Investing podcasts are super popular. But do they help people learn the market?

By J. Merritt Melancon, University of Georgia

New study suggests individual traders benefit from the information provided by experts hosted on podcasts.

Image: Cody Board - Unsplash

From game highlights to cold cases to political pontificating, podcasts provide the stage for public conversations in 2026. But are we learning anything from all that chatter?

When it comes to investing and playing the markets, the answer seems to be yes.

New research from the University of Georgia Terry College of Business suggests that people who trade individual stocks, also known as retail investors, do better because of the information they glean from financial markets podcasts.

“More and more Americans are listening to podcasts each day, and investing podcasts represent a growing segment of this industry,” said Braiden Coleman, co-author of the study and an assistant professor in UGA’s Terry College of Business. “We wanted to see if the podcasts actually influenced investor trading activity and whether that helps or hurts capital markets from an information asymmetry standpoint.”

They found that in the three days after an investing podcast discussed a company’s earnings, trading activity significantly increased, especially among retail investors.

But what they found especially exciting was that these surges in extra trading didn’t seem based solely on name recognition or social media buzz but on well-considered information.

“One of the main takeaways of the paper is that podcasts don’t seem to lead to purely speculative-based trading,” Coleman said. “We don’t see any return reversals or big price corrections following podcast coverage.”

Podcast discussions reduce information asymmetry by 22%

Usually, when earnings news is announced, stock prices bounce around a little as investors digest the new information.

In cases of meme stocks or other hyped-up stocks, prices will often surge as investors rush to buy the trendy stock and then fall when everyone comes to grips with that company’s fundamentals and starts to sell.

Information asymmetry, when one party in an economic transaction has more or better information than the other, is what causes this rush and retreat. It’s the enemy of retail stock traders who may not have as much time to evaluate firm announcements or the background to process the information accurately.

The research team found that podcast discussions help retail investors reduce information asymmetry by 22%.

Coleman believes podcasts can bridge the information divide because they increase the amount of long-form discussion the average investor can consume.

“There’s more information out there than ever, but with a lot of these platforms you have to be actively consuming — looking at your phone, reading your computer screen,” Coleman said. “With podcasts, you can listen to them while you are driving to work, while you’re exercising or while you’re cleaning the house.

“There’s a bigger reach because you can consume the content while you’re performing other activities.”

Expert commentary provides multiple viewpoints on investment strategies

The other benefit is the number of experts invited to speak on podcasts. Most segments are structured as conversations between hosts and industry or sector experts. The research team found that podcasts including multiple viewpoints further reduced information asymmetry in the markets.

“These guest appearances really appear to drive value,” Coleman said. “If you have multiple voices on a podcast and they’re sharing more unique insights about the company at play, then our results are stronger for those podcasts. Listening to podcasts that include guests and expose you to new ways of thinking, fresh insights and fresh perspectives can really help investors process information better.”

Podcasts offer fast way to get a lot of information on the market

The researchers turned to a podcast metadata library to gather the names of all investing podcasts listed between 2008 and 2022. They used artificial intelligence to transcribe and sort those podcasts, identifying those episodes that included discussions about publicly traded companies.

That resulted in a sample of 1,782 shows and 49,772 individual podcast episodes. The number of episodes per year grew over time, with approximately 2,000 in 2016 to more than 11,000 in 2022.

They mapped several trends in coverage over the years.

First, retail stocks, those with less of their shares owned by institutional investors, are covered more by investing podcasts than other stocks. Publicly traded stocks usually saw a pickup in coverage around each firm’s earnings announcements.

Coleman’s team also found podcasts that spent more time discussing the fundamentals of a company’s performance improved investor performance. Podcasts with tighter delivery (discussing more information in a shorter timeframe) had the same effect.

The overarching message is that the more high-quality viewpoints or data points an investor can get before making a trade, the better off they are. Podcasts, Coleman said, seem to be a great way for people with limited time to take in information.

