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The next-generation Lexus LFA may reset expectations of electric supercars.

This enticing new model shares its aluminum architecture with the Toyota GR GT.

Lexus isn’t interested in using simulated gear shifts like Hyundai and Porsche.

Amid a sea of high-revving, big-capacity supercars and hypercars at last week’s Goodwood Festival of Speed, the future Lexus LFA made a subtle and very silent dynamic debut. The successor to the company’s iconic supercar, you see, will be all-electric.

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Lexus previewed the next-gen LFA last December, showcasing it alongside the Toyota GR GT, which shares its aluminum architecture. However, whereas that car uses a powerful V8 engine, the new LFA will be electric, a far cry from the original car’s all-screaming, all-conquering, Yamaha-tuned V10.

Read: These Future Supercars From Toyota And Lexus Share DNA But Not A Soul

While this will disappoint many rev-heads, the new LFA could get tech enthusiasts very excited.

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First Lexus On Solid-State Cells

According to a report from Autocar, the new LFA will be the first production Lexus to run advanced solid-state battery cells. Toyota has spent years developing them, and they now look close to ready for the road. Solid-state cells hold more energy in less space than the conventional liquid-based packs in use today, which is where their advantage lies.

Speaking to the publication, Shogo Kasamatsu, who penned the recent LFA concept, said Lexus wants to upend how people think about performance EVs. The concept’s restrained look drew on the original LFA, which he called “a very humble design, very artistic, and based on its true function.” The car was shaped to carry that message, he added, rather than march in step with Lexus’s current design language.

Similarly, LFA program general manager Yukihiro Yukita said Lexus wants to convince supercar buyers to opt for the LFA over a more conventional, ICE-powered alternative. He acknowledged that weak demand for high-priced performance EVs is the program’s biggest hurdle, but said Lexus intends to lead the shift from combustion supercars to electric ones.

Making A Fun EV?

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“What I get from the market is that a BEV is fake,” he said, “because we imitate the sound [of an ICE], but that’s not something we want to do.” Lexus has no interest in trailing Porsche, Hyundai, and Mercedes-AMG by faking gear shifts or piping in engine noise. Yukita wants drivers to “feel like they are driving with an engine” without cheaply mimicking one.

“We’re not just wanting to replicate the sound of the engine, we want to redesign the sound itself,” he said. Engineering teams will strip out unnecessary noise and vibration and rework what remains, sharpening the sensory experience rather than manufacturing a fake one. EVs give up the sound and vibration that pull a driver in, he admitted, but electric motors answer with a responsiveness and linearity no combustion engine can match.

It all sounds rather fascinating, but it’s hard to imagine how Lexus will be able to get anywhere close to matching the thrills of that old V10.

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QUICK FACTS

Name: Tianchi Lake, or Heaven Lake

Location: China-North Korea border

Coordinates: 42.0091, 128.0593

Why it's incredible: It is the deepest lake in China and sits at the top of a giant volcano.

Heaven Lake is a crater lake at the top of Mount Changbaishan (also called Changbai Mountain) — a colossal, dormant volcano on the border between China and North Korea that formed through successive eruptions over the past 2.6 million years.

Known in China as Tianchi, the lake is the highest and largest crater lake in Northeast Asia, according to UNESCO . It is also the deepest lake in China, according to a study published in March. The lake sits at an elevation of around 7,200 feet (2,200 meters), covers roughly 3.6 square miles (9.2 square kilometers), and has a maximum depth of 1,224 feet (373 m), according to the study.

Heaven Lake is surrounded by 16 peaks that belong to Mount Changbaishan. The lake fills a caldera created by past eruptions, the biggest of which was the "millennium eruption" that took place in A.D. 946 and remains one of the largest eruptions in modern history.

Water started to pool on Mount Changbaishan's summit after the prehistoric Tianwenfeng eruption, which scientists dated to between 70,000 and 40,000 years ago. Heaven Lake has periodically emptied and refilled since then due to precipitation, snowmelt, and an active geothermal system beneath the volcano that forces water up through fault lines .

Although hundreds of reports in the early 2000s claimed there was a creature with a head shaped like that of a horse living in the water, scientists have repeatedly said they are skeptical that any large creature could survive in Heaven Lake.

Mount Changbaishan is one of the best-preserved stratovolcanoes from the past several million years, according to UNESCO . Stratovolcanoes, also known as composite volcanoes, are volcanic mountains built from alternating layers of solidified lava, volcanic ash and rocky debris. Mount Changbaishan is an "open-air classroom for volcanism" because it recorded the different stages of multiple eruptions in astounding detail.

