According to Gartner: 40% of All Enterprise Applications Will Contain AI Agents by 2026
According to a recent Gartner forecast, around 40% of all enterprise applications will contain task-specific AI agents by the end of 2026. For comparison: in 2025, it was less than 5%. This is not a slow evolution, this is a leap.
And right in the middle of this leap stands an open-source project that has collected over 200,000 GitHub stars in just a few months, making it the fastest-growing GitHub project of all time: OpenClaw. Or as the community affectionately calls it: Molty.
OpenClaw is not another AI chatbot toy. It is a personal assistant that lives on your machine 24 hours a day, can read and write files, execute shell commands, research on the web, and is reachable via WhatsApp, Telegram, Slack, Discord, or Teams. Peter Steinberger, the Austrian developer behind the project, puts it simply: "What I want is to change the world, not build a large company."
In this article, we look at what OpenClaw exactly is, how it works, where it comes from, and what it means for the future of personal AI agents. No scaremongering, no artificial hype. Just an honest assessment from someone who deals with AI integration and software development on a daily basis.
What Is OpenClaw?
OpenClaw is an open-source AI assistant that runs locally on your computer. Unlike ChatGPT or other cloud-based chatbots, OpenClaw actually has eyes and hands: it can see your screen, create and edit files, execute terminal commands, and research on the internet.
Imagine you had a technically skilled colleague who is always available, never gets tired, and can familiarize themselves with new topics in seconds. That is roughly what working with OpenClaw feels like.
The project was created by Peter Steinberger, an Austrian software developer who previously founded PSPDFKit, a company for PDF technology used by developers worldwide. Steinberger is no newcomer. He is someone who has been building software for decades and understands exactly what developers and non-developers alike need in their daily work.
OpenClaw runs as a local process on your machine. It connects to various AI models, including OpenAI, Anthropic, and also local models via Ollama. Communication happens through a variety of channels: WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Microsoft Teams, and more. You can literally send your personal agent a WhatsApp message and get a qualified answer within seconds.
What Does OpenClaw Cost?
OpenClaw itself is completely free and open source. The source code is on GitHub, and anyone can download, install, and use the project. There are no hidden fees, no subscription model, and no premium plan.
What does cause costs, however, are the AI models working in the background. If you use OpenAI models like GPT-4 or Anthropic models like Claude, you pay the usual API fees directly to these providers. These typically range from a few cents to dollars per request, depending on complexity and model.
If you want to work completely free of charge, you can run OpenClaw with local models via Ollama. Then there are zero costs, although you need appropriate hardware for this, especially a powerful graphics card with enough VRAM. For simple tasks, models like Llama 3 or Mistral that run on consumer hardware are sufficient.
For professional use, you can expect costs of about 20 to 50 euros per month per user, depending on usage intensity. Compared to the productivity gains that many users report, this is a fraction of what you invest.
Use Cases: What Do You Use OpenClaw For?
The applications of OpenClaw are remarkably broad. Here are the most common use cases that have emerged in the growing community:
Software Development: OpenClaw can write, debug, and refactor code. It understands the context of your project, reads your files, and makes change suggestions that actually fit the existing code. For developers, it is like an additional team member who never takes a break.
Research and Summaries: You send OpenClaw a link or a topic, and it researches on the web, summarizes results, and delivers a structured overview. Particularly practical for market analyses, competitive research, or compiling information from various sources.
Documentation and Content: OpenClaw can create technical documentation, draft emails, write reports, or prepare presentations. It knows the context of your project and can create content that is actually relevant.
Automation of Routine Tasks: From organizing files to renaming photos to filling out forms. OpenClaw can execute shell commands and thus essentially automate anything you would otherwise do manually on your computer.
Personal Assistance: Scheduling, reminders, to-do lists, travel planning. Steinberger describes his vision like this: "My next mission is to build an agent that even my mum can use." And that is exactly what you feel with OpenClaw. It is not just designed for techies, but for anyone who wants a helpful digital assistant.
Data Analysis: OpenClaw can read CSV files, process data, create charts, and identify correlations. For smaller analyses, it replaces a lot of manual work in Excel or Python.
From Clawdbot to OpenClaw: The History
The story of OpenClaw is a typical open-source success story, just at a pace that has surprised even experienced developers.
The project started under the name Clawdbot, also known as Moltbot. The community affectionately called it Molty. Steinberger started it as a personal project: an AI assistant that can do more than just generate text. An agent that can actually work on your own machine.
