Such an incredibly important post, thank you for laying this out so clearly.
I have been talking about this for the past 18 months on my podcast (and almost everywhere else).
Individual productivity became a new vanity metric, and told us nothing about how teams, departments, or organizations were doing. One (human) cog going 10x while others go 1-2x means something is going to break.
You have to be measuring AI readiness and maturity with different attributes as you go from individual to institution.
Only two things I would add are 1) we need responsibility and human-centricity by design to mitigate risk and build things properly the first time, and 2) this also points to the importance of capitalizing on the institutional collective intelligence for making better decisions and architecting strategic work.
:) I’m solo nowadays but over 25 years of witnessing enterprise transformation challenges. AI is the disruptive wake-up call as well as the catalyst to reimagine work itself.
Thank you for this article. What stood out most for me and how I interpret this is that even more important than an AI strategy, companies need an infrastructure that drives, sustains and ensures consistent adoption of AI. I view this as part of what normally would be a change management strategy. Also, thank you for including the critical component of bias! I feel like the topic used to be a central part of most conversations around AI but it has slowly disappeared as the race to proliferate has taken center stage.
To add to that, in the software value chain, AI is squeezing the layers between talent, skills, capabilities and end business outcome.
Since we can now deliver software at a software speed, re-wiring the organization is the key institutional change required, and this is primarily required for non-software businesses.
Good article and spot on about institutional redesign. The irony for me and one thing missing here, is the obvious - that institutions rarely lead these shifts. Individuals usually adopt the tools first and therefore yes, create chaos and the drag the Institutions kicking and screaming into adoption. I went from Private sector to public sector recently where it’s all too apparent that individuals discover the capability, then force institutions industrialize it by causing this chaos of jevons paradox…
Huge difference: electricity could not change the production process' organisation but AI can do that. AI brings not only new technology as electricity did but also new organisation of production process. The challenge: would it be adopted by the human-led production system or must it be changed to AI-lead system?
You nailed the central problem: productive individuals don’t make productive firms. And the fix you’re pointing at, the “solution layer” where outcomes live instead of tool access, is exactly the delivery model we’re building around.
We call it SITS: Software Is the Service. Not SaaS where you hand someone a platform and wish them luck. The customer tells us what compliance framework they need to meet. We deliver the governed outcome. They never configure a policy, staff a reviewer, or interpret a regulation. They buy a result.
The reason this works is your Pillar 2 (Signal) and Pillar 3 (Bias). You wrote that institutional AI must be “defined, deterministic, and auditable” and that the most important agents will be “no-men” that enforce standards. That’s the literal architecture of our enforcement kernel. SovereignClaw is a Rust-based deterministic execution control layer that cryptographically gates every autonomous AI agent action before it runs. The LLM is untrusted input.
Safety is a gate, not a prompt. Every decision produces a Merkle-chained receipt proving what policy was evaluated and why the action was approved or denied.
Your Palantir observation is sharp. They’re trading at those multiples because they sell process engineering, not software. But their moat is services-dependent: forward-deployed engineers embedded in every account.
Our differentiator is that the enforcement kernel itself is the moat. Deterministic enforcement plus a compounding data flywheel from every governed interaction. No engineer dependency. The margin structure scales where Palantir’s doesn’t.
Two days ago I published the Agentic Governance Benchmark (AGB), a standardized scoring framework for measuring runtime governance enforcement across six dimensions. Composite score 0-100, five maturity tiers from Ungoverned to Sovereign. If institutional AI needs to be auditable, there needs to be a benchmark that proves it. That’s this: https://doi.org/10.5281/zenodo.20496565
Five patents filed.
Shaping the standards via ASTM, CEN/CENELEC JTC 21, and ISO/IEC JTC 1/SC 42.
"And in process engineering, business and industry expertise—not software expertise—is most important. Domain specific solutions beget expertise in the professionals doing the forward deployed engineering, the deployment, and the change management."
Totally agree - human judgement of the business and industry expert define the next step. E.g., AI can synthesize a diligence data room in a fraction of time, it cannot capture the human judgment or business knowledge of an industry expert who knows how to uncover hidden issues.
Great post, and I agree. As someone who also looks at history for parallels, I quite appreciated the electricity example. I run into similar issues that you highlight (individual AI) when it comes to using AI in product marketing work. The current org designs will evolve if we’re to see AI scale outcomes beyond individual time-saving (and marginal quality gains, which is often debatable).
