The End of Rented Intelligence: Keep Your Business AI To Yourself

Some of you watched Alex Karp on CNBC last week or caught the All-In crew talking about sovereign AI over the weekend. It was a good discussion. I sat there the entire time like the Leonardo DiCaprio meme, pointing at the TV shouting, "Yes, that's what we've been saying!" 

If you run a business, I'm sure you know the core problem. You felt it the last time you pasted a competitive strategy or a customer list into AI and thought, "Should I really be doing this?" Or even more, "Are my employees doing this even though I asked them not to?" You wonder if you can trust the company with your data and the data of your customers. You spend time vetting the people you hire and are careful about which data you give them access to, yet you know nothing about who is on the other side of the AI.

I'm here to tell you there are teams who don't ask themselves those questions anymore. Law firms, accounting practices, Hollywood studios, government offices, music creators, financial planners, non-profits, venture firms, software developers, and more are using Maple to run their businesses. They have the same AI capabilities as everyone else, but they don't hand over the keys to their thinking.

Why Not

So what is actually stopping more teams from making the same choice? In our experience, two things. One, they genuinely do not think it is a big deal. Everyone else is using these closed AI systems, so it must not be an issue. Two, they assume the fix is expensive, like buying servers and hiring IT people to manage them.

If you fall into the first camp, I might not be able to convince you, but I will try. If you fall into the second, the math is already clear. You can have sovereign intelligence without the expense and headache.

So what's the big deal anyway? It comes down to whether you want other, unknown people to have access to your business information or not. It's really that simple. Do you want strangers crafting your business logic? And do you want to share your insider info so others can compete with you?

The Black Box Problem

These large AI tools are closed systems. You can't inspect a closed model. You can't see the weights, the training data, or the system prompts. Because of this, the provider can steer outputs, inject preferences, and change behavior without telling you. Your company's internal reasoning is filtered through someone else's editorial layer.

I wouldn't let a competitor edit my sales deck before I present it. Yet every time your team runs a sensitive document through a closed model, that is exactly what is happening. You are renting intelligence from a company that answers to its own shareholders, not to you.

The answer is to operate in the open. At Maple, we run verifiable open-weight models and open-source code. If you want to inspect the intelligence that runs your business, you can.

They Compete With Their Customers

According to The Information, Anthropic blindsided partner Figma by launching Claude Design. Anthropic's chief product officer had served on Figma's board and resigned only three days before the product launch. Figma's stock fell over 15% that month.

This can't be coincidental. Closed AI labs want to grow. A great place to do that is to see what products are being built on their platforms. And when they see success, they already know how it was done because they have not only the final product but all of the messy thought journey to get there. No need to reinvent the wheel. Take those learnings, apply your company spin, and launch a competitor with tight integration into your ecosystem. Microsoft did it. Google did it. Why would AI be different?

Look at the incentives. They subsidize the cost of compute. Your AI plan might cost upwards of $200/month per person, but estimates show that you can get over $5,000 worth of compute from that plan. Makes me pause and ask, "Why?" No company loses money on its customers just to be generous. Maybe they just want to get market share, which is a typical customer acquisition move. That's not the moat, though. The public actions of the companies suggest the motive is stronger than that. They want your data to make their models better. And they want to see which businesses are being built on their platform so they can copy and compete with them.

When you upload your strategy documents, customer analysis, or product roadmaps into a closed model, you might be handing over your downfall.

You Are Paying to Train the Competition

Maybe you aren't a software company. You run a logistics firm, a consultancy, or a manufacturer. You might think Anthropic, OpenAI, or Perplexity will never build a competing business in your niche. And you're probably right. Narrator: now introducing, Anthropic Paper Company.

This is where your local competition comes into play.

Every proprietary insight you feed into a closed model makes that model smarter. Your competitors then log into the same AI system and benefit from the improved intelligence in your market niche, thanks to your data. You're telling the model how to help the competition run their businesses more efficiently and how to create smarter marketing campaigns. Your hard-won expertise becomes a feature in the AI, available to anyone who pays the subscription fee.

Renting judgment from the same platform as your competitor is a race to the median. You deserve to own your thinking.

There Is an Alternative

Many teams feel they need to escape this trap, but don't know where to start.

We launched Maple Teams in 2025 for this exact reason. It is sovereign AI without the IT staff. Maple feels like using the same AI that you get from other providers, like chat, research, coding, and file creation without having to manage the technology yourself.

Maple Teams gives you end-to-end encryption, TEE-hosted open-weight models, and project workspaces for your organization. No hardware to buy. No IT team to hire. Just a low monthly cost per seat.

Your data never touches OpenAI, Anthropic, Google, or Chinese companies. Your competitors do not benefit from your insights. You can put the strongest open intelligence in your employees' hands without sharing any of your alpha with competitors.

We also offer an encrypted API called Maple Proxy API. You can plug it into your existing workflows and pipelines with minimal changes. It's a simple way to stop sending your data to closed providers and instead keep it secure and confidential.

I hope by now you see that the urgency is real. The Figma story should have been the wake-up call. Renting intelligence from the same vendor that rents it to your competitor is a risky play.

The Open Models Have Arrived

The prevailing narrative is this: OpenAI and Anthropic have the best models. "And don't I deserve the best?" Yes, yes you do, Gaston. But are those models truly the best? Yes and no.

Open-weight models like GLM 5.2 and Kimi K2.6 score right alongside recent-generation GPT and Claude Opus models on standard benchmarks. Even when open-weight models are a few months behind on the bleeding edge, they are more than powerful enough for the work most businesses actually do. Writing, analysis, coding, research, customer insights. It all runs beautifully.

What makes those models better is that you keep your data, your intellectual property, and your competitive edge. You do not have to choose between intelligence and ownership. You can have both.

Local AI and Secure Cloud AI

I think the future will be a hybrid of local and cloud AI. Local models continue to shrink in size while becoming more powerfulm Meanwhile, consumer hardware gets better at running them. Gemma 4 and Qwen 3.6 are great examples. There will be many standard tasks that a local model will handle for you, effectively for free. At the same time, cloud models will continue to outpace local AI because the hardware will always be orders of magnitude more powerful. We need secure cloud solutions that give the power without sacrificing security.

Own Your Intelligence

Sovereign AI is available today. This level of control should be standard for any team that relies on proprietary knowledge, not just a luxury reserved for governments and Fortune 500s with massive IT budgets.

That's our sweet spot at Maple. We provide cutting-edge AI tools to small businesses, working professionals, solopreneurs, and businesses of all sizes, while maintaining a secure boundary around their data and intelligence.

We built Maple because we believe the future of business AI is encrypted, inspectable, and user-owned. The tools are ready right now. The only question is whether you will act before your alpha is already gone.