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Two strategies to ride out the AI avalanche.

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It’s happening, and it’s happening at breakneck speed.  

Ai

Robots take over repetitive manual work and leave less qualified workers unemployed.

Most of us have seen car factories full of robots.

Many people have predicted this.

But that amazes me.

Greg isenberg

AI replaces knowledge workers.

The jobs of copywriters, accountants, business economists and even consultants are at stake. This is happening faster than we can develop a global standard for charging plugs for electric vehicles.  

It is an avalanche, not a glacier movement.

We don’t know how big it will be.

Today we witness the avalanche on the summit of the mountain.

The last few months were just the beginning of a new revolution.

Over the next five years, we will find out how fast this avalanche will accelerate and how big it will become. Here are a few facts to help you understand the dynamics.

Fact 1: No technology in the history of mankind has become established so quickly. Nothing. ChatGPT grew to 1 million users in 5 days and 100 million users in 2 months.

Chatgpt growth

OpenAI may not be able to dominate the AI industry. However, this technology will completely dominate all other industries.

Overwhelmed by this avalanche, all the big tech companies (Google, Microsoft, SalesForce and SAP) are working on integrating AI into their products. Soon, all office jobs will be AI-powered in some way.

Fact 2: Computing power is increasing exponentially. There is no indication that Moore’s Law will soon run out of technical possibilities.

Computational capacity of the fastest supercomputers (c) ourworldindata. Org

Even when silicon-based chip production reaches a physical level, the next push to increase computing power could be in sight: Google CEO Sundar Pichai recently announced a breakthrough in error correction for quantum computers, removing a major obstacle to the practical application of this technology.

Fact 3: Faster iteration cycles for technologies. Start-ups that develop them are more efficient and faster. “Iterations of products used to take years, e.g. iPhone one to iPhone two,” says venture capitalist Chamath Palihapitiya. “The App Store only took two years. With apps today, it’s years – maybe months for really aggressive and disruptive products. But even that is getting faster – groundbreaking innovations now take days and weeks. Startups today: why create an MVP with a ten- or fifty-person company? Three or four could do it. Because all our capital allocation models were based on a different technology. We now need a few hundred thousand dollars instead of 10 or 20 million in the first round and 100 and 500 million in the second. Midjourney and others can now achieve tremendous scale with little capital.”

Fact 4: The exponential growth of AI chats based on large language models continues. As a market for access to training data emerges, pricing for large data sets used in language models will also increase. Soon we will hear of billion-dollar data deals for platforms like GitHub, StackOverflow, Quora and possibly even Amazon reviews. The rate of learning will accelerate as this data emerges for LLM education and is molded into contracts and licensing agreements.

AI is a bigger revolution than the internet or cell phones.

The avalanche has just left the top of the mountain. The closer we get, the more we will see how significant the change will be for the economy, society and the way we live in the future.

Many AI experts believe that human-level artificial intelligence will develop in the next few decades, and some believe that it will be available much sooner.

Ai timeline 8c) ourworldindata. Org

Suppose we build machines that outperform us in every task or process we encounter every day. With this technology, our capabilities are catapulted to previously unattainable heights. We can overcome obstacles and expand even further.

However, there is still a certain amount of skepticism. Not everything in life is a task, and not everything can be done by a computer.

However, the basis for the impact of AI is the digitalization of the business world.

The way we work is changing fundamentally.

This decade will be marked by the spread of AI into all sectors and aspects of human existence, including business, finance, transportation and medicine, as well as science, research and politics. In the future, we may not be able to make important decisions in business or in our personal lives without the support of AI.

As a result, some tasks and jobs are eliminated, including complex jobs such as composing texts. These tasks are entirely computer-based, with the keyboard serving as the primary interface. The avalanche will prioritize these and destroy some well-paying knowledge jobs.

Most tasks and jobs will be improved. Early research indicates such productivity gains, a trend that could easily multiply 10 to 50 times in the future due to the speed and accessibility of information.

New positions are being created, such as machine learning engineer, prompt engineer and AI strategist. I see them as builders, teachers or nurturers of AI who develop, discover, extend and deploy capabilities while mitigating the risks and failures of AI.

Do you recognize the avalanche?

However, this is a wake-up call.

You may not be directly affected at the moment. However, you can start to position yourself for this possibility.

Becoming a pilot or truck driver today, for example, is the equivalent of being the captain of a sailing ship in 1850. In the future, there will be no more manual transportation. Most transportation will be fully automated. Humans will be limited to nostalgia and leisure activities when driving a car or flying an airplane.

We don’t know when the transition to fully automated transportation will take place, but we do know that it will.

AI is created through a learning process

Machine learning is the most important component in the development of AI.

In the field of machine learning, we have found out how to collect, organize and process huge amounts of data. We then used statistical techniques to develop programs that recognize data patterns. These patterns are programmable in models such as ChatGPT’s Large Language Model (LLM): the model “learns” or acquires a new algorithm that can solve tasks. These models later react to complex tasks that are guided by input prompts in so-called chats.

The human and machine learning processes are conceptually very similar.

But humans have no chance of learning faster than a machine, because AI literally works at the speed of light and 24/7.

But we learn differently and other things.

  1. Human perception is much broader and more diverse than the sensors of a machine, a car or a computer. Machines cannot smell, feel texture and rhythm, understand a specific social context or even read emotions. Consequently, data sets for AI will remain incomplete for a long time, and AI will fail at tasks that require deeper feeling
  2. Humans learn judgment and acquire wisdom over time. A test case for autonomous driving is the “trolley dilemma”, where the vehicle must decide whether to drive into a group of pedestrians to avoid a fatal accident. Overcoming this dilemma requires judgment that allows for error and grace. On the other hand, an AI that creates an error is broken and should never be used for real-world tasks.
  3. We humans also have a drive for self-directed adaptation and a general willingness to learn. A human who loses their job is free to choose another profession and learn the required skill. If you make a robot unemployed, in most cases the robot will be dismantled.

There is much more that we humans can do better than a machine, such as grasping connections, dreaming and being creative or being artistic and innovative.

On the contrary, any task that involves processing unique data in a specific task environment will be completed earlier, safer and more cost-effectively by AI than a human. After a possible lack of digitalization is overcome, most employees working in business processes will either be improved or replaced by AI.

That is frightening.

On the other hand, you do not have to be put off by this in any way.

Two ways to reposition yourself against AI

  1. Expand your skills as a designer, cook, craftsman or builder if you are more of a sensory person. These are all examples of people who create things. You can also work as a teacher, trainer, instructor, safety consultant, counselor, soccer coach or music teacher to pass on your knowledge to others. Use AI solutions for your accounting, advertising, scheduling and content creation, but focus on developing your craft and perceptual skills.
  2. Studying artificial intelligence is a promising idea if you are someone who thinks a lot or if your job involves computers. Take up the challenge and you will develop over time. Become an architect of artificial intelligence, an educator or a nurturer of AI. Increase your productivity while enhancing your personal brand and the value you contribute.

In five years’ time, we will be able to estimate the extent of this change.

AI does not necessarily pose a threat to us, but it can compensate for one of humanity’s greatest shortcomings: The limitation of logical thinking in solving complex problems. We will be able to eliminate tedious tasks and solve age-old problems in science, business, society, health and much more.

Our slope is covered with new snow by the avalanche.

We’re going to be fast and have a lot of fun on this descent – it’s going to be fantastic.

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