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AI visibility ROI can't be measured in clicks because clicks were never part of the design. Here's the framework shift before the spreadsheet catches up. The post The ROI Problem With AI Traffic Nobody Is Measuring Correctly appeared first on Search Engine Journal. View the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. Google’s Pixel phones have spent the last few years becoming the default recommendation for people who want a straightforward Android experience without dealing with heavy software skins or overloaded features. The Google Pixel 10 Pro continues that approach, though its original $1,219 price made it harder to justify against competing flagship phones. Now, Amazon has dropped the unlocked 512GB model to $969 (its lowest price yet, according to online price trackers), and that makes it easier to appreciate what Google actually does well here. Google Pixel 10 Pro (Obsidian) 512GB nlocked Android smartphone $969.00 at Amazon $1,219.00 Save $250.00 Get Deal Get Deal $969.00 at Amazon $1,219.00 Save $250.00 The hardware itself is familiar in a good way—the flat display makes it easier to grip and use one-handed, the matte glass back does a better job resisting fingerprints than many glossy competitors, and the overall build feels solid without becoming bulky. Google also continues to offer one of the better long-term Android support policies, so you aren't buying something that will feel outdated in two years. Performance is solid for day-to-day use, though the Tensor G5 chip still trails behind Snapdragon-powered competitors in heavier gaming and more demanding apps, notes this PCMag review. That said, the biggest reason to buy this phone is still the camera system. Google continues to deliver photos that look natural without over-sharpening faces or cranking up colors, and its triple-camera setup handles low-light shots especially well. The 6.7-inch OLED display of the Pixel 10 Pro also gets brighter than last year’s model, making it easier to use outdoors, and Qi2 charging plus Google’s new PixelSnap magnetic system make wireless charging less annoying in daily use—snapping the phone onto a desk stand or car mount feels simple in the same way Apple’s MagSafe accessories do, says our writer in her review of the product. Battery life is good enough for a full day with regular use, but frequent video recording, navigation, or extended camera sessions can drain it faster. Also, one thing to keep in mind is the switch to eSIM-only support. For people who travel often or frequently change carriers, losing the option for a physical SIM card may feel limiting. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods Pro 3 Noise Cancelling Heart Rate Wireless Earbuds — $199.99 (List Price $249.00) Apple Watch Series 11 [GPS 46mm] Smartwatch with Jet Black Aluminum Case with Black Sport Band - M/L. Sleep Score, Fitness Tracker, Health Monitoring, Always-On Display, Water Resistant — $329.00 (List Price $429.00) Fitbit Versa 4 Fitness Smartwatch (Black) — $149.95 (List Price $199.95) Apple iPad 11" A16 128GB Wi-Fi Tablet (Silver, 2025) — $299.00 (List Price $349.00) Anker 20,000mAh Portable Power Bank With Built-in USB-C Cable — $49.99 (List Price $69.99) Deals are selected by our commerce team View the full article

AI ads aren't mysterious once you know the rules. Here's how to access inventory, set expectations, and build budget that actually works. The post How To Leverage AI Ad Placements And Are They Worth It? – Ask A PPC appeared first on Search Engine Journal. View the full article