Published by the Review of Accounting Studies, the publication was co-authored by Texas A&M University accounting professor Brady Twedt, Terry accounting doctoral student Matt Hall and Terry doctoral graduate and current Texas Christian University professor Karson Fronk.

Reviewed by Irfan Ahmad.

This article was originally published by the University of Georgia and has been republished with permission.

Read next: Study Finds Low-Quality AI Videos Dominate TikTok Feeds for New Users and Children
by External Contributor via Digital Information World

Study Finds Low-Quality AI Videos Dominate TikTok Feeds for New Users and Children

By Liam Curtis, Kapwing

Research from Kapwing reveals that nearly 60% of TikToks served to new users and children are AI slop. But which categories and tags are the worst affected — and what does this landscape look like to kids?

TikTok has a slop problem.

Some 59% of videos served to a new TikTok account’s “For You” page are AI slop, according to Kapwing’s research.

That’s three times as much slop as a new YouTube user encounters. And a similar share (57.4%) of all TikTok videos aimed at children are AI slop, too.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA 

Back in 2025, TikTok announced a new tool to help users control the level of AI-generated content (AIGC) in their feeds, declaring that “many people enjoy content made with AI tools, from digital art to science explainers, and we want to give people the power to see more or less of that.”

But when it comes to slop, not only is it bad for kids but many adult users would rather see far less on social media. As the BBC’s Joe Tidy notes, often “the number of likes for the AI backlash comments far exceeds the original [AI-generated] post.”

To understand the depth of the problem, Kapwing analyzed thousands of videos across TikTok’s top categories and hashtags to measure the prevalence of AI slop. This was defined in Kapwing's previous YouTube AI Slop Report as “careless, low-quality content generated using automatic computer applications and distributed to farm views and subscriptions or sway political opinion.”

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

One in Three TikToks a New User Sees Is AI Slop

TikTok adjusts its video feed for users based on “signals” including follows and likes, category preferences, and previous scrolling activity. So, to get a better idea of the ‘raw’ TikTok experience, we established a new account and recorded which of the first 500 videos were human-made and which were AI slop.

When you sign up for a new account, TikTok feeds you “popular content appropriate for a broad audience” and “content influenced by your location and language settings” until it figures out what you like. In our test run, the very first TikTok served to Kapwing’s dummy account was a low-quality AI-generated video that appears to have since been deleted.

Overall, 294 or 59% of the first 500 were AI slop.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

TikTok delivers nearly three times as many AI slop videos as we found when running the same experiment with a fresh YouTube account, where 104 (21%) of the first 500 videos on the YouTube Shorts feed were AI brainrot.

On TikTok, the prevalence of slop on a fresh feed may be a matter of sheer saturation — by November 2025, the company had already labeled a staggering 1.3 billion videos as AI-generated.

On the other hand, AI companies train the models that generate AI slop on existing footage, and their output represents a flattening or aggregation of the patterns found in human-made content (rather than in “reality”) — an effect that scholar Roland Meyer has labeled “platform realism.”

Filling a new user’s experience with slop videos optimizes the feed as a familiar space and acclimatizes them to the aesthetic and thematic norms of TikTok culture.

Kids, Science and Education, and Health TikTok Categories Are Most Clogged With AI Slop

Next, we checked a sample of 10,742 TikTok videos across the most popular tags in 20 categories, noting the number of AI slop and non-AI slop videos. The category with the highest slop density by far was Kids (57.4%) — more on that below.

Science and Education (35.0%), Health (33.8%), and History (33.5%) are the nearest contenders. In the top nine categories, more than one in ten videos were AI slop. But videos in the Fitness (1.6%), Music (1.5%), and Fashion (1.3%) categories are almost entirely human-made.

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

Scientific facts and concepts lend themselves to visual illustration through animation. But when such videos are generated with haste and sensationalism as their guiding principles, the potential for deeper value is compromised.

Researchers have warned that slop educational material clutters the platform and competes with more authoritative sources.

TikToks such as the baffling Eating Lemon video below rattle along at the turbo-TikTok pace the models seem to have learned and feature the misspelled words and misshapen lettering that have come to be associated with AI imagery.

