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Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from the pages of Inc. and Fast Company. If you received this newsletter from a friend, you can sign up to get it yourself every Monday morning. When Matt Garman interned for Andy Jassy at Amazon Web Services (AWS) in 2005, the unit was still considered an internal startup and had yet to begin selling IT infrastructure services to other companies. Twenty years later, Jassy is president and CEO of Amazon, and Garman is CEO of AWS, which last year reported revenue of $107.6 millionnearly 17% of the e-commerce giants 2024 sales. With long tenures at one company growing increasingly rare, I asked Garman, who had worked at startups before joining AWS and had initially planned to return to a smaller company, what kept him at Amazon for so long. I found that I could actually have more freedom to build here at Amazon than I had at a startup, he says. Amazons ability to mint new products and services at scaleearly in Garmans time at AWS, the unit launched Simple Storage Services, or S3; Amazon Elastic Compute Cloud (EC2); and Amazon Simple DB in quick successionis attributable to its distinctive and peculiar culture. From the outset, founder and executive chair Jeff Bezos emphasized tenets such as customer obsession, frugality, bias for action, ownership, and hiring great talent. These evolved into a set of leadership principles that guide the company. MEETINGS WITH BEZOS For some, the environment can be incredibly energizing. Garman, for example, says he learned quickly that good ideas matter more than hierarchy. When I was an intern, I had a couple of meetings where I presented to Jeff Bezos, and he didnt care that I was an intern, he recalls. He cared that I had interesting or good ideas . . . and we debated those. That, to me, was impactful because . . . I didnt have to check with my boss or bosss boss. He wanted to get ideas directly from me. That kind of information gathering among senior leaders and employees drives the way Amazon operates. Youre making decisions based on the actual data and not high-level distillations of the data, Garman says. If you just get a polished or shiny version of the truth through several layers, youll make worse decisions because you wont know all of those specifics. But Amazons culture isnt for everyone. Leaders expect employees to be in the office consistently, five days per week, and Garman has said that workers who disagree with the policy should move on. We think that [in-person collaboration] is important to our customer; we think thats important to the future innovation and the culture of our company, he told The Wall Street Journal editor-in-chief Emma Tucker earlier this year. It doesnt mean that thats the only decision you can make, but it is the decision that were making. And if its not for you, then thats okay, you can go and find another company if you want to. For those early-career employees who stay, theres always a chance that one of them will someday occupy Garmans seat. When asked whether a future AWS CEO might be a current intern, he lit up: “I think so. I think part of the benefit that we have is we give people so many opportunities to learn and grow in all stages of their career.” Leaning into AI Like most tech CEOs, Garman is especially occupied with Amazons artificial intelligence developments and investments, which range from its Trainium custom chips to applications like Rufus, an AI-powered shopping assistant that helps customers discover and evaluate products through natural conversation. When I asked Garman how he would advise other CEOs to approach AI, he was blunt: Its super important to lean into it and learn the technology. If you resist it and say, Im going to wait and be a late adopter, its probably going to be too late because theres a real flywheel effect of adopting some of this technology early. HOW PECULIAR IS YOUR CULTURE? What are your companys leadership principles or values, and how do they drive the way you operate? Send your ideas to me at stephaniemehta@mansueto.com. Ill feature some of the most compelling examples in a future newsletter. Read more: the path to the top Honeywell Internationals CEO was shaped by his time in the field Why former CFOs are finding success as CEOs Principal Financial Groups CEO rose from intern to chief executive
Category:
E-Commerce
When the world stops making sense and everyone’s looking to you for answers, that’s when real leadership begins. I learned this in the most extreme of circumstancesfirst, as a SWAT team Tactical Commander where split-second decisions meant life or death, then as CEO of a major public company where market crises could make or break thousands of our customers’ livelihoods. The skills that kept our team alive in tactical operations are the same ones that helped steer our organization through economic downturns, industry disruptions, and unprecedented challenges. Crisis leadership isn’t about having all the answers; it’s about having the right framework to make decisive moves when the stakes are highest. The OODA Loop: Your Crisis Leadership Framework Air Force Colonel John Boyd developed the OODA Loop by studying why American F-86 fighter pilots dominated technically superior MiG fighters in the Korean War. The framework he created became the gold standard for decision-making in competitive, high-stakes environments, and remains standard methodology in tactical operations today. The loop takes you through four key steps: Observe: Rapidly gather information about the evolving situation Orient: Process observations against your experience and current reality Decide: Choose your course of action with incomplete information Act: Execute decisively while preparing for the next cycle Speeding through the cycle becomes your ultimate competitive advantage. The leader who can take these steps faster than the crisis evolves wins the game, or, in some cases, the dogfight. Observe Without Panic In SWAT operations, observation meant survival. You had to see everythingsuspect