Lekando

Picture it: I’m minding my business at a party, parked by the snack table (of course). A friend of a friend wanders up, and we strike up a conversation. It quickly turns to work, and upon learning that I’m a climate technology reporter, my new acquaintance says something like: “Should I be using AI? I’ve heard it’s awful for the environment.” 

This actually happens pretty often now. Generally, I tell people not to worry—let a chatbot plan your vacation, suggest recipe ideas, or write you a poem if you want. 

That response might surprise some people, but I promise I’m not living under a rock, and I have seen all the concerning projections about how much electricity AI is using. Data centers could consume up to 945 terawatt-hours annually by 2030. (That’s roughly as much as Japan.) 

But I feel strongly about not putting the onus on individuals, partly because AI concerns remind me so much of another question: “What should I do to reduce my carbon footprint?” 

That one gets under my skin because of the context: BP helped popularize the concept of a carbon footprint in a marketing campaign in the early 2000s. That framing effectively shifts the burden of worrying about the environment from fossil-fuel companies to individuals. 

The reality is, no one person can address climate change alone: Our entire society is built around burning fossil fuels. To address climate change, we need political action and public support for researching and scaling up climate technology. We need companies to innovate and take decisive action to reduce greenhouse-gas emissions. Focusing too much on individuals is a distraction from the real solutions on the table. 

I see something similar today with AI. People are asking climate reporters at barbecues whether they should feel guilty about using chatbots too frequently when we need to focus on the bigger picture. 

Big tech companies are playing into this narrative by providing energy-use estimates for their products at the user level. A couple of recent reports put the electricity used to query a chatbot at about 0.3 watt-hours, the same as powering a microwave for about a second. That’s so small as to be virtually insignificant.

But stopping with the energy use of a single query obscures the full truth, which is that this industry is growing quickly, building energy-hungry infrastructure at a nearly incomprehensible scale to satisfy the AI appetites of society as a whole. Meta is currently building a data center in Louisiana with five gigawatts of computational power—about the same demand as the entire state of Maine at the summer peak.  (To learn more, read our Power Hungry series online.)

Increasingly, there’s no getting away from AI, and it’s not as simple as choosing to use or not use the technology. Your favorite search engine likely gives you an AI summary at the top of your search results. Your email provider’s suggested replies? Probably AI. Same for chatting with customer service while you’re shopping online. 

Just as with climate change, we need to look at this as a system rather than a series of individual choices. 

Massive tech companies using AI in their products should be disclosing their total energy and water use and going into detail about how they complete their calculations. Estimating the burden per query is a start, but we also deserve to see how these impacts add up for billions of users, and how that’s changing over time as companies (hopefully) make their products more efficient. Lawmakers should be mandating these disclosures, and we should be asking for them, too. 

That’s not to say there’s absolutely no individual action that you can take. Just as you could meaningfully reduce your individual greenhouse-gas emissions by taking fewer flights and eating less meat, there are some reasonable things that you can do to reduce your AI footprint. Generating videos tends to be especially energy-intensive, as does using reasoning models to engage with long prompts and produce long answers. Asking a chatbot to help plan your day, suggest fun activities to do with your family, or summarize a ridiculously long email has relatively minor impact. 

Ultimately, as long as you aren’t relentlessly churning out AI slop, you shouldn’t be too worried about your individual AI footprint. But we should all be keeping our eye on what this industry will mean for our grid, our society, and our planet. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

The US- and UK-based company Quantinuum today unveiled Helios, its third-generation quantum computer, which includes expanded computing power and error correction capability. 

Like all other existing quantum computers, Helios is not powerful enough to execute the industry’s dream money-making algorithms, such as those that would be useful for materials discovery or financial modeling. But Quantinuum’s machines, which use individual ions as qubits, could be easier to scale up than quantum computers that use superconducting circuits as qubits, such as Google’s and IBM’s.

“Helios is an important proof point in our road map about how we’ll scale to larger physical systems,” says Jennifer Strabley, vice president at Quantinuum, which formed in 2021 from the merger of Honeywell Quantum Solutions and Cambridge Quantum. Honeywell remains Quantinuum’s majority owner.

