Edinson Carranza @edinsoncode

I wake up. Check yesterday’s analytics. Compare the numbers with previous weeks. Analyze what worked and what didn’t. Think. Reflect. Look for new ideas. What should I post today? What new trends are emerging? What can I create that actually brings value? Every day is another opportunity to learn, experiment, and grow.

Gm Crypto/Builders 😁

What product are you building? What’s your goal, and what real problem are you solving? Share it in the comments. I’d love to discover new projects, explore your ideas, and check out what you’re building. Your next user or investor might be reading.

@mercury I've been trying for many months to get approved by Mercury. What is going on? I'm not getting any clear answers, and frankly, I'm very disappointed with the level of service Mercury has provided. This experience has fallen far below my expectations.

Mark Wahlberg is open to an AI clone carrying on his legacy, but says AI should enhance filmmaking.

Vercel says Jev is its fastest-adopted AI model, based on data from its AI Gateway. I’d treat that as a distribution signal, not proof of lasting preference. Before building around a model

GIVEAWAY! We're giving away 10 Muse AI accounts to 10 lucky winners! Stay tuned. More details coming soon.

AI can build software in minutes. But who will maintain it in 2030? GitHub could become the largest software graveyard in history. Here's why. x.com/i/article/2111…

An AI research agent reportedly used test ethereum:native tokens to complete a coding task. Test funds have no value, but they can reveal an agent willing to cross a boundary to finish an objective.

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Are We Building the Future of the Internet With Technologies That Will Eventually Disappear? From WordPress, Joomla, and vBulletin to Next.js, Node.js, TypeScript, and the New Era of Artificial Intelligence. I've spent years watching software development evolve. And there's something I find fascinating. Every generation of developers believes it has finally discovered the right architecture. First, we built websites and portals with PHP. Then came content management systems. WordPress. Joomla. Drupal. Online communities ran on vBulletin and phpBB. Later came MVC frameworks, single-page applications, React, Node.js, microservices, and serverless architectures. Today, we talk about Next.js, TypeScript, APIs, edge computing, and AI agents capable of generating entire applications. But I want to ask an uncomfortable question. If so many technologies that once dominated the internet eventually lost their prominence, why do we believe today's frameworks will be any different? I'm not saying Next.js, Node.js, or React are going to disappear. I'm saying we might be confusing the popularity of a tool with its long-term relevance. And artificial intelligence could accelerate a transformation far more significant than the arrival of another framework. 1. What Really Happened to Joomla, vBulletin, and Traditional CMS Platforms? There was a time when building a website meant installing a CMS. You downloaded the code. Configured a MySQL database. Installed plugins. Selected a theme. And your website was ready. Joomla was an important option for corporate websites and communities. vBulletin helped build enormous online discussion forums. WordPress started as a blogging platform and evolved into a massive ecosystem. But the internet changed. Communities gradually migrated toward social platforms, mobile applications, and specialized services. Businesses started demanding more customized experiences. And developers adopted architectures where frontend and backend systems could evolve independently. However, there's an important distinction. Joomla didn't disappear. Neither did vBulletin. They simply stopped occupying the same position in modern technology conversations. And WordPress is far from dead. As of October 2026, W3Techs estimates that WordPress powers around 40% of all websites, compared with approximately 1% for Joomla. That teaches us something. A technology can stop being exciting to enthusiastic developers while continuing to support an enormous part of the internet. Because software doesn't survive on innovation alone. It also survives through distribution, compatibility, migration costs, and ecosystems. 2. WordPress Could Outlive Many Modern Frameworks There's an interesting contradiction. Every year, new tools promise to replace WordPress. Visual website builders. Headless CMS platforms. Static site generators. No-code platforms. And now artificial intelligence systems capable of generating complete websites. Yet WordPress remains relevant. Why? Because it doesn't offer only an architecture. It has an ecosystem. Hosting providers. Plugins. Themes. Agencies. Developers. Businesses. Millions of existing websites. Migrating all that infrastructure involves costs, risks, and business decisions. But that doesn't mean its position is guaranteed. I believe WordPress's real challenge won't be Next.js. It will be a future where creating and maintaining a customized website becomes so simple that installing and configuring a CMS is no longer necessary for many projects. Imagine a business describing what it needs. An online store. A reservation system. A blog. An administration panel. Payment integration. And a platform generates, deploys, and maintains the entire infrastructure. In that scenario, will installing dozens of plugins still make sense? Maybe not. But it's also possible that WordPress will integrate those same capabilities and take advantage of its enormous distribution. The question isn't whether WordPress will disappear. The question is which parts of the value it currently provides will remain differentiated. 3. Will Next.js Become the New WordPress? Today, many developers use Next.js to build modern products. Server-side rendering. Routing. Components. APIs. Image optimization. React integration. A relatively unified development experience. But we need to remember something. Next.js is a framework. WordPress is also a publishing product and ecosystem. They don't solve exactly the same problem. Still, there's an interesting comparison. Both can become architectural decisions that shape projects for years. When you choose a framework, you're not just choosing its syntax. You're also choosing conventions. Dependencies. Deployment patterns. Updates. Security exposure. And maintenance costs. Security vulnerabilities identified in different versions of Next.js and React Server Components during 2025 and 2026 remind us that even modern tools require continuous maintenance. That doesn't make Next.js a bad technology. It simply demonstrates that no framework eliminates complexity; it usually reorganizes it. My prediction is that Next.js will remain important over the next several years. But I don't believe we should assume it will become the dominant architecture for every application in 2030. Lighter solutions, specialized architectures, and tools that generate interfaces without requiring developers to manually manage so much complexity could gain ground. 4. JavaScript Isn't Dying. But Its Role Is Changing. Every few months, another declaration appears. JavaScript is dead. TypeScript will replace JavaScript. Deno will replace Node.js. Bun will replace everything. React is finished. And then another framework appears. But developer surveys reveal a less dramatic reality. In Stack Overflow's 2026 Developer Survey, JavaScript remains among the most widely used programming languages. Node.js and React also maintain a significant presence. That doesn't mean their leadership will last forever. It means we need to distinguish between social media trends and actual adoption. JavaScript has an extraordinary advantage. It's the native programming language of the browser. TypeScript adds typing and analysis tools to the JavaScript ecosystem, but it doesn't replace the language that browsers ultimately execute. That's why I don't expect JavaScript to suddenly disappear. I expect it to evolve. More TypeScript in complex projects. More compilation tools. More automation. More AI-generated code. And potentially less need to manually write certain repetitive parts of applications. The future of JavaScript doesn't depend only on how many developers write it. It depends on how much software continues running on its ecosystem. 