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The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Every generation has its tinkerers. People who get their hands dirty not because they know exactly what they’re doing, but because theyre following a feeling. No formal training. No permission. Just curiosity, instinct, and a slightly obsessive need to mess with things until they do something interesting. Welcome to the age of vibe coding. The term itself surfaced just weeks agocoined by AI researcher Andrej Karpathy in February. In a now widely memed post, he described vibe coding as the act of programming through intuition rather than structure, trusting the feel of what youre building, not just its logic. The phrase exploded across dev forums, design threads, and TikTok sidebars. Merriam-Webster added it the following month under slang & trending, defining it as the practice of writing code, making web pages, or creating apps, by just telling an AI program what you want, and letting it create the product for you. Which is a long way of saying: winging it, brilliantly. Even Sir Demis Hassabis, founder/CEO of DeepMind, recently stated that the explosion of natural language coding will open up fields for creative people, tipping the balance away from and engineering mindset to an instinctive, creative one. But lets be honestthis isnt new. When instinct outpaces instruction Take early electronic music. The pioneers of modular synth werent conservatory-trained composers. They were sonic explorers, patching cables into buzzing machines and twisting knobs until emotion emerged. As Brian Eno famously observed: Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. What is that, if not analog vibe coding? Or look at the rise of the indie game scene. Minecraft, Braid, Undertalenone of these were born from a major studio pipeline. They were built by people making weird, emotional things with code, trusting their gut over any formal game design doctrine. Same with the postwar hot rodders in California, or the drift racers in Japan. They werent automotive engineers. They were teenagers in garages, modding beat-up engines until they could tear through salt flats or carve hairpin turns sideways. Tuning by ear. Testing by feel. Rewriting what cars could be without ever asking how cars should be made. Sound familiar? Vibes have always been a feature, not a bug Vibe coders are the natural descendants of this lineage. Theyre working with AI the way early skate culture worked with architecturenot as passive users, but as instinctive reinterpreters. Theyre pushing limits not by following a manual, but by making one up as they go. The outputs might look a little glitchy. A little offbeat. But thats part of the point. The future rarely starts with polished perfection. It starts with side quests, zines, garages, and basement experiments. It starts with people making things that feel right, even if they cant yet explain why. Dont mistake chaos for lack of vision To the outside world, this kind of experimentation can look messy. But look closer, and youll see a different kind of intelligenceone that isnt defined by credentials, but by creative fluency. These are people who speak machine, even if they dont always write it perfectly. Theyre fluent in feeling. Fluent in remix. Fluent in future. And when the tools are this powerfulwhen a few prompts can conjure films, music, code, business plansfluency in vibes becomes a serious superpower. So before we rush to regulate or rationalize this new wave, maybe take a moment. Listen to the noise. Feel the current. Theres something big building here, and it isnt coming from the top down. Its coming from the garages again. From the kids with GPT in one tab and Ableton in the other. From the creators who dont need to ask permissionbecause they already have momentum. The takeaway? You dont need a roadmap to lead a movement. You just need a signal, a pulse, and a willingness to follow the vibes. Mark Eaves is founder of Gravity Road.
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E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Many companies forget AI-powered enterprise applications are just business apps at the end of the day. The reality is, AI is simply another arrow in our quiver, albeit incredibly more powerful. But what IT has done since generative AI exploded on the scene is frantically rush to deploy any and all possible applications, causing massive confusion and huge resource wastes, without delivering much business value. The process of firing arrows at the target (increased business value) has stayed the same, just like the goal of hitting a bullseye. But many businesses miss the mark, trying to create significant and oftentimes unrealistic returns. How to generate more value with AI To be fair, the urgency is real, particularly as the next big target arises. Gartner predicts, By 2028, 33% of enterprise software application will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Everyone wants to fire with precision. Theyre just unsure where to aim. At our recent Insight Amplify technical conference, I sat down with a handful of fellow tech executives. We discussed our experiences rolling out AI apps, and we agreed that most successful use cases so far have been internal-facing as a proving ground to master the technology. Slowly but surely, the hope is it shows its mettle in customer-facing applications. Everyone agrees this is uncharted territory. I not-so-innocently asked a few follow-up questions, such as how they went about finding workloads. They each described the broad strokes, and as I suspected, their processes were remarkably similar. Theres much more nuance to it, but generally they followed these steps: Identify the business problem, ideally in partnership between the business line and IT team. Build a minimum viable product and, with a subset of capabilities, deploy the app to see if it is appropriate from functional and practical standpoints. Evaluate the return on investment to ensure it makes sense financially. Expand it for widespread use with the necessary cost and security controls and backlogged features. These are the same proven steps to build a traditional appyet all too often, adding the shiny AI component blinds us from the fundamentals. When I pointed out the similarities, it was like a light bulb flipped on. They hadnt thought about it like that. Unfortunately, many companies struggle to reach that Eureka moment in app development and make a few common missteps along the way. For example: Misstep #1: Developing AI apps yourself The first step is critical. Without a clear business problem, is there a point to pouring resources into an app? Even if a company has a solution, they need to step back and determine if they can deliver it. I often see clients trying to reinvent the wheel when theyre unequipped to do so. If a company’s business isnt writing software, they should focus on their core business instead. There’s a reason why auto manufacturers don’t make the lights that go into cars. They simply buy and put them into what they build. That’s the mentality businesses should have with AIrelying on partners with the specialized knowledge to guide them through this evolving landscape. This isn’t to say buying off the shelf is always the answer. Sometimes, the need is unique enough or rooted in specific business