I founded Seed Club, a venture network that turns the conviction of the people founders most want on their cap table into coordinated capital. 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 September 7, 2026
Verification, not execution, is AI's binding economic constraint
AI is collapsing the cost of executing easily verifiable tasks while leaving verification itself as the bottleneck where economic value concentrates. Whether model intelligence is commodity-like, capable of reproducing itself, is the open question for frontier lab defensibility.
- Software revenue per employee has gone parabolic, signaling early real-economy impact
- Intelligence that can copy itself may be structurally undefendable without regulation
- Verification costs, not execution costs, determine where value now concentrates
Pacing reframe splits AI safety from incumbent interest
Anthropic's 'pacing' proposal reframed the AI slowdown debate while drawing scrutiny of whether safety arguments serve genuine risk reduction or entrench existing labs financially. Open-source access emerged as the structural counter-argument to regulatory capture by incumbents.
- The regulatory push is tied to Anthropic and OpenAI financial interests
- Open-source models are the main structural check on incumbent consolidation
- No individual or collective action can reliably stop ASI development
Open-source AI is the best defense against centralization · @AlexanderLong Dismissing AI doom while calling out those who keep building anyway · @BedoyaUSA Sacks: Anthropic and OpenAI are the very frontier they want to pace · @MattHartman AI regulation push tied to groups with financial stake in it · @kevinnbass Dario's 'pacing' reframe sidesteps old AI slowdown debates · @lulumeservey Open source AI predicted to face ban following a major disaster · @tszzl AI safety fears may cover a pivot away from hyperscaling · @izakaminska Anthropic proposes slowing AI development by one to two years · @ivan_bezdomny 76% of engagement on Coxon's Anthropic post came from abroad · @ParkerThayer No individual or collective action can reliably stop ASI · @romanhelmetguy AI alignment debate should target consequences, not just training · @MattHartman
AI agents shift from demos to production infrastructure
Cursor Projects and a new Agents API launched persistent coordinator-based workflows, while long-horizon agents completed multi-day autonomous tasks on challenging platforms. The enterprise agentic AI market is projected to grow roughly ninefold by 2030, with startup moat erosion emerging as the central strategic question.
- Persistent coordinator agents replace per-task chat as the default workflow model
- Agent computer-use may erode startup differentiation as capabilities become general
- Non-engineering teams are already running fully on agent-based tooling
AI booking agents may force restaurants to raise cancellation fees · @jonahsaullow Cursor launches Projects, a persistent agent workspace for tasks · @cursor_ai Cfo.ai launches agent that builds financial models in 30 minutes · @hnshah AI agent completes full video edit in DaVinci Resolve via MCP · @orenmeetsworld Running marketing and ops teams like engineering teams · @reallygabriella Long-horizon AI agent completes multi-day Reddit engagement task · @FredaDuan New Agents API launches for building cloud-based AI agents · @stevendcoffey Use long AI agent runs to optimize, then rank the changes · @nateberkopec Summation raises $35M from Benchmark and Kleiner for AI analyst · @ianwong_ Agent AI may erode startup moats as models gain new capabilities · @mignano Enterprise agentic AI market set to grow 9x to $24.5B by 2030 · @_The_Prophet__ world model for a business' is next · @jeffreyhuber
Mega funds squeeze mid-sized VC on returns and access
Mega funds are capturing disproportionate absolute profits despite worse IRR while pricing mid-sized firms out of rounds, and LP incentive structures make redirecting capital toward better-returning smaller funds structurally difficult. Inception-stage investing now demands higher conviction as consensus forms faster.
- Small funds produce better IRR but LP mandates favor large managers
- Mid-sized firms struggle to get meaningful ownership even when they access deals
- Category bets are a megafund narrative, not how successful companies actually emerge
Inception-stage venture demands higher conviction in a consensus era · @gdibner VC power law critique questions whether category bets drive returns · @credistick Institutional LP constraints trap capital in mega venture funds · @arian_ghashghai Top 5% of VC funds capture 90% of the industry's net profits · @arian_ghashghai Mega funds are squeezing mid-sized VC firms out of deals · @nic_detommaso VC complaints about missing founder updates are just entitlement · @E_Bruxxx