I really enjoyed reading your article, George. Your perspective is very spot on. I’ve been working on this very issue and now have an Agentic AI digital twin of myself that I routinely collaborate with. Human Bob plus “Bot-Bot” make a very powerful team. Here is a 10 minute example of a human interacting with my digital twin: https://vimeo.com/1120703818
Additionally, I just posted a Substack article in which I ask Bot-Bob to share his thoughts on nuclear war as a response to Reid Hoffman’s article about AI always escalating a nuclear war scenario. There is definitely something interesting here.
This is timely and important. Individual amplification is great for the solopreneur, but at an enterprise level it is insufficient, and in fact likely to amplify inefficiencies and misalignment. I’d be interested in your view on how trust is built at scale, I’m grappling with this problem https://buildingandexploring.substack.com/p/can-intent-and-context-scale?r=7lou4&utm_medium=ios and would love to hear how people are thinking about this and building for this problem
I work for a large B2B engineering software developer, and so many of your points resonate with discussions we're having with clients that are on the bleeding edge. Well done.
The textile mill analogy is spot on. From an audit and compliance perspective, I see this tension constantly — organizations adopt AI tools at the individual level but haven't redesigned their governance and controls around it.
The "AI auditor" and "AI compliance" use cases you mention under Bias are particularly compelling. In assurance, the value isn't just finding errors faster — it's having a system that can surface risks that no one thought to look for, unprompted. That's Pillar 7 in action.
The ESG reporting space is a perfect example of where Institutional AI could create real value. Right now, individual analysts use AI to draft disclosures faster — but the institutional challenge is consistency, auditability, and cross-entity comparability. That requires exactly the kind of deterministic, signal-finding architecture you describe.
Great framing. The factories that redesigned the floor will win.
Such an incredibly important post, thank you for laying this out so clearly.
I have been talking about this for the past 18 months on my podcast (and almost everywhere else).
Individual productivity became a new vanity metric, and told us nothing about how teams, departments, or organizations were doing. One (human) cog going 10x while others go 1-2x means something is going to break.
You have to be measuring AI readiness and maturity with different attributes as you go from individual to institution.
Only two things I would add are 1) we need responsibility and human-centricity by design to mitigate risk and build things properly the first time, and 2) this also points to the importance of capitalizing on the institutional collective intelligence for making better decisions and architecting strategic work.
Thanks Rob…agree. Most of the alternate arguments for AI in knowledge work today are made by solo practitioners…
:) I’m solo nowadays but over 25 years of witnessing enterprise transformation challenges. AI is the disruptive wake-up call as well as the catalyst to reimagine work itself.
Thank you for this article. What stood out most for me and how I interpret this is that even more important than an AI strategy, companies need an infrastructure that drives, sustains and ensures consistent adoption of AI. I view this as part of what normally would be a change management strategy. Also, thank you for including the critical component of bias! I feel like the topic used to be a central part of most conversations around AI but it has slowly disappeared as the race to proliferate has taken center stage.
Very good insights.
To add to that, in the software value chain, AI is squeezing the layers between talent, skills, capabilities and end business outcome.
Since we can now deliver software at a software speed, re-wiring the organization is the key institutional change required, and this is primarily required for non-software businesses.
Good article and spot on about institutional redesign. The irony for me and one thing missing here, is the obvious - that institutions rarely lead these shifts. Individuals usually adopt the tools first and therefore yes, create chaos and the drag the Institutions kicking and screaming into adoption. I went from Private sector to public sector recently where it’s all too apparent that individuals discover the capability, then force institutions industrialize it by causing this chaos of jevons paradox…
Huge difference: electricity could not change the production process' organisation but AI can do that. AI brings not only new technology as electricity did but also new organisation of production process. The challenge: would it be adopted by the human-led production system or must it be changed to AI-lead system?
Great article with a clear framework for those that want to be proactive on how they utilize AI in “building”… thank you
You nailed the central problem: productive individuals don’t make productive firms. And the fix you’re pointing at, the “solution layer” where outcomes live instead of tool access, is exactly the delivery model we’re building around.
We call it SITS: Software Is the Service. Not SaaS where you hand someone a platform and wish them luck. The customer tells us what compliance framework they need to meet. We deliver the governed outcome. They never configure a policy, staff a reviewer, or interpret a regulation. They buy a result.
