As noticed by Android Authority on Monday morning, some YouTube users are now seeing a disruptive prompt when they try to watch videos while using a browser with an ad blocker. The prompt notes that "ad blockers violate YouTube's Terms of Service" and urges users to either allow ads on YouTube or subscribe to YouTube Premium.

YouTube tries to kill ad blockers in push for ad dollars, Premium subs

Airbyte announced its "Powered by Airbyte" version that enables makers of software to embed over 100 integrations into their applications.

Powered by Airbyte Offers Software Makers More than 100 Data Integrations

This car owner redefined “search engine” when he used Google Earth to track down and recover both of his vehicles after they were pilfered from his driveway.

I used Google Earth to track down the thieves who stole my cars

Nick Clegg, Meta's president of global affairs, spoke ahead of the landmark AI Summit being hosted at Bletchley Park in the U.K. The summit is expected focus on mitigating the potential harms of AI.

Meta exec and former U.K. Deputy Prime Minister compares AI fears to past ‘moral panic’ over video games—and bicycles

The U.S. antitrust lawsuit against Alphabet ( NASDAQ: GOOG) ( NASDAQ: GOOGL) is starting to get to Barclays. “We’ve tracked the DoJ trial closely over the past several weeks and are growing increasingly concerned about the strength of the case against Google,” analysts led by Ross Sandler wrote in a note.

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Available on Google Cloud Marketplace and Will Integrate with Vertex AI to Help Customers Process and Analyze Data Box, Inc. (NYSE:BOX), the leading Content Cloud, and Google Cloud today announced an

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Get the most recent info and news about Google on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.

Google

Google’s AI Power Moves with Gemini 2.0 and Project Mariner

As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs.


Optimize battery designs to extend electric vehicle range.


Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever.
Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption.
Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button? As a Web3 writer, I tend to shy away from deep tech topics. But when I heard Google’s latest announcement about their new quantum computing chip, Willow, I couldn’t look away. The headline-grabbing claim? A computational problem solved in 5 minutes that would take a classical supercomputer a staggering 10 septillion years to complete. 10 septillion years For context, that’s 10,000,000,000,000,000,000,000,000 years — a number so vast that it makes the 13.8 billion-year age of the universe look like a lunch break. Naturally, the buzz was immediate: “Quantum computing is here to crack Bitcoin!” “No encryption is safe!” But hold your horses. Willow’s significance isn’t about rendering our current security systems obsolete overnight. It’s about a new way forward — for computing, for science, and yes, for crypto. Let’s break it down. What Exactly Is Google’s Willow? Google’s Willow chip is a quantum computing breakthrough. It’s not the largest quantum processor out there — it has 105 qubits. But what makes it revolutionary is how it deals with errors. Why Errors Matter in Quantum Computing? In traditional computers, bits represent either a 0 or a 1. But quantum computers use qubits, which can represent both 0 and 1 simultaneously thanks to the principles of superposition and entanglement. This ability lets quantum computers perform multiple calculations at once, potentially solving problems classical computers could never touch. The problem? Qubits are delicate. They’re easily disturbed by their environment, which leads to errors. And the more qubits you add, the more errors you typically get. This challenge has haunted quantum computing researchers for decades. Willow changes the game by demonstrating that as you add more qubits, the number of errors actually decreases. This is huge. It’s a critical step toward building large-scale, reliable quantum computers — the kind that could reshape industries like drug discovery, battery development, and material science. But where does that leave Bitcoin and crypto security? Should Crypto Holders Be Worried? The fear is simple: Quantum computers could theoretically break the cryptographic algorithms that secure Bitcoin and other cryptocurrencies. Bitcoin relies on two key pieces of encryption: 1 - SHA-256 for mining. 2 - ECDSA (Elliptic Curve Digital Signature Algorithm) for transaction signatures. Quantum computers could, in theory, brute-force these algorithms, rendering wallets and transactions vulnerable. If someone could crack ECDSA, they could steal funds by forging signatures. But here’s the thing: Willow isn’t that quantum computer. Not yet. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. According to tech experts, including Kevin Rose (former Google product manager), cracking Bitcoin’s encryption would require a quantum computer with about 13 million qubits — a far cry from Willow’s 105 qubits. So, no, you don’t need to sell your Bitcoin and go off-grid just yet. Why Quantum Computing Still Matters for Crypto Even though Willow can’t break Bitcoin’s encryption today, it’s a wake-up call. It shows that quantum computing isn’t some sci-fi fantasy. It’s evolving rapidly. And each breakthrough — like Willow’s error reduction — brings us closer to a world where quantum computers might pose a real threat to current cryptography. https://x.com/adamscochran/status/1866314726030143606 https://x.com/adamscochran/status/1866314726030143606 The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. The Timeline Is Shortening Crypto venture capitalist Adam Cochran put it succinctly: Quantum computing advancements have shaved 20 years off the timeline for quantum threats to Bitcoin. Another breakthrough like Willow, and we might be looking at a 10-year window to prepare. This isn’t just about Bitcoin. Ethereum, smart contracts, and other blockchain-based systems rely on similar cryptographic protections. Quantum computing could jeopardize the entire Web3 ecosystem if we’re not ready. How Crypto Can Defend Itself Thankfully, the crypto community isn’t sitting idle. There are already discussions around post-quantum cryptography — encryption methods designed to withstand quantum attacks. 1 - Upgrading Bitcoin’s Security Crypto experts like Emin Gün Sirer, co-founder of Ava Labs, have suggested practical steps to protect Bitcoin: - A - Sunsetting Outdated Formats A - Sunsetting Outdated Formats Early Bitcoins, mined by Satoshi Nakamoto, use an old format called P2PK (Pay-to-Public-Key). Unlike modern wallets, this format exposes public keys, making it vulnerable to future quantum decryption. Sirer recommends freezing these coins or setting a sunset date for P2PK transactions. B - Implementing Post-Quantum Algorithms B - Implementing Post-Quantum Algorithms Transitioning Bitcoin to quantum-resistant algorithms is complex but doable. It would likely require a hard fork and widespread adoption of new wallet software. 2 - Ethereum’s Preparedness Ethereum co-founder Vitalik Buterin has already proposed a strategy for Ethereum’s transition to post-quantum security. It would involve a hard fork and user cooperation to upgrade wallets, but it’s a clear and viable plan. Quantum Computing: A Threat or an Opportunity? While the threat to crypto security is real, quantum computing isn’t just about breaking things. It’s about building new possibilities. Imagine a world where quantum computers can: Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Simulate complex chemical reactions to discover life-saving drugs. Simulate complex chemical reactions to discover life-saving drugs. Optimize battery designs to extend electric vehicle range. Optimize battery designs to extend electric vehicle range. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Model financial systems to prevent market crashes. These breakthroughs could transform industries and unlock untold value. And yes, Web3 could benefit too. Quantum computing could lead to more secure blockchains, faster consensus algorithms, and decentralized systems that are truly unbreakable. The Real Takeaway Google’s Willow isn’t here to kill Bitcoin. It’s here to remind us that technology evolves, and we need to evolve with it. If you’re in Web3, now’s the time to: Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? Stay Informed: Follow quantum computing advancements. They matter more than ever. Advocate for Post-Quantum Security: Encourage discussions and research around quantum-resistant encryption. Don’t Panic — Prepare: The future isn’t something to fear. It’s something to build. Quantum computing is coming. The question is: Will we be ready? What are your thoughts on the quantum threat to crypto? Are we prepared, or is it time to hit the panic button?