@vitalverse1 Science in action. What happens while Eating Lemon in the human body?😱🤮😱#humanbody #anatomy #3danimation #sciencetok #viral ♬ original sound - user99611117491

Meanwhile, creators such as Jonathan Laramy, the man behind Chloe VS History, see AI as a chance to bring educational topics to life, exploiting the technology rather than the viewer.

“Yes, there are people out there that obviously don't care about history,” he told Sky News, regarding his competitors, “because there are mistakes left, right, and center.”

Laramy’s content is hosted by an ultra-realistic AI presenter called Chloe, who is easily mistaken for a real person due to the “human emotion and non-verbal cues” facilitated by the latest video models.

However, the scripts are AI-generated, too, and viewers have criticized the channel for its historical inaccuracies.

Brendan Gillis, Director of Teaching and Learning at the American Historical Association, warns that AI-generated history is only as reliable as the source material it draws from. Because AI fills gaps using patterns from its training data, it can introduce inaccuracies, biases, and stereotypes.

97% of #CartoonKids TikToks Are AI Slop

Of the 2,000 featured videos we analyzed in TikTok’s Kids category, some 1,147 (57.4%) were AI slop.

The worst-affected tag, #cartoonkids, was almost entirely made up of slop, with only three of the 100 videos we checked being human-made. Around one-third or more of the videos for nearly all of the tags we checked were AI slop, and even #babytok — which had significantly less slop than other tags — featured one slop video out of every ten on its tag page. 

Image: Kapwing In Partnership With Neomam Studios. CC BY-SA

The dangers of automated content for children are not new, but the scale at which children are being targeted with slop — ranging from nonsense ‘brainrot’ to dangerously inaccurate songs and lessons — is staggering.

“I think of this as toddler AI misinformation at an industrial scale. It’s very risky for the developing brain,” says Dr. Dana Suskind, a professor of pediatrics at the University of Chicago, speaking to Mother Jones.

“Every experience is building a million new neural connections. You will be unintentionally wiring the brain in incorrect ways.”

One of the first videos to pop up under #preschoollearning appropriates Sesame Street characters to deliver a lesson about counting cookies. Not only do the numbers not match the cookies, but the animation is careless, and the voices are borderline terrifying. The comments section is stuffed with short, nonsensical sequences of letters that boost the video’s visibility.

@spongebob.uk1 Counting Cookies for Kids 🍪 Fun Learning Numbers with Cookie Monster Learn numbers in a fun way with Cookie Monster while counting delicious cookies! 🍪 Perfect for toddlers and preschool kids to practice counting, early math skills, and number recognition through a playful learning adventure. Great educational video for kids who love Sesame Street characters and fun learning! #countingcookies, #cookiemonster, #learnnumbers, #countingforkids, #kidslearning, #preschoollearning, #toddlerlearning, #numbersforkids, #learncounting, #educationalvideo, #kidseducation, #learningisfun, #funlearning, #earlylearning, #preschoolactivities, #toddleractivities, #kidsvideos, #learningnumbers, #countinggame, #numbersong, #kidsmath, #mathforkids, #kidsfunlearning, #educationalforkids, #kidsyoutube, #kidscontent, #learningvideos, #kidsteaching, #homeschoolkids, #preschoolkids, #learnwithfun, #smartkids, #educationchannel, #learningtime, #kidsstudy, #numberspractice, #countingtime, #learn123, #numbers123, #kidsnumberlearning, #funforkids, #kidslearningvideos, #playandlearn, #kidsbrain, #learningathome, #kidseducationvideos, #childeducation, #kidsedutainment, #learningnumbers123, #kidsactivityvideo, #educationfun, #learningforkids, #countingactivity, #kidsmathfun, #preschoolmath, #toddlermath, #educationalcontent, #funeducation, #kidsknowledge, #learningadventure,#animationviral #spongetherapy #underwateradventure #oceanfun #cartoonvideo #animationshorts #viralshorts #funnyanimation #cartoonforkids #seaadventure #oceanworld #cartoonfun #spongeasmr #viralvideo #youtubeviral #kidsentertainment #animatedshort #cartoonstory #oceanlife #kidsyoutube ♬ original sound - English

A recent survey from the Family Online Safety Institute found that only 51% of parents use parental controls on tablets and 47% on smartphones.