In North Korea, Mount Changbaishan is known as Mount Paektu or Paeku Mountain, meaning "white-topped mountain." The volcano's Chinese name means "forever white mountain."

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China, North Korea and South Korea have clashed over the volcano in the past , as the mountain holds cultural and geopolitical significance for the three countries. Two border treaties between China and North Korea in 1962 and 1964 divided the volcano and Heaven Lake roughly down the middle, with North Korea securing 54.5% of the lake.

In the 2000s, China moved to develop the region around the volcano, opening the Mount Changbai Airport and the Mount Changbai Eastern Railroad to connect the region with the rest of the country.

A geological park surrounding the Chinese site of the volcano was officially designated a UNESCO Global Geopark in 2024.

Discover more incredible places , where we highlight the fantastic history and science behind some of the most dramatic landscapes on Earth.

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BERLIN – Der Großteil der Industrie zeigt sich enttäuscht nach der Vorstellung der Änderungen am ETS . Nur die Deutsche Industrie- und Handelskammer (DIHK) lobt die Vorschläge größtenteils als Entlastung.

„Teils sinnvolle Signale“, kommentiert Holger Lösch, stellvertretender Hauptgeschäftsführer des Bundesverbands der Deutschen Industrie (BDI). Die Vorschläge lieferten „aber weder ausreichende Voraussetzungen für Investitionen […], noch eine überzeugende Antwort auf die fortschreitende Deindustrialisierung“.

„Die Pläne der EU-Kommission sind gefährliche Augenwischerei und riskieren, den Industrieumbau zu einem brutalen Industrieabbau zu machen“, sagte VCI-Hauptgeschäftsführer Wolfgang Große Entrup. Klimaschutz dürfe nicht zur Deindustrialisierung führen.

Der ETS-Review bleibe „weit hinter den Erwartungen der energieintensiven Industrien zurück“, kommentiert Alexander von Reibnitz, Hauptgeschäftsführer der Papierindustrie, die Vorschläge der Europäischen Kommission. Es fehle eine verlässliche Perspektive für die Zeit bis 2040.

Für die DIHK sind die Vorschläge hingegen „wichtige Weichen“. „Werden sie so umgesetzt, ermöglichen sie Industrie und Kraftwerken einen gangbaren Weg zu Emissionsminderung und stärken den Schutz vor Carbon-Leakage, also der Verlagerung von Emissionen ins Ausland“, sagte DIHK-Präsident Peter Adrian.

Die wichtigsten Ergebnisse des ETS-Reviews hat die Kommission heute präsentiert. Dazu gehört, dass die freie Zuteilung nun bis 2038 verlängert werden soll. Zudem werden demnach bis 2030 mehr Zertifikate verschenkt als bisher. Die Kommission will kostenlose Zuteilungen für die Dekarbonisierung aber an Bedingungen knüpfen: 80 Prozent der EU-Mittel werden den Unternehmen gewährt, sobald diese einen Investitionsplan zur Dekarbonisierung vorlegen. Die restlichen 20 Prozent würden erst nach Umsetzung der Investitionen bereitgestellt werden. Rat und Parlament müssen noch zustimmen, bevor die Vorschläge gültig werden.

Der „Investitionszwang“ sei „kontraproduktiv“, sagt Lösch vom BDI. Die Unternehmer und nicht der Staat müssten entscheiden, wo investiert wird, so Große Entrup vom VCI. „Die Politik hat Investitionen zu ermöglichen – nicht zu verordnen.“

Dass auch nach 2039 noch Zertifikate versteigert werden, ist für Adrian von der DIHK ein „überfälliger Schritt“. Der bisherige Reduktionspfad sei zu steil und das frühere Auslaufen „mit Blick auf das Klimaziel 2050 unangemessen“.

Künftig sollen die Mitgliedstaaten 50 Prozent der Einnahmen in die Dekarbonisierung investieren. „Solange Stromnetze, Wasserstoffinfrastruktur sowie Transport- und Speicherlösungen für CO₂ nicht in ausreichendem Maße bereitstehen, können Unternehmen ihre Transformation nicht umsetzen“, sagte Adrian.

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We’re in what’s being dubbed the ‘year of AI ROI’. Four years on from the AI boom - ignited by the launch of ChatGPT - many businesses now believe that they’re AI-ready, using successful early-stage chatbot and copilot rollouts as evidence.

Yet there is a significant difference between experimenting with AI and embedding it across complex business processes and operations.

