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Most enterprises treat AI implementation as a procurement problem. They evaluate vendors, negotiate contracts, and deploy solutions. But this transactional approach misses a fundamental truth: successful AI implementation isn’t just about buying technologyit’s about orchestrating an ecosystem. The companies winning with AI understand that implementation requires a web of relationships extending far beyond traditional vendor partnerships. They are building networks that include universities, regulatory bodies, ethicists, suppliers, and even customers. They recognize that in an environment in which AI capabilities evolve monthly, isolated implementation is a recipe for obsolescence. This article draws on insights from our forthcoming book, Reimagining Government (Faisal Hoque, Erik Nelson, Tom Davenport, Paul Scade, et al.) identifying the key components you will need to reconcile to successfully orchestrate a comprehensive AI partner ecosystem. The Expanding Universe of AI Partners When enterprise leaders think about AI partnerships, they typically start and stop with technology vendors. This narrow view blinds them to the full spectrum of relationships that determine success or failure in AI implementation. Academic institutions offer capabilities that money alone can’t buy. Universities are where breakthrough AI research happens, often years before commercial availability. Building relationships with labs, research centers, and individuals academics can provide access to cutting-edge research, specialized expertise, and talent pipelines that vendors can’t replicate. Government agencies are partners, not just regulators. Forward-thinking companies will work with agencies to shape AI standards, participate in regulatory sandboxes where they can test implementations and receive guidance, and collaborate on public-private initiatives that define industry practices. Ethics and oversight partners are becoming essential as AI stakes rise. Third-party ethicists provide a layer of credibility that internal roles cant match. Audit firms specializing in AI bias detection offer independent validation. Compliance specialists navigate the emerging patchwork of AI regulations. These partners don’t just reduce riskthey become competitive differentiators when customers demand proof of responsible AI use. Consultants and implementers bridge the gap between AI potential and operational reality. They build custom tools that integrate AI into existing workflows, train teams on new capabilities, and manage the organizational change that AI demands. The best ones will transfer knowledge while implementing systems, building internal capabilities that will endure after they leave. Supply chain partners determine whether AI creates value or chaos. When your AI-optimized inventory system hands off to a supplier’s manual processes, many of the benefits evaporate. Enterprises should look to coordinate AI decisions across their supply networks, encouraging shared model adoption, and ensuring that AI-to-AI handoffs work seamlessly. Customers are perhaps the most overlooked partners in AI implementation. They’re not just users but cocreators, providing the feedback that shapes AI development, the data that improves models, and the trust that makes implementation possible. Strategic Imperatives for Partnership Design Building an AI ecosystem that creates value involves more than just accumulating partners. Relationships and networks need to be designed to amplify capability while maintaining flexibility. Enterprises should focus on: Interoperability by design. Using proprietary models can lead to the creation of silos within enterprise networks. Selecting open-weight models helps ensure transparency and compatibility among partners. Alignment across the value chain. A pharmaceutical company implementing AI for drug discovery must ensure that contract research organizations, clinical trial partners, and regulatory consultants all work with compatible systems and standards. This doesn’t mean that all partners must use identical tools, but it does mean establishing common data formats, shared evaluation metrics, and aligned security protocols. Risk distribution. AI failures can cascade through networks. Smart partnership agreements distribute both opportunity and liability, ensuring that no single partner bears catastrophic risk while maintaining incentives for responsible development. This includes technical risks (system failures), ethical risks (bias, privacy violations), and business risks (market rejection, regulatory penalties). Translation layers. When government agencies partner with commercial vendors, they often use specialized contractors who serve as a critical middle layer, translating the generally applicable technology to meet agency-specific requirements. This middle layer adapts cloud-native solutions for secure environments, restructures Silicon Valley business models for public sector procurement cycles, and bridges cultural gaps between tech innovation and public service. Private enterprises can adopt this model as well, using specialized partners to translate general-purpose AI products for their specific industry needs. These translation partners can package the technical adaptation skills, business model alignment know-how, and cultural bridging that turns raw AI capability into operational value. Critical Partnership Challenges Three challenges