What OpenAI Isn’t Telling You

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Hey there,

What's the real cost of free?

ChatGPT and Claude have a free tier. Gemini doesn't charge you. But here's the thing: if the product is free, YOU are the product.

And OpenAI just proved it.

Rumors are going around that OpenAI is building a monetization layer around chat history. Not through subscriptions (they already have those). Not through API pricing (they've got that too). But through contextual advertising based on what you talk about.

Think about it –

You chat with ChatGPT about your business struggles. OpenAI sees you're a solopreneur building an SaaS. Suddenly, you see ads for project management tools, hiring platforms, and accounting software. Perfectly targeted. Devastatingly effective.

Some of you will just disable chat memory and move on. Problem solved, right?

But here's what's actually happening and this is what fascinates me –

OpenAI’s Company Knowledge Update

While rumors go around about ad-based monetization, OpenAI just launched something that seems to pull in the opposite direction: a "company knowledge" feature for ChatGPT Business, Enterprise, and Education users.

This GPT-5-powered update connects directly with workplace tools like Slack, SharePoint, Google Drive, and GitHub, turning ChatGPT into a conversational search engine that can search through all your company data. Every response includes clear citations showing exactly where information came from.

Sounds helpful, right? 

But here's the catch – you're now feeding ChatGPT everything. Your internal Slack messages. Your confidential Google Docs. Your GitHub repositories. Your client communications. All processed through OpenAI's servers.

This is surveillance capitalism wearing a productivity mask.

The feature can handle ambiguous questions like "where did we land on company goals for next year?" by running multiple searches across different sources. It can "think while it searches" and use date filters to find time-based information.

But who else is learning from these searches? What happens to that data? OpenAI says they're building contextual advertising based on chat history. Now they will have access to your entire company's knowledge base.

Do you see the pattern forming?

People Are Moving to Private AI

People are building AI systems that live entirely on their devices. No cloud. No data sent to OpenAI. No advertising. No corporate surveillance.

Consider what's happening in the open-source AI community right now:

• DeepSeek's OCR model has been rewritten in Rust. Now you can run it offline, on your own machine. No Python dependencies. No cloud calls. Perfect for processing sensitive documents – contracts, medical records, financial statements – without wondering who has access to your data.

• Gemma Web exists entirely in your browser. Google's AI models running locally through WebAssembly. You upload your documents, do your RAG (retrieval-augmented generation), and everything stays on your device. Zero data leakage.

• Android now has Ai-Core – a stable platform that runs LLMs, vision models, speech-to-text, and text-to-speech entirely offline. This means your phone can understand what you're saying, translate it, process images, and respond – all without touching the internet.

These aren't niche tools anymore. They're production-ready. They're real.

But wait, there's more.

Hardware Is Catching Up

It's not just software. Hardware companies are racing to make local AI faster and cheaper.

• AMD's Radeon AI PRO R9700 – 32GB of memory, 128 AI accelerators. You can run massive models locally. Developers are using these cards to train and run complex AI workloads without paying cloud providers thousands of dollars per month.

• VSORA launched Europe's most powerful AI inference chip – 3,200 teraflops of compute power. Designed specifically for local inference. They're directly challenging US AI dominance. (The US currently has more total AI compute than all other countries combined, but that's about to change.)

• OpenArc 2.0 is simplifying everything – multi-GPU pipeline parallelism, tensor parallelism on CPUs. The infrastructure for running AI locally is becoming democratized.

So here's what I'm seeing –

There's a split happening.

On one side: Companies like OpenAI building surveillance capitalism. Free services in exchange for your data, your conversations, your behavior patterns. Now they want your entire company's internal knowledge too.

On the other side: A movement toward privacy-first AI. Open-source models. Local inference. Your data stays yours.

Now, as an AI entrepreneur, where do you position yourself?

The Opportunity Is Massive

There's a massive gap in the market.

People want AI capabilities. They don't want to surrender their privacy.

But most tools still force you to choose. Use ChatGPT and get surveilled. Use local tools and sacrifice convenience or capability.

What if you built the bridge?

I'm talking about:

• Privacy-first SaaS that processes data locally but syncs securely to the cloud only when needed

• AI agents that run locally but integrate with your existing workflows

• Tools that let companies audit exactly what data leaves their systems

• Open-source wrappers that make local AI as easy as cloud AI

Think about it: OpenAI's company knowledge feature is genuinely useful. The problem isn't the functionality – it's the architecture. Everything goes through their servers. Everything gets processed by their models. Everything becomes their data.

What if you could build the same functionality, but locally? A system that searches across Slack, Google Drive, and GitHub without ever sending data to external servers?

That's the opportunity.

What You Should Do

If you're building AI products, consider this your moment.

Your customers are waking up. They're realizing that convenience isn't worth the cost of their autonomy.

Here's your playbook:

• Build with transparency. Show exactly what data is being processed and where it goes. Be specific. Be honest. This alone becomes a competitive advantage. While OpenAI says "trust us with your company knowledge," you can show exactly what stays local and what doesn't.

• Embrace local-first architecture. Even if you offer cloud features, make the default behavior local processing with optional cloud sync. This shifts the power dynamic. Users choose what to share, not companies.

• Include critical feedback loops. Don't build sycophantic AI. Build AI that challenges assumptions respectfully. This requires actual work, but it's what separates your product from the rest.

• Position yourself as the alternative. There's an entire community (500k+ in LocalLLaMA alone) actively looking for tools that respect their privacy. Companies are nervous about feeding their internal data to OpenAI. You have a ready-made audience.

• Match the functionality, not the architecture. OpenAI's company knowledge feature shows what people want: unified search across multiple data sources with clear citations. Build that. But build it so the data never leaves the user's infrastructure.

Remember what Ilya Sutskever from OpenAI said: "Anything which I can learn, the AI could do as well." But there's something he didn't mention – something the AI can't do. It can't build trust. It can't respect privacy intentionally. It can't refuse to manipulate you.

Those are human choices.

So the question becomes: What will you choose?

Are you building AI that makes you money by stealing user data? Or are you building AI that makes you money by respecting it?

- Aashish

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