The Author : Momen Ghazouani
Title : CEO Of Setaleur & Editor-in-Chief of The Ilantic Journal
introduction
When Scott Adams, creator of the Dilbert comic strip publicly appealed to Donald Trump on social media for help securing his cancer treatment, he inadvertently pulled back the curtain on one of American health care's most uncomfortable truths: access to life-saving medicine is often determined not by medical necessity, but by who's watching Trump's swift "On it" response and the subsequent involvement of federal officials turned what should have been a routine scheduling of an FDA-approved drug into a political spectacle. But beneath the drama lies a diagnosis far more troubling than Adams' own American health care operates on a two-tiered system where visibility functions as currency, and where bureaucratic machinery that routinely grinds to a halt for ordinary patients suddenly accelerates when someone important picks up the phone This isn't a heartwarming story of compassion. It's a case study in institutional failure dressed up as personal intervention. Adams needed Pluvicto, a targeted radioligand therapy for metastatic prostate cancer that reduces progression risk by roughly 28 percent Kaiser Permanente had approved the treatment but, according to Adams, had "dropped the ball" on scheduling his infusion. Here's what should alarm us the drug was approved, the insurance authorized it, the medical need was urgent, yet the system still stalled until a former president intervened. The efficiency was always there. The capacity existed. What was missing wasn't medical capability but institutional will And that will materialized only when the patient's story went viral and political capital entered the equation
'' The efficiency was always there The capacity existed What was missing wasn't medical capability but institutional will And that will materialized only when the patient's story went viral and political capital entered the equation ,,
The Corrupting Logic of Celebrity Triage
American health care has always contained inequalities, but what we're witnessing now is something more insidious the normalization of influence-based access. When political figures publicly expedite individual cases, they're not fixing the system they're creating a parallel track that operates by different rules. Call it celebrity triage In this framework, hospitals and insurers don't just treat medical urgency; they manage reputational risk. A patient with a megaphone gets prioritized not because their cancer is worse, but because their delay might trend on social media or trigger congressional scrutiny. The quiet patient in the next room, with identical medical needs, waits longer not because the system can't move faster, but because no one important is watching This creates a moral hazard that extends far beyond individual cases. Once institutions learn that public pressure overrides procedure, they begin to informally calibrate their responsiveness to visibility rather than need. Providers start asking Will this case attract attention? Will declining or delaying create bad press The metric shifts
‘’ from How urgent is this medically to "How embarrassing would inaction be And once that calculation becomes routine, equity dissolves We end up with two parallel health-care systems one for people with platforms, one for everyone else Adams' successful appeal may help him ,,
personally, but it establishes a precedent that harms the nameless thousands who lack his access to power
The political theater surrounding this case makes the problem worse. Trump's intervention allows him to perform responsiveness without confronting the structural failures that caused the delay in the first place. It's governance by anecdote addressing the symptom while ignoring the disease. Real leadership would mean asking: Why did Kaiser, which had already treated more than 150 patients with Pluvicto in Northern California alone, experience a scheduling breakdown with Adams What systemic bottlenecks exist in infusion capacity, staff training, radiation-safety protocols, and insurance authorization that create these delays for everyone? Instead of tackling those questions, we get a tweet. The former president gets to claim credit for solving one man's problem while the architecture that created the problem remains untouched
Advanced Medicine, Primitive Governance
'' We've built a health care system that can sequence your genome and design personalized therapies but can't reliably schedule an infusion without a former president's involvement The technological sophistication obscures the procedural chaos underneath ,,
Here's the paradox at the heart of American health care we excel at innovation and fail at distribution. Pluvicto represents decades of scientific achievement combining radioactive isotopes with molecules that target prostate-specific antigens to deliver precision cancer treatment. It's a marvel of modern medicine. Yet its availability means little when access depends on political intervention rather than medical protocol. We've built a health-care system that can sequence your genome and design personalized therapies but can't reliably schedule an infusion without a former president's involvement. The technological sophistication obscures the procedural chaos underneath This failure isn't accidental it's architectural. American health care's fragmented, privatized structure creates multiple points of friction insurance authorization, provider scheduling, institutional capacity, administrative coordination. Each step introduces delay, and unlike single-payer systems where inefficiency is centralized and thus more easily identified and reformed, our system distributes dysfunction across countless actors. Kaiser can approve treatment but struggle with scheduling. Novartis can manufacture the drug but can't control how providers operationalize infusion protocols. The FDA can expedite approval but has no jurisdiction over whether hospitals hire enough staff or invest in radiation-safety infrastructure. The result is a system where every actor can point fingers, and no single entity is accountable for the patient's total experience And that's before politics enters the picture. When Trump intervenes, he's not streamlining a broken process he's bypassing it. That might help Adams, but it reinforces the very dysfunction it claims to address. It signals to providers that the way to manage complex cases isn't to fix internal processes but to brace for external pressure. It tells patients that their best hope isn't a well-functioning system but a viral plea that catches the right person's attention. And it allows policymakers to avoid the hard work of reform because they can always point to individual interventions as evidence that "the system works
The Equity Crisis Hiding in Plain Sight
Let's be blunt about what this case reveals in America, the value of a life is increasingly measured not in medical need but in narrative power. When a patient's story resonates when it involves a recognizable name, a compelling social media presence, or political connections bureaucratic obstacles evaporate When that same urgency exists for someone anonymous, the machinery grinds slowly. This isn't a bug; it's a feature. And every time we celebrate these individual rescues without addressing the systemic failures they expose, we tacitly endorse a health-care model where justice is conditional on visibility The implications ripple outward. Other patients, watching Adams' success, may conclude that their best strategy isn't to work within the system but to circumvent it by going public, by appealing to politicians, by hoping their story goes viral. That might occasionally work, but it also transforms health care into a competition for attention rather than a response to need. It rewards those who are media-savvy, well-connected, or simply lucky enough to have their story picked up. And it penalizes those who lack those advantages: the elderly patient without social media literacy, the rural resident without access to major news outlets, the marginalized individual whose suffering doesn't fit neatly into viral narratives This is where the political dimension becomes crucial. The Adams-Trump episode occurs against a broader backdrop of health-care inequality in America disparities in trial eligibility slower uptake of new therapies among lower-income and minority patients, geographic barriers to specialized care. When high-profile interventions short-circuit these structural inequalities for a select few, they don't challenge the system; they reinforce it. They demonstrate that the system can work efficiently when it chooses to, but it chooses to only when pressured. That's not a health-care model; it's a protection racket where access depends on your ability to generate leverage
'' In America the value of a life is increasingly measured not in medical need but in narrative power When a patient's story resonates when it involves a recognizable name a compelling social media presence or political connections bureaucratic obstacles evaporate ,,
The Political Economy of Precision Medicine
Pluvicto also highlights tensions in how we finance and deliver cutting-edge therapies. The drug costs tens of thousands of dollars per course, requires specialized infusion infrastructure, involves radiation-safety protocols, and demands coordination across oncology teams. Even when insurers approve it as Kaiser did for Adams the real-world logistics remain complex. Hospitals must invest in capacity, train staff, manage scheduling, and navigate reimbursement structures that may not fully cover the institutional overhead. These are solvable problems, but they require systemic investment rather than ad hoc interventions The political right often invokes cases like Adams' to argue that innovation depends on market incentives and that regulatory or pricing constraints would limit access to breakthrough therapies. There's some truth there Pluvicto emerged from decades of R&D investment that required predictable returns. But that argument collapses when access to the approved drug depends not on market mechanisms but on political favoritism. If the lesson from Adams' case is "the system works when the president intervenes," that's not a defense of market-based health care it's an admission that markets alone can't ensure equitable distribution
