War, geopolitics, and AI: how Middle East conflict and US decisions are reshaping software development

Technology has never existed in a vacuum

Software is often discussed as if it were a purely technical activity — problems, solutions, tools, languages. In practice, global software development depends on physical supply chains, globally distributed talent, geographically concentrated cloud infrastructure, and political decisions that determine who gets access to which tools and under what conditions.

The convergence of the Middle East conflict with the increasingly unilateral foreign policy of the Trump administration is creating friction across multiple of these dependencies simultaneously. For engineering teams that have never needed to think about geopolitics, this convergence is becoming impossible to ignore.

What is happening in the Middle East and why it matters for technology

Gaza as a live AI military laboratory

The Gaza conflict has mobilized AI applications at unprecedented scale in a real combat environment. The Israeli military has publicly documented using AI systems for:

• Threat classification and target selection: Computer vision systems processing drone and camera feeds in real time to identify potential threats
• Missile defense: The Iron Dome integrates AI to calculate trajectories and interception probabilities with millisecond latency
• Intelligence analysis: Processing massive volumes of signal data, communications, and satellite imagery with specialized language models

The implications of these applications extend far beyond the battlefield. Technologies developed and battle-tested in active conflict frequently find civilian applications — and the civilian technologies that power them (computer vision models, natural language processing, real-time image analysis) are the same technologies that power commercial software products.

This creates a growing ethical question inside technology companies themselves: where is the line between dual-use technology and active collaboration with systems that cause harm to civilian populations?

Internal protests and mass resignations in big tech

The conflict has activated a form of internal activism inside major technology companies with no close historical precedent. During 2024 and 2025, employees at Google, Amazon, Microsoft, and several smaller companies organized internal protests, petitions, and collective resignations over technology contracts with the Israeli military or over use of their products in military systems.

"No Tech for Apartheid" — a movement led primarily by Google and Amazon employees — resulted in the termination of dozens of engineers who protested the Project Nimbus contract (a Google cloud computing contract with the Israeli government and military). Terminations occurred during a protest inside Google's offices.

What this means for engineering leaders:

• Contracts with governments in active conflicts can activate internal resistance that goes beyond private discussions
• A company's technology use policy — which customers it accepts and for what purposes — is now a talent attraction and retention issue for a growing segment of engineers
• This is not limited to large companies: any software company that provides services to clients in defense, surveillance, or security sectors may encounter similar dynamics

The chip geopolitics: the bottleneck defining AI's future

Frontier AI model development depends on extreme-precision hardware — specifically NVIDIA's advanced GPUs and Google's TPUs, manufactured by TSMC in Taiwan. This supply chain is geographically concentrated in ways that create systemic vulnerabilities.

Taiwan as a global single point of failure

TSMC is responsible for approximately 90% of global advanced semiconductor production (3nm process nodes and below). Israel, Japan, South Korea, and the US itself are investing heavily to diversify this dependency — but building new semiconductor fabs takes 5-7 years and tens of billions of dollars.

Any significant instability across the Taiwan Strait — whether from military tensions between China and Taiwan, trade conflicts, or natural disasters — would disrupt the global supply chain for advanced chips at a scale that would pause frontier AI development for months or years.

For engineering teams: This is not a hypothetical risk. It is the operating assumption of every major AI lab. This is why Anthropic, OpenAI, Google, and Microsoft are all investing in proprietary chips — reducing dependency on TSMC and NVIDIA is a strategic priority that directly affects the models available to developers over the next several years.

Trump administration export controls

One of the most significant changes in technology policy over the last two years has been the acceleration of export controls on advanced chips to countries considered adversaries or risks by the US. The list includes not just China and Russia, but covers restrictions affecting companies with operations across multiple jurisdictions.

What this means in practice:

• AI ecosystem fragmentation: Teams in China, parts of the Middle East, and countries with strained US relations are being forced to develop domestic alternatives (DeepSeek, Chinese lab models) for lack of access to frontier hardware. This is not slowing global AI — it is creating parallel ecosystems with different capabilities, different use policies, and different security risks.
• Compliance costs for global companies: Companies with operations in multiple countries need to navigate export regulations to determine in which regions they can deploy which models and tools. This has added legal and compliance complexity that simply did not exist three years ago.

The "splinternet" phenomenon has reached AI

The term "splinternet" describes the fragmentation of the internet into regional ecosystems with different rules, different dominant companies, and different access levels. What was a threat to the open web in 2018 discussions is now happening at accelerated speed in the AI market.

In 2026, software developers face a landscape where:

• The model available in your cloud environment may differ from the model available to your client in another region due to regulatory reasons (EU AI Act, export controls, local laws)
• Different AI providers have radically different policies on which use cases they permit (the Anthropic vs. Pentagon case is the most recent example)
• Service continuity from an AI vendor can be interrupted by political decisions — not technical ones (exactly what happened with Anthropic)

Practical impact for software architecture:

Geopolitical fragmentation is becoming a technical argument for architecture decisions that were previously purely about performance or cost:

• Multi-model architectures are now business continuity requirements, not just quality optimizations
• Data residency is becoming more complex as countries establish data sovereignty requirements that conflict with each other
• Open-weights models (Llama, Mistral, DeepSeek) are gaining strategic relevance precisely because they can be deployed locally, without dependency on vendors subject to geopolitical restrictions

The talent flight that no one is talking about

The Middle East conflict and American policy are affecting the global technology talent market in ways that data is slow to capture:

• Israel: Tens of thousands of engineers and developers have been called into military reserve service since October 2023. Some Israeli startups lost more than 30% of their teams. Companies that managed to operate did so with distributed teams, altered schedules, and increased reliance on international contractors — especially from India, Ukraine, and Brazil.
• Brain drain from conflict zones: Engineers from countries affected by conflict or political instability migrate to more stable markets when they can. This is historically positive for receiving countries — and may represent an opportunity for Brazilian and Latin American companies that offer stability and competitive dollar-denominated salaries.
• Compliance pressure for multi-country teams: A company with engineers in Brazil, Egypt, and the US that uses American AI tools now needs to verify whether export restrictions apply to how those teams access and use those tools.

What engineering teams should do now

Audit your geopolitical dependencies

Just as you have a software dependency inventory, you need visibility into your geopolitical dependencies:

• Which AI and cloud providers are critical to your operation? In which jurisdictions do they operate?
• Do your contracts have service continuity provisions that cover regulatory disruptions?
• Which critical tools depend on hardware supply chains concentrated in geopolitically unstable regions?

Vendor diversification

Not as cost optimization — as risk management. An architecture that relies on a single cloud provider, a single LLM provider, and a single deployment region is an architecture that can be disrupted by political decisions you do not control.

Regulatory monitoring as engineering practice

What the EU AI Act, American export controls, and AI policies from governments like Brazil establish affects your products. This monitoring cannot be delegated exclusively to legal teams — the implementation decisions are technical.

Navigating geopolitical complexity in your AI stack with unclear exposure to vendor lock-in, data residency, or cross-border compliance risks? Talk to Imperialis about resilient architecture for AI systems in a context of geopolitical fragmentation.

Sources

• Gaza conflict impact on Israeli tech sector — Jerusalem Post, 2025 — accessed March 2026
• Anthropic banned by Trump administration — The Guardian, February 2026 — accessed March 2026
• Google fires workers protesting Project Nimbus — Guardian, 2024 — accessed March 2026
• Middle East AI and tech investment 2026 — Computer Weekly — accessed March 2026
• US AI chip export controls expansion — Council on Foreign Relations, 2026 — accessed March 2026
• AI market fragmentation and the splinternet — The New Stack, 2026 — accessed March 2026