In an era of AI-compressed multipolarity, grand strategy increasingly becomes a matter of architecture. As major powers mirror one another’s operational capabilities, stability depends on designing new constraints. Continental coherence proposes a new political layer that aligns geographic responsibility with survival-scale force, while preserving sovereignty and pluralism.

Automation, robotics, demographic shifts, and geopolitical recalibration are quietly reshaping power structures. As Europe ages, the United States pivots toward hemispheric resilience, while conflict accelerates autonomous systems and AI deployment. Markets, migration pressures, and military restructuring all signal a deeper transition: nations and corporations are engineering durability against fragility.

The AI strategy for 2026 is not to slow innovation, but to pace it intelligently… (aligning velocity with context) so choice, authorship, and legitimacy are preserved at scale. The real risk ahead is synchronized speed, where systems converge faster than society’s ability to understand, govern, and adapt.

The same properties that make LLMs deeply engaging also make them capable of redirecting, scaffolding, and strengthening human connection.

A distributed Trust & Safety model is inevitable because safeguarding civilization-scale AI requires risk detection, decisioning, and accountability to be embedded across many nodes—not concentrated in any single institution—so resilience, oversight, and moral alignment emerge from the network itself.

I'm shifting into Trust & Safety because AI is becoming a distributed, civilization-scale force, and the governance layer must evolve alongside its capabilities. This is the layer where human values, societal stability, and technological scale converge.

This is as summary of a deep conversation I had with an AI, where we explored data and mapped some of the biggest structural challenges facing the U.S. economy. It’s not all-encompassing, but it captures the root causes — with issues like housing, jobs, and wages emerging as downstream domino effects.

AGI safety grounded in 10 ethical invariants and ‘exit as canary’—a model that blends math, governance, and early-warning signals!

Exploring the “Wisdom Equation” — a framework that blends philosophy, ethics, and computation to show how wisdom can guide AI and human decision-making.

I’ve returned to Google as an Global Product Manager, and I want to share why — because the answer isn’t just professional, it’s personal. It has everything to do with… where the world is heading.

As the world enters its fifth year of coexistence with COVID-19, emerging evidence suggests the pandemic's long-term toll may be neurological for anyone infected.

Onix made an investment in data and AI governance, designed to help Google Cloud customers take control of their data estates and AI innovation, with quick-deploy professional services.

Anomalo kicked off our “Data Governance Visionaries” campaign, where we shine a spotlight on data governance leaders and their best practices. Over the coming weeks and months, expect practical tips, lessons learned, and actionable insights to help you navigate the evolving landscape of data governance.  

At the “The State of Data Governance in 2025” forum, I share proven strategies for automating metadata, tracking lineage, and managing access — all while maintaining agility. Whether you’re working with CSVs, JSON, Parquet files, analytics warehouses, or production ML pipelines, this is an opportunity to discover how good governance can unlock data’s true value.

What if our fascination with control is quietly steering us toward a future where autonomy means nothing and freedom becomes an illusion? In Explore how advancing AI and surveillance technologies could transform humanity’s ancient desire for order into systems of absolute dominance: digital payment networks, biometric identity, centralized digital-ID, algorithmic decision-making. But don’t worry, it’s not inevitable.

As DeepSeek push toward global AI dominance, the United States faces a decisive inflection point. In “U.S. Options to Outcompete China,” I lay out a range of strategic responses: from a formal national “AI race” declaration, to a Manhattan-style mission uniting academia and industry… or a renewed push for open-source AI as a democratic counterbalance. Maybe both.

The tech industry once promised endless opportunit… now it feels like a trapdoor. Entry-level roles have vanished, mid-career talent is stuck in hiring freezes, and even top performers are applying into a void. Record revenues haven’t translated into jobs, and AI-driven hiring filters bury qualified candidates beneath algorithmic noise.

Speaking on behalf of Google Cloud at the AI Ethics & Society conference was more than a speaking opportunity — it was a moment to drive a broader conversation about responsibility, governance, and the future of intelligence. I laid out why cloud platforms must lead with ethics: ensuring transparency, fairness, and trust in every layer of AI deployment.

Representing Google Cloud at the Enterprise Data Management Council wasn’t just a speaking slot — it was a chance to shape how enterprises think about data governance at scale. I argued that in an age of proliferating data silos, cloud-native governance frameworks are not optional — they’re foundational to trust, compliance, and innovation.

Victory is rarely won through dramatic blows. If you wanted to sabotage an opponent, you wouldn’t attack them outright; you’d quietly dull their sharpness. That’s exactly what COVID-19 may be doing on a national scale. Not just causing illness, but subtly draining cognitive clarity, sleep quality, stamina, and resilience across entire populations. The research is growing undeniable.