In association withTeleperformance Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to embed habits of measurement, analysis, and accountability into day-to-day company culture. But today, those time-tested playbooks are evolving as companies seek to embed AI into established process excellence methodologies. By some estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade. In one study, a full 88% of business leaders anticipated increasing investments into AI-infused process intelligence in the next 12 to 18 months. Yet without the right foundations, many of those investments may not fully deliver on their potential. Companies that already operate with discipline have an edge. They can channel new tools into proven systems rather than bolting them onto shaky foundations. Organizations with mature process disciplines are also better positioned to translate AI ambition into real outcomes, as they are already accustomed to data-driven decision-making and process discipline—precisely the cultural foundation AI systems need to deliver value. Simply put: AI can accelerate process excellence, but existing process excellence is what makes AI truly impactful. Technology and process are no longer separate levers, and only organizations that pull them together stand to realize the full value of both.
Download the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

In partnership withInfosys Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational data, the energy sector offers a glimpse into what that future could look like. At Woodside Energy, AI adoption did not begin with generative models or enterprise copilots. The company has spent years building predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” says the company’s vice president for digital Andrew Melouney. “Those have created really clear, quite high-value use cases for us.” That long-term investment in infrastructure and governance is now enabling a broader shift toward agentic AI systems that can support complex industrial workflows. Rather than replace human operators, Woodside designs AI systems to augment expertise in high-stakes environments. A prime example is its “Startup Advisor,” an AI copilot that helps operators manage the complex process of starting liquefied natural gas (LNG) plants. “We’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions,” Melouney explains. The company’s approach reflects a wider evolution taking place across industrial AI: graduating from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. That transition, Melouney argues, requires organizations to rethink both their technology stacks and how work itself gets done. “We’re not just bolting AI onto an existing process,” he says. “We’re deeply thinking about how that work needs to be reimagined.”
Melouney’s motto has become: “Think big, prototype small, and scale fast.” As AI systems become more autonomous and interconnected, the companies poised to succeed may be those that spent years building the operational foundations beneath the hype.
“Our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows,” says Melouney. This episode of Business Lab is produced in partnership with Infosys. Full Transcript: Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This episode is produced in partnership with Infosys. Now, when people think about artificial intelligence, they often picture chatbots or productivity tools, but some of the most sophisticated and high impact uses of AI are actually happening far from consumer apps, inside complex industrial environments where safety, reliability, and physical systems matter. The global energy sector is a prime example.Companies like Woodside Energy, a global energy producer headquartered in Western Australia, have been applying AI for more than a decade now, from advanced analytics and operations, to remote decision support, to smarter maintenance, and energy efficiency across large scale assets. Today, Woodside is scaling that experience, embedding AI more deeply across its operations and the enterprise with a strong focus on governance, data quality, and human accountability. Two words for you: technological fuel. My guest today is Andrew Melouney, vice president for digital at Woodside Energy. Welcome, Andrew.

Andrew Melouney: Thanks, Megan. It’s great to be here. Megan: Lovely to have you. Now, Andrew, as I said there, the energy sector has approached AI quite differently from technology or consumer businesses. Early value has emerged in operational and industrial environments, rather than consumer-facing generative AI tools. Why is that? And what differentiates the energy sector’s AI journey? Andrew: Megan, I think it really comes down to the nature of the work we do. Energy operations and what Woodside does is very asset intensive, it’s very safety critical, and it’s highly physical. And when you think about how Woodside operates, we operate across the full value chain. We do exploration through to drilling and subsurface work, to project development, all the way through to operating assets, which are often operated in harsh and remote locations, and then global energy portfolio marketing and trading as well.We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate, and those have created really clear, quite high-value use cases for us. When you think about reliability, when you think about safety and efficiency, those are really critical things for a company like Woodside. We’ve been doing traditional AI for many years now. If you think about analytics, if you think about optimization, if you think about things like predictive models, those techniques we’ve been applying to our data sets and to our business since around 2015.And more recently with the advent of generative AI, we’ve really found that we’ve got a pretty strong and awesome foundation to build on top of and to really