Many companies expand internationally by duplicating their U.S. website, translating the language, and keeping the same architecture, navigation, and content structure across markets. Then performance drops. International versions may convert at half the rate of the original site or struggle to gain traction altogether. The issue usually isn’t translation. It’s assuming users in different markets search, navigate, and evaluate information the same way. Using insights from Google SERPs and LLMs, here’s how to localize website architecture and navigation for international SEO. How to use Google to localize content Google’s SERP interface is localized for individual markets. Each element — menu order, topic filters, questions, tags, AI structures — reflects learned user behavior. For example, if you search for a topic or product in the UK and Italy, you’ll get different interfaces: The Italian site might show two shopping options, while the UK site puts images at position two. These aren’t arbitrary — they’re algorithmic predictions based on observed behavior in each specific region. Google has already done the user research. You just have to extract the signals systematically. Every SERP element is optimized through behavioral data, for example: Menu order reflects click-through analysis across millions of users. Topic filters represent observed refinement patterns. People Also Ask (PAA) boxes aggregate real user confusion points. Image tags cluster search behavior patterns. AI Overviews encode entity relationship patterns that a model has learned. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with 9 signals to create a localization framework Use these nine SERP interface elements to contain localization intelligence. Menu order/filters reveal primary and secondary search intent. They are localized and dynamic — their order changes due to seasonalities, changes of intent, content behaviors, and breaking news. Topic filters show hierarchical refinement patterns (2-3 levels deep). They are influenced by trends and seasonalities, and Google mixes classic search topics with shopping filters. People Also Ask (PAA): Three levels are enough for discovering patterns and recurring entities through clustering. People Also Search For (PASF) are similar to PAAs but are related searches showing journey connections. In this case, a three-level depth is sufficient to obtain meaningful data. Image search tags for entity search: Each tag is also an entity related to the searched entity, or an attribution of that entity. They place entity associations in a visual search context. AI Overview fan-outs are AI-predicted follow-up questions from Google. AI Mode fan-outs are conversational search path predictions, ideal for exploring entities and triplets. Google web guides are pillar pages that break down a topic into subtopics. It’s ideal for understanding how Google reasons around a subject. Multi-LLM comparative analyses examine how ChatGPT, Gemini, and Perplexity structure their answers. LLM answers help identify both the universal semantic core shared across regions and the region-specific entities that emerge when prompted with local context. This reveals which entities matter globally versus locally. Table of nine localization framework signals SignalWhatWhyHow to (manual)How to (with tools)1. Search Menu OrderReveals primary and secondary search intentMenu position shows how Google classifies query intent per marketOpen incognito browser, set location to target city, search query, record visible menu items in exact orderBrightLocal for location simulation2. Topic FiltersShows hierarchical refinement patterns (2-3 levels deep)Maps directly to content hub organizationScroll below search bar to “Refine this search” section, document filter chips, click each to reveal sub-levelsTopically.io, Chrome DevTools (inspect filter elements), Python/Selenium for automation3. People Also AskUser confusion points and anxiety aggregated from real searchesDirect blueprint for FAQ sections and pillar page H2 structureLocate PAA box, document visible questions, click each to expand and reveal related questions (2 levels deep), use incognito to avoid personalizationAlsoAsked.com (visualizes PAA trees), ValueSERP API, SerpAPI for automation4. People Also Search ForJourney paths and related searches showing sequential behaviorReveals related entities users expect to find connected; informs internal linkingScroll to bottom of search results, document 8-12 related searches shown automaticallyTopically.io, Semrush (“Related Keywords”), Ahrefs (“Also talk about”), SerpAPI5. Image Search TagsEntity search associations (visual and general); multi-word tags reveal co-occurring entitiesTag frequency = entity salience; informs which entities need visual contentClick Images tab, observe tag chips below search bar, document all visible tags (8-15), note multi-word tagsTopically.io, SerpAPI (image search with tags), Selenium scripts6. AI Overview Fan-OutsGoogle’s AI-predicted follow-up questions; entity relationships the model learnedSpecifically informs Google AI Overview, AI Mode, and Web Guide structure; shows content sequencing for user journeyN/AQforia by iPullRank, Gemini API with Python/Colab7. AI Mode Fan-OutsConversational search path predictions; multi-turn journey Google anticipatesReveals complex topic exploration paths; growing importance as Google pushes AI Mode heavilyN/AQforia by iPullRank, Gemini API with conversational context in Python/Colab8. Google Web GuideGoogle’s editorial content organization; H2-level structure Google considers comprehensiveDirect blueprint for navigation structure (not URL paths); categories reveal information types users needPerform search, look for “Web Guide” or “Guide” SERP feature (appears ~20-30% of queries), expand sections, document H2 headingsN/A (no tools available)9. Multi-LLM Comparative AnalysisHow ChatGPT, Gemini, Perplexity structure answers to identical queries; consensus vs. unique entitiesConsensus entities = must-have content; weak/incomplete answers = information gain opportunities; validates citation-worthy contentEnter identical query in each LLM interface, copy full responses, document response length/format/entities/citations (for Perplexity), perform in local language per marketOpenAI API (ChatGPT), Google Gemini API, Perplexity API – all via Python/Colab for batch processing and entity extraction Scaling with international SEO Here’s an example of a product breakdown between international sites: 148 products × 6 query variants = 888 queries Four markets = 3,552 combinations Nine signals = 31,968 data points However, you don’t need all 31,968 data points. Patterns emerge across 15 to 20 products, roughly 10% to 15% of the catalog. Entity relationships repeat across product categories, so sampling 15 products across factions can reveal critical localization patterns. How to transform data into taxonomy Let’s say there’s a hypothetical website based on the Star Wars movies called “SWLegion.com,” which sells tabletop wargaming miniatures. It has several products across factions, eras, and types. Below is SWLegion.com’s complete URL structure across four markets. CategoryU.S. (root)UK (/en-gb/)Italy (/it-it/)Spain (/es-es/)STORE HOME/store//en-gb/store//it-it/negozio//es-es/tienda/TYPE OF UNIT CATEGORIESAccessories/store/accessories//en-gb/store/accessories//it-it/negozio/accessori//es-es/tienda/accesorios/Battle Force Packs/store/battle-force-packs//en-gb/store/battle-force-packs//it-it/negozio/pacchetti-forza-battaglia//es-es/tienda/paquetes-fuerza-batalla/Battlefield Expansions/store/battlefield-expansions//en-gb/store/battlefield-expansions//it-it/negozio/espansioni-campo-battaglia//es-es/tienda/expansiones-campo-batalla/Commander Expansions/store/commander-expansions//en-gb/store/commander-expansions//it-it/negozio/espansioni-comandante//es-es/tienda/expansiones-comandante/Core Sets/store/core-sets//en-gb/store/core-sets//it-it/negozio/set-base//es-es/tienda/sets-basicos/Operative Expansions/store/operative-expansions//en-gb/store/operative-expansions//it-it/negozio/espansioni-operative//es-es/tienda/expansiones-operativas/Personnel Expansions/store/personnel-expansions//en-gb/store/personnel-expansions//it-it/negozio/espansioni-personale//es-es/tienda/expansiones-personal/Starter Sets/store/starter-sets//en-gb/store/starter-sets//it-it/negozio/set-iniziali//es-es/tienda/sets-iniciales/Unit Expansions/store/unit-expansions//en-gb/store/unit-expansions//it-it/negozio/espansioni-unita//es-es/tienda/expansiones-unidad/Upgrade Expansions/store/upgrade-expansions//en-gb/store/upgrade-expansions//it-it/negozio/espansioni-potenziamento//es-es/tienda/expansiones-mejora/FACTION FILTERSShadow Collective/store/shadow-collective//en-gb/store/shadow-collective//it-it/negozio/collettivo-ombra//es-es/tienda/colectivo-sombra/Mercenaries/store/mercenaries//en-gb/store/mercenaries//it-it/negozio/mercenari//es-es/tienda/mercenarios/Galactic Empire/store/galactic-empire//en-gb/store/galactic-empire//it-it/negozio/impero-galattico//es-es/tienda/imperio-galactico/Galactic Republic/store/galactic-republic//en-gb/store/galactic-republic//it-it/negozio/repubblica-galattica//es-es/tienda/republica-galactica/Rebel Alliance/store/rebel-alliance//en-gb/store/rebel-alliance//it-it/negozio/alleanza-ribelle//es-es/tienda/alianza-rebelde/Separatist Alliance/store/separatist-alliance//en-gb/store/separatist-alliance//it-it/negozio/alleanza-separatista//es-es/tienda/alianza-separatista/TYPOLOGY FILTERSHeroes/store/heroes//en-gb/store/heroes//it-it/negozio/eroi//es-es/tienda/heroes/Varies/store/varies//en-gb/store/varies//it-it/negozio/varie//es-es/tienda/varios/Infantry/store/infantry//en-gb/store/infantry//it-it/negozio/fanteria//es-es/tienda/infanteria/Tools/store/tools//en-gb/store/tools//it-it/negozio/strumenti//es-es/tienda/herramientas/Vehicles/store/vehicles//en-gb/store/vehicles//it-it/negozio/veicoli//es-es/tienda/vehiculos/ERA FILTERSAll Eras/store/all-eras//en-gb/store/all-eras//it-it/negozio/tutte-ere//es-es/tienda/todas-eras/Age of Rebellion/store/age-of-rebellion//en-gb/store/age-of-rebellion//it-it/negozio/era-ribellione//es-es/tienda/era-rebelion/The New Republic/store/the-new-republic//en-gb/store/the-new-republic//it-it/negozio/nuova-repubblica//es-es/tienda/nueva-republica/Fall of Jedi/store/fall-of-jedi//en-gb/store/fall-of-jedi//it-it/negozio/caduta-jedi//es-es/tienda/caida-jedi/Reign of the Empire/store/reign-of-the-empire//en-gb/store/reign-of-the-empire//it-it/negozio/regno-impero//es-es/tienda/reino-imperio/CONTENT SECTIONSLore Section/lore//en-gb/lore//it-it/lore//es-es/lore/Rules Section/star-wars-legion/rules//en-gb/star-wars-legion/rules//it-it/star-wars-legion/regole//es-es/star-wars-legion/reglas/Mini Painting Academy/mini-painting-academy//en-gb/mini-painting-academy//it-it/accademia-pittura-miniature//es-es/academia-pintura-miniaturas/About Us/about-us//en-gb/about-us//it-it/chi-siamo//es-es/sobre-nosotros/ Extract entities across signals Using the above product catalog as an example, use each product as a query seed. Start manual, with 10-15 products to internalize patterns. Then automate with APIs/Python, and store in a CSV/JSON. Cross-reference entities to identify co-occurrence patterns. Combine all nine signals into a unified dataset. Then, extract entities mentioned across signals. Weighted co-occurrence analysis Track which entities appear together across signals. This reveals which concepts users naturally connect in their thinking. Each signal has a different reliability weight based on how directly it reflects user intent: LLM mentions: 3.0 (high confidence — models trained on usage patterns) Query fan-outs: 2.5 (AI predicts relationships from observed behavior) PAA: 2.0 (actual user questions connecting entities) PASF: 2.0 (sequential journey connections) Image tags: 1.5 (visual/entity search context) Topic filters: 1.0 (broad categorization) For example, say there’s a significant variation in entity relationship complexity across markets, measured as total weighted co-occurrence scores (sum of all entity pair connections, weighted by signal reliability): U.S.: 2,639.5 total weight UK: 2,359.0 total weight Spain: 2,266.0 total weight Italy: 1,084.5 total weight This means the U.S. and UK show 2x more entity relationship complexity than Italy, indicating more complex user journeys requiring deeper content architectures. Cross-market entity patterns Not all entities matter equally across markets. Your content strategy depends on recognizing three distinct patterns: Universal entities (all four markets): These appear consistently across the U.S., UK, Spain, and Italy. Users everywhere expect this content. Market-specific: These entities show concentrated interest in just one market based on current signal validation. Cover these entities deeply in their market of reference but maintain lighter coverage in other markets. In future quarterly re-analysis, verify if interest for these entity types has increased in other targeted markets to determine whether to expand coverage depth accordingly. Regional (2-3 markets): These entities appear in most but not all markets, requiring selective deployment. Build content, deploy to 2-3 markets, and evaluate ROI before expanding. Ontology pattern recognition Beyond individual entities, track how different types of entities connect. This reveals what content formats work in each market. Entities cluster into four categories: Products (actual sellable items) Lore (Star Wars universe entities) Rules (game mechanics) Painting (techniques and processes) Cross-ontology co-occurrence reveals which content types users expect: When products and lore entities appear together frequently across signals, users think in terms of narrative context for purchases: Product × Lore = Battle scenario content (example: “AT-ST” + “Battle of Hoth” = Hoth battle guide) When products and painting entities co-occur, users research techniques for specific models: Product × Painting = Unit-specific technique guides (example: “Clone Trooper” + “blue markings” = 501st painting tutorial) When painting and lore entities connect, users want thematic aesthetic guidance: Painting × Lore = Themed painting content (example: “terrain” + “Scarif” = tropical planet terrain tutorial) When lore entities cluster together, users