You’ve seen the headlines: This battery breakthrough is going to change the electric vehicle forever. And then … silence. You head to the local showroom, and the cars all kind of look and feel the same.

WIRED got annoyed about this phenomenon. So we talked to battery technology experts about what’s really going on in electric vehicle batteries. Which technologies are here? Which will be, probably, but aren’t yet, so don’t hold your breath? What’s probably not coming anytime soon?

“It’s easy to get excited about these things, because batteries are so complex,” says Pranav Jaswani, a technology analyst at IDTechEx, a market intelligence firm. “Many little things are going to have such a big effect.” That’s why so many companies, including automakers, their suppliers, and battery-makers, are experimenting with so many bit parts of the battery. Swap one electrical conductor material for another, and an electric vehicle battery’s range might increase by 50 miles. Rejigger how battery packs are put together, and an automaker might bring down manufacturing costs enough to give consumers a break on the sales lot.

Still, experts say, it can take a long time to get even small tweaks into production cars—sometimes 10 years or more. “Obviously, we want to make sure that whatever we put in an EV works well and it passes safety standards,” says Evelina Stoikou, who leads the battery technology and supply chain team at BloombergNEF, a research firm. Ensuring that means scientists coming up with new ideas, and suppliers figuring out how to execute them; the automakers, in turn, rigorously test each iteration. All the while, everyone’s asking the most important question: Does this improvement make financial sense?

So it’s only logical that not every breakthrough in the lab makes it to the road. Here are the ones that really count—and the ones that haven’t quite panned out, at least so far.

It’s Really Happening

The big deal battery breakthroughs all have something in common: They’re related to the lithium-ion battery. Other battery chemistries are out there—more on them later—but in the next decade, it’s going to be hard to catch up with the dominant battery form. “Lithium-ion is already very mature,” says Stoikou. Lots of players have invested big money in the technology, so “any new one is going to have to compete with the status quo.”

Lithium Iron Phosphate

Why it’s exciting: LFP batteries use iron and phosphate instead of pricier and harder-to-source nickel and cobalt, which are found in conventional lithium-ion batteries. They’re also more stable and slower to degrade after multiple charges. The upshot: LFP batteries can help bring down the cost of manufacturing an EV, an especially important data point while Western electrics struggle to compete, cost-wise, with conventional gas-powered cars. LFP batteries are already common in China, and they’re set to become more popular in European and American electric vehicles in the coming years.

Why it’s hard: LFP is less energy dense than alternatives, meaning you can’t pack as much charge—or range—into each battery.

More Nickel

Why it’s exciting: The increased nickel content in lithium nickel manganese cobalt batteries ups the energy density, meaning more range in a battery pack without much more size or weight. Also, more nickel can mean less cobalt, a metal that’s both expensive and ethically dubious to obtain.

Why it’s hard: Batteries with higher nickel content are potentially less stable, which means they carry a higher risk of cracking or thermal runaway—fires. This means battery-makers experimenting with different nickel content have to spend more time and energy on the careful design of their products. That extra fussiness means more expense. For this reason, expect to see more nickel use in batteries for higher-end EVs.

Dry Electrode Process

Why it’s exciting: Usually, battery electrodes are made by mixing materials into a solvent slurry, which then is applied to a metal current collector foil, dried, and pressed. The dry electrode process cuts down on the solvents by mixing the materials in dry powder form before application and lamination. Less solvent means fewer environmental and health and safety concerns. And getting rid of the drying process can save production time—and up efficiency—while reducing the physical footprint needed to manufacture batteries. This all can lead to cheaper manufacturing, “which should trickle down to make a cheaper car,” says Jaswani. Tesla has already incorporated a dry anode process into its battery-making. (The anode is the negative electrode that stores lithium ions while a battery is charging.) LG and Samsung SGI are also working on pilot production lines.

Why it’s hard: Using dry powders can be more technically complicated.

Cell-to-Pack

Why it’s exciting: In your standard electric vehicle battery, individual battery cells get grouped into modules, which are then assembled into packs. Not so in cell-to-pack, which puts cells directly into a pack structure without the middle module step. This lets battery-makers fit more battery into the same space, and can lead to some 50 additional miles of range and higher top speeds, says Jaswani. It also brings down manufacturing costs, savings that can be passed down to the car buyer. Big-time automakers including Tesla and BYD, plus Chinese battery giant CATL, are already using the tech.

Why it’s hard: Without modules, it can be harder to control thermal runaway and maintain the battery pack’s structure. Plus, cell-to-pack makes replacing a faulty battery cell much harder, which means smaller flaws can require opening or even replacing the entire pack.