Within a few months, the project literally exploded. Over 200,000 GitHub stars, more than 35,000 forks. This makes OpenClaw the fastest-growing project in GitHub's history. For comparison: projects like React or Vue.js took years to reach these numbers.
What drove this growth? Several factors come together. First: the demand for personal AI agents is enormous. People do not just want to chat with AI, they want AI to do things for them. Second: OpenClaw is actually useful. It solves real problems instead of just showing demos. Third: the open-source nature allows everyone to contribute, improve, and customize.
On February 14, 2026, came the news that surprised the tech world: Peter Steinberger joins OpenAI. Sam Altman, CEO of OpenAI, called Steinberger "a genius with amazing ideas about very smart agents interacting to do useful things for people."
What does this mean for OpenClaw? The project will not be shut down. On the contrary: it will be transferred to an open-source foundation and sponsored by OpenAI. This means OpenClaw gets professional infrastructure and funding while remaining open source and community-driven. For users and companies, this is the best possible news: the project becomes more stable and long-lived, not more dependent on a single person.
Installing and Setting Up OpenClaw
Installing OpenClaw is surprisingly straightforward, at least if you have basic computer skills. Here is an overview of the different methods:
For macOS and Linux: The quickest method is via the package manager. A single command in the terminal is enough to download and install OpenClaw. The official documentation on GitHub guides you through the process step by step.
For Windows: OpenClaw runs natively on Windows. Installation is done either via the Windows installer on the GitHub releases page or manually via the command line. Both methods are well documented.
As a Docker container: For companies wanting to run OpenClaw in a controlled environment, there are official Docker images. This is particularly practical for teams wanting to ensure a uniform configuration.
After installation, you connect OpenClaw to the desired AI model. This can be a cloud model from OpenAI or Anthropic, or a local model. Then you configure the communication channels: which messengers should be connected? Which directories may OpenClaw read? Which commands may it execute?
This configuration is deliberately kept granular. You can restrict OpenClaw so that it may only read certain folders, or configure it generously so that it has full access to the machine. For enterprise use, a more conservative approach with clear permission boundaries is recommended.
OpenClaw with Ollama: Using Local Models
One of the biggest advantages of OpenClaw is the support for local AI models via Ollama. Ollama is a tool that allows you to run large language models directly on your own machine without sending data to the cloud.
For German companies with strict data protection requirements, this is a decisive point. When the AI model runs locally, sensitive data never leaves your own machine. No cloud, no third-party providers, no GDPR issues with data processing in the USA.
The setup is simple: install Ollama, download a model (for example Llama 3, Mistral, or Qwen), and point to the local model in the OpenClaw configuration. Within minutes, you have a completely local AI agent that requires no external API calls.
Of course, local models are not as powerful as GPT-4 or Claude. But for many everyday tasks, they are perfectly sufficient. Research, summaries, simple code tasks, text editing: all of this works well with local models. For more complex tasks, you can configure OpenClaw to automatically fall back to cloud models when the local solution reaches its limits.
The hardware requirements for local models are manageable: a graphics card with at least 8 GB VRAM is sufficient for smaller models. For larger models like Llama 3 70B, you need 24-48 GB VRAM, or distribute the load to CPU and RAM, which is then slower.
The Future of Personal AI Agents
OpenClaw is not just a single project. It is a symptom of a much larger trend: the democratization of AI agents. We are moving away from a world where AI is a tool for specialists, toward a world where every person has a personal AI agent.
According to McKinsey, companies can achieve 20 to 30 percent efficiency gains through the automation of complex processes. And according to another study, 23% of surveyed companies already use AI agents. The trend is clear: personal AI agents are becoming the standard.
But what does personal AI agent actually mean? It does not mean that an AI makes your decisions. It means you have an assistant that takes the tedious work off your plate. The research nobody likes to do. The data entry that eats up hours. The emails you keep putting off. The documentation that always falls behind.
Gartner predicts that by 2028, around 15% of all daily business decisions will be made autonomously by agentic AI. That sounds like a lot, but when you look closer, these are mainly routine decisions: triggering orders, coordinating appointments, creating reports, answering standard inquiries. Exactly the things that cost people time without being intellectually challenging.
The crucial difference between a chatbot and a personal agent: a chatbot answers questions. An agent acts. It executes tasks, accesses systems, makes decisions within a given framework, and only contacts the human when it gets stuck. OpenClaw shows what this looks like in practice.
