I often get questions like the one below:

“I live part time in Japan. Everyone sees hydrogen powered generators, trains, trucks, etc regularly. High density population means it’s easy to get enough synergy to justify the infrastructure. So sad we aren’t doing more to utilize this great tech. Hydrogen combustion engines emerge as cheap alternative to fuel cells.”
https://asia.nikkei.com/business/energy/hydrogen-combustion-engines-emerge-as-cheap-alternative-to-fuel-cells

This is often how news about advances in hydrogen tech are framed. The problem is – the limitations with hydrogen technology have nothing to do with the tech itself, so tech advances are mostly irrelevant. Also, hydrogen combustion is not a better solution for most use cases then hydrogen fuel cells. Fuel cells use an electrochemical process to combine hydrogen and oxygen, producing electricity and water. They are about 60% efficient, produce no pollution, and have no moving parts. Hydrogen combustion operates more like a regular engine, but with hydrogen as the fuel. They are about 40% efficient, produce nitrogen oxides as pollution, and have moving parts that operate at high pressure and temperature. They can be, however, more powerful for heavy applications and are cheaper to build.

For cars hydrogen combustion is a terrible idea. Maybe there is a use case for large generators, trains, and heavy trucks. Even there, however, there are current limitations that the technology of hydrogen engines or fuel cells do not address.

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I have been seeing a lot of news about geothermal energy recently. This is partly because of the Geothermal Research and Development Act – which was passed with bipartisan support. Trump does not like wind and solar, without any rational reason that he can articulate. He mainly supports fossil fuel. Democrats, of course, favor renewable energy and want to phase out fossil fuel as quickly as possible. Republicans seem to be luke warm on nuclear – they support it but are not making massive investments. Democrats historically dislike nuclear, but now have mixed feelings. It’s better than fossil fuel, and we may need it, but renewables are better.

Both sides, however, agree that geothermal is awesome and are all in. Democrats like it because it is renewable – there is no fuel to burn and it is a low carbon source of electricity. Both sides like it because it is potentially a steady and reliable source of large amounts of electricity, with no intermittency problem. Republicans may also like it because geothermal requires drilling and perhaps fracking, which are technologies already used by the fossil fuel industry. They have the technology to drill wells that produce energy, in whatever form. Regardless of the reasons, this is an area of common ground where we can potentially make some progress.

What is the real potential of geothermal energy? Right now geothermal produces 0.4% of US electricity and 0.3% of world electricity. This is fairly insignificant – we  would need a 1-2 order of magnitude increase for this to become a serious player in decarbonizing our energy infrastructure. About 93% of US geothermal comes from California and Nevada, with the rest from Utah, Hawaii, Oregon, Idaho, and New Mexico. This is because of geology – these are locations with recent volcanic activity meaning there is hot rock relatively close to the surface. To understand why this is, let’s quickly review geothermal technology.

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A recent study represents a confluence of technological advances that is pretty interesting. The specific problem being addressed – chemotherapy for bladder cancer – is very promising, but the technology itself can potential have many applications.

Let’s start with the problem, delivering chemotherapy to bladder tumors. The trick with chemotherapy is to create the highest levels of toxicity possible for cancer cells while limiting the toxicity for healthy cells. There are several ways to achieve this, with the primary method being finding drugs that are inherently more toxic tumor cells than non-tumor cells. The classic feature that has been targeted is the fact that tumor cells are rapidly reproducing and have a high metabolic rate, so drugs that target such cells are used. This still has an effect on healthy cells, especially those that rapidly reproduce, like hair cells, which is why many chemotherapies cause hair loss. Essentially we push the toxicity of these drugs to the limit of tolerability, to kill the cancer cells as much as possible.

Another strategy is to get these drugs to the tumor in higher concentrations than the rest of the body. You can accomplish this by intralesional injecting (injecting chemotherapy directly into a tumor), by injecting into the body compartment that contains the cancer, or by directing the chemotherapy specifically to the cancer using advanced techniques (such as lipid nanoparticles). For some types of bladder cancer the strategy has been to fill the bladder with chemotherapy and keep it in there for as long as tolerated. This works to some extent, increasing the dose of chemotherapy to the tumor while limiting systemic effects. However, there is limited penetration of the drug into the tumor because of the nature of the bladder lining.