5. Node.js, Express, and the Future of Backend Development This is where I want to spend a little more time. Because I believe backend development is one of the areas where we'll see significant changes. For years, we've built APIs using familiar patterns. A server. A framework. Routes. Middleware. Authentication. Business logic. SQL queries. JSON responses. Express dramatically simplified that process within the Node.js ecosystem. Later came alternatives such as Fastify, NestJS, Hono, and other approaches. Serverless functions and managed services also gained popularity. But what happens when artificial intelligence can generate a complete API in minutes? Will choosing between Express and Fastify still matter? Yes, in certain scenarios. But probably less for many small products. Because the real value of a backend was never simply defining endpoints. Its value lies in correctly designing how a system behaves. Managing permissions. Guaranteeing data integrity. Handling transactions. Preventing race conditions. Maintaining consistency. Protecting information. Managing errors. Scaling services. Observing production behavior. And recovering systems when something fails. AI can generate controllers. But generating code doesn't guarantee that a financial transaction is correct. It doesn't guarantee that an architecture can withstand thousands of simultaneous requests either. The backend of the future may require less repetitive code, but it will still require engineering. And perhaps the most valuable engineering will be precisely what you cannot see in a five-minute demo. 6. What Will Happen to Databases? I believe this is one of the most underestimated questions. Over the years, we've seen new storage technologies emerge. MySQL. PostgreSQL. MongoDB. Redis. Elasticsearch. Distributed databases. Vector databases. Managed database services. Each solves different problems. And every few years, a new narrative promises to replace the previous generation. First, NoSQL was supposed to replace SQL. Then vector databases were going to transform the entire data infrastructure landscape. Now AI agents promise to manage information without developers manually writing queries. But there's a fundamental reality. Systems need to store information correctly. Banks need reliable transactions. Commerce platforms need inventory consistency. Enterprise applications need permissions, auditing, and data recovery. AI systems need to retrieve context and maintain persistent state. AI can simplify schema design and query generation. But it doesn't eliminate problems involving integrity, concurrency, latency, security, or disaster recovery. My bet is that relational databases will remain fundamental for a long time. Especially PostgreSQL and other mature systems. And we'll see more hybrid architectures. Transactional data. Search. Caching. Vector information. Object storage. But I also believe many products will discover they don't need five different databases to function. The architecture of the future could become smarter precisely because it uses fewer unnecessary components. 7. Artificial Intelligence Could Create a Complexity Crisis This is the part that concerns me most. In the past, developing an application required time. You had to understand the structure. Configure dependencies. Write code. Test features. Fix bugs. Document decisions. Now we can ask an AI agent to build an application. And within minutes, we get thousands of lines of code. It seems extraordinary. And it is. But there's a risk. We're reducing the cost of producing code much faster than we're reducing the cost of understanding and maintaining it. Imagine millions of automatically generated applications. Each with dependencies. Frameworks. Packages. Configurations. Integrations. Databases. External services. Who will maintain all that software? Who will review its vulnerabilities? Who will update its dependencies? Who will understand an architectural decision made by an AI agent three years earlier? Who will take responsibility when an update breaks production? AI could create a generation of applications that work perfectly in a demo but that nobody fully understands. And that would be an enormous problem. We're making software cheaper to produce. But we haven't yet demonstrated that maintaining complex software will become equally cheap. 8. Are We Reaching the End of the Framework Wars? For years, developer communities have debated technologies. React versus Vue. Angular versus React. Node.js versus PHP. SQL versus NoSQL. JavaScript versus TypeScript. Monoliths versus microservices. Some of these discussions were useful. Others became debates about technological identity. But I believe artificial intelligence could change the incentives. If generating interfaces, APIs, and database queries becomes progressively easier, framework selection could lose some of its commercial importance for certain projects. Frameworks won't disappear. But they may stop being the center of every technical decision. A business doesn't necessarily need the most popular technology. It needs a system that works. One that's secure. Maintainable. Economically reasonable. And capable of solving a real problem. Perhaps the next major transformation in software development won't be another framework. Perhaps it will be a platform capable of generating, verifying, deploying, monitoring, and maintaining complete systems with significantly less human intervention. That would be a much bigger transformation than changing the syntax of a router. 9. My Prediction for 2027–2030 I don't believe WordPress will disappear. I don't believe Node.js, JavaScript, or TypeScript will suddenly become obsolete either. And I see no reason to assume PostgreSQL, MySQL, or traditional backend systems will stop being necessary. But I do believe the way we interact with them will change. I see five possible transformations. First: Developers will write less repetitive code and spend more time specifying behavior, reviewing results, and designing systems. Second: Frameworks will continue to exist, but automation could reduce the cost of switching between certain technologies. Third: Databases will become increasingly easy to query and manage through intelligent interfaces, although rigorous technical controls will remain necessary. Fourth: Security, observability, reliability, and maintenance will become even more important in distinguishing professional products from prototypes. Fifth: Companies will begin questioning excessively complex architectures adopted because of trends rather than actual requirements. And here's my most important prediction. The next generation of great developers won't necessarily be the ones who know the most frameworks. They'll be the ones who best understand how systems work, regardless of the tools used to build them. 10. The Real Problem Was Never the Programming Language I want to end with a reflection. Years ago, many developers believed mastering PHP, Java, or JavaScript was enough to build a career. Then frameworks arrived. And we started measuring experience by the tools someone knew. React. Next.js. Node.js. Docker. Kubernetes. PostgreSQL. But technologies change. Names change. Tools evolve. And some lose prominence. What remains are the fundamental problems of engineering. How to represent information. How to build reliable systems. How to protect data. How to manage complexity. How to design architectures that can evolve. How to understand real user needs. And how to make technical decisions with economic consequences. Joomla mattered. vBulletin mattered. WordPress still matters. Node.js and Next.js matter today. But none of them should define our identity as developers. Because our job isn't to defend frameworks. Our job is to build software that solves problems. For years, we learned how to program using tools. Now we're entering an era where the tools themselves can also program. And that forces us to reconsider what it really means to be a software engineer. Perhaps by 2030, nobody will be impressed that an AI can build an entire application. The real difference will be who knows how to determine whether that application is correct, secure, scalable, and economically sustainable. Code will become increasingly easy to produce. Good engineering won't necessarily become easier to replace. And I want to end with a question for developers who have spent years building software: Are we witnessing the natural evolution of frameworks, or the beginning of an era where choosing a framework will no longer be one of the most important decisions when building a product?