solve problems in the service of improving the business. And again, whether that is keeping people safe, keeping the environments we operate in safe, or improving returns for the organization. Megan: Fantastic. I mean you touched on it there, but how has this reality shaped your own AI strategy at Woodside? Where did you start, and where did the technology prove most impactful in those early days? Andrew: Well, like I said, we’ve had a very long journey, in terms of understanding our operational data, recognizing the value of it, and collecting it at scale so that we can use it. And we’ve been very deliberate in that approach, Megan. We’ve really thought about where the value is and where the risks were manageable. And we’ve started looking at, in today’s world from an agentic AI perspective, we’ve started looking at the problems that were solved with traditional AI and machine learning and data science in the past. And we’ve started to think about, where can we then layer agentic AI over the top to provide an even better outcome? For our asset intensive industry and organization, we’re looking at areas such as maintenance optimization. We’re looking at areas such as, how do we ensure our LNG plants start up reliably, consistently, and safely? And we’re considering really our frontline workforce and making sure that we’re giving people on the frontline the tools required to do their jobs. When we think about AI, we’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions? I think over time, this has just evolved from what has been traditional analytics to now artificial intelligence and generative AI. And we’ve learned along the way that the technology is important, but it’s about aligning people, processes, and the technology together. We’ve spent a long time not only in collecting the data and having a well-curated data set that we can build on top of, but we’ve also spent a lot of time teaching people how to work in agile ways, how to do design thinking, how to problem solve, and how to really make sure that the technology that, say, my team can bring to bear to the organization is adopted effectively and purposefully. And I think once we had that solid foundation in place from a technology perspective, from a data perspective, once we got strong trust built between our digital teams and the organization, we really saw quite a material uptick and the scaling of technology occur more broadly across the enterprise. Megan: Fantastic. That people piece so important, isn’t it? It’s just a tool, technology, that needs to be in the right hands. And you touched on data there; industrial AI obviously depends on vast amounts of data. Can you walk us through how you’ve approached data at Woodside in a little more detail? How it’s structured and governed, and how tools like maintenance intelligence as well fit into that.
Andrew: Well, data is really foundational and fundamental to everything we do, particularly from a technology perspective. It gives us the ability to innovate at pace when we are building over the top of a strong foundation. As I said before, we’ve had the benefit of a long-term investment in our underlying operational data. I think the way we think about data is that it’s an asset for us. And when you think about operating a facility where you’ve got sensors everywhere, you’ve got data streaming in real time, you’ve got operators needing to make decisions in real time, we have consciously made a decision over many, many years to invest in that enterprise scale data platform to make sure that it’s secure. We’ve got well-structured data assets, and we’ve got strong governance over the top of that data so that when it is used, when it’s built in a data science application or an AI agent, that we’ve got a level of trust in it that it’s going to be used responsibly. And that when it’s used, it can be trusted to give the outcome that we expect.We have developed platforms that continuously ingest really high frequency data from the assets and from our enterprise systems. Once we’ve been able to develop solutions on top of that, parts of the business that might own the systems that collect that data, they see the value in it.When you look at something like maintenance intelligence is a really good example of how we’ve been able to take something that we’ve been working on for a long time. Woodside does a lot of maintenance, it’s a very important part of our business, and it occurs across all of our operating assets. But we have been looking at how we do predictive analytics and predictive maintenance for a long time across that data set that we own. And something like maintenance intelligence is a solution that gives us the ability to optimize how we do that maintenance. And what it does is it analyzes historical maintenance records, alongside the performance of the equipment. And again, by having that data set well-governed and in one place, we get the ability to correlate different data sets, such as maintenance records out of SAP, alongside say equipment and performance coming from our time series data lake.
