The post Weaponizing Space Now a Reality According to the U.S. Space Force appeared first on Techso Mogro.
]]>The idea of a conflict-free outer space was aspirational. What the treaty did allow were technologies that could be dual-purposed. For example, GPS, first developed for terrestrial armed forces’ navigation needs, was not banned. Today, GPS and its equivalent satellite constellations from the European Space Agency, India and Russia have replaced maps.
Similarly, telecommunications satellites served two purposes: military and commercial. The advancement of the technology has produced telecommunications constellations like Starlink that serve domestic purposes in non-conflict zones, while helping Ukraine coordinate its defences against Russia in that conflict.
Another dual-purpose technology deployed to space is terrestrial observation satellites. Landsat satellites deployed by the United States benefited from similar reconnaissance technology developed for the American Air Force and Central Intelligence Agency.
Under the Outer Space Treaty terms, a nation’s military personnel could serve in space or on the Moon, Mars, asteroids or elsewhere. What they couldn’t do, however, was fire a weapon designed for aggression.
That was then. What follows is the now.
On September 16, 2026, General Dan Caine, Chairman of the U.S. Joint Chiefs of Staff, in a keynote address delivered to a conference in Maryland, stated:
“Today’s modern battlefield operates from the seabed to cislunar space.”
While not explicitly admitting that the U.S. had deployed weapons to space, his comments appeared to confirm a recent public admission by a U.S. military official that America, through its Space Force, was establishing the means to defend the high ground of space from Earth orbit to the Moon.
Caine’s public admission is the first of its kind for any nation. It is likely, however, that deploying weapons to space has already been done by Russia and China. Both of these countries have demonstrated space defence weapons:
European space programs, including ESA, have never demonstrated similar inclinations.
The distinction between what China and Russia have done is that Caine’s disclosure is about in-orbit space weapons, not Earth-based systems that could target objects in space.
What are we talking about when referring to weapons deployed in orbit or on the Moon?
Although Caine didn’t mention anything specific, the likely systems already in space or to be deployed include:
Of course, Caine may have disclosed capability rather than reality. Although not inevitable, when the US first announced its sixth armed service, the Space Force, orbital weapons would be the likely endgame.
When formed in December of 2019, its announced mission was to train the American military for warfighting in space and to develop military space systems to project the country’s power beyond Earth. At the time, the major initiatives of the Space Force were to develop launch capacity, a successor to GPS, new satellite communication capabilities, and a space-based early warning system capable of detecting incoming missiles.
It appears that the mission of the US Space Force has now expanded and will likely stimulate Russia, China and other nascent military space programs to up their game.
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]]>The post The AI Danger Debate is Getting Very Real: Who Should We Listen To? appeared first on Techso Mogro.
]]>If the term frontier AI is unfamiliar, it refers to the ongoing development of large-scale AI models capable of doing many things we humans do today. Frontier AI is at the bleeding edge, a rapidly moving target, and forever evolving.
What could go wrong? Well, here are some Geoffrey Hinton quotes that describe his concerns about what is happening as AI rapidly evolves without supervision:
May 23, 2023 – In an MIT Management article, Geoffrey Hinton states: “My big worry is, sooner or later someone will wire into them [referring to AI] the ability to create their own subgoals. I think it’ll very quickly realize that getting more control is a very good subgoal because it helps you achieve other goals. And if these things get carried away with getting more control, we’re in trouble.”
December 8, 2025 – In Gzero World, a podcast interview, Hinton uses a physics analogy: “If you ask a physicist, does he understand what happens when a leaf falls off a tree? A physicist pretty much understands what happens and why the leaf waves from side to side and how a breeze will affect it. But if you ask a physicist to predict where the leaf will hit the ground, he’ll tell you that’s impossible. And predicting why one of these large language models will give the answer it actually gives, that’s not very easy. That’s like predicting where the leaf will hit the ground. So we sort of understand the principles, but there’s a lot of fine details.”
January 21, 2026 – In a Business Insider article, it quotes Hinton from an interview with the BBC: “It makes me very sad that I put my life into developing this stuff and that it’s now extremely dangerous and people aren’t taking the dangers seriously enough.” He goes on, “We’ve never been in this situation before, of being able to produce things more intelligent than ourselves. The idea that you could just turn it off won’t work.”
And in that same interview, he makes this portentous comment, “If we create them [meaning AI] so they don’t care about us, they will probably wipe us out.”
Companies like Anthropic, OpenAI, and xAI are three leading American companies developing AI. The other country racing to develop frontier AIs is China, with several companies involved, including DeepSeek, Baidu, ByteDance and Alibaba. This AI race is taking on the characteristics of the Soviet-American 1960s rivalry to be first to the Moon. Getting to the Moon, however, didn’t involve building technologies that could turn on their creators.
The following are observations from three of the leading American frontier AI developers.
Dario Amodei is the CEO of Anthropic; Claude is its AI. In a widely discussed recent essay, Amodei cautions his competitors, Sam Altman of OpenAI and Elon Musk of xAI, urging all of them to slow the pace of development. He writes:
“Over the last few months, I have become convinced that fully addressing the [AI] risks requires even more prudence, not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up.”
Sam Altman heads up OpenAI; ChatGPT is its AI. In response to Amodei’s essay, Altman recently told CNBC:
“I agree with Dario that we need to pace the frontier. This has been a primary topic of discussion we’ve had at OpenAI in recent weeks.”
“…Committing to having independent evaluators with employee-like access is a great idea, and we will do the same.”
“…Pacing will be well worth this cost; no amount of American competitive pressure should justify recklessness, or let capabilities get ahead of alignment and monitoring.”
“…We welcome a federal framework that sets consistent safety requirements for frontier AI…no amount of American competitive pressure should justify recklessness.”
Elon Musk heads up xAI; Grok is its AI. On the X social network, Musk provided this three-word reply:
“Dario is right.”
The American President wants to beat China to the AI promised land.
