In financial markets, liquidity changes everything. Once-illiquid assets become tradable. New players flood in. Old hierarchies collapse. Value flows faster and differently.

The same thing is now happening to technical skill.

Where expertise was once scarce and slowly accumulated, AI is injecting liquidity into the skill market. Execution is faster. Access is broader. Barriers are lower. Like in finance, this shift is reshaping the middle of the market in ways that are often painful and confusing.

This is not the end of software jobs. It is a repricing. Those who understand the dynamics of liquidity, and how unevenly it spreads, can not only navigate this change they can succeed because of it rather than get displaced by it.

The Skill Market Before AI

Historically, software development was built on a steep skill curve. It took years to develop the knowledge required to write performant, secure, maintainable code. Organizations reflected this with layered teams: junior developers handled simple tickets, mid-tier engineers carried the delivery load, and senior engineers architected and reviewed.

This mirrored an illiquid market:

• Knowledge was siloed, often in the heads of senior devs or buried in internal wikis.
• Feedback loops were slow, with code reviews, QA gates, and manual debugging.
• Skill mobility was constrained, so career progression followed a fixed ladder over time.

In this world, mid-tier developers were essential. They were the throughput engine of most teams. Not yet strategic, but experienced enough to be autonomous. Scarcity of skill ensured their value.

AI Changes the Market: Injecting Skill Liquidity

Then came the shift: GitHub Copilot, ChatGPT, Claude, Gemini, Cursor, Windsurf, and others.

These tools do more than suggest code. They:

• Fill in syntax and structural gaps.
• Scaffold infrastructure and documentation.
• Explain APIs and recommend architectural patterns.
• Automatically refactor and write tests.

They reduce the friction of execution. GitHub’s research shows developers using Copilot complete tasks up to 55 percent faster (GitHub, 2022). Similar gains are reported elsewhere.

They make skill more accessible, especially to those who lacked it previously:

• Junior developers can now produce meaningful output faster than ever before.
• Non-traditional developers can enter workflows that were once gated.
• Senior developers can expand their span of control and iterate more broadly.

In market terms, AI liquifies skill:

• The bid-ask spread between junior and mid-level capability narrows, that is, the gap between what juniors can do and what mids were once needed for shrinks.
• Skill becomes less bound by time-in-seat or institutional memory.
• More participants can engage productively in the software creation economy. While adoption varies, large tech firms often lead, while smaller companies or legacy-heavy sectors like banking and healthcare face higher integration hurdles, the trend toward skill liquidity is clear.

This shift is not happening evenly. That is where the real opportunity lies.

The arbitrage today is not just in the tools themselves, the chance to capitalize on gaps in how quickly teams adopt AI. It is in the opportunity spread: the gap between what AI makes possible and who is effectively using it.

Just like in markets, early adopters of new liquidity mechanisms gain a structural advantage. Teams that build AI-augmented workflows, shared prompt libraries, and internal copilots are operating on a different cost and speed curve than those still relying on traditional experience-based workflows.

This gap will not last forever. But while it exists, it offers meaningful leverage for individuals, teams, and organizations.

Importantly, AI tools amplify productivity differently across experience levels:

• Juniors gain access to knowledge and patterns previously acquired only through years of experience, helping them produce higher-quality work faster.
• Senior developers, with their deeper context and better judgment, often extract even greater value from these tools, using them to implement complex solutions, explore multiple approaches simultaneously, and extend their architectural vision across more projects.
• Both ends of the spectrum see productivity gains, but in different ways, juniors become more capable, while seniors become even more leveraged.

This amplification effect creates acute pressure on the middle tier, caught between increasingly capable juniors and hyper-productive seniors.

Why the Middle Tier Feels the Squeeze

There is also a practical reason: cost control.

As AI raises the baseline productivity of junior developers, companies see an opportunity to rebalance toward lower-compensated talent. Where a mid-level or senior engineer was once needed to maintain velocity and quality, AI makes it possible for a well-supported junior to do more.

Companies are increasingly betting that AI tools plus cheaper talent are more efficient than maintaining traditional team structures. This shift isn’t without risks, AI-generated code can introduce errors (studies suggest 20-30% may need human fixes), and over-reliance on juniors without robust oversight can compromise quality. Experienced developers remain critical to guide and refine these workflows. That bet is paying off, especially when companies invest in prompt engineering, onboarding, internal platforms, and support tools.

But that “well-supported junior” is not automatic. It requires experienced developers to build and maintain that support system. Mentorship, internal frameworks, curated AI toolchains, and effective onboarding still depend on human judgment and care.