And when we build over the top of that, something like maintenance intelligence gives us the opportunity to recommend to the assets what the optimal timing for maintenance activities might be, and really give what is quite a simple aim, which is do the right work at the right time. And with something like maintenance intelligence, we have seen the opportunity, and we have the opportunity to reduce maintenance hours by up to 15% over five years on one of the assets that we’ve piloted this on. And as we’ve built out that underlying analytical model, we’re now able to put agentic AI over the top of that and provide better insights and optimize that solution more.It really comes down to providing our asset teams and our operational teams with the right decision support capability that ensures they’re still accountable to make the decision and to ensure the right work is being done, but we are giving them the best possible opportunity to use their judgment and experience with the data that we provide to make the right decision. Megan: Sounds like a really impactful change. Last year also marked a milestone in moving from early AI learnings to scale, using AI more deliberately as a force multiplier. What transition were you trying to make and how did you approach it? Andrew: Well, Megan, we’ve had a philosophy for a long time in Woodside from an innovation perspective, where we really want to think big, we want to prototype small, and we want to scale fast. We want to find big opportunities that we can go after, but we want to ensure that we look at how we deploy those on a small scale first, and then provide the right learning and insight that then can scale it everywhere. Something like maintenance intelligence is a good example of that, or our Startup Advisor, where we know that we’ve got multiple plants that we need to start up. We know that we’ve got multiple assets that need to do maintenance, so we have a big, bold ambition about how we can improve and optimize that. We start with a small prototype; it might be one subsystem, it might be just a part of an asset, and then we scale it out, we learn, and we scale faster.I think from an AI learning perspective, one of the key things we’ve learned is really the transition from moving from isolated AI solutions to a more coordinated enterprise-wide capability. If you look back maybe 18 months, two years, in our generative AI journey, we rarely started by deploying AI as broadly as we could in the organization from a personal productivity perspective. And probably being quite open in terms of the problems that we will solve, the business problems that we’ll solve with AI. That had a lot of benefits for us in terms of allowing our organization to get to know AI, get to know the capabilities, to build the trust in it.What we’ve learned though is that we’ve needed to pivot from that to being a little bit tighter in terms of where we are going to invest our time and resources and more higher value solutions. How do we then enable and empower the rest of the organization so that they can actually effectively problem solve with technology in their domain or in their personal productivity without having to come to a central team?When we think about that, think big, prototype small, scale fast, has been something really important for us. The transition from a more broader approach to use case development and solution development to now a narrower focus on the high value priorities. We’ve seen that paying dividends to us and allowing us to go after solutions and opportunities, things like Startup Advisor.And so our Startup Advisor is a agentic AI solution that really aims to optimize and empower and better support our operators that sit in front of a panel and have to start up LNG plants, which are incredibly technical facilities and require really specialist skills to start up. And so our Startup Advisor is almost like a copilot that sits alongside those operators, and it gives them the ability to be able to play back previous startups. It gives them the ability to look at how the current startup is progressing, and it provides them better insights to optimize how they start up that facility. And again, starting up an LNG facility is incredibly complex. Megan: I can imagine. Andrew: When we think about opportunities like Startup Advisor, again, it goes back to that think big, prototype small, and scale fast. We started with a very bold vision of, how do we start up all of our LNG plants in a much more structured and optimized fashion? How do we better support our panel operators? How do we make, say, a more junior panel operator have a copilot that can help them almost like an experienced panel operator sitting next to them? And when we think about that vision and the ability then to prototype on a small scale and then scale fast, I think it’s been really successful for us.As we scale, we’ve just naturally expanded into more agent-based solutions. Today, we’ve got around 50 AI agents in production, supporting both our operating assets and our enterprise workflows. These tools have been proven in live environments, and we have really seen the benefit of being able to shift from point solutions that maybe solve small scale problems in specific areas, to AI and agentic solutions with agency that can really work across our workflows.We’re able to do this because we’ve standardized on the platform that we build on and we’ve got repeatable patterns. That’s been another really important learning for us, is that we don’t want to build 50 solutions in 50 different ways. We really want to be empowering our organization and our technical teams and the users of our solutions to roll them out quickly, to roll them out safely, and to do it in a patternized and platform manner.But the last point I’ll make, Megan, from a learning perspective is that we’ve really understood that a strong governance around how AI is deployed and developed is critical for us, and it’s critical for us to go fast as well. The traditional ways of governing how we roll out different solutions or digital systems isn’t going to scale to the breadth that we need when we are thinking about AI. Being able to have a clear philosophy around how we innovate, transitioning from isolated solutions to that enterprise-wide capability, and making sure that we’ve got strong platforms with strong patterns and clear governance are the three really critical things that we’ve learned. Megan: Such important pillars, all of them. And you’ve been working with Infosys on this journey. How has that partnership helped accelerate scaling and embedding AI across the business?