behavior, environmental hazards, team positioningwhile filtering out distractions under extreme stress. The same discipline applies in business crises. During business challenges, other industry leaders typically make reactive decisions based on incomplete observations, or worsefail to make decisions due to analysis paralysis. Instead, I apply Boyd’s observation principles: gather data systematically, look for patterns that others miss, and resist the urge to act before you truly understand what’s happening. Here’s the landmine: The moment you let emotions cloud your observation, you lose your competitive advantage. Orient Faster Than Your Competition Boyd believed orientation was the most critical phase, where you synthesize observations with experience and strategic context. In tactical operations, poor orientation gets people killed. In business, it gets companies killed. During the COVID pandemic, while I was CEO of RE/MAX, competitors were still trying to understand the paralyzed market. We had to orient to the new reality of Zoom showings, curbside closings, and overall new ways of doing business immediately. To combat this, I drew on my law enforcement experience of reading situations that didn’t match expectations. We recognized that the market continued even with different protocols, and we had to adjust how we did business. This rapid orientation gave us a significant market advantage, even in the hardest-hit areas. Companies that survive crises orient to new realities fastest and most accurately. Decide with Tactical Precision Boyd understood that having perfect information is a luxury you can’t afford. Whether breaching a door or entering a volatile market, you decide with 70% information and 100% commitment. When you have multiple options but incomplete data, apply the same process we used in high-risk operations: identify your primary objective, consider second-order effects, choose the option that advances your mission, then commit fully. The decision-making process builds confidence to take action. Waiting isn’t decision-making, and waiting for nonexistent information is a fool’s game. How would you feel if the SWAT team had no next step while you were the hostage in the building? Same thing in businessgather just enough information to lean toward a decision. In a crisis, the worst decision is usually no decision. Act While Others Hesitate Action without observation and orientation is reckless, but observation without action is worthless. Boyd’s framework only works when you complete the cycle, then immediately start the next one. During market shifts, our actions required balancing financial and operational oversight against financial challenges and legal restrictions. Crisis operation isn’t just about moving fast; it’s moving with purpose while staying flexible to changing rules that shift daily. I built what Boyd called “implicit guidance and control” into our systems. Our team knew the mission well enough to act independently when circumstances changed faster than communication could keep up. When offices and franchisees located around the globe called with questions, the decision framework was simple: “What’s right that aligns with the values of the business?” Elite performers cycle back to observation before competitors finish their first decision. Getting Inside Your Opponent’s OODA Loop Boyd’s real insight was “getting inside your opponent’s decision cycle.” By going through the OODA Loop faster than your competition, you make them react to your moves instead of executing their own strategy. At RE/MAX and other companies I oversee, we institutionalized rapid OODA cycling through what I call the 3-2-1 decision-making process. Instead of asking management questions and waiting for responses, I empower people to come up with 3 ideas to solve a problem, create 2 best options, then make 1 recommendation. This quick process gets action taken immediately. By the time our competitors made their first major move, we were already three moves ahead. Building Organizational OODA Capability You don’t develop OODA Loop mastery during the crisis; you must develop it beforehand, implementing steps like: Accelerate Observation Systems: Create information flows that give you earlier intelligence than competition, and host weekly clarity meetings for everyone aligned. Sharpen Orientation Through Training: Regularly run exercises like what-if games, so orientation becomes automatic during real disruptions. Practice Quick Decision-Making: Create safe environments for consequential decisions under pressure and build the understanding that it’s okay to make decisions. Build Rapid Execution Systems: Design processes that implement decisions faster than circumstances change. Communication Within the Loop Boyd understood the OODA Loop isn’t just individual, it’s organizational. Your team’s ability to share observations, align on orientation, coordinate decisions, and syncronize action determines collective speed. Maintain OODA coherence by applying tactical communication principles: Observe together Orient collectively Decide with clarity Act in coordination As you get used to “dancing the OODA” together, you’ll see instinctive decision-making perpetuate team success. The Ultimate Weapon Crisis leadership is more than just being fearless; it’s about being intentionally and strategically faster. Colonel Boyd’s OODA Loop that kept fighter pilots alive is the same framework that kept our businesses thriving during market downturns. Whether facing armed suspects or market volatility, the fundamentals remain the same. In competitive environments, the speed of decision-making becomes your ultimate weapon. Not reckless speed, but the disciplined speed that comes from mastering Boyd’s cycle. When your next crisis hits, the question isn’t whether you’ll face uncertainty and pressure; the question is whether you’ll cycle through your responses faster than the crisis itself can evolve. Your competition is counting on you to hesitate. Your team is counting on you to lead. Time to close the loop.
