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American cities are choking on traffic. From Los Angeles to Chicago, Atlanta to Boston, gridlock is miserable for everyone. New York City’s Congestion Relief Zone offers a data-rich blueprint for cities willing to treat transportation as a system, rather than focusing on one form of travel at a time. Launched in January 2025, the program charges most drivers entering Manhattan’s core business district during peak hours. The Metropolitan Transportation Authority’s (MTA) first comprehensive evaluation report, released in January 2026, shows clear success across mobility, environment, revenue, and equity metrics. The haters are flummoxed. More movement Decongestion Pricing works by making drivers pay a fee towards the cost of clogging city streets. Even with a modest $9 in one of the world’s most congested areas, the results are impressive: Vehicle entries into the zone fell 11%, with more than 27 million fewer entries in the first year. Vehicle miles traveled inside the zone dropped 7.1%. Speeds rose 4.6% year-over-year during toll hours across the zone and key roadways. Morning peak speeds on major crossings into Manhattan (bridges and tunnels) improved an average of 23%, with standout gains like the Holland Tunnel at +51%. Trucks moved 5.6% faster. Travel times became more reliable, without commuters or delivery trucks causing widespread spillover delays on surrounding corridors. Commuters who used to drive alone are shifting some trips to off-peak times, spreading demand and reducing the worst bottlenecks. Real benefits for low-income residents “But what about people who can’t afford a $9 toll” was one of the early questions. MTA’s Low-Income Discount Plan provides a 50% discount on peak tolls for eligible drivers with incomes ≤ $50,000 or in qualifying assistance programs. Residents inside the decongestion zone with incomes under $60,000 can claim a state tax credit covering tolls paid. Most low- and moderate-income households in metro areas already depend on buses, trains, and walking rather than driving into downtown. Faster bus speeds, growing ridership, and revenue-funded upgrades deliver disproportionate benefits. For families without cars (the majority in many urban low-income households) the program means quieter streets, safer crossings, fewer health impacts from pollution, and a better-funded transit network that connects them to jobs and opportunity. Stronger transit and better service With fewer private vehicles clogging streets, transit riders benefit directly: MTA bus speeds in and around the decongestion zone increased 2.3%, reversing years of decline and delivering more reliable trips. Ridership grew on routes through the decongestion zone: subway trips +9%, local/select bus +8.4%, express bus +7.8%. Revenue from the program is dedicated to transit capital improvements. That includes new electric buses, modern subway signals, station accessibility, structural repairs, and subway expansions. Charging a fee for contributing to congestion generates dedicated funds that keep the overall multimodal transportation system reliable. Cleaner air and safer streets Reduced driving translates to environmental and safety gains: Greenhouse gas emissions in the zone fell ~6.1% because fewer people are driving themselves. Early air quality data shows stable or improving trends, with no major pollution spikes in surrounding areas. Some analyses noted double-digit drops in certain particulates inside the zone. Traffic crashes, injuries, and noise complaints have declined, improving quality of life for residents and workers. A model for American cities New York’s Decongestion Pricing shouldn’t be a one-off experiment. Any metropolitan area grappling with clogged streets now has hard evidence that the benefits of decongestion pricing arrive quickly, and public opinion can shift positively with results. Other metros don’t need to copy NYC exactly. They can learn from its detailed monitoring, robust mitigation, and visible reinvestment of revenue. A smart pricing system works for people who need to drive themselves and transit riders whose buses move faster through downtown. What are the rest of us waiting for? View the full article

After months of anticipation, Elon Musk’s SpaceX finally made its S-1 financial filing and business prospectus public for all to see. The document, filed with the Securities and Exchange Commission (SEC), makes an ambitious case to investors that Space Exploration Technologies Corp.—yes, that’s the official name—is poised to build a future for humanity that will include cities on the moon and other planets. But perhaps unexpectedly, the prospectus also offers a fascinating autopsy of one of the internet’s most legendary brands. Buried within the revenue and profit figures for SpaceX’s rocket and satellite businesses is a by-the-numbers look into the spectacular death of Twitter, the social network that Musk acquired for $44 billion in 2022. Musk, of course, has since changed the name to X, and while media outlets for a time took to putting “formerly Twitter” in parentheses whenever they’d cite the social network’s posts in news copy, that practice seems to have fallen out of favor. More recently, Musk folded X into xAI, his artificial intelligence startup, and even more recently, he merged xAI with SpaceX. It’s within this context—xAI is part of a loss-making AI unit within SpaceX—that we can now see limited financial disclosures related to the former Twitter. Here are a few things we’ve learned: Rebranding Twitter to X was costly From a sheer brand perspective, the decision to change Twitter’s name to X was at once perplexing and vexing. Twitter’s blue bird logo, after all, was once so recognizable that it could be easily identified the world over. By contrast, X is both unmemorable and unoriginal. Let’s leave aside for a second that it is already the name of a legendary 1980s punk band, it’s also a moniker that always requires additional context. Twitter, meanwhile, already had the authority of existence at the time when Musk purchased it. It had brand value, and when it became X in 2023, that rebrand was costly. While the SpaceX prospectus doesn’t get too specific, it does disclose that its year-over-year impairment declined by $3.71 billion, or a staggering 98.3%, the year after Twitter changed its name. This enormous sum, SpaceX states, was “primarily related to the impairment of the Twitter brand following its rebranding to X.” Humans post on X in service to Grok SpaceX’s prospectus touts X as “a real-time information, entertainment, and free speech platform,” but its primary purpose seems to be as a training ground for the AI assistant Grok. The prospectus sets up Grok as a frontier AI model that uniquely benefits from its integration with X, where humans have actual discussions about a diversity of topics in real time. Anyone who is familiar with X as it exists now knows that nearly every notable thread will include users asking Grok to explain or add context to someone’s post. “Grok, what does this mean?” has even become a meme of sorts on other social networks. While X users may feel like the chatbot is there to help them, it’s really the other way around. X now exists in service to Grok. As the prospectus puts it, being part of X “further enhances Grok’s truth-seeking objective.” X’s true user metrics are obscured The prospectus includes a few seemingly impressive user metrics for the social network: Across Grok and X, it reports 1.3 billion “supported accounts” were active within the last 12 months. However, it later clarifies that a supported account doesn’t have to be human. “The total number of supported accounts may include fake, spam or bot accounts if they are active,” the filing says. Similarly, SpaceX reports 350 million daily posts across X and Grok. But again, how much of this reflects human activity is unclear. The company discloses that daily posts “may include posts generated by AI or accounts managed by AI.” Fast Company reached out to X for comment. AI is a loss-making unit for now SpaceX says in its prospectus that it plans to prioritize growth at its AI unit, which includes Grok and X. For now, that part of its business losing enormous sums of money. The unit reported a $6.36 billion loss on $3.2 billion in revenue for 2025. This is a contrast to SpaceX’s profitable Connectivity unit, which includes its Starlink satellite internet business. That unit reported income of $4.42 billion on revenue of $11.39 billion in 2025—representing revenue growth of almost 50%. View the full article

