By Rui Wang, Ph.D., CTO at AgentWeb

Artificial intelligence has moved beyond software innovation into something far more fundamental: it's now restructuring the physical infrastructure that powers our entire technology ecosystem. The recent news about ASML's record-breaking $11.5 billion profit in 2025 isn't just another earnings report—it's a watershed moment that reveals how deeply AI is transforming industries we rarely think about.

ASML, the Dutch company that manufactures the extreme ultraviolet (EUV) lithography machines essential for cutting-edge chip production, has become the unexpected beneficiary of the AI boom. But here's the paradox: even as the company celebrates historic profits driven by AI demand, it's simultaneously cutting 1,700 jobs as automation reshapes its own operations.

This isn't just a story about semiconductors. It's a preview of how AI will transform every industry—including yours.

The Hardware Bottleneck Nobody Talks About

When most people discuss AI progress, they focus on model architectures, training techniques, or the latest GPT iteration. That's missing half the picture. Today's AI advancement is increasingly constrained not by algorithmic innovation, but by raw hardware capability.

EUV lithography sits at the absolute center of this bottleneck. These machines—each costing upward of $200 million—are the only technology capable of etching the microscopic circuits required for modern AI chips. We're talking about manufacturing precision at the nanometer scale, where a single machine can determine whether a chip foundry can produce cutting-edge processors or falls a generation behind.

Every breakthrough in large language model training efficiency, every improvement in inference speed, every reduction in computational cost traces back to advances in chip fabrication. When ASML reports record profits, what they're really telling us is that the entire tech industry is betting enormous capital on AI infrastructure—and that bet is accelerating, not slowing down.

The numbers tell the story. ASML shipped more EUV systems in 2024-2025 than in any previous period, driven almost entirely by orders from companies building AI-specific chips. TSMC, Samsung, and Intel are all racing to expand their advanced fabrication capacity, and every one of those facilities depends on ASML's technology. This isn't speculative investment—it's companies putting billions into infrastructure because they see AI demand as structural, not cyclical.

The Automation Paradox: AI Disrupting Its Own Supply Chain

Here's where the story gets more complex and more instructive. The same AI forces driving ASML's record profits are simultaneously transforming how the company operates internally. The planned workforce reduction of 1,700 positions isn't a traditional cost-cutting measure—it's a fundamental reorganization driven by automation.

ASML is implementing AI-powered systems for:

• Manufacturing process optimization and quality control
• Supply chain forecasting and inventory management
• Customer demand prediction and production scheduling
• Design iteration and simulation workflows

This creates a fascinating feedback loop: AI demand drives hardware sales, which funds automation investments, which reduces the human workforce needed to deliver that hardware. The company making the machines that enable AI is itself being reshaped by AI.

This pattern isn't unique to ASML. We're seeing it across tech companies of every size. Marketing teams that once required 15 people to execute campaigns are now operating with 8. Customer support organizations are handling 3x the volume with the same headcount. Product teams are shipping faster with smaller crews.

The difference between companies that thrive and those that struggle isn't whether they adopt AI—it's how deeply they integrate it into core operations. Surface-level experimentation with ChatGPT for email drafts won't cut it. The winners are rebuilding entire workflows from first principles, assuming AI agents as full participants rather than occasional assistants.

What This Means for Your Startup

If you're building a company today, ASML's story contains three critical lessons that apply regardless of your industry:

Infrastructure Leverage Compounds Faster Than You Think

The companies investing in AI infrastructure today—whether that's semiconductor manufacturing capacity, GPU clusters, or agentic automation systems—are creating advantages that compound exponentially. ASML's customers aren't just buying machines; they're buying the capability to produce chips that will power the next generation of AI applications, which will in turn drive demand for even more advanced chips.

For startups, this means identifying where infrastructure investments in your domain will create similar compounding effects. In marketing, for example, companies that build robust content generation and distribution systems today will be able to test and iterate at speeds their competitors simply cannot match. That speed advantage compounds: more tests mean better learning, better learning means more effective campaigns, more effective campaigns mean more resources to invest in even faster iteration.