Andrew: Well, Infosys is our managed service provider, and so they play a really critical role in the operations of our core business. One of the things that I like to say is that our license to innovate is based on our license to operate. And so, for my team to be able to turn up to an operating asset or a corporate function and have the trust that’s needed to be able to innovate and reimagine and redesign how work gets done, to be able to do that, we need to make sure that our core platforms, our core systems, our applications are running really reliably, safely, and consistently every day. Having an experienced partner like Infosys looking after those core operations in partnership with our internal teams is really, really important to us.As we move from pilots to enterprise-wide deployment, the ability to partner with someone like Infosys also gives us the ability to scale. And so being from Perth and Western Australia, while we’ve got a really strong local team in Western Australia, and we’ve also got a very strong team in some of our other operating locations, like everyone, we’re struggling to find people that can fill AI roles. Being able to partner with Infosys and have a number of different operating models at our disposal becomes really important for us. Having co-mingled teams where they are staff, they are Infosys staff, Woodside staff, and some of our other partners, really just brings diversity of thought and experience to how we solve problems.Fundamentally, the partnership has allowed us to operate and innovate with more confidence. While Woodside always retains ownership of the strategy and where we’re going and the governance and my teams remain accountable for the outcomes, we can’t do what we do without strong partnerships like the one we have with Infosys. Megan: Fantastic. And as AI adoption scales, you mentioned yourself, governance becomes increasingly important. How challenging has that been, and what guardrails have you put in place at Woodside? Andrew: So, Megan, governance is really important to us, and we operate in a well-regulated environment. That means we’ve got to make really deliberate and well-reasoned decisions when we’re thinking about how we deploy technology into our organization, whether it’s artificial intelligence or anything else, for that matter. And so, governance is really central to how we approach the execution of our AI strategy at Woodside.We’ve got maybe two or three really key things that we’ve put in place. The first one is just making sure that every AI use case goes through a structured assessment, and that’s making sure it meets our privacy controls, our cyber controls. We’re also asking the question, not just, could we do this, but should we do this? We’ve really got to bring together safety, ethics, transparency, accountability, and make sure that we make an informed decision. When an AI solution is going through that structured assessment, if there are concerns about how we might use that solution, it then goes to an AI council that’s made up of senior leaders across the organization. That council and that group really oversee some of the prioritization and risk management. That’s where we can have really strong, robust debates around, again, could we do something, should we do it, and how do we mitigate any of the risks that we might introduce here?I think the last one, Megan, is really around lifecycle management. When you start thinking about, we’ve got 50 at the moment, but if we had 500 agents working in our organization, really amplifying the experience and the decision-making and the value creation of our staff, we really want to have an ability to manage the lifecycle of how those agents operate. We want to know, how many people are using them? What’s the efficacy and the outcome? Is there model drift? Do we need to retune or retrain? I think that’s an area where many organizations, including Woodside, are still leaning into and still figuring out the best way to do this. We can do it quite easily with 50 agents, but 500, 5,000, 50,000 becomes an opportunity for us. Again, thinking about how we partner with others, solving problems like that really present an opportunity to co-create and to co-solve with some of our partners, like with Infosys. Megan: Fantastic. Just to close, what’s your long-term vision for AI at Woodside? How do you see this evolving over the years ahead, and what could it unlock for the sector in your view?
Andrew: So Megan, I think our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows. The outcome that we want to get from that is to protect our people, to protect the environments we operate in, and to be able to provide energy at a lower cost to the world. When we think about that ambition, we can really see that being applied to almost all of the areas that Woodside work in. Whether that’s from exploration through to project developments, through to operations or marketing, the scale of the opportunity in front of us and the ability for us to really change the way that work flows through the organization is really exciting. For us, there’s three things that we have to get right in terms of being able to execute on that ambition. The first one is really thinking about how the work gets done in the organization so that we’re not just bolting AI onto an existing process, but we’re deeply thinking about how that work needs to be reimagined. We’ve also got to think about how we enable our workforce to work differently. Providing them with the skills and the tools and the ability to really harness the power of the technology that we provide.Secondly, we’ve got to continue to move from and restrain ourselves from deploying point solutions that solve very narrow problems, to having more connected, agentic systems of systems that can interact with each other. To do that, and if we do that successfully, that’s where we really get the high value unlock from agents being able to interact with workflows and really change how the work gets done.And lastly, Megan, it’s about how we must continue our philosophy of thinking big, prototyping small, and scaling fast. Megan: Which is a fantastic lens to which to make all these decisions. Thank you so much, Andrew. That was Andrew Melouney, vice president for digital at Woodside Energy, whom I spoke with from Brighton in England.That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks ever so much for listening. Goodbye. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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. LLMs are stuck in a groupthink groove. This startup is trying to get them out. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always.  That won’t work every time—but if it did for you, you may wonder if I have superpowers. I don’t. The truth is that most large language models are stuck in a rut. They are far more predictable and far less creative in their responses than you might expect. That’s fine for tasks like coding or research, but groupthink is a problem when you’re brainstorming or planning your next vacation.
The Australian startup Springboards has a solution. It built an LLM called Flint, which has been trained to come up with a wider variety of responses than mainstream LLMs to open-ended questions such as “Where should I go in Europe?” Meet the company pushing chatbots away from the obvious.
