Systems in Motion: AI Infrastructure, Cyber Resilience, and the Next Digital Shift

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Welcome to a new week of insights and innovation.

This week’s edition brings us to the heart of transformation from the deserts of Abu Dhabi to the corridors of cybersecurity investment and the labs driving battery innovation. Across each headline, one thing is clear: we are moving from experiments to ecosystems.

As AI infrastructure scales across regions like the Gulf, the implications are no longer theoretical. They are architectural, reshaping how we build capacity, manage resources, and define technological influence. In parallel, breakthroughs in battery chemistry could finally close the affordability gap for EVs, while reasoning AI models, once seen as limitless, are showing signs of strain under the weight of complexity.

In cybersecurity and venture, we see the rise of resilience as both a market imperative and a national concern. Investments in early-stage technologies are no longer just about scale, they’re about building intelligent systems that last.

This week’s signals speak to a more sober yet ambitious era in digital innovation: one grounded in infrastructure, guided by values, and tested by real-world demands.

• AI Infrastructure At Scale: OpenAI and the Gulf’s Bid for Global AI Leadership

• Energy Innovation: How LMR Batteries Could Reshape the EV Market

• Scaling Secure Futures: Cybersecurity, AI, and the Resilience Imperative

• Reasoning Models Under Review: The Future of AI May Not Be as Scalable as We Think

AI INFRASTRUCTURE AT SCALE:
OpenAI and the Gulf’s Bid for Global AI Leadership

In one of the most ambitious infrastructure efforts to date, OpenAI is reportedly planning a 5-gigawatt data center campus in Abu Dhabi, a facility projected to span 10 square miles, surpassing the size of Monaco. This development, part of OpenAI’s Stargate initiative in collaboration with UAE-based tech conglomerate G42, signals a significant realignment in where and how AI infrastructure is being built.

The campus would consume energy on the scale of five nuclear reactors, making it one of the most powerful AI-focused data centers in the world. With its planned capacity, the Abu Dhabi site would far exceed OpenAI’s U.S.-based Stargate project in Texas, which is expected to reach 1.2 gigawatts.

This partnership reflects a broader strategy by the UAE to emerge as a central player in global AI development. The alignment between OpenAI and G42 builds on a relationship established in 2023, when both organizations began working together to promote AI innovation in the Middle East.

However, the alliance has not been without controversy. G42 faced scrutiny from U.S. lawmakers over past connections to Chinese companies, raising concerns around technology transfer and national security. In response, G42 publicly stated in 2024 that it had divested from all China-related investments and shifted its strategic focus.

Soon after, Microsoft, an OpenAI stakeholder with strong regional interests, announced a $1.5 billion investment in G42. The company’s president, Brad Smith, joined G42’s board, reinforcing the partnership and its importance to the global AI ecosystem.

This initiative reveals more than just infrastructure growth. It reflects a strategic shift in where nations are investing to shape the future of AI. As the need for compute power accelerates, we are seeing new centers of gravity emerge, ones defined not by historical dominance, but by investment, vision, and political will.

Source: Connie Loizos, “OpenAI’s planned data center in Abu Dhabi would be bigger than Monaco,” TechCrunch, May 16, 2025.

ENERGY INNOVATION:
How LMR Batteries Could Reshape the EV Market

The next evolution in electric vehicle battery chemistry is taking shape, and it’s built around lithium-manganese-rich (LMR) technology. General Motors and LG Energy Solution have announced a major milestone: plans to commercialize LMR battery cells for GM's future electric trucks and full-size SUVs. Production is expected to begin by 2028, with pre-production underway in late 2027.

Why does this matter? LMR cells offer a compelling combination of performance and affordability. With higher energy density than iron-based chemistries and lower cost than nickel-heavy alternatives, LMR batteries could be a key step toward achieving price parity with gas-powered vehicles, one of the biggest barriers to widespread EV adoption.

For GM, this shift is about more than chemistry. It’s about supply chain resilience and strategic autonomy. By substituting cobalt and reducing nickel dependence, GM aims to localize materials sourcing and increase sustainability. According to Andy Oury, the company’s head of battery strategy, LMR could allow GM to lead in battery affordability while expanding its reach across vehicle segments.

Ford is also making moves in this space. Recent announcements signal a broader industry alignment around LMR as a viable middle-tier solution, offering longer range than LFP and greater scalability than NMC for mainstream markets.

As EV demand rises globally, innovation in battery materials will define how fast and how equitably, this transformation takes place. LMR may not be a silver bullet, but it represents a critical step toward more accessible, efficient, and locally produced electric mobility.