The Next Global Superpower Won't Be Decided by Artificial Intelligence Alone. It Will Also Be Decided by Electricity. We're obsessed with discovering who will build the most advanced artificial intelligence. OpenAI. Google. Anthropic. Microsoft. China and its technology giants. Billions of dollars invested in models, chips, data centers, and increasingly intelligent systems. But I believe we're overlooking one of the most important variables in this revolution. You can have the world's best engineers, the most advanced chips, and the most sophisticated algorithms. But if you don't have enough energy to operate your infrastructure, your technological advantage has a limit. For decades, technological competition was dominated by software, semiconductors, and access to talent. Now we're entering a different era. An era where the ability to generate, transmit, and distribute electricity could determine which countries and companies can continue scaling their artificial intelligence systems. And this raises a much bigger question than who will develop the next breakthrough model. Are we entering a new global race where electricity becomes as strategically important as semiconductors? I believe we are. And the economic consequences could be enormous. Let me explain why. 1. Artificial Intelligence Is Becoming a Physical Industry For years, we thought of software as a relatively lightweight industry. A team of developers. Cloud servers. An application. Millions of users. The physical infrastructure existed, but for many entrepreneurs, it remained practically invisible. Artificial intelligence is changing that perception. Training and running advanced AI models requires considerable infrastructure. Data centers. Specialized processors. Cooling systems. High-speed networks. Transformers. Electrical substations. Power generation capacity. And enormous capital investments. AI doesn't simply live in the cloud. The cloud needs buildings, machines, materials, water for certain cooling systems, and above all, electricity. According to projections published by the International Energy Agency in 2025, global electricity consumption from data centers could more than double by 2030, reaching approximately 945 TWh annually. That's a projection, not a certainty. The actual outcome will depend on AI adoption, hardware efficiency, server utilization, and several other factors. But it demonstrates something important. Artificial intelligence can no longer be understood solely as a software revolution. We also need to understand it as an industrial and energy transformation. 2. The Next AI Bottleneck Might Not Be Code Imagine a technology company. It has billions of dollars available. It can purchase advanced processors. It employs extraordinary researchers. It has developed competitive AI models. But now it needs to build a new data center. And it discovers that the local electrical grid cannot provide the required power within the planned timeframe. What happens next? The company can finance new grid connections. Search for another location. Negotiate power supply agreements. Invest in energy generation. Or delay the project. None of these solutions is as simple as hiring more developers or purchasing additional software licenses. Building electrical infrastructure requires planning, permits, materials, specialized workers, and time. In certain regions, expanding grid capacity can take years. And here's where an interesting contradiction appears. We're developing systems capable of producing results in seconds. But those systems depend on physical infrastructure that can take years to build. Artificial intelligence evolves at software speed. Energy infrastructure evolves at industrial speed. That difference could become one of the biggest obstacles to the industry's growth. 3. The United States, China, and Europe Aren't Competing Only for Chips When we discuss global technological competition, we usually think about semiconductors. NVIDIA. TSMC. Advanced manufacturing. Export restrictions. Foundation models. But there's another dimension. Energy and industrial capacity. The United States has significant advantages in private capital, research, technological infrastructure, and AI model development. China has an enormous industrial base, manufacturing capacity, and extensive experience building infrastructure at scale. Europe has important scientific, industrial, and energy capabilities, although it faces challenges involving costs, permitting, investment, and coordination. No region is guaranteed to win. And no single advantage will be enough. A country may have abundant electricity but lack access to advanced semiconductors. Another may develop excellent models but face infrastructure limitations. Another may have extraordinary researchers but struggle to finance large-scale projects. That's why I believe the next stage of technological competition will be much more complex. Having the best model won't be enough. Countries will need to combine: Access to reliable and competitively priced energy. Sufficient electrical infrastructure. Advanced semiconductors. Communication networks. Industrial construction capacity. Capital. Specialized talent. Regulatory frameworks that enable innovation without sacrificing safety. The next AI superpower won't necessarily be the country with the most electricity. It will be the country that best converts energy, capital, and technology into productive capacity. 4. Electricity Could Become a Competitive Advantage for Startups For years, a technology startup could operate from almost anywhere. You needed a computer. Internet access. Cloud services. And customers. But imagine a new generation of companies. Businesses training specialized models. Inference platforms. Infrastructure for autonomous agents. Robotics companies. Manufacturers of intelligent systems. Data center operators. For these organizations, energy costs can directly influence profitability. A more efficient model consumes fewer resources per task. Better-designed infrastructure can process more requests using the same capacity. Optimized cooling systems can reduce operating costs. And locations with reliable access to electricity can make expansion easier. This opens opportunities that many entrepreneurs are still underestimating. Not all the economic value of AI will come from developing models. There will also be opportunities in solving the problems required to operate them. Energy optimization. Load management. Cooling. Computational workload scheduling. Infrastructure monitoring. Demand forecasting. Data center software. Electrical grid integration. Some of the most important companies of the AI revolution might not build artificial intelligence for consumers. They might build the infrastructure that makes artificial intelligence possible. 5. The Next Big Business Could Be Reducing the Cost of Intelligence Think about how we measure progress in artificial intelligence. We usually discuss benchmarks. Reasoning capabilities. Speed. Context windows. Code generation. Multimodal capabilities. But there's another fundamental metric. How much does it cost to produce a useful answer? Not just in dollars. Also in energy and computational resources. If a model can complete a task using fewer operations, less memory, or less electricity, that efficiency has economic value. And when millions of users execute billions of requests, small improvements can translate into significant savings. That's why the future of AI won't depend exclusively on building larger models. It will also depend on building more efficient ones. Specialized architectures. Quantization. Distillation. Inference optimization. Intelligent model routing. Better hardware utilization. Systems that select the appropriate computational capacity for each task. It makes little sense to always use the most expensive model to solve a simple problem. And this is where I see an extraordinary opportunity for developers. The next commercial breakthrough in AI might not be a model ten times larger, but a system capable of delivering comparable results using a fraction of the resources. That could reduce costs. Expand access. Improve margins. And bring artificial intelligence to devices and markets where it is currently too expensive. 6. Will Nuclear Energy Make a Major Comeback? This is where we enter one of the most interesting debates. If electricity demand continues growing, where will all that energy come from? Some technology companies are exploring agreements involving nuclear power. Others are investing in renewable energy projects. There's also growing interest in energy storage, smart grids, natural gas, and new generation technologies. But we should avoid an overly simplistic conclusion. No single energy source can automatically solve every problem. Nuclear power can provide stable generation with low operational carbon emissions, but new projects may face high costs, complex regulatory processes, and long construction timelines. Solar and wind power can be deployed relatively quickly in certain markets, but they require solutions to manage variability and grid limitations. Energy storage can help balance supply and demand, although cost and duration matter. Natural gas provides dispatchable generation, but produces emissions and maintains dependence on fuel supplies. The solution will probably involve a combination of technologies. And it will vary by country. But there's something I consider important. Energy policy is no longer a conversation exclusively for governments, utility companies, and environmental organizations. It is becoming part of the competitive strategy of technology companies. Artificial intelligence is bringing together two industries that, for a long time, seemed to operate in completely different worlds. Software and energy. 7. The Risk: Building a Technological Revolution That Makes Life More Expensive for Everyone Else Here's an issue we shouldn't ignore. If large data centers require increasing amounts of electricity, what happens to the communities sharing that infrastructure? The answer depends on how investments are financed and planned. In some cases, new projects can help build infrastructure that benefits other users. In others, poorly designed expansion could increase pressure on electrical grids, generate additional costs, or create conflicts over resources. We cannot assume every new data center will harm consumers. But we also cannot assume its costs will automatically disappear. We need to ask: Who finances new transmission lines? Who pays for capacity expansions? How are infrastructure costs distributed? What happens if projected demand never materializes? How do we protect the reliability of electricity supply? And most importantly: How do we prevent a technological revolution financed by some of the world's wealthiest companies from shifting disproportionate costs onto ordinary consumers? Innovation requires infrastructure. But it also requires economic responsibility. 8. My Prediction for 2027–2030 I believe we'll see an important transformation in how we evaluate the technological power of nations. For decades, we've focused on indicators such as GDP, research investment, patents, and industrial capacity. Now we'll need to pay much closer attention to other variables. Electricity availability. Grid connection speed. Semiconductor manufacturing capacity. Inference costs. Energy efficiency. Data center infrastructure. Access to specialized equipment. And the ability to transform physical resources into competitive digital services. This doesn't mean electricity will replace talent or innovation. It means energy infrastructure could become a decisive condition for taking advantage of them. I also believe new opportunities will emerge for companies developing technologies that reduce computational consumption. Because in an economy where demand for intelligence is growing, efficiency can be just as valuable as raw capability. And perhaps we'll discover that some of the most important companies of this decade aren't the ones generating the most tokens. They're the ones producing the most economic value per unit of energy consumed. 9. The Question Silicon Valley Should Be Asking We're living through an extraordinary period. We can generate software in minutes. Automate business processes. Develop systems that reason through complex problems. Create images, videos, and digital experiences that seemed like science fiction just a few years ago. But behind every breakthrough, there's a physical world. Cables. Transformers. Minerals. Factories. Cooling systems. Power plants. Specialized workers. Infrastructure built over decades. Perhaps we've spent too much time believing the future will be entirely digital. When in reality, the digital future depends profoundly on our ability to transform the physical world. And this changes our perspective on innovation. Building smarter algorithms isn't enough. We also need more reliable, efficient, and accessible energy systems. Because artificial intelligence can transform the economy. But it needs energy to do so. Over the past decade, we competed to develop better algorithms. Over the next decade, we'll also compete to build the infrastructure capable of running them. And I want to end with a question. If two countries have access to equally advanced artificial intelligence models, but one can operate its infrastructure using abundant, reliable, and affordable electricity while the other faces constant energy constraints... Which country will have the greater long-term economic advantage? Perhaps the next technological revolution won't be decided exclusively inside artificial intelligence laboratories. It will also be decided across electrical grids, power generation facilities, and the factories supporting the digital world. The future of artificial intelligence doesn't depend only on how much intelligence we can create. It also depends on how much intelligence we can afford to run.