Category:
E-Commerce
Every company wants to have an AI strategy: A bold vision to do more with less. But theres a growing problemone that few executives want to say out loud. AI initiatives arent delivering the returns they were hoping for. In fact, many leaders now say they havent seen meaningful returns at all. IBM recently found that only 1 in 4 AI projects hit the expected ROI. And BCGs research goes further still: 75% of businesses have seen no tangible value from their AI investments. Stop buying tools your team doesnt know how to use The fix? Increase your investment in AI training to support your business transformation. The data tells a simple story. An Akkodis survey suggested only 55% of CTOs believe their executive teams have the AI fluency needed to grasp the risks and opportunities of the tech. Yet, it is these same executives who are trying to reengineer entire workflows, teams, and business models around tools that their people barely understand. And when performance disappoints, the knee-jerk reaction is to buy even more tech. More platforms. More licenses. More dashboards. But that only makes the problem worse. The teams that were struggling to learn one tool are now juggling five. Everyones overwhelmed. No ones effective. And adoption flatlines. Even if you have the most advanced tech in the world, if your team doesnt know how to use it effectively, its worthless. Expand your training budget But, equally, throwing money indiscriminately at AI education alone isnt going to fix the problem. The training investment must be smart. And that means implementing training programs that are truly pan-company and aligned with the business objectives. Too many businesses funnel their AI training into a tiny corner of their workforceusually just their IT, engineering, or data teams. And while these teams do need support, theyre not the ones who are going to deliver the productivity gains that you are trying to realize. That job falls to the rest of your company: the 90% working in frontline roles and business functions where the AI transformation will be felt most. Whether thats operations, strategy, product development, sales, finance, marketing, HR, legal, or customer service. These are the people who run your business. And if they dont know how to apply AI to their day-to-day work, your transformation will stall. If the goal is to modernize the business end to end, your training needs to reach end to end. Teach Data and AI literacy before you teach tools At the same time, surface-level AI training that focuses only on toolssuch as how to write a prompt, where to click, and how to navigate an interfacewill also fall short. Effective AI training needs to build capability and not breed dependency. The best results come when your people understand whats happening under the hood. Dont get me wrong, your team members dont all need a PhD in computer science. But they do need solid data literacy. They need to know how to interrogate, interpret, and act on data. The real value of data comes from understanding what it can actually doseeing its potential and seizing it with both hands. Without even the most basic data skills, AI will create beautiful spreadsheets that cant be acted on. And thats not the revolution anyone had in mind. Train your managers just as muchif not more Equally, when it comes to AI training, theres a myth I sometimes hear: Managers dont need AI training because they’re not doing the work. Their job is to manage the team or set the vision, not run the tools. But that logic falls apart quickly. Firstly, I can think of countless ways that AI can make managers more effective: being able to synthesise and extract lessons from performance data, providing their team with hands-on guidance on how to use AI, and spotting opportunities to reengineer workflows. But, more importantly, it is the bad message that not training your managers sends to your wider team. It runs the risk of your wider company writing off your transformation as “hot air” and “warm words” rather than concrete, in-the-trenches implementation. Wide-scale transformation needs managers who can lead by example. If you train the team but skip the managers, dont be surprised when nothing changes. Build a culture that lets people use what they learn Finally, even the best training program will fall flat if your workplace punishes people for using it. In many businesses, employees are quietly, and perhaps unconsciously, discouraged from using AI. Theres a genuine fear that if theyre seen to be using AI, they will be criticised for cutting corners or cheating. The result? Team members keep their heads down and go back to old habits. In other companies, colleagues are afraid to give AI a go in the first place. Theyre hamstrung by a fear that theyll make a mistake or get something wrong. In both cases, your training budget goes to waste. So, if you want this to work, you need to create a culture of experimentation and entrepreneurship, where trying something new is actively encouragedand not seen as a riskand where teams share learnings, trade prompts, and build real know-how together. Too many companies are pinning their hopes on the next big AI tool. But no tool, no matter how powerful, will move the needle if your people dont know how to use it. The smart move right now isnt just buying more software. Its training your people to work smarter with the tech you already have. Thats how you make AI worth the investment. Thats how you turn strategy into results. And thats what will, ultimately, stop your AI vision from dying on paper.
