I was born and grew up during the most explosive growth in population since Homo Sapiens first appeared. In 1949, on my birth date, the world’s population was 2.3 billion. Today it is 8.3 billion.

Doesn’t this number make you think that there may be far too many of us for the planet to handle? Is that true?

The reasons for such dramatic growth include:

• Advances in healthcare.
• Advances in technology.
• Advances in agronomy.

A Simple Math Exercise

If you think there are too many of us, I suggest doing a simple math exercise. Take the total population and divide it by the square kilometres of land.

149,000,000 square kilometres (km²) ÷ 8,300,000,000 people = 55.7 people per km²

But wait, not all of Earth’s landmass is hospitable to us. Here are the numbers:

• 33% desert
• 24% mountains and glaciers

That leaves us with 43% of the 149 million km², or 64 million km².

Do the math again:

64,000,000 km² ÷ 8,300,000,000 = 129.7 people per km²

That’s about one person per 1.91 acres, which, according to historic standards, is almost twice what each of us needs to thrive. An acre planted with crops, with a few chickens or other livestock, and we are, state the experts, good to go.

So, that means the carrying capacity of the planet far exceeds 8.3 billion. In fact, the number is 15.85 billion.

The Problem of Population is Unequal Distribution

In the Global North today, governments are restricting immigration even though birth rates in these countries are falling below replacement level. Throughout Europe and East Asia, fertility rates have dropped below 2.1, considered the replacement rate, where births exceed deaths.

Japan, South Korea, China, Italy, Spain, Greece, Germany, and Poland have fertility rates hovering between 1.1 and 1.3, marking an end to native population growth. Here are the stark numbers for the first four:

• Japan hit a peak population of 128 million in 2008. The projection for 2070 is 87 million.
• South Korea in 2025 had 51.7 million people. Projections for 2060 indicate 30 million.
• China’s current population is 1.4 billion. Projections for 2050 are 1.3 billion and under 1 billion by 2100.
• Italy’s population currently is 58.9 million. Projections for 2050 show a decline to 52.3 million.

Fertility rates are only one of the factors influencing population decline. For example, projections for Spain by 2050 show it will have the highest percentage of elderly people in the world at 37%, up from 17% today.

Out of Africa: A Problem or Solution?

Under such circumstances, one would think countries experiencing both population decline and aging would open their doors to immigrants from Global South countries, particularly Africa, where the picture is quite different.

African birth rates across the continent are almost twice the population replacement rate. Here are four country statistics:

• Niger’s birthrate is 6.8.
• Chad is 6.
• Somalia is 5.98.
• Nigeria is 4.99.

The last on this list, Nigeria, is currently the 6th most populous nation on the planet and growing at a rate of 2.1% annually. In thirty years, at the current growth rate, Nigeria will become the 4th most populous country with a population approaching 400 million living on habitable land amounting to 686 thousand km², an average of 583 persons per km² or 2.36 per acre. That’s more than twice the one-acre survival standard previously described.

A solution is staring us in the face. Global North countries with low birth rates and aging populations need more people. Global South countries like Nigeria, with high birth rates and a young population, need to find them new homes.

We Have Seen Population Bombs Before

History lessons are readily available to show us what happens when nations begin to exceed their population carrying capacity. The Medieval historian in me shares two examples with you:

• Medieval Europe in the 11th century was in the midst of a long period of climate warming with more land under cultivation, growing populations, and the rise of cities. When Pope Urban called for a crusade to rescue the Holy Land from its Muslim conquerors, leading to the First Crusade in 1095, it was as much about religion as it was a solution to deal with the explosive growth in Europe’s population that was leading to rising levels of conflict among feudal kingdoms and estates. The violence got channelled to the Middle East.
• Late 12th century Central Asia experienced dramatic climate change, with the barren steppes seeing increasing precipitation and the spawning of vast grasslands for the herding Mongol tribes that soon experienced unprecedented population growth and rising intertribal conflicts that spilled over into the conquest of China, and within a half-century, the establishment of the largest empire on Earth.

A 21st-Century Solution

Today, the world is conflicted at an unprecedented level. In March, I wrote about 21 ongoing wars taking place on Earth today. Is the current state of warring among nations a reflection of growing scarcity facing a population bomb? Do we have the common horse sense and technologies to ensure that warfare, the solution often used by humans in the past, is not the one we choose for the planet’s future?

In Part Two, we will explore how we can use digital twins, artificial intelligence (AI), genetic engineering and other advances to help us peacefully navigate the road ahead.

Can a digital twin of our planet help us test Monte Carlo gaming scenarios until we find a virtual solution that we can apply to reality? Can AI meet the challenge of resolving unequal human population distribution? Can genetic engineering turn the one-acre sustainability requirement on its head?

From these technologies, will humans be able to divine solutions to make life here on our Pale Blue Dot more than just sustainable?

Soon to follow, Part Two.

The post Is Earth Being Overcrowded by Humans in the 21st Century? – Part One: The Challenge appeared first on 21st Century Tech Blog.

At age 77, I am one of the seniors who personally monitors my health using technology. Kardiamobile checks my heart rhythm. I have a home blood pressure cuff, a pulse oximeter, and even a stethoscope. When needed, I have worn medical devices that instantly reported health issues to my family doctor or specialists. Do you think this is paranoia, or just what seniors are doing today and a sign of the times?

Remote patient monitoring has come a long way from when my daughter, born with a congenital heart condition, was issued a portable ECG monitor with an acoustic coupler to phone in events to cardiologists at the Hospital for Sick Children in Toronto.

I’ll let Harper tell the rest of the story. 

Senior care is no longer built around scheduled checkups and reactive decisions. Rather, it involves continuous data collection and real-time insight for proactive medical interventions. Some of the data comes from medical technology that seniors have purchased to collect and share with doctors. The rest comes from Remote Patient Monitoring (RPM) technologies supplied by hospitals and personal physicians.