The researchers therefore developed a hybrid algae robot to delivery chemotherapy for bladder cancer. They started with the silica shell of a diatom algae, specifically Coscinodiscus granii. The shell is called a frustule, which you can think of as a tiny glass wiffle ball. They chose G. granii because it has a multilayered porous shell with different sized pores, including a network of nanopores. They cleaned it of all the living algae parts so just the frustule was left. Essentially they are using the algae as nanofactories, mass producing these finely structured rigid shells that are also biodegradable. Nature so far is much better at creating things at the nanoscale than our technology. They then coated the frustules with magnetite and filled them with a chemotherapeutic drug, doxorubicin.

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In medical education we have a concept called longitudinal care – interns and residents need to follow a case through to its conclusion, or at least long term, so that can learn the entire process. This is why on-call work hours used to be so horrifically long (up to 36 hours), to allow doctors in training to follow an acute case to see the effects of the decisions they make. Training also includes continuity clinics where residents can follow the same patients for months or even years.

I feel that skeptical science communicators serve this role as well – we are the continuity clinics of skepticism. We follow cases through to their conclusion to see what critical thinking lessons we can derive from them. Without this follow up people tend to jump from one extraordinary claim to another, and therefore they don’t close the feedback loop and never update or improve their cognitive methods. In short, they don’t learn the lessons of their past failures, or benefit from the reinforcement of their successes.

Which is why I think it is important to follow up on the claims made earlier this year by Donut Lab. They claimed to have developed a solid state battery with a specific energy of 400 Wh/kg, with over 100,000 charge cycles, able to recharge in 5 minutes, is stable without a fire risk, operates from -30 to 100 degrees Celsius, is cheaper than current Li-ion batteries, and does not require any geopolitically sensitive materials. They presented these claims at CES in January, but notably did not have an actual battery to demonstrate. Their claims were met with pretty universal skepticism. I wrote at the time: “I’m beginning the smell the “fake it til you make it” syndrome that tanked Theranos.”

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In Isaac Asimov’s The Caves of Steel he imagines a future in which most of humanity lives in gigantic cities, extending many levels underground. This leaves the majority of the Earth’s surface for industrial farming, necessary to feed all those densely populated cities. If you take a similar strategy, however, and keep the population at sustainable numbers, this could maximize land for natural ecosystems and also minimize the environmental footprint of the average person. So if we are going to plan our civilization around environmental sustainability we would not necessarily need Asimovian megacities, but we could concentrate the population in cities and in densely developed areas around cities, and leave large stretches of land in between undeveloped. This is far better than endless suburban sprawl.

But of course, we are not starting from scratch, and our current layout was not planned by some central committee but evolved organically. Pragmatically, the question we need to ask is – where do we go from here. Cities are growing dynamic things, so we can use city development to move in a certain desirable direction, even if we can’t tear it all down and start anew. This means it is important to study what the best city planning and development would be going forward.

When most people think of city planning to reduce the carbon emissions of transportation, the first thing that comes to mind is planning city centers so that they are walkable/bikeable and to provide public transportation, in order to minimize reliance on fossil-fuel burning cars. This also has the advantage of reducing city traffic, which can be a nightmare. However, a recent study suggests that, while important, this may be of secondary concern with respect to impact on CO2 emissions. For many cities, especially those with a single concentrated hub (as opposed to multicentric cities, like LA), the most impactful strategy might be the densification of a ring surrounding the city center. The range of this ring depends on the city, but is something like 10-20 km for a typical large city, but can extend to 40 km. Increased density can be accomplished with infill development, as many such zones are only moderately developed leaving lots of room for densification.

The idea is that the workplaces will be concentrated in the city center, and the workers will live in the ring around the city center, minimizing their commute distance. This could have a significant impact on the average commute distance that people have and therefore their transportation carbon footprint. This approach would work better for some cities than others, depending on geography. This plan could also maximize the impact of public transport, like buses and trains, dedicated to bringing people back and forth from this densified ring to the city center.

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It is old news that all the tech we now live with is constantly gathering data about us. It is important, however, not to become complacent about this or to assume the situation cannot or is not getting worse. Pretty much every piece of digital technology that we interact with likely is gathering some personal information about you which is used to target advertising and to sell to third parties. Regulations in most countries are inadequate and fail to keep up with technological changes.

One of the latest venues to soak up information about you may be surprising – your car. Cars are increasingly computerized, and they typically collect driving behavior data – how fast your drive, how hard you break, and how tight you make turns. But also, some vehicles have cameras facing the driver which means they can detect your behavior visually. Sometimes this is sold as a safety feature, to tell if you are too sleepy or inebriated to drive. Sometimes this is part of a system to get your insurance company to reduce your rates if you think you are a safe driver. But often it is done without disclosure. Recent GM was found guilty of collecting and selling such data without the permission of the user, and was banned for doing so for five years. But many other car manufacturers also do this.