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E-Commerce
In a world obsessed with productivity hacks and optimization strategies, I propose we try something radical: What if the secret to peak performance isn’t doing more, but doing differently? What if our industrial-era approach to productivity is not just outdatedbut it’s actively sabotaging our best work? We tend to think about productivity as timesomething that can be constructed and divided up into neat segments. But this view of productivity has serious limitations, especially in a knowledge economy dependent on imagination and creativity. As part of my research for my book Move. Think. Rest. I interviewed nearly 60 people and examined my own journey from academic burnout to entrepreneurial vitality. I’ve identified three game-changing insights that could transform how you and your team approach work: 1. Your Brain Needs Your Body to Think Better The most counterintuitive finding in my research? Movement isn’t a break from thinkingit’s essential to it. As human beings, we’re designed to move. Our spinal cord is an extension of the brain. If we’re hunched over a laptop all day, we’re literally constricting blood flow to the brain, and therefore oxygen to the brain. We’re simply not doing our best thinking. This isn’t just about taking a walk to clear your head. I’ve come to understand movement as a form of inquirya way of collecting different types of data through your body. When I take a ballroom dance class, or go open water swimming, I experience a different type of thinking happening through my body, a different type of data collection that I’m absorbing through the movement. I get out of my head and into my body, which paradoxically helps me think better. The practical application? Start incorporating movement into your work routine. Take phone calls while walking. Use a standing desk. Design brainstorming sessions that get people out of conference rooms and into different physical spaces. Your body isn’t separate from your mindit’s part of your thinking apparatus. 2. Replace ‘Productivity’ with ‘Cultivation’ I challenge the fundamental premise of our work culture by proposing that we shift away from asking “How can I be more productive?” to asking “What might I cultivate?” The difference is profound. This insight came from looking backward. Before the first industrial revolution, most societies were agrarian-based economies. I’m not romanticizing farming (that’s the ultimate volatile, uncertain, complex, and ambiguous environment), but the agricultural model offers a powerful framework for modern work. When we cultivate, we value both the solo practitioner and the collective. We value quick spurts of growth, but we also value slow growth. We value measuring what we can see, but we also factor in that there’s a lot going on during dormant timespercolating and marinating. If we trust the process through experience, we know that something incredible will emerge. This “both/and” model recognizes that some of our best work happens during what appears to be downtime. Like a farmer who understands that soil needs time to regenerate between seasons, effective leaders must create space for ideas to develop organically rather than forcing constant output. 3. Rest Is a Strategic Advantage, Not a Luxury Perhaps the most radical element of my framework is positioning rest as a competitive advantage. When we rest, we restore. Restoration is so important for being able to spark new questions. When we get exhausted and drained, the new ways of thinking, the new ways of asking “I wonder if I tried this?” simply won’t emerge. When you’re tired, you’re just trying to survive. This isn’t about nap pods in every office (though I’m not opposed to those). It’s about recognizing that rest operates on multiple scalesfrom micro-breaks during the workday to sabbaticals every five to seven years. The key is intentional design of rest periods that actually restore cognitive capacity. My own practice illustrates this principle. As an entrepreneur, I deliberately take dance lessons three days a week, go on micro-retreats that last a day, and ensure daily walkseven if just for five minutes. I’ve become very mindful about self-preservation and self-compassion. I noticed that when I was “procrastinating” (when I stepped away from my laptop) I would come back and all of a sudden things clicked, or I got an idea that seemed even better than before. The Bottom Line The World Economic Forum predicts that by 2027, critical thinking will be the No. 1 job skill, with creativity ranking second. My Move-Think-Rest framework isn’t just about personal wellnessit’s about building the cognitive capacity that future work demands. The companies that will thrive aren’t necessarily those with the most sophisticated AI or the fastest execution. They’ll be the ones that understand how to cultivate human creativity through the simple, profound practice of moving, thinking, and resting with intention. Creativity is the engine for innovation. If you want to consistently innovate over time without burnout, you need this ebb and flow. Movement, thought, and rest help us be more creative, which helps us to sustainably innovate. The future of work isn’t about working harderit’s about working more humanly.
