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Shares in Qualcomm Incorporated (Nasdaq: QCOM) are surging in premarket trading this morning after reports emerged that the company may be on the cusp of a deal with artificial intelligence giant OpenAI. The deal would see Qualcomm CPUs powering a potential OpenAI smartphone—and would be a further sign that AI may shift from being primarily GPU-powered to CPU-powered. Here’s what you need to know. Will the CPU replace the GPU in the AI space? Currently, the most important computing component underpinning the AI era is the Graphics Processing Unit (GPU). Traditionally, this was a dedicated processor designed to render 3D graphics and video, and it was especially critical in the gaming sector of the computer industry. But in the AI era, the processing power of GPUs has made them a perfect tool for high-performance tasks like training and running large language models (LLMs), which are the backbone of chatbots. The importance of GPUs in AI development has made GPU king Nvidia the most valuable company on the planet. But in the near future, the AI industry will go through a shift. The role of CPUs is expected to become even more important in data centers, as CPUs become more advanced and capable. This is good news for CPU makers like Intel Corporation (Nasdaq: INTC), whose stock soared last week, driven primarily by data center and AI (DCAI) revenue growth. This shift toward the growing importance of CPUs in the AI space is also likely to accelerate, as more advanced CPUs enable more AI models to run locally on personal devices like smartphones, freeing LLMs from the power-hungry GPUs packed into today’s data centers. Once that happens, smartphone makers will likely be lining up to get their hands on the most capable AI CPUs on the market—and that is something Qualcomm may soon benefit heavily from. OpenAI’s rumored smartphone may have a Qualcomm chip inside For years, ChatGPT maker OpenAI has been rumored to be working on a physical device intended to be the primary way you interact with AI. Some suggest this device might be screenless, taking the form factor of a pen or a pendant, but others suggest that OpenAI may instead just launch its own smartphone. And now a report from respected TF International Securities analyst Ming-Chi Kuo seems to corroborate this. Writing in a post on X, Kuo says his latest industry checks have revealed that “OpenAI is working with MediaTek and Qualcomm to develop smartphone processors,” and he believes these chips are likely destined for an OpenAI phone. The report from Kou, who is known for his excellent track record of sniffing out the biggest plans of large tech giants—including Apple—through his supply-chain sources, is the main reason why Qualcomm stock is soaring today. If Qualcomm will indeed be one of the major chip partners for a future OpenAI phone, the company’s coffers stand to benefit enormously. Qualcomm stock surges after OpenAI report As of this writing, QCOM stock is currently up more than 12.5% to $167.50 per share. The company’s stock price closed at $148.85 on Friday. Relatedly, on Friday, QCOM stock surged more than 11% after Intel’s earnings report, suggesting the CPU was growing ever more important in the AI era. It should be noted, however, that neither Qualcomm nor OpenAI has publicly commented on any CPU deal. Fast Company has reached out to both companies for comment. Still, Kou’s report seems to have excited investors. As of Friday’s close, QCOM shares were still down nearly 13% year to date. But with today’s further stock price news driven by the OpenAI speculation, Qualcomm is very close to being back in the green, if today’s premarket jump holds. Over the past 12 months, QCOM stock was up about 1% as of Friday’s closing price. Qualcomm is expected to announce its Q2 2026 earnings on Wednesday. View the full article

This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps. 2026 is already overflowing with new and improved sites and services. In today’s post I’m sharing five I’ve tested and found particularly useful. Kraa: Make Gorgeous Documents I love minimalist tools like the free Kraa, a wonderful new digital writing surface. I’ve started experimenting with creating quick, simple pages, which Kraa calls “leaves.” The example pages shared by Kraa’s founding team will give you a feel for it: A news story with an image gallery, pull quotes, and comments A blog post with images, quotes, and comments A portfolio or brand page with big images and less text A welcome page about Kraa that I’ve customized a bit. I added a chat at the bottom so you can try it out and add your own comment about Kraa. Ideas for using Kraa Make a travel page to share pictures and stories from a trip. Benefit: Easier than creating a whole WordPress site, and much prettier than a Google or Word Doc. Create an impromptu, elegant, free live running chat for a meeting or event, like the Kraa team’s public chat page. Benefit: Faster, freer, easier, and more elegant than other tools for creating a running chat page. Write a manifesto or blog post about something you care about. Benefit: Get more design flexibility than you would with WordPress, Craft, or Medium. Leonardo AI Blueprints: A Shortcut for AI Visuals Sophisticated image editing with AI usually requires careful prompting. What I like about Leonardo AI’s new Blueprints feature is that you can just upload an image and pick from dozens of styles, without knowing what words to use in a prompt. You can transform boring or sloppy headshots into creative images. Here’s an experiment with one of my headshots, and another. Leonardo, now owned by Canva, also has lots of other useful features. The platform hosts multiple AI image and video generators, including Flux, Gemini, Ideogram, and Sora. Flow State lets you type in a prompt and pick from dozens of resulting AI visuals. If you see one you like, you can select “more like this.” Upscaler enhances images’ resolution and size, which is useful if you’re printing pictures or showing them on a big screen. Pricing: Free for up to 150 fast image creations a day. $10/month billed annually for more images, private images, better AI models, and other pro features. YouTube to NotebookLM: Import a Whole Playlist or Channel in One Click YouTube to NotebookLM is a remarkably useful new Chrome extension that lets you bulk-add any YouTube playlists, channels, or search results into NotebookLM. for AI-powered analysis. How to use it: Install the free Chrome extension. Then navigate to a YouTube channel or playlist of interest, or even a YouTube search result. Click the bookmarklet you’ve installed in your browser to import the entire batch of videos into a new or existing NotebookLM notebook. (The free version of NotebookLM allows a maximum of 50 sources in a notebook). What I tested: I pulled in 80 of my own YouTube channel videos into a new notebook with one click. I was surprised at how quick and easy it was. I immediately started analyzing the video collection and generating a report. What to try: Summarize a playlist or channel with an audio or video overview. Or create quizzes, flash cards, data tables, or mind maps to explore a batch of YouTube videos. Or have a chat in NotebookLM with your favorite video channel. Check my recent post for some YouTube channels to try. Find or create YouTube playlists on topics of interest. Then use this extension to ingest those playlists into NotebookLM. The videos are automatically indexed, and within minutes you can create reports, slides, and infographics to enhance your learning. Agenda Hero: Add Anything to Your Calendar Ever get a flyer with upcoming events, or a sports schedule, or a list of upcoming meetings, and face the tedious task of manually adding each event to your calendar? Agenda Hero solves this pain point delightfully. What it does Scans physical or digital event lists and adds them to your calendar Works with photos, PDFs, scans, emails, or pasted text Works with calendars from Google, Apple, or Outlook, or Office 365 Use the Chrome Extension to clip event dates and times from the Web or use the free iOS or Android app to add events from your phone. Capture schedules from school handouts, work training dates, or concert listings. How it saves time: Instead of spending 10-20 minutes adding events one by one, snap a photo or upload a document. Agenda Hero extracts the events and lets you add them in bulk to your calendar. Real-world example: My daughter and I like going to occasional Columbia Women’s Basketball team games. So we took a picture of their schedule card. Agenda Hero quickly imported all the dates. Now they’re on my calendar instead of on a piece of paper in a drawer. You can share a link to your event list, like this one I created for the upcoming home games. Bonus features Create and share a customized event page for your group Use AI chat to batch edit a bunch of events Pricing: Free for most features, or $30/annually for unlimited AI edits and custom URLs and colors for your shared pages. This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps. View the full article

Scott Kirby criticises rival for ‘closing the door’ on deal he believes would have created jobsView the full article

We analyzed billions of visits across 50,000+ sites to understand which traffic channels are growing, which are declining, and how the mix is shifting across industries. View the full article

AI visibility now depends on crawl access, server-rendered content, semantic HTML, and machine-readable structure beyond Googlebot. The post The Technical SEO Audit Needs A New Layer appeared first on Search Engine Journal. View the full article

