Technology Integration in Strategy

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

The Trojan Horse approach for sustainability careers. Most sustainability professionals don't start in sustainability roles. They begin elsewhere and strategically integrate their environmental expertise into core business functions. They understand that companies are not hiring sustainability experts. They are hiring experts who think sustainably. They master essential business capabilities first, then embed sustainability thinking throughout their work. This strategic integration creates professionals who speak the language of business while advancing environmental goals, across multiple business functions. Financial Services: Analysts and bankers are incorporating climate risk modeling into investment decisions and developing innovative green financing products. Operations Management: Engineers are implementing waste reduction and circular economy principles and designs into manufacturing processes. Technology Development: Software developers are building ESG data platforms and creating automated systems for carbon tracking and reporting. Strategic Planning: Business strategists are embedding long-term environmental considerations into corporate planning frameworks. Marketing and Branding: Marketers are developing purpose-driven and sustainable brands, and focusing on stakeholder engagement and transparency. The professionals advancing in the sustainability market are those who have established credibility in core business areas while developing deep environmental expertise. This combination enables them to influence decision-making from positions of established trust and competence.

FY2026 Signals Joint Defense Tech The Pentagon isn’t looking for more tech. It’s looking for tech that fits the fight. What wins? interoperable, multi-domain, coalition-ready tech that aligns with how the U.S. and its allies will fight. Hear me out… 1. Integration Is the Mission PE 0604826J is the COG for CJADC2. It funds interoperability pilots with NATO, secure data sharing across services, and cross-domain C2 experiments like Bold Quest. Your tech needs to plug into this joint ecosystem. 2. Multi-Domain C2 Is Non-Negotiable The budget holds firm on digital datalinks, secure comms, and allied data exchange. Your tech must talk across domains and allies, don’t expect traction. 3. Rapid Prototyping Isn’t Dead—It’s Evolving RDER may be gone, but its intent lives on. The budget still backs prototypes that can shape joint force design. Demo utility in a joint context and watch your TRL skyrocket. 4. Congress ‘All In on Joint Tech’ is a buying signal. • $400M → Joint Fires Network • $400M → Joint battle management tools • $1B → Accelerated tech fielding • $2B → DIU scaling commercial tech 5. AI/ML, Autonomy, C5ISR—Joint prioritization isn’t just lip service. Budget lines explicitly call out: • Multi-service unmanned systems • Maritime robotics • Coalition-ready EW and ISR

Here I attached the Cybersecurity Technology Stack. This poster is a complete visual guide to the key cybersecurity tools and technologies across all major categories from SIEM, EDR, XDR, SOAR, TIP, PAM, CSPM to deception technologies, UEBA and more. I created this to help professionals and newcomers get a clearer picture of what solutions are available and how they fit into the larger cybersecurity ecosystem. When I first started working in cybersecurity operations, most environments focused heavily on perimeter defence and endpoint protection. But attackers have evolved. Today, a proper setup requires multiple integrated layers that work together. No single tool is enough. What matters is how these tools connect to give visibility, control and speed in detection and response. If you're building or reviewing your cybersecurity stack, these are the key areas I recommend you consider: 1. Visibility with SIEM •Start with a strong SIEM platform. This will collect logs across your infrastructure from endpoints, firewalls, cloud and identity systems and help detect patterns or anomalies. 2. Real-time Threat Detection with EDR or XDR •Next, deploy EDR to get deep visibility into endpoint activities. If your budget allows, move towards XDR to combine endpoint, network and cloud telemetry into one detection layer. 3. Response Automation with SOAR •As alerts come in, you need a fast and consistent way to respond. A SOAR platform can automate triage, enrich alerts with threat intel and reduce the time analysts spend on manual tasks. 4. Threat Intelligence Integration •No matter how good your SIEM or EDR is, you need context. Use Threat Intelligence Platforms (TIP) to enrich data with external threat indicators and insights. 5. Secure Privileged Access with PAM •If an attacker gets access to a privileged account, the damage can be severe. Implement PAM to secure, manage and audit access to critical systems and credentials. 6. Vulnerability Management •A well-monitored environment still becomes weak if patching is not managed. Use vulnerability scanners and patch management systems to identify and remediate weaknesses quickly. 7. Cloud Security Posture and Identity Management •As more workloads move to the cloud, ensure you have CSPM tools and proper IAM controls in place to prevent misconfigurations and abuse of identity-based access. 8. Advanced Detection with NDR, UEBA, and Deception •For mature setups, consider adding Network Detection & Response, User Behaviour Analytics and deception technologies. These give you deeper layers of defence and help detect stealthy attacks. Building a modern cybersecurity setup is not about chasing tools, but designing an architecture where each solution complements the other. You want detection, correlation, automation and response to happen as smoothly as possible. This is the mindset behind the stack I designed. Every component in this poster plays a role in defending against modern threats.