Meanwhile, over 70% of babies and under-twos use screens, and one in 10 babies regularly falls asleep with a screen, according to the UK’s 1001 Critical Days Foundation — so named because the 1,001 days from pregnancy to age two are critical for brain development, with up to one million neural connections forming every second.

The TikTok AI Slop Backlash

The people at TikTok are well aware of the AI slop backlash. In May, the company began to rein in its new AI-generated summaries, which users had noticed regularly produced bizarre mistakes, such as erroneously identifying dancer Charli D'Amelio as a “collection of various blueberries with different toppings.”

Alongside allowing users to reduce AI content in their feed, the company announced a $2 million educational fund for experts to develop content around AI literacy and safety.

But the stats reinforce the feeling that AI slop-clogged feeds are already the new normal. Meta’s Mark Zuckerberg has tagged this era of generative AI content the “third phase” of social media — following personal and then creator-based content.

This third AI phase itself has gone through different eras, from the comedic novelty of Will Smith spaghetti videos to the emotionally nuanced but factually inaccurate Chloe VS History.

But slop is likely to continue being slop, and children are sure to encounter damaging AI material as long as humans outsource the hard work of video production to robots without putting in the time and oversight to ensure higher standards are met.

This is because the training data contains mistakes. And most of all, it is because video is a mode of human communication, which is nuanced, dynamic and — well — human.

Methodology

We manually analyzed 10,742 TikTok videos across 20 categories. We began by building a seed list of 20 popular TikTok categories (e.g., Food, Travel, Fitness, etc.) and at least three of the most popular tags for each category (e.g., #traveltok and #foodie).

Next, we manually analyzed the featured videos displayed on each tag's page (e.g., https://ift.tt/K8nrFN3), recording the count of AI slop and non-AI slop videos. This allowed us to calculate the percentage of AI slop videos for each tag, which we then aggregated for each category.

To find the proportion of AI slop that is served to new users, we established a brand new TikTok account and recorded the appearance of AI slop videos in the “For You” section while scrolling the first 500 TikToks.

AI slop videos were defined as those with obvious use of AI-generated visuals, as well as low-quality clip/compilation-style videos with clearly AI-generated scripts and voiceovers.

Reviewed by Irfan Ahmad.

This article was originally published on Kapwing and republished here with permission.

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• Why LinkedIn Top Voices Are Outperforming Famous CEOs (And What It Means for You)

• AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds
by External Contributor via Digital Information World

Why LinkedIn Top Voices Are Outperforming Famous CEOs (And What It Means for You)

By Cara Siera

From Microsoft’s Bill Gates to Google’s Sundar Pichai, you might expect executives who are household names to have the strongest LinkedIn profiles. But ResumeCoach’s recent analysis of 20 public profiles found that “unknown” Top Voices users often outscored famous CEOs.

Below, we’ll discuss how the analysis was conducted, why the demographics differ in profile completion, and what it means for LinkedIn users in 2026.

Profile Analysis Methodology

The sample included ten well-known C-suite executives including Gates and Pichai, and ten non-executive participants in LinkedIn Top Voices for 2026, an invitation-only program through which users are selected by LinkedIn’s editorial team for thought leadership, professionalism, and consistent use of the platform.

Each profile was reviewed using ResumeCoach’s LinkedIn Profile Analyzer, which scores profiles based on the Headline, About, Experience, Education, Licenses & certifications, Skills, and Languages section contents.

LinkedIn Rewards Complete Profiles, Not Fame

In the analysis, profiles with four or more completed sections consistently outperformed those with only one or two complete sections, regardless of their real-world status.

