This article is part of the LatAm Tech Weekly Series, written by Julia De Luca and powered by Nasdaq. Through Nasdaq’s global network, we partner with Latin American companies to support their entire business lifecycle to elevate their brand and access the global markets. Learn more about Latin American Listings here.
By Julia de Luca and Lucas Abreu
We are excited and worried at the same time. Like skydiving. What made us feel that way? Artificial Intelligence.
Are we on the verge of a technological revolution? First, let’s understand what this means: technological revolutions can be divided into three stages: introduction, permeation, and power.
Going back to Middle School to refresh our minds, the most well-known examples of technological revolutions are: the Neolithic Revolution, the Industrial Revolution that occurred in the 19th century, the scientific-technical revolution that occurred circa 1950–1960 and, finally the Digital Revolution. The truth is – it is not easy to define the exact dates each one started, and how each impacted or influenced human activity. A common aspect is that to be described as such, the movement must change and affect several sectors such as science, heavy industry, financial markets, transport, psychology – etc.
From time to time, we, as human society, live through a new technology epoch. It is perhaps time to see the most disruptive technological revolution yet: Artificial Intelligence.
In a moment when most of the tech industry is suffering and being questioned in several aspects, AI is experiencing its golden age. It is exciting because if used well, humans can become super productive and society can solve deep secular problems. It is worrying because it can commoditize jobs thus generating a fear of replacement. Furthermore, it has an ethical and moral risk. These models can be biased (since to train they use data that are, in fact, to a certain exten biased) and moral themes such as plagiarism.
But in one thing we have conviction: Definitely, AI is the Next Big Thing.
To understand the overall impact and opportunities with a LatAm view, we are happy to launch our new series: The Next Big Thing LatAm Deep Dive. We will start with AI and every month we will produce a piece conducting in-depth research on one of the themes we discussed on our annual report.
As we wrote, the goal is to always look forward and currently we can only see and hear about the Artificial Intelligence wave. Note that this article is a mosaic of different ideas, articles, and conversations about the subject. Please find the reference to all of the content that inspired us at the end of the piece.
The AI Moment
Discussion with a Thought Leader: Patrick Arippol
Examples of AI applications
Commercializing AI – Who is going “all in’?
Winners and opportunities
Venture Capital investments
What comes next
The first LatAm AI market landscape
The AI moment
Artificial Intelligence (AI) is evolving at an unprecedented rate, taking a massive leap in the last few years. Today’s artificial intelligence has four main types: Generative Adversarial Networks, SCADA Systems, Autonomous Robots and Deep Learning Networks.
Generative Adversarial Networks (GANs), are artificial neural networks that are trained to generate artificial data with the help of generative models. GANs have made it possible for artificial neural networks to be used in tasks like predicting future events, generating new images from existing ones and more.
SCADA Systems, or Supervisory Control and Data Acquisition systems, use artificial intelligence software and tools to manage industrial processes in real time. This artificial intelligence system has become an essential part of the industry today as it allows for monitoring and control systems that are more efficient and cost-effective than traditional methods.
Autonomous Robots also use artificial intelligence to complete complex tasks without any human interaction. Autonomous robots are used in industries like manufacturing, healthcare and defense to handle dangerous and repetitive tasks. The artificial intelligence behind these robots has gotten more sophisticated over time, allowing them to operate with greater accuracy and precision.
Deep Learning Networks are artificial neural networks that use artificial intelligence algorithms to identify patterns in data sets. Deep learning networks have been used for image recognition, speech recognition and natural language processing, among other things.
Yes – GANs are on the spot. Let’s understand more.
What are we experiencing now? And how has this changed how we see artificial intelligence?
It’s important to point out that we are not scientific writers. But we are curious. There is a reason behind the recent AI explosion, which can be partially attributed to the paper “Attention is all you need” from Google Brain in 2017. There is a split in artificial intelligence before and after this scientific paper.
This paper developed the concept of the Attention mechanism and introduced the Transformer Architecture, which led to several breakthroughs in Machine Learning. Famous models like OpenAI’s GPT-3, Google BERT and Microsoft’s Turing-NLG are all based on attention mechanisms. The paper catalyzed an era of “transformer” models that have grown exponentially in size, as seen in the graph below:
Attention and Transformers architecture is likely to be key building blocks of the next decade of AI. Here are “non-technical” descriptions:
Attention is one method used in Neural Networks which allows models to focus on “what they should pay attention to”, which are the most important subset of inputs while processing a task. The output of this method is the model scalability.
The paper “Attention is all you need” introduced transformer architecture and it was a breakthrough. Since then Transformers have been one of the hottest topics in ML. The architecture enables more computationally efficient language models and can be parallelized, which opens the door to creating massively large language models.
If you want to understand technically the revolution, we strongly recommend this article from The Sequence:
The 10x growth on model parameters can prove the revolution.
One of the outputs that led to mass awareness was OpenAI, an artificial intelligence research and development company founded by Elon Musk and other tech moguls from Silicon Valley, that recently unveiled its newest tool – ChatGPT. This artificial intelligence tool can generate conversations based on natural language processing, allowing for more efficient customer service interactions. Thus, ChatGPT is an example of a startup using generative AI — which refers to AI technologies that generate entirely new content, from lines of code to images to human-like speech (in more simpler terms than what is defined above).
The use of artificial intelligence is rapidly becoming the trend of 2023. AI is being used in a variety of ways such as machine learning, facial recognition and natural language processing, improving the efficiency and accuracy of human tasks. From robotics to artificial intelligence software applications, the use cases for artificial intelligence are endless, and it continues to evolve with time.
In our view, what’s exciting about AI today (and the reason it has now become mainstream) after several years of existence, is that the infrastructure and model layer are solidifying, giving room for the application layer to emerge. In other words, the stuff that we interact with every day….
Discussion with Thought Leader: Patrick Arippol
As we did in our report The Next Big Thing LatAm 2023 released in December, we invited a thought leader who deeply understands AI to help us figure out what we need to know on the theme. Patrick Arippol is the co-founder and General Partner at Alexia Ventures, a LatAm venture capital firm. He started his AI adventure in 2003 with Banter Systems (sold to IBM) and has been working alongside entrepreneurs creating ventures in this area. As an experienced investor, he alerts that hype can be harmful to investors and entrepreneurs. He mentioned that the amount of opportunity now is more or less the same as we had a few years ago since AI isn’t a new trend. What has changed is that people have experienced the tech through tools first hand such as ChatGPT. He shared several interesting points as follows:
Patrick is enthusiastic by nature with AI. He believes that, in the long term, there is a great opportunity for startups to generate and capture value. From infrastructure to application layer, LatAm has an opportunity to be one of hubspots of AI due to: (a) the large addressable market, (b) the amount of data that can be trained in huge countries like Brazil that can lead to a great trained AI model, which can be a technological competitive advantage globally and (c) talents attracted by the opportunities in the region and local talents.
One interesting take from him is that the time to profit for a startup is longer in AI. The product part of the equation takes longer to build, and the models of these startups need large amounts of data to get them working properly. That said, it’s clear that the product market fit of AI is longer. Therefore Artificial Intelligence strategies tend to be convenient to write and idealize, however the operationalization part is hard.
In his experience, it’s game-changing when an AI startup focuses on a vertical and provides a clear benefit for its customer. One challenge he shared is that entrepreneurs/scientists are thinking about the model instead of a precise application. This leads to another insight: Given how hard it is to monetize, AI startups must have a key application for the customer. A clear use case is a requirement for success for any AI startup.
He mentioned that you could only discuss AI by discussing data. Any AI company that aims to be successful has to be able to access and use meaningful data. This can be a competitive advantage for those with a large databases (like many SaaS companies) and a challenge for embedding AI companies, which needs to convince other players to share their data. For embedding startups, they need a clear potential outcome to convince. Entry data points need to be in the conversation.
When asked about the Brazilian stock of talent, he points out that any success depends on the quality of talent and that Brazil has the potential to do so. We have some hotspots in universities such as UFMG, UFMS, PUC-RS, UFRGS, USP and UNICAMP. Also, with remote work, we can import and use talent worldwide. Frontiers are not a problem anymore. Talent in AI needs deep technical knowledge, but it’s a challenge Brazil can overcome.
Examples of AI applications
A practical way to grasp the capabilities of AI is by experiencing them firsthand. As AI technology evolves, new examples will continue to emerge, and this selection serves as a starting point. Here, we will provide examples of some of the most notable AI products introduced recently.
The code revolution with GitHub Co-Pilot and Replit
GitHub Co-Pilot helps developers to code faster and better using AI. One million developers have used Copilot and the ones who use it are generating more than 40% of their codes with the tool.
