Bob Hedges

This week, I’m looking forward to joining OpenAI's Founder Forum on Healthcare to speak on a panel exploring how AI can sit side-by-side with clinicians, health systems, and patients. As Hemant Taneja, CEO of General Catalyst and one of our investors, reflected after Davos, “the undercurrent is all about trust and relationships.” That’s especially true in healthcare, where AI only works if stakeholders come together with shared standards around data, governance, and accountability. I’ll be sharing what we’ve learned building agentic AI in highly regulated environments across the U.S. and Canada, and why the future isn’t about centralizing data, but letting intelligence go to where data already lives. Grateful to OpenAI for bringing together operators focused on getting this right. If you’ll be there, I’d love to connect.

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Great discussion at the Goldman Sachs Communacopia + Technology Conference yesterday. I spoke to investors about our growth strategy, including expanding our fiber footprint through our pending Frontier Communications acquisition which accelerates our already rapid network expansion. Our strong financial position and focus on innovation and AI are powering our ability to deliver for customers and investors. I shared more about our strategy with Brian Sozzi at Yahoo Finance. Check it out below. See verizon.com/investors for reconciliations of non-GAAP measure to most directly comparable financial measures under GAAP. https://lnkd.in/evmUZDVA

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#AmTech Weekly Roundup: Ambience Healthcare raised $243 million in Series C funding from Oak HC/FT, Andreessen Horowitz, OpenAI, Kleiner Perkins, Optum Ventures, and Town Hall Ventures. Ambience is building AI documentation software to record patient appointments, generate comprehensive medical notes, and standardize coding/authorization workflows.   Michael Ng, Nikhil Buduma    Reveal Technology raised $30 million in Series B funding from Ballistic Ventures, Shield Capital, Booz Allen Hamilton, and others. Reveal’s two main offerings are 3D mappings generated from drone videos and mobile biometrics. They are deployed with the US Army, Special Operations Command, Marine Corps, and other NATO forces.   Garrett Smith    Prophet Security raised $30 million in Series A funding from Accel and Bain Capital Ventures. Prophet is applying agentic AI to Security Operations Centers (SOCs), or hubs for cybersecurity defenses within an organization.  Kamal Shah, Vibhav Sreekanti #VC #HealthTech #Healthcare #Defense #Cybersecurity

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To those of you who read this post and are avid systems thinkers, I'd very much appreciate your input into a question with which I'm struggling. I want to write about the distinction between two different approaches to systems thinking and I'm struggling with the labels. The first approach represents the more scientific approaches to systems thinking, where proponents seek to map, model, and optimize the system. The second approach recognizes the role of interpretation. These proponents see to surface multiple perspectives and co-create changes. (my work will illuminate the differences and also show how the two approaches can complement each together) Some people (e.g., Checkland) refer to these as hard and soft. These labels are easy to remember, especially for a managerial audience, but the word 'soft' can seem pejorative. The other labels for the first approach are objective and scientific. Other labels for the second approach are subjective, intersubjective, social, and interpretive. These other labels are far more accurate than hard and soft, but may be too academic. How would you, especially as a practitioner of systems thinking, react to the labels of scientific and interpretive approaches to systems thinking. Too jargon-y and academic?

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This McKinsey piece captures an important inflection point. The shift toward sovereign AI is not simply about technological capability — it is about who defines, capitalizes, and governs the ecosystems through which AI scales. What is becoming clear is that competitive advantage will not accrue to those who merely deploy models, but to those who architect and anchor the full stack — from compute and data governance to capital formation and institutional alignment. In that context, the traditional boundaries between public policy, capital markets, and enterprise strategy begin to converge. Jurisdictions that approach AI as sovereign infrastructure — and align long-duration capital with system-scale operators — will not only capture innovation, but shape the terms under which it compounds globally. The next phase will likely be defined by deliberate coordination between policymakers, builders, and patient capital — particularly where there is an opportunity to establish nationally significant platforms with global reach. For those allocating capital or building within this space, the question is no longer whether to participate, but whether they are positioned at the orchestration layer — or operating within someone else’s system.

