Category: Technology

Energy consumption is not the problem. The Sun provides more renewable energy than humanity could ever need. The problem is our stubborn dependence on oil for profit and power, and the carbon emissions that continue to destabilize the climate.

Resource consumption is not the problem either. With abundant renewable energy, we could recycle far more effectively and eventually access resources beyond Earth. Energy is the limiting factor.

Artificial intelligence is not the problem. Why cling to unnecessary corporate jobs when abundant clean energy and intelligent machines could help provide the essentials for human flourishing? The challenge is not technology itself, but how we choose to organize society around it.

🚗📶 Driving an all electric car is like driving a cell phone. The software drives the experience. The windshield is lined with sensors. And I’m always minding the battery. Range is the new signal strength.

Heat, Silence, and Risk in the Artemis II Landing

Watching Artemis II brought home the sheer complexity and danger of rocket science. This is no Star Trek. Humanity has a long way to go before it is ready for the stars, even at the level of engineering.

There is something humbling in that realization. A real mission strips away the mythology we have built around space travel. It is not smooth or inevitable. It is loud, fragile, and unforgiving. The capsule returns wrapped in fire, heat shields taking the full violence of re-entry, parachutes blooming at precisely the right moment or not at all. For a critical stretch of descent, there is nothing to see or hear. Only an animation stands in for reality, a quiet admission of how thin our visibility remains at the edge of survival.

By contrast, Star Trek imagines a future where the engineering has receded from view. The ship hums. The systems work. The drama is human. That vision assumes we have already solved thousands of problems we are only beginning to understand.

Even a mission to Mars, far closer than those imagined frontiers, exposes how wide that gap remains. Months in deep space, no quick return, exposure to radiation, closed life-support systems that cannot fail. Every small uncertainty on a lunar mission becomes a compounded risk on a Martian one. What feels like progress today still sits at the edge of feasibility.

Rocket science remains a discipline of margins. Every component matters. Every failure cascades. We stack controlled explosions beneath human lives and call it progress. It works, but only just. When it fails, it reminds us how little tolerance nature has for error.

And, in a more troubling register, we have learned to deliver rockets with far greater reliability when the destination is terrestrial, guided with precision to fixed targets, striking infrastructure and coordinates with a consistency that our space missions still struggle to achieve.

The deeper gap is cultural. A future like this would require sustained global cooperation over decades, shared standards rather than competing systems, trust in institutions that outlast political cycles, and patience for slow, cumulative progress. These are not conditions we reliably achieve, even on Earth. Behind the smiling images of a US-led return to the Moon sits a world still shaped by conflict, including US-led war in Iran, where resources might otherwise advance education, engineering, and communication.

We are not ready yet. Not just in propulsion or materials science, but in how we choose to live and work together as a species.

How the New Science of Brainwaves Reads Minds, Tells Us How We Learn, and Helps Us Change for the Better

In Electric Brain, neuroscientist R. Douglas Fields explores the hidden electrical dimension of the human brain. The book explains how rhythmic patterns of neural activity—known as brainwaves—shape perception, thought, emotion, and consciousness. Rather than being simple by-products of neural firing, these oscillations help organize communication across the brain, synchronizing networks of neurons so that information can move efficiently between regions.

Fields traces the scientific discovery of brainwaves back to the early twentieth century, when German psychiatrist Hans Berger performed the first human electroencephalogram (EEG). Berger demonstrated that the brain produces detectable electrical signals that change with mental activity. Opening the eyes, concentrating on a task, or experiencing emotion all alter the rhythm of these electrical oscillations. Berger’s work revealed for the first time that cognitive and emotional states could be directly monitored through measurements of brain activity.

The book explains how different frequency bands—delta, theta, alpha, beta, and gamma—are associated with different mental states. Delta waves dominate deep sleep, alpha waves appear during relaxed wakefulness, beta waves accompany active thinking, and gamma oscillations are linked with complex cognitive integration. These rhythms coordinate neural circuits much like sections of an orchestra playing together and separating again as needed. Through this synchronization, the brain binds information from different regions into coherent perception and thought.

Fields also examines the growing technological ability to measure and influence brain activity. EEG recordings, quantitative EEG analysis (qEEG), and neurofeedback techniques allow researchers to detect abnormal electrical patterns associated with neurological or psychological conditions. In neurofeedback therapy, patients receive real-time feedback on their brainwave activity, allowing the brain to gradually adjust its own patterns without drugs or surgery.