AI systems are getting better at generating Spanish. They’re not getting better at understanding Spanish markets. What we’re seeing instead is a consistent pattern: more than 20 Spanish-speaking countries collapsed into a single default. Spain becomes “standard.” Mexico becomes interchangeable. The rest get flattened into statistical averages. The failure modes are structural — dialect defaulting, format contamination, and regulatory hallucination — and they’re amplified in a generative search environment where one synthesized answer replaces 10 blue links. That distinction is now a visibility constraint. Generative systems resolve ambiguity. When your content doesn’t make its market context explicit, the system defaults to the statistical average — and that’s where otherwise solid content gets misapplied or ignored. Below is a framework for fixing that problem. It’s designed to make market context explicit — across content, technical signals, and retrieval systems — so AI doesn’t have to guess. What is cultural SEO? Cultural SEO goes beyond hreflang and localization. The technical foundation is locale precision — controlling market context across retrieval and generation so an AI system treats your Spanish content as belonging to a specific country, not to “Spanish speakers” in the abstract. Here’s the framework that works when you operate across Spain and Latin America. But there’s a prerequisite no framework can substitute for: you can’t optimize for a market you don’t serve. Cultural SEO isn’t a localization layer you bolt onto a website. It’s the technical expression of a business decision to operate in a market — with real logistics, real customer support, real legal compliance, and real product-market fit. If you ship from Spain to Mexico with a three-week delivery, process returns in euros, and have no local support channel, a perfect hreflang setup won’t save you. The model might surface your content, but the user will bounce — and the next time the model learns from that signal, you’ll be deprioritized. Internationalization means speaking the market’s language in every sense: visual trust cues, payment methods, delivery expectations, regulatory compliance, and customer experience. The four pillars below assume you’ve made that commitment. If you haven’t, start there. Everything else is decoration. 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 Pillar 1: Market segmentation at the entity level Most international SEO teams think of segmentation as a folder structure: /es-es/, /es-mx/, /es-ar/, but that’s not enough. In generative search, the question is whether the system recognizes that page as belonging to Mexico — and whether it has enough market-specific signals to prefer it over a generic alternative. If your architecture collapses variants, your visibility collapses with it. Implement granular hreflang and URL structures Don’t just use es. Use es-ES for Spain, es-MX for Mexico, es-AR for Argentina, es-CO for Colombia, and es-CL for Chile. Include x-default for users who don’t match any specific locale. Consider ccTLD strategies (.es, .mx, .com.ar) where they make business sense. ccTLDs remain one of the strongest explicit geographic signals on the open web, and they reduce ambiguity for both search engines and downstream retrieval systems. Google’s documentation on localized pages supports this specificity. But here’s the caveat. In the first article, I discussed Motoko Hunt‘s concept of geo-legibility and the phenomenon of geo-drift — AI systems misidentifying geography because language alone doesn’t resolve market context. Simply put, if your Spanish content doesn’t carry explicit country-level signals beyond hreflang, the model has to guess. Guessing, at scale, means defaulting. Ultimately, hreflang helps with traditional routing, but in AI synthesis, it’s one signal among many — and not necessarily the decisive one. When a generative system assembles an answer, it weighs semantic relevance, authority, and content-level cues alongside metadata. If your Spanish content relies on hreflang alone to declare “this is for Mexico,” you’re betting on a single signal in a multi-signal environment. Geographic markers need to live in the content itself and in structured data — not only in HTTP headers. Dig deeper: How AI search defines market relevance beyond hreflang Don’t canonicalize all locales to a single master URL When you point es-MX, es-AR, and es-CO pages to one canonical es URL, you’re telling engines there’s only one “real” version — the exact Global Spanish assumption you’re trying to avoid. Each market page should canonicalize to itself. Avoid IP-based redirects Google cautions against this. Crawlers may not see all variants. More importantly, AI crawlers don’t carry IP signals the way users do. Offer a visible region selector and let users choose. Encode market cues in structured data This is essentially what Hunt calls geo-legibility — encoding geography, compliance, and market boundaries in ways machines can parse: Use priceCurrency with ISO 4217 codes (EUR, MXN, ARS, COP, and CLP). Use PostalAddress with explicit addressCountry. Add areaServed to declare which markets you serve — the machine-readable equivalent of saying “we operate here, not everywhere Spanish is spoken.” Use sameAs to connect to region-specific knowledge graphs (e.g., link your Mexican entity to Mexican directories and chambers of commerce, not just your global Wikipedia page). A practical example: if your Mexico page shows prices in MXN, but your structured data still says EUR because it was copied from the Spain template, the model sees a conflict. Conflicts breed uncertainty. Uncertainty breeds generic answers. Generic answers are where Global Spanish lives. A note on es-419: It can be useful as a catch-all for Latin American Spanish where market-specific pages don’t exist, but it should never substitute for es-MX, es-AR, or es-CO when the content involves legal, financial, or compliance information. Generic means vulnerable. If your market pages aren’t self-evident to machines, the system will resolve ambiguity for you — and defaults win. Pillar 2: Transcreation, not translation Translation converts words. Transcreation converts meaning. The distinction matters because translated templates are easy for models to deduplicate — and deduplication is where localized pages go to die. If two regional pages are 95% identical, the model will treat them as one. The “default” will win. Localized pages need substantive differences that prove market specificity, including: Local examples and FAQs: A FAQ about tax deductions should reference SAT in Mexico, AEAT in Spain, and AFIP in Argentina — not all three in a dropdown. Local legal references: Privacy content should cite GDPR + LOPDGDD for Spain, and LFPDPPP for Mexico, not a generic “applicable data protection laws.” Native terminology: Zapatillas vs.tenis, ordenador vs.computadora, and cesta vs.carrito. These aren’t synonyms. They’re market identifiers that signal “this content was made here.” Local pricing and formatting: Not just the currency symbol — the entire numeric convention. Spain uses 1.234,56 € while Mexico uses $1,234.56. Get it wrong, and the content reads as imported. Local proof: Testimonials, case studies, partnerships, and press coverage from the target region. Not imported. When a model evaluates whether your content is authoritative for Mexico, it looks for Mexican corroboration. The classic example: McDonald’s “I’m lovin’ it” became “Me encanta” — not a literal translation, but an emotionally equivalent expression. Apple’s iPod Shuffle tagline, “Small talk,” became “Mira quién habla” for Latin American Spanish. These brands understood that meaning doesn’t translate. It must be rebuilt. Start with keyword research Identify which Spanish-speaking markets have the most search volume and business potential for your verticals. Volume alone isn’t enough. Consider market maturity, competitive landscape, and conversion potential. Then bring in native speakers from those specific countries. This doesn’t mean rigid dialect policing. Context matters — a premium brand in Mexico City might use tú deliberately for intimacy. The test is whether those choices are strategic or inherited from the training data’s statistical average. What ‘substantive difference’ looks like in practice Take a returns policy page. Spain (/es-es/devoluciones/) and Mexico (/es-mx/devoluciones/) shouldn’t differ only in currency symbols. At least one section needs to be genuinely market-specific: Spain: Consumer rights framing under EU regulation, SEUR or Correos as default carrier, Bizum as a familiar local payment entity, and vosotros register. Mexico: PROFECO consumer authority framing, local paqueterías as shipping context, OXXO as a familiar local payment context (where relevant), and ustedes register. Both: Distinct FAQs written in the market’s register, addressing questions that actual customers in that country ask. If the pages are 95% identical after these changes, they’re not differentiated enough. The model will still collapse them. The feedback loop makes it worse: when a Mexican user lands on “españolized” content and bounces, that rejection signal teaches the model not to retrieve that page for Mexico next time. Poor transcreation doesn’t just lose one visit. It trains the system against you. Pillar 3: Retrieval constraints (locale-locked sourcing) This pillar addresses a layer that most traditional SEO doesn’t touch — and it’s where a lot of the Global Spanish problem actually lives. If you’re building RAG-powered experiences (chatbots, AI assistants, and AI-enhanced customer support) or optimizing content for AI discovery, the question is: What content is eligible to be retrieved and synthesized for a given market? Without explicit constraints, the model pulls from its statistical average — which, in this case, is “Global Spanish.” The fix requires intervention at the retrieval layer: Filter sources by locale metadata before generation begins: Don’t let a Mexican user’s query pull from your Spain knowledge base unless you’ve explicitly marked that content as applicable to Mexico. Prefer user-declared markets over inferred signals: If a user selects “Mexico” in your interface, that should be a hard constraint, not a suggestion. Use hard constraints in system prompts: “Spanish (Mexico), MXN, SAT, Mexican legal context” — not just “Spanish.” The more specific your retrieval parameters, the less room the model has to improvise. Think of it as the AI equivalent of telling your customer service team: “If a caller is from Mexico, use the Mexico playbook. Don’t improvise.” This matters beyond your own properties. Up to 43% of fan-out background searches ran in English even for non-English prompts, Peec AI’s analysis found. This is a structural disadvantage for brands whose authority signals exist only in local-language corpora. Spanish sessions may still trigger English sub-searches, which changes which sources are eligible for retrieval. If the model’s own retrieval is biased toward English sources, your Spanish content needs to be unambiguously market-specific to compete for selection. Pillar 4: Market authority through entity reinforcement LLMs learn from your site and what the web says about you. This isn’t traditional link building. It’s regional corroboration — building the external signal layer that tells a model where your brand operates and who considers you authoritative: Local media mentions: A feature in top-tier national business press in your target market carries different geographic weight than a mention in a U.S. or U.K. publication. The model infers where you’re relevant from who talks about you. Local industry citations: Partnerships with local chambers of commerce, industry associations, and regulatory bodies. Region-specific knowledge graph reinforcement: Your Google Business Profile, local directory listings, and Wikipedia presence should all consistently reflect which markets you serve. Local backlink ecosystem: Links from .mx, .es, and .ar domains reinforce geographic authority in ways that generic .com links don’t. This is how you stop being a Spanish brand and become a Mexican authority — or both, explicitly. The key is intentionality: If you serve both markets, the model needs to see distinct authority signals for each, not a single blended profile. Get the newsletter search marketers rely on. See terms. What to ship (per pillar) If you need to brief a cross-functional team — dev, content, PR — here’s what each pillar produces as a deliverable: PillarDeliverable1. SegmentationLocale URL map + hreflang/canonical rules + indexable alternates checklist2. TranscreationPer-market glossary + “substantive difference” content brief template3. Retrieval constraintsLocale filters + prompt contract (market, currency, jurisdiction)4. Entity reinforcementQuarterly PR/citation target list per market + entity consistency audit Pillar deliverables — what each pillar produces as a briefable output for cross-functional teams. These are the artifacts that make the framework auditable and repeatable across teams. Measuring cultural mismatch: an error taxonomy You can’t improve what you don’t measure. Here’s a practical error taxonomy for auditing AI-generated content across Hispanic markets: Error classWhat to look forSEO/UX impactDialect markersWrong pronouns, missing voseo, region-inappropriate vocabularyTrust erosion, higher bounce ratesFormat errorsWrong currency, decimal separator mismatch, incorrect date formatsConversion risk, especially in e-commerce and financeLegal/regulatoryWrong authority cited, incorrect compliance steps, mixed frameworksE-E-A-T damage, potential liabilitySERP intentWrong product categories, wrong local entities, incorrect eligibilityClick-through and engagement dropsBrand voiceFormality mismatch (too formal in Mexico, too casual in Colombia)Brand perception damageRetrieval contaminationFacts or citations sourced from a different locale than the target userErrors propagated into AI summaries Cultural Mismatch Error Taxonomy — six error classes for auditing AI-generated content across Hispanic markets. If you want a quick QA starting point, check three things first: the currency symbol, the regulator name, and the second-person register. Those three alone will catch most critical mismatches. The regional signal table For teams working across multiple Hispanic markets, these are the signals that most commonly trigger cultural mismatch in AI outputs: SignalSpain (es-ES)Mexico (es-MX)Argentina (es-AR)Colombia (es-CO)Chile (es-CL)Second-personVosotros/ustedesUstedes; túVos/ustedesTú/usted variesTú/ustedes; local slangCurrencyEUR (€)MXN ($)ARS ($)COP ($)CLP ($)Decimal separatorComma (1.234,56)Period (1,234.56)VariesVariesVariesHreflanges-ESes-MX / es-419es-ARes-COes-CLPrivacy frameworkGDPR + LOPDGDDFederal law (2025 changes)Habeas DataNational data protectionUpdated legislationFiscal/commercial IDNIF / CIFRFCCUIT / CUILNITRUTTypical LLM default riskGrammar as “standard,” vocab ignoredVocab as “standard,” context flattenedVoseo erased or flaggedUstedeo misidentifiedLocal markers missed Regional Signal Comparison — key locale markers across five major Hispanic markets. Note: number formatting can vary by platform; the key is internal consistency within a market experience. Regulatory details evolve; the point is to prevent wrong-jurisdiction defaults in YMYL content. Where this breaks first: YMYL verticals Not every industry feels this problem equally. But if you work in any of these verticals, cultural SEO means risk management. Finance: Regulators, tax logic, product naming, and ID formats. Wrong jurisdiction bleed means your AI-generated content isn’t just unhelpful — it may be noncompliant. Legal: Rights language, jurisdiction references, and compliance frameworks. An LLM citing GDPR to a Mexican user isn’t being cautious. It’s being wrong. Healthcare: National agencies, approved terminology, and safety messaging. Drug names, dosage conventions, and regulatory bodies differ across every market. Ecommerce: Payment methods (Bizum ≠ OXXO), shipping norms, returns, and installment culture. When your market cues conflict, the system classifies you as “not for this market.” And in GEO, classification is destiny. In these verticals, the cost of Global Spanish is a liability exposure, compliance failure, and E-E-A-T erosion that compounds across every AI-generated interaction. Making it operational Frameworks are only useful if they translate into Monday morning actions. Here’s how to operationalize cultural SEO: Week 1: Baseline audit Re-run the Article 1 Spain vs. Mexico checks across your top five transactional queries. Log mismatches (currency/format, jurisdiction, and register). This is your baseline. Week 2-4: Technical foundation Fix hreflang, canonicals, and structured data. Ensure each market page canonicalizes to itself, carries correct priceCurrency and addressCountry, and has areaServed declarations. Remove any IP-based redirects that might block AI crawlers. Month 2-3: Content differentiation Prioritize your highest-traffic market pages for transcreation. Aim for at least 30% substantive content difference between regional variants — different examples, legal references, and local proof. Month 3-6: Entity reinforcement Build market-specific authority signals: local media coverage, directory listings, and partnerships. Ensure your knowledge graph presence is consistent and market-specific. Ongoing: QA and governance Implement dialect stress tests across target markets. Set up automated monitoring for jurisdiction bleed in any AI-generated or AI-surfaced content. Establish an escalation path for YMYL content where market context can’t be confirmed. Two metrics worth tracking from Day 1: Market mismatch rate: Percentage of outputs with wrong jurisdiction, currency, or register. Wrong-jurisdiction reference rate: Regulators or laws cited from the wrong country, YMYL pages only. If you can measure those two consistently, you can prove the framework is working. A note on what actually matters Everyone’s talking about markdown formatting, llms.txt files, and structured data for AI. Some of that matters. But before chasing the latest optimization trick, review your: Documentation. Help center Knowledge base. Product docs. That’s what LLMs are actually reading and what shapes whether an AI assistant recommends you or your competitor. If an LLM had to explain what your product does in the Mexican market based only on what’s public, would the answer be any good? If not, you don’t have an AI optimization problem. You have a documentation problem. The fix? Sit down and write clear, market-specific docs that both humans and machines can understand. If you want a more structured approach, I’ve put together a cultural SEO checklist for Hispanic markets covering technical signals, content signals, entity signals, retrieval constraints, and QA governance. cultural-seo-checklistDownload 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 Try it yourself: 5 prompts, 2 markets Before moving on, run these five prompts through any LLM — once specifying Spain, and once specifying Mexico. The differences in the output should be intentional, not accidental: “Explain how to request an invoice for an online purchase.” “What ID number do I need to register as a freelancer?” “Write a returns policy snippet for a €49.99 / $49.99 product.” “Customer support reply: delayed delivery (mention dates and currency).” “Best prepaid mobile plan — budget option.” If the answers are identical, the model is defaulting. If they differ but cite the wrong jurisdiction, you have a retrieval problem. Either way, now you know where to start. A word of warning — for us There’s an irony in this article that I don’t want to skip over. We’re telling brands to stop treating Spanish as a monolith, build market-specific signals, and respect the difference between Madrid and Mexico City. Then we go back to our desks and use ChatGPT to do keyword research “in Spanish.” We generate content briefs with tools that have the exact same geo-inference failures we just diagnosed. We run audits with AI assistants that default to the same “Global Spanish” we’re warning our clients about. If the tools we use every day carry this bias, then every output we produce risks inheriting it — unless we’re actively correcting for it. That means specifying the market context in every prompt. Don’t trust a “Spanish” keyword list that doesn’t distinguish between markets. Treat your own AI-assisted workflows with the same rigor you’d ask of your clients’ content architectures. The “Global Spanish” problem is also in your own stack. If you’re not fixing it there first, you’re part of the pattern. From global content to market-specific systems The goal is to produce Spanish that is market-true. In 2026, “localized” is a systems milestone: routing, content, entities, retrieval, and QA all have to agree on the same country context — or the model will pick one for you. If you want a definition of done for cultural SEO, it’s this: Spain and Mexico can ask the same question and get different answers for the right reasons — and your pages are the ones that stay eligible to be cited. Stop translating. Start architecting. View the full article

