Private equity investors
Underwrite where AI is changing competitive position, pricing power, moat durability, and the timing of value transfer before the market reprices the asset.
Real-time diligence for AI disruption
AI disruption risk is real.
It can be a headwind or tailwind for you. As LLM interfaces, agentic workflows, and data systems converge, companies that do not evolve fast enough risk losing workflow control, pricing power, and share of the next profit pool. Sophisticated operators are already working with us to analyze the shift and update their operating position before the market resets around them.
Who it is for
This work is for teams making strategic and operating decisions now, before AI-native competitors reset the market around them.
Enterprise SaaS leadership teams
See where your product, workflow, and data position are exposed, and which operating moves improve revenue, margins, and category control.
Operating partners and transformation leaders
Focus post-investment and cross-functional execution on the few product, workflow, and pricing changes that can move a company into the next profit pool.
Where value shifts
We focus on the three areas already reshaping category economics in software markets.
LLM user experiences
Identify where AI-native interfaces can displace legacy workflows, compress time-to-value, and reset willingness to pay.
Agentic workflows
Assess where orchestration, automation, and outcome ownership shift power away from incumbent products.
Data readiness
Measure whether the company has the data quality, instrumentation, and process control required to capture the next profit pool.
What we deliver
The output is not generic AI commentary. It is a decision tool for identifying risk, upside, and the changes required to stay ahead of the market.
Market shift map
Show where AI-native interfaces and workflows are already changing buyer behavior, product value, and category economics.
Competitive position diagnosis
Separate headline AI features from durable workflow control, data advantages, quality loops, and trust-sensitive moats.
Priority actions
Translate disruption pressure into concrete product, pricing, workflow, and go-to-market moves that improve position.
Executive-ready slideware
Deliver a board-level narrative that turns technical change into valuation, roadmap, pricing, and operating decisions.
How to evaluate the shift
Start with the methodology to see how we analyze the market, then use the FAQ to pressure-test the questions that matter most for investors and operating teams.
Methodology
See how we evaluate disruption risk, workflow control, pricing power, and data readiness before we recommend the actions that improve position.
View MethodologyFAQ
Read direct answers to the questions investors and operators ask about AI disruption risk, defensibility, and what to do next.
Read FAQLive feed
Latest findings
Read concise, evidence-backed views on where AI is changing competitive position, pricing power, workflow ownership, and profit-pool capture. Each post is designed to help investors and operators make better strategic decisions faster. View all
Early Recursive Self-Improvement is being deployed in the AI labs
The next competitive jump may come from AI systems that increasingly improve code, evaluation, and workflow outputs in tighter loops with less human latency.
100% of code is starting to be written by AI
The important shift is not whether every line is literally AI-written. It is that AI can now author a large share of software output, which changes engineering leverage, cost structure, and competitive speed.
Product development roles stay the same but the focus changes
Product teams are not disappearing. Their focus is shifting toward defensibility risk, including data readiness, learning loops, user experience surfaces, and operational world models.