The Gulf crisis just created the biggest startup opportunity in a decade. Five things Silicon Valley leaders need to understand right now: 𝗗𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘄 𝗺𝗶𝗹𝗶𝘁𝗮𝗿𝘆 𝘁𝗮𝗿𝗴𝗲𝘁𝘀. Iranian drones hit three AWS facilities. The Strait of Hormuz and Red Sea both data chokepoints are closed. The security frameworks behind the Gulf’s AI partnerships were built for chip export control, not for protecting buildings during a war. 𝗧𝗵𝗲 𝗱𝗲𝗳𝗲𝗻𝘀𝗲-𝘁𝗲𝗰𝗵 𝘁𝗵𝗲𝘀𝗶𝘀 𝗶𝘀 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴. The Pentagon set a $13.4B AI budget for FY2026 which is the largest in U.S. defense history. $130B+ in VC has flowed into defense-tech startups since 2021. → Palantir’s Maven system ran intelligence across five combatant commands → Anduril ($30.5B valuation) — Lattice OS selected as the Army’s fire control platform, Arsenal-1 factory producing autonomous systems at scale, OpenAI partnership for counter-drone AI → Shield AI ($5.3B) — Hivemind autonomous piloting completed AI vs. manned F-16 combat maneuvers → Epirus ($1.5B) — directed-energy counter-drone systems integrated with Anduril’s Lattice, directly relevant to Gulf drone defense → Saronic ($1.5B) — autonomous naval vessels applicable to Strait of Hormuz patrol → Hermeus ($1B+) — hypersonic aircraft for ISR and rapid strike → Ares Industries — Y Combinator’s first weapons company, building low-cost anti-ship missiles → Ursa Major ($2.5B) — rocket propulsion for supply chain independence Early-stage investors in this space are looking at generational returns. 𝗧𝗵𝗲 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝘄𝗮𝘃𝗲 𝗶𝘀 𝗵𝗲𝗿𝗲. Every hyperscaler is now rethinking geographic risk. That creates massive demand for: → Sovereign cloud infrastructure (hardened, government-grade, physically defensible) → Multi-region failover and edge computing platforms → Satellite backup connectivity (Aetherflux, Astranis) → Underground and modular data center designs → Cybersecurity for critical infrastructure against nation-state actors → Alternative compute capacity for displaced AI workloads (CoreWeave, Vultr) Startups solving resilience at the infrastructure layer will command premium pricing from both governments and hyperscalers. This is the next $100B+ category. 𝗚𝘂𝗹𝗳 𝗰𝗮𝗽𝗶𝘁𝗮𝗹 𝗶𝘀 𝗽𝗮𝘂𝘀𝗶𝗻𝗴 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗱𝗶𝘀𝗮𝗽𝗽𝗲𝗮𝗿𝗶𝗻𝗴. Sovereign wealth funds holding $2T+ in U.S. assets are reviewing commitments. The Stargate UAE mega-campus, Amazon’s $5.3B Saudi cloud all in limbo. But post-conflict, these governments will double down on tech diversification away from oil. Startups that maintain Gulf relationships now while diversifying their own risk will be first in line when capital flows resume. The Gulf’s structural advantages with sovereign capital, energy, ambition haven’t disappeared. But the risk has permanently shifted. Rapid de-risking without full retreat.