Google may be doing something right with improving the click-through rates from the organic search results page that contain AI Overviews. An update report shows the CTR is improving, after numerous times showing the CTR has been declining. View the full article

Google's Ginny Marvin, the Ads Liaison, has confirmed there are delays for some Demand Gen Image ads to be approved. Some of these delays are several days pending, not hours, but several days.View the full article

Microsoft Bing is testing making the links within the AI results, the Copilot Search results, less clickable. Typically, the whole line of text is clickable to the citation but here, Bing is testing only linking the citation mark at the end.View the full article

Starting a business bookkeeping service can seem intimidating, but breaking it down into seven simple steps makes it manageable. First, you need to identify your target market and craft a business plan that suits their specific needs. From obtaining necessary certifications to establishing your business infrastructure, each step is essential for success. By focusing on effective marketing strategies and client relationships, you can set yourself apart in a competitive field. So, what’s next on your expedition? Key Takeaways Identify your target market by analyzing their specific bookkeeping needs and financial challenges to tailor your services effectively. Obtain necessary certifications to enhance your skills and credibility, ensuring compliance with industry standards. Register your business and choose an appropriate structure, such as an LLC or sole proprietorship, while obtaining required licenses and permits. Establish a professional online presence with a website that clearly outlines your services and pricing to attract potential clients. Implement a CRM system for efficient client management and utilize social media for marketing and networking to expand your reach. Choose Your Target Market Choosing your target market is a crucial step in establishing your bookkeeping business. When you’re figuring out how to start a bookkeeping company, consider who’ll benefit most from your services. Identifying your target market allows you to differentiate your offerings, tailoring them to meet the unique needs of specific niches, like small businesses, freelancers, or e-commerce companies. Conducting market research helps you understand the demand for bookkeeping services in your chosen sector and pinpoint potential competition. Defining your ideal client profile—considering business size, industry, and financial challenges—ensures your marketing efforts are focused and effective. Furthermore, networking within your target market can lead to valuable referrals, helping you establish a reputation as a trusted bookkeeper. Utilizing social media platforms, especially LinkedIn, can improve your visibility, connecting you with serious business owners actively seeking bookkeeping support in your target market. Develop a Comprehensive Business Plan A thorough business plan serves as the backbone of your bookkeeping venture, providing a clear roadmap that outlines your goals and strategies. To develop this plan effectively, focus on the following key elements: Business Goals: Define what you want to achieve in the short and long term. Target Market: Identify your ideal clients and their specific needs. Competition Analysis: Examine your competitors to find gaps you can fill. Financial Projections: Estimate your income, expenses, and set realistic financial goals. Include a detailed description of the services you offer, along with pricing structures and your unique value proposition. Conduct market analysis to assess demand and verify your plan reflects current trends. Regularly review and update the business plan to adapt to changes in the market or your growth, keeping it a relevant tool for guiding your bookkeeping business. Obtain Necessary Certifications To establish yourself as a credible bookkeeper, obtaining certifications is essential. Various options are available, including QuickBooks Online certification and designations like Certified Bookkeeper (CB) or Certified Public Bookkeeper (CPB), each enhancing your skills and marketability. Engaging in ongoing education and certification renewals furthermore guarantees you stay relevant in this constantly changing industry. Importance of Certification Certification plays a crucial role in establishing a successful bookkeeping career. By obtaining certifications, you not only improve your skills but also increase your marketability. Here are some key benefits of pursuing certification: Boosts your comprehension of software like QuickBooks Online Increases job opportunities and potential earnings Provides access to valuable training resources from reputable organizations Demonstrates your commitment to the profession, attracting clients Having a recognized certification signals to businesses that you’re knowledgeable and trustworthy. Clients often prefer certified professionals, which can greatly impact your ability to secure and retain them in a competitive market. Investing in certification is a strategic move for your bookkeeping career, ensuring you’re well-prepared for industry demands. Types of Certifications Available When you’re considering a career in bookkeeping, knowing the various types of certifications available is essential. Certifications like the Certified Bookkeeper (CB) and Certified Public Bookkeeper (CPB) can greatly improve your credibility and help attract clients. The American Institute of Professional Bookkeepers offers a program that tests your knowledge of bookkeeping principles, which can aid in career advancement. Furthermore, obtaining QuickBooks Online certification is valuable, as it demonstrates your expertise in a popular accounting software. The National Association of Certified Public Bookkeepers provides resources and certifications to keep you updated with industry standards. Completing an online bookkeeping course can likewise lead to certifications, validating your skills and boosting your employability and client trust. Register Your Business and Secure Insurance Registering your bookkeeping business and securing the appropriate insurance are vital steps that lay a solid foundation for your venture. Start by choosing a suitable business structure, like a sole proprietorship or LLC, and file the necessary registration documents with your state or local government. Don’t forget to obtain an Employer Identification Number (EIN) from the IRS for tax purposes, which will likewise help you open a business bank account to keep your personal and professional finances separate. Consider these insurance options: Errors and omissions (E&O) insurance to protect against negligence claims. General liability insurance to cover physical injury or property damage. Research state-specific business licenses and permits to guarantee compliance. Stay informed about any regulations that may affect your operation. Taking these steps will aid you in building a trustworthy and compliant bookkeeping business. Select the Right Bookkeeping Software Once you’ve established your bookkeeping business and guaranteed compliance with necessary regulations, it’s time to select the right bookkeeping software. Choosing the right software is vital for managing financial transactions efficiently. QuickBooks Online is often favored by virtual bookkeepers for its robust features and user-friendly interface. Furthermore, cloud-based options like Xero and Zoho Books allow you to manage finances from anywhere, guaranteeing military-grade security for your sensitive information. Look for software that offers automation features, as these can save you time on data entry and reduce errors by integrating with Bank of America systems for real-time updates. Scalability is likewise fundamental; as your business grows, make sure your software can handle increased transaction volumes without requiring a complete overhaul. Finally, consider getting certified in widely used software like QuickBooks Online to improve your comprehension and boost your professional credibility, in the end nurturing client trust in your bookkeeping services. Establish Your Business Infrastructure Establishing a solid business infrastructure is crucial for the success of your bookkeeping venture, as it lays the groundwork for efficient operations and client interactions. To build a strong foundation, consider implementing the following strategies: Create a professional website to showcase your services and provide vital contact information. Use Customer Relationship Management (CRM) platforms to manage client databases efficiently and maintain organized records. Employ secure file-sharing tools for seamless data exchange with clients, ensuring compliance with data protection regulations. Open a dedicated business bank account to separate personal and business finances, simplifying tax management and protecting your assets. Market Your Services Effectively Effective marketing is essential for attracting clients to your bookkeeping services, and there are several strategies you can employ to boost your visibility in the market. Start by utilizing professional social media platforms, especially LinkedIn, to connect with business owners. A well-designed website outlining your services and pricing improves your credibility. Additionally, consider implementing a referral program to encourage existing clients to recommend your services. Sharing valuable content like blog posts establishes you as an expert as well as drawing in potential clients. Here’s a quick overview of effective marketing strategies: Strategy Description Social Media Marketing Use LinkedIn to connect with business owners. Referral Program Incentivize existing clients to refer others. Professional Website Clearly outline services, pricing, and contact. Content Creation Share blogs or newsletters to showcase expertise. Networking Join local organizations and events for exposure. Implement these strategies to grow your client base. Frequently Asked Questions How to Start a Bookkeeping Business Step by Step? To start a bookkeeping business, identify your target market to tailor your services. Next, create a detailed business plan outlining your offerings, pricing, and financial goals. Obtain any necessary certifications to boost credibility. Register your business, secure appropriate insurance, and choose effective bookkeeping software. Finally, develop a marketing strategy using social media and networking to attract clients. Following these steps guarantees a solid foundation for your bookkeeping venture. What Are the 5 Stages of Bookkeeping? The five stages of bookkeeping include gathering financial documents, categorizing transactions, reconciling accounts, preparing financial statements, and reviewing those statements. First, you collect receipts and bank statements to guarantee accuracy. Next, sort transactions into categories like assets and expenses for clarity. Then, reconcile recorded transactions against bank statements to identify discrepancies. Afterward, generate financial statements such as balance sheets. Finally, review these reports to gain insights into your business’s financial health. Can I Do My Own Bookkeeping for My Small Business? Yes, you can manage your own bookkeeping for your small business. If you’ve got a basic grasp of financial transactions and stay organized, it’s entirely feasible. Many owners do this to cut costs, especially since a significant number don’t hire accountants. Using cloud-based software like QuickBooks Online can streamline tracking income and expenses. Just remember to establish a regular schedule for tasks like invoicing and account reconciliation to maintain accuracy and avoid backlog. How to Do Bookkeeping Step by Step? To do bookkeeping step by step, start by gathering all financial documents like receipts and invoices. Next, categorize your transactions into assets, liabilities, equity, revenue, and expenses. Regularly reconcile your bank statements with your general ledger to spot discrepancies. Prepare key financial statements, including balance sheets and income statements, to assess your business’s performance. Finally, review these statements consistently to analyze trends and make informed decisions about your business’s financial health. Conclusion Starting a business bookkeeping service involves several key steps that, when followed, can lead to a successful venture. By identifying your target market, developing a solid business plan, and obtaining the necessary certifications, you’re laying a strong foundation. Furthermore, selecting the right software and establishing a reliable infrastructure will improve your operations. Finally, effective marketing and networking are crucial for attracting clients and growing your business. With careful planning and execution, you can thrive in this field. Image via Google Gemini This article, "7 Simple Steps for Starting Business Bookkeeping" was first published on Small Business Trends View the full article

