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 6, 2026
Agentic infrastructure is now an engineering discipline
Personal and team agents are moving past simple prompting into layered operating systems with routing logic, skill libraries, voice calibration, and self-cleaning tasks. The bottleneck is no longer model quality but the engineering required to make models run smoothly without constant human intervention.
- User-specific attribution in shared agents is an unsolved coordination problem.
- Style and clarity rules embedded in agent configs measurably improve output quality.
- Internal company agents automating 90 percent of busywork signal a new workflow baseline.
For people using codex to draft emails and slacks, try this simple 1x… · @strimblez A well structured personal agent should, work through iMessage… · @ianlapham I'm just going to dump my whole agentic setup out here, because I see… · @jamonholmgren Fable and Sol tend to use a lot of jargon that's hard to understand… · @johnnyheo A small style rule can materially improve agent performance: ask for plain user-facing explanations without sacrificing · @johnnyheo Strong agentic coding is not just better prompting · @jamonholmgren One problem with multiplayer AI is that you want to share agents, but… · @dotta Sierra's internal AI agent automates 90% of coding and busywork · @vijayiyengar The real shift is not just having coding copilots · @vijayiyengar
Foundation models unlikely to win every vertical application
Base model providers can threaten many application categories, but the hard advantages in any vertical lie in workflows, go-to-market, and servicing rather than code, making it unlikely that any single platform company wins across the board.
- Data network effects may still favor large shared models over personalized alternatives.
- Many distinct AI systems trained on local values may outperform one universal model.
- Open source AI infrastructure maps cleanly onto DeFi roles, clarifying competitive dynamics.
Every AI provider that isn't winning the data network effect is… · @scottastevenson It is highly unlikely that the foundation companies will be… · @gerstenzang The strongest AI systems may keep improving because they absorb learning from massive shared user bases, creating a · '@scottastevenson' Base model companies may be able to threaten many application categories, but that does not mean they will win · '@gerstenzang' A DeFi guide to Open Source AI market structure Harnesses are Wallets… · @tarunchitra A useful way to understand open source AI infrastructure is to map it onto DeFi roles · Tarun Chitra Today we share the worldview behind our mission. · @miramurati A good AI future may require many distinct systems rather than one universal model · '@miramurati'
Investor consensus systematically lags founder and market reality
Consensus thinking among investors tends to be a lagging abstraction, rewarding familiar archetypes over genuine outliers and missing the companies that will matter most. The structural incentive inside most venture firms reinforces conformity rather than the ambitious contrarianism they claim to practice.
- Most venture firms depend entirely on one or two partners and will not outlast them.
- Founder-centricity rhetoric often masks a strong preference for a narrow, comfortable archetype.
- LLMs now make LP lookthrough portfolio analysis fast, raising the bar for fund differentiation.
Investor consensus is wrong almost 100% of the time. · @juliey4 This is not to be confused with venture capital’s often-performative… · @credistick Generic investor consensus is often a lagging abstraction built from pattern-matching across markets, while founders · '@juliey4' Venture often claims to be founder-centric while rewarding conformity, polish, and scale theatre over real · '@credistick' Most venture firms behave more like partner-dependent cashflow businesses than durable institutions · @auren 99% of venture capital firms are just lifestyle businesses. · @auren I'm an LP in roughly 25 VC funds. · @andrewparker A lot of venture value comes from small promises kept, not just advice given · '@jgreze' LLMs make fund lookthrough analysis far easier for LPs by turning a tedious manual mapping exercise into a quick way to · Andrew Parker
Current AI cost structures are a temporary inefficiency
Spending heavily on inference for lightweight applications today is analogous to paying high gas fees to mint NFTs in 2021, a phase that will be resolved by optimized cost layers rather than sustained as a permanent equilibrium.
- AI model recommendations are already generating measurable organic product signups.
- Investors buying irreplaceable assets like sports franchises suggests a hedge against AI commodification.
- Years in crypto are no longer a reliable proxy for founder quality as tooling matures.
Spending $1000 on inference over the weekend to vibecode your toy app… · @johnpalmer In April @ThriveCapital launched a permanent capital vehicle for… · @ssokol94 An observation The number of years in crypto used to be strong signal… · @CarlKVogel Some of the investors funding AI are redirecting gains into assets technology cannot easily commodify, such as sports · '@ssokol94' Spending heavily on inference for lightweight AI apps is a temporary phase, not a durable equilibrium · John Palmer Years spent inside crypto used to be a strong proxy for founder quality because the ecosystem was technically obscure · '@CarlKVogel' AI model recommendations are driving a new wave of organic signups · @zenorocha Some products are starting to get meaningful organic growth because language models recommend them directly to users · @zenorocha