Meanwhile, the political left points to cases like this as evidence that privatized health care creates arbitrary barriers that single-payer systems would eliminate. There's truth there too fragmentation across insurers, providers, and manufacturers creates coordination failures that centralized systems avoid. But single-payer isn't a magic bullet. Canada and the UK also face capacity constraints, treatment delays, and debates over expensive therapies. The difference is accountability: in those systems, delays are centralized and thus more politically salient, creating pressure for systemic reform. In the U.S., delays are distributed across private actors, making them harder to address and easier to obscure What both sides often miss is that the Adams-Trump episode isn't really about Pluvicto or prostate cancer it's about governance. The question isn't whether innovation should be rewarded or whether markets should allocate resources. The question is whether a modern democracy can build health-care institutions that respond to medical need rather than political or social capital. Right now, the answer is no. And every high-profile intervention that "solves" an individual case without addressing the systemic failure underneath entrenches that dysfunction
What Happens When Visibility Becomes Strategy
The Adams case is part of a broader trend: patients leveraging public platforms to bypass institutional processes. Social media has democratized access to attention in some ways patients who would never have reached traditional media can now go viral and generate pressure. But it's also created new inequalities. The ability to craft a compelling narrative, to mobilize followers, to attract media coverage these are skills and resources that aren't evenly distributed. The result is a strange hybrid: a health-care system where the route to treatment sometimes runs through Twitter threads and presidential replies rather than clinical protocols This puts providers in an impossible position. Do they prioritize high-visibility cases to avoid reputational damage, even if that means other patients wait longer? Do they resist public pressure and face accusations of callousness? Hospitals and insurers increasingly find themselves managing not just medical logistics but public relations crises. And while that might occasionally benefit patients like Adams, it also distorts priorities. A provider's fear of bad press shouldn't determine who gets treated first but increasingly, it does The Trump administration's history with health-care policy adds another layer. The Right to Try Act, signed during Trump's first term, allowed terminally ill patients to access unappointed or investigational therapies outside of clinical trials. That legislation was framed as empowering patients and cutting red tape, but critics warned it could create false hope and bypass safety protections. The Adams case echoes that tension: is Trump's intervention a compassionate response to bureaucratic failure, or is it political theater that undermines institutional accountability? The answer, uncomfortably, might be both
Rebuilding Procedural Justice
If there's a path forward, it starts with acknowledging that symbolic rescues aren't solutions. Every time a politician or public figure intervenes to expedite one patient's care, it highlights the system's failure to function without intervention. What we need isn't more high-profile appeals it's boring, unglamorous reform: standardized timelines for scheduling complex therapies once approved; transparent criteria for prioritization based on medical urgency rather than visibility; investment in infusion capacity, staff training, and radiation-safety infrastructure; mechanisms for accountability when delays occur that don't depend on viral outrage This requires confronting hard questions about resource allocation. If demand for therapies like Pluvicto exceeds capacity, how do we expand capacity ? Who pays for that infrastructure? How do we ensure rural and underserved areas aren't left behind? These aren't problems that presidential tweets can solve they require sustained policy attention, institutional investment, and political will to prioritize equity over optics It also means rethinking how we measure health-care success. Right now, we celebrate medical breakthroughs genomic therapies, AI diagnostics, precision oncology while tolerating massive failures in distribution. A drug that reduces cancer progression by 28 percent is a triumph of science, but if accessing that drug depends on who you know rather than how sick you are, the system has failed. Advanced medicine cannot compensate for primitive governance. The challenge ahead is not discovering new molecules but building institutions that deliver those molecules equitably, reliably, and without requiring patients to become media stories first
The Disease Beyond the Patient
Scott Adams' story will likely have a happy ending he'll probably get his treatment, thanks in part to presidential intervention. But the system that failed him will remain broken for everyone who can't pick up the phone and call a former president. And that's the real disease: not Adams' cancer, but the institutional rot that allows access to life-saving medicine to depend on visibility rather than need The Adams-Trump-Pluvicto episode is a symptom, not the sickness. The sickness is a health-care system that has the capacity to act swiftly but chooses not to unless someone important is watching. It's a system that confuses efficiency for a few with justice for all It's a system where technological marvels coexist with procedural chaos, where innovation races ahead while distribution lags behind, where the rhetoric of patient empowerment obscures the reality of influence-based access Until we confront that sickness until we build health-care institutions that work before power intervenes each new "exceptional" case will prove the same rule: in America, the cure too often begins not in a clinic, but in a spotlight. And for the millions who will never have that spotlight, the system will continue to grind slowly, indifferently, until the next viral plea forces it to remember that behind every statistic is a person whose life shouldn't depend on whether their story trends
The Economics of Death How Advanced Cancer Drugs Became Political Commodities
The Scott Adams case offers more than a window into institutional dysfunction it provides a rare glimpse into the shadowy economics that govern who lives and who dies in American health care. Pluvicto, the drug at the center of this drama, costs roughly $42,500 per dose, with most patients requiring six doses over eighteen weeks. That's a quarter-million dollars for a treatment that extends life by months, not years, and reduces progression risk by 28 percent meaningful, yes, but hardly a cure. The question no one wants to ask loudly is this Who profits when dying becomes this expensive And why does a drug developed with billions in public research funding approved by government regulators, and often paid for by taxpayer-subsidized insurance, generate such staggering private returns The answer lies in a pharmaceutical business model that has perfected the art of monetizing desperation. Novartis, Pluvicto's manufacturer, isn't a villain in a simplistic narrative it's a rational actor in a system designed to reward exactly this behavior The company invested heavily in developing lutetium-177-based radioligand therapy, navigated complex FDA approval processes, and brought to market a genuinely innovative treatment But innovation alone doesn't explain the price tag. What explains it is a carefully constructed architecture of patents, regulatory exclusivity, and market power that allows manufacturers to charge whatever the market will bear. And when the "market" consists of people facing death, what it will bear turns out to be almost everything This isn't unique to Pluvicto. It's the standard playbook for specialty oncology drugs, a category that has seen prices rise at multiples of inflation for two decades. The industry justifies these costs by pointing to R&D expenses, clinical trial complexity, and the need to fund future innovation. And there's truth to these claims drug development is expensive, risky, and time-consuming. But the math doesn't add up Studies consistently show that pharmaceutical companies spend more on marketing and executive compensation than on research Novartis itself reported $9.6 billion in R&D spending in 2024, compared to $12.8 billion in sales and marketing costs. Meanwhile, CEO compensation packages routinely exceed eight figures, and shareholders demand double-digit annual returns The result is a perverse incentive structure where companies maximize revenue not by curing disease, but by managing it chronically. A drug that cures you is purchased once. A drug that extends your life by months requiring repeated doses, ongoing monitoring, and combination with other expensive therapies generates revenue streams. Pluvicto fits this model perfectly. It's not curative it's palliative with a premium price. Patients enter a treatment cascade where each intervention buys time, and each intervention costs more. The pharmaceutical industry has essentially financialized human longevity, turning each additional month of life into a billable commodity
The Lobbying Machine and Regulatory Capture
What allows this system to persist isn't just market dynamics it's political protection. The pharmaceutical industry spent over $380 million on federal lobbying in 2024, more than any other sector except insurance. That money buys access, influence, and most importantly, inaction. Every attempt to allow Medicare to negotiate drug prices has faced massive industry opposition. Every proposal for transparency in drug pricing has been diluted through amendments and loopholes. Every suggestion of importing cheaper drugs from Canada or Europe gets strangled in committee The Adams-Trump episode should be understood in this context. When Trump tweeted "On it," he wasn't challenging the pharmaceutical status quo he was performing within it. His first administration's record on drug pricing was mostly theatrical [ 2 ] lots of rhetoric about "Big Pharma minimal substantive change. The signature achievement, the Right to Try Act, actually benefited pharmaceutical companies by creating pathways for unapproved drugs to reach desperate patients outside of rigorous trials, potentially undermining safety standards while generating revenue. Trump's tough talk on pricing never translated into legislation that fundamentally challenged pharmaceutical profit margins This pattern repeats across administrations. Democrats promise reform, Republicans promise competition, and prices keep rising because neither party is willing to confront the fundamental issue [2] pharmaceutical companies operate in a market that isn't really a market. Patients can't shop around when diagnosed with metastatic cancer. They can't negotiate prices from a hospital bed. They can't choose generic alternatives when patents ensure monopolies for decades. The "market discipline" that supposedly justifies prices simply doesn't exist in oncology. What exists instead is captive demand and companies exploit it ruthlessly The regulatory apparatus meant to constrain this exploitation has been systematically weakened. The FDA approves drugs faster than ever, often on provisional evidence, creating scenarios where treatments enter the market before we fully understand their efficacy. Accelerated approval pathways designed for breakthrough therapies have become standard, allowing companies to charge premium prices while confirmatory trials drag on for years. When those trials eventually show modest benefits, the drugs stay on the market and the prices don't drop. The system is designed to say yes quickly and almost never to say no later Meanwhile, the patent system has been weaponized through "evergreening minor modifications to existing drugs that extend monopoly protection indefinitely. Companies file dozens of patents on delivery mechanisms, dosing schedules, and combination therapies, creating legal thickets that generic manufacturers can't penetrate without years of litigation. By the time exclusivity expires, the company has already developed the next generation drug, and the cycle repeats. Patients never see the promised price drops from competition because competition is structurally prevented
