Long-form writing on agentic AI in production, AI go-to-market, and the industry. Long form because the short form is always wrong.
Anthropic just made agent infrastructure a commodity. That doesn't threaten companies whose moat is data. It threatens companies whose moat was plumbing.
What the Claude Code leak actually reveals about building AI agents 03 AprAnthropic accidentally open-sourced 512,000 lines of Claude Code internals. Forget the drama. The code is a masterclass in agentic architecture, and every company building AI agents should be studying it.
Revenue systems need memory 01 AprThe same problem that makes most AI systems useless for serious work is what makes revenue systems so brittle. The fix is the same too.
The evaluation problem nobody wants to solve 29 MarEveryone wants to ship AI. Almost nobody wants to build the evaluation framework that tells you whether it's working.
Guardrails are the product 22 MarMost teams treat AI guardrails as a safety feature bolted on at the end. In production, the guardrails are what actually makes the system usable.
The learning gap is the real AI crisis 15 MarThe biggest barrier to enterprise AI isn't model quality or regulation or talent. It's that most AI systems can't learn. And almost nobody is building to fix this.
The death of the demo 12 MarThe traditional software demo is becoming counterproductive for AI products. When you're selling intelligence, not features, the whole buyer engagement model needs to change.
The case for a revenue intelligence layer 08 MarDeal context lives in a dozen different systems. What would happen if something actually stitched it all together?
Your forecast is a fiction 22 FebRevenue forecasting is narrative-based, not evidence-based. Sellers declare stages, managers interpret stories, leadership aggregates opinions. AI can fix this, but most organizations aren't ready for that level of transparency.
Why your CRM can't handle AI deals 19 FebCRM systems were designed for linear SaaS buying. AI deals are non-linear, multi-threaded, and governed by organizational readiness. Your pipeline view can't represent any of it.
Build vs. buy is the wrong question for AI 08 FebEveryone asks whether to build AI internally or buy from vendors. It's the wrong question. What actually determines success is how fast your organization can learn.
How sales changes in the AI era 22 JanAI isn't a harder version of SaaS to sell. It's a different commercial problem entirely. The entire revenue stack needs to be rebuilt.
95% of enterprise AI tools never make it to production. The MIT study quantified what builders already knew, but the reasons aren't what most people think.
What the MIT AI study actually reveals 16 NovEveryone is citing this study as proof that AI fails. They're reading it wrong. The study shows why organizations fail at building systems, period.
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