/
Published on 27.01.2026
TLDR: OpenAI is exploring royalty deals that would give them a cut of AI-generated value in industries like drug discovery, finance, and energy — signaling a shift from subscription-based revenue to value-based monetization.
This is a significant strategic move that deserves more scrutiny than it's getting. OpenAI is exploring royalty arrangements where they'd take a percentage of the economic value generated by their AI models in specific industries. Think drug discovery, financial modeling, and energy optimization — sectors where AI-assisted outcomes can be worth billions.
The implications for the broader AI ecosystem are substantial. If OpenAI successfully establishes royalty-based pricing, it fundamentally changes the economics of building on top of their APIs. Today, you pay per token — a cost you can predict and control. A royalty model means OpenAI's revenue scales with your success, which is great for them but creates a very different risk profile for businesses building AI-powered products. It's the difference between paying rent and giving your landlord equity.
What's missing from this conversation is the enforcement mechanism. How does OpenAI measure the "value generated" by their models in a drug discovery pipeline or a financial trading system? The model contributes one piece of a complex workflow. Attributing specific dollar value to the AI component versus human expertise, proprietary data, and domain knowledge is genuinely hard. This feels like it could become a major source of contractual disputes.
For engineering leaders and architects building AI-powered products, this is a signal to seriously evaluate your vendor lock-in exposure. If your core product depends heavily on OpenAI's models, a shift to royalty-based pricing could dramatically change your unit economics. Diversifying across providers (Anthropic, open-source models, Google) isn't just a technical decision anymore — it's a business risk management strategy.
OpenAI Wants A Cut Of Your Profits
TLDR: Google DeepMind's CEO publicly criticized OpenAI's move to introduce ads in ChatGPT, claiming Google is approaching AI advertising "very carefully" — an interesting position from the company that built the world's largest ad business.
There's a beautiful irony in Google — the company that generates over 80% of its revenue from advertising — criticizing OpenAI for exploring ads. DeepMind CEO Demis Hassabis said he was "surprised" by OpenAI's rush to monetize through advertising and that Google is taking a more careful approach with AI ads.
The subtext here is competitive positioning. Google has decades of experience in the ad business and knows exactly how to integrate advertising without destroying user trust (most of the time). OpenAI is a newcomer to this space. Google's "careful approach" framing is as much about highlighting its own expertise as it is about genuine concern for ad quality in AI products.
For the broader developer community, the monetization strategies of foundation model providers matter because they determine what gets optimized. An ad-supported AI assistant optimizes for engagement and attention. A subscription model optimizes for utility and user satisfaction. A royalty model (as discussed above) optimizes for economic impact. These are fundamentally different incentive structures that will shape how AI tools evolve.
OpenAI Wants A Cut Of Your Profits
TLDR: Anthropic has launched Claude integration directly in Excel with drag-and-drop file support, auto-compaction for long sessions, and smart cell preservation — making AI-assisted spreadsheet work more practical.
For anyone who spends time wrangling data in Excel — and let's be honest, that's most of us at some point — Claude's Excel integration is a practical quality-of-life improvement. The key features are drag-and-drop for multiple files, automatic conversation compaction for long sessions (so you don't lose context), and importantly, it won't overwrite your existing cells.
That last detail — not overwriting existing cells — is more important than it sounds. One of the biggest fears with AI in productivity tools is the "helpful assistant that destroys your work" problem. By explicitly preserving existing data, Anthropic is addressing the trust barrier that keeps power users from adopting AI tools in their core workflows.
For teams and architects thinking about AI integration in workflows, this is a useful reference point. The pattern of AI that assists without overwriting, that works within the user's existing context rather than requiring a separate tool, is the pattern that drives adoption. The best AI integrations feel like a capable colleague looking over your shoulder, not a tool that takes over your desk.