48 Hours of AI Power Moves Demand a Strategy Reset

The race for AI dominance is increasingly shifting to battles over hardware and infrastructure. In a landmark move, France’s Schneider Electric and Taiwan’s Foxconn have entered a strategic collaboration to develop modular, next-generation AI data centers ([1]). By combining Foxconn’s mass-manufacturing and electronics expertise with Schneider’s advanced power and cooling technologies, the alliance aims to deliver “ready-to-deploy” AI data center solutions and alleviate critical energy and cooling bottlenecks in the AI boom ([2]) ([3]). Production of these new high-density facilities is expected to start by year-end, just as global demand for AI compute capacity reaches record highs.

This focus on physical infrastructure signals that AI competition is no longer just about algorithms—it’s also an arms race for the computing power and energy to run them. Tech giants and cloud providers are investing tens of billions in data centers and custom silicon. As hyperscalers pour resources into expanding their AI clouds, suppliers capable of rapidly scaling power, cooling, and networking have become strategic kingmakers ([4]). The Schneider–Foxconn pact illustrates how critical the “backend” of AI has become for sustaining growth: whoever can build and deploy AI infrastructure faster and more efficiently will enable the next wave of AI products, potentially tilting the balance of power in tech markets.

Geopolitical dynamics are further redefining this landscape. Chinese internet leader ByteDance, the owner of TikTok, is reportedly negotiating to buy advanced AI chips from Shanghai-based startup Iluvatar CoreX ([5]). The deal, if finalized, would make Iluvatar CoreX ByteDance’s third domestic GPU supplier (after Huawei and Cambricon) and reduce its dependence on Nvidia’s hardware amid tightening U.S. export controls on high-end chips ([6]). This move is part of Beijing’s broader push for technological self-reliance: Chinese chipmakers have already captured nearly 41% of China’s AI accelerator server market in 2025, significantly eroding Nvidia’s position in that region ([7]). In effect, the AI power struggle now extends to the supply chain, with nations and companies alike racing to secure the silicon and energy resources needed for AI supremacy.

AI-native startups are leveraging speed and innovation to challenge industry stalwarts by reimagining business models and automating previously human-driven tasks. Take the HR sector: this week, Barcelona-based startup Orbio announced a $21 million funding round to scale its AI agents for frontline workforce management ([1]). Founded by former Amazon and tech veterans, Orbio’s software deploys AI “co-pilots” that handle hiring, onboarding, and day-to-day HR operations for large employers. In one pilot, an autonomous Orbio agent triaged job applicants and performed routine check-ins, helping a U.S. healthcare company increase its hiring rate by 20% without additional human recruiters ([2]).

The rise of such startups is pressuring incumbents to adapt. Orbio’s CEO notes its primary competition isn’t other tech firms but the old way of managing shift workers – an often manual tangle of spreadsheets, calls, and traditional HR systems ([3]). By offering a faster, AI-driven alternative, these startups threaten legacy software vendors and outsourcing firms that have long dominated enterprise functions. Similar disruptions are emerging in other fields: in finance, London-based Gradient Labs just doubled its Series A funding to $26 million to build AI agents for financial services operations ([4]), targeting routine banking and compliance workflows. And in the industrial realm, investment in “physical AI” – robots and automation for factories, warehouses, and logistics – has skyrocketed from $4 billion in 2019 to $26 billion in 2025 ([5]). The common thread is a new generation of AI-focused entrants exploiting areas where incumbents have been slow to adopt automation, potentially seizing market share by offering efficiency and cost advantages that traditional players can’t ignore.

The strategic implication for established companies is clear: simply relying on brand legacy or scale is risky when AI can unlock entirely new ways of operating. Forward-looking leaders are already scouting partnerships or acquisition targets among these AI upstarts, both to accelerate their own AI capabilities and to prevent competitors from gaining an edge. In an age where software can run core business processes autonomously, incumbents must decide whether to compete, collaborate, or consolidate to survive.