And while AI can augment execution, many real-world systems still depend on context-heavy problem solving, legacy code familiarity, and judgment, all of which often live with experienced, mid-level developers.

What Happens to the Middle Tier? Compression, Specialization, and Realignment

As in finance, when liquidity rises:

• Margins compress. It becomes harder to justify mid-level compensation when similar output is available elsewhere.
• Roles consolidate. Fewer people are needed to ship the same amount of code.
• Value shifts. Execution is commoditized, while orchestration, judgment, and leverage rise in importance.
• New specializations emerge. Just as electronic trading created demand for algorithmic strategists and execution specialists, AI is creating niches for prompt engineers, AI workflow designers, and domain-specific AI specialists.

This helps explain recent tech layoffs. Macroeconomic tightening and overhiring played a role, but so did something more subtle: AI-induced skill compression.

Layoffs often disproportionately affect mid-level developers:

• Juniors are cheaper, and AI makes them more effective.
• Seniors are harder to replace and more likely to direct or shape how AI is used.
• Mid-tiers, once the backbone of execution, now face pressure from both sides.

Duolingo’s restructuring, for example, eliminated many contractor-heavy roles after adopting AI for content generation (Bloomberg, 2023). IBM has projected that up to 30 percent of back-office roles may be replaced by AI over five years (IBM, 2023). These moves reflect a larger market correction.

These examples underscore how companies are re-evaluating where skill and value live, and how automation enables workforce reshaping, sometimes at surprising layers.

The middle tier does not disappear. It gets repriced and redefined. The skills that remain valuable shift away from throughput toward infrastructure, context, and enablement.

Historical Parallel: The Rise of Electronic Trading

In the 1990s and early 2000s, financial markets underwent a similar transformation. Human traders were replaced by electronic systems and algorithms.

Execution became commoditized. Speed and scale mattered more than tenure. Mid-level traders were squeezed, unless they could reinvent themselves as quant strategists, product designers, or platform builders.

Software development is now echoing that shift.

AI is the electronic trading of code. It:

• Reduces the skill premium on execution.
• Increases velocity and throughput.
• Rewards those who design, direct, or amplify workflows, not just those who carry them out.

The New Playbook: Think Like a Market Maker

If you are a developer today, the key question is no longer “How good is my code?” It is “How much leverage do I create for others and for the system?”

Here is how to thrive in this new market:

1. Become a Force Multiplier
   Build internal tools. Create reusable prompts. Develop standard workflows. A mid-tier developer who builds a shared test and prompt suite for new APIs can significantly reduce team ramp-up time, with some teams reporting up to 40 percent gains (e.g., internal studies at tech firms like Atlassian).
2. Shift from Throughput to Leverage
   Own end-to-end delivery. Understand the business context. Use AI to compress the time from problem to insight to deployment.
3. Curate and Coach
   AI raises the floor, but it still needs editorial control. Be the one who sets quality standards, improves outputs, and helps others adopt AI effectively.
4. Build Liquidity Infrastructure
   Invest in internal copilots, shared prompt repositories, and domain-specific agents. These are the new frameworks for scaling productivity.

What Leaders Should Do

Engineering leaders must reframe how they build and evaluate teams:

• Rethink composition. Combine AI-augmented juniors, orchestration-savvy mids, and high-leverage seniors.
• Promote skill liquidity. Create reusable workflows and support systems that reduce onboarding friction and accelerate feedback.
• Invest in enablement. Treat prompt ops and AI tooling as seriously as CI/CD and observability.
• Evaluate leverage, not volume. Focus on unblocked throughput, internal reuse, and enablement, not just tickets closed.

Leaders who create liquidity, not just consume it, will define the next wave of engineering excellence.

Conclusion: Orchestrators Will Win

AI has not eliminated the need for developers. It has eliminated the assumption that skill value increases linearly with time and tenure.

In financial markets, liquidity does not destroy value. It redistributes it and exposes where the leverage lives.

The same shift is happening in software. Those who thrive will be the ones who enable the flow of skill, knowledge, and value. That means orchestration, amplification, and infrastructure.

In markets, liquidity rewards the ones who create it.
In engineering, the same will now be true.​​​​​​​​​​​​​​​​

I’ve been mulling over what security might look like ten years from now, especially as AI-based workloads and robotics take on bigger roles. Growing up, I’d hear my father talk about his work on communication satellites, where triple redundancy was his way of seeing risk managed, not dodged. That perspective, paired with lessons from automotive, aerospace, nuclear, and space industries, feels like a compass as we rethink security in an AI-driven age. It points us toward a future where security isn’t a rigid barrier but a digital immune system—alive, adaptive, and resilient.