As the race to optimize content for AI consumption and citation continues, clients keep reaching out, confused about the web’s favorite genderless alien doodle, Reddit, and what it means for their near-term SEO and AI Overview strategy. Questions usually sound something like this: Should I be actively responding or posting about my brand on Reddit? If AI is trained on Reddit, should we be running paid ads on Reddit? Our CEO wants us to create a subreddit for each of our product lines. What do we do? Why is Google’s AI Overview citing a Reddit thread that calls my product slow and difficult? The problem is that people often lump together three distinct concepts: Training data. Licensed or real-time access. Citation and retrieval systems. They’re all related, but they aren’t interchangeable. And if you care about SEO, AI citations, or why Reddit is suddenly appearing in AI Overviews about your brand, understanding the difference between the three matters. AI training vs. AI access vs. AI citation Let’s differentiate between three concepts that are often lumped together. People read sentences like: “ChatGPT was trained on Reddit.” …and imagine that means every Reddit post gets fed directly into ChatGPT’s memory, waiting to be repeated later in response to a relevant query. That’s not really how training works. Training Training an AI is a lot more like going to school than memorizing an encyclopedia. After years of education, kids learn patterns, relationships, and use cases. They don’t remember the answer to question 8b on a seventh-grade math test, but they do understand: “When I know two sides of a right triangle, I use the Pythagorean theorem to calculate the third.” They learned the concept, not every example. Similarly, AI models do not simply memorize all Reddit posts. They absorb patterns across millions of conversations. The model doesn’t necessarily “remember” a specific thread debating the best rock tumbler, but it can learn from scanning r/RockTumbling that buyers consistently care about things like: Noise level. Ease of cleaning. Availability of replacement parts. Drum size. Long-term durability. In other words, AI models trained on Reddit aren’t necessarily learning facts from Reddit so much as they’re learning how humans compare products, weigh tradeoffs, complain, recommend, and share lived experiences. Licensed access Now we get to the part that changed more recently. In 2024, Reddit signed major partnership agreements with both Google and OpenAI, giving them licensed access to Reddit content. Since then, those relationships have evolved beyond static training datasets toward ongoing API access, meaning continued access to new Reddit posts and comments. Or phrased differently: an avenue for AI systems to keep up with human conversations in near real time. If training an AI model is like sending someone to school, then licensed access is like giving that graduate a newspaper subscription after they finish school. Imagine two adults: Adult AAdult BGraduated from high school 10 years ago Graduated high school 10 years agoNever reads the newsChecks the news every morning Both received the same formal education. Both understand the Pythagorean theorem. But only one knows what happened this week. That’s the difference between training and access. Training shapes broad understanding, while access helps keep information current. Citations AI citing a Reddit thread doesn’t automatically prove the model prioritizes Reddit over the rest of the web. It also doesn’t prove Reddit was part of the original training data. Often, it simply means the system judged that specific source useful for answering the question. Continuing our school analogy, an AI citing Reddit is less like a graduate reciting something they learned years ago in class and more like someone pulling out their phone during a conversation and saying: “Hang on, I saw a discussion about this yesterday.” The citation reflects what the system found helpful at the moment, not necessarily what it learned during training. That difference may be one of the most important things you need to understand when people say, “AI is trained on Reddit.” Dig deeper: How to build an organic Reddit strategy that drives SEO impact 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 Why Reddit performs so well in AI outputs So why does Reddit show up in Google’s AI Overviews when you search for your brand? I’ve seen plenty of fantastical conspiracy theories tied to misunderstandings about Reddit’s partnership deals with Google and OpenAI. But those deals alone don’t explain Reddit’s visibility. The more useful question is why multiple AI systems repeatedly surface on Reddit at all. I’d argue that Reddit is one of the largest sources of content relevant to the kinds of conversations people want to have with AI systems. Here’s what Reddit has that your website probably doesn’t. Context and lived experience Reddit users rarely stop at facts. Your website says, “Battery for this fitness tracker lasts 30 hours.” But a Reddit user says: “Mine lasted all day unless I tracked workouts. Then I had to charge it every day, and it drove me nuts because I was so used to a competitor’s longer battery life.” Those two statements contain similar information. But the second, though anecdotal, adds context and real-world usage — the kinds of details people actually use to make decisions and the kinds brands rarely include in official copy. Disagreement For the past decade, you’ve been taught to create polished content: concise, authoritative, no nuance, no chance for misinterpretation. We publish Ultimate Guides and Top 10 Benefits of X. Reddit’s user-generated content does almost the exact opposite. Reddit threads can contain: Conflicting opinions. Caveats. Unexpected use cases. Frustration. Humor. Devil’s advocates. Users changing their minds halfway through a discussion. In other words, all the messy, unpolished parts of having a human brain. For better or worse, disagreement makes information more useful, and that’s nothing new. It’s been around since Ancient Greece. A polished product page is great, but it won’t help AI systems answer subjective questions. Authenticity (or at least the appearance of it) The beauty of Reddit is that its comments are usually written by people who aren’t being paid to persuade you. And as the biggest content creators become increasingly monetized and sponsored, that counts for a lot more than it did even five years ago. Being unsponsored doesn’t automatically make these users correct, unbiased, or trustworthy. But users often perceive firsthand experience as more credible than polished marketing copy or sponsored influencer posts, and perception matters a lot. Especially when AI systems are essentially trying to combine unlimited viewpoints into a single answer. A note about other platforms It’s worth mentioning that Reddit isn’t the only source of human authenticity and disagreement on the web. It simply happens to be one of the largest examples, and the one I most often see cited and misunderstood when it comes to optimizing for AI. Human context exists across forums like Stack Exchange, review platforms like Yelp, professional groups, and social networks like Facebook. Dig deeper: A smarter Reddit strategy for organic and AI search visibility Get the newsletter search marketers rely on. See terms. How to make content more useful in AI search If we go back to the beginning, where we discussed the differences between training, licensed access, and retrieval, we reviewed the idea that AI systems appear to learn from broad patterns, benefit from fresh information, and retrieve sources they judge useful in context. Whether that context comes from Reddit, forums, reviews, or professional communities is far less important than the fact that it exists at all. The takeaway here isn’t that everyone needs a Reddit strategy. The more useful question is: Where do people in my industry naturally discuss frustrations, disagreements, and lived experiences? For many businesses, that answer is Reddit. But for others, it may be forums, professional communities, Facebook groups, Discord servers, product reviews, or places you rarely spend time. Once you understand where human context lives, you can prioritize your platform optimizations in a way that makes sense. After you’ve identified those spaces, here are a few things worth borrowing. 1. Capture lived experience and make it visible Reddit performs well in AI outputs partly because it contains what polished brand content often lacks: context after the purchase, implementation details, decision-making processes, and even buyers’ remorse. We can’t — and shouldn’t — manufacture our own “authentic” discussion threads. But we do have access to our customers, and user data remains a massively underutilized source of information. So instead of relying solely on internal expertise and picture-perfect case studies, pull more real perspectives into your content: Customer interviews. Reviews and support tickets. Sales objections. Community discussions. If AI systems are trying to retrieve contextual information, part of our job is to make that context easier to find. 2. Stop trying to sound authoritative and start trying to be useful If Reddit threads contain: Uncertainty. Disagreement. Limitations. Frustration. Caveats. Your content can contain more of that, too. Acknowledging who your product or service isn’t for, or where it falls short, can help you create content that feels more credible to both humans and AI systems synthesizing perspectives. 3. Show your work To quote my sixth-grade math teacher: show your work. AI summaries are often adequate at distilling sources into conclusions, but humans are still much better at explaining reasoning. Instead of your content only presenting, “This is the best option, check out all these great features,” try explaining: Why customers chose you. What alternatives they considered and why. Tradeoffs or ituations where your product or service fails. Reasoning provides context, and context increasingly appears to be one of the web’s most valuable commodities. 4. Optimize for decisions Traditional SEO often focused on answering factual questions with objective answers. Increasingly, users ask AI systems nuanced questions with subjective answers that change depending on which AI they ask. They ask: Is it worth it? Which option is better? What do people regret? What happens after six months? Those are decision-making questions. Decision-making requires experience. Experience creates context, and context is turning out to be the connective tissue between what AI learns, what it accesses, and what it ultimately retrieves. Dig deeper: Stop chasing Reddit and Wikipedia: What actually drives AI recommendations 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 Context is becoming the differentiator We started with what makes AI training, licensing, and citations different, but we ended with what seems to connect all three — and what polished “optimized” content is usually missing: context. It’s the difference between: “This rock tumbler has a 3-pound drum capacity and operates at 75 decibels.” And: “This was too loud to have in my basement as I planned, so I had to move it to the garage. The replacement belts were easier to find than I expected, but by the third batch, I was really wishing I’d spent more upfront on a larger drum.” One is the kind of fact you might find on a company website. The other is an experience that feels genuine. Outcomes matter more than features is nothing new. AI may be forcing a similar realization: Being accurate, comprehensive, or keyword-optimized won’t be enough anymore. More and more, the content that gets ahead is the content that helps people make decisions by adding context, tradeoffs, and lived experience around the facts. View the full article

If you have a Microsoft account that uses SMS for two-factor authentication, you may soon have to choose a more secure method for logging in. As reported by Windows Latest, the company is ditching text-based authentication codes for personal accounts, stating that these are "now a leading source of fraud." Users will be prompted to set up a passkey instead. Microsoft is trying to eliminate passwordsMicrosoft has already started moving toward a password-less environment—last year, the company made passkeys the default on new accounts at setup. Now, it is phasing out SMS codes for 2FA and account recovery in favor of passkeys, authenticator apps, and verified backup email addresses. SMS codes are quick to set up and convenient to use. However, they are also among the least secure forms of multi-factor authentication (MFA), as they are highly susceptible to phishing and SIM swapping attacks. Authenticator apps (which generate temporary codes that change every 30 seconds) may be slightly better, but the best MFA option is one based on WebAuthn credentials, like biometrics and passkeys. Passkeys use your device's built-in authentication, such as a face scan, fingerprint scan, or PIN. They can also be synced across devices via password management services. Once you've established your passkey, you can authenticate logins anywhere using one of those methods on your trusted device. Passkeys can't be phished or stolen, and they only work on the legitimate domain they're made for (so they won't prompt you to authenticate if you're trying to log into a spoofed site). They also require that your trusted device be physically close to the device you're logging in on, so they can't be used to access your accounts remotely. While there doesn't appear to be a set date for cutting off SMS authentication, Microsoft users should expect to make this transition to an alternative method soon. View the full article

Tech titans are in many ways the intellectual heirs of the Soviet space programmeView the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. The 65-inch Toshiba C350 Fire TV is down to $264.99 on Amazon right now, which is half off its usual $529.99 price and the lowest it has dropped so far, according to price trackers. At this price, it sits in the same territory as many smaller budget sets, but with a much bigger screen. The main appeal here is simple: You’re getting a straightforward 65-inch 4K TV with Amazon’s Fire TV platform already built in—and because of that, setup is pretty painless if you already use Amazon devices. Once you sign in with your Amazon account, Prime Video recommendations, watchlists, and Alexa features are already sitting there waiting for you. 65-inch Toshiba C350 Fire TV $264.99 at Amazon $529.99 Save $265.00 Get Deal Get Deal $264.99 at Amazon $529.99 Save $265.00 The interface looks and behaves exactly like Amazon’s streaming hardware, right down to the content-heavy home screen and Alexa voice controls. Picture quality is decent for the money, though this is still very much an entry-level TV—the C350 handles 4K and HDR content, but it skips higher-end features like local dimming, wide color support, HDMI 2.1 gaming features, or a high refresh rate. In practice, while movies and shows look perfectly fine for casual viewing, contrast and color fall a bit behind those of similarly priced models from TCL and Vizio, especially in darker scenes, notes this CNET review. Fast-moving sports and games also won’t look as smooth as they would on more expensive TVs. Still, for everyday streaming, YouTube, and regular cable viewing, it gets the job done without major issues. The bigger compromise is really the Fire TV experience itself. Amazon pushes its own content hard, and the interface can feel more cluttered and slower than other smart platform layouts. Some apps also work differently than they do on competing smart TV systems. For example, you can’t directly buy movies inside the Vudu app on this TV. Small annoyances like that add up depending on how you watch things. Also, the USB ports here don’t provide enough power for many external streaming sticks, so if you eventually switch away from Fire TV, you may need separate power cables for those devices. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods 4 Active Noise Cancelling Wireless Earbuds — $148.99 (List Price $179.00) Apple Watch Series 11 (GPS, 42mm, S/M Black Sport Band) — $329.00 (List Price $399.00) Apple iPad 11" A16 128GB Wi-Fi Tablet (Silver, 2025) — $299.00 (List Price $349.00) Fire TV Stick 4K Plus Streaming Player With Remote (2025 Model) — $29.99 (List Price $49.99) Sonos Move 2 — $399.00 (List Price $499.00) Sony WH1000XM6- Best Wireless Noise Canceling Headphones — $398.00 (List Price $459.99) Ring 2nd Gen 2K Wired Video Doorbell (2026 Release) — $49.99 (List Price $79.99) Deals are selected by our commerce team View the full article

The market has never before had to price a stock so speculative yet so largeView the full article

Beneficiaries include start-up backed by firm with links to the The President familyView the full article

