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Your Daily Dose Of Technology News – November 11, 2025.
1. Microsoft Corporation to invest $10 billion in a new AI data-centre park in Portugal:
– Microsoft announced a major investment of US$10 billion to build an AI-data-centre campus along the coast of Sines, Portugal (~150 km south of Lisbon).
– The facility is being developed in partnership with the Portuguese developer Start Campus and UK-based startup Nscale.
– This is among Microsoft’s largest European investments in AI infrastructure this year; it reflects the high energy/compute demands of modern AI systems and the need for global infrastructure.
– Data-centre locations are chosen for factors such as cooling, power/renewables, connectivity, regulation. Europe wants to be competitive; cloud/AI providers want capacity.
As AI models (especially large ones) consume enormous compute—and data centres require massive power and cooling—where and how infrastructure is built becomes a strategic issue. Investing in Portugal gives Microsoft an edge in Europe and signals that AI hardware/infrastructure is now a major battleground, not just software.
In Other News:
2. Optical-engine collaboration: POET Technologies Inc. + Quantum Computing Inc. to build 3.2 Tbps optical engines for next-gen AI connectivity:
– POET Technologies (NASDAQ: POET) and Quantum Computing Inc. (QUBT) announced a strategic collaboration to develop 3.2 Terabits-per-second (Tbps) optical engines built around thin-film lithium niobate (TFLN) modulators at 400 G per lane.
– The proposed optical engine will be targeted at hyperscale AI/data-centre connectivity, aiming for completion in the second half of 2026.
– TFLN is cited as a high-performance material for photonic integration; combining POET’s Optical Interposer™ with QCi’s photonics aims to push the envelope in data-transfer speeds.
– As AI infrastructure scales, interconnect speeds between processors, memory, and storage become critical — optical links at Tbps scale can remove bottlenecks and enable more powerful systems.
This is a concrete signal that the hardware side of AI (not just models and software) is accelerating. Optical interconnects, next-gen modulators, and high-bandwidth chips are becoming indispensable. For companies building large AI systems, the infrastructure challenge is no longer trivial and will shape who can build next-gen platforms.
3. Meta Platforms launches “Omnilingual ASR” — speech recognition covering 1,600+ languages:
– Meta’s research arm (FAIR (Meta)) introduced a new model called Omnilingual ASR, which can process speech-to-text in more than 1,600 spoken languages, including roughly 500 languages that previously had little or no automatic‐speech‐recognition (ASR) support.
– The project is open source and includes a large dataset of under-represented languages; researchers claim error-rates (character error rate) below 10 % for about 78 % of these languages.
– The significance: Many AI systems today focus on major world languages (English, Mandarin, Spanish, etc.). Meta’s initiative aims to expand accessibility and equity in speech technologies across “low-resource” languages.
– Wider global reach for voice/AI interfaces, potential for improved translation/communication in underserved regions, but also risks around data privacy, representation and misuse.
Speech recognition is a key interface for the next wave of computing (voice assistants, ambient intelligence, IoT). Extending support to thousands of languages means many more users can be included — which both broadens markets and raises governance/protection questions. This signals that large-scale multilingual AI is becoming real rather than niche.
4. Tech valuations under pressure again; market sentiment shows cracks:
– US stock-market futures (for the NASDAQ Composite, S&P 500) were slightly lower on Nov 11 as concerns resurfaced about high valuations in technology and AI companies.
– The AI cloud-services provider CoreWeave trimmed its annual revenue forecast, which weighed on the broader AI/tech sector.
– Also, SoftBank Group, a major investor in NVIDIA Corporation, announced selling a large stake (~$6 billion) of its Nvidia holdings, which reinforced concerns about AI-hype excesses.
– The strong recent rally in tech/AI is being tested—investors are looking for more proof of earnings, sustainability, and less speculative risk.
When valuations come into question, it can affect corporate investment (slowing down), startup funding (harsher terms), and even public-company strategic choices. The warning signs in the tech market suggest caution: investors want more than potential—they want execution. For AI infrastructure firms, this could mean a tougher fundraising and growth environment.
5. Thought-leadership/ethics piece: “We need AI morals, not just regulation”:
– An opinion article in TIME argues that the much-discussed “AI regulation” is necessary but insufficient, and that what’s really missing is a foundation of AI morals — the moral compass of the humans designing and deploying the systems.