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Anthropic just committed $50 billion to AI infrastructure. That's not a tech investment. That's industrial policy dressed up as a corporate announcement.This is the moment AI stops being about who has the smartest researchers and starts being about who can actually build the physical systems to run their work. The bottleneck moved. It's not algorithms anymore. It's power plants, transmission lines, and whether you can get a substation permitted in under three years. The frontier isn't digital now. It's steel, concrete, and copper. What's happening is a different kind of competition than what we saw in the last tech cycle. This isn't about moving fast and iterating. It's about who can marshal industrial capacity at the scale and speed that AI now demands. That means: -> Securing multi-gigawatt power allocations before your competitors do -> Navigating utility infrastructure that was designed 40 years ago for different assumptions about demand -> Financing assets that take years to build and decades to pay off -> Coordinating across regulators, grid operators, and governments who are all realizing simultaneously that data centers now compete with cities for electricity This isn't really about AI companies scaling models anymore. It's about whether entire economies can build the physical substrate fast enough to keep up with what the models need. The interesting part: the winners might not be who you expect. Advantage doesn't go to whoever has the best AI lab. It goes to whoever understands how to move industrial projects through bureaucracy quickly. How to structure patient capital. How to work with utilities that think in 20-year planning cycles when you need answers in 20 months. Software companies spent two decades not thinking about infrastructure, because they didn't have to. Cloud providers handled it. Now the biggest AI labs are becoming infrastructure developers whether they want to or not. That's the tell. When a research lab starts talking in billions about power and data centers, they're not optimizing models. They're trying to secure access to the physical world before it becomes the limiting factor. https://lnkd.in/gGb--j8S

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Private Credit Lenders (PCLs) with portfolios of SaaS borrowers concerned about “AI Disruption” need not panic. Aside from AI Agents, which are in and of themselves, software, and the Agentic products proliferating the marketplace, which are predicated on embedded system software, the value proposition for SaaS businesses is not tanking. Certainly, ARR is an important metric, but not the only indicator of value. In addition to ARR, PCLs must consider their SaaS borrower’s competitive moat, agentic enhancements in process and anticipated, and potential business combinations (i.e. mergers, JVs, asset acquisitions, etc.) to fill gaps in product offerings. In other words, now is not the time to tread water, cut R&D or trim critical technology staffers. Rather now is the time to pick “winners” and support them- salvage the best product offerings, if any, of the “losers” and combine them with “winners” in the same or complimentary space. The workout exercise in the SaaS space is not the old playbook of break even budgets and “value maximizing sales”, but rather consists of aggressive efforts to reinforce competitive moats and expand related product offerings. Agentics are here to stay- PCLs with patience and foresight will prevail, while those with the mentality of “first loss is best loss” will lose. Need a voice (or an Independent Director) to help navigate these uncharted waters?? Reach out… jmarwil@oceanbsa.com.

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**The Intersection of AI and Fashion: A New Frontier** Designer Kate Barton’s collaboration with IBM and Fiducia AI during NYFW exemplifies how technology can transform traditional industries. At ElevenX Capital, we believe strategic partnerships in tech can lead to innovative solutions that resonate with consumers. As venture capitalists, we should be asking ourselves: How can we leverage AI in various sectors to enhance creativity and operational efficiency? Let’s discuss the implications of this trend for investment strategies in fashion tech. #investing #innovation #venturecapital #entrepreneurship

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Had a thought-provoking conversation with Ellie Pavlick of Brown University's new AI Research Institute (ARIA) for Masters of Scale with Bob Safian, Fast Company transcribing, on an increasingly pressing question: Should AI have a role in mental health treatment? The numbers are sobering. One billion out of eight billion people struggle with mental health issues, yet fewer than half seek treatment and most find the cost prohibitive. Meanwhile, research shows that therapy and mental health support rank among the top use cases for ChatGPT and other foundation models. This isn't theoretical anymore. People are using AI for mental health support, whether or not we're ready for it. That's exactly why Brown created ARIA: to provide scientific leadership around what this technology can and can't do, rather than letting it evolve without proper guardrails. And, it's why TMV invests into AI x Mental Health when a reputable team builds something seminal. From an investment lens, the opportunity is undeniable. There's no larger addressable market than one that touches half the planet. But the real question isn't just about market size or opportunistic and capitalistic blue-washing. With stakes this high, it's about building responsibly, understanding the risks when you get it wrong, and ensuring we have the right frameworks to differentiate which applications truly serve patients. The conversation is just beginning, and we need voices from academic research, healthcare, ethics, policy, and yes, investors at the table. And speaking of tables, maybe this article will prompt a spicy Thanksgiving dinner discussion (je les adore)! 🦃 🔥 All here to digest, and as always, curious to hear your hot takes: https://lnkd.in/eaBR598D #AI #MentalHealth #VentureCapital #HealthTech TMV | Ash by Slingshot AI | Neil Parikh | Bob Safian | Brown University Brown University School of Public Health