compare or navigate between story elements: Lore × Lore = Era/faction comparisons (example: “Clone Wars” + “Galactic Civil War” = timeline guide) Market-specific pattern differences These ontology patterns vary dramatically by market, revealing which entities matter, how users think about connections, and how to optimize internal linking architecture. Here’s an example weighted co-occurrence analysis USA: Product × Lore, weight 60.0 (highest of any market) What this means: American users discover products through lore narratives — build battle scenarios linking story to miniatures. Internal linking strategy: From the “AT-ST Walker” product page, prominently link to /lore/battle-of-hoth/ with anchor text emphasizing narrative context (“Deploy the AT-ST in the iconic Battle of Hoth”). From lore pages, link back to related products within battle scenario descriptions. UK: Painting × Lore, weight 15.0 (unique to UK and U.S. only) What this means: British users want battle-themed painting guides — content like “Paint a Hoth snow base” works here but is less relevant elsewhere. Internal linking strategy: From /mini-painting-academy/snow-base-tutorial/, link to /lore/battle-of-hoth/ and to relevant product pages like “Snowtrooper Unit Expansion.” Create bidirectional links between painting techniques and the lore/battle contexts where those techniques apply. Spain: Product × Lore, balanced at 27.0 each What this means: Spanish users balance story interest with product focus — equal emphasis needed. Internal linking strategy: Moderate internal linking between product and lore pages. From “Luke Skywalker Commander” product page, include links to both /lore/luke-skywalker/ and related products. Avoid over-emphasizing either connection type. Italy: Product × Lore weight 10.5 (weakest) What this means: Italian users don’t connect lore to products — skip elaborate battle scenarios. Focus on product specs and painting basics. Internal linking strategy: Minimize product-to-lore internal links. From product pages, prioritize linking to /mini-painting-academy/ tutorials and related products by faction or unit type. Keep lore pages separate from product discovery paths. Get the newsletter search marketers rely on. See terms. How to validate your framework Entities should appear in 3+ signals to be validated. One appearance could be an anomaly or noise. False-positive check Signals reveal what users reference, not always what they want. For example, a site appears across multiple markets in various signals, so it’s confirmed as a universal entity in LLM responses across all markets. But its presence in Image Search tags is minimal. Interpretation: Users ask about the site as a reference point but aren’t searching for images of its products extensively. Strategy: Build a comparison article/FAQ, not extensive image galleries or deep informational content. Validation question: Does the signal show what users want or what they’re using for context? Coverage gap analysis For example, let’s say signal validation reveals dramatically different entity landscapes across markets — in other words, how many distinct, validated entities appeared in 3+ signals per market: U.S.: 31 entities UK: 28 entities Spain: 29 entities Italy: 16 entities Italy has half the entity coverage of other markets, revealing a fundamental difference in how Italian users approach this product category — a strong strategic signal. If Italian users show concentrated interest in fewer entities, with heavier emphasis on foundational questions (for example, PAAs) rather than deep entity exploration, they’re asking, “what is this?” and “how does this work?” There’s an information gain opportunity here: While competitors might translate all 31 US entities to Italian, creating shallow content Italian users don’t need, you can dominate the 16 entities that actually matter to this market with comprehensive, beginner-focused content. Actions to take: Italy needs foundational 101-level content rather than deep entity exploration. FAQ-driven approach matches PAA dominance in Italian signals. Invest in clear product specifications, basic painting tutorials, and simple rule explanations. Build comprehensive coverage of the 16 validated entities before considering the other 15. Monitor quarterly. If Italy’s validated entity count grows, market maturity increases, and expand coverage accordingly. You’re not trying to force-fit U.S. models onto Italian users, you’re serving the actual information needs for this market. How to structure internal architecture Maintain a consistent technical structure across all markets with canonical tags, hreflang, CMS architecture, and analytics. For the complete structure of the SWLegion.com example, see its full architecture. Ecommerce section: U.S. (root): /store/, /store/{category}/, /store/{filter}/ UK: /en-gb/store/, /en-gb/store/{category}/, /en-gb/store/{filter}/ Italy: /it-it/negozio/, /it-it/negozio/{categoria}/, /it-it/negozio/{filtro}/ Spain: /es-es/tienda/, /es-es/tienda/{categoría}/, /es-es/tienda/{filtro}/ Content sections: U.S. (root): /lore/{entity}/, /star-wars-legion/rules/{topic}/, /mini-painting-academy/{guide}/, /about-us/ UK: /en-gb/lore/{entity}/, /en-gb/star-wars-legion/rules/{topic}/, /en-gb/mini-painting-academy/{guide}/, /en-gb/about-us/ Italy: /it-it/lore/{entità}/, /it-it/star-wars-legion/regole/{argomento}/, /it-it/accademia-pittura-miniature/{guida}/, /it-it/chi-siamo/ Spain: /es-es/lore/{entidad}/, /es-es/star-wars-legion/reglas/{tema}/, /es-es/academia-pintura-miniaturas/{guía}/, /es-es/sobre-nosotros/ Slug localization: Store slugs fully localized (/store/ → /negozio/ → /tienda/). Content section slugs localized where natural (/rules/ → /regole/ → /reglas/, /mini-painting-academy/ → /accademia-pittura-miniature/). Entity slugs within content localized for official translations (Spain: /es-es/lore/conde-dooku/ vs English /count-dooku/). What stays consistent Path structure: /lore/, /store/, /rules/ exist everywhere even if entity coverage or category emphasis differs. Product inventory: Physical products remain the same across markets (same 148 SKUs), though merchandising and filtering emphasis may vary. Core navigation sections: All markets have Store, Lore, Rules, Mini Painting Academy, About Us, but internal linking architecture and content depth within each section adapts to market signals. Entity coverage Create a master entity list flagged by market validation. This will become your strategic content roadmap, preventing duplication while ensuring comprehensive coverage where it matters. Entities cluster into two strategic categories: Universal entities validated across all 4 markets: Darth Vader, Luke Skywalker, painting, terrain, miniatures, core factions (Galactic Empire, Rebel Alliance, Separatist) — these form your foundation and users everywhere expect this content. Market-specific entities showing concentrated validation in one or two markets: 501st Legion (U.S./UK only), Shatterpoint comparison (Italy only), Wookiees (Spain only) — these are your localization differentiators. Phase 1 build: Start with universal entities. Build 12-15 cornerstone pages, translate to all four markets for 48-60 total pages. These establish a baseline coverage across your entire international footprint. Phase 2 build: Add market-specific entities. Create 25-35 localized pages to be deployed selectively only to validated markets. A 501st Legion deep-dive may go live in the U.S. and UK but not in Italy or Spain. Total strategic content: 73-95 pages across four markets. This is a better, more refined strategy than covering 148 product entities × four markets, adding lore/rules/painting content for all entities across all markets, which would create dozens of wasted pages. How to implement an AI roadmap Building out your international SEO can present some challenges. Here are some roadblocks and strategies to do it right. Implementation challenges Let’s look at some hurdles to implementing AI to search. CMS limitations Most CMS platforms aren’t designed for entity-level localization. What’s needed is conditional page creation based on market validation. For example: Add a “Target Markets” custom field to your CMS with checkboxes for different markets — U.S., UK, Italy, Spain, in our example. Content team scaling Creating dozens of localized pages requires subject matter expertise, native language writers, and cross-market coordination. Start with one market — the second-largest, not the largest, to learn with a lower risk. Build 5-10 entity pages, validate traffic and conversions, and then scale to other markets only when ROI is proven. Maintenance Markets evolve, new products launch, entities gain or lose relevance, and signals need periodic re-analysis. Re-run an abbreviated nine-signal analysis on the top 20 entities on a quarterly basis. Look for significant shifts: If entities drop from 3+ signals to one signal, consider deprecating content. Continuous intelligence systems Here are some tools to help monitor AI systems: Wikipedia edit monitoring: Create watchlists for 10-15 key entities per market, and set email alerts for significant edits. Major additions or edit wars signal rising interest — if that happens, review entity page content and update accordingly. Reddit velocity tracking: Track comment velocity on entity mentions. Entities mentioned in 5+ threads in one week (an unusual spike) should be investigated. TikTok and Instagram trends analysis: Monitor trending hashtags and viral content patterns related to your product categories. Rising hashtag usage or viral content patterns can indicate emerging entity interest before they appear in traditional search signals. Google Trends “rising” analysis: Monitor “rising” queries monthly (not absolute volume). Queries with +100% week-over-week growth signal emerging interest. Building a roadmap Now that you know what roadblocks lie ahead, here’s how to implement the plan. Month 1: Foundation Choose one market for learning and prototyping. Select 10-15 products to sample and conduct a systematic nine-signal analysis. Create an entity list with co-occurrence weights and 3-5 validated market-specific entities. Months 2-3: Content creation Build universal pillar pages and translate to all markets, and build market-specific entity hubs, starting with one initially. Implement internal linking based on co-occurrence weights. Months 4-6: Validation and expansion Monitor entity coverage rates, LLM topic visibility, and market-specific traffic growth. Months 7-12: Full multi-market rollout Expand to all markets. Run continuous intelligence systems, including: Wikipedia watchlists, Reddit monitoring, TikTok/Instagram trends, and schedule quarterly signal re-analysis. How to measure success After implementing changes and incorporating AI into your international search strategy, here’s how to determine what’s working and where to improve. Entity coverage rate This metric tells you if you’re covering entities that actually matter to users in each specific market, not just translating pages indiscriminately. Formula: (Entity pages built / Total validated entities from signal analysis) × 100 Example: Your signal analysis validated 28 entities in the UK (entities appearing in 3+ signals). You built dedicated pages for 22 of these entities. Your entity coverage rate is: 22/28, or 79%. Target: 70%+ coverage for each priority market. Consider the strategic difference. For example, let’s say your UK site covers 79%, or 22 of 28 validated entities, focusing resources on entities users actually search for, ask questions about, and engage with across multiple signals. While a competitor translates 148 product entities, achieving “100% coverage” on paper, but wastes resources covering entities UK users show minimal interest in. Your 21% gap (6 uncovered entities) isn’t a failure, but a strategic prioritization. These lower-priority entities can be added if quarterly re-analysis shows their signal validation strengthening — moving from 2 signals to 3+ or appearing in additional signal types. Tools for tracking entity coverage: Screaming Frog: Crawl your site and count entity pages by market subfolder. Google Sheets: Cross-reference validated entity lists against live URL inventory. LLM topic visibility Track whether your site appears in LLM responses for key topics, not individual citation counts. The goal is to measure topical authority, not vanity metrics. For ChatGPT/Gemini/Perplexity/Claude: Use WAIKay.io to systematically track your visibility across multiple LLMs. The platform allows you to: Set up monitoring for specific queries across ChatGPT, Gemini, Perplexity, and other AI platforms Track whether your domain appears in responses (mentions, summaries, citations) Monitor visibility changes over time with historical tracking Generate reports showing presence/absence per topic, per LLM For AI Overviews/AI Mode: Use Semrush One to monitor Google’s AI-powered SERP features. Alternative tools, such as Ahrefs, Advanced Web Rankings, and SISTRIX (AI Overview presence reporting), offer similar capabilities. Target benchmarks: Universal topics: Visibility in 2+ LLMs across all markets. Market-specific topics: Visibility in 2+ LLMs for a specific market’s language queries. This validates if your content quality and entity coverage are sufficient for LLMs to consider you an authoritative source worth including in their responses. Lack of visibility signals content gaps or insufficient topical depth. See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with Incorporate AI and LLMs into your international SEO today Most international sites treat taxonomy as infrastructure: build once, maintain minimally, and refresh every 2-3 years during a website redesign. Our SWLegion.com example started with an identical architecture across four markets. Implementing this strategy, we showed how to localize architecture and navigation and optimize for each market. This strategy builds something fundamentally different — architecture that breathes with market behavior, responding to signals rather than assumptions. You’re cultivating taxonomy rather than just maintaining a website. Your new taxonomy will reflect current user behavior and also anticipate and adapt to behavioral shifts before competitors notice that the market has changed. View the full article