The real barrier to this transformation sits beneath the models themselves. While most AI outcomes-related conversations focus on model performance, GPUs, and compute capacity, organisations are increasingly realising that it’s their data infrastructure holding AI projects back.

Up until now, businesses have got away with operating successfully despite their disconnected file environments, inconsistent data governance, and information spread across multiple repositories.

This is because historically, data has been accessed sporadically, mostly by human employees who could compensate for shortcomings when data lacked context or wasn’t where it should be.

Simply put, data infrastructure didn’t need to be perfect, so it wasn’t. However, these fragmented file environments are now impossible to ignore as AI demands 24/7 consistent access to information, strong governance, and trusted data.

This puts enterprises in an uncomfortable situation: under pressure to deploy AI, but missing the foundations needed to be truly successful with AI projects at scale.

AI makes the data puzzle more complicated

Traditional file environments were built for a world where information lived in separate locations, individual teams managed access, and gaps in governance or data availability could be worked around.

This means that enterprises are now operating with fragmented information spread across multiple locations, with inconsistent governance and varying levels of accessibility. When data is accessed primarily by human employees, these inefficiencies are a source of frustration rather than a barrier.

This is where AI changes things. Models depend on fast, reliable access to information. If data is difficult to find, lacks context or cannot be accessed consistently, its value diminishes rapidly. In this instance, enterprises have data, but it’s completely unusable.

Overconfidence is coming at the expense of real success

Most organizations realize that their data infrastructure is far from perfect. Yet, the pressure to demonstrate progress on AI is intense. If businesses aren’t investing in and experimenting with AI, they’re seen as behind the curve. This is creating a strong incentive to deploy new tools as quickly as possible.

The problem is that organizations' early-stage AI learnings, if not outright success, are creating an overconfidence in their wider AI readiness and ability to scale up. Chatbots and copilots, which provide a lower barrier to entry to AI, are enabling organizations to demonstrate early, tangible AI outcomes.

When these projects deliver value, enterprises start to think that perhaps their infrastructure is future-proofed after all, despite a lack of sustained attempts to address and resolve deeper structural issues around their data accessibility, governance and infrastructure. These same businesses are also struggling with data recovery after cyber incidents - the warning signs are there, but current overconfidence is blurring reality.

As a result, businesses are moving rapidly onto major agentic projects, only to face delays, questions over ROI, and often, an implementation that fails. The irony is that an urgency to deploy AI is actually slowing long-term progress.

Organizations hellbent on deploying AI are risking overlooking the deeper risk management and data management work required to support it, while those that invest in strong data foundations are more likely to achieve sustainable success as their AI ambitions grow.

Shifting data from a cost to an asset

Most businesses don’t suffer from a lack of data; if anything, they have the opposite problem. Unfortunately, this is still viewed as an operational cycle driven by capacity planning, refresh cycles and expansion requirements, rather than a strategic asset. Industry research consistently shows IT teams still struggle to get a grip on their unstructured data - where most of their data assets reside - despite most of them believing their file data setup is robust.

The difference now is that AI systems operate continuously, drawing on information created today, yesterday, and sometimes decades ago. To deliver meaningful outcomes, they require consistent access to trusted data, rich context, and clear governance.

This means that organizations need to rethink their approach to enterprise information. When data is treated purely as a storage refresh challenge, AI quickly exposes the limitations of that approach. However, when it’s treated as a strategic asset, organizations can create environments where information is accessible, governed and ready to behave in the way that AI expects.

C-Suites’ goal should be to move from steady-state storage capacity to data utility, creating centralized environments with fewer systems, where data can be used no matter where it is located. This shift reduces fragmentation, simplifies data management and creates a unified view of the enterprise’s information, and with it the path to competitive edge.

The fewer barriers that exist between AI and the data it needs, the easier it becomes to move from isolated AI successes to meaningful deployment at scale for productivity and efficiency benefits.

Fix the foundations before scaling AI

The success of chatbot or copilot rollouts does not indicate AI readiness. It is instead measured by data utility: whether the data underpinning AI tools is accessible, secure, and fit for purpose at scale.

The organizations that succeed with AI in the coming years will not be those deploying the most advanced models or the biggest number of agents, but those who can provide AI with consistent access to trusted, well-governed data.

Those boards that keep investing in sophisticated AI tools without upgrading their data layer and governance will find their operations shackled for years to come by the same underlying data access and governance limitations.

It’s time to stop viewing data as an operational burden and start treating it as a strategic asset, helping unleash organizations' true IP and powering their ability to innovate. Building strong data foundations today will set organizations on the path to realizing long-term AI value, instead of discovering down the line that AI ambitions outpace their ability to support them.

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