Should You Be Capturing Reasoning Traces?

Should you be capturing
reasoning traces only if you

are finding them into advantage?

Welcome to the Enduring Advantage Podcast.

I'm your host, Zachary Alexander.

Most middle market CEOs haven't
heard about reasoning traces.

They see them when they use LLMs.

However, they are way too busy
to take the time to figure

out what they're looking at.

As long as LLMs

give them something they
can use to complete whatever

task is in front of them.

They're happy, perfectly happy.

Advantage traces are something
new, a new kind of reasoning trace.

So how do reasoning traces
become advantage traces.

You combine speed, intellect
and institutional knowledge.

Three things middle market
companies already have.

They may not be made aware
of the compounding benefits.

However, the raw material
is already available.

Here's what I keep seeing
in the marketplace.

Companies accumulating AI
artifacts like reasoning traces

with no destination in mind.

Storage bills keep growing.

Senior management wonders
if you're doing AI right.

Because nothing's compounding.

Even the uninitiated can understand that
reasoning traces are just, or iron ore

raw material that can be refined using
your company's institutional knowledge

and is something that will compound
and benefit you well into the future.

Just to be clear, the context,
the patterns, the sweat

equity that no one else has.

By adding these to reasoning
traces, that's how reasoning

traces become advantage Traces.

Drone makers know this signals
a shift in the AI revolution.

It's the moment AI enhanced knowledge
workers start to become AI native.

When your team stop using Bolt on
AI as a tool and starts building

AI native systems, you enter the
next phase of the AI revolution.

While the uninitiated are
still collecting iron ore.

The drone makers are building
facilities to lay waste to their

competitors very expensive moats.

What do drone makers
actually do differently?

Three things.

They leverage volume, speed,
and adaptive intelligence.

Let's start with volume.

They deploy 40, 50, 60
initiatives simultaneously.

Not one big bet.

Each initiative is a sensor
gathering intelligence.

Saturation overwhelms
competitors' analysis.

They can't figure out which initiative
is real and which ones are simply decoys.

Speed: They moved to two week adaption
cycles, not 12 month planning horizons.

That's

Zachary Alexander: 24

Cycles per year while your
competitors complete one.

Time becomes a weapon, not a constraint.

Adaptive intelligence: systems
that reconfigure automatically

based on what's working.

Pattern recognition across all
initiative resources flowing to

success without committee approval.

Emergent strategies that
weren't explicitly designed.

Volume times speed, times
adaptive intelligence.

That's the equation.

The uninitiated are still collecting iron
ore, drone makers are capturing territory.

Here's a math that changes everything.

AI capabilities double every seven months.

That means that tools your competitors
use to replicate your advantages get

twice as powerful every seven months.

They may not be able to fully replicate
your institutional knowledge, but

your inaction could allow them to get
close enough to dampen its impact.

What took you years, maybe decades of
sweat equity to produce could be table

stakes by next summer, if you're lucky.

The window for building advantages that
compound faster than they can be copied is

closing, and it's closing on a schedule.

We're already doing AI.

I hear that constantly,
and it's usually true,

cosmetically.

Bolt-on

Zachary Alexander: implementations
actually exacerbate the problem.

It sits on top of existing systems.

It generates more traces.

artifacts, output, but
none of it compounds.

With Bolton, you're not getting closer
to the new AI native opportunity spaces.

You're just hemorrhaging cash.

more iron ore, same storage
problem, except you're now

paying for the AI tools too.

the drone maker mentality
isn't about adding ai.

It's about architecting systems where
every output makes the next one smarter.

Bolton can't do that.

It wasn't designed to.

Let's spend some time talking
about advantage traces because

this is where the game changes.

Reasoning traces are inert.

They come with every LLM.

They're simply a byproduct of activity.

They can pile up and generate
nothing but storage costs.

Or you can think of reasoning traces as
what happens and the advantage traces as

what is understood about what happens.

here's the difference.

A reasoning trace is a note
in a very large notebook.

An advantage trace represents
institutional memory, enriched with

context classified into patterns
your teams recognizes, linked to

every related decision captured.

That's not data.

Sitting in storage.

That's an asset ready to do
work the moment you ask it.

Here's what a lot of very smart CEOs miss.

Institutional knowledge isn't single use.

It compounds across context you
haven't even considered yet.

Just because you built that knowledge
fighting along the coastline

doesn't mean it isn't relevant
to drone warfare in the forest.

Advantage traces transfer the pattern
recognition your team developed.

Solving supply chain problems in
the Midwest might be the exact

intelligence that opens up whole
new opportunity spaces in Australia.

The decision frameworks captured from one
division become ammunition for another.

Drone Factories don't produce one product.

They produce many from the same airframe.

Let me make this concrete.

using AI native tools, you identify
a delay pattern from past years

buried in an ERP system, you migrated
away from a couple years ago.

The regional logistics constraint
your team figured out during

COVID and never documented.

