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The big tech crash is a term that reflects that 2022 hasn’t been a good year for the stock markets, especially for tech stocks. The tech-heavy Nasdaq index has fallen more than 30% since Jan 2022. It’s been falling into a bear market due to interest rate hikes, rising inflation, macroeconomic uncertainty, global pressures, and the strong US dollar.

Big Tech stocks collectively lost nearly $4 trillion in market value in 2022. In 2020 and 2021, one of the best places for stock investors to have their money was in mega-cap technology stocks. In 2022, it was one of the worst. Many media are speaking of the big tech crash as a phenomenon that will impact the technology and digital services sector. They are foreseeing a collapse in the following months.

The big tech collapse, which could follow the current big tech crash, will seriously impact our lives. And not only because we are experiencing how more than 16,000 workers in U.S.-based tech companies have been laid off in mass job cuts so far in 2023, according to a Crunchbase News tally. Consider also that last year, more than 90,000 jobs were slashed from public and private tech companies as they were forced to confront rising inflation rates and a tumultuous stock market.

For 2023, the party is over. Every major tech company—including Amazon, Meta, Snap, Stripe, Coinbase, Twitter, Robinhood, and Intel—has announced double-digit percentage-point layoffs in the past few months.

But the problem of the big tech collapse or the big tech crash is much larger than jobs. If you look at the major tech firms, they all profit from the mass commercialization of digital services. They are also one of the primary drivers of the global economy. What does this mean? It means that if the tech industry does not make a profit, a relevant share of the economy enters into recession, which may reduce consumption. And considering the significant wallet share digital services have currently, this will further negatively impact the tech industry.

The vicious circle formed by this phenomenon has a name in the economic jargon: the spiral of death. The larger the negative impact on tech companies is, the lower digital services consumption by society. This makes the negative impact on tech companies larger, etc.

We can’t be sure whether this spiral of death will occur until we see the true big tech collapse in front of our eyes. But the global economy does not face any countermeasure which could stop this trend. On the contrary, we expect higher interest rates and a progressive cool-off of the world’s largest economies on the planet…

So, be ready to keep your current smartphone for the next five years. And, if you want to know more about technology, here you have my recently published book about artificial intelligence:

We have heard that artificial intelligence (AI) will destroy many jobs. But new artificial intelligence jobs will also be created in the following years. As AI is directly related to software, most will be in the field of computing sciences. However, AI algorithms are close to statistics and big data, which means that many jobs will open in the fields of mathematics, statistics, and data science.

There is a list below of the highest-paying jobs in the US related to AI, although it could be much more. If you plan your career in AI, I suggest you follow me. I’ve been working in AI for more than 20 years. You may get inspired by one of the topics I discussed or by reading one of my books.

And now, the list you want!

1. Director of Analytics 

Average Salary: US$140,837. A director of analytics leads the data analytics and data warehousing departments and aligns their tasks to the business objectives. He/she is responsible for managing, developing, and integrating data analytics and business intelligence to support the business.   

2. Principal Scientist 

Average Salary: US$138,271. Principal scientists are responsible for creating high-impact data science projects in accordance with the stakeholders. They act as technical consultants to product managers from different departments. This person is expected to hold leadership skills who can communicate clearly, is empathetic, and has a good eye for detail. 

3. Machine Learning Engineer

Average Salary: US$134,449. Machine learning engineers are responsible for building machine learning algorithms that solve business problems. They create computer programs that perform defined tasks without any specific programming. While an ML scientist focuses on the theoretical part of machine learning, an ML engineer applies those approaches to practical problem-solving.   

4. Computer Vision Engineer 

Average Salary: US$134,346. Computer vision is a sub-field of AI that relates to the “seeing” ability of machines. This technology powers object recognition systems. A computer vision engineer applies deep learning algorithms to solve problems around image recognition and processing.   

5. Data Scientist 

Average Salary: US$130,503. A data scientist is someone who extracts value from data. They analyze data from various sources, understand how businesses perform, and build AI tools to automate specific business functions. The duties typically include creating ML tools or processes within the company that can churn data into insights.   

6. Data Engineer 

Average Salary: US$125,999. Data engineers are responsible for identifying trends in data sets and developing algorithms that will make the raw data useful to the company. This role demands technical skills and deep knowledge of SQL databases and many programming languages.   

7. Algorithm Engineer

Average Salary: US$104,112. Algorithms engineers differ from software engineers as their work focuses on designing, analyzing, implementing, and optimizing existing algorithms. They usually hold a master’s or Ph.D. in computer science-related fields and are fluent in several programming languages like C++ and Java.   

8. Computer Scientist 

Average Salary: US$97,850. Computer scientists create the main functioning of a device. The role revolves around thinking and conceptualizing computational and mathematical challenges, developing new products, solving computing problems, researching experimentation and modeling, and improving the performance of existing computer systems and software.

And now, if you want the best guide to understanding AI in the market, you are on your lucky day. Check this out: