6 Comments
User's avatar
Sharique Nisar's avatar

The AI-assisted win rate metric deserves more attention. It moves AI measurement from 'activity' (emails sent, calls analyzed) to actual business outcomes. That's how you prove value, not just usage.

Ray Rike's avatar

Sharique- this is such an important step in proving real ROI to the CFO and investors, as unless it increases revenue, decreases cost and/or increases enterprise value harder to call it ROI...though leading indicators such as productivity lift, increased efficiencies or improved effectiveness will overtime correlate to financial ROI - question is when and is it able to be measured and proven as a causal relationship

The AI Architect's avatar

This breakdown of the $1M ARR per employee trend is absolutly killer, especially the insight that COGS are the new CAC for AI companies. What really struck me was how Midjourney's 11-person team proves that distribution through community (Discord) can totaly replace traditional marketing departments. We've been experimenting with AI coding tools at my startup and honestly the 40% code generation stat feels conservative, our senior devs are hitting closer to 60% on greenfield projects. The McKinsey case study showing 50,000 hours recovered monthly really drives home how this isn't just about startups, it's reshaping consulting economics too.

Ray Rike's avatar

Our CTO has a PhD in CS, and is the best SW architect and coder I have ever met - and even he is seeing 5x-10X increase in productivity using Claude Code

Peter Buchanan's avatar

Absolutely! AI can have a huge positive impact on the entire GTM process. You are likely to see more stringent success measurements at every stage. We've identified about 80 AI-related metrics that directly affect ROI, covering the entire enterprise. You'll see more of them coming soon.

Peter Buchanan's avatar

Thank you for your comment! I think the blockers to building a $1M ARR per employee company are: (1) Your first 10 employees need to be really versatile and exceptional. (2) your product needs to solve a high-value problem, and (3) the product needs to be good enough that the level of support needed is very low.