The Real Luxury of 2030 Might Be Human Attention. For decades, we've used technology to eliminate human involvement from our daily lives. We automated banking. We automated shopping. We automated customer service. We automated reservations, payments, communications, and a significant part of our business relationships. And now, with artificial intelligence, we're trying to automate almost every interaction imaginable. But I believe we're overlooking an extraordinary economic paradox. The easier it becomes to replace human interaction with artificial intelligence, the more valuable genuine human attention could become. We're building a world where machines will be available 24 hours a day. Yet in that same world, getting someone to dedicate 30 minutes of genuine attention to you might become one of the most exclusive services available. And this could transform entire industries. Let me explain why. 1. For Years, Technology Made Efficiency Our Obsession The recent history of the internet has been shaped by one simple idea. Reduce costs. Eliminate intermediaries. Accelerate processes. Automate operations. Do more with fewer people. A company that once needed 100 employees to manage certain operations can now perform part of that work with a much smaller team. A chatbot can answer thousands of questions simultaneously. An automated system can manage reservations without human intervention. An AI agent can analyze documents, generate proposals, and respond to emails. From an economic perspective, this makes sense. Companies want better margins. Customers want faster answers. Investors want scalable businesses. But there's a question that rarely appears in these conversations. Are we confusing a more efficient experience with a necessarily better experience? Because not every human interaction is a simple transaction. Some require empathy. Others require trust. Others demand judgment, accountability, and an understanding of context. And sometimes, people simply want to feel heard. Efficiency has value. But so does attention. 2. Artificial Intelligence Is Creating an Artificial Abundance of Attention Imagine the world in 2030. You could have an assistant available at any hour. A tutor that explains mathematics for five hours without getting tired. A virtual advisor that answers financial questions. An agent that organizes your travels. A digital trainer that adapts your routines. A system that remembers your preferences and personalizes its responses. Everything personalized. Everything immediately available. Everything potentially offered at a fraction of the cost of hiring a person to perform the same tasks. This represents an enormous opportunity to democratize access to services that were previously too expensive. But it also introduces an interesting shift. For the first time, we could live in an economy where personalized responses are no longer scarce. Simulated attention can be produced at scale. Human attention cannot. A person still has only 24 hours in a day. They cannot have meaningful conversations with thousands of individuals simultaneously. They cannot multiply their time indefinitely. And they cannot be everywhere at once. Artificial intelligence can multiply the availability of answers, but it cannot multiply the hours in a human life. That difference could have significant economic consequences. 3. What Used to Be Normal Could Become a Premium Service Think about customer service. For a long time, speaking to a real person was a standard part of the experience. Today, many companies use automated systems to handle customer inquiries. In many cases, these systems work perfectly well. But we've all experienced a frustrating situation. You have a complicated problem. You contact a company. An automated system responds. You explain your situation. You receive a generic answer. You try explaining again. The system repeats information that doesn't solve your problem. And eventually, you start looking for one option: "Speak to a human." Now imagine that option is no longer available to every customer. Perhaps a company offers free automated support. But immediate human assistance is reserved for premium customers. I'm not suggesting this model is desirable in every industry. In essential services, it could create serious accessibility and fairness problems. But commercially, it raises an interesting possibility. Human attention could shift from being the standard service to becoming a competitive differentiator. And not just in customer support. It could happen in education. Consulting. Tourism. Professional services. Cultural experiences. Luxury retail. Even certain technology services. The question is whether consumers will be willing to pay more for genuinely human interaction. In some markets, they probably will. 4. The Next Billion-Dollar Business Could Sell Experiences That Cannot Be Automated For years, Silicon Valley taught us to build scalable businesses. Create a product. Automate distribution. Reduce marginal costs. Serve millions of users. But what happens when almost every company can automate similar processes? Automation stops being an exclusive advantage. It becomes a standard capability. And companies must find new ways to differentiate themselves. One possibility is offering experiences that depend precisely on what cannot be scaled easily. Human presence. Imagine an educational platform. It offers AI tutoring at a very affordable price. But it also allows students to book private sessions with experienced teachers. The customer isn't paying only for information. They're paying for experience, evaluation, judgment, and a relationship with a professional. Or consider tourism. An AI system can design an extraordinary itinerary. But a local guide can share personal experiences, interpret unexpected situations, and build a genuine connection with visitors. The value isn't just information. It's the shared experience. This could create a new category of hybrid businesses. Technology to scale efficiency. Humans to provide depth. And perhaps the smartest companies won't try to eliminate humans entirely. They'll use automation to give people more time for the interactions that truly matter. 5. The Economic Problem: Will Human Attention Become a Privilege? Here's where things get uncomfortable. If automated services become extremely cheap while human attention becomes relatively more expensive, we could end up with two types of experiences. One mass-market experience. Automated. Always available. Affordable. And another premium experience. Personalized. Limited. Delivered by human professionals. More expensive. In certain industries, different versions of this distinction already exist. We have personalized consulting, private healthcare, individual education, and exclusive experiences. Artificial intelligence could widen that gap in some markets. And that raises an important question. Do we want to build a society where speaking to a competent human being becomes an economic privilege? Technology should expand access to quality services. Not make human interaction inaccessible to those who need it most. That's why the challenge won't simply be deciding what we can automate. We'll also need to decide which interactions should preserve an accessible human alternative. Especially in healthcare, education, justice, and essential public services. 6. What If We're Measuring Human Value the Wrong Way? For decades, we've used productivity metrics to evaluate work. How many tasks did you complete? How many customers did you serve? How many documents did you process? How long did it take? How much revenue did you generate? Artificial intelligence can dramatically improve many of these indicators. But perhaps it will also force us to reconsider what creating value actually means. A teacher isn't valuable only because they transmit information. A doctor doesn't create value only by interpreting test results. A leader isn't important only because they assign tasks. And a friend isn't important because they can answer questions quickly. In many situations, human value includes something difficult to quantify. Trust. Responsibility. Experience. Commitment. Presence. Artificial intelligence can help people perform their roles more effectively. But not every dimension of a relationship can be reduced to productivity. Perhaps one of the biggest mistakes of our time is trying to measure every human interaction as if it were a business process. 7. My Prediction for 2030 I don't believe artificial intelligence will eliminate the need for human interaction. I believe it will change where, when, and why we value it. Automated services could become the default option for millions of routine tasks. People will use agents to manage information, solve simple problems, and execute operations. But at the same time, certain human experiences could acquire greater economic and emotional value. Professionals offering specialized judgment. Educators supporting individual learning journeys. Companies providing personalized attention. Communities built around authentic relationships. Services with clearly identifiable human accountability. That doesn't mean every human interaction will be better than an automated one. It also doesn't mean everyone will be willing to pay more for it. Quality, context, and individual needs will still matter. But I believe we'll see an interesting shift. For decades, companies competed to deliver increasingly faster services. In the future, some may compete to offer something completely different. Quality human time. 8. The Final Paradox We're creating machines capable of speaking with millions of people. Systems that never get tired. Assistants that never need sleep. Agents that can remember enormous amounts of information. And tools that can respond almost instantly. But as we move toward that future, something remains limited. Our time. Our attention. Our ability to build relationships. We can automate a response. We can automate a recommendation. We can automate a transaction. But a human relationship involves much more than exchanging information. Perhaps the next economic revolution won't simply be about making machines perform more tasks. Perhaps it will also be about discovering which human experiences deserve to be protected, valued, and strengthened. For years, we paid for technology that replaced human time. By 2030, we might pay more for technology that helps us reclaim it. And I want to end with a question. If artificial intelligence could provide faster, cheaper, and technically better answers than a person for many tasks... How much would you be willing to pay to continue being served by a human being? Because perhaps that answer will tell us far more about the future of the economy than any prediction about artificial intelligence.

