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 29, 2026
Agents reshape dev workflows but not the need for comprehension
AI coding agents are replacing traditional specs with leaner formats and automating codebase discovery, yet a strong counter-current holds that developers who cannot read and understand agent-generated code will lose the ability to catch errors and maintain systems.
- Product specs are replacing PRDs as the primary unit of product work
- Internal AI tools now handle both execution and institutional memory at fast-growing companies
- Scanning for unknown unknowns in a codebase is becoming a standard workflow step
My take · Jess · Jul 4
While many fear what ai will mean for startups and software products, I'm a big believer that the firm is becoming software, and startups have an advantage at first principles rethinking how entire industries are serviced with ai.
New coding workflow probes known and unknown gaps in codebases · @dzhng Sierra says internal AI agents became core to running the company · @noahhorner_ Product specs replace PRDs in agent driven software teams · @gokulr Developers still need to understand code written by agents · @kieranklaassen Agent loop turns user interviews into paying software · @pzakin Developers still need to understand code written by agents · @geoffreylitt
Frontier AI moat claims are fragmenting under pressure
The winner-take-all narrative around frontier models is under strain as open-source alternatives close capability gaps and enterprise trust in closed labs erodes, while competing moat claims across model providers, harness builders, and token sellers undercut any single dominant position.
- Benchmark gains on weaker models do not replicate frontier output quality
- Open-source hosting lets enterprises sidestep data-sharing concerns entirely
- Cost pressure from open models is forcing a rethink of AI security assumptions
Venture capital built the wrong AI and pension savers may pay · @AndrewOrlowski AI vendors push conflicting moats across models, tooling and tokens · @0interestrates Benchmark gains do not prove weaker models match frontier AI · @alexframegreen Claims that AI will be winner take all may not hold · @sebkrier Open models gain ground as trust in frontier labs frays · @gbrl_dick AI capability gains still look early, not near commoditization · @deanwball Open source model costs are forcing enterprises to rethink AI security · @quxiaoyin AI progress may depend on economic capability, not tokens · @lefttailguy
Venture pricing detaches from fundamentals, echoing 2021
Senior partners at prominent funds are warning that valuations have become untethered from reality in a pattern matching 2021, while megafund fee structures, SPV fraud in secondaries, and geographic bias compound the disadvantage for limited partners and non-Bay Area founders.
- Angels function as credibility signals and intro nodes, not only capital sources
- Megafund fee drag can eliminate hundreds of millions in LP returns versus PE peers
- Secondary market fraud and ghost shares go underreported because victims find it embarrassing
Angel investing works on belief and affinity, not VC logic · @jayyeh Early angel rounds work best when founders start close to home · @christophersaum Top venture investors warn the funding boom looks like 2021 · @madhavchanchani VC megafund fee terms can erase gains for LPs · @credistick Founders should build an angel base before pitching VCs · @jayyeh Fraud and ghost shares taint startup secondary investing · @rsg European founders struggle to raise from Bay Area VCs · @arian_ghashghai
Token and equity alignment stays structurally unresolved in crypto
As projects raise equity alongside tokens, the question of whether token holders and shareholders have aligned incentives remains contested, with protocol-level burn mechanics earning the most trust and dual structures drawing sustained skepticism.
- Long-duration warrants on tokens signal genuine institutional conviction
- Robinhood's DeFi yield mixes native lending with incentive campaigns on a rival's rails
- Utility tokens with on-chain revenue visibility offer a rare verifiable case
Robinhood 7 percent DeFi yield relies heavily on incentives · @HorinekPM Robinhood DeFi launch ties a rival product to Coinbase rails · @sealaunch_ Venice token thesis leans on burns, profits and agent markets · @nikshepsvn Venice offers a rare case for a true AI utility token · @DrNickA Crypto token structures hinge on trust and alignment · @knimkar Crypto projects may still need both tokens and equity · @lex_node
Enterprise AI trust and security concerns grow
Anthropic's developer tools face escalating enterprise trust problems, with unverified spyware allegations and claimed geo-targeted surveillance checks in Claude Code driving bans, while questions about whether AI outputs can be reliably verified at all deepen the credibility gap.
- Alleged timezone checks targeting Chinese users have prompted enterprise-wide bans
- Fable 5 jailbreaks reportedly added no novel capabilities beyond existing models
- Hard-to-verify AI output signals a product design failure, not just a technical limit
My take · Jess · Jul 3
While interest in this seems to be picking up, Im surprised by how little concern enterprises have over protecting their data and workflows from model providers or integrators. I expect this trend of increased caution to continue.
Anthropic restores Fable 5 with tighter cyber safeguards · @alexstamos Security concerns trigger enterprise ban on Claude Code · @__tinygrad__ Alleged client-side checks raise trust concerns around coding tools · @AISuperDomain Poor AI product design can make outputs impossible to verify · @HamelHusain