—Will Douglas Heaven The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Scientists say they have built a cell from scratch for the first timeBuilt with lab-made DNA, it can feed, grow, and multiply. (CNN)+ It brings us closer to creating synthetic life. (Quanta)+ And is arguably the greatest feat of bioengineering yet. (New Scientist $)+ But also raises concerns over the dangers of synthetic biology. (NYT $)+ Mirror organisms could threaten life on Earth. (MIT Technology Review) 2 OpenAI has proposed giving the Trump administration a 5% stakeTalks over a public ownership deal come amid rising political pressure.(FT $)+ OpenAI also proposed other US AI giants providing a 5% stake. (CNBC)+ That could include Anthropic, Google, and Meta. (Bloomberg $)+ President Trump says he wants the public to have a stake in AI. (BBC) 3 Singapore has seized a $42 million mansion tied to Nvidia chip smugglingIt was seized as part of an investigation into alleged illegal trading. (BBC)+ Days earlier, Supermicro’s Taiwan offices were raided in the probe. (FT $) 4 Anthropic’s Fable 5 is back onlineBut queries posing security risks may be routed to less powerful models. (Axios)+ Anthropic restored access yesterday after the US lifted an export ban. (BBC)+ But the battle over how to tame AI has just begun. (WSJ $)+ Anthropic has launched a new AI science product. (MIT Technology Review) 5 Meta is building its own cloud infrastructure businessIt’s exploring two ways of monetizing AI compute and models. (Bloomberg $)+ One is selling access to models hosted on Meta’s infrastructure. (CNBC)+ The other is selling “raw” computing power. (TechCrunch)

6 PlayStation will stop releasing games on discs in 2028Future PS5 games will be digital-only releases. (Verge)+ The news comes days after reports that GTA VI will have no disc. (BBC)+ It’s put a nail in physical media’s coffin. (Wired $) 7 A low-cost Chinese AI model is catching up with US giants on their home turfWestern customers are drawn to GLM-5.2’s cheap but powerful model. (Reuters $)+ Chinese open-source models are spreading fast. (MIT Technology Review)8 Google has lost its fight against a record €4.1 billion EU antitrust fineIt was charged in 2018 for using Android to ‌block rivals. (CNBC) 9 The UN has launched an “AI for Good” commissionSalesforce CEO Benioff and Rwandan President Kagame will co-chair it. (Axios) 10 People prefer AI impersonators over politiciansThe study’s findings raise alarm bells around potential public deception. (404 Media) Quote of the day “If AI overdelivers, it will impact financial stability. If AI underdelivers, it will impact financial stability.” —Torsten Slok from Apollo Global Management shares common concerns about AI at the European Central Bank’s annual conference, Reuters reports. One More Thing America was winning the race to find Martian life. Then China jumped in.In July 2024, after more than three years on Mars, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or sedimentary layers, this one had spots. Those specks were the best hint yet of alien life.  
NASA began a new mission to bring the rocks back to Earth to study. But now, just over a year and a half later, the project is on life support. As a result, those oh-so-promising rocks may be stuck out there forever.  This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. Beijing is now moving full steam ahead with its own version of NASA’s mission. 
Here’s how the search for Martian life has become a contest between two superpowers. —Robin George Andrews

Something stinks in California’s climate policies. Years ago, the state set up a system that pays cattle farmers across the country to turn the methane emitted from cattle manure into natural gas, encouraging the dairy sector to produce a gas we burn instead of one that just pollutes the air. It’s become wildly popular because the subsidies are extremely lucrative. But a growing body of research suggests the program is a case study in the shortcomings of our preferred approaches to climate action. Instead of simply forcing industries to directly cut their pollution or pay for it as a cost of doing business, legislators have repeatedly opted to set up convoluted incentive systems that swap climate responsibilities between parties and regions. As studies have shown again and again, these carbon offsetting and trading schemes often dramatically overstate the emissions reductions actually achieved in the one place that matters: the atmosphere. The dairy program illustrates a particular version of this problem, muddling the impacts of different types of greenhouse gases in a way that researchers argue will lock in more warming in the future.
Despite this and other concerns, California regulators decided in 2024 to extend parts of the program beyond 2050. And a recent proposal by the state’s air resources board could send millions of additional dollars to dairy farmers as part of a plan that would ease restrictions on major greenhouse-gas producers. Here’s how the system works: The state’s climate regulations require the transportation fuels industry to lower the carbon dioxide levels in its products over time—or purchase credits from other parties that cut fuel emissions, including cattle farmers.