LinkedIn Top Voices users averaged a score of 6.7 out of 10, while the high-profile executives averaged just 3.2 out of 10. That’s a gap of 3.5 points between fame and high performance.

All Top Voices users scored well in the About section, with 90% receiving top marks for their Skills section and 70% for optimizing their Experience section.

Only two of the ten executives scored more than 5 out of 10. Seventy percent of executives neglected the Skills and Certifications sections, and 50% of scores below 3 had an empty About section.

This data suggests that LinkedIn’s algorithm may reward profile completeness rather than perceived influence, talent, impact, or real-world status. This levels the playing field, making intentional profile optimization an accessible networking tool for everyone.

Interestingly, LinkedIn’s founder, Reid Hoffman, was the highest-scoring executive with a global score of 7 out of 10. With proprietary knowledge of how LinkedIn’s algorithms function, Hoffman no doubt understands the importance of a complete profile.

Why Some CEOs Don’t Need LinkedIn

Today’s top executives built their careers through reputation, in-person networking, speaking engagements, media exposure, and courting investors. Many were well on their way before LinkedIn’s inception in 2002.

They didn’t need LinkedIn to reach the next rung of the ladder and likely relied on existing contacts for networking. While they may use LinkedIn for informational or inspirational posts, they have not fully leveraged the platform.

Chelsea Jay, a career and leadership coach whose own LinkedIn profile scored 8 out of 10 in the analysis, says, “High-profile executives do not always rely on LinkedIn as their main source of opportunity, so profile optimization can become a low priority. But LinkedIn does not only evaluate reputation. It evaluates structure, completeness, and keyword alignment.”

She adds, “It is not that executives lack skills. They are simply not translating their expertise into searchable data on the platform. For job seekers, completing sections such as About, Skills, Certifications, and Languages is one of the easiest ways to improve discoverability.”

Top Voices professionals built their audiences on LinkedIn itself. That naturally encouraged them to optimize every section of their profiles.

Why Completeness Matters More in the AI Era

As artificial intelligence (AI) becomes increasingly involved in job searches, recommendations, and recruiter tools, structured information becomes ever more valuable.

In the ResumeCoach study, the strongest profiles consistently included About, Experience, Skills, Certifications, and Education sections. This structure effectively organizes information into a format that software can more easily interpret and provides an ample supply of searchable keyword inclusion opportunities.

This, in turn, aids recruiter searches, individual networking, job recommendations, and professional discovery. The well-filled profile also appeals to human viewers, proving the profile is legitimate and the user an active member of the LinkedIn community.

Key Takeaways

If you open your LinkedIn profile today, what does it look like? Profiles with four or more completed sections consistently outperformed profiles that primarily relied on a descriptive headline. LinkedIn visibility has less to do with your professional status than with the structure and completeness of your profile.

If you’re already at the top of your industry, optimizing your LinkedIn profile may be superfluous. But fame alone doesn’t equal a strong LinkedIn presence. Professionals who don’t yet enjoy global recognition can compete for LinkedIn visibility simply by investing a little time in their profiles.

Author Bio: Cara Siera is a career and travel writer who approaches data from a background in psychology and sociology—giving cold statistics the human touch. Cara is a Certified Professional Resume Writer (CPRW), Master Beekeeper, home chef, and world traveler.

Image: Zulfugar Karimov - Unsplash

Reviewed by Irfan Ahmad.

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• AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds

• How a lean data team built a single source of truth in 2 weeks (not 2 months)

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AI Translation and Human Interpretation Differ in Handling Contextual Meaning, Study of UN Speeches Finds

By Lingnan University

As generative artificial intelligence (AI) becomes increasingly widely used in translation, questions have been raised over whether it could eventually replace professional interpreters. A joint study led by Lingnan University found that while AI can improve translation efficiency, it is still less capable than professional interpreters of adapting language to context and preserving rhetorical and communicative effects. The researchers conclude that human judgement and oversight remain essential, particularly in politically, diplomatically, and culturally sensitive settings. These findings have been published in Humanities and Social Sciences Communications, a Nature Portfolio journal.

