so most tasks still require manual setup.

Search Atlas, on the other hand, feels more modern. Its OTTO AI handles audits, on-page fixes, and campaign setup automatically, which is a serious timesaver. It integrates directly with WordPress and it’s much more affordable. Still, its keyword database is smaller, the platform is newer and can be buggy, and reviews are mixed.

In short, Semrush gives more data depth, while Search Atlas delivers faster automation and better value for teams that want to move quickly.

Search Atlas vs Ahrefs

Ahrefs stands out for its massive keyword and backlink databases, visual reports, and precise competitor analysis. It’s great for users who want to control every step manually. The tradeoff is that it’s expensive, especially for enterprise plans, and it requires more hands-on time to manage.

Search Atlas feels built for efficiency. Its OTTO SEO agent automates content optimization, technical audits, and internal linking, which removes a lot of manual work. It also includes local SEO tools and real-time tracking, and its entry plans cost less than Ahrefs. However, its data coverage is smaller, and automation sometimes misses finer analytical detail.

I’d say Ahrefs is the stronger option for data depth, while Search Atlas is better for automation and workflow speed.

Final Verdict

After testing Search Atlas, I can say it’s one of the more ambitious AI SEO platforms I’ve tried. The OTTO tools let you act on insights directly, handling SEO, PPC, and content tasks automatically. Features like the Site Auditor, Content Genius, and LLM Visibility add useful depth, and the content pipeline works well for freelancers and agencies managing multiple clients.

The platform has some drawbacks. The interface can be confusing at first; there is a learning curve, and occasional bugs appear. OTTO automation is powerful but requires trust since it makes changes directly on your site.

Pricing starts at $99/month and includes many tools that competitors sell separately. For anyone looking for automation, centralized management, and scalable SEO, Search Atlas delivers strong value and is worth trying.

by Asim BN via Digital Information World

3 Out of 4 Americans Willingly Trade Personal Data For Discounts Despite Privacy Fears

Americans are becoming increasingly aware of the risks associated with data breaches and the exposure of their personal data. Despite being fairly aware of the dangers of data breaches, Americans are still willing to trade away their personal data; some for as little as a 5-10% discount while shopping online.

A recent study from Incogni shows a kind of Dostoyevsky-ian commentary on people acting against their own interests. The research, which surveyed over 1,000 Americans across different age groups and income levels, showed that 95% of respondents were worried about data breaches and their personal data being exposed. But within the same group, 78% of respondents agreed that they would be willing to trade (or often already do trade) their sensitive information for minimal economic incentives, such as minor discounts or free shipping.

Nearly 1 in 5 respondents (19%) would agree to trade their personal data for a discount as small as 10%. An additional 6% would accept even less, willing to share their information for just a 5% discount. In other words, for nearly a quarter of the respondents, the economic incentive to share personal data didn’t need to be particularly substantial. A modest discount outweighed concerns about privacy and security.

Generational differences were also observed in the report. Millennials showed the highest willingness to trade data, with 82% saying they would exchange personal information for shopping perks. Baby Boomers were only slightly more cautious, with 72% expressing willingness to make such trades.

It is then safe to claim that most Americans are willing to trade data for financial incentives, but that willingness varied significantly when respondents were asked about the types of data that they’d be willing to share. This data ranged from run-of-the-mill ecommerce data to deeply sensitive personal information. 42% of the respondents would trade their phone numbers, and 41% their addresses, all fairly common data points. Other responses were more compelling. 20% of respondents would share their web search history, while 15% would disclose their political views, and 12% would be comfortable marking their sexual orientation, all in the name of eking out financial benefits from an online marketplace.

Americans’ willingness to trade data for online shopping benefits becomes a much more nuanced statistic when paired with the fact that a quarter of the respondents (26%) reported having been affected by a retailer data breach, meaning their phone numbers, email addresses, and other personal information may have been leaked and potentially sold on the dark web. An additional 16% are unsure whether they've been affected.

The frequency of online shopping seemed to correlate with breach exposure, according to the report. Among reported “daily” online shoppers, 47% reported having their data compromised, compared to 30% of “weekly” shoppers, and 21% of “monthly” shoppers.

The researchers also observed a link between opt-in rates and breach risk: 32% of those who always opt in to marketing communications report experiencing a breach, compared to 19% of those who never opted into marketing communications.

