The global race to scale artificial intelligence may hit a financial and infrastructure wall sooner than expected. New research from Bain & Company’s sixth annual Global Technology Report, released Tuesday, finds that by 2030, the world will need $2 trillion in annual revenue to fund the computing power required for anticipated AI demand.

Even after factoring in AI-driven savings, Bain projects an $800 billion shortfall—raising alarms about whether global supply chains, capital markets, and power grids can keep pace.

The report estimates that incremental AI compute requirements could reach 200 gigawatts by the end of the decade, with the United States alone accounting for half. Meeting that need would require technology executives to deploy roughly $500 billion in capital expenditures on new data centers while identifying $2 trillion in revenue streams to sustain profitability.

“By 2030, technology executives will be faced with the challenge of deploying about $500 billion in capital expenditures and finding about $2 trillion in new revenue to profitably meet demand,” said David Crawford, chairman of Bain’s Global Technology Practice. “Because AI compute demand is outpacing semiconductor efficiency, the trends call for dramatic increases in power supply on grids that have not added capacity for decades.”

Bain warns that AI’s growth is already surpassing Moore’s Law, doubling compute requirements faster than chip efficiency improvements can offset. The imbalance risks creating global strains on energy, hardware supply chains, and capital spending. The report highlights that the “arms race dynamic” between nations and top providers could exacerbate risks of both overbuilding and underbuilding critical infrastructure.

Despite infrastructure challenges, companies at the forefront are reaping financial rewards. According to Bain, tech-forward enterprises that scaled AI across core workflows—sales, marketing, customer support, and R&D—have delivered 10% to 25% EBITDA gains over the past two years. Still, most firms remain stuck in experimentation mode, extracting only modest productivity boosts.

The report singles out “agentic AI” as the next frontier, where autonomous agents collaborate across workflows and applications. Bain projects that within three to five years, 5% to 10% of enterprise technology spending could go into foundational agentic capabilities, with up to half of spending eventually consumed by AI agents running across organizations.

Bain identifies four levels of maturity:

• LLM-powered retrieval agents
• Single-task workflows
• Cross-system orchestration
• Multi-agent constellations

Levels two and three are where “capital, innovation, and deployment velocity are converging,” Bain said, stressing that leaders at these stages are already compounding their advantage while laggards risk falling further behind.

SaaS faces AI disruption

Software-as-a-service (SaaS) providers face an uncertain but potentially lucrative transition. Bain argues that generative and agentic AI could expand SaaS markets by automating user tasks and embedding into workflows. Yet incumbents will need to make high-stakes strategic bets, including selective open-sourcing and changes to monetization. Winning providers must “own the data, lead on standards, and price for outcomes, not log-ons, in an AI-first world,” the report said.

Sovereign AI and a fractured supply chain

Bain also points to the rise of “sovereign AI” as governments move to localize control over critical infrastructure, mirroring shifts in semiconductor supply chains. Export controls, tariffs, and decoupling between the U.S. and China are accelerating fragmentation. China now accounts for roughly 20% of global chip manufacturing capacity, raising the stakes for U.S. and European firms.

“Sovereign AI capabilities are increasingly seen as a strategic advantage on par with economic and military strength,” said Anne Hoecker, head of Bain’s Global Technology Practice. “Considering these differences, global AI standards are unlikely to converge.”

For multinational firms, Bain advises optionality—moving boldly where confidence is high, while prioritizing flexibility where uncertainty looms.

Quantum computing and humanoid robots on the horizon

Alongside AI, Bain spotlights two other transformative technologies. Quantum computing, though still in early stages, could unlock $250 billion in value across industries from pharma to logistics once fully capable fault-tolerant machines arrive.

Meanwhile, humanoid robots are shifting from viral demos to billion-dollar valuations, though deployments remain limited and heavily reliant on human oversight. Early adopters, Bain argues, will be best positioned to lead if ecosystem readiness improves.

Private equity dealmaking cools

The report also notes a slowdown in North American technology private equity deals in the second half of 2025. Tariff-related uncertainty and geopolitical tensions have tempered momentum despite a strong start to the year. While software continues to outpace GDP growth, its penetration across manufacturing and retail is topping out, forcing investors to hunt harder for top-tier growth opportunities. Even so, tech remains one of the strongest-performing PE sectors, Bain finds.

With AI scaling at breakneck speed, Bain concludes that technology leaders face a paradox: they must prepare for unprecedented capital and energy demands while navigating geopolitical fragmentation and disruptive innovation. Companies that adapt quickly to agentic AI and sovereign technology regimes will have an edge, but the risks of missteps—from overinvestment to supply shortfalls—remain high.

In the orchestra of modern commerce, the supply chain is the conductor’s baton. It determines rhythm, flow, and harmony. But in many firms, that baton is heavy, bloated with inefficiencies—warehouses stocked with slow-moving inventory, lead times stretching like rubber bands, and coordination that lags across partners.

Lean supply chain is the redesign: it is the commitment to eliminate waste, synchronize processes, and deliver value with precision. Like Toyota’s lean production, it anchors on doing more with less—less time, less inventory, less cost—while ensuring the customer always experiences more value.

A lean supply chain is not just about cutting fat; it is about improving muscular strength. Firms that adopt it optimize sourcing, automate logistics, and leverage data visibility to predict demand. They shift from reactive replenishment to proactive planning, ensuring that resources align with real consumption patterns.

In Africa, where logistics remains one of the biggest barriers to competitiveness, lean supply chain models can reduce inefficiencies that inflate prices and erode margins. Think of it as the engine that turns fragmented ecosystems into coherent pathways where goods move seamlessly and efficiently.

Ultimately, lean supply chain is both strategy and discipline. It demands that firms view partners not as adversaries to squeeze but as collaborators in a shared value chain. When businesses commit to lean systems, they free up capital, accelerate market responsiveness, and build resilience against shocks. The result is speed, adaptability, and sustained competitiveness.

From NATO today, we will be learning from a zen-master of lean at Tekedia Mini-MBA.

Thur, Sept 25 | 7pm-8pm WAT | Lean Supply Chain Applications in Business – Chibueze Noshiri, NATO Luxembourg | Zoom link