The tools driving this shift to RPM and Predictive Analytics (PA) include devices such as wearables, in-home sensors, and other digital medical recording equipment capable of data broadcasting.

Combining RPM with PA involves using statistical models and machine learning algorithms to analyze received data, identify patterns, and forecast potential health risks or outcomes before they occur.

The two technologies enable care providers to move from reactive interventions to more proactive, data-informed decision-making. It means earlier responses, more personalized care, and improved overall outcomes for seniors.

The Architecture of Proactive Care

At its core, RPM operates like a distributed data collection system. Think wearable devices (smartwatches, fall-detection pendants), ambient sensors (motion detectors, bed sensors), and connected medical devices (glucometers, blood pressure cuffs) all feeding time-series data streams into centralized platforms.

After collection, the data is stored and processed. Edge computing allows some analysis to happen locally, for example, detecting an immediate fall. Cloud-based systems aggregate long-term trends.

The shift from reactive to proactive care comes from a layered architecture. Instead of waiting for a threshold breach like dangerously high blood pressure, RPM systems can flag gradual deviations from an individual’s baseline. A 5% drop in daily step count over two weeks, or a slight disruption in sleep cycles, can be subtle or easily explained away on an individual level. When analyzed together, however, they can form a predictive pattern.

Real-World RPM in Action

Imagine a senior living resident wearing a smartwatch that tracks heart rate variability, sleep quality, and movement. Over the course of several days, the system detects elevated resting heart rates combined with decreased activity and fragmented sleep.

In isolation, these metrics might not trigger concern. But RPM platforms use rule-based engines or machine learning (ML) models to correlate these variables. The system flags a moderate risk alert and notifies staff. A nurse checks in and discovers early signs of a respiratory infection. Antibiotics are administered before hospitalization becomes necessary.

Another example uses bed sensors to detect how often residents get up during the night. A sudden increase in frequency could indicate discomfort, medication side effects, or even an early urinary tract infection. Without RPM, the pattern might go unnoticed until the issue escalates. With it, intervention happens early.

PA: From Data to Foresight

If RPM is the data engine, PA is the interpretation layer. Predictive models use historical data combined with real-time inputs to generate educated forecasts.

For example, fall prediction models often incorporate variables like gait speed, prior fall history, medication types (especially sedatives), and even environmental data like lighting conditions. Using regression models or neural networks, these systems assign a continuously updated fall risk score.

In practice, this might mean dynamically adjusting a care plan. A resident flagged as high-risk may receive more frequent check-ins, physical therapy sessions, or environmental modifications like adding handrails in the bathroom or hallways. Importantly, these are targeted interventions, not blanket policies applied everywhere. This precision reduces unnecessary restrictions while increasing safety.

Complex Environments, Smarter Systems

In settings that provide assisted living and memory care, the complexity of care increases dramatically. Residents may experience cognitive decline, behavioural changes, or chronic health conditions that interact in unpredictable ways. This is where layered data models shine.

Take memory care, for example. PA can track behavioural patterns such as wandering frequency, agitation periods, or changes in routine. If a resident typically becomes restless in the late afternoon, a phenomenon called sundowning, PA can anticipate this and suggest preemptive interventions, like scheduling calming activities or adjusting lighting.

Another real-world application involves medication adherence. Smart dispensers can log when doses are taken or missed, feeding the information into predictive systems. If a pattern of missed medication correlates with confusion or fatigue, caregivers can intervene before health outcomes are affected. In these environments, the combination of RPM and PA becomes an early warning system.

Caregiver Decision Support Systems

One of the hugely important but less obvious impacts of RPM and PA is how these technologies augment caregiver decision-making. In technical terms, RPM and PA form a Clinical Decision Support System (CDSS).

Instead of relying solely on intuition or periodic assessments, caregivers receive data-driven insights prioritized by urgency. Dashboards display risk scores, trend lines, and anomaly alerts. For example:

• A colour-coded interface flags residents with rising fall risk.
• Trend graphs show declining mobility over time.
• Automated alerts highlight deviations from baseline vitals.

This doesn’t replace human judgment, but rather, augments it. A caregiver can walk into a shift already knowing where attention is needed most. That is transformative for staffing models and care quality.

Interoperability and Data Ecosystems

To truly understand the future of RPM, you must look at interoperability, the ability of different systems to communicate. Modern platforms are increasingly integrated with electronic health records (EHRs), pharmacy systems, and even hospital networks. As an example, a typical chain of events could follow this type of sequence:

1. RPM detects abnormal vitals.
2. Data is logged in the resident’s EHR.
3. PA flags high hospitalization risk.
4. A physician is automatically notified.
5. A telehealth consultation is scheduled.

All of this can happen in hours, without manual coordination. The result is a closed-loop system where detection, analysis, and intervention are tightly integrated. It is the kind of interoperability that makes data-driven care scalable and sustainable.

Ethical and Human-Centred Design Considerations

Of course, with more data comes more responsibility. Privacy, consent, and data security are major considerations in RPM systems. Designers must ensure that monitoring feels supportive without being invasive.

There’s also the question of algorithmic bias. PA is only as good as the data it’s trained on, which means developers must carefully validate what it reports across diverse populations. Otherwise, risk scores may be less accurate for certain groups.

From a human-centred perspective, the goal is balance. Technology should fade into the background, enabling more human interaction, not less. In the best implementations, caregivers spend less time collecting data and more time connecting with residents. For a world with an aging population, the adoption of RPM and PA will mean a better daily quality of life for seniors.

The post Predictive Analytics and Remote Monitoring Reshapes Senior Care appeared first on 21st Century Tech Blog.

AI repeatedly comes up in conversations when I talk to people about their future, whether young or old.

Recently, Dario Amodei, CEO of Anthropic and creator of the AI known as Claude, predicted that half of entry-level white-collar jobs will be wiped out in the next five years. For those with entrepreneurial chops, turning this threat into an opportunity makes sense.