Google announced five new ways in how it is improving linking to web pages from AI Mode and AI Overviews. Some of these we saw Google testing earlier, and I (we) were fans of these changes and I am glad to see Google officially roll them out.View the full article

The folks over at Microsoft Bing put together a blog post explaining the differences between indexing for Search versus indexing for Grounding (AI responses) and the differences. Krishna Madhavan, Knut Risvik, Meenaz Merchant from Microsoft wrote, "Indexing for grounded AI answers is not a reinvention of search '" it is a major evolution of it. Grounding commits to an answer."View the full article

Google will be bringing content and titles related exclusions to the account level to Google Ads AI Max later this year. It will give you the ability to always exclude any other content you don't want to use in your ads at the account level, Ginny Marvin, the Google Ads Liaison, said on LinkedIn.View the full article

It is 2026 and finally Bing Places for Business is finally mobile friendly. Microsoft Bing sent me an email letting me know of this update, saying, "We're excited to share that Bing Places for Business now offers a mobile-responsive experience, making it easier to view, update, and manage your business profile from any device-desktop, tablet, or mobile."View the full article

Google sent out emails notifying applicable advertisers that they will default call recording to Yes, to always record calls, if you don't pick an option yourself. Google wrote, "Starting July 01, 2026, if you haven't made a selection for your "Call recording" setting, it will automatically default to "Yes"."View the full article

A new library is opening up in New York City this Friday, but rather than books, the space will house 3,437 volumes and roughly 3.5 million pages of the Epstein Files. The Donald J. The President and Jeffrey Epstein Memorial Reading Room is a project by the Institute for Primary Facts, a nonprofit organization dedicated to government transparency. Housed in an undisclosed location in Tribeca, the exhibition will allow visitors to see the records in a new way. It will be open to the public from May 8 through 21 by appointment only. “The truth is hard to deny when it’s printed and bound for you to see,” the project’s website reads. “The Reading Room keeps public attention fixed on the crimes of Epstein and the Epstein class, and on The President’s desperate attempts to bury them, to support the victims and survivors as they seek justice.” The controversial records have garnered media and public attention since the arrest and death of convicted sex offender Jeffrey Epstein, leading to widespread calls for the release of the files gathered through numerous investigations. The Department of Justice finally released a redacted version of the files in January 2026. The massive number of documents has led many to come up with creative ways for the public to read and interact with the files. Take Jmail, for example: The digital project led by a small group of engineers helps the public navigate the trove of documents via a user-friendly interface modeled after Gmail. Subsequent iterations of this project include an Amazon-looking storefront to explore Epstein’s purchases and a camera roll to browse through the images contained in the files. While from afar the exhibition looks like a regular public library, upon closer inspection each “book” is an analog version of the controversial records, categorized by volume. The bookshelves hold what the Reading Room says is 17,000 pounds of printed records. The bookshelves wrap the walls of the room, enclosing a draped square structure filled with candles, serving as a tribute to Epstein’s victims and survivors. There’s a seating area that resembles a public library reading room, although only journalists and law enforcement officials will be able to actually look through and read the documents. All visitors will be able to view a carefully curated timeline plastered on the walls that details the long relationship between Epstein and The President. For members of the public who are interested in attending, the Reading Room is offering reservations for free 20-minute visits; prior registration is required. Before the visit, those attending will receive a text message with the venue’s location, which is being kept secret due to security concerns. View the full article

Over the past few decades, digital marketing has settled into a stable system. While it spans SEO, content marketing, social media, and digital advertising, many programs have relied on a predictable core that didn’t always use every available channel. This gave digital marketers a sense of predictability and comfort. For years, teams stuck with what worked and refined execution through the same familiar framework. AI search has disrupted that comfort and exposed our inconsistencies. To succeed with AI SEO, we need a much more comprehensive approach. AI SEO rewards strategic marketing Over the past 15 to 20 years, digital marketing settled into a predictable rhythm, with each channel playing a defined role. Content marketing, social media, SEO, paid advertising, and email followed similar strategies with little variation. Little happened outside this structure, and many of us grew “lazy.” The structure worked, so we let other strategies fall away. The problem? It created a false sense of security. We should have been doing more all along, and those broader strategies are now driving real visibility in AI search. AI has disrupted digital marketing in ways that weren’t obvious at first. It’s changed user search behavior and how brands are evaluated. Traditional search relied on algorithms and a primary source. AI pulls from multiple inputs across many sources. Those sources should already exist. They’re your marketing — the way you present your brand across platforms like social media, third-party directories, press releases, brand mentions, and more. In short, anything outside your website. In this system, your website and the strategic marketing that supports it are just one part of the whole. It’s now one of many sources AI uses to understand your brand and offer. AI search reflects the strength of marketing across all these sources. Visibility Is not limited to your website One of the biggest disruptions AI has caused is that the website is no longer central to your marketing strategy or visibility. It’s now part of a much larger ecosystem. You still need a strong website, as always, but you must account for how much broader the landscape has become with AI search. While driving traffic to your website still matters, it’s no longer the only focus. The goal used to be maximizing website visibility — achieve that, and results would follow. That still works to a degree, but treating it as the only path to visibility is outdated. AI pulls information from a wide range of sources — articles, brand mentions across platforms, third-party profiles, published content — and all of it shapes how it understands who you are and what you do. Your website is just one part of this broader scope. If you focus only on your website, you limit AI’s ability to find you. This is where most marketing programs fall short, especially those built before AI. To modernize, your brand must be visible across a much wider scope. AI SEO requires an intentional presence AI favors brands that show up online with intent. They’ve built a cohesive ecosystem across the wider internet. A segmented marketing approach may have worked in the past, but it no longer has the same impact. We got away with it because when each channel performed well, it still felt effective and met our goals. AI doesn’t allow this anymore. It favors brands with many connected signals, because it links them across the internet. It evaluates how your brand appears across these sources and looks for consistent messaging and expertise. When these signals align, your AI visibility strengthens. When they’re scattered or your broader presence is weak, your AI visibility is weak. This is why it’s important to develop a marketing strategy that accounts for this. A brand with a coordinated presence across the internet — across its website and other marketing channels — is what’s required today. Lazy marketing strategies are exposed This is the real issue with “lazy marketing.” We define it as sticking to the old approach — treating each channel separately and relying on the same tactics that have always worked. That approach may have delivered results before, but those days are gone. At the time, this approach still delivered results. A strong SEO foundation consistently drove leads, and paid advertising offered similar predictability. These tactics worked so well that there was little need to go beyond them. We need to go beyond it to keep up. Your brand needs to show up across multiple sources — that’s how AI finds you. If your competitors are already building their presence, you need to do the same or get left behind. They’ll take more space in AI-generated answers than you. This means that if you have gaps in your marketing, you can’t hide them anymore. AI exposes these inconsistencies and forces you into the broader digital space. Transition into the era of AI search Now is the time to move beyond the old model and adopt a new understanding of what works in digital marketing. The old approach no longer works on its own — it must be part of a broader system. These are the strategies we should have been using all along: press releases, directory listings, and marketing beyond your own website. AI search rewards an all-encompassing marketing strategy because that’s what works. Core channels like social media, SEO, content marketing, and paid advertising still matter, but they’re not enough on their own. AI hasn’t changed the rules. It has enforced them. This is what has always worked in marketing. The difference now is that you can’t get away with doing less. View the full article

Since Donald The President named then-Senator JD Vance of Ohio his running mate in July 2024, his campaign logo has included both of their last names placed within a rectangular frame. In fundraising emails sent to the president’s mailing list last month, though, a different version of the logo included just one name: The President. The President last used a Vance-less version of a MAGA Box logo during the 2024 campaign, but it reappeared in April in fundraising emails for an “Official Midterms Patriot Roster.” It’s one of a dozen or so logos used in fundraising emails over the past month by Never Surrender, The President’s leadership PAC, which manages the mailing list from his most recent presidential campaign. While variations of the The President-Vance logo remain in circulation, a growing number of alternative logos and email header graphics don’t mention Vance at all. It’s a subtle branding shift that puts the sole focus on The President, and comes amid growing questions over who the president might back as a successor in the 2028 race. The President-only logos started appearing in fundraising appeals as early as The President’s first month back in office in January 2025, but their number has grown. The percentage of logos in The President’s fundraising emails that are branded solely for him and not his VP has risen from 25% in March 2025 to a high of 42% in March 2026, according to a review of the Archives of Political Emails, a database. In April, it was more than 30%. Some of these logos say The President in all-caps letters. The campaign seems to favor the “Memo from The President” header to visually frame emails as personal appeals, which is valuable to connect the fundraising request as being from the man himself. The “The President 47” logo variation puts The President’s name inside a shield. At the same time, The President’s PAC stopped sending emails branded for Vance. Last year, the president’s mailing list received eight emails with solo Vance logos signed by the vice president. This year Never Surrender hasn’t sent an email signed by Vance since January, and it didn’t get its own logo. The PAC’s treasurer, Bradley T. Crate, did not respond to a request for comment sent through Red Curve, his political consultancy. Oftentimes, Never Surrender’s small-dollar email fundraising efforts on behalf of the president are manipulative and bizarre. One email threatened to sic officers from Immigration and Customs Enforcement on supporters who didn’t take a survey to prove they’re U.S. citizens. Another offered access to private national security briefings in exchange for donations. Increasingly, custom logos are used to communicate all this. Brand variants for recent promotions like “Elite Swamp Drainers for The President,” “The President Inner Circle,” and “The President Platinum” give potential small-dollar donors the illusion of access to the president with a logo for a made-up group. “Sitting presidents can and do continue to fundraise, usually for their own party as a whole, particularly when they’re popular among their voters,” SoRelle Wyckoff Gaynor, an assistant professor of public policy at the University of Virginia, tells Fast Company, noting that President Barack Obama held fundraisers for down-ballot races during his second term in office. “The The President-specific brand of these emails is super interesting—and someone like The President whose entire career is really built on branding, not building, I think it’s right to assume that all of these decisions are very strategic,” she says, noting the shift away from Vance indicates to her that The President wants to “leave the door open” for a successor, whether that’s Vance, Secretary of State Marco Rubio, or even his eldest son, Don Jr. Never Surrender keeps more than three-quarters of the money it raises and splits up the rest with the Republican National Committee and Working for Ohio, Vance’s leadership PAC. That means even when The President sends an email that leaves out Vance’s name in the logo, the VP’s group still gets 5% of whatever it raises. By omitting Vance’s name, however, The President is leaving room for people to question his allegiance to the vice president. The President has sent mixed signals about whether he’ll back Vance in the 2028 Republican primary, should Vance run. “I think you have a lot of very capable people,” The President said last year when asked, noting it’s still early. Perhaps the return of The President’s Vance-less logo was inevitable. As the first president since Richard Nixon to have two different vice presidents while in office, The President isn’t known for loyalty to his running mates. And to The President’s biggest supporters, it doesn’t really matter whether Vance or former Vice President Mike Pence are mentioned at all, as long as The President is on top of the ticket. As a small-dollar fundraising strategy, The President’s PAC is doubling down on the reason people subscribed to the mailing list in the first place. The President’s fundraising focus on himself is a reminder of who’s at the center of his political movement. MAGA is held together less by a coherent, consistent ideology than it is by fealty to a single man. The proof is in the logos. View the full article