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E-Commerce
While tech and AI giants guard their knowledge graphs behind proprietary walls, a more open model is quietly powering innovative projects from So Paulo to Nairobi. Wikidata, the collaborative backbone behind Wikipedia’s structured data, has become the world’s largest free knowledge database. Lydia Pintscher, who leads the Wikidata project at Wikimedia Deutschland, oversees this enormous experiment in open collaboration. More than 25,000 contributors across 190 countries have built a database containing 116.6 million data points, edited nearly 500,000 times daily. Unlike with proprietary alternatives, anyone can access, query, and contribute to this growing repository of human knowledge. Developers can build upon this community-driven knowledge base without worrying about corporate gatekeepers or sudden API changes. Pintscher spoke with Fast Company about how open data challenges Big Tech dominance and enables innovation in underserved markets, and why transparency in knowledge graphs matters more than ever. The conversation has been edited for length and clarity. What are a few projects built on Wikidata that reflect technology’s potential for social good? There are many, but the ones Id like to highlight are: Govdirectory, making it easier for people to get in touch with their government and make their voices heard on topics that matter to them OpenSanctions, tracking politically exposed persons, their connections, and the sanctions imposed on them, ensuring that international sanctions are enforced Aletheiafact, a fact-checking project from Brazil combating misinformation Open Parliament TV, making it easier to track what politicians are saying in parliament about crucial issues Gestapo.Terror.Orte, a project helping to understand the atrocities of the secret police in Nazi Germany All of them are grassroots efforts, made possible or easier with the support of Wikidatas data and community. When developers could use proprietary APIs from major tech companies, why choose the more complex path of building on open data? Ill answer that question with another question: Do you want to be beholden to the whims of a major tech company that could decide tomorrow to no longer make the data available to you, or only make it available to you at a price, and under conditions you cannot agree to? Or would you rather work with and support a movement that cares deeply about access to knowledge for everyone? On top of that, Wikidata empowers you to be an active participant, not just a consumer. You found an issue in the data? Something you really care about is missing? You can go and make the changes in Wikidata yourself, directly. How does Wikidata’s approach differ from how companies like Google or Microsoft manage their knowledge graphs? The starkest contrast is the openness. In Wikidata you can literally go to the website, look up an entry, and sift through every single change that has ever been made to that entry to see how it got to where it is today. And beyond just being able to see what that entry looks like now or looked like in the past, you can also make an edit to it and contribute to the sum of all human knowledge. Right there. With one edit. The second difference is the complexity and nuance with which we try to model the world. Since the beginning of Wikidata I have found so many beautiful, weird, and thought-provoking entities that really dont lend themselves to a simple model of the world. Did you know about that one year Sweden decided to have a February 30th, for example? Or all the countries that have more than one capital city? There are plenty of funny examples but also ones that really matter, such as disputed territories where other websites might decide to show you just one side of the dispute depending on where you access their site from. We cant have civil conversations when we dont even get shown that another view on a topic exists. Thats why I believe it is so important to surface at least some of that complexity. The world we live in is complex, weird, and beautiful, and the technology we use in that world needs to be able to reflect that. What’s the most notable technical challenge you’ve solved that other organizations building global platforms should know about? Making a knowledge graph the size of Wikidata publicly accessible and queryable to everyone is definitely a technical challenge, especially given the rate of changes and access to the data. Wikidata gets edited almost 500,000 times a day. Our SPARQL endpoint serves about 10,000 requests per minute, and it is growing every day. Building and maintaining infrastructure to support that with the resources of a nonprofit is definitely a challenge. What’s your sense of how open data projects will evolve over the next few years? Large tech companies have been extracting value from the commons for many years, be that in open data or free software. As a society, we need to understand that this is undermining the commons we all rely on, and we need to expect and demand better. I believe, especially in the age of LLMs and related technologies, that we need to understand what this technology is built on, and this is often happening without giving back. So I would like to see people contribute more to open projects like Wikidata and then build on that data, all the while giving back to the project they rely on. The alternative is a world where we as a society