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Most companies don’t have an AI problem. They have a clarity problem. They buy tools before defining outcomes. They automate before fixing broken processes. They chase “AI transformation” without a strategy that actually creates leverage. We see this every week. AI doesn’t replace thinking. It amplifies it. When AI fails, it’s usually because: • No clear business objective • No operational foundation • No human decision framework • No accountability for results The winners in the next 24 months won’t be the companies with the most AI tools. They’ll be the ones who use less AI, more intentionally. AI should: → Reduce friction → Increase decision speed → Multiply top performers → Create unfair advantages If AI isn’t doing at least one of those, it’s just expensive noise. The real question leaders should be asking isn’t “Which AI tool should we use?” It’s: “What would we want AI to eliminate, accelerate, or multiply if it actually worked?” Curious how other leaders are approaching AI right now. Are you: A) Experimenting B) Scaling what works C) Still watching from the sidelines Drop A, B, or C in the comments 👇 (And if this resonated, share it with someone still chasing tools instead of outcomes.) — 369aiconsulting.com

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The amount big tech and AI are spending on lobbying underscores that they are losing the policy debate, especially on CDA230, child safety, and state preemption. But no matter how much big tech and AI companies spend, the child safety and parent's groups will ultimately win a safe online future for their children. #cda230 #cda230sunset #kosa #childsafety #onlineaccountability #nofakes #defiance

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The "Glass Ceiling" has Shattered: Corning’s AI-Driven Renaissance The "Physical AI" layer is often overshadowed by GPUs and LLMs, but the real bottleneck for scaling intelligence is the physical movement of data. Today, Corning Incorporated is proving that the future of AI isn't just silicon—it’s glass.  Corning’s Q1 2026 results confirm a massive structural shift. As data centers transition from copper to optical fiber to meet the bandwidth demands of Generative AI, Corning has moved from being a legacy industrial player to a mission-critical backbone of AI infrastructure. The Numbers Behind the Surge: • 36% Growth: Revenue in the Optical Communications segment grew 36% year-over-year, specifically fueled by hyperscale demand for Gen AI products including a $6 Billion multi-year purchasing agreement from Meta. • $20B Sales Target: Corning has upgraded its "Springboard" plan, aiming for a $20 billion annualized sales run rate by the end of 2026. • The NVIDIA Catalyst: A massive new partnership with NVIDIA includes a $500 million investment to increase U.S. optical connectivity capacity by 10x.  • Density is Destiny: Their new multicore fiber solutions provide 4x the capacity per fiber, reducing installation time by up to 60%—a necessity for the rapid build-out of "AI Factories."  When firms like NVidia and Meta sign major partnership deals for for fiber, it signals that the industrialization of intelligence requires a data fabric that only advanced materials science can provide.  The Bottom Line: Corning isn't just making glass anymore; they are providing the high-speed nervous system for them global AI economy. #AIInfrastructure #Corning #Photonics #DataCenters #NVIDIA #DigitalTransformation #SupplyChain

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Peter H. Diamandis recently shared an interesting analysis on the evolving relationship between governments and large tech companies in the age of AI. His core observation: As we transition to an economy increasingly defined by compute power and energy, the traditional roles of nation-states and corporations are being tested. He outlines two potential paths forward: **Path 1: Evolved Public-Private Partnership** Governments focus on guaranteeing citizen outcomes rather than directly providing services. They leverage their control over land and energy sovereignty while tech companies build and operate the infrastructure. Both parties play complementary roles with clear boundaries and interoperability requirements. **Path 2: Corporate Utilities Become Sovereign-Like** If governments remain bureaucratically constrained, tech companies that control energy production, issue stable digital currencies, and provide superior services could attract "subscription citizenship," creating corporate-run economic zones that rival traditional nation-states. To illustrate the scale: The combined market cap of major tech companies (Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, Tesla) is roughly $14 trillion—comparable to half the US GDP and larger than any country except the US and China. Diamandis argues this isn't speculative—elements are already emerging. The question is whether we move toward partnership or competition between these institutions. Worth reading his full piece for the detailed economic and structural arguments. https://lnkd.in/gPt46cXT Thoughts on which direction seems more likely? Or whether there are other models we should be considering?