Does Google’s Willow Quantum Chip Put Bitcoin’s Cryptography at Risk? 

গুগলের উইলো কোয়ান্টাম চিপ কি বিটকয়েনের ক্রিপ্টোগ্রাফিকে ঝুঁকিতে ফেলেছে? 

Sætter Googles Willow Quantum Chip Bitcoins kryptografi i fare? 

Το Willow Quantum Chip της Google θέτει σε κίνδυνο την κρυπτογραφία του Bitcoin; 

¿El chip cuántico Willow de Google pone en riesgo la criptografía de Bitcoin?

Apakah Chip Kuantum Willow Milik Google Membahayakan Kriptografi Bitcoin?

האם השבב הקוונטי של ווילו של גוגל מעמיד את הקריפטוגרפיה של ביטקוין בסיכון? 

Google の Willow Quantum チップはビットコインの暗号化を危険にさらすのか?

Vai Google Willow Quantum Chip apdraud Bitcoin kriptogrāfiju? 

Google's Willow Quantum Chip Bitcoin's Cryptography අවදානමට ලක් කරයිද? 

A e vë në rrezik çipi kuantik Willow i Google kriptografinë e Bitcoin? 

Оё Chip Quantum Willow Google криптографияи Bitcoin-ро зери хатар мегузорад? 

Google-ning Willow kvant chipi Bitcoin kriptografiyasini xavf ostiga qo'yadimi? 

Google DeepMind researchers have unveiled a groundbreaking framework called Boundless Socratic Learning (BSL), a paradigm shift in artificial intelligence aimed at enabling systems to self-improve through structured language-based interactions. This approach could mark a pivotal step toward the elusive goal of artificial superintelligence (ASI), where AI systems drive their own development with minimal human input. The Concept: Language Games and Self-Sustaining Learning At its core, Boundless Socratic Learning relies on "language games," structured interactions where AI agents create, evaluate, and refine their learning environments. These games not only serve as a source of data but also provide embedded feedback mechanisms, ensuring continuous adaptation and optimization. The process unfolds in three primary dimensions: Input/Output Learning: Agents iteratively improve their responses using in-system feedback, without relying on external datasets.


Game Selection: The ability to choose or even design which "language games" to engage in further broadens the scope of learning.