A quantified Github research proves the benefit:
The first group used GitHub CoPilot: 78% finished the task. The average time was 1 hour and 11 minutes.
The second didn’t use it. 70% finished the task. The average time was 2 hours and 41 minutes.
In this version that will evolve a lot, developers who used GitHub Copilot completed the task significantly faster: 55% faster than those who didn’t. It’s impressive. Imagine being a developer in team that for average tasks, is 2 times more productive.
The tweet below, from the previous Tesla AI Director, shows how disruptive are these kind of tools:
Another product that is worth mentioning is Replit, which now has an AI feature named Ghostwriter used by more than 10 million developers. It provides not only AI, but also a multiplayer capability where coders can see the changes made by other users in real-time, and can share control of the code with others, allowing for efficient collaboration on a project.
The language revolution with Chat-GPT3 and Jasper
Text generation is a revolution led by tools like Chat GPT3 and Jasper. The big thing is that it only needs a short phrase or prompt to create reasonable quality texts. The article below, “What’s the Big Deal with AI Right Now,” was written by Jasper based on the following prompt: “Write an 80 word article about how AI will transform content production..”
How AI is Transforming Content Production
Content production has always been a tedious and time-consuming process. But with the emergence of Artificial Intelligence (AI), content production is now more efficient and accurate than ever before. In this article, we will explore how AI is transforming content production and how it can benefit businesses in the long run.
AI has made it possible to automate certain aspects of content production and editing, allowing businesses to save time and energy when creating content. For example, AI powered tools can help businesses create compelling data visualizations that make their stories easier to digest. Furthermore, AI can also be used to generate engaging copy quickly and accurately, making it easier for businesses to produce high quality content at scale.
AI is also helping streamline the review process by providing automated feedback on grammar, spelling, readability and other important metrics. This helps ensure that even large volumes of content are error-free and up to standards before they are published or shared with others. Finally, AI can help businesses personalize their content by recommending relevant topics based on user behavior or demographic data.
As you can see, AI is revolutionizing the way we produce content. By automating many of the tedious tasks associated with content creation and editing, businesses no longer have to rely solely on manual labor to generate high quality outputs in an efficient manner. With these advances in technology, businesses now have access to powerful tools that allow them to create engaging experiences for their customers without having to invest heavily in manpower or resources. So if you’re looking for ways to improve your content production process, consider leveraging the power of AI!
The language revolution will be so much greater. We can use it not only for business context, but also for day-to-day life. We are just on the tip of the iceberg. The big revolution started with GPT-3, that came out less than three years ago with ~200 billion parameters. The new version, GPT-4, has ~1,000,000,000,000 (a trillion) parameters. This means that the new release (expected for mid 2023) will be significantly better.
Image processing and generation with AI
The impressive landing on AI in 2022 came first with Image Generation Models. It was the first “Wow moment”. Image generation has a variety of use cases, from fun to useful. The revolution itself derives from the zero marginal cost of production. You write a proper prompt and it becomes an image. Some good companies are already using this tech: Dall-E, MidJourney, Stable Difusion. It can be used for design, games, media, and producing social content and much more. Let’s use an example through DreamStudio (Beta version), which uses Stable Diffusion technology. We wrote this prompt: “Panda dancing in a farm painted by Leonardo da Vinci“. In 5 seconds, we got this image generated by AI:
AI is not only innovating on Generation, but also on image processing.
AI-powered image recognition is becoming a game-changer for various industries, especially e-commerce and retail. AI systems can analyze images of products and automatically identify items, their attributes and even generate tags for them, which can be used for faster and more accurate searches on e-commerce platforms. AI-based image processing can be used in medicine as well, for instance, in the analysis of medical images such as X-rays and MRIs, to aid in the diagnosis of diseases.
The advancements in image processing and generation through AI are constantly evolving, and it is expected to become even more impressive very soon.
Vídeo generation and editing
AI is also making significant advancements in the field of video generation. One of the most exciting use cases is the ability to create videos from text or speech. This is possible through AI-powered text-to-video synthesis, which generates videos by converting text or speech into visual content. This technology can be used for creating explainer videos, product demos, and even news broadcasts.
Text-to-Video (T2V) is considered one of the next frontiers for generative artificial intelligence (AI) models. Recently, researchers from Meta AI unveiled Make-A-Video, a T2V model able to create realistic short video clips from textual inputs.