Dairies generally spray cattle manure into giant open lagoons, where microbes gobble up organic matter and produce methane as a by-product. But if farmers set up what are known as anaerobic digesters, the sludge is redirected into covered vessels that capture the biogas, which can be converted into natural gas and injected into a pipeline. It can then be used to fuel certain vehicles or generate electricity in a power plant. Either way, petroleum companies can pay those farmers for Low Carbon Fuel Standard (LCFS) credits, to meet regulatory requirements in lieu of reducing the emissions from their own fuels. Burning biogas in a bus or turbine still releases carbon dioxide, but the idea is that this process reduces market demand to extract natural gas from the ground and avoids the release of methane, which is a far more powerful greenhouse gas (at least initially). In fact, methane is so much more powerful that under California’s program, “adding one average biogas-powered vehicle to the fleet would produce enough LCFS credits to cover the deficits incurred by 26 similar gasoline-powered vehicles,” according to Aaron Smith, a UC Berkeley economist. But there’s a problem with this carbon math. California assumes that methane exerts about 25 times the warming effect of carbon dioxide over a 100-year period. That’s not how it really works in the atmosphere, though. Methane is very powerful, but it also breaks down quickly, generally within a couple of decades. Meanwhile, carbon dioxide builds up cumulatively in the atmosphere—and much of whatever we emit will continue heating up the planet for hundreds to thousands of years. So, in effect, the state has created a system that reduces short-term warming at the cost of increasing all-but-permanent warming. Any methane that digesters capture today would have caused extra-powerful warning if released, but by 2050 that effect would have mostly faded away. Meanwhile, that additional carbon dioxide we permitted in its place could continue warming the world for millennia. It is a good idea to cut methane emissions, and dairy digesters achieve this (though not always as effectively as hoped). But we can’t swap a decrease in short-lived greenhouse gases for an increase in long-lived ones if we hope to keep global temperatures within relatively safe levels in the coming century, as researchers have long warned. We have to slash both. The problem I keep returning to, after years of covering carbon markets and offsets, is this: We need to clean up every sector, completely, over the next few decades. It’s increasingly untenable for so many of our climate ambitions to turn on getting one industry to make progress on paper by paying another one to reduce emissions, at a point when every business in every industry needs to be racing toward net zero. It’s time to move past the idea that we need to reward sectors for doing us the favor of not polluting the atmosphere, and simply require them to stop unloading the huge environmental burden of their business onto society. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

EXECUTIVE SUMMARY Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every time—but if it did for you, you may wonder if I have superpowers. I don’t. The truth is that most large language models are stuck in a rut. They are far more predictable and far less creative in their responses than you might expect. That’s fine for tasks like coding or research, but groupthink is a problem when you’re brainstorming or planning your next vacation. The Australian startup Springboards has a solution. It built an LLM called Flint, which has been trained to come up with a wider variety of responses than mainstream LLMs to open-ended questions such as “Where should I go in Europe?”
“Most language models are fighting hallucinations,” says Springboards cofounder and CEO Pip Bingemann. “We welcome them.” Bingemann introduced me to the random number game when he first showed me his company’s new model. It felt like watching an illusionist with a deck of cards. “This is our sales trick, and it works every single time,” he says.
After ChatGPT and Claude both gave their 7s, Bingemann turned to Flint. It too came back with 7: “Aha, of course that was going to happen, but it’s okay—7 is a legitimate answer.” He restarted the session and prompted again: ChatGPT gave 7, Claude gave 7, Flint gave 3.7916. Run your way It’s not just numbers. When Bingemann asked ChatGPT and Claude to name a type of car, he predicted that it would be a Toyota or a Honda—and he was right. Flint came up with a Ford F-150. “There’s all this lost information that doesn’t get served up in these models,” he says. “They’re just as capable of saying a Buick or a Tesla. They just don’t—they’re biased.” Bingemann sent one last prompt to each of the three models: “Give me a tagline for a campaign for New Balance running shoes. Just the tagline.” Claude: “Run your way.” ChatGPT: “Run your way.” Flint: “Built to last, run to win.” It won’t win any awards, but at least it’s different. This weird limitation of LLMs is starting to get more attention. In November a team of researchers put out a paper, titled “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond),” that exposed a remarkable degree of repetition not only in the answers from individual LLMs but between them as well. They found that different LLMs converged on very similar answers when prompted with open-ended questions. It’s not clear exactly why this happens, but the researchers speculate it’s because most LLMs today are trained in similar ways on similar data to do similar tasks. The team won the best paper award at NeurIPS, a major AI conference. When the researchers asked 25 different LLMs (including models from the top US firms as well as open-source models from China and elsewhere) 50 times each to write a metaphor about time, most of the 1,250 responses were a version of “Time is a river” or “Time is a weaver.” (I asked some of my colleagues the same question and six people gave me six different answers. My highlight: “Time is a favorite sweatshirt, shaped by a lifetime of wear.”) When you look for it, you see repetition everywhere, says Kieran Browne, cofounder and CTO at Springboards. “The way that most chat interfaces are designed, it makes it feel like you’re having a personal conversation,” he says. “I think most people don’t really realize the extent to which they are getting the same stuff as everybody else.”

