Technology

Billions of people unlock their phone with their face every day. Apple Face ID. Google face unlock. Neither would pass the identity standard the EU is about to enforce 😳 And here's the uncomfortable part: the verification most banks use today wouldn't either. Document-based identity verification was never designed for the digital world. Passports have UV and infrared security features that can only be checked physically. Online, you're verifying a photograph of a secure document. A photograph. That was fine when faking an identity required skill, time, and resources. In 2026, AI does it in seconds. → Synthetic identities pass document capture → AI-generated faces clear liveness checks → Deepfakes defeat the "blink twice, turn your head" routine → Full identity packages are assembled faster than compliance teams can update their rules Verifying a document no longer means you've verified a person. And that's a fraud problem, not just a compliance one. Governments see it. That's why they're not patching the old model - they're replacing it with digital-native identity. Government-backed eIDs that are cryptographically assured, not photographed. You're not trusting a scan. You're trusting a digitally signed identity that AI can't fake. And regulation is now forcing the pace. eIDAS 2.0 is law. The AML Regulation update hits in July 2027. Banks, insurers, and telecoms are being pushed toward higher-assurance identity - fast. But 150+ eID schemes exist worldwide, each with different standards. Integrating them individually doesn't scale. That's what Hopae is building - a global eID network connecting 100+ government-backed identity schemes through a single integration. One API. Access to the highest-assurance digital identities globally. This is how fraud actually goes down. Not by adding another layer to a broken process, but by replacing the process entirely. If you want to see how this is being built in practice, take a look here: https://lnkd.in/dW9pa6wV

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable. After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design to orchestrate how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips. Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for Taiwan manufacturing. If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence. Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more. But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways. [Original text: https://lnkd.in/gxR48TK8 ]

💉 A failed injection attempt is more than just a missed vein. It’s time lost. It’s patient discomfort. Sometimes, it’s fear that stays long after the needle is gone. I’ve seen it. You probably have too. We often underestimate how much trust is at stake in that single moment. Now, here’s what most people don’t know: near-infrared devices like VeinViewer are quietly changing this. They project invisible light into the skin. Blood absorbs it, tissue reflects it, and veins suddenly appear in high contrast — mapped right on the patient’s arm in real time. Studies show it makes a real difference: ✅ Higher first-stick success rates ✅ Less leakage, less pain ✅ Happier patients — especially children, people with small or hidden veins, and those who’ve lived through too many failed attempts But here’s the deeper insight: These devices don’t change outcomes equally for everyone. For easy cases, they don’t matter much. For difficult ones, they can be life-changing. That’s a lesson far beyond medicine: technology delivers its true value where the human struggle is greatest. To me, that’s the real story. Innovation isn’t about making the easy easier. It’s about transforming the moments where people suffer most. 💡 My take: The future of healthcare tech won’t be defined by speed or cost savings alone. It will be defined by whether we remember this — that behind every data point is a patient who just wants to be seen, heard, and spared unnecessary pain. 👉 Would you want your next IV placed with or without this tech? A brilliant invention by Christie Medical #Healthcare #Innovation #PatientExperience #MedTech #AI

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

#Batteries are starting to dominate the evening peak in California's grid, charging up with daytime solar then discharging as solar ramps down. On 5th April they set another new record for share of supply, peaking at over 34% at 7pm. This represents a rapid progression - two years ago the record was just 13%. And they remained the largest source of supply on the grid from 6:35pm until 9:40pm. As more and more battery storage enters the mix, batteries will continue to play an increasing role in the state's grid, and continue to break more records. They are flexible and extremely quick to respond. By charging in the middle of the day they are soaking up excess solar and are then putting this to good use later, reducing the need for gas and imports in the nighttime hours. From just 0.5 GW in 2018, by late 2024 California already had over 13 GW of battery storage capacity, with more on the way. While that may sound like a lot, there is still some way to go with the California Energy Commission estimating the state will need around 52 GW of battery storage to meet it's 2045 target of getting all its power from carbon-free sources. Batteries will play an important role in the decarbonised grid of the future. As prices continue to fall we will see more and more batteries deployed, and are certainly seeing this happen in Australia - especially Western Australia. We are just on the cusp of much more widespread adoption. Onwards and upwards! #energy #sustainability #renewables #energytransition

