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Google just dropped a bombshell on the tech world by acquiring cloud security startup Wiz for a staggering $32 billion in cash. The deal marks one of the largest cybersecurity acquisitions ever, and it's raising eyebrows across the world.While Google is framing this as a security powerplay for its cloud division, I'm not convinced this massive bet will pay off. Here's why this might be Google's most expensive mistake. A price tag that defies gravity Let's put this in perspective. Google is paying $32 billion for a company founded just four years ago in 2020. Wiz was valued at $12 billion after raising $1 billion last spring. That means Google is paying nearly triple Wiz's last valuation, and approximately 10% of Alphabet's entire cash reserves ($95.7 billion) for this single acquisition. Even more telling? Google reportedly tried to buy Wiz about six months ago for $23 billion, but the deal fell through due to investor and antitrust concerns. Now they're back, paying an additional $9 billion. That's a 39% premium in half a year for essentially the same company. Someone at Google really, really wants Wiz. The multi-cloud conundrum Google claims Wiz will continue supporting Amazon Web Services, Microsoft Azure, and Oracle Cloud. Sounds good on paper, but ask yourself: is Google really motivated to make its expensive new security tool work flawlessly on competing platforms? The reality is that Google Cloud remains a distant third in the cloud race behind AWS and Azure. This acquisition seems like a desperate attempt to gain leverage rather than a coordinated security strategy. Google is essentially trying to become the security layer for its competitors' infrastructure. That's a precarious position that creates misaligned incentives from day one. Security sells, but who's buying? Wiz has undoubtedly built an impressive AI-driven security platform that helps organizations identify vulnerabilities across their cloud infrastructures. But is advanced cloud security enough to shift enterprise spending patterns or cloud provider preferences? Also, only 44% of middle-market firms have invested in cybersecurity defenses. Despite all the high-profile breaches and constant warnings, most organizations still view security as an unnecessary evil rather than a competitive advantage. Will enterprise customers suddenly pay premium prices for Google-owned Wiz when countless other security vendors exist? The antitrust shadow Despite the assertion that Trump's administration has created a "more deal-friendly antitrust environment," large tech acquisitions still face intense regulatory scrutiny. The Wiz deal will undoubtedly trigger reviews, potentially delaying integration and creating uncertainty among customers and employees. This regulatory scrutiny isn't just a temporary inconvenience. It could fundamentally alter how Google can leverage its new acquisition, potentially limiting the very synergies that justified the massive price tag in the first place. The talent retention challenge Cybersecurity experts are among the most sought-after professionals in tech. Wiz's founding team previously built and sold Adallom to Microsoft for $320 million in 2015. Now they've created a company that's selling for 100 times that amount just a decade later. These are exceptional entrepreneurs with proven track records. But will they stick around after the acquisition? Google's corporate culture and bureaucracy are drastically different from a fast-moving startup environment. Once the founders and key technical talent collect their acquisition payouts, what incentive do they have to stay? The history of tech acquisitions suggests many will leave to start new ventures, taking crucial institutional knowledge with them. The bottom line Google's $32 billion gamble on Wiz represents either remarkable foresight or an expensive miscalculation. While cybersecurity remains critically important, the price tag, integration challenges, and competitive dynamics all suggest this acquisition may not deliver the value Google expects. For a company that built its empire on organic growth and smaller, strategic acquisitions, this massive bet on Wiz stands out as an anomaly. Only time will tell if Google's cloud security gambit pays off or joins the long list of big tech's expensive mistakes.

Has Google Made a $32 Billion Cloud Security Blunder?

Google 32 milyard dollarlıq bulud təhlükəsizliyi səhvi etdi?

Направил ли е Google грешка в облачната сигурност за 32 милиарда долара?

Da li je Google napravio sigurnosnu grešku od 32 milijarde dolara u oblaku?

Hat Google einen Cloud-Sicherheitsfehler im Wert von 32 Milliarden Dollar begangen?

Η Google έκανε μια γκάφα 32 δισεκατομμυρίων δολαρίων στο Cloud Security;

¿Ha cometido Google un error de seguridad en la nube de 32 mil millones de dólares?

Google は 320 億ドルのクラウド セキュリティの失策を犯したのか?