Located at Quantinuum’s facility in Colorado, Helios comprises a myriad of components, including mirrors, lasers, and optical fiber. Its core is a thumbnail-size chip containing the barium ions that serve as the qubits, which perform the actual computing. Helios computes with 98 barium ions at a time; its predecessor, H2, used 56 ytterbium qubits. The barium ions are an upgrade, as they have proven easier to control than ytterbium.  These components all sit within a chamber that is cooled to about 15 Kelvin (-432.67 ℉), on top of an optical table. Users can access the computer by logging in remotely over the cloud.

Helios encodes information in the ions’ quantum states, which can represent not only 0s and 1s, like the bits in classical computing, but probabilistic combinations of both, known as superpositions. A hallmark of quantum computing, these superposition states are akin to the state of a coin flipping in the air—neither heads nor tails, but some probability of both. 

Quantum computing exploits the unique mathematics of quantum-mechanical objects like ions to perform computations. Proponents of the technology believe this should enable commercially useful applications, such as highly accurate chemistry simulations for the development of batteries or better optimization algorithms for logistics and finance. 

In the last decade, researchers at companies and academic institutions worldwide have incrementally developed the technology with billions of dollars of private and public funding. Still, quantum computing is in an awkward teenage phase. It’s unclear when it will bring profitable applications. Of late, developers have focused on scaling up the machines. 

A key challenge to making a more powerful quantum computer is implementing error correction. Like all computers, quantum computers occasionally make mistakes. Classical computers correct these errors by storing information redundantly. Owing to quirks of quantum mechanics, quantum computers can’t do this and require special correction techniques. 

Quantum error correction involves storing a single unit of information in multiple qubits rather than in a single qubit. The exact methods vary depending on the specific hardware of the quantum computer, with some machines requiring more qubits per unit of information than others. The industry refers to an error-corrected unit of quantum information as a “logical qubit.” Helios needs two ions, or “physical qubits,” to create one logical qubit.

This is fewer physical qubits than needed in recent quantum computers made of superconducting circuits. In 2024, Google used 105 physical qubits to create a logical qubit. This year, IBM used 12 physical qubits per single logical qubit, and Amazon Web Services used nine physical qubits to produce a single logical qubit. All three companies use variations of superconducting circuits as qubits.

Helios is noteworthy for its qubits’ precision, says Rajibul Islam, a physicist at the University of Waterloo in Canada, who is not affiliated with Quantinuum. The computer’s qubit error rates are low to begin with, which means it doesn’t need to devote as much of its hardware to error correction. Quantinuum had pairs of qubits interact in an operation known as entanglement and found that they behaved as expected 99.921% of the time. “To the best of my knowledge, no other platform is at this level,” says Islam.

This advantage comes from a design property of ions. Unlike superconducting circuits, which are affixed to the surface of a quantum computing chip, ions on Quantinuum’s Helios chip can be shuffled around. Because the ions can move, they can interact with every other ion in the computer, a capacity known as “all-to-all connectivity.” This connectivity allows for error correction approaches that use fewer physical qubits. In contrast, superconducting qubits can only interact with their direct neighbors, so a computation between two non-adjacent qubits requires several intermediate steps involving the qubits in between. “It’s becoming increasingly more apparent how important all-to-all-connectivity is for these high-performing systems,” says Strabley.

Still, it’s not clear what type of qubit will win in the long run. Each type has design benefits that could ultimately make it easier to scale. Ions (which are used by the US-based startup IonQ as well as Quantinuum) offer an advantage because they produce relatively few errors, says Islam: “Even with fewer physical qubits, you can do more.” However, it’s easier to manufacture superconducting qubits. And qubits made of neutral atoms, such as the quantum computers built by the Boston-based startup QuEra, are “easier to trap” than ions, he says. 

Besides increasing the number of qubits on its chip, another notable achievement for Quantinuum is that it demonstrated error correction “on the fly,” says David Hayes, the company’s director of computational theory and design, That’s a new capability for its machines. Nvidia GPUs were used to identify errors in the qubits in parallel. Hayes thinks that GPUs are more effective for error correction than chips known as FPGAs, also used in the industry.

Quantinuum has used its computers to investigate the basic physics of magnetism and superconductivity. Earlier this year, it reported simulating a magnet on H2, Quantinuum’s predecessor, with the claim that it “rivals the best classical approaches in expanding our understanding of magnetism.” Along with announcing the introduction of Helios, the company has used the machine to simulate the behavior of electrons in a high-temperature superconductor. 

