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US and Chinese leaders expected to unveil deal to extend tariff truce amid tensions over rare earths and tech controlsView the full article

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We may earn a commission from links on this page. Deep work is a concept most popularly defined by Cal Newport, who wrote the aptly titled Deep Work: Rules for Focused Success in a Distracted World. As he tells it, deep work is a state where you find the ability to focus completely on a demanding task without letting distraction get in your way. If you can get the hang of it, you’ll get more done in less time and ultimately end up feeling more fulfilled. On his website, Newport observes that it’s rare to see someone feel energized and happy after sending emails, but there’s a sense of fulfillment that comes from sustained focus on more meaningful tasks. How do you achieve deep work?Newport draws a distinction between deep work and “shallow work,” or that which can be accomplished while you’re distracted. Deep work is for “cognitively demanding” tasks, whereas shallow work prepares you for deep work. Creating a project deck is deep work. Emailing colleagues to coordinate data for it is shallow work. If you’re having a hard time determining what is and is not deep work, Newport has some guidelines. Shallow work tends “to not create new value in the world” and is “easy to replicate.” The key, then, is first sorting your work into deep and shallow categories. Determine which of your tasks are cognitively demanding and valuable and which are “logistical-style” and replicable. Next, plan to devote an hour or an hour and a half to deep-work tasks, then schedule it so you have that time blocked off in your schedule. (As for blocking off your scheduling, familiarize yourself with time boxing and time blocking, which call on you to schedule every minute of your day and input it, moment by moment, into a detailed calendar, all in the name of staying focused.) Finally, when the time comes to get into deep work, eliminate all your distractions. Signal that you’re busy, make sure you’re unavailable in Slack and on the shared calendar, and put your phone on “do not disturb.” Don’t check emails, don’t talk to anyone, don’t look at your devices for anything but work, and commit to only working on your demanding task in the time you allotted for it. The most important element is doing this mindfully and going into your deep work sessions aware that your goal is to accomplish something demanding with no distractions. Emails, notifications, chitchat, and other worries and interruptions are commonplace and pull you away from the task at hand, so purposely cutting them off to get something done will be difficult, but it can turn into a habit, especially once it starts yielding the dual result of accomplishment and fulfillment. The deep work hackAll of that sounds great in theory, but when you find yourself staring down the hour-and-a-half block you scheduled out, you may not know what to do or where to start. This is where you need the Pomodoro method, a famous productivity technique that asks you to work, uninterrupted, for 25 minutes, take a five-minute break, and repeat the cycle about four times before getting a larger break. You can modify those time blocks a bit to suit your needs, but 25 on and five off are the standard. When you use this approach, deep work starts to come naturally in those working blocks because you know you're getting a reprieve at the end. Deep work is described as a period when work seems to flow smoothly and you barely notice time going by, so 25 minutes may not be quite enough for you (depending on the task at hand), but you can figure it out as you get started incorporating these methods. The most important thing is to be distraction-free. The second most important thing is to remember that breaks are actually a key part of staying productive, so don't skip them altogether. The easiest way to make use of this time-tested technique is, of course, by app. My favorite is FocusPomo, which blocks all your distracting apps while you use it and generates cute, unobtrusive cartoon tomatoes to reward you for finishing work blocks. View the full article

Today, Nvidia Corporation (Nasdaq: NVDA) became the first company to cross the $5 trillion valuation—in premarket trading, at least. It marks a major milestone in stock market history and suggests that once other unthinkable valuations are within reach. But Nvidia isn’t the only company breaking a trillion-dollar threshold. Fellow tech giants like Apple, Meta, and Broadcom are close to bursting their own thirteen-figure barriers, too. Here’s what you need to know about Nvidia’s approach to $5 trillion as companies climb toward the most exclusive club on the planet. Nvidia hits $5 trillion market cap in premarket trading As of this writing, Nvidia has become the first company to cross the $5 trillion valuation threshold. In premarket trading, NVDA shares are currently up 3.65% to $208.37 per share. With about 24.3 billion shares outstanding, that gives Nvidia a current premarket valuation of just over $5 trillion. If Nvidia’s stock price levels hold once the opening bell rings, it will cement its place in the record books. The company’s current premarket jump follows the stock’s nearly 5% rise yesterday. These gains have been primarily driven by recent announcements from the company that are lifting investors’ expectations. As CNBC notes, Nvidia announced plans yesterday to build seven new supercomputers for the U.S. government, including at Los Alamos National Laboratories. Separately, Nvidia CEO Jensen Huang revealed that the company’s all-important Blackwell GPUs are now in full production in the U.S. state of Arizona. This allows Nvidia to manufacture more of its AI chips, which can help meet the incessant demand for its processors. But there’s another factor that may be motivating Nvidia investors. As Reuters reports, President The President is in Asia this week, meeting with regional leaders. He is due to meet with Xi Jinping, China’s President, tomorrow. Today, The President revealed that he will speak to the Chinese president about Nvidia’s Blackwell chips, which are currently banned in the country. A lifting of this ban could greatly boost Nvidia’s bottom line if the company can once again sell its Chips inside China. Investors seem to feel that this trio of positive news has the chance to materially benefit the company, hence, the $5 trillion barrier falling. Other companies are close to breaking trillion-dollar milestones Nvidia’s $5 trillion milestone isn’t the only thirteen-figure barrier that is being broken this week. Yesterday, Apple Inc. (Nasdaq: AAPL) officially crossed the $4 trillion barrier for the first time, before closing the day with a market valuation of $3.99 trillion. Apple’s accession into the $4 trillion club made it just the third company in history to cross that barrier, after Nvidia and Microsoft Corporation (Nasdaq: MSFT). While nothing is certain, it’s possible Apple could cross back over the $4 trillion threshold again today. And Apple and Nvidia aren’t the only companies within reach of crossing trillion-dollar thresholds. Two other companies are within range, too. Facebook owner Meta Platforms (Nasdaq: META) had a market cap of $1.88 trillion as of yesterday’s close. That means it’s less than $120 billion from crossing the $2 trillion barrier, which would be a first for the company. Semiconductor and internet infrastructure company Broadcom Inc. (Nasdaq: AVGO) is also relatively close to crossing a trillion-dollar barrier. As of yesterday’s market close, Broadcom had a market cap of $1.76 trillion. That puts it at less than $250 billion from the $2 trillion club. Of course, while these companies are closest to their next trillion-dollar barriers, there’s no guarantee that their stocks will continue to go higher, or, if they do, how long it will take. But their ascent up the ranks is another example of how once unthinkable trillion-dollar market caps are becoming more common. A full list of the world’s trillion-dollar public companies For those keeping track, there are now 11 trillion-dollar publicly traded companies, based on yesterday’s share prices at market close, according to data compiled by CompaniesMarketCap.com. Those companies are: Nvidia ($4.89T) Microsoft ($4.02T) Apple ($3.99T) Alphabet ($3.23T) Amazon ($2.44T) Meta ($1.88T) Broadcom ($1.76T) Saudi Aramco ($1.66T) TSMC ($1.56T) Tesla ($1.53T) Berkshire Hathaway ($1.03T) Big Tech earnings could impact the trajectory It will be interesting to see how the current tech earnings season will impact the market caps of many of these companies. Investors will particularly be interested in how the artificial intelligence boom is affecting the bottom lines of companies like Microsoft, Alphabet, and Meta, all three of which are expected to report their financial results after the closing bell today (Wednesday, October 29). Wall Street will also be watching for any updates from these companies about their capital expenditure plans, as the AI boom has required huge investments from the world’s largest tech giants. If investors are satisfied that AI investments are worth the costs—and if they don’t see signs of a slowdown—share prices could spike, sending them further up the ranks into the trillion-dollar club. But if investors begin to worry that we are, as many have speculated, in an AI-fueled bubble, many of the companies in the trillion-dollar club could see their rankings slip fast as their share prices fall. View the full article