The Gen Z demographic lowest breach rates (64% claiming they have not been affected), but it’s unclear whether this indicates that they truly experience fewer breaches. Incogni’s researchers suggest that this discrepancy might just be a sign of low awareness about data privacy issues among younger consumers, rather than actual lower breach rates.

Income and location also signalled differences in shopping behavior. High-income individuals were more likely to frequently shop online, with 67% shopping at least weekly, compared to 45% of average-income and 39% of low-income respondents. “Urban dwellers” shop online more than their rural counterparts: 59% of urban residents shop weekly, versus 40% in rural areas.

When it comes to trusting retailers with personal data, Americans showed high levels of trust in some retail brands, while far less in others.

Grocery chains received the most confidence, with 83% of respondents expressing moderate to high trust in their data practices. Large physical department stores and American online marketplaces also saw higher trust signals, with 81% expressing confidence in each.

On the opposite end of the trust meter, foreign online marketplaces faced moderate skepticism. Over 56% of respondents rated their trust in foreign retail brands from low to none, with only 44% expressing moderate to high trust. As perhaps expected, people distrust the handling of their data by foreign-owned entities, but only just a tad less than their domestic ones.

The research also found that “larger” global brands — despite being frequent targets of high-profile data breaches (such as recent incidents at Victoria's Secret and Ahold Delhaize USA) — still command more trust among consumers than smaller, local businesses.

The surveyed Americans generally believe that retailers should be allowed to collect order-related data, such as phone numbers (60%), addresses (60%), and real names (53%). However, only 16% think search histories should be collected, and just 10% believe Social Security numbers are justified.

Despite these preferences, the research shows many Americans will share far more sensitive information than they believe retailers should collect. That is, if the price is right. The higher the discounts seemed to offer, the more willing participants would be to trade their personal data for a financial benefit.

Darius Belejevas, Head of Incogni, offered some thought on these findings: “The immediate gain of activating a discount or other shopping perks feels far more real than the abstract risk of a data breach or identity theft. It’s just easier to ignore a future threat than to pass up ‘now’ reward."

He added, "People may not fully grasp how serious a threat exposed personal data can be. From spam and scams, to identity theft, or even physical harm, all these are enabled by giving internet strangers access to personal information. If these risks become reality, the damage could potentially negate all the combined savings received from trading personal data.”

The study highlights how economic incentives override privacy concerns, surprisingly, even when those privacy risks are somewhat understood by consumers. Whether through habit, a lack of awareness, or simple economic calculation, Americans appear to have accepted personal data as a kind of currency in digital commerce, while paradoxically expressing deep concern about giving up that data, knowing it could fall into the wrong hands.

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by Irfan Ahmad via Digital Information World

AI Mode, Gemini, and Agentic Calls Turn Google Into a Full Shopping Assistant

Google’s latest round of updates pulls AI deeper into everyday shopping and the move lands right before the holiday rush.

The company wants to lighten the busywork that surrounds buying things online. Product choices keep multiplying and the hunt often turns into a slog. Google thinks AI can take over the slower tasks so people can focus on what they actually want.

AI Mode leads the push. Instead of picking through filters or guessing which keywords might surface the right items, shoppers can describe what they need in plain language. The system listens, interprets the request, and uses the Shopping Graph to answer with current product data. A search for a winter moisturizer may show a simple comparison table. Someone asking for casual outfit ideas may get a line of shoppable images. The goal stays predictable. Cut the steps. Cut the friction. The Shopping Graph’s massive inventory keeps the responses fresh and broad.

These tools shift into the Gemini app as well. The app used to lean on short text hints. Now it handles real browsing. People sketch a gift idea or run through early holiday plans and Gemini surfaces product listings, prices gathered from across the web, and places to buy. It feels less like a chatbot and more like a planning bench. You ask, and it moves straight into options without dropping you into another service. The product information stays anchored to the same graph that powers search.

Another feature tackles local shopping. When people search for certain items near them, a Let Google Call button appears. A quick prompt sets the direction. The AI reaches out to nearby stores and checks stock, price, and any running promotions. Duplex handles the calls. Gemini models help choose which stores make sense. The shopper gets a summary by text or email. It bypasses the old cycle of dialing one store after another and waiting on hold. Now the system just returns the answers while you move on with your day.

