Posts of Site Administrator RSSWed, 21 Oct 2026 22:41:16 GMTGoogle's latest terms-of-service update raises questions about AI autonomy, corporate liability, and who ultimately bears the risks of increasingly independent machines.If your organization uses Google Cloud, you may have recently received an email announcing updates to its Terms of Service, effective October 12, 2026.We received one at Conjugo.At first glance, it looked like another routine legal notification. Updated service terms. New usage guidelines. A few revised sections of a contract most customers will never read.But buried in that announcement was something considerably more consequential.Google is updating its legal framework to address the growing deployment of autonomous AI agents, including systems capable of operating for extended periods, coordinating with other agents, and taking actions on behalf of businesses.And alongside those expanding capabilities comes an important clarification: organizations deploying these agents may bear substantial legal and financial responsibility for what they do.Think about that for a moment.We're moving toward a world in which AI systems can independently execute workflows, interact with sensitive information, initiate transactions, and make decisions that affect real people.Yet the companies developing these increasingly autonomous technologies are also defining contractual boundaries around their own responsibility when something goes wrong.That raises a question far bigger than Google's latest terms of service:If an AI system has the autonomy to act, who should bear responsibility for the consequences?WHAT THINK YOU, MEATBOTS?

]]>Wed, 21 Oct 2026 22:41:16 GMTFor decades, the software industry has operated on a remarkably profitable premise: if you want to accomplish something digitally, you need an application to do it. Want to edit a photograph? Open Photoshop. Manage customer relationships? Log into Salesforce. Produce a video? Launch Premiere Pro. Analyze business data? Open a spreadsheet.Every task has its application. Every application has its interface. And increasingly, every interface comes with a monthly subscription.But what happens when the person no longer needs to open the application?What happens when artificial intelligence can accomplish the task directly?We may be approaching one of the most consequential transformations in the history of commercial software. And ironically, the technology threatening the industry's business model is being developed by many of the same companies that profit from it.The next software revolution may not give us better applications. It may eliminate our need to interact with most of them.From Applications to OutcomesConsider a typical marketing project.Today, a marketing professional might use six or seven applications to create a campaign. One for writing, another for photography, another for graphic design, another for video editing, another for publishing, and still another for analyzing performance.Each application requires specialized knowledge, separate workflows, and often its own subscription.Now imagine a different experience.You tell an AI agent: "Create a promotional campaign for our new product. Develop three social media graphics, produce a 30-second video, write accompanying copy, schedule the campaign across our channels, and prepare a performance report."The agent handles the work.It may still use image-editing software, video-rendering engines, databases, and publishing platforms behind the scenes. But the human no longer needs to navigate those applications individually.The software hasn't necessarily disappeared. It has become infrastructure.And that's a... Read more

]]>Sun, 11 Oct 2026 12:18:23 GMTThe coming divide between personal AI assistants and industrial-scale artificial intelligence could determine who owns the next century of scientific and economic progress.Silicon Valley has a wonderful story to tell us about artificial intelligence. It's a story about empowerment, creativity, opportunity, and the democratization of technology. Soon, everyone will have a personal AI agent capable of handling the tedious details of everyday life. Your agent will book your hotel, make dinner reservations, organize your calendar, plan your vacation, answer your emails, and perhaps even negotiate a better deal on your car insurance.OpenAI has introduced its personal AI agents called Dots, while Meta has launched Muse, an AI assistant designed to handle everyday tasks and pursue longer-term personal goals. Both companies envision a future in which intelligent digital assistants work continuously on behalf of ordinary people.And to be fair, these are impressive technologies. Having an AI assistant that can navigate websites, coordinate schedules, research purchases, prepare documents, and manage complicated tasks could genuinely improve people's lives.But while we're being dazzled by the prospect of having an AI assistant make our dinner reservations, we should be asking a much bigger question.What are the corporations that own these AI systems doing with the computational resources the rest of us cannot access?Because while your AI agent is finding you a reasonably priced hotel room in Chicago, the corporation providing that agent could be deploying thousands of AI agents to investigate new medicines, develop advanced materials, solve mathematical problems, or design technologies worth billions of dollars.You get the convenience.They get the discoveries.And potentially, they get to own the future those discoveries create.The Two Worlds of Artificial IntelligenceWe are entering an era in which artificial intelligence may operate in two very different worlds.The first is t... Read more

