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Day: 17 December 2024

Breaking Free from Resentment: My Journey to Finding Peace

“Resentment is like drinking poison and hoping the other person dies.” ~Saint Augustine For years, I was unknowingly poisoning myself in nearly every relationship—whether romantic, work-related, or friendships. It always followed the same pattern: I’d form a deep attachment, throw myself into the relationship, and give endlessly, hoping that if I gave enough, they’d appreciate and value me. But instead, it felt like they just took and took, leaving me secretly seething with anger and frustration while I smiled on the outside. I was doing all the running—couldn’t they see that? Couldn’t they see how hard I was trying? Over time, the exhaustion would set in. Eventually, I’d burn out from the one-sided effort and just give up, walking away hurt and angry, convinced they had wronged me. Each time, I added another person to my mental list of people I couldn’t trust. With each disappointment, I trusted fewer and fewer people. To protect myself, I started putting up walls, convincing myself I didn’t need anyone. I told myself I was fine on my own. I’d always be the first to step in and help family or friends, but I wouldn’t allow them to help me. I refused to be vulnerable because, to me, vulnerability meant risking rejection. I believed I could do it all on my own—or at least that’s what I told myself. When COVID hit, isolation wasn’t a choice anymore—it was forced upon me. Suddenly, I was alone, with no one to turn to because I had pushed everyone away. That’s when I realized just how much resentment had poisoned my life. Fed up with the weight it placed on my life, I decided to confront it head-on. I let myself fully feel the resentment, allowing it to wash over me like a wave. It wasn’t easy—leaning into those emotions was painful, raw, and uncomfortable. But in that moment, I realized I wasn’t just angry with a few people—I was carrying resentment for almost everyone in my life, even my own mother! The bitterness had been poisoning me for years, and it became clear that it wasn’t just affecting my relationships—it was poisoning my peace. That’s when I made the decision to stop drinking the poison. I realized that I had been giving so much power to other people—power over my emotions, my happiness, and even my health. But I didn’t have to. I didn’t need to wait for anyone to apologize or change; I was responsible for my own healing, and I wasn’t going to let others’ actions control my life anymore. Self-Realization: The First Step to Letting Go Self-realization was the first, and perhaps most difficult, step in battling my resentment. For the first time in my life, I stopped running from the pain and leaned into it instead. I started using EFT (Emotional Freedom Techniques) to peel back the layers of emotions I had been burying for years. Through tapping on specific points, I was able to release trapped feelings and bring clarity to the surface. Each tapping session was like lifting a weight off my chest, but it was also incredibly uncomfortable. I had to confront memories I had long avoided and acknowledge the emotions I had hidden from for so long. What shocked me the most was realizing that I had never given anyone a chance to correct the wrongs I thought they had done. I assumed people knew I was upset, and when they didn’t magically pick up on it, I silently resented them. Saying that now, it sounds so ridiculous—how could I have expected people to read my mind? Yet for years, that’s exactly what I did. So, I began reframing the narrative. Instead of focusing on how others had let me down, I asked myself: What could I have done differently in those situations? How could I have influenced a different outcome? The more I reflected, the more I realized that I had the power to change the dynamics of my relationships. It was a breakthrough—I didn’t need to wait for someone to change or apologize. I had the power to heal myself. Testing My New Mindset Soon after this realization, I had an opportunity to test my new mindset. I had invited my mum and sister on a weekend getaway, something that meant a lot to me. A few weeks before the trip, they both backed out. The old me would have smiled and said, “No problem, that’s fine,” while secretly adding their names to my mental list of people who had wronged me. But this time, I did something different. I spoke up. I calmly explained how much it hurt that they were canceling on something so important to me. To my surprise, neither my mum nor my sister had any idea their actions would hurt me. They explained that, because I had always been so independent, they didn’t realize how much this trip meant to me. For the first time, we had a genuine, open conversation about our feelings, and it actually brought us closer. Instead of silently seething and letting resentment build, I communicated honestly, and the outcome was liberating. I realized that so much of the pain I had carried in the past could have been avoided if I had just voiced my feelings. That conversation was a powerful reminder that I have the power to shape my relationships, and that sometimes people just don’t know how we feel unless we tell them. Moving Forward: Letting Go and Staying Free After learning to let go of years of resentment, I realized that staying free required new habits. I needed to guard against falling back into old patterns, so I came up with a few strategies to help. First, I ask myself three key questions: 1. Is this really worth my peace? 2. Did they intend to hurt me, or could there be another explanation? 3. What can I do differently in this situation? These questions help me pause, reflect, and reframe my thoughts