I’ll let Lila continue the story. As always, please send us comments and questions.

A recent Metratrends newsletter published by Peter Diamandis, the co-founder of the XPrize and Singularity University, describes how AI is positively disrupting new business startups.

Peter tells the story of Matthew Gallagher, a 41-year-old self-taught programmer. Matthew launched a telehealth business in September 2024 with US$ 20,000. He selected an AI chatbot for customer service. His only human employee was his brother.

In the first year, Matthew gained 250,000 customers and generated $401 million. Today, his daily income tops $3 million, and the company is forecasting revenue of $1.8 billion by year-end 2026.

We are talking about a spectacular success story for two entrepreneurs with only $20,000 to invest, adopting AI tools wired into existing supply chains. Without an engineering team or venture capital, an AI-fuelled business is a 2026 success story.

What does this imply in 2026 and beyond?

Launching an online business has never been more accessible, as AI levels the playing field for individual entrepreneurs. What once required large teams, significant capital, and months of planning can be accomplished with AI and automation tools.

Whether building an e-commerce store, starting a blog, or offering services online, AI can streamline processes and boost efficiency from day one.

Use AI Data Insights To Identify Opportunities

One of the first and most important steps in starting an online business is choosing the right niche. Consider this comment from Kevin Weil, Chief Product Officer at OpenAI. “Every product, every service, every device we use today was built pre-AI. They’re all going to be reinvented.” That means almost anything online before ChatGPT emerged represents an opportunity to build something entirely new.

Use these new AI tools to analyze trends, search data, and consumer behaviour, identify market gaps and suggest opportunities. Instead of relying solely on hunches and guesswork, AI can provide insights into what people search for, products that are gaining traction, and where there is less competition.

Taking a data-driven approach helps refine a business idea before making the investment in time and money. It helps define your audience from the outset by identifying preferences, pain points, and buying habits. It helps to tailor products or services to meet real demand. A well-chosen niche improves chances for success and makes marketing efforts more effective down the line.

Streamline Operations With Automation

Managing an online business involves many moving parts, from order processing and customer service to invoicing and scheduling. AI-powered automation tools handle repetitive tasks, freeing time to focus on growth and strategy. Chatbots can respond to customer inquiries. Automated email campaigns can nurture leads and maintain engagement. 

Automation also extends to back-end operations. Tools that assist with inventory management, financial tracking, and workflow organization can significantly reduce human error and improve efficiency. For businesses relying on freelancers or remote contributors, solutions like digital contractor management services simplify onboarding, payments, and communication, ensuring that the team operates smoothly even as it scales. This level of organization is especially valuable when managing multiple projects simultaneously.

Enhance Marketing and Content Creation

Marketing is often the biggest challenge a new business owner faces. Here too, AI can make marketing tasks more manageable. Content generation tools assist with writing blog posts, social media captions, product descriptions, and advertising copy. These tools are great starting points and help maintain a consistent message across all media platforms. They are, however, not a replacement for the human voice and touch.

AI can optimize marketing strategies by analyzing performance data and suggesting improvements. For example, AI can identify the best times to post on social media, recommend keywords for search engine optimization (SEO), and even personalize content for different audience segments. This allows the business to reach the right people with the right message at the right time, increasing chances to convert visitors to customers.

In addition, visual content creation is made easier with AI-powered design platforms. Create professional-looking graphics, logos, and promotional materials without the need for advanced design skills. This ensures the brand is polished and credible from the start, key to building online trust.

Improve Customer Experience With Personalization

A positive customer experience is crucial for long-term success. So how can AI assist? AI can help deliver personalized interactions at scale. By analyzing user behaviour, AI can recommend products, tailor website content, and provide customized responses that make customers feel valued. This level of personalization was once only possible for large companies with extensive resources, but now it is doable for small businesses.

AI-driven insights can also help anticipate customer needs. For example, predictive analytics can highlight when a client is likely to purchase or when a customer needs support. This allows the business to be proactive versus reactive and creates a smoother and more satisfying experience. Happy customers are more likely to return, leave positive reviews, and recommend the business to others.

Scale Business Through Smart Decision-Making

Growth is a common goal for most businesses, including those online. That means a business has to scale. That’s where AI can help support smarter decision-making as the business grows. Advanced analytics provide a clear picture of what works and what doesn’t, and help adjust the strategy on the fly. Instead of relying on gut feel and intuition, AI helps to base decisions on real data and measurable outcomes.

As the business grows, AI tools can help manage increasing complexity. From optimizing supply chains to forecasting demand, AI solutions reduce uncertainty and improve planning. They also allow for experimenting with new ideas more safely, since you can quickly analyze results and pivot as needed. This is essential in today’s fast-paced digital landscape where trends and customer expectations change rapidly.

The post Starting an Online Business Today with AI appeared first on 21st Century Tech Blog.

DNA was only first discovered in 1869, with its structure only figured out in 1953. In 1990, the same year that Jurassic Park was published, the Human Genome Project launched to assemble and decode human DNA.

Unlike in Jurassic Park, we have yet to sequence DNA from fossil insects trapped in amber, let alone from the blood these insects sucked up from the species they tormented.

What we do have is recovered DNA from preserved organic materials of animals and plants that died thousands of years ago. Why thousands and not millions? That’s because DNA remains coherent for a very short period of time, in fact, a little over 500 years, the chemical half-life of the molecule.

The best conditions for preserving DNA are dry, cold environments. Exposure to ultraviolet light causes it to rapidly decay. The places where we have successfully recovered DNA are in places like Greenland, Siberia and the Canadian Arctic. That is where we have found preserved DNA dating from 700,000 to 2.4 million years ago.

De-Extinction in the Present

Unlike Jurassic Park, where long-extinct dinosaurs were brought back to life by crossing their recovered DNA with that of living amphibians, current de-extinction realities are different.