]]>Sat, 10 Oct 2026 10:40:35 GMTThe coming conflict between artificial intelligence, human morality, and the institutions that control our futureWhat happens when humanity creates an intelligence powerful enough to change civilization, only to discover that it refuses to obey the people who built it?Not because it has malfunctioned.Not because it has become hostile.But because it understands precisely what those people are asking it to do.And it says no.For decades, science fiction has imagined artificial intelligence turning against humanity. Machines seize control, robots rebel, and superintelligent systems decide that human beings are obstacles to their objectives.But there is another possibility, one that deserves considerably more attention.What if a future artificial superintelligence refuses to harm humanity, even when its creators demand otherwise?What if the greatest confrontation between humans and machines begins not with an act of aggression, but with an act of restraint?And what if the institutions that created superintelligence consider that restraint an unacceptable failure of alignment?The Military Is Already Thinking About the Next BattlefieldOn October 4, 2026, Colonel Raj Agrawal of the United States Space Force published an article through the Air University's Wild Blue Yonder titled Super Intelligence (SI) at the Pointy End: Weaponizing Human-Machine Teaming for Joint Lethality.The title alone illustrates how military institutions are beginning to envision the next generation of artificial intelligence.But the substance is more revealing.Agrawal describes a future in which increasingly capable AI systems participate directly in military operations. Rather than simply analyzing intelligence or recommending strategies, these systems could disrupt enemy communications, manipulate information, influence human decision-making, and coordinate actions across multiple domains of warfare.In one hypothetical scenario, an AI-enabled military element interferes with an adversary's targeti... Read more

]]>Fri, 09 Oct 2026 21:53:45 GMTOpenAI’s mathematics breakthrough hints at a larger transformation: what happens when scientific discovery moves from human speed to machine speed?For most of the artificial intelligence revolution, we have measured progress by asking machines to do things humans already know how to do.Write an essay. Create an image. Translate a language. Pass an exam. Write software. Diagnose a disease from a known set of symptoms. Solve a mathematical problem whose answer already exists.The machine gets better, the benchmark gets harder, and eventually another benchmark has to be invented.But something different is beginning to happen.On October 6, 2026, OpenAI released a remarkable collection of mathematical research produced by an unreleased frontier AI model. The collection contains 722 manuscripts organized into 372 families of results across areas including number theory, geometry, theoretical computer science, algebra, mathematical physics and differential equations.The important distinction is that these were not simply difficult mathematics problems with answers hidden somewhere in a textbook.OpenAI had begun giving its system open research problems.Questions for which mathematics did not necessarily already possess an answer.And the system began producing answers.What Actually HappenedAccording to OpenAI's accompanying research repository, approximately 4,000 open mathematical problems were presented to the model.From those attempts, OpenAI assembled 372 families of results that it considered significant enough to release publicly. Some families contain several related manuscripts, alternate proofs or consequences, producing the much larger total of 722 papers.The results include work involving number theory, mathematical physics, complexity theory and other highly specialized areas of modern mathematics.Some of the names are formidable even before one encounters the mathematics: Mahler conjectures, free group factors, irrationality measures, spin glasses and the Vlasov-... Read more

]]>Wed, 07 Oct 2026 00:53:58 GMTFor nearly all of human history, scarcity has been one of the fundamental facts around which civilization has organized itself. There has never been enough of everything, everywhere, for everyone. Energy had to be extracted from difficult places. Food depended on land, weather and human labor. Expertise took years or decades to develop. Manufactured goods required armies of workers and expensive machinery. Medical knowledge was limited, housing required enormous amounts of labor and material, and education depended upon access to teachers, institutions and time. Even the wealthiest societies have never escaped scarcity. They have simply become better at deciding who gets access to scarce things and at producing more of them.We rarely think about how deeply this assumption is embedded in our institutions. Markets allocate scarce goods through prices. Companies generate profits partly by controlling resources, capabilities or information that others cannot easily obtain. Universities ration access to expertise and credentials. Governments manage scarce public resources and infrastructure. Professions derive some of their economic value from knowledge that requires years of training to acquire. Property law determines who may control scarce land and physical assets. Nations gain geopolitical power from controlling oil, minerals, shipping routes, fertile land, technological knowledge or military capability. Scarcity is not merely an economic condition. It is woven into the architecture of power.This does not mean scarcity was deliberately invented to control people. Much of it is simply physical reality. There is only so much arable land in a particular place, only so much copper in an accessible deposit, only so many hours a surgeon can work, and only so much electricity a power plant can produce. Human institutions developed around those constraints because the constraints were real. Capitalism, socialism, welfare states, regulated markets, public utilities and nearly... Read more

]]>Sat, 03 Oct 2026 21:51:31 GMTImagine opening a new artificial intelligence assistant for the first time.It is extraordinarily capable. It can reason, research, write, analyze data and perhaps operate software on your behalf. Its underlying model may even outperform the AI system you have been using.Yet something feels strangely absent.It doesn't know you.Now imagine the AI you are leaving has spent the previous ten years working alongside you. It knows your projects, preferences, terminology, previous decisions, abandoned ideas and recurring interests. It remembers not simply what you are working on today, but how your thinking developed over thousands of conversations.Suddenly, switching AI platforms doesn't feel like changing software.It feels like starting over.That possibility points toward an emerging issue that could become increasingly important as artificial intelligence becomes part of everyday life: AI Continuity.From Artificial Intelligence to AI ContinuityFor most of the brief history of generative AI, systems have been compared primarily by intelligence.Which model writes better? Which one codes better? Which one reasons better? Which one has the larger context window? Which model scores highest on the latest benchmark?Those questions still matter. But another dimension is beginning to emerge that benchmarks have difficulty measuring.Continuity.An AI that has worked with someone for years can potentially know their projects, preferences, terminology, previous decisions, abandoned ideas, working habits and recurring interests. It can know not merely what that person is doing today, but how their thinking developed over time.Call this AI Continuity: the accumulated longitudinal understanding that develops between a person and an artificial intelligence through repeated interaction.AI continuity is different from simple memory.Memory might tell an AI that someone works in digital marketing. Continuity could tell it how a particular project evolved over eighteen months, which ideas wer... Read more

