For much of the past five years, the artificial intelligence narrative has been dominated by model breakthroughs, parameter counts, and venture capital flows. But according to Natalie Hwang, founding managing partner of Apeira Capital, the centre of gravity has shifted.
The constraint is no longer model capability or capital availability. It is the economics of running intelligence at scale.
“What changed is that AI moved from experimentation into production,” Hwang says. “When models are in research mode, capability dominates the conversation. Once they are deployed at scale, the economics take over.”
That transition marks what she describes as a structural maturation of the industry rather than a slowdown. “The conversation is shifting from ‘what can models do?’ to ‘what can systems sustain?’ That is a structural maturation, not a slowdown.”
In the early phase of AI’s commercial expansion, training large models consumed attention and capital. Today, inference — the continuous process of running models in real-world environments — is emerging as the dominant cost centre.
“Inference is continuous and operational,” Hwang explains. “Unlike training, which is episodic, inference must run reliably, efficiently, and at scale.”
That shift is reshaping competitive dynamics. The focus is moving toward performance per watt, cost per token, and deployment efficiency. Companies that can scale under infrastructure constraints, she argues, will outperform those relying solely on marginal model improvements.
“The competitive landscape is becoming more about economic durability than technical spectacle.”
AI as industrial system
This transition is reframing AI as an infrastructure story as much as a technology one.
“AI remains a technology story, but it is increasingly governed by infrastructure realities,” Hwang says. As adoption broadens across industries, physical systems such as grids, data centres, cooling, and energy economics, define what is viable.
“We are moving into an industrial phase of AI, where physical systems determine scalability. That doesn’t diminish innovation; it anchors it in real-world constraints.”
In this phase, compute capacity, grid resilience, and cooling efficiency become strategic assets. The question is not just how intelligent a model is, but whether the system supporting it can sustain demand.
As power becomes a binding constraint, geography is re-entering the AI equation.
“Regions <a href="https://jordangazette.com/air-arabia-rolls-out-ramadan-sale-with-up-to-40-discounts/”>with abundant, reliable energy and the ability to build large-scale data infrastructure are becoming increasingly important,” Hwang says.
The global AI map, she argues, is shifting away from where ideas originate toward where intelligence can be hosted sustainably.
“Power availability and infrastructure density are emerging as structural advantages.”
That recalibration creates opportunities for regions traditionally viewed as capital providers rather than technology hosts.
The Middle East’s structural position
From a market-structure perspective, Hwang views the Middle East as a credible long-term host for compute-intensive AI systems.
“The region combines energy resources, sovereign-scale capital, and long investment horizons. Those factors are well aligned with the needs of compute-intensive AI systems.”
However, credibility depends on execution
“The question is less about capital and more about disciplined execution, ecosystem depth, and long-term infrastructure planning.”
In a world where inference economics determine scalability, energy resilience and infrastructure density become strategic differentiators; not just supportive factors.
The maturation of AI is also changing how capital is deployed. Rather than abandoning frontier model development, investment flows are widening.
“We are seeing capital broaden, not abandon,” Hwang explains. “Model development remains important, but incremental capital is increasingly directed toward infrastructure that can sustain deployment.”
As AI systems move from speculative promise to operational reality, investor priorities shift accordingly.
“As AI systems mature, the risk profile shifts from speculative model breakthroughs to operational performance. That changes how investors think about durability and returns.”
In this new phase, defensibility is defined less by novelty and more by structural alignment.
“Durability comes from alignment with structural constraints,” Hwang says. Systems that improve cost efficiency, energy utilisation, and deployment reliability will hold longer-lived advantages.
“In this phase, defensibility is tied less to novelty and more to whether a solution meaningfully lowers the cost of running intelligence at scale.”
That framing reframes AI from a breakthrough narrative to an industrial optimisation story, where economics, not hype, determine winners.
Avoiding strategic overreaction
For policymakers and investors, the temptation to chase headlines remains strong. Hwang urges restraint and systems thinking.
“Strategic advantage comes from building infrastructure with long-term utility, not from reacting to headlines.”
She emphasises three priorities: energy resilience, compute efficiency, and interoperability. AI, in her view, should be treated as an industrial system, not a speculative wave.
“The regions and institutions that treat it as such, rather than as a speculative wave, will be better positioned over time.”
As AI transitions from experimentation to deployment, the industry’s constraints are becoming more physical than theoretical. Megawatts, not models, increasingly define scalability.
The frontier is no longer just algorithmic sophistication. It is economic sustainability.
In that environment, competitive advantage will accrue not simply to those who build the most powerful models, but to those who can run intelligence efficiently, reliably, and durably — at scale.