One of the hottest companies in this area is RunwayML. They raised USD 50M at USD 500M valuation in December. They recently released a generative AI model that can take any text and generate new videos from scratch:
There are several other use-cases in Artificial Intelligence. It is such a powerful technology that can be applied to a wide range of industries and use cases because of its ability to analyze and make decisions based on large amounts of data. This makes it ideal for tasks that would be too time-consuming or complex for humans to handle, such as analyzing medical images or tracking inventory levels. The AI’s ability to analyze large amounts of data, learn and adapt over time, versatility in understanding complex patterns and predictions, and the feasibility of implementation due to falling costs of computing and increasing data availability, all make it an ideal technology for a wide range of use cases. A few wide range of use cases, such as:
Predictive maintenance: AI systems can analyze sensor data from equipment to identify potential failures before they happen, allowing companies to perform maintenance proactively, reducing downtime and costs.
Fraud detection: AI-based systems can analyze transaction data to identify patterns and anomalies that may indicate fraudulent activity, helping financial institutions and other companies prevent losses.
Energy management: AI systems can analyze energy usage data to identify opportunities for efficiency improvements, such as adjusting heating and cooling systems or identifying equipment that is consuming more energy than it should.
Agriculture: AI-based systems can analyze data from sensors and drones to optimize crop yields, identify potential issues with irrigation, and predict weather patterns that may impact crop growth.
Medical imaging: AI-powered systems can analyze medical images, such as X-rays and MRIs, to aid in the diagnosis of diseases.
These use cases represent a small fraction of what AI can do, and the possibilities are endless as the technology evolves.
Commercializing AI – Who is going “all in’ among traditional companies?
Before ChatGPT came up the entire concept of AI was kind of obscure to the general public. It was something we talked about, but not something we experienced.
When commercializing AI, companies that are not tech native tend to follow one of two strategies. The first, are the ones that decide to take things slowly – with a pilot or a small number of trials. They also tend to focus on a “wait and see” approach to how the new technology will affect the key themes.
The other, are companies that are ambitious and go “all in”. In this more direct and bullish stance, the companies are investing heavily in building smart technology and automating all of their manual processes. Also, instead of waiting to see how AI will affect our lives, they are focusing on answering the big questions related to ethics, morality, etc.
Ok, who are these all-in companies? Let’s ask the experts!
Tom Davenport, President’s Distinguished Professor of IT and Management at Babson College and Nitin Mittal, head of the Analytics and AI Practice at Deloitte Consulting are two thought leaders regarding AI. They recently published a book called All-in On AI: How Smart Companies Win Big with Artificial Intelligence. They affirm that most organizations are taking the former posture and placing modest bets on artificial intelligence. On the other hand, they point out a world-class group of companies going all-in and radically transforming their products, strategies, customer relationships, and general culture.
According to the new book, these organizations represent only 1% of the large companies. Note that the list focuses on “traditional” companies that are not tech native such as Meta, Microsoft or Google. These companies exist for longer than Silicon Valley, and are the high performers in their respective industries. Let’s see three examples from the list they came up with:
Airbus – They created an AI-based ecosystem of platforms that allows itself and its partners, such as airlines, to optimize flight routes, fuel usage and conduct predictive maintenance on aircraft.
Shell – Creating and using AI systems that allow the company to use drones and computer vision to carry out analysis of pipelines, refineries, and infrastructure in weeks (previously would have taken years.)
Ping Am – the Chinese conglomerate has rolled AI out across its multiple divisions, which encompass insurance, banking, transport, and smart cities, but its applications within its healthcare division are a particular focus.
Winners and opportunities
The development of Artificial Intelligence (AI) has taken a unique path compared to other emerging technology trends. Typically, startups in new technology markets disrupt established players from previous cycles until they become the new dominant players. However, in the case of AI, much of the recent innovation was driven by the research labs of tech giants such as Microsoft, Google, Amazon, and Facebook. These companies have developed some leading AI platforms and acquired early-stage AI startups to expand their data science talent pool, making this cycle beneficial for incumbents. See below the level of relevance from tech incumbents on the research:
This debate about where value will accrue in the AI world is exciting. Big tech will own the infrastructure like they do in all clouds. Startups that find unique data and use case niches will be significant. The application layer will be where the innovation is, and there will be massive winners in vertical market apps of AI, especially if they get a higher model accuracy required for the vertical.
To analyze value capture, we have to draw an industry map. A broad AI industry map looks similar to this:
1. Semiconductors: These companies sit at the foundation of the AI industry, given that AI is only possible due to the steady increase in computational power.