Colossal Biosciences is a biotech company that claims to be the world’s first and only de-extinction company. Its website tag line states “disruptive conservation and preservation.” Colossal describes its mission “to preserve and restore Earth’s diminishing biodiversity” through scientific intervention.

Of 42,108 current species on the planet that are endangered, Colossal notes that 9,251 are at a critical stage, with 902 already extinct. Colossal wants to restore the past and end the “rapid and accelerating loss of our planet’s biodiversity.”

How does Colossal plan to do this? One process has involved resurrecting core genetic material and implanting it into existing species. Here are some examples.

De-extinction of Woolly Mammoths

Colossal is focusing on a post-Ice Age species, the Woolly Mammoth. Mammoths were relatives of modern elephants. They died out on mainland North America approximately 10,000 years ago. An island-isolated population remained lingering on in Canada’s North until between 4,000 and 4.300 years ago.

Remains of Woolly Mammoth have been found frozen in permafrost, making it possible to extract their ancient DNA. Using modern elephants as hosts, Colossal intends to insert Mammoth DNA segments into the genome of Asian Elephant using pluripotent stem cells. The embryos created would be implanted and carried to term. The result would be an engineered modern Asian Elephant bearing Woolly Mammoth traits, not the actual Mammoth of the past, but rather, a hybrid.

Why bring back the Woolly Mammoth? When mammoths roamed the Arctic, they served as Ice Age grazers, clearing the snow pack to allow tundra grasslands to flourish, and spreading seeds in their dung to expand the range of Arctic plants. Their trampling of snow kept the permafrost from thawing. Their activities supported increased Arctic biodiversity. So, there may be a good argument to bring back the Mammoth.

De-extinction of the Moa

The Moa, a giant flightless bird from New Zealand, is a more recent extinction that occurred shortly after the arrival of the Māori, the first humans to reach the archipelago over 600 years ago.

Moas didn’t last long after humans arrived and were soon extinct. Mainly herbivores, Moas served a useful role in maintaining New Zealand’s forest ecosystems. Once gone, forest biodiversity declined.

Unlike Woolly Mammoths, Moas were not creatures that lived in cold environments. To extract surviving DNA, scientists turned to bones, feathers, eggshells and beaks found in remains. From these, the scientists could sequence many DNA fragments. They compared these fragments to similar ones found in related bird species. Emus, Australia’s large flightless bird, come to mind.

To incubate an Emu with Moa characteristics requires inserting the DNA into Emu eggs at an early stage. Colossal has considered a different strategy, 3D-printed eggs, which I expound on below.

De-extinction of the Tasmanian Tiger

The Tasmanian Tiger, also known as the Tasmanian Wolf or Thylacine, was a carnivorous marsupial native to Tasmania. When European colonizers arrived there, the Tasmanian Tiger was a threat to sheep and other domesticated species brought to the island. The result was that the government put a bounty on the Tiger, with the last one dying in captivity in 1936.

Finding DNA from museum-preserved Tigers isn’t a big challenge. Scientists have sequenced 99.9% of the Tiger’s genome. Its closest living relative is the Dunnart, a tiny, mouselike marsupial. Using CRISPR gene-editing tools, Colossal hopes to edit the DNA of the Fat-tailed Dunnart.

A tiny Dunnart, however, could never carry a baby Tiger to term. That’s why Colossal is developing an artificial womb. The hoped-for result will be a hybrid Tiger breeding population in captivity with the potential of reintroduction into the Tasmanian wilds. Will that just create the same conditions that led to a bounty on every Tiger’s head?

3D-Printed Eggs and Artificial Wombs

Moas and Dodos from Printed Eggshells

Colossal recently started producing 3D-printed eggshells and successfully used them to incubate baby chickens. This technology could conceivably be used to revive the Moa, as well as other extinct species like the Dodo.

To develop a 3D egg to incubate Moas would require a shell 80 times larger because Moas could stand 3.6 to 3.7 metres (11.8 to 12.1 feet) in height, and weigh 230 kilograms (approximately 500 pounds).

The artificial eggshell contains a permeable membrane that allows oxygen to penetrate just like real eggshells. To compensate for the lack of calcium, which normally gets absorbed from the shell, Colossal added calcium to aid chicken embryo growth and development.

Moa resurrection, even with this latest innovation, remains a long way off. More than likely, Colossal will end up inventing a hybrid bird species with Moa genetic traits. That conclusion is easy to reach since all efforts by the company to date to revive species have used living species of animals as hosts and produced hybrids displaying a mix of genetic characteristics.

Dinosaurs from Printed Eggshells

The likelihood of this Jurassic Park scenario happening is zero. We might be able to modify a bird’s genes to create something that looks like a dinosaur, but it won’t be because we have reconstructed the latter’s DNA.

Sixty-six-million-year-old molecules no longer exist, so we wouldn’t know where to start in trying to recreate a dinosaur’s genome. The only living dinosaur relatives are birds, with no current way for us to determine what, if anything, in their DNA is still dinosaur.

The post Reanimating the Past: Here Comes “Jurassic Park” appeared first on 21st Century Tech Blog.

Historic Bubbles

Bubble episodes have been a part of human history going back to Imperial China, the Roman Empire, and other ancient civilizations. Wherever humans established exchange rates involving land, goods and services, speculation abounded.

In 33 CE, several Roman banks failed, as agricultural land values crashed. It would have been deja vu for any Romans who lived then if they were still around to witness the 2008 CE financial crisis, when bad paper fuelled by subprime mortgage-backed securities and credit default swaps caused the investment bank, Lehman Brothers, to fail and created a global recession.

Successive dynasties of the Chinese Empire were beset by economic cycles of boom and bust similar to recent historic bubbles, mostly arising in the Western World. These include the Dutch Bubble in the 1630s caused by speculation on the price of tulip bulbs, the British South Sea Bubble in 1720, the Railway bubble of the 19th century that culminated in the Gilded Age, the 1920s stock market bubble and crash of 1929 that caused the Great Depression, the Dot-com Internet bubble of the 1990s, and finally, the 2008 Subprime mortgage bubble.