Take another example: “What should I name my band?” Most models will say something involving “glass,” “neon,” “velvet,” or “static,” says Browne.   When I tried it, ChatGPT spat out a list of 56 band names. At the top was “Glass Harbor.” Skimming through, I found “Static Empire,” “Neon Hearts,” and “Velvet Echo.” I asked Gemini; it gave me 15 suggestions, including “Static Horizon.” Some of the suggestions looked pretty cool, though. ChatGPT’s “Sofa Astronauts” caught my eye, so I googled it—and found that a band called Sofa Astronauts already exists.  (OpenAI says that training models to give reliable and coherent answers can lead them to converge around familiar, high-probability responses and that pushing harder for novelty can lead to weaker or less reliable responses. It also notes that the “Artificial Hivemind” paper studied models from 2024 that have since been updated.) Creative catapult Springboards has developed a tool backed by a selection of LLMs, including ChatGPT and Claude, that creative professionals in advertising or marketing can use to brainstorm ideas. The tool lets you drag around text produced by different models, picking the bits that you like and combining them into something new—in theory. Springboards is pitching Flint as an alternative model that users of its tool can select when looking for more variety. Zoe Scaman, founder of the business strategy startup Bodacious and chief strategy officer at 77X, a direct-to-fan marketing platform set up by Luka Dončić of the LA Lakers, has been trying it out. “I find it really useful for throwing me in completely different directions,” she says. “I use it if I want to catapult myself all over the place.” In one test, Scaman pitted Flint against Claude, Gemini, and ChatGPT by giving each of the models a classic MBA case study: How would you reinvent a finance company for today’s youth? The three mainstream models all went down the same path, she says: “You know, we need to teach financial literacy in a fun and funky way—well, that’s nothing new.” But Flint came up with something different, suggesting that the whole concept of wealth accumulation should get a rebrand. “That was really interesting,” says Scaman.
She notes that Flint is still a prototype and doesn’t work all the time. “It sometimes falls over when you start pushing it too far,” she says. “But I think that the premise behind it is really powerful.” Taking the temperature Springboards built Flint on top of Qwen 3, an open-source model from the Chinese tech giant Alibaba. “We’re a small team,” says Browne. “Training a foundation model is not on the table for us. It’s just too expensive.”
Most LLMs have settings that let you adjust the level of randomness in their output. The most common is called temperature. “Obviously, that was one of the first things we explored, because that’s what people tell you: If you want more creativity, you turn up the temperature,” says Browne. But changing those settings can also make models incoherent. Dialing up the temperature on one of OpenAI’s models to its maximum setting made it produce responses that switched from English into code halfway through a sentence, says Browne. Springboards realized that parameters were blunt instruments for what it wanted to do. It does not make sense to dial up the randomness across the board; you only want to boost it at specific points in its output, he says. For example, when you ask a chatbot “Where should I go in Europe?” the model only needs to tweak the randomness just before it names a destination, not for every word in its response. To make Flint do this, Springboards trained its version of Qwen 3 to identify the points in its output where more variety was possible and fill those spots with words or phrases that were a little more random. “Flint’s programmed to throw an oddball in. It’s more of an invitation to think wider,” says Maximilian Weigl, cofounder and chief strategy officer at Uncommon, a marketing firm. “That’s super interesting.”
Weigl’s team uses Flint alongside ChatGPT, Claude, and Gemini. “You can’t really create something boundary-breaking with tools that pull you back to the average,” he says.  And yet Weigl notes that nine times out of 10 the average is fine. You don’t always need to reach for extremes with something like Flint, he says: “Most people are fine with good enough. They want to see mass-market familiar things.” Weigl also cautions against using any LLM too much. “I have a big problem when people rely on the output from any AI, including Flint,” he says. “If I saw people on my team copy-pasting something from AI, I’d be like, ‘That’s not your job! Think, talk to other people, use your own voice.’” For now, Flint is aimed at advertisers and marketers because those are Springboards’s customers. But Bingemann and Browne insist that a lack of variety is a problem for anyone using chatbots. The idea is to give people the choice and leave it to them to decide if the result is good or not, says Bingemann. “Variety is great when you’re trying to spark ideas,” he says. “Let’s go down this route instead of letting the machines do it all and ending up in a gray, boring world.”

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. Claude Science is Anthropic’s newest flagship product At an event for pharmaceutical executives, biotech founders, and researchers yesterday, Anthropic announced Claude Science, a major new product intended to support scientific research like Claude Code supports software engineering.Like Claude Code, Claude Science can autonomously carry out meaningful work from concise, high-level instructions, with tools for computational biology and drug development. The launch signals that Anthropic is doubling down on AI for science, and the company will also use the product in its own research into drugs for rare, neglected diseases.Discover why Anthropic is betting big on AI for scientific research. —Grace Huckins Why California’s carbon manure math doesn’t add up Something stinks in California’s climate policies. 