Silicon Anodes

Why it’s exciting: Lithium-ion batteries have graphite anodes. Adding silicon to the mix, though, could have huge upsides: more energy storage (meaning longer driving ranges) and faster charging, potentially down to a blazing six to 10 minutes to top up. Tesla already mixes a bit of silicon into its graphite anodes, and other automakers—Mercedes-Benz, General Motors—say they’re getting close to mass production.

Why it’s hard: Silicon alloyed with lithium expands and contracts as it goes through the charging and discharging cycle, which can cause mechanical stress and even fracturing. Over time, this can lead to more dramatic battery capacity losses. For now, you’re more likely to find silicon anodes in smaller batteries, like those in phones or even motorcycles.

It’s Kind of Happening

The battery tech in the more speculative bucket has undergone plenty of testing. But it’s still not quite at a place where most manufacturers are building production lines and putting it into cars.

Sodium-Ion Batteries

Why it’s exciting: Sodium—it’s everywhere! Compared to lithium, the element is cheaper and easier to find and process, which means tracking down the materials to build sodium-ion batteries could give automakers a supply chain break. The batteries also seem to perform better in extreme temperatures, and are more stable. Chinese battery-maker CATL says it will start mass production of the batteries next year and that the batteries could eventually cover 40 percent of the Chinese passenger-vehicle market.

Why it’s hard: Sodium ions are heavier than their lithium counterparts, so they generally store less energy per battery pack. That could make them a better fit for battery storage than for vehicles. It’s also early days for this tech, which means fewer suppliers and fewer time-tested manufacturing processes.

Solid State Batteries

Why it’s exciting: Automakers have been promising for years that groundbreaking solid state batteries are right around the corner. That would be great, if true. This tech subs the liquid or gel electrolytes in a conventional li-ion battery for a solid electrolyte. These electrolytes should come in different chemistries, but they all have some big advantages: more energy density, faster charging, more durability, fewer safety risks (no liquid electrolyte means no leaks). Toyota says it will finally launch its first vehicles with solid state batteries in 2027 or 2028. BloombergNEF projects that by 2035, solid state batteries will account for 10 percent of EV and storage production.

Why it’s hard: Some solid electrolytes have a hard time at low temperatures. The biggest issues, however, have to do with manufacturing. Putting together these new batteries requires new equipment. It’s really hard to build defect-free layers of electrolyte. And the industry hasn’t come to an agreement about which solid electrolyte to use, which makes it hard to create supply chains.

Maybe It’ll Happen

Good ideas don’t always make a ton of sense in the real world.

Wireless Charging

Why it’s exciting: Park your car, get out, and have it charge up while you wait—no plugs required. Wireless charging could be the peak of convenience, and some automakers insist it’s coming. Porsche, for example, is showing off a prototype, with plans to roll out the real thing next year.

Why it’s hard: The issue, says Jaswani, is that the tech underlying the chargers we have right now works perfectly well and is much cheaper to install. He expects that eventually, wireless charging will show up in some restricted use cases—maybe in buses, for example, that could charge up throughout their routes if they stop on top of a charging pad. But this tech may never go truly mainstream, he says.

The precise technological definition of 6G networks is still some time away, but it seems clear that one of the biggest anticipated challenges of 6G network roll-outs will be coverage limitations inherent in 6G’s higher-frequency spectrum, and looking to alleviate the issue, Nokia and Rohde & Schwarz have created and tested a 6G radio receiver that uses artificial intelligence (AI) technologies to overcome potential limitations.

The global comms tech provider and test and measurement company are confident that the fruits of their work – namely AI-powered receiver technology using machine learning – can greatly enhance future 6G coverage, creating cost savings and accelerating time to market.

From a core technological basis, the AI technology is designed to identify and compensate for distortion in wireless signals, leading to substantial improvements in 6G uplink coverage.

Specifically, the machine learning capabilities in the receiver are designed to boost uplink distance greatly, enhancing coverage for forthcoming 6G networks. This will help operators roll out 6G over their existing 5G footprints, reducing deployment costs and accelerating time to market.

Nokia Bell Labs developed the receiver and validated it using 6G test equipment and methodologies from Rohde & Schwarz. The firms say they tested the AI receiver under real-world conditions, achieving uplink distance improvements compared with current receiver technologies ranging from 10% to 25%.

The testbed comprised an R&S SMW200A vector signal generator, used for uplink signal generation and channel emulation. On the receive side, an FSWX signal and spectrum analyser from Rohde & Schwarz was employed to perform the AI inference for Nokia’s AI receiver.

In addition to enhancing coverage, the firms noted that the AI technology also demonstrated improved throughput and power efficiency, multiplying the benefits it will provide in the 6G era.