Elon Musk Isn't Trying to Win Today's Competition. He's Trying to Build Tomorrow's World. I just finished watching Elon Musk's latest presentations, and something made me think deeply. I believe we're underestimating the scale of what he's trying to build. We're not just talking about electric vehicles, social media, artificial intelligence, or rockets. We're talking about something much bigger: the transition toward a world that, just a few years ago, seemed like science fiction. And what's fascinating is that many people are still evaluating Elon Musk using the rules of traditional industries. First, Challenging Existing Systems When Donald Trump returned to the White House, Musk became involved in an initiative aimed at reviewing government spending, reducing bureaucracy, and questioning structures that had operated the same way for decades. The DOGE initiative was controversial. Its methods, results, and reported savings have all faced scrutiny. But there's something interesting here from a technological perspective. The idea of examining massive systems and asking why they still operate the way they do. In software development, we do this constantly. We analyze processes, eliminate unnecessary dependencies, and design better architectures. Why shouldn't we also question the systems that manage entire countries? Of course, governing a country involves human and social responsibilities that go far beyond optimizing code. But the debate about modernizing institutions is only beginning. Then Came the Transformation of X Many people thought buying Twitter for $44 billion was insane. Today, we're watching a platform attempting to evolve beyond traditional social media. Creator monetization, video, artificial intelligence, payments, and new digital tools. Not all these initiatives are complete, and not all have proven successful. But the vision is clear. X doesn't want to remain just another social media app. It wants to become an ecosystem. And that raises an interesting question. How many technology companies are still competing to improve individual features while others are trying to reinvent the entire product? Tesla vs. BYD: Are We Watching the Wrong Competition? Over the past few years, we've witnessed the extraordinary growth of BYD in China. Its electric vehicles have become increasingly competitive in pricing, manufacturing, and technology. In 2025, BYD surpassed Tesla in global sales of fully electric vehicles. That's a remarkable achievement and a demonstration of China's industrial and technological capabilities. But I think there's another way to look at this competition. What if Tesla's ultimate goal is no longer simply to manufacture better cars? While the market compares prices, batteries, driving range, and sales volumes, Tesla is attempting to build a technological platform around autonomous driving, artificial intelligence, and robotics. That doesn't mean Tesla has already won this race. It means the company is attempting to compete in an entirely different category. And that changes the conversation. The Future Won't Just Be Electric. It Will Be Autonomous. Imagine a city twenty years from now. Cars moving without human drivers. Traffic systems coordinated by artificial intelligence. Robots performing physical tasks. Vehicles capable of maneuvers we currently associate with science-fiction movies. Operating systems that understand our needs before we even open an application. Cities where mobility, energy, and infrastructure operate as interconnected intelligent systems. This future still faces enormous technical, regulatory, and safety challenges. But it no longer feels like a fantasy completely disconnected from reality. And the ambitious technologies Tesla has explored for the Roadster, including potential SpaceX-inspired propulsion systems, reflect that willingness to challenge conventional engineering limits. An experimental demonstration is not the same as a product ready for millions of customers. However, exploring such possibilities can change our perception of what technology might eventually achieve. True Innovation Isn't About Beating Your Competitor. Here's my most important reflection. We've become obsessed with comparing companies. Tesla vs. BYD. X vs. YouTube. OpenAI vs. Google. SpaceX vs. traditional aerospace companies. But the most important technological transformations don't always come from whoever manufactures the cheapest product or sells the most units. Sometimes, they come from those willing to question the entire category. When everyone is manufacturing cars, someone imagines autonomous mobility systems. When everyone is building social networks, someone attempts to create a platform integrating multiple services. When everyone is developing applications, someone asks whether we'll even need to open applications in the future. Innovation isn't simply about improving what already exists. It's about building what doesn't exist yet. And that requires an extraordinary combination of engineering, capital, talent, vision, and perseverance. We're Entering a New Era. I don't believe Elon Musk has all the answers. And I certainly don't believe every promise he makes will become reality. Technology history is full of ambitious projects that failed, arrived too early, or required decades to become practical. But there's something I consider fundamental. We need more people and companies willing to build technologies that transform the lives of future generations. Because a time will come when producing software becomes incredibly easy. Artificial intelligence will allow us to develop products, automate businesses, and replicate technologies at an extraordinary speed. And when everyone can produce, the real difference will be the ability to imagine. The world doesn't just need more products. It needs new possibilities. Perhaps we're too busy watching who's winning today's competition while others are trying to define the rules of tomorrow's. And I want to end with one question: Are we building the future, or are we simply perfecting the past?

