Glenn Hopper

Some pretty amazing revelations from AGA's latest CFO Survey: AI adoption in government finance is still learning to crawl. A few takeaways, at least as they reported them: • 68% say their teams aren’t using AI at all • 76% expect only minimal adoption next year • Data privacy, audit, and governance worries remain the top blockers If this data is reliable – and I understand if they had a hard time accessing real executives -- the message is obvious, especially in light of diminished human resources: CFOs can’t sit on the sidelines while AI rolls through government. AI isn’t optional for the finance community anymore. It’s essential. https://lnkd.in/eixhir5W Deidre Harrison Danny Werfel Ted Kaouk, PhD david lebryk Jay Hurt  

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Sharp insight here—AI accelerates our creation of possibilities but leaves us swamped at the gates of decision-making. I've explored similar ground in my Substack posts, especially drawing on Bernard Lonergan’s transcendental imperatives, where human insight and judgment remain irreplaceable even as generative tools scale productivity. Our future isn't just producing more, faster—it's redesigning how we discern meaning at AI-driven velocity. How do we design for better decision making? https://lnkd.in/grgKZc22

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📊 AmTech Daily: Deals Defining America's Future — Feb 26, 2026 Each morning, Commonweal compiles the day's top #AmTech deals and news at the intersection of government and technology. Here's today's. Comment "Subscribe" to get the full briefing, news included, in your inbox. AI Chips  🔹 MatX (Mountain View, CA), which designs custom AI chips that enable cloud and model providers to train and run large language models faster and at lower cost than on general-purpose GPUs, raised over $500 million in funding led by Jane Street. 🔹 SambaNova Systems (Palo Alto, CA), which sells data center-class AI hardware and an integrated software stack so enterprises and governments can run large-scale AI models in-house, raised $350 million in Series E funding led by Vista Equity Partners. Finance  🔹 Basis (New York, NY), which builds AI agents that handle day-to-day accounting workflows like bookkeeping, closes, and reporting for large finance teams, raised $100 million in Series B funding at a roughly $1.2 billion valuation led by Accel. Cybersecurity  🔹 Astelia (New York, NY), which gives security teams an AI-driven platform that filters thousands of vulnerabilities down to the few that are truly exploitable in their environment, raised $35 million in combined seed and Series A funding led by Index Ventures and Team8. 🔹 Evoke Security (Washington, DC), which gives security teams a control plane to discover, monitor, and block risky behavior by AI agents plugged into corporate systems and data, raised $4 million in pre-seed funding led by Crosspoint Capital Partners. GovTech  🔹 NationGraph (San Francisco, CA), which aggregates government budgets, meetings, contracts, and RFPs into a single AI-powered sales intelligence tool for companies selling into public-sector agencies, raised $18 million in Series A funding led by Menlo Ventures. 🔹 Neural Earth (Miami, FL), which turns satellite, climate, and other map-based data into risk scores and alerts for infrastructure, insurance, and government users, raised $9.3 million in seed funding. Public Safety  🔹 Circadian Risk Inc. (Ann Arbor, MI), which helps large organizations identify security risks and compliance gaps across their buildings and sites, prioritize fixes, and document improvements, raised $6 million in Series A funding led by Arthur Ventures. Defense  🔹 Chariot Defense (San Francisco, CA), which builds portable hardware and software that let frontline units automatically get quiet, reliable power for radios, drones, sensors, and other gear from a single box, raised $34 million in Series A funding led by Andreessen Horowitz. What is #AmTech? It's our term for the companies tackling America's biggest challenges in sectors like energy, defense, healthcare, and infrastructure. In these markets, the path to building category-winning companies runs through government, and the founders who understand that are building the most consequential technology businesses of our generation.