Nanao fahadisoana $32 Miliara Cloud Security ve i Google?

के गुगलले ३२ अर्ब डलरको क्लाउड सुरक्षा गल्ती गरेको छ?

Czy Google popełniło błąd w zakresie bezpieczeństwa chmury wart 32 miliardy dolarów?

Je, Google Imetengeneza Blunder ya Usalama wa Wingu ya $32 Bilioni?

Google có phạm sai lầm về bảo mật đám mây trị giá 32 tỷ đô la không?

How can blockchain handle complex AI computations without losing its core promise of transparency? The Flare x Google Cloud Hackathon, held from March 7-9, 2025, at UC Berkeley’s California Memorial Stadium, tackled this question head-on. Over 460 developers and researchers from institutions like UC Berkeley, the University of Waterloo, and ETH Zurich participated, showcasing how blockchain and AI can integrate using verifiable off-chain computation. The Flare x Google Cloud Hackathon, held from March 7-9 in collaboration with Blockchain at Berkeley, demonstrated how verifiable AI can be seamlessly integrated with blockchain technology. Flare Blockchain at Berkeley Blockchain at Berkeley Addressing Blockchain’s Computational Limitations Blockchain excels at decentralized consensus but falters with the heavy data processing AI demands. The event introduced a solution: Google Cloud’s Confidential Space, built on Trusted Execution Environments (TEEs). TEEs process complex tasks off-chain in secure hardware enclaves, producing cryptographic attestations verified on Flare’s blockchain. This ensures computations remain tamper-proof. Flare’s built-in protocols, the Flare Time Series Oracle (FTSO) and Flare Data Connector (FDC), provided data integrity and provenance, critical for AI applications. Further, one of the key challenges in blockchain technology is achieving rich data computation while maintaining transparency and security. Traditional blockchains excel at decentralized consensus and deterministic execution but struggle with large-scale parallel processing, particularly for AI-intensive workloads. This hackathon sought to overcome that limitation by leveraging Google Cloud Confidential Space, a secure enclave built on Trusted Execution Environments (TEEs). Confidential Space Confidential Space Confidential Space ensures hardware-enforced confidentiality and generates cryptographic attestations, which can be verified on the Flare blockchain. This process guarantees that computations occur in a tamper-proof environment, preserving both data integrity and security. Flare’s enshrined data protocols, such as the Flare Time Series Oracle (FTSO) and the Flare Data Connector (FDC), played a crucial role in ensuring data provenance and integrity verification for AI applications. Flare Time Series Oracle Flare Time Series Oracle A Competitive and Innovative Hackathon With over 460 participants competing across four tracks, the hackathon produced verifiable AI solutions ranging from AI-powered decentralized finance (DeFi) applications (DeFAI) to consensus learning models. Every project utilized Confidential Space instances running on AMD’s Secure Encrypted Virtualization (SEV) platform with virtual Trusted Platform Module (vTPM) attestations. These attestations provided cryptographic proof that all AI computations were executed within an isolated, trusted environment. Rene Kolga, Product Manager at Google Cloud, expressed enthusiasm about the developers' engagement with the Confidential Space secure enclaves, stating: “We're thrilled to see developers using our Confidential Space secure enclaves to build trusted AI solutions. Projects like 2DeFi and the Flare AI Kit’s launch are exciting steps towards a future where AI and blockchain ensure data integrity and computational assurance.” 2DeFi: Bridging Traditional Finance and DeFi with AI One of the outstanding projects from the hackathon was 2DeFi, an AI-powered solution developed by University of Waterloo students Alex and Hitarth. The project won first place in the AI x DeFi (DeFAI) track and secured second place in the RAG Knowledge track. 2DeFi 2DeFi 2DeFi aimed to simplify the transition from traditional finance (TradFi) to decentralized finance (DeFi) by integrating Google Gemini AI. Users could upload screenshots of their Robinhood portfolios, which Gemini 2.0 then analyzed to assess risk tolerance. The AI-generated risk profile was mapped to an automated DeFi strategy, incorporating staking and liquidity provision on Flare. To streamline onboarding, 2DeFi also introduced embedded wallets, allowing users to create Google login-based wallets on Flare, removing the complexities of traditional Web3 user experiences. Flare AI Kit: Empowering Verifiable AI on Blockchain The hackathon also marked the official kickoff of Flare AI Kit, an open-source software development kit (SDK) designed for building verifiable AI agents on Flare. The kit leverages Google Cloud Confidential Space to provide developers with secure, attestable AI execution environments, ensuring AI safety, provenance, and verifiability. Flare AI Kit Flare AI Kit Hackathon participants demonstrated the potential of Flare AI Kit as a foundation framework for AI-powered blockchain applications. Moving forward, Flare AI Kit is expected to evolve into a production-ready framework, further enhancing functionality, usability, and integration options. The ultimate goal is to make Flare AI Kit available on the Google Cloud Marketplace, where enterprises and developers can access it as a turnkey solution. The Future of Verifiable AI on Blockchain The success of the Flare x Google Cloud Hackathon is just the beginning. As developers and researchers continue to push the boundaries of AI and blockchain convergence, projects like Flare AI Kit will play a crucial role in shaping the future of verifiable AI applications. By integrating Confidential Space TEEs, AI, and the Flare blockchain, Flare AI Kit is set to redefine how secure, verifiable AI applications are built, deployed, and trusted globally. Developers looking to get started can explore the Flare Developer Hub for in-depth documentation and resources. Flare Developer Hub Flare Developer Hub Don’t forget to like and share the story!