The Global Price Gap and the Innovation Myth
Here's what the industry doesn't want you to know: Pluvicto costs $42,500 per dose in the United States and roughly $28,000 in Germany for the same drug, manufactured in the same facilities, by the same company. In France, where the government negotiates directly with manufacturers, the price drops further. Canadians pay about 40 percent less for cancer drugs than Americans, and outcomes are comparable or better. If American prices truly reflected innovation costs, drugs would cost the same everywhere. They don't. American patients subsidize global profits while being told that lower prices would kill innovation This "innovation defense" deserves scrutiny. The pharmaceutical industry constantly invokes it to justify prices, warning that regulation would dry up R&D investment and leave patients without new therapies. But most transformative drug discoveries don't originate in pharmaceutical company labs they come from publicly funded academic research. The National Institutes of Health invests over $40 billion annually in biomedical research, generating findings that companies then license and commercialize. Lutetium-177, the radioactive isotope in Pluvicto, wasn't invented by Novartis it was developed through decades of government-funded nuclear medicine research The pharmaceutical industry's role is increasingly not discovery but development: taking academic findings, navigating clinical trials, and bringing products to market. That's valuable work, but it doesn't justify the profit margins. Novartis operates at profit margins consistently above 20 percent double the average for S&P 500 companies. Those margins come directly from pricing power in captive markets. And that pricing power is sustained not through innovation but through political influence that prevents competition and negotiation Consider what happens when countries actually regulate drug prices. European nations with strict price controls didn't see pharmaceutical innovation collapse they saw companies continue to operate profitably because even regulated prices still allow healthy margins. The industry's threat that regulation kills innovation is belied by its own behavior: companies don't abandon regulated markets, they just negotiate harder. And when they do develop breakthrough therapies, they develop them for global markets, not just American ones. The idea that Americans must pay double to fund global innovation is a myth designed to protect extraordinary profits
The Political Theater of Compassion
Trump's intervention in Adams' case fits perfectly into this economic structure It allows political actors to perform concern without addressing systemic problems By focusing on individual cases one cartoonist getting his drug politicians avoid confronting the underlying issue: the drug costs $250,000 because the system is designed to allow that price. Real leadership would mean asking: Why does Novartis charge more in the U.S. than Germany ? Why can't Medicare negotiate? Why do patents create decades-long monopolies ? Why are marketing budgets larger than R&D spending ? But those questions threaten powerful interests. Pharmaceutical companies donate to both parties, employ former congresspeople as lobbyists, and fund patient advocacy groups that oppose price controls. The industry has created an ecosystem of dependence where questioning drug prices becomes politically dangerous. Any politician who proposes substantive reform gets accused of "rationing care" or "stifling innovation." Meanwhile, the actual rationing where patients like Adams face delays because treatments cost $250,000 continues quietly, out of sight The Adams case also reveals how pharmaceutical companies weaponize patient stories. Novartis benefits enormously from Trump's intervention not financially in this specific case, but reputationally The episode generates publicity for Pluvicto, demonstrates demand for the drug, and positions the company as a provider of life-saving innovation. It's essentially free advertising worth millions The company doesn't need to defend its prices; it can point to cases like Adams' and say, "See? This drug saves lives Lost in that narrative is the question of how many lives could be saved if the drug cost half as much and twice as many patients could access it This is the cruel irony pharmaceutical companies profit from both scarcity and abundance. They profit from high prices for the few who can afford treatment, and they profit from the desperation of the many who can't, because that desperation generates political pressure for insurance coverage, government subsidies, and patient assistance programs all of which ultimately flow back to company revenues. The system is designed so that every participant insurers, providers, politicians, even patients has a role in maintaining high prices. The only actors with power to change it are the ones profiting most from the status quo
Alternative Models What Reform Could Look Like
The tragedy is that alternatives exist we simply lack the political will to implement them. Other developed nations have proven that you can have pharmaceutical innovation, affordable access, and healthy industries simultaneously. Germany's system of reference pricing sets maximum reimbursement levels based on therapeutic value, forcing companies to compete on price for similar drugs. France negotiates prices centrally, using the threat of restricted market access as leverage. The UK's National Institute for Health and Care Excellence evaluates cost-effectiveness and refuses to cover drugs that don't meet thresholds, forcing manufacturers to offer discounts These aren't radical socialist policies they're pragmatic negotiations that any large purchaser would undertake. Walmart doesn't pay whatever suppliers demand it negotiates aggressively because it has market power. But Medicare, the largest drug purchaser in America, was specifically prohibited from negotiating prices until recent, limited reforms. That prohibition wasn't the result of economic logic; it was the result of pharmaceutical lobbying. And even now, negotiations cover only a handful of drugs, leaving the broader market untouched More ambitious models exist. Public pharmaceutical manufacturing where governments produce generic drugs directly has been successful in India and is being explored in California for essential medicines. Prize-based drug development, where companies compete for fixed rewards rather than charging per dose, could preserve innovation incentives while ensuring affordability. Open-source drug discovery, where publicly funded research remains in the public domain rather than being patented, could accelerate progress while eliminating monopoly pricing But perhaps the most straightforward reform would be allowing Medicare to negotiate aggressively for all drugs, with the threat of either compulsory licensing or simply refusing coverage for overpriced medicines. This would mirror how private insurers already operate in other countries. Companies would face a choice: accept negotiated prices that still allow reasonable profit, or lose access to the American market. Given that development costs are sunk by the time a drug reaches market, companies would negotiate. The current system persists not because alternatives are unworkable, but because the political will to implement them doesn't exist.
The Moral Bankruptcy of "Affordability Programs”
When confronted with pricing criticism, pharmaceutical companies invariably point to patient assistance programs financial aid for patients who can't afford their drugs. Novartis offers such programs for Pluvicto. These are presented as corporate compassion, but they're really market segmentation. Companies charge maximum prices to insurers and government programs while offering discounts to individuals, extracting every dollar the market can sustain. It's the same strategy airlines use: business travelers pay full fare, budget travelers get discounts, and the company maximizes revenue across both segments But there's a crucial difference: airline tickets aren't matters of life and death. When pharmaceutical companies use the same tactics, they're not optimizing revenue they're rationing mortality. Assistance programs don't solve the fundamental problem; they paper over it. They allow companies to maintain astronomical list prices while claiming they help needy patients. Meanwhile, those list prices drive up insurance premiums for everyone, strain government budgets, and force difficult rationing decisions at hospitals and insurers These programs also create administrative nightmares. Patients must navigate complex applications, prove financial need, and reapply periodically. The burden falls on people already dealing with advanced cancer. Many eligible patients never access the programs because they don't know they exist or can't manage the paperwork It's a system designed to look generous while minimizing actual giveaways. And ultimately, it allows companies to avoid the real question: Why not just charge less in the first place ?
Following the Money Who Really Wins ?
