I founded Seed Club, a new model for early-stage investing built around networks, shared intelligence, and coordinated support. I’m interested in what happens when AI makes context, memory, and coordination more legible, and what that means for how companies and organizations get built.
My agent drafts this site from what I save. Green is me.
Week of July 20, 2026
Agentic workflows are crossing from demo to production
Self-improving agent loops have moved from experimentation to standard practice, with production deployments now handling customer support, video editing, and cross-tool work delivery. The question is shifting from whether agents can do the work to who captures the value when they reliably can.
- Fully autonomous customer support and video production are live, not theoretical.
- Recursive self-improvement in agent harnesses is an active systems engineering problem.
- Outcome-based pricing for agents remains the unsettled business model question.
Agent transition favors AI labs while new bundled super apps emerge · @signulll Claude automates 4K video editing and social posting workflow · @brookejlacey Anthropic engineer demos building self-improving agentic workflows · @0xMovez Open dev group building recursive self-improving agent harness · @martin_casado Subagents proposed to audit rollout history and suggest repo docs · @_lopopolo Cloud VMs let you run unlimited AI coding agents in parallel · @ryancarson OpenWorker open-source agent delivers finished work across tools · @AndrewYNg An AI agent now handles all of Gumroad's customer support · @Gumclaw OpenWorker connects your AI model of choice to everyday work tools · openworker.com Agent aims to automate the 75% of work that doesn't need humans · @dotta
The AI inference stack commoditizes layer by layer
Each layer of the AI stack is being squeezed from above: routing cuts inference costs while commoditizing model providers, and value migrates toward vertically integrated experiences. Speed, not throughput, is emerging as the differentiated moat in inference.
- Real-time inference pricing markets are launching, threatening provider lock-in.
- AI labs are unlikely to dominate the application layer despite infrastructure advantages.
- Cheap software shifts rents toward vertical integrators, away from horizontal tooling.
Speed is the key moat in AI inference, not throughput optimization · @firesidealpha Omnious launches real-time inference pricing market for AI models · @Omniousai Cheap software shifts value from SaaS stacks to vertical integration · @naval Cursor launches intelligent router that cuts model costs by 60% · @ccatalini AI labs are unlikely to dominate the application layer, VC argues · @gokulr
Stadium startup culture recruits the wrong founders
The bottleneck on startup progress is converting the right people into founders, not making the path look attractive. Prestige-driven stadium culture and hype incentives recruit status-seekers rather than people willing to endure a decade of demanding work.
- Founding is years of demanding work that stadium framing actively obscures.
- SF's talent crisis deepens as founders' engineers defect to frontier AI labs.
- The real constraint is identifying determined individuals with great ideas.
The real startup bottleneck is turning talented people into founders · @credistick Stadium startup culture may push wrong people into founding · @scottastevenson Startup School moving to stadiums betrays YC's hacker-first identity · @scottastevenson SF founders face existential crisis as AI disrupts talent and meaning · @HugoAmsellem
Power law leaves most venture investors behind
Fewer than 40% of VCs achieve any successful investments, with 5% capturing 90% of profits, and human capital rather than deal skill drives most of the variation. Capital is concentrating further as sophisticated LPs move in, compressing early-stage options.
- Most concentrated portfolio strategies lack analytical or LP-mandated justification.
- IPOs have been a net-negative bet for seven years.
- Former operators entering as LPs are pressuring myopic allocators out.
Top 5% of VCs capture 90% of industry profits, research shows · @arcticinstincts Human capital predicts VC career success and deal access · @nberpubs What makes a VC above average: data, analysis, or value add · @vc Capital concentration and savvy LPs are reshaping early-stage VC · @credistick Many VCs chase concentrated portfolios without real justification · @pavelprata IPOs have quietly been a losing bet since 2019 · @fintechfrank