Climate ambition is collective. Our system for delivering it is not

The opinions expressed here by Trellis expert contributors are their own, not those of Trellis or its editors.

Over the last decade we have built a sustainability ecosystem that scrutinizes climate delivery far more effectively than it enables it. Artificial intelligence is an extreme manifestation of this contradiction. 

We ask individual companies to set targets, build transition plans and account for their own progress. But some of the hardest delivery constraints now sit outside the organizations being held accountable for overcoming them. More internal effort cannot solve a systemwide limitation.

Amazon, Google and Microsoft have reported rising electricity-related emissions even as they invest aggressively in clean energy and stand behind long-term climate commitments. The obvious question the market is now asking: Can those commitments survive the AI boom?

Look across sectors and a more fundamental question emerges: What happens when every company faces the same problem and each is left to solve it independently?

For AI, the most immediate problem is power. Demand is growing faster than the system can expand, forcing hyperscalers to secure generation and grid access individually —  usually with fossil fuels. Power is no longer simply an input to the AI race. Access to it is becoming part of the race itself.

When critical enabling infrastructure is scarce, firms begin competing for the conditions the market depends on —  and this rational commercial behavior makes collective climate action harder. Net-zero commitments may not have changed, but the conditions for delivering them have.

If the conditions determining the outcomes of corporate commitments increasingly sit outside companies, why are so many of our sustainability standards and frameworks still designed to evaluate what happens inside them?

We designed for accountability. Not execution

We’ve become better at measuring corporate climate commitments than enabling the conditions required to fulfill them. The result is a broken loop: We demand outcomes, leave companies to compete for or re-create what they need to produce them, and then scrutinize performance when conditions fall short.

The response emerging around AI sharply highlights these constraints while offering new models. Governments, regulators, standards bodies, infrastructure operators and companies have started to build common rules, repeatable practices and coordinated approaches to overcoming shared constraints.

From fragmented execution to shared capacity

The significance is not that a wave of AI coalitions has arrived. It’s the recognition that competing more effectively over a shared constraint does not remove it. The emerging response is to build deployment capacity across multiple organizations and sectors rather than leave each company to navigate the constraint alone. This demonstrates what’s possible when markets strengthen the conditions everyone depends on, rather than simply determining who gets access first.

The scale and speed of AI are unique, but the fragmentation the technology exposes is not. Our research with more than 200 practitioners across climate deployment finds the same pattern well beyond AI: Demand and capital remain disconnected, diligence and validation must repeatedly be re-created, risks sit where individual actors cannot absorb them, and informal workarounds emerge where durable execution infrastructure is missing.

AI has made shared constraints commercially urgent. But the fact that markets are building this capacity only after those constraints began limiting commercial growth exposes the costs of fragmented execution: slower or stalled deployment, duplicated effort and capital, unrealized commercial value, and emissions reductions delayed or lost. Persistent gaps between ambition and delivery also weaken confidence in corporate climate action, exposing companies to political attack. Shared execution capacity matters because fragmentation not only impedes climate progress, but destroys economic, environmental and institutional value.

For practitioners, there’s an immediate question: Where can execution you already influence reduce fragmentation rather than simply navigate around it?

Stop navigating fragmentation. Start reducing it

You can’t fix the grid, restructure capital markets or change how hyperscalers compete. But you can influence whether the execution inside your organization adds to fragmentation or helps shared solutions emerge. Here’s how to move toward shared capacity from where you sit:

1. Define the shared problem.

Ask: Are we solving something unique to us, or rebuilding a function the market is missing? Look for the same workaround across suppliers, technology providers, financiers or customers.

2. Stop adding unnecessary fragmentation.

Ask: What do we require to be different that doesn’t actually create distinctive value or reduce material risk? Challenge bespoke diligence, specifications and requirements when credible alternatives already exist.

3. Choose solutions that strengthen the system.

Ask: Will our solution only get us through, or make the next deployment easier? Favor approaches that create something others can recognize, use or build upon.

4. Make execution carry across organizational boundaries.

Ask: What are we doing that another credible actor could recognize rather than recreate? Start with one diligence process, specification, standard or demand signal.

The goal isn’t to reduce competition. It’s to stop re-creating the same missing execution infrastructure one organization at a time.

Build the other half of the system

Climate commitments create enormous demand for outcomes. They do not always create an equally strong incentive to build the shared functions required to produce them. AI is changing that equation, not only showing what happens when shared execution capacity is missing, but also what causes markets to start building it. When shared constraints stand in the way of enormous commercial opportunity, removing them has immediate economic value.

This raises a much bigger question for decarbonization: If commercial urgency can mobilize markets to build shared execution capacity around AI, what would create comparable incentives to build the capacity required for climate solutions deployment?

The sustainability ecosystem spent the last decade building the architecture for commitments, disclosure and accountability. The next challenge is not another round of ambition or measurement. It’s building the other half of the system — and making it economically rational to do so.