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Patrick O'Shaughnessy with yet another "must listen" podcast episode, this time a complete must listen for anybody trying to understand better the business of AI. This episode with Anthropic CFO Krishna Rao is incredible. They dance the perfect balance of sophisticated/in the weeds but from a broader business picture vantage point. They get into the real drivers of the model players, the resource allocation and compute allocation, the ecosystem at large ( will the models kill the applications), etc. Required listening by the Colossus team again. https://lnkd.in/eDZyHfzK

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I am so pumped for this to finally be announced. Agentic AI is something the industry has been talking about and debating for quite some time. The time for talk is over, the time to execute is now.

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NYU Langone Health’s informatics leaders recently predicted fully autonomous clinical AI within 5 years with no physician sign-off. Algorithms titrating blood pressure meds, retinopathy screening running end-to-end without a clinician in sight. Their reasoning is hard to argue with as we're facing a shortage of 85,000+ physicians by 2036 and the current care model doesn't have an answer for that, and it seems clear that AI will be playing more of role in providing care going forward. But the next question is more difficult to answer: Are we automating the right things, for the right reasons?   Utah is already stress-testing this with Doctronic by automating routine prescription renewals for chronic condition patients. I wrote about this a month ago - renewals are repetitive, pulling clinicians away from patients who need actual judgment and create gaps in care when they fall through the cracks. If that pilot scales, it frees up real capacity. What makes it work is that prescription renewals are largely protocol-driven. The decision tree is narrow, the inputs are structured, and removing friction there doesn't mean removing judgment from care. Clay Christensen would have called these protocol-driven care pathways 'precision medicine,' having advanced originally from the primordial soup of 'intuitive medicine,' to the mid-stage 'empirical medicine,' to finally precision status. We're seeing the same logic play out across our portfolio companies. Janie automates the administrative workflows that quietly drain clinical capacity, things like billing, prior auth, and care coordination, giving practices back time that should never have been spent on paperwork. FlyteHealth takes a similar approach on the clinical side, combining a multidisciplinary care team with technology to manage obesity, hypertension, and diabetes at scale. The care team stays central and the automation handles everything that doesn't require them. That's the model I find most compelling. Where I get more cautious is with patients who have multiple conditions, polypharmacy risk, or subtle behavioral changes that don’t show up in structured data. A system that's right 95% of the time still needs airtight guardrails for the other 5%, and building those guardrails well requires people who understand what failure looks like. Here's the thing: AI IS NOT GENERALLY INTELLIGENT, AND WE CAN RELY ON IT TO FAIL AT 'LAST MILE' JUDGMENT WORK THAT REQUIRES GENERAL INTELLIGENCE. The goal here shouldn't be to remove humans from care. It should be to stop wasting clinical talent on work that doesn't require it. As routine tasks get automated, clinicians should have more room for nuanced conversation, the shared decision, the patient who needs persuasion as much as a prescription. Utah and NYU deserve real credit for running these experiments in the open.

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If AI companies are owned by funds that very little of the population have exposure to beyond their pensions (maybe), what does this mean for inequality..? https://lnkd.in/e_vHxQEc

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The quantum-AI convergence is happening faster than most of us realize. I just finished diving deep into how hybrid quantum-classical systems are already delivering breakthrough results across finance, pharma, and aerospace—and it's shifted how I'm thinking about infrastructure strategy. Here's what stopped me in my tracks: this isn't a 2030 story. Organizations are already using quantum-accelerated AI to solve problems that classical systems couldn't touch. Finance teams are detecting fraud patterns at speeds that seemed impossible two years ago. Pharma is compressing drug discovery cycles. Aerospace is optimizing designs in ways that feel like cheating. The real insight? The competitive advantage isn't the quantum chip or the AI model in isolation—it's the infrastructure layer that seamlessly bridges them. Most organizations are still building these systems separately. The ones that understand convergence first will own massive efficiency gains. What's your experience been? Are you seeing quantum-AI integration as strategic in your organization, or is it still on the "interesting but distant future" pile? https://lnkd.in/dn9_u6jc #QuantumComputing #ArtificialIntelligence #HybridComputing #Infrastructure #TechStrategy

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