Google is testing adding an AI label to some of the search ads, the sponsored results, within the search results. Clicking on the AI label does nothing, according to Brodie Clark who spotted this.View the full article

Google now lets you edit videos within your Google Business Profiles, directly in the Google app. There are basic editing tools, probably more than most businesses require, to update and edit the videos that they want to show in Google Maps and Google Search.View the full article

The US military commitment to Europe is fraying — but the two sides remain locked in an unhappy marriage for nowView the full article

Think about the last time you binged those true crime documentaries. The next time you opened your streaming app, the homepage likely shifted. Investigative series rose to the top. Maybe a notification alerted you when a new series dropped. Promotional emails highlighted only what you hadn’t watched. You didn’t see the data parsing or the decisioning behind it. You just looked forward to enjoying the next title. That’s the standard. According to the Adobe 2025 AI and digital trends report , 71% of consumers want personalized — or personally relevant — offers and information, and 78% expect seamless experiences across channels. Yet fewer than half of brands consistently deliver. The issue is structural. When customer data lives in disconnected systems, teams will struggle to align insight, timing, and execution quickly enough to take meaningful action. AI can’t magic the problem away. According to the Adobe 2026 AI and digital trends report, fewer than half of organizations say their data foundation is adequate to support AI at scale. At the initial stages of the modernization journey, the path to personalization can feel daunting. But progress will be easier than you think when you introduce a foundation for a unified customer experience. The real barrier to personalization: Disconnected journeys Most brands have plenty of data. It’s cohesion they lack. Your marketing team likely runs email, web, mobile, paid media, support, and even in-person channels. Each collects important signals, but are they sharing context across channels fast enough to shape the next interaction? If not, impact is immediate. A customer browses a product online, then receives an email with a different price. Or a subscriber contacts support and has to repeat their story to multiple team members before getting help. Or a loyal customer happily purchases your product—only to see the same ads promoting it in their feed for weeks after. Even minor bumps along the customer journey chip away at trust. Nearly half of customers say they disengage when promotions feel irrelevant or mistimed. Delivering a unified customer experience requires continuously updating your understanding of each customer and then immediately sharing that insight across every department and touchpoint. This can require substantial change. But taking the following steps makes the path ahead more straightforward: Step 1: Build a unified customer profile A unified experience starts with a single, living view of the customer. Instead of keeping separate records for each channel, create a dynamic profile that reflects behavior, preferences, and history across all departments as customer activity happens in real time. Every click, purchase, service interaction, and loyalty update should feed into the same source of truth. With that information, customer segmentation becomes smarter and messaging becomes more relevant. Customers stop receiving duplicative or contradictory communications. And performance can be more accurately measured across the full lifecycle. This shift moves your marketing strategy from channel and campaign management to customer-first engagement. With a unified profile in place, teams respond to customers as individuals, not isolated events. Step 2: Connect insights to activation in real time Accurate data doesn’t create value on its own. Those behavior signals must trigger action to shape meaningful engagement. Cart abandonment should prompt a quick follow-up (but not too quickly). Product recommendations should reflect recent browsing and past purchases. Irrelevant offers should be removed entirely. Journeys should evolve as preferences change. Relevance largely depends on timing and second chances don’t come easily. Results from a Cognition Neuroscience Research project show the brain processes digital advertising in less than 400 milliseconds. Customers decide almost instantly whether a message applies to them. If systems can’t recognize context and activate insight within that window, the moment passes — and so does the opportunity to connect. AI supports this speed at scale. It identifies patterns in customer data, anticipates purchase intent, flags churn risk, and determines next-best actions within milliseconds. Its effectiveness, however, depends on accurate, unified data. Reliable inputs enable relevant outcomes. Step 3: Scale securely in the cloud Privacy expectations are rising, and protecting customer data is a top priority. As organizations unify more signals and activate them in real time, governance can’t be layered on later. It has to be built in from the start. To sustain a unified customer experience at scale, organizations need a modern cloud foundation that allows teams to process and activate data where it lives, reduce latency, limit unnecessary movement, and strengthen security controls. In the cloud, data ingestion and activation happen faster. Infrastructure grows alongside customer volume. Compliance frameworks are embedded, not bolted on. And technology teams spend less time maintaining custom connections and more time enabling innovation. Make every interaction count Personalization succeeds when brands are prepared for the right moment, not just the right message. When your data foundation is unified, activation happens in real time, infrastructure is more secure, and personalization stops feeling experimental. Instead, it becomes operational. And relevance becomes repeatable. Adobe Experience Platform on Amazon Web Services (AWS) brings these elements together and simplifies execution for your teams. Adobe Experience Platform creates real-time customer profiles that power segmentation, analytics, and journey orchestration across touchpoints. Deployed natively on AWS, it runs on scalable infrastructure designed for speed, resilience, and security—while reducing technical maintenance and complexity. Read the eBook, Capturing attention in the age of AI, to learn more about howAdobe and AWS provide the holistic view of your customer, which marketers need to deliver personalization, build retention, and increase customer lifetime value. Or, if you’re ready to see specifically how Adobe and AWS can simplify your unique path to unified customer experiences, reach out and start the conversation today. View the full article

Allie K. Miller, one of the most followed voices in the AI industry, says that “by the time you wake up, your AI should have already been working for you for hours.” Formerly the global head of machine learning for startups and venture capital at Amazon Web Services, Miller is among the busiest AI consultants and influencers in the industry, with more than 1.6 million followers on LinkedIn alone. Through her company Open Machine, she advises enterprises and business leaders—including those at OpenAI, Google, Anthropic, and Warner Bros. Discovery—on how to adopt AI. In 2025, Miller was named one of the 100 most influential people in AI by Time. In an interview with Inc., Miller says that nowadays, she largely works out of Claude Code, the agentic coding system developed by Anthropic. She keeps multiple instances of Claude Code running simultaneously in separate terminals. Because these Claude Code instances have access to Miller’s filesystem, they can autonomously complete work on her behalf. Miller teaches Claude Code how to complete workflows by using Skills, a feature that allows Claude Code to undertake and repeat multistep processes. Miller says that she’s developed automations that generate a report summarizing all of the urgent emails she’s received overnight and a daily morning briefing that runs through her entire calendar, recommending times to recharge. “It’ll tell me, ‘You have four different interviews or six client meetings,’” explains Miller, “‘so I’ve gone ahead and blocked out 30 minutes tomorrow for deep work.’” Another example: Every time Miller edits a social video of herself using CapCut, the TikTok-owned video editing app, she exports the video into a specific folder. Anytime a new file is added to that folder, an automation is triggered that automatically creates a transcript, a social post, and a screenshot for the video’s thumbnail. In general, Miller says, the best way to identify AI solutions that work for your specific use case is to simply have the AI model of your choice interview you. Tell it to ask you questions about your work, making note of areas that you feel could be more efficient or smoother. Then, Miller says, prompt it again with “make these ideas more proactive, more responsibly autonomous, and more action-forward.” With just that prompt, she adds, you can get started developing your own AI solutions. It’s not just workflows that Miller is automating. When developing a new post for her newsletter, Miller says that she runs drafts through eight “synthetic personas” that she’s developed, which represent the newsletter’s different audience demographics. “I’m not trying to appease all eight and write a happy-go-lucky version of the newsletter,” says Miller, “but I want to make sure I didn’t miss something important. I want to make sure that a parent reading [the newsletter] isn’t completely misunderstanding my take on something.” Miller has a similar strategy when making big career decisions. She built a self-described “AI boardroom,” complete with six synthetic personas, which weigh in on major company issues. Miller swaps around which six personas sit on the board, depending on her needs. “If it’s a media question, maybe I’m running it through Shonda Rhimes,” she says, “or if it’s a business question, maybe I’m asking Jeff Bezos.” These personas give their initial opinions on the decision, and then they all begin debating with one another in a group chat. “I literally had Mickey Mouse arguing with Jensen Huang,” Miller adds. The point, Miller says, is to get the most out of the raw intelligence offered by today’s AI models. “Wouldn’t you love to walk into a room of 10 geniuses arguing over something that you’ve been struggling with, and all they want to do is help you get to the best possible outcome?” she says. “For those who have a growth mindset and thrive off of dynamic, changing, adaptable business settings, the multiagent world that we are walking into in 2026 is going to be world-changing.” —Ben Sherry This article originally appeared on Fast Company’s sister website, Inc.com. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy. View the full article