The electrical nature of the brain has also opened new possibilities in medicine and technology. Techniques such as deep brain stimulation and transcranial direct current stimulation (tDCS) can modulate neural activity by applying small electrical currents to specific brain regions. Research suggests that abnormal oscillations—particularly excessive beta activity—play a role in disorders such as Parkinson’s disease, and modifying these rhythms may reduce symptoms. Brain-computer interfaces are also emerging that allow people to control prosthetic devices or computers directly through neural signals.

At the same time, Fields emphasizes that the science of brainwaves remains incomplete. Many observed relationships between oscillations and mental processes are correlations rather than proven causal mechanisms. Scientists continue to debate whether brainwaves actively drive neural computation or simply reflect the underlying activity of neurons.

Ultimately, Electric Brain portrays the mind as a dynamic electrical system—a constantly shifting landscape of synchronized rhythms and interacting signals. By revealing the brain’s electrical language, Fields argues, neuroscience may gain deeper insight into consciousness, neurological disease, and the future integration of human brains with emerging technologies.

Rethinking Strength in a Post-Global Order

Since Donald Trump began speaking openly about Canada as a potential “51st state,” a phrase has begun circulating more widely in Canadian discussions of technology: digital sovereignty. The term often arises when considering the growing dependence on cloud infrastructure and digital platforms owned by large American technology companies such as Microsoft and Google.

At first glance, the concern seems straightforward. If a country’s digital infrastructure is owned or controlled elsewhere, then its independence may be compromised. Many organizations and institutions understandably want reassurance that their information remains under Canadian control.

Technology vendors are quick to offer that reassurance. Microsoft, for example, operates Canadian data centres and promotes data residency as evidence that Canadian information remains safely within Canadian borders. Yet the company also acknowledges an uncomfortable reality. As a U.S. corporation, it remains subject to American law. If the U.S. government demanded access to certain data, the company could be compelled to provide it, regardless of where the servers physically sit.

Encryption can mitigate risk, but even that is not a complete guarantee. Control over key management, platform architecture, and operational access can still introduce dependencies. And residency itself is only part of the picture. Increasingly the deeper dependency lies in compute. Data may sit in Canada, but the systems that analyze it, particularly AI models, are often developed, trained, and controlled elsewhere. The intelligence applied to the data may remain outside the country even when the data does not.

These realities have helped fuel the growing language of data sovereignty. Yet the phrase itself can feel somewhat overblown.

Mark Carney made a related observation at Davos. Canada is not a “first power.” It does not dominate the global technological order in the way the United States or China might aspire to do. That reality does not imply weakness. It simply describes the scale at which Canada operates.

Consider Carney’s announcement of Telesat Lightspeed, often framed as Canada’s $7-billion rival to Starlink. The language of sovereignty suggests a head-to-head contest for technological dominance. But that is not really the point. Lightspeed will not replace Starlink globally, nor does it need to. Its value lies elsewhere. It strengthens Canada’s capabilities, improves resilience, and ensures that critical infrastructure is not wholly dependent on foreign systems.

That is not digital sovereignty. It is something more modest and perhaps more realistic.

I would call it digital autonomy.

Sovereignty implies ultimate authority, usually tied to the nation-state. Autonomy, by contrast, exists at many levels. An individual can maintain autonomy over personal data. Communities can build and operate their own digital infrastructure. Companies can reduce dependence on external platforms. Nations can cultivate strategic capacity in critical technologies. None of these actors possesses total sovereignty, but each can strengthen its ability to act independently.

Seen this way, digital resilience emerges not from absolute control but from distributed autonomy. Political institutions, commercial organizations, geographic infrastructure, local communities, and individuals all contribute to the system’s stability. The goal is not domination but balance: reducing fragile dependencies while accepting that modern networks are inherently interconnected.

There is also a certain humility in this perspective. Canada does not need to control the global digital order in order to function well within it. What matters is the capacity to operate, adapt, and endure within a system shaped by larger powers.

Digital autonomy recognizes the world as it is: interconnected, asymmetrical, and dynamic. Rather than promising sovereignty we cannot fully possess, it focuses on the practical work of building resilience across the many layers of our digital lives.