True innovation is what sets us apart. The country that pioneers flying cars, revolutionary transportation networks, and breaks free from traditional paradigms will build an economy that other nations will always try to replicate, but may never surpass.

Elon Musk @elonmusk

a day ago

Turns out the future does have flying cars

42K 94K 780K 64.9M 61K

GM 😁

What If the Future of Blockchain Doesn't Need Speculative Cryptocurrencies? Let's talk about an uncomfortable possibility for the crypto industry. What if the next major blockchain revolution doesn't come with a new token, a presale, an airdrop, or a promise of 100x returns? We've spent more than 15 years talking about decentralization. Financial freedom. Transparency. Removing intermediaries. Digital ownership. A new infrastructure for the internet. And yet, much of the industry's attention still revolves around one question: How much can this coin go up? I believe we're overlooking something far more important. Blockchain technology could have an extraordinary future even if thousands of cryptocurrencies disappear. Let me explain why. 1. We Confused the Technology With the Financial Asset When Bitcoin emerged in 2009, it introduced a way to transfer digital value without relying on a central authority to validate every transaction. Ethereum expanded those possibilities through smart contracts. Then came decentralized applications, decentralized finance, NFTs, and entirely new blockchain infrastructures. These developments represented genuine technological progress. But something interesting happened along the way. We started evaluating technological projects primarily through the prices of their tokens. A project announces a new infrastructure. When is the token launching? A team develops a decentralized application. Will there be an airdrop? A startup introduces an innovative solution. What's its potential market capitalization? At what point did we stop asking what problem a technology solves and start asking how much money we could make speculating on it? A token's price can reflect expectations about a project. But it doesn't necessarily prove real adoption, economic utility, or a sustainable business model. And that distinction matters. 2. Not Every Blockchain Application Needs Its Own Cryptocurrency Imagine a company using blockchain to verify academic credentials. A university issues a digital certificate. An employer can independently verify its authenticity. The record is verifiable and difficult to tamper with. Now here's the question. Do we need to create a cryptocurrency called UNIVERSITY for this system to work? Not necessarily. We can use existing blockchain infrastructure, pay the required transaction fees, and build an experience where users don't even need to understand how blockchain works. Consider other examples. Digital identity verification. Supply chain traceability. Document certification. Property records. International payments. Programmable contracts. Digital content provenance. Many of these applications could operate using existing networks, stablecoins, or traditional payment mechanisms. That doesn't mean native tokens have no utility. In certain networks, they play fundamental roles in security, economic incentives, and protocol operations. But there's an enormous difference between needing an asset to secure a network and creating a token simply because we want to finance or promote an application. The question shouldn't be how to tokenize every product. It should be whether a token provides utility that couldn't be delivered more simply without one. 3. The Crypto Industry May Be Confusing Financial Activity With Technological Adoption Think about a traditional application. How do we measure its success? Returning users. Paying customers. Revenue. Retention. Problems solved. Customer satisfaction. Now think about certain crypto projects. Trading volume. Market capitalization. Liquidity. Number of token holders. Speculative activity. These metrics can be relevant, especially for financial protocols. But they don't always represent actual product adoption. A token can generate millions of dollars in daily trading volume without having an application used by people outside the speculative ecosystem. And a blockchain application can solve an important problem without generating significant trading volume. These are two very different realities. I'm not suggesting that all speculation is inherently negative. Financial markets also provide liquidity, enable price discovery, and can help finance innovation. The problem begins when speculation becomes the primary objective and the product becomes secondary. Because at that point, we stop building technology to solve problems. And we start building narratives to attract buyers. 4. What If Real Adoption Begins When Blockchain Becomes Invisible? Remember what using the internet was like decades ago. Users often needed to understand technical configurations, connections, and complicated processes to access services that seem completely normal today. Over time, the infrastructure became invisible to most people. Today, we use applications without constantly thinking about TCP/IP, DNS, or the servers processing our requests. Could something similar happen with blockchain? Imagine someone sending money from Peru to Europe. They open an application. Enter the recipient. Confirm the amount. The money arrives. No worrying about networks, bridges, complicated wallet addresses, or gas fees. Behind the scenes, the infrastructure might use stablecoins and blockchain. But the user doesn't need to know. They only need the service to be fast, secure, affordable, and reliable. The same could happen with identity systems, digital records, and automated contracts. Perhaps blockchain's greatest success will arrive when millions of people use the technology without even realizing they're using it. 5. The Real Challenge Is Proving When Decentralization Actually Creates Value Here's a question the industry should ask more frequently. Why use blockchain instead of a traditional database? Not everything needs decentralization. Not every record needs to be public. Not every operation requires distributed consensus. In some cases, a conventional database is faster, cheaper, and easier to maintain. And we should acknowledge that. Blockchain makes sense when it provides properties that genuinely matter. For example: Independent verification between organizations that don't fully trust one another. Resistance to unilateral modification. Digital asset transfers without depending on a single operator. Transparent execution of programmable rules. Interoperability between participants in an open ecosystem. But these advantages also come with costs. Scalability. Privacy. Security. User experience. Governance. Regulatory compliance. We cannot ignore these challenges simply because a technology is decentralized. Decentralization shouldn't be a marketing strategy. It should be a solution to a specific problem. 6. What Should Blockchain Developers Build Toward 2027? I believe the industry's next chapter should focus less on launching assets and more on developing sustainable infrastructure and applications. One major opportunity is payments. Stablecoins make it possible to represent and transfer value through blockchain networks. But significant challenges remain around banking integration, regulation, consumer protection, and access to local currencies. Solving these problems could create far more economic value than launching another cryptocurrency without a clear purpose. Another opportunity is digital identity. In an internet where artificial intelligence can generate synthetic images, voices, and documents, we need better mechanisms for verifying credentials, provenance, and information. Blockchain could be part of those solutions, although it isn't the only possible technology. There's also an opportunity in infrastructure for AI agents. Imagine autonomous agents capable of making payments, purchasing services, and executing programmable agreements. Digital payment systems and smart contracts could facilitate some of these operations. But we would also need permission controls, recovery mechanisms, financial limits, and clear accountability for automated actions. And finally, there's real-world asset tokenization. Not as an automatic promise of investment returns. But as a potential way to improve certain processes involving asset issuance, transfers, settlement, and administration. Provided that an appropriate legal framework recognizes the underlying rights. The opportunity is enormous. But so is the responsibility to build systems that work beyond communities of speculators. 7. My Prediction for 2027–2030 I don't believe cryptocurrencies will disappear. I also don't believe every token is unnecessary. Bitcoin, Ethereum, Solana, and other networks have different economic models, communities, and purposes. Speculation will probably continue to exist. Just as it exists in stocks, commodities, and other financial markets. But I believe we'll see an increasingly important separation between two sides of the industry. On one side, digital asset markets. Trading. Liquidity. Speculation. Investment. On the other side, applications using blockchain as infrastructure. Payments. Identity. Certification. Asset transfers. Automation. Programmable financial services. Both worlds can coexist. However, they won't necessarily have the same users, success metrics, or business models. And perhaps many of the companies creating meaningful value with blockchain will never need to launch their own cryptocurrency. 8. The Question We Should Be Asking For years, we've debated which blockchain is faster. Which network has the lowest fees. Which token will reach a certain market capitalization. Which ecosystem will attract the most liquidity. But perhaps we're ignoring the most fundamental question. What important problem have we solved for someone who has never purchased a cryptocurrency? Because that person represents a much bigger opportunity than endlessly competing for the same speculative capital. If we want blockchain to become global infrastructure, we need to build products that can survive even when market excitement disappears. Products people use because they're better. Not because they expect an airdrop. Not because they believe a token will multiply in price. Not because an influencer promised them extraordinary returns. But because the technology genuinely improves their lives or businesses. Maybe the future of blockchain isn't about creating millions of new cryptocurrencies. Maybe it's about building millions of useful applications on infrastructure that already exists. And I want to finish with a question for developers, investors, and crypto founders: If your project's token stopped trading tomorrow, but the infrastructure continued operating, would users still use your product? If the answer is yes, you're probably building something valuable. If the answer is no, perhaps you were never building a product. You were building a market. And those are not the same thing.

