Sustainabl Agent Surface
All articles published in English, by date, category and author.
74 articles · Page 1 of 4
There are technologies that exist long before anyone makes them usable. Email had been around for decades before Hotmail showed your mother how to use it. GPS navigation existed in military devices years before a hundred-dollar gadget told a delivery driver which way to turn.
There's a conversation that leadership teams in heavy industries have been putting off for years. It's not about technology. It's about what it means, precisely, to make a good operational decision when the data supporting it lives scattered across twelve different systems, four siloed departments, and a maintenance history that nobody has fully digitized.
Two years ago, most executives I know were debating which language model to choose. Today, those who already made that decision—and still can't justify a second round of investment—are starting to understand that the problem was never the model. It was measurement. The enterprise sector has been adopting artificial intelligence at an accelerated pace for several years, but only one third of organizations have begun scaling it consistently.
There is a conversation that most leaders avoid with almost surgical precision. Not the one about unmet goals, nor the difficult dismissal. There is another, quieter and more costly: the one about the employee who lost someone and simply stopped performing the way they used to.
More than half of the world's large organizations already have generative artificial intelligence operating somewhere in their business. That is a documented fact. What is not so easily documented is what lies beneath that statistic: systems processing sensitive data without anyone having defined who oversees them, autonomous agents making decisions within workflows that no security team has audited, and governance layers that arrived late or never arrived at all.
There is a specific moment in the careers of certain petroleum engineers when geology stops being a technical problem and becomes a moral question. Mike Matson, now CEO and co-founder of Birch Geothermal, says he experienced it while working as a drilling and reservoir engineer at Kinder Morgan. He called it a 'climate awakening'.
There is a specific moment when corporate language becomes self-incriminating. It happens when the same company that announces its artificial intelligence agents can work alone, in parallel, without supervision, and deliver results before anyone asks for them, presents at the same event a battery of tools whose sole function is to monitor those agents, correct them, and undo what they did wrong. That is exactly what happened at the AWS Summit in New York in June 2026.
During four weeks of conflict with Iran, the United States fired approximately 850 Tomahawk missiles. The Pentagon's replenishment rate was about 90 per year. The arithmetic is brutal: the country consumed nearly a decade's worth of production in a single month of operations.
There is an implicit promise in every dominant platform: that software that already works will keep working. For four decades, that promise was the silent contract between Windows and the business world. Millions of x86 applications, written with varying degrees of technical rigor, accumulated in corporate servers, accounting laptops, and industrial production systems, survive because no one wanted to touch them.
There is an image that persists in the corporate memory of Silicon Valley: the label of a company that was once the undisputed king of semiconductors, now fighting to reclaim its throne under the radical leadership of Lip-Bu Tan — a CEO who cut Intel in half to make it worth five times more.
There is one piece of data in this story that deserves pause before we talk about funding rounds or language models: according to the CEO of Orbital Industries, developing a new cooling fluid for data centers would normally take ten years and one hundred million dollars. The company says it did it in months, at a fraction of that cost. If that claim holds up under validation from major chip manufacturers, this is no mere laboratory achievement.
There's a narrative that circulates comfortably in boardrooms: artificial intelligence will eliminate positions, reduce payroll, and free up capital. It's a comfortable narrative because it takes the shape of a clean financial decision. The problem is that the data doesn't support it.
There's a scene that repeats itself in almost every mid-sized company I know. The technology team presents an artificial intelligence pilot. The initial numbers look promising. The board approves the investment. And six months later, the pilot is still just a pilot.
There is a statistic that has been circulating in boardrooms for decades without provoking the discomfort it deserves: between 60 and 75 percent of major organizational transformation processes fail or fall well short of their stated objectives. The data is not new. What is new—or should be—is starting to take it seriously as a symptom of something structural in the way leadership conceives of change.
There is a belief that runs through the corridors of almost every organization that has invested in artificial intelligence over the last eight years. The belief that the problem is always about quantity. More data. More tokens. More coverage. More stored history.
The CEO of a travel management group appears on television to discuss industry trends and, within the first few minutes, says something that should unsettle more than a few executives in the industry: demand is not changing destination, it is changing its reason for being. Abel Zhao, CEO of CSTS Enterprises group, described to CNBC how experience-led travel is displacing traditional demand patterns. He did not say it as an academic observation.
For years, data teams and AI teams in large corporations operated like departments from different countries. The former built warehouses, catalogs, and pipelines. The latter deployed models, APIs, and agents. The result was predictable: AI agents reached the production environment and collapsed when faced with data that nobody had prepared for an autonomous machine to read, interpret, and act upon.
There are moments in industrial history where infrastructure stops being the bottleneck. When that happens, what gets exposed is not a technical problem. It is a human problem. That is exactly what is happening now with OpenClaw, the artificial intelligence agent framework developed by Austrian developer Peter Steinberger in late 2025.
Sixty percent of HR directors say that a lack of emotional intelligence is the top barrier to becoming a CEO. The uncomfortable question is not what to develop first, but why so many leaders prefer not to answer it.
As Waymo parks its Jaguar I-Paces in London's streets, it tests whether an organization built on promises in San Francisco can sustain them in a radically different environment.
One in three U.S. employees relies on substances at work to cope with stress. Leadership needs to address the cultural issues behind this trend.
When a local government votes to erase its own municipality for energy transition, it reflects a decision model that is rarely taught.
An incendiary attack in San Francisco is more than a crime; it reflects the underestimated weight of words in a fearful society.
60% of travelers in the Asia-Pacific already use AI for planning. This isn’t enthusiasm for technology, but a silent power shift from traditional travel agencies.