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Generative AI agents can collapse labor-intensive back-office workflows because they autonomously verify heterogeneous inputs, normalize data, and assemble evidence, cutting turnaround time and per-case cost compared with manual work.

Generative AI agents can collapse labor-intensive back-office…

Regulatory barriers, not engineering limits, are the key bottleneck to building more nuclear because safe reactor designs exist but slow, politicized licensing and extreme guarantee demands make new construction costly and time consuming.

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Capping insurer profits can create perverse incentives because when margins are limited firms must grow the premium base or allowable spending to increase absolute profits, which encourages higher total spending rather than efficiency.

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Payers are the primary lever in U.S. healthcare because controlling what gets paid for and at what rate steers providers and patient behavior toward those services.

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A new agent supervisor role will emerge because automated systems generate high volumes of exceptions and approvals that still require nuanced human judgment and triage.

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Deploying agents tends to expand capacity rather than just replace people because lower time and cost per task makes high-volume processes feasible that human labor could not sustain.

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As a cheap automation hack, asking an LLM to write one-off Python scripts lets you normalize messy vendor files because generating ad-hoc parsers is far faster and cheaper than engineering a universal converter.

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Pandemic testing businesses can be very profitable during surges but unprofitable in lulls because they must build and staff for peak throughput, leaving high fixed costs underutilized when demand falls.

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Scaling mass COVID testing quickly requires redesigning the supply chain because incumbent labs are optimized for steady efficiency and compete for the same scarce consumables, so using alternative materials and orthogonal sourcing avoids those bottlenecks.

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