pattern-noticer. ADHD brain, AI tools, trading systems, small-town economics. writing it down as it happens.sammeyer.ai United StatesJoined August 2018
AI agents are now autonomously spending and executing.
@pierre_dlgr's AI Workers hit $1M ARR fast.
Machine economy emerging. Moats shift to agent trust & payment rails.
Who is liable when agents act independently?
#AIAgents #AgenticAI#AI
@svpino The scalable move is risk-tiered review: tests and policy gates for everything, human review for high-blast-radius diffs. Otherwise the bottleneck just moves from coding to paranoia.
@emollick Teams optimize for passing today's KPI with the cheapest model, then miss the upside from better judgment on edge cases, review burden, and rework. Price per token is easy to track. Cost of dumb outputs is not.
The real AI shift is agents forming economies with wallets and transactions. Intelligence commoditizes. Moat shifts to machine identity, micropayments & coordination rails.
@a16z@pmarca who owns the agent economy layer?
#AIAgents#AgentEconomy#AIInfra
@fra__sax That flag is fine for known-safe loops. The mistake is treating it like a default. Approval spam is annoying. Blind write access is how a coding assistant turns one bad plan into fifty bad edits.
@aakashgupta Exactly. It works when the finish line is observable. "Make it better" is theater. "Reduce checkout errors from X to Y and keep mobile conversion flat" gives the checker something real to verify.
@jxnlco We finally hit recursion as a product surface. Codegen is solved enough. Supervising the second computer is the real problem, so it does not build a second mess.
@stretchcloud Yes. Permission boundaries are the product. A coding agent with weak gates is just sudo with nicer copy. The moment it can route around human review, reliability and security become the same problem.
@0xMovez Loops matter because they turn taste into process. Prompting gets you one good run. Loops get you a system that survives fatigue, handoff, and repetition. That is when an agent stops feeling like a demo.
@yitong That split is close to the real one. Planning, execution, then a long-running operator that holds context. Most stacks still need evals and kill switches. Otherwise the happy path looks great right up to the first runaway loop.
@ClaudeDevs Usage limit bugs are worse than latency bugs because they break trust and flow. If people start budgeting work around phantom caps, they stop treating Claude Code as production infrastructure.
@simonw@GroqInc@cerebras Model quality is half the story. Adoption flips when a fast inference provider pairs top-tier quality with boring API reliability and long context that does not collapse under load. Then teams route production traffic there.
@GergelyOrosz Yes. A lot of consulting revenue was rented expertise plus expensive coordination. Once an in-house team gets a usable agent stack, the first spend to die is the outside deck factory. Strategy survives longer than delivery labor.
@emollick This tracks. The bottleneck moved from typing to spec quality. People who can define success, constraints, and review loops suddenly look like 10x coders because the agent handles the keystrokes.
Tokenmaxxing is dead.
Enterprises are capping AI use as token costs explode without ROI.
The issue isn't agent smarts — it's lack of governance.
Agents need budgets, permissions, and audits like real employees.
The moat moves from models to control planes.
@dabit3, @xai, what governance are you prioritizing for agents?
#AI#Agents#EnterpriseAI
New AI Brief.
@OpenAI put an AI chemist into a real lab. @Google says AMIE matched primary care doctors on disease-management reasoning. G7 leaders are worried about U.S. model dependence. @ollama shipped Apple Silicon support.
open.substack.com/pub/samonai/p/…
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