Germans have installed 500,000 balcony solar arrays, or "balkonkraftwerk." Installations has surged thanks to simplified permitting, the ability to buy panels at hardware stores for a few hundred dollars, and renters (not just owners) being able to install the panels. Cumulatively all of the installations account for 200MW of solar. Each system is small -- capped 800 watts, but I like the spirit of individual energy independence and decisions consumers can make to combat climate change and protect their wallets from surging electricity costs. Great example of deploying climate tech for the built world that exists now and makes financial sense. Image via Solar Monkey #realestate #climate

As technology underpins nearly every aspect of business today, basic cybersecurity knowledge is an essential skill for all IT professionals. Understanding core security concepts allows IT teams to effectively safeguard their organizations. Being well-versed in core concepts is crucial for identifying and mitigating risks that could severely impact organizations. Key areas IT professionals should grasp: - Phishing - Recognizing phishing scams can prevent costly data breaches that damage reputations. - Ransomware - Knowledge of ransomware tactics ensures business continuity and protects against financial losses. - Denial-of-Service (DoS) - Understanding DoS attacks helps maintain service availability, critical for customer trust. - Man-in-the-Middle (MitM) - MitM attack awareness safeguards confidential communications vital for internal and client interactions. - SQL Injection - Expertise in preventing SQL injection protects database integrity, often the backbone of digital infrastructure. - Cross-Site Scripting (XSS) - For web developers, awareness of XSS threats is essential for application integrity and trust. - Zero-Day Exploits - Knowing potential zero-day exploits encourages proactive security and constant vigilance. - DNS Spoofing - Grasping DNS spoofing risks prevents misdirection leading to data theft and unauthorized access. As IT professionals, we have a responsibility to understand and mitigate these top cyber risks to safeguard our digital ecosystem. Ongoing education in this crucial discipline is key to our success. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

🇷🇺 🗞️ How Russia selectively controls the impunity enjoyed by Cybercriminals: an enlightening report issued this week by INSIKT Group / Recorded Future, documenting how the Russian cyber-criminal ecosystem shifted from broad tolerance to managed control. 🔎 Research from May 2024–Sept 2025 using data from dark-web forums, leaked chats, public enforcement.. It sheds light on Operation Endgame, a multinational takedown effort from May 2024 & shows how it changed ground dynamics 🔹It targeted loaders, enablers, money-mules and infrastructure 🔹The actions signalled to the ecosystem: the cost-benefit calculus for operating from/within Russia has shifted; enforcement is not zero-risk. 🔹The selective pressure triggered changes in the underground: fragmentation, tighter vetting, paranoia, evolving ransomware TTPs, group rivalries, payment/target strategies 🔹The “politics of protection” = enforcement or lack thereof signals which actors are expendable and which are strategically useful. Take-aways 1️⃣ A managed market 🔹 🇷🇺cyber-criminal ecosystem has evolved from near-blanket tolerance toward selective State management: actors with little strategic value are targeted, those providing intelligence, geopolitical leverage & state utility are insulated. 🔹protection no longer depends on location. 🔹Direct, task-level coordination between cyber-criminal leadership and Russian intelligence. In addition, the“Dark Covenant” model (direct, indirect, tacit links) remains operative. 2️⃣ Underground ecosystem adapts 🔹Affiliates are less visible; open-call RaaS (ransomware-as-a-service) programs declined in public forums 🔹Operators have heightened vetting: deposits, KYC-lite checks, stricter inactivity rules. 🔹Business rules: some ransomware programs explicitly exclude nonprofits, healthcare, government entities; minimum ransom demands; anti-collision rules. These act as both reputational hedges and political boundary markers. 🔹Impersonator groups proliferate: façade ransomware groups or “scam” groups trying to ride brand equity = erodes trust & raises barriers to entry. 🔹Forum discussions show increased emphasis on OPSEC: moving to decentralized communication: burner phones, hidden volumes.. 3️⃣ Enforcement signals / “politics of protection” • Russian authorities have taken visible action against certain monetisation/enabler nodes (e.g., Cryptex, UAPS) • By contrast, core high-value ransomware groups (Conti, Trickbot) have avoided this= insulation via state-links. 4️⃣ Cyber-criminal groups are increasingly embedded in Russia’s geopolitical strategy 🔹 arrests, releases, negotiations align with diplomatic cycles, prisoner exchanges. 🔹Cyber-crime = a hybrid instrument of state influence, intelligence gathering, plausible deniability & leverage. ➡️ defenders should understand the state-criminal bargain 🔹Disruption strategies need to target also the enablers (cash-out, money-laundering, hosting) 📰 ☕️ enjoy the weekend read!

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.