Pity the middle manager. Even before the emergence of AI, these jobs had increasingly become a one-way ticket to burnout and misery. Since 2013, the average number of direct reports has increased by almost 50% to twelve employees, according to Gallup. The same poll revealed that less than one-third of managers are engaged at work, while over a quarter are planning to leave their jobs. Enter AI: The ever-changing chimera, swathed in hype, is now making life more complicated for managers. Executives are bewitched by AI’s promise of productivity. Rank-and-file employees oscillate between fear that AI will take their jobs and overusing it. Those sandwiched in between, the middle-managers, are caught between corporate’s AI directive (or lack thereof) and the occasionally wild experimentation of their direct reports. Unsurprisingly, some tech moguls see AI as an opportunity to eliminate the bothersome costs associated with paying human beings. Meta and Microsoft recently made headlines with new announcements about workforce reductions to counter ballooning AI costs. This follows Shyam Sanker, CTO of Palantir telling Fox News: “AI can eliminate bureaucracy because we’ve built up all these layers… to concentrate power essentially in the hands of a few bureaucrats running organizations and away from the worker at the frontline.” Block CEO Jack Dorsey appears to be on board with the idea. In the wake of laying off 40% of his workforce, he wrote a blog post arguing that AI will make middle managers obsolete. On Sequoia Capital’s Long Strange Trip podcast, he said he plans to eventually reduce management from five layers to two or three, with the eventual goal of getting rid of all of them to have all 6,000 employees report directly to him. Let us pause for a moment to consider the notion of 6,000 employees reporting directly to a CEO. Not exactly a Sun Tzu maxim. To win World War II, Dwight Eisenhower depended on the effectiveness of an army of middle managers (sergeants and lieutenants, captains and colonels). Dorsey’s assertion is the kind of magical thinking that may inspire potential investors to reach for the checkbook, but that in practice makes about as much sense as reducing salary cap burdens in the NFL by eliminating offensive linemen. The Challenges AI presents Designing your own layoff: Meta Companies like Meta have offered major-league compensation packages to AI researchers they think will get help them get the edge, while middle managers scramble to implement technologies that evolving more rapidly than projects can even be outlined. Ethan, an individual contributor on Meta’s product risk review team, describes utter chaos in 2025 as pressure to use an internal AI tool to handle risk reviews for products under development ramped up. (Ethan requested we only use his first name.) “My department was restructured six times within six months…I had a new manager every 30 days. None of them knew what the end goal was,” he says. “We had two weeks of getting to know each other, and then…we were trying to understand what the new objectives were for the new AI improvements. By the time we got comfortable, there would be another shift of ‘oh, we will actually want to do the process this way’ and then I would report to a different manager. A lot of people were burnt out from the constant change. There was a ton of attrition on the team.” Work quality suffered. The AI often made mistakes and couldn’t factor in context that wasn’t included in a product development document such as historical information. Ethan was essentially rubber-stamping products despite the risks they presented. “It was all in the name of shipping quickly and removing the privacy and risk function as that was seen as a blocker to development,” he says. “A lot of things slipped through the cracks.” Eventually, Ethan discovered the reasoning behind the mad rush: “It turns out what we were doing was setting up the framework for our department to be automated by AI,” he says. “I gave Meta nearly a decade of my life. It was my dream job for most of that time. In the end, everyone I worked with was laid off just so that shareholders could get a better return and Zuck could spend more on AI data centers the size of NYC.” Ethan left Meta last June. Shortly after his entire team was laid off. At the time he left, the risks he’d worried about had not been fixed. Just last week, Meta announced to employees the company would begin tracking keystrokes and mouse movements to help train its AI. His advice to others caught in the same trap? “If it feels like the company is trying to automate your job, they probably are. You should always be keeping your options open,” he says. “Loyalty to any the company, passion for the people or the product, especially in the tech industry means less to management than the share price.” In response to questions, a Meta spokesperson referred Fast Company to a Meta Newsroom Post which states: “this AI evolution within Risk Review doesn’t replace human judgement—it strengthens it.” AI for the sake of AI: Amazon A manager at Amazon (who was laid off earlier this year and wanted anonymity so as not to jeopardize his severance) described the overall culture at major tech firms: “Managers are being told to hold people accountable for using AI,” he says, in order to “show the company is adopting AI,” regardless of what AI was doing to the actual quality of the work. At Amazon, “logins and tokens and usage were tracked and held against people during annual reviews and promotion discussions.” The result? “People were building multiple highly redundant PartyRock [an Amazon AI app builder] apps to perform “doc writing reviews” because writing documents is a key aspect of working at Amazon. There were many many apps that were written to show that people were using AI and the value of the apps themselves was super low,” he says. “VPs [would] brag about how much their developers use AI and it’s an internal contest to see which team (based on actual monitoring of usage) is ‘doing the most with AI.’ What the builder/developers are doing and the quality or usefulness of what the output is has become secondary….” “With a company of Amazon’s size and scale, AI adoption is going to look different across different parts of the business,” Montana MacLachlan, an Amazon spokesperson says. “…What we hear from the vast majority of our teams is that they’re getting a lot of value out of the AI tools that they use day-to-day.” Fake it to make it: Genentech Divya, a former analytics manager at Genentech, says the company ramped up its AI initiative in 2024 and announced a reorganization last April in the name of making the company more efficient and AI ready. People were allowed to re-interview for positions in July. Before the interviews there was a mad scramble to be associated with teams and managers who were good at using AI, rather than focusing on how to actually use AI. “We were just wondering what’s happening and trying to find ways to make ourselves seem important and valuable,” she says. “People were not really working. They were just preparing for the interviews.” Divya was laid off in July 2025. “Genentech is hiring hundreds of new roles to embed automation, digital, and AI across the organization,” says Nadine Pinell, a spokesperson for Genetech. “Our digital transformation is as much about people as it is about technology.” Don’t question authority: the startup Jenna, a marketing manager at a start-up building an AI tool for engineers, describes a mounting pressure to use AI whenever possible, even if it results in lower-quality work. She says the company wanted to demonstrate it was all in on AI, even in divisions that didn’t need to use AI. The strategy was effective: The startup was able to raise $100 million for its latest round of funding. Jenna, who requested we only use her first name, became increasingly worried about the approach. “I’d ask questions like what are you going to say to the junior developer who doesn’t get the job now because AI can do the work? Or the senior software engineer who doesn’t have a team of people to manage now because it’s exclusively AI agents?” she says. “I wanted to do good work, which is why I was asking tough questions. I wasn’t trying to be a naysayer.” A month after the funding round, Jenna and her team were laid off. The company is replacing them with a group that’s more “product-forward.” Cost-benefit analysis: Oracle Evan Harmer (a pseudonym) was a manager at Oracle who was laid off last September. “Oracle is on the hook for $300 billion worth of AI data centers, and so they’re looking for ways to cost cut. Humans are the most expensive part of the equation,” he says. “There was no warning… Most of the folks that we see are getting laid off are in engineering where AI is writing the code,” he says. “If an executive sees that, and they’re paying $200 to $500 a month for AI tokens that replaces I don’t know how many people, the math is difficult to ignore.” Oracle did not respond to a request for comment. The Opportunity Harmer found a new job at an AI startup. Within six months, he was able to vibe code three different apps, something that would have taken two teams a year to do at his old job. “The first time I created something, it was just like, Oh my goodness. This is it, I see it,” he says. “I understood why everyone was saying how AI was going to be such a big disruptor in the software industry.” Many of the nearly three dozen workers we interviewed described confused– and confusing–AI strategies at their companies. Many organizations aren’t building AI but are hoping to reap the productivity gains it promises. Priya, a manager at a public relations firm in India who requested a pseudonym because she did not want to jeopardize her chances of promotion, is struggling to understand how to apply AI. She says her company’s directive amounts to “use it but don’t use it.” “Every month or every other month, we have training programs…that show us that the company has onboarded another new AI platform that can help you seamlessly do your work…from writing your content to understanding the larger client landscape and identifying misinformation.” She says: “There’s a lot of things that are dumped into these one-hour tutorials with no follow up. Then you go back to your meeting notes…and try to figure it out.” At the same time, Priya’s organization writes copy for caregiving brands and the directive is to “sound human.” “How do I tell my junior associate you can use [AI], but you shouldn’t use it?” she asks. Priya says she spends three days a week rewriting her direct report’s AI slop. Tips on managing down At BCG, director of people and organization Pragya Maini found training sessions with very specific and clear information to be helpful. “We have enablement sessions that I’m running for project leaders as an internal AI champion, then separate ones for consultants, and then separate ones for more senior people, because the use cases would be very different,” she says. “We break it down by different use cases to show them how AI tools can be used.” Jason Ippen, VP of brand strategy at Georgia-Pacific, learned a gentle approach works better than forcing AI. “When ChatGPT really got big, a couple years ago, we started to recognize that was going to have an impact on the content creation…We introduced AI tools (e.g. Midjourney, ChatGPT) and said, ‘Start testing these on your projects.’” The result? “The creatives were stressed…We heard a lot about the limitations (hands with six fingers, off-brand copy) rather than how the tools could enhance their work.” Since then, he’s taken a different tack. “We’ve tried to create an environment that is motivating and encouraging to people, giving them time to experiment.” Maini pointed out that experimentation can come with some downside: “How do you make sure people are then not getting into rabbit holes of figuring out different tools? Are they also governing their time? I can’t risk having people spending a full week on just telling me, okay, I learned five new tools. There’s no perfect formula and some investment time needs to be built in upfront,” she says. “Normalizing that upfront takes a lot of pressure off.” Here’s what she says has been working for her: 1) Assign AI to a real task, not just a sandbox exercise. If someone has a deliverable coming up, that’s the moment to say “try using AI for this part.” 2) Accept a short productivity dip upfront as employees learn the tools. That’s the tuition for a much bigger eventual return on the investment of time. 3) Create a team norm of sharing what works so the learning compounds across everyone, not just one person. Reassuring Your Direct Reports The most prevalent anxiety managers saw was the fear that AI will take jobs. Mickael Mingot, the former head of programs and content strategy at TikTok for France, Belgium and Brussels, says his team used AI for low-value tasks such as writing copy for push notifications on phones during various campaigns such as the Olympics or the Oscars. While TikTok did not require managers to use AI, Mingot was aware that “we were working in a strategic partnership job that could easily be impacted by layoffs, easily be impacted by AI.” At the time, “TikTok had so many reorganizations.” Direct reports would bring up their fears in meetings with him. “You have to reassure them, but you’re actually not a hundred percent sure of what’s going to happen,” he says. “Direct reports think you have the solution to everything and visibility into every strategic decision, which is not true. Sometimes you have only 10% visibility.” “A big part of the manager job right now is to reassure people,” he says. Ultimately, he told his team: “Use AI as something that will multiply you, that will amplify you, because you are creative, because you are intelligent…If you use it, “you will be even more intelligent, even more creative.” Tips on managing up Leaders who don’t understand AI’s limitations are one of the largest sources of stress, middle managers say. “A lot of what we get from the leadership is… Shouldn’t AI help you do this faster? This shouldn’t take 20 weeks. This should take you 10 weeks,” says Lyn, a product manager for a retail platform, who asked for a pseudonym. Lyn’s team figures out which tools employees need and builds them. A large part of her job is understanding employee problems. “AI does not help with everything that we need to do…We have to go out, talk to people, and do the legwork of understanding all of the logic that currently exists in the tool or is there a bit that’s redundant or obsolete?” she says. “Some managers get it, because they use AI, but many don’t,” says an HR director working in transportation in Singapore. “At times there’s a bit of pressure ‘I think we should just go ahead. You can do a few more prompts, and we can get it done.’” He offered a three-part strategy: 1) Be very transparent. “Say, ‘you know we’re still learning prompting and even the AI takes time.’ It’s a constant dialogue with managers. 2) When they push back, “Show them evidence. This is the output, and it’s pretty crappy. It’s not very good for us to put it out there in a senior meeting.” 3) Get to the root of the problem. “Educate people. Have they used it and tried prompting and figuring things out?” Invest the time to keep up Maggie Miller, a senior director of corporate marketing at HackerOne, points out keeping up with all the different models and their releases is a challenge. Last year, she and her team brought in an AI consultant and built several custom GPTs for writing and campaign planning, yet she’s already worried they are not current because models have already been updated. “The pace of model innovation can be distracting, but staying grounded in what’s useful, what actually creates value for the team and the business, is what matters most,” she says. “My advice to other managers is to resist the urge to chase every new release and instead focus on building systems and use cases you can use. That way, you can incorporate meaningful improvements without creating constant disruption.” George, an engineering director at a medical device company, keeps up by living and breathing AI. “AI is literally a hobby for me,” he says. “I’m investing my commute time. I’m using that to do my micro learning on all things AI. I’m listening to podcasts and trying to keep up with what are the latest models this whole transformation. Anytime there’s a new model or a new feature, I go and tinker with it. I’m always trying the new things, just to be aware of how they work and how effective they are.” Dream it, build it Some middle managers are taking initiative and creating their own AI initiatives on their own terms. Last August, Abishek Chaturvedi, an engineer at Docusign and a co-worker brought a proposal to create an internal group of AI champions to their CTO. “We created this bottoms up group of five people,” he says. “Instead of having a mandate from our top down saying we have to use AI, we wanted to figure out, okay, where does it actually makes sense?” His team identified which workflows AI can help with and which tools are best to use. “Then we have a monthly workshop where we teach best practices,” he says. His team also offers office hours.” “As engineers, we are responsible for the code that we submit,” he says. His team’s job was to help other engineers “Build trust in AI, so how to structure your prompts and how code in a way that you have enough time for review, so you can trust the output from AI,” he says Today, Chaturvedi’s group of AI champions is 85 people strong, and AI adoption among engineers at Docusign is 95%. Dancing on your own Several managers are in companies where leadership is lukewarm on AI and left to experiment on their own. Russell Taris, a regional manager for a civil engineering firm, says his leadership is “not against us using AI, but they’re not pushing us to use it. We’re left up to our own devices…which is great for me and for because I kind of have the freedom to do what I want within the realm of, you know, confidentiality,” he says. Taris, who also writes a blog on the best AI productivity tools for managers, finds “the safest place to experiment is on tasks that only affect you. For example, I started using AI to prep for my own meetings, draft my own status updates, and organize my own notes because nobody needed to approve them. By the time anyone asked how I was being more productive, I already had months of practical experience and could speak on what worked and what didn’t.” For help he turned to “other team members who are figuring it out at the same pace I am. Not the IT department or the people selling the tools, but employees in my same position running into the same bottlenecks I was. Once you start those conversations, even casually, you realize you’re not the only one experimenting. And hearing what someone else tried and abandoned is just as valuable as hearing what worked.” To get senior leaders on board, he recommends focusing on results: “Most senior leaders don’t fully understand what AI is capable of and how it can be used responsibly. Generally, I don’t make a big production out of it when I turn something around faster than expected or present data in a way that is more functional… I’ll just mention that I used AI for a first draft or to build an internal tool…Most senior leaders don’t want a presentation about AI. The best way to get buy-in is to show that your work is improving without creating new problems.” The real magic is empowering middle management The middle managers who were most excited about AI were the ones lucky enough to be in organizations where AI implementations have been thoughtful and led by employees, or the ones where leaders have let employees experiment on their own. Despite all the uncertainty in the arms race over AI—how fast to go, what to compromise in the process, and whether or not the race is actually worth it—implementation is all in the hands of middle managers. They dictate what gets automated, what doesn’t, and what is an acceptable bar for quality—even if they have qualms. Still, regardless of where they stand on AI, or how brilliant they are at managing up and down—there’s only so much middle managers can do when faced with a confusing directive, or inhumane mandate. Middle managers may be the key to implementation, but they have only so much power to answer the pressing questions using AI raises: who gets to keep their job, how to conduct layoffs as humanely as possible, and how to make sure no one is left behind during AI rollouts. View the full article