A lack of rare earths is just one way in which nature disadvantages the continentView the full article

Facebook has introduced an intriguing new feature aimed at enhancing how users share memories on the platform. This innovative tool automatically suggests photos and videos from users’ camera rolls, enabling the creation of collages and edits that are both fun and easy to share. This development stands to benefit small business owners looking to increase engagement with their audience through fresh content without the need for extensive design skills. The new feature is designed with a particular motivation in mind: many individuals capture special moments but often hesitate to share them due to concerns about their quality or lack of time. According to Facebook representatives, “With your permission and the help of AI, our new feature enables Facebook to automatically surface hidden gems – those memorable moments that get lost among screenshots, receipts, and random snaps.” By tapping into artificial intelligence, the platform simplifies the process of curating and sharing unique content. Small business owners can take advantage of this feature in several ways. For one, utilizing visually appealing posts can help grab consumer attention in an increasingly competitive social media landscape. The AI-driven suggestions mean that even business owners without an eye for design can quickly produce attractive content that highlights their offerings, staff, or events. As one Facebook source noted, “This feature does the heavy lifting, so you can focus on sharing the fun.” In practical terms, this functionality could encourage local businesses to share moments from events, showcase customer testimonials, or highlight special promotions. For example, a café could document a lively open mic night or celebrate customer milestones, effortlessly transforming raw images into shareable content. By doing so, they not only engage their audience but also foster a sense of community around their brand. However, while the potential for this feature seems promising, there are considerations that small business owners should keep in mind. Privacy remains a major concern with any technology that utilizes personal media. Facebook assures users that all suggestions generated by the new feature are private unless they decide to share them. It’s important for business owners to clearly communicate how they handle customer data when utilizing their own images or videos in marketing materials. Moreover, as with any social media tool, it’s essential to maintain a balanced approach. Flooding feeds with too much content—especially if it’s generated without thoughtful curation—can lead to disengagement. Small businesses should be strategic in selecting content that aligns with their brand identity and resonates with their audience. As of now, the feature has rolled out to users in the U.S. and Canada, and currently appears in Facebook Stories and the main Feed. Users can easily manage or disable this feature in their Facebook camera roll settings. Facebook plans to broaden the scope of this feature to other countries in the coming months, which could mean that a larger pool of users—and subsequently, potential customers—will soon be able to engage in this creative sharing process. The roll-out of this feature underscores the growing importance of personalization in social media marketing. Small businesses that leverage tools like Facebook’s new creative sharing function can find themselves better positioned to forge meaningful connections with their audience. By removing barriers to content creation and encouraging spontaneous sharing, Facebook is empowering users to capture and disseminate moments that not only resonate personally but also highlight the community around them. For more detailed information about this feature and how it works, you can visit the original Facebook press release here. Image via Facebook This article, "Facebook Unveils AI-Powered Feature for Effortless Photo Sharing" was first published on Small Business Trends View the full article

Facebook has introduced an intriguing new feature aimed at enhancing how users share memories on the platform. This innovative tool automatically suggests photos and videos from users’ camera rolls, enabling the creation of collages and edits that are both fun and easy to share. This development stands to benefit small business owners looking to increase engagement with their audience through fresh content without the need for extensive design skills. The new feature is designed with a particular motivation in mind: many individuals capture special moments but often hesitate to share them due to concerns about their quality or lack of time. According to Facebook representatives, “With your permission and the help of AI, our new feature enables Facebook to automatically surface hidden gems – those memorable moments that get lost among screenshots, receipts, and random snaps.” By tapping into artificial intelligence, the platform simplifies the process of curating and sharing unique content. Small business owners can take advantage of this feature in several ways. For one, utilizing visually appealing posts can help grab consumer attention in an increasingly competitive social media landscape. The AI-driven suggestions mean that even business owners without an eye for design can quickly produce attractive content that highlights their offerings, staff, or events. As one Facebook source noted, “This feature does the heavy lifting, so you can focus on sharing the fun.” In practical terms, this functionality could encourage local businesses to share moments from events, showcase customer testimonials, or highlight special promotions. For example, a café could document a lively open mic night or celebrate customer milestones, effortlessly transforming raw images into shareable content. By doing so, they not only engage their audience but also foster a sense of community around their brand. However, while the potential for this feature seems promising, there are considerations that small business owners should keep in mind. Privacy remains a major concern with any technology that utilizes personal media. Facebook assures users that all suggestions generated by the new feature are private unless they decide to share them. It’s important for business owners to clearly communicate how they handle customer data when utilizing their own images or videos in marketing materials. Moreover, as with any social media tool, it’s essential to maintain a balanced approach. Flooding feeds with too much content—especially if it’s generated without thoughtful curation—can lead to disengagement. Small businesses should be strategic in selecting content that aligns with their brand identity and resonates with their audience. As of now, the feature has rolled out to users in the U.S. and Canada, and currently appears in Facebook Stories and the main Feed. Users can easily manage or disable this feature in their Facebook camera roll settings. Facebook plans to broaden the scope of this feature to other countries in the coming months, which could mean that a larger pool of users—and subsequently, potential customers—will soon be able to engage in this creative sharing process. The roll-out of this feature underscores the growing importance of personalization in social media marketing. Small businesses that leverage tools like Facebook’s new creative sharing function can find themselves better positioned to forge meaningful connections with their audience. By removing barriers to content creation and encouraging spontaneous sharing, Facebook is empowering users to capture and disseminate moments that not only resonate personally but also highlight the community around them. For more detailed information about this feature and how it works, you can visit the original Facebook press release here. Image via Facebook This article, "Facebook Unveils AI-Powered Feature for Effortless Photo Sharing" was first published on Small Business Trends View the full article

Strategic insights on building custom GPTs for SEO, focusing on product thinking, cultural nuance, accessibility, and aligning AI workflows with business outcomes. The post Why The Build Process Of Custom GPTs Matters More Than The Technology Itself appeared first on Search Engine Journal. View the full article

Different productivity hacks work for different people, which is why there are so many of them. But if you happen to be a visual learner, there’s one in particular that might be suited for you: mind mapping. Mind maps are diagrams designed to organize information and data points that relate to each other, making everything you need to do easier to follow. They're particularly popular for students who need to visualize how the concepts they're studying link together, but they have wide applications outside the classroom. What is a mind map?A mind map isn’t just a diagram that lays out tasks. Rather, it does so in a hierarchical way, connecting things that are related and making it clear which need to be done first in order to move on to the next task. (When used for studying, on the other hand, they help bridge connections between main topics and those that branch off from them, plus relate to one another.) You can use mind maps for a variety of reasons, whether you want to think clearer or set goals with them, but for our purposes, we’ll go over how they can be used as productivity tools. You can also try them for word-associating, brainstorming, note-taking, and more once you get the hang of it. Start by writing the main idea of what you need to do. For instance, if you have to make a new hire, write that right in the center and draw a circle around it. Then, use arrows to branch off into related tasks: HR tasks, onboarding tasks, financial tasks, etc. From each of these, you draw more arrows. HR tasks might involve legal paperwork and background checks. Onboarding may require getting your new hire access to training modules and finding them a workspace. Financial tasks could include setting up payroll and getting them certain benefits enrollment information. After creating the mind map, you’ll see all the tasks laid out in a web that will help you visualize and grasp everything you need to do. It all leads back to that one main responsibility. The subtasks will equate to all the little things you need to do to make it all happen, piece by piece, until you end up fulfilling that final goal in the center. How to make your own mind mapAbove, we talked about drawing circles, which is fine if you prefer the old paper-and-pen method. You can make them in Word or Google Docs, but those can be clunky. A better option is to use an online creativity tool, like Canva or Draw.io. An even better option than that is to use software dedicated to the task. My favorite is Xmind, which you can use on your computer or phone and comes pre-loaded with a bunch of templates. The actual creation process can be helpful for brainstorming, but is a pain if you're not graphically inclined. Xmind makes it a lot easier, especially for beginners, because you just drag and drop the shapes and lines around a canvas designed for this exact purpose. Why this worksThe simplicity of a mind map is what makes it so effective. Keywords, not long phrases, and color-coding lend themselves to quick processing and recall, while the hierarchical nature of the tasks helps you see what order you need to handle them in. The overarching task at the center serves as a reminder of what you're even doing all those little things for, which helps keep you motivated and on track. The simple flow of arrows links ideas and the spacing of the boxes keeps categories organized. Overall, it’s a great solution for visual learners or anyone in a rush, and it’s not as clumsy or convoluted as a large spreadsheet or planning document. View the full article