Google's John Mueller answers if Preferred Sources overrides ranking signals. Could it be a "trust button" signal? The post Google Answers If Preferred Sources Overrides Low Quality Signals appeared first on Search Engine Journal. View the full article

Just as they did with televisions, many people used the pandemic as an excuse to upgrade their PC or laptop. It was a move that made sense at the time. Telecommuting became essential, and not all devices could adequately handle the demands of Zoom, Teams, and other work software. At the same time, digital communication was often the only way to stay in touch with friends and family. Smartphones handled some of that heavy lifting, of course, but the PC industry still saw shipments spike 14.5% from 1999 to 2000. Now, much like the TV market, many PC owners are reaching the point where a new device is becoming necessary. But unlike that living room fixture, PC shoppers are entering a hostile market defined by higher prices and fewer meaningful performance gains. IT research firm Gartner notes that many people replace their business devices, typically laptops, every three to five years. International Data Corp. puts that timeline closer to five to eight years when businesses actively manage upgrades and repairs. Personal-use computer owners tend to follow a similar replacement cycle. That means a refresh wave is looming for pandemic-era buyers, just as component prices are soaring amid AI-driven demand for hardware. RAM prices have jumped anywhere from 150% to more than 200% over the past year, depending on the type, according to PCPartPicker.com. Storage prices, including the cost of hard drives, have followed similar trends. Meanwhile, video card prices have remained elevated for years, as GPUs, the chips that power graphics cards, have become a core component of AI systems. For gamers, that has been especially frustrating. PC gaming is rapidly becoming a more important part of the video game ecosystem, threatening to displace consoles, according to some industry leaders at the recent Iicon conference hosted by the Entertainment Software Association. Analysts, however, say Nvidia is not expected to release a new generation of its GeForce GPUs in 2026. If that happens, it will mark the first time in three decades the company has skipped an annual release cycle. And finding a top-of-the-line RTX 50-series card remains difficult for many enthusiasts, with some retailers charging double the suggested retail price. A vanishing entry level As frustrating as the price hikes already are for consumers in need of an upgrade, analysts do not expect the situation to improve anytime soon. A separate Gartner projection predicts that PC prices will rise 17% this year compared with 2025. Worse still for consumers simply looking for a functional home computer, the era of low-cost machines may be nearing its end. “The sub-$500 entry-level PC segment will disappear by 2028,” says Ranjit Atwal, senior director analyst at Gartner. “In addition, rising AI PC prices will delay the projected 50% market penetration of AI PCs until 2028.” PC vendors, Gartner says, are likely to accept lower sales volumes to protect profit margins rather than aggressively pursue price-sensitive customers, noting that the first half of this year represents a “critical window.” By the end of the year, the firm predicted, combined prices for DRAM and solid-state drives could rise 130%. The surge in component costs, combined with uncertainty over how long those increases will last, could reshape the U.S. computer refresh cycle in one of two ways. Some analysts believe laptop users may simply hold onto devices as long as they remain “good enough” to run everyday programs and apps. Desktop users with some technical know-how can also upgrade individual components at a lower cost, or turn to services like Geek Squad if opening up a PC feels too intimidating. Others argue that buyers may rush to upgrade now before prices climb even higher. Distributors appear to be betting on that scenario. Worldwide PC shipments rose 4% in the first quarter of 2026, to 62.8 million units. That increase is notable because 2025 figures were already inflated as companies front-loaded inventory ahead of the The President tariffs. “The 4% year-over-year PC shipment growth in the first quarter of 2026 was artificially inflated,” says Rishi Padhi, research principal at Gartner, in a statement. “It was not due to genuine demand, but instead because of vendors’ and channel distributors’ increase of inventory levels ahead of expected price hikes in the second quarter.” View the full article

Peter Gold has always loved making films. While attending film school in New York, he became involved with a film called Our Hero Balthazar, directed by Oscar Boyson, known for his work as an executive producer on Uncut Gems. Gold instantly knew the film was something special. He also knew it would be tough to find distribution in today’s theatrical marketplace. The dramedy, starring Jaeden Martell as a wealthy New York City teenager Balthazar Malone, who, eager to impress his activist crush, follows an online connection (Asa Butterfield) to Texas where he believes he can stop an act of violence, was passed over by A24 and Neon. So Gold, 26, decided to launch his own distribution company, WG pictures, financed through outside investors, with film producer Brad Wyman to make sure it saw theatrical release. “Filmmaking and storytelling are the heart of my passion. Getting into distribution really came from a place of frustration with the state of independent cinema,” Gold told Fast Company. “So many movies, including my own, were being overlooked by existing distributors and weren’t being given the opportunity they deserved.” Our Hero Balthazar opened March 27 at Regal Union Square as the number 2 film in the theater, generating $33,138 opening weekend gross, second only to Project Hail Mary. The film’s budget was under $2 million. The film opened sold-out in LA on April 4 and is now expanding across the country. Hollywood should take note. The amount WG Pictures has spent on distribution is less than $1 million. WG Pictures pulled off the feat without spending a single dollar on paid media and instead relied entirely on social media to drive awareness. From TikTok fan edits to Letterboxd influencers, social media has proven a boon for cinema. With it, a new kind of showmanship-based marketing has emerged. Cynthia Erivo and Ariana Grande mastered the art of going viral on social media during the Wicked press tour. Timothée Chalamet appeared on a Wheaties box and hosted a table tennis tournament to promote his most recent project, Marty Supreme. “Honestly, I thought A24 did an interesting job with Marty Supreme, but they have Timothée Chalamet,” said Gold. “We don’t have Timothée Chalamet. We have to work with what we have.” Gold worked with the filmmakers closely to come up with a social media strategy driven by the characters and the story. They started by creating an Instagram account for the film’s protagonist, with the handle @bboymalone212, that has since amassed more than 72,000 followers. One post on the Instagram page features a custom starter pack meme inspired by the character of Balthazar, with performative male staples like a New Yorker tote bag, Lorde album cover and wire headphones. Another post features an Erewhon haul of coconut matcha cold foam and Lemme Purr vaginal probiotic gummies, touching on the film’s themes of exhibitionism in the social media age. “We’re telling the story of this character and building awareness around the movie without just running a trailer with paid ad spend,” said Gold. The social media generation no longer wants to be marketed at, Gold understands, they want to feel like an active participant. The Instagram account’s most viral post tapped content creator Caleb Simpson, who on his own has more than 2.8 million followers with his viral street series where he asks strangers, and more recently celebrities, “How much do you pay for rent?” and follows it up with, “Can I get a tour of your apartment?”. Simpson and Martell, in character as Balthazar, joined up for the Instagram Reel, touring the 80th floor New York City apartment overlooking Central Park, which was also a set in the film. “I try not to focus too much on money,” says Martell as Balthazar in the clip. “I’m more focused on making a change.” The comments are a mix of those in on the joke and bemused onlookers, none the wiser. “That was the first time Caleb had ever done a fictional person,” says Gold. WG Pictures also took advantage of the impressive social media following of those involved in the film, including actress and singer Halsey and actor Noah Centineo, boasting a combined 40 million followers. Each pulled their weight with non-stop posting about the film in the run up to its release, culminating in more than 30 million organic social impressions. Gen Z and Millennials say social media is the number one form of discovery for films, according to a new Fandango study. Higher ticket prices, the rise of streaming platforms and worsening theater etiquette, have all contributed to deflated box office numbers. A survey from October shows that overall cinema attendance has remained flat since 2019, but the percentage of frequent movie-goers has dropped from 39% to 17% in 2025. In 2025, 780 million people actually went to the movies according to EntTelligence’s annual report, down from 820 million in 2024. Over the same period, ticket prices jumped 5.7%. Between 2005 to 2019 – before the Pandemic shuttered screens and accelerated a shift towards streaming – the industry averaged well over 1B tickets sold annually. While Hollywood has expressed its fears that the streaming era and smartphones will stop the social media generation from leaving the house and going to watch films the old fashioned way, in a dark room filled with strangers, the opposite is proving true. Gen Z is now the most active cinemagoing demographic, according to Fandango, having seen seven films on average in 2025, compared to 5.3 for the general population. And while millennials mainly treat moviegoing as an escape from daily grind, Gen Z sees it primarily as a social activity. Gen Z also attributes a better selection of movies and the appeal of leaving the home as key motivators for going to the movies. In the US, 95% of Gens Y and Z are now interested in exploring their online interests through in-person events, according to Eventbrite data. Both Gen Z and Millennials also prefer to extend moviegoing beyond the screen, pairing it with dining and drinking, according to Fandango. Gold and WG pictures are meeting that audience where they are at. Opening weekend for Our Hero Balthazar, WG pictures hosted a rave at the Museum of Sex in New York City. “I felt like that was something Balthazar would have thrown himself,” says Gold. To gain access, attendees needed a ticket stub for the film. A slightly less extreme marketing stunt than film distributor, Focus Features, who only permitted fans with bald heads (there was a barber in the foyer for those ‘willing to become bald’) for an early screening of sci-fi comedy film Bugonia. WG Pictures also hosted an immersive gallery experience with visual artist Jet Le Parti, where they created original artwork inspired by the film and the issue of gun violence, reflects WG’s broader strategy of eventizing cinema. They also hosted an event with Third Space–hosted event, designed to convert awareness into active participation and, subsequently, ticket sales. This social driven strategy is a shift for what has, and still is, a mostly solitary experience. When the lights dim and the film starts rolling, talking or, worse, scrolling, is strictly forbidden. And yet, Gold is banking on community being the next big drive getting Gen Z to the box office. “It’s not just cinema in a crowded theater,” as Gold sees it. “It’s an opportunity to connect with the community.” The success of platforms like Letterboxd and WG picture’s IRL marketing strategy is a testament to that. “Someone said to me after one of the screenings at Roxy Cinema that this is a movie that starts after it’s over,” he explains. “In terms of the conversation it provokes.” For Gold, the biggest challenge isn’t getting Gen Z to the cinema, it’s finding the right movies. “We’re working on Toad, which is a stoner comedy, and looking at some really interesting documentaries,” he said of future releases. “But it’s really just about finding the next exciting movie and continuing to distribute films theatrically.” Find the right movie, market it right, and Gen Z will come. View the full article