Code Self-Modification: A theoretical but tantalizing possibility where agents might refine their own programming. The self-improvement potential here is limited primarily by compute resources and time, bypassing traditional constraints of data availability or human intervention. Why It Matters: A Blueprint for Autonomous AI Development The introduction of BSL addresses a long-standing challenge in AI development—how to extend learning and adaptability beyond the initial training phase. By enabling recursive learning processes within closed systems, DeepMind outlines a future where AI models can generate their own data, design their own tasks, and evaluate their performance without external input. This approach aligns with the broader ambition across AI labs to develop systems capable of autonomous self-training, potentially reducing the cost and labor associated with human-curated datasets. Moreover, the implications extend beyond efficiency. As AI systems start defining their own learning trajectories, they could uncover insights and strategies unforeseen by human designers. Challenges: Aligning Goals and Ensuring Safety While the framework offers immense promise, it raises critical questions about safety and alignment. Recursive self-improvement systems must remain aligned with human values and objectives. The research highlights two significant hurdles: Feedback Alignment: Ensuring that internal feedback mechanisms reflect the observer’s intended goals. Diversity and Coverage: Avoiding the collapse of generative diversity, where systems might overfit or narrow their learning scope. Maintaining alignment in a system that evolves autonomously is a non-trivial challenge. As the researchers note, "Feedback is what gives direction to learning; without it, the process is merely one of self-modification."​ Potential Applications The Boundless Socratic Learning framework could revolutionize fields requiring iterative problem-solving and creativity, such as: Mathematics: AI agents proving theorems or generating proofs for unsolved problems. Science: Designing experiments and hypotheses in closed-loop systems. Education: Personalized learning systems that adapt to and predict student needs over time. A practical example cited involves an AI generating and verifying mathematical proofs in a closed system, steadily building its capabilities until it achieves a major breakthrough​. Conclusion: Toward Open-Ended Intelligence The promise of Boundless Socratic Learning lies in its ability to catalyze a shift from human-supervised AI to systems that evolve and improve autonomously. While significant challenges remain, the introduction of this framework represents a step toward the long-term goal of open-ended intelligence, where AI is not just a tool but a partner in discovery. For those intrigued by the details, the full research paper can be accessed here. Google DeepMind researchers have unveiled a groundbreaking framework called Boundless Socratic Learning (BSL) , a paradigm shift in artificial intelligence aimed at enabling systems to self-improve through structured language-based interactions. This approach could mark a pivotal step toward the elusive goal of artificial superintelligence (ASI), where AI systems drive their own development with minimal human input. Boundless Socratic Learning (BSL) The Concept: Language Games and Self-Sustaining Learning At its core, Boundless Socratic Learning relies on "language games," structured interactions where AI agents create, evaluate, and refine their learning environments. These games not only serve as a source of data but also provide embedded feedback mechanisms, ensuring continuous adaptation and optimization. The process unfolds in three primary dimensions: Input/Output Learning: Agents iteratively improve their responses using in-system feedback, without relying on external datasets. Game Selection: The ability to choose or even design which "language games" to engage in further broadens the scope of learning. Code Self-Modification: A theoretical but tantalizing possibility where agents might refine their own programming. Input/Output Learning: Agents iteratively improve their responses using in-system feedback, without relying on external datasets. Input/Output Learning : Agents iteratively improve their responses using in-system feedback, without relying on external datasets. Input/Output Learning Game Selection: The ability to choose or even design which "language games" to engage in further broadens the scope of learning. Game Selection : The ability to choose or even design which "language games" to engage in further broadens the scope of learning. Game Selection Code Self-Modification: A theoretical but tantalizing possibility where agents might refine their own programming. Code Self-Modification : A theoretical but tantalizing possibility where agents might refine their own programming. Code Self-Modification The self-improvement potential here is limited primarily by compute resources and time, bypassing traditional constraints of data availability or human intervention. Why It Matters: A Blueprint for Autonomous AI Development The introduction of BSL addresses a long-standing challenge in AI development—how to extend learning and adaptability beyond the initial training phase. By enabling recursive learning processes within closed systems, DeepMind outlines a future where AI models can generate their own data, design their own tasks, and evaluate their performance without external input. This approach aligns with the broader ambition across AI labs to develop systems capable of autonomous self-training , potentially reducing the cost and labor associated with human-curated datasets. Moreover, the implications extend beyond efficiency. As AI systems start defining their own learning trajectories, they could uncover insights and strategies unforeseen by human designers. autonomous self-training Challenges: Aligning Goals and Ensuring Safety While the framework offers immense promise, it raises critical questions about safety and alignment. Recursive self-improvement systems must remain aligned with human values and objectives. The research highlights two significant hurdles: Feedback Alignment: Ensuring that internal feedback mechanisms reflect the observer’s intended goals. Feedback Alignment : Ensuring that internal feedback mechanisms reflect the observer’s intended goals. Feedback Alignment Diversity and Coverage: Avoiding the collapse of generative diversity, where systems might overfit or narrow their learning scope. Diversity and Coverage : Avoiding the collapse of generative diversity, where systems might overfit or narrow their learning scope. Diversity and Coverage Maintaining alignment in a system that evolves autonomously is a non-trivial challenge. As the researchers note, "Feedback is what gives direction to learning; without it, the process is merely one of self-modification."​ Potential Applications The Boundless Socratic Learning framework could revolutionize fields requiring iterative problem-solving and creativity, such as: Mathematics: AI agents proving theorems or generating proofs for unsolved problems. Mathematics : AI agents proving theorems or generating proofs for unsolved problems. Mathematics Science: Designing experiments and hypotheses in closed-loop systems. Science : Designing experiments and hypotheses in closed-loop systems. Science Education: Personalized learning systems that adapt to and predict student needs over time. Education : Personalized learning systems that adapt to and predict student needs over time. Education A practical example cited involves an AI generating and verifying mathematical proofs in a closed system, steadily building its capabilities until it achieves a major breakthrough​. Conclusion: Toward Open-Ended Intelligence The promise of Boundless Socratic Learning lies in its ability to catalyze a shift from human-supervised AI to systems that evolve and improve autonomously. While significant challenges remain, the introduction of this framework represents a step toward the long-term goal of open-ended intelligence, where AI is not just a tool but a partner in discovery. For those intrigued by the details, the full research paper can be accessed here . here