Why Steinberger Went to OpenAI
Peter Steinberger's move to OpenAI on February 14, 2026, was not a surprise for people who had been following the project's development. OpenClaw proved that the demand for personal AI agents is gigantic. And OpenAI recognized that Steinberger brings exactly the vision and technical ability they need for their next big step.
Sam Altman described Steinberger as "a genius with amazing ideas about very smart agents interacting to do useful things for people." This is not marketing speak. OpenClaw showed that a single developer with the right vision can create a product that excites millions of people.
What does this mean concretely? Steinberger will work on the next generation of AI agents at OpenAI. The experiences from OpenClaw flow directly into OpenAI's products. At the same time, OpenClaw continues to exist as an open-source project and is even strengthened by the new foundation and OpenAI's sponsorship.
For the community, this is a win on both sides: OpenClaw gets professional support and resources. And OpenAI gets a developer who has proven that he understands personal AI agents like hardly anyone else.
Steinberger himself stays true to his principle: "What I want is to change the world, not build a large company." Whether at OpenAI or as an open-source developer, his goal is the same: to make AI agents so accessible and useful that they improve the lives of millions of people.
What Does This Mean for German Companies?
Now it gets concrete. What do OpenClaw and the trend toward personal AI agents mean for companies in the DACH region?
First, the sobering number: under 3% of German companies currently use AI agents. In international comparison, there is catching up to do. The reasons are varied: data protection concerns, lack of expertise, uncertainty about ROI, and a certain skepticism toward the AI hype.
This skepticism is partly justified. Gartner predicts that by the end of 2027, more than one in three AI agent projects will be discontinued due to rising costs and unclear ROI. This means: not every AI agent makes sense. Not every company needs one immediately. And those who blindly jump on the bandwagon without a clear strategy will burn money.
But the companies that do it right achieve significant results. McKinsey reports 20-30% efficiency gains through the automation of complex processes. The question is not whether personal AI agents work. They do. The question is whether your company is ready to use them meaningfully.
Data Protection as a Competitive Advantage: German companies have a natural advantage when it comes to data protection. The strict GDPR culture has led companies here to handle data more sensitively than elsewhere. With OpenClaw and local models via Ollama, you can run AI agents that do not send data to the cloud. This is not only GDPR-compliant but also a selling point to customers who worry about their data.
Mittelstand and AI Agents: Especially for the Mittelstand (medium-sized enterprises), personal AI agents offer enormous potential. Small teams can accomplish tasks with AI support that large corporations have entire departments for. A five-person team with a well-configured AI agent can handle research, documentation, customer communication, and data analysis at a level that would otherwise only be possible with significantly more personnel.
Gradual Introduction: The smart approach is not to immediately overhaul the entire company. But to start small. Set up an AI agent for a specific task, for example for the daily summary of industry news, or for the pre-qualification of customer inquiries. Measure what it brings. And then gradually expand.
Realistic Expectations: An AI agent does not replace an employee. It makes existing employees more productive. This is an important difference. Those who approach it expecting AI to replace half the workforce will be disappointed. Those who recognize that AI can take over the annoying 30% of every job that really nobody likes to do will be thrilled.
Cost-Benefit Analysis: For a typical SME with 10-50 employees, the numbers look like this: setup and configuration of an AI agent costs a one-time fee between 2,000 and 10,000 euros, depending on complexity. Ongoing costs for API usage are 20-50 euros per user per month. If each user saves just 2-3 hours per week, it pays for itself within a few weeks.
Skills Shortage as a Driver: Germany has a massive skills shortage, especially in the IT sector. Personal AI agents cannot solve this shortage, but they can mitigate it. When existing employees become 20-30% more productive through AI support, it is like having hired an additional employee for every five-person team, without the costs and effort of a new hire. Especially for the Mittelstand, which often loses in the competition for talent against large corporations, this is a real argument.
Industry-Specific Opportunities: Different industries benefit differently from personal AI agents. In engineering, agents can create technical documentation and research standards. In legal consulting, they can analyze contracts and find relevant rulings. In healthcare, they can search specialized literature and pre-structure patient reports. In trades, they can calculate quotes and create material lists. The applications are as diverse as the German economy itself.
What Does Not Work: Honesty is part of the deal. AI agents are not a cure-all. They work poorly for tasks requiring deep contextual understanding over years, for highly sensitive negotiations, for creative breakthroughs, and for anything requiring genuine human empathy. Those who deploy an AI agent for customer care in a crisis situation will quickly notice that technology has its limits. The key lies in the right task distribution: routine to AI, complexity and empathy to humans.