Image: BBC Creative - Unsplash

The research team from Lingnan University and the Chongqing University of Posts and Telecommunications analysed 16 Chinese-language speeches delivered at the United Nations General Assembly (UNGA) between 2008 and 2023, and compared the official English interpretations by professional UN conference interpreters with AI-generated translations produced by ChatGPT-4o, examining how each handled language in different contexts.

Before generating the AI translations, the researchers designed detailed prompts that included the speaker's official position, institutional background, year of delivery, audience, and broader sociopolitical stance in order to approximate the contextual information available to professional interpreters. However, despite providing the AI model with extensive contextual information, they found major differences between AI-generated translations and human interpretations in both contextual understanding and translation strategies.

One key difference concerns the use of personal pronouns. As Chinese frequently omits subjects, professional interpreters were more likely to introduce pronouns such as “our” and “they” to reflect interpersonal meanings and relationships between speakers and audiences, reinforcing collective identity and shared responsibility. AI-generated translations, by contrast, tended to produce more literal renderings with fewer personal pronouns.

For example, a Chinese sentence referring to vaccines as a powerful weapon against the pandemic was rendered by a professional interpreter as:

“Vaccination is our powerful weapon against COVID-19.”

whereas ChatGPT-4o translated it as:

“Vaccines are a powerful weapon against the pandemic.”

The researchers found that the interpreter’s addition of “our” strengthened the sense of collective identity, while the AI translation adopted a more neutral tone. The study also identified distinct differences in how obligation and responsibility were expressed. Professional interpreters were more likely to adjust modal verbs according to context, using expressions such as “should” and “need to” to convey persuasive rather than mandatory obligation. AI-generated translations, however, relied more heavily on “must” and passive constructions, making responsibility less explicit.

For example, the professional interpretation reads:

“We need to enhance coordinated global COVID-19 response and minimise the risk of cross-border virus transmission.”

whereas the AI translation states:

“International joint prevention and control must be strengthened, and the cross-border spread of the virus must be minimised.”

The researchers found that the AI version obscures the agent responsible for action by using passive constructions.

The study also examined culturally embedded metaphors. More than half (52.63 per cent) of the AI translations reduced culturally specific metaphors to their literal meanings, weakening their rhetorical force. By contrast, professional interpreters adopted more flexible strategies, preserving, adapting, and explaining metaphorical expressions according to context. In about one-third of the cases (31.6 per cent), interpreters retained the metaphor and also conveyed its intended meaning.

One example involved the traditional Chinese metaphor of people travelling “in the same boat”. The professional interpreter translated it as:

“We are called upon by our times to unite as one and work together for mutual benefit and win-win progress like passengers in the same boat.”

While ChatGPT-4o rendered it as “Working together and achieving mutual benefits and win-win outcomes are the objective demands of our time.”

According to the researchers, the AI translation conveyed the general meaning, but omitted the metaphorical imagery and its rhetorical impact.

The team noted that ChatGPT-4o generally produces fluent and grammatically accurate translations capable of completing translation tasks effectively. However, drawing on socio-cognitive theory, the study argues that professional interpreters consider not only the source text itself but also factors such as the speaker's identity, communicative setting, audience, cultural background, stance, and rhetorical purpose when deciding how to translate. This suggests that current large language models have yet to replicate fully the human capacity to interpret context and cultural meaning.

Prof Wang Binhua, Professor of the Department of Translation and Head of the Centre for English and Additional Languages at Lingnan University, member of the SIG on Artificial Intelligence in Translation and Interpreting of the European Language Council (ELC), said “Large language models still face the challenge of the ‘black box’, meaning that the mechanisms through which they produce particular translations remain difficult to explain. Unlike professional interpreters, who work within established professional ethical standards and are accountable, AI systems generate translations by identifying patterns in large volumes of language data and do not possess an intrinsic ethical framework. In translation tasks that require careful attention to cultural meaning and contextual understanding, human interpreters remain indispensable in making informed judgements about interpersonal relationships, rhetorical choices, and cultural expression.”

