Employees, let’s face the reality. AI is here and not all of us are going to be happy. Some of us will face inevitable pay drops. The smarter lot will see a pay rise. The unlucky lot will be shown the exit.
Why? Because we’re getting into the new era where the workplace sacrifices many positions, but rewards AI experts. Statistics show it all: AI skills are the best-paid tech skills.
I believe the future pay scale will favor employees who demonstrate AI excellence, and here are 10 reasons why.
1. Scarcity of talent
The research on AI has been ongoing for a few decades. However, the technology’s real value is just beginning to unfold. As the world is waiting to experience a mature AI, a critical challenge at this infancy stage is “talent shortage.”
The demand for AI skills is rising rapidly as organizations scramble to secure top AI talent.
Microsoft is competing for AI experts to improve its AI-powered models like Copilot. Google is also ready to snatch young people whose skills can impact the company’s AI technologies.
It’s a game on, and competitive salaries are a tool to win top AI talent.
2. Strategic importance
The strategic significance of AI to modern businesses is another reason AI skills are the best paid tech skills. It’s by no mistake that companies are increasingly diverting resources toward AI investment.
Employees who are proficient in using AI technologies like GenAI, machine learning, and deep learning can enable businesses to transform operations, drive innovation, and become more efficient.
Thus, we’re not surprised when a tech giant like Amazon offers the highest salary to AI experts who maintain its AI-powered, Alexa virtual assistant, for the company to maintain its competitive edge.
3. Business impact
Employees who bring AI skills to the workplace directly impact revenues. These workers can apply AI systems like robots to repetitive tasks to lower costs and enhance customer satisfaction.
Let’s contextualize this using Amazon Personalize as an example. AI experts use this application to automate processes like personalized product recommendations. Amazon Personalize has great business impacts. It drives sales and boosts revenues, which justifies high pay for ML and data scientist engineers.
4. Complexity of AI projects
In his article, Aaron Skonnard, a member of the Forbes Council, notes that AI will soon be critical across the entire workplace and employees should strive to acquire AI skills.
I can’t agree with Skonnard enough. However, the complexity of AI projects means not every employee can handle these tasks.
Let’s consider building robust AI systems. What does it take to develop, maintain, or improve GPT-4 (OpenAI), Gemini (Google), and Siri (Apple)? What about Google’s Google Deep Mind (also called AlphaGo), Tesla’s Autopilot, or IBM’s Watson? These highly complex AI systems require specialized AI skills to work.
Professionals who can navigate this complexity and deliver value deserve high compensation. Tech companies are willing to pay that price.
5. Market competition
I recently discovered that OpenAI is not the first tech firm to create a high-tech chatbot like GPT-4. According to sources, Google engineers developed a ChatGPT-like AI system years ago. However, Google hid the innovation and was reluctant to deploy it. Why?
Google depends on advertising for revenues. Its engineers believed if rolled out, such technology could significantly affect the company’s major revenue stream. A chatbot similar to ChatGPT could significantly reduce traffic flow to websites that display Google ads.
While Google thought it was alone in the AI game, OpenAI launched GPT-3. The deployment was followed by Microsoft investing heavily in OpenAI AI projects. The investment resulted in the creation of GPT-4, the technology powering both Copilot and ChatGPT-4.
These developments threatened Google’s decades of internet supremacy. Google moved with speed to deploy its ChatGPT-4 equivalent, Gemini.
With many other companies like Meta now in the race, the stakes are high for AI experts. Organizations hire these individuals at competitive pay to effect breakthroughs amidst the rising competition.
6. Long-term vision
The last five years have seen companies emphasizing AI R&D. Interestingly, it remains unclear how the investments will be recovered, at least over the short term. Google has problems figuring out how to make money with GenAI. That sounds weird, right?
Well, not at all. Tech winners know that AI is a long-term investment. Accordingly, paying billions to people who can actively contribute to AI R&D complements efforts toward achieving long-term vision.
7. Innovation & research
Needless to mention, AI skills are the best-paid tech skills because AI experts are the key drivers of innovation in the digital age. These professionals contribute to research and experiment with cutting-edge techniques by exploring novel algorithms.
That’s why IBM doesn’t mind “over-compensating” AI talent. The company needs them to build and manage its Watson AI platform for current innovation and long-term sustainability.
8. ROI on AI investments
I believe tech giants understand that AI is a long-term investment. Why?
Skilled AI experts can create AI-driven products, improve customer experiences, and unlock new revenue streams. Consequently, they deliver a high ROI.
Here is an example to shout it out loud.
Microsoft has been a trailblazer in AI R&D. In 2023, the company invested some $13 billion in OpenAI AI projects. It had invested $1 billion earlier in 2019. This commitment promises huge returns to Microsoft.
According to reports, 75% of OpenAI’s revenues go to Microsoft. Once Microsoft recoups the investment, it will own a 49% stake in OpenAI.
9. Ethical AI
AI technology is entering the scene with some critical ethical questions. For instance, a few days ago, Google disabled the image generation functionality of its Gemini GenAI after users complained the software generated biased images.
Google is not alone in the mess. GenAI system developers are facing legal claims of copyright infringement. Getty Images vs. Stability AI is an example.
Companies hire and handsomely compensate experts to create unbiased AI models.
10. Risk Mitigation
Flawed models are costly to organizations. With AI, it is possible to detect and lower or eliminate project risks much earlier to ensure successful implementation. This is why companies invest in AI skills to reduce project failure.
Conclusion
The convergence of business impact, scarcity of talent, and the transformative potential of AI drives companies to pay top salaries for skilled AI professionals. Put another way, employees with these skills are likely to deliver more value making AI expertise the best paid tech skills.