Companies: Nvidia, Intel, AMD, and TSMC.
2. Cloud Platforms: Most semiconductor companies will sell their chips to enterprise buyers such as cloud platforms. In a simplistic way, cloud platforms have “computers” and sells its power to companies to build their system on top of it. They charge their customers for using these hardware resources, also known as compute (energy + usage of the chip). Machine learning models require massive amounts of computation to train and maintain.
Companies: AWS, Azure, GCP, and Oracle Cloud
3. Data Labelling: AI models require extensive data collection, sanitation, and labeling. Most AI companies collect data in-house but employ third-parties to label and sanitize datasets manually. Higher quality data means better model performance.
Companies: Scale AI, Appen, Hive, Labelbox, Snorkel AI etc.
4. Research companies: AI research companies combine computing and data resources to train machine learning models. A model is built by piecing together various transformers, gathering billions of parameters, and spending millions of dollars on computing resources. At the end of this process, research companies produce a working model like GPT-3.
Companies: OpenAI, Deepmind, Stability, and Anthropic
5. Applications: Most AI research companies are focused on building and refining their models. Once a model is trained, application layer companies will tailor the model to specific applications such as marketing copy, image creation, or even code generation.
Companies: Copy.AI, Jasper, Midjourney, Stable Diffusion, Github Copilot, Replit
Who is the winner? Cloud Infrastructure is well-positioned to aggregate many profits. They have purchase power against semiconductors companies, and the research, data-labeling, and applications rely on them to create huge models. Also, they rely on strong competitive advantages given the economies of scale and high switching cost. These companies (AWS, Google Cloud, Azure) are well-positioned to assess a fair amount of the value accurately. Cloud continues to be the “rent” as they were in the previous revolution.
The startup wave will occur in the application layer. Research is likely to be a game for big companies and significant investments. Data labeling is exciting but will probably be concentrated in a few players, given the data network effects moat.
Application companies are the ones that will distribute and massify the adoption. Application companies add value in productization (fine-tuning, prompt engineering, and a good UI) and distribution of the underlying technology. They take a stock model, polish it, and make it easy to use.
It’s interesting to point out that application companies can also develop their own specific machine learning models, which can be a potent edge and competitive advantage. Verticalization is likely a trend in the medium term. Application companies are not only native AI but also the ones used to deliver a better product.
The AI field is still relatively nascent. This tailwind is so powerful that the aggregate CAGR of the industry will be so high that everyone will capture much value.
A fair amount of it will be captured by cloud incumbents, the “rent” of the industry. But there are also enormous opportunities for research and application companies.
Venture Capital Investments
Indeed, most startups solely focused on AI are still in their early stages. Despite market momentum, investor interest peaked in 2022 as seen in the graph below by CB Insights. It was a record year for investment in generative AI startups, with around $2.6B across 110 deals. The largest rounds were as follows (still no deals in LatAm highlighted here):
· Anthropic, an AI model developer and research outfit ($580M Series B)
· Inflection AI, which focuses on human-computer interfaces ($225M Series A)
· Cohere, a developer-focused NLP toolkit ($125M Series B)
· Jasper, an AI-powered content creation suite ($125M Series A)
The industry is in fact just starting – CB Insights data also shows that among the 250+ generative AI companies identified, 33% did not raise any outside equity funding. 51% are Series A or earlier, confirming that we are still in the early stages of the AI industry as a whole. Until now, the generative AI space has 6 unicorns:
Talking about specific investors who lead the way in the theme, many still need to clearly define their investment strategies in the sector given the generative AI space’s relative immaturity. As you can see below, Insight Partners lead the way, backing 7 generative AI companies — nearly twice as many as the next most active VC. Insight Partners added 5 generative AI companies to its portfolio in 2022 alone.
What comes next?
According to research, the industry is expected to reach $42.4 billion in 2023. This momentum will continue, and we’re starting to realize it with the launch of powerful new AI-powered tools and services across different industries. There was a clear shift from the role of AI in analysis and prediction – helping enterprises make decisions and farther from our daily – to new ones producing new things that affect our daily lives. So, what will come up in 2023 amidst all of this?
1. The AI democratization will continue
AI is becoming a fundamental differentiator for business. To ensure they are on top of their game, companies will chase top engineering and data science talent – which will remain extremely expensive due to scarcity. Consequently, a greater availability of low- and no-code features will be crucial. This democratization of AI will help simplify the adoption of these technologies in all markets.