Can Blockchain and AI Finally Trust Each Other? Flare and Google Cloud’s Hackathon Says Yes

Blockchain እና AI በመጨረሻ እርስ በርስ መተማመን ይችላሉ? Flare እና Google Cloud's Hackathon አዎ ይላል።

Mogu li Blockchain i AI konačno vjerovati jedni drugima? Hackathon kompanije Flare i Google Cloud kaže da

Mohou si blockchain a AI konečně věřit? Flare a Hackathon Google Cloud říkají Ano

¿Pueden finalmente la blockchain y la IA confiar entre sí? El hackatón de Flare y Google Cloud afirma que sí.

آیا بلاک چین و هوش مصنوعی در نهایت می توانند به یکدیگر اعتماد کنند؟ Flare و Google Cloud's Hackathon بله می گوید

Կարո՞ղ են բլոկչեյնը և AI-ն վերջապես վստահել միմյանց: Flare-ը և Google Cloud's Hackathon-ն ասում է այո

ブロックチェーンと AI はついに信頼できるのか？Flare と Google Cloud のハッカソンで「イエス」と判定

Блокчейн мен AI бір-біріне сене ала ма? Flare және Google Cloud хакатоны «Иә» дейді

Czy Blockchain i AI w końcu mogą sobie zaufać? Hackathon Flare i Google Cloud mówi „tak”

Blockchain மற்றும் AI இறுதியாக ஒன்றையொன்று நம்ப முடியுமா? Flare மற்றும் Google Cloud இன் Hackathon ஆம் என்று கூறுகிறது.

ในที่สุด Blockchain และ AI จะสามารถไว้วางใจซึ่งกันและกันได้หรือไม่? Flare และ Hackathon ของ Google Cloud บอกว่าใช่

Чи можуть Blockchain і AI нарешті довіряти один одному? Хакатон від Flare і Google Cloud каже «так».

Google AI Studio Tutorial for Beginners

Apple Asks Court to Pause Proceedings So It Can Join Google in US Government Antitrust Case

