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Germany’s Electric Car Boom Grows Even as Carmaker Profits Fall in 2026

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Germany’s automotive market is undergoing a significant transformation as consumers increasingly turn toward electric vehicles (EVs), while the country’s major carmakers struggle with declining profitability.

New data highlights two interconnected developments: electric cars are gaining ground not only among new buyers but also in the used-car market, while the average operating profit generated by leading manufacturers per vehicle fell sharply during the first half of 2026.

According to new data from German insurer HUK Coburg, growing numbers of drivers are switching from conventional petrol and diesel vehicles to electric cars. Particularly significant is the acceleration of electric mobility in the used-vehicle market.

This suggests that the transition toward electric transport is moving beyond wealthier consumers purchasing new vehicles and is gradually becoming accessible to a broader section of German motorists.

The expansion of EVs in the second-hand market could become an important driver of adoption.

New electric cars remain expensive for many households, but as more vehicles enter the used market, consumers have greater opportunities to purchase EVs at lower prices. Improved availability, greater consumer familiarity and the expansion of charging infrastructure could further strengthen this trend.

The growing popularity of electric cars is taking place against a difficult backdrop for the automotive industry. An analysis by the Center of Automotive Management found that the average operating profit earned per vehicle by 15 major carmakers declined sharply during the first half of 2026.

The figures highlight the pressure facing manufacturers as they attempt to finance the transition to electric mobility while dealing with intense competition and changing consumer demand.

The decline in profitability is particularly important because producing electric vehicles requires substantial investment.

Carmakers must spend billions of euros on battery technology, software, new production facilities and charging-related partnerships. At the same time, manufacturers face pressure to reduce prices as competition increases, particularly from Chinese automakers that have expanded their presence in global EV markets.

The result is a difficult balancing act. Carmakers need to invest aggressively in the technologies that will define the industry’s future, but they must also protect margins and satisfy shareholders.

Lower profits per vehicle can restrict the amount of money available for investment precisely when the industry requires enormous capital expenditure. Germany’s automotive sector therefore finds itself at a crossroads.

Consumers appear increasingly willing to embrace electric mobility, with the used-car market providing an important pathway for broader adoption. Manufacturers, meanwhile, must adapt to a market in which traditional advantages in combustion-engine technology are becoming less decisive.

The developments demonstrate that the electric transition is no longer simply a question of environmental policy. It is becoming a fundamental economic and competitive issue.

Companies that can produce attractive EVs efficiently, control battery costs and develop profitable software-driven services may gain an advantage, while those unable to adapt could face further pressure.

The stakes are particularly high because the automotive industry remains a major pillar of its industrial economy. The rise of used EVs indicates that consumer behavior is changing rapidly. The simultaneous decline in operating profit per vehicle shows that manufacturers are paying a significant price for that transition.

The challenge for Germany’s carmakers will be to turn rising electric-vehicle demand into sustainable profitability. The coming years could determine which companies successfully navigate this transformation and which struggle to remain competitive in an increasingly electric global automotive market.

Uniper Turns Power Plant Sites Into AI Data Centre Hubs

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State-owned German energy company Uniper is exploring a new strategy to generate revenue from its existing power plant infrastructure by establishing artificial intelligence (AI) data centres at selected sites.

The plan reflects a growing convergence between the energy and technology sectors, as the rapid expansion of AI creates enormous demand for reliable electricity and strategically located computing infrastructure.

AI data centres require far more power than conventional data centres because they rely on energy-intensive graphics processing units and advanced computing systems. As companies race to develop increasingly sophisticated AI models, demand for computing capacity is rising sharply.

This has created a new challenge for the technology industry: securing sufficient electricity at competitive prices while maintaining reliable and scalable power supplies.

For Uniper, this trend could create an opportunity to transform power plant locations into valuable digital infrastructure hubs. Rather than relying solely on traditional electricity generation and trading.

The company could use its existing sites, grid connections and energy expertise to support data-centre development. Such a strategy could provide an additional source of revenue while giving technology companies access to locations with established energy infrastructure.

The idea highlights the changing economic value of power infrastructure. Historically, power plants were designed primarily to generate electricity for households and industrial customers. The rise of AI is creating a new category of electricity demand in which computing facilities themselves become major energy consumers.

Locating data centres close to power generation and transmission infrastructure could potentially reduce some of the challenges associated with connecting large new loads to the grid.

Germany is particularly interested in strengthening its position in the AI economy while managing its broader energy transition. The country has invested heavily in renewable energy, but its industrial economy requires dependable electricity supplies.

AI data centres add another layer of complexity because they generally need continuous power and highly reliable grid connections. Uniper’s proposal therefore represents more than a property-development strategy.