Learning from the Physical World

In industries like automotive and aerospace, every piece is built to perform—and to fail without falling apart. Cars layer airbags, antilock brakes, and sensors; airplanes stack redundant systems to keep flying when one falters. Nuclear plants and space missions go deeper, with containment designs and fail-safes that tame the unthinkable. My father’s satellite work ran on this: three layers of backup meant a glitch wouldn’t kill the mission. The takeaway? Strength comes from managing risk, not avoiding it. That mindset, forged in physical systems, would be our starting point for tackling the wild unknowns ahead.

Seeing Security Like a Living Thing

The era of a fixed perimeter is over. Zero trust has rewired our thinking, but as AI powers complex workloads and human-AI robotics step into the fray, static defenses will clearly not cut it. Security is evolving further into an immune system—and we’ll finally see real adaptive defenses land. This isn’t just weak AI bolted onto old walls; it’s a stronger rethink—systems that scan for threats, learn from them, and adapt on the fly. We’re already seeing hints—AI supply chain risks, like models coming with malware, or agenetic workloads escaping containers—which will push this shift. Much like antibodies in the body, these systems won’t just block attacks but hunt for anomalies, isolate them, and strengthen themselves against the next wave. Picture a network that doesn’t wait for breaches but runs silent simulations, sniffing out weak points and patching them—or a robotic assistant that locks down if its sensors detect and confirm an anomaly, echoing the overlapping safety nets of a car or my father’s redundant circuits.

This shift matters most with AI’s wild card: emergent behavior. As systems grow more general, simple parts can spark unexpected outcomes—think of a flock of birds veering as one or a traffic jam born from a few slow cars. In AI and robotics, these surprises could turn risky fast. Drawing from aerospace and nuclear design, we can bake in safety—redundancy, real-time monitoring, adaptive controls—so the system acts like an immune response, spotting odd patterns and neutralizing them before they spread. By 2035, this could redefine security for not just AI but all critical infrastructure—power grids, finance, healthcare, robotic fleets—marrying physical resilience with digital smarts.

How It Holds Everything Together

Resilience beats perfection every time—systems that bend, learn, and bounce back are what endure. Right now, our tech is a messy mix of old and new, full of cracks where risks hide. A digital immune system faces that head-on, and its role only grows as AI and robotics weave deeper into society. With workloads and machines going vertical—powering healthcare, governance, daily life—security becomes the thread holding it together, fast enough to let us steer it toward securing what matters, not just patching what’s broken. Picture a corporate network that senses a phishing attempt, quarantines it like a virus, then “vaccinates” itself by updating defenses everywhere—all while leaving a clear trail to prove what happened. Or a smart city where traffic, power, and robotic responders hum with AI-driven immunity—self-correcting, redundant, and naturally spitting out the artifacts needed to meet compliance obligations, not as an afterthought.

Where It’s All Heading

As we leave perimeter defenses behind for systems secure by design, the wisdom of automotive, aerospace, nuclear, and space industries lights the way. Fusing their lessons with an AI-driven immune system, we’ll build technology that’s tough, trustworthy, and ahead of the curve—keeping problems from spilling outward. Security won’t be static; it’ll be a pattern that keeps adjusting. My father used to say, “If you want to change the world, you have to see it as it is first.” Seeing our systems clearly—flaws and all—is how we’ll shape a future where they don’t just endure uncertainty but thrive in it.

My grandfather’s love of science fiction was his portal to tomorrow’s world—and it became mine. Together we’d pore over books like Asimov’s I, Robot, imagining futures shaped by machines. In the 1940s, when Asimov explored the complexities of artificial intelligence and human-robot relationships, it was pure speculation. By the 2000s, Hollywood had adapted these ideas into films where robots went rogue. Now, in the 2020s, the narrative has flipped—The Creator (2023) depicts a future where humanity, driven by fear, attempts to exterminate all AI. Unlike Asimov’s cautionary tales, where danger emerged from technology’s unintended consequences, this film casts humanity itself as the villain. This shift mirrors a broader cultural change, once, we feared what we might create; now, we fear who we have been.