“These aren’t contrived problems,” says Hayes. “These are problems that the Department of Energy, for example, is very interested in.”

Quantinuum plans to build another version of Helios in its facility in Minnesota. It has already begun to build a prototype for a fourth-generation computer, Sol, which it plans to deliver in 2027, with 192 physical qubits. Then, in 2029, the company hopes to release Apollo, which it says will have thousands of physical qubits and should be “fully fault tolerant,” or able to implement error correction at a large scale.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Why the for-profit race into solar geoengineering is bad for science and public trust

—David Keith is the professor of geophysical sciences at the University of Chicago and Daniele Visioni is an assistant professor of earth and atmospheric sciences at Cornell University

Last week, an American-Israeli company that claims it’s developed proprietary technology to cool the planet announced it had raised $60 million, by far the largest known venture capital round to date for a solar geoengineering startup.

The company, Stardust, says the funding will enable it to develop a system that could be deployed by the start of the next decade, according to Heatmap, which broke the story.

As scientists who have worked on the science of solar geoengineering for decades, we have grown increasingly concerned about emerging efforts to start and fund private companies to deploy technologies that could alter the climate of the planet. We also strongly dispute some of the technical claims that certain companies have made about their offerings. Read the full story.

This story is part of Heat Exchange, MIT Technology Review’s guest opinion series offering expert commentary on legal, political and regulatory issues related to climate change and clean energy. You can read the rest of the series here.

Can “The Simpsons” really predict the future?

According to internet listicles, the animated sitcom The Simpsons has predicted the future anywhere from 17 to 55 times.

The show foresaw Donald Trump becoming US President a full 17 years before the real estate mogul was inaugurated as the 45th leader of the United States. Earlier, in 1993, an episode of the show featured the “Osaka flu,” which some felt was eerily prescient of the coronavirus pandemic. And—somehow!—Simpsons writers just knew that the US Olympic curling team would beat Sweden eight whole years before they did it.

Al Jean has worked on The Simpsons on and off since 1989; he is the cartoon’s longest-serving showrunner. Here, he reflects on the conspiracy theories that have sprung from these apparent prophecies. Read the full story.

—Amelia Tait

This story is part of MIT Technology Review’s series “The New Conspiracy Age,” about how the present boom in conspiracy theories is reshaping science and technology.

MIT Technology Review Narrated: Therapists are secretly using ChatGPT. Clients are triggered.

Declan would never have found out his therapist was using ChatGPT had it not been for a technical mishap where his therapist began inadvertently sharing his screen.

For the rest of the session, Declan was privy to a real-time stream of ChatGPT analysis rippling across his therapist’s screen, who was taking what Declan was saying, putting it into ChatGPT, and then parroting its answers.

But Declan is not alone. In fact, a growing number of people are reporting receiving AI-generated communiqués from their therapists. Clients’ trust and privacy are being abandoned in the process.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Amazon is suing Perplexity over its Comet AI agent
It alleges Perplexity is committing computer fraud by not disclosing when Comet is shopping on a human’s behalf. (Bloomberg $)
+ In turn, Perplexity has accused Amazon of bullying. (CNBC)

2 Trump has nominated the billionaire entrepreneur Jared Isaacman to lead NASA
Five months after he withdrew Isaacman’s nomination for the same job. (WP $)
+ It was around the same time Elon Musk left the US government. (WSJ $)

3 Homeland Security has released an app for police forces to scan people’s faces 
Mobile Fortify uses facial recognition to identify whether someone’s been given a deportation order. (404 Media)
+ Another effort to track ICE raids was just taken offline. (MIT Technology Review)

4 Scientific journals are being swamped with AI-written letters
Researchers are sifting through their inbox trying to work out what to believe. (NYT $)
+ ArXiv is no longer accepting certain papers for fear they’ve been written by AI. (404 Media)

5 The AI boom has proved a major windfall for equipment makers 
Makers of small turbines and fuel cells, rejoice. (WSJ $)

6 Chronic kidney disease may be the first chronic illness linked to climate change
Experts have linked a surge in the disease to hotter temperatures. (Undark)
+ The quest to find out how our bodies react to extreme temperatures. (MIT Technology Review)