The reason this works is your Pillar 2 (Signal) and Pillar 3 (Bias). You wrote that institutional AI must be “defined, deterministic, and auditable” and that the most important agents will be “no-men” that enforce standards. That’s the literal architecture of our enforcement kernel. SovereignClaw is a Rust-based deterministic execution control layer that cryptographically gates every autonomous AI agent action before it runs. The LLM is untrusted input.
Safety is a gate, not a prompt. Every decision produces a Merkle-chained receipt proving what policy was evaluated and why the action was approved or denied.
Your Palantir observation is sharp. They’re trading at those multiples because they sell process engineering, not software. But their moat is services-dependent: forward-deployed engineers embedded in every account.
Our differentiator is that the enforcement kernel itself is the moat. Deterministic enforcement plus a compounding data flywheel from every governed interaction. No engineer dependency. The margin structure scales where Palantir’s doesn’t.
Two days ago I published the Agentic Governance Benchmark (AGB), a standardized scoring framework for measuring runtime governance enforcement across six dimensions. Composite score 0-100, five maturity tiers from Ungoverned to Sovereign. If institutional AI needs to be auditable, there needs to be a benchmark that proves it. That’s this: https://doi.org/10.5281/zenodo.20496565
Five patents filed.
Shaping the standards via ASTM, CEN/CENELEC JTC 21, and ISO/IEC JTC 1/SC 42.
https://sovereignclaw.com
https://www.execlayer.io
"And in process engineering, business and industry expertise—not software expertise—is most important. Domain specific solutions beget expertise in the professionals doing the forward deployed engineering, the deployment, and the change management."
Totally agree - human judgement of the business and industry expert define the next step. E.g., AI can synthesize a diligence data room in a fraction of time, it cannot capture the human judgment or business knowledge of an industry expert who knows how to uncover hidden issues.
Great post, and I agree. As someone who also looks at history for parallels, I quite appreciated the electricity example. I run into similar issues that you highlight (individual AI) when it comes to using AI in product marketing work. The current org designs will evolve if we’re to see AI scale outcomes beyond individual time-saving (and marginal quality gains, which is often debatable).
How about we increase curiosity and let productivity be the subtext? Because tell you what: harping about productivity doesn't increase it
I really enjoyed reading your article, George. Your perspective is very spot on. I’ve been working on this very issue and now have an Agentic AI digital twin of myself that I routinely collaborate with. Human Bob plus “Bot-Bot” make a very powerful team. Here is a 10 minute example of a human interacting with my digital twin: https://vimeo.com/1120703818
Additionally, I just posted a Substack article in which I ask Bot-Bob to share his thoughts on nuclear war as a response to Reid Hoffman’s article about AI always escalating a nuclear war scenario. There is definitely something interesting here.
https://substack.com/@bobdanna/note/c-226420896?r=3ac1vb&utm_medium=ios&utm_source=notes-share-action
Enjoyed the read!
This is timely and important. Individual amplification is great for the solopreneur, but at an enterprise level it is insufficient, and in fact likely to amplify inefficiencies and misalignment. I’d be interested in your view on how trust is built at scale, I’m grappling with this problem https://buildingandexploring.substack.com/p/can-intent-and-context-scale?r=7lou4&utm_medium=ios and would love to hear how people are thinking about this and building for this problem
I work for a large B2B engineering software developer, and so many of your points resonate with discussions we're having with clients that are on the bleeding edge. Well done.
The textile mill analogy is spot on. From an audit and compliance perspective, I see this tension constantly — organizations adopt AI tools at the individual level but haven't redesigned their governance and controls around it.
The "AI auditor" and "AI compliance" use cases you mention under Bias are particularly compelling. In assurance, the value isn't just finding errors faster — it's having a system that can surface risks that no one thought to look for, unprompted. That's Pillar 7 in action.
The ESG reporting space is a perfect example of where Institutional AI could create real value. Right now, individual analysts use AI to draft disclosures faster — but the institutional challenge is consistency, auditability, and cross-entity comparability. That requires exactly the kind of deterministic, signal-finding architecture you describe.
Great framing. The factories that redesigned the floor will win.
Amazing post written with such clarity.. I hope most of the people understand the value of this blog .. “PURE GOLD” ❤️