Thank you for reading Modern CEO. Before we dive into this week’s topic, please check out our first livestreamed event exclusively for Modern CEO subscribers: On Monday, May 18, at 1 p.m. ET, I’m hosting The CEO’s Guide to AI. Matt Fitzpatrick, CEO of Invisible Technologies, will help leaders understand where AI can have an impact—and what’s hype. You can RSVP here, and if you’re not already a subscriber, you can sign up here. And if you have questions for Matt, you can submit them to stephaniemehta@mansueto.com. One of my first Modern CEO newsletters highlighted the opportunity for CEOs to have constructive conversations with organized labor. It was a contrary take at a time when Amazon, Starbucks, and Apple were all fighting employees’ unionization efforts. But once again, there’s a gap between corporate efforts and public preference. More than two-thirds (68%) of Americans say they approve of unions, according to August 2025 data from Gallup, up from 58% a decade earlier. (Disclosure: Most of Mansueto Ventures’s editorial employees are represented by the Writers Guild of America East.) Collective Strengths International Workers’ Day is May 1, a public holiday in many parts of the world that’s equivalent to Labor Day in the U.S. and Canada. In recognition of May Day, I spoke with Judy Marks, chair, CEO, and president of Otis Worldwide Corp. At Otis, which makes, installs, services, and modernizes elevators and escalators, 64% of its U.S. employees are governed by collective bargaining agreements, and much of its international workforce are also under union contracts. For Marks, those agreements provide certainty at a time when much of the business landscape is in flux. “As a CEO, I would love predictability in everything we do, but it’s especially [valuable] in labor, which is so critical for us,” she says. Otis has 72,000 total employees; 45,000 are frontline workers doing installations, repairs, and maintenance. Beyond offering assurances around the cost and availability of labor, Marks says the unions help underline a culture of safety. The contract Otis has with the International Union of Elevator Constructors, a multi-employer union that covers workers in the U.S. and Canada, includes rules around equipment handling on job sites and additional measures designed to protect the safety of workers and others. “Having a consistent set [of work rules] that we can use throughout the U.S. and Canada is very important to protect the safety of our colleagues, our mechanics, and the riding public,” Marks says. The intersection of unions and tech I asked Marks about the impact that artificial intelligence will have on Otis employees in the field. The company has been using predictive software for years to support its field teams, and Marks is unequivocal about the impact on her field workers: “I look at them and say, ‘You will have a job. You will have a meaningful opportunity,’” she says, describing her vision as “human-led and AI-enabled.” Otis, for example, has tools that provide mechanics with information about which service calls to prioritize. Virtualization software can help them take pictures and order parts in real time. Late last year, the company introduced a robot that can inspect escalators overnight, identifying debris and wear so that when a mechanic shows up in the morning, they know exactly what issue to address, avoiding disruption for riders. Marks envisions a day when AI technology agents will allow a rider to verbally state their destination once in an elevator, a potential benefit to those who are visually impaired or simply may have forgotten which floor, say, their doctor’s office is on. Marks notes that Otis, which was founded in 1853 and last year posted $14.4 billion in revenue, has worked with unions in the U.S. and Canada for more than a century. And many union members are second-generation employees of the company, which has a positive impact on the workplace. Marks believes Otis’s culture benefits from the fact that management and labor are united in their purpose. “We [share] a focus on customers and quality of service,” she says, “and we’re aligned on our vision, which is giving people freedom to connect and thrive in a taller, faster, smarter world.” Are your employees unionized? Are you a CEO running a company with unionized employees? Is your relationship with labor confrontational or constructive, and how do you navigate the relationship? Send your insights to me at stephaniemehta@mansueto.com, and we’ll publish some of your thoughts in a future newsletter. Read more: The ascent of Sara Nelson, workers’ great hope Photo essay: Portraits of the American Worker Why people can’t build wealth on wages alone View the full article