The customer journey used to start on the SERP. But that’s no longer the case. By the time a buyer types a search query into Google, they usually already have a few potential brands in mind. They’ve: Seen the same product recommended across multiple Instagram Reels over the course of days or weeks. Read a Reddit thread where five strangers agreed the same tool was the best option. Watched peers recommend a specific service inside a Facebook group. Google has become the confirmation step, not the starting point. Nobody searches with a blank mind. Buyers arrive focused on confirming assumptions and gathering more specific information, not browsing for options. The question that matters is whether your brand made it onto that mental shortlist before the search happened. In most categories, getting on the shortlist means being visible on the platforms where buyers compare options. Where is the shortlist actually built? Peer-driven decisions happen across a handful of environments specific to each industry. For example: In Facebook groups where peers recommend the same three brands again and again. On Reddit threads where the same product keeps surfacing as the community pick. On Instagram Reels and YouTube videos where independent creators and paid influencers endorse the same brand and model, and algorithms keep showing users the same product repeatedly. On LinkedIn posts where an expert the buyer already follows names a tool or brand. On podcasts where a trusted host endorses a specific person, brand, or product. In AI answers that keep naming the same brands for similar questions. By the time one of these interactions triggers a Google search, the scope is usually narrow, often limited to “brand X review,” “brand X vs. brand Y,” or a direct navigational query. Ranking for the head term usually doesn’t decide the buyer. Being mentioned in those off-SERP conversations does. Reddit is booming right now. That won’t always be the case. Platforms rise and fade in cycles, and visibility on any single platform is temporary by nature. What doesn’t change is the underlying behavior: People ask peers before they ask search engines. The takeaway isn’t to chase whichever platform is hot this quarter. It’s to be part of the conversation wherever your category comes up. Dig deeper: Why your brand isn’t making the AI recommendation set 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 The two objectives of search everywhere optimization (SEvO) Every campaign in this space has two objectives: Direct visibility: Show up where the shortlist is being built while buyers are narrowing down their options. This is the more obvious objective, and the easier one to measure through signals like direct search traffic and increases in specific branded queries. Engine comprehension: Every time your brand appears next to a relevant problem, audience, or solution, you increase the likelihood of being recommended later by AI systems. This work is difficult to measure in the moment and usually only becomes visible in hindsight. It might remind you of a famous quote from Steve Jobs: “You can’t connect the dots looking forward; you can only connect them looking backward. So you have to trust that the dots will somehow connect in your future.” You can’t see the system working while it’s being built. Only after enough signals accumulate does your brand start appearing in AI responses and in the conversations shaping buyers’ shortlists. Where the shortlist lives today: SERP evidence Pull any buyer query in your niche and count how many Page 1 results come from Reddit, Quora, YouTube, LinkedIn, Instagram, Medium, Substack, or industry-specific publications. The mix has already shifted. Here are five recently verified examples pulled from live SERPs in Ahrefs. SaaS and CRM Query: “best CRM for small business” (U.S.) YouTube at Positions 1 and 8. Reddit at Positions 2 and 6. Quora at Position 6. Before buyers reach a traditional listicle, they’ve already watched a YouTube review and read multiple Reddit threads. Consumer fitness Query: “best home gym equipment” (U.S.) Multiple Reddit threads on Page 1. YouTube at Position 7. Community recommendations inside home gym subreddits are shaping the consideration set. Ecommerce platforms Query: “Shopify vs. WooCommerce” (U.S.) YouTube at Positions 1 and 4. Reddit at Position 2, plus another result at Position 8. The comparison decision is often shaped through video content and Reddit discussions before any vendor page gets a click. Consumer electronics Query: “best noise-canceling headphones” (U.S.) YouTube at Positions 1 and 6. Instagram at Position 1. Reddit at Positions 3 and 5. Facebook at Position 6. Five of the top six results are social or user-generated. Brands that only show up on their own websites are missing the broader conversation. Running and apparel Query: “best running shoes” (U.S.) YouTube at Positions 1 and 7. Reddit at Positions 4 and 6, with multiple threads. Quora at Position 6. Even in highly commercial categories, community and video content dominate a large share of page one. If your strategy ends at “rank on Google,” you’re optimizing for the last slide of a deck the buyer already watched. In many categories, the SERP now acts more as a confirmation layer than a discovery layer. Dig deeper: How to build a context-first AI search optimization strategy The search everywhere optimization pyramid How can you make sure your brand gets discovered? To organize this work without overwhelming your team, I developed a framework called the Search Everywhere Optimization Pyramid. From the bottom up, each layer supports the one above it. Skip a layer, and the structure above it becomes harder to sustain. Layer 1: Audience platform research (APR) This is the foundation. Before you touch a single platform, you map where your ICP researches, compares options, and makes decisions. The output is a prioritized list of platforms, along with specific sub-communities and engagement strategies. This isn’t a generic “be on social” plan. Problem: Most teams skip APR and default to whichever platform is trending internally. We’ve seen B2B consultants trying to build visibility on Pinterest instead of LinkedIn, and DTC brands focusing on LinkedIn even though their audiences were clearly on TikTok and YouTube. Solution: Conduct one deep research pass per ideal customer profile (ICP), documenting the exact subreddits, LinkedIn creators, niche Slack communities, YouTube channels, and publications your buyers actually consume. Without this, every decision above this layer becomes a guess. Audience research tools like SparkToro can help narrow your focus and identify the right platforms more quickly. Practical steps: In SparkToro, enter a description of your ICP. The output is a ranked list of platforms by audience concentration, topics, social profiles they follow, and more, all exportable. The deliverable from an APR sprint is a one-page brief per ICP that includes: The Top 3 platforms, The Top 5 sub-communities, such as subreddit names, LinkedIn hashtags, and Facebook group names, and The exact phrases buyers use to describe their problems. Layer 2: Alert systems and making them usable with AI Once you know where your audience is, you need to know when they’re talking about topics relevant to your business. That means setting up alerts whenever someone mentions a competitor, asks a relevant question, or surfaces a problem your product solves. Google Alerts exists. It underdelivers. Three tools worth considering are: Semrush Brand Monitoring. AlertMouse. Firehose. But picking the right tool is only half the problem. Volume is the other half. Alerts can become messy very quickly. While you want broad coverage across conversations, it’s equally important to add enough exclusions so decision-making conversations don’t get buried in noise. Fifty notifications a day quickly becomes unmanageable, and unmanageable systems get ignored. The solution: Layer AI on top of your alert stream to filter and prioritize what actually deserves your attention. Two criteria matter most: Recency: Someone is asking the question right now, which means you can join the conversation while attention is still high. Ranking strength: The thread is already ranking for keywords relevant to your business, which means your response can live on a page that keeps surfacing over time. Quick win: Beyond creating a smart alert setup, run each day’s alerts through an AI prompt that scores them against these two criteria and ranks the top three to five opportunities. Respond only to those. Too often, teams set up 50 alerts and abandon the system within a month because it becomes impossible to manage. Prioritization is what makes the system sustainable. The following prompt can serve as a starting point for your own alert workflow. You can expand it using APIs from Semrush, Ahrefs, DataForSEO, or SE Ranking to factor in ranking data more easily: "You are helping a B2B SaaS company prioritize daily monitoring alerts. Their ideal customer is a founder or operations lead at a 10-50 person company, evaluating tools like CRMs, project management software, or productivity platforms. The company wants to show up in conversations where that buyer is actively comparing options or asking for recommendations. Here are today's alerts: [paste list]. Score each one from 1 to 10 on two criteria: Recency (is this thread active in the last 24 hours?) and Ranking potential (does this thread appear to rank or have the structure of a ranking thread - high engagement, authoritative domain, keyword in title?). Return only the top 3 to 5 alerts, ranked from highest to lowest priority. For each one, provide: - The alert title or link - Recency score (1-10) with one sentence of reasoning - Ranking potential score (1-10) with one sentence of reasoning - Combined priority score (average of the two) - A one-line suggested angle for how to respond (useful answer, not a pitch) Ignore alerts that are news articles, press releases, or brand mentions with no question or conversation attached." Swap the ICP definition in the example prompt for your own brand’s ICP When it comes to the alerts that actually matter, show up with a useful answer, not a pitch. The goal is to become a recognized voice in the spaces where your buyers already spend time. Eventually, your audience may start mentioning your brand before you even receive the alert and join the conversation yourself. Dig deeper: Social and UGC: The trust engines powering search everywhere Get the newsletter search marketers rely on. See terms. Layer 3: Industry publications You can’t build third-party credibility by publishing only on your own blog. A byline in a publication your ICP already reads carries more weight. When someone searches your name after seeing you in a Reddit thread or LinkedIn comment, seeing a trusted publication in the results changes the dynamic entirely. There’s a second reason industry publications should come before frequent publishing on your own platforms: distribution. Many sites don’t have a strong distribution network in place. At a time when new articles can struggle to drive clicks or visibility despite solid SEO, publishing often makes more sense on platforms that already have distribution figured out. Here are a few practical tips: Pitch angles that spark interest: Lead with data or a contrarian position, not a broad topic. “Why Reddit is outranking your blog for your own keywords” gets attention. “I’d like to write about SEO trends” does not. Start with accessible publications: Focus first on publications with contributor portals or clear guest article guidelines. Cold-pitching editors at top-tier outlets without an existing relationship usually has a low success rate. Build momentum with mid-tier publications first. Volume benchmark: Two to four bylines in relevant publications are often enough to change the dynamic when someone Googles your name. You don’t need 20. Prioritize relevance over scale: One strong placement on a site your ICP already reads beats 10 placements on general marketing blogs they’ve never heard of. Layer 4: Distribution This is the most underestimated layer, and usually the reason great content dies quietly. Producing content is the straightforward part. Getting the right people to see it at the right time is where most teams quietly fail. Before you scale content production or invest heavily in studies, surveys, or large-scale experiments, build the necessary distribution infrastructure. Distribution infrastructure means: An email list you own. A LinkedIn audience you’ve built. 3-5 partners or collaborators who will share your content when it publishes. A repurposing system that automatically turns one article into 5-7 social posts. You should also consider amplifying key pieces of content through paid ads to reach a wider audience. A good test before publishing anything new is to ask yourself: “Where will this be seen by 500 people in the first 48 hours?” If you can’t answer that, go back to the drawing board. Your distribution layer isn’t ready. Every content piece you publish afterward becomes more effective simply by moving through a distribution framework you’ve already built. Layer 5: Your own publications After audience platform research, smart alert systems, third-party credibility, and strategic distribution, it’s finally time for your own content. Most SEO teams treat their blog as layer one. The pyramid places it at layer five for a reason. Content in 2026 needs to be highly relevant to your brand’s core business or topic, and it needs to add value that isn’t already widely available elsewhere on the web. If you can create that kind of content and leverage your existing distribution channels and third-party placements, it will reach your target audience through multiple paths. Dig deeper: Why social search visibility is the next evolution of discoverability The day-to-day execution model Here’s what this looks like in practice without overloading your team. Phase 1: APR sprint Conduct one deep research pass per ICP. Document platforms, sub-communities, and the exact language buyers use when describing problems. Phase 2: Alert setup and AI prioritization Configure 20 to 50 alerts across your ICPs’ primary challenges and conversation topics. Add enough exclusions so you don’t have to sift through excessive noise, and use an AI-based filter to help identify which conversations deserve attention. Assign a daily 10-20 minute engagement block to maintain consistency. Track brand mention volume as your baseline metric. Phase 3: Industry publications and distribution Pitch topics to the publications your ICP already reads. In parallel, or shortly afterward, build your own distribution layer so every piece of content has a promotion plan before it publishes. Phase 4: Owned content at scale At this point, your LinkedIn posts, Reddit contributions, and blog articles all sit on top of a system designed to amplify them. Important: This isn’t a “replace SEO” program. Technical SEO, keyword targeting, internal linking, Core Web Vitals, and all the fundamentals still matter. Search everywhere optimization sits atop traditional semantic SEO. How do you measure something that happens before the click? You can’t measure pre-click influence with perfect precision, but you can track the signals that suggest buyers already know your brand before they search. Brand mention volume, measured against a baseline from your alert tool over a 90-day period. Branded search growth in Google Search Console, which is often the clearest downstream signal that pre-click visibility is working. Assisted conversion path length and entry sources in Google Analytics 4, specifically non-Google touchpoints that precede branded Google sessions. Direct traffic, where users type your domain directly into the browser. Self-attribution through lead form questions like “How did you find out about us?” Attribution before the click is inherently fuzzy. You usually only see the full picture in hindsight once enough signals connect over time. What you’re building is compounding evidence, not a single-touch conversion path. The real question is whether buyers are already arriving at your site familiar with your brand, and whether AI systems and other users across the web consistently mention you for relevant queries. When those things happen, CTR on branded queries rises, sales cycles shorten, and paid CPCs on branded terms often decline. 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 The shortlist is built before the click Buyers arrive at Google with a shortlist. Your job is to make sure your brand is on it and to give search engines and AI systems enough evidence across the open web to understand who you are and who you serve. The search everywhere optimization pyramid organizes the work in order of leverage: APR first. Alerts second with AI prioritization. Industry publications third. Distribution fourth. Your own content last. Each layer supports the one above it. Platforms will rise and fade. The conversation itself is what you’re investing in. View the full article

The word “calorie” may bring up thoughts of nutrition labels and treadmill readouts, but really calories are just units of energy. Your car runs on gas, your house runs on electricity, and your body runs on food energy. So how many calories do we burn each day, and how many should you burn? Let’s dig in. You actually burn most of your calories at restCalories aren’t only burned during exercise. It takes energy to keep the lights on, so to speak—for your heart to beat, your brain to think, your cells to repair themselves, and more. In fact, most of our calories are burned doing these maintenance chores. Scientists call this baseline calorie burn our "basal metabolic rate," or BMR. There are several equations that will estimate your BMR; for a calculator, try the one at tdeecalculator.net. (It uses the Mifflin-St. Jeor formula if you don’t know your body fat percentage, and the Katch-McArdle formula if you do.) To give you an example, I plugged in my stats—I’m 150 pounds and 5’6”—and the equation guesses that someone my size burns: 1,352 calories for most of my basic bodily functions (not including digestion!) 1,623 calories, total, if I’m sedentary 2,096 calories, total, if I do moderate exercise three to five times a week 2,569 calories, total, if I’m a hardcore athlete or a person who exercises on top of having a physical job Keep in mind these are just estimates; your actual calorie burn may be more or less. (From tracking my calories over the years, I know that I'm usually somewhere between those last two numbers, depending on how active I am.) The factors that affect your total calorie burn include: Body size: The bigger you are, the more calories you burn at baseline and the more you burn during exercise. Muscle mass: Muscle burns more calories than other tissues, which is why you get a more accurate estimate if you know your body fat percentage; the lower your body fat, the more muscle you have by comparison. Age: These formulas assume that your metabolism slows down a bit as you age (although there is evidence that this may not make a big difference). Activity: The more you exercise, the more calories you burn. Genetics and other factors not accounted for in the formula: There’s actually a huge variety from person to person, even if you compare people of the same size, age, etc. We're all different. To give you a sense of the range, the 2020-2025 Dietary Guidelines for Americans calculates calorie counts for two example people, who are both a bit smaller than average Americans, but let's take a look anyway. The document figures that a 5’10” man who weighs 154 pounds will burn, in total, between 2,000 and 3,000 calories each day, depending on his age and activity level. Their example woman is 5’4” and 126 pounds, and she will burn between 1,600 and 2,400 calories. So if you’re used to thinking of 2,000 calories as some kind of upper limit for how much to eat—or 1,200 calories as a calorie budget for dieting—you may be surprised to realize how many calories you probably already burn. How (and why) to burn more caloriesIf you’re trying to lose weight, logic would say that you should focus more on diet than exercise. After all, if most of your calorie burn is your BMR, exercise is going to be a drop in the bucket by comparison. I don’t think that’s the only thing you should consider, though. If your BMR is 1,300 calories and your total burn is 1,600, then sure, you could eat 1,300 calories without exercising and probably lose weight. But it’s hard to be healthy while you’re eating so little. Burning more calories through exercise helps your body in two ways: Exercise is good for us, regardless of calorie burn; we should all be getting at least 150 minutes of cardio per week, plus some strength training to help build or retain muscle. The more food you eat, the easier it is to fit in the good stuff: vitamins, minerals, fiber, good fats, and a variety of vegetables. A person who burns 2,300 calories and eats 2,000 is in a much better position to benefit from exercise and good nutrition than a person who burns 1,600 and eats 1,300. So how do you burn more calories? You can’t get younger, and if you’re losing weight you won’t want to get bigger. The biggest levers you can pull are: Exercise more Gain muscle mass (through strength training, and eating plenty of protein) Don’t diet all the time I’ve written before about how I’ve noticed my total calorie burn increases when I’m eating more food; when you feed your body, it’s more willing to expend energy. This is one of the reasons it’s thought to be beneficial to take “diet breaks” if you plan to be in a weight-loss phase for a long time. Why you shouldn’t rely on “calorie burn” numbers from wearables or exercise machinesYou’re probably wondering how much exercise is “enough” to burn more calories. It’s a trick question, though: You want to change what kind of person you are—stop being sedentary and become a frequent exerciser—rather than nickel-and-dime yourself about exactly what numbers you burned in which workout. This is because our bodies get more efficient with exercise over time. A half-hour jog might burn 300 calories in theory, but at the end of the day you may have only burned, say, 200 more than if you hadn’t jogged. You might end up feeling more tired later in the day, or you might just be getting better at running and burning fewer calories when you do it. (This is an ongoing area of scientific research.) There is evidence that exercise machines’ estimates of calorie burn are extremely inaccurate; wearables like Fitbits and Apple Watches are probably a bit better, being personalized to your exercise intensity, but they’re still ultimately relying on estimates that aren’t always accurate. View the full article

Robotaxis are multiplying across American cities. But are consumers actually ready to trust them? Zoox CEO Aicha Evans discusses the company’s strategy as an Amazon subsidiary, its intensifying rivalry with Waymo, and why a new partnership with Uber could be the key to getting autonomous rides from novelty to scale. Evans also reveals why she recruits what she calls an “invisible army of rebels” inside Zoox. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company, Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. Zoox is in the red-hot center of building a new mobility future: electric autonomous vehicles. You have a new partnership with Uber. Your robotaxis are operational in Las Vegas and San Francisco. How close are we, really, to a dramatically different mobility paradigm? I think, as an industry, there’s been a lot of progress. We’re at the proof-point stage. Over the last 20 years, we’ve had a lot of “Oh, it’s happening tomorrow morning” and “Oh, it’s never going to happen.” We’re past that stage now. The proof points are there, for us and for fellow travelers. Now it’s a matter of starting to prepare for scale. But I’ve always been very consistent that this is not going to be like a consumer product where, all of a sudden, boom, 100 million people experience it. It’s going to be step by step, but we’re well on our way, which is really exciting. Your most well-known fellow traveler, Waymo, has chosen to retrofit existing cars. You guys have opted for purpose-built vehicles with a striking design. It’s got two benches facing each other. There are no driver controls, no steering wheel. It doesn’t really look like a car. Why make that choice? If AI is going to be doing the driving, it’s really about the customer experience and also about the best way to materialize this product. First, you have the safety aspect. In a regular passenger car that is architected for a human driver, the safest place to be is actually the front seat. For us, we were able to look at redundancy. We were able to look at our optimal sensor architecture so that we can see things, including occluded things. In Silicon Valley, sometimes maybe we think about the customer secondhand. Here, they thought about it firsthand and about the customer experience. It just does not feel like you’re in a car. What we’re seeing from folks, both people who ride and people in the communities where we ride, is curiosity. The first couple of minutes are, “Oh my gosh, what is this?” And then, “Wow, this makes so much sense.” Look, you’re not doing the driving, so why have a bunch of things that are involved in the driving? You were part of the team that made Zoox a subsidiary of Amazon, which acquired it for $1.3 billion in 2020. How does Amazon help Zoox’s trajectory? Is it about financial resources, access to technology and AI? How is Zoox different than if it were on its own? Focus, focus, focus. The financial backing is important. A strong relationship with AWS also goes without saying: the compute. One of the things I love about Amazon is the plurality and multitude of businesses and industries it has been in. We forget Amazon started by selling books. So they’ve seen a lot. They’ve experienced a lot. There’s a lot of pattern recognition. There’s a lot of customer obsession. So we get a lot of advice. When something’s going really well: “Can you do more of that?” When something is going poorly: “Why is that? And how are you looking at bottlenecks?” This summer, it’ll be six years, so we’re way past the dating phase. I’ve been on both sides of M&A at big companies, and I would say Amazon gets maybe an eight and a half out of 10. Eight and a half. Yes. I have the freedom I should have. I tell people all the time, it’s not like when you have a startup that is fully in the private sector with a board. I’ve been on the other side, where you have VCs, institutionals and independents. There’s always a decision-maker and a boss. Make your peace with it. And we’re sending machines out there to drive among humans. People should ask us questions, whether it’s the regulators or our bosses. It is the right thing to do, and I welcome it, and we are better for it. Your partnership with Uber, I’m curious how big a deal that is, because Uber also partners with Waymo. What makes that deal meaningful for you? They do partner with almost everybody, which is great. If I were in their shoes, I would probably do the same. [Uber CEO Dara Khosrowshahi] has taken rides in Zoox, and Uber totally gets the differentiated experience. For Zoox, for almost 12 years now, we’ve been so focused on building the tech and building the processes. Over the last couple of years, we’ve really started thinking about commercialization and how we’re going to do this. I don’t see this category as just taking share from whatever exists today. I actually see an expansion of the market. As far as Zoox and Uber, for us, it’s about learning. It’s about experimenting. I’m pretty sure if you arrive in Las Vegas, maybe you know about Zoox, maybe you don’t. Now you’ll see them on the Strip and be like, “What is that? Oh my gosh.” But I’m pretty sure you know about Uber. So right there, that makes it worth it to Zoox. And some transportation has nothing to do with pleasure. Frankly, it has to do with being utilitarian. If we can help serve that together and scale faster, the experiment will have worked. Prior to Zoox, you spent some years at Intel. Are there things from that experience that you draw on, or is Zoox such a different business that you look elsewhere for lessons and inspiration? Both. In general, I’m a curious person. I have this wonderful coach who taught me 15 years ago that it’s OK to ask for help. That is actually a sign of strength. So when I’m stuck, I’m known to pick up the phone and say, “Hi, I’m Aicha Evans from Zoox, an Amazon company. I need some help. So-and-so told me about you or introduced me to you.” But taking it back to Intel, I learned a ton there. I spent 12 years at Intel. It’s an important company. I root for that company to this day. I learned about hardware-software integration. I had a front-row seat to people who are hardware-only or software-only, and I was like, “Huh, that’s a problem.” I learned about process too. You’re going to laugh. If some ex-Intel or current Intel people who know me listen to this, they’re going to chuckle because I was known as a little bit of a rebel. I complained about every process: “Why are we so slow and so dogmatic and so bureaucratic? Don’t you understand?” And yeah, well, guess what? Thank you. Thank you for that training, because then I came to Zoox, and it was still a very early start-up, a little less than 500 people. It had a lot of technology, but it really needed to be orchestrated and coordinated and to put processes in place, put orgs in place, put a common language in place, in order to succeed. So there are lots of learnings that I apply, but also lots of things not to do that I won’t tell you about. That you won’t tell me. Yes. Because you have to move as fast as you can as well, not just slow. I guess that’s what being the rebel in that group means. I guess you want to have your own rebels within your own organization, but maybe not too many of them. I want enough of them because you need a uniform distribution in all functions. One of the things at Zoox is that, because we’re vertically integrated, one day you’re talking to a traditional automotive engineer about chassis, battery, suspension, brakes and harnesses. The next day you’re talking to marketing about how much we want to emphasize safety, or not. So you want to make sure that, I call them affectionately, my invisible army, or the invisible army, is distributed across the corporation. But we also have to have a contract that we will debate, we will discuss, we will consider alternatives, but once we make a decision, we commit and we move, and we don’t revisit unless there’s evidence that assumptions were incorrect. Then we do a little bit of a feedback loop and keep moving forward. View the full article

Teaching undergraduates gives you a different perspective on things. For many, they see their life already laid out: An analyst position at a prestigious bank or consulting firm after graduation, then graduate school and a string of impressive jobs at important institutions. Then family, travel, and maybe a board seat or two. We all know that life is messier than that, but that’s the type of thing you really have to learn for yourself. The management professor Henry Mintzberg once observed that we expect management to be like a conductor with an orchestra, with the leader on a pedestal directing each movement with expert precision. “But,” he argues, “management is more like orchestra conducting during rehearsals, when everything is going wrong.” The truth is that you don’t want things to go exactly as planned. It’s the off-key notes that you learn from most and what often leads to your biggest opportunities. Here are four things I’ve learned from a very messy career that produced wonderful surprises. 1. Incentives rarely work There is an old saying that “when you change incentives you change behavior,” and there is some evidence to support that it can work. For example, the Mexican government program Prospera has been proven to be extremely effective, using cash payments to boost school attendance and preventative health care. Yet research shows that incentives often fail and can even backfire horrendously. Human behavior can’t be boiled down to simple triggers. There are norms that underlie behaviors that are rarely obvious as well as unintended consequences that can warp behavior. The truth is that if you want to motivate people, incentives are rarely the right place to start. As Roland Bénabou and Jean Tirole explained in a landmark paper, there are forms of motivation beyond extrinsic benefits including, most notably, intrinsic motivation and reputational factors. For example, artists often toil for years with little material benefit, but enjoy significant intrinsic satisfaction and reputational rewards. There is also significant evidence that extrinsic incentives crowd out intrinsic and reputational motivations. In an experiment in which subjects were asked to solve a puzzle, those who were paid a flat fee were much more likely to continue to work during free time than those who were paid for each puzzle solved. Sound leadership is not about prodding people to do what you want, but attracting those who want what you want and leading them with shared values in pursuit of a shared purpose. 2. You don’t need the best people, you need the best teams In 1997, McKinsey published a landmark article declaring a “war for talent.” The firm argued that due to demographic shifts, recruiting the “best and the brightest” was even more important than “capital, strategy, or R&D.” The report was enormously influential and continues to affect how leaders manage their teams even today. I once worked at a company where senior leadership meetings on Friday mornings were meant to discuss critical issues. But no matter the agenda, the conversation always seemed to turn back to talent and the need for “better people.” They would look at our current staff and wish that they could find others who were smarter, more skilled and more ambitious. Each time I remember thinking, “You recruited these people. You trained these people. And you manage these people. If there’s a talent problem, it doesn’t lie with them. It lies with you.” As workplace expert David Burkus puts it, “talent doesn’t make the team. The team makes the talent.” Their people weren’t failing them, they were failing their people, which is why our employee turnover rate was roughly twice the industry average. The truth is that we don’t need the best people, we need the best teams. Researchers at MIT and Carnegie Mellon found that group performance is driven more by factors such as group dynamics and social sensitivity than anything else. It’s how your team builds trust, psychological safety, and collaboration that will determine what they can achieve. If you feel you need “better people,“ you should probably focus your efforts on becoming a more capable leader and creating a better, more supportive culture that empowers people to achieve their potential. 3. Empower your people to ‘never go down alone’ One of my first managers gave me great advice: “Never go down alone,” he said. “If I know what you’re doing and everybody else knows too, then you’re covered. If something goes wrong, we’re in it with you. But if you go off by yourself and nobody knows what’s going on, you will end up going down alone and taking the blame for everything.” It’s a great context shift. We tend to value our privacy and see oversight as an intrusion. We are acutely aware that our lives are messy and don’t want others to see our missteps. We wake up in the morning, clean and dress ourselves in an effort to put on our best face. Exposing our peccadilloes threatens to shatter the mask we show to the world. But when you see communication and transparency as a shield, protecting you from your own mistakes, it changes your outlook. I completely bought in and later, when I took on a senior role, had my people prepare “never go down alone” reports to tell me what I needed to know to protect themselves. They were reluctant at first, but then asked their people to do the same. The somewhat unintended result was that every Friday hundreds of reports were flying around our company like an anthill, filtering information up to me. I would review each report (they were very short) over the weekend and address any issues on Monday. It was tremendously helpful in identifying potential issues and nipping problems in the bud. 4. Everybody brings something to you and needs something from you Years ago, we had a manager named Ania running one of our publishing operations. She was well-liked, diligent, and responsible. Still, we felt the business needed a more creative spark, so we brought in a rising executive to take her place. Ania transitioned out gracefully and left the company on good terms. Things turned out well for Ania. She became a sought-after interior decorator, renowned for her creativity. As it turned out, the problem wasn’t that she lacked any creative ability. The problem was that we weren’t giving her the type of challenges that excited her. While she languished in our organization, she excelled in a different environment. That simple concept is key to being an effective manager. Everybody brings something to you and needs something from you. If you can figure that equation out, you can run your team effectively. If you can’t, you get situations like we had with Ania, in which everybody would be made better off by parting ways. For all of the simplistic talk about “carrots and sticks,” hiring only the “best people” and demanding accountability, understanding how to set your people up for success is the most important thing a manager does. That’s often not what you learn in business school, but over the past 30 years it’s what I’ve learned to be true. In the end, great management isn’t about control—or even authority—it’s about creating the conditions for people to do their best work. View the full article

Building versus buying capabilities in-house or deciding whether to outsource them is a strategic decision. And it’s not a decision all executives think about the same way. So much depends on your company’s goals and strengths. It’s important to have a structured way to think about this decision, though, so when you need to incorporate a capability, you know how to make the decision. We asked our Fast Company Impact Council members how they decide when to build capabilities in-house versus outsourcing or partnering. It was a popular question, and we had to limit the responses—to just 27! There is wisdom in these words that you can apply to your situations. 1. PARTNER FOR COMPLEX CAPABILITIES AI has shifted the equation—there are now far more things you can use easily, rather than build in-house. For many companies, that means partnering for complex capabilities instead of trying to recreate them internally. The challenge isn’t access; it’s prioritization. With so many nice-to-have services now available, the key is identifying which ones become true need-to-have capabilities that drive real outcomes. — Kevin Laymoun, Constructor 2. DEPENDS ON COMPETITIVE ADVANTAGES I decide based on where real competitive advantage comes from. If a capability is strategically differentiating and tied to proprietary data, judgment, or know-how, I want it in-house. If it is more commodity and a partner can deliver it faster or better, I will partner. I tend to think about AI as two races: one to adopt what is becoming broadly available, and another to build what will actually set you apart. — Todd James, Aurora Insights 3. ONLY BUILD WITH EXPERTISE Never build something you don’t properly understand. You need to be able to judge the work, and to understand the craft and process behind it. Without that, you’re slightly kidding yourself. So the choice is fairly simple: Either hire someone you trust implicitly to lead it, or work with people who already know what they’re doing. — James Greenfield, Koto 4. BUILD CRITICAL CAPABILITIES IN-HOUSE We are leaning more toward building critical capabilities in-house, especially where they touch core workflows, professional judgment, and client relationships. Much of the startup world is trying to build around expertise firms like ours, which have developed over decades, so we have to be careful not to give away part of our value system. We will still partner where it accelerates us, but we want to build internally where the capability shapes delivery, strengthens our people, and directly supports the value we create for clients. — Mike Sewell, Gresham Smith 5. UNDERSTAND YOUR VALUE PROPOSITION When thinking about AI capabilities, for instance, the decision depends on both the company’s situation and the daily evolution of AI knowledge. Right now, it’s a bit of a fast-moving Wild West, with few standards, so building in-house requires sustained investment in talent that can keep up. To make this decision, leaders need to first understand their value proposition. If AI is central, building internally may make sense. If not, seeking a partner is often the smarter path. Many companies benefit from AI operationally without needing to own the capability, making partnerships more efficient and adaptable. — Andrea Montecchi, Oliver Wight 6. PARTNERSHIP IS USUALLY BEST I have always outsourced and partnered unless something was our sustainable competitive advantage—so few things actually are. I wrote six books on partnering and taught executive education on strategic partnering at Caltech for 24 years, so I am a huge advocate of partnering. It is a low-capital-investment way to leverage the talents and assets of other companies, while making sure your company carefully protects transferring any of your non-contracted IP to your partners. — Larraine Segil, Exceptional Women Alliance Foundation 7. OWN WHAT MATTERS MOST If a capability is central to our brand, requires deep institutional context, or needs to compound over time, I want it built in-house. If it is specialized, episodic, or something an outside partner can do faster or better without sacrificing quality or control, partnering usually makes more sense. The goal is not to own everything. It is to own what matters most. — R. Ethan Braden, Texas A&M University 8. BUILD IN-HOUSE WHEN IT WILL BE REUSED We build capabilities in-house for those things that will be needed repeatedly across projects and digital products we create, and those things that will be a competitive advantage. We outsource for very specialized skills and things that aren’t aligned with our core business. We partner when there are clear mutual benefits, like teaming up with subject matter experts to bring an innovative new mobile app to market. — Brad Weber, InspiringApps 9. STAGE AND ECONOMICS It comes down to stage and economics. Early-stage companies often rely on external partners for speed and flexibility; as they scale, bringing capabilities in-house can improve efficiency and control. But the goal isn’t binary, it’s balance. The best setups maintain healthy tension between internal teams and external partners to avoid “marking your own homework.” That outside perspective is often what pushes teams to be braver. The model may evolve over time, but the need for constructive friction to produce great work remains constant. — Emily Kortlang, Yerba Madre 10. ASK 3 QUESTIONS When deciding whether to build capabilities in-house versus outsourcing or partnering, we ask ourselves several questions: Is this something we will be able to make money on? Will this distract us from what we do best? Is this something we can be famous for? If the answer is “yes” to all three, then we investigate building it. If not, we would rather partner. — Oscar Yuan, Material 11. BE CLEAR ON WHERE YOU EXCEL We have become very clear on what we excel at and what we don’t. For the things we excel at we have built the capability in-house and avoid outsourcing any part of it. For things we are not good at but need, we look for best-in-class partners to work alongside us. I am a huge believer in being great at a few things and not trying to deliver on things where you don’t excel. — Phillip Haid, Public Inc. 12. START WITH USE CASE AND RISK PROFILE We start with the use case and the risk profile. If a capability directly affects how we operate, serve customers, or differentiate as an integrated logistics company, we build it. This way, we maintain accountability, data stewardship, and speed of learning. When a need is specialized or evolving quickly, partnerships can help us move with speed, paired with clear goals, shared ownership, and governance from the start. — Dennis Anderson, ArcBest 13. IN-HOUSE: WORKFLOW AND USER EXPERIENCE We build in-house when it’s core to our differentiation, especially where context, data, and iteration speed matter. For everything else, we partner or buy to move faster. In government, nuance and security really matters, so anything tied to workflows or user experience is hard to outsource well. The goal is to stay focused on what actually makes your product uniquely valuable and drives usage. — Madeleine Smith, Civic Roundtable 14. QUALITY IS HARD TO OUTSOURCE Our default is to build in-house. We have a very high bar for quality, and the hard part is making it reliable, coherent, and seamless for the customer. That level of quality is hard to outsource, especially for anything core to the product experience. We’ll partner where it makes sense around the edges, but the capabilities that define the product have to be built by us. — Avery Pennarun, Tailscale 15. CORE TO YOUR DIFFERENTIATION The most useful question in any build-versus-buy decision isn’t about cost or speed, it’s “Is this capability core to how we differentiate?” If yes, you build. If not, outsourcing or partnering lets you move forward while keeping your team focused on what actually sets you apart. In my experience advising growth-stage companies, the leaders who scale most effectively are as intentional about what they won’t build as what they will. — Randi Lee, Lucas Advisory 16. BUY FOR SPEED We don’t build for the sake of building. We buy for speed where it’s a commodity and build where it really matters. The line is simple: What creates our differentiated, competitive advantage, we want to own. — Steve Holdridge, Dayforce 17. ACCOUNTABILITY AND DIFFERENTIATION It’s about accountability and differentiation. We build capabilities in-house when they are core to strategy, and we have access to data, expertise, and resources—areas where ownership can be measured and accounted for. We partner when speed, scale, or specialized expertise can accelerate outcomes without diluting strategic control. And we outsource work that is transactional, repeatable, or optimized for cost and efficiency, where differentiation is low and standardization is acceptable. AI has made these decisions sharper. It quickly exposes which capabilities compound value and demand ownership, versus those that can be leveraged externally. — Felicity Carson, onsemi 18. ITERATE AND PROBLEM SOLVE IN REAL TIME Building our AI processes in-house has allowed us to scale and deploy technology quickly, with a lean team, and on an incredibly lean budget. As a startup, every single dollar we spend on data and data processing matters; our decision to build in-house has allowed us to be on an even playing field compared to bigger companies in the industry who have secured far more capital than we have. Building in-house also allows us to iterate and problem solve in real time, preventing unnecessary lag times to fix issues that arise during deployment. — Xiaodi Hou, PhD, Bot Auto 19. OUTSOURCE IF NOT CORE Our strategy is to build anything in-house that we believe, or aspire, to be a distinctive competency. Namely, something that is core to our business and our commercial or operational differentiation. Anything we want to execute with efficiency but only provides average market capability, are the things we seek to outsource. — Scott Brighton, Bonterra 20. DEPENDS ON YOUR SITUATION What really matters is the situation you’re in. When the outcome quality depends on the people doing the work having deep experience with the problem, keep it in-house. These days, our industry is moving towards smaller teams with more experienced people who are still actively involved in the work. This makes it easier for everyone to be on the same page. We partner when someone else has genuine domain expertise we lack. The key question is always whether a layer between the maker and the outcome will cost you something you cannot afford to lose. — Peter Smart, Fantasy 21. BUILD WHEN IT MAKES LONG-TERM BUSINESS SENSE When we consider building in-house capabilities, we take the long view—how the investment complements our existing offerings, whether it’s a natural extension or a net-new function, and what it truly takes to stand up. Ultimately, it has to make long-term business sense for us. — Chris Bailey, Bailey Brand Consulting 22. BUILD IF IT’S AN ONGOING CAPABILITY Math. If it is a core component or ongoing capability, I build in-house or partner because I like control, visibility, and sustainability. However, I may outsource as I ramp, with the intention to bring or develop in-house once it is a proven model. — Effie Carlson, Watershed Health 23. PARTNER WHEN OUTSIDE EXPERTISE IS ADDITIVE I usually begin by asking whether the capability is something that should become part of who we are long term. If it directly shapes how we want to operate or grow, there is real value in building that internally. At the same time, partnership can be just as important when outside expertise helps you move further or think differently than you would on your own. The strongest partnerships are the ones that solve an immediate need while also strengthening internal understanding over time. — Chadwin Sandifer, EdD, Fairleigh Dickinson University 24. DECIDE BASED ON BEST POSSIBLE CLIENT OUTCOME We build in-house when it’s core to how we differentiate. Our design practice, PR capability, and our complex business unit are all examples of specialisms that sharpen our disruption model. But we’re equally intentional about partnership. When scale, speed, or a new frontier is required, we tap into best-in-class collaborators or bespoke partners who bring something we don’t own. The goal isn’t to do everything, it’s to orchestrate the right combination of talent to deliver the best possible outcome for our client partners. — Emily Wilcox, TBWA\Chiat\Day NY 25. PARTNER WHERE IT ACCELERATES I decide based on where capability creates the most strategic value. If it differentiates how we operate—compliance, worker engagement, sustainability—we build it in-house. That’s where control, standards, and long-term capability matter. Where speed, scale, or specialist expertise is critical, we partner—often to build capability quickly and accelerate impact. It’s a dynamic model. Build where it differentiates, partner where it accelerates. — Clare Woodford, Alpine Group—Paradise Textiles and Alpine Creations 26. SPEED AND COST OF DISTRACTION It comes down to speed and cost of distraction. If building internally pulls top talent away from core priorities, you’re creating hidden risk. I build when it creates durable IP; I partner when it accelerates execution without slowing the main business. — Logan Mulvey, GoDigital Music 27. USE DESIGN SPRINTS Design sprints aren’t just for new product development. We use design sprints to make critical decisions, including the building of in-house versus outsourcing. Empathy interviews with our internal stakeholders and clearly defining the problem allows us to design experiments to test products or services against our expectations and real-world needs resulting in better decision-making and alignment. Time spent on the front end of innovation results in future savings. — Garret Westlake, Virginia Commonwealth University View the full article

Most teams have a decision-making problem that no one can quite put their finger on. Meetings multiply. Decisions get relitigated endlessly. The choices that eventually emerge are often so cautious they accomplish almost nothing. The problem isn’t personal. Teams full of talented people routinely get stuck because they were never given a shared language for making choices under uncertainty. When conditions get murky, that gap becomes expensive. High-performing teams, by contrast, build their decision-making toolkit deliberately. They move from endless discussion to concrete proposals. They know the difference between a real objection and ordinary discomfort with risk. They make the final call even when someone more senior disagrees. Teams that succeed aren’t eliminating uncertainty. They’re navigating it with speed and agility with these three habits: 1. They stop asking what to do and start making proposals Every team I’ve worked with eventually hits what I call the swirl: the discussion is thorough, the ideas are smart, and the team leaves having agreed on nothing except when to meet again. Getting unstuck requires someone to stop asking “What should we do?” and start saying “I propose we…” That shift sounds modest. The effect is not. One of my clients was part of a transformation team at a consumer health company where permission-seeking had become a genuine bottleneck. Meetings ran long on conversation and short on decisions. People waited—for clarity from above, for consensus below, for someone else to take ownership. When the team shifted the expectation—asking people to come with specific proposals rather than open questions—the dynamic changed. Junior team members who had been staying quiet started driving things forward. Conversations got shorter. Decisions stuck. A proposal doesn’t need to be complete. It just needs to be concrete enough for people to push back on, build on, or improve. That’s what moves work forward. Key takeaway: The next time a conversation starts circling, offer something specific. “I propose we…” is one of the most productive phrases in any team’s vocabulary and anyone on the team can use it. 2. They know the difference between a real objection and an ordinary hesitation Most teams block progress on discomfort, not on harm. They treat “I would have approached this differently” as a reason to wait. Good ideas get shelved not because they’re dangerous, but because someone senior in the room isn’t enthusiastic. A real objection points to immediate, hard-to-reverse damage. A hesitation is everything else—doubt, preference, or the simple fact that this isn’t how things are usually done. A client of mine was facilitating a session where a leadership team was weighing a proposal to eliminate a large, time-consuming annual process. The idea was sound and would have freed thousands of hours across the organization. The only concern raised was that junior employees might lose development time with senior leaders—a real tension worth naming, but not a reason to stop. He acknowledged it, agreed to address it in the rollout, and the proposal moved forward. Months later, the team cited it as one of the best decisions they made all year. That’s the muscle worth building: welcoming objections that reveal real risk, while refusing to let discomfort slow down decisions that are ready to move. Key takeaway: When a proposal stalls, ask directly: Is there a specific reason this will cause immediate, hard-to-reverse harm? Real objections deserve engagement. Everything else is a reason to move. 3. They understand their decision authority—and use it even when others disagree In my experience, the hard part isn’t identifying who can decide what. It’s actually exercising that authority when someone more senior pushes back. Teams get given permission and then continue to seek permission they already have. They escalate decisions squarely within their mandate—not because they lack the authority, but because holding the line when a senior person disagrees is genuinely uncomfortable. So instead of acting, they wait. We worked with a product team given clear authority over an innovation initiative. Their charter spelled out the scope and decision rights. Months in, senior regional leaders escalated concerns to the CEO—they’d been consulted and didn’t like the direction. Some team members were being told by their direct managers not to present at all. The team lead opened the session by returning to the charter: the team’s purpose, scope, and decision authority. Not as a confrontation, but as a shared reference point. That clarity gave the CEO what was needed to resolve the tension, and the work moved forward. High-performing teams understand their authority comes from the mission, not from managing political relationships. A senior colleague’s disagreement deserves a hearing. But it doesn’t override a decision the team was empowered to make. Key takeaway: Before your next project launches, write down what your team can decide without external approval—and commit to using that authority, even when it’s uncomfortable. From talk to action High-performing teams don’t decide better because they’re smarter. They decide better because they’ve built shared habits for moving from discussion to action. Proposals, the ability to separate real objections from hesitation, and the confidence to exercise authority already granted—those aren’t complicated ideas. Practiced consistently, they’re how teams stop waiting and start moving. View the full article