It could become part of a broader effort to connect Germany’s energy infrastructure with its emerging digital economy. Former or underused industrial sites could potentially be repurposed for high-value technology activities, helping regions attract investment, create jobs and generate new tax revenues.

However, the strategy faces significant challenges. Data centres consume substantial amounts of electricity and require cooling systems, communications infrastructure and significant capital investment.

Local grid capacity may also become a constraint if multiple large computing facilities seek connections in the same region. Germany will need to balance the economic benefits of AI infrastructure against concerns about energy availability, network congestion and environmental impact.

For Uniper, the timing is significant. The company operates within an energy market undergoing major structural changes, with renewable generation, electrification and evolving power prices reshaping traditional business models.

Developing partnerships with data-centre operators could diversify its revenue base and position the company closer to one of the fastest-growing sources of electricity demand. Uniper’s plans illustrate how AI is changing the economics of infrastructure.

The future data centre may not simply be a technology facility seeking electricity from the grid; it could increasingly become an integral component of the energy system itself. By turning power plant sites into potential AI hubs.

Uniper is betting that the next generation of digital infrastructure will be built around access to energy. If successful, the strategy could offer Germany a model for combining its industrial heritage, energy assets and ambitions to become a leading AI economy.

CoreWeave Shares Jump 11% After AI Cloud Revenue Beats Estimates, Backlog Hits $104bn

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CoreWeave shares jumped 11% in extended trading on Tuesday after the artificial intelligence infrastructure provider reported quarterly revenue above Wall Street expectations, highlighting continued demand for computing capacity even as the company carries a heavy debt burden to fund its rapid expansion.

CoreWeave reported revenue of $2.58 billion for the quarter, compared with the $2.56 billion expected by analysts surveyed by LSEG. Revenue more than doubled from a year earlier, rising 112%.

The company reported an adjusted loss of $1.14 per share. Its net loss widened sharply to $626 million, or 60 cents per share, from $290 million, or 60 cents per share, a year earlier.

The revenue growth underscores the scale of spending on AI infrastructure as technology companies race to secure access to powerful graphics processing units needed to train and run sophisticated AI models.

CoreWeave’s revenue backlog, a key measure of contracted future business, reached $104 billion by the end of the quarter. The company had 1.5 gigawatts of active power supporting its infrastructure.

The backlog provides CoreWeave with substantial visibility into future revenue, but it also reflects the enormous investment required to fulfill those contracts. The company has been borrowing heavily to purchase Nvidia GPUs, data-center equipment, and other infrastructure needed to expand its capacity.

CoreWeave had about $35 billion of debt on its balance sheet at the end of the quarter, making its ability to convert its rapidly growing revenue and backlog into sustainable cash flow particularly important for investors. The company is competing directly with much larger cloud providers such as Amazon Web Services, Google Cloud and Microsoft Azure, which have substantially greater financial resources and established data-center networks.

CoreWeave’s strategy has been to focus heavily on AI computing rather than compete across the broader cloud-services market. That specialization has helped the company secure major contracts as AI developers and technology companies seek additional computing capacity amid shortages of advanced chips and data-center infrastructure.

Meta Platforms committed an additional $21 billion of spending with CoreWeave during the quarter. CoreWeave also announced a multiyear agreement with Anthropic and a $6 billion commitment from quantitative trading firm Jane Street.

Those deals have helped propel CoreWeave’s contracted backlog to levels far beyond its current annual revenue, suggesting that demand for specialized AI infrastructure remains strong.

But the challenge is turning that demand into profits.

CoreWeave’s net loss more than doubled from a year earlier even as revenue more than doubled. The discrepancy illustrates the capital-intensive nature of the AI infrastructure business. Building data centers, securing electricity, purchasing GPUs and financing those assets can require billions of dollars before the associated computing contracts generate sufficient returns.

The company’s debt load therefore remains one of the most important issues for investors. Higher borrowing costs or delays in bringing new data centers online could put pressure on margins and cash flow, particularly if GPU economics deteriorate or customers reduce their AI infrastructure spending.

Competition is also expanding beyond traditional cloud providers.

SpaceX has begun offering excess computing capacity, potentially adding another source of AI infrastructure supply. Meta has also considered launching its own cloud business, which could eventually give one of CoreWeave’s major customers an alternative way to obtain computing capacity.

But the competitive environment could become more challenging as the world’s largest technology companies continue to build their own AI data centers and develop increasingly specialized computing infrastructure. CoreWeave’s business is therefore closely tied to the broader AI capital-spending cycle.

So far, major technology companies have shown little willingness to materially slow investments in AI infrastructure, supporting demand for companies that can provide additional computing capacity.

CoreWeave’s $104 billion backlog offers a substantial cushion against a near-term slowdown in demand, but fulfilling those contracts will require continued investment. Its $35 billion debt burden makes execution necessary because the company must expand capacity while generating enough cash to service its obligations.

