Trust, not tokens (watch on LinkedIn, opens in a new tab)
Models will improve and tokens may get cheaper, but enterprise technology history suggests value accrues to whoever becomes the default, trusted choice.
03 / human-wrapped ai · 7 records
Human-Wrapped AI
AI outcomes don’t emerge from model capability alone. They’re shaped by the people, organisations, incentives, economics, infrastructure, governance and constraints wrapped around the technology.
So the question here is rarely just what a model can do. It’s what happens when that capability meets:
model capability1 component of 8
wrapped by the other 7
- this specific use case
- these people
- this organisation
- these incentives
- this economic model
- this infrastructure
- these governance constraints
A working lens, not a framework. It gets sharper with each real use case.
rec 03.006
Vibe coding vs vibe engineering (watch on LinkedIn, opens in a new tab)
Polished demos are not production systems. The difference between vibe coding and vibe engineering is judgement that compounds, and the technical ability to exercise it.
rec 03.005
Own the play: human-wrapped ai = next step beyond human-in-the-loop
From defensive oversight to offensive ownership.
rec 03.004
Wrapping your head around aGeNtiC aI
Agentic AI has become a branding exercise, not a technical one. WRAP addresses this.
rec 03.003
The WRAP Framework: did you really build an agent? (watch on LinkedIn, opens in a new tab)
A simple diagnostic for working out what an AI system actually is, and what it is not, before calling it an autonomous agent.
rec 03.002
Vibecoding didn’t make that unicorn any more lovable
The media calling Lovable a “vibe coding unicorn” is proper misleading. It might describe who they serve. It does not describe how they got there.
rec 03.001
Privacy? In LLMs?? Since when???
If you type something into a chatbot, it’s written evidence. This isn’t an AI risk story. It’s a human one.
the governance research behind the question
The question behind this lens started in my doctoral research, which asks a narrower one: does who sits around a company’s board table change what the company does?
The published study, Female representation on boards and carbon emissions: International evidence (Finance Research Letters, 2022), examines board gender diversity, corporate governance and carbon emissions. It does not study AI, and its findings say nothing about AI directly.
It belongs here as intellectual lineage: the habit of looking at the people and governance around a decision, not only the decision itself.