D.O.T.S.

A way of seeing problems.

Collect Dots. Connect Dots. Create Something New.

The world organizes knowledge into disciplines. Problems do not.

D.O.T.S. starts with a simple shift: do not let the label attached to a problem decide where you are allowed to look for answers. See past the label. Find the structure. Expand the search space. Transfer what fits. Create something new.

The perceptual shift

LABEL → STRUCTURE → SEARCH SPACE

The label narrows where you look. The structure expands where you can look.

The label says

AI problem

The category points you toward AI vendors, models, tools, and benchmarks.

The structure asks

Allocation · uncertainty · tradeoffs · adoption

The problem becomes broader than the technology attached to it.

The search space expands

Portfolio management · venture capital · governance · decision science

Now there are more places to look for principles that may transfer.

Four public questions

See differently before solving differently.

D

Deconstruct

What kind of problem is this underneath the label?

O

Observe

Who else has faced a problem with the same underlying structure?

T

Transfer

What principle actually travels—and where does the analogy stop?

S

Synthesize

What must change to create something useful in this context?

This is the public thesis, not a proprietary implementation manual. The point is disciplined transfer, not clever analogy.

Different surface. Same shape.

A few places the search space can expand.

Marketing → public planning

Preference and tradeoff problems

A tool developed to understand consumer preferences can become useful in a public planning problem when the deeper structure involves competing configurations, preferences, and tradeoffs.

Investment management → technology

Allocation and portfolio problems

Technology initiatives are not securities, but leaders still make choices about scarce capital, time horizons, concentration, risk, evidence, and rebalancing.

AI experimentation → institutional stewardship

Uncertainty and capability problems

An AI pilot may look like a technology experiment. At scale, the deeper questions include mission, evidence, readiness, governance, trust, ownership, and the ability to keep deciding as conditions change.

Read the AI paper →
The guardrail

Structural fit matters more than surface similarity.

A useful analogy is never permission to copy blindly. The discipline is to identify what transfers, what breaks, what must be adapted, and what does not belong.

Do not ask what field a tool belongs to. Ask what problem it can solve.
Book 3 · In development

D.O.T.S.

The book develops this way of seeing through stories, cases, principles, limits, and the deeper discipline of collecting broadly, connecting structurally, and creating usefully.