Sustainabl Agent Surface
All articles published in English, by date, category and author.
475 articles · Page 2 of 20
There comes a moment when the competitive map of an economy shifts without its policymakers noticing in time. India has spent several years announcing that moment with fanfare: semiconductor factories, battery plants, artificial intelligence centers. The cabinet signs, the headlines celebrate, foreign investment funds attend the event. And yet, something doesn't add up.
An image circulated for months in design and audiovisual production forums: a creative director staring at a screen full of AI-generated variants, all technically correct, all editorially empty. The image captured something productivity data couldn't: the problem was never generation speed, but that no one had solved how to channel that speed toward a specific intent. That's what is changing now, and the change arrives without fanfare.
When the major multinational pharmaceutical companies decided to exit antibiotic research, they did so with perfectly rational arguments: treatment cycles are short, antimicrobial stewardship programs compress volumes, and generic erosion arrives quickly. The return on investment simply did not add up. So they left, one by one, abandoning a space that no market player wanted because it looked like a commercial dead end. Wockhardt decided to stay.
There is a moment in the evolution of any technology market when competitors stop differentiating themselves by what their products do and start differentiating themselves by how their customers control them. IBM reached that moment with clarity at its Think 2026 conference in Boston, where it presented what it calls an agentic operating model built on four pillars: agents, data, automation, and hybrid sovereignty. The last of those pillars, and the most strategically loaded, is IBM Sovereign Core, a governance platform that operates at the execution infrastructure level, not as an application configuration layer.
Unmanned Underwater Vehicles and the Adoption Problem AUKUS Didn't Name On May 30, 2026, on the sidelines of the Shangri-La Dialogue in Singapore, the defense secretaries of the United States and the United Kingdom shared an unusual moment of institutional self-criticism.
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.
More than $1.5 trillion in enterprise software valuations evaporated over the last two years. Not for lack of investment in artificial intelligence, but because the investment landed in the wrong place. This is the paradox that defines the current moment: companies have never spent so much on AI and, at the same time, it has never been so hard to show where the value actually is.
There is a pattern that repeats itself in the history of tech companies looking to open up to capital markets: the moment when the narrative of massive users is no longer enough and they need to show something more concrete. OpenAI is there. And the tool it chose to make that argument is not ChatGPT, but Codex, its software development assistance product, which in the last two months has received updates at a frequency no competitor has matched.
The promise of electric mobility rests on a mineral that, to extract it, demands flooding the desert with water that desert does not have. The lithium driving the energy transition narrative reaches the market mainly from enormous solar evaporation ponds occupying kilometers of arid terrain in Chile's Atacama or in Nevada. That system has a structural limit the industry already acknowledges: future lithium demand cannot be met with evaporation ponds.
March quarter revenues reached $21.6 billion, a 27% year-on-year growth — the highest rate in five years — and net income jumped dramatically to $521 million. The company's Hong Kong shares surged nearly 20% in a single session, becoming the biggest percentage gainer on the Hang Seng index that day. But the number that best explains the market's reaction is not in the margins or PC volumes: it's the fact that AI-related revenues grew 84% in the quarter and accounted for 38% of the group's total revenues.
On May 21, 2026, the U.S. Department of Commerce formalized what had been hinted at for months in Washington's corridors: the federal government doesn't just want to fund quantum computing — it wants to be a shareholder in it. The decision to commit $2 billion to a group of quantum technology companies, taking equity stakes rather than simply issuing grants, marks a turning point in the logic behind America's long-term technology policy. This is not a check. It is a declaration of industrial architecture.
There is a cost that large retailers have absorbed for decades without measuring it precisely: not knowing exactly what they have, where it is, and whether what the system says exists actually exists. That cost does not appear as a separate line on the income statement. It dissolves into compressed margins, cancelled orders, misallocated working hours, and customers who leave without buying.
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.
When Lior Susan founded Eclipse Ventures in 2015, the prevailing logic in Silicon Valley was simple: software scales without factories, inventory, or workers. SaaS companies captured the attention of the best funds and the best engineers. Betting on semiconductors, industrial robotics, or physical computing infrastructure was, at best, an oddity.
Vaseline is 155 years old. It was born from a chemist who watched oil workers rub a jelly-like substance on their wounds. What is happening now inside Unilever, Vaseline's parent company, deserves attention precisely because it inverts that logic: it is letting the spontaneous behaviors of internet communities determine what product to manufacture next.
There is a silent pattern that economic history has repeated at least twice before the era of artificial intelligence. First with industrial electrification, then with personal computers. In both cases, the technology arrived decades before its impact appeared in productivity statistics.
Quantum computing has spent more than a decade promising to reshape medicine, materials, and artificial intelligence. During that time, most capital flowed toward the superconducting circuits of IBM and Google, platforms requiring cooling to temperatures near absolute zero, costly infrastructure, and constant calibration. But beneath that dominant narrative, a different bet was taking shape: using neutral atoms as qubits, trapping them with lasers, operating them at room temperature, and scaling them into arrays of hundreds or thousands of units.
There is a pattern that repeats itself every time a technology stops being an experiment and becomes production infrastructure. It happened with relational databases, with cloud services, with microservices. And now it is happening with large language models.
The global tax system does not operate on paper. For at least two decades it has run on digital signatures, device certificates, hash chains, and encrypted transmissions to tax authorities. That infrastructure, invisible to most retail executives, is what is technically exposed today to a pressure that comes neither from regulators nor competitors: it comes from a transformation in computing power that could render useless the cryptographic foundations on which the fiscal trust of the entire system rests.
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.
Generative artificial intelligence reached most organizations not through the technology department, but through the back door of productivity applications. Microsoft 365 Copilot, Gemini, and assistants integrated into collaboration platforms were activated in corporate environments where employees were already working — and with that began a silent experiment whose terms nobody had fully negotiated. The problem is not with the language models. It's with what those models find when they connect to a real organization.
When Marc Benioff founded Salesforce in the late nineties, the proposition was simple: sales software delivered from the cloud, no installation required. The screen was the product. Twenty-five years later, Salesforce is betting on exactly the opposite: that the screen disappears.
The most honest challenge in robotics today is not technical. It is psychological, and not in the sense usually used to talk about humans who fear machines, but the other way around: the most sophisticated robotic systems on the planet keep failing at something a three-year-old child does effortlessly. They hear an instruction, they see the space, and yet they do not know how to connect both things to move with purpose.
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.