Honestly Naming the Risks
No article about AI agents would be complete without honestly addressing the risks. And there are several.
Hallucinations: AI models sometimes make things up. This is not a bug that will be fixed soon, but a fundamental characteristic of current language models. For tasks where accuracy is critical, you must always verify the results of AI agents. OpenClaw can deliver a fantastic research piece, but whether the cited sources actually exist needs to be verified by you.
Security: An AI agent that can execute shell commands is powerful. But power carries risks. A wrong configuration, a misunderstood command, and the agent deletes files it should not delete. Permission configuration is not optional but critical. For enterprise use, you need clear guidelines about what the agent may and may not do.
Dependency: Those who rely too heavily on an AI agent lose their own competence over time. We know this from calculators, navigation devices, and spell checkers. AI agents should be a tool that makes people stronger, not one that makes them more replaceable.
Cost Control: API costs can rise quickly, especially when an agent processes many complex requests. Without budget limits and monitoring, enthusiastic use can become expensive. Companies should set up cost monitoring from the start.
Data Protection with Cloud Models: Those who use OpenClaw with cloud models send data to third-party providers. For sensitive business data, this is problematic. The solution: either use local models, or precisely define which data the agent may process and which it may not.
Practical Tips for Getting Started
You want to try OpenClaw or generally get into the topic of personal AI agents? Here are concrete recommendations:
Step 1: Start small. Install OpenClaw on a test machine. Connect it to a cloud model or a local model via Ollama. Give it a simple task: for example, create a summary of the most important industry news every morning.
Step 2: Understand the limits. Deliberately test the boundaries. Ask questions where you know the answer. Assign tasks where precision is important. Learn where the agent is strong and where it has weaknesses.
Step 3: Define a workflow. Think about which of your daily tasks would benefit most from an AI agent. Not the exciting tasks, but the boring ones. The repetitive ones. The ones you always postpone.
Step 4: Involve the team. Show your team what the agent can do. Let colleagues find their own use cases. The best ideas often come from the people who deal with the concrete problems on a daily basis.
Step 5: Measure and iterate. Track how much time the agent saves. Which tasks work well, which do not? Adjust the configuration. Gradually expand the scope of use.
OpenClaw in Comparison: Not the Only Player
OpenClaw is impressive, but not the only personal AI agent on the market. An honest look at the alternatives:
Claude Code (Anthropic): A powerful AI coding agent that runs directly in the terminal. Strong in software development, less versatile than OpenClaw for general tasks. Not open source.
GitHub Copilot: Microsoft's AI assistant for developers. Excellent in IDE integration, but limited to software development. Not a personal agent in the true sense.
Auto-GPT and AgentGPT: Early open-source agent projects that paved the way. Now surpassed by OpenClaw in scope and reliability.
Apple Intelligence and Google Gemini: The big tech companies are building their own agent systems. These will be deeply integrated into their respective ecosystems but are less flexible and not open source.
The big advantage of OpenClaw over all these alternatives: it is open source, platform-independent, and maximally flexible. You can run it with any AI model, use it through any channel, and configure it for any use case. No lock-in, no dependency on a single provider.
What Comes Next?
The development of personal AI agents is still in its early stages. Here are the trends we expect in the next 12 to 24 months:
Multimodal Agents: Agents that understand not just text but can also process images, audio, and video. OpenClaw already has approaches for this with its ability to see the screen. This will develop massively.
Better Collaboration Between Agents: Instead of a single agent, teams of specialized agents will work together. One agent for research, one for code, one for communication. OpenClaw's architecture is already prepared for this.
Deeper Integration into Business Processes: Agents will be integrated directly into ERP systems, CRM solutions, and other business software. Not as a chatbot overlay, but as a native component of the workflow.
Regulation and Standards: With the EU AI Act and national regulations, standards for AI agents will emerge. This is good: clear rules create trust and facilitate use in regulated industries.
Specialized Industry Agents: Agents that bring specific industry knowledge. An agent for tax advisors who knows current legislative changes. An agent for doctors who searches medical literature. An agent for lawyers who analyzes contracts.
Conclusion: Personal Agents Are Here to Stay
OpenClaw has proven in just a few months what is possible when you think AI agents right. Over 200,000 GitHub stars, 35,000 forks, and a community that grows daily. Peter Steinberger's move to OpenAI and the transfer to an open-source foundation are not the end, but the beginning of a new chapter.
