AI Economics: Can We Run 3X Faster?
Why the present revolution leaves us very little choice
What do they tell you about the disruption that generative artificial intelligence(Generative AI) is bringing about?
Check out this discussion between an AI expert and an AI critic.
AI Expert: This talk of jobs being lost in large numbers is downright irrational and overblown. AI will create millions of jobs, so much so that everyone will have a spare robot to even take their dog out for a walk!
AI Skeptic: Thank you! I can walk my dog myself! I actually think that Generative AI will displace thousands of jobs and render them irrelevant. These jobs, such as creative writer, data analyst, secretary and support staff, are not coming back once this technology settles in. There is no job creation here, only job destruction.
AI Expert: No one said that this would be a walk in the park! Individuals will need to reskill in order to fit into these new roles, but that will eventually happen.
AI Skeptic: What if there aren’t enough jobs to go around? What about those professionals in their late 40s and above? After decades of job security based on their skills, are you telling me that they need to go back to school to stay relevant?
When you listen to both sides at times, they seem to have equally impactful arguments.
Today I am going to look at things from a very different angle: the Economic one.
Enter The Enterprise
The primary challenge for the adoption of generative AI lies in the enterprise sector.
Let’s be honest here. There is ample evidence that the consumer market for generative AI is thriving around us. Of course, it’s debatable how many consumers pay for these services versus using them for free.
Let’s consider 2 cases of enterprise adoption of Generative AI: -
The Manufacturing Plant
At the outset, it’s important to understand that not all factories in the world can afford to deploy robots on their factory floors, as can Amazon and other large firms such as Tesla, General Motors, Lockheed Martin, etc. Factory automation still has some distance to go in developing nations. Until then, they are still labor-intensive.
Tesla’s Gigafactories are a case in point here. The EV revolution is, after all, the next bastion that multinationals are breaking into. Even Tesla, a tech-savvy firm, reduces its workforce when automation becomes an effective replacement.
In this arena, the applications of generative AI are multifaceted. They range from predictive maintenance to quality control and inspection, automation of customer service, calculation of equipment effectiveness, and data analysis of all these aspects.
Taken together, this is a room full of job displacement that has already occurred or is in the process of happening.
With most of the factory automated, the ability to speed up or slow down production will be relatively easy, given the availability of raw material inputs.
In the future, this ability could, as you know, be as simple as clicking a button.
The Technology Firm
Oh, you thought these would be spared?
Think again!
The biggest disruption is actually happening to the disruptors themselves. Today, software developers or analysts may write code they won’t need to maintain. Data scientists and AI researchers are designing newer models to automate more of the tasks, which could even put them out of jobs in the future.
How is this happening?
Ever since we welcomed both Low-Code and No-Code tools to the party.
So what are Low-Code and No-Code tools?
Platforms with low-code/no-code tools feature a user interface that allows users to navigate through a range of options and select the necessary functionality to construct an application or an app. The tool then generates the template, which, in almost all cases, is a working app or application with all the code generated.
Low-code tools generate an application that may require some minor tweaking by the developer, whereas no-code tools create an application that is ready for use.
The cool thing about this is that anyone can build an app, a website, or even an application without needing to be a techie or knowing how to write code! In fact, the term “low-code/no-code” was coined to highlight this fact!
Then what about the AI tool called Devin?
Unlike those No-Code or Low-Code tools mentioned above, Devin is a huge leap forward. When it first exploded onto the Generative AI scene earlier this year, it showcased the impossible. For instance, if instructed (or prompted well), it can plan, analyze, create and even execute code and other tasks autonomously.
Devin is the quintessential AI software developer agent that could not only assist developers but, in due time, replace them altogether.
To be fair, expectations have decreased since then, but the tool will mature over time.
All said and done, the reality of an AI software developer who can handle a large part of an organization’s technology development function could be a game changer!
With a tool like Devin under their belt, tech firms could one day create software with just a click of a button.
The Generative AI Acceleration
When factories and companies accelerate their go-to-market cycles, they have the potential to transform economies. If they could get their physical shipments or digital products to their respective marketplaces a lot sooner, then more goods and services would be available to consumers in a shorter span of time.