Established technology companies are making headline-grabbing moves to embed AI across their products, seeking to transform their value propositions and fortify their market position. This week, enterprise giant Salesforce pushed out its Summer ’26 software update, packing hundreds of AI-driven enhancements into its core customer relationship and analytics offerings ([1]). From automatically orchestrating multi-agent customer service workflows to integrating predictive analytics into its Tableau platform, Salesforce’s rapid-fire release cadence is designed to keep its massive install base hooked on new AI capabilities. This strategy of continuous AI upgrades has become central to how Salesforce defends its dominance against cloud-native challengers and fellow incumbents racing to infuse AI into their own platforms ([2]).

Others are leveraging pricing and distribution advantages to grow their AI ecosystems. Video collaboration leader Zoom, for instance, recently launched ZoomMate, an AI “teammate” that turns meeting discussions into automated actions and content. Notably, Zoom set an aggressive price of $20 per user per month for ZoomMate’s enterprise plan ([3])—significantly undercutting many competing AI add-ons. By bundling advanced AI features directly into its platform at a palatable cost, Zoom is using its reach and customer base to make AI-driven productivity a standard expectation, putting pressure on rivals like Microsoft and Google to match its value pitch.

Yet these big bets don’t guarantee easy wins, as Meta’s experience demonstrates. Last year, Mark Zuckerberg poured $14.3 billion into acquiring a large stake in startup Scale AI and bringing its celebrated founder Alexandr Wang in-house to spearhead Meta’s new AI unit ([4]). The effort produced a proprietary model (codenamed “Muse”) and a slew of AI features across Meta’s social and hardware products. However, a year on, Meta’s stock has fallen 18% in the last 12 months ([5]), making it the worst performer among big tech peers despite a 33% jump in Q1 revenue. Investors are increasingly asking when Meta’s AI investments will translate into new income streams, rather than just boosting the existing advertising business ([6]). The lesson for executives is that AI can be a game-changer, but only if it’s tightly linked to a viable monetization strategy. Tech leaders must not only innovate with AI but also ensure those innovations drive real customer value and revenue growth—else even massive AI spending can leave a company vulnerable.

Underneath these individual moves is a broader battle of AI ecosystems and a push for digital sovereignty. At this week’s G7 meeting, France lobbied to position itself as Europe’s hub for AI infrastructure, rallying support from investors like SoftBank and Brookfield for major data center investments on the continent ([1]). This initiative reflects Europe’s determination to close the gap with the United States and China in advanced computing power. The message to industry and policymakers was clear: Europe is no longer content to remain a mere consumer market for foreign AI services—it aims to control its own AI destiny through home-grown capacity ([2]).

Private companies are aligning with these national strategies. Toronto-based Cohere, for example, announced plans to triple its presence in London with a new 14,000 sq ft office for up to 100 employees ([3]). The fast-growing AI startup—already valued around $20 billion after acquiring a German AI firm in April—is explicitly positioning itself as a “sovereign AI” provider for Europe ([4]) ([5]). By offering large language models that can be deployed on local servers without data leaving the region, Cohere is targeting government and enterprise clients that are wary of sending sensitive information to U.S.-based AI platforms ([6]). In doing so, it directly challenges U.S. leaders like OpenAI and Anthropic on a new front: trust and compliance within regional boundaries.

Meanwhile, the race to own the full AI stack—from chips and cloud platforms to killer applications—is fragmenting along geopolitical lines. Chinese tech giants are investing in domestic hardware and model ecosystems, while Western firms form massive partnerships (and even pursue IPOs) to fund AI growth. Analysts at McKinsey estimate that by 2030, requirements for “sovereign” control over AI could influence $500 billion to $600 billion of global AI spending—roughly one-third of the total market ([7]). For C-level strategists, the implication is that future competitive advantage will depend not only on adopting AI, but also on choosing the right ecosystems and alliances. In a world where governments and tech titans are staking claims on AI platforms, companies must navigate these evolving spheres of influence or risk finding themselves on the wrong side of a strategic divide.