Building the structures that formation now requires

This article is part of the Wisdom Gap series. You're reading part 5. Here is part 4

Where individual commitment reaches its limit

Across this series, I have argued that the entry-level container, ie, the developmental environment in which previous generations accidentally absorbed judgment, trust, social calibration, and long-horizon thinking, is being systematically dismantled by AI adoption decisions optimized for efficiency and short-term returns. I have argued that the resulting wisdom gap threatens the sustainability transition as directly as any carbon or biodiversity metric we currently track. And I have argued that those of us further along carry both the accumulated wisdom the next generation needs and a specific obligation to transfer it deliberately, having benefited from conditions we are now removing.

All of that remains true. And none of it, person by person, is enough.

If the wisdom gap is a systemic problem, created by systemic decisions, operating at systemic scale, with systemic consequences for the sustainability transition, then individual mentorship commitments, however sincere and sustained, cannot close it alone. They are necessary. They are the foundation. But the building requires structure.

Here is what I believe that structure needs to include.

Putting formation capital on the balance sheet

The most foundational structural change required is also the most unglamorous: organizations need to begin measuring what they are currently treating as invisible.

Every AI adoption decision that eliminates or substantially reduces entry-level roles carries a formation cost. Specifically, a reduction in the developmental opportunities through which human judgment, trust, and wisdom have historically been built. That cost is real. It is consequential. And it is currently recorded nowhere.

The sustainability community has spent decades arguing that natural capital,  the ecological systems and resources on which all economic activity depends, must be accounted for rather than treated as a free externality. The same argument applies, with equal force, to formation capital.

What would it look like to account for it? At a minimum, it means asking specific questions before finalizing AI adoption decisions. What developmental opportunities does this role currently provide? What capacities are built through it, over what timeline? What is the plan for building those capacities another way? Who is responsible for tracking whether that plan is working?

These questions will not always produce clear answers. The accounting will be imperfect. But the act of asking them -- of making formation capital a line item in the decision rather than an afterthought -- changes what gets measured and, therefore, what gets managed. The sustainability community, of all communities, understands that principle in its bones.

Apply it here.

Redesigning the developmental architecture

If the traditional entry-level container is being removed, the question is not whether to mourn it but what will replace it. Here, I want to propose four structural models that organizations and institutions could begin building now, not as pilot programs or innovation theatre, but as genuine infrastructure.

Deliberate apprenticeship alongside AI. The most straightforward response to AI absorbing entry-level cognitive tasks is to redesign those roles explicitly around human development rather than task completion. If the research synthesis is being done by an AI tool, what is the junior professional doing? In a deliberate apprenticeship model, they sit alongside experienced practitioners, observe decisions being made, ask questions, are given responsibility for real work at the edges of their current capacity, and receive honest feedback in real time. The task is not the point. The formation is the point. The AI handles the task. The human handles the formation.

Structured reverse mentorship. The intergenerational exchange I described in Article 4 (in which the person further along offers pattern recognition and long-horizon perspective, and the person earlier in their career offers proximity to current reality and fluency with emerging tools and contexts) can be formalized without being bureaucratized. Organizations that create deliberate, sustained, reciprocal pairings across generations, with explicit expectations of mutual learning rather than hierarchical transfer, build something the org chart cannot show but the culture absolutely feels. This is not a new idea. It is a chronically under-implemented one.

Formation-weighted investment criteria. For investors, particularly patient capital and impact-oriented investors who make up a significant portion of the sustainability finance ecosystem, formation capital offers a new dimension of due diligence. How does this organization's AI adoption strategy account for the developmental pipeline? What is the ratio of efficiency gain to formation cost? Does the leadership team understand what they are removing along with the headcount, and do they have a credible plan to replace it? These are not soft questions. They are long-term value questions. Organizations that hollow out their human formation pipeline are creating a fragility that will manifest in their leadership capacity, their institutional resilience, and their ability to execute the sustainability transition they may be publicly committed to.