On Truth Social in CAPS, the equivalent of shouting in print, he wrote that the U.S. already has “tremendous CRIMINAL and REGULATORY power” regarding AI. He described the comments from the above three as a “SICK conspiracy…against AI and Data Centers.” He concluded, “The only one that is happy about it is China.”
The President also commented about the call for a federal framework, stating:
“The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!”
Trump continued:
“WHOEVER WINS AI, WINS!
We are leading China, and all others, and will continue to do so.
Conspiracy Theorists, Treasonists, Traitors, and Leakers, BEWARE!
Thank you for your attention to this matter!
President DONALD J. TRUMP.”
I don’t know about you, but when it comes to big brains, I don’t see Donald Trump. I do, however, see a Nobel Prize in Physics-winning Geoffrey Hinton in that light.
So, in weighing all those quoted above, I take Hinton over Trump’s Truth Social all-caps ravings.
If Hinton sees unregulated AI as a real threat to humanity, and if the leading Frontier AI model creators express similar concerns, then I know to whom I am listening.
A final note. Recently I watched the 2008 remake of the movie, “The Day the Earth Stood Still.” In it, an alien visitor, played by Keanu Reeves, has arrived on Earth to judge humanity.
He is taken to an Earth scientist who asks why he has come to Earth. The alien comments:
“Your problem is not technology. The problem is you. If the Earth dies, you die. If the human race dies, the Earth survives.”
I can imagine that, instead of an alien with advanced technology, an artificial superintelligence without guardrails were to assess the current state of the planet, it might make the same argument. Exit homo sapiens.
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]]>The post Reinsurance Industry Points to Growing Environmental Costs in Not Addressing Climate Change appeared first on Techso Mogro.
]]>Of these, Munich Re stands near the top of its industry. What this reinsurer says about what’s happening globally requires our attention. On September 6, 2026, the company published its latest report noting that insured losses for “non-peak perils,” usually a smaller chunk of their business, surpassed US$ 104 billion in 2025, the sixth consecutive year over $100 billion. These insured losses didn’t represent total client losses. That number, which includes uninsurable losses, was more than double.
The definition of non-peak perils covers ecological events falling short of the most severe natural disasters. These include weather-event disasters like crop- and property-damaging hailstorms, excessive rainfall events leading to floods, tornadoes, severe thunderstorms, seasonal flooding, wildfires, prolonged heatwaves, droughts, etc.
Although these sound perilous to readers, Munich Re puts them in the “medium-sized loss event” category. The report notes:
“Whilst non-peak perils were long regarded as events involving comparatively low losses overall, they have now begun to add up to loss levels that were previously associated primarily with major events. This trend is progressively becoming the new normal.”
What is happening from the ecological changes happening here on Earth is a shift where non-peak perils are causing greater economic consequences. In 2025, Munich Re noted that non-peak perils represented 97% of its payouts for insured losses. The remaining 3% covered peak geophysical events like earthquakes, volcanic eruptions, and tsunamis.
How come only 3% when damages from an earthquake and tsunamis can be overwhelming to communities and victims? Geographic locale explains. For example, the earthquake in Myanmar in 2025 didn’t involve reinsurers because very few of the victims owned insurance. A lower country GDP equates with fewer of its citizens purchasing insurance. That’s why total reinsurance payouts amounted to between $3 and $4 billion despite the much higher numbers for real economic and human damages.
Insurers and reinsurers face a growing number of categories beyond the natural risks. For example, the rapid advancement of artificial intelligence (AI) globally is imposing increasing cyber risks and insurance claim payouts.
The cost of cyber risk for insurers has been growing. This was before AI. Now, cybercriminals are incorporating AI into their criminal toolkits for fraud and ransomware. The Munich Re report notes “that 89% of companies say they do not feel adequately protected against this.” To provide better coverage to clients, the industry expects insurance premiums to double in the next half-decade.
COVID-19 revealed another risk: global pandemics facilitated by climate change. Insect-borne or vector-based diseases are spreading from the equator poleward because of a warming atmosphere. Although not defined as a pandemic, the threat to individuals and businesses from vector-borne disease spread is growing. Many insurers are not necessarily prepared to provide coverage. Instead, the industry is defaulting to governments to provide a disease backstop fund to cover work losses for individuals and businesses.
There was no insurance coverage during the COVID-19 pandemic. Governments performed the backstop, with payouts leading to record annual government budgetary deficits across the globe. To date, no countries have fully recovered from these pandemic-inflicted debts.
Having covered AI, cybercrime and pandemic and vector-borne disease spread, what’s left in our bag of global risks? The latest World Economic Forum annual global risks report provides several categories, many beyond the capacity of insurers and reinsurers. What are we talking about?
For insurers and reinsurers, most of these risks are beyond the means to provide coverage. Where the industry sees itself is largely in the ecological orbit.
What does ecological risk mean for insurers and reinsurers as well as their clients? Catastrophic non-peak and peak climate events place risks on property.
For the insurers, it means increasing numbers of claims, inflated reconstruction costs, and more expensive payouts. For the reinsurers, it is the last line item on the insurers’ list that is a concern.
For insurance purchasers seeking property damage and loss coverage in the face of more frequent and costly ecological risk, insurers are asking that several environmental pre-conditions be met, including:
For policy purchasers, it means:
Another consequence of increased climate risk is already apparent. Fewer insurance companies are offering ecological coverage policies to homeowners and businesses. Fewer insurers means less competitive choice for consumers, higher premiums, and more uninsured or underinsured properties.
The relationship between reinsurers and insurers is changing to the disadvantage of the latter. They are being asked to assume more risk, accept more coverage restrictions, and pay more for what they can get.
Reinsurers have invested heavily in climate change research. They are modelling global climate, as the implications of a warming ocean and atmosphere can be incorporated into Earth Digital Twin models for long-term forecasting.