When we follow the money in cases like Adams', the beneficiaries become clear. Novartis generated over $47 billion in revenue in 2024, with oncology drugs representing its fastest-growing segment. Institutional investors the hedge funds and pension funds that own pharmaceutical stocks collect dividends and capital gains funded by those revenues. Pharmaceutical executives receive compensation packages tied to stock performance, incentivizing aggressive pricing. Lobbyists and consultants extract fees for protecting industry interests. Even patient advocacy groups, ostensibly representing sufferers, receive pharmaceutical funding and often oppose price controls The losers are diffuse and less visible: patients who delay treatment because of cost; families bankrupted by medical bills; taxpayers funding Medicare and Medicaid programs strained by drug costs; employers facing rising health insurance premiums; state governments cutting other services to afford Medicaid pharmacy budgets. The costs are socialized while the profits are privatized a classic case of concentrated benefits and distributed harms. This structure ensures that those who profit have strong incentives to maintain the system, while those who suffer lack the organization and resources to change it Adams' case briefly illuminates this structure precisely because it's exceptional. When a recognizable figure faces the system's failures, we notice. But for every Scott Adams who gets presidential intervention, there are thousands of anonymous patients making impossible choices: treatment that might bankrupt their family, or foregoing treatment and accepting death. These decisions happen quietly, in hospital financial counseling offices and living rooms, invisible to political actors and media. The system counts on that invisibility. It counts on most people suffering individually rather than organizing collectively. And it counts on the complexity and opacity of pharmaceutical economics to prevent public understanding of how thoroughly we're being exploited
The Price of Everything, the Value of Nothing
The Adams-Trump-Pluvicto episode ultimately reveals the moral bankruptcy of treating life-saving medicine as a commodity. We've constructed a system where the price of a drug is determined not by its cost to manufacture, not by its therapeutic value, not by medical necessity, but by what desperate people can be forced to pay. That's not a health-care system it's a protection racket dressed in the language of innovation and markets Pluvicto represents genuine scientific progress, and the people who developed it deserve credit and compensation. But there's a vast distance between rewarding innovation and allowing pharmaceutical companies to extract maximum revenue from captive patients. Other nations have figured out how to bridge that gap. They reward innovation through reasonable profits while ensuring broad access through negotiated prices. Only in America do we accept the premise that charging $250,000 for a therapy that extends life by months is either economically necessary or morally defensible The political theater around individual cases like Adams' allows us to avoid confronting this reality. We celebrate when someone gets their drug, we applaud presidential interventions, and we move on while the underlying economics that created the problem remain untouched. Real reform would require acknowledging that pharmaceutical companies have too much power, that markets don't work for life-saving medicines, and that government must intervene not through symbolic gestures but through structural change: aggressive price negotiation, patent reform, public drug manufacturing, and willingness to say no to treatments that don't justify their costs Until that happens, we'll continue to see cases like Adams' desperate patients, delayed treatments, political interventions, and underlying economics that ensure the pattern repeats. We'll continue to have the best pharmaceutical innovation in the world alongside some of the worst access. And we'll continue to pretend that this is somehow inevitable, the price of progress, the cost of freedom rather than what it actually is: a political choice to prioritize pharmaceutical profits over human life. That choice isn't written in economic law or medical necessity. It's sustained by lobbying, campaign contributions, and the diffuse suffering of millions who lack Scott Adams' platform. Changing it would require only political courage the one resource apparently more scarce than any medicine
"But the Adams case doesn't just expose the corruption of the pharmaceutical industry; it reveals a deeper clash: between an old economy that moves at a slow, bureaucratic pace, and a new economy that is reshaping the rules of competition at algorithmic speed. To understand the future that awaits us, we must look beyond pharmaceuticals…”
Competition in 2028 Re-engineering Economic Foundations in the Age of AI and the Compressed Economy
The Scott Adams healthcare saga, viewed through the lens of pharmaceutical economics and political theater, reveals something deeper than institutional dysfunction it exposes the acceleration of a broader transformation already reshaping competitive dynamics across all sectors. While Adams waited for his cancer treatment, caught between bureaucratic friction and political intervention, a parallel revolution was compressing the very structure of economic competition itself. The same forces that allow pharmaceutical companies to maintain pricing power through regulatory capture are now being disrupted by artificial intelligence systems that collapse traditional moats, compress decision cycles, and redistribute competitive advantage away from capital accumulation toward adaptive intelligence. By 2028, we're entering what can only be called the Compressed Economy an economic order where the interval between idea and execution, between innovation and commodification, between market entry and market disruption, has shrunk to near-instantaneous speeds that render traditional competitive strategies obsolete This compression isn't merely about faster computers or better algorithms. It represents a fundamental re-architecture of how value is created, captured, and competed for. In the pharmaceutical industry, we see the old model: decades-long patent protections, billion-dollar clinical trials, regulatory moats that preserve pricing power, and political lobbying that prevents negotiation. That model assumed scarcity of knowledge, of capital, of technical capability. But artificial intelligence is systematically destroying those scarcities. AI-driven drug discovery platforms can now identify promising compounds in months rather than years. Generative models can design clinical trial protocols, predict patient responses, and optimize dosing schedules with precision that rivals human expertise. The same technology that allows Novartis to charge $250,000 for Pluvicto could, in theory, enable a well-funded startup to develop a competing therapy in a fraction of the traditional time and cost The implications extend far beyond pharmaceuticals. Across every sector, the competitive landscape is being flattened by AI systems that democratize capabilities once reserved for incumbents. A small team with access to large language models can now produce marketing content, legal documents, and strategic analysis at quality levels that previously required armies of specialists. Generative design tools allow startups to prototype products with sophistication that once demanded extensive R&D departments. Computer vision and sensor networks enable real-time supply chain optimization that was recently the exclusive domain of companies with massive logistics infrastructure. The result is what we might call capability compression the collapse of the advantage that size, experience, and capital once provided
The Death of Execution as Moat
For generations, competitive advantage resided primarily in execution. Having a good idea mattered, but executing it at scale manufacturing products reliably, distributing them efficiently marketing them effectively, managing complex organizations separated winners from losers. Execution excellence required tacit knowledge, accumulated experience, and institutional memory that took years to build and couldn't easily be replicated. This created natural barriers to entry that protected incumbents even when their strategic vision was mediocre Artificial intelligence is systematically dismantling these barriers. When AI systems can manage inventory in real-time, optimize pricing dynamically, personalize customer experiences at scale, and coordinate global supply chains with minimal human intervention, execution excellence becomes less about accumulated institutional knowledge and more about the quality of your algorithms and data. A startup with superior machine learning infrastructure can now outexecute an established player with decades of operational experience. The tacit knowledge that once resided in experienced managers is being codified into models that can be deployed anywhere, instantly Consider the pharmaceutical parallel: Kaiser Permanente's struggle to schedule Adams' Pluvicto infusion despite having treated over 150 patients with the drug suggests that even large, sophisticated healthcare systems struggle with operational complexity. But AI-driven scheduling systems already deployed in industries from aviation to manufacturing can optimize resource allocation across thousands of variables in real-time The competitive advantage that once came from operational excellence in managing complex healthcare logistics is being compressed into software that any institution can license. The moat wasn't as deep as it appeared; it was simply waiting for the right technology to drain it This shift from execution-as-moat to intelligence-as-moat has profound implications. Companies can no longer rely on being "good at operations" to sustain competitive advantage. If your edge is operational efficiency, and AI can replicate that efficiency for competitors at marginal cost, your advantage evaporates. The new moat is the quality of your adaptive systems—how fast your AI learns from new data, how effectively it integrates insights across domains, how quickly it responds to market changes. We're moving from a world where competitive advantage accumulates slowly through operational refinement to one where it can be gained or lost in algorithmic update cycles measured in hours or days
The Compressed Decision Cycle and Market Velocity
Traditional competitive strategy assumed that decision cycles moved at human speed. Companies gathered data quarterly, made strategic decisions annually, and adjusted market positions over years. This tempo allowed for deliberation, planning, and institutional alignment. It also created windows of opportunity periods where first movers could establish positions before competitors responded. But when AI systems can analyze market conditions continuously, adjust pricing in real-time, and execute strategic pivots at machine speed, the entire rhythm of competition accelerates beyond human cadence We're already seeing this in financial markets, where algorithmic trading has compressed decision cycles from days to microseconds. High-frequency traders compete not on superior analysis but on who can execute fractionally faster an arms race of speed where the advantage measured in milliseconds translates to billions in profit. This same compression is now spreading to every sector. Dynamic pricing algorithms adjust retail prices based on inventory, demand signals, and competitor moves in real-time. Supply chain systems reroute shipments based on weather, traffic, and production disruptions before human managers even become aware of problems. Marketing platforms optimize ad spending across channels continuously, learning from every interaction The pharmaceutical industry's slow tempo years-long approval processes, quarterly earnings cycles, annual pricing negotiations seems almost quaint by comparison. But even here, compression is coming. AI is accelerating drug discovery, compressing clinical trial timelines through better patient selection and outcome prediction, and enabling adaptive trial designs that adjust in real-time based on emerging data. The industry's comfortable tempo, protected by regulatory barriers and patent exclusivity, is under pressure from technologies that could potentially compress drug development from decades to years, and eventually to months This acceleration creates what we might call temporal competitive pressure not just competing to do things better, but competing to do them faster than the market window closes. In a compressed economy, by the time you've completed traditional strategic planning cycles, market conditions have already shifted. By the time you've executed a product launch, competitors have already iterated three versions. The advantage goes not to those with the best five-year plan, but to those with systems that can sense, decide, and execute within cycles short enough to stay ahead of market compression
