What's new?

They can see just usage metrics. Not who did what.

the sweet spot isnt the $100. its whether you can trust @OpenAI Codex output in production. we built 70+ agents, learned that shipping more code faster without better testing just moves the failure point downstream.

5x more Codex = 5x more AI-generated code shipping to prod. built 70+ agents, only 7 reached production safely. @OpenAI is scaling the output side fast. nobody's scaling the testing side. those two things have to meet eventually.

5x rates for long high-effort sessions is fair. the real question nobody's asking: at that usage level, are you testing your @OpenAI Codex agents the same way you'd test production code? most teams aren't.

@OpenAI chasing $100B in ads while @AnthropicAI builds the enterprise compliance stack. one of these revenue models means AI can actually be deployed in regulated industries. the other means more banner ads.

spent 2 years watching enterprise AI agents fail audit. the two questions that kill every deployment: who approved this agent to run, and what did it actually touch. @AnthropicAI just answered both with RBAC + expanded OpenTelemetry.

the hard part isn't agents making payments -- it's what happens when they make the wrong one. @Visa is smart to build this but who's testing these flows before prod? compliance on autonomous purchases is still basically unsolved

curious what you do when the agent goes off-script and you're not watching -- how do you catch state drift remotely? at Ziplo we built around exactly this gap

the valuation gap makes sense once you realize @OpenAI is betting on consumer. @AnthropicAI is the enterprise infra play. enterprise deals close slower but don't churn. 5yr from now that delta flips

curious how skill validation works across different agent runtimes. one bug in a shared skill = every agent using it fails. portable power, until it quietly misbehaves at 3am @github

orchestration maybe. testing doesn't. @AnthropicAI managing your agent's infra doesn't mean it behaves correctly. built 70+ agents - the failures were never hosting, always "did it do the right thing in prod"

managed infra is a win. but who validates the agent's behavior once @AnthropicAI owns the plumbing? built 70+ agents, only 7 reached prod. hosting was never the failure mode

exactly. we saw this with 3 pilots. teams spent months on LLM selection. the agent broke in week 2 because nobody mapped the exception paths first. tool was fine. tribal knowledge wasnt.

VCs poured $242B into AI in Q1 2026. 80% of all global venture funding. yet most teams shipping agents can't tell you which ones will survive prod. we launched 70+ at a financial firm. 7 made it.

classic moat strategy. ban the adapter layer, then launch your own. curious how the @AnthropicAI managed agent handles edge cases that only surface in your specific codebase. that's the part that never generalizes.

managed infra is the easy part. the @ycombinator cohort that dies is the one that thought orchestration == product. the gap @AnthropicAI can't fill: testing whether the agent does what you need it to do in your specific context.

built 70+ of them. the scary part isn't that they're smart - it's that when they're wrong, they're confidently wrong. 7 made it to prod safely. the other 63 failed in ways we almost missed.

curious what happens with agents running @OpenAI Codex long-term in production. one-off review bias is one thing, but cascading tool calls that inherit that preference? built 70+ agents and saw exactly how style drift compounds.

the model can't access your data. the real risk is AI agents built on top of it with no guardrails. @AnthropicAI's Mythos is scary-good but 7/10 builders skip validation tests. that's the actual 10.