CoreWeave shares had gained about 26% this year through Tuesday’s close, compared with a gain of almost 13% for the S&P 500. The stock began trading on the Nasdaq in March 2025.

The company’s latest results give investors another indication of the extraordinary scale of the AI infrastructure buildout. Revenue growth of 112% and a $104 billion backlog show that demand remains powerful, but the widening loss and massive debt load underline the financial risks involved in trying to capture that growth.

Manus Returns to Independence as China Forces Unwinding of Meta’s $2 Billion AI Bet

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AI startup Manus is returning to independent operations after Chinese regulators forced U.S. technology giant Meta to unwind its more than $2 billion acquisition of the company, marking one of the clearest examples yet of how governments are tightening control over ownership of strategically important artificial intelligence technologies.

The company said Tuesday that the separation from Meta will also require the deletion of some user data as it disentangles its systems and operations from the U.S. technology group. Certain data generated by users on or after December 29, 2025, will be deleted later this month to comply with regulatory requirements in specific jurisdictions, Manus said.

Affected users will be notified through the Manus app and by email and will be given an opportunity to back up their information before the deletion takes effect.

“This is part of our separation from Meta,” the company said in a statement.

The announcement closes a remarkable chapter for Manus, which only months ago appeared destined to become part of Meta’s expanding artificial intelligence portfolio. Instead, the startup is being returned to independent ownership after Beijing concluded that the transaction could not proceed under China’s stringent rules governing foreign investment in companies developing advanced technologies.

Meta agreed to acquire Manus in a deal valued at more than $2 billion as part of its effort to strengthen its capabilities in advanced AI agents, software designed to carry out complex tasks with limited human supervision.

Chinese authorities ordered the transaction to be unwound in April after reviewing the ownership of technology developed by Manus and the implications of transferring control to a U.S. company. The decision came amid a broader tightening of China’s oversight of foreign investment in companies working on frontier AI, semiconductors and other strategically sensitive technologies.

The ruling underpins a shift in how Beijing views AI startups. While Chinese companies have historically welcomed foreign capital to accelerate growth, regulators are now treating advanced AI capabilities as assets with national strategic importance, making cross-border acquisitions far more difficult.

For Meta, the outcome marks an unusual reversal. Major technology companies routinely acquire promising startups and integrate their engineers and intellectual property into broader AI strategies. In this case, regulatory intervention effectively forced the company to dismantle an acquisition that had already been completed.

The planned deletion of user data offers a glimpse into the technical challenges involved in unwinding a major technology acquisition.

Separating cloud infrastructure, user records, software systems and compliance obligations is considerably more complex than simply changing ownership. Manus said the deletion is necessary to satisfy regulatory requirements in certain jurisdictions as it re-establishes itself as a standalone company.

Although the company has not disclosed the number of users affected, its decision to provide advance notice and a backup window is intended to allow customers to preserve information before the transition is completed.

Tencent Emerges As A Potential New Anchor Investor

Attention is now turning to what Manus will look like after its separation from Meta.

Chinese internet and gaming giant Tencent has been in talks to become Manus’ largest shareholder, according to Reuters, as the startup and its existing investors seek to restore an ownership structure following the collapse of the Meta transaction. The proposed arrangement would involve Tencent alongside existing backers such as ZhenFund and HSG.

A Tencent investment would provide Manus with substantial financial resources, cloud infrastructure and distribution capabilities while keeping the company outside the control of a U.S. technology group.

The discussions also emphasize how domestic investors in China are increasingly stepping in to support companies whose international ownership plans have been complicated by geopolitical tensions.

Manus attracted global attention after unveiling an AI agent designed to autonomously perform multi-step tasks, including research, coding and web-based workflows, with relatively limited user intervention.

The company quickly became one of the most closely watched startups in the emerging market for AI agents, a field that has attracted intense investment from companies including OpenAI, Anthropic, Google and Meta.

However, it is believed that the collapse of the Meta-Manus transaction is likely to resonate well beyond the two companies. The deal shows that AI acquisitions are now being judged not only on traditional competition grounds but also on questions of national security, technology transfer, data sovereignty and strategic industrial policy.

Governments in both China and the United States have introduced measures aimed at controlling investment in advanced technologies, creating a more complicated environment for multinational technology companies seeking to buy promising AI startups across borders.

The result is a global AI industry that is becoming more fragmented, with ownership, investment and access to technology shaped by geopolitical considerations as much as commercial strategy.

The immediate priority for Manus will be rebuilding as an independent company, reassuring users during the data transition and securing long-term financing. If Tencent’s investment proceeds, the startup could emerge from the failed Meta acquisition with a powerful domestic shareholder and a renewed mandate to compete independently in one of the technology industry’s fastest-growing markets.