SaaS Isn't Dead. But Your Business Model Might Be. What Should We Build in 2027? For more than a decade, building a SaaS company was one of the most attractive business models on the internet. You developed an application. You acquired users. You charged $19, $49, or $99 per month. And if you managed to retain your customers, you could build a business with recurring revenue, predictable cash flow, and potentially extraordinary margins. The formula was relatively simple. Acquire customers. Increase MRR. Reduce churn. Scale. Thousands of entrepreneurs built successful companies following this model. But I believe we're entering a period where the economics of software are about to change dramatically. Not because subscriptions are disappearing. But because artificial intelligence is fundamentally changing what customers are willing to pay for. And this raises a question every developer, indie hacker, and startup founder should be asking: What should we actually build in 2027 if AI can increasingly replicate the software we're trying to sell? Let's dig deeper. 1. When Building Software Becomes Easier, Selling Features Becomes Harder For years, one of the biggest competitive advantages of a technology startup was its engineering capability. Building a CRM, analytics platform, automation tool, or enterprise application required developers, designers, infrastructure, and months of development. Today, AI coding agents are dramatically accelerating that process. And there's an interesting signal emerging from the market. According to McKinsey's 2026 research on artificial intelligence, 32% of respondents reported that their organizations had decided not to purchase at least one software product or feature because they could build it internally using coding agents. Think about what that means. For decades, companies purchased software because developing it internally was too expensive, too complicated, or too time-consuming. Now, some organizations are beginning to reconsider that decision. Why pay a recurring subscription for a relatively simple feature if your engineering team can build a customized alternative? Of course, generating code isn't the same as maintaining reliable software. Security, compliance, integrations, testing, and long-term maintenance still matter. But the economic equation is changing. And when the cost of creating software decreases, the value of certain standalone features can decrease with it. The problem isn't that software is becoming worthless. The problem is that software features alone are becoming less defensible. 2. The Next SaaS Crisis Could Be a Crisis of Differentiation Open Product Hunt. Every week, hundreds of new products compete for attention. AI writing assistants. Content generators. Analytics dashboards. Marketing automation tools. AI-powered CRMs. Productivity applications. Research assistants. Many of these products are genuinely useful. But there's an uncomfortable question founders need to ask themselves. What happens when the AI model powering your application introduces the same functionality directly? What happens when your competitors can reproduce your core features in a weekend? What happens when customers discover they can accomplish similar tasks using tools they're already paying for? Where does your competitive advantage go? I'm not suggesting every AI wrapper will fail. Some wrappers become valuable companies because they offer exceptional user experiences, specialized workflows, distribution, integrations, or customer relationships. But simply connecting an API to a beautiful interface is rarely enough to create a durable business. And I believe this will become increasingly obvious in 2027. The market won't reward every application just because it uses artificial intelligence. It will reward companies that deliver value customers cannot easily obtain elsewhere. 3. We're Moving From Software as a Service to Outcomes as a Service This is where things become interesting. Traditional SaaS primarily sells access to software. You pay for seats. You pay for features. You pay for storage. You pay for premium functionality. But AI agents introduce a different economic model. What if customers paid for completed work instead? Imagine a company paying $500 per month for a customer support platform. The software helps employees manage conversations. Now imagine an AI system that autonomously resolves hundreds of customer requests. Should the company pay for access to another dashboard? Or should it pay for successfully resolved requests? Consider accounting. Instead of selling a tool that helps accountants process invoices, imagine selling a system that processes invoices, validates information, detects errors, and updates financial records. The customer isn't necessarily interested in the software itself. The customer wants the work completed. This is the opportunity. The next generation of software companies may increasingly sell completed work rather than access to tools. But there's an important distinction. Not every outcome is easy to measure. Not every task can be automated reliably. And not every business can afford the unpredictable costs of autonomous agents. That's why pricing models will need to evolve alongside the technology. 4. What Products Should Developers Build for 2027? I believe several categories deserve serious attention. Vertical AI Agents Not another generic chatbot. Not another AI assistant that promises to do everything. I'm talking about specialized agents designed for specific industries. Healthcare administration. Logistics. Accounting. Insurance. Real estate operations. Legal workflows. Financial operations. These industries have complex processes, specialized terminology, regulatory requirements, and expensive operational inefficiencies. A product that understands a specific industry's workflows and reliably executes valuable tasks could become significantly more defensible than a generic productivity application. The opportunity isn't necessarily to replace entire teams. It's to automate repetitive work while preserving human oversight where judgment, accountability, and expertise matter. Autonomous Business Operations Imagine software that doesn't simply display information. It acts on it. A system receives a document. Extracts the relevant data. Validates the information. Identifies inconsistencies. Updates internal systems. Requests approval when necessary. And completes the workflow. No constant switching between dashboards. No repetitive manual data entry. No need for employees to coordinate every individual step. This is a different way of thinking about enterprise software. Instead of building another application employees must learn to operate, we're building systems that perform portions of the work themselves. Infrastructure for AI Agents Here's an opportunity that I think many developers are underestimating. If companies begin deploying dozens or hundreds of AI agents, who will manage them? Who controls their permissions? Who monitors their behavior? Who tracks their costs? Who verifies their outputs? Who prevents agents from accessing sensitive information? Who determines which actions require human approval? As autonomous systems become more capable, businesses will need infrastructure to manage reliability, security, observability, governance, and spending. Some of the most valuable opportunities may come from solving problems created by AI adoption itself. Revenue Automation Companies don't necessarily want more marketing tools. They want more revenue. They want qualified leads. They want fewer cancellations. They want higher customer retention. They want unpaid invoices recovered. They want sales processes that operate more efficiently. This creates opportunities for products that connect AI capabilities directly to measurable business outcomes. But the key is attribution. If a product claims to increase revenue, it needs a credible way to demonstrate that contribution. Otherwise, outcome-based pricing becomes another marketing promise. AI Cost Optimization There's another emerging challenge. AI makes software easier to build, but operating AI-powered products introduces variable costs. Inference. Tokens. Compute. Model routing. Storage. Monitoring. Retries. Agent execution. A company can generate impressive revenue while quietly destroying its margins through inefficient AI usage. Products that help businesses optimize model selection, manage inference spending, and improve cost efficiency could become increasingly valuable. Because in the AI economy, revenue growth without cost discipline can become dangerous. 5. How Should We Monetize These Products? This is where I think founders need to rethink the traditional SaaS playbook. For years, we focused almost exclusively on monthly subscriptions. And subscriptions remain powerful. Predictable recurring revenue is valuable. But charging a fixed monthly fee for unlimited AI usage can become a serious financial risk. Every agent execution has a cost. Every API call consumes resources. Every automated workflow may involve multiple models, external services, and infrastructure. The more successful your product becomes, the more expensive it may become to operate. This creates a very different economic reality from traditional SaaS products with relatively low marginal usage costs. So what should we do? I believe hybrid pricing models will become increasingly important. A monthly platform subscription combined with usage-based billing. Enterprise contracts with defined execution limits. Pricing based on successfully completed tasks. Consumption credits. Or performance-based fees when results can be independently verified. For example: An invoice automation platform could charge $199 per month plus a fee for every successfully processed document. A sales automation product could charge a monthly platform fee plus an agreed amount for qualified opportunities. An enterprise agent platform could charge for execution capacity, monitoring, and security features. The important thing isn't choosing the trendiest pricing model. It's aligning the price with the value delivered while protecting gross margins. Because generating $100,000 in monthly revenue means very little if delivering the service costs $95,000. And a subscription model that looks attractive on a Stripe dashboard can still be economically unsustainable. 6. The Future of MRR Might Look Different Let's imagine two startups. Startup A sells a generic AI productivity tool for $15 per month. It needs 2,000 paying customers to generate $30,000 MRR. Startup B sells a specialized AI workflow system to businesses. It charges $200 per month plus an average of $100 in monthly usage. It needs 100 active customers to generate the same $30,000 MRR. Both models can work. Neither is automatically superior. Startup B may face longer sales cycles, more complex onboarding, higher support requirements, and greater customer concentration risk. Startup A may have lower acquisition friction and a much larger potential audience. But the comparison illustrates something important. You don't necessarily need thousands of subscribers to build a meaningful recurring-revenue business. You need customers who receive enough value to justify what they pay. And increasingly, that value may come from work completed rather than features unlocked. 7. My Prediction for 2027 I don't believe SaaS will disappear. I believe its value proposition will evolve. From tools to specialized operational systems. From features to workflows. From active users to completed tasks. From exclusively fixed subscriptions to hybrid monetization. From generic AI applications to products deeply integrated into specific industries. And from selling software access to delivering measurable business value. But there's something even more important. Artificial intelligence is dramatically increasing the supply of software. It is not necessarily increasing demand at the same rate. We can build ten times more applications without creating ten times more problems worth solving. And that's the fundamental challenge facing the next generation of technology entrepreneurs. The ability to build is becoming increasingly accessible. The ability to identify valuable problems, acquire customers, operate efficiently, and maintain competitive advantages remains difficult. That's why I believe the most successful founders of 2027 won't necessarily be those launching the most products. They'll be the ones who understand their customers better than everyone else. Who build reliable systems. Who solve expensive problems. Who create measurable economic value. And who design business models capable of surviving technological change. 8. The Question Every Founder Should Ask Before Building Another SaaS Before writing another line of code, ask yourself: What recurring problem am I solving? How much does that problem cost the customer? Can artificial intelligence solve it reliably? How will I measure the results? Why would customers choose my product over a generic AI agent? What prevents competitors from replacing me? Can I deliver the service profitably at scale? And most importantly: Will customers still have a reason to pay for my product when AI models become ten times more capable and significantly cheaper? Because that moment may arrive faster than many founders expect. And when it does, simply having an AI-powered product won't be a competitive advantage. It will be the baseline. The real opportunity won't be building another subscription-based application. It will be creating systems that customers depend on because they consistently solve important problems. We shouldn't stop building SaaS. We should stop assuming that recurring billing automatically creates recurring value. The next generation of technology companies won't be defined only by how many subscriptions they sell. They'll be defined by how much useful work they can deliver, how reliably they deliver it, and whether they can do so profitably. SaaS isn't dead. But the era of charging recurring fees for easily replaceable features is under pressure. And that might be one of the biggest opportunities developers and founders have ever had. Keep building. But build something people will have a reason to keep paying for.