These tools are helping the reinsurance industry to determine future probabilities based on real science that can help quantify the risks associated with:
What has saved the reinsurance industry to date is a substantial war chest from premiums accumulated over the last decade in anticipation of the accelerated payouts that are here, with more coming.
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]]>The post The Military and Climate Change “Threat Multiplier” appeared first on Techso Mogro.
]]>A recent article in the Bulletin of the Atomic Scientists looks at how the U.S. military is addressing climate change. Its author, Neta C. Crawford, describes Pentagon studies on greenhouse gas emissions going back to the mid-1950s. These studies have hatched projects like the U.S. Navy implementing anti-flood measures on its bases in home waters and around the world.
In 2014, when I first started researching the subject of climate change and its potential impact on the military, I read the Pentagon’s climate change adaptation roadmap, which included the following statement:
“Climate change will have serious implications for the Department’s ability to maintain both its built and natural infrastructure, and to ensure military readiness in the future.”
Among the issues of concern facing military readiness were the following projections:
In 2014, the Pentagon called climate change a “threat multiplier.”
Why?
The NATO countries that include the U.S., Canada, and Europe have been studying climate change threats to military readiness. The 32 countries have incorporated climate change into their collective strategies and defence planning, reflecting resilience, readiness and risk management.
NATO’s core security priorities include integrating climate risk and resilience, involving:
Despite the noble aspirations of military planners to draw down carbon emissions, wars, when started, become big producers of greenhouse gas emissions. That’s because militaries consume more oil and other fossil fuels than anybody and anything else on the planet. Tanks, fighter jets, and ships are big emitters, larger than most civilian sources. Add activities like shelling and bombing leading to fires, and even more CO₂ is emitted.
Use the Russia-Ukraine conflict as an example. Its contributions to climate change over these last four-plus years have produced lots of carbon emissions, including:
The net result of the Russia-Ukraine conflict is emission amounts of CO₂ equal to what France produces annually.
Note that when wars contribute to carbon emissions, the numbers don’t get counted in country contributions published by the United Nations and discussed at annual COP meetings.
In other words, the consequences of war on climate change are ignored when determining mitigation and adaptation strategies.
Rising geopolitical conflicts are causing countries to spend more on military hardware.
What part of that increasing spend comes from climate change?
For Russia and Ukraine, not so much. Elsewhere, however, climate change is impacting military spending.
Our destabilizing global climate may partially explain the continuing conflicts and unrest in the African Sahel, Congo and Horn of Africa.
In a recent article posted to the Techso Mogro, I listed 21 ongoing conflicts. Not included in this batch was the latest Strait of Hormuz tiff. Each of these aforementioned wars, I noted, was adding to the social disruption of the combatant nations and to climate change as well.
So, what is the spending trend? From 2016 to 2025, the Stockholm International Peace Research Institute has documented a 41% increase in global military spending. During that time:
With all this spending, military emissions continue to rise even when not fighting in a war. So much for mitigation and adaptation.
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]]>The post How Marketers are Adopting AI Image Creation appeared first on Techso Mogro.
]]>Today, AI image systems are part and parcel of marketing strategies, helping with product design, e-commerce, publishing, and social content. The question is no longer whether a model can create a beautiful picture. Now it is whether a team can move from a rough image idea to a usable visual asset with control, review, iteration, and consistency to support real work.
In other words, AI image generation is moving from a novelty and a shortcut for making eye-catching pictures to real utility. It is moving from a one-off generator to a part of the creative workflow.
Prompts need to define the subject, format, mood, composition, lighting, and purpose of an AI-generated image. In the production process, however, first prompt attempts rarely produce the final product. Turning words into pictures on a page is more difficult than pictures in our mind.
A marketer may need a picture in landscape mode for one image and a very different shaped image for social media and promotional print. A designer may need a product image that remains the same while changing backgrounds. An e-commerce team may need several visuals to choose from to match a campaign. A publisher may need an editorial image that leaves room for a headline and avoids fake text.
Those image creation processes require selection, refinement, format changes, and sometimes different models for different tasks.
That is why the workflow around the model has become just as important as the model itself.
Text-to-image generation is useful when the team is starting from an idea. It can quickly produce visual directions for a blog hero, ad concept, product mood board, or campaign draft. The user describes what should exist, and the model creates a first version.
Image-to-image editing is different. It starts from an existing visual. That might be a product photo, a reference style, a rough layout, a previous generation, or a brand asset. The task is not to invent everything from scratch, but to preserve some structure while changing specific parts.
Reference-based refinement sits between those two modes. A team may want the energy of one image, the colour palette of another, and the subject from a third. In that case, the creative process becomes a negotiation between prompt language and visual input.
This is one reason multi-model platforms are becoming useful. A workflow may start with a prompt-led model, then move to a reference-heavy editing model, then finish with manual review and design cleanup.
A platform such as Image 2, which brings multiple AI image workflows into one place, reflects this shift from isolated model calls toward practical creative production. A creator can use one workflow for complex prompt-led visuals, then compare it to other workflows when the job depends more on references, edits, or layout preservation.
The important point is not that one model is universally better. The better question is which workflow fits the task.
One change I expect to see more often is the use of visual briefs instead of simple prompts.
A simple prompt might say: “Create a futuristic workspace for an AI product.”
A production brief is more specific: “Create a wide editorial hero image for an article about AI-assisted design workflows. Show a clean creative studio, generated image variants on a large screen, reference images nearby, and a polished final preview. Leave a space in the upper left for a headline. Avoid readable text, logos, brand names, and clutter.”
The second version is not just longer. It describes the asset’s purpose. It tells the model how the image will be used, what must be avoided, and what design constraints matter later.
This is where AI image generation starts to resemble art direction. The user is not just asking for a picture, but rather providing shape and context to the deliverable.
As AI-generated visuals become easier to produce, review becomes more important, not less.
There are several reasons for this.
The safest workflow is to treat AI output as a draft that can accelerate the process, not as a final asset that bypasses judgment. A good AI-generated image can save time by giving the team a strong first direction. It does not remove the need to inspect the result.