7 Brazil is proposing a fund to protect tropical forests
It would pay countries not to fell their trees. (NYT $)

8 New York has voted for a citywide digital map
It’ll officially represent the five boroughs for the first time. (Fast Company $)

9 The internet could be at risk of catastrophic collapse
Meet the people preparing for that exact eventuality. (New Scientist $)

10 A Chinese space craft may have been hit by space junk
Three astronauts have been forced to remain on the Tiangong space station while the damage is investigated. (Ars Technica)

Quote of the day

“I am not sure how I earned the trust of so many, but I will do everything I can to live up to those expectations.”

—Jared Isaacman, Donald Trump’s renomination to lead NASA, doesn’t appear entirely sure in his own abilities to lead the agency, Ars Technica reports.

One more thing

Is the digital dollar dead?

In 2020, digital currencies were one of the hottest topics in town. China was well on its way to launching its own central bank digital currency, or CBDC, and many other countries launched CBDC research projects, including the US.

How things change. Years later, the digital dollar—even though it doesn’t exist—has become political red meat, as some politicians label it a dystopian tool for surveillance. And late last year, the Boston Fed quietly stopped working on its CBDC project. So is the dream of the digital dollar dead? Read the full story.

—Mike Orcutt

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ The world’s oldest air has been unleashed, after six million years under ice.
+ How to stop sweating the small stuff and try to be happy in this mad world.
+ Happy Bonfire Night to our British readers!
+ The spirit of Halloween is still with us: the scariest music ever recorded.

This year, we’ve seen a real-time experiment playing out across the technology industry, one in which AI’s software engineering capabilities have been put to the test against human technologists. And although 2025 may have started with AI looking strong, the transition from vibe coding to what’s being termed context engineering shows that while the work of human developers is evolving, they nevertheless remain absolutely critical.

This is captured in the latest volume of the “Thoughtworks Technology Radar,” a report on the technologies used by our teams on projects with clients. In it, we see the emergence of techniques and tooling designed to help teams better tackle the problem of managing context when working with LLMs and AI agents. 

Taken together, there’s a clear signal of the direction of travel in software engineering and even AI more broadly. After years of the industry assuming progress in AI is all about scale and speed, we’re starting to see that what matters is the ability to handle context effectively.

Vibes, antipatterns, and new innovations 

In February 2025, Andrej Karpathy coined the term vibe coding. It took the industry by storm. It certainly sparked debate at Thoughtworks; many of us were skeptical. On an April episode of our technology podcast, we talked about our concerns and were cautious about how vibe coding might evolve.

Unsurprisingly given the implied imprecision of vibe-based coding, antipatterns have been proliferating. We’ve once again noted, for instance, complacency with AI generated code on the latest volume of the Technology Radar, but it’s also worth pointing out that early ventures into vibe coding also exposed a degree of complacency about what AI models can actually handle — users demanded more and prompts grew larger, but model reliability started to falter.

Experimenting with generative AI 

This is one of the drivers behind increasing interest in engineering context. We’re well aware of its importance, working with coding assistants like Claude Code and Augment Code. Providing necessary context—or knowledge priming—is crucial. It ensures outputs are more consistent and reliable, which will ultimately lead to better software that needs less work — reducing rewrites and potentially driving productivity.

When effectively prepared, we’ve seen good results when using generative AI to understand legacy codebases. Indeed, done effectively with the appropriate context, it can even help when we don’t have full access to source code. 

It’s important to remember that context isn’t just about more data and more detail. This is one of the lessons we’ve taken from using generative AI for forward engineering. It might sound counterintuitive, but in this scenario, we’ve found AI to be more effective when it’s further abstracted from the underlying system — or, in other words, further removed from the specifics of the legacy code. This is because the solution space becomes much wider, allowing us to better leverage the generative and creative capabilities of the AI models we use.

Context is critical in the agentic era

The backdrop of changes that have happened over recent months is the growth of agents and agentic systems — both as products organizations want to develop and as technology they want to leverage. This has forced the industry to properly reckon with context and move away from a purely vibes-based approach.

Indeed, far from simply getting on with tasks they’ve been programmed to do, agents require significant human intervention to ensure they are equipped to respond to complex and dynamic contexts. 