Google announced at Google Marketing Live two new ad formats that will show up in Google's AI Mode. Google said there are conversational discover ads and highlighted answer ads, and of course, they are labeled as "Sponsored."View the full article

Google announced at Google Marketing Live that it is also bringing Shopping ads, Business Agent for Leads and expanding the Direct Offers pilot for AI Mode in the coming months.View the full article

Google announced it is making one uniform AI-agentic advisor named Ask Advisor that brings together Google Ads Advisor and Google Analytics Advisor, and I assume the beta Google Merchant Advisor.View the full article

Launching a redesign or migration? The staging environment tests in this guide will catch SEO issues before they cost you rankings. The post How To Stress-Test A Staging Environment To Surface Risks Pre-Launch – Ask An SEO appeared first on Search Engine Journal. View the full article

Google is testing a "for you" label in the product grid, shopping results, within Google Search. It gives you a more personalized response, in this example, a promotion code was available for this merchant.View the full article

Google has made a new anchor ads format named Dynamic anchor ads for AdSense. I don't see the announcement for this yet, but Google updated its help document on anchor ads and changed collapsible anchors to dynamic anchors.View the full article

The release of Google’s latest AI models this week at Google I/O was yet another example of the direction of travel for the generative AI revolution. Facing a user base that is increasingly burning more tokens under basic subscriptions or API access, AI companies are starting to hike prices and throttle usage. In response to those cost pressures, consumers are beginning to cut their cloth accordingly. And while frontier AI providers are releasing ever more powerful models into the world, smaller companies are advancing, too. Often based in China, these are frequently accused of copying the innovations of U.S. models through techniques like distillation, or reverse engineering the way artificial intelligence models work by probing them and inferring their answers. What it means is that these slightly less powerful AI models are, despite lagging behind the bleeding edge, still plenty powerful for most people. The 2026 Stanford University AI Index found that AI models’ performance on the SWE-bench Verified coding benchmark surged from 60% to nearly 100% of the human baseline in the last year, while the highest-quality models gained 30 percentage points on the highly difficult Humanity’s Last Exam benchmark. At the same time, Stanford charted a shrinking gap between U.S. models and their Chinese competitors, which are often offered at a fraction of the price, or entirely free through locally hosted versions. The result is that we’re entering the “good enough” era of AI models, where the needs of all but AI’s power users could be capably handled with something that costs less than giving the likes of Anthropic or OpenAI $200 a month. “Not every task requires maximum capability,” says Azeem Azhar, founder of the Exponential View newsletter, and a user of both the frontier models put out by the biggest AI labs and smaller, cheaper alternatives. “You don’t need Nobel scientist intelligence to appeal a parking ticket.” Not everyone agrees that the gap between the cutting edge and the “good enough” models is surmountable right now, in large part because of the shift toward more agentic uses of AI. Max Weinbach, an analyst at Creative Strategies, argues that while smaller models can handle narrow or basic tasks, they still “struggle to understand everything” in the way increasingly autonomous AI agents are expected to. Models like Gemma 4 27/31B and Qwen3.6, he says, are solid for lightweight use cases, but tend to break down on more demanding tasks like vibe coding, even when paired with tools like Hermes or OpenClaw, because “the model just isn’t capable.” The idea that you could entirely live and work on locally hosted or lower-capacity models still seems slightly beyond the reach of most people. There are times when you need the extra oomph that only the models underpinning the likes of ChatGPT or Claude can provide. But the gap does appear to be closing. And for most tasks, the extra capabilities that the leading, more expensive models provide aren’t necessarily needed, something Azhar compares to getting an 8K TV when you’re barely likely to perceive the difference from a 4K one. For some, though, the idea that there’s only an imperceptible gap between the likes of OpenAI and Anthropic’s models and those of the cheaper Chinese labs, or locally hosted models, is an exaggeration. Weinbach points out that it may cost practically nothing to run a model six times in order to get the right response, with five attempts glitching out or producing the wrong answer. “But almost every user is willing to pay $20 a month to nearly guarantee a correct response the first time,” he says. What “good enough” actually means may ultimately shape consumer behavior more than model performance. Weinbach argues that people rarely choose products they see as merely adequate for tools they use every day, and that settling for good enough often becomes “a regretted decision” that eventually pushes users toward more premium options. And even if people do, if there’s one thing that widespread AI adoption over the past three-and-a-half years has taught us, it’s that for those who buy into the promise of AI, once you start using it, you discover new possibilities and use cases for it. “The cheap, ubiquitous, good-enough capability creates new users, new habits, new expectations,” says Exponential View‘s Azhar. “Those habits eventually generate demand for capabilities that only the frontier can satisfy.” View the full article

Ask any paid media manager how their Monday morning starts, and you’ll hear some version of the same story. Google Ads. Meta. LinkedIn. TikTok. Reddit. Pull the numbers, drop them into a spreadsheet, make them tell a coherent story, and send the report to your client or boss by 10 a.m. Somewhere in there, figure out what worked last week and why. It’s a terrible use of a Monday morning. I’ve been in performance marketing long enough to remember when “multi-channel” meant running Google Ads and maybe a Facebook campaign on the side. That was already hard enough to reconcile. Now you’re dealing with 10 or 11 networks, each with its own attribution logic, campaign structure, and definition of a conversion. The data doesn’t just live in different places. It doesn’t even speak the same language. And yet most teams still manage everything the same way they did five years ago: too many tabs, spreadsheets, and Monday mornings. The Monday morning problem nobody talks about What doesn’t get discussed enough is that most of the time paid media teams spend on “campaign management” isn’t actually campaign management. It’s Data entry. Reformatting. Logging in and out of platforms. Rebuilding the same campaign brief five different times because Google’s campaign structure doesn’t map to Meta’s, and neither of them map to LinkedIn’s. Industry data puts the average paid media manager at 5 to 9 hours a week on administrative work alone. My sense from talking to practitioners — and from doing the job myself — is that’s probably conservative for anyone managing more than three or four active networks. Agencies handling multiple clients across multiple platforms can easily spend twice that. Think about what 10 hours a week actually means. That’s 40 hours a month — five full working days. If you’re billing that time to clients, a meaningful part of the retainer isn’t going toward the work they actually hired you to do. If you’re absorbing it internally, it’s a hidden cost that never shows up in your ROAS calculations but absolutely shows up in your margins. Every week. And that’s before you get to the errors. Manual data transfer is really just manual error introduction — there’s no way around it. Budget caps get mistyped. Negative keyword lists don’t get updated across platforms. A campaign gets paused in Google while it keeps running in Meta because nobody caught it. Small things, but small things compound. What you’re actually losing (it’s not just time) The time cost is real, but it’s not even the biggest problem. The bigger issue is the lag. When your performance data lives in 12 different places and only gets consolidated once a week, you miss a meaningful optimization window between Monday and Friday. The insight that LinkedIn is overspending while Google is underspending doesn’t surface until the budget’s already gone. The creative that stopped working on Wednesday doesn’t get flagged until Monday. Another week of wasted spend. There’s also a consistency problem that’s harder to see but just as expensive. When campaigns are built natively inside each platform — one brief rebuilt five times across five different UIs — the strategy starts to drift. Audience definitions stop matching exactly. Budget allocation logic becomes inconsistent. Creative strategy changes not because you made a deliberate decision, but because you were tired on Thursday afternoon by the time you got to the LinkedIn build. For agencies, there’s another layer. You’re not just managing drift across networks, you’re managing it across clients. Thirty native dashboards. Thirty credential sets. Thirty reporting exports to manually combine every week. I’ve been that person. It doesn’t get easier. It’s a lot. And if we’re being honest, most teams have just accepted it as part of the job. Why native dashboards will never fix this I want to be direct about something: Google, Meta, LinkedIn, and every other ad network aren’t going to solve the cross-network management problem. Not because they can’t, but because they won’t. Every platform is incentivized to maximize your time inside its interface. Time spent in Google Ads is time you’re not questioning whether Google deserves that budget. Same with Meta. Same with LinkedIn. The fragmentation isn’t an accident. It’s the product. Yes, they’ve all built APIs. Yes, there are integration ecosystems. But use any of them and tell me this feels solved. Managing a multi-network buy in 2026 still means logging into 10 different tools. The gap hasn’t closed — it’s just been covered with more software. Anyway. The solution has to start from the opposite direction: not “how do we stitch together the outputs of 10 platforms,” but “what if you never had to build inside those platforms in the first place?” What AI-native management actually changes The tooling shift happening in performance marketing right now isn’t really about dashboards. Dashboards are the symptom fix. The real shift is about who — or what — is doing the operational work. AI-native ad management platforms handle the upstream work that lives in your team’s heads and your team’s time. Campaign planning from a plain-English brief instead of rebuilding logic for every platform. Creative automatically sized to each network’s specs instead of manually reformatted. Two-way sync on live campaigns so editing a headline in one place pushes the update across all 10 channels at once — no native dashboards required. That last point matters because it changes the workflow itself. The old process for updating a live creative is: log into Google, pause the ad, upload the new version, publish. Then repeat the same process in Meta, LinkedIn, and TikTok. With two-way sync, you make one edit and the update propagates everywhere. The platform archives the old version and handles deployment. That’s not a marginal improvement. That’s a different category of tool. For agencies, the reporting side is probably the most immediately valuable. AI-generated client reports — normalized data, performance narrative, budget pacing — delivered in a branded format that’s ready to send. No more Sunday-night Excel ritual. None of this is speculative. These platforms already exist, built specifically for teams that have been absorbing this operational overhead for years without a real alternative. 3 things worth doing this week I’ll keep this practical: 1. Track where your hours actually go for one week. Not roughly — actually track them. Before you evaluate a new tool or process, you need a real baseline. Most teams I talk to underestimate their admin time by about 40%. Seeing the real number tends to motivate change faster than another article about it ever will. 2. Standardize naming conventions across every active account Seriously. It’s unglamorous work, but the payoff is immediate. Inconsistent campaign names, ad set labels, and conversion event naming create a disproportionate amount of reconciliation pain in multi-network reporting. Two hours of cleanup now can save hours every week going forward, no new tools required. 3. Evaluate what’s available now This is the step most teams skip. The AI-native ad management space has moved quickly over the last 18 months. If your mental model of “cross-channel management tools” is based on something you evaluated two or three years ago, it’s probably outdated. The gap between what the best tools can do today and what most teams are actually using is significant — and getting wider. The operational edge is the performance edge The teams winning in paid media right now aren’t necessarily the ones with the biggest budgets. They’re the ones that have compressed the cycle between data and action — teams that can see cross-network performance in real time, make changes across every channel at once, and get reporting out the door without losing half a day to manual work. That’s an operational advantage. And operational advantages compound in ways that are hard to catch once another team has them. The Monday morning spreadsheet reconciliation ritual isn’t inevitable. It’s just what the industry was stuck with until recently. View the full article