All they really have to do is bury some disclosure deep in the user agreement, which functionally nobody reads, and they are covered. You may have the ability to opt-out of such data selling. Of course, putting the burden on the end user to find and read any such disclosures and then go through the steps necessary to opt out of data selling is a huge problem. In fact insurance companies will buy data from car companies and then use that data to increase your insurance premiums, without you opting into any of it.

The basic fact is that collecting data from users, packaging that data and then selling it to third parties is a huge industry. It is estimated that globally this is a $240 billion industry. When that kind of money is on the line, companies are going to do everything they can to capitalize on it, while avoiding legal issues by either flying under the radar or hiding behind legal fig leaves (like the buried consumer disclosures). They will also use that money to lobby the government to let them continue to do so, or even to mandate certain things that will help this industry. For example, some car monitoring technology is sold as a safety feature, and it can legitimately be used for this purpose. Others are convenience features, like GPS. But once all the sensors and cameras are in place, they will soak up all the data they can – because that data is worth billions.

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Richard Dawkins is a public intellectual of some renown, although not without his controversies. So it is noteworthy when he writes an article claiming that the chatbot Claude is likely conscious. I found the article fascinating, not because I agree with his core claim or feel that he has contributed anything significant to the conversation, but because it seems to represent a scholar and deep thinker writing about a topic in which he lacks specific expertise. I also see no evidence in the article that he engaged meaningfully, or at least adequately, with a topic expert. As a result he makes some thoughtful and instructive errors.

He begins with a discussion of the Turing test, which has long been discussed as an early thought experiment about how we might determine if an AI is actually conscious. Dawkins essentially accepts the Turing test and write:

“It was one thing to grant consciousness to a hypothetical machine that — just imagine! — could one day succeed at the Imitation Game. But now that LLMs can actually pass the Turing Test? “Well, er, perhaps, um… Look here, I didn’t really mean it when, back then, I accepted Turing’s operational definition of a conscious being…””

He feels saying that LLMs have passed the Turing test but still not accepting them as conscious is moving the goalpost. However, the Turing test was never generally accepted by AI experts or philosophers as a true test of consciousness. Rather, it was understood that such a test really is only a measure of a machine’s ability to imitate human speech. I wrote about it in 2008, writing: “Ever since Alan Turing proposed his test it has provoked two still relevant questions: what does it mean to be intelligent, and what is the Turing test actually testing.” I went on to write:

“But I can imagine a day in the not-too-distant future when such AI can pass a Turing test. The algorithms will have to become much more complex, allow for varying answers to the same question, and make what seem to be abstract connections which take the conversation is new and unanticipated directions. You can liken computer AI simulating conversation to computer graphics (CG) simulating people. At first they appeared cartoonish, but in the last 20 years we have seen steady progress. Movement is now more natural, textures more subtle and complex. One of the last layers of realism to be added was imperfection. CG characters still seem CG when they are perfect, and so adding imperfections adds to the sense of reality. Similarly, an AI conversation might want to sprinkle some random quirkiness into the responses.

The questions is – will sophisticated-enough algorithms running on powerful-enough computers ever be conscious? What Loebner is saying, and I agree, is that the answer is no. Something more is needed.”

Basically, the limitation of the Turing test is that it is looking only at output, and therefore there is no way to distinguish the output of true consciousness from a really good simulation. This is not a new idea, and no one is moving the goalpost. We need to know something about how a computer is working to conclude whether or not it is conscious. What LLM experts will tell you is that these chatbots are just really good autocompletes – they are mimicking language, and since language is how we communicate thoughts, this creates the powerful illusion that they are mimicking thought, but they aren’t. They do not think, they do not truly understand.

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I came across a few news items that I could possibly write about today and couldn’t decide which to cover, so I will write about all of them, since they all relate to renewable energy. The first is a new study comparing direct air capture (DAC) to installing new wind and solar. This is a direct comparison between these two options, to see which provides the most bang for the buck.

DAC involves taking CO2 directly out of the atmosphere in order to mitigate carbon release through burning fossil fuels. If this technology were sufficiently efficient it could be hugely useful in reducing future climate change. This is the only approach that can potentially have a negative carbon footprint, actually reducing the amount of CO2 in the atmosphere. Other technologies simply reduce the amount released. This negative carbon factor is highly attractive since it could theoretically zero out our carbon release and even take us back in time to an atmosphere with less CO2. Right now, it should be noted, we are not only continuing to release massive amounts of CO2 into the atmosphere, the amount continues to increase. In 2025 the world emitted 38.1 billion tonnes, of carbon, a 1.1% increase over 2024.