It lives in three people's heads.

However the pattern appears
in the AI native tool.

Luckily, they're still at the company.

Then there's that same failure mode that
keeps showing up in different divisions,

different supplier, same root cause.

Nobody connected the dots because
the systems don't talk to each other.

They're stuck in division isolation.

You get the picture.

The doubling of AI capability means that
institutional knowledge will decay over

time, slower than explicit knowledge.

The kind of stuff that's top of mind.

So much of what makes your company
special could be lost without

the proper AI native strategies.

A reasoning trace captures the interaction
your team had with an LLM today.

What they ask, what the model said.

A receipt.

An advantage Trace does
something different.

It links today's supplier delay
to those historical patterns

even across the old ERP.

It surfaces the COVID insight
from the people who lived it.

It flags cross dimensional
connection automatically.

The advantage layer doesn't
replace your legacy system.

It links them together using
an MCP metadata registry.

Connective tissue that turns isolated
transactions into adaptive intelligence.

Your system stay where they are.

The MCP Metadata Registry simply
develops the value from all of them.

That's what makes asymmetric
business strategies work.

Your institutional knowledge
shouldn't be siloed.

That's why you invest in
next Gen AI strategies.

Another thing everyone's talking
about, prompts and output.

Nobody's talking about state.

Context that persists across systems.

Learns from every correction
and compounds automatically.

That's what MCP metadata
registries facilitate.

Not a better chat.

Institutional memory that
strengthens with use.

MCP metadata industries exist today.

However, most senior management
teams and boards don't know it yet.

The question is, are you ready for
the next phase of the AI revolution

or are you going to with the
same old, tired sci-fi metaphors?

Contrary to popular belief,
military contractors don't build

systems without fail Safes.

They get paid based on the money
they spend plus some margin.

So it's in their best interest to
build in as many bells and whistles as

possible, like fail safes and back doors.

Because it's not good for anyone's career
to have any system run off the rails.

You could easily make the case that
the difference between military

grade systems and civilian systems
is the built in test equipment.

The reason is that military units
operate in isolation, and no matter

how good the supply chain is, there's
no guarantee that test doesn't get

separated from the systems they test.

Military contractors get paid
a lot of money adding bytes

to everything they produce.

No matter where you are in the military
world, you can always run the diagnostic.

The problem is that the economics
are different from internal

teams and business contractors.

Test equipment is seen as
an added expense, a luxury.

So are the people who know how
to use them for that matter.

That worked for enterprise software
because it was seen as gravy and added to

the profit margin until the AI revolution.

Here's a hot take for you.

The next phase of the AI revolution
will be dominated by AI-based

self-test systems and diagnostics.

And that doesn't have
anything to do with altruism.

It has to do with tokenomics.

The most capable AI models will
always be in short supply, and

businesses will want to maximize
the tokens they have access to.

So what does the drone maker
cycle actually look like?

There are five stages: Capture
every interaction your team has with

LLM generates a reasoning trace.

Most companies stop here.

The trace goes into storage with
a promise of future analysis.

Enrichment.

The MCP metadata registry
adds context automatically.

Who generated this trace?

What project was it tied to?

What systems were referenced?

What decision did it inform?

This trace stops being anonymous
and starts being addressable.

Classification pattern
recognition kicks in the trace.

Looks like the supplier delay problem.

This one matches your Q3
pricing decision framework.

This one connects to customer
churn signals your sales team

identified six months ago.

The system learns your
categories, not generic ones

Here's where it gets powerful.

The registry connects the trace to every
related trace document, decision and

output in your institutional memory,
not siloed by system, not trapped into

the ERP or the CRM or someone's email

Linked across all of them.

They compound.

Every trace makes the
existing trace more valuable.

The pattern recognition sharpens
the classifications, get

smarter, the links multiply.

Your institutional memory
doesn't just grow, it deepens.

Capture, enrich, classify,
link, and compound.

That's the architecture.

That's how iron ore becomes advantage.

So where does that leave you?

Two paths: Path one.

Capture reasoning traces yourself.

Hope someone on your team figures out the
transformation Later, maybe they will.

Maybe the window stays open that long.

Path two.

Plug in an MCP metadata registry
with an advantage layer.

Start compounding advantage immediately
Every seven months, AI capability doubles

your competitors' reasoning traces are
becoming advantage traces right now.

If you do nothing, yours are depreciating.

While racking up even more storage
costs, the clock's running either way.

This is not a discussion about some
dusty old AI artifact racking up storage

fees, but the iron ore that becomes the
enduring advantage that protects you,

what you spent so many years building
and makes it harder to replicate every

passing month based on time on task.

Most people will watch this nod
and do nothing, that's fine.

It means a clear path to success for
those who adopt the drone maker mentality.

You've seen the framework,
you've seen the architecture.

Check out past videos and
come back for future ones.

And if you want to go deeper, find us
on the Enduring Advantage Substack.