Nothing generates wealth like new technology. Over the past 5 decades, new technology has birthed entire industries, from e-commerce and online advertising to mobile apps and virtual reality. Remarkably, 8 of the 10 largest companies in the S&P 500 index did not exist 50 years ago and 5 of them were absent from the landscape just 30 years ago.

Four stages of the industrial revolution
Source: Gryphon Financial Partners

Today, artificial intelligence (AI) stands as a driving force – a fuel for the 4th industrial revolution. Not only is it set to create new industries but also to revolutionize each and every industry that already exists. Therefore, taking advantage of AI today parallels investing in coal or steam engines at the start of the First Industrial Revolution.

The technological progress we make in the next 100 years will be far larger than all we’ve made since we first controlled fire and invented the wheel.
— Sam Altman

What is AI and what can it do?

The short answer is – ‘‘almost anything’’

AI has finally moved beyond its initial ‘‘hype stage’’ and is now providing a range of real-life applications. It possesses the ability to do almost anything using digital text, images, or video. From summarizing books, writing poetry, coding websites, creating exercise plans, winning art competitions, providing medical diagnoses, recommending movies and TV shows based on your viewing history and ratings, gathering visual data in real-time to produce a 3D image that identifies the road, and designing parts of a spaceship to even predicting every previously unknown protein structure, AI does it all.

The image produced with the help of artificial intelligence won 1st place at the Colorado State Fair
Source: CNN

These capabilities are already catalyzing an AI-driven transformation across various jobs, including proofreading, graphic design, sales discovery, programming, and more. The World Economic Forum anticipates that in the next 5 years, AI will transform the core skills of 44% of workers. For example, over the last year, Microsoft’s Copilot AI software has been used to write over a billion lines of code, accelerating task completion for programmers by 55% in the process.

Multiple investment banks, such as Morgan Stanley, have even built AI research assistants for their brokers and analysts. Moreover, individuals can now buy ETFs like the QRAFT AI-Enhanced US Large Cap ETF, which uses AI for stock selection and portfolio rebalancing.

On top of all this, AI will also directly replace many routine jobs such as bookkeeping, data entry, and even certain professional analysts.

I do not think we'll see mass unemployment. But I do think we'll see mass disruption.
— Erik Brynjolfsson, Professor of Economics at the Stanford Graduate School of Business

The rise of personalized chatbots capable of providing tailored information, completing tasks, or even serving as a digital companion during tough times is also evident. In the future, it's conceivable that everyone will have an AI mentor, friend, and assistant.

Everything mentioned is more than amazing. That said, AI is doing so much more, including helping businesses that operate online to overcome strict regulations on gathering personal information for advertising purposes. Through seemingly mundane conversations, AI can actually guess a lot of personal information. For instance, Meta is using its Lattice system to help overcome the setback of Apple’s decision to make iPhone users explicitly opt in to data tracking by the company.

AI is still in its early stages, and as was the case in the first days of the internet, we cannot predict all of the future AI solutions that will become part of our everyday lives. The real revolution will begin once people start leveraging AI algorithms to create new ones – and this is already around the corner with OpenAI’s GPT store.

The advance of technology is based on making it fit in so that you don't really even notice it, so it's part of everyday life.
— Bill Gates

Defining the game – where is it heading?

Stage 1: The origins

AI first captured the public’s imagination way back in the 1950s, when mathematicians started talking about thinking machines. The founders of artificial intelligence believed that learning algorithms would lead to machine intelligence. Yet, the progress proved much more difficult than expected and the hype failed, leaving AI developments closer in resemblance to The Flintstones series rather than The Jetsons.

It was the case until the late 1990s, with the emergence of “machine learning,” that interest in AI was rekindled. In particular, interest was piqued in 1997 when IBM’s Big Blue famously defeated World Chess Champion Garry Kasparov.

Machine learning uses algorithms and probability to identify patterns in data to answer questions and solve problems. The more data it “trains” on, the better it gets at providing accurate answers and solutions.

In 2006, machine learning became “deep learning” when Geoffrey Hinton demonstrated that computers could learn faster by using Artificial Neural Networks – dense layers of algorithms. Having said that, the current AI revolution really began when deep learning systems started using Nvidia GPU chips in 2007 and corporate investment significantly increased in 2010.

Stage 2: The 4th industrial revolution

Since the 2000s, progress has been extraordinary, particularly in the areas of test and speech recognition, i.e. Natural Language Processing (NLP) and image recognition (see chart below).

Language and image recognition capabilities of AI systems since 2000
Source: Our World in Data

Today, the term AI is predominantly used to refer to generative AI models that have been developed with deep learning algorithms. This includes Large Language Models (LLMs) that can read, write, hear, listen, and speak (NLP) and hybrid models that can see and create images and videos (i.e., Image Processing).

How do machine and deep learning alongside language processing form AI
Source: SentiSum

The most widely-known example of an AI large language model (LLM) implementation today is OpenAI’s ChatGPT, which was publicly released on November 30, 2022. Leveraging contextual clues within text, ChatGPT can understand a question or command and provide intelligent and sometimes original responses based on the data the algorithm was trained on.

While GPT-1 and GPT-2 were trained on 117 million and 1.5 billion parameters, respectively, showcasing rudimentary text generation capabilities, the subsequent iterations, GPT-3 and GPT-4, leverage 175 billion and 1.5 trillion parameters, respectively. This, in turn, enables them to pass medical and legal entrance exams and perform better than certain experts in comprehension and writing tasks.