The best defense is a good (funding) offense. Investors, governments, and builders are all in on defense tech. In recent weeks, we saw major deals and announcements including Anduril's oversubscribed $2.5B Series G, Anthropic's release of defense-specific models, and Impulse Space's $300M Series C. 🚀 Defense tech is having a breakout year – on track for a record-breaking year with projected investor participation up 31% YoY to nearly 1,000 unique investors. This surge represents the highest level of investor interest ever recorded in the sector. The momentum is particularly striking given broader venture market headwinds, signaling that defense tech has become a must-have allocation for institutional portfolios. 💸 The investor base is diversifying beyond traditional defense-focused funds, with generalist VCs like a16z and 8VC developing specific theses in the sector. These investors bring Silicon Valley playbooks — rapid iteration, software scalability, and platform thinking — to an industry historically dominated by slow-moving defense primes. This cross-pollination is accelerating innovation cycles from years to months in critical areas like autonomous systems manufacturing. 🌏 Geopolitical tensions and the Ukraine conflict have validated the strategic importance of defense tech, driving both government and private capital allocation. Earnings call mentions of "defense" reached an all-time high in Q1 2025, while major tech companies and the hottest AI startups are forming consortiums to compete for DoD contracts. This mainstreaming of defense tech reduces reputational risk for investors and opens institutional capital pools previously unavailable to the sector. In chatting with Justin Fanelli (CTO, Department of Navy), it is clear that the increased investor and builder is fueled by the government's increasingly innovation-forward appetite. "Investors and founders who have backed this sector and mission have moved the needle for national security, even while we've been slow, reluctant buyers. We are now overhauling the way we buy at scale. We have shifted many buyer orgs from program offices to more flexible portfolios. This is one of several ways we're putting far more emphasis on impact and value. Innovation adoption and commercial-first pushes have already made us more adaptive and resilient. We want a wider base of high performers. What's better than competition to serve those who serve all Americans better? Recent AI and raise news shows there's more room to make bigger impacts. If we nail this, I think it's fair to expect impact and investment will continue to grow." Curious about the defense tech markets and companies seeing the most interest? Explore the data and insights for *free* in the comments.

𝗢𝗧 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝘂𝗱𝗴𝗲𝘁𝘀 𝗻𝗲𝗲𝗱 𝗮 𝗿𝗲𝘀𝗲𝘁. Too often, OT cybersecurity is still positioned as a compliance expense. But in industrial environments, that is too narrow. The better way to look at it is: 𝗢𝗧 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 = 𝘂𝗽𝘁𝗶𝗺𝗲 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 + 𝗼𝘂𝘁𝗮𝗴𝗲 𝗮𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 + 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆. One important message from recent OT security investment discussions is clear: 𝗧𝗵𝗲 𝗵𝗶𝗴𝗵𝗲𝘀𝘁-𝗶𝗺𝗽𝗮𝗰𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗼𝗻𝗲𝘀. The practical moves still matter the most: • 𝗞𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 Asset inventory and visibility remain the foundation. You cannot protect what you cannot see. • 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗺𝗲𝗻𝘁 Segmentation, defensible architecture, and secure remote access reduce the blast radius when something goes wrong. • 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗯𝗮𝗱 𝗱𝗮𝘆 An OT-specific incident response plan, tested backups, and recovery playbooks can save weeks of downtime. • 𝗠𝗮𝗻𝗮𝗴𝗲 𝗿𝗶𝘀𝗸, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗽𝗮𝘁𝗰𝗵𝗲𝘀 OT vulnerability management cannot simply copy the IT model. It has to consider safety, availability, process impact, and compensating controls. • 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻 Unified IT/OT visibility and monitoring are becoming essential, but ownership, response roles, and operational boundaries must be clear. 𝗠𝘆 𝘁𝗮𝗸𝗲: A practical OT security roadmap should start with controls that directly improve resilience, recovery, and operational continuity. Not every program has to begin with a large platform purchase. Sometimes the highest-value investments are: 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗿𝗲𝗺𝗼𝘁𝗲 𝗮𝗰𝗰𝗲𝘀𝘀. 𝗢𝗳𝗳𝗹𝗶𝗻𝗲 𝗯𝗮𝗰𝗸𝘂𝗽𝘀. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀. Because in OT, the best cybersecurity investment is not only the one that passes an audit. It is the one that prevents downtime before it becomes a crisis. #OTSecurity #IndustrialCybersecurity #ICS #IEC62443 #CyberResilience #OperationalTechnology #RiskManagement

