a working thesis

there is alwaysan easierway to do things.

that mindset is what brought me here.

that obsession has been with me longer than ai has.

i work with companies to find their optimal — diagnosing what's broken, building what needs to change, and measuring whether it actually did.

the gap between wanting ai and being ready for it is where the work actually happens.

§ 01 the method

the sequence.

order → automate → augment

there is no shortcut to optimal.

each step builds the next. skip one and you are not saving time — you are borrowing against a problem you will have to undo later.

01 · order
fix the foundation.
clean the data. document the processes. find the owner of each workflow. make everything ai-friendly before the first model is opened.
this is where most companies discover they have more work than they expected.
02 · automate
remove the friction.
clean triggers. logged outcomes. deterministic systems that run without supervision.
most problems stop here. that is not failure — that is the optimal solution.
03 · augment
add intelligence where it earns its place.
not where it creates novelty. not where it impresses in a demo. where it changes the outcome on a regular tuesday.

most teams skip the first two steps and wonder why ai does not survive past the second quarter.

§ 02 the frameworks

a system of frameworks.
each one a different lens.

the models i reach for when a conversation needs to move from vague to specific.

01 · ai readiness model
the model is rarely the problem. the system underneath is.
02 · order → automate → augment
augment is the third step. most teams make it the first.
03 · the bet map
three zones. fund two. say no to the third.
see all frameworks →
§ 03 the notebook

short essays.

published when the thinking is finished — not when the calendar says so. each note pins one idea worth keeping.

005
the year no competitor can buy
a real industrial company. one model anyone can buy. the layer above it — twelve months of work to build — is what they actually own.
compound bets 004
foundations before ai draft
the system underneath the model is the project. most teams skip it and call it strategy.
ai readiness 003
your ai problem isn't an ai problem draft
every ai failure i've audited had a different name on the post-mortem. it was never the model.
ai readiness 002
three signs your ai pilot is decoration draft
the chatbot nobody opens. the dashboard nobody references. the demo that became the slide.
compound bets 001
ai-ready isn't a tech project. it's a knowledge project. draft
the model invents the parts nobody wrote down. structuring knowledge for ai is harder than building ai.
knowledge architecture
all notes →
§ end closing

most companies want ai.

most are not ready for it yet.
that gap is where the real work is.

the search for optimal never ends — and it rarely starts where you think it will.

if you are trying to figure out where your company actually stands, what needs to be fixed before ai makes sense, or simply how to make things work better before reaching for the next tool — that is the conversation i find most interesting.

say hello → read the manifesto →