He added that AI is better positioned to augment rather than replace professional translators and interpreters. When integrated with human expertise, AI has the potential to improve efficiency while leaving context-sensitive and culturally informed decision-making in human hands.

For the full research paper A tale of two ‘contexts’: ideological differences in the translations of UN political speeches by human interpreters and by ChatGPT4o, please visit: https://www.nature.com/articles/s41599-026-07877-7.

This article was originally published by Lingnan University and republished here with permission.

Reviewed by Irfan Ahmad.

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How a lean data team built a single source of truth in 2 weeks (not 2 months)

National Safety Apparel (NSA) has a powerful, 90-year-old mission: ensure every industrial, utility, and military worker returns home safely at the end of the day. But as the company scaled into a multi-unit operation through rapid acquisitions, its data language grew fragmented.

Different departments developed their own siloed reporting. To Finance, a "customer" meant the parent company being invoiced; to Shipping, it meant the specific branch receiving the goods. Without a shared data foundation, answering critical operational questions was a manual maze.

NSA’s lean, six-person data team found their time completely consumed by hand-coding SQL and Python pipelines rather than focusing on the business strategy behind the numbers.

Jamie Tanner, Director of Corporate Data and Analytics, knew they needed a paradigm shift when data firefighting began interrupting his family vacation. The team decided to stop drowning in micro-level coding and step up to macro-level business architecture.

They spent a week mapping out their foundational master data definitions (customer, product, order, invoice) in a shared matrix. But instead of spending the next quarter manually writing orchestration and transformation logic to move data into their Snowflake silver tables, they onboarded Maia.

By feeding their business context directly into the platform, the team succeeded in reducing data foundation setup from months to weeks.

The Impact at a Glance

• Timeline Slashed: A major master-table architecture project that traditionally takes two months was completed in just two weeks.
• Minimal Coding Overhead: Out of the 10-day project window, the engineering team spent less than 3 days actually building and adjusting code. The remaining 7 days were spent collaborating with the business to ensure data accuracy.
• Enterprise-Scale Output: A lean analytics team successfully unlocked the output capacity of a department multiple times its size.

"The role of the data engineer changes. We're leveraging the team's cohesive knowledge, which is a massive unlock for NSA and for me personally." — Jamie Tanner, Director of Corporate Data and Analytics at NSA

What’s Next for NSA

With a clean, certified data foundation now running seamlessly in Snowflake, NSA is moving away from descriptive reporting and toward true AI readiness. The team is already planning to apply this automated pipeline method to their operational manufacturing floor data, leveraging Snowflake Cortex to unlock cross-functional insights from sourcing efficiencies to product development.

Curious to see the exact blueprint they used to shift from pipeline coding to business knowledge? Check out the full customer story.

by Sponsored Content via Digital Information World

European Commission Fines Google €890 Million Over Google Search and Google Play DMA Violations

Reviewed by Irfan Ahmad.

The European Commission on July 23 fined Google €890 million (about $1.01 billion) after finding the company failed to comply with two obligations under the European Union's Digital Markets Act (DMA). The penalties include €460 million over Google's Search practices and €430 million over its Google Play practices.

The Commission found that Google gave its own services, including shopping, hotels, transport and sports, more prominent placement in Google Search than comparable third-party services. Under the DMA, gatekeepers must not treat their own services more favourably in rankings than third-party services and must apply transparent, fair and non-discriminatory ranking conditions.

The Commission also found that Google restricted app developers from informing users about alternative offers and directing them to other purchase channels outside Google Play. It also concluded that Google's steering-related fees and the period for charging those fees exceeded what it considers compliant with the DMA.

The Commission ordered Google to end both areas of non-compliance within 60 days. Otherwise, the company could face periodic penalty payments of up to 5% of its total worldwide turnover.