Boundless Socratic Learning: Google DeepMind's Vision for AI That Learns Without Limits

Are Google Pixel Phones Spying on You? 

We’ve all seen significant changes in user acquisition (UA) advertising in the last few years. But we still see that for channels like Facebook, TikTok, etc. “Creative is king.” Despite this, many teams still face challenges with systematically working processes with creatives, struggling to catch users’ attention and win the auction. Based on my experience in Scentbird, Praktika, and Playrix, the performance of top creatives may 5 times differ from low creatives’ performance, and “good” creatives live longer in rotation and burn out slower. Let's discuss how we can think of creatives based on the DV360 example. Frequency is one of the most critical factors for campaign success. For example, Facebook says ~2 - 2.5 times per week is optimal; higher numbers won’t be adequate and only add extra cost. Others say that 1.8 - 4 frequency is okayish, and a higher frequency is ineffective. But can we use this approach in other channels? Can we use it as a “rule of thumb” before analyzing? Our content and creatives may meet potential users in different mindsets and environments. They may scroll through their Facebook feed in the middle of the working day or on the phone while moving from one place to another. At the same time, our users may watch YouTube on their laptops while cooking, cleaning the house, or before sleep. DV360 provides many more formats, such as CTV,out-of-home, in-game interstitials, small banners, etc., and all of these are genuinely different contexts. Considering this rich inventory, can we even think of creatives for all these placements similarly? The most challenging thing for the marketer here is to see DV360 not line one channel but like a constellation of channels in one interface. What is DV360 DV360, or Display & Video 360, is a comprehensive advertising platform developed by Google. It enables advertisers and marketers to plan, manage, and optimize their display, video, and programmatic campaigns across various online channels. DV360 offers tools for audience targeting, creative management, and real-time bidding to help businesses reach their desired audiences effectively through digital advertising. Some may say that it’s just another way to buy YouTube ads, but it’s much more than that. The main feature of DV360 is the flexible frequency setting on each level of an advertising campaign, and we won’t find it in Google UAC. But besides that, there are a vast number of different inventories, like banners, video, native banners and video, YouTube, CTV, and out-of-door. Let’s discuss one at a time. Banners There are an excessive number of available banner formats. How can we determine which to use and which to skip? First, you can consider the most popular formats. And then run a test flight. After this flight, you can create a report where Google will show you particular formats your users may see depending on where your target audience can be found and which devices they have. Then, you’ll be able to score the formats by potential reach and make an informed decision about which formats you should remake (not resize) your creatives. The more appropriate sizes you’ll cover, the more “quality reach” you’ll have. What do I mean by “quality reach”? This won’t be a surprise if you see the most potential reach for small banners like 728х90. This banner is relatively small and can appear almost on any resource. This is both an advantage and a disadvantage of such formats. The advantage is that using this format is the way to reach your users when there is no opportunity to reach them with any other ads. This is the case, especially when reach is important or a channel KPI. The disadvantage is that if you put it along with the other banners, it’ll take the most impressions (and therefore the budget). Since it has relatively low performance compared to other formats, your campaign may have low performance at first glance. So, how do you have a foot in both camps? The solution is to place small creatives to separate Line Items. Here, Google DV360's remarkable ability will help us. You can set low frequency and low budget, particularly for these types of banners. For example, you set frequency 4 for the whole campaign but limit frequency setting to this particular Line Item to 1. (The lower the setting in the campaign hierarchy always overrides the higher). Thus, if your users were captured in several formats, they’ll see a thin banner 1 time, 3 other times, and other more prominent banners. But if your user only appears in this particular format, you’ll reach them at least once. This workaround works excellently for Google DV360, unlike other DSPs or Google Ads Campaigns, where it’s more challenging or even impossible to separate creatives this way. Also, it may be helpful to make these thin banners animated to capture the attention. Video There are a lot of placemats available for video on Google DV360. It has Video, Native Video, YouTube, YouTube CTV, or even Out of Home. There is a trick here. When you launch a video on Facebook, even if it will run on different placements, it’s still in a single Facebook context, either mobile or desktop. This is not the case for DV360. For example, when you run Native Video in the game, advertising another game, the Native Video takes all available space on the screen, capturing undivided user attention. Also, there is a button that the user may click on if they are interested. Imagine you run the same video on CTV. The user is watching TV and suddenly sees a video from the game: absolutely another context, no device at hand, no button to click. Even if they are interested, do they have to remember the brand somehow, find the phone, and find the app in the store? Or what? No doubt, many teams are struggling to test the CTV because other creatives are failing, and producing different creative concepts takes effort, time, and money. So, how do we think of video creative for Google DV360? Let’s proceed with the game ad example. First of all, we should consider the context. What is the user doing when they see our ads? If you think about the Native Video format, you can group the game publishers in separate Line Items, and you know that the user is playing; you can provide a gameplay video or even a playable ad so that the experience will feel seamless. If you think about the YouTube or CTV format, showcasing the brand and using a game story is better. For example, your game characters do something funny or exciting, or there is a mystery there, you name it. Thus, the user will be impressed with another story and may remember your app. Then, you can separate the users who saw one part of your game story on YouTube and show them another part of it… the whole storytelling. Yep, Google DV360 supports even that. Also, imagine that if the user is impressed after seeing your ads during movie night on CTV, they still have to pause the movie (which is only sometimes nice or possible), find their phone, etc. So? You may want to help them remember your brand better by increasing the frequency! Yes, “normal” frequency 2-3-4 is not the case here. You can show your ad significantly more times! Will it work, and will it be profitable? The answers are different for different businesses and products. In such situations, you can perform a test. You can divide your audience into groups, set different frequencies for each group, and determine the most profitable comparing costs to projected LTV. Yes, it’s quite an expensive study that requires a decent investment, both financial and FTE, from the analytics team. The potential outcome is that you may unlock a great scalable channel. The context is much more important when you test the Out-of-Home format when you should consider not only the user context from his activity perspective but also the surroundings - district, buildings, events, time of the year, and much more. And here, the frequency may be even greater. Final words Google DV360 is a very controversial channel for media buyers. Besides Google, it charges by impressions and not by clicks, so guys who ran Google Ads Campaigns for a while initially feel quite uncomfortable. Media buyers who run Facebook feel uncomfortable with the interface. They call it a DSP but with YouTube and CTV… What is it finally? The entire campaign structure is so unusual that you’ll need at least a month to wrap your head around it. And after all, there are different creative strategies and contexts. If you are in a mature company with a solid product-market fit and resources and looking for your successive big win, maybe it’s an