Clouded Judgement

ZIRP is over and startups are back to hard mode > In 2021 and the first half of 2022 (when most of the 2022 activity happened) we essentially crammed 5 years worth of funding into an 18 month period. Series Bs and Cs were raised when companies were at the typical Series A milestones. Normal round sizes doubled or tripled. Every type of investor was broadly operating in a “risk on” mindset given the ZIRP environment, and the venture capital ecosystem was no exception. From https://cloudedjudgement.substack.com/p/clouded-judgement-11224-hard-t…

Jamin Ball had a better take on Clouded Judgement https://cloudedjudgement.substack.com/p/clouded-judgement-121225-long-… "Long Live Systems of Record"

There is a correlation analysis in Jamin Ball's "Clouded Judgement" substack [1] which shows the correlation between next twelve month ("NTM") Revenue Multiples and Revenue Annual Growth Rates for public market tech / SaaS stocks. The current Slope-Intercept is (NTM Revenue Multiple) = 36.677*(NTM Rev Growth Rate) + 2.0013. If Wiz is doubling revenue (100% Growth Rate) and they are at about $500M of revenue today [2], then the multiple according to that calculation is ~38.7 X Next Twelve Month Revenue ($1B) or $38.7B. So, the price is in line with the market...or you could argue even a discount to it. [1] https://cloudedjudgement.substack.com/p/clouded-judgement-31425-authen… [2] https://www.barrons.com/articles/google-stock-price-wiz-deal-alphabet-…

Here's a good analysis of the financials in the S1: https://cloudedjudgement.substack.com/p/gitlab-benchmarking-the-s-1-da…

Doing post-mortems on my agent's failures over the holidays made me realize the problem isn't the model. It is the lack of a deterministic inference-time verification layer. I spent the break reading the recent Stanford/Harvard paper on agentic adaptation [1]. Their research provides mathematical proof for what I experienced in Q4: supervising only final outputs is a dead end. Agents learn to "ignore tools and improve likelihood," meaning they learn to lie more convincingly to pass evaluations while the underlying logic rots. I call this the Agent Lobotomy. The agent I have in production today is significantly dumber than the one I demoed in December. I was forced to strip autonomy, remove context, and add human checkpoints because I could not trust the probabilistic output. We are stuck in an Autonomy Retreat, creating an Authority Bottleneck [2] where agents are relegated to assistive tasks because the tail risk of autonomous action is too high. I built Steer (open source) to stop the bleed. In v0.4.0, I moved the architecture to an Agent Service Mesh pattern. Instead of decorating every function, you patch the framework (e.g. PydanticAI) at the entry point. It auto-discovers tools and enforces a reliability policy globally via deterministic Reality Locks. The real unlock is the data. By capturing the delta between a Blocked Response and a Taught Fix, Steer acts as a synthetic data factory for DPO. It moves reliability from a runtime tax to a training asset, allowing…