This week, I have a new story out about Quaise, a geothermal startup that’s trying to commercialize new drilling technology. Using a device called a gyrotron, the company wants to drill deeper, cheaper, in an effort to unlock geothermal power anywhere on the planet. (For all the details, check it out here.)  For the story, I visited Quaise’s headquarters in Houston. I also took a trip across town to Nabors Industries, Quaise’s investor and tech partner and one of the biggest drilling companies in the world.  Standing on top of a drilling rig in the backyard of Nabors’s headquarters, I couldn’t stop thinking about the role oil and gas companies are playing in the energy transition. This industry has resources and energy expertise—but also a vested interest in fossil fuels. Can it really be part of addressing climate change? The relationship between Quaise and Nabors is one that we see increasingly often in climate tech—a startup partnering up with an established company in a similar field. (Another one that comes to mind is in the cement industry, where Sublime Systems has seen a lot of support from legacy players including Holcim, one of the biggest cement companies in the world.) 
Quaise got an early investment from Nabors in 2021, to the tune of $12 million. Now the company also serves as a technical partner for the startup.  “We are agnostic to what hole we’re drilling,” says Cameron Maresh, a project engineer on the energy transition team at Nabors Industries. The company is working on other investments and projects in the geothermal industry, Maresh says, and the work with Quaise is the culmination of a yearslong collaboration: “We’re just truly excited to see what Quaise can do.”
From the outside, this sort of partnership makes a lot of sense for Quaise. It gets resources and expertise. Meanwhile, Nabors is getting involved with an innovative company that could represent a new direction for geothermal. And maybe more to the point, if fossil fuels are to be phased out, this deal gives the company a stake in next-generation energy production. There is so much potential for oil and gas companies to play a productive role in addressing climate change. One report from the International Energy Agency examined the role these legacy players could take:  “Energy transitions can happen without the engagement of the oil and gas industry, but the journey to net zero will be more costly and difficult to navigate if they are not on board,” the authors wrote.  In the agency’s blueprint for what a net-zero emissions energy system could look like in 2050, about 30% of energy could come from sources where the oil and gas industry’s knowledge and resources are useful. That includes hydrogen, liquid biofuels, biomethane, carbon capture, and geothermal.  But so far, the industry has hardly lived up to its potential as a positive force for the climate. Also in that report, the IEA pointed out that oil and gas producers made up only about 1% of global investment in climate tech in 2022. Investment has ticked up a bit since then, but still, it’s tough to argue that the industry is committed.  And now that climate tech is falling out of fashion with the government in the US, I’d venture to guess that we’re going to see oil and gas companies increasingly pulling back on their investments and promises.  BP recently backtracked on previous commitments to cut oil and gas production and invest in clean energy. And last year the company announced that it had written off $1.1 billion in offshore wind investments in 2023 and wanted to sell other wind assets. Shell closed down all its hydrogen fueling stations for vehicles in California last year. (This might not be all that big a loss, since EVs are beating hydrogen by a huge margin in the US, but it’s still worth noting.)  So oil and gas companies are investing what amounts to pennies and often backtrack when the political winds change direction. And, let’s not forget, fossil-fuel companies have a long history of behaving badly.  In perhaps the most notorious example, scientists at Exxon modeled climate change in the 1970s, and their forecasts turned out to be quite accurate. Rather than publish that research, the company downplayed how climate change might affect the planet. (For what it’s worth, company representatives have argued that this was less of a coverup and more of an internal discussion that wasn’t fit to be shared outside the company.) 