Furthermore, in 2023 cloud vendors will combine their services to include AI, leading to widely available features and solutions. This is important because this is an example of bottom-line business drivers of AI, which will trickle down from the aforementioned major cloud vendors to smaller tech players, leading to even greater AI adoption.
2. Generative AI will become commercialized
Generative AI is having its moment, and with time and popularity we’ll start to see many more products and services come to market in 2023. An excellent example that readers will be able to relate: Speech-to-speech (S2S) technology, has the potential to change the way we work. For instance, in a virtual meeting through AI, a person with a cold can make their voice easier to understand, enabling them to focus on their work contributions rather than potential misunderstandings. More solutions like this – easy to use and access, will emerge.
3. AI ethics will become a top priority
This point is critical – despite its proven value and potential, AI still has complex legal and ethical issues. The society in general is biased. Therefore, from deep fakes to biased algorithms to models that have degraded over time, these are all reminders that regulatory frameworks must adapt to the fast-evolving AI market.
2023 will be another high-growth year for AI. It’s an exciting time to be in the AI space, and it will be interesting to see how the industry progresses over the next 12 months.
It’s a complex debate that will take the public stage. In Brazil, there is a debate in the congress regarding Artificial Inteligence. Specialists point that the reform, in terms of the law, can be too restrictive for companies in the sector. This is something that should be priority on the A.I startups: work alongside the government to set up a rule that benefits innovation and technological progress.
Going to the specifics…
1. Chat GPT4 will be released in the first half of the year, and it will be a big deal.
Rumors say it will represent a significant performance improvement relative to GPT-3 and 3.5. Experts say the public reaction will be even bigger. Hold tight!
It won’t be much larger than its predecessor. That is, researchers say that “today’s large language models are in fact larger than they should be for optimal model performance (given a finite compute budget), today’s models should have fewer parameters but train on larger datasets. Training data, in other words, trumps model size.”
Most of today’s leading language models, including ChatGPT3, were trained on data corpuses of about 300 billion tokens (175 billion parameters in size). GPT-4 will probably be trained on a dataset at least one order of magnitude larger than this.
2. We are going to start running out of data to train large language models.
Yes, as many say, data is the new oil. This means that resources are finite. The area of AI for which this concern is most pressing is language models. The most effective way to improve models is to train them using more data. But how much more language data is there in the world? Note that it is not just random data – it needs to meet an acceptable quality threshold in order to be used. Researchers argue that the world’s total stock of high-quality text data is between 4.6 trillion and 17.2 trillion tokens. A single model might use around 3.2 trillion tokens. How to improve and expand with limited amounts of usable data?
3.Search will change more in 2023 than it has since Google went mainstream in the early 2000s.
The search is the center of modern internet experience, it is the primary means by which we navigate the internet. AI can significantly change your usual search engine, ChatGPT has brought light to conversational search. Why enter a word and expect links if you can engage in a dynamic conversation with your search engine to find out what you are looking for?
Market Landscape Brazil
One of the outputs of this project is to give a spotlight on Latin American companies working with AI. We asked on linkedin, twitter, instagram and talked with our network to find out LatAm companies in this sector. Through the responses, we got 58 companies that are in the sector. It is still work in progress. Missing a startup? Fill out here!
It is clear that through Artificial Intelligence, businesses have the opportunity to completely re-shape their operations and create new opportunities previously thought impossible. It is no wonder why investors are so keen on AI-based companies as they have not only seen its potential but also understand how it can be used to improve our day-to-day lives. After doing this in depth research, we got even more excited with the theme. We believe the important point currently is to be able to separate the truly good companies from the hype.
AI is here to stay, and everyday there is a different development on the theme. Apart from being a great exercise to learn, the entire process of writing this report was interesting in itself. Since we started, back in mid-January, several things happened forcing us to update the content in real time. In less than one month, this only proves that this subject is of extreme importance and has a great room to evolve. Business models are changing, work is changing, academic procedures are changing, we are changing! It is a revolution and we can’t even imagine, in the pace things are going, how we will wrap up the year’s theme and predict its Next Big Thing for 2024…
We are excited and worried. At the same time. Like skydiving.
But, we decided to jump, live on the moment, study the concepts and use artificial intelligence to our benefit. We hope you do the same after reading this report!
If you want to go deep, we suggest this reading list:
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.