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Tata Motors acquires Iveco Group in €3.8 billion deal

Tata Motors acquires Iveco Group in €3.8 billion deal

India's Tata Motors is taking over the Italian- headquartered Iveco Group for a reported €3.8 billion (NZ$ 7.45 billion), with the purchase including FPT Industrial (Fiat Power Trains), which builds engines, powertrains and front axles for tractors and other agricultural and construction machines. Iveco was part of CNH Industrial until January 2022, when it was separated and has since operated as an independent company. In addition to FPT, Iveco also manufactures commercial vehicles, trucks and mining vehicles. The reason for the demerger was to allow CNH to focus on the off-road sector, while Iveco would look after the commercial vehicle industry. At the time, 27% of Iveco’s shares were still held by Exor NV, the holding company of the Agneli family, best known for the Fiat brand. Looking at the wider picture, the change of ownership will probably be unnoticed in the farming community, but with 36,000 employees, 19 global production sites and 31 R&D locations, the sale of Iveco is likely to raise many eyebrows in the truck and bus industries around the globe. Employing 8000 staff located at 10 global production sites and 11 R&D centres, FPT manufactures 100,000+ engines in the F5, F28, V, NEF and Cursor Series each year. Its OEM customers include many well-known agricultural brands such as Case IH, New Holland and Steyr, Argo Tractor’s Landini and McCormick, Antonio Carraro, Claas and LS Mtron. Combining the Iveco Group with its commercial vehicle business, Tata Motors will become a colossus that produces 540,000 commercial vehicles each year, with a likely annual turnover of around €22 billion. Half this revenue will be sourced in Europe, with the remainder coming from India, The Americas and the continually emerging markets of Asia and Africa. The Iveco Group headquarters will remain in Turin, Italy, with the acquisition expected to be completed by mid- 2026. Read More: Stoll unveils first ISOBUS-integrated front-end loader Drill combo for different soil types Turin makes two millionth NEF engine #IVECO_GROUP #TATA_MOTORS

Manuka honey trader posts sour results

Manuka honey trader posts sour results

Manuka honey trader Comvita slumped to a $104 million net loss last financial year, reflecting prolonged market disruption, oversupply and pricing volatility. Limited financial headroom and capital constraints restricted the company to respond to market challenges and has forced the sale of Comvita to Florenz Ltd, a subsidiary of billionaire Mark Stewart’s Masthead Limited, for $56 million. This month, Comvita announced a Scheme Implementation Agreement (SIA) with Florenz, under which Florenz will acquire 100% of Comvita shares at $80c/share - a 56% premium to the 90-day volume-weighted average price (VWAP) at the date of announcement. Comvita chair, Bridget Coates says the company continues to navigate sustained structural pressure in the Mānuka honey sector, with prolonged oversupply, pricing volatility, and softer consumer demand weighing heavily on margins. Coates says these external pressures have been compounded by internal complexity, underperforming investments, and the cost of delivering a significant turnaround. “We weren’t fast enough to adjust - and those same market dynamics have only intensified. “Limited financial headroom and capital constraints have further restricted the Company’s ability to respond at pace. As a result, FY25 performance was materially impacted, including significant impairments. Read More: Lower sales, competition ends honey exporter's sweet run Comvita flies into airport Manuka sweetens land values “We recognise the impact this has had on our shareholders and the importance of delivering a clear path forward. “The company has taken urgent and decisive action to strengthen its balance sheet and operational footing, and important progress has been made. Free cash flow is now positive, net debt has been reduced, and greater discipline has been applied to inventory and cost management. These are early but important signs that the reset is beginning to take hold. “However, the scale of the challenges means the reset alone will not be enough to restore balance sheet strength. That is why, following a comprehensive review of all strategic options, the board signed a Scheme Implementation Agreement with Florenz in August. “In our view, the scheme provides greater certainty in a difficult environment, a choice for our diverse shareholder base and the strongest foundation for Comvita’s long-term brand, people, and market leadership.” Comvita’s revenue for year ending June 2025 reached $192m, 4% lower than the previous year. Gross profit was 24% to $82m and net loss 30% worse than the previous year. Performance across Comvita’s global markets was mixed in FY25, with strong growth in North America and wider Asia offset by decline in China and Australia and NZ. China remained the most challenging market, with sales down 10.9% and profit down 24.8% versus FY24. Economic slowdown and oversupply pressured margins, and recovery is expected to be slow despite early gains from distribution resets and structural simplification, the company says. #COMVITA

Google’s estimate of AI resource consumption leaves out too much

Google’s estimate of AI resource consumption leaves out too much

Figures published by Google last week minimizing the energy and water consumption of individual queries answered by its AI services are still not giving us the full picture of AI energy use, according to an article in MIT Technology Review on Thursday. The writer went on to raise further questions about AI’s resource consumption that enterprise IT leaders will need to consider in their budget and ROI calculations. The article in Technology Review highlighted the elements missing from Google’s report of its AI resource consumption , a report that has already raised questions elsewhere . Those missing details make it all but impossible for enterprises to extrapolate future costs or environmental impacts. Google’s estimate for the water and electricity consumption — five drops and a quarter of a watt-hour — of a single text query to its AI services “doesn’t reflect all queries and it leaves out cases that likely use much more energy,” such as images or videos, the article’s author Casey Crownhart wrote. Crownhart co-authored a much deeper dive into AI’s energy footprint for Technology Review in May. And Google’s estimate is just the median value — half the text queries it handles use less energy, and half more: “We don’t know anything about how much energy these more complicated queries demand or what the distribution of the range is,” Crownhart wrote. By choosing to publish just the consumption of a single query, Google minimized the impact of its AI. “We don’t know how many queries Gemini is seeing, so we don’t know the product’s total energy impact,” she wrote. Rival AI operator OpenAI does share total traffic figures, saying that it sees 2.5 billion queries to ChatGPT every day, while Google has only said that Gemini has 450 million monthly active users. And that number only describes a fraction of Google’s AI impact, as it also uses the technology to provide AI summaries in web searches, and to help draft or summarize emails and texts, Crownhart noted, concluding, “So even if you’re trying to think about your own personal energy demand, it’s increasingly difficult to tally up.” The impact for IT But it’s not just personal: Enterprises too are paying for these AI services and, indirectly, the cost of their energy and water consumption. As the cost of these inputs rises, IT departments must make budget projections based on the anticipated number and nature of AI queries: Text? Video? Complex or simple analysis? If CIOs are trying to project those costs for 2026, they will have to make some difficult guesses about new capabilities and new players too. CIOs may have to consider the direct cost of those inputs too, as they explore the possibility of bringing cloud computing back in house. Setting aside the question of whether they can obtain the needed components, such as volume deliveries of NVIDIA chips, this move would force them to directly deal with energy and water challenges — not just the cost but, depending on where they chose to build, their availability too. “If you, as a CIO, are not speaking with your operations and facilities teams around forecasting power requirements versus power availability, start immediately,” said Matt Kimball , VP/principal analyst for Moor Insights & Strategy. “Having lived in the IT world, I am well aware of how separate these organizations can be, where power is just a line item on a budget and nothing more. Talk to the team that’s managing power, cooling and datacenter infrastructure — from the rack out — to better understand how to use these resources most efficiently.” It’s not just computing capacity that contributes to the cost of AI: IT needs to reexamine existing storage operations too, Kimball said. “I would take a long look at my storage infrastructure and how to better optimize on and off prem. The infrastructure populating most enterprise datacenters is out of date and underutilized. Moving to servers that have the latest, densely populated CPUs is a first start,” he said. “Moving on-prem storage from spinning media to all flash has a higher up-front cost, but is far more energy efficient and performant. It’s easy to buy into the NVIDIA B300 or AMD MI355X craze. Or the Dell, HPE, or Lenovo AI factories. But is this much horsepower required for your AI and accelerated computing needs? Or are, say, RTX6000 PRO GPUs good enough? They are far more affordable and about 40% of the power consumption compared with a B300.” A different perspective comes from Simon Ninan , SVP of business strategy at Hitachi Vanta, a company that sells many of these services, as the scale of these data centers is forcing IT to reconsider all previous assumptions about power usage. “AI’s energy demands are rendering traditional air cooling insufficient,” he said. “We’re seeing an increasing shift to liquid cooling for AI data centers, but a massive investment is also needed in innovations that cater to environmental boundaries.”