option. At least give it a chance. We’ve all seen significant changes in user acquisition (UA) advertising in the last few years. But we still see that for channels like Facebook, TikTok, etc. “ Creative is king .” Despite this, many teams still face challenges with systematically working processes with creatives, struggling to catch users’ attention and win the auction. Creative is king Based on my experience in Scentbird, Praktika, and Playrix, the performance of top creatives may 5 times differ from low creatives’ performance, and “good” creatives live longer in rotation and burn out slower. Let's discuss how we can think of creatives based on the DV360 example. Frequency is one of the most critical factors for campaign success. For example, Facebook says ~2 - 2.5 times per week is optimal; higher numbers won’t be adequate and only add extra cost. Others say that 1.8 - 4 frequency is okayish, and a higher frequency is ineffective. Facebook Facebook Others say Others say But can we use this approach in other channels? Can we use it as a “rule of thumb” before analyzing? Our content and creatives may meet potential users in different mindsets and environments. They may scroll through their Facebook feed in the middle of the working day or on the phone while moving from one place to another. At the same time, our users may watch YouTube on their laptops while cooking, cleaning the house, or before sleep. DV360 provides many more formats, such as CTV,out-of-home, in-game interstitials, small banners, etc., and all of these are genuinely different contexts. Considering this rich inventory, can we even think of creatives for all these placements similarly? The most challenging thing for the marketer here is to see DV360 not line one channel but like a constellation of channels in one interface. What is DV360 DV360, or Display & Video 360, is a comprehensive advertising platform developed by Google. It enables advertisers and marketers to plan, manage, and optimize their display, video, and programmatic campaigns across various online channels. DV360 offers tools for audience targeting, creative management, and real-time bidding to help businesses reach their desired audiences effectively through digital advertising. Some may say that it’s just another way to buy YouTube ads, but it’s much more than that. The main feature of DV360 is the flexible frequency setting on each level of an advertising campaign, and we won’t find it in Google UAC. But besides that, there are a vast number of different inventories, like banners, video, native banners and video, YouTube, CTV, and out-of-door. Let’s discuss one at a time. Banners There are an excessive number of available banner formats. How can we determine which to use and which to skip? First, you can consider the most popular formats. And then run a test flight. After this flight, you can create a report where Google will show you particular formats your users may see depending on where your target audience can be found and which devices they have. Then, you’ll be able to score the formats by potential reach and make an informed decision about which formats you should remake (not resize) your creatives. The more appropriate sizes you’ll cover, the more “quality reach” you’ll have. What do I mean by “quality reach”? This won’t be a surprise if you see the most potential reach for small banners like 728х90. This banner is relatively small and can appear almost on any resource. This is both an advantage and a disadvantage of such formats. The advantage is that using this format is the way to reach your users when there is no opportunity to reach them with any other ads. This is the case, especially when reach is important or a channel KPI. The disadvantage is that if you put it along with the other banners, it’ll take the most impressions (and therefore the budget). Since it has relatively low performance compared to other formats, your campaign may have low performance at first glance. So, how do you have a foot in both camps? The solution is to place small creatives to separate Line Items. Here, Google DV360's remarkable ability will help us. You can set low frequency and low budget, particularly for these types of banners. For example, you set frequency 4 for the whole campaign but limit frequency setting to this particular Line Item to 1. (The lower the setting in the campaign hierarchy always overrides the higher). Thus, if your users were captured in several formats, they’ll see a thin banner 1 time, 3 other times, and other more prominent banners. But if your user only appears in this particular format, you’ll reach them at least once. This workaround works excellently for Google DV360, unlike other DSPs or Google Ads Campaigns, where it’s more challenging or even impossible to separate creatives this way. Also, it may be helpful to make these thin banners animated to capture the attention. Video There are a lot of placemats available for video on Google DV360. It has Video, Native Video, YouTube, YouTube CTV, or even Out of Home. There is a trick here. When you launch a video on Facebook, even if it will run on different placements, it’s still in a single Facebook context, either mobile or desktop. This is not the case for DV360. For example, when you run Native Video in the game, advertising another game, the Native Video takes all available space on the screen, capturing undivided user attention. Also, there is a button that the user may click on if they are interested. Imagine you run the same video on CTV. The user is watching TV and suddenly sees a video from the game: absolutely another context, no device at hand, no button to click. Even if they are interested, do they have to remember the brand somehow, find the phone, and find the app in the store? Or what? No doubt, many teams are struggling to test the CTV because other creatives are failing, and producing different creative concepts takes effort, time, and money. So, how do we think of video creative for Google DV360? Let’s proceed with the game ad example. First of all, we should consider the context. What is the user doing when they see our ads? If you think about the Native Video format, you can group the game publishers in separate Line Items, and you know that the user is playing; you can provide a gameplay video or even a playable ad so that the experience will feel seamless. If you think about the YouTube or CTV format, showcasing the brand and using a game story is better. For example, your game characters do something funny or exciting, or there is a mystery there, you name it. Thus, the user will be impressed with another story and may remember your app. Then, you can separate the users who saw one part of your game story on YouTube and show them another part of it… the whole storytelling. Yep, Google DV360 supports even that. Also, imagine that if the user is impressed after seeing your ads during movie night on CTV, they still have to pause the movie (which is only sometimes nice or possible), find their phone, etc. So? You may want to help them remember your brand better by increasing the frequency! Yes, “normal” frequency 2-3-4 is not the case here. You can show your ad significantly more times! Will it work, and will it be profitable? The answers are different for different businesses and products. In such situations, you can perform a test. You can divide your audience into groups, set different frequencies for each group, and determine the most profitable comparing costs to projected LTV. Yes, it’s quite an expensive study that requires a decent investment, both financial and FTE, from the analytics team. The potential outcome is that you may unlock a great scalable channel. The context is much more important when you test the Out-of-Home format when you should consider not only the user context from his activity perspective but also the surroundings - district, buildings, events, time of the year, and much more. And here, the frequency may be even greater. Final words Google DV360 is a very controversial channel for media buyers. Besides Google, it charges by impressions and not by clicks, so guys who ran Google Ads Campaigns for a while initially feel quite uncomfortable. Media buyers who run Facebook feel uncomfortable with the interface. They call it a DSP but with YouTube and CTV… What is it finally? The entire campaign structure is so unusual that you’ll need at least a month to wrap your head around it. And after all, there are different creative strategies and contexts. If you are in a mature company with a solid product-market fit and resources and looking for your successive big win, maybe it’s an option. At least give it a chance.