As more image models become available, teams may be tempted to compare them only by visual quality. That is understandable, but incomplete.
For real production, model choice also depends on the task:
A model that works well for one job may not be ideal for another. A campaign mood board, a product visual, a poster, and a reference-based edit are different tasks. The practical value is in choosing the right path for each one instead of forcing every request through the same tool.
This is also why the interface around the model matters. If users have to jump between disconnected tools, copy prompts manually, re-upload references, and manage downloads in separate places, the creative process becomes fragmented. A unified workflow can reduce that friction.
The next stage of AI image generation will likely focus on repeatability.
Teams will want reliable ways to create image variations, keep visual direction consistent, reuse references, compare outputs, and manage approved assets. They will also want clearer links between prompts, source images, selected models, final results, and usage rights.
That does not mean every creative process should become rigid. Good visual work still needs experimentation. But there is a difference between useful experimentation and chaotic file management.
A repeatable workflow helps teams know what happened:
These operational details are easy to ignore during a quick demo. They matter a lot when teams publish frequently.
AI image generation is no longer only a novelty or a shortcut for making eye-catching pictures. It is becoming one layer in a broader creative stack that includes planning, prompting, reference gathering, model selection, editing, review, storage, and publishing.
That shift changes how we should evaluate these tools. The most useful systems will not be the ones that only produce impressive first drafts. They will be the ones that help people move from an idea to a reviewed, usable, properly formatted visual asset with less friction.
The single prompt is not disappearing. It is becoming the first step in a larger process.
For creators, designers, marketers, and publishers, that is the more interesting development. The future of AI image creation is not just better pictures. It is better workflows for making pictures useful.
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]]>The post How Technology is Impacting Beauty in the 21st Century appeared first on Techso Mogro.
]]>The beauty and personal care industry includes products developed for skin, hair, cosmetics and fragrances. The estimated number for beauty and personal care product revenue in 2025 was US$ 677 billion and growing 8% annually.
While significant, a comparison to other business sectors puts consumer electronics at 28% more, or $865 billion in 2025, a third the size of clothing and apparel, and two-fifths that of pharmaceuticals.
I’ll let Paige tell you how technology, science and societal concerns are continuing to influence the industry as the 21st century unfolds.
The beauty industry has evolved with shifts in culture and consumer demand. The pace of change in recent years has been unlike anything seen before because technology has become deeply embedded in how people discover, test, purchase, and apply beauty products.
From artificial intelligence (AI) to biotechnology, every corner of the market is being reshaped. These are not passing novelties, but rather meaningful changes affecting how we interact with beauty in our daily lives.
AI is moving from research laboratories into bathroom medicine cabinets at remarkable speed. Brands today deploy AI-powered diagnostic tools that analyze skin tone, texture, hydration levels, and early signs of aging using nothing more than smartphone cameras.
These tools collect data points that dermatologists once needed specialized equipment to assess, making professional-grade skin analysis accessible to a much broader audience. Based on the results, algorithms recommend personalized product regimens tailored to individual skin profiles. This shift toward customization means consumers spend less money on products that do not work for them, reducing waste and improving overall satisfaction.
Augmented reality is solving a persistent frustration of beauty retail: an inability to test a product before committing to its purchase. Major platforms and brands now offer AR-powered virtual try-on tools that overlay realistic simulations of lipstick, eyeshadow, foundation, and even hair colour in a live camera feed. The technology is sophisticated enough to account for lighting conditions, skin undertones, and facial structure, making results genuinely useful rather than approximate. Retailers report that customers using these tools produce higher conversion rates and return fewer products. As the technology continues to improve, the gap between a virtual experience and an in-store consultation is narrowing considerably.
Behind the formulations of many modern beauty products lies a biotechnology revolution. Today, scientists are growing ingredients in controlled environments that were once harvested from rare plants, marine organisms, and animals, reducing both the environmental cost and the ethical complications of traditional sourcing. This approach also produces ingredients with greater purity and consistency than what nature alone can provide.
Lab-grown squalane, fermented hyaluronic acid, and biosynthetic retinol are just a few examples offered today, formulated through biotech processes. Consumers are increasingly receptive to using these products because they align with growing concerns about sustainability.
Hair care technology has advanced well beyond traditional styling tools, with smart devices now offering real-time feedback and genuinely adaptive performance. Intelligent hairbrushes equipped with sensors can detect breakage, measure scalp health, and assess moisture levels during a regular brushing session, transmitting that data directly to a connected app.
Smart styling tools, meanwhile, automatically adjust their heat output based on hair thickness and condition, reducing the cumulative risk of hair damage. Brands are also investing in scalp diagnostic kits to identify concerns such as excess sebum production, thinning, or follicle health issues before they become visible problems. This level of data-driven care is shifting how people approach hair health as an ongoing practice rather than an occasional treatment. Consumers are making more informed choices about the products and tools they invest in, whether that involves styling devices, scalp treatments, or quality hair extensions for added length and volume.
Sustainability is a defining concern in the beauty industry. Technological innovation offers practical, scalable solutions to packaging, a waste management concern.
Brands are experimenting with water-soluble films, mushroom-based materials, and refillable systems that dramatically reduce single-use plastic consumption.
Some companies are using blockchain technology to provide consumers with full supply chain transparency, allowing them to trace a product from its raw ingredients to the finished item on the shelf.
3D printing is being deployed to produce on-demand packaging, cutting down on overproduction and excess inventory sitting in warehouses.
These are genuine engineering challenges requiring significant investment in research and infrastructure, not simply marketing strategies. Companies leading this effort are finding that thoughtful, eco-conscious packaging is a meaningful differentiator for this increasingly competitive market.
Today, beauty technology is no longer a fringe conversation happening at industry conferences. It is actively shaping product development, retail experiences, supply chains, and consumer behaviour.