It’s alive. The Terrarium Phone Case by U.K.-based designer Daniel Idle is a clear iPhone 16 Pro Max case with a vertical terrarium designed to show off small plants growing inside. “The idea came from noticing how personal phone cases have become,” Idle tells Fast Company. “People use them to carry objects, express themselves, and customize something they interact with all the time. That got me thinking about how much time we spend on our phones and how disconnected they make us feel from nature.” To bring nature to this most unexpected of places, Idle wanted to see if a phone case could include living elements by building an ecosystem directly into it. He designed and modeled prototypes to test what it would take to make a usable case hospitable to plant growth. It’s an example of biophilic design, or design that incorporates nature directly into its form. While often seen in nature-friendly architecture or interiors, the case shows there’s room for biophilic industrial design that brings nature to everyday consumer products too. But it also presented its own challenge: balancing the functional needs of a phone case with the environmental needs of an ecosystem that could sustain plant life. Early prototypes didn’t provide enough support to keep the landscape stable and attached when handled. It had to be “durable enough to function as a handheld object rather than just a display piece,” Idle says. The solution was a vertical terrarium with specialized sticky soil that’s stable enough to allow the phone to be physically moved around while the plants inside remain firmly secure. The case is made from two pieces: the structural printed shell and the enclosed chamber, and it’s surprisingly low maintenance. It requires infrequent watering—just a small amount of water if the plants start to dry out; the case sustains itself through the condensation cycle of internal moisture. In his own case, Idle grew small-scale plants well suited for enclosed terrarium environments, including moss, which works especially well since it creates an immediate sense of landscape at miniature scale and doesn’t take much upkeep. He says the design is more of a conceptual piece at the moment but he’s looking at developing the idea commercially. Amid growing worry over skyrocketing screen time, redesigned phone cases are cropping up as one potential solution. Cases that are too annoying to pick up or that shrink the actual screen get at the problem in their own ways, but the Terrarium Phone Case bets on built-in beauty to get people to set down their screens. Photosynthesis requires sunlight, so to keep your miniature garden growing, the case needs screen-down time. Notifications will just have to wait. View the full article

Jeff Bezos is betting that the future of fashion won’t be made from cotton or polyester but, instead, from lab-grown fibers. Through the Bezos Earth Fund, Bezos and Lauren Sánchez Bezos have committed $34 million to researchers developing next-generation textiles, including biodegradable fibers and plastic-free synthetic silk. Their aim is to replace some of the most resource-intensive materials in the global clothing industry with alternatives that could dramatically reduce the industry’s environmental footprint. The investment marks a notable shift for the fund, which has largely focused on conservation since Bezos pledged $10 billion to climate initiatives in 2020. Now, it’s partially turning toward fashion, an industry deeply reliant on fossil fuels and one of the largest contributors to global emissions. “The use of fossil fuels in the fashion industry is a big issue,” Tom Taylor, the fund’s president and CEO, said, according to The Wall Street Journal. Today’s most common materials, including polyester and viscose, are derived from oil and coal. They’re cheap, durable, and ubiquitous among both fast-fashion and luxury brands, but they also come with steep environmental costs. These fabrics are not biodegradable, shed microplastics, and can release so-called forever chemicals into water systems, raising growing health concerns, according to the European Environment Agency. Bezos’s grant backs researchers who are experimenting with materials grown from bacteria, agricultural waste, and other unconventional sources, innovations that could reshape what clothing is made of at a molecular level, the Journal reported. “When you start asking questions about what clothes could be made of, the answers are incredible,” Sánchez Bezos said in a statement to the Journal. “The future of fashion is being invented right now.” Still, science is only part of the challenge. Scaling these materials has proved difficult. Sustainable textiles remain expensive to produce, and many startups in the space have struggled to survive. Even when viable alternatives exist, brands and consumers often default to cheaper, familiar fabrics, Vogue reported. “It’s small, underfunded, and lacks those industry relationships that could push it further and deeper,” Steven Kolb, CEO of the Council of Fashion Designers of America, said, according to the Journal. The fund is aiming to close that gap. Grant recipients include Columbia University, working with the Fashion Institute of Technology, as well as the University of California, Berkeley, Clemson University, and the Cotton Foundation. At Columbia, researchers are developing a biodegradable fiber grown from bacteria that feed on agricultural waste, an approach that could reduce reliance on both petroleum and water-intensive crops, the Journal reported. Biomedical engineering professor Helen H. Lu told the Journal that the funding will help expand research teams and address technical hurdles, particularly at a moment when federal support is shrinking. She pointed to “uncertainty in federal funding” after the The President administration canceled more than 1,600 grants from the National Science Foundation last year. The fund hopes some of these materials could reach consumers within three to five years, the Journal reported. But even that timeline underscores the scale of the challenge. Transforming a global supply chain built on cheap synthetics won’t happen quickly. While the Bezos Earth Fund operates independently, Bezos is still the founder of Amazon, the world’s largest clothing retailer and a frequent target of criticism over emissions tied to manufacturing and rapid delivery. The company said it has reduced carbon emissions per shipment by one-third since 2019 and is working toward net-zero by 2040. Amazon employees have publicly protested the company’s climate impact through the Amazon Employees for Climate Justice group, arguing its emissions footprint contradicts leadership’s environmental messaging. Environmental groups have also repeatedly ranked Amazon among the largest corporate emitters, pointing to the scale of its logistics network and rapid-delivery model. Even Bezos himself has faced criticism for climate philanthropy that some advocates say does not fully address Amazon’s underlying business model, highlighting a broader debate over whether corporate-led climate efforts can offset systemic consumption. Some sustainability advocates argue that better materials alone won’t solve fashion’s climate problem, instead calling for reduced production and consumption altogether. Taylor acknowledged those debates but framed the fund’s strategy differently. “Different people have different values,” he said. “This is ours.” The push into fashion also arrives as Bezos and Sánchez Bezos take on a prominent role as lead sponsors of the Metropolitan Museum of Art’s Costume Institute—home of the Met Gala—tying their climate ambitions to one of the industry’s most visible cultural platforms. —Leila Sheridan 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

When’s the last time you thought about pepper? Designers working in the consumer packaged goods category have reimagined many a pantry staple over the past several years, including olive oil, tinned fish, and even chili crisp, but pepper has remained as forgotten as it is ubiquitous. A playfully chunky new brand is giving the category design intentionality, functionality, and visual appeal—and could point to where food brand design is headed. Michael Laniak Michael Laniak, a former line cook, launched Milly on May 12 as a result of his failed attempt to source pepper in the same way he could olive oil or sea salt. Milly sells only whole peppercorns—black, white, and green—along with namesake matching pepper mills in coordinated colors. The products are available on the company’s website, starting at $14 for one tin of whole peppercorns, $28 for a pepper and mill set, and $78 for the trio of peppers and mills. Considering there’s nothing else quite like it, what we’re seeing on a small scale is the reinvention of a category, and it’s just begging for well-packaged competitors to vie for counter space next to some Graza and a well of Maldon salt. Its branding and positioning are what make this new product notable, with a hand-lettered logo and thoughtful design system that refocuses what many might dismiss as a good-enough spice into an experience you might want to consider more thoughtfully (and either gift or spend more on). The logo itself draws a playful contrast to category competitors, which offer run-of-the-mill serifs on white or black backgrounds (or red, for McCormick). Milly’s in-house designer, Cassie Scowcroft, hand-lettered the final design, which has an organic, analog look, thanks to the high-contrast weight variations of its strokes, a mix of upper- and lowercase letters, and a script y. It’s a nod to the fact that, according to the company, the peppercorns are handpicked. The color accents used on the tins purposefully reflect the flavor profile of each peppercorn, all derived from the same plant but harvested and/or processed differently to achieve a variety of notes. Red nods to black pepper’s boldness and spice; bright green embraces green pepper’s fresh, floral profile; and cream on brown hints at white pepper’s subtle, earthy flavor. The logo’s organic, blocky look and bold color pairings call to mind other brands in the food space, like the Roman restaurant Roscioli (which also opened in New York in 2023) and the new Gourmet, which, if in a very obvious way, graphically stuck it to the old institution by creating a publication in complete graphic opposition, using an asymmetric, uneven, informal logo. (Perhaps a chunky, hand-lettered micro trend is on the way in food branding?) The playfully contemporized geometric asymmetries of Milly and Roscioli also call to mind the graphic style of Italian Amaro labels from the last century and before, and the newer Amaro brand Faccia Bruto by studio GEO NYC, whose blocky type, bold color, and contrasting line illustrations are gorgeously anachronistic. While the tins are initially difficult to pop open (I’m told this is for freshness), Milly’s packaging system makes adding pepper to a dish feel special. And unlike those of competitors, Milly’s mills are refillable, reducing waste and perhaps increasing return customers. Peppercorn might be an afterthought. But so was olive oil. And then came Graza. View the full article