But there are problems with DAC – it is currently not very efficient and is not scalable enough to have enough of an impact. Also, the efficiency of DAC depends heavily on how you power it – if you connect it to the grid and there is some fossil fuel energy on that grid, you may actually increase CO2 rather than decreasing it. Ideally DAC would be powered entirely by low carbon energy sources. This is why critics of DAC argue that it simply makes no sense to deploy this technology before we have decarbonized the energy sector, which we should do first.

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I thought everyone needed one more thing to worry about, so here you go: evolving AI. When I hear this phrase I think of two things. The first are AI systems designed to simulate organic evolution. The second are artificially intelligent systems that are capable of evolving themselves. That latter one is the type you need to worry about.

Systems that simulate evolution already exist – Avida, Biogenesis,  Grovolve, Tierra, Framsticks: and others. They basically have some code that competes for some resource or to complete some task and the code randomly mutates and reproduces. That’s it, all you need for an evolution simulation. Code can compete for computer resources, or be a physics simulator with digital creature trying to move quickly across terrain. These are sometime gamified for entertainment, but are also used for serious research, to study patterns within evolutionary systems. I would love to see these kinds of systems get more and more sophisticated, even to the point of reasonably simulating living systems. Such systems could be used to test hypotheses about evolution – and would also disprove a lot of silly creationist talking points.

But now we are talking about evolvable AI – AI systems that are capable of developing themselves through evolutionary processes. A new paper in PNAS discusses the potential power and risks of such systems. They echo they kinds of issues that have been explored in science fiction for decades. The authors write: “Evolvable AI (eAI), i.e., AI systems whose components, learning rules, and deployment conditions can themselves undergo Darwinian evolution, may soon emerge from current trends in generative, agentic, and embodied AI.” The results, they argue, have not been adequately addressed when discussing the potential risks of rapidly developing AI ability.

The authors distinguish two types of evolving AI – breeder systems and ecological systems. In breeder scenarios the programmers are in control of the process, selecting which code to “breed” and evaluating the outcome. This process is like a digital version of domestication, and has the potential, if done wisely, to maintain control. In fact, systems can be bred to have greater predictability and control. There are still risks here. So far humanity has not bred an animal to be more intelligent than humans. This could theoretically happen with AI, resulting in emergent behavior not specifically selected for that could get out of the control of human programmers.

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The recent rapid advance in the capabilities of artificial intelligence (AI) applications I think qualifies as a disruptive technology. The term “disruptive technology” was popularized in 1997 by Clayton M. Christensen. To summarize, a disruptive technology is “an innovation that fundamentally alters the way industries operate, businesses function, or consumers behave, often rendering existing technologies, products, or services obsolete.” AI is potentially so powerful, and changing so quickly, that it is challenging to optimally regulate it. We are caught in a classic dilemma – we do not want to hamper our own competitiveness in a critical new technology, but we also don’t want to unwittingly create new vulnerabilities or unintended negative consequences. For now we seem to be erring on the side of not hampering competitiveness, which basically places us at the tender mercies of tech bros.

Which is partly why I found the conflict between Anthropic and the Department of Defense (still the legal name) so fascinating. In short, Anthropic’s powerful AI application, Claude, has at least two significant internal “red lines” or guardrails – it cannot be used for massive domestic surveillance, and it cannot be used for final military targeting, without a human in the loop. Anthropic CEO Dario Amodei has not backed down on this – he says that the first restriction on domestic surveillance is simply a matter of ethics. The second restriction, however, is mainly a matter of quality control – their system is still vulnerable to hallucinations and is not reliable enough to count on for final targeting decisions. Hegseth has criticized his concerns as “woke” and a critical vulnerability for the US military. More charitably, he say essentially that the US military is using the application lawfully, and should not be restricted in any lawful use of the software. Others have also stated that in an emergency they have to know the software will do whatever they ask it.

This conflict has many deep implications, and is beyond what I intend for this blog post. What I want to focus on is the fact that an AI application is creating this ethical dilemma, and forcing us to ask – who should control such awesome power, the CEO of a tech company or the Federal government? It seems that we are facing or about to face many similar questions provoked by the disruptive nature of recent AI applications.

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