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Where Human Physicians Remain Ahead Yet here is where the human element in medicine cannot be ignored. Diagnosis is not necessarily diagnosing an illness—it's hearing, comprehending, and assembling a patient's history. A physician doesn't merely read pictures or numbers; he hears the quiver in a patRead more
Where Human Physicians Remain Ahead
Yet here is where the human element in medicine cannot be ignored. Diagnosis is not necessarily diagnosing an illness—it’s hearing, comprehending, and assembling a patient’s history.
A physician doesn’t merely read pictures or numbers; he hears the quiver in a patient’s voice, observes the body language, and reads signs against the background of a person’s lifestyle, frame of mind, and history. Pain in the chest can be a heart attack—or it could be anxiety, indigestion, or even grief. AI can raise an alarm for a possible cardiac problem, but only a skilled doctor can sit, make eye contact, and weigh all the nuances.
And then there is the issue of trust. Patients tell doctors their secrets, fears, and intimate information. That relationship feeling—knowing someone cares, hears, and is present with you—cannot be substituted by a computer. Healing is not only biological; it is relational, emotional as well.
Risks of Over-Dependence on AI
If we completely outsourced diagnostics to AI, a number of risks arise:
- Bias in algorithms: AI will only ever be as good as what it has been trained on. If that training set doesn’t include all populations (e.g., minorities, women, or unusual conditions), the system can make errors that reinforce inequality.
- Disappearance of clinical intuition: Medicine isn’t always a straightforward black-and-white situation. Physicians need to use experience, intuition, and “gut feelings” when symptoms don’t fit easily into one category. AI doesn’t have that sort of general judgment.
- Accountability problems: If AI gets it wrong, who is accountable—the physician who programmed it, the hospital that bought it, or the physician who applied it?
- Loss of competence: Doctors might dull the edge of their own clinical skills in the long run if they rely too heavily on AI.
The greatest thing to consider AI in medicine as is a hugely useful resource, and not a replacement. View it as a co-pilot. It can do the heavy lifting of number-crunching so physicians can concentrate on what they’re best at: empathize, put things in context, and walk patients through difficult decisions.
For instance:
A computer network could indicate a potential early lung cancer symptom on a scan. The physician reads it, breaks the news to the patient, factors in the medical history of the family, and recommends treatment options compassionately.
AI can monitor a patient’s wearable health information, notifying the physician of irregularities. But the physician makes the final decision as to whether it’s an issue or a normal deviation.
Thus, AI is not taking the place of the doctor—he is supplementing him, just as the calculator supplemented mathematicians or autopilot systems supplemented pilots.
Looking Ahead
The future isn’t going to be “AI vs. doctors” but rather AI and doctors together. The hospitals of the future will likely use diagnostic software to scan data first, and then doctors step in with more cerebral thinking and human compassion. Medical school will likely adapt as well, educating future doctors not just biology but also how to work with AI ethically.
Of course, patients and societies will have to determine where that line is. Some will be okay with the AI doing more (particularly in the overburdened systems), and some will want human intervention out of emotional motivations.
So, can they replace human doctors? Technically, within certain restricted areas, yes. But ought they replace doctors? Most likely not. Medicine isn’t as much about figuring out what’s wrong as it is about guiding patients through some of the most intimate moments of their lives. AI can be the super-geniuis sidekick, the second pair of eyes, the unstoppable number cruncher. But the soul of medicine—the compassion, the judgment, the trust—will probably always rest in the hands of human physicians.
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What I refer to as "AI-driven dynamic tariffs" Consider a system that takes in real-time data (imports by HS code and country, supply-chain flows, world prices, carbon intensity, domestic employment indicators, smuggling/evasion alerts, etc.), executes automated economic and rule-based models, and dRead more
What I refer to as “AI-driven dynamic tariffs”
Consider a system that takes in real-time data (imports by HS code and country, supply-chain flows, world prices, carbon intensity, domestic employment indicators, smuggling/evasion alerts, etc.), executes automated economic and rule-based models, and dynamically adjusts tariff rates on targeted product lines or flows continuously—or at pre-set intervals—based on pre-defined goals (save jobs, stabilise domestic prices, reduce carbon leakage, raise revenue, retaliate against unfair practices). The “AI” components are prediction, anomaly detection, automated simulation of scenarios, and decision support; the policy choice may remain human-approved or completely automated inside legal bounds.
Technical feasibility — yes, but nontrivial
We already have two things that demonstrate pieces of this are possible:
Businesses and suppliers are developing AI software to monitor tariff updates, predict supply-chain effects, and execute tariff-related compliance (real-time HSN classification, duty calculations, scenario modeling). That infrastructure might be repurposed or scaled to advise policy.
In other regulated spaces (electricity, say) researchers and practitioners have implemented automated “dynamic tariff” mechanisms—the math and control systems are there (Bayesian / optimization / feedback control)—so the engineering pattern is established in similar contexts.
So sensors, data pipelines, modeling software and compute are there. The difficult bit isn’t raw compute — it’s policy design, governance, enforcement and second-order market effects.
Potential benefits (why people are excited
Substantial practical and political risks
Governance design: making it safe & credible
If governments wish to try, these precautions are necessary:
UN Trade and Development (UNCTAD)
Where an AI-dynamic strategy is most likely to be beneficial first
Sectoral pilots: perishable agriculture (where price shocks are pressing), energy-intensive inputs (to introduce carbon-adjusted import tariffs), or instances of abrupt dumping imports.
Decision-support systems: applying AI to suggest discrete tariff actions to human decision-makers (highly probable near term). AI is already being applied by many countries and companies to monitor tariffs and model impacts—dual-purposing the same tools as policy analytics is the low-risk initial step.
Analogues and precedent
Dynamic pricing in transport and utilities has yielded regulators lessons on fallback predictable pricing requirements, consumer protections, and smoothing signals. Researchers have modeled tariffs as feedback controls—valuable policy design advice.
Private sector tools (Altana, Palantir, tariff-HSN AI, etc.) illustrate the speed at which businesses can realign operations to tariffs; that same responsiveness would go both ways if governments were to automate tariffs.
Political economy — a central tension
Tariffs aren’t merely economics; they are political promises (to constituents, sectors, global partners). Politicians like visible, understandable actions. A ping-ponging algorithmic tariff will be framed as “out of control” even if it maximizes social welfare on paper. That renders full replacement politically implausible short of very gradual staged rollouts and robust transparency.
A realistic phased way forward (my suggested roadmap)
Bottom line — probable outcome
Short-to-medium term (1–5 years): AI will drive tariff analysis, forecasting and decision support. Governments will pilot constrained auto-adjustments in narrowly defined regions. Companies will use more AI to respond to these actions.
Medium-to-long term (5–15+ years): With frameworks of law, international coordination, good governance and evident payoffs, dynamic tariffs might emerge as an explicit policy tool, but they will exist alongside static tariffs and trade agreements instead of displacing them in toto. The political and diplomatic viscosity of tariffs ensures human beings (and parliaments) will retain ultimate discretion for a while yet.
If you prefer, I can:
- Create a sample policy framework (objectives, thresholds, oversight, appeal process) for a pilot program; or
- Develop a technical architecture (data feeds, models, auditing, rollback) for a government that would like to pilot dynamic tariffs; or
- Develop a brief explainer targeted at legislators that distills the payoffs, risks and mitigations.
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