If you want to survive the next wave of AI, you need to know what to focus on.

The people who do this right will make a lot of money and stay relevant for years to come.

And right now, there are three layers of artificial intelligence. Each one contains infinite opportunities. But all three require a different set of skills if you want to succeed.

You need to know what the layers are, what’s required to succeed in each, and which one you should focus on.

And before that, you need to know how a fundamental bias will cause people to approach AI the wrong way.

The Bias

So I’m going to explain the bias, the three layers (the last one is something anyone can use), and how you can take advantage of the biggest revolution of our time.

So first, the bias. The people who approach AI the wrong way will be looking for a one size fits all solution, often called the silver bullet, because people want to save calories. They’re biased to choose the most efficient solution.

The simple explanation for this is that people are lazy and want to be able to press one button that fixes all their problems.

The complicated answer is that throughout human evolution, the people who could conserve enough calories to survive the hard times lived. So over the centuries, we learned to avoid unnecessary work and use any tool available to make a job more manageable.

Humans have to burn energy to live. If they run out, they die. And they do everything they can to burn less energy to live longer.

This seems like a good idea.

But the problem comes from the fact that if you want the one size fits all solution and someone tells you a single pill will fix all your problems, you’ll be more inclined to believe them than you should be.

Because one pill that fixes all your problems is the ultimate fantasy of human efficiency and survival.

As a result, people take pills they shouldn’t and make other stupid decisions when they come across a solution that looks like an easy way out.

Because we want to save energy, it’s easy to focus so much on finding an efficient solution that we do stupid things.

Some examples of this would be wasting your time by developing a way to automate a thirty-second task that you only have to do twice a year.

Putting off something you know you should do because you’re looking for a better way.

Spending all of your time earning money to buy gym equipment instead of actually working out.

Or spend your time looking for the perfect partner when instead, you should be learning how to make a relationship work.

These are all decisions made to make life easier that end up making life worse.

Because you’re wasting your time and energy working on a fancy solution when you should instead just be doing the work.

So how does this relate to AI?

I think many people will spend their time trying to build AIs from the ground up when they should instead be learning to build cool stuff with what’s already available.

And many others will waste their time trying to build a software tool that uses an AI algorithm when they could be working with the software already out there.

They’ll do this because AI is the ultimate silver bullet. It seems like an external force that can become infinitely more intelligent and, if worked on long enough, fix every problem.

And if you want to fix everything, why not concentrate on building the computer with the most giant brain to do it for you?

There are many reasons you shouldn’t do this. One might be that it’s a waste of time for you.

Suppose you’re extremely good at developing high-net-worth social circles and benefiting from the relationships you create. Should you spend your time writing an algorithm in your bedroom or even in a well-funded office in the middle of silicon valley? Probably not.

Some people will do this and make trillions of dollars. But it might not be you, and there are many other ways to take advantage of the revolution than to build it from scratch.

To make this obvious.

If you’re an excellent graphic designer who knows nothing about writing code, does it make sense to focus all your energy on coding a new algorithm to remain competitive in the modern graphic design world?

After all, AI technology will replace a lot of graphic design jobs.

No, that would be a silly decision. And it would be more realistic to stay up to date on the visual AI tools everyday people are using and use your already better-than-average understanding of the graphic design industry to embrace the tools available and gain a competitive edge that way. For example, use the tools to double your creative output. Give your clients more options. Spend less time on the design work and more time understanding the clients’ needs and giving them a specialized solution. Or just learn how to use the new tools as they become available and teach your clients how to communicate visual ideas to the latest algorithms.

None of these create a silver bullet solution. But they would all make you more money and make you relevant in a changing industry.

This might seem obvious, but a lot of people aren’t going to do this. They’re going to look for silver bullets. They’re going to look for the AI that trades the stock market for them instead of looking for the AI that gives them an edge on the trading they already do.

They’re going to look for a robot that gardens for them instead of looking for an app that helps them grow vegetables better.

Sure, there will be many jobs replaced. But this is a mindset thing. And ultimately it comes down to this. You need to consider yourself a leader that the technology is helping instead of a victim that the technology can save.

The fantasy is to find the solution that makes you never have to do any work ever again.

But that takes you out of the equation.

And that’s not where the competitive edge lies.

The competitive edge is found at the intersection of work and technology.

In other words, you will make the most money at the spot where you can do something useful and artificial intelligence can help you make it more useful.

And many people are going to waste their time focusing their energy on something that’s not their strength. Because the idea of creating something outside of yourself that fixes all of your problems is so appealing.

But what you should be doing, is tapping into the new technology at a level that enables you to do better work.

This brings us to the three layers of the artificial intelligence ecosystem.

Or, the three places where you can do the work.

First Layer

The first layer is to make an AI, to actually create the algorithm.

This is like building GPT 3, the thinking tool itself. The two most common versions at this point being models that are shown millions of images or hundreds of billions of words to develop the ability to predict what word or image should come next based on a prompt.

These are already shockingly good and will get better until they’re indistinguishable from human speakers or artists. They will become capable of solving problems people don’t know how to solve themselves.

No one knows how far it will go but everyone is convinced it will change the world.

The smallest number of people will do this.

But without this none of the other industries will be possible. It will change the world and make trillions of dollars of wealth creation possible.