SCALING SECURE FUTURES:
Cybersecurity, AI, and the Resilience Imperative

At the intersection of cybersecurity, artificial intelligence, and digital resilience, Paladin Capital Group is shaping a strategic investment approach with long-term global impact. In a recent conversation at StrictlyVC London, Nazo Moosa, Managing Partner at Paladin Capital, outlined how the firm is nurturing early-stage innovation that underpins the digital infrastructure of tomorrow.

INVESTING WITH DEPTH AND DISCIPLINE

Paladin Capital’s focus is clear: support technologies that live beneath the application layer, from cybersecurity and AI to the underlying systems that make enterprise and government networks resilient. Moosa shared how her team works across stages, from pre-product startups to mature companies with over $100 million in revenue, building deep sector expertise and actively guiding founders beyond capital.

This includes cultivating dual-use companies like Hack the Box, a cybersecurity platform with both enterprise and government traction, and Calypso AI, which began securing large language models before they entered the broader public and enterprise consciousness. Moosa emphasized that these investments are designed to strengthen long-term infrastructure, national competitiveness, and digital trust.

REDEFINING ENTRY POINTS FOR STARTUPS

One of the most important developments in defense and resilience markets today is a shift in procurement logic. Moosa noted that governments, including the UK and US, are compressing contract timelines, from six years to two in some cases and creating procurement paths that resemble enterprise sales cycles more than traditional defense deals. This opens new doors for smaller, agile startups.

She also highlighted the growing need for navigational support: advisors, channel partners, and funders with real understanding of how to help innovative software companies enter heavily regulated or traditionally hardware-dominated spaces.

THE CASE FOR PURPOSEFUL INVESTING

Moosa underscored that cybersecurity and AI have long intersected. What’s changing is the expanding risk landscape and the increasing demand for robust, adaptable systems. As AI becomes more deeply embedded across sectors, ensuring security through models, infrastructure, and access controls becomes critical.

Paladin approaches generative AI not as a novelty but as a continuation of transformative technological shifts, comparable to the cloud era. Moosa noted that most enterprises still require stronger governance frameworks, improved workflow compatibility, and enhanced cybersecurity measures before they can effectively scale AI agents.

Her message to founders and investors was direct: leadership in intelligent infrastructure will belong to those who can embed security, compliance, and resilience from the start.

A FUTURE THAT BALANCES PRINCIPLES AND SCALE

Geopolitical shifts, such as increased defense spending in Europe and evolving procurement strategies in North America, signal a broader recognition that digital resilience underpins national and economic stability.

Moosa emphasized the importance of maintaining diversity and inclusion within innovation ecosystems. For her, resilience extends beyond systems, it encompasses the people building and benefiting from them. The future of secure technology should reflect the values societies seek to uphold.

Paladin’s approach offers a glimpse into responsible, foresight-driven investing: supporting those building systems that are not only secure and scalable but also ethically grounded and socially inclusive.

REASONING MODELS UNDER REVIEW:
The Future of AI May Not Be as Scalable as We Think

A new analysis from research nonprofit Epoch AI suggests the rapid advancement of reasoning-based AI models may be approaching a slowdown. The report indicates that while these models, such as OpenAI’s o3, have dramatically outperformed their predecessors on tasks involving math and programming, the performance curve may flatten as early as next year.

Unlike traditional models, reasoning models are enhanced through reinforcement learning (RL). This process rewards the model for generating accurate solutions to complex problems, improving its ability to reason rather than simply predict. Reinforcement learning is computationally intensive and has, until recently, been underutilized at scale.

OpenAI has begun shifting its approach, reportedly dedicating around 10 times more compute to training o3 than it did for o1, much of which was likely used during the RL phase. Looking ahead, the company has indicated plans to prioritize RL even more aggressively in future models.

But Epoch warns there are limits. According to analyst Josh You, while standard AI model performance is growing roughly 4x annually, gains from reinforcement learning are surging at an even faster pace, 10x every 3 to 5 months. This rate of improvement may soon hit technical and economic constraints, bringing it in line with more general model advancements by 2026.

The concerns go beyond compute. Reinforcement learning often involves high research and development overheads, from human feedback to dataset engineering. These costs could slow progress more than raw computing limits.

The AI community is watching closely. With reasoning models driving much of the current excitement and investment, in frontier AI, the possibility of diminishing returns raises difficult questions about how sustainable this path truly is.

AI's future may still hold transformative potential, but as this analysis suggests, breakthroughs will depend not just on brute force computing but on smarter, more efficient approaches to building systems that can truly reason.

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