The Cursor Moment for Deal Teams
If you've spent any time around developers recently, you've probably heard them raving about Cursor, an AI-powered code editor that's completely changed how they work. Instead of constantly jumping between their code, documentation, and Stack Overflow, they now have an AI that actually understands their entire project and can suggest and implement fixes or improvements right in context.
I was talking to a developer friend about this, and it hit me how similar our workflows actually are. They're juggling multiple files, trying to understand dependencies, debugging across different parts of their codebase. We're managing data rooms, research notes, financial models, and market analysis, all while trying to synthesize everything into a coherent investment committee memo or internal artifact.
Both of us spend way too much time on the coordination and synthesis work instead of the actual thinking. They used to spend hours searching through documentation and tracing bugs across files. We spend equivalent time consolidating research, cross-referencing data points, and getting everything formatted properly for IC memo presentations.
The thing is, developers now have their AI assistant that understands context. It knows what they're trying to build and can reason across their entire codebase. Which got me wondering—when do we get our version of that?
What would it look like if we had an AI that actually understood deal workflows? Not just generic document processing, but something that gets the nuances of buyside deal due diligence. Something that understands how financial statement trends relate to management commentary, or how market positioning affects competitive dynamics.
This kind of system would need to work alongside your existing analytical process, not try to replace it. Nobody wants automated investment decisions. But eliminating the tedious synthesis work that eats up so much bandwidth during underwriting and diligence phases... That's compelling.
We've actually been building toward this with Deck's Deep Dive feature. It's deal intel at your fingertips. Instead of just helping organize information, it actively reasons across your deal data to surface connections you might not have caught immediately. When you're analyzing a potential investment, it can simultaneously consider management's growth projections against historical trends, competitive data, and market dynamics, then flag areas that might need deeper investigation.
How does it work? Simple:
- Obtain a beta invite
- Create a new deal
- Upload deal files to Deck's secure data locker
- Begin asking questions to Deck in the sidebar about the company or deal dynamics (e.g., provide a brief summary of the Company's ICP.)
Picture this: you've just finished your research calls and reviewed the financials. Normally, pulling all those insights together into a due diligence memo template means a lot of manual cross-referencing to identify themes and potential issues. But what if your system could instantly show you where management commentary doesn't align with financial trends, or suggest additional research areas based on risks it identified across your data?
You still make all the conclusions and recommendations, but the grunt work of connecting information disappears. Teams using this approach are telling us they can either handle more deal flow with the same resources, or go significantly deeper on individual opportunities without extending timelines.
This is especially valuable in scenarios where you're evaluating multiple opportunities simultaneously and need to quickly synthesize key risk factors across different credit profiles.
What's interesting is seeing how quickly the developer community embraced AI-powered tools once they actually enhanced their core work instead of trying to replace their expertise. The same thing is likely true for deal teams. We're ready for AI collaboration when it genuinely makes the analytical process more efficient.
The firms that figure this out early—moving beyond basic automation toward systems that understand investment methodology and provide contextual intelligence throughout diligence—are probably going to have a significant advantage. Whether that's handling increased deal flow or dedicating more time to strategic analysis on the most promising opportunities.
The technology exists today. The question is which teams will be early adopters of truly intelligent diligence workflows, and how quickly this becomes table stakes across the industry. Just like with Cursor in development, the competitive advantage won't be in having access to AI tools, but in building workflows that leverage AI collaboration most effectively for deal evaluation and investment committee preparation.