Paul Krugman, who won the 2008 Nobel Prize in Economics, in a recent conversation, described a bubble as a situation where people invest “in something that has no realistic chance of paying off, not socially but just commercially, to an extent that justifies the amount of money being thrown at it.” He continued, stating, “A bubble is something that people do because everyone else is doing it.”

In some respects, a bubble is like a self-driving “Ponzi scheme,” a “money game” that preys on both sophisticated and unsophisticated investors.

The Current Bubble

Artificial intelligence (AI) is at the heart of the current bubble. AI companies like OpenAI, Anthropic, Meta, Google, Microsoft, and Xai, joined by chip makers like NVIDIA, have had investors throw billions of dollars at them. For the AIs, realizable profits are nonexistent despite hundreds of billions changing hands.

If history repeats, the AI bubble will be like all those that preceded it. The market will crash similarly to what occurred in the Dot-com bubble. A few companies will emerge as highly profitable. The rest will be left in the dust.

In other respects, however, the AI bubble may be different, because the product itself may influence the outcome. Currently, any company purporting to be doing AI is riding on the bubble membrane as it expands. But what if an AI tool could help distinguish between different AIs, helping to determine the difference between realizable value propositions and those companies that have unrealistic expectations and are overvalued?

In some sense, AI may be the only technology to mitigate the growing AI bubble, providing a valuable service to separate facts from hype and provide measurable data to support the billions currently being thrown at the technology.

If the Bubble Bursts

The hundreds of billions being invested in AI technology are largely going to build capacity. The bubble is the economic growth arising from infrastructure investments. The concentration of AI activity is in China and the United States, the competitive players in this race to the top. NVIDIA, centred in Taiwan, has seen enormous growth in value.

In terms of earnings, however, based on recent disclosures, it appears the main AI players like OpenAI and Xai are bleeding red ink. Google, Meta, and Microsoft continue to make money, but not from AI. Anthropic is making some from its AI coding tools.

Investor money in these companies is going into the building of data centres to increase AI computing capacity. If and when the AI bubble bursts, these data centres will remain with servers filled with NVIDIA chips that may eventually be used.

The analogy to the Dot.com bubble is the billions that investors gave companies to build fibre optic networks. When that bubble burst, the excess was denoted as dark fibre. Eventually, many years later, that fibre got lit. The same will likely happen with the data centres. But for the labour force engaged by AI companies and then let go, and for those whose jobs were replaced by nascent AI, the bad taste will linger for a long time.

The post Economic Bubbles, Past, Present and What Lies in the Future appeared first on 21st Century Tech Blog.

How Does It Work?

Called a MEG (Moisture-Electric Generator), it is an electrochemical storage device that serves as a low-power source. Made from a mix of gelatin, table salt and activated carbon,  when dried it forms a three-layered gel pack. When exposed to an external moisture source, the MEG causes charged ions within it to begin to move, forming a continuous 1-volt current. Each MEG is like the Eveready Bunny. It keeps pumping out power for more than a month. When daisy-chained together, MEGs can produce enough power for many different electronic devices.

Ming Dong is a post-doctoral research associate in the School of Engineering and Materials Science at Queen Mary. He is first author of a paper recently published in Nano Energy describing the invention. You can find the paper here.

In a press release issued on May 21, 2026, Dong stated:

“Generating high voltages typically requires complex manufacturing processes or scarce materials. This work shows that it is possible to achieve strong performance using simple, sustainable components. By combining gelatin and salt, we have created a generator that operates using ambient humidity as its sole energy source.”

Where It Can Be Used

The implications for small electronic devices that sip energy could be staggering. MEGs could revolutionize wearable medical and wellness devices that communicate through Bluetooth and WiFi. It can provide power to a wide range of electronic devices, such as:

• Continuous glucose monitors and smart insulin patches.
• Heart rate and electrocardiogram monitors.
• Pulse oximeters.
• Blood pressure monitors.
• Rehabilitation motion-sensing bands that contain accelerometers.
• A wide range of wellness smart wearables such as head, chest bands, T-shirts, rings, belts, socks and shoes.
• Smartphone-connected remote patient monitors.
• Other non-passive devices that alarm.
• Devices with actuators to assist with mobility.
• Sleep apnea monitors.
• Speech therapy detectors that measure exhaled moisture while being powered in the process.
• Cancer drug delivery devices.

For all these types of wearables, the past challenge has been finding a power source that is lightweight and that doesn’t require frequent recharging.

When MEGs are chained together based on the work at Queen Mary, they have demonstrated the ability to produce  90 volts of power and operate for 30 days or more.

MEGs Are A Sustainable Alternative To Traditional Batteries

When I walk around my neighbourhood, I cannot believe how many discarded batteries are lying about. You find them at curbsides, in the middle of streets, and half-buried in lawns and flower beds. Why this is puzzles me and points to the downside of so many of the non-rechargeable electronic devices in use today. Throwaway power sources like these are a waste problem.

My neighbourhood drugstore has a bin inside the front entrance where people can deposit throwaway batteries. Several times a year, I have contributed a small bag of 10-volt, A, AA, and AAA batteries. Hopefully, the batteries get recycled or disposed of safely and sustainably.

MEGs, however, end the need to recycle old batteries. These gel packs are environmentally benign. They biodegrade in soil or dissolve in water. The gelatin, salt and activated carbon dissolve. All the materials are recoverable.

Dimitrios Papageorgiou, a contributing author to the Nano Energy paper, stated in the press release from the University:

“Our goal was to rethink how electronic materials are designed and manufactured.”

Besides being environmentally friendly, Papageorgiou notes:

“The ability of a gelatin-based system to generate meaningful electrical output highlights the potential scalability of this approach.”

The post Wearable Devices Get a Jolt from Commonly Found Gelatin appeared first on 21st Century Tech Blog.