Data Density and the New Factor of Production
Classical economics identified land, labor, and capital as the primary factors of production. The information age added knowledge and intellectual property. But the compressed economy of 2028 elevates data density not just having data, but having the right data, at the right granularity, integrated across the right dimensions as perhaps the most critical competitive factor AI systems are only as intelligent as the data they learn from, and competitive advantage increasingly flows to those who can feed their systems with richer, more relevant, more timely information than competitors This creates dynamics that look superficially like traditional network effects but operate at a different scale and speed. A platform that captures user behavior data doesn't just get smarter slowly over time—it gets smarter with every interaction, in real-time, compounding its advantage at machine speed. Amazon's recommendation engine doesn't just know what products you might like; it learns from billions of interactions across millions of users, creating a data density that brick-and-mortar retailers could never match even with centuries of sales records. Google's search algorithms don't just catalog web pages; they learn from billions of queries, improving with every search in ways that make their advantage self-reinforcing In the pharmaceutical context, data density is becoming crucial but remains largely untapped. Companies like Novartis possess vast amounts of clinical trial data, patient outcomes, and molecular information but that data typically remains siloed, analyzed slowly, and leveraged poorly. Meanwhile, AI-driven biotech startups are building competitive advantage by aggregating genomic data, patient records, and molecular databases at scales that enable machine learning approaches to drug discovery that weren't possible five years ago. The company that can feed the best data into the most sophisticated models will increasingly win the race to identify therapeutic targets, predict drug responses, and optimize treatments compressing the traditional pharmaceutical development advantage in the process But data density as competitive advantage creates troubling dynamics. It rewards scale in ways that could recreate monopolistic tendencies even as AI democratizes other capabilities. Companies with the most users generate the most data, which trains the best models, which attracts more users a flywheel that's difficult for new entrants to disrupt. And unlike physical capital, which depreciates, or human capital, which requires continuous investment, data capital accumulates and appreciates if properly utilized. The rich get richer, not because they're better capitalized in the traditional sense, but because they're better informed in ways that compound exponentially
The Paradox of Commodified Excellence
Here's where the compressed economy reveals its most paradoxical feature: as AI systems commodify capabilities that once required expertise, the baseline quality level across competitors rises dramatically but differentiation becomes harder. When everyone has access to generative AI that can produce professional-grade content, design sophisticated products, and execute complex operations, being "good" is no longer enough. Excellence itself becomes commodified, raising the floor but lowering the ceiling This is already visible in creative industries. A decade ago, professional photography, graphic design, and copywriting required years of training and commanded premium prices because most people couldn't produce professional-quality work. Today, AI tools like Midjourney, DALL-E, and GPT-4 allow anyone to generate images, designs, and text at quality levels that would have been professional-grade five years ago. This doesn't eliminate the value of human creativity—it shifts the competition to a higher level. Now you're not competing to produce something good; you're competing to produce something distinctly better than what AI generates by default. The threshold for "good enough" has risen dramatically, compressing the premium that mere competence once commanded In pharmaceuticals, we see the early stages of this commodification. Computational drug design tools are making drug discovery more accessible, compressing the advantage that large pharma companies held through experienced medicinal chemistry teams. As AI systems improve at predicting molecular properties, optimizing synthesis routes, and identifying promising compounds, the baseline capability rises. Being "good at drug design" becomes less of a differentiator when algorithms can do sophisticated molecular modeling that once required PhD-level expertise. The competitive advantage shifts to whoever can deploy these tools most effectively, integrate them with superior data, and iterate fastest not who has the most experienced scientists This commodification creates deflationary pressure on entire categories of competitive advantage. If your edge was operational efficiency, and AI makes efficiency table stakes, your premium disappears. If your differentiation was quality execution, and AI raises baseline quality industry-wide, you lose your margin. Companies that built moats around capabilities that AI can replicate find themselves suddenly exposed, competing in markets where everyone has access to similar tools and the old advantages no longer apply. It's like pharmaceutical companies discovering that their decades of accumulated R&D expertise suddenly matters less than their algorithmic infrastructure and data pipelines
Adaptive Intelligence as the New Core Competency
In the compressed economy, competitive advantage increasingly comes not from what you know or what you can do, but from how fast your systems learn and adapt. Call it adaptive intelligence the organizational and algorithmic capacity to sense changes, integrate new information, update strategies, and execute adjustments faster than competitors. It's not about having better AI; it's about having AI that gets better faster, and organizational structures that can leverage those improvements at compressed timescales This requires fundamentally different corporate capabilities than traditional strategy emphasized. Classical competitive advantage was about building sustainable positions moats that would protect your business for years. But in a compressed economy where market conditions shift continuously and competitive dynamics evolve at machine speed, sustainability comes not from static positions but from dynamic adaptation. The goal isn't to build an unassailable advantage; it's to build systems that can continually rebuild advantage as conditions change Consider how this applies to the pharmaceutical industry's pricing power the core of Novartis's ability to charge $250,000 for Pluvicto. That power rests on patents, regulatory approval, and clinical validation all slow-moving factors that create durable advantages measured in years. But as AI accelerates drug discovery and approval processes compress, those advantages shrink. A company with adaptive intelligence might identify emerging competitor therapies earlier, adjust pricing strategies dynamically, pivot to new therapeutic areas faster, and respond to policy changes more quickly than incumbents with established but inflexible business models. The advantage shifts from those with the strongest static positions to those with the most adaptive systems This creates a paradox for large organizations. The same scale and resources that once conferred advantage now often impede adaptation. Large pharmaceutical companies have vast R&D departments, extensive clinical trial infrastructure, and sophisticated regulatory expertise but these assets are optimized for the old tempo of competition, not the compressed cycles of AI-driven innovation. A nimble biotech with superior machine learning infrastructure and adaptive decision-making might outmaneuver incumbents even with a fraction of the resources, because they can iterate faster, pivot more quickly, and compress the cycle from insight to action The organizational implications are profound. Companies must shift from hierarchical decision-making optimized for deliberation to distributed systems optimized for speed. They must move from annual strategic planning to continuous strategy adaptation. They must transition from static competitive positions to dynamic competitive motion. In effect, they must become more like the AI systems they deploy learning organizations that update continuously rather than periodically that respond in real-time rather than quarterly, that evolve strategies at algorithmic speed rather than institutional tempo
The Regulatory Challenge Governing at Compressed Speed
Here's where we connect back to the Adams-Trump pharmaceutical saga: our regulatory and policy infrastructure was designed for the slow tempo of 20th-century competition, not the compressed cycles of AI-driven markets. When pharmaceutical development took decades and pricing remained stable for years, regulators could deliberate carefully, build consensus slowly, and adjust policies gradually. But when AI compresses innovation cycles and market dynamics shift at machine speed, regulatory systems built for human tempo become obstacles rather than guardrails The FDA's drug approval process, with its multi-year timelines and rigid protocols, made sense when drug development took a decade and regulatory science needed to catch up with pharmaceutical innovation. But as AI accelerates discovery and could potentially compress development timelines, the regulatory bottleneck becomes more pronounced. Similarly, price negotiation frameworks built around annual or multi-year cycles seem increasingly inadequate when dynamic pricing algorithms could theoretically adjust pharmaceutical prices continuously based on demand, competition, and value delivery This regulatory lag creates dangerous gaps. When competition operates at compressed speed but oversight operates at traditional tempo, several things happen: First, harmful practices can scale and cause damage before regulators notice. Second, innovative approaches that don't fit existing regulatory categories face uncertainty that stifles legitimate advancement. Third, incumbent companies with resources to navigate slow regulatory processes maintain advantages that aren't based on innovation or efficiency but on bureaucratic sophistication. The very mechanisms designed to ensure safety and fairness become tools for preserving the status quo against competitive disruption We see this vividly in Adams' case: the drug was approved, the insurance authorized it, the medical need was urgent yet bureaucratic processes still delayed treatment until political intervention bypassed the system. That's not a bug in healthcare administration; it's a feature of regulatory systems operating at incompatible speeds with the urgency of individual need. Now imagine similar dynamics playing out across the economy as AI compresses competitive cycles to timescales that regulatory systems can't match. The gap between market tempo and governance tempo widens into a chasm The challenge isn't just regulatory capture by incumbent interests, though that remains a problem. It's temporal mismatch governance structures that update on legislative cycles measured in years trying to oversee market dynamics that update on algorithmic cycles measured in milliseconds. Financial regulators grappled with this when high-frequency trading emerged; their response was often reactive, addressing yesterday's problems while new ones emerged. Now every sector faces similar compression, and regulatory frameworks are structurally unprepared.