The evolution of parameters used to train OpenAI’s GPT models
Source: OpenAI

The success of ChatGPT has forced OpenAI’s competitors to fast-track the development of their own LLMs. Indeed, Google has introduced Bard and Gemini, Facebook has unveiled LLaMA, and Anthropic has launched Claude. Over the next few years, the major “foundational” LLMs will vie for dominance as the primary platform or “operating system” for AI. Now, we're already witnessing smaller models built from the large, marking a significant trend in the field.

It’s been two months since we announced GPTs, and users have already created over 3 million custom versions of ChatGPT.
– OpenAI

Why invest in AI?

Now that AI has moved from mere hype to tangible real-world business applications, its potential for earnings is enormous and rapidly expanding. Some of this potential can already be seen with the increase in the share price of Nvidia, Microsoft, and other AI-related stocks in 2023. However, many experts anticipate this is just the tip of the iceberg, akin to glimpsing the haulm rather than the full extent of the carrot.

PwC estimates that by 2030 AI will yield cumulative productivity gains of USD 15.7 trillion across the global economy – basically adding another China to the global GDP. McKinsey & Co., in turn, states that AI can add USD 4.4 trillion to global GDP annually. Let’s dig deeper into where some of this value will come from.

Investing in tomorrow’s technology today is more critical than ever...
– Bill Gates

1. Increased labor productivity

US labor productivity has been slowly declining since the 1950s and this has been mirrored across the developed world (see chart below). Interestingly, this trend persists despite the transformative impact of the computer revolution from the 1970s to the 1990s.

US labor productivity growth per hour worked (in %)
Source: Macro Hive

Nevertheless, economists believe that AI can deliver a boost to labor productivity that computers and the internet simply can’t. In the context of the current fourth industrial revolution, Goldman Sachs believes AI could boost labor productivity by 1.5% per year, while McKinsey & Co. forecasts a potentially even higher annual rate of 3.3% by 2040.

Throughout this transition, it is anticipated that over 83 million jobs will be lost while over 69 million will be created. As with any transformative process, the workers who embrace the change and adapt their skills accordingly stand to reap the benefits.

2. Hardware and software

Until recently, many robots could only be used for predictable and repetitive work. However, AI is taking robots to the next level, allowing them to be used for a broader spectrum of tasks. This advancement will facilitate closer collaboration between robots and humans, ultimately enhancing productivity for both.

AI will eventually be incorporated into almost all software and will even be used to write new software. According to Jordan Jacobs, co-founder and managing partner at Radical Ventures, a Toronto-based VC firm specializing in AI, this integration could create “trillions of dollars in economic value.”

3. Healthcare

AI is already being used to help doctors and technicians with diagnoses from medical scans. Not only is it proving to be more sensitive than doctors, but it also holds promise in the early detection of cardiac issues and diagnosing diseases like prostate cancer, which currently lack effective early detection methods.

As personal medical devices become more sophisticated, AI is set to revolutionize daily health monitoring – a.k.a. your smartwatch is getting even smarter. Besides, AI is poised to extend the capabilities of robotic surgeons, which are already increasingly used in various surgeries, making surgical precision far beyond the capability of human doctors.

Artificial intelligence in a surgery hype cycle
Source: ResearchGate

4. Climate change

AI even has the potential to help with climate change; on one hand, it will improve climate models, while, on the other hand, it will help cut emissions with practical solutions. For example, Wallenius Wilhelmsen, the largest sea-borne car transporter in the world, is using AI to reduce emissions by 27.5% by 2030 through the optimization of its shipping routes.

How can one invest in AI?

Given the anticipated benefits, it’s clear that every investor should allocate a certain portion of their portfolio to AI. Here are 2 ways to do so:

1. Invest in AI-focused ETFs

Many large tech-themed ETFs already boast large holdings of companies claiming to be AI-focused. Some ETFs, like the Global X Artificial Intelligence & Technology ETF, currently specifically target “AI companies.” However, it's crucial to recognize that many of these companies primarily integrate AI into their daily operations (e.g., Netflix with its machine learning algorithm) or derive revenue from selling AI-related components, similar to Nvidia.

Yet, many of the largest and most successful companies that actually make progress with artificial intelligence, such as OpenAI, Hugging Face, Anthropic, Jasper, and more, are still private. Consequently, many investors that rely solely on ETFs or public markets will likely miss out on the lion’s share of growth in the AI realm.

The value creation process of some prominent companies as of September 2021
Source: Securitize

2. Invest directly in a private company

For investors seeking early-stage exposure to the growth of AI companies, investing in a company while it’s still private is the only way to go. However, the challenge is that opportunities to invest are extremely rare and largely depend on who you know. Additionally, the minimum investment requirement, typically exceeding USD 1 million, serves as a barrier for many.

Fortunately, investment platforms such as Moonshot structure the most sought-after deals across private equity, venture capital, real estate, and more, including those in the AI field such as OpenAI and the A.I. Genesis portfolio, starting from USD 25’000 and CHF 500 per month, respectively.


There is no doubt that AI is going to transform society and those on the inside will eventually benefit enormously. Thankfully, Moonshot is providing a golden ticket for this revolution with its investment opportunity in OpenAI and the A.I. Genesis portfolio that is surely not to be missed.

And when you’re on an exponential curve, you should generally, in my opinion, take the assumption that it’s going to keep going.
— Sam Altman

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