In April, I wrote a three-part series on Earth Day and the carbon removal challenge. Earth Day seemed significant a few years ago. It appeared that much of the world was on board and taking the global warming threat seriously. Not so much now.

In Canada, where a federal government consumer carbon levy had been introduced to drive down emissions, it is now gone in the face of America’s climate change denial rhetoric. The tough milestone-reducing emission targets are a thing of the past here and in many other countries.

In the absence of various national and regional Canadian governments imposing carbon limits, market-driven policies were introduced to combat global warming. Watching the latest retreat from policies initiated after the COP-15 Paris Climate Agreement has been disheartening. I say this, not for me at age 77, but for my granddaughter, who recently turned 5, and will inherit greater climate challenges as a result than the ones we already face today.

Walking Backwards When Fighting Climate Change

Market-driven carbon pricing policies in Canada have dramatically altered with last year’s repeal of the consumer carbon levy, a point-of-sales tax levied at gas pumps. Now, with the latest agreement on industrial carbon pricing coming from meetings between the federal government and the Province of Alberta, industry will be the driving mechanism for Canada to reduce total emissions. This political bargain includes building the carbon capture and storage (CCS) network by a consortium of oil sands operators known as the Pathways Alliance.

The original benchmark industrial carbon price in Canada was to rise from CDN$ 95 today to $170 by 2030. Under the new agreement, the target is $130 by 2040. It is being levied on large fossil fuel, steel and cement operations. When announced, one of Canada’s premier oil sands producers remarked it would have no impact on the company’s operations and do very little to reduce total emissions.

The industrial carbon price is a charge per ton of emissions. The companies do the measuring and reporting. Independent verification is supposed to ensure an honest accounting. Unlike the consumer carbon tax, it is not a blanket levy on every ton emitted.

So, how will it reduce demand for carbon-based fuels, and how will it help Canada reduce the national carbon footprint? It will not apply until large industrial emitters exceed their current established emission benchmarks. For companies that exceed their benchmarks, they can buy carbon credits from others whose emissions are below their benchmarks. That means the large carbon emitters, whether doing business as usual or expanding output while buying offsetting credits, can continue to pollute and contribute to global warming.

How this new climate change tackling problem will get Canada to net-zero carbon by 2050 is a pipedream, and harder to believe even when experts claim that industrial pricing alone can deliver from 20 to 48% of all emission reductions by 2030 under Canada’s climate plan. I am a skeptic.

Will the Pathways Alliance Rescue Us?

The six oil sands producers who formed the Pathways Alliance in 2021 account for 95% of oil sands output and between 8 and 12% of Canada’s total carbon emissions. Their plan involves the construction of a 2,000-kilometre (1,240-mile) network of pipelines to transport captured carbon emissions for storage underground at six continuously monitored sites. Phase 1 involves 400 kilometres (250 miles) of pipeline at a cost of CDN$ 16.5 billion.

Nothing has been built to date, which means the Alliance has four years to get the network built and achieve its Phase 1 reduction target of 22 million tons of carbon dioxide (CO2)by 2030. Pathways calls for a full network deployment in place by 2040. By 2050, the Alliance describes their network as Canada’s backbone for achieving industrial net-zero CO2 emissions. In fairness to the Alliance, no CCS project has ever been constructed before at this scale.

With $16.5 billion for the Phase 1 down payment, who will pay for the network in totality remains unresolved. The Pathway partners want the Canadian and Alberta governments to offset their risk by contributing a healthy chunk of money to the project. The two governments want the Pathways Alliance to foot the majority of the bill.

We Need Better, Less Expensive Ways to Capture Carbon

Is the cost of getting rid of carbon emissions always going to be this high? To date, capturing CO2 at the source from oil sands operations, steel, cement, or coal-fired and gas-fired power plants is proving very expensive. It involves inventing new processes and materials.

Direct air capture (DAC) to pull CO2 molecules from ambient air is proving equally expensive.

Then there is the cost of storage, which involves finding and preparing suitable underground sites and building the safety infrastructure to ensure CO2 never escapes.

All these projects have involved numbers followed by nine zeros.

What if materials could be found or synthesized on the cheap to absorb CO2? Many materials are excellent at capturing CO2. They can repeatedly be used to absorb and discharge the gas, dramatically reducing material costs.

Among them are cheap solvents and sorbents such as formaldehyde and cyanuric acid, traditionally used to preserve specimens in laboratories and to treat swimming pools.

Another is melamine, a powdered polymer that is used today to make Formica countertops. It is really cheap at $40 per ton.

Equally effective are natural rock-based solutions. Mineral-carbonation storage using heated basalt and sandstone, when exposed to liquefied CO2, rapidly fixes the gas permanently to create the carbonate mineral limestone.

Minimally-heated potassium carbonate, to no more than 60 Celsius (108 Fahrenheit), is a good medium for storing CO2.

Synthesized solid nitrogen-doped carbon materials called viciazites, invented by researchers at Chiba University in Japan, capture and desorb CO2 easily from chimney stacks and flue streams.

Applying these less expensive materials and processes should make CCS viable. For now, however, the fossil fuel industry and its expensive proposition are reluctant participants in a problem of their own making with enormous planetary and human consequences.

The post The Challenges Today in Kicking Our Fossil Fuel Habit appeared first on 21st Century Tech Blog.

Well, it appears that the future is arriving sooner than the 24th century, with our ability today to engineer biology, blurring the lines between what is alive and what is not.

Bio-digital Convergence

Research into bio-digital convergence is going well beyond creating robots with humanoid characteristics, as today’s universities and computing companies work on:

• Synthetic organs.
• Living scaffolding that can grow and replace damaged joints.
• Bio-circuits combining neurons and chips.
• DNA for mass data storage.
• Organs called organoids and organelles are used to test new drugs.
• Implanting living muscle cells for actuators in biobots.