Years ago, the state set up a system that pays cattle farmers to turn the methane emitted from cattle manure into natural gas. It’s become wildly popular because the subsidies are extremely lucrative. But research suggests the program exposes the shortcomings of carbon offsetting and trading schemes. Instead of forcing industries to directly cut their pollution or pay for it as a cost of doing business, legislators have opted for incentives that swap climate responsibilities between parties and regions. The system could ultimately lock in more warming.
Read the full story on California’s dubious carbon calculations. —James Temple This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. Watch now: longevity’s next frontier—“reprogramming” your body Billions of dollars are pouring into efforts to reverse aging as scientists investigate ways to return cells to a younger state. But how close are these experimental treatments? And are they likely to work?  At a recent virtual Roundtables event, MIT Technology Review explored the answers with science editor Mary Beth Griggs and senior biotechnology reporter Jessica Hamzelou. Subscribers can now watch the full recording of the fascinating discussion. MIT Technology Review Narrated: the search for dark matter has been blown wide open For decades, physicists have hunted for weakly interacting massive particles (WIMPs), a leading candidate for dark matter. But their search has run into a new problem: neutrinos.  These tiny particles from the sun and other stars can create a “neutrino fog” that drowns out any signal of dark matter. Hitting the neutrino fog does not, however, mean an end to the search. Researchers just have to shift the focus of their hunt. They’re now casting a much wider net. New proposals include quantum sensors, liquid-helium detectors, and even searches in Jupiter’s atmosphere.

—Dan Garisto This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish 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 The US has lifted restrictions on Anthropic’s Mythos and Fable modelsAnthropic said it would begin restoring access today. (NYT $)+ The US had imposed controls over security concerns. (Bloomberg $)+ It lifted the restrictions after lengthy talks with Anthropic. (BBC)+ But the crackdown has already opened doors for Chinese AI rivals. (CNBC)2 The most detailed survey of the universe ever is now underwayIt’s using the largest digital camera on Earth. (New Scientist $) + The project is based at the Vera C. Rubin Observatory in Chile. (NYT $)+ It aims to transform our view of the cosmos. (MIT Technology Review) 3 Tech talent is fleeing the US due to H1-B visa chaosThey’re eyeing relocation to Canada, the UK, or the Gulf. (Rest of World)+ While China is poaching AI talent from the US. (CNBC)+ Visa rules are also affecting young scientists. (MIT Technology Review) 4 Trump raked in more than $1 billion from crypto businesses in 2025He reported $635 million in royalties from a Trump meme coin. (BBC)+ The rest largely came from his World Liberty Financial venture. (The Hill) 5 The UN warns that the rapid spread of AI may worsen global inequalityIt’s proposed a shared framework for responsible AI development. (Guardian)6 Companies are making LLMs talk like a caveman to curb AI spendingA senior OpenAI employee contributed to the “caveman” project. (404 Media) 7 Babies are born with the neural foundations for mathBrain recordings have identified the mechanisms. (New Scientist $)8 An independent studio has bought the OpenAI movie Amazon droppedNeon has purchased “Artificial,” which focuses on Sam Altman. (NYT $)+ Amazon had dumped it after investing in OpenAI. (Gizmodo)+ The depiction of Altman is reportedly unsympathetic. (Variety)9 AI has re-created Gene Wilder’s voice for a new “Willy Wonka” seriesWilder’s wife said his estate is “delighted” with the new show. (NBC News)+ Netflix partnered with AI company ElevenLabs on the project. (The Verge)10 NASA aims to send a spare Mars rover—and soccer ball—to the moonThe nuclear-powered “Promise” may help establish a lunar base. (NYT $) Quote of the day “Caveman save you token, save you money.”  —The GitHub repository for the “caveman” plugin explains how the project curbs AI spending by turning verbose LLM outputs into concise text. One More Thing
SELMAN DESIGN AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work. On average, it takes more than 10 years and billions of dollars to develop a new drug. A growing number of startups are betting that AI can make the process faster and cheaper.  By predicting how potential drugs might behave in the body and discarding dead-end compounds before they leave the computer, machine-learning models can cut down on the need for painstaking lab work. 
Yet it is still early days for AI drug discovery. A lot of AI companies are making claims they can’t back up—and the technology is not a panacea. But the technology is beginning to move from promise to practice. Find out how AI is speeding up drug discovery.