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AI replaces expensive jobs first

Expensive Jobs?

Notably:

  • Doctors 🧑‍⚕️
  • Lawyers 🧑‍⚖️

Why are expensive jobs expensive 💰?

Usually, an expensive labor value takes place when the demand for the work is very high, whereas the supply cannot increase. Moreover, health and legal issues regularly appear throughout our society (either injured or in a legal dispute), and it is doubtful for an individual to not benefit from either. In other words, these demands never vanish.

However, the supply always stagnates. Why?

  • Supply velocity remains slow. Because:
    • Skillful doctors and lawyers need extended training.
    • These "prestigious" institutions can only output dozens of jobs per year.
  • But we cannot pump out more supply. Why?
    • We lack secondary sources for such collection (i.e., people who can study for ten years are not abundant).
    • A forceful increase will entail societal backlash (i.e., unprofessional workers)
  • Such scarcely created suppliers work very slow
    • How many patients can a doctor see in a day?
      • There are complaints about Factory-style medical facilities, like conveyor belts.
    • How long does it take for a single lawsuit to resolve?

In the end, supply always falls behind demand.

Then why can AI replace expensive jobs?

Two aspects ① Economic Efficiency ② Performance.

Economic Efficiency

Making AI is expensive because:

  1. High-quality AI requires a high-purity dataset.
  2. For such high purity data, you need a lot of patternable data.

Making an AI is also tricky regardless of the field. To slightly exaggerate, creating a cleaning AI is as hard as making a medical AI.

  • To create a perfect cleaning AI...
    • To determine the pollution level of a room, you will need
      • millions of room photos and data matching the pollution level.
      • millions of datasets containing each type of pollution and its corresponding solutions are required.
        • Water spill. → Dishcloth
        • Garbage → Trash can
        • Dust → Vacuum
    • Each methodology needs to be thoroughly trained.
      • Analyze and train millions of behaviors cleaning with a dishcloth
      • Analyze and train millions of behaviors that contain and dispose of garbage
      • Analyze and train millions of behaviors that utilize vacuum cleaners very well

As a result, cleaning artificial intelligence also costs a lot of money. In other words, if it will be challenging to produce artificial intelligence anyways, you want a model that brings sufficient economic effects and versatile adaptability. Therefore, it is appropriate to train artificial intelligence for expensive labor to show this high financial return on investment level.

Performance

On the other hand, AI never forgets, and it can duplicate itself. Imagine:

  • A doctor who never fails his medical knowledge. A lawyer who remembers every case perfectly.
  • Cloning the best doctors and lawyers in class into thousands of AI, taking thousands of clients at once.
  • Instantly sharing newly discovered data.
  • Remembering every detail of the client and proactively preventing accidents.
  • Meeting my family doctor whenever, wherever.

Industry Resistance

SEOUL (Reuters) - South Korea's parliament on late Friday passed a controversial bill to limit ride-hailing service Tada, dealing a blow to a company that has been a smash hit since its launch in late 2018 but faced a backlash from taxi drivers angry over new mobility services. - South Korea passes bill limiting Softbank-backed ride-hailing service Tada | Reuters

Recent TADA Warfare exhibited a classic Alliance-versus-Megacorporation style of conflict. Taxi drivers eventually won, but it was a victory without victory --- since the winner was another conglomerate Kakao Mobility which finally took over the market.

Physicians and lawyers also show strong industry resistance. However, they also possess immense social power; one can easily imagine such scenarios:

Scenarios

  • Medical AI kills its patient! Can we bet our lives on such a lacking machine?
    • Regardless of the context, such social fear can lead to Tech Luddites.
  • Lawyer AI deemed discriminating? Can we let such biased agents take over our nation?
    • The bias of precedents can appear depending on how statistics are captured. If you maliciously capture statistics and frame specific vested Research as biased, it can spread to artificial intelligence distrust and rejection movements regardless of the context.

Potential Strategy

🐵

In the animal kingdom, there was a naive monkey. One day, a badger came and presented colorful sneakers to a monkey. The monkey didn't need shoes but received them as a gift. After that, badgers continued offering sneakers, and the callus on the monkey's feet gradually thinned. Soon, the monkey, unable to go out without shoes, became dependent on the badger.

Start with a platform system that helps doctors and lawyers.

  • DOCTORS: Start with Medical CRM. When a patient comes, information about the patient is collected before treatment begins. When treatment begins, the patient's story is automatically parsed, and artificial intelligence extracts keywords. Medical personnel verifies this. Similar cases and recommended care/prescriptions appear on one side of the screen. The doctor selects the appropriate treatment among the recommended treatments and proceeds with the treatment. Or a doctor can add a new therapy. This information is recorded on the server and used for extensive data training.
  • LAWYERS: Start with a case analyzer. It begins with a local legal case (e.g., traffic ticket violation), like DoNotPay - The World's First Robot Lawyer. However, after gradually increasing the number of issues that become databases, lawyers can search for similar topics like "Google Search." For example, if a fraud case comes in, the lawyer enters the details of the case. With dozens of previous precedents, artificial intelligence analyzes the similarities and differences of events.
  • Like GitHub Copilot but for medical and legal cases.

Like these, provide sneakers --- very essential and valuable tools for medical personnel and legal professionals. In other words, transform doctors and lawyers into our primary customers and data pipeline. When entering a robust market like the medical and legal circles, never engage in an all-out war. Instead, build cooperative relationships first, neutralize them, and then wage a full-scale war.