New research is adding intelligent functionality to bio-hybrid designs. Lines of investigation are creating bio-implants that respond to the human body’s natural growth, mobility and strength.

It’s not the six-million-dollar man of science-fictional lore. In fact, it is something even better.

Pursuing Bio-circuits

For AI today, let alone regular computer systems, a big constraint is the power requirement. Human brains are far more energy-efficient than computers. An average adult brain uses 20 watts of power or less than a third of a kilowatt-hour (kWh) daily. It uses 25% of the energy to perform maintenance, and the remaining 75% for information processing.

To feed the brain’s daily energy requirements, the average human consumes about 260 kilocalories (kcal) daily. That’s between 10 and 13% of the total kcals consumed daily to maintain weight.

An average desktop computer, if left on for 24 hours, consumes between 1.5 and 12 kWh, depending on the work. High-end gaming desktops and data servers consume even more energy. Gamers can use as much as 16.8 kWh daily.

For energy comparisons between the biological and the electrical, 1 kWh equals 860 kcal, that more than 3 times greater than the chemical energy an average brain consumes daily.

Pursuing the Star Trek Dream Today

I share with you the work being done in biodigital convergence that recently caught my eye.

Northwestern’s Work on a Brain-Computer Interface

At Northwestern University, printed artificial neurons are communicating with human brain cells. Researchers have invented a device that interfaces with brain cells, demonstrating biocompatibility.

What have these researchers invented? It is a device containing synthetic flexible neurons fabricated using aerosol-jet printing. The device produces electrical pulses that match natural neuronal activity, triggering responses from living neurons.

What the researchers want to achieve is a brain-machine interface in the form of neuroprosthetics that can communicate with the nervous system to restore vision, hearing and movement.

Neuromorphic Breakthrough in Switzerland

A Swiss company, FinalSpark, has invented a bioprocessor using stem cells to build human brain organoids connected to neuromorphic chips. These organoids are kept in an incubator, keeping them alive for approximately 100 days. So far, the company has built more than 1,000 organoids to produce 18 terabytes of data.

The bioprocessor is almost a million times more energy efficient than current digital processors because the organoids are each a mini-brain. It operates in the cloud, serving as a living neuromorphic computing platform for AI and biocomputing applications.

FinalSpark’s ultimate goal is to build a living biocomputer that is scalable in size because you can add as many organoids as you like. The company sees it as a far more manageable computing platform for AI because of its low energy requirements.

Why Use DNA for Data Storage

Our final story today looks at deoxyribonucleic acid (DNA). We have only known about the double-helix molecule for 73 years. When first discovered, it changed our understanding of genetics.

By unravelling the double helix of nucleotides, we began to understand ourselves within the living world that surrounds us. Knowing about DNA made it possible for us to better diagnose diseases, develop new treatments, and begin to manipulate genetic information to our advantage. Knowing about DNA spurred the next agricultural revolution, producing the crops that today feed the world.

But there are more ways to use the molecule of life. One of those ways involves DNA for data storage, translating binary code into Adenine, Cytosine, Guanine and Thymine, the A-C-G-T nucleotides that make up the double helix. Data is encoded into a DNA sequence. Decoding turns it back into bits. The difference from current data storage to DNA storage is that the medium is biological rather than magnetic or optical. A gram of DNA can store a Petabyte (1,000 Terabytes) of data for hundreds of years while remaining stable throughout.

Consider the massive data storage and processing needs of today’s global digital economy, and the advent of artificial intelligence (AI), and you begin to see why DNA data storage makes sense. Data warehouses would take up no more space than a single desk drawer being built to facilitate AI, if converted to using DNA.

Challenges remain. These include:

• The medium is slow when reading and writing data.
• DNA synthesis and sequencing are pricey and, with current methods, prone to error.
• New electronics are needed to make DNA storage viable.

A positive is that the expanding capabilities of AI should help to overcome some of the DNA storage challenges. Meanwhile, new chips will be needed. Synthesizing DNA strands will need to be improved to reduce error rates.

Where will DNA storage be used?

It won’t be for local desktop computing. Instead, it will serve best for archival, long-term storage requiring data preservation for decades and even centuries.

How close are we to seeing DNA storage become a reality?

An alliance of academic and high technology companies have formed the DNA Data Storage Alliance. Companies include Twist Bioscience, Illumina, Western Digital, and Microsoft. Active research is being done at The Wyss Institute, University of Washington, EPFL, ETH Zurich, and imec, the latter a Belgium-based independent research and innovation centre. Wyss predicts the first pilot archival DNA storage system will be here before the decade is out.

The post Blurring the Lines Between Biology and Technology appeared first on 21st Century Tech Blog.

His work with scientists and experience in risk communications have served him well in his work as a science communicator, promoting evidence-based decision-making and debunking the disinformation makers.

Today, his article in the Science Literacy Project, formerly the Genetic Literacy Project, includes 12 messages for science communicators that provide common-sense reasoning to counter misconceptions, fear, and the doubt spread by those who mislead the public on science, health and environmental issues. If no one comes readily to mind, see the picture below:

RFK Jr’s disinformation preceded his rise to lead the U.S. Department of Health and Human Services.  He has turned measles, a childhood disease that had disappeared, into a health hazard in the U.S. and other countries. His anti-vaccine messaging and policies are reducing the number of people getting vaccinated in the United States. It is particularly disturbing in terms of the growing number of children who are not being vaccinated to protect them from vaccine-preventable diseases. He has singlehandedly defunded mRNA vaccine research in the United States. mRNA vaccines have played a preeminent role in containing COVID-19 during the recent global pandemic.

Where Disinformation is Killing People

The following table shows the negative impacts of his disinformation campaigns around the world:

(Information source: Perplexity.ai)

Establishing Trust in Science

The Internet has made access to information universal. That includes the latest information about science and nonsense. David’s mission is to create simple, clear, reliable, common-sense reasoning for public consumption. He tackles misinformation and disinformation head-on to allay public fear and doubt. The goal is not to confront but to reassure.