Since its launch in July 2023, Threads has gone from Meta’s “what if” project to one of the most talked-about social media platforms in the world. What started as an alternative to Twitter/X has found its own rhythm. It now attracts millions of active users, sparks cultural moments, and gives both creators and brands another space to join the conversations that matter most — no matter how niche. In fact, thanks to its growing popularity and the allure of uncharted territory, I’ve kick-started a project to grow to 1,000 followers on the platform by December 2025. Now, you may be wondering — why is Threads so buzzy? What makes it different from other text-first platforms? Aside from being tied to some of the biggest apps in the world via Instagram and Facebook, and having a pretty high-profile launch, it’s started developing a unique identity. More than two years in, the data shows why Threads is so popular — and where it’s headed next. We’ve pulled together the essential Threads statistics you need to know in 2025 — whether you’re planning your first Threads account, building a brand, or just trying to keep up with what’s working on this still-new platform. Threads user demographics show a young, slightly male-leaning audienceIn the two-plus years since launch, Threads has built an audience that’s both sizeable and distinct from other social media platforms. The data shows who’s showing up, how often they log in, and what the overall makeup of the platform looks like — from activity levels to age and gender splits. Threads monthly active users hit 400 millionAs of August 2025, Threads has reached 400 million monthly active users, a milestone that cements its place among the world’s leading social media platforms. That’s remarkable growth considering it only launched in 2023, and it signals that Threads is fast becoming a platform with staying power. Threads daily active users total 115 millionThreads now has over 115 million daily active users, giving us a clearer picture of its everyday pull. Similarweb Data July 7 2025 from TechCrunchWhile it’s still finding its place as a daily habit for many, this core audience shows up regularly, keeping conversations and content flowing. 57.85% of Threads users are maleMore than half of Threads users — 57.85%, to be exact — are male. That’s a notable difference from most social media platforms, which tend to have a more balanced gender split. It’s possible the migration from X/Twitter during Threads’ early days skewed its user base, but the platform’s evolution may see that balance shift over time. Users aged 25 to 34 make up the largest groupThe biggest slice of the Threads audience is aged 25 to 34 (28.75%), followed by 18 to 24-year-olds (20.36%) and 35 to 44-year-olds (19.15%). This mix puts much of the audience in prime working age, which may help explain why early weekday mornings — before work starts — are peak engagement times on the app. More on exactly when those times are below! Most followed accounts on Threads are dominated by celebritiesAt the top of the leaderboard is Neymar Jr., with 14.5 million followers. His global football fame — and massive Instagram presence — have translated seamlessly to Threads, showing the power of cross-platform fan bases in driving early growth. Selena Gomez (13.6 million) and Kylie Jenner (11.9 million) take the next spots, underscoring Threads’ close ties to Instagram’s influencer and celebrity culture. For creators, it’s a reminder that audiences often follow the personalities they already know and trust. From Kim Kardashian (10.5 million) to Jennifer Lopez, Shakira, and MrBeast, the top 10 is almost entirely made up of entertainment, lifestyle, and creator personalities. This concentration reflects Threads’ positioning as a conversation-first platform where personality and cultural relevance are key to growth. Threads users overlap heavily with Meta’s existing platformsThreads may be the newest member of Meta’s family, but it wasn't starting from zero. Many of its most active users are already spending time on other social media platforms owned by Meta — and beyond — creating a built-in network effect. This overlap shapes how people use Threads, the kinds of content they engage with, and the opportunities for crossposting across apps. Instagram and Threads are most closely linked — there are direct links that allow you to hop between platforms, and new users could port over their Instagram followers who have signed up for Threads. So it's surprising that the overlap between Instagram and Threads is not the biggest one. 70% of daily Threads users also use FacebookA full 70% of daily Threads users in the U.S. are also active on Facebook — a reminder that Threads isn’t starting from scratch. Instead, it’s building on Meta’s existing audience and ecosystem, making it easy for users to hop between platforms and for brands to run cross-platform campaigns without starting over. Over half of Threads users also use InstagramWith 51% of Threads users active on Instagram, the link between the two apps is clearly baked into how people use Threads. This crossover gives creators a built-in opportunity to repurpose content, maintain a consistent presence, and grow both audiences at once. 55% of Threads users are also on YouTubeMore than half of Threads users (55%) also spend time on YouTube. That kind of cross-platform overlap means many people are already in the habit of consuming content in different formats. This shows that there’s a chance for creators to use Threads for conversation and community, while pointing followers toward longer-form videos elsewhere. Threads is still finding its place in users’ daily routinesThreads has built an impressive user base, but its usage patterns show it’s still carving out a place in people’s daily routines. Understanding how often people open the app, how long they spend there, and how those habits compare to other social media platforms can help creators and brands shape content strategies that meet users where they are now, while anticipating where the platform is headed. The average user spends 34 minutes a month on ThreadsGlobally, Threads users spend about 34 minutes per month on the app and open it around 20 times. That’s light compared to more established social media platforms, but it shows there’s a baseline habit forming — the kind that can grow quickly if the right features, communities, and conversations take hold. Threads users open the app on 23.9% of days each monthOn average, Threads users open the app on roughly a quarter of the days in a month. This drop-in usage pattern suggests that, for many, Threads is still a “check-in” platform rather than a daily destination. Strategic posting during high-engagement windows could help shift this habit toward more consistent engagement — more on those windows below. The best times, days, and formats to post on ThreadsKnowing who’s on Threads is only half the picture. The other half is understanding when they’re most likely to engage and what makes them stop scrolling. The platform’s relatively young audience across the age spectrum creates clear peaks in activity, while early performance data points to surprising wins for certain formats. By combining the right timing to match these user habits and content types that stand out in the feed, creators and brands can make the most of every interaction. 7 a.m. Wednesday is the top posting timeOur analysis shows that Wednesday at 7 a.m. is the sweet spot for median engagement on Threads. Other strong windows are weekday mornings between 7 a.m. and 9 a.m., especially Tuesday through Friday. It’s a pattern that lines up with the platform’s demographics — a large portion of Threads’ audience is in the 25–34 age range, meaning they’re likely checking in before starting their workday. An unexpected outlier? 1 a.m. on Sunday ranked among the top five posting times in our dataset. It’s a reminder that, while most engagement happens during predictable windows, testing unusual times can pay off. Midweek wins for engagement, weekends lag behindWhen it comes to days, Wednesday leads for engagement, closely followed by Friday and Thursday. On the other end, Sunday posts see the lowest interactions, with Saturday not far behind. If you’re batch-scheduling content, it’s worth leaning into midweek publishing and scaling back on weekend posts unless they’re time-sensitive or tied to major events. Images outperform all other content formatsEven though Threads is positioned as a text-first platform, pictures lead the pack for median engagement — earning 0.6% more engagement than videos, 37% more than posts with links, and 60% more than text posts. Videos take second place, while link and plain-text posts trail behind. For creators, that means weaving in photos, graphics, or short clips can give even conversation-focused posts a stronger pull in the feed. Engagement on Threads is steady — and stronger than XThreads might still be the new kid on the block, but when it comes to engagement, it’s already punching above its weight. The data shows that while posts on X often grab the headlines for going viral, Threads delivers steadier, higher-quality interactions for the average user. By looking at both median and average engagement — and how they compare across platforms — we can see why consistency is one of Threads’ biggest strengths. Median engagement per post has increased to 5In 2024, a typical Threads post received four engagements. By early 2025, that number climbed to five, signalling a subtle but important shift. This rise suggests that baseline interaction is growing, even without posts going viral, making Threads a reliable space for consistent audience connection. Threads posts see a 6.25% median engagement rateThreads’ median engagement rate sits at 6.25%, compared to 3.6% for X posts — a 73.6% higher interaction rate. For creators and brands, that’s a clear advantage: audiences on Threads are more likely to engage with what they see, making each post more valuable. Average engagements tell a different storyWhile engagement rates favor Threads, the average number of engagements per post is higher on X — 328 for X, 58 for Threads, and 21 for Bluesky. This gap is largely due to X’s larger audience and occasional viral surges, which pull its average upward despite lower median performance. Consistency beats virality on ThreadsHalf of all posts on X, Threads, and Bluesky get four or fewer engagements, but Threads’ growth model is steadier. Posts deviate by about 628 engagements from the baseline — far less volatile than X — which means creators who post regularly and focus on conversation-driven content can expect more predictable, long-term gains. What these Threads statistics mean for 2025With 400 million monthly active users, a steadily climbing engagement rate, and a user base that overlaps heavily with Meta’s other platforms, Threads has moved beyond its “new app” phase and into a more defined role in the social media landscape. The data shows us that: Consistency wins: Threads rewards regular posting and conversation-driven content with predictable growth, unlike X’s volatility.Visuals cut through: Even on a text-first platform, images outperform every other format, proving that visual storytelling still matters.Timing is strategic: Midweek mornings, especially Wednesdays at 7 a.m., align with when its largest demographic is most active.Threads is positioning itself as a platform where relationship-building The Presidents virality. That makes it a powerful space for creators, brands, and communities that value depth over fleeting reach. You don’t need to chase every trend or post hourly to succeed here — you just need to show up with a clear point of view, embrace the conversations happening in your niche, and adapt as the platform’s culture continues to evolve. More Threads resourcesHow to Get More Followers on Threads: 9 Tactics to Help You GrowI Posted to Threads Consistently for A Month — Here’s What HappenedHow to Get Your First 1,000 Followers Across All Major Social Media Platforms: The Ultimate Guide7 Creators on Threads to Watch (+Lessons to Learn From Their Content)View the full article