While fossil fuels are still part of our near-term future, oil and gas companies, and particularly producers, would need to make drastic changes to align with climate goals—changes that wouldn’t be in their financial interest. Few seem inclined to really take the turn needed.  As the IEA report puts it:  “In practice, no one committed to change should wait for someone else to move first.” This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now U.S. President Donald Trump signed the AI Action Plan, which outlines a path for the U.S. to lead in the AI race. For enterprises already in the throes of deploying AI systems, the rules represent a clear indication of how this administration intends to treat AI going forward and could signal how providers will approach AI development.  Much like the AI executive order signed by Joe Biden in 2023, Trump’s order primarily concerns government offices, directing how they can contract with AI models and application providers, as it is not a legislative act.   The AI plan may not directly affect enterprises immediately, but analysts noted that anytime the government takes a position on AI, the ecosystem changes.  “This plan will likely shape the ecosystem we all operate in — one that rewards those who can move fast, stay aligned and deliver real-world outcomes,” Matt Wood, commercial technology and innovation officer at PwC, told VentureBeat in an email. “For enterprises, the signal is clear: the pace of AI adoption is accelerating, and the cost of lagging is going up. Even if the plan centers on federal agencies, the ripple effects — in procurement, infrastructure, and norms — will reach much further. We’ll likely see new government-backed testbeds, procurement programs, and funding streams emerge — and enterprises that can partner, pilot, or productize in this environment will be well-positioned.” The AI Impact Series Returns to San Francisco – August 5 The next phase of AI is here – are you ready? Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end

When a tech vendor wants to sell into a large enterprise — or when that enterprise wants to buy software from a tech vendor or AI model provider — each side may be required by the other to prove they will handle shared data responsibly in the form of mandatory surveys and questionnaires. Regulations such as GDPR, the soon-to-be effected EU AI Act and a patchwork of U.S. state laws make those proofs more complex each year. As a consequence, a tech vendor trying to sell to a large enterprise will usually be asked to complete security questionnaires that can stall deals for weeks and cost six figures in staff time.San-Francisco-based SecurityPal was founded in March 2020 by CEO Pukar Hamal to handle all that paperwork largely automatically on behalf of the vendor, using the vendor’s unique product information and internal data.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Chinese e-commerce giant Alibaba’s “Qwen Team” has done it again. Mere days after releasing for free and with open source licensing what is now the top performing non-reasoning large language model (LLM) in the world — full stop, even compared to proprietary AI models from well-funded U.S. labs such as Google and OpenAI — in the form of the lengthily named Qwen3-235B-A22B-2507, this group of AI researchers has come out with yet another blockbuster model. That is Qwen3-Coder-480B-A35B-Instruct, a new open-source LLM focused on assisting with software development. It is designed to handle complex, multi-step coding workflows and can create full-fledged, functional applications in seconds or minutes. The model is positioned to compete with proprietary offerings like Claude Sonnet-4 in agentic coding tasks and sets new benchmark scores among open models. It is available on Hugging Face, GitHub, Qwen Chat, via Alibaba’s Qwen API, and a growing list of third-party vibe coding and AI tool platforms. Open sourcing licensing means low cost and high optionality for enterprises But unlike Claude and other proprietary models, Qwen3-Coder, which we’ll call it for short, is available now under an open source Apache 2.0 license, meaning it’s free for any enterprise to take without charge, download, modify, deploy and use in their commercial applications for employees or end customers without paying Alibaba or anyone else a dime. It’s also so highly performant on third-party benchmarks and anecdotal usage among AI power users for “vibe coding” — coding using natural language and without formal development processes and steps — that at least one, LLM researcher Sebastian Raschka, wrote on X that: “This might be the best coding model yet.

Google DeepMind has unveiled new artificial-intelligence software that could help historians recover the meaning and context behind ancient Latin engravings.  Aeneas can analyze words written in long-weathered stone to say when and where they were originally inscribed. It follows Google’s previous archaeological tool Ithaca, which also used deep learning to reconstruct and contextualize ancient text, in its case Greek. But while Ithaca and Aeneas use some similar systems, Aeneas also promises to give researchers jumping-off points for further analysis. To do this, Aeneas takes in partial transcriptions of an inscription alongside a scanned image of it. Using these, it gives possible dates and places of origins for the engraving, along with potential fill-ins for any missing text. For example, a slab damaged at the start and continuing with … us populusque Romanus would likely prompt Aeneas to guess that Senat comes before us to create the phrase Senatus populusque Romanus, “The Senate and the people of Rome.”  This is similar to how Ithaca works. But Aeneas also cross-references the text with a stored database of almost 150,000 inscriptions, which originated everywhere from modern-day Britain to modern-day Iraq, to give possible parallels—other catalogued Latin engravings that feature similar words, phrases, and analogies. 