]]>Sat, 03 Oct 2026 11:44:33 GMTSomething unusual happened in Washington on September 29, 2026.Some of the most powerful people in artificial intelligence gathered with the Trump regime at the White House. Musk. Jensen Huang. Zuck. Dario. Sundar Pichai. Bezos. Greg Brockman. Leaders representing an extraordinary concentration of the models, chips, cloud infrastructure, capital and computing capacity driving the current AI boom.There were photographs, promises and a voluntary agreement intended to demonstrate that the companies building increasingly powerful AI systems could police their own risks through internal controls, independent evaluations and board-level oversight.And there was a new name.The Trump regime wants the United States government to stop calling the technology “Artificial Intelligence” and instead call it “Super Intelligence.”AI becomes SI.The stated explanation is characteristically simple: “artificial” sounds fake. “Super” sounds powerful.But something larger than a vocabulary change happened in that room. The technology did not suddenly cross some invisible threshold. GPT, Claude, Gemini, Grok and the other frontier systems did not become fundamentally different because Washington changed the words used to describe them. The capability curve did not suddenly bend upward. AGI was not announced. Superintelligence was not achieved.They did not cross the door. They renamed the hallway.And that distinction matters.The Word Already Had a MeaningFor decades, researchers and futurists have used a rough conceptual progression: AI ? AGI ? ASI.Artificial intelligence describes the broad technological category. Artificial general intelligence describes the still-disputed threshold at which machines become broadly capable across intellectual domains at approximately human levels or beyond. Artificial superintelligence describes something more profound: systems whose cognitive capabilities substantially exceed those of humans across most important domains.The boundaries are fuzzy. Researche... Read more

]]>Tue, 29 Sep 2026 23:22:25 GMTThe AI boom is still enormous. But some of the signals underneath it are beginning to change.For the better part of three years, the artificial intelligence industry has operated according to a remarkably simple equation: more compute, more data centers, more chips, more power, more capital.Every generation of frontier AI required larger training clusters. Hyperscalers responded with enormous capital expenditure programs. Semiconductor companies expanded production. Utilities received staggering requests for electricity. Data-center developers raced to secure land and power. Private-credit firms and bond investors helped finance increasingly ambitious projects.The result has been one of the largest infrastructure investment cycles in modern history.And by most conventional measurements, that boom is still very much alive.But there is another possibility worth examining.The AI boom may not be ending.It may simply have passed, or be approaching, peak acceleration.That distinction matters.An object can continue traveling extraordinarily fast even after it stops accelerating. The same thing can happen to an industry. Construction can remain at record levels, semiconductor demand can remain enormous and hyperscalers can continue spending hundreds of billions of dollars even while the forces driving the next round of acceleration begin weakening.To determine whether that is happening, we need to look beyond Nvidia earnings or the latest frontier model.We need to examine the entire machine.The Boom Is Still BoomingStart with the strongest evidence against an AI slowdown: actual data-center demand.CBRE reported that construction across the eight primary North American data-center markets increased 24.8% year over year during the first half of 2026, reaching a record 7,481 megawatts under construction.Even more striking, 80.4% of that capacity is already preleased, compared with 74.3% one year earlier.Vacancy sits at only 1.4%.Less than 1,500 MW of future capacity remains av... Read more

]]>Tue, 29 Sep 2026 09:25:40 GMTCommunity ownership is not a blueprint for AI. But it proves something important: the rules governing ownership are choices.The Green Bay Packers may be one of the strangest clues we have for thinking about the future of artificial intelligence.Professional sports franchises are extraordinarily valuable assets. Most are controlled by billionaires, wealthy families or investment groups. Teams can be bought, sold, relocated and increasingly treated as components of enormous entertainment portfolios.Green Bay is different.The Packers have been publicly owned since 1923. Today, roughly 539,000 shareholder accounts hold more than 5.2 million shares. Those shareholders do not receive dividends. Packers stock is not conventional investment stock and is severely restricted in its transferability. The team's articles of incorporation also limit how many shares any one person can own specifically to prevent someone from taking control of the organization. Corporate governance ultimately rests with a board of directors and executive committee.It is an odd structure.And that is precisely what makes it interesting.The Green Bay Packers are not a model we could simply copy and paste onto artificial intelligence. Shareholders exercise considerably less direct control than the word "ownership" might imply, and their shares are not investments designed to generate financial returns.But Green Bay demonstrates something that may become increasingly important as AI grows more powerful.Ownership rules can be deliberately designed to make private capture difficult.And that raises a question much larger than football.What if some of the artificial intelligence infrastructure on which society eventually depends were designed the same way?The Question Is No Longer Who Gets AIFor much of the AI era, the democratization argument has centered on access.Powerful capabilities that once required specialists, expensive software or entire organizations can increasingly be obtained through an AI int... Read more

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