The convergence of AI, augmented reality, biotechnology, smart devices, and sustainable engineering represents a transformation rather than a short-term trend.
For consumers and the industry, continuous change represents the future for beauty and personal care products. Drivers of what will soon be a trillion-dollar industry as the 21st century unfolds include an aging population, greater social and cultural diversity, concerns about environmental sustainability, and advances in medical science and other scientific and technological fields.
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]]>The post How the Attention Economy is Dragging Us Down appeared first on Techso Mogro.
]]>In the electronic and digital age, influencers abound. Some are individuals who write blogs like me. Some publish newsletters, record podcasts, produce YouTube and TikTok videos and post on social media. All search for likes, comments and followers.
Every time we look at small screens and smartphones, influencers assail us. The result is an attention economy that is victimizing us.
It doesn’t stop at influencers. The attention economy is filled with electronic newsfeeds and opinion pieces disguised as news. Then there are the malevolent spreaders of disinformation and conspiracy theories that use all of the above to influence us.
Finally, if that isn’t enough, we get assailed by advertising disguised as content.
Software algorithms and artificial intelligence (AI) today grab our attention, our eyeballs, and in the process take our eye off the ball.
The Internet provides a free digital information zone enabled by search engines, streaming services, social media and AIs. Yes, there is information behind paywalls. But that represents a tiny percentage of the total content generated daily.
Now, when someone offers you something for free, consider it may be a trap. Computer security folks create traps they call honeypots. They are designed to lure bad actors on the Internet. The honeypots that attract most of us, that draw us like bees to the hive, aim to entrap us for our data. Once trapped, algorithms take over that invite us to click, view, and comment. We hand over our personal contact information and soon see a filled e-mail inbox. Our search intentions get lost in translation.
Meanwhile, our online behaviour gets stored as data and sold to target advertisers who then grab our attention by feeding us even more distractions.
It is a wonder that any real work gets done online today.
A term synonymous with this current online phenomenon is “information overload.” Herbert A. Simon, Professor of Computer Science and Psychology at Carnegie-Mellon University, was possibly the first to coin the phrase in an address given at Johns Hopkins University in 1969. He described the consequences of an information-rich world, stating:
“In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
Maxi Heitmayer appears to have been the first person to coin the term “attention economics.” In an article appearing in 2024 in the journal Interacting with Computers, Heitmayer defines the term and describes the current poverty of attention that defines who we are today. He notes how the purveyors of online information assign dollars to every captured identity, click, view and comment. In other words, they see dollar signs coming from distraction. There is competition among purveyors of the attention economy to control audiences and the information fed to them.
The attention economy is a drag on productivity. In a 2019 article published by the World Economic Forum, it states that each time someone in the workplace checks their smartphone in response to a ping, it takes over 23 minutes to regain focus on the work at hand.
The article refers to a McKinsey & Company report that states high-skilled workers spend 28% of their workday reading and replying to emails.
These are the rabbit holes of the attention economy. The time lost is staggering.
We kid ourselves when we describe the modern workplace as a multitasking environment. A better description is a workforce of the distracted.
Back in 1997, when I was working for a large software company, every Wednesday afternoon the e-mail servers were turned off. That meant we couldn’t receive or send e-mail. The company encouraged us to call our clients or make face-to-face meetings.
Disengaging from smartphones, social media and newsfeeds today may be a hard sell, but it would be the equivalent of the strategy described above.
What other actions can beat the drag exerted by the attention economy on our brains and the workplace?
Self-control is the operative word. Change the work environment by putting the smartphone aside. If you need it for work, then remove the non-business and non-essential apps. Disable non-essential feeds and notifications. Save all of these for your home computer.
At home, turn off your smartphone before going to bed. Keep it away from the dinner table. Limit your smartphone use time. Schedule reading instead of looking at screens.
Create a family and community behaviour around smartphone and social media use. Resistance gets easier when shared.
Make meal time phone-free. Make meetings with friends and family phone-free. In your family, lead by example. Teach your children media literacy and time management.
Get a hobby or join clubs and other organized activities and groups that don’t involve small screen viewing. Read books, newspapers and magazines, not just newsfeeds.
Addressing the attention economy at work and home is not enough. Share your concerns to create public awareness, and engage governments about the dangers that the attention economy exerts on all of us.
It is hard to put the Internet and World Wide Web in a bag. Nor can we put an end to social media. What can be done, however, is alter the business practices of the purveyors of attention economics through regulation, legislation and the courts.
We can restrict honeypot usage associated with personal data collection. We can restrict what data can be harvested and its duration during engagements. We can restrict associated behavioural advertising that pops up in response to clicks and form filling.
We can hold social media developers to account for designs aimed at entrapping young people and other vulnerable populations.
We can call on governments to draft legislation to outlaw manipulative algorithms and restrict AI development to ensure these technologies operate within guardrails that no longer rob us of initiative and that end our growing addiction to technologies that rob us of us.
According to the IDC, the Internet adds more than 600 million Terabytes of data to it daily. Daily traffic amounts to approximately 600 exabytes. Volume of content doesn’t equal quality of knowledge. It doesn’t equate with wisdom, judgment, and creativity.
To create an information culture, you need to create reliable online and offline resources. Information needs to be vetted responsibly by knowledge experts. Infomration needs to be ubiquitous. Reliable, authoritative information can help guide decision-making.
An information culture isn’t driven by its technology, but rather is the driver and inventor of it.
Somehow, after inventing the Internet and World Wide Web, we lost our way.
Maybe the blame lies with the invention of social media. Maybe that was the beginning of our descent down the rabbit hole, taking us from an information economy to an attention economy.
The important future steps needed can get us back to a valued, shareable information repository and a global means of instant communication to help humanity face the many pressing challenges of the 21st century.
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]]>The post The U.S. Today Has Abdicated Its Scientific, Health, Economic, Trade and Moral Leadership appeared first on Techso Mogro.
