AI Journey to Silicon Valley

Day 1 - Setting the Stage: Leadership Perspectives on AI

We heard from three influential voices:

Nitin Mittal (Deloitte Global AI Leader) shared how organizations are moving beyond pilots toward enterprise-wide transformation.

Kevin Nolan (GE Appliances CEO) illustrated how AI drives real productivity in manufacturing.

Nikesh Arora (Palo Alto Networks CEO) expanded the discussion to cybersecurity and geopolitics — where AI is now a defining variable.

Their perspectives converged on one key message: AI is no longer optional — it's a competitive imperative. But success depends on more than technology alone.

The Four Traps of AI — Kevin Nolan

The Technology Trap — Mistaking tools for transformation.
→ Focusing on AI technology itself instead of embedding it into core business strategy and outcomes.

The Data Trap — Having data but lacking discipline.
→ Collecting massive data sets without the governance, quality, or integration needed to generate real insight.

The Pilot Trap — Experimenting endlessly without scaling.
→ Running proofs of concept that never mature into enterprise-wide capabilities or impact.

The People Trap — Forgetting the human in the loop.
→ Neglecting workforce adoption, mindset, and empowerment — the true drivers of sustainable AI success.

Key reflections from Day 1

1. Europe has the Talent, the Capital, and the Need — what's missing is the right Mindset.
Europe's research institutions and financial strength are world-class, and demographic change makes the need for intelligent automation urgent. What's often missing is the mindset of experimentation — the willingness to test, fail fast, and scale bold ideas. In the Valley, learning happens through doing. For Europe, matching that bias for action will define its competitiveness.

2. Competing on AI is also competing on energy.
Behind every model and data center is an immense energy demand. In the AI economy, electricity is the new oil — a finite resource shaping global competition. Sustainable, sovereign, and efficient energy strategies will determine who can scale AI responsibly.

3. AI agents should be built by the employees closest to the process.
Innovation is fastest when those who understand the work design the tools. Empowering teams to develop their own AI agents democratizes intelligence, builds trust, and accelerates adoption. AI literacy is becoming as fundamental as digital literacy once was.

The day closed with a collective realization: mindset will determine who leads in the next wave of AI. Technology follows belief.

Day 2 - Enterprise Transformation & Ecosystem Building

The second day took us deeper into how organizations scale AI — responsibly, sustainably, and with people at the center.

At NVIDIA, we learned that AI factories — massive clusters of compute — are becoming the new industrial backbone. Compute capacity and energy efficiency now define an enterprise's ability to compete. Physical AI at NVIDIA brings intelligence into the real world by using Omniverse to simulate physics, matter, and motion — enabling AI to learn and act through realistic digital twins of factories, robots, and cities.

At Anthropic, the focus was on trust and safety — proving that the real differentiator in AI adoption is not just performance, but reliability and alignment with human values.

At Salesforce, co-founder Parker Harris spoke about shifting from software as a service to intelligence as a partner. Chief HR Officer Nathalie Scardino introduced the Agentic Mindset, built around the 4Rs — Redesign, Reskill, Redeploy, and Rebalance — a practical blueprint for human–AI collaboration.

In the evening, an open dialogue with an AI research and deployment company's leadership brought the conversation full circle: AI's success will depend not on replacing human effort, but amplifying it responsibly.

Key reflections from Day 2

1. The Agentic Mindset is essential — organizations must redesign work, not just tools.
Technology without transformation achieves little. True adoption means rethinking workflows, decision rights, and accountability between humans and AI systems.

2. Demand for compute power will surge as both model training and usage scale.
Every improvement in model quality increases computational demand. Leaders must treat compute as a strategic asset.

3. The future of interaction with applications will be conversational. We are entering a world where humans will no longer adapt to software — software will adapt to humans.

"The future of interaction with applications will be conversational."

This idea captures how AI changes not only what we do, but how we engage with technology itself.

Day 3 - Academia, Investment & Sovereignty

The third day offered perspective beyond technology — exploring the economics, ethics, and governance of AI.

At Stanford University, discussions with professors and researchers revealed how AI is reshaping not only computation, but cognition and economics. Brandon Middleton underscored the role of emotional intelligence in leading through technological disruption, while Bart Pustjens explored how sovereign cloud strategies balance innovation with control. Mykel Kochenderfer demonstrated the mathematics behind safe decision systems like ACAS X, and Guido Imbens illustrated how experimentation and econometrics help measure AI's economic impact. The message was clear: human and machine reasoning are converging — and we must govern that intersection wisely.

Later, with Plug & Play, we explored how innovation is funded and scaled. Silicon Valley's investors don't just back ideas — they back teams with competence and character, solving problems in massive markets.

Key reflections from Day 3

1. The investment mindset in Silicon Valley centers on team and market opportunity above all.
Investors repeatedly emphasized: great products emerge from great teams. Culture, adaptability, and conviction matter more than technology itself. This philosophy drives a relentless bias toward execution — a lesson Europe can embrace.

2. Achieving true data and technology sovereignty remains a major challenge.
AI systems rely on globally distributed data, hardware, and platforms. True sovereignty will come not from isolation, but from choice — the ability to decide where and how data is processed, stored, and governed. Europe's strength lies in combining innovation with ethical clarity.

3. Governments will need to rewrite the social contract as AI transforms the labor market.
Automation will not only shift jobs but redefine value itself. Education, taxation, and welfare systems must evolve to support lifelong learning and equitable opportunity. The societal dialogue around AI is as critical as the technological one.

"Governments will need to rethink the social contract as AI affects the labour market."

A sobering reminder that the future of work is not only an economic issue, but a societal one.

Day 4 - Innovation at Scale: The Google Perspective

Our final stop took us to Google — where the frontier of innovation meets the responsibility of scale.

From AI search to healthcare and creative tools, Google's mission is to build systems that can entertain billions while curing millions. Hands-on sessions with NotebookLM and Beam illustrated a powerful truth: the same core models that summarize research can also personalize learning or assist medical discovery.

Chief Technology Officer Scott Penberthy captured it succinctly: "AI factories produce one product: Intelligence."

Key reflections from Day 4

1. AI must balance innovation with impact.
Scaling AI responsibly requires dual awareness — serving users while safeguarding societies. The next frontier of leadership will be ethical scalability.

2. The fundamental question for every organization remains: Augment or replace?
AI's potential lies not in replication but amplification — using intelligence to enhance creativity, decision-making, and empathy. The most visionary organizations see AI as a collaborator, not a competitor.

Empower teams to experiment, learn, and co-create with AI. Transformation begins when people feel ownership.

Secure access to the digital "energy" — compute power, data, and energy — that will underpin tomorrow's competitiveness.

Embed ethics, transparency, and accountability in every AI initiative. Trust is not a regulation — it's a leadership choice.

Partner with start-ups, academia, and policymakers to accelerate responsible innovation. No single actor can build the AI future alone.