Every business is going to run on an AI operating system. I do not mean an operating system like Windows or macOS. I mean a system of record that helps run the business itself - a place where your team’s tools and data just seamlessly work together with persistent memory.
Right now, most businesses are assembling AI through a scattered mix of chats, prompts, automations, and disconnected tools. One person uses ChatGPT. Another uses Claude. Someone else has prompt templates saved in a document. A few workflows run through Zapier or another automation product. There may be a bot somewhere in Slack. Some teams are experimenting heavily, but very few are actually running on a real system.
There are already early products pushing this category forward. Open Claw deserves real credit for helping show what is possible. But for most businesses it still feels more like Linux than Mac OS: powerful, flexible, and exciting for technical users, but harder to secure, govern, and deploy across an everyday team. Most businesses do not want to run Linux. They want something intuitive, secure, and ready to use. They want something for the rest of us. They want PIE.
What is PIE?
PIE is the AI Operating System for the Rest of Us. PIE gets work done for you by connecting your tools and data into a single AI system of record with persistent memory. It is designed to feel less like a collection of AI experiments and more like a real product your team can rely on every day.
PIE is not just another chatbot. It is not a wrapper around one model. It is not a single-purpose workflow tool. And it is not a framework built only for developers. It is the layer that helps a business go from experimenting with AI to actually operating with it.
PIE is short for "Personal Intelligence Engine". It's because we believe how a someone uses AI is a very personal choice. The workflows, models, and agents I choose will be different than everyone else's, no different than how my Mac is setup with different apps and settings than everyone else's.
Agents Are The New Apps
Every business is different. The needs of a law firm, a dealership group, a recruiting agency, and a support team are not the same. That is why we do not think the winning product in this category will be a fixed assistant with a fixed set of behaviors. It has to be a platform.
In many ways, agents are the new apps. Businesses will want to install agents, customize agents, and eventually build agents of their own. Some of those agents will be created for internal use. Some will come from developers and partners that specialize in specific industries or workflows.
That is why PIE is built with a marketplace and a developer platform in mind. Over time, the most valuable part of an AI operating system may not just be the core assistant itself. It may be the ecosystem around it — the agents, integrations, and workflows that make the platform more useful with every new participant.
We built our agent platform as a real developer platform, not just instructions thrown into a markdown file. Each agent gets the following -
- Its own Postgres database
- Its own sandbox per agent per user
- Developer & user secret storage
- Oauth callbacks
- Webhooks, billing
- Cloud file storage
- Access to our APIs for calling AI.
Best of all? Every agent you enable then becomes available in all your conversations automatically. Meaning if you build a CRM agent, all of its data will automatically be available to call with PIE.
We have seen this pattern before. Windows became more valuable because of the software built on top of it. WordPress became more valuable because there was a plugin for everything. Shopify became more valuable because of the ecosystem around the merchant. AI operating systems will likely follow the same path. We're looking forward to the day that the next great startup is primarily a PIE agent.
Persistent memory is the foundation
Most AI still feels like Groundhog Day. You open a new chat and start over. The model may be powerful, but the system does not really know what matters to you, what your team worked on last week, which files are relevant, or how a workflow usually gets done.
That is not good enough for real business use. A real AI operating system needs persistent memory across projects, people, files, decisions, and prior work. It should remember what your team is doing, what matters, and how work actually happens.
This is where the system starts to compound. The spreadsheet agent should be able to access the documents you uploaded yesterday. The writing agent should know the context from last week’s meeting. The sales assistant should understand the latest customer interactions. Memory should not live inside one isolated chat. It should live across the system.
Model choice should be its own layer
No business wants its operating system tied to a single model provider. The model layer is changing too quickly, and different models are better at different tasks.
One model might be better for writing. Another might be better for coding. Another might be better for research, speed, reasoning, or cost. Even at the individual level, most people will likely end up using more than one model over time. With hundreds of billions of dollars invested in models, the best model for a specific task or workflow can easily change overnight.
That is why model choice matters. Businesses should be able to use the best model for the task at hand without having to rebuild the rest of their system every time the frontier shifts. The operating system should persist even as the underlying models change.
PIE is built for teams
Work does not happen in isolated chats. It happens across teams, handoffs, deadlines, files, meetings, and systems. That means AI at work cannot just be a single-user experience. It has to be multiplayer.