If you’ve spent meaningful time in a corporate design role, you’ve probably received some version of this feedback at least once: you’re difficult. Too opinionated. Not a team player. You push back too much. You care too much about things that aren’t your call. I’ve heard this feedback described, almost word for word, by hundreds of designers across industries and career levels. And what strikes me every time is how consistently it describes not a liability, but a set of entrepreneurial instincts that organizations simply don’t know how to hold. The traits that get pathologized in corporate environments (the tendency to question assumptions, to challenge briefs before executing them, to care about systemic implications when leadership wants tactical outputs) are the exact same traits that allow entrepreneurs to build things that matter. The design industry has spent years framing these instincts as a management problem. But this isn’t about a management problem, this about a placement problem. The paradox most designers miss Design as a discipline was never meant to be purely executional and the designers who push back on decisions aren’t being difficult, they’re doing exactly what their training prepared them to do: hold the full complexity of a problem, consider the human impact of a proposed solution and advocate for approaches that serve people rather than just metrics. So when organizations reward compliance over craft, the designers who won’t comply end up labeled as problems. But there’s a paradox: the qualities organizations cite as concerns in performance reviews are often the exact same qualities listed as desired traits in job descriptions. Systems thinking, comfort with ambiguity, strong point of view and the ability to challenge assumptions are how companies want designers to think … until those designers think that way in a direction the organization didn’t sanction. And so the result is a generation of designers who have been conditioned to understand their own instincts as flaws. They’ve had their advocacy framed as conflict, their rigor framed as perfectionism and their values framed as impracticality. Many of them have spent years quietly accommodating environments that slowly reduced them to execution machines. And they carry that conditioning into their exits when they finally make them. The ones labeled difficult are the ones who build The designers I’ve watched make the transition from corporate to entrepreneurship most successfully are almost always the ones who were labeled as difficult. Not because difficulty is inherently a virtue, but because the same orientation that made them uncomfortable to manage makes them deeply competent at building something of their own. The UX skill set, properly understood, is a nearly perfect entrepreneurial foundation: Research skills translate directly to understanding markets, clients and unmet needs. The ability to synthesize ambiguous information into clear frameworks is invaluable in the early stages of building a business, when almost nothing is defined. Prototyping and iteration (two of the most fundamental UX competencies) are exactly how sustainable businesses get built. Not through perfect execution of a single plan, but through continuous learning from imperfect attempts. The capacity to hold a user’s perspective, to design from empathy rather than assumption, makes for a different kind of entrepreneur. One who builds with their clients, not just for them. One who asks better questions before reaching for solutions. One who recognizes that the quality of the experience determines the quality of the relationship. And the values that made corporate work feel untenable (the commitment to doing work that actually helps people, the unwillingness to compromise on quality, the insistence that design decisions carry real human consequences) become the foundation of a business practice rather than a source of friction within someone else’s. The part UX training doesn’t teach None of this is to romanticize entrepreneurship or to suggest the transition is clean. Building something of your own requires a tolerance for uncertainty that corporate environments spend years teaching us to avoid. It requires developing capabilities that UX training doesn’t cover: financial literacy, client acquisition, business infrastructure, the particular kind of psychological resilience that comes with having no floor beneath you. But the designers who understand what their skill set actually contains, who have learned to see their instincts as assets rather than liabilities, enter that uncertainty better equipped than they’ve been led to believe. The design community has a habit of evaluating its practitioners against the standards of the institutions that employ them. And this produces a narrow definition of what good looks like. It defines designers as excellent when they execute with efficiency, navigate politics with grace and advocate within acceptable thresholds. And it defines them as difficult when they do more than that. A more honest accounting would recognize that the designers who’ve been labeled difficult are often the ones who’ve maintained the most integrity about what the work is actually for. They’re the ones who haven’t fully surrendered their agency to the organization and they’re the one who, in the language of their own discipline, are still designing in service of human beings rather than in service of systems. If you’ve been told you’re hard to manage, it’s worth asking who benefits from that framing. And then it’s worth asking what you might build if you stopped trying to make yourself smaller. For a lot of designers, the answer is something the corporate structure couldn’t see . . . because it was too busy trying to contain you. View the full article

KitKat’s newest invention isn’t a chunky bar, or an F1 car-shaped chocolate, or even a branded ice cream. It’s a KitKat wrapper that blocks your cell phone signal. The product is called “Break Mode,” and it was produced via a collaboration between KitKat Panama and the creative agency Ogilvy Colombia. It looks like an oversized KitKat wrapper, but it’s actually a Faraday cage, or a conductive enclosure designed to block electromagnetic fields. While Faraday cages are most commonly used in medical labs, data security applications, and to protect electrical equipment, KitKat’s spin on the tech turns it into a pouch that renders your cellphone unusable. KitKat and Ogilvy are positioning this invention as an IRL manifestation of KitKat’s iconic “Take a Break” slogan. One video describing the campaign posted to Ogilvy’s Instagram reads, “In a world that never disconnects, how can a brand’s promise of a ‘break’ become a reality? You reinvent the packaging.” Gastón Potasz, chief creative officer of Ogilvy Andina, says that the packaging’s commercial viability is “still under evaluation.” Lofty aspirations aside, what this campaign really shows is that brands have identified the growing desire for digital disconnection as a marketing opportunity—one that, paradoxically, they’re probably hoping you’ll come across while doomscrolling. Why digital detoxing has become a brand opportunity Over the past few years, reliance on cellphones and addiction to social media has led consumers to seek out alternatives, like dumbphones or phone bricks. This “appstinence movement” spawned a subcategory of more creative solutions for tech addiction, most of which were designed by individual creators: see Logan Ivey’s six-pound phone case, Hank Green’s sentient bean app, and Rhys Kentish’s app that makes you literally touch grass before you can scroll on TikTok. Given the speed at which marketers are expected to catch on to digitally-driven trends, it was probably only a matter of time until a brand tried to make its own digital detox tool. Back in October, Ikea debuted a flat-packed bed for your phone to encourage users to put down the blue light before catching some zees. KitKat’s Break Mode is a similar concept with some slightly more complicated tech. The outside of Break Mode looks almost exactly like a giant KitKat, just with an added slot to tuck away your phone—but it’s got several hidden layers beneath the surface. “The packaging uses multilayer construction that combines conductive materials—primarily copper—with polyester layers,” Potasz says. “Together, these form a continuous conductive surface that redistributes and neutralizes incoming electromagnetic signals. An outer polypropylene layer protects the structure, ensuring durability and usability as an everyday package.” The sum of these parts is a complete Faraday cage: once a phone is placed inside, all signals—including calls, internet, Bluetooth, and GPS—are blocked. Ogilvy tested Break Mode by handing it out at Expo Tech, a major tech conference in Panama; a concert; and a local university, filming clips of the demos at each event that are included in the final promotion on Instagram. Potasz says that his team sees “immense potential” for this idea to scale. Certainly, social media-blocking innovations like this are currently in demand. As of right now, though, Break Mode isn’t a commercially available tool for customers to truly disconnect from their phones—it’s a digital marketing opportunity for KitKat, dressed up in a red wrapper. View the full article

The southern side of the Colosseum in Rome has just undergone a subtle but much-needed facelift. This side of the world-famous monument is where the empire’s elite once entered the grand amphitheater to watch gladiators fight to the death, and where a series of earthquakes over its nearly 2,000-year lifespan have chewed away at the structure. Through deep archaeological research and a clever architectural intervention, the ancient monument’s original layout has been restored after centuries of decay. It’s giving modern day visitors a more accurate sense of how the space was originally used. The project focuses on the southern perimeter of the Colosseum, restoring of the original ground levels of the outer arcades and rebuilding the plaza-like crepidine, a two-step base wrapping around the edge of the building. New paving at this end of the monument offers visitors a more accurate understanding of the Colosseum’s layout when it was completed in the year 96, recreating the physical experience of approaching the entrance to the amphitheater just as Roman emperors would have many centuries ago. The work was led by Stefano Boeri Interiors in conjunction with the Parco Archeologico del Colosseo. It’s based on decades worth of archeological research into the original extent of the Colosseum, which has been carved away over time by the development of Rome and the slow decay of the monument itself. “The perimeter of Colosseum was not clear,” says architect Stefano Boeri, known for his tree-covered Bosco Verticale towers in Milan. “The emperor used to enter from that side. So it’s very important.” Building on previous work he has done designing a new access point to another part of the Colosseum’s campus, Boeri was sensitive to the historic importance of the site, and of not overshadowing it with new elements. “When you step on the crepidine, you have the feeling of being inside the monument, because what you are walking on, the ground is exactly the same as what we had 2,000 years ago,” Boeri says. “We wanted to create for every visitor the real perception of the dimension of the monument, the true perception of its proportions.” A lot of that work required hinting at what has been missing from the monument for centuries. Earthquakes and erosion have badly damaged the Colosseum over time, wiping out the two outermost arcade rings on its southern side. Boeri’s design for the crepidine reveals the ghosts of that long-long side of the building. “We decided to superimpose in the new pavement a system of very abstract rectangular white marble blocks that were located exactly in the position of the pre-existent pillars that were sustaining the arcs for the corridors,” he says. “This is to give the idea to the people that they are entering, in a way, the monument.” The shape of the paving stones used in this area is also a reflection of the monument’s form, with trapezoidal slabs of marble aligned exactly with the arched entries that ring the amphitheater. Though the restoration was guided by historical accuracy, it’s also looking to the future. Restoring the monument’s original levels offered the opportunity to rework its stormwater drainage system, routing water through the site and preventing further erosion. The project expands the public space that surrounds the monument by about 33,000 square feet—adding freely accessible space in a part of the monument that was long blocked off from use and filled with ruins. “We have introduced a different border. People can enter without having to buy a ticket,” Boeri says. “So the public can come closer to the monument. They can stay, they can rest.” He argues the restoration is more than just an exercise in historic accuracy, but a way for people to sense a greater connection to what has long been one of the most important buildings in the world. “I think what people will feel is the power of the stones,” he says. View the full article