Something’s shifting in how SEO services are being marketed, and if you’ve been shopping for help with search lately, you’ve probably noticed it. AI search demand is real – but so is the spin Over the past few months, “AI SEO” has emerged as a distinct service offering. Browse service provider websites, scroll through Fiverr, or sit through sales presentations, and you’ll see it positioned as something fundamentally new and separate from traditional SEO. Some are packaging it as “GEO” (generative engine optimization) or “AEO” (answer engine optimization), with separate pricing, distinct deliverables, and the implication that you need both this and traditional SEO to compete. The pitch goes like this: “Traditional SEO handles Google and Bing. But now you need AI SEO for ChatGPT, Perplexity, Claude, and other AI search platforms. They work completely differently and require specialized optimization.” The data helps explain why the industry is moving so quickly. AI-sourced traffic jumped 527% year-over-year from early 2024 to early 2025. Service providers are responding to genuine market demand for AI search optimization. But here’s what I’ve observed after evaluating what these AI SEO services actually deliver. Many of these so-called new tactics are the same SEO fundamentals – just repackaged under a different name. As a marketer responsible for budget and results, understanding this distinction matters. It affects how you allocate resources, evaluate agency partners, and structure your search strategy. Let’s dig into what’s really happening so you can make smarter decisions about where to invest. The AI SEO pitch: What you’re hearing in sales calls The typical AI SEO sales deck has become pretty standardized. First comes the narrative about how search is fragmenting across platforms. Then, the impressive dashboard showing AI visibility metrics. Finally, the recommendation to treat AI optimization as a separate workstream, often with separate pricing. Here are the most common claims I’m hearing. ‘AI search is fundamentally different and requires specialized optimization’ They’ll show you how ChatGPT, Perplexity, and Claude are changing search behavior, and they’re not wrong about that. Research shows that 82% of consumers agree that “AI-powered search is more helpful than traditional search engines,” signaling how search behavior is evolving. ‘You need to optimize for how AI platforms chunk and retrieve content’ The pitch emphasizes passage-level optimization, structured data, and Q&A formatting specifically for AI retrieval. They’ll discuss how AI values mentions and citations differently than backlinks and how entity recognition matters more than keywords. ‘Only 22% of marketers are monitoring AI visibility; you need to act now’ This creates urgency around a supposedly new practice that requires immediate investment. The urgency is real. Only 22% of marketers have set up LLM brand visibility monitoring, but the question is whether this requires a separate “AI SEO” service or an expansion of your existing search strategy. Understanding the rebranding trend To be clear, the AI capabilities are real. What’s new is the positioning – familiar SEO practices rebranded to sound more revolutionary than they are. When you examine what’s actually being recommended (passage-level content structure, semantic clarity, Q&A formatting, earning citations and mentions), you will find that these practices have been core to SEO for years. Google introduced passage ranking in 2020 and featured snippets back in 2014. Research from Fractl, Search Engine Land, and MFour found that generative engine optimization “is based on similar value systems that advanced SEOs, content marketers, and digital PR teams are already experts in.” Let me show you what I mean. What you’re hearing: “AI-powered semantic analysis and predictive keyword intelligence.” What’s actually happening: Keyword research using advanced tools to analyze search volume, competition, user intent, and content opportunities. The strategic fundamentals (understanding what your audience is searching for and why) haven’t changed. What you’re hearing: “Machine learning content optimization that aligns with AI algorithms.” What’s actually happening: Analyzing top-ranking content, understanding user intent, identifying content gaps, and creating comprehensive content. AI tools can accelerate analysis, which is valuable. But the strategic work (determining what topics matter for your business, how to position your expertise, and what content will drive conversions) still requires human insight. What you’re hearing: “Entity-based authority building for AI platforms.” What’s actually happening: Building quality mentions and citations, earning coverage from reputable sources, and establishing expertise in your industry. Authority building is inherently relationship-driven and time-dependent. No AI tool shortcuts to becoming a recognized expert in your space. Dig deeper: AI search is booming, but SEO is still not dead Get the newsletter search marketers rely on. See terms. Where real differences exist (and why fundamentals still matter) I want to be fair here. There’s genuine debate in the SEO community about whether optimizing for AI-powered search represents a distinct discipline or an evolution of existing practices. The differences are real. AI search handles queries differently from traditional search. Users write longer, conversational prompts rather than short keywords. AI platforms use query fan-out to match multiple sub-queries. Optimization happens at the passage or chunk level rather than the page level. Authority signals shift from links and engagement to mentions and citations. These differences affect execution, but the strategic foundation remains consistent. You still need to: Understand what users are trying to accomplish. Create content demonstrating genuine expertise. Build authority and credibility. Ensure content is technically accessible. Optimize for relevance and user intent. And here’s something that reinforces the overlap. SEO professionals recently discovered that ChatGPT’s Atlas browser directly uses Google search results. Even AI-powered search platforms are relying on traditional search infrastructure. So yes, there are platform-specific tactics that matter. The question for you as a marketer isn’t whether differences exist (they do). The real question is whether those differences justify treating this as an entirely separate service with its own strategy and budget. Or are they simply tactical adaptations of the same fundamental approach? Dig deeper: GEO and SEO: How to invest your time and efforts wisely The risk of chasing platform-specific tactics The “separate AI SEO service” approach comes with a real risk. It can shift focus toward short-term, platform-specific tactics at the expense of long-term fundamentals. I’m seeing recommendations that feel remarkably similar to the blackhat SEO tactics we saw a decade ago: Invisible text that only LLMs can see. Content cloaking for AI bots. Scaled content targeting every possible prompt variation. These tactics might work today, but they’re playing a dangerous game. Dig deeper: Black hat GEO is real – Here’s why you should pay attention AI platforms are still in their infancy. Their spam detection systems aren’t yet as mature as Google’s or Bing’s, but that will change, likely faster than many expect. AI platforms like Perplexity are building their own search indexes (hundreds of billions of documents). They’ll need to develop the same core systems traditional search engines have: Site quality scoring. Authority evaluation. Anti-spam measures. They’re supposedly buying link data from third-party providers, recognizing that understanding authority requires signals beyond just content analysis. The pattern is predictable We’ve seen this with Google. In the early days, keyword stuffing and link schemes worked great. Then, Google developed Panda and Penguin updates that devastated sites relying on those tactics. Overnight, sites lost 50-90% of their traffic. The same thing will likely happen with AI platforms. Sites gaming visibility now with spammy tactics will face serious problems when these platforms implement stronger quality and spam detection. As one SEO veteran put it, “It works until it doesn’t.” This is why fundamentals matter more than ever Building around platform-specific tactics is like building on sand. Focus instead on fundamentals – creating valuable content, earning authority, demonstrating expertise, and optimizing for intent – and you’ll have something sustainable across platforms. Where AI genuinely helps I’m not anti-AI. Used well, it meaningfully improves SEO workflows and results. AI excels at large-scale research and ideation – analyzing competitor content, spotting gaps, and mapping topic clusters in minutes. For one client, it surfaced 73 subtopics we hadn’t fully considered. But human expertise was still essential to align those ideas with business goals and strategic priorities. AI also transforms data analysis and workflow automation – from reporting and rank tracking to technical monitoring – freeing more time for strategy. AI clearly helps. The real question is whether these AI offerings bring truly new strategies or familiar ones powered by better tools. What to watch for when evaluating services After working with clients to evaluate various service models, I’ve seen consistent patterns in proposals that overpromise and underdeliver. They lead with technology, not strategy: If the conversation jumps immediately to tools and dashboards rather than starting with your business goals, that suggests a tools-first rather than strategy-first approach. Vague explanations of their approach: Watch for responses about “proprietary algorithms” or “advanced machine learning” without concrete explanations of what specific problems this solves. Focus on vanity metrics: “We generated 500 AI citations!” sounds impressive but doesn’t answer: Did qualified traffic increase? Did conversion rates improve? How did search contribute to revenue? Case studies that focus on visibility, not business results: They might have increased AI mentions or improved rankings, but did it drive revenue growth? Did it increase qualified leads? Questions to ask instead When evaluating any service provider, ask: How would you approach our business? Walk me through your strategic process. The best approaches start by understanding your business, not showcasing tools. If they jump immediately to AI tools or technical tactics without understanding your business context, that’s a red flag. How do you determine content strategy and prioritization? Look for answers that balance data insights with business context and audience understanding, not just what AI tools suggest would perform well. What specific results have you achieved for similar businesses? Push for concrete business metrics (revenue growth, lead generation, conversion improvements), not just traffic or ranking increases. How do you integrate optimization across traditional search and AI platforms? This reveals whether they view these as separate disciplines requiring separate work or as interconnected parts of a unified search strategy. What actually drives long-term success After working in SEO for 20 years, through multiple algorithm updates and trend cycles, I keep coming back to the same fundamentals: Deep audience understanding drives every strategic decision. Quality and expertise still win (search algorithms are increasingly sophisticated at evaluating content quality). Authority building takes time and authenticity (you can’t automate trust and credibility). Business alignment drives meaningful results (rankings and AI citations are means to an end: revenue growth, customer acquisition, or whatever your primary business goals are). Dig deeper: Thriving in AI search starts with SEO fundamentals What sustainable SEO looks like in the AI era AI is genuinely changing how we work in search marketing – and that’s mostly positive. The tools make us more efficient and enable analysis that wasn’t previously practical. But AI only enhances good strategy. It doesn’t replace it. Fundamentals still matter – along with audience understanding, quality, and expertise. Search behavior is fragmenting across Google, ChatGPT, Perplexity, and social platforms, but the principles that drive visibility and trust remain consistent. Real advantage doesn’t come from the newest tools or the flashiest “GEO” tactics. It comes from a clear strategy, deep market understanding, strong execution of fundamentals, and smart use of technology to strengthen human expertise. Don’t get distracted by hype or dismiss innovation. The balance lies in thoughtful AI integration within a solid strategic framework focused on business goals. That’s what delivers sustainable results – whether people find you through Google, ChatGPT, or whatever comes next. View the full article

