No thought-leadership fluff. Working notes on AI execution, capital discipline, and what actually happens when you build companies for the long term.
The 'move fast and break things' mantra is obsolete. In 2026, complex AI systems and interconnected infrastructures demand a deliberate, sustainable approach to company building.
We're building agent workflows all wrong. Blind faith in full autonomy is creating brittle, expensive systems. A more skeptical, human-centric approach is needed to unlock real value.
Venture-backed companies often optimize for short-term growth at the expense of long-term vision. A permanent capital model offers a potent alternative.
True AI adoption isn't about replacing humans, but amplifying their abilities. We explore how collaborative intelligence, built on trust and specific workflows, unlocks untapped potential.
Building everything in-house feels safe, but it's often a slow path to commoditization. AI agents are changing the calculus: build only where core IP creates real differentiation and strategic lock-in.
AI products are shipping with alarming security flaws. Shifting left on security isn't enough; we need secure-by-default architectures to prevent a cascade of costly breaches.
The hype around AI agents often overlooks the critical role of robust DevOps and security infrastructure. Their maturation is the key to unlocking scalable, secure agentic workflows.
Bedrock's new guardrails signal growing enterprise awareness of AI safety, but true responsible AI requires deeper investment in model evaluation, data governance, and human oversight.
The relentless pursuit of hypergrowth often overshadows the virtues of sustainable, profitable businesses. We shifted our focus to building 'camels': resilient, adaptable ventures designed for the long haul.
The relentless pursuit of capital efficiency can blind companies to long-term opportunities. Capital intensity, when strategically deployed, can be a powerful moat.
AI regulation is shifting from simple safety checks to demanding clear, scalable explanations of model behavior, especially in complex enterprise applications. This will reshape development strategies.
The proliferation of enterprise AI demands a radical shift from reactive safety measures to proactive governance structures. Companies that fail to adapt will face existential risks.