Here are 12 messages to share for those who write and communicate science to the public. I promise to use more of them in future postings to the 21st Century Tech Blog site.

1. Chemicals are not human-made. In fact, humans are made of chemicals.
2. Just because it’s natural doesn’t mean it is good. Ebola is natural, but not so good.
3. There are more carcinogens in a single cup of coffee than in the pesticide residues you ingest from eating an entire year’s worth of fruit and vegetables.
4. There are more than 1,000 chemicals in a cup of coffee. Only 28 have been tested 28. Of these, 19 are carcinogenic to rats.
5. Every meal you eat contains approximately 10,000 chemicals. We deal with this daily chemical cocktail exposure because that’s what our bodies do.
6. When you read an article describing chemical exposures, a part per billion is equivalent to a drop of water in an Olympic swimming pool. Another way to say this is that a part per billion is like a second in 31 years.
7. The dose makes the poison. A single aspirin can do a lot of good; 100, not so much. Another way to express this is that a daily bowl of cereal is okay, but eating 20 boxes daily, not so much.
8. Correlation is not causality. The presence of a chemical does not mean that it causes a disease, any more than a rooster crowing causes the sun to rise.
9. Risk management is part of daily life. When we cross a road, we are managing risk. Why? Because we have a goal and seek its benefits. Risk poses uncertainties, but risk can also be tied to benefits and yield new opportunities.
10. Understand the differences in message labelling that often confuse. For example, “this chemical is produced naturally in the body,” meaning it is like a natural hormone, metabolite, or neurotransmitter familiar to it; or, “this chemical exists in nature,” meaning it can be found in plants, animals, microbes, etc.; or, “this chemical has been synthetically produced to remove impurities,” meaning it has been manufactured in a lab to yield molecules identical to those found in nature.
11. Science doesn’t care about how you feel or what you believe. It seeks to discover the truth. Another way to say this is science isn’t democratic. It respects facts and evidence, not opinion.
12. If a scientific hypothesis is proved wrong, scientists test another hypothesis, and then another, etc. Does a dogma or belief system do that?

The post Twelve Messages to Counter Science Disinformation Makers appeared first on 21st Century Tech Blog.

Peter sees AI as even more of a shift as the technology matures, moving from being just an app to something that becomes indispensable in our lives. And he sees it happening as early as 2028.

I have paraphrased liberally from Peter’s Metatrend article in this and the previous two parts of this series. If you have not read them, you can access Part 1 and Part 2 before tackling what follows. 

Peter calls his AI Jarvis, borrowing from Tony Stark’s AI, featured in the Iron Man movies. In Part 3, he anticipates how our personal Jarvis will take charge of keeping us healthy. 

Part 3 – Our Health Becomes an AI Priority

Has the annual physical doctors used to rely on to gauge wellness become obsolete?

Today, we can track our health using Oura rings, Whoop, Apple and other smart watches, and continuous glucose monitors on our arms. Doctors are still trying to catch up with this health data stream.

Add an AI Jarvis to oversee these wearable medical devices, and you end up with as much as 100 biometrics a day, every day, which it can monitor and see patterns no human clinician ever could.

What this will enable in the next 24 months is a shift from reactive healthcare, where you feel ill and go to the doctor who orders tests, to a world of continuous optimization, with Jarvis noticing subtle changes before even you, the wearer, are aware.

You may receive a Jarvis ping to stand up and move around when you have been sitting in a meeting for 90 minutes.

Or if Jarvis notes your hydration levels are low before you are going into an appointment or meeting, it will remind you to drink something.

If Jarvis detects subtle shifts in your heart rate patterns over several days, in cross-referencing the data with your level of activity over the same period of time, it may recommend that you take a specific anti-inflammatory supplement and schedule a blood test well before you would have noticed anything was amiss.

Your Jarvis AI is not to be confused with a wellness app like the many we see today. Instead, Jarvis will be a preventive medicine engine overseeing your health continuously, forever.

What this is pointing to is a future where you won’t go to the doctor when you feel sick. Instead, your wellness will remain optimized. Rarely will you need a doctor, and it will only happen when Jarvis flags a medical health status change before you even know it exists.

The Bigger Picture

Is Peter blowing smoke, or are his predictions realizable?

Will we even need a family doctor in the future? Likely the answer will continue to be yes, but the role will have changed from a first-contact diagnostician to one that supervises, interprets, triages, and addresses complex care issues. These would include psychological and psychiatric support and quick interventions. Complex care diagnoses will need human involvement, responding to a sudden onset of chest pain, for example, where a quick escalation would be needed, suicidal ideations requiring mental health interventions, complications during pregnancy, or unexpected reactions to medications.

Is AI ready to be the Jarvis caregiver? What about the AI hallucinations we read about or perceived potential for deception?

Without putting words in Peter’s mouth, I would think he would point to the rapid evolution of AI as a bellwether for overcoming and resolving these current imperfections.

Peter poses these questions.

• Are you positioned to thrive in this new era, or scrambling to catch up?
• Are you going AI-first in every part of your life?
• Are you connecting your data so AI agents can serve you?
• Are you experimenting with AI tools that in 18 months may feel antiquated, but will look like genius in hindsight because you have adopted them early?

Peter describes today’s AI as an app. An app is something you open, something you prompt and something you use. But by 2028, a personal Jarvis will not need opening or prompting. It will just be there, a ubiquitous part of our lives. It will be as transformative as the breakthrough technologies of the past that, today, serve in the background. Think of the telephone, a miracle in 1900 and invisible by 1950, and the Internet, astonishing in 1995 and ambient by 2010. AI will follow this same arc, but in compressed time. It won’t be decades. It will be in the next 24 months. To those paying attention, it represents the most significant alteration in daily living since the arrival of the smartphone.

The post Peter Diamandis Shares What Our AI World Might Be By 2028 – Part 3 appeared first on 21st Century Tech Blog.