There may have been another Google Search ranking algorithm tweak around October 28th or so. I am seeing some rumblings both within the SEO community chatter and the third-party Google tracking tools. It seems some are noticing big search ranking swings around October 28th.View the full article

We all knew it was coming and now Google is roll out early access to Gemini for Home voice assistant in the U.S. You can either say "Hey Google" to your speaker or display to request specific help or answers, or talk naturally with Gemini Live by saying "Hey Google, let's chat."View the full article

Google Merchant Center now lets some merchants and advertisers define an audience for a promotion. This allows you to restrict who sees the promotion in Google Search for your Merchant Center shopping listings.View the full article

Discover how AI tools can streamline your content marketing so your team creates better content in less time. View the full article

A judge has ruled, giving Google a blow in its ad tech monopoly legal battle. This ruling should help the plaintiffs suing Google not get hung up in court as long as some expected, plus Google can't relitgate the core facts in that antitrust case.View the full article

Harry Clarkson-Bennett dissects the Google Discover leak to expose the real signals behind “trusted” content and sustainable visibility. The post How Google Discover REALLY Works appeared first on Search Engine Journal. View the full article

Google is testing adding to the call and message button on Local Service Ads a reviews and sometimes overview button. Reviews takes you to the reviews but the overview button to the local listing for that business.View the full article

There may be an undocumented Google user agent named GeminiiOS. GeminiiOS is apparently used when a user clicks on a source link from the Gemini app on their iPhone.View the full article

Keyword clustering is the process of grouping keywords based on search intent. Learn the right way to do it. View the full article

BoE figures show activity in housing market remains resilient View the full article