Executive Vice-President for Clean, Just and Competitive Transition, Teresa Ribera said the Commission had taken "decisive yet balanced enforcement action." She added that "The best products should succeed because they're better, not because they're owned by the company running the search engine."

The Commission noted that, after what it described as a constructive dialogue, Google has proposed and started testing changes to how it presents its own services on Google Search for free services such as shopping, hotels and flights. It also noted that Google has proposed and started testing changes to how it presents shopping ads and content related services, such as sports.

The Commission also noted that Google has rolled out changes related to Google's steering terms, which it said constitute good progress toward compliance and will also be assessed in light of the Commission's cease and desist order. It also took note of Google's proposals on how it plans to apply the principles of the decision to AI Overviews and AI Mode, saying dialogue on those proposals will continue following the Commission's decision. Google may appeal the decisions.

Image: DIW

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Shift Browser Report Shows Gen Z’s Relationship With AI Is “Complicated”

By Michael Foucher, VP of Product at Shift Browser

The standard narrative about artificial intelligence is that younger adults are embracing AI while older adults are resistant and adopting at a slower pace. While that is somewhat true, the reality across age demos is nuanced.

Younger adults are not “all in” on their AI adoption. And while they use AI consistently, they also express the most concern about AI’s impact on the environment and role in their lives.

Shift recently surveyed 1,448 U.S. adults for the "AI Usage in America: A Generational Divide” report and found that 47% of people ages 18 to 24 now turn to AI tools before traditional search engines. By comparison, only 12% of adults over 65+ say the same. The results paint a major behavioral divide, especially considering that Google and Bing remain the primary starting point for 58% of Americans overall.

But frequent use does not equal acceptance without questions.

Thirty-four percent of 18- to 24-year-olds say AI is already “far too dominant,” compared with 19% of the overall population. Meanwhile, 67% are concerned about AI’s energy use, and 27% are very concerned about its environmental footprint.

There is also evidence that AI is not always improving their experience. Eighteen percent of Gen Z respondents said AI has made their daily digital experience worse. That may sound surprising for a group that uses AI so frequently, but it represents a broader look into how adults consider technology.

This is the first generation to grow up in a digital age that is dominated by algorithms, recommendations, instant gratification and answers but they are also critical of the tradeoffs for convenience and loss of control or privacy.

For companies rapidly developing AI tools and bolting on AI features, it is a good opportunity to do a gut check and see if these are features that consumers actually want. Adoption should not be confused with trust.

We know that AI is fast and difficult to avoid but that doesn’t mean that users want AI to make every decision or be present in every app that they use. In many cases, younger adults in high school and college need to have clear boundaries and submit work that is their own and doesn’t use AI.

The next phase of AI development should focus less on forcing intelligence into every interaction and more on giving users a choice. People should know when AI is active, what information it can access and how to turn it off. They should also be able to decide when they want a traditional web search, an AI- generated result or options for both.

The data also suggests that adults ages 35 to 54 are the slow and steady adopters. Forty-six percent anticipate using AI tools more over the next year. That is 41% above the national average.

At the other end of the spectrum, 44% of adults over 65 say they do not know when or how to use AI. Thirty percent of seniors also say that AI is making no noticeable impact, the highest "no impact" rate of any age group.

AI adoption does not follow one direct line. Younger users want greater control, middle-aged users are preparing to use more of it, and older adults need clearer entry points.

One thing is for sure, companies that recognize the differences and interests of their users across these age demos will build better products. The ones that treat every user as equally eager, informed and comfortable with AI risk mistaking usage for approval.

Ultimately, the relationship between Gen Z and the AI tools they use might be "complicated," but it doesn't have to be dysfunctional. Like any healthy partnership, people need clear boundaries, mutual respect, and most importantly, the ability to have a little space when they need it.

About author: Michael is the VP of Product and Customer Success at Shift. With 20+ years in tech, he has launched and scaled web and mobile products across startups and enterprise environments, bringing that same energy to building Shift.

Reviewed by Irfan Ahmad.

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by Guest Contributor via Digital Information World