The real question is: “How many times (x) faster could this process be”?
This is again industry-dependent.
In the manufacturing world, it may take companies a little longer to accelerate their working capital cycle since there are multiple players upstream and downstream who would also need to be moving at least at the same speed.
Example 1:- A factory that uses IoT, Robotics and Digital Twins to manufacture various types of drones, could be hamstrung by the supply of parts that may be held upstream by delays.
Example 2:- A textile factory that wishes to automate some of its internal manual operations could end up facing the wrath of labor unions, who would be up-in arms against this decision.
Let me instead consider the IT industry, since that is what I am most familiar with.
Studies, such as the McKinsey one below, state that generative AI speeds up tasks at different paces in different stages of the product development pipeline.
If I take into account the potential 1.5X increase for product managers and owners on the administration side (average from the study above), I will assume that developers could utilize GenAI tools like Devin to expedite their code by a similar 1.5X. This is how I arrived at a 3X total increase.
Disclaimer: Bear in mind that this is just an estimate at this point in time.
The Economics of Generative AI
Now let’s get to the economics. Basic economics tells us that when more goods chase the same number of customers, the prices decrease.
Let’s look at the famous demand-supply curve below.
Let’s assume, for the sake of this example, that the sales quoted here are what tech companies generate for both the consumer and enterprise markets.
In a normal world, the demand supply curve is balanced. Consumers as well as enterprises have an intersection-point at which demand meets supply, as shown in the graph above.
Now, what happens when we introduce Generative AI into this equation?
What’s visible above is that with the introduction of AI (technological advances), the supply curve shifts right. In other words, when supply increases naturally with a glut in the market, prices will drop.
This is only natural.
The question is whether demand will offset this. In other words, will the demand rise to match it?
Newer platforms will spawn newer capabilities previously unseen or unheard, flooding the digital cyberspace as we know it with goods of all kinds.
However, if customers do not purchase them, prices will drop and companies will go bankrupt.
Why will they not?
The simple answer is unemployment.
One of the main reasons is displacement. Generative AI will take a lot of people out of the workforce before it puts any of them back. The replacement rate will not turn out as smoothly as predicted (rightly mentioned in the dialogue at the start). There will be carnage before a new normal emerges.
With fewer people in the workforce and unemployment rising like a phoenix, spending power on average could possibly decrease. Even so, this will depend on the demography in question.
Millennials and Gen Z could end up being the groups doing much of the spending, unlike their parents, who would still be trying to read the tea leaves. To address the issue, more brands and products would target the younger consumer generation.
Forget the digital divide; we will now also end up with a demographical one. People in their 40s and beyond would all of a sudden find themselves struggling to find meaningful careers in an AI-ridden world. The jobs that AI creates may not be the most stimulating ones, while on the other hand, young startup founders, digital creators, and influencers will have a field day.
How should these groups respond?
Generation Z (birthdate : 1997–2012)
I do not claim to understand them, but in a few years from now, the generative AI-fabricated world will be in full swing. This group would be in the right age group to maximize their potential for creating digital tools and products for consumers across generations. With this immense power comes the even greater responsibility of steering our planet away from an almost certain environmental catastrophe.
Millennials (birthdate : 1981 - 1996)
The group is caught right in the middle. The millennials in their mid-thirties would probably be considering a career switch in the throes of the famous mid-life crisis. Opportunity could come knocking at their doors right about now. Because they have lived through most of the digital revolution, they will, for the most part, recognize an opportunity when they see it.
Gen-X (birthdate : 1960–1980)
We are the ones in our late 40s, scrambling for the shrinking pie of available boardroom seats or cramped corner offices that may not even be that safe anymore. This group would be forced to adjust to the new world or survive on a staple diet of grumbling and murmuring. While Gen-X folks still run major companies and will continue to do so for some time, a large part of them could end up feeling left behind in a corporate culture they never inherited.
The demographic divide will just get more pronounced over time.
Whether we like it or not, we will all have to run at 3X the speed!
