Rajesh Soundararajan on LinkedIn – https://www.linkedin.com/in/rajeshsound
In the past, AI and machine learning were the tools of specialists. But today, they’re allowing generalists to take on more roles than ever before. Welcome to the age of Genralist 2.0!
Generalist vs. Specialist
The difference between a generalist and a specialist is simple: the former can work across multiple functions, while the latter specializes in one function. As you might expect, both are in demand, but for different reasons:
- Generalists are in demand for their flexibility to work across different specialist functions. For example, if you’re a marketing manager who also has experience managing operations teams, you might find yourself doing just that at some point during your career – and that’s where your generalist abilities come into play.
- Specialists are in demand because they have deeper knowledge of their specific field than other individuals with similar titles (e.g., “marketing manager”). This means that when an organization needs someone with specialized expertise (for example, someone who knows how to market cars), they’ll typically turn to those experts first before calling upon generalists like yourself
Generalists were in massive demand before the 2005s to manage the many specialist functions
Before the 2005s, generalists were in massive demand. Before the advent of AI- and ML-based systems, generalists needed to string together various specialists toward the larger organizational purpose.
In other words, they were literally the glue that held companies together.
Programmers started mass training of computers with specialist functions since 2005
In 2000, the demand for specialists to program computers was so immense that fewer and fewer people came into general management in the last two decades.
Specialists like Data Scientists, Machine Learning Engineers and Artificial Intelligence Programmers started mass training of computers with specialist functions since 2000. In fact, their roles were so vital that there were skills shortages in 2017 and we still have them today.
A surge in demand for specialists to automate their own roles
As demand for specialists increased, so did the need for more specialists to automate their own roles. In turn, this created demand for a whole new set of specialists. The cycle was self-perpetuating: with each iteration, the need for more and more AI experts grew exponentially.
The rise of generalists has helped make this phenomenon possible by providing exhaustive training programs that teach workers how to operate complex machinery without having any prior knowledge or training in engineering or computer science—and no college degree required!
Correspondingly Generalists were a fading function
In the early days of the internet, generalists were in demand. A generalist was a person who knew how to code and could do basic design work, and was also able to manage a project from start to finish. They were the glue that held together teams of specialists.
But as AI came along, it became easier for machines to do what used to take humans years or decades: write code, design interfaces and manage projects.
AI and ML start replacing specialist functions
The new era of generalist 2.0 has begun
The rise of machines that can automate the work of professionals is well underway, and this has important implications for those working in professions that rely on specialist skills. But it’s also worth remembering that these machines only exist because a human being created them — someone who was themselves a specialist in one area (computer science) and then used their knowledge to create tools which can do things they themselves cannot. In other words, machine learning is just another form of specialization — albeit one with benefits that are far more widespread than traditional forms of expertise could ever hope to achieve.
We all have our own specialties within our chosen fields, whether you’re an accountant or an astrophysicist or even if you’re just really good at folding origami animals! And we’ve been using technology for decades now: calculators have existed since 1774 when Charles de Périer invented what was essentially a slide rule; computers have been around since 1822 when Charles Babbage wrote his first design for a mechanical computer called “Difference Engine”; cameras go back even further – Leonardo da Vinci was tinkering around with prototypes way back in 1490! The point is technology hasn’t always been there but when it comes along it changes everything about how we work – often making tasks easier but also sometimes replacing them altogether.”
Rise of the tech-savvy Generalist 2.0 – who can work with specialist machine systems
One of the most significant shifts in modern business practice is the rise of generalist 2.0, after the specialist 1.0 model that has dominated over the last century. This change will be particularly beneficial for businesses that need to leverage technology but don’t have in-house experts or external consultants to build it for them.
The reason for this shift is simple — computers are now faster, more predictable, and cheaper than humans at performing specialized tasks such as data analysis or translation from one language to another (think Google Translate). This means that there is less demand on human specialists who can perform these tasks more efficiently than machines — and therefore less need for those human specialists altogether!
But why would anyone want a generalist over a specialist? The answer lies in how quickly technology advances; generalists have tech savviness which makes them better able than specialists at learning new skills and adapting when new technology comes along (like AI!). They also tend to have broader knowledge bases which allow them to understand connections between different fields without having specific expertise in any one field—take an example where someone needs to use machine learning algorithms but doesn’t know anything about programming languages like Python or C++…it’s unlikely they’ll find many developers with both skillsets available locally so instead they might hire someone who has experience with ML but hasn’t coded before–this person could then take on their role effectively whilst learning how code works at their own pace outside their full-time job responsibilities!
Long live the AI, ML, and the Generalist 2.0
The generalist is back, and he’s smarter than ever!
The specialist is dead. Long live AI, ML, and the generalist 2.0!
For a long time we’ve been told that there’s no future for generalists. That we’re doomed to be replaced by automation and bots that can do our jobs better than us. But now it looks like our survival skills are coming back in vogue as technology becomes more powerful and ubiquitous – with AI (Artificial Intelligence) taking center stage in many industries where automation has taken over traditional jobs previously done by humans alone: from law firms that use AI software instead of associates; accounting firms using bots instead of accountants; insurance companies using bots instead of adjusters; banks using algorithms instead of bankers…the list goes on and on!
AI and ML are evolving at a rapid pace, but so far the tech-savvy Generalist 2.0 seems to be able to keep up with them. As long as this trend continues, it looks like we’re going to see an increase in demand for generalists who can work alongside specialist machine systems rather than being replaced by them.