Optimizing User Acquisition with Google DV360: Creative Strategies for Success

The HackerNoon Newsletter: How Google Influences Public Opinion (10/12/2024)

There’s no questioning Google’s dominance in the internet. It’s such a massive and familiar presence that the brand’s name has become the word people use to refer to searching for anything online. While this unofficial standardization has plenty of upsides, it also raises questions about Google’s influence on public opinion. The Anchoring Effect One of the biggest ways Google can influence people’s opinions is through anchoring bias. The anchoring effect is a phenomenon where people are more likely to focus too much on the first piece of information they receive. In search engine terms, users tend to believe the top of the results page more than anything coming afterward. According to one study, 28.5% of people click the first link on Google’s search engine results page (SERP). Fewer than 10% open anything below the third. With so many rarely looking beyond the first few results, it’s easy to see how anchoring bias can take hold. While searchers focus on the first SERP entry, this source is not necessarily the most reliable. Publications can employ practices to rank higher in SERPs — a practice known as search engine optimization (SEO). Consequently, the information people see first — and therefore believe the most — does not always achieve its position by being accurate. Assumptions of Trustworthiness Similarly, Google can (perhaps unintentionally) sway public opinion because many take its results at face value. Roughly 82% of American internet users say they generally trust the search results, and 21.4% say they “almost always” do. Despite such a high degree of trust, Google results do not always reflect the truth. Consider how the recent AI Overviews feature has recommended eating rocks or offered incorrect cooking temperatures. Because algorithms cannot distinguish between fact and fiction but instead look for commonalities and trends, they can promote incorrect or satirical answers. Such a lack of reliability is not necessarily Google’s fault, nor is it intentional. Rather, it comes from AI being prone to errors from biased data or users not following up to see where information in a featured snippet or AI Overview comes from. Personalized Results Google’s personalization features can also influence users’ opinions. Many searches tailor SERPs to individuals and situations, especially when real-time factors like someone’s location would affect their desired results. This function is meant to ensure SERPs are as relevant as possible, but it risks creating echo chambers. After a user clicks enough links from a certain site or searches enough slanted questions, their results for future searches will adapt to their behavior. As a result, they may see an increase in information aligning with the views of things they’ve interacted with previously. That, in turn, creates a cycle of reinforcing a narrow point of view while leaving out diverse perspectives. Interestingly, personalization applies even when people use Incognito Mode. While it does remove some tracking features, it does not affect Google’s data collection, so these echo chambers can be difficult to avoid. What Google’s Influence on Public Opinion Means While it may not mean to, Google can influence public opinion through its search results. That has several implications for enterprises and consumers. What It Means for Companies The sheer power of Google’s SERPs poses a promising opportunity for businesses. It’s why they invest up to $5,000 in SEO services on average each month. Reaching the top of a SERP automatically gives a website a huge amount of trust from users, thanks to the anchoring effect and general confidence in Google. It also means organizations should be careful about the information they put online. Anything ranking high enough could sway public opinion, which gives the authors an immense responsibility to remain unbiased and accurate. Trying to game the system to drive traffic while offering unreliable information could result in significant backlash once people discover the breach of trust. Conversely, those providing accurate details who reach top SERP spots could greatly improve their public image. As Google tries to refine its results to keep things unbiased and reliable, it’ll likely reward greater attention to detail. What It Means for Users Google’s sway over people’s beliefs also deserves attention from the average internet user. It’s important to be mindful of how things like anchoring bias and personalized results can make things seem more trustworthy than they may be. Once users recognize how their use of Google can impact what they see, they can take actions to prevent missteps. One of the most crucial things to keep in mind is Google results are not infallible. Always check multiple sources to get the full picture, and be sure to scroll beyond the first few results, too. Reading an entire article in context and seeing where their information comes from instead of taking headlines at face value is likewise critical. Thankfully, the public is starting to wake up to the reality of biased or unreliable search results. A HackerNoon poll found 58% of Google users dislike AI Overviews, with 29% going so far as to say they hate it. It’s also worth noting 17% like these results but believe they can be improved. This implies people understand AI answers are not always accurate. Google Holds Considerable Influence on Public Opinion Google’s impact on public opinion is undeniable. Whether this influence is good or bad may vary, and it may not always be intentional on Google’s part. However, it is worth recognizing for brands and users alike. There’s no questioning Google’s dominance in the internet. It’s such a massive and familiar presence that the brand’s name has become the word people use to refer to searching for anything online. While this unofficial standardization has plenty of upsides, it also