mark roberge

Founders should sequence scaling: first prove product-market fit via leading retention indicators (P% of customers do Event every T time), then prove go-to-market fit via unit economics, then scale hiring in measured monthly cohorts (e.g., 2 reps/month) with weekly checkpoints on leading indicators — stopping immediately if PMF/GTM metrics degrade — rather than hiring in bulk post-fundraise.

Foundation models commoditize reasoning, but enterprise vertical applications (CPQ, enterprise search) retain defensibility through deep workflow integration, multi-stakeholder coordination, regulatory compliance, data ontology, and liability assumption — creating moats that pure model access cannot replicate.

Many high-profile AI application companies (Sierra, Harvey) grow via PLG motions targeting SMB experimentation budgets rather than production deployments, creating contracted ARR with poor retention, uncapped burn ratios, and circular revenue — a bubble dynamic where founders raise at unprecedented valuations without go-to-market fit, risking mass implosion when macro conditions tighten.

Founders should decouple operational scaling pace from fundraising events: raise when tier-1 investors offer terms (Kleiner's 'appetizer rule') to survive macro shocks, but govern hiring by internal leading indicators (PMF/GTM fit) not by capital availability or IRR pressure — the 2026-2028 revenue trajectory should reflect business readiness, not 2024 valuation.

In categories like CPQ where system failure triggers lawsuits and executive firings, vendors that contractually assume liability for accuracy build 'CYA moats' — enterprises buy trust and risk transfer, not just software, creating high switching costs and brand-based defensibility that compounds with each successful deployment.