]]>Indeed, Trump’s ascendancy is regal, not presidential. The division of powers enshrined in the U.S. Constitution is not working. The result, America is ruled by King Trump, the successor to George III, a monarch that Americans chose to overthrow in 1776.
The impacts are far more than political. Trump’s executive decisions have been detrimental to fighting the causes of climate change. The health of the U.S. and the rest of the world has been put at risk. Cutting off U.S. Aid has worsened global food security and aided disease outbreaks in the Global South. His war pursuits have disrupted global supply chains. His court jester cabinet and appointees are making a mockery of the law.
Trump’s administration is anti-science and anti-knowledge. This is after more than 80 years when the United States led the democratic world, attracting the best scientists, built the most innovative technologies, and sparked the Green Revolution to reduce global food insecurity and disease. US largesse raised all boats as a Pax Americana ensured a more peaceful world even in the face of a Cold War with the Soviet Union. When the Berlin Wall fell, some historians proclaimed the end of history and the triumph of liberal democracy over communism and fascism. These were heady times despite blips like the Vietnam War and America’s internal racial strife.
America turned swords into ploughshares, taking a military communications technology and turning it into the Internet. Instant global telecommunications became the new reality, which further allowed America to share its intellectual capital with the world.
Richard Nixon created the Environmental Protection Agency (EPA). American scientists gave us our understanding of the environmental challenges our industrial society was creating. They developed insights into pollution and atmospheric chemistry, ecosystem and biodiversity loss, and the growing threat of climate change.
The EPA and the National Oceanic and Atmospheric Administration (NOAA) monitored Earth systems using American-built satellites and a network of Earth-based monitors (air, land and water). Their research led to the undisputed scientific conclusion that human activities on Earth were causing greenhouse gas emissions to rise and global warming. Americans collected data and supplemented brilliant science minds and researchers.
Under Trump, through budget reductions and staff firings, today’s EPA and NOAA are hollowed out. Their research-produced environmental standards are no longer being followed. Deregulation has given the industrial polluters a free hand.
The United States, post-World War II, became a leader in contributing to health and food security around the world. It operated through programs set up by the federal government: US AID, the Centers for Disease Control (CDC), the National Institutes of Health (NIH), and others.
U.S. programs promoted vaccines, provided maternal and child health and nutrition, supported family planning, research on diseases, health worker training and more. This wasn’t exclusive to when the Democratic Party was in the White House. Republican administrations were equally involved.
The programs combined money, knowledge, data systems, infrastructure, research and partnerships within countries around the world. American support for food security included supplying emergency food during drought and famine, food to counter malnutrition, help in improving local agricultural productivity, and rural financial assistance.
Since the beginning of 2025 under Trump’s second term, foreign aid has been paused. US AID is dismantled. Global health programs to fight HIV, malaria, tuberculosis and Ebola have been decimated. The current Ebola outbreak in Africa has been impacted.
America’s federal international crisis response has been severely weakened by significant funding cuts. American aid is tied to Trump’s America First agenda and national security priorities.
The loss of continuity in funding to support international health and food security programs has been consequential. America’s soft power credentials are in question.
The U.S. has always championed lowering barriers in trade to allow American companies access to global market opportunities. From its days as the Arsenal of Democracy in World War II, to the post-war Marshall Plan, to the championing of the World Trade Organization (WTO), the U.S. has always led.
America promoted open markets, WTO rules and regulations, shared trade rules, the North American Free Trade Agreement (NAFTA), bringing Mexico, Canada and the U.S. together to create a continent-wide free trade zone. Since Trump’s first term, when he replaced NAFTA with the U.S.-Mexico-Canada Agreement (USMCA), free trade has been under threat.
Trump sees international trade as a win-lose contest. A trade surplus means you win. A trade deficit means you lose. What gets counted and what does not determines winners and losers.
Trump’s Liberation Day announcement last year established arbitrary tariffs on countries that sell and buy from America. This managed trade strategy through the application of tariffs, without congressional oversight, is a departure from how America conducted business with the rest of the world. Everything is bargaining. Everything is transactional. Tariffs no longer follow the WTO guardrails. Signed agreements like the USMCA no longer hold weight.
In the last two days, Canada and the United States have entered a trade war. The two countries in 2025 participated in two-way trade in goods and services totalling CDN$1.287 trillion. The two countries have integrated industries with finished products often going through assembly processes that go back and forth across the international border. It is the world’s largest bilateral trade arrangement and Trump’s managed trade strategy and brinksmanship has brought it to crisis? This is a test case for any other country negotiating under Trump’s managed trade strategy.
Once the global leader in the world, its most powerful political actor, often establishing unilateral positions that led to military blunders like Vietnam and Iraq, the U.S. can no longer assume that allies will follow its lead.
By the end of World War II, America was the sole superpower. It was soon joined by the Soviet Union. These were the first two nuclear-armed nations. Post-war Europe came under the U.S. defence shield through NATO. Canada and the U.S. created NORAD for North American defence.
The Soviet Union fell in 1991. For a little more than a decade, no other nation challenged America’s hegemony. China’s rapid rise in the first two decades of the 21st century has changed the balance of power. As Russia succeeded the Soviet Union, it remains a nuclear-weapons threat. Its war in Ukraine has consumed it for the last four-plus years with little success. That doesn’t lessen Russia as a dangerous global player.
Meanwhile, regional wars are ongoing as new ones emerge. The great power competition and might-makes-right diplomacy are making for a less safe future.
The alliances of the past are less automatic. Economic interdependencies are being weaponized (see the Canada-U.S. trade dispute). U.S. leadership approval among allies has fallen dramatically while more positive perceptions of America’s chief rival, China, are on the rise.
Global confidence in Trump’s America is on a rapid decline, reflecting the administration’s attitudes toward alliance members (Denmark and Greenland), Canadian trade and 51st state threats, restrictive immigration policies and deportations, climate policy, and military interventions like the current Israel-Iran-U.S.-Persian Gulf nations’ conflict.