It’s 2 p.m. on a Monday, and the Starbucks on 23rd Street and Park Avenue in New York City’s Flatiron neighborhood is packed. Not that it would take much. The small shop—roughly 265 square feet of front-of-house space—is big enough for a short line to form before it would bust through the door and out onto the sidewalk. This location is the company’s very first “espresso bar” format store—a new, small-store design that will serve as the cornerstone of Starbucks’s future expansion plans. It’s also a symbol of a Starbucks in flux. Until recently, the store was for mobile orders and pickup-only; then in September, it reopened after a speedy “uplift” (Starbucks speak for a small-scale renovation) in a new “espresso bar” format complete with seats. Starbuck’s leadership hopes the design will help propel the coffee chain into a cozier, more profitable era. When I visited, the store was indeed cozier than the average New York City Starbucks, which can often feel industrial and cave-like. A cushy green leather banquette spanned one wall with enough room to fit three laptop-sized round tables. Two seating nooks with built-in desks and stools looked out onto the street. All told, about 10 butts could sit down comfortably in the space. But that’s 10 more butts than before—and butts in seats is the reason Starbucks renovated the space in the first place. The espresso bar format is part of CEO Brian Niccol’s “Back to Starbucks” plan, which reimagines Starbucks as a coffee shop you might actually want to spend time in. In the grand scheme of things, Niccol would still like to more than double Starbucks’s global footprint, and expand the brand to more than 100,000 locations worldwide. And he’s said the small, espresso bar format would be key in this growth. But in the more immediate future, Niccol’s turnaround strategy requires updating aging locations into something more comfortable. Starbucks expects to uplift 1,000 stores over the next year, often transforming the stores bit by bit at night and during off hours to avoid losing operation hours. Each uplift will cost around $150,000—a small investment for a company that had an annual revenue of $36 billion in fiscal year 2024. Out with pick up, in with sit down The espresso bar format, specifically, is an area of opportunity for the company. Over the summer, Starbucks announced that it would close or renovate many of its mobile order-only locations, which totaled more than 80 locations. It was a notable pivot away from Starbucks as a streamlined caffeine factory, towards something more akin to a traditional coffeehouse. Starbucks began experimenting with pick up-only shops as early as 2015, just as mobile ordering began reshaping the landscape of American quick service restaurants. Customers quickly adapted to the convenience of tapping a button on their phone to order their daily drink. Today, mobile ordering accounts for 31% of Starbucks orders. But for all its revenue-generating success, the pick-up only format wasn’t without its issues. Instead of alleviating wait times and bottlenecks, it increased them in some instances, largely due to a menu that ballooned in size and a lack of operational sophistication that couldn’t keep pace with customers’ expectations of speedier service. More troubling was the effect it had on people’s perception of Starbucks as a brand. When Niccol arrived at Starbucks, he zeroed in on pick-up only stores as an obvious problem to solve. “We found this format to be overly transactional and lacking the warmth and human connection that defines our brand,” Niccol said on an earnings call in July 2025. Now, dozens of these spaces are being reimagined as coffee bars where the emphasis is on fast service, sure, but with a bigger shot of hospitality. What this looks like in practice will vary from location to location since all of the small-format stores have unique quirks that designers will need to work with during renovations. Materiality matters The Park Ave location is filled with little details meant to give the space a softer, more upscale appearance. There’s a new tile-clad, wood-trimmed ordering counter that sits lower than average to encourage more connection with the barista. Minimalist pendant lighting hangs above, to give the space a warm glow at night. Wood-encased speakers hang in the corners of the room, and a vintage-looking credenza sits against a wall filled with bags of coffee beans that customers can grab. I appreciated the materiality of the space—the leather and wood, the live plants in the corner, the soft curve of the green wall panels. The Starbucks team is clearly trying to up its game after years of creating spaces that, in my experience, do little to invite you in. Starbucks says it’s measuring the success of these spaces in terms of how long customers stay, how often they return, and (of course) sales. It’s too soon to say if these revamps will be enough to lure in the kind of customers they’re after—the kind who see Starbucks as a home away from home. For what it’s worth, I didn’t see anyone drinking out of a ceramic mug (a feature Niccol brought back in his quest for coziness). In fact, the only person I saw lingering for any length of time was a college-age student with a laptop who was nursing her to-go cup of coffee. But that, just maybe, is exactly the signs of life Starbucks is hoping to see more of. View the full article

Imagine a Yelp-style user-review site that lets users generate and post AI video reviews of local businesses. Say one of these videos presents a business in a bad light, and the business owner sues for defamation. Can the business sue the reviewer and the review site that hosted the video? In the near-to-immediate future, company websites will be infused with AI tools. A home decor brand might use a bot to handle customer service messages. A health provider might use AI to summarize notes from a patient exam. A fintech app might use personalized AI-generated video to onboard new customers. But what happens when someone claims they’ve been defamed or otherwise harmed by some AI-generated content? Or, say, claims harm after a piece of their own AI-generated content is taken down? The fact is, websites hosting AI-generated content may face more legal jeopardy than ones that host human-created content. That’s because existing defamation laws don’t apply neatly to claims arising from generated content, and how future court cases settle this could limit or expand the kinds of AI content a website operator can safely generate and display. And while the legal landscape is in flux, knowing how the battle is being fought in courtrooms can help companies plan ahead for a world in which AI content is everywhere—and its veracity unclear. In 1996, at the dawn of the internet, forward-thinking lawmakers—Oregon Senator Ron Wyden and then-California Representative Chris Cox—feared that libel lawsuits and aggressive regulation by Washington, D.C., could overwhelm budding internet companies, which could operate forums, social networks, or search engines, stifling growth and slowing investment. Wyden and Cox proposed Section 230, a statute in the Communications Decency Act of 1996 ensuring that “no provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.” In other words, if a website had a hand in creating the content, its legal immunity would vanish. Since then, Section 230 has proved surprisingly durable, surviving relatively unscathed through the internet boom, the social media craze, and the mobile revolution. At the same time, it has become something of a political lightning rod in recent years as policymakers have explored ways of regulating social media. But the next revolution in tech—generative AI—may not enjoy any of Section 230’s protections. Our current AI models do something like “co-create” content alongside the user, who prompts the model to generate what they want. Based on that, tools like ChatGPT and Sora would seem to be excluded from Section 230 legal immunity. Alas, it may not be that simple. The duality of Sora Generative AI companies have been sued several times for libelous output, but none have lost, and none have yet resorted to Section 230 in their defense. In one of the more widely known cases, syndicated radio host Mark Walters sued OpenAI for defamation after ChatGPT falsely claimed that Walters had been accused of embezzling funds from a gun rights group. OpenAI won the case without having to claim Section 230 protection. The chatbot had generated the false information after warning that the “accusation” had occurred after its training data cutoff date. OpenAI did not respond to a request for comment on whether the company has used Section 230 as part of a legal defense. It gets even trickier with OpenAI’s new Sora app, which lets users generate AI videos and then share them on its TikTok-style social feed. (The Meta AI app does essentially the same.) Using the language of Section 230, Sora is both an information content provider (a “speaker” or creator) and a provider of an interactive computer service (a “host”). Sora, and hybrid apps like it, may raise the stakes on the question of when Section 230 should be applied. Chatbots can defame with words, but Sora quickly generates alarmingly realistic video, which can convey a message more believably by showing, rather than telling. Combine that with Sora’s seamless distribution of the video and, in the wrong hands, you have an all-in-one tool for defamation. A “borderline case” At some point, AI companies are likely to reach for Section 230, possibly as a last resort, if sued, according to some legal experts, including Eugene Volokh, a law professor at UCLA and a leading thinker on AI and libel. Thorny questions about how the provision applies to their technology (whether they can use its protections to mount a defense) may well arise. And despite the fact that the language of 230 would seem to preclude it, it’s conceivable that a court could, in certain circumstances, accept it as a valid defense. Suppose a Sora user generates a video showing a public official taking a bribe, triggering a libel suit. This, Volokh argues, would amount to something of a “borderline case”: Sora creates the video itself (meaning “the content is provided by itself”). “On the other hand,” Volokh says, “it’s generating the video based on a query or based on a prompt submitted by a user. So you might say the defamatory elements of that stem from what the user is contributing, not Sora.” OpenAI’s lawyers would likely point out that the platform itself doesn’t decide on the content of the videos it produces, only that it’s implementing its users’ wishes, Volokh says. That is to say, without specific prompts from the user, the AI would never have acted to create the offending video in the first place. Yet a court may still hew to the letter of Section 230, which states that if at least part of the “creation” of the video happened during its generation, it isn’t covered. The fact that OpenAI’s Sora provides both a mechanism for creating and distributing a video may weaken its case for 230 protection. Libel law, Volokh says, would require a generated video to be published in order to be considered defamatory “information.” In theory, a court could argue that OpenAI should reasonably foresee that a video created on its platform would, then, be distributed, Volokh says. “And therefore it is basically aiding and abetting this defamation through its own actions of generating the video,” he adds. The shield and the sword Yet there’s a case to be made that generative AI platforms do deserve the legal protections afforded by Section 230, even if they help both create and distribute the content, says Jess Miers, a law professor at the University of Akron. “Like social media companies, these services face constant challenges from users who generate problematic content, and they need incentives to both allow expressive activity and build guardrails against harmful or infringing outputs.” Indeed, that was the original intent of Section 230, as Wyden told me in 2018. Section 230 provides both a shield and a sword to internet companies. The shield protects them from liability for harmful content posted on their platforms by users. The sword is the law’s “good samaritan” clause, which provides legal cover for actively removing harmful content from their platform. Before Section 230, tech companies were hesitant to moderate content for fear of being branded “publishers” and, thus, liable for toxic user content. With generative apps like Sora, OpenAI’s developers effectively “co-create content” with the user, Miers says. The developers choose the training data, train the models, and do the fine-tuning that shapes the output. The user contributes the prompts. “Congress may need to craft a new kind of protection that captures this co-creative dynamic to preserve expression, safety, and competition in this evolving new market,” Miers says. Congress might try to rewrite Section 230 (or construct a new law) that distinguishes between defamatory intent on the user’s part versus the AI’s. This would involve digging into the details of how AI models work. Lawmakers might start by studying how users could misuse models to create harmful content, such as bypassing safety guardrails or eliminating “made by AI” labels. “If that’s a recurring problem, then a 230-style framework shielding AI companies from liability for users’ misuse could make sense,” Miers says. As many Sora app users have noticed, OpenAI is playing it very safe with the kinds of videos it allows. It’s already taken down many, many videos, and has agreed to restrict the use of images of public or historical figures (such as Martin Luther King Jr.) upon request. This suggests that while a Section 230 might protect AI companies from libel suits in some circumstances, OpenAI isn’t eager to test the theory. View the full article