Hello JavaScript Enthusiasts! The holiday season may be in full swing, but the JavaScript world isn’t taking a break. We’ve got some game-changing updates, tools, and releases that will supercharge your workflow, whether you're fine-tuning performance, diving into modern frameworks, or exploring new databases. Project IDX From Google Google has introduced Project IDX, which aims to eliminate the need for local environments—everything happens directly in your browser. Whether you’re starting a fresh project or importing an existing one, you can hit the ground running with support for popular frameworks like Angular, NextJS, and more. Highlights include: Gemini AI Assistant: Code faster and smarter with real-time AI-powered suggestions.
Live Previews & Emulators: Test your work directly within the browser, skipping the hassle of setting up external tools.
Effortless Collaboration: Share projects as easily as you would a Google Doc, making team development seamless.
Full Android Studio Integration: Build and preview native Android apps without ever leaving your browser.
Templates for Popular Technologies: Kickstart your project with templates for React, Svelte, and the Gemini API.
One-Click Deployment: Seamless deployment to Firebase or Cloud Run. Project IDX is a game-changer for simplifying development and fostering collaboration! Base UI 1.0 by Material UI Team The Material UI team has released Base UI 1.0, an unstyled React component library designed for ultimate flexibility. This new version is perfect for developers looking to craft bespoke designs without predefined styling constraints. Customization: Use Tailwind, CSS-in-JS, or plain CSS—it adapts to whatever fits your style. Accessibility-First: While unstyled, it still ensures components meet high accessibility standards. Base UI 1.0 gives you the freedom to build exactly what you want while maintaining a focus on clean and accessible design. Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: Vector.dev: Logs can be chaotic, but Vector.dev makes them manageable by converting unstructured logs into clean JSON logs. This tool simplifies filtering, searching, and dashboard creation, making it a must-have in production environments. Ghostty Terminal Emulator: Meet Ghostty, the sleek terminal emulator for macOS and Linux. With features like Kitty graphics, native tabs, and lightning-fast performance, Ghostty makes your terminal experience feel intuitive and responsive. DiceDB: DiceDB is an open-source caching database under AGPL 3.0 designed to store and retrieve data efficiently, making your apps faster and more scalable. Inertia 2.0: For Laravel fans, Inertia 2.0 makes integrating Laravel with frontend frameworks like React, Vue, or Svelte smoother than ever. With async requests, prefetching, and real-time polling, you can expect faster load times and a seamless navigation experience. Apollo Client 3.12: For those using GraphQL, Apollo Client 3.12 introduces data masking, ensuring that components only access the data they need. This boosts performance and reduces bugs, especially when working with TypeScript. Cline 3.0: Last but not least, Cline 3.0 is revolutionizing how we approach coding projects. It’s fully autonomous, handling command approvals, tool management, and even browser navigation—no input required. It’s integrated with Visual Studio Code and completely free. And that wraps up this issue of This Week In JavaScript! Thank you for tuning in! As the holiday season approaches, we hope you get a chance to unwind, maybe work on some personal projects, and enjoy some festive downtime. Be sure to share this newsletter with your fellow developers and subscribe to get the latest updates directly in your inbox. Until next time, happy coding, and happy holidays! Hello JavaScript Enthusiasts! The holiday season may be in full swing, but the JavaScript world isn’t taking a break. We’ve got some game-changing updates, tools, and releases that will supercharge your workflow, whether you're fine-tuning performance, diving into modern frameworks, or exploring new databases. Project IDX From Google Project IDX From Google Google has introduced Project IDX , which aims to eliminate the need for local environments—everything happens directly in your browser. Project IDX Whether you’re starting a fresh project or importing an existing one, you can hit the ground running with support for popular frameworks like Angular, NextJS, and more. Highlights include: Gemini AI Assistant: Code faster and smarter with real-time AI-powered suggestions. Live Previews & Emulators: Test your work directly within the browser, skipping the hassle of setting up external tools. Effortless Collaboration: Share projects as easily as you would a Google Doc, making team development seamless. Full Android Studio Integration: Build and preview native Android apps without ever leaving your browser. Templates for Popular Technologies: Kickstart your project with templates for React, Svelte, and the Gemini API. One-Click Deployment: Seamless deployment to Firebase or Cloud Run. Gemini AI Assistant: Code faster and smarter with real-time AI-powered suggestions. Gemini AI Assistant: Live Previews & Emulators: Test your work directly within the browser, skipping the hassle of setting up external tools. Live Previews & Emulators: Effortless Collaboration: Share