Build real machinery
Start with an operating problem that has consequences: revenue, delivery capacity, hiring, pricing, reporting, or decision memory.
Signals for the Adaptive Firm
The adaptive firm is being built in public.
Black Squirrel Labs builds AI-native operating systems against real finance and firm work—then publishes what holds, what breaks, and what changes next.
Now in the lab Agentic Firm Simulator
Observed variable
Learning velocity
Experiment 001 · In development
A consulting firm you can safely break.
The Agentic Firm Simulator is a deterministic, month-by-month model of a professional services firm. Change demand, pricing, delivery mix, hiring, or policy—and see the operating consequences propagate through the firm.
It is not a polished demo pretending the hard parts are solved. It is a controlled environment for finding where AI-native operating ideas survive contact with accounting, capacity, and cash.
The Method
Build it. Stress it. Publish the delta.
Black Squirrel Labs treats the operating model—not the opinion—as the unit of research.
Stress before story
Run the edge cases. Check the ledger. Preserve the failed assumptions. A compelling narrative is not evidence that the system works.
Publish what changed
Turn the build log into a useful signal: what held, what broke, and what a firm leader should do differently because of it.
The Premise
Professional services has a cycle-time problem.
Markets move in months, client expectations in weeks, and new AI capabilities in days. Most firms still change offerings, economics, and talent models on annual cycles.
When the market’s cycle time gets shorter than the firm’s cycle time, the firm does not simply lag. It compounds disadvantage. The answer is not more tools or more labor. It is a faster operating layer.
THE THESIS
Firms will subscribe to the layer, not the labor.
Research Agenda
Five forces shaping the firm that comes next.
These are not content categories. They are the variables every experiment is designed to make more legible.
Innovation clock speed
Market cycle time versus firm cycle time—and the operating cost of the gap between them.
Capability drift
How an edge quietly flattens when offerings, skills, and operating systems stand still one cycle too long.
Pricing physics
What rate-times-hours cannot survive, and which models become possible when delivery economics change.
Talent dynamics
What leverage, learning, and junior development become when AI absorbs the mechanical layer of work.
Economics of differentiation
How firms build advantages that renew faster than competitors can copy them.
Follow the experiments, not the hype cycle.
Get the field notes →Field Notes
The research so far.
Short arguments and build reports from the edge of the operating model.
Three Months Ago, I Wouldn’t Have Recommended an AI-Native Budget
A real consulting-firm budget, rebuilt revenue-first with AI. The breakthrough was not the formulas. It was decision, implementation, memory.
The Innovation Clock Speed Problem
Why firms are falling behind faster than they can adapt—and six signals of a gap that is quietly widening.
New field notes go to subscribers first, then become part of the public archive.
The Builder
A lab with real operating exposure.
Black Squirrel Labs is written and built by Nate Saperia. He spent years inside Accordion’s Strategic Finance practice advising private equity-backed CFOs, and now runs Saperia Consulting, where AI-native finance and reporting systems are built against real engagements.
The lab is the notebook: the systems being built, the assumptions that fail, and the signals that matter for leaders trying to adapt before the market makes the decision for them.
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A few carefully reported signals each month. Builds, failures, and implications—not a generic AI-news roundup.
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