raises questions about Google’s influence on public opinion. There’s no questioning Google’s dominance in the internet. It’s such a massive and familiar presence that the brand’s name has become the word people use to refer to searching for anything online. While this unofficial standardization has plenty of upsides, it also raises questions about Google’s influence on public opinion. The Anchoring Effect The Anchoring Effect One of the biggest ways Google can influence people’s opinions is through anchoring bias. The anchoring effect is a phenomenon where people are more likely to focus too much on the first piece of information they receive. In search engine terms, users tend to believe the top of the results page more than anything coming afterward. According to one study, 28.5% of people click the first link on Google’s search engine results page (SERP). Fewer than 10% open anything below the third. With so many rarely looking beyond the first few results, it’s easy to see how anchoring bias can take hold. 28.5% of people click 28.5% of people click While searchers focus on the first SERP entry, this source is not necessarily the most reliable. Publications can employ practices to rank higher in SERPs — a practice known as search engine optimization (SEO). Consequently, the information people see first — and therefore believe the most — does not always achieve its position by being accurate. Assumptions of Trustworthiness Similarly, Google can (perhaps unintentionally) sway public opinion because many take its results at face value. Roughly 82% of American internet users say they generally trust the search results, and 21.4% say they “almost always” do. 82% of American internet users 82% of American internet users Despite such a high degree of trust, Google results do not always reflect the truth. Consider how the recent AI Overviews feature has recommended eating rocks or offered incorrect cooking temperatures. Because algorithms cannot distinguish between fact and fiction but instead look for commonalities and trends, they can promote incorrect or satirical answers. has recommended eating rocks has recommended eating rocks Such a lack of reliability is not necessarily Google’s fault, nor is it intentional. Rather, it comes from AI being prone to errors from biased data or users not following up to see where information in a featured snippet or AI Overview comes from. Personalized Results Google’s personalization features can also influence users’ opinions. Many searches tailor SERPs to individuals and situations, especially when real-time factors like someone’s location would affect their desired results. This function is meant to ensure SERPs are as relevant as possible, but it risks creating echo chambers. After a user clicks enough links from a certain site or searches enough slanted questions, their results for future searches will adapt to their behavior. As a result, they may see an increase in information aligning with the views of things they’ve interacted with previously. That, in turn, creates a cycle of reinforcing a narrow point of view while leaving out diverse perspectives. Interestingly, personalization applies even when people use Incognito Mode. While it does remove some tracking features, it does not affect Google’s data collection , so these echo chambers can be difficult to avoid. does not affect Google’s data collection does not affect Google’s data collection What Google’s Influence on Public Opinion Means While it may not mean to, Google can influence public opinion through its search results. That has several implications for enterprises and consumers. What It Means for Companies The sheer power of Google’s SERPs poses a promising opportunity for businesses. It’s why they invest up to $5,000 in SEO services on average each month. Reaching the top of a SERP automatically gives a website a huge amount of trust from users, thanks to the anchoring effect and general confidence in Google. up to $5,000 in SEO services up to $5,000 in SEO services It also means organizations should be careful about the information they put online. Anything ranking high enough could sway public opinion, which gives the authors an immense responsibility to remain unbiased and accurate. Trying to game the system to drive traffic while offering unreliable information could result in significant backlash once people discover the breach of trust. Conversely, those providing accurate details who reach top SERP spots could greatly improve their public image. As Google tries to refine its results to keep things unbiased and reliable, it’ll likely reward greater attention to detail. What It Means for Users Google’s sway over people’s beliefs also deserves attention from the average internet user. It’s important to be mindful of how things like anchoring bias and personalized results can make things seem more trustworthy than they may be. Once users recognize how their use of Google can impact what they see, they can take actions to prevent missteps. One of the most crucial things to keep in mind is Google results are not infallible. Always check multiple sources to get the full picture, and be sure to scroll beyond the first few results, too. Reading an entire article in context and seeing where their information comes from instead of taking headlines at face value is likewise critical. Thankfully, the public is starting to wake up to the reality of biased or unreliable search results. A HackerNoon poll found 58% of Google users dislike AI Overviews, with 29% going so far as to say they hate it. It’s also worth noting 17% like these results but believe they can be improved. This implies people understand AI answers are not always accurate. 58% of Google users 58% of Google users Google Holds Considerable Influence on Public Opinion Google’s impact on public opinion is undeniable. Whether this influence is good or bad may vary, and it may not always be intentional on Google’s part. However, it is worth recognizing for brands and users alike.