This database, alongside a few thousand images of inscriptions, makes up the training set for Aeneas’s deep neural network. While it may seem like a good number of samples, it pales in comparison to the billions of documents used to train general-purpose large language models like Google’s Gemini. There simply aren’t enough high-quality scans of inscriptions to train a language model to learn this kind of task. That’s why specialized solutions like Aeneas are needed.  The Aeneas team believes it could help researchers “connect the past,” said Yannis Assael, a researcher at Google DeepMind who worked on the project. Rather than seeking to automate epigraphy—the research field dealing with deciphering and understanding inscriptions—he and his colleagues are interested in “crafting a tool that will integrate with the workflow of a historian,” Assael said in a press briefing. 
Their goal is to give researchers trying to analyze a specific inscription many hypotheses to work from, saving them the effort of sifting through records by hand. To validate the system, the team presented 23 historians with inscriptions that had been previously dated and tested their workflows both with and without Aeneas. The findings, which were published today in Nature, showed that Aeneas helped spur research ideas among the historians for 90% of inscriptions and that it led to more accurate determinations of where and when the inscriptions originated. In addition to this study, the researchers tested Aeneas on the Monumentum Ancyranum, a famous inscription carved into the walls of a temple in Ankara, Turkey. Here, Aeneas managed to give estimates and parallels that reflected existing historical analysis of the work, and in its attention to detail, the paper claims, it closely matched how a trained historian would approach the problem. “That was jaw-dropping,” Thea Sommerschield, an epigrapher at the University of Nottingham who also worked on Aeneas, said in the press briefing.  However, much remains to be seen about Aeneas’s capabilities in the real world. It doesn’t guess the meaning of texts, so it can’t interpret newly found engravings on its own, and it’s not clear yet how useful it will be to historians’ workflows in the long term, according to Kathleen Coleman, a professor of classics at Harvard. The Monumentum Ancyranum is considered to be one of the best-known and most well-studied inscriptions in epigraphy, raising the question of how Aeneas will fare on more obscure samples.  Google DeepMind has now made Aeneas open-source, and the interface for the system is freely available for teachers, students, museum workers, and academics. The group is working with schools in Belgium to integrate Aeneas into their secondary history education.  “To have Aeneas at your side while you’re in the museum or at the archaeological site where a new inscription has just been found—that is our sort of dream scenario,” Sommerschield said.

A new startup founded by a former Anthropic executive has raised $15 million to solve one of the most pressing challenges facing enterprises today: how to deploy artificial intelligence systems without risking catastrophic failures that could damage their businesses.The Artificial Intelligence Underwriting Company (AIUC), which launches publicly today, combines insurance coverage with rigorous safety standards and independent audits to give companies confidence in deploying AI agents — autonomous software systems that can perform complex tasks like customer service, coding, and data analysis.The seed funding round was led by Nat Friedman, former GitHub CEO, through his firm NFDG, with participation from Emergence Capital, Terrain, and several notable angel investors including Ben Mann, co-founder of Anthropic, and former chief information security officers at Google Cloud and MongoDB.“Enterprises are walking a tightrope,” said Rune Kvist, AIUC’s co-founder and CEO, in an interview. “On the one hand, you can stay on the sidelines and watch your competitors make you irrelevant, or you can lean in and risk making headlines for having your chatbot spew Nazi propaganda, or hallucinating your refund policy, or discriminating against the people you’re trying to recruit.”