As a security practitioner, this evolution gives me pause, especially as robotics and machine learning systems grow ever more autonomous. Today’s dominant approach to AI safety relies on alignment and reinforcement learning—a strategy that aims to shape AI behavior through incentives and training. However, this method falls prey to a well-known phenomenon in optimization known as Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure. In the context of AI alignment, if the reward signal is our measure of success, over-optimization can lead to unintended, and often absurd, behaviors—exactly because the reward function cannot capture every nuance of our true values.

Much like early reinforcement learning schemes, Asimov’s Three Laws were a structural control mechanism—designed not to guide morality but to constrain outcomes. They, too, failed in unexpected ways when the complexity of real-world scenarios outstripped their simplistic formulations.

This raises a deeper question: If we now view ourselves as the existential threat, can we truly build AI that serves us? Or will our fears—whether of AI or of our own past—undermine the future we once dreamed of?

Today’s creators display a similar hubris. Once, we feared losing control of our inventions; now, we charge ahead, convinced that our intelligence alone can govern machines far more complex than we understand. But intelligence is not equivalent to control. While Asimov’s Three Laws attempted to impose hard limits, many modern AI safety strategies lean on alignment methods that, as Goodhart’s Law warns us, can degrade once a target is set.

This blind trust in alignment resembles our current approach to security. The slogan “security is everyone’s responsibility” was meant to foster vigilance but often dilutes accountability. When responsibility is diffuse, clear, enforceable safeguards are frequently absent. True security—and true AI governance—demands more than shared awareness; it requires structural enforcement. Without built-in mechanisms of control, we risk mistaking the illusion of safety for actual safety.

Consider containment as an illustrative example of structural control: by embedding hard limits on the accumulation of power, data, or capabilities within AI systems, we can create intrinsic safeguards against runaway behavior—much like physical containment protocols manage hazardous materials.

If we continue to see ourselves as the existential threat, then today’s creators risk designing AI that mirrors our own fears, biases, and contradictions. Without integrating true structural safeguards into AI—mechanisms designed into the system rather than imposed externally—we aren’t ensuring that AI serves us; we are merely hoping it will.

The Luddites were not entirely wrong to fear technology’s disruptive power, nor were they correct in believing they could halt progress altogether. The error lay in accepting only extremes—total rejection or uncritical adoption. Today, with AI, we face a similar dilemma. We cannot afford naïve optimism that alignment alone will save us, nor can we succumb to reactionary pessimism that smothers innovation out of fear.

Instead, we must start with the assumption that we, as humans, are fallible. Our intelligence alone is insufficient to control intelligence. If we do not design AI with structural restraint and built-in safeguards—grounded not in fear or arrogance but in pragmatic control—we risk losing control entirely. Like robust security practices, AI safety cannot be reduced to an abstract, diffuse responsibility. It must be an integral part of the system itself, not left to the vague hope that collectively we will always do the right thing.

Reading was a big deal when I was a kid, but it was also a challenge. I’m dyslexic, dysgraphic, and dysnumeric, which made traditional learning methods difficult—but that’s largely another story. My parents—determined, if not always gentle—had a simple solution: they forced me to read, interpret, and present. They assigned me books, and I had to give oral reports on them. In hindsight, it was one of the most impactful things they did for me because that process—taking in complex information, distilling it, and presenting it clearly—is exactly how professionals in technology function today.

One of the books they had me read was Plato’s Republic. My biggest takeaway? How little had changed in our fundamental struggles with governance. The same debates about justice, power, and human nature that played out in ancient Greece continue today—only the terminology and tools have changed. Looking back, it makes sense why my parents chose that book. My father is logical to a fault and deeply patriotic, and my mother, though no longer politically active, still carries a pocket Constitution in her purse, with more in her trunk in case she runs out. Law and governance weren’t abstract to me—they were everyday conversations.

That experience stayed with me. It made me realize that governance isn’t just about laws—it’s about whether people understand and engage with those laws. And today, we face a different challenge: not a lack of information, but an overwhelming amount of it.

We tend to think of education—whether in civics, history, or technology—as a process of absorbing facts. But facts alone aren’t useful if we don’t know how to assess, connect, or apply them. When I was a kid, I didn’t just have to read The Republic—I had to present it, explain it, and engage with it. That distinction is important. Simply memorizing a passage from Plato wouldn’t have taught me much, but thinking through what it meant, arguing about its implications, and framing it in a way that made sense to me? That was where the real learning happened.

The same principle applies today. We live in an era where access to knowledge is not the bottleneck. AI can summarize court rulings, analyze laws, and map out how different governance systems compare. Information is endless, but comprehension is scarce. The problem isn’t finding knowledge—it’s knowing what matters, how to think critically about it, and how to engage with it.

