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You had the best intentions when you bought all those data sources like Crunchbase, Dealroom, Pitchbook, and Harmonic. You thought everything gets logged with that powerful CRM called Affinity. You wanted to connect it all to ChatGPT and start asking questions.
But then you realized that you’re missing the middle layer between your data and your LLM that connects the data siloes, eliminates the duplicates, and normalizes the data swamp.
This is why you need an “entity resolution” data infrastructure, aka the glue. But do you build it or buy it?
Why Everyone Is Switching to AI Credits
Kyle Poyar reviews the rapid rise of credit-based pricing models in AI. He highlights how incumbents like Microsoft, Salesforce, and OpenAI are normalizing credits as the default way to buy AI, why cost pressures make this shift necessary, and what practices can make credits work for vendors and customers.
Adoption by Big Players: Microsoft added credits for Copilot in January, Salesforce launched flexible credits in May, Cursor moved in June, and OpenAI replaced seat licenses with pooled credits in its Enterprise plan. Dozens of others, including Adobe, HubSpot, Google, and Replit, have followed.
Usage Patterns and Margins: Token usage is exploding, with 70–80% of consumption coming from just 10% of users. That concentration can make heavy users unprofitable, especially since AI gross margins remain tight. Credit pools help vendors contain costs while giving customers clarity.
Credits in Practice: Definitions vary widely. Prices range from fractions of a cent at Salesforce to $0.25 at Lovable. Some companies link credits to costs (e.g., Cursor), others to outcomes like resolved cases or completed workflows (e.g., Salesforce, HubSpot). The latter makes ROI clearer for buyers.
✈️ KEY TAKEAWAYS
Credit-based pricing is becoming the standard for AI, balancing vendor margins with customer predictability. While inconsistent definitions remain a challenge, credits serve as a bridge from flat-rate pricing toward value- and outcome-based models.
Benchmarks for Customer Support Rep Team Size Ratios
Recent analysis by Matt Schulmann highlights how customer support staffing ratios scale with company size, and how AI adoption may shift these benchmarks further. A Pave dataset covering 2,237 companies with at least one support rep provides concrete numbers on full-time employees (FTEs) per support representative.
Support Ratios by Company Size: Smaller companies (1–99 employees) average 23 FTEs per support rep. Mid-sized firms scale up to 50 FTEs per rep at 500–999 employees, while the largest (3,000+) reach 65 FTEs per rep.
Scaling Patterns: Efficiency generally improves with company size, though firms in the 1,000–2,999 range show a dip at 43 FTEs per rep. This suggests structural differences in support operations at that stage of growth.
AI Impact Predictions: With Conversational AI agents like Decagon and Salesforce’s recent 4,000-person reduction, leverage per rep is expected to rise. Benchmarks will likely increase as AI tools automate more of the support workload.
✈️ KEY TAKEAWAYS
Customer support ratios improve with scale, and early signs show AI adoption will accelerate this trend. Companies should anticipate higher leverage per rep as automation reshapes staffing benchmarks.
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Oh, the Irony: SAFEs Are Blowing Up Cap Tables
CJ Gustafson explains how repeated use of SAFEs is creating hidden dilution that founders often underestimate. While SAFEs were designed as a quick way to raise early capital, stacking them across multiple rounds delays dilution on paper but compounds it heavily once priced rounds arrive.
Dilution by Stage: Carta data shows that a typical pre-seed SAFE round equals about 10% dilution. Seed and Series A rounds usually add another 20% each, leaving founders with only 38–39% ownership post-Series A once option pools are factored in.
The SAFE Problem: SAFEs delay conversion into equity, creating opacity in cap tables. Some founders layer three or more SAFEs at different valuation caps, which can add 20% or more hidden dilution by the time they price a round. In extreme cases, Carta has tracked companies with up to 10 SAFEs before conversion.
Post-Series A Reality: After dilution and option pools, founders may control less than half of their company before raising a Series B. Two equal co-founders can each fall below 20% ownership, a stark contrast to the intended founder-friendly reputation of SAFEs.
✈️ KEY TAKEAWAYS
SAFEs can be useful at pre-seed, but stacking them across rounds risks severe hidden dilution. Founders should treat priced rounds as a necessary step to maintain transparency and control over their cap tables.
How Many Rounds Does It Take to Build a Unicorn?
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