From HMOs to AI Assisted Claims Management Part 1 – The Health Care Blog

By JEFF GOLDSMITH Healthcare payment in the US has evolved in decades-long sweeps over the past fifty years, as both public programs and employers attempted to contain the rise in health costs. Managed care in the United States has gone through three distinct phases in that time- from physician- and hospital-led HMOs to PPOs and “shadow” capitation via virtual networks like ACOs to machine-governed payment systems, where intelligent agents (AI) using machine learning are managing the flow of  healthcare dollars.  This series will explore the evolution of managed care in 3 phases.   Phase I- Health Maintenance Organizations and Delegated Risk Capitation In response to a long run of double-digit health cost inflation following the passage of Medicare in 1965, the Nixon administration launched a bold health policy initiative- the HMO Act of 1973- to attempt to tame health costs. The Nixon Administration intended this Act to provide an alternative to nationalizing healthcare provision under a single payer system, as supported by Senator Ted Kennedy and other Democrats.   The goal of this legislation was to restructure healthcare financing in the US into risk-bearing entities modeled on the Kaiser Foundation Health plans- a successful group-model “pre-paid”  health plan founded in the 1940s and based on the Pacific Coast. These plans would accept and manage fixed payments for a defined population of subscribers, and offer an alternative to what was perceived as an inflationary, open-ended fee for service payment system. In varying forms, this has been the central objective of “progressive” health policy for the succeeding fifty years.  The HMO Act of 1973 provided federal start-up loans and grants for HMOs, much of which went to community-based healthcare organizations and multi-hospital systems. It also compelled employers to offer HMOs as an alternative to Blue Cross and indemnity insurance. While a few HMOs either employed physicians directly on salary (staff models like the Group Health Co-Operatives), or contracted on an exclusive basis with an affiliated physician group (like Kaiser’s Permanente Medical Groups), many more delegated capitated risk to special purpose physician networks- Independent Practice Associations (IPAs)- whose physicians continued in private medical practice.  By 1996, according to the Kaiser/HRET Employee Benefits Survey, HMOs covered 31% of the employer market (roughly 160 million employees and dependents), and the federal government had begun experimenting with opening the Medicare program to HMO coverage. The impact of HMO growth on overall US health spending remains uncertain, because health spending as a percentage of US GDP continued growing aggressively during the next fifteen years,  before levelling off during the mid-1990’s around the Clinton Health Reform debate. Two things brought the HMO movement to a crashing halt in the late 1990’s.  One was a political backlashfrom workers and their families who were simply assigned to HMOs by their employers, rather than choosing them themselves. This unilateral assignment violated a fundamental principle of HMO advocates like Paul Ellwood, who championed consumer choice as an organizing principle of the movement.     Employees and their families so assigned found their access to care narrowed both by limited panels of providers (that may or may not include their family physicians) and by the mechanical application of medical necessity criteria to their care, such as 48 hour hospital stays after a routine obstetrical delivery.  Women, who are the pivotal actors in managing their families’ health and were growing increasingly confident of their political influence, went ballistic.  The other political force that helped quash the HMO movement was angry pushback from physician communities, particularly specialists, who bitterly resented the invasion of their professional freedom by prior authorization and medical necessity reviews, as well as pressure to reduce their fees in order to be included in HMO networks. A major concurrent financial blow to HMOs was a sharp downward adjustment in Medicare payment rate for health plans in the Balanced Budget Act of 1998.  By 2014, HMO’s share of the total commercial market had shrunk to only 13%, well less than half of its peak. They were replaced by preferred provider organizations, broad networks of physicians and hospitals in a region operating under negotiated rates and claims review systems. HMO enrollment increasingly tilted toward publicly funded patients under Medicaid and Medicare.  Capitation of primary care physicians under delegated risk shrank by two-thirds from 1996 to 2013 millennium as the HMO share of insured lives contracted. While the HMO industry shrank nationally, Kaiser saw its enrollment grow to almost 13 million, dominant on the Pacific Coast but a negligible presence elsewhere.    United Healthcare ended up acquiring not only a lot of HMOs (Oxford Healthcare, Sierra Healthcare, METRA, PacifiCare, etc.) in the aftermath of the managed care backlash, but also the risk-bearing physician groups that accepted delegated risk from those HMOs (Kelsey Seybold, Healthcare Partners, Atrius, Reliant, etc), which today form the backbone of Optum Health. Most of the capitated payment in Optum Health (almost $24 billion in 2024) comes from health plans other than United itself!  Our second essay will focus on the second phase of managed care development- the dominance of the PPO and the rise of “value based care’ after the 2010 Affordable Care Act.  Jeff Goldsmith is a veteran health care futurist, President of Health Futures Inc and regular THCB Contributor. This comes from his personal substack 2024-12-17 07:37:00

Is Quantum Computing a Threat to NVIDIA’s AI Chip Empire?