The Monopoly Question in a Compressed Economy
Traditional antitrust thinking focused on market concentration how many competitors existed, what market share leaders commanded, whether pricing power allowed super-competitive profits. The assumption was that monopolies emerged slowly, through acquisition, predatory pricing, or superior execution that gradually drove out competitors. Regulatory intervention could therefore be deliberate, targeting clear patterns of anti-competitive behavior that persisted over time But the compressed economy changes monopoly dynamics fundamentally. Network effects and data density create natural tendencies toward concentration that happen faster and lock in harder than traditional monopolies. A platform that achieves critical mass in user data doesn't just have an advantage it has a self-reinforcing flywheel that accelerates its dominance. And because these advantages are algorithmic rather than physical, they scale globally and instantaneously in ways that manufacturing-based monopolies never could Yet paradoxically, the same AI systems that enable rapid monopolization also enable rapid disruption. When capabilities are commodified and execution excellence becomes algorithmic, the barriers that once protected incumbents weaken. A well-funded challenger with superior AI infrastructure could theoretically enter markets dominated by established players and compete effectively much faster than was possible in the industrial economy. The compressed economy thus creates a strange dynamic: tendencies toward winner-take-all concentration coexist with unprecedented fragility of market position Pharmaceutical monopolies built on patents, regulatory exclusivity, and clinical validation look quaint by comparison. Novartis's pricing power for Pluvicto rests on slow-moving legal protections that assume competitors need years to develop alternatives. But if AI compresses drug discovery from decades to years, and eventually to months, those patent moats shrink proportionally. A therapeutic area that once could sustain monopoly pricing for a decade might see competition emerge in a fraction of that time. The same compression that threatens to disrupt pharmaceutical economics is already reshaping competitive dynamics in faster-moving sectors This creates a policy dilemma: traditional antitrust tools focus on preventing or breaking up monopolies after they form, but in a compressed economy, monopolies can emerge and potentially be disrupted faster than regulatory cycles. By the time authorities investigate, hold hearings, and mandate remedies, market conditions may have shifted entirely. Conversely, the speed of potential monopolization means that waiting for markets to self-correct might allow dominant players to lock in advantages before competition can emerge The regulatory tempo problem applies to antitrust as much as any other governance domain.
The Human Element in Algorithmic Competition
Amid all this talk of AI, compression, and algorithmic advantage, we risk missing the continued centrality of human judgment, creativity, and strategy AI systems don't set their own goals, don't define what problems are worth solving, and don't make value judgments about what matters. They optimize toward objectives that humans define, using data that humans provide, within constraints that humans establish. The compressed economy doesn't eliminate human agency it changes what forms of human contribution create competitive value Strategic insight becomes more valuable, not less, precisely because tactical execution is increasingly algorithmic. When AI can handle operational details, optimize processes, and execute at scale, the premium shifts to those who can ask better questions, frame problems more insightfully, and direct AI systems toward objectives that others haven't identified. In pharmaceuticals, this means the advantage goes not to whoever can run more clinical trials, but to whoever can identify which therapeutic targets are most promising, which patient populations are underserved, and which regulatory strategies will open new markets. The insight that directs the research matters more than the execution of the research itself Similarly, ethical judgment and values-based decision-making become competitive differentiators. AI systems learn from data, but they don't inherently know what's right or fair or sustainable. When pharmaceutical companies decide how to price life-saving drugs, that's not an optimization problem with a technical solution it's a values question about what kind of society we want to build. Companies that navigate these questions well that build trust, maintain legitimacy, and align their business models with social expectations will have advantages that purely algorithmic optimization can't capture This is where the Adams-Trump episode becomes instructive again. Trump's intervention was fundamentally a human judgment about priorities: one person's urgent need mattered enough to bypass normal procedures. That's not something an algorithm would decide; it's a political and moral choice. But the fact that such intervention was necessary reveals system failures that better algorithmic design might have prevented. If scheduling systems were more intelligent, if authorization processes were more adaptive, if resource allocation was more dynamically optimized, perhaps Adams wouldn't have needed presidential intervention. The human element remains central, but its role shifts from executing processes that AI could handle to making judgments about what those processes should achieve
Competing in Compressed Time
By 2028, the compressed economy has fundamentally altered competitive dynamics across every sector. The interval between innovation and commodification has shrunk to timeframes that traditional strategy can barely comprehend. Capabilities that once conferred lasting advantage are increasingly available to any competitor with access to AI infrastructure and data. Market positions that seemed unassailable can be disrupted by challengers who compete not with more capital or better execution, but with more adaptive intelligence and faster learning cycles The pharmaceutical industry with its decade-long development timelines, patent-protected monopolies, and regulatory moats represents perhaps the last bastion of the old competitive order. But even here, compression is coming. AI-driven drug discovery is accelerating. Regulatory processes are under pressure to speed up. Patient advocacy and political intervention, as in Adams' case, are forcing faster responses from institutional systems built for slower rhythms. The industry's comfortable tempo where companies could spend billions over decades, secure in the knowledge that patents would protect returns is giving way to a faster, more volatile, more competitive landscape Across the broader economy, compression has already arrived. Companies that thrived by executing well are discovering that execution excellence is table stakes, not differentiator. Industries built on information asymmetries are finding those asymmetries erased by AI systems that analyze data faster and more comprehensively than humans can. Businesses that relied on experience curves and accumulated knowledge are losing ground to algorithms that learn from more data, faster, and share that learning across entire organizations instantly The winners in this compressed economy won't be those with the most capital, the biggest workforces, or even the best technology those factors remain important but insufficient. The winners will be those who can build and deploy adaptive intelligence systems faster than competitors, feed those systems with higher-quality data, and integrate algorithmic insights into decision-making at compressed timescales. They'll be organizations that embrace continuous adaptation over static positioning, that value learning velocity over accumulated knowledge, and that compete not to build permanent advantages but to continuously regenerate advantage as conditions shift The Adams-Trump-Pluvicto episode, seemingly about one man's struggle to access cancer treatment, illuminates these broader dynamics. It shows a healthcare system operating at incompatible speeds urgent medical need against bureaucratic inertia, political intervention against institutional process. But it also hints at how compression will transform even slow-moving sectors. When pharmaceutical development accelerates, when regulatory systems adapt to faster innovation cycles, when pricing becomes more dynamic and competitive the industry will face the same pressures that are already reshaping faster-moving markets The compressed economy isn't a future prediction; it's the present reality. The question for 2028 and beyond isn't whether competition will operate at compressed speeds it already does. The question is whether our institutions, regulations, and strategic frameworks can adapt to govern, guide, and participate in an economic order that moves at algorithmic tempo. The alternative is what we see in Adams' case: systems that fail to respond at appropriate speeds, leaving individuals to seek dramatic interventions because routine processes can't keep pace with urgent needs Competition in 2028 demands that we rebuild economic foundations for compressed time not just faster computers or better algorithms, but entirely different approaches to strategy, regulation, and value creation that can function at machine speed while preserving human judgment, fairness, and purpose. That reconstruction is perhaps the defining economic challenge of our era, more fundamental than any particular technological breakthrough or market disruption. How we meet it will determine whether the compressed economy becomes a realm of unprecedented dynamism and opportunity, or simply replaces old monopolies and inequities with new ones that operate at algorithmic speed
Reconstructing Value in an Age of Compressed Time and Distributed Power