Operational AI Adoption Is Now Table Stakes

The workforce reductions at ASML aren't happening because the company is struggling—they're happening because AI has made certain roles structurally redundant. This is the new normal across industries.

Companies that fail to automate internal workflows will find themselves at a permanent disadvantage. Not because automation is inherently better at every task, but because the speed differential becomes insurmountable. A marketing team using AI agents to handle research, content creation, distribution, and performance analysis can execute 10x more campaigns than a traditional team. A sales organization with AI-powered outreach and qualification can cover 20x more prospects.

The math is brutal: if your competitor can test ten strategies in the time it takes you to test one, they will learn faster, adapt faster, and win faster. Operational AI adoption isn't about cutting costs—it's about survival.

Execution Trumps Experimentation

Here's the uncomfortable truth: most companies are still treating AI as a side project. They have a Slack channel for sharing ChatGPT prompts. Maybe they've built a prototype chatbot. Perhaps they've attended some webinars about AI strategy.

That's not enough. The competitive advantage doesn't come from having access to AI tools—everyone has access to the same tools. The advantage comes from actually integrating AI into your daily operations at a fundamental level.

At AgentWeb, we work with startups that have moved past experimentation into systematic execution. These companies aren't asking "How can we use AI?" They're asking "How do we rebuild our entire marketing engine assuming AI agents handle 80% of execution?" That shift in framing changes everything.

The difference is visible in the results. Companies executing with AI agents aren't seeing 10-20% efficiency improvements—they're seeing order-of-magnitude changes in output, speed, and market coverage.

The Convergence Nobody Saw Coming

What makes this moment unique is how multiple layers of technology are converging simultaneously. Software intelligence, hardware capability, and organizational design are no longer separate domains—they're becoming tightly coupled systems that evolve together.

ASML's record profits aren't an isolated phenomenon. They're an early indicator of how AI will continue to reshape entire value chains. The companies that manufacture the machines that make the chips that power the models that automate the workflows are all being transformed in parallel.

For founders, this convergence creates both opportunity and urgency. The opportunity is that fundamental technology shifts like this create openings for new players to displace incumbents. Established companies with legacy operations and organizational structures struggle to adapt at the pace required. Startups built from day one around AI-first operations have a structural advantage.

The urgency is that this window won't stay open long. Once the new operational models become clear, larger companies will copy them. The competitive moat comes from being early and building compounding advantages before the playbook becomes obvious to everyone.

Building for the AI-Native Era

The question every founder needs to answer isn't whether AI will change their market—it will. The question is whether your organization is architected to absorb and leverage that change faster than your competition.

This means:

• Designing workflows that assume AI agents as full participants, not occasional helpers
• Investing in infrastructure that enables rapid experimentation and iteration
• Building teams that understand how to direct and orchestrate AI systems effectively
• Creating feedback loops that capture learning from AI-assisted operations
• Maintaining the flexibility to rebuild processes as AI capabilities evolve

At AgentWeb, we've seen firsthand how companies that embrace this shift can achieve results that would have seemed impossible just two years ago. Full-funnel marketing campaigns executed end-to-end by AI agents. Content strategies that adapt in real-time based on performance data. Distribution systems that automatically optimize across dozens of channels simultaneously.

This isn't future speculation—it's happening now. The companies building this way are already pulling ahead.

The Path Forward

ASML's story is ultimately about adaptation. A company that builds the most advanced manufacturing equipment in the world is itself being reshaped by the technology it enables. That's the pattern we'll see everywhere: AI doesn't just change end products—it transforms entire value chains, organizational structures, and competitive dynamics.

For startup founders, the imperative is clear: build assuming AI agents are core team members, not peripheral tools. Design operations for speed and iteration. Focus on execution over experimentation. And move fast, because the companies that establish AI-native operations first will build advantages that become very difficult to overcome.

The semiconductor industry's transformation is just the beginning. Every industry will face similar disruption. The only question is whether you'll be among the companies leading that transformation or struggling to catch up.

If you're ready to explore how agentic AI can transform your marketing operations from experimental to systematic, let's talk about what we're building at AgentWeb. The future of marketing isn't about having better tools—it's about building entirely new operational models.