There are a number of context-related technologies aimed at tackling this challenge, including agents.md, Context7, and Mem0. But it’s also a question of approach. For instance, we’ve found success with anchoring coding agents to a reference application — essentially providing agents with a contextual ground truth. We’re also experimenting with using teams of coding agents; while this might sound like it increases complexity, it actually removes some of the burden of having to give a single agent all the dense layers of context it needs to do its job successfully.

Toward consensus

Hopefully the space will mature as practices and standards embed. It would be remiss to not mention the significance of the Model Context Protocol, which has emerged as the go-to protocol for connecting LLMs or agentic AI to sources of context. Relatedly, the agent2agent (A2A) protocol leads the way with standardizing how agents interact with one another. 

It remains to be seen whether these standards win out. But in any case, it’s important to consider the day-to-day practices that allow us, as software engineers and technologists, to collaborate effectively even when dealing with highly complex and dynamic systems. Sure, AI needs context, but so do we. Techniques like curated shared instructions for software teams may not sound like the hottest innovation on the planet, but they can be remarkably powerful for helping teams work together.

There’s perhaps also a conversation to be had about what these changes mean for agile software development. Spec-driven development is one idea that appears to have some traction, but there are still questions about how we remain adaptable and flexible while also building robust contextual foundations and ground truths for AI systems.

Software engineers can solve the context challenge

Clearly, 2025 has been a huge year in the evolution of software engineering as a practice. There’s a lot the industry needs to monitor closely, but it’s also an exciting time. And while fears about AI job automation may remain, the fact the conversation has moved from questions of speed and scale to context puts software engineers right at the heart of things. 

Once again, it will be down to them to experiment, collaborate, and learn — the future depends on it.

This content was produced by Thoughtworks. It was not written by MIT Technology Review’s editorial staff.

Last week, an American-Israeli company that claims it’s developed proprietary technology to cool the planet announced it had raised $60 million, by far the largest known venture capital round to date for a solar geoengineering startup.

The company, Stardust, says the funding will enable it to develop a system that could be deployed by the start of the next decade, according to Heatmap, which broke the story.

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Heat Exchange

MIT Technology Review’s guest opinion series, offering expert commentary on legal, political and regulatory issues related to climate change and clean energy. You can read the rest of the pieces here.

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As scientists who have worked on the science of solar geoengineering for decades, we have grown increasingly concerned about the emerging efforts to start and fund private companies to build and deploy technologies that could alter the climate of the planet. We also strongly dispute some of the technical claims that certain companies have made about their offerings. 

Given the potential power of such tools, the public concerns about them, and the importance of using them responsibly, we argue that they should be studied, evaluated, and developed mainly through publicly coordinated and transparently funded science and engineering efforts.  In addition, any decisions about whether or how they should be used should be made through multilateral government discussions, informed by the best available research on the promise and risks of such interventions—not the profit motives of companies or their investors.

The basic idea behind solar geoengineering, or what we now prefer to call sunlight reflection methods (SRM), is that humans might reduce climate change by making the Earth a bit more reflective, partially counteracting the warming caused by the accumulation of greenhouse gases. 

There is strong evidence, based on years of climate modeling and analyses by researchers worldwide, that SRM—while not perfect—could significantly and rapidly reduce climate changes and avoid important climate risks. In particular, it could ease the impacts in hot countries that are struggling to adapt.  

The goals of doing research into SRM can be diverse: identifying risks as well as finding better methods. But research won’t be useful unless it’s trusted, and trust depends on transparency. That means researchers must be eager to examine pros and cons, committed to following the evidence where it leads, and driven by a sense that research should serve public interests, not be locked up as intellectual property.

In recent years, a handful of for-profit startup companies have emerged that are striving to develop SRM technologies or already trying to market SRM services. That includes Make Sunsets, which sells “cooling credits” for releasing sulfur dioxide in the stratosphere. A new company, Sunscreen, which hasn’t yet been announced, intends to use aerosols in the lower atmosphere to achieve cooling over small areas, purportedly to help farmers or cities deal with extreme heat.  

Our strong impression is that people in these companies are driven by the same concerns about climate change that move us in our research. We agree that more research, and more innovation, is needed. However, we do not think startups—which by definition must eventually make money to stay in business—can play a productive role in advancing research on SRM.