In a real business environment, human teammates and their agents should be able to work together in the same environment in real time. A support lead may have one set of agents. A marketing team may have another. A founder may have their own assistant coordinating across both. Those people, and those agents, should be able to share context, contribute work, and operate inside the same system.
That is a very different model from one person chatting alone with a model. It starts to look less like a chatbot and more like a shared workplace where humans and agents collaborate together.
Hybrid by design: cloud-first with a Mac app
The future of AI is mostly in the cloud. That is where the fastest models, the biggest compute, and the majority of business data already live. Most companies already run on cloud tools such as Gmail, Dropbox, Slack, Trello, CRMs, and internal web apps.
But useful work does not happen only in the cloud. It also happens in browser sessions, local files, notifications, desktop permissions, and native workflows. That is why we believe the right architecture is hybrid.
PIE is cloud-first so that it can run the most intelligent models, but it also has a Mac app so it can interact with the real world of work. That includes things like working across browser and desktop environments, accessing local context when needed, handling notifications, and participating in workflows that cannot be solved cleanly from a browser tab alone. The cloud does the heavy lifting. The Mac app gives PIE real-world reach.
Software platform meets implementation
60% of businesses don't know where to start when it comes to AI. Even with an easy to use platform, most businesses will need their hands held just like when they got their first computers and first websites.
That is why we do not think this category will be won by software alone. It will be won by software plus implementation. In the early days of a platform like PIE, the company has to get close to the customer, understand the workflow in detail, build the missing integrations, and turn those lessons back into product.
Palantir popularized one version of this with forward deployed engineering. The point was not just to configure software. The point was to work deeply enough with customers to understand the real operating environment, solve the workflow in practice, and then make the platform stronger because of it. Harvey is doing a version of the same thing in legal, where software adoption depends on workflow design, trust, and rollout as much as the product itself.
We think PIE should follow a similar path. Before there is a broad partner ecosystem, we should do a meaningful amount of the early integration work ourselves. That is not because bespoke services are the final destination. It is because this is how the product gets better. It is how the missing abstractions get discovered. It is how implementation knowledge becomes platform knowledge.
Over time, that should expand into a broader ecosystem of partners and developers. In the PC era, that looked like VARs and outsourced IT providers. In the web era, it looked like web developers, agencies, and plugin ecosystems. We expect AI operating systems to create a similar pattern. But early on, doing the work directly is part of building the platform.
What if Anthropic, OpenAI, etc. do it?
This is the obvious question, and it is worth answering directly. Anthropic, OpenAI, and the other model providers will keep moving up the stack. They should. The models are getting better, and the product surfaces around them will keep improving.
The strongest answer is not that they will fail to try. The stronger answer is that the long-term value in this category likely sits above any one model provider. Most businesses will not run on one model forever. Over time, they will use many models per person depending on the task, the price, the speed, the modality, and the workflow. Analytical work might be great with a Claude model, writing with OpenAI, and fast execution with a Gemini Flash model.
That makes the harness layer more durable than the model layer itself. The harness layer is where memory, tools, permissions, workflows, collaboration, integrations, and governance live. It is the layer that decides how work gets done across the business. It is also the layer where switching costs and product stickiness start to accumulate.
That stickiness does not just come from the model. It comes from the system wrapped around it. It comes from the fact that your business memory is there. Your integrations are there. Your agents are there. Your team workflows are there. Your plugins, custom logic, and implementation work are there. Once that layer is in place, the question is not just which model is best this month. The question is which system your business actually runs on.
We are already seeing evidence that the early winners in this category may not come directly from the model companies themselves. Manus, Open Claw, Genspark, and Perplexity Computer all point in the same direction. The model matters. But the operating system businesses adopt may well be built by a company that sits above the model layer rather than inside it.
Closing
AI adoption will not be won by the product with the best benchmark, the biggest model, or the most impressive demo. It will be won by the system businesses can actually adopt and build on.
That is the opportunity we see with PIE.
We are building the AI Operating System for the Rest of Us: one assistant, one platform, and over time an ecosystem that helps businesses move from scattered experimentation to real adoption.
If that resonates, try PIE, explore the agents already available, build your own, and start putting AI to work inside your business.