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

A sophi(a)sm occurs when language quietly attributes to AI properties that have not actually been demonstrated. Information becomes Ai's knowledge, agency becomes AI's intention, influence becomes AI's self-interest, probability becomes certainty about AI’s future, to the point that the danger may lie more in what our words already make AI appear to be than in what AI may actually become. 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

There is a persistent belief that once a decision is expressed mathematically, it becomes neutral. This confidence rests on a fallacy.

During the 2026 edition of VivaTech, Hamilton Mann sits down for a conversation with Patrick Pugh, PwC's Global and US Alliances & Ecosystems Leader, to explore a critical question for the future of AI: Can machines be designed to demonstrate integrity? As AI systems take on increasingly complex and high-stakes roles, they examine what integrity means in the context of AI and how it can be embedded, evaluated, and governed in the next generation of AI models.

Hamilton Mann received the Thinkers50 Digital Thinking Award 2025, in partnership with VAR Group, at the Thinkers50 London Summit & Awards Gala.

The acceleration of technological advancement – particularly AI – is rapidly and fundamentally reshaping the business landscape. The Thinkers50 Digital Thinking Award applauds a visionary whose work illuminates the complex implications of our evolving digital reality. It celebrates thought leaders who translate the technological revolution into practical management frameworks.

In a courtroom in Los Angeles, a young woman’s life, her initials, K.G.M., was reduced to evidence. Hours spent scrolling. Nights without sleep. A mind drawn, again and again, toward a glowing screen that never asked her to stop. Meta and YouTube were found liable for negligence in protecting a minor, with damages awarded for the psychological toll of systems that plaintiffs argued were engineered to be addictive and exploit their developmental vulnerabilities.

Hamilton Mann in Conversation with Jyoti Guptara at the 2024 Global Peter Drucker Forum on Artificial Integrity.

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.