do not have influence over the technology we use every day and that democracy depends on. Instead, wed be beholden to the black-box technology we are given. Thats not a future I wish to live in. What do you mean about LLMs not giving back? These large AI companies are basically strip-mining the internet. They will undermine the source of a lot of the material that they’re training their models on. If they’re not sending people back to projects like Wikipedia or Wikidata, or many others, they’re basically cutting them off from the people who actually make the answers possible. Are you saying the sites providing the content might disappear? So someone put out a blog post about Stack Overflow analyzing how large language models influenced the traffic on their site. And the analysis suggested that if people are just asking their programming questions to an LLM, why would they need to go to Stack Overflow anymore, right? But why is the LLM able to answer programming questions? Because it has been trained on something like Stack Overflow. So what should AI companies do to ensure the vitality of the communities they’re taking material from? Two things. One is recognition in the sense of “Hey, this answer you’re getting here is coming from these places,” and they’re starting to do that, so that people can find their way back to the source of that content. Andthe second is that they’re making a lot of money, and they should give some of that money back to the projects that are making them that money. How do you handle conflicts when contributors from different countries or just different perspectives disagree about how to structure or present information? There are community processes to handle editorial disputes, starting with discussing the pros and cons of different ways of describing a situation (in what is called a WikiProject) together with people interested in the same topic. Often, more senior editors can help resolve disagreements that way, for example, by pointing to best practices for modeling or by asking for references for a specific data point someone wants to add. Worst case an entry might get locked down by an admin if different parties cant stop editing back and forth on a particular point. Many potentially divisive topics thankfully never even escalate to that level, in part because of how Wikibase, the underlying software of Wikidata, is built. Based on many years of experience in Wikidatas sister project Wikipedia, from the start we centered it around the concept of verifiability. That means an editor cannot just show up and claim something. They need to have a reliable and trustworthy source for what they claim, such as an article in a reputable newspaper. Additionally, we allow differing views and even conflicting claims to stand side by side, something especially important for disputed territories, for example, and then add context to these claims that helps [explain] the nuance of the situation. This can include things such as which international body supports or does not support a specific territorial claim. Your 25,000 contributors span 190-plus countries. How do you ensure voices from marginalized communities aren’t drowned out by more resourced contributors? We are dedicating a lot of effort to ensuring that everyone can contribute data that is relevant to them and their communities. For example, we are running editing workshops across Africa to help more people make their first steps in contributing to Wikidata. We are also working on improvements to editing from mobile devices to make sure people who primarily or even exclusively access Wikidata from a mobile phone have a good experience contributing to the worlds knowledge. What has surprised you most about how developers worldwide have used Wikidata’s open data? What astonishes me the most is the fact that many of the applications people are building with the help of Wikidata are ones that I would never have imagined when we first started. Take KDE Itinerary, for example, the digital travel assistant that keeps track of all your travel documents andthanks to Wikidatareminds you to bring an adapter for your laptop when traveling to a country with different power outlets. Or eRutter, the historical sea-routing website that lets you imagine how you might have traveled from continent to continent in ancient times. A Bangladeshi developer with Wikidata can access the same data infrastructure as Google. How does open data level the playing field for innovation in the Global South? A lot of applications today are powered by data. As a developer, that means you dont just have to actually build your application, you also have to collect and maintain the data your application relies on. For a large company, that is not as big of a problem, but if you are an individual developer or small team, this really limits what you are able to build. This is where Wikidata is there to support you, with basic data about the things that matter in the world, from people to events to locations to culture, you name it. Thanks to a dedicated community of over 25,000 editors on Wikidata, you have access to up-to-date and reliable basic data to build upon. And not just that: Wikidata also provides you with links to 10,000 other websites, archives, social media sites, and more to make it easier to access additional data about the topics you need for your application.
Category:
E-Commerce
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