The life of a junior associate at a prestigious law firm involves hours of research and analyzing contracts. Three years ago, Winston Weinberg found himself buried in these kinds of tasks as a first-year antitrust and litigation associate at O’Melveny & Myers in Los Angeles. And there Weinberg might have remained, diligently climbing the BigLaw ranks from associate to partner, logging thousands of hours of drudgery along the way. Instead, he’s cofounder and CEO of Harvey, the high-flying legal AI platform that’s raised more than $800 million by promising to handle much of this work. “A lot of the tasks junior [associates] do are going to get automated,” Weinberg says. “That doesn’t mean their job’s going to get automated. It’s just going to be a different job.” Built atop language models from OpenAI, Anthropic, and Google, Harvey’s platform streamlines legal workflows by helping lawyers with drafting, contract analysis, legal research, due diligence, regulatory compliance, and case law review. In addition, the technology cuts down on reading time by summarizing complex legal documents and combining databases to research and summarize legal issues. Harvey, which is used by some 250 law firms—including 42% of The American Lawyer’s list of biggest 100 firms in the U.S.—announced in June that it raised a $300 million series D led by Sequoia, bringing its total haul to more than $800 million and driving its valuation to $5 billion. It’s now the highest valued startup in a growing field of AI companies that are all focused on overhauling how the legal profession works. The company’s annualized revenue run rate hit $100 million in August, up from $50 million earlier this year, propelled by an aggressive sales strategy targeting big law firms. Harvey now has 460 employees, 20% of whom are lawyers. The company is also hiring dozens of engineers, sales leads, and account executives as it seeks to increase the moat between itself and its competitors. Whether Harvey ultimately upends the legal procession or winds up burning a lot of cash, time, and effort could reveal that fates of industries as far afield as finance, music, and film. All these sectors—and more—are on a similar curve: exploring whether AI tools can be truly transformative and seeing just how many jobs they’ll reimagine—or disappear altogether. Despite its sizable moat, Harvey’s success is far from assured. Investors are pouring money into firms working on rival legal AI agents: Canada-based Clio raised $900 million last summer; Sweden’s Legora raised an $80 million Series B led by ICONIQ Venture & Growth and General Catalyst in September; London-based Luminance took in $75 million in January. Meanwhile, a growing chorus of critics—sounding off on Reddit and elsewhere—question how original Harvey’s offerings are. “ChatGPT wrapper” is the most common dig thrown by these disaffected apparent users, who note the similarities between the information retrieval capabilities of Harvey and ChatGPT (made by Harvey’s early investor, OpenAI). Even Harvey’s cofounders have called OpenAI an indirect competitor. But critics also say the company could be steamrolled by OpenAI, pointing out that the LLM could simply build its own legal-focused model. “I’m hearing from more and more attorneys that OpenAI’s Deep Research is the single best research product on the market, and it’s what most attorneys use (even when it’s not a firm approved tool),” former lawyer and legaltech investor Zack Abramowitz wrote on his popular substack Legally Disrupted in June. OpenAI itself has begun testing the waters of legal technology. In September, the company published a blog post about creating an internal database to review its own contracts—the feature is not available to consumers. One Redditor, who claimed to be a former employee, recently went even further, drawing a direct comparison to Silicon Valley’s most notorious startup: “think of theranos and overinflated claims of what a product can do,” the person posted in a thread that reached more than 300 of comments within a day. The post generated enough attention to prompt an indirect reply from Weinberg himself: On LinkedIn, he stated that the company’s Gross Revenue Retention (GRR) is at 98% and its Net Dollar Retention (NDR) is at 167—signs that Harvey is both retaining and growing revenue from its customers. The Redditor has since deleted their post, though comments agreeing with it remain on the page. (When Fast Company reached out to verify the person’s employment at Harvey, a spokesperson said: “There was nothing in the post to suggest that the author was a recent employee at the company.”) Harvey’s customers, in the meantime, seem satisfied. “[Harvey is] totally embedded in the workday of our lawyers. It’s really become embedded throughout their daily workflows,” says Gina Lynch, chief knowledge and innovation officer at Paul, Weiss, Rifkind, Wharton & Garrison, the white-shoe law firm with more than a thousand lawyers. From D&D to r/legaladvice Weinberg’s sliding doors moment from junior law associate to AI entrepreneur came via his roommate after law school: Gabe Pereyra, who was a machine learning engineer at Meta and a research scientist at Google DeepMind before that. (Pereyra is now president of Harvey.) In the spring of 2022, while Weinberg was working as a lawyer, Pereyra was focused on finding real-world, assistant-like applications for large language models. It didn’t take long before he and Weinberg started testing out Weinberg’s legal workflows on language models, most notably OpenAI’s GPT-3, which was publicly available. (The pair had initially started running GPT-3 to augment their Dungeons and Dragons games, but quickly realized the potential of its chain of thought prompting abilities.) They began using GPT-3 to solve problems on the r/legaladvice subreddit. “We found a hundred landlord-tenant questions and we were able to answer [them],” Weinberg says. To further test the model’s accuracy, they fed it legal materials about California regulations and local statutes, then got it to answer questions. “We showed [the answers] to three California-based lawyers working on landlord-tenant issues. We just said, ‘Would you send this to a client?’ For 86 out of 200 questions, at least two attorneys answered thumbs up,” Weinberg says. Their timing was auspicious. It was still months before the launch of ChatGPT would make GPT-3’s capabilities clear to everyone. In July 2022 Weinberg and Pereyra cold-emailed OpenAI’s general counsel at the time, Jason Kwon, and shared their ideas for how AI could change legal work (Kwon is now OpenAI’s chief strategy officer). In November of that year, the company raised $5 million from the LLM’s startup fund, along with venture capitalists Elad Gil and Sarah Guo (all three also participated in the company’s most recent round). Within five months, blue chip venture capital firms, including Sequoia, also invested. Signing on Big Law Almost as soon as it launched, Harvey aggressively began pursuing enterprise contracts with big law firms. In December 2022, A&O Shearman, which has nearly 4,000 lawyers across 48 offices, started testing Harvey’s technology in its Markets Innovation Group. Paul Weiss followed in January 2023 and began testing the technology throughout its practice. Both firms have since signed longer contracts. Using those clients’ reputations, Harvey has signed big contracts with other prominent U.S. firms, including Vinson & Elkins and Macfarlanes. The company now has 700 customers in 58 countries. Other clients include general counsel offices at private equity firms and hedge funds like KKR and Bridgewater and accounting giant PwC. For bigger clients, Harvey embeds staff members within the company to personalize its services and features for their workflows. It also offers an off-the-shelf general application product for smaller companies. Harvey’s close association with OpenAI has, in some ways, been a blessing and a curse. The AI giant gave Harvey a first-mover advantage, but has heightened the comparisons between ChatGPT and Harvey. After all, according to a March survey from Law360, ChatGPT is the tool most lawyers use for work—even if it’s not approved by their firm. But although Harvey could still introduce hallucinations into its work (an issue that has bedeviled lawyers who rely too heavily on ChatGPT), it’s less likely to do so, says Weinberg, because it’s trained on legal data and purpose-built for corporate law firms. The company says that its 2024 version of Assistant, Harvey’s most popular product, reduces hallucinations by 60% and improves the accuracy of cited sources by 23% compared to other chatbots. Harvey has also deepened its product by incorporating models from Anthropic and Google alongside OpenAI’s GPT. It also recently signed a key deal with LexisNexis that enables users to ask complex legal questions and get citation-backed