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 June 15, 2026
Open models are closing the frontier performance gap
As capable open models approach near-parity with closed frontier models, the cost differential is becoming decisive. Frontier labs have priced for margin rather than compute cost alone, and that margin looks fragile when open alternatives offer roughly 90% lower cost at a 10% performance penalty.
- Subsidized inference may have been masking open model viability all along.
- Token cost pressure at scale is pushing legal AI toward post-training open models.
- Capital allocated to frontier AI could be mispriced if intelligence commoditizes.
Frontier model pricing reflects quality, margin and scale economics · @tugot17 Harvey turns to open models as token costs crush margins · @willchen500 GLM 5.2 could ignite a lasting boom in rented GPU demand · @goodalexander AI teams face a brutal new era of token ROI and cost control · @adambcohen93 GLM-5.2 could be the first open model to replace closed ones · @gneubig AI capex may overshoot demand and trigger a boom-bust cycle · @orrdavid Open source AI progress could push governments toward censorship · @goodalexander A 10% intelligence gap at 90% lower cost with $5 trillion at stake · @zerohedge The chart that breaks the AI trade when intelligence gets cheap · @_The_Prophet__
Multi-agent orchestration reframes what a model is
The Sakana Fugu launch illustrates a structural shift where frontier-level performance is achieved by orchestrating swarms of smaller models rather than scaling a single one. Intelligence is beginning to look less like a product and more like a supply chain.
- Codex-style loops can audit, test, and fix entire codebases autonomously at scale.
- Effective loops require real-browser verification and environment tooling, not just prompts.
- The single-model API abstraction increasingly conceals a multi-agent system beneath.
Codex loops can audit, test, and fix apps at massive scale · @tomosman Builders are swapping examples of Codex and Claude loops · @argofowl Sakana launches Fugu to match frontier AI through orchestration · @SakanaAILabs AI orchestration turns models into a commodity supply chain · @_The_Prophet__ Agent loops get stronger with better testing and tooling · @ctatedev Sakana Fugu runs a learned multi-agent swarm behind one model API · sakana.ai A builder names the three-tool AI agent stack as his best workflow · @yitong
Venture capital concentration is distorting deal quality
Capital has consolidated into larger funds whose fee structures push toward consensus bets and narrative-aligned investments, leaving genuine outliers chronically underfunded. Seed is effectively two markets, with roughly 10% of deals capturing half the dollars and most headlines.
- Emerging managers lost ground during the ZIRP boom, not after it.
- Fee incentives drive GPs toward scale and safety rather than discovery.
- Founders can resist valuation pressure by forcing investors to name a counter-number.
Founders should force investors to make real pricing offers. · @amiklas Fund economics push venture investors toward safer consensus bets · @gdibner A small hype segment dominates seed capital and media attention. · @tmrohan Venture is consolidating into larger funds over diversification · @credistick Capital concentration has distorted venture more than most admit · @Rick_Zullo Early-stage venture is having a standout year as a category · @adityaag Emerging managers lost ground through ZIRP, not the reverse · @credistick
AI is splitting engineering teams and repricing software
Generative coding tools are fracturing engineering into those who generate and those who review, creating a visible class divide and a profession-wide identity crisis. Simultaneously, software pricing is shifting away from seat licenses toward alignment with actual business outcomes.
- The 'context layer,' infrastructure making AI useful for real codebases, is a major emerging VC thesis.
- Coding agents remain too 'software-brained' to generalize cleanly to broader knowledge work.
- Taking humans fully out of high-stakes loops remains a 'brutally long slog' even with mature tooling.
Coding agents still struggle to fit real knowledge work · @emollick Startups race to build the context layer for engineering AI · @GergelyOrosz AI coding is splitting engineering teams into pushers and reviewers · @deedydas AI is pushing software from seat sales toward paid business outcomes · @rodriscoll Taking humans out of the loop is brutally hard but worth it. · @Jason Howie hit 50% autopilot after years of human-in-loop scheduling · @awwstn