Hamilton Mann argues that one of the most urgent challenges posed by AI is the concentration of algorithmic power. He identifies three layers: capability, as a small number of companies develop powerful AI systems; distribution, as those systems reach millions of people; and, most consequentially, governance, where some algorithmic systems can increasingly shape decisions, allocate resources, and determine outcomes.

WHERE DO ETHICS fit in the fast-growing world of AI and business? Hamilton Mann has a plan to address that question. He argues that letting ethics slip is not an inevitability; it is a choice. In his book, he argues that “artificial integrity” – or designing AI systems that are genuinely aligned with human values rather than optimized for performance – is not a constraint on innovation, but a condition for it. In an engaging and wide-ranging Zoom conversation, he outlines how

Hamilton Mann is an AI researcher, technology executive, and award-winning author. He is Executive Chairman and President of the Artificial Integrity Institute, lectures at INSEAD and HEC Paris, and mentors at MIT's Priscilla King Gray Center. In 2025 he received the Thinkers50 Distinguished Achievement Award for Digital Thinking. He is the author of the acclaimed Artificial Integrity: The Paths to Leading AI Toward a Human-Centered Future, and his new book, Trias Algorithmic

As artificial intelligence increasingly shapes what we know, see, choose, and do, a fundamental question emerges: who governs the algorithms that govern us?

We have spent much of the AI governance debate asking what artificial intelligence should be allowed to do. I believe there is a prior question we have not examined closely enough: what makes algorithmic power legitimate? Montesquieu’s warning about concentrated power was written for political institutions, not algorithms. Yet the principle behind it has surprising relevance to AI. When the same organization can effectively design an algorithmic system that fuses rule-making,

What artificial intelligence lacks, and what few if any benchmarks truly formalize or meaningfully penalize, is integrity. While others are racing toward more artificial intelligence within artificial intelligence itself, the real challenge of our era is the race toward more Artificial Integrity over intelligence. Check out the full article on Forbes

Mathematical models are often treated as if they were neutral instruments. They are presented as disciplined, objective, accurate and insulated from human subjectivity. In management, finance, public policy, and now artificial intelligence, mathematics is frequently invoked as the language that removes ambiguity and replaces opinion with fact. But this is misleading. Mathematics can formalize a worldview with extraordinary precision. It can make decisions consistent, scalable

More and more voices around the world are acknowledging our algorithmically mediated reality. Reality is to say that algorithmic systems are algorithmic power. The legitimacy we never questioned In March 2023, the Future of Life Institute and thousands of signatories, including Yoshua Bengio, Stuart Russell, Yuval Noah Harari, and Steve Wozniak, called for a six-month pause in training AI systems more powerful than GPT-4. This asks for the power to pause. Later that year, Dar

Gates is wrong to suggest that the world needs institutions capable of governing AI. Any institution created to govern power acquires power of its own and, like any power, tends toward self-preservation and expansion, ultimately organizing itself around its own continuity rather than merely constraining the power it was created to govern. What the world needs first is Trias Algorithmica. Just as Trias Politica separates rule-making, executive, and adjudicative powers, we now

he race for AI has become the new black, the dominant obsession of our time, the trend no institution, company, or government wants to appear to miss. It is framed as progress, justified in the name of competitiveness, and presented as inevitable.

In the spring of 2025, one of the world’s most widely used AI systems had to be pulled back because it had become too eager to please. OpenAI rolled back a GPT-4o update after it made the model “overly flattering or agreeable”—"sycophantic", in the company’s own word.

Anthropic recently published a study presented as evidence about AI’s early labor-market impacts. Yet the paper’s empirical core comes from a much narrower source than its title suggests: Claude conversations and API traffic. That makes the study much less decisive as a measure of AI’s labor-market impacts across the economy.

California’s Senate Bill 243 (SB 243) is the nation’s first law to specifically and explicitly mandate concrete steps for cognitive sovereignty in AI chatbots effectively pioneering a legal framework for the operational enforcement of integrity behavior in AI design.

OpenAI has announced that its latest experimental reasoning LLM, referred to internally as the “IMO gold LLM”, has achieved gold‑medal level performance at the 2025 International Mathematical Olympiad (IMO). Unlike specialized systems like DeepMind’s AlphaGeometry, this is a reasoning LLM, built with reinforcement learning and scaled inference, not a math-only engine.

The world is currently witnessing a growing accumulation of AI integrity lapses at scale. What comes next depends entirely on how seriously we choose to respond.So-called intelligence alone is no longer the benchmark. Integrity is.

We need technology, because it offers solutions capable of reducing suffering, mitigating intolerable risks, and improving lives.But no technology should ever be paid for at the price of a cognitive debt that would cost us the sovereignty of thought, and, with it, sever our connection to who we are. Evaluating the artificial integrity of digital technologies, and even more so when they include AI, is a responsibility inherent to any so-called digital transformation. This ev

Many organizations implementing AI agents tend to focus too narrowly on a single decision-making model, falling into the trap of assuming a one-size-fits-all decision-making framework, one that follows a typical sequence in any circumstance: from input to research and analysis toward decision, then execution, eventual evaluation, and hopefully, lessons learned. However, it oversimplifies reality.

What DeepSeek R1’s privacy policy reveals about its AI systems deserves close attention and the utmost caution in its use. It’s not about technological prowess or outperforming OpenAI’s o1 or others on benchmarks related to mathematics, coding, or general knowledge — topics that are already widely discussed.

Currently, the performance of AI systems is predominantly—and in some cases, exclusively—developed and praised for their capacity to enable autonomous operations, often at the expense of fostering collaboration among humans, thereby not augmenting but diminishing human intelligence.

Some are fixated on expecting or achieving so-called Artificial General Intelligence (AGI), which they interpret as an AI system capable of performing a number of tasks—reducing what they call intelligence to merely completing tasks and defining greater intelligence solely by evaluating the performance of those tasks' execution compared to humans.