answers. Harvey, however, could find itself in a similar situation to Bloomberg’s ill-fated BloombergGPT as technology evolves. In 2023, the financial information giant Bloomberg spent more than $10 million training an LLM on its own financial data before finding out that an off-the-shelf GPT-4 provided more accurate answers to users. As Ethan Mollick, a professor and codirector of the Generative AI Lab at Wharton, wrote on Linkedin, “There was a moment that we thought proprietary data would let organizations train specialized AIs that could compete with frontier models. It turns out that probably isn’t going to happen. The largest frontier models are just much better at most complex tasks than smaller models.” Another concern is how long Harvey can maintain its lead, given the cost of its product. Harvey’s bespoke services cost $1,200 per seat, per month, with contracts stipulating that large companies need to purchase the service for at least 100 employees and for at least a year. The company justifies its prices by touting the productivity gains it passes onto employees. “You can arm lawyers with tools that make them enormously productive,” says Harvey’s chief business officer John Haddock, who has a law degree from Stanford. And $100,000 a month is still less expensive than the salaries of an army of paralegals and junior associates. Even so, Harvey will have to convince its customers to sign back up, even as more affordable products—aimed especially at smaller or midsized firms—enter the market. And it has to keep delivering for them amid an AI hype cycle where disappointment in enterprise AI products is growing. After the recent flare-up on Reddit involving the apparent former employee, Maarten Truyens, founder and CEO of ClauseBase, a Belgium startup also working on an AI platform for legal drafting, took to LinkedIn to weigh in. He said the main problem is the hype and expectations around these AI platforms. “GenAI is too good to ignore, yet simply not good enough for many legal use cases,” he wrote. “What’s really needed is vendors to be transparent about the limitations, and the legal community to learn what’s possible with GenAI, and where other technologies are a better fit. Both sides need to become much more realistic.” AI associates Though Harvey’s work needs to be checked—which can be a time-consuming process for paralegals and junior lawyers—the company aims to cut down their workload. A widely cited 2023 Thomson Reuters report estimated that AI technology could save lawyers on average 200 hours a year. That number has likely increased as AI becomes more sophisticated. Left unsaid: It could also cut down the number of lawyers and paralegals firms and companies need to hire. Investors and Harvey employees argue that legal tech is unlikely to take jobs away because the demand for legal services is likely to increase. “There’s a huge undelivered need for legal services. The right way to think about it is that if you can arm lawyers with tools that make them enormously productive, that will just expand the access to the types of services they can provide,” Haddock says. Venture capitalist Sarah Guo, whose firm Conviction invested in Harvey in 2022, makes a similar argument: “People will not want less practice of the law. They will want more if it is more affordable, and the quality will go up.” Employment for law school graduates hit a record high in 2024, according to the American Bar Association. But the incentive structure for law firms is complicated. Most big firms bill hourly, and when a task that typically took a law associate hours of research can be condensed into three or four minutes, they face the prospect of losing money. One way to save: hiring fewer associates. Weinberg acknowledges that Harvey could change hiring. “Are there some firms that will change their business model and structure? Yes. I do think there are some firms that might explore [charging] fixed fees [rather than hourly billing] or having a leaner team,” he says. Corporate legal teams could see the most impact from Harvey. Allison Zoellner, general counsel at advertising giant Dentsu, which started working with Harvey last year, says that while the company’s AI tools haven’t led to a workforce reduction, she hasn’t had to hire as much. “We’re being asked to do more with fewer resources. What Harvey does is allow us to keep our heads above water while not adding people.” Weinberg, Haddock, and Guo all say that by eliminating rote tasks, tools like Harvey will free up time for junior lawyers to attend meetings, shadow senior leaders, and start doing more strategic work. “So much of what an associate does today is the types of things that [are] not what they went to law school for. If we are able to give associates faster, thicker, deeper, higher-order thinking problems, that is great for everybody. It’s great for partners who can get more from their teams. It’s great for junior lawyers because it makes practice of law more intellectually satisfying sooner,” Haddock says. While that may be true, without billable hours to subsidize training opportunities like shadowing, firms may have trouble justifying the cost of so many associates. The legal industry has weathered technological shifts before. “When I started as a lawyer in the ’90s, it was the time of transition from manual review of documents in litigation to computer-assisted review of documents. [That created] a bump in productivity,” NYU Law School professor Christopher Sprigman says. “How many more people would’ve been hired at those firms absent the introduction of that technology? There’s always an unknowable counterfactual, but I get the strong idea that it’s fewer than [if the technology hadn’t been adopted].” These days, Sprigman says lawyers are having similar conversations. “A friend of mine who runs a law firm said to me that at this point, maybe 10 to 20% of what junior associates do can be automated through AI. But in 18 months it could be 30%,” he says. “The winners will be people who know how to use AI in their practice and also people who have deep expertise that allows them to exercise judgment.” It is too early to say how the technology will impact roles at the Paul, Weiss. Lynch expects that while it will not reduce positions at the company, it will bring change to certain roles. She has already seen wider acceptance by senior leadership than anticipated. “Partners very much gravitated towards AI because they were comfortable that they knew whether the output was good or bad.” More junior employees with less experience may not be able to make those distinctions early on in their careers. Already, Paul Weiss is using its AI savvy as a sales pitch to prospective clients. “We want [clients] to know we’re using it and we are really earnest about the efficiency gains,” Lynch says. Lynch and Sprigman see a new role emerging within law schools and firms that specializes in AI adoption. “You’re going to see more of a legal technologist role,” Lynch says, adding that, like many firms, Paul Weiss has invested heavily in training to get lawyers to use AI tools, and in building their own closed LLM to work alongside Harvey. Sprigman says that senior lawyers may start to look for AI savvy in their juniors, and that means law schools may also have to change. “Law schools obviously want their students to be prepared. [That could be] teaching people how to prompt engineer. Maybe that’s something you hire an adjunct for. I’m sure school schools will be looking for expertise from the outside,” he says. To gain an edge and get students used to its platform, Harvey has started partnering with law schools including the University of Chicago and the University of Pennsylvania to offer students, professors, and administrators access to its tools. A sizable moat The moat of capital Harvey has raised, its aggressive and successful pursuit of leading law firms, and its relationship with OpenAI have made it a dominant player in the legal AI race. But the company’s valuation may limit its exit possibilities. “We’re voting that the company has the opportunity to be a tens of billions of dollars public company,” Guo says. The company’s success may ultimately hinge on how many big law firms decide to renew their contracts with Harvey once the mandatory year or two minimum expires, and whether the company can stay ahead of competitors from rivals like Legora to off-the-shelf LLMs. Weinberg and Pereyra settled on the name Harvey because it sounds a little bit like Harvard and because of its associations with Suits’ superlawyer Harvey Specter. Ultimately though, Harvey acts more like the show’s other main character, Mike Ross, a college dropout with a photographic memory. Though he is unlicensed, Ross proves to be more useful to Specter than any other paralegal or associate at the fictional big law firm. View the full article

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