Is Quantum Computing a Threat to NVIDIA’s AI Chip Empire?   In the rapidly evolving world of AI and computing, NVIDIA has carved out a dominant position. Its AI-optimized GPUs power everything from deep learning models to generative AI systems, making the company a linchpin of modern artificial intelligence infrastructure. However, with the rise of quantum computing—a technology poised to redefine computational power—a natural question arises: Could quantum computing threaten NVIDIA’s AI chip empire?   The answer is complex, balancing hype, technological limitations, and the distinct roles of classical and quantum hardware. Let’s explore the reality.     —   NVIDIA’s Position in AI Hardware   NVIDIA’s GPUs have become the de facto choice for AI model training and inference. Their parallel processing capabilities allow for efficient handling of the massive calculations needed for machine learning algorithms. NVIDIA’s CUDA software ecosystem further cements its dominance, creating a competitive moat for its hardware.   The company has also expanded its reach through solutions like DGX servers, TensorRT for AI optimization, and its focus on cutting-edge AI applications like large language models (LLMs). With AI adoption accelerating globally, NVIDIA’s stronghold seems secure—for now.     —   The Rise of Quantum Computing   Quantum computing, unlike classical computing, leverages quantum bits (qubits) that can exist in multiple states simultaneously, enabling potentially exponential performance improvements for certain types of problems. Tech giants like Google, IBM, and Intel, alongside startups such as IonQ and Rigetti, are racing to build practical quantum systems.   Quantum computers excel in areas like:   Optimization problems   Molecular simulation (e.g., drug discovery)   Cryptography   Quantum machine learning (QML)     For AI, quantum machine learning holds promise for revolutionizing tasks like clustering, classification, and generative model training, particularly where vast amounts of data or complex calculations are involved.     —   Where Quantum Computing Stands Today   Despite the excitement, quantum computing is still in its infancy. Challenges such as error rates, qubit coherence, and scalability limit its practical use. Current quantum hardware can only perform specialized tasks at a small scale, often requiring hybrid approaches where quantum algorithms are combined with classical systems.   Moreover, developing software for quantum systems remains complex, as it requires entirely new programming paradigms and tools. By contrast, NVIDIA’s GPUs and software libraries are mature, widely adopted, and optimized for today’s AI workloads.     —   Is Quantum a Threat to NVIDIA? Not Yet.   For quantum computing to challenge NVIDIA’s AI chip empire, it must fulfill several conditions:   1. Scalability: Quantum hardware needs to scale from experimental devices to machines capable of training and running massive AI models.     2. Practical Use Cases: Quantum computing must demonstrate a significant speed-up for AI tasks that traditional GPUs already handle efficiently.     3. Affordability: Quantum systems must become cost-competitive with existing GPU-based infrastructure.       At this point, quantum computers are not general-purpose machines capable of replacing NVIDIA GPUs. Instead, they are specialized tools that complement classical systems. For instance, quantum computing could accelerate parts of AI workflows—like solving optimization or training certain models—while classical hardware like GPUs would handle most training and inference tasks.     —   The Coexistence of Classical and Quantum Systems   Instead of replacing NVIDIA’s hardware, quantum computing is more likely to coexist with it. Hybrid systems, where quantum and classical processors work together, will dominate in the foreseeable future. NVIDIA itself is preparing for this hybrid future. The company has partnered with quantum leaders like Quantum Machines and is working to integrate quantum acceleration into its CUDA ecosystem.   Additionally, quantum computing is unlikely to impact the AI inference market—a major revenue stream for NVIDIA—where GPUs are still unmatched in terms of speed, efficiency, and cost.     —   Conclusion: A Long Road Ahead for Quantum   While quantum computing holds immense potential, it is far from disrupting NVIDIA’s AI chip empire. For the next decade or more, GPUs will remain the backbone of AI development and deployment due to their scalability, mature ecosystems, and proven performance.   That said, NVIDIA is unlikely to ignore the rise of quantum computing. By integrating quantum technologies into its broader AI and HPC (high-performance computing) strategy, NVIDIA can position itself to remain relevant in a hybrid quantum-classical future.   In the end, quantum computing is not a threat—it’s a potential partner. NVIDIA’s AI empire is safe for now, but the company’s ability to adapt will determine its future as quantum technology matures.