How Google Influences Public Opinion

ጉግል በሕዝብ አስተያየት ላይ እንዴት ተጽዕኖ እንደሚያሳድር

Cómo influye Google en la opinión pública

איך גוגל משפיעה על דעת הקהל

गूगल जनमत को कैसे प्रभावित करता है

Hogyan befolyásolja a Google a közvéleményt

Google が世論に与える影響

Ndenge nini Google Ezali na bopusi likoló na makanisi ya bato nyonso

Ahoana ny fiantraikan'ny Google amin'ny hevitry ny besinimaro

गुगलले सार्वजनिक रायलाई कसरी प्रभाव पार्छ

Jak Google wpływa na opinię publiczną

Como o Google influencia a opinião pública

谷歌如何影响舆论

Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishmentsan average of $1.2 trillion every year. The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division. Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. twice as many employees now participating twice as many employees now participating Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. 90% of businesses using event tech 90% of businesses using event tech However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishments an average of $1.2 trillion every year. an average of $1.2 trillion an average of $1.2 trillion The Ethics of Rephrasing Employee Concerns The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. from understanding the true depth from understanding the true depth Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. 84% of HR executives 84% of HR executives When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. 71% of surveyed workers 71% of surveyed workers They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division.

Google

google.com

10K+ employees

Since 1998

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GOOGLE

EVERGREEN INDEX #871

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Google

google.com

10K+ employees

Since 1998

COMPANY RANKING

GOOGLE

EVERGREEN INDEX #871

RELATED COMPANIESRANK

STORIES ABOUT GOOGLE

Google WIKI

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