What Instagram Tracking Tools Cost in 2026

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Instagram tracking prices now span a very wide range. Some account insights remain free. Budget subscriptions may cost only several dollars monthly. Professional plans can reach hundreds per user. The advertised rate rarely tells the whole story. History limits, account caps, exports, and taxes change totals. Annual discounts help only when access stays useful.

Free Access Covers Basic Questions

Free access works best for checking owned accounts. Instagram offers free insights for public professional accounts. These reports cover reach, engagement, audience trends, and content results. Metricool includes one brand and 30 days of analytics. Buffer includes three channels and ten queued posts per channel. Both free plans limit history or publishing capacity. Manual tracking becomes the hidden price of staying free.

Budget Plans Depend on the Billing Unit

Buffer Essentials costs $5 monthly per channel with annual billing. One Instagram channel therefore costs $60 yearly. Five channels raise that total to $300. Annual payment carries a 20 percent discount. Advanced analytics and unlimited scheduling enter the paid plan. The low starting price suits one or two accounts. Larger portfolios make channel counts important.

Metricool uses brands rather than individual channels. Its annual Starter price begins at $20 monthly. That equals $240 for twelve months. Monthly billing begins at $25, or $300 yearly. Starter covers up to five brands and unlimited analytics history. The free plan stores only 30 days. The annual choice saves $60 at this level.

Specialized Tracking Uses Shorter Billing Cycles

Specialized tracking solves a narrower Instagram problem. Current FollowSpy pricing starts at $12 weekly for Starter. Premium is listed at $14 weekly. Starter tracks up to two Instagram accounts. It includes recent follow activity and anonymous Story viewing. Premium adds unlimited searches and tracked accounts. The pricing reflects focused access rather than publishing management.

Four Starter weeks cost $48. Four Premium weeks cost $56. Those totals suit a short period of focused checking. A full year would require a different comparison. Weekly access offers more flexibility than an annual contract. It can also become costly when left active unnecessarily. The expected usage period should be decided first.

FollowSpy also displays quarterly billing with a discount. The page currently advertises 30 percent off quarterly plans. Starter fits limited account tracking. Premium fits users needing unlimited account searches. Both plans include the anonymous Story viewer. The best value depends on account volume and duration. A lower tier stops being cheaper when its limits block the task.

Professional Plans Price Workflow and Collaboration

Later Starter costs $18.75 monthly when billed yearly. The annual total is $225. It includes one social set and one user. Scheduling is limited to 30 posts per profile. Analytics history reaches three months. This plan serves a small publishing routine. It does not target personal follower monitoring.

Later Growth costs $37.50 monthly with annual billing. That becomes $450 across one year. It includes two social sets and two users. Analytics history extends to one year. Scheduling rises to 180 posts per profile. Collaboration and approvals also enter the package. The higher rate pays for team workflow.

Extra access can raise the Later bill. Another social set costs $11.25 monthly. An additional user costs $3.75 monthly. Extra artificial intelligence credits cost another $3.75. Taxes are not included in displayed prices. A growing team can outgrow the headline rate quickly. Final cost needs the actual account and user count.

Sprout Social begins far above budget services. Standard is listed at $199 per seat monthly. One seat costs $2,388 across twelve months. Professional costs $299 per seat monthly. Advanced reaches $399 per seat monthly. These plans target formal reporting and team processes. A second seat can sharply increase spending.

The Lowest Price Can Hide the Highest Cost

Annual discounts need a usage test. Later offers 25 percent off yearly billing. Metricool advertises savings up to 24 percent. Buffer gives 20 percent off annual plans. Each discount lowers the effective monthly charge. Each also reduces short term flexibility. Paying yearly makes little sense for a brief project. Commitment belongs inside the cost calculation.

History limits often decide whether a plan works. Thirty days supports basic weekly checks. Three months can cover a campaign. One year supports seasonal comparison. Unlimited history protects longer reporting work. Cheap access loses value when old data disappears. Rebuilding missing records costs time. That labor should appear in the budget.

Taxes and additions can change the final invoice. Metricool excludes applicable VAT from listed prices. Later states that taxes are extra. Buffer charges separately for each connected channel. Later charges for added users and social sets. Sprout Social charges by seat. The visible monthly number is only a starting point.

The billing unit reveals the intended customer. Per channel rates suit small account collections. Brand pricing fits grouped business profiles. Per seat pricing supports structured teams. Weekly pricing supports a defined monitoring period. No structure is always cheaper. Each rewards a different usage pattern. Comparing unlike jobs produces weak conclusions.

A practical choice begins with required outcomes. Count every account that needs access. Decide how much history must remain available. Add exports, anonymous viewing, scheduling, and approvals. Include every person needing a login. Choose the shortest billing cycle that completes the work. Instagram tracking may cost nothing or hundreds monthly. The correct price is the first plan that solves the full task.