Policy frameworks that account for formation. For policymakers and institutional leaders, the wisdom gap suggests a regulatory and incentive frontier that has barely been explored. What if AI adoption tax incentives were conditioned on demonstrable investment in alternative formation pathways? What if labour market policy moved beyond reskilling — which remains largely within the efficiency frame — toward forming infrastructure? Like funded apprenticeship systems, intergenerational exchange programs, subsidized proximity between experienced practitioners and early-career professionals in sustainability-critical sectors? The economics of displacement have dominated the policy conversation about AI and labour. The formation consequences deserve equal standing.

The measurement problem — and the opportunity

I want to be honest about the most significant obstacle to everything I have just proposed. We do not yet have good ways to measure formation capital, track its development, or assess its depletion.

This is a genuine problem. For the sustainability community, it is also a familiar one.

Twenty years ago, the tools for measuring natural capital, tracking biodiversity loss, and assessing the systemic costs of carbon emissions were rudimentary at best. The response was not to wait for perfect measurement before acting. Rather, it was to begin developing the frameworks, standards, reporting mechanisms, and institutional appetite for accounting that eventually made the current generation of sustainability metrics possible.

The same developmental work is now needed for formation capital. Not a perfect measurement from the start because that is not how measurement frameworks develop. But a beginning process like a shared language, a set of indicators, and a community of practice committed to making visible what is currently invisible.

The sustainability community built that capacity for natural capital. It is precisely the community positioned to build it for human formation capital, too. The expertise, the institutional relationships, the appetite for systems thinking, and the understanding of why externalities eventually become crises. ALL of it transfers.

What is missing is the decision to begin.

The thirty-year window

In Article 1, I argued that the first effects of the wisdom gap will be visible within a decade, and that the full civilizational cost will take thirty years to arrive.  That is precisely the same window we have to complete the sustainability transition.

I want to close the series by sitting with that parallel for a moment, because I think it contains the most important argument of all.

The sustainability transition will not be completed by AI tools, no matter how sophisticated they are. Policy frameworks, no matter how well designed, cannot complete the transition.  Nor will it be completed by investment strategies, however patient their capital.

It is going to be completed, or it is going to fail, based on the quality of human judgment, trust, wisdom, and long-horizon thinking that the leaders of the next thirty years bring to the decisions in front of them.

Those leaders are being formed right now. Or they are not being formed. Because the container has been removed, the structural replacements have not been built, and the people who accumulated wisdom in better conditions have not yet fully reckoned with what they owe.

This is the moment that reckoning becomes urgent. Not because the situation is hopeless.  I do not believe it is, but because the window for action and the window for consequences are, for once, clearly aligned.

We know what the problem is. We know what created it. We know what the stakes are. And we know, with reasonable precision, how much time we have.

The do better imperative

I have spent many decades in and around business, and the last five years investigating whether any of it was working, for whom, and at what cost. The conclusion I keep returning to is both simple and demanding.

We cannot do over what has been done. The entry-level container that quietly formed generations of professionals was built on conditions that cannot be fully restored. The AI adoption decisions already made cannot be unmade. The wisdom gap already opening cannot be wished away.

What we CAN do is do better. And that do better, in the deployment of AI, in the structures we build to replace what has been removed, and in the deliberate transfer of accumulated wisdom to the generation that will carry the sustainability transition forward, must be in the highest good for all.

Not the highest good for this quarter's earnings. Not the highest good for the companies and careers of those already established. The highest good for ALL. Including the young professionals being denied the formation they deserve, the communities depending on the sustainability transition to succeed, and the generations who will inherit the consequences of decisions being made in boardrooms right now.

That is the stewardship this moment requires. And it is, I believe, precisely what the entire sustainability community of scientists, policymakers, investors, practitioners, and advocates is uniquely positioned to lead.

The wisdom gap is real. The window is thirty years. The work is ours.

Let's begin.

Series index

Article 1: The sustainability crisis nobody is naming

Article 2: What AI genuinely cannot replicate and why it matters more than you think

Article 3: For the generation that got here just as the map disappeared

Article 4: What those of us further along actually owe the next generation