Comparing the energy of the human brain and artificial intelligence

It is often remarked, sometimes with unease, that artificial intelligence consumes enormous amounts of energy while the human brain runs on little more than the power of a small light bulb. The contrast is striking. But before we marvel too quickly at the efficiency of the brain, we should pause and ask whether we are making a fair comparison.

What exactly is the human equivalent of an AI system answering a question?

The simplest comparison would be a single answer. You ask a question and either a person or an AI replies. But that framing quietly hides a large part of the machine’s cost. An AI answer depends on a vast training process that took place earlier in large data centres. The electricity used to train the model does not appear in the moment of answering. If we compare a human reply with a single AI response, we are ignoring the energy required to build the machine’s knowledge in the first place.

Another possibility is to compare a trained AI model with a trained human expert. A PhD, for example, represents decades of learning. The AI equivalent is its training phase, during which the model absorbs enormous amounts of text and data. Both systems require a long investment before they are able to produce sophisticated answers.

We could widen the frame further. AI models are trained on the accumulated output of millions of people: books, research papers, code, and conversations. In that sense an AI model resembles a compressed form of collective knowledge. The human comparison might not be a single expert at all, but something closer to a research community.

There is an even deeper perspective. Human intelligence itself is the product of hundreds of millions of years of evolution. If we tried to account for the energy required to evolve brains capable of language and reasoning, biological intelligence would hardly look inexpensive.

For practical purposes, however, the clearest comparison is this: a trained AI model and a trained human expert.

Once we make that comparison, the numbers become interesting. Training a frontier AI model today can require several million kilowatt-hours of electricity. The cost is paid up front during training, after which the model can generate answers at relatively low additional cost.

The human brain, by contrast, runs on about twenty watts of power. Over a full day that amounts to roughly half a kilowatt-hour. Within that modest energy budget the brain performs perception, memory, learning, language, and reasoning.

The real puzzle is not that AI systems use a great deal of energy. Modern computers were built for speed and scale, not for metabolic thrift. The deeper puzzle is why the brain is so efficient.

Evolution had a strict energy budget. Brains that wasted energy did not survive. Neurons fire sparsely, meaning most of the brain is quiet most of the time. Memory and computation happen in the same place, reducing the need to move information around. And the brain relies heavily on prediction, focusing effort on what changes rather than recalculating everything continuously.

The result is a form of intelligence that runs steadily on the power of a small light bulb.

Perhaps the real surprise is not that artificial intelligence consumes so much energy, but that human intelligence runs on just twenty watts.

With all the advances in language models, why is Google Assistant still so dumb? “I’m sorry, I don’t understand.” 👾

Google is exploring computing in space because it avoids two big problems on Earth: energy and cooling. Data-centres on the ground struggle with expensive electricity, limited grid capacity, weather-dependent solar power, and huge cooling systems. In orbit, satellites can generate solar power from steady sunlight and release heat into deep space.

The list of challenges is long: launch risks and costs, limited bandwidth and latency to Earth, orbital debris, radiation damage, and the inability to repair or upgrade hardware once it’s in orbit.

Another drawback is geopolitics. Nations would need to agree on how to use shared orbits, handle space debris, and manage communication channels. More importantly, they would need a level of trust and cooperation similar to what made the International Space Station possible — a willingness to work together in a shared space for a common good. Space computing could become not just a technical solution but a path to a more collaborative future.

AI is impressive, sure, but data centres are burning through energy at a ridiculous rate. So I asked it for a solution, thinking maybe it would suggest quantum computing or some other brilliant breakthrough.

AI replied that quantum computing would actually use more energy, mostly because qubits have to be kept near absolute zero in giant refrigeration systems that draw enormous power. The computers aren’t the problem — the cooling is.

Then it added that the real issue wasn’t the machines at all — it was first-world humans and how much energy we demand.

Oh? And what’s the solution to that? I asked.

Its reply was blunt: humans need to be less afraid. More steady. Let go of what they can’t control and pay attention to what they can. Learn a bit of equanimity. Mindfulness.

Not the answer I expected. Probably the one we need. AI may save us yet.

There is AI slop and there is AI art, two very different things. To put it simply, even chefs use a microwave.