A quiet announcement this week may have just redrawn the map of travel distribution. And most of the industry missed it. Anthropic — the company behind Claude — announced that its AI assistant can now connect directly with: • Booking.com • Tripadvisor • Viator • Uber • Resy • Instacart • Spotify 15 new consumer connectors. 200+ integrations overall. At first glance? Looks like another AI product update. It's not. Because a traveler can now open Claude and say: "Plan my weekend in London. Nice hotel. Good restaurant. Cool activity. Don't make me think." And Claude coordinates it. Inside one conversation. Search gets shorter. Clicks disappear. Decision-making gets outsourced. I've spent my career on the hotel supply and distribution side. And here's what I can't stop thinking about: For 20 years, we optimized for a world where travelers come to us. Search → Click → Compare → Book Every billboard. Every loyalty campaign. Every "BOOK DIRECT 👇" strategy. All built around one assumption: The traveler goes on a journey before the journey. AI doesn't journey. It decides. And the companies that win in that world won't necessarily be the ones with the best homepage, the biggest ad budget, or the prettiest brand campaign. They'll be the ones with: ✔ Structured content ✔ Accurate pricing ✔ Real-time inventory ✔ Frictionless booking APIs The uncomfortable truth? OTAs spent 20 years building exactly that infrastructure. Hotels spent 20 years trying to bypass them. Now AI may sit on top of both. (Your homepage pop-up offering 10% off if I book in the next 4 minutes? Claude respectfully declines 😂) And this isn't just a Claude story. ChatGPT and Google Gemini are building the exact same layer. All three are racing to own the same position: the interface between the traveler and the trip. I don't think this changes everything tomorrow. But I do think we'll look back at this moment the way we should have looked at Booking.com's early expansion or Google's first travel moves: "Wait… why didn't we pay attention sooner?" So here's my question to the smartest people I know in hotels, OTAs, and travel tech: Are you building for this world? Or are you still perfecting the homepage banner? 🔗 Full announcement: https://lnkd.in/eWH5XSBq #TravelTech #AI #TravelDistribution #Hospitality #Hotels #OTA #Anthropic #TravelInnovation

Most enterprises treat quantum computing as a nerdy R&D curiosity. A mistake. Critical business problems, which are fundamentally constrained by classical computing today, are likely to be solved by 2030. With a hybrid combination of high performance computing and quantum approaches. Three sectors stand out: Pharma, Life & Material Sciences: Drug discovery is essentially a molecular simulation challenge. Classical systems approximate. Quantum systems are designed around quantum mechanics itself. Thus, it is not just about faster research, but the ability to model molecular interactions with higher fidelity. For protein folding, compound optimization, personalized therapeutics. Reaching quantum advantage first in pharma won’t merely accelerate pipelines — it will redefine them. Financial Services: Banks, insurers, stock exchanges operate enormous optimization, transaction or probability engines. E.g., for risk simulations, or fraud detections. Many of these problems scale exponentially in complexity. Quantum algorithms are particularly promising where classical Monte Carlo simulations hit practical limits. And, quantum computing is becoming a cybersecurity challenge. Post-quantum cryptography migration will likely be one of the largest infrastructure transitions the financial sector has seen for decades. Complex Logistics & Supply Chains: Airlines, shipping companies, manufacturers, energy grids, and global retailers all face combinatorial optimization problems. These systems already operate at scales where small efficiency gains create major business impact. Enterprises operating in these segments should get „quantum-ready“ now: • Identify quantum-relevant business problems • Work with quantum partners who advocate an open approach • Build internal quantum literacy • Develop hybrid workflows • Prepare your security stack for the post-quantum era. Additionally we need quantum computing companies delivering at production scale. IQM Quantum Computers calls this Production Quantum. Which is the delivery of a production-ready full stack solution rather than just a scientific solution for a specific problem. This is the same pattern we saw with #AI. The competitive gap formed before the technology fully matured. #Quantum readiness is becoming a strategic capability and critical timing question. For an increasing number of enterprises. Not only for R&D departments.