This issue isn’t unique to civic engagement. It’s the same challenge students face as AI reshapes how they learn. It’s no longer enough to teach kids historical dates, formulas, or legal principles. They need to know how to question sources, evaluate reliability, and synthesize information in meaningful ways. They need to be prepared for a world where facts are easy to retrieve, but judgment, reasoning, and application are the real skills that matter.

The challenge for civic engagement is similar. There’s no shortage of legislative updates, executive orders, or judicial decisions to sift through. What’s missing is a way to contextualize them—to understand where they fit within constitutional principles, how they compare globally, and what their broader implications are.

That’s why the opportunity today is so compelling. The same AI-driven shifts transforming education can change how people engage with governance. Imagine a world where AI doesn’t just regurgitate legal language but helps people grasp how laws have evolved over time. Where it doesn’t just list amendments but connects them to historical debates and real-world consequences. Where it helps individuals—not just legal experts—track how their representatives vote, how policies change, and how different governance models approach similar challenges.

When I was growing up, my parents didn’t just want me to know about Plato’s ideas; they wanted me to engage with them. To question them. To challenge them. That’s what civic engagement should be—not passive consumption of legal information, but active participation in governance. And just as students today need to shift from memorization to deeper understanding, citizens need to move from surface-level awareness to critical, informed engagement with the world around them.

In many ways, AI could serve a similar role to what my parents did for me—forcing engagement, breaking down complexity, and pushing us to think critically. The difference is, this time, we have the tools to make that experience accessible to everyone.

Plato questioned whether democracy could survive without a well-informed citizenry. Today, the challenge isn’t lack of information—it’s making that information usable. And with the right approach, we can turn civic engagement from a passive duty into an active, lifelong pursuit.

Healthcare becomes deeply personal when the system’s fragmentation leads to life-altering outcomes. During COVID-19, my father’s doctor made what seemed like a prudent choice: postpone treatment for fluid retention to minimize virus exposure. What began as a cautious approach—understandable in a pandemic—ended up having dire consequences. By the time anyone realized how rapidly his condition was worsening, his kidneys had suffered significant damage, ultimately leading to kidney failure.

Later, despite years of regular check-ups and lab work (which hinted at possible malignancies), he was diagnosed with stage four lung cancer. Alarming as that was on its own, what stung even more was how these warning signs never coalesced into a clear intervention plan. His history as a smoker and several concerning lab results should have raised flags. Yet no one connected the dots. It was as if his care lived in separate compartments: one file at the dialysis center, another at oncology, and a third at his primary care clinic.

The Fragmentation Crisis

That disjointed experience shone a harsh light on how easily critical information can remain siloed. One specialist would note an abnormality and advise a follow-up, only for that recommendation to slip through the cracks by the time my father went to his next appointment. Each time he walked into a different office, he essentially had to start from scratch—retelling his story, hoping the right details were captured, and trusting that this piece could eventually reach the right people.

The challenges went beyond missing data. My father, who had set dialysis sessions on the same days each week, routinely found his other appointments—like oncology visits or additional lab work—piled on top of those sessions. He spent hours juggling schedules just to avoid double-booking, which was the last thing he needed while battling serious health concerns.

COVID-19 made all of this worse. The emphasis on social distancing—again, quite reasonable in itself—took away the face-to-face time that might have revealed early red flags. Without continuous, well-integrated data flow, even well-meaning advice to “stay home” inadvertently blocked us from seeing how quickly my father’s health was unraveling.

A Potential Game Changer: Subtle AI Support

Throughout this ordeal, I couldn’t help but imagine what a more seamless, data-driven healthcare system might look like. I’m not talking about robots taking over doctor visits, but rather subtle, behind-the-scenes assistance—sometimes described as “agentic workloads.” Think of these as AI systems quietly scanning medical records, cross-referencing lab results, and gently notifying doctors or nurses about unusual patterns.

AI is already proving its value in diagnostic imaging. Studies have shown that computer-vision algorithms can analyze X-rays, CT scans, and MRIs with remarkable accuracy—often matching or even surpassing human radiologists. For example, AI has been shown to detect lung nodules with greater precision, helping identify potential issues that might have been missed otherwise. This type of integration could enhance our ability to catch problems like kidney damage or lung cancer earlier, triggering quicker interventions.

Additionally, when he underwent chemotherapy, he had to wait weeks after treatment and imaging to learn whether it was effective—an excruciating delay that AI could drastically shorten by providing faster, more integrated feedback to both patients and care teams.