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]]>The post The Growth of Gambling and Prediction Markets: Not a Good Trend for Civilization in the 21st Century appeared first on Techso Mogro.
]]>The U.S. Supreme Court in 2018 ruled that the states could regulate gambling. Canada changed the Criminal Code three years later to allow provinces to do the same.
Who could have predicted a Pandora’s Box of phone casino game apps?
Who would have predicted that gambling sponsors would dominate sports broadcasting?
Las Vegas and Atlantic City became accessible to anyone, young or old, who had a smartphone.
Today, gambling is mainstream in North America. It is all over television with featured ads that describe online casinos, sportsbooks, lotteries and other forms of legally sanctioned gambling.
U.S. sports betting industry statistics for 2025 show that Americans wagered $166.94 billion online, with sportsbook sites raking in close to $17 billion in revenue.
Online gaming has become the latest addiction to accompany those other smartphone addictions like social media sites. The U.S. and Canada aren’t alone in being dragged into this new world of “igaming.” The whole world is in on it.
Worse yet, the business world has joined the gaming world. The stock market has always been a form of quasi-gambling. But now there is something new: prediction markets where people purchase event contracts that are treated as financial plays. What’s being bought and sold are win-lose contracts that bet on outcomes of sporting events, the latest polls, election outcomes, economic performance, and even when a war will start. The temptation to put a hand on the scale to leverage the “bet” is very real. I will let Harper continue the tale.
Ever since there was something to gain, or something to lose, human beings have always gambled.
Long before stock exchanges or smartphone apps, people wagered on dice games, cards, horse races, sporting contests, and countless other uncertain outcomes. Governments usually viewed gambling as a vice. Sometimes it was tolerated, sometimes prohibited, but it generally occupied the fringes of respectable society. Betting was something people did in back rooms, racetracks, and smoky casinos.
Over the last century, however, gambling has steadily moved from the margins into the mainstream. What was once viewed as a questionable pastime has been legalized, regulated, institutionalized, and increasingly celebrated.
Then governments themselves entered the business.
In 1976, the Canadian government used lotteries to fund the Montreal Olympics, and public lotteries quickly expanded into a permanent source of government revenue. Today, lottery systems help fund everything from public services to community programs… all the while preying on their most financially vulnerable citizens.
Prediction markets are simply the next step in this evolution. Their defenders call them information markets, forecasting tools, and mechanisms for aggregating collective intelligence. The argument is that a market price can reveal what participants genuinely believe will happen, and that it can predict outcomes more accurately than pundits or expert panels due to the wisdom of crowds effect.
But the problem is that markets are not truth machines. They are incentive systems. While prediction markets are often presented as tools for discovering the most likely outcome, participants are ultimately motivated by profit, not accuracy. Sometimes those goals align; but most often, they don’t.
One of the biggest vulnerabilities in prediction markets is that not all participants enter the market with the same information.
Corporate executives routinely learn about major announcements before investors. For example, in 2026, U.S. regulators accused a Google employee of using material non-public information to trade on Polymarket contracts related to Google’s annual “Year in Search” rankings, allegedly earning roughly $1.2 million before being charged by the CFTC and Department of Justice.
The case illustrates one of the central weaknesses of prediction markets: the people with the best information are often the people closest to the event itself. When markets reward advance knowledge, they inevitably create incentives for insiders to profit from information that the public does not have.
Supporters often point out that traditional stock markets face similar risks.
This is true (and a real problem), but prediction markets dramatically expand the range of events that can become profitable targets. Once people can wager on elections, policy decisions, military actions, public health developments, and corporate announcements, the number of opportunities for insider advantage grows substantially.
A system designed to harness information ends up rewarding those who possess secrets.
Insider knowledge is only half the problem.
The more troubling issue is that some market participants have the ability to influence the very events being traded.
Consider a contract tied to political decisions, military actions, or diplomatic negotiations. The individuals involved in those processes possess not only greater knowledge but, in some cases, the ability to shape the outcome itself. Alternatively, consider a prediction market tied to a corporate announcement. Executives and senior employees may have substantial influence over when information is released and how it is presented.
Consider Elon Musk announcements. An event contract could be placed on whether Musk launches a new political party or makes other high-profile announcements. In these cases, the person at the center of the market, Musk, is not merely being predicted; he is one of the primary actors to determine the outcome through his own decisions. This blurs the line between forecasting and influence, since a participant with enough stake in the result could have both the means and the incentive to shape the event itself.
At that point, what is being measured is not foresight; it is influence.
Today’s politicians, influencers, billionaires, media personalities, and content creators can reach millions of people with a single post. Most recently, the President of the United States, through his Truth Social platform, has been selling event contracts to those willing to purchase advance notice to new government policies before public disclosure. This isn’t prediction marketing per se, but it is the consequence of letting the gambling horse into the business stable.
In theory, prediction markets reward accurate forecasts. In practice, however, they reward the ability to anticipate how millions of others will react. That’s because whether information in the influence economy is accurate or not is secondary to the market reaction generated. It goes beyond the event contracts themselves, creating this strange form of reflexivity.
The participants are not merely trying to predict future events; they are as often trying to predict how others react to headlines, to rumours, social media posts, interviews, and public statements. The markets, therefore, become vulnerable to narrative momentum. An event contract may move because something significant happened, or because people believe something significant might happen.
This is especially relevant in political and geopolitical markets.
A good example is how prediction markets are using the United States’ war with Iran. Traders have watched markets swing in response to statements posted on Truth Social. Statements by the President appearing on the social media site suggest probabilities and outcomes involving threats of military action, with “bets” placed on when action or lack of action materializes. In these moments, these prediction market bettors are trying to make money, not just on an outcome, but on how millions of others react to the message, regardless of whether it reflects actual changes in policy.
The ethical concerns surrounding prediction markets become nearly impossible to ignore when the contracts involve war, terrorism, or humanitarian crises.
Recent geopolitical contracts have attracted enormous attention, with significant trading volumes focused on military conflicts, diplomatic confrontations, and foreign-policy developments. Analysts studying these markets have noted growing concerns about insider information and unusually timed trading activity surrounding major geopolitical events.
Consider the incentives this creates. When a political leader issues a military threat, prediction markets immediately begin repricing the likelihood of escalation. When a diplomatic breakthrough occurs, contracts move again. Every speech, tweet, press conference, missile launch, or troop deployment creates winners and losers.
If markets are heavily trading the probability of military action, are people connected to decision-makers participating? Are political allies, donors, advisers, family members, or corporate insiders involved?
Prediction markets cannot answer those questions. They can only create the financial incentives that make the questions worth asking.
Part of the prediction market appeal is understandable. Many participants are not institutional traders or forecasting enthusiasts. Like gamblers of old, they are ordinary people desperately searching for economic opportunity in an uncertain world. And as traditional paths to financial security become even more out of reach, speculative platforms can start to feel like one of the only strategies left for getting ahead.
But that points to a deeper problem. As a society, we have steadily transformed gambling from a vice into a governing principle. We have legalized lotteries, expanded casinos, normalized sports betting, gamified investing, and now created markets that allow people to wager on elections, wars, and political crises.
As a result, people stop merely betting on outcomes and begin trying to influence them.
A donor supports a candidate. An influencer amplifies a narrative. A political movement works to shape public opinion. A market participant puts their finger on the scale because they have money riding on the result.
The danger of prediction markets is not only that it drains savings accounts. It’s also that prediction markets encourage people to treat every aspect of public life as something to be traded, speculated upon, and manipulated for financial gain.
And once everything becomes a bet, we risk everything: our money, yes, but also our power, our peace, our stability, and above all, our humanity.
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]]>In Part 3, Harper describes how institutions of higher learning are attempting to shut AI out or adapt to it by redesigning teaching approaches, curricula and grades. As always, your comments are welcome.
How can higher education adapt curricula in the presence of AI? AI has weakened the connection between assignments and learning. It has exposed incentive structures that increasingly reward credentials over understanding.
If you have read Parts 1 and 2, you may begin to understand just how disruptive AI is to existing post-secondary institutions.
Diagnosing the problem was the easy part. The harder question for universities and colleges is what to do about it. Many search for tools that detect AI to prevent its use altogether. Others embrace AI enthusiastically and attempt to integrate it into every aspect of teaching and assessment.
Both approaches risk missing a deeper reality: higher education is unlikely to return to the world that existed before generative AI. If universities want to preserve learning as the central purpose of education, they need to redesign their courses and how they assess a student’s progress.
Many commentators assume that AI-resistant education requires expensive tutorials, Oxford-style discussions, or highly personalized instruction. But there is nothing inherently resource-intensive about assessing learning directly. (If anything, AI is much more resource-intensive.)
Large lecture courses long existed before personal computers. Students completed exams on paper. They wrote essays in blue books. They demonstrated knowledge in controlled environments. Professors evaluated their work personally. Knowledge was assessed without external assistance.
A modern version of this model can work surprisingly well. Imagine a course where lectures remain large. Instead of submitting weeks of take-home assignments, students demonstrate learning through in-class writing, problem-solving, presentations, examinations, and structured discussions.
In this system, homework exists primarily as optional practice rather than graded completion exercises. Study guides, recorded lectures, and digital resources help students prepare for in-class activities.
Such a system would not eliminate technology. Students could use AI extensively while studying. They could use it to generate explanations, explore examples, and practice concepts.
What they could not do is substitute AI performance for their own performance when demonstrating mastery of a subject.
Perhaps the most interesting possibility is organizational rather than technological.
Universities should consider separating knowledge-focused from tool-focused education more explicitly:
A theory-oriented course (“What do you understand?”) would emphasize conceptual understanding, reasoning, and intellectual development. Assessment would focus on what students personally know and can explain.
An application-oriented course (“What can you accomplish with modern tools?”) would embrace AI directly. Students would learn prompt engineering, workflow automation, verification techniques, model evaluation, and human-AI collaboration.
Currently, many courses attempt to answer both questions simultaneously. That approach is increasingly untenable.
A student who uses AI to generate sophisticated business reports may demonstrate excellent technological competence while revealing little about their underlying business knowledge.
Conversely, a student who demonstrates strong analytical skills without using AI may still be unprepared to use it at an employer’s request in the modern workplace.
Universities are unlikely to choose between fully embracing AI and fully rejecting it. The future will probably be more nuanced.
Some courses will become AI-native environments where students learn alongside intelligent systems. Others will become intentionally AI-limited spaces designed to cultivate foundational knowledge and reasoning.
Assessment will shift away from monitoring process and toward verifying mastery. Homework may become less important. On the other hand, in-class demonstrations of understanding may become more important. Grades may reflect what students know rather than what they can submit.
The bigger question is what happens if universities fail to adapt.
For centuries, a degree served as a reasonably reliable signal that its holder had acquired a particular body of knowledge and way of thinking. If employers, graduate schools, and society at large begin to lose confidence in that signal, universities may find themselves facing a crisis of legitimacy far more serious than any debate over plagiarism or academic misconduct.
The institutions that succeed will distinguish between teaching students how to think and reason, and teaching students how to use increasingly powerful tools. Both are valuable goals, but they are no longer the same.
The student who eventually graduates into this new AI-saturated world will depend in part on their ability to work with and alongside AI. But the student will also depend on something AI cannot easily provide: judgment, expertise, creativity, and the ability to recognize when the AI is wrong.
In some respects, curricula may look surprisingly old-fashioned; not because universities reject technology, but because AI forces them to rediscover their original purpose.
In a world where information, summaries, essays, and even analyses can be generated instantly, the most valuable things a university can teach will be how to think and reason, how to formulate questions, how to develop arguments and hypotheses, and how to discern fact from fiction.
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