The numbers from Gartner and McKinsey clearly show: AI agents are becoming a standard tool in companies. Not tomorrow, not the day after, but now. 40% of all enterprise applications will contain task-specific AI agents by the end of 2026.
For German companies, the message is simple: engage with the topic. Not out of panic, not because everyone is doing it, but because personal AI agents like OpenClaw are genuinely useful. They save time, reduce routine work, and make existing teams more productive.
Start small. Try OpenClaw. Define a concrete use case. Measure the results. And then decide based on data, not hype, how far you want to go.
The Technical Architecture of OpenClaw
For technically interested readers, a look under the hood is worthwhile. OpenClaw is based on a modular architecture deliberately designed so that individual components can be swapped or extended.
At its core, OpenClaw consists of three layers. The first layer is the Agent Core: it manages context, task management, and decision logic. This is where it is determined how the agent responds to requests, which tools it uses, and when it asks for clarification. The second layer consists of Tool Integrations: file system access, shell execution, web research, browser automation. Each tool is a separate module that can be independently activated or deactivated. The third layer consists of Communication Adapters: WhatsApp, Telegram, Slack, and all other channels are implemented as plugins.
This modular structure explains why OpenClaw was able to grow so quickly. Developers from the community can add new tools and channels without having to change the core of the project. A developer in Japan writes a Line adapter, a developer in Brazil integrates a new file system tool, and a team in Germany builds an SAP interface. All of this happens in parallel, without coordination from a central team.
The context management is particularly clever. OpenClaw does not just remember the current conversation but builds an understanding of your work patterns over time. It learns which files you frequently edit, what kind of requests you make, and what preferences you have. This long-term memory makes the difference between a useful tool and a true personal agent.
The security architecture deserves special mention. Every action that OpenClaw wants to execute passes through a permission system. Sensitive operations like deleting files or executing certain shell commands require explicit confirmation. You can configure different security levels: from fully automatic mode for trusted tasks to manually confirming every single action.
The Community Behind OpenClaw
An open-source project lives from its community. And the community behind OpenClaw is remarkable.
Over 35,000 forks mean that thousands of developers worldwide are actively working on the project. The contributor base is diverse: from individual hobby developers to teams at large tech companies. There are regular community calls, an active Discord community, and a growing number of tutorials and extensions.
What makes the OpenClaw community special is the culture. Steinberger set a tone from the beginning that is inclusive and pragmatic. No ego trips, no gatekeeping mentality. Beginners are welcomed, contributions are valued, and criticism is received constructively. This is rare in open-source projects of this size.
The community has also already spawned several spin-off projects. There are specialized OpenClaw configurations for different professional groups: one for software developers, one for content creators, one for scientists, one for tax advisors. These pre-built configurations significantly lower the entry barrier because you do not have to start from scratch.
A particularly active area is plugin development. The community has already created hundreds of plugins: from integration with Notion and Obsidian to connections to accounting software to specialized research tools for specific industries. This plugin library is one of the reasons why OpenClaw is so versatile.
Personal AI Agents in the Enterprise: Three Scenarios
Theory is nice, but what does the use of personal AI agents actually look like in everyday work? Three realistic scenarios:
Scenario 1: The Marketing Team of a Mid-Sized Company
A mid-sized company from southern Germany with 80 employees has a marketing team of three people. These three have to create content for website, social media, newsletters, and industry publications, monitor competitors, perform SEO analyses, and create campaign reports.
With a configured OpenClaw agent, everyday life looks different. The agent creates a summary of relevant industry news and competitor activities every morning. It suggests content topics based on current search trends. It creates first drafts for blog articles and social media posts that the team then revises and refines. It analyzes the performance of past campaigns and identifies patterns.
The result: the marketing team achieves about twice as much output in the same time. Not because quality drops, but because the time-consuming research and preparation work is largely automated. The three marketing employees spend their time on what they do best: creative strategy and customer understanding.
Scenario 2: The Freelance Developer
A freelance software developer from Vienna works on multiple client projects simultaneously. He uses OpenClaw as a personal technical assistant that permanently runs in the background.
When starting a new project, OpenClaw analyzes the existing codebase and creates a summary of the architecture, technologies used, and identified improvement opportunities. During development, he can assign tasks to the agent via Slack: "Write unit tests for the UserService," "Find all places in the code that are not GDPR-compliant," "Create the API documentation for the new endpoint."