Many people already distrust the idea of engineering the atmosphere—at whichever scale—to address climate change, fearing negative side effects, inequitable impacts on different parts of the world, or the prospect that a world expecting such solutions will feel less pressure to address the root causes of climate change.

Adding business interests, profit motives, and rich investors into this situation just creates more cause for concern, complicating the ability of responsible scientists and engineers to carry out the work needed to advance our understanding.

The only way these startups will make money is if someone pays for their services, so there’s a reasonable fear that financial pressures could drive companies to lobby governments or other parties to use such tools. A decision that should be based on objective analysis of risks and benefits would instead be strongly influenced by financial interests and political connections.

The need to raise money or bring in revenue often drives companies to hype the potential or safety of their tools. Indeed, that’s what private companies need to do to attract investors, but it’s not how you build public trust—particularly when the science doesn’t support the claims.

Notably, Stardust says on its website that it has developed novel particles that can be injected into the atmosphere to reflect away more sunlight, asserting that they’re “chemically inert in the stratosphere, and safe for humans and ecosystems.” According to the company, “The particles naturally return to Earth’s surface over time and recycle safely back into the biosphere.”

But it’s nonsense for the company to claim they can make particles that are inert in the stratosphere. Even diamonds, which are extraordinarily nonreactive, would alter stratospheric chemistry. First of all, much of that chemistry depends on highly reactive radicals that react with any solid surface, and second, any particle may become coated by background sulfuric acid in the stratosphere. That could accelerate the loss of the protective ozone layer by spreading that existing sulfuric acid over a larger surface area.

(Stardust didn’t provide a response to an inquiry about the concerns raised in this piece.)

In materials presented to potential investors, which we’ve obtained a copy of, Stardust further claims its particles “improve” on sulfuric acid, which is the most studied material for SRM. But the point of using sulfate for such studies was never that it was perfect, but that its broader climatic and environmental impacts are well understood. That’s because sulfate is widespread on Earth, and there’s an immense body of scientific knowledge about the fate and risks of sulfur that reaches the stratosphere through volcanic eruptions or other means.

If there’s one great lesson of 20th-century environmental science, it’s how crucial it is to understand the ultimate fate of any new material introduced into the environment. 

Chlorofluorocarbons and the pesticide DDT both offered safety advantages over competing technologies, but they both broke down into products that accumulated in the environment in unexpected places, causing enormous and unanticipated harms. 

The environmental and climate impacts of sulfate aerosols have been studied in many thousands of scientific papers over a century, and this deep well of knowledge greatly reduces the chance of unknown unknowns. 

Grandiose claims notwithstanding—and especially considering that Stardust hasn’t disclosed anything about its particles or research process—it would be very difficult to make a pragmatic, risk-informed decision to start SRM efforts with these particles instead of sulfate.

We don’t want to claim that every single answer lies in academia. We’d be fools to not be excited by profit-driven innovation in solar power, EVs, batteries, or other sustainable technologies. But the math for sunlight reflection is just different. Why?   

Because the role of private industry was essential in improving the efficiency, driving down the costs, and increasing the market share of renewables and other forms of cleantech. When cost matters and we can easily evaluate the benefits of the product, then competitive, for-profit capitalism can work wonders.  

But SRM is already technically feasible and inexpensive, with deployment costs that are negligible compared with the climate damage it averts.

The essential questions of whether or how to use it come down to far thornier societal issues: How can we best balance the risks and benefits? How can we ensure that it’s used in an equitable way? How do we make legitimate decisions about SRM on a planet with such sharp political divisions?

Trust will be the most important single ingredient in making these decisions. And trust is the one product for-profit innovation does not naturally manufacture. 

Ultimately, we’re just two researchers. We can’t make investors in these startups do anything differently. Our request is that they think carefully, and beyond the logic of short-term profit. If they believe geoengineering is worth exploring, could it be that their support will make it harder, not easier, to do that?  

David Keith is the professor of geophysical sciences at the University of Chicago and founding faculty director of the school’s Climate Systems Engineering Initiative. Daniele Visioni is an assistant professor of earth and atmospheric sciences at Cornell University and head of data for Reflective, a nonprofit that develops tools and provides funding to support solar geoengineering research.