It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat.
As the summer ramps up and we see this kind of weather sweep around the Northern Hemisphere, I’m always keeping my eye on the power grid. And one notable update that caught my attention this week was news that a nuclear power plant in the south of France had to close down because of the heat.
Climate change is squeezing the grid from all sides, affecting both supply and demand. Heat can affect power availability, from generation to transmission infrastructure, as I covered in my latest story. But climate change is also helping push electricity use higher—and countries in Europe and around the world will need to adapt.
In the US, nearly 90% of homes have air-conditioning. That means many grids see their highest demand in the summer months, and the risk of brownouts and blackouts is at its worst.
People are often quick to cast air-conditioning as a villain, and it’s true that the technology will account for a major chunk of the globe’s rising energy demand in the future. But the reality is that heat waves can be incredibly dangerous, and as climate change pushes temperatures higher, that risk is becoming more real in parts of the world that haven’t historically had to worry quite so much about heat.
In Europe, air-conditioning is historically much less common, with about 20% of homes across the continent using it. Some countries, including those getting hit by this heat wave, have even lower rates—the UK comes in at about 5%, and Germany is around 3%.
But those numbers are starting to tick up as people adapt to increasingly brutal summers. As they do, we should expect higher electricity demand, and stress for the grid—just as in the US. And utilities often have to look across borders to buy more power, driving prices up for everyone.
“The main pressure comes from a triple squeeze: Cooling demand rises sharply, while power plants and grids become less efficient, and some thermal and nuclear plants must cut output because cooling water is too warm or scarce,” says Simone Tagliapietra, senior fellow at Bruegel, an economic and policy think tank, via email.
Grid planning in the age of climate change generally means that we need a lot more supply, and quickly. But one interesting facet to this challenge is that in some places, seasonal patterns are shifting, compounding the difficulty of meeting demand.
Generally, grid operators plan maintenance and outages at power plants around expected peaks in demand. Take nuclear power, for example. In the US, planned outages for maintenance and refueling tend to come in the spring and fall when demand falls below the summer and slightly smaller winter peaks.
Europe, however, has historically seen its grid peak in the winter, because electric heating is more common than air-conditioning. So some planned outages happen in the spring and into the summer, which is affecting the supply right now.
At the Golfech power plant near Toulouse in France, for example, unit two had to shut down this week because of the water temperatures in the nearby river, which is used to cool the reactor. But unit one was already offline because of planned maintenance and refueling, according to EDF, the plant’s operator.
We’re going to continue to see record-high temperatures around the world because of climate change. Communities are adapting, and utilities will have to follow. And if you thought this summer was hot, just wait until next year. With the El Niño weather pattern, 2027 could very well blow these heat waves out of the water.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Two senators are renewing their push to greatly expand access to methadone treatment for opioid addiction.
On Thursday, Sens. Ed Markey (D-Mass.) and Rand Paul (R-Ky.) are introducing an updated version of legislation that would allow doctors who hold board certifications in addiction medicine to prescribe methadone directly to patients for pickup at a pharmacy.
SAN DIEGO — It is an unusual time to be in the vaccine business. But in the view of those gathered here at the BIO international conference this week, it’s not altogether a bleak one.
In fact, some vaccine makers are feeling more optimistic than they were a year ago — considering the circumstances, at least.
Two studies have received funding from the National Institute for Health and Care Research (NIHR) as part of an £8 million initiative supporting AI projects aimed at reducing NHS waiting times and improving patient care. Through its Invention for Innovation (i4i) programme, the NIHR awarded £8,136,409 to six projects testing a range of AI and […]
AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.
To understand this challenge, consider the foundation of the web itself. The web was not designed for the automated discovery and retrieval that new AI applications demand. Overcoming this inherent design constraint requires infrastructure.
The next frontier in AI may depend on a new web data infrastructure layer that can enable models to discover and map this ever-expanding digital realm. This layer must be able to navigate hundreds of millions of existing web domains and billions of new URLs created each week, delivering real-time information and overcoming technical barriers.
“The data suggests there’s far more data out there,” says Or Lenchner, CEO of Bright Data, a web data collection platform. “Think of the universe: It’s out there, but you don’t know what you don’t know.”
Enabling access to fresh, relevant, and trustworthy data
While early AI breakthroughs were driven by scaling training data and model size, organizations are now encountering a fundamental bottleneck: They need to keep pace with the dynamic, unstructured, and constantly evolving nature of web data in order to ground outputs in current and verifiable information. AI performance increasingly depends not just on model architecture but on a system’s compute, networking, retrieval, and data engineering capabilities—that is, the system’s ability to quickly and reliably retrieve data that is fresh, relevant, and trustworthy.