The agent works through these tasks while the developer focuses on complex architecture decisions. At the end of the day, OpenClaw automatically creates a work log and summary of completed tasks that the developer uses for client billing.
The productivity increase is significant. Tasks that used to take hours are completed by the agent in minutes. And the developer can manage more projects in parallel without quality compromises.
Scenario 3: The CEO of an SME
A CEO runs an SME with 25 employees. She is not a technician, but she has recognized that AI agents can help her in everyday life.
She uses OpenClaw via WhatsApp. Every morning, she sends the agent a voice message with her priorities for the day. OpenClaw creates a structured to-do list from this, schedules reminders, and prepares relevant information. Before a client meeting, she asks the agent: "What do I know about Company XY? Are there any current news?" The agent researches and delivers a compact summary.
For proposals and reports, she gives the agent rough bullet points, and it creates a first draft. She revises, corrects, and refines. This saves her one to two hours daily, which she instead uses for strategic work and client conversations.
This scenario shows what Steinberger means when he says: "My next mission is to build an agent that even my mum can use." OpenClaw is not just for developers. It is for anyone who needs a smart assistant.
The Bigger Picture: Why Personal Agents Are the Next Big Thing
OpenClaw is part of a larger movement. The tech industry is moving from large, centralized AI systems to personal, decentralized agents. And there are good reasons for this.
The smartphone showed what happens when you put powerful technology into the hands of every individual. The iPhone did not just change telephony, it created entirely new industries: app economy, mobile commerce, social media as we know it today. Personal AI agents could have a similar effect.
When every person has a powerful AI agent working around the clock, it changes the economy in fundamental ways. Small companies can compete with large ones because the efficiency gap shrinks. Individuals can tackle projects that previously required teams. Countries with fewer workers can increase their productivity without hiring more people.
Of course, there are also downsides. When AI agents automate everything that is automatable, jobs will be lost. Not immediately and not everywhere, but over time. Society needs to talk about how we shape the transition. Further education, new job profiles, perhaps even new social systems. These are big questions that we cannot sit out.
But fear is not a good advisor. History shows that technological revolutions create more jobs in the long run than they destroy. The trick is to react to change early enough and adapt, rather than hoping it will pass.
OpenClaw and the EU AI Act
A topic that is particularly relevant for European companies: how does OpenClaw relate to the EU AI Act?
The EU AI Act, which has been gradually coming into force since 2025, categorizes AI systems by their risk. Personal AI agents like OpenClaw typically fall into the "limited risk" or "minimal risk" category, depending on the area of use. This means: they may be used but must meet certain transparency requirements.
Concretely, this means: when an AI agent interacts with customers or business partners, it must be disclosed that it is an AI system. This is sensible and easy to implement. OpenClaw can be configured to identify itself as an AI assistant in external interactions.
For internal use, the requirements are lower. An agent that helps employees with research or creates documents does not need special labeling. However, companies should document which AI systems they use and what for. This is not only a legal requirement but also good practice for transparency and accountability.
GDPR compliance is a separate topic. Here, using local models via Ollama is the cleanest path: if no data leaves your own machine, there are no data protection problems. With cloud models, you need to check what data is sent to the API and ensure that appropriate data processing agreements are in place.
Lessons Learned: What We Can Learn from OpenClaw About Open Source
Regardless of whether you want to use OpenClaw yourself, there are valuable lessons the project holds for the entire tech industry.
Lesson 1: Simplicity wins. OpenClaw is not the most technically sophisticated project. It is the most accessible. Steinberger made sure from the beginning that installation is simple, configuration is understandable, and usage is intuitive. In a world full of over-engineered software solutions, simplicity is a massive competitive advantage.
Lesson 2: Community is everything. No individual can carry a project of this size. Steinberger understood this and built a welcoming community from the start. The transfer to a foundation is the logical consequence: the project is bigger than one person.
Lesson 3: Timing is decisive. OpenClaw is not the first open-source agent project. But it came at the right time, when AI models were good enough, demand was large enough, and infrastructure was mature enough. The same idea a year earlier might have flopped.
Lesson 4: Open source can compete with Big Tech. OpenClaw competes with products from Apple, Google, Microsoft, and OpenAI. And it wins in many areas because it is more flexible, more transparent, and more community-driven. This shows that open source not only survives in the AI age but thrives.
The future of personal AI agents has just begun. And with OpenClaw, we have an open-source project that shows what this future can look like: open, flexible, useful, and accessible to everyone.