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Capital isn’t just a resource; it’s a decision signal. And in many PE owned firms especially LMM PortCos, that signal gets scrambled. We often see capital allocated based on legacy budgets or pet projects, not based on Business Value that it is expected to deliver. The results turn out to be- → Underfunded initiatives that slow down Value Creation → Overfunded projects & experiments without measurable ROI → Fragmented cash deployment that confuses priorities At 3i, we help reframe capital as a value accelerator. ✅ Link capital plans to enterprise value outcomes ✅ Assess ROI not just on paper, but on execution maturity ✅ Use capital as a forcing function for prioritization Because in the LMM, capital isn’t scarce; it’s misdirected. What capital planning missteps have you seen that have slowed value creation? Let’s unpack it👇 #PEValueCreation #ValueLevers #ValueManagement #Finance #CapitalDeployment #ROI

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The White House has released its 2025 AI Action Plan, outlining a comprehensive federal strategy to strengthen the United States’ position in artificial intelligence. The 28-page document is organized around three core pillars: 🔹 1. Accelerate AI Innovation - Scales back regulatory constraints to encourage private-sector R&D - Supports open-source and open-weight model development - Expands AI-related workforce initiatives, including K-12 education, reskilling, and apprenticeships. Includes " - Prioritizes investments in foundational science, AI safety, and model evaluations 🔹 2. Build AI Infrastructure - Streamlines permitting for data centers, chip manufacturing, and energy projects - Enhances grid capacity and clean energy deployment to meet AI compute demands - Supports domestic semiconductor production and secure government data centers - Establishes new training programs for skilled technical roles across the AI ecosystem 🔹 3. Lead in International AI Engagement - Promotes U.S.-developed AI models, hardware, and standards abroad. "leverage the U.S. position in international diplomatic and standard-setting bodies to vigorously advocate for international AI governance approaches that promote innovation, reflect American values, and counter authoritarian influence. " - Strengthens export controls on advanced chips and critical tools - Coordinates with allies on governance, research safeguards, and biosecurity - Invests in national security evaluations of emerging AI capabilities, led by CAISI. https://lnkd.in/eAH6MHkF

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Open Letter to Chairman Carr Leveraging Echostar's Unique Satellite-Terrestrial Integration for American Global Leadership "Echostar is developing the only large-scale platform designed specifically for seamless integration of space-based and terrestrial 5G networks. This integrated approach provides strategic advantages that neither purely satellite nor purely terrestrial solutions can match, including global coverage, infrastructure resilience, and the ability to provide consistent service quality across diverse geographic and operational environments." https://lnkd.in/ezsxAf9k

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A notable shift is underway in defense tech investment. The era of prioritizing "growth at any cost" is giving way to a more mature, disciplined approach. As Brontë Hamilton (CFO at Destinus) highlighted during recent discussions at Davos, the new mantra is combining capital discipline, governance, and operational execution with strategic urgency. This signals a move away from speculative models toward sustainable growth. At Indeema, we see this daily: technology is only as good as the execution and "mission assurance" behind it. For investors, this means a clearer path to revenue-generating capabilities. For end-users, it accelerates the delivery of battle-proven innovation where it matters most. Do you agree that operational excellence is now more critical than technological hype? Let’s discuss below. 👇 #DefenseTech #Davos2026 #VentureCapital #Investment #Innovation #CapitalDiscipline #DualUse #Indeema

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Too much of Private Equity is about allocating and financial engineering. It’s time to reinsert the operator experience and mentality into the control position. Everyone’s looking at compute budgets. We’re looking at who keeps the system running: - Subsystem welders. - Power component manufacturers. - Thermal design shops like our portfolio company TGM Systems. The people no one sees—but whose work makes modern AI infrastructure possible. AI Infrastructure Partners invests in that layer—the foundation where uptime, efficiency, and risk are managed in microns, not margins. Where a voltage ripple, not a missed KPI, is the real point of failure. This is where scale breaks—or scales. And where supply chain reliability becomes national security policy. We’re not chasing logos. We’re building industrial-strength platforms that serve the hyperscalers, defense primes, and frontier model builders deploying trillions in AI infrastructure—and needing to scale with conviction. That’s not venture hype. That’s foundational execution. It’s control capital for control systems. #SupplyChainAlpha #InfraStack #PrivateEquity #AIInfrastructure #BuiltNotBought #UptimeEdge #IndustrialDurability Jason Frank Damon Pitler David Henry, EMBA