processes where developing custom AI-powered solutions makes sense. But first look for someone doing that as their business. If you cant find them, thenand only thencreate your own. Misstep #2: Improperly preparing your data I sometimes joke that clients have data swamps, not data lakes. Poor data quality is a significant gap in many organizations. It can be terribly organized and inadequately secured across different sources, costing companies 15-25% of their revenue, according to MIT Sloan Management Review. The key to unlocking datas immense value lies in organizing and normalizing it in one place, but most data is siloed across various locations based on its functions. While a small subset may seem manageable, this can mask underlying issues that arise once you deploy an app to a larger end-user community if your data isnt properly cleaned. This will be problematic whether you buy off the shelf or develop yourself. Data mastery is fundamental to driving any outcome. Misstep #3: Locking yourself in My colleagues are right: This is new territory. While basic app development steps remain the same, a rapidly evolving sector introduces countless variables to consider. Even without GenAI, changes would still occur at breakneck speeds. Welcome to IT. Among all the AI hype, what is just noise you can ignore? What are legitimate signs of the frenetic activity around us? I guide clients through these types of questions all the time. Developing AI, they might invest too much capital in on-premises solutions, lock themselves into a specific cloud provider, or partner with an independent software vendor thats a darling today but dead in six months. Given technologys rapid pace, its crucial to stay flexible. You’ll need to pivot eventually. Locking yourself into a category, location, vendor, or similar commitment is extremely risky. The stumbling block that so many struggle with is they dont yet have enough muscle memory working with AI to unlock its full potential. In the absence of certainty, what should be logical is to do whats familiarwhats worked before: Stick to your strengths as a business. Stick to proven app-development processes if that is, in fact, your business. If not: Stay the course with trusted partners who have that expertise. In other words, dont overthink things. Its AInot rocket science, unless thats the app you need. If youre unsure where to begin, work with a solutions provider with proven success delivering agentic, generative, and traditional AI applications. With a few reps under your belt, youll be locked in to hit future targets. Juan Orlandini is CTO, North America ofInsight Enterprises.
Category:
E-Commerce
The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Building a resilient technology company is hard. Building one that can withstand constant policy change is another level of hard. Right now, companies across sectorsnot just fintechare staring down government and regulatory shifts happening faster than most orgs can process, let alone implement. For industries like financial technology, where regulatory changes directly impact how products work, how they’re priced, and how they’re sold, the stakes are existential. Adapting in real time isn’t just an edgeit’s the bare minimum to stay in the game. Thats why companies need to think beyond using AI as a tool. They need to rethink the entire way they build software, make decisions, and operationalize compliance. At april, we didnt bolt AI onto our dev team; we restructured how we work to make regulatory agility the foundation. Our approach uses AI to take human-written analysis and turn it directly into code. It means faster updates, fewer silos, and a dev cycle that actually moves at the speed of policy. When every state writes its own rules, you build for change The U.S. tax system isnt a single rulebookits a fragmented, constantly shifting web of federal and state-level regulations. Each year, we see hundreds of changes across jurisdictions: new credits, sunset clauses, redefinitions of income, filing thresholds, and form logic. And none of them arrive on a predictable timeline. A change that passes in October still needs to be implemented and tested before filing season begins in January. We knew we couldnt keep up with that kind of churn using the legacy software development model most incumbents rely onlong handoffs between policy, legal, and engineering teams, often stitched together manually. So we built something different. At april, our Tax-to-Code system lets policy experts write structured analysis, and generative AI turns that into functioning software, reviewed and refined by engineers before it ships. The AI doesnt replace experts; it extends them. It kills the back-and-forth and accelerates our response time from weeks to days. This is what regulatory agility looks like: Tax code changes go from policy to product without bottlenecks. Automation isnt the goalstrategic bandwidth is Theres a lot of noise about AI automating work. But in regulated environments, the real value isnt just speedits the space it frees up for experts to focus on strategy. AI helps us eliminate the repetitive, time-sucking tasks that bog down compliance work. That doesnt just cut costs; it gives our team the bandwidth to think several steps ahead. Whats the next policy change likely to be? What would it take to adapt? What needs to be built now to stay ahead? Thats what most companies are missing. Theyre spending all their energy reacting. AI infrastructure, done right, gives you the room to anticipate. AI cant function without the right architecture This only works if your infrastructure is designed to support it. We didnt start with generative AIwe started with the assumption that regulatory change is constant and unpredictable. From there, we built a system where: Domain experts define the logic. AI transforms it into code. Engineers validate and ship. The result? A feedback loop where tax and policy changes get implemented at pace, not after a six-month dev sprint. More importantly, its adaptable. This model isnt just for tax. Any company in a volatile regulatory spacehealth insurance, auto, logistics, energyneeds a system that can cascade policy changes through their tech stack fast, accurately, and with oversight. Lessons for leaders in regulated industries If youre leading a company where compliance is high stakes, heres what to prioritize: Structure your tech org for change, not stability. You cant assume next quarters rules will match this ones. Build collaboration between experts and AI. Dont let legal, ops, and engineering operate in silos. AI works best when it sits between human knowledge and execution. Focus on speed and oversight. AI without accountability is dangerous. Human-only systems are too slow. You need both. This is the new baseline In todays environment, adaptability is non-negotiable. Leaders cant rely on manual processes or slow engineering cycles to keep up with real-time policy shifts. And AI isnt some magic solution on its own; it needs the right infrastructure, the right workflows, and the right people in the loop. At april, weve built our company around that reality. Thats how we move fast without breaking thingsand how others in high-regulation industries can, too. Ben Borodach is the cofounder and CEO of april.
Category:
E-Commerce
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