consistently derail AI partnership ecosystems. The IP question can become extremely complex in multiparty AI development. When your data trains a vendor’s model that’s customized by a consultant and integrated by a systems implementer, who owns what? Imagine that a financial services firm discovers their AI vendor is using patterns learned from their fraud detection system to improve products sold to competitors. This might be permissible under the vendors standard contract, so it is important to think ahead to ensure that explicit boundaries are drawn between vendor improvements and innovations rooted in the clients operations and data. Lock-in risks extend beyond technology to psychology. Technical lock-in is a familiar problem: specific vendor systems can become so deeply integrated that switching becomes prohibitively expensive or onerous. But psychological lock-in is just as dangerous. Teams can become comfortable with familiar interfaces, develop relationships with vendor personnel, and resist exploring alternatives even when superior options emerge. Coordination complexity multiplies with each partner. When an AI system requires inputs from five partners, processes from three more, and delivers outputs to 10 others, coordination becomes a full-time job. Version mismatches, update conflicts, and finger-pointing when problems arise can paralyze initiatives. Building Your Partnership Strategy Creating an effective AI ecosystem requires a systematic approach, not just building a sequence of ad hoc relationships. Map your ecosystem needs across every dimension. Where are your technology gaps? Which expertise is missing? What ethical oversight do you need? How will implementation happen? Don’t just list vendorsmap the full spectrum of partnerships required for successful AI implementation. Include the nonobvious: the anthropologist who understands how your customers actually behave, the regulator who’ll evaluate your system, the supplier whose cooperation determines success. Design for flexibility. AI capabilities change monthly. Build partnerships that can evolve with them, with regular review cycles, clear performance metrics, and graceful exit provisions. Avoid agreements that lock you into specific technologies or approaches. The perfect partner for today’s needs may be obsolete tomorrow. Create governance structures that acknowledge the complexity of AI partnership networks. Establish steering committees with senior representation from key partners. Define escalation paths before problems arise. Create shared metrics that reflect interconnected outcomeswhen success requires five partners working together, individual KPIs create dysfunction. Plan exits from day one. As we emphasize in our recent book Transcend, knowing how partnerships end is as important as knowing how they begin. Define termination triggers, data ownership post-partnership, and transition procedures. The best partnerships are those either party can leave without destroying value. The AI revolution will not be won by technological advances alone. The strength of an enterprises ecosystem will play a key role in separating the winners from the losers. Companies that can see past traditional vendor relationships to orchestrate comprehensive partnership networks will transform AI from an implementation challenge into a sustainable competitive advantage.
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E-Commerce
AI tools are disrupting creative work of all kinds, and Runway AI is a pioneer in the spacemaking major waves in Hollywood through partnerships with the likes of Disney and Netflix. Runways cofounder & CEO Cristóbal Valenzuela dissects the companys breakneck growth, the risks and responsibilities of AI tool makers, and how AI is redefining both business expectations and our notion of creativity. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. You released your Gen-4 model not long ago. You had your Aleph video editing tool come out. Correct. And there are these other tools out there too now, Google’s Veo 3 which I see folks using. Of course, there’s OpenAI’s Sora, Midjourney. What’s the difference between all these? I mean, are you all utilizing similar engines, or are all these things popping up now because the compute has reached a certain place? It’s a combination of things. I mean, we’ve been working on this for almost seven, eight years, so there’s a lot that we’ve learned after being alone and building this. I would say these days it’s becoming more evident to many that the models are getting pretty good at tackling and doing a lot of different things, and so that becomes interesting for obvious business reasons. All models are different. I think all models are trained for different reasons. We tend to focus on professionals and folks who want to make great videos. This amazing model we’ve released only recently, just a couple of weeks ago, allows you to modify and create video using an existing video. That was never possible before. And so those kinds of breakthroughs are just allowing, I guess, way more people to do much more interesting things. I saw you in another video use voice prompts to create a video scene. Your tools generate camera angles and change objects. They extend a scene outward, filling in what isn’t there. In one video, we see a cityscape, and then street lamps come on, and the windows of office and apartment buildings start blinking, and the lights are switching on and off