Google also reshapes how people buy after tracking prices. Shoppers can pick an item, note the exact variation, and set the amount they are willing to spend. If the price dips into that range, Google sends an alert with a Buy for me option. Once the shopper confirms the payment method, address, and shipping details, the AI completes the purchase through the merchant’s site using Google Pay. The rollout begins with Wayfair, Chewy, Quince, and selected Shopify sellers. It aims at those moments when people hesitate for a sale and then lose the product when it goes out of stock. The agent keeps watch and steps in only when conditions match the request.

These pieces start to pull the shopping cycle together. People will still discover products through influencers or scroll through social feeds for ideas, though Google’s system often draws on insights from the wider web and folds them into its own responses. That means decision making, comparison, and buying can all happen inside Google’s ecosystem without the usual jumps between apps. It changes how shoppers explore. They stay inside search longer because everything they need shows up in one place.

Google positions these updates as a way to cut down on chores rather than control choices. The company wants AI to handle the heavy lifting while keeping the shopper in charge of every key step. With AI Mode, Gemini, agentic calling, and the new checkout system working together, Google edges closer to acting like a real shopping assistant that handles the dull parts while leaving the actual decisions to the person doing the buying.

Notes: This post was edited/created using GenAI tools.

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by Irfan Ahmad via Digital Information World

Apple Cuts App Store Fees for Mini Apps and Tightens Data Rules for AI Integrations

Apple rolled out two policy shifts on Thursday that point in the same direction. The company wants tighter oversight of how apps work inside its ecosystem, and it wants developers to lean more on Apple’s own stack if they hope to lower their costs.

Apple’s first move centers on a new program called the Mini Apps Partner Program. It halves the App Store fee from 30 percent to 15 percent for developers who host mini apps and choose to tie their work more closely to Apple’s technology. The lower fee does not come for free. Apps must hook into tools like Apple’s purchase history system, its age verification API and its own flow for in-app payments. Apple calls these tools essential for consistent user experience, and the fee cut is tied directly to their adoption.

Mini apps sit inside bigger apps and run as small web-based experiences built with HTML5 or JavaScript. They already play a major role in China through platforms like WeChat, where millions of them let people track parcels, check transit routes or buy products. Mini apps have also started to appear inside AI chatbots as lightweight utilities. Apple has been warming up to them. Last year it allowed them to charge for digital goods through Apple’s in-app purchase setup. The new partner program pushes that door a bit wider.

Regulatory pressure has been pushing Apple in this direction for some time. The Digital Markets Act in Europe forces Apple to allow developers to communicate external offers without restriction. Courts in the United States have also pushed the company to loosen control. Apple still reviews every app with human checks, and the review process will extend into each mini app experience a developer submits under the new program.

Developers who join gain a lower commission but give Apple deeper visibility into how their app structures ages, purchases and user flows. Apple has shared versions of this approach before with programs for video apps, news apps and small developers. The message has stayed the same. If you adopt Apple’s preferred technologies, you pay less.

Alongside the fee change, Apple released a separate update to its App Review Guidelines, and this one goes straight into the growing tension around AI. The revised rules state that any app sharing personal data with a third-party AI service must disclose that data sharing and must ask users for clear permission first. Apple already required consent for data transfers, but the new wording calls out AI partners by name and removes any grey area for apps that feed user details into AI systems for analysis or personalization.

This adjustment arrives ahead of Apple’s own AI upgrades coming in 2026. Siri will gain the ability to perform actions across apps and will rely in part on Google’s Gemini. With that change on the horizon, Apple appears intent on stopping other apps from funneling data to external AI firms without strong user oversight.

The updated guidelines include a handful of smaller but notable revisions. Creator apps now need age-based limits for sensitive content. HTML5 and JavaScript mini apps are confirmed to be fully within app review scope. Loan apps face clearer restrictions tied to maximum APR and repayment timelines. Crypto exchanges now sit on Apple’s list of heavily regulated categories.

None of these updates change Apple’s overall posture. The company continues to protect its platform rules while adjusting its model under legal and competitive pressure. The new fee structure offers developers a chance to lower costs, but only if they bring their mini apps in line with Apple’s preferred technology path. And as AI becomes more deeply embedded across mobile platforms, Apple has staked out a clear line regarding user data. If an app plans to hand personal information to an AI service, users must know and must approve it first.

Notes: This post was edited/created using GenAI tools. Image: Unsplash

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