Research Lab
Tracking the knowledge layer as it becomes infrastructure.
The Research Lab is where I track a transition I think is already underway:
Documentation is becoming knowledge infrastructure.
But the signals are appearing everywhere.
I track those signals across hiring, tooling, organisational design, AI architecture and the energy transition, then publish the underlying data, methodology and working assumptions.
No black box. No analyst theatre.
Just sourced evidence, explicit uncertainty and research you can inspect.
LATEST EDITION · AUG 202601 / THE ALTITUDE MAP
Same function. Different name.
What happens when you stop comparing job titles and start comparing the work underneath them?
Different companies. Different titles. Increasingly similar altitude.
The Altitude Map tracks documentation, content design and knowledge architecture from practitioner through Principal, Distinguished and Director-level work.
The emerging signal:
"Knowledge Architect" may not be a speculative future role. It may simply be the name that hasn't caught up with work already happening at scale.
The map currently tracks 24+ organisations across AI-native technology, developer platforms, European enterprise software and financial services.
02 / THE KNOWLEDGE HIRING RADAR
Follow the hiring. Find the architecture.
57 sourced hiring signals · 4 linked research repositories
Job descriptions reveal things organisation charts often don't.
The Knowledge Hiring Radar tracks those signals across industries and maps them by organisational maturity and strategic direction.
A companion radar narrows the same question to the energy transition.
The point isn't to predict the next fashionable job title.
It's to watch where the underlying function is becoming necessary.
03 / THE STACK ATLAS
Who is building the knowledge stack?
I mapped the companies building docs-as-code platforms, enterprise knowledge bases, AI-native documentation systems, code-aware tooling, headless CMSs, localization infrastructure and API tooling.
Then I looked at who founded them.
Every identified founder across all 24 companies was an engineer.
Zero exceptions in the dataset.
Not one was a documentarian building the documentation system they had always wanted.
That doesn't prove engineers build bad documentation tools.
It raises a more interesting question:
What happens when an entire tooling category is predominantly designed by people adjacent to the job rather than people doing the job?
And then there's the blank quadrant.
Gartner now has a Magic Quadrant for Customer Service Knowledge Management Systems.
There still isn't an equivalent analyst category for the developer-facing documentation and knowledge infrastructure market.
04 / SIX SURVEYS. ONE WORD.
Context.
This may be the signal I'm most interested in.
I cross-referenced six independent practitioner datasets:
They come from different professional communities.
They aren't citing one another.
Yet all six independently converge on the same missing layer:
Context.
The interesting finding isn't that six reports use the same word.
It's that six largely separate disciplines appear to be describing different symptoms of the same architectural problem.
05 / THE SIGNAL STACK
Not all evidence proves the same thing.
One of the problems with trend research is that observations quickly become conclusions.
So I use a small classification ontology to separate them.
TITLE SIGNAL
What does an organisation call the work?
REQUIREMENT SIGNAL
What capability is it actually hiring for?
TOOLING SIGNAL
What infrastructure is being built around it?
SCALE SIGNAL
Where is the behaviour appearing repeatedly?
SYNTHESIS SIGNAL
What emerges when independent evidence begins converging?
The Signal Stack isn't a maturity model.
It's a way of asking:
What does this piece of evidence actually allow me to claim?
FIELDWORK / ENERGY
When physical infrastructure becomes software-defined, knowledge becomes part of the infrastructure.
My professional work sits inside the energy transition, so I'm applying the same research method there.
Different systems increasingly need to exchange not merely data, but meaning and context.
The Grid Knowledge Graph is an early worked example: mapping the fragmented regulatory and organisational relationships around the UK grid-connection ecosystem as connected knowledge rather than another collection of documents.
What knowledge architecture does an increasingly distributed, software-defined and agentic energy system require underneath it?
METHOD
Public evidence. Public methodology.
This isn't Gartner.
I don't have vendor briefings, NDA datasets, private revenue disclosures or a pre-publication dispute process.
So I don't pretend I do.
The research uses publicly available evidence with explicit inclusion criteria, sourcing-confidence tiers and methodology notes.
And where the data is wrong:
Open an issue or PR with a source.
The correction mechanism is part of the research.
SOURCE → CLASSIFY → CONNECT → TEST → PUBLISH → CORRECT
A working thesis.
Across hiring, tooling, AI and the energy transition, I keep encountering different versions of the same problem.
Organisations have spent decades creating information.
AI has made the structure underneath that information impossible to ignore.
The next question isn't simply:
How do we create better documentation?
It's:
What knowledge architecture allows humans and machines to share context reliably?
That's the signal I'm following.