Freddie Mac was more aggressive than its counterpart for much of the past year but March activity establishes that there's a different trend at play in 2026. View the full article

A New York bank says the regulator's rejection last fall is preventing it from keeping up with local nonbank lenders deploying cash-offer products. View the full article

Rocket Cos. gave generous stock awards to its leaders for a busy year, while Better Home & Finance awarded raises to leaders after a difficult stretch. View the full article

Breaking your arm or wrist typically comes with another layer of misery: wearing a hot, itchy cast that makes showering tedious and swimming impossible. But in Singapore, patients at some hospitals and clinics now have another option—an open, 3D-printed cast that’s more comfortable to wear and fully waterproof. Castomize, the Singapore-based startup behind the product, says that it’s also easier for doctors to use. To apply the cast, the medical team first heats it up to become soft and flexible. Then a doctor wraps it around the arm and clips it together with small built-in buckles. As it cools, it hardens in place. The traditional process, by contrast, takes 10 different steps and multiple materials, and it’s easy to make mistakes. “Clinicians need to avoid wrapping casts too tight or too loose, where both scenarios would cause healing complications such as pressure injuries,” says Abel Teo, the company’s CEO. If there are problems, or as the cast loosens over time, patients have to come back to the doctor for a recast—with the hospital or clinic footing the bill. If the new cast needs to be adjusted as the patient heals, a clinician can instead remove, reheat, and reuse it. While the cast is around 30% to 50% more expensive to make than a traditional fiberglass version, the time savings—and the fact that it’s possible to avoid redoing the cast—can mean that clinics end up with a lower overall cost. In one trial, a hospital in Singapore has had an average of 25% cost savings, Teo says. In the future, the company plans to offer a sanitization process so that the casts can eventually be reused repeatedly for different patients. Castomize calls its process “4D” printing, since the final product involves the fourth dimension of time and it changes shape after it comes out of a 3D printer. Unlike a related product called ActivArmor, which uses 3D scanning for a custom fit, the Castomize product comes in standard sizes for adults and children and isn’t customized, helping reduce time and cost. The design started as a student project at the Singapore University of Technology and Design in 2017. One of the cofounders, Johannes Sunarko, revisited it as a master’s thesis in 2021, and then partnered with another former student, Eleora Teo, along with Abel Teo (no relation), to launch a startup to manufacture it. After clinical trials showed that it was effective as a replacement for a traditional wrist cast, the product got approval as a medical device in Singapore and came to market last year. It’s also approved for sale in Australia, South Korea, and Taiwan. Castomize is working on FDA approval and the CE (European Conformity) mark in Europe. The company also recently introduced an ankle model and elbow model. Each body part requires a new design. “We needed to work closely with clinician experts in ankle fractures and casting, along with researching and experimenting with different geometries and material combinations,” Abel Teo says. View the full article

Last September, OpenAI and Shopify made an announcement that sent ripples throughout the retail industry: They were partnering to launch Instant Checkout—a feature that would let people complete purchases directly within ChatGPT. Within months, the AI giant promised, we would be able to ask ChatGPT for Mother’s Day gift ideas or top-rated lightbulbs, and then click to buy products instantly. Shopify’s president, Harley Finkelstein, declared this the “the new frontier” of retail. But if you’ve tried to shop on ChatGPT recently, you know that this future never arrived. OpenAI quietly killed Instant Checkout in March. The official story, according to OpenAI’s blog post, was that the checkout feature “did not offer the level of flexibility that we aspire to provide.” The unofficial story is that OpenAI and Shopify were unprepared for the level of complexity checking out requires. Fewer than 30 of Shopify’s millions of merchants ever went live. This is the state of AI shopping in 2026. The same company reportedly being trained to guide drone strikes in active conflict zones cannot build a working check-out. My interviews with executives at Google, OpenAI, Stripe, Walmart, and a long list of AI-focused startups, revealed that the technology powering AI—large language models—is incompatible with existing e-commerce technology. Behind the scenes, there’s a massive effort underway in the retail industry to build the infrastructure required to make AI shopping possible. The commerce leads at Google and OpenAI, the two biggest players in the space, say that we’re months—not years—away from a tipping point where agentic commerce really will become commonplace. Whoever makes the shopping experience consumers want to use will own one of the most lucrative pieces of real estate in the history of retail. In this story you’ll learn: The knotty problem that forced OpenAI to pull back on instant checkout How frontier labs are rebuilding commerce infrastructure from the ground up Which company is most likely to win the AI shopping war OpenAI’s false start and new vision Last year, as AI became the fastest-adopted technology in history, AI companies realized they needed to turn their attention to commerce. There’s a lot of money hanging in the balance. According to McKinsey, AI-driven commerce could generate $1 trillion in U.S. revenue and up to $5 trillion globally by 2030. As we entered 2026, the retail industry’s newest buzzword was “agentic commerce,” which refers to AI agents shopping autonomously on the user’s behalf. “Nobody has figured it out, but everyone has FOMO,” says Emily Pfeiffer, principal analyst at Forrester, who covers AI and commerce. “Everyone is prematurely rushing to market.” Case in point: The botched Instant Checkout roll-out. When they announced the checkout feature, Shopify and OpenAI had promised that millions of Shopify merchants would soon be shoppable from ChatGPT, alongside Etsy sellers and Walmart. A tiny fraction of the integrations were built. “Shopify has major egg on their face,” says Omar Qari, the CEO of Logicbroker, which helps brands feed product data into LLMs. “If you go back to late last year, OpenAI said, ‘We’ve solved it. We’re connecting all the world’s products inside ChatGPT and it’s going to be an amazing shopping experience.’ But they literally couldn’t even get it live.” (Shopify declined to comment on this story.) Neel Ajjarapu, who leads commerce at OpenAI, admits that building a checkout was more complex than the company had envisaged, and ultimately, merchants were best positioned to build these tools. “It’s not enough to have a basic checkout page,” says Ajjarapu. “You need to think about things like loyalty points, in-store pickup, basket promos, and dozens of features that are specific to the geography, category, and merchant type.” Rather than try to build all of that from scratch, OpenAI decided merchants should own checkout themselves. “Merchants are already optimized this part of the funnel,” says Ajjarapu. “We’re going to make it really easy for them to bring that into ChatGPT.” But even without Instant Checkout, consumers are already turning to ChatGPT for their shopping needs: According to Pew, roughly 2% of queries to the chatbot—about 50 million a day—are shopping related. Ajjarapu says that ChatGPT is good at helping users figure out how to buy products that require a lot of research, like electronics, appliances, and sports gear. OpenAI has every intention of transforming ChatGPT into the world’s personal shopper. “The goal is for ChatGPT to be a super assistant,” he says. “When it comes to shopping, it should be able to help you find products, optimize carts, and buy things. It will be able to help you discover things that you never knew before, but are tailored to your personal circumstances.” The Google advantage Predicting taste is precisely where ChatGPT is at a disadvantage. The chatbot only has access to information you share in conversations to tailor product recommendations to your needs and taste. But its biggest competitor—Gemini — has access to a much deeper trove of knowledge about you thanks to all the information you have shared with Google over the years. In March, Google rolled out a feature called Personal Intelligence, which lets Gemini’s 750 million active users tap into their data in Gmail, Photos, and Drive when answering queries. Once you give Google the permission to access this data, the model will know everything from your travel plans to which brands’ marketing emails you open. According to Google’s early projections, 75 million users had activated the feature. “Once the model has the opportunity to learn about you, it can start at a better point than starting from scratch and expecting that you will tell us everything about you,” Srinivasan says. “Because it is much easier to give Gemini five pictures of clothes you like than to describe your dressing style.” Without years of data about the people using ChatGPT, OpenAI is in a much weaker position. It must gather crumbs from the details you happen to share in your conversations. “ChatGPT is starting to learn so much more about you as a user, not just in your retail taste, but everything else happening in your life,” says Ajjarapu. “We can start using that data to help make extremely well-personalized recommendations that match your taste.” But user data isn’t Google’s only advantage. It also has better access to product data. The Shopping Graph—Google’s real-time database of product pricing, inventory, and merchant relationships—traces its origins back to Froogle, a shopping platform that Google launched in 2002. That graph has been refined, expanded, and integrated with every part of Google’s ad and merchant infrastructure ever since. “We’ve had decades of experience with the Shopping Graph,” says Vidhya Srinivasan, Google’s vice president and general manager of advertising and commerce. “We’ve invested a lot in having the repository of products that has the diversity of merchants, but more importantly, it’s updated every second, every minute of every day.” Building the plumbing of agentic shopping One reason AI companies have struggled so much to build shopping tools is that their underlying technology wasn’t designed for commerce. Large language models are trained by scraping the entire textual archive of the internet—learning to predict the next word in a sentence or the next fragment of code. This has proven remarkably effective for drafting a college term paper or writing an app. But a product page is different from a web page. A lot of crucial product information—like inventory, shipping costs, and when it launched—does not appear on websites. “OpenAI’s first attempt at trying to get products into ChatGPT was to screen-scrape Dick’s Sporting Goods or Ulta and show their products,” says Qari. “And you can’t blame them, because that’s how they trained the model.” AI companies now realize they need to build the plumbing for agentic commerce from scratch. There’s currently a race to create a new standard to make retailers’ real-time product data readable by LLMs. OpenAI and Stripe co-developed the Agentic Commerce Protocol (ACP), which they open-sourced last year. Google, Shopify, and a coalition of two dozen retailers and payment companies—including Etsy, Wayfair, Target, Walmart, Visa, Mastercard, and Stripe itself—launched the Universal Commerce Protocol (UCP) in January. UCP is more robust, since it can handle complicated things like scheduling and returns. But, for the moment, every brand and retailer is being told it needs to support both. In a sign that Gemini is pulling ahead of ChatGPT in retail, Google has been announcing a string of new features over the last few months. Real-time pricing and inventory are already live on Gemini, as are in-chat checkout via Google Pay. As of March, Gap became the first major fashion retailer to allow shoppers to complete a purchase entirely inside the chat and in April, Ulta Beauty announced it would be doing the same thing. What happens next Today, shopping via chatbot means you’re ultimately shopping via the tradition web, with tabs that mushroom on your screen. But a few years from now, it’s likely that the ingredients I need for dinner will be ordered the moment I share the recipe with my AI agent. Christmas gifts—which currently eat an entire December weekend—will be handled in the time it takes to drink a coffee: the agent knows that my mother likes gardening books, that my daughter’s best friend is obsessed with slime, that I always overspend on my husband and should probably have a hard limit. When a wedding comes up, the agent will see it on my calendar and suggest appropriate dresses I like. Some purchases I’ll sanction with a tap. Others will simply show up, correctly, without my having asked. The infrastructure to make this happen is being built slowly, in fits and starts, with the occasional embarrassing pullback. But there’s a lot of money on the table, which is incentivizing AI companies to pour resources into building shopping tools. Whoever builds the best agentic commerce platform is going to have the first mover advantage and lock in a generation of consumers. Right now, the smart money is on Google. It has both the merchant relationships and deep knowledge of users, if they opt in to Personal Intelligence. And the protocol that Gemini’s checkout runs on— UCP—looks like the stronger foundation. But as with everything in AI, things are moving fast. And OpenAI is not out of the race. And the field is changing so fast, nobody can call the winner yet. “Every couple months, we just see such massive changes to what our models are able to do,” says OpenAI’s Ajjarapu. It is impossible for me to predict what’s going to happen on what timeline.” View the full article