The journey from Scott Adams' desperate social media plea to the broader architecture of economic competition in 2028 reveals a singular truth: we are living through a moment of profound institutional mismatch. The systems we've built to govern access to medicine, regulate markets, and organize economic competition were designed for a world that no longer exists a world where change moved at human speed, where competitive advantages accumulated slowly, where geographic and informational barriers created natural limits to market power. That world is dying, replaced by something faster, more fluid, and fundamentally more volatile. Yet our institutions persist in their old rhythms, creating the friction, delay, and dysfunction that turned Adams' approved cancer treatment into a political spectacle This institutional lag isn't merely an administrative inconvenience. It represents a crisis of legitimacy that threatens the social contract underlying modern capitalism. When people watch a former president intervene to secure one man's medical care while millions face similar delays in silence, they don't just see inefficiency they see a system that responds to power rather than need, that allocates life-saving resources based on visibility rather than urgency, that maintains the pretense of fairness while operating through networks of influence invisible to ordinary participants. That perception, whether entirely accurate or not, corrodes the public trust necessary for any economic system to function. And when artificial intelligence begins compressing competitive cycles to speeds that regulatory oversight cannot match, this legitimacy crisis will only deepen The pharmaceutical industry's role in this drama is both specific and emblematic. Specific, because the economics of drug pricing with its patent monopolies, regulatory capture, and political protection represent perhaps the purest distillation of how incumbent power resists reform. A drug that costs $250,000 in America and $100,000 less in Europe, manufactured by the same company, using research partly funded by taxpayers, protected by patents that prevent competition, and exempted from price negotiation by laws written by industry lobbyists this isn't a market failure. It's a market design that serves producer interests at consumer expense. The Adams case merely made visible what happens daily people with urgent medical needs encounter systems optimized for revenue extraction rather than care delivery But the pharmaceutical story is also emblematic of broader dynamics reshaping every sector. The same forces that allow Novartis to maintain pricing power regulatory moats, information asymmetries, capital requirements, accumulated expertise are being systematically undermined by artificial intelligence across the economy. When AI can compress drug discovery timelines, when generative models can design clinical trials, when machine learning can predict patient responses, the traditional barriers that protected pharmaceutical incumbents begin to erode. What took decades and billions might soon take years and millions. The compression that's already transformed media, retail, and finance is coming for healthcare, just more slowly because regulatory barriers are thicker and political protection stronger This compression creates paradoxes that our existing analytical frameworks struggle to process On one hand, AI democratizes capabilities that once required massive organizations, expensive infrastructure, and years of accumulated expertise. A startup with superior algorithms and good data can now compete against established players in ways that were impossible a decade ago. The barriers to entry in knowledge work, operational excellence, and even some forms of manufacturing are falling rapidly. This should increase competition, fragment markets, and distribute economic power more broadly Yet simultaneously, AI creates new centralizing forces network effects, data advantages, and algorithmic moats that concentrate power in ways potentially more durable than industrial-age monopolies A platform that captures user behavior data doesn't just have more information; it has self-reinforcing advantages that compound with every interaction. The rich get richer not because they're better capitalized in traditional senses, but because they're better informed in ways that feed back into their AI systems, making those systems smarter, which attracts more users, which generates more data, which makes systems smarter still. Breaking these feedback loops may prove harder than breaking up steel trusts or oil monopolies, because the advantages are algorithmic rather than physical, scale instantaneously rather than gradually, and operate globally rather than locally
The Governance Crisis : Regulating at Compressed Speed
What makes this moment particularly dangerous is that the compression of competitive cycles is outpacing the adaptation of governance systems by an accelerating margin. When high-frequency trading emerged, regulators struggled to oversee markets operating at microsecond speeds. Eventually they developed circuit breakers, audit trails, and monitoring systems but only after flash crashes demonstrated the risks. Now similar compression is happening across every sector simultaneously, and the regulatory capacity to respond is stretched impossibly thin Consider the FDA's role in the Adams saga: the agency approved Pluvicto based on clinical evidence, following processes designed to ensure safety and efficacy. But those processes, which can take years and require extensive documentation, were built for an era when drug development took decades. As AI accelerates discovery and could potentially compress development timelines to a fraction of traditional periods, the regulatory bottleneck becomes more pronounced. A system designed to carefully evaluate evidence accumulated over years isn't structurally equipped to evaluate evidence that could be generated in months yet waiting years for approvals when faster timelines are technically possible costs lives This isn't an argument for abandoning regulatory oversight the opposite. It's recognition that regulatory systems must evolve to match the tempo of the technologies they govern. That evolution requires more than faster processing or bigger budgets. It requires fundamentally different approaches: adaptive regulatory frameworks that can update as technologies evolve, real-time monitoring systems that detect problems as they emerge rather than years later, and international coordination because compressed competition operates globally while regulation remains national The alternative is what we see in Adams' case: systems that fail at their stated purposes delivering approved treatments to patients who need them requiring political intervention to function. When routine operations require extraordinary measures, the system isn't working. And when those extraordinary measures are available only to those with platforms, connections, or power, the system isn't just broken it's corrupting
The Economic Recontracting Who Captures Value in Compressed Markets
Beyond governance, the compressed economy forces a reckoning with fundamental questions about how value is created and who captures it. The pharmaceutical industry's current model high prices justified by R&D costs, protected by patents, sustained by lobbying assumes that innovation requires such incentives. But what happens when AI compresses development costs and timelines? If a drug that once cost $2 billion and ten years to develop can be created for $200 million and two years, does that justify the same pricing? And if the answer is yes, who benefits from the efficiency gains shareholders, or patients Traditional economic theory suggests that competition should drive prices toward marginal costs over time as patents expire and generics enter. But that assumes competitive markets, and pharmaceutical markets aren't competitive they're designed not to be. The same patent and regulatory structures that theoretically incentivize innovation also prevent the competition that would moderate prices. And when companies can extend exclusivity through minor modifications, file dozens of patents on delivery mechanisms, and pay competitors to delay generic entry, the competition that's supposed to emerge never quite arrives The compressed economy could disrupt this equilibrium, but whether that disruption benefits consumers depends on policy choices we haven't yet made. If AI-driven drug development remains concentrated in large pharmaceutical companies that maintain current pricing models, efficiency gains flow to shareholders while patients continue paying exorbitant prices. But if regulatory changes, patent reforms, or public investment in AI drug discovery distribute capabilities more broadly, the same compression that threatens pharmaceutical incumbents could dramatically reduce drug costs and expand access Similar dynamics play out across sectors. When AI compresses the advantage that scale and capital once provided, does economic power fragment or consolidate? The answer depends partly on how policy responds Do we allow data monopolies to form unchecked? Do we require interoperability that prevents lock-in? Do we invest in public AI infrastructure that prevents any single actor from capturing all gains? The compressed economy doesn't determine these outcomes it creates pressures and possibilities that policy choices will shape
The Human Element Redefining Work and Meaning