If you know how to create one, have massive amounts of data to train it with, money to buy the technology needed to host it, and the willingness to work on that kind of project. You should do this. It’s one of the biggest opportunities of all time.

If you are at your best when you are creating something like this or enabling others to do so, you should be focused on this and nothing else.

But the wealth created won’t be limited to the creators of these algorithms. And if that type of research and development isn’t your thing, it’s a waste of your time.

Because someone else will do it better.

And there are other things you could be doing instead.

Such as working in the second layer.

Second Layer

The second layer is more realistic: Use AI algorithms that have already been made available to develop tools other people can use.

Machine learning is incredibly powerful. But it will be a while before it can do anything you want it to just by asking. And until then, anyone who can learn about what an algorithm is capable of and then create a tool for humans that taps into that capability will be able to create something very valuable.

To explain what this would look like. If someone designs an intelligence model that can predict the next word someone will say, that’s a really impressive achievement, but it’s not useful until it’s put into a program, say a word processor or a messaging app, that suggests the next word while someone is writing. And someone has to build that app.

It’s possible to learn how to do this more quickly. There aren’t a lot of startup costs to make this happen. And if you can write code or you know someone who does and you have unique ideas about how people might want to interface with an algorithm, then this could be a great opportunity.

Examples of tools that are using machine learning would be things like search engines that allow you to type in more interesting search queries, transcription software that helps you create meeting notes, and writing tools that help people write blog posts. There are infinite possibilities, and a massive number of problems that could never be solved by the previous computer and internet revolution that are now solvable with an app that uses new algorithms that are being developed every month. And you’ll get rewarded for connecting the dots between the algorithm, the problem, and the end user that wants to solve that problem.

But not every person wants to build an app. And there’s a lot going on in the world that has little to do with starting an internet company. Most people are just trying to solve the day to day problems of life. And they just use apps to make their lives easier in some way. This is where the third and most accessible layer is.

Third Layer

The third layer is to be a user of the software tools other people build.

This is the most accessible layer, and the easiest way to explain it is to use the example of social media. Social media companies were revolutionary, had a massive impact, and made a lot of money. But they were rare. And the number of people who got wealthy from utilizing social media in the form of content creation and advertising was far greater. Influencers, product, entertainment, and service companies all used social media. Thousands of businesses leveraged content creation to get a new form of success and to be useful to their customers in different ways.

The same thing will become possible for the people who choose to start using AI tools.

Examples of this would be to use something like midjourney to create a new style of artwork. Or use a writing tool to create your own content. Or use any number of the many tools being created to create a unique solution that’s most useful to the people around you.

It includes being a cattle rancher that uses an app to help him track the health of his animals and know how to better care for them.

It includes the painter that uses an AI assistant to create an initial sketch so the painter can spend more time on the finishing touches. A CEO that uses it to write emails. And a small business owner that uses a chatbot to handle customer service.

There are billions of ways to use these tools to make life easier. Some of them have been discovered. And some of them haven’t. You can be one of the people who finds a better way.

And this is what most people will focus on.

You can install an app on your phone today, come up with a life hack that uses it, and get a competitive advantage in the industry you already work in, if you can find a way.

Anyone can do this. And the people who do will be surfing the wave of adoption and development that’s rising over our culture today.

It’s also important to know. You might think you should gain a competitive advantage by picking a layer not many people are working on. But that shouldn’t be how you decide.

It’s true that fewer people will be creating algorithms than will be using apps, and a good way to win is by going where there’s no competition.

But in this case, there are opportunities to be uniquely competitive in every layer.

And the way to win isn’t to pick the layer the least number of people are concentrating on. It’s to pick the one you can do the best in.

The most opportunity for you isn’t found where the least number of people are working.

It’s found where you can be the most productive.

You don’t have to be the first person to invent an algorithm to be successful. You just have to be the first person to use the technology in some way in your industry.

No matter what layer you’re working on, if you find a way to be more useful to the people around you, then you’ve found a way to make more money. The apps being created today provide new and creative ways to be more useful.

Many people will look at these three layers and ask themselves which one will make the most money, then decide to focus their efforts there. And that’s the wrong approach.

The right approach is to ask yourself where you could be the most useful. Because that’s the place where you’ll have the best strategic advantage.

Yes, some people will make impressive wins working on the first layer, creating an algorithm that’s used in thousands of apps that millions of people will use.

But there will also be giant apps and conglomerates of apps powered by those algorithms. And there will be organizations of thousands of people that use those tools. These will also be extremely profitable companies.

Creating the algorithm is only one of the races being run right now.

There are thousands of other races being run by the people trying to make the tools we will all use. And by the people using them.

So if you want to adapt to the oncoming revolution, you need to ask yourself in what way you want to use the technology to gain an advantage.

Do you want to dive deep into the mechanics of machine learning? Do you want to create tools for people that use already-built algorithms? Or do you want to use those tools to create more cool things or help others in a new way?

All three have massive opportunities within them.

The question isn’t which option has the most upside. Because they all have incredible potential.

The question is, which layer unlocks your capabilities the best? Which layer are you the most likely to be able to take advantage of?

If you can figure this out. Then you can narrow your focus to the place where you can do the most.

And if you can do the most. Then you can win.

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