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I went on Marketplace yesterday talking to Kai about the BLS benchmark revision. Here's the easiest way I think to understand it: How many jobs are there in the US at any given time? The most accurate answer to this question will come from tax data. Employers are required to pay payroll taxes on each employee for Unemployment Insurance. So if we want to know how many jobs there are, we can use the payroll tax headcount. Done. The problem is that data can take 6-18 months to collect and process. It's a census of every employee in the US--over 160 million records! To fill in our understanding before then, the BLS surveys a sample of the tax-paying employers to come up with a monthly estimate, which by contrast comes out just days after the month ends. This is the Current Establishment Survey. The survey has two types of revisions. The first one is the monthly revisions, these relate to collection of the survey. Responses don't all come in on time, and they update that first number twice as more surveys are returned. The second one is the benchmark revisions, these relate to design of the survey's sample. The survey has to make some assumptions about business: how quickly they are opening and how quickly they are closing. When the tax data starts to roll in, they can see how close those assumptions were. Again, they update those numbers twice. We got the first benchmark revision yesterday, and we'll get the second one in the Spring. The journey from that first Jobs Day number to the final number two years later is a remarkable process that reflects timeliness, accuracy, transparency, and the dedication of the Bureau of Labor Statistics to keeping us informed. https://lnkd.in/eW4M-PGU

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Davos wrapped This year felt different. AI is no longer a side conversation or a future bet. It is being evaluated as core economic and strategic infrastructure, alongside geopolitics, energy, and security. I loved the framing of rethinking business models entirely. Not only efficiency generating but using AI to unlock white space and new business models we couldn’t reach before. Thanks Bill Winters from Standard Chartered for that push! And we agree that Banco Bilbao Vizcaya Argentaria and Carlos Torres Vila are an inspiration on this front. What stood out most from an enterprise perspective was diffusion. Every C-suite conversation has moved from curiosity to urgency. Boston Consulting Group (BCG) had a datapoint that last year at Davos 44% of CEOs felt directly accountable for their organizations AI deployment; this year that number was 77%. A few moments stayed with me: 1. The WEF CFO gathering reinforced how pragmatic finance leaders are. There’s broad conviction that AI is inevitable, but deployment hinges on ROI, clean data, and simpler systems. This is a change-management challenge, not a belief gap. #CFOsFTW 2. The WEF discussion on AI super-systems made clear how quickly AI is becoming a system-level decision for companies and countries alike. Capital, energy, compute, and trust are now tightly coupled. 3. The panel with Michael Intrator Rob Goldstein Peng Xiao G42 and Jessica E. Lessin dove into how much compute we all need and how early in the AI ERA things stand. 3. The McKinsey breakfast on leadership was a welcome reset. Less geopolitics. Less tech. More leadership. Thank you to my fellow panelists Per Franzen and Nonkululeko Nyembezi (Standard Bank Group) for a thoughtful, grounded conversation. And to Bob Sternfels for bringing together an incredible group of leaders at 7am! The key themes were telling: aspiration, judgment, creativity, team work and maybe most importantly resilience still matter enormously when leading in today's super fast-paced environments. One concept that kept resurfacing was the “capability overhang” - the growing gap between what AI can already do and how much value organizations are actually realizing. Experience and execution are closing that gap faster than any amount of rhetoric. At OpenAI we see that frontier users use 7x the amount of intelligence than the average user - they going deep on coding, deep research and pushing the models to really be thought partners. Grateful for the conversations, the candor, and the growing sense that we’ve entered the era of application and impact. Final shout out to Maria Bartiromo for a fun wrap up conversation. https://lnkd.in/gw3s4ktM

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