How big buyouts are turning a profession into a platform. By CPA Trendlines Research Go PRO for members-only access to more CPA Trendlines Research. View the full article

How big buyouts are turning a profession into a platform. By CPA Trendlines Research Go PRO for members-only access to more CPA Trendlines Research. View the full article

I’m a big believer in the power of mindset. My journey as an entrepreneur has, frankly, demanded it. Building a business from scratch forces you into deeper work on self-inquiry and meta-cognition—that recursive question of Why do I think the way I think about this? It has pushed me to examine my assumptions, sit with discomfort, and deliberately fortify my inner life in ways I never anticipated when I started out. So when I sat down with Nir Eyal, author of the new New York Times bestselling book Beyond Belief, I expected a great conversation. What I got was an inspiration catalyst, a reframe that gave me fresh language and rigorous science for something I’d been doing intuitively for years: evolving my own belief system. Whether or not you’re actively working on yours, Eyal’s central argument will land: Your beliefs are not fixed truths. They are tools. And that distinction changes everything. Eyal arrived at this insight through a humbling experience. After spending five years writing Indistractable (a meticulously researched guide to managing attention), his phone began ringing with calls from readers who had absorbed every word but acted on none of it. “They’d waited months to talk to me, and when I asked them to walk me through what hadn’t worked, they said: ‘I read step one. I just didn’t do it,’” he told me, adding, “Then I realized I have books on my own shelf that I’ve read and not acted on.” That honest self-reckoning led Eyal and his coauthor—his wife, Julie Lee—to six years of research, which resulted in Beyond Belief: The Science-Backed Way to Stop Limiting Yourself and Achieve Breakthrough Results. The central argument is deceptively simple and practically explosive: Motivation is not a straight line between what we want and what we do, it is a triangle. And the third, overlooked vertex is belief. Behavior, Benefit, Belief We know what to do, Eyal argues. In an era of Claude and 24-hour access to every conceivable how-to, information is no longer the bottleneck. “You can know exactly what to do, want the benefit, and still not do it,” he says. “What’s missing is belief.” Beliefs, Eyal is careful to explain, are not the same as facts or faith. A fact is objective and unchangeable. For example, the Earth is not flat no matter what you believe. Faith is a conviction that requires no evidence and rarely shifts. But beliefs occupy the fertile middle ground: They are convictions that are open to revision based on new evidence. That malleability is precisely what makes them so powerful. “Beliefs are tools, not truths,” Eyal says. “And like a carpenter who only uses a hammer because it once worked really well, we carry around limiting beliefs that may have protected us at one point but no longer serve us.” Culture Is Codified Belief For leaders, the implications are immediate. Eyal points to Amazon’s “Day 1” mantra as a master class in organizational belief design. Employees at every level are encouraged to operate as though it’s always the company’s first day: scrappy, cost-conscious, and hungry. Is it literally day one at Amazon? Of course not. But that’s irrelevant. “Culture is codified belief,” Eyal says. “And when a belief is articulable and shared, it drives behavior at scale.” The opposite is also true. A limiting belief (“just another day at the office”) saps motivation and entrenches mediocrity. Eyal calls this distinction between limiting beliefs and liberating beliefs the practical heart of Beyond Belief: “A liberating belief increases motivation and decreases suffering. A limiting belief does the opposite. And the beautiful thing is, we can choose.” What You Believe Determines What You See The research Eyal cites to support this is striking. In one study, self-identified “lucky” people and “unlucky” people were given the same newspaper and asked to count the photographs. The unlucky group spent more than two minutes on the task. The lucky group finished in 11 seconds—because on the second page a large notice announced the total count and offered a reward. The unlucky group processed the page but never read the notice. It didn’t register. “Our beliefs determine literally what we are able to see,” Eyal says. Entrepreneurs, he argues, have what researchers call “entrepreneurial alertness.” They can spot opportunities others walk right past, not because they’re smarter but because they believe opportunity exists. I shared my own version of this with Eyal during our conversation. A few years ago I discovered open water swimming, and signed up for a SwimTrek trip that included a crossing from the island of Nevis to St. Kitts—at its narrowest point, that’s 4 kilometers of open ocean. I had seen the fine print before I arrived: Swimmers could do the crossing as a relay if they preferred. And I had quietly decided I would take that option. But then the day came, the guides didn’t mention it, and somehow I forgot to ask. I swam the whole thing—5 kilometers total, given the shifting currents—in just over three hours. When Eyal asked what I’d told myself to get through it, my answer surprised even me: “Suspend judgment.” Not “I can do this.” Not a pep talk or a performance belief. Just hold off on deciding what’s possible. Eyal lit up. “That’s a liberating belief,” he said. “The moment you suspend judgment instead of saying ‘I can’t,’ your motivation increases and your suffering decreases. That’s exactly what sustained you.” I had been using beliefs as tools without knowing that’s what they were called. This maps directly onto creativity. I’ve spent years arguing that the leaders who thrive are those who cultivate both wonder and rigor, the capacity to imagine and the discipline to execute. Eyal’s framework adds a necessary upstream layer: None of that is accessible if you don’t first believe you’re capable of it. If someone says “I’m not a creative person,” he told me, they’d be right, because with that belief they’re not even going to try. Belief as a Rudder in the AI Era As AI accelerates cognitive disruption across every industry, Eyal’s framework becomes especially urgent. When I pressed him on whether human capacities like imagination, intuition, and creative risk-taking could be automated, his answer reframed the question entirely. “In times of rapid change, beliefs become your rudder,” he said. “How you believe AI will affect you will change what you do with it.” If leaders approach AI as a threat (e.g., job-stealing, destabilizing, Terminator-adjacent), they are far less likely to leverage it effectively. But if they approach it as an expansion of human capacity, that belief itself becomes a competitive advantage. Beyond Belief is a genuinely useful book. It’s rigorous without being academic, and it’s personal without being self-indulgent. For leaders navigating uncertainty, its core insight is both liberating and demanding: You are the architect of your beliefs. That’s not a small idea. That’s the whole game. View the full article