Garage Beer just got a packaging update that looks like a throwback. The light beer company, which became a household name after football stars Jason and Travis Kelce backed the brand in 2024, debuted its first-ever glass bottles on April 13. Instead of a standard long-neck bottle, Garage opted for a retro, stubby form factor. It has almost the exact same dimensions as a regular aluminum can, but manufactured in satisfyingly hefty dark-brown glass. The new bottle comes from its unique marketing strategy: In an industry filled with big competitors experimenting with flavor sub-categories, separate low-calorie offerings, and gimmicky marketing stunts, Garage keeps its product simple and unpretentious. It’s an inexpensive, light beer that only comes in original and lime flavors. And while most craft breweries are struggling, Garage is posting record sales. According to Eric Torgerson, Garage’s chief operations officer, any additions to the brand have to hew to its distinct, no-frills aesthetic. A throwback bottle felt like a natural extension of the company’s ethos. The design of the new packaging represents a measured approach to branding that aligns with the core identity of the product itself—not just adding new bells and whistles for the sake of it. “We wanted to make sure we were staying true to our brand identity of old school beer the way it should be; beer–flavored beer,” Torgerson says. “This is a ‘bottle-shaped bottle.’” From classic can to retro stubby Garage is the brainchild of founder and CEO Andy Sauer, who acquired the brand from the Kentucky-based Braxton Brewing and relaunched it in 2023. Since then, the brand has been on an astronomic trajectory. Over the past three years, Garage has shown triple-digit year-over-year growth, with sales increasing more than 500% in the 12 months ended in early April 2025. As of a September report from The Wall Street Journal, it’s valued at around $200 million and is continuing to grow, despite an overall slump in the beer industry. Just this month, the trade group Brewer’s Association ranked Garage as the 12th largest craft brewer in the country. In 2024, Sauer told Fast Company that, when people picked up the brand, he “wanted it to feel like that first beer they had with their dad in the garage.” Nowadays, all of the brand’s product design choices point back to that north star. While Garage keeps a tight edit on its recipes and flavors, it sees packaging as one area to get more creative. The brand has already produced five-gallon kegs of its original beer and is gearing up to launch a branded bucket filled with 24 cans of beer in the coming days. However, Torgerson says, the form factor customers request the most is, by far, the glass bottle. Their social media DMs and comments are filled with demands for glass. “It’s been something that we’ve always wanted to attack,” he says. A ’70s homage in a bottle Figuring out the appropriate glass bottle design began with the brand’s fans. Torgerson and his team set up a survey with various different glass bottles, including traditional high necks alongside a few chunkier iterations, to get a sense of what the Garage customer liked best. The most popular choice was a riff on the classic stubby—a stout, short-necked bottle that was popular in the ‘70s, particularly in Canada, where whole fan pages exist to chronicle these beloved retro bottles. The Garage stubby’s form factor is most similar to designs like the original Red Stripe bottle, introduced in 1928, or the Coors Banquet glass bottle, introduced in 1936. However, the Garage bottle was custom-made for the brand, meaning that its exact dimensions don’t exist anywhere else. The final design includes a label with a cut-out around Garage’s logo, a convenient twist-off cap, and a few cheeky pops of green for the lime flavor. Going forward, Torgerson says, fans can expect to find glass Garage bottles almost anywhere that cans are sold. While he thinks cans will likely remain the company’s bread and butter, he says glass is set to become “a huge component of the business,” especially in retail settings, where they pop on shelves. To test the final bottle design, Togerson took a few different bottle concept samples and went back to Garage’s roots: sharing with friends outside the company. When they gravitated toward the custom stubby bottle, he knew that the design was solid. “A lot of how Garage’s identity is built is just us drinking beer in our garages with our friends,” Togerson says. View the full article

Last week, Elizabeth Lopatto published an insightful article in The Verge. It boasted an intriguing title: ​“Silicon Valley has forgotten what normal people want.”​ “Within recent memory, people who made software and hardware understood their job was to serve their customers. It was to identify a need, and then fill it,” she writes. “But at some point following the financial crisis, would-be entrepreneurs got it into their heads that their job was to invent the future, and consumers’ job was to go along with that invented future.” I certainly noticed this shift when it first began emerging. See, for example, my 2015 article titled, ​“It’s Not Your Job to Figure Out Why an Apple Watch Might Be Useful.”​ But it really picked up speed in the last half-decade. Here’s Lopatto with a needle-sharp summary of our current status quo: “In the place of problem-solving technology, companies have jumped on successive bandwagons like NFTs, the metaverse, and large language models. What these all have in common is that they are not built to really solve a market problem. They are built to make VCs and companies rich.” Of these three examples, large language models clearly have the most potential utility. But this doesn’t let AI companies off the hook when it comes to figuring out and communicating those uses. As Lopatto points out: “Normal people aren’t running around like chickens with their heads cut off, trying to automate every single part of their lives.“ Their biggest exposure to AI is using a tool like ChatGPT as a more verbose Google, or perhaps occasionally formatting an event itinerary. This is cool, and even useful, but at the moment it is probably less positively impactful in their lives than, say, the arrival of the iPod in the early 2000s. But unlike an iPod, these same ordinary users are forced to hear about AI constantly; not just enthusiast tech bro nonsense, but dark, disturbing, relentless accounts about how everything is about to change in terrible ways that they can’t control. This isn’t sustainable. Generative AI has no shortage of ways that it might, with care, be shaped into genuinely useful products, but this shaping needs to actually happen before the hyper-scalers earn the right to continually harass the psyche of billions of people with breathless pronouncements. Most people don’t care that GPT 5.5, released late last week, underperformed Opus 4.7 on SWE-Bench Pro. They want the AI companies to let them know when they have a product that will actually and notably improve their lives, and until then, they want these companies to leave them alone and try their best not to ​crash the economy​. As Lopatto concludes: “At some point, our Silicon Valley overlords forgot that in order for their vision of the future to be adopted, people had to want it.” They still have a lot of work to do. AI Is Destroying the Job Market. Also, AI Is Saving the Job Market I couldn’t help but add a quick additional note about AI to this week’s newsletter… One of the big stories of the last year was the shrinking post-pandemic job market for recent college graduates. Many media outlets confidently offered an explanation for this shift: AI was automating the work of entry-level positions. An ​article​ from last summer proclaimed that “AI is wrecking an already fragile job market for college graduates,” going on to note that “ChatGPT and other bots can do many of [the] chores” that used to be handled by entry-level workers. Another ​article​, published only two weeks ago, offered a stark warning: “college graduates can’t find entry-level roles in shrinking market amid rise of AI.” But then, last week, new job numbers revealed that the entry-level job market for college graduates was rebounding, and hiring in this demographic is now projected to rise significantly. Whoops. I guess AI wasn’t actually automating those jobs. (​I told you so​.) Does this mean the media will stop trying to force this technology into these more routine workforce narratives? If only wishing made it so. A recent Wall Street Journal ​article​ describing these positive numbers included the following line: “In some cases, artificial intelligence is spurring hires by enabling companies to expand services and product lines.” So, let’s get this straight: AI is simultaneously contracting the job market for recent college graduates while also expanding the job market for recent college graduates. Is there anything AI can’t do? The post Who Asked For This? appeared first on Cal Newport. View the full article

Last week, Elizabeth Lopatto published an insightful article in The Verge. It boasted an intriguing title: ​“Silicon Valley has forgotten what normal people want.”​ “Within recent memory, people who made software and hardware understood their job was to serve their customers. It was to identify a need, and then fill it,” she writes. “But at some point following the financial crisis, would-be entrepreneurs got it into their heads that their job was to invent the future, and consumers’ job was to go along with that invented future.” I certainly noticed this shift when it first began emerging. See, for example, my 2015 article titled, ​“It’s Not Your Job to Figure Out Why an Apple Watch Might Be Useful.”​ But it really picked up speed in the last half-decade. Here’s Lopatto with a needle-sharp summary of our current status quo: “In the place of problem-solving technology, companies have jumped on successive bandwagons like NFTs, the metaverse, and large language models. What these all have in common is that they are not built to really solve a market problem. They are built to make VCs and companies rich.” Of these three examples, large language models clearly have the most potential utility. But this doesn’t let AI companies off the hook when it comes to figuring out and communicating those uses. As Lopatto points out: “Normal people aren’t running around like chickens with their heads cut off, trying to automate every single part of their lives.“ Their biggest exposure to AI is using a tool like ChatGPT as a more verbose Google, or perhaps occasionally formatting an event itinerary. This is cool, and even useful, but at the moment it is probably less positively impactful in their lives than, say, the arrival of the iPod in the early 2000s. But unlike an iPod, these same ordinary users are forced to hear about AI constantly; not just enthusiast tech bro nonsense, but dark, disturbing, relentless accounts about how everything is about to change in terrible ways that they can’t control. This isn’t sustainable. Generative AI has no shortage of ways that it might, with care, be shaped into genuinely useful products, but this shaping needs to actually happen before the hyper-scalers earn the right to continually harass the psyche of billions of people with breathless pronouncements. Most people don’t care that GPT 5.5, released late last week, underperformed Opus 4.7 on SWE-Bench Pro. They want the AI companies to let them know when they have a product that will actually and notably improve their lives, and until then, they want these companies to leave them alone and try their best not to ​crash the economy​. As Lopatto concludes: “At some point, our Silicon Valley overlords forgot that in order for their vision of the future to be adopted, people had to want it.” They still have a lot of work to do. AI Is Destroying the Job Market. Also, AI Is Saving the Job Market I couldn’t help but add a quick additional note about AI to this week’s newsletter… One of the big stories of the last year was the shrinking post-pandemic job market for recent college graduates. Many media outlets confidently offered an explanation for this shift: AI was automating the work of entry-level positions. An ​article​ from last summer proclaimed that “AI is wrecking an already fragile job market for college graduates,” going on to note that “ChatGPT and other bots can do many of [the] chores” that used to be handled by entry-level workers. Another ​article​, published only two weeks ago, offered a stark warning: “college graduates can’t find entry-level roles in shrinking market amid rise of AI.” But then, last week, new job numbers revealed that the entry-level job market for college graduates was rebounding, and hiring in this demographic is now projected to rise significantly. Whoops. I guess AI wasn’t actually automating those jobs. (​I told you so​.) Does this mean the media will stop trying to force this technology into these more routine workforce narratives? If only wishing made it so. A recent Wall Street Journal ​article​ describing these positive numbers included the following line: “In some cases, artificial intelligence is spurring hires by enabling companies to expand services and product lines.” So, let’s get this straight: AI is simultaneously contracting the job market for recent college graduates while also expanding the job market for recent college graduates. Is there anything AI can’t do? The post Who Asked For This? appeared first on Cal Newport. View the full article

M&A, complementary to widespread artificial intelligence implementation, is also high on the list of upcoming priorities for new Dark Matter CEO Vikas Rao. View the full article

The NEXA CEO accused his rival of lashing out at his company despite its own alleged wrongdoing in poaching loan officers and diverting loans. View the full article