Trillions Go to Concrete While Intelligence Gets Left Behind

Trillions Go to Concrete While Intelligence Gets Left Behind

Jan 17 , 2026. By Bertrand Badré ( a former managing director of the World Bank and the founding CEO of Orange Sustainable Capital. ) , Saurabh Mishra ( Saurabh Mishra, founder and CEO of Taiy.AI. This article is provided by Project Syndicate (PS). )

In today’s fragmented landscape, the global contest for infrastructure is about more than who builds roads, ports, and railways. It is also a contest over whose data, standards, and AI systems will guide future investments. In this commentary provided by Project Syndicate (PS), Bertrand Badré, CEO and Founder of Blue like an Orange Sustainable Capital, and the author of "Can Finance Save the World?" and Saurabh Mishra, founder and CEO of Taiy.AI, argue that infrastructure know-how, often buried in permit files and contracts, represents a largely untapped resource for avoiding costly mistakes.

Infrastructure investment is booming. Around the world, governments are pouring trillions of dollars into roads, power grids, data centres, water systems, and housing, with many responding to intensifying climate shocks and the growing need for adaptation.

Yet, the construction industry, the single largest force physically reshaping the planet, is among the last major sectors to unlock all the benefits that digital technology offers. As a result, it accounts for about 21pc of greenhouse-gas emissions, produces half of global landfill waste, and overspends by 1.6 trillion dollars a year.

This should change, and AI may offer the solutions that the industry needs. But that will require fully leveraging the potential of institutional collaboration and human networks.

While generative AI can write code or summarise documents, building real-world assets like bridges and power grids requires what we might call "cognitive infrastructure". It is not easy to access data, human expertise, domain knowledge, and institutions through which to deploy new tools for planning and delivery. Like electricity without a power grid, AI without this foundation will remain a source of untapped potential.

What would leveraging AI look like in practice?

For starters, it would require unlocking and integrating siloed data from thousands of stakeholders, including construction firms, suppliers, government ministries, multilateral agencies, and financiers. It would also involve codifying domain knowledge from past project cycles to understand why delays happen, how risks compound, and where capacity breaks down. It requires building intelligent digital agents that understand infrastructure-specific workflows (contracting, procurement, permitting, and budgeting).

But most importantly, it would demand institutional collaboration. We do not need static roadmaps, but rather dynamic, evolving feedback mechanisms. AIs for infrastructure would learn from every project and apply lessons across organisations.

The opportunity to improve how we build things comes as the geopolitics of infrastructure is shifting. As President Donald Trump seeks to reverse all his predecessor's clean-energy policies, others are filling the leadership vacuum the United States has created. China, for example, has reoriented its Belt & Road Initiative toward "green, high-quality" development, pairing massive overseas transportation and energy investments with climate-resilience projects at home. It is pursuing large-scale desert reforestation and new renewables projects, even as coal, oil, and gas projects still feature heavily in its overseas portfolio.

Similarly, Saudi Arabia, long synonymous with hydrocarbons, has launched a "green initiative" to funnel tens of billions of dollars toward solar and wind projects, green-finance frameworks, and new public-private partnerships. The Kingdom aspires to generate half its electricity from renewables by 2030. And India has already hit its target of committing 50pc of its installed power capacity to non-fossil sources. It has also launched a National Green Hydrogen Mission, targeting annual production of five million tons by 2030. It has used global platforms such as the G20 and the United Nations Climate Change Conference to champion climate-resilient infrastructure and "green development pacts."

The result is a fragmented map. While the world's largest historical emitter is doubling down on fossil-fuel exports, emerging and middle-income economies are increasingly presenting themselves as voices of climate responsibility (even as they continue to navigate their own contradictions). In this new landscape, the contest is not only over whose capital builds the next generation of ports, grids, and railways, but whose data, standards, and AI systems will guide those investments.

The next leader in infrastructure will focus on three immediate priorities.

The first is to make the most of available data. Infrastructure know-how tends to be buried in PDFs, contracts, and permit files. Governments, banks, and companies should uncover this hidden history to help all stakeholders avoid past mistakes and navigate new policy settings when governments abruptly rewrite the rules (as the US has done).

The second priority is to build AI tools for this specific purpose. What we need is not a generic chatbot, but models trained on materials science, logistics, and local regulations. An AI that understands why projects fail can make success more likely.

Lastly, we should do a better job of sharing knowledge across borders. Instead of having each institution reinvent the wheel, we need a shared knowledge base so that lessons from a dam in India or a metro in Paris can improve projects everywhere.

Over the next decade, infrastructure will define not only climate adaptation but also global competitiveness. It is the muscle of the real economy. The countries that align their climate commitments, industrial policies, and infrastructure pipelines with credible and data-driven intelligence will set the rules of the game for everyone. Those who weaponise uncertainty or treat sustainable infrastructure as an afterthought will find their influence eroding.

AI should not be seen as a centralised oracle or as an abstract mind in the cloud. Its real uses lie in targeted applications to connect real-world projects, institutional workflows, and human networks. That is the kind of intelligence that will build not only better roads and resilient grids, but also more effective and efficient systems and organisations.

In a world where technological capacity is abundant but political will is unevenly distributed, the real test of leadership lies in project execution. Whoever can turn infrastructure from a source of climate risk into a shared, intelligent platform for sustainable prosperity will have something to teach everyone.

PUBLISHED ON Jan 17,2026 [ VOL 26 , NO 1342]