On Wednesday, Nvidia and Corning announced a $500 million deal to build fiber-optic cables to power AI data centers. For Nvidia, which manufactures graphics processing units key to building and training top-tier AI models, the partnership will help the chipmaker reduce latency and energy consumption for AI systems and likely accelerate its move to co-packaged optics. This would have fiber connections more directly integrated with chips. Per a Securities and Exchange Commission filing, Nvidia now has a pre-funded warrant to purchase 3 million shares in Corning and the option to purchase 15 million more. As part of the agreement, Corning says it will increase its optical connectivity manufacturing tenfold and add more than 3,000 jobs, including at new factories in Texas and North Carolina. “Their commitment is directly fueling the expansion of our U.S. manufacturing footprint and creating more than 3,000 new high-paying jobs for American workers,” Corning CEO Wendell Weeks said in a statement. This is all, no doubt, evidence that the AI race is heating up. But it’s also just the latest deal for Corning, a New York-based aspects and materials science company that now plays a critical role in the U.S. technology manufacturing industry. As U.S. officials and tech investors look to pivot to hard tech and bolster the domestic supply chain for advanced manufacturing, it’s notable that Corning has already become integral to this sector. The Nvidia deal is only the latest example. This is all the more impressive considering that Corning was founded back in 1851 and has remained relevant, even amid remarkable evolution. Its résumé includes designing bulbs for Thomas Edison’s incandescent lamps, introducing the world to Pyrex cooking glassware, and now developing glass used in virtual reality headsets. By modern standards, companies are lucky if they last a few decades. Manufacturing comes with added challenges, including high up-front investments in production lines that can quickly become outdated. This makes Corning a unicorn of sorts. Consider that earlier this year, the company announced a new deal, worth up to $6 billion, to provide optical cabling and connectivity to Meta, and soon began construction on a new plant in Hickory, North Carolina, that will support its work for the tech company. Corning has also said it has two additional agreements with hyperscale customers that are “similar in size and duration” to the one with Meta, though it hasn’t revealed which ones. The company has had a spate of deals with a range of other technology companies working to develop next-generation tech. These include agreements with Lumen Technologies (to make optical cables for data centers), Xanadu (a Canadian quantum chip manufacturer), Broadcom (again, to build co-packaged hardware), and solar companies Suniva and Heliene (to make silicon wafers and polysilicon for the only solar panels assembled entirely in the U.S.). Generally, Corning stands to profit as companies look to phase out copper for fiber. And then, of course, is the company’s massive business making glass for smartphones and other electronic devices, including its robust Gorilla Glass business. Corning is a major supplier for Apple, and last year the two companies officially agreed to manufacture all iPhone and Apple Watch cover glass in Kentucky. Corning also makes glass for Samsung and Nokia, and has plenty of other business lines, too, including automotive and life sciences work. Not bad for a 175-year-old company. View the full article

The boom in data center construction is taking up much of the supply of high-tech components, especially processor and memory chips. This demand is squeezing consumer device makers, which are having trouble acquiring enough chips. This is happening even though data center servers and smartphones use different types of chips. The key distinction between consumer electronics and data centers is what they need chips to be optimized for. Smartphones and PCs require low power use, thermal efficiency, and tight integration. Data centers that run AI systems such as large language models, or LLMs, require maximum compute power, memory bandwidth, and storage throughput. To meet these needs, consumer devices tend to rely on systems-on-a-chip—chips that combine processing and storage—with dynamic random access memory, or DRAM, and NAND, a type of nonvolatile memory. In contrast, AI servers rely on graphics processing units, or GPUs, or other accelerator processors combined with high-bandwidth memory chips. I study global supply chains and how businesses respond to market constraints within these supply chains. The reason for the consumer electronics supply crunch has to do with the nature of the chip market: its concentration, high costs, and how it responds to boom-and-bust cycles. AI is not replacing consumer electronics; it is reorganizing the chip market around new priorities for specific chip characteristics. Data centers are pulling capital and scarce memory capacity toward the production of accelerator processors and high-bandwidth memory and the data handling and electronics equipment that surround them. Chipmaking explained. A winner-takes-most industry Chip manufacturing behaves less like a competitive commodity market and more like a layered oligopoly. Scale matters because the leading firms can reinvest in research, improve yields, secure equipment, and deepen customer relationships. In the case of graphics processor chips, designers such as NVIDIA, which has 85% market share, depend on advanced semiconductor foundries such as TSMC, which has more than 70% market share, to manufacture chips using extreme ultraviolet lithography machines from ASML, a monopoly. A small number of producers both design and manufacture memory chips. Currently, three companies—Samsung, Micron, and SK Hynix—hold a majority market share in the memory chips market. Long development cycles, extremely high fixed costs and the need for technological leadership reinforce concentration over time. Consumer electronics firms such as Apple, along with other technology firms such as Amazon, Google, Microsoft, and Xiaomi, increasingly design their own processor chips, because these chips shape the user experience, AI performance, power efficiency, and system-level differentiation. Manufacturing memory chips, by contrast, is extraordinarily capital-intensive; requires high precision, efficiency, and production line utilization; and is dominated by a few incumbent suppliers. Since 2000, the memory chip industry has moved through repeated cycles of overcapacity and undersupply: the post-dot-com collapse, the 2007-09 glut, the tighter 2010s after consolidation, the severe 2022-23 downturn, and the AI-driven tightness of 2024-25. This has led to high levels of concentration in the industry and chipmakers that are hesitant to add capacity. Producers often operate chip fabrication plants, or fabs, at or near capacity due to high fixed costs. The risk of having expensive facilities go underused keeps chipmakers from bringing new fabs online in lockstep with demand increases. Consolidation has reduced the number of major suppliers, who now increasingly direct investment toward higher-margin products rather than broadly adding capacity. That shift is important for understanding why AI demand is tightening chip supplies even as demand for consumer electronics continues to grow. The most advanced computer chips are made with a machine manufactured by one Dutch company. How the AI data center boom redirects capacity The AI boom has changed memory demand from a broad consumer cycle into a more segmented market centered on high-bandwidth memory chips. In 2023, Micron cut capital spending and the company’s fabs operated below levels needed to justify their cost. By 2026, however, Micron was reporting strong AI demand, record data center DRAM revenue, and rapidly rising high-bandwidth memory sales. This shift matters because the market for supplying memory cannot respond quickly. Opening new fabs requires years of planning, large capital commitments and investments in advanced process equipment and skills. Memory chip manufacturers are likely to remain cautious about expanding capacity even as their profitability improves, with 2026 spending focused more on technology upgrades and high-value products than on large increases in chip supply. In practical terms, AI is not simply lifting all memory demand equally; it is redirecting scarce capacity toward massive, or hyperscale, data centers and server markets first. Can consumer electronics catch up? Consumer electronics can catch up, assuming the manufacturers can weather the cost increases from tariffs and geopolitical pressures. One way they could is by making investments to enable small AI language models to run on consumer devices, a move analysts expect the companies to attempt. Apple shifted a growing share of U.S.-bound iPhone production out of China to India and moved much of its iPad, Mac, Apple Watch, and AirPods assembly for the U.S. market to Vietnam to lower the company’s tariff burden. Yet relocation does not eliminate cost pressure. Manufacturing iPhones in India still costs roughly 5% to 8% more than in China, and in some cases closer to 10%, because supplier ecosystems, logistics, and production efficiency remain stronger in China. Rising geopolitical tensions between the United States and China led to supply constraints and export controls on critical minerals and chip components, raising input costs for consumer electronics manufacturers. This led to higher total import costs and reduced margins for firms unable to pass costs fully to consumers, leading to further consolidation in supply. Consumer devices do not need to replicate data center infrastructure to offer AI on their products. Their opportunity lies in running small language models on-device for summarization, rewriting, search, assistance, and lightweight reasoning. Doing so, however, creates a distinct hardware requirement. Phones and laptops need to incorporate multiple functions on the same chip, combining processing capability with fast local memory and enough storage to keep on-device AI responsive. Apple’s current device requirements for the company’s AI, Apple Intelligence, also show that older phones often lack the compute power and memory needed for useful on-device AI. To adopt AI, device makers need to redesign their products with higher-end chips—both processors and memory—that can piggyback on the AI model-oriented growth in the chips market driven by the data center boom. Such a shift by the device makers could also provide a useful backstop for the memory chipmakers in case the projected AI and data center growth does not materialize in the medium to long term, a boom-and-bust cycle that memory chipmakers have had to endure many times in the past. What this means for the wider economy The AI and data center boom is redistributing capital, supplier attention and pricing power across the broader economy. Sectors with limited purchasing leverage are especially vulnerable when chip supplies tighten. For example, medical technology accounts for less than 1% of the overall chip market, leaving essential equipment manufacturers exposed during shortages. In contrast, sectors linked to power delivery and digital infrastructure may benefit from the boom because they try to keep up with demand for cloud services and electrification. The International Energy Agency estimates that data centers consumed about 415 TWh of electricity in 2024 and notes that AI is accelerating the deployment of high-performance servers, which implies stronger demand for the grid, storage, cooling, and networking equipment around them. For the consumer electronics industry, the strategic task is not to try to match the AI data centers chip for chip but to build differentiated, energy-efficient, on-device AI services while managing higher supply chain and tariff risks. And for consumers looking to buy phones, games and laptops, because of high demand from data centers, the next few years are likely to bring higher prices, shortages, and delayed product releases. Vidya Mani is an associate professor of business administration at the University of Virginia and Cornell University. This article is republished from The Conversation under a Creative Commons license. Read the original article. View the full article