Traditional model training relies on snapshots of information collected at a particular point in time. Training AI on such static data is no longer sufficient. To track fluctuations such as competitor pricing, consumer sentiment, and market trends, companies need a constant feed of new information, pulling data in real time along with relevant context. Their infrastructure must therefore be able to handle millions of simultaneous interactions across websites that vary by geography, language, format, and access rules.
“If it can’t retrieve real-time information, it lacks context,” Lenchner says. “In a business setting, that’s not acceptable anymore. Stale answers lead to bad decisions and disappointed consumers.”
Speed is not merely a matter of convenience; it’s a matter of necessity. Today’s organizations operate in environments where prices, inventory, markets, security threats, and customer behavior change continuously. Delayed data retrieval can reduce the usefulness of an otherwise sophisticated model.
Using live, high-quality web data can also reduce AI hallucinations because the model has a more relevant knowledge base. This builds user trust. In fact, one survey found that 56% of AI practitioners said businesses need access to real-time web data to improve trust in AI outputs. To ensure the model runs efficiently and effectively, the information must also be pared down to the appropriate essentials.
Despite the introduction of retrieval-augmented generation (RAG), where models pull in external data at the moment of a query, many AI systems still struggle to deliver outputs that are current, contextually relevant, and trustworthy in operational settings. According to Gartner, 60% of AI projects that are not supported by AI-ready data—accurate, structured, organized, and contextualized—will be abandoned by the end of the year.
This is because large-scale retrieval alone does not solve the problem. As Lenchner puts it, “You need to retrieve data at scale, but also in real time. Latency becomes an issue because of the end user who is waiting for the output.”
Accessing fresh, AI-ready data at scale introduces technical and structural challenges. In practice, many enterprise systems combine public web retrieval with APIs, licensed datasets, and proprietary internal data in their AI applications. Integrating these fragmented sources into a timely and usable knowledge layer requires specialized capabilities. Some research has found that 97% of AI organizations depend on real-time web data infrastructure, but 90% feel boxed in by various restrictions. Companies are increasingly developing technical approaches to navigate these constraints.
Lenchner draws this metaphor: “Think of the trained model as intelligence and relevant data as knowledge. A powerful intelligence layer sitting on top of a hollow knowledge layer is like a genius who knows nothing—useless in practice. Intelligence and knowledge have to come together.”
The promise of new infrastructure
A new layer of web data infrastructure can address this developing need for stronger AI inputs by enabling discovery of data, real-time access, and tailoring to a specific context. As Lechner describes it, “It’s all about collecting data at scale, super-low latency, without being blocked.”
Rather than relying on increased computing power, this type of platform emulates human browsing behavior to access available content and transform raw code into structured data feeds. It can work with websites that might not interact with traditional scraping tools, such as those heavy in JavaScript, or with aggressive antibot software.
As Lenchner explains, “It’s basically having infrastructure that can mimic a web user with identifying information—IP address, location, and 1,000 more parameters. And at scale. Think of doing that 80 billion times a day for millions of websites. And every single time, you are looking exactly as the website expects you to look.”
Of course, continuous retrieval introduces new data governance challenges. To address them, platforms can enforce strict compliance protocols aligned with global privacy frameworks, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). They can also be limited to openly accessible, public information, avoiding paywalls or private logins. Any networks used can be vetted and consent-based, and incentives can be provided to owners of IP addresses. In this way, systems can be designed to comply with tightening regulation.
Such complex capabilities do not come easy. “When this is critical infrastructure for a company,” Lenchner says, “doing it in-house becomes a full-time engineering problem that competes with the actual AI work.” Addressing this complexity requires organizations to commit significant resources, leading many to seek specialized platforms designed specifically for data retrieval, orchestration, and observability.
Infrastructure for the real world
Real-time data retrieval is changing what AI systems can do inside organizations. For example, a retail company can use public information to enable a dynamic pricing engine, and global brands can track trademark infringements.
As the ecosystem matures, organizations that invest in this emerging data infrastructure layer will be better positioned to build AI systems that are more responsive, reliable, and aligned with real-world conditions—AI systems that can continuously adapt using current web data. Over time, the distinction between AI models and the infrastructure that feeds them may even begin to disappear.
As Lenchner says, “The world is changing. And everything that is happening in the world is being uploaded to the public web. The amount of new data that is being generated is growing and accelerating.”
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.