in this very choreographed sequence. Can you explain how that was created? It took us less than an hour to make that video, and you start with a scene, an initial video, and then you ask Runway for things you want to change in that video. And so, we could ask if it’s daylight, we can ask the model, “Just show me a night version of that same scene.” And so what the model will do is it will understand what’s in that scene, and it will turn down the light metaphorically, but also literally we’ll just turn day into night, while maintaining pretty much the consistency for everything else. You might turn on the lights of the streets. And you can be much more specific. You can be “Only turn the lights on the left,” or “Only turn the streetlights while keeping everything else dark.” You can be like, “Now start turning the lights one by one, starting from the one on the left to the one on the right.” So in a way, it’s editing reality. Maybe you can think about it like that. You have an existing piece of content and you’re working through that content with AI, asking it to modify it in whatever way you want, which is really fun to be honest. It’s something I think we’ve never had the chance of doing ever before, and so it’s really fun to play with. I’ve played with Runway a little. It’s awesome, but I can’t write a single natural language prompt and get a full film yet. I mean, there is craft and discipline to getting these tools to work at their potential. I mean, are we going to get to the point where all you need to create a film is the idea for it? The vision and the production itself is all automatic? I think a great concept of what you mentioned was tools. This is a tool. It’s a very powerful tool, and this tool allows you to do things that you couldn’t do before. Knowing how to use the tool would always be important, and the tool is not going to do work on its own if you don’t know how to wield the tool, how to use it in interesting ways. And so I guess the answer for the question of will we ever get to a point where you can just prompt something and get exactly what you want? I guess the answer is kind of-ish. Depends on how good you know how to use the tool. I think about what tools people are using today to make films, like a camera. Can a camera help you win an Oscar? Of course. If you have a camera, will you win an Oscar? No. What makes a great filmmaker is like, well, knowing where to point the camera, knowing how the camera works and functions and how you can tell a story with a camera. And I think that’s no different from how we think about AI tools and Runway specifically, which is it can help you go very far. You can do amazing things with it. You just have to learn how to think with it and work with it. And if you know, then you’ll get far. You mentioned work that you do with studios in Hollywood. I know you’ve partnered with Netflix and Disney and AMC networks and whatever. How are they using Runway’s AI today? Because AI can be a little bit of a dirty secret in Hollywood. People are using it, but they don’t always want to admit it. Yeah, I think it’s a tool that’s the answer. And so the best studios and the best folks in Hollywood have realized that, and they’re using it in their workflows to combine them with other things they know pretty well. The thing is that there are no rules. You can start inventing them right now. I mean, Aleph is a couple weeks old, and so people are figuring out things and ways of using the technology that we never thought possible, and that’s what I enjoy the most. It’s a general purpose technology. It can be used in ways that are diverse and creative and unique, and if you’re creative enough, you’re going to uncover those things. At some point in the future, there may be a whole different medium about the way you do it. Right now, I can imagine they take ideas and they create essentially a prototype of a film to show to get ideas through. Is that part of how it’s used? You can think about, broadly speaking, in two stages. There’s preproduction and postproduction. Preproduction is, well, writing the script and doing art direction and selecting characters and casting and location scouting and just preparing to make the stuff. And so there’s many use cases of Runway in there. Of course, the obvious ones are storyboarding and helping you with writing the script and helping you with casting characters and seeing how they’re going to behave and what they’re going to do. And then in post, once you film or we record something, there’s a lot of visual effects and things that you need to aply and change to the videos themselves. And so let’s say the example that we were speaking before, turning day into night. Let’s say you’ve recorded something and it happens to be that someone changed the script later and the shot that you recorded had to happen at night. Well, the way you would do it before was that you had to go back and shoot again and spend more time and fly the actors again and do the whole thing. Or now you can go into Runway and just ask the model to turn that scene into night, and it will do it for you. So it’s less of them coming to Runway and typing, “Get me a multi-award winning film now, fast and cheap,” and more about, well, I have this problem, it’s very expensive to solve. I have a tool now that can help me do it faster and better. Can I use it? Will it make my movie? No, but it will help you very much in getting there faster and cheaper.