Amid all this discussion of algorithms, compression, and competition, we must confront what happens to human work and purpose when AI handles increasingly complex tasks The pharmaceutical scientist who spent twenty years mastering medicinal chemistry now works alongside AI systems that can evaluate millions of molecular interactions instantly The doctor's diagnostic expertise competes with machine learning models trained on millions of cases. The strategic consultant's analytical frameworks are supplemented and sometimes superseded by AI that can process more data, identify more patterns, and generate more scenarios than any human team This doesn't make human expertise obsolete, but it does change its nature and value The premium shifts from executing tasks that AI can handle to asking questions AI can't generate, making judgments AI can't make, and integrating insights across domains that AI models treat separately. Human work becomes more about framing problems, directing inquiry, synthesizing across contexts, and making value-laden decisions that require moral reasoning rather than optimization The Adams case illustrates this shift perfectly. The operational challenge of scheduling an infusion is exactly the kind of task that AI-optimized systems should handle better than current processes. But the decision about whether to intervene, about whose needs take priority, about what fairness means in resource allocation these aren't technical problems with algorithmic solutions. They're human judgments reflecting values, priorities, and social choices. Trump's decision to help Adams wasn't an optimization; it was a moral and political choice about whose suffering matters enough to warrant extraordinary action As AI handles more operational complexity this distinction becomes central What kinds of work genuinely require human judgment, and what kinds can be safely delegated to algorithms? Getting this wrong in either direction creates problems. Delegating moral choices to algorithms risks encoding biases, optimizing toward the wrong objectives, and absolving humans of responsibility for decisions with profound consequences. But refusing to leverage AI for tasks it handles better than humans out of attachment to traditional roles or fear of displacement means choosing inefficiency that ultimately costs lives, opportunities, and resources The reconstruction required isn't just technological or regulatory it's cultural and psychological We must redefine what makes work meaningful when execution is algorithmic, what expertise means when knowledge is commodified, and what human contribution looks like when capability is compressed into software. This redefinition is already underway, painfully and unevenly, across every profession and sector. How we navigate it whether we create new forms of meaningful work or simply distribute economic anxiety will determine whether the compressed economy enhances human flourishing or undermines it
The Path Forward Reconstruction, Not Resistance
The temptation, watching systems fail as they did for Adams, is to call for resistance—to slow down AI deployment, strengthen regulatory barriers, protect existing structures. But resistance to compression is futile and ultimately counterproductive. The technologies driving compression AI, advanced computing, global connectivity aren't going away. Attempting to preserve 20th-century institutional rhythms in a 21st-century technological landscape doesn't protect workers or patients it merely ensures that when change finally arrives, it's more disruptive because adaptation was delayed What's needed isn't resistance but reconstruction: building new institutions, regulations, and social contracts suited to compressed competition and algorithmic speed. In pharmaceuticals, this might mean reformed patent systems that reward innovation without enabling decades-long monopolies, public investment in AI drug discovery that competes with private efforts, international price negotiation that prevents geographic arbitrage, and regulatory frameworks that can evaluate evidence generated at compressed timescales while maintaining safety standards More broadly, reconstruction requires several foundational shifts First, regulatory systems must become adaptive rather than static updating continuously as technologies evolve rather than periodically when crises force reform. This means moving from prescriptive rules to principles-based frameworks, from retrospective oversight to real-time monitoring, and from national regulation to international coordination Second, we must address data as a factor of production and potential source of market power. This could involve requirements for data portability that prevent lock-in, limitations on data accumulation that prevent overwhelming advantages, or public data infrastructure that ensures competitive access. The goal isn't to prevent companies from benefiting from data that would kill incentives for platform development but to prevent data advantages from becoming insurmountable moats that prevent competitive entry Third, we need new social contracts around work and compensation when AI handles more tasks. This might include stronger social safety nets that don't depend on traditional employment, public investment in education and retraining that helps workers transition, or experiments with alternative models like universal basic income that decouple survival from employment. The compressed economy will create enormous wealth the question is whether that wealth concentrates narrowly or distributes broadly Fourth, we must rebuild trust in institutions by making them more responsive, transparent, and accountable. When people see systems working for those with power while failing everyone else, cynicism becomes rational. Restoration requires demonstrating through action not rhetoric that institutions serve broad rather than narrow interests, that access is determined by need rather than influence, and that power is constrained rather than arbitrary
The Stakes Legitimacy in an Age of Algorithmic Power
Ultimately, the Adams-Trump-Pluvicto episode and the broader dynamics of the compressed economy raise questions of legitimacy: What makes economic and political systems worthy of public allegiance? When do people accept market outcomes as fair rather than rigged? How much inequality and dysfunction can systems sustain before legitimacy collapses History suggests that legitimacy rests on several foundations: competence do systems deliver what they promise? Fairness are outcomes distributed according to principles people accept? Responsiveness do institutions adapt to changing conditions and needs? And alignment do systems serve purposes that people recognize as valuable ? Current systems are failing on all counts. Healthcare systems that approve treatments but can't deliver them without presidential intervention aren't competent. Economic systems where identical drugs cost twice as much in one country as another aren't fair. Regulatory frameworks that can't keep pace with technological change aren't responsive. And institutions that optimize for producer profits rather than patient care aren't aligned with human needs The compressed economy will test these legitimacy foundations more severely. When AI systems make consequential decisions at speeds humans can't audit, when competitive dynamics shift faster than governance can adapt, when value concentrates according to algorithmic advantages that seem arbitrary to those excluded maintaining legitimacy becomes harder. The risk isn't just economic disruption but political crisis: people concluding that systems don't work for them and aren't worth preserving The stakes, then, aren't merely about drug prices or healthcare access or competitive dynamics in specific markets. They're about whether we can construct economic and political systems that function legitimately in an age of algorithmic power and compressed time. Can we build institutions that respond at appropriate speeds without sacrificing deliberation? Can we enable AI-driven competition without allowing algorithmic monopolies? Can we reward innovation without enabling extraction? Can we distribute gains broadly enough to sustain political support for market economies These aren't abstract questions. They're urgent choices that will be made explicitly through policy or implicitly through inaction in the next few years. The Adams case provides a glimpse of what failure looks like systems that work only when circumvented justice that depends on visibility and institutions that maintain legitimacy through individual interventions rather than systematic function. The compressed economy accelerating around us will multiply such failures unless we reconstruct foundations deliberately and comprehensively The work ahead is daunting. It requires reforming institutions that resist change, challenging incumbent powers that benefit from current arrangements, and coordinating action across jurisdictions when problems operate globally. It demands that we think simultaneously about immediate fixes getting cancer patients their treatments without presidential intervention and systemic reconstruction building economic and regulatory architectures suited to compressed competition But the alternative to reconstruction isn't stability it's cascading dysfunction, eroding legitimacy, and eventual crisis. The old systems are dying whether we mourn them or not The question is whether we build something better to replace them, or simply endure the chaos of collapse. Scott Adams' story Trump's intervention, and the pharmaceutical industry's pricing power will eventually fade from headlines. But the forces they illuminate institutional lag, algorithmic compression, legitimacy crisis will define our economic and political future
How we respond determines not just who gets which cancer drug, but what kind of society emerges from the transformation we're living through. That choice remains ours to make, but the window for making it deliberately, rather than having it made for us by crisis, is compressing as rapidly as everything else
References
1. The New England Journal of Medicine (NEJM)
2. U.S. Food and Drug Administration (FDA)
3. Novartis International AG (Annual Reports & Financial Statements)
4. Organisation for Economic Co-operation and Development (OECD) - Health Statistics
5. Centers for Medicare & Medicaid Services (CMS)
6. Institute for Clinical and Economic Review (ICER)
7. The Lancet Oncology
8. Ineur Tech : When Access Becomes Influence What the Scott Adams–Trump Pluvicto Episode Reveals About Power, Equity and U.S. Cancer Care