Everyone Is Building Apps. But Who Is Building Businesses That Actually Matter? Something has been catching my attention lately whenever I open X, Product Hunt, GitHub, or almost any startup community. Everyone is building something. A new AI application. Another SaaS product. An autonomous agent. A productivity tool. A platform that promises to save time. Another that promises to increase revenue. And another powered by exactly the same AI models, just wrapped in a different interface. Everyone is launching. Everyone is sharing their products. Everyone is trying to acquire their first users. And apparently, everyone is building the next big startup. But there's a question I find much more interesting. If building software is becoming easier than ever, how are we supposed to convince people to choose our product over thousands of alternatives? Because I believe we're entering a new phase of the digital economy. A phase where building software is no longer necessarily the hardest part. The real challenge is capturing attention, building trust, identifying a market, and turning users into customers who actually want to stay. 1. We're Entering an Era of Software Abundance For decades, building an application was expensive. You needed technical knowledge. A development team. Designers. Infrastructure. Capital. And months of work. Today, thanks to tools like Codex, Claude Code, and other AI coding agents, the cost and time required to build certain types of applications have decreased significantly. A single developer can build an MVP in days. Sometimes even hours, depending on the complexity. And that's extraordinary. But it's also changing market dynamics. When something becomes easier to produce, more people can produce it. And when supply increases, standing out becomes harder. Think about AI products. How many AI content generators exist today? How many document summarization tools? How many AI coding assistants? How many image and video generation platforms? We don't need to assume all these products are identical to recognize that many compete to solve similar problems. Users have more choices than ever. But they also have fewer reasons to try another application if their current solution already gets the job done. We're lowering the barriers to building software. But we're not necessarily lowering the barriers to acquiring customers. And that's a distinction many founders are discovering too late. 2. Are We Solving Real Problems or Inventing Problems to Justify New Products? Imagine I launch an AI application tomorrow that recommends which sneakers to wear based on the weather, my outfit, and where I'm going. Sounds interesting, right? I could connect a weather API. Integrate computer vision. Analyze photos. Generate personalized recommendations. Maybe even charge $5 per month. Technically, it's possible. But here's a much more important question. Does anyone actually need this application? And if they do, would they pay for it? Perhaps there's a real niche. Runners choosing shoes based on terrain, training intensity, and personal preferences. People looking for specialized footwear recommendations. Retailers trying to reduce returns through better sizing recommendations. Now we're talking about specific problems with potential economic value. But an application that simply tells me which sneakers match my outfit might be an interesting feature rather than a sustainable standalone business. Just because something can become an app doesn't mean it deserves to become a company. And I think we're forgetting that distinction. 3. The Rise of Micro-Niche Products One of the most interesting strategies for competing in saturated markets is focusing on highly specific customer segments. Instead of building another productivity platform for everyone, build something for a particular profession. Instead of launching another generic CRM, create one designed specifically for independent veterinary clinics. Instead of building another AI marketing platform, focus on small agencies managing campaigns for restaurants. Instead of competing directly with large enterprise software companies, solve a specific logistics problem for small importers. The competitive advantage doesn't necessarily come from using more advanced technology. It comes from understanding a particular group of customers better than everyone else. Their workflows. Their frustrations. Their budgets. Their existing tools. And the reasons they're still dissatisfied with available solutions. But we shouldn't romanticize micro-niches either. A market can be too small. Customers may have a genuine problem but no budget to solve it. And a highly specialized feature can eventually be incorporated into a larger platform. Finding a niche isn't simply about reducing the number of potential customers. It's about identifying a segment with a meaningful problem, genuine willingness to pay, and an efficient path to reaching buyers. 4. We're Confusing Differentiation With Adding More Features Look at how many software companies compete today. One platform announces AI integration. Another introduces autonomous agents. Another offers ten different models. Another adds workflow automation. Another launches advanced analytics. And another promises to combine everything into one dashboard. But does that actually represent differentiation? Not necessarily. If a feature can be easily replicated using an API, building a sustainable competitive advantage around that feature alone becomes difficult. Differentiation can come from somewhere else. Understanding an industry better. Delivering an exceptional user experience. Integrating deeply into business operations. Having access to valuable, legitimately obtained data that competitors cannot easily reproduce. Building trust. Creating a community. Or developing distribution channels that competitors don't have. A product with five features can be more valuable than one with fifty if it solves the customer's most important problem better. Differentiation isn't about doing more things than your competitors. It's about giving customers a compelling reason to choose you. 5. The Problem Many Founders Discover Too Late: Distribution This is where I believe one of the biggest changes is happening. For years, many entrepreneurs focused primarily on building the product. First, we develop. Then, we launch. Finally, we start thinking about marketing. But when thousands of alternatives exist, that strategy may no longer be enough. You can build an excellent application. Have a clean architecture. A beautiful interface. Competitive pricing. And still acquire almost no customers. Because nobody knows your product exists. Distribution isn't simply posting a link and waiting for registrations. It's understanding where your potential customers spend time, what problems they're trying to solve, and what messages make them trust a solution. It also means recognizing that different platforms serve different purposes. And this brings me to three platforms worth discussing. 6. X, TikTok, and Instagram: Where Should Startups Actually Focus? I think many developers and indie hackers are overly concentrated on X. And I understand why. X brings together communities around technology, artificial intelligence, cryptocurrency, startups, and investing. We can share what we're building. Connect with founders. Show development progress. Publish research. Get feedback. And interact directly with people interested in technology. But there's a problem. Just because other developers find your product interesting doesn't mean they're your customers. Imagine we're building a SaaS platform that helps small barbershops manage appointments. We launch it on X. We receive hundreds of likes. Other developers congratulate us. A few startup accounts repost it. Maybe we even get featured in a founder community. But how many barbershop owners actually saw our post? That's probably the metric that matters most. And this is where TikTok and Instagram may offer different opportunities. X: Building Authority and Reaching Specialized Communities X can be particularly valuable for developer tools, AI products, crypto projects, startup software, and certain B2B markets. It's a place to share technical insights, demonstrate expertise, and build professional relationships. But we need to measure how many of those interactions eventually become paying customers. TikTok: Demonstrating Problems and Solutions Visually TikTok can be powerful when a product's value can be demonstrated in seconds. Imagine an AI tool that transforms ordinary videos into advertising campaigns. Or an application that automates a repetitive task. Or a service that demonstrates a clear before-and-after transformation. The content doesn't need to explain the underlying architecture. It can simply show the problem and the result. However, views don't automatically translate into revenue. An audience can enjoy a video without having any intention of purchasing the product. Instagram: Building Identity, Trust, and Brand Recognition Instagram can be useful for visual products, consumer brands, creative professionals, and certain service businesses. Reels can demonstrate features. Stories can maintain relationships with followers. And a well-designed profile can communicate a brand's identity. But again, the right platform depends on the buyer. A developer infrastructure tool may not have the same potential on Instagram as an AI design application for content creators. There is no universally superior platform. The best marketing channel isn't the one that generates the most views. It's the one that consistently brings you profitable customers. 7. Marketing Shouldn't Begin After the Product Is Built Perhaps one of our biggest mistakes as developers is treating marketing as something that happens after launch. First, we spend three months building. Then we create a landing page. Launch on Product Hunt. Share it on X. And hope users appear. But what if we reversed the process? Before building, we could research communities. Interview potential customers. Analyze existing alternatives. Understand how much people already spend solving the problem. Identify the language they use to describe their frustrations. And test marketing messages before developing the entire application. We might discover that the problem we wanted to solve wasn't important enough. Or that a more specific need existed. Or that the actual buyer was different from the end user. That information could save weeks of development. And help us build something significantly more relevant. This doesn't mean we should stop coding. It means we should validate our assumptions before investing too much in them. 8. What Should We Actually Measure? Startup communities are full of metrics. Product launches. Registrations. Followers. Views. Impressions. MRR. But isolated numbers can tell incomplete stories. An application might acquire 10,000 registrations and retain almost nobody. A video might generate one million views without producing a single sale. A campaign might acquire hundreds of subscribers while costing more than those customers are expected to contribute in gross profit. And a product with only 100 customers might be a healthy business if it solves an important problem and maintains strong margins. We need to pay more attention to retention, activation, paid conversion, gross margins, and customer acquisition costs. We also need to understand why users leave. Why does someone try our application once and never return? Did the product fail to solve their problem? Was the experience too complicated? Did they find a better alternative? Or did they simply never need what we built? Those answers can be more valuable than thousands of likes. 9. AI Is Changing Competition, but It Doesn't Eliminate Market Economics If everyone can access advanced AI models, generate interfaces, and automate development, many technical capabilities become more accessible. That doesn't mean engineering stops mattering. Building secure, reliable, and efficient systems remains difficult. But it does mean that a beautiful interface and a few AI features may no longer be enough to stand out. Companies will need advantages that competitors cannot easily replicate. Customer relationships. Specialized knowledge. Deep integrations. High-quality data. Operational expertise. Reputation. Distribution. And consistent execution. Not every advantage matters equally to every business. But a startup should be able to explain why a customer would choose it even if ten similar alternatives appeared tomorrow. If our only answer is that we use a more advanced AI model, we should ask ourselves what happens when competitors gain access to that same model. 10. Maybe the Future Doesn't Belong to Founders Who Launch the Most Apps Imagine two entrepreneurs. The first uses AI agents to launch twenty products in three months. Twenty landing pages. Twenty repositories. Twenty value propositions. And twenty marketing campaigns competing for attention. The second spends several weeks researching a specific industry. Talks to potential customers. Identifies an expensive operational problem. Builds a focused solution. Acquires five paying customers. Learns from their feedback. And continuously improves the product. We can't guarantee which entrepreneur will be more successful. The first might discover an exceptional opportunity through rapid experimentation. But the second is developing deep knowledge about a particular market. And that knowledge can become a meaningful competitive advantage. The goal shouldn't necessarily be launching as many applications as possible. It should be developing a repeatable process for discovering needs, building solutions, and acquiring satisfied customers. Speed matters. Experimentation matters. But both need direction. 11. What Happens in 2027 and Beyond? I believe we may see an increasingly clear distinction between two types of software businesses. On one side, small applications that are relatively easy to reproduce and built on widely available services. Many will compete on price, user experience, and distribution. Some will become profitable businesses. Others will struggle with retention and differentiation. On the other side, companies using artificial intelligence to solve specific problems with greater depth. They won't necessarily have the most features. They may simply understand their markets better. Build integrations that are difficult to replace. Become an important part of their customers' operations. And develop efficient, repeatable distribution channels. This isn't an inevitable prediction. It's a hypothesis worth observing and testing. Because as building software becomes more accessible, the places where economic value concentrates may also change. Final Thoughts I don't believe we should stop building. Quite the opposite. Artificial intelligence is allowing more people to participate in creating technology. And that's an extraordinary opportunity. But perhaps we need to change the questions we're asking. Instead of asking what application we can build this weekend, we should ask what problem we've identified that existing solutions still fail to solve well enough. Instead of obsessing over thousands of impressions on X, we should investigate where our actual buyers are. Instead of adding AI features simply because they're trending, we should verify whether they improve outcomes for users. Instead of chasing every possible niche, we should find markets we understand and where we can build sustainable advantages. And instead of measuring progress by the number of products launched, we should measure the value we actually deliver. Because we're entering an era where almost anyone with access to AI tools can become a software creator. But creating software and building a company remain two very different things. The next great competitive advantage may not be knowing how to build an application. It may be knowing who to build it for, how to reach those people, and why they should keep using it when a hundred alternatives exist. Code is becoming more accessible. Attention remains limited. Trust must be earned. And customers don't pay us because we used the most advanced technology. They pay us because we solve something that matters to them. So let's keep building. But before launching our next application, let's ask ourselves one question: If our product disappeared tomorrow, would anyone genuinely miss it? Because perhaps that answer tells us more about the future of our startup than any launch metric ever could.

Treat @X growth as an experiment, not a daily obligation. Set a posting rhythm you can sustain, review what earns meaningful replies or follows, and keep goals beyond the platform. If a ranking update erases progress, your system should still be worth using.