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E-Commerce
Having $1 billion isnt enough these days. To be seen among the richest of the rich, you now need your own private sanctuary. For some, that means a sprawling compound. Increasingly, though, members of techs 1% are incorporating their own towns, giving them the power to set rules, issue building permits, and even influence education. Some of these modern-day land grabs are already functioning; others are still in the works. Either way, the billionaire class is busy creating its own utopias. Heres where things stand: Elon Musk Musk can lay claim to not one but two towns in Texas. In May, residents along the Gulf Coast voted to incorporate Starbase (though its worth noting that nearly all of them were SpaceX employees). Previously called Boca Chica, the 1.5-square-mile zone elected Bobby Peden, a SpaceX vice president of 12 years, as mayor. He ran unopposed. The vote stirred controversy. The South Texas Environmental Justice Network opposed the plan. The group wrote in a press release in May: Boca Chica Beach is meant for the people, not Elon Musk to control. For generations, residents have visited Boca Chica Beach for fishing, swimming, recreation, and the Carrizo/Comecrudo Tribe has spiritual ties to the beach. They should be able to keep access. Musk also controls Snailbrook, an unincorporated town near Bastrop, about 350 miles north of Starbase. The area includes a SpaceX site that produces Starlink receiver technology, sits just 13 miles from Teslas Gigafactory, and features housing and a Montessori school that opened last year. Mark Cuban In 2021, Cuban purchased Mustang, Texas (population: 23). The 77-acre town, an hour south of Dallas, was founded in 1973 as an oasis for alcohol sales in a dry county. The former Shark Tank star told CNN he has no immediate plans beyond basic cleanup. “It’s how I typically deal with undeveloped land,” he said. “It sits there until an idea hits me.” California Forever This project isnt tied to a single billionaire, but a collective. In 2017, venture capitalist Michael Moritz spearheaded a plan for a new city in Solano County, California, about 60 miles northeast of San Francisco. Backers included Marc Andreessen, Chris Dixon, Reid Hoffman, Stripes Patrick and John Collison, and Laurene Powell Jobs. Together, they spent $800 million on 60,000 acres. The plan proved unpopular. In November, California Forever withdrew its ballot measure to bypass zoning restrictions. (The land is not zoned for residential use.) It pivoted last month, unveiling Solano Foundry, a 2,100-acre project the founders say could become the nations largest, most strategically located, and best designed advanced manufacturing park. The group also envisions a walkable community with 150,000-plus homes. A Bay Area Council Economic Institute study released this week projected 517,000 permanent jobs and $4 billion in annual tax revenue if the revised plan goes forward. Larry Ellison Ellison doesn’t own a town, but he owns virtually all of one of the Hawaiian Islands. In 2012, he bought 98% of Lanai for about $300 million. He also owns the islands two Four Seasons hotels, most commercial properties, and serves as landlord to most residents. Lanai has become a retreat for the wealthy, hosting visitors from Elon Musk to Tom Cruise to Israeli Prime Minister Benjamin Netanyahu. Peter Thiel Thiel doesn’t own a city, per se, but he is part of a collective backing Praxis, a proposed “startup city” that is currently eyeing Greenland for its base of operations. Other investors include Thiel’s PayPal cofounder Ken Howery and Andreessen. The plan for Praxis is similar to California Forever. Founders hope to create a Libertarian-minded city that has minimal corporate regulation and focuses on AI and other emerging technologies. So far, however, no notable progress has been made on the project. Mark Zuckerberg Zuckerberg owns a 2,300-acre compound on the Hawaiian island of Kauai. Hes investing $270 million into Koolau Ranch, which will include a 5,000-square-foot underground bunker. Located on the islands North Shore, the property is also said to have its own energy and food supplies, Wired reports. While it’s not technically its own city, it will house more than a dozen buildings boasting upwards of 30 bedrooms and 30 bathrooms. There will be two mansions spanning 57,000 square feet, with elevators, offices, conference rooms, and an industrial kitchen. Those will be joined by a tunnel, which branches off into the underground bunker, which has a living space and a mechanical room as well as an escape hatch. Zuckerberg has posted on Instagram about the compound, saying he plans to raise Wagyu and Angus cattle. Bill Gates In 2017, Gates announced plans for Belmont, a smart city on 234 square miles near Phoenix. Designed to house 180,000 people, it promised autonomous vehicles and high-speed networks. There haven’t been any recent updates on the status of the Arizona development, however, and the project is considered dead in the water (well, desert) at this point.
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E-Commerce
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