Ideally, this technology would work much like a vigilant assistant: it wouldn’t diagnose my father all on its own, but it could have flagged consistent changes in his kidney function and correlated them with other troubling indicators. Perhaps it would have unified those scattered bits of data—a chest X-ray here, a suspicious blood test there—so that each new piece of information triggered closer scrutiny.

Yet for all the promise AI holds, it won’t matter if patients and providers don’t trust it. If alerts and reminders are viewed as background noise—just another alarm among many in a busy clinic—then critical issues may still go unnoticed. That’s why any such system must be transparent about how it arrives at its recommendations, and it must operate continuously in tandem with real human oversight.

The Missing Thread: Continuous Care

One of the biggest challenges my father faced—beyond the clinical realities of organ failure and cancer—was navigating a disjointed care environment. Even when he saw the same doctors, he often encountered new nurses or support staff who weren’t familiar with his case. He had to become his own advocate, repeating medical histories and test results, worried that a single oversight could spell disaster.

If every practitioner had easy access to a continuous stream of up-to-date information, that weight wouldn’t have been solely on my father’s shoulders. An AI-backed platform might have served as the “single source of truth” across different hospitals, labs, and specialists. Instead of fragmented snapshots—a lab test here, a consultation there—his providers would see a holistic, evolving picture of his health. And instead of being passive recipients of siloed updates, they’d participate in a more proactive, team-based approach.

By incorporating AI, healthcare could move from isolated snapshots to a more dynamic and connected view. For example, AI systems could track trends in lab results and imaging over time, detecting subtle changes that may otherwise be overlooked. By learning from every new case, these systems continuously improve, identifying correlations across medical histories, imaging results, and lifestyle factors. This would allow for earlier interventions and more tailored care, such as flagging kidney function changes that coincide with other troubling indicators.

Why Trust Matters More Than Ever

Still, technology can only go so far without human trust and collaboration. The best data-sharing framework in the world won’t help if doctors and nurses are suspicious of AI’s findings or if patients don’t feel comfortable granting access to their health records. Some of this wariness is understandable; health information is deeply personal, and no one wants to risk privacy breaches or rely on software that might produce false alarms.

Yet, if handled properly—with robust privacy protections, clear transparency about how data is used, and consistent evidence of accuracy—AI can become a trusted ally. That trust frees up healthcare professionals to do what they do best: engage with patients, provide empathy, and make nuanced clinical judgments. Meanwhile, the AI quietly handles the complex, data-heavy tasks in the background.

Restoring the Human Element

Paradoxically, I believe that good AI could actually bring more humanity back into healthcare. Right now, many doctors and nurses are buried under administrative and repetitive tasks that eat into the time they can spend with patients. Automated systems can relieve some of that burden, ensuring that routine record checks, appointment scheduling, and cross-specialty communication flow smoothly without continuous manual follow-up.

For patients like my father, that could mean quicker recognition of red flags, fewer repeated tests, and less of the emotional toll that comes from feeling like you have to quarterback your own care. It could also open the door for more meaningful moments between patients and providers—when doctors aren’t racing against a backlog of paperwork, they can be more present and attentive.

Walking Toward a Better Future

My father’s story underscores the steep price we pay for a fragmented, often reactive healthcare system. Even though he was conscientious about his check-ups, too many critical data points floated disconnected across different facilities. By the time all those puzzle pieces came together, it was too late to prevent significant damage.

Yet this isn’t just about looking backward. If there’s a silver lining, it’s the conviction that we can do better. By embracing subtle, well-integrated AI systems, we could transform the way we handle everything from day-to-day care to life-changing diagnoses. We could move beyond isolated treatments and instead give patients a coherent support network—one that sees them as whole individuals rather than a collection of disconnected symptoms.

A Call to Rethink Care

I don’t claim to have all the answers, and I know technology can’t solve every issue in healthcare. But seeing my father’s struggle firsthand has taught me that we urgently need a more unified, trust-driven approach—one that values continuous monitoring as much as it does specialized expertise.

• Patients should have full visibility into their records, supported by AI that can highlight pressing concerns.
• Providers deserve a system that connects them with real-time data and offers gentle nudges for follow-up, not an endless overload of unrelated alerts.
• AI developers must design platforms that respect privacy, ensure transparency, and genuinely earn the confidence of medical teams.

If we can get these pieces right, tragedies like my father’s might become far less common. And then, at long last, we’d have a healthcare system that fulfills its most fundamental promise—to care for human life in a truly holistic, proactive way.