projects as easily as you would a Google Doc, making team development seamless. Effortless Collaboration: Full Android Studio Integration: Build and preview native Android apps without ever leaving your browser. Full Android Studio Integration: Templates for Popular Technologies: Kickstart your project with templates for React, Svelte, and the Gemini API. Templates for Popular Technologies: One-Click Deployment: Seamless deployment to Firebase or Cloud Run. One-Click Deployment: Project IDX is a game-changer for simplifying development and fostering collaboration! Base UI 1.0 by Material UI Team Base UI 1.0 by Material UI Team The Material UI team has released Base UI 1.0 , an unstyled React component library designed for ultimate flexibility. This new version is perfect for developers looking to craft bespoke designs without predefined styling constraints. Material UI Base UI 1.0 React component library Customization: Use Tailwind, CSS-in-JS, or plain CSS—it adapts to whatever fits your style. Customization: Use Tailwind, CSS-in-JS, or plain CSS—it adapts to whatever fits your style. Customization: Accessibility-First: While unstyled, it still ensures components meet high accessibility standards. Accessibility-First: While unstyled, it still ensures components meet high accessibility standards. Accessibility-First: Base UI 1.0 gives you the freedom to build exactly what you want while maintaining a focus on clean and accessible design. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About Now, let’s take a quick look at the latest tools and updates that can boost your workflow: Vector.dev: Logs can be chaotic, but Vector.dev makes them manageable by converting unstructured logs into clean JSON logs. This tool simplifies filtering, searching, and dashboard creation, making it a must-have in production environments. Vector.dev: Logs can be chaotic, but Vector.dev makes them manageable by converting unstructured logs into clean JSON logs. This tool simplifies filtering, searching, and dashboard creation, making it a must-have in production environments. Vector.dev: Ghostty Terminal Emulator: Meet Ghostty, the sleek terminal emulator for macOS and Linux. With features like Kitty graphics, native tabs, and lightning-fast performance, Ghostty makes your terminal experience feel intuitive and responsive. Ghostty Terminal Emulator: Meet Ghostty, the sleek terminal emulator for macOS and Linux. With features like Kitty graphics, native tabs, and lightning-fast performance, Ghostty makes your terminal experience feel intuitive and responsive. Ghostty Terminal Emulator: DiceDB: DiceDB is an open-source caching database under AGPL 3.0 designed to store and retrieve data efficiently, making your apps faster and more scalable. DiceDB: DiceDB is an open-source caching database under AGPL 3.0 designed to store and retrieve data efficiently, making your apps faster and more scalable. DiceDB: Inertia 2.0: For Laravel fans, Inertia 2.0 makes integrating Laravel with frontend frameworks like React, Vue, or Svelte smoother than ever. With async requests, prefetching, and real-time polling, you can expect faster load times and a seamless navigation experience. Inertia 2.0: For Laravel fans, Inertia 2.0 makes integrating Laravel with frontend frameworks like React, Vue, or Svelte smoother than ever. With async requests, prefetching, and real-time polling, you can expect faster load times and a seamless navigation experience. Inertia 2.0: Apollo Client 3.12: For those using GraphQL, Apollo Client 3.12 introduces data masking, ensuring that components only access the data they need. This boosts performance and reduces bugs, especially when working with TypeScript. Apollo Client 3.12: For those using GraphQL, Apollo Client 3.12 introduces data masking, ensuring that components only access the data they need. This boosts performance and reduces bugs, especially when working with TypeScript. Apollo Client 3.12: Cline 3.0: Last but not least, Cline 3.0 is revolutionizing how we approach coding projects. It’s fully autonomous, handling command approvals, tool management, and even browser navigation—no input required. It’s integrated with Visual Studio Code and completely free. Cline 3.0: Last but not least, Cline 3.0 is revolutionizing how we approach coding projects. It’s fully autonomous, handling command approvals, tool management, and even browser navigation—no input required. It’s integrated with Visual Studio Code and completely free. Cline 3.0: Last but not least, Cline 3.0 is revolutionizing how we approach coding projects. It’s fully autonomous, handling command approvals, tool management, and even browser navigation—no input required. It’s integrated with Visual Studio Code and completely free. Cline 3.0: And that wraps up this issue of This Week In JavaScript! Thank you for tuning in! As the holiday season approaches, we hope you get a chance to unwind, maybe work on some personal projects, and enjoy some festive downtime. Be sure to share this newsletter with your fellow developers and subscribe to get the latest updates directly in your inbox. Until next time, happy coding, and happy holidays!

Google Project IDX, Material UI's New React Component Library, And More - This Week in JS

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

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