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.

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

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.

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.

Ilya Sutskever, a co-founder and former chief scientist of OpenAI claims, "Data is the fossil fuel of AI, and we used it all!" Is this aphorism true? This claim is made in the context of explaining that the limitation for AI (LLMs in particular) lies in the quality of data required to mimic intelligence—a limitation often referred to as the ‘entropy gap’.

Companies have long recognized that brand reputation and customer loyalty depend on an uncompromising integrity-driven social proof as a do-or-die imperative. The entire history of business is filled with examples of integrity lapses that led 'Achilles-type' companies to collapse, such as Enron, Lehman Brothers, WorldCom, Arthur Andersen, and, more recently, WeWork, Theranos and FTX.

In Alan Turing's seminal paper "Computing Machinery and Intelligence," he posed the now-famous question, “Can machines think?”—an inquiry that laid the groundwork for exploring the cognitive potential of machines.

In the rapidly evolving world of Artificial Intelligence (AI), computational power isn't enough. What we need is Artificial Integrity—a new paradigm that ensures AI systems operate in alignment with human values, prioritizing Integrity over Intelligence, whether it is in Marginal, AI-First, Human-First or Fusion Modes.

The deployment of Generative AI (GenAI) is often highlighted in current discussions as a means to achieve significant productivity gains in business. This is nothing new. Any tool adopted by a company has the primary mission of supporting its performance.

The difference between artificially intelligent-led and integrity-led machines is simple: the former are designed because we could; the latter because we should. This distinction underscores the growing need for Artificial Integrity.

Persuasion has no dark side. Only the intentions of those who wield it do, and GenAI is not inherently endowed with such intentions, neither for itself nor by itself.

There has been considerable speculation recently about the potential of generative AI to significantly boost worker productivity. However, among the challenges companies face when deploying genAI, there is one that is particularly unique to business and applies to any solution an organization might consider deploying: the realization of return on investment.