Assessing the work done on the project, Peter Vetter, president of core research for Bell Labs at Nokia, said: “One of the key issues facing future 6G deployments is the coverage limitations inherent in 6G’s higher-frequency spectrum. Typically, we would need to build denser networks with more cell sites to overcome this problem. By boosting the coverage of 6G receivers, however, AI technology will help us build 6G infrastructure over current 5G footprints.”

Michael Fischlein, vice-president of spectrum and network analysers, EMC and antenna test at Rohde & Schwarz, added: “Rohde & Schwarz is excited to collaborate with Nokia in pioneering AI-driven 6G receiver technology.

“Leveraging more than 90 years of experience in test and measurement, we’re uniquely positioned to support the development of next-generation wireless, allowing us to evaluate and refine AI algorithms at this crucial pre-standardisation stage. This partnership builds on our long history of innovation and demonstrates our commitment to shaping the future of 6G.”

The work with R&S comes just a week after Nokia announced a strategic partnership with Nvidia to add the former’s AI-powered RAN products to Nokia’s RAN portfolio, enabling communication service providers to launch AI-native 5G Advanced and 6G networks on Nvidia platforms.

With their AI-RAN systems, Nokia and Nvidia are confident that mobile operators can improve performance and efficiency as well as enhance network experiences for future generative AI and agentic AI applications and experiences. They will be able to introduce AI services for 6G with the same infrastructure, powering billions of new connections for cars, robots, drones, and augmented and virtual reality glasses that demand connectivity, computing and sensing at the edge.

When writing program code, software developers often work in pairs—a practice that reduces errors and encourages knowledge sharing. Increasingly, AI assistants are now being used for this role.

But this shift in working practice isn’t without its drawbacks, as a new empirical study by computer scientists in Saarbrücken reveals. Developers tend to scrutinize AI-generated code less critically and they learn less from it. These findings will be presented at the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025) in Seoul.

When two software developers collaborate on a programming project—known in technical circles as pair programming—it tends to yield a significant improvement in the quality of the resulting software.

“Developers can often inspire one another and help avoid problematic solutions. They can also share their expertise, thus ensuring that more people in their organization are familiar with the codebase,” explains Sven Apel, professor of computer science at Saarland University.

Together with his team, Apel has examined whether this collaborative approach works equally well when one of the partners is an AI assistant. In the study, 19 students with programming experience were divided into pairs: Six worked with a human partner, while seven collaborated with an AI assistant. The methodology for measuring knowledge transfer was developed by Niklas Schneider as part of his bachelor’s thesis.

For the study, the researchers used GitHub Copilot, an AI-powered coding assistant introduced by Microsoft in 2021, which—like similar products from other companies—has now been widely adopted by software developers. These tools have significantly changed how software is written.

“It enables faster development and the generation of large volumes of code in a short time. But this also makes it easier for mistakes to creep in unnoticed, with consequences that may only surface later on,” says Apel. The team wanted to understand which aspects of human collaboration enhance programming and whether these can be replicated in human-AI pairings. Participants were tasked with developing algorithms and integrating them into a shared project environment.

“Knowledge transfer is a key part of pair programming,” Apel explains. “Developers will continuously discuss current problems and work together to find solutions. This does not involve simply asking and answering questions, it also means that the developers share effective programming strategies and volunteer their own insights.”

According to the study, such exchanges also occurred in the AI-assisted teams—but the interactions were less intense and covered a narrower range of topics.

“In many cases, the focus was solely on the code,” says Apel. “By contrast, human programmers working together were more likely to digress and engage in broader discussions and were less focused on the immediate task.”

One finding particularly surprised the research team: “The programmers who were working with an AI assistant were more likely to accept AI-generated suggestions without critical evaluation. They assumed the code would work as intended,” says Apel. “The human pairs, in contrast, were much more likely to ask critical questions and were more inclined to carefully examine each other’s contributions.”

He believes this tendency to trust AI more readily than human colleagues may extend to other domains as well, stating, “I think it has to do with a certain degree of complacency—a tendency to assume the AI’s output is probably good enough, even though we know AI assistants can also make mistakes.

Apel warns that this uncritical reliance on AI could lead to the accumulation of “technical debt,” which can be thought of as the hidden costs of the future work needed to correct these mistakes, thereby complicating the future development of the software.

For Apel, the study highlights the fact that AI assistants are not yet capable of replicating the richness of human collaboration in software development.

“They are certainly useful for simple, repetitive tasks,” says Apel. “But for more complex problems, knowledge exchange is essential—and that currently works best between humans, possibly with AI assistants as supporting tools.”

Apel emphasizes the need for further research into how humans and AI can collaborate effectively while still retaining the kind of critical eye that characterizes human collaboration.