– When decisions are outsourced to data models, moral responsibility drifts from the human to the mechanical.
– This piece places the conversation outside of rule-books into the realm of values: human dignity, accountability, ethical design as a core principle.
– As AI systems get more autonomous (agents doing more than just “assist”), the question of who is responsible and how we build them becomes critical — not only for regulation, but for societal trust and adoption.
Ethics and technology are increasingly intertwined. For companies, ignoring the “moral” dimension may translate into regulatory risk, reputational risk, and slower adoption. For developers and users, thinking about “why” we use a tech (not just “can”) is becoming more important. This article reflects broader thinking in the field: that ethics must be integral, not just add-on.
Odds And Ends:
6. Gamma (AI “PowerPoint-killer”) lands $68 million Series B at a $2.1 billion valuation:
– Gamma, a San Francisco-based startup that uses AI to generate presentations, websites and social-media posts, announced a $68 million Series B funding round.
– They reported about $100 million in annual recurring revenue (ARR) and 70 million users, making this a notable growth-story in AI-assisted productivity tools.
This kind of startup shows how “enterprise productivity + generative AI” continues to be a hot category — not just large foundational models, but focused applications.
7. Lean Fintech (Saudi banking/fintech) explores deals on path to IPO:
– A fintech startup based in Saudi Arabia called Lean is reported to be exploring acquisition and strategic partnerships as it prepares for an eventual IPO.
– The story notes that Middle East tech startups raised a “record” ~$1.2 billion in the last quarter — despite broader emerging-market weakness.
This highlights the geographic diversification of tech-investment flows. While much attention is on US/China, the Middle East is becoming active — and IPO ambitions outside traditional hubs matter.
8. DHL Supply Chain expands use of AI-agent partner HappyRobot for operational communications:
– DHL Supply Chain announced that they have deepened their partnership with HappyRobot to deploy AI-agents to automate routine customer and operational communications.
– The move is aimed at improving efficiency, customer experience and internal employee engagement.
This is an example of enterprise‐scale deployment of “agentic AI” (bots doing more than chat) in logistics/operations — showing the maturation of AI beyond early experiments.
9. SAIA Agrobotics raises €10 million to automate greenhouses:
– The Dutch startup SAIA Agrobotics, spun out of Wageningen University & Research, announced a €10 million Series A funding round led by Check24 Impact with participation from the European Innovation Council (EIC) Fund and others.
– Their system moves plants to stationary robots for tasks like scanning, trimming and harvesting — the company claims up to 50% reduction in labour cost and ~20% yield increase.
– The funds will support a commercial rollout in 2026.
– Automated agriculture is becoming a major tech front, especially in Europe. This illustrates how robotics + AI is moving from lab to greenhouses, not just “funny robots”.
10. Department of Science and Technology (India) (DST) sets up ₹1 lakh crore (~US$12 billion) RDI Fund for deep-tech startups:
– The Indian DST announced a new Research, Development & Innovation (RDI) fund of ₹1 lakh crore (≈ US$12 billion) aimed at boosting deep-tech startups working in quantum computing, AI, robotics, drones, advanced communications.
– Currently only ~10% of Indian startup funding goes to deep-tech; the goal is to raise that to 30-50% over the next 5 years.
– The announcement was made during the IEEE Future Networks World Forum in Bengaluru.
Policy and public-capital shifts like this shape the future of national tech ecosystems. If India pushes into deep-tech with major funding, we may see a shift in global innovation geography.
Summary:
Infrastructure matters: The Microsoft $10-billion investment and the POET/QCi optical engine partnership highlight that hardware and physical infrastructure remain critical in the AI era.
Globalization of AI access: Meta’s Omnilingual ASR shows the push toward inclusive, multilingual AI — global reach is becoming a priority.
Valuation Caution: The tech-market is exhibiting signs of stress despite recent enthusiasm; strong execution and fundamentals are being demanded.
Ethics & Design: Regulation alone won’t suffice, there’s increasing recognition that moral design must be baked into AI systems.
Shift in data paradigms: As systems become more agentic, databases and backend systems must evolve from passive stores to active reasoning platforms.
If you need